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airbnb/knowledge-repo

[KP] support more sort by & UI improvement

Description of changeset: as title Test Plan: local dev  Reviewers:

2023-03-14 06:42:17+00:00

2023-03-19 03:03:33+00:00

knowledge_repo/app/templates/index-base.html

{% extends "base.html" %} {% macro format_authors(authors) %} {% for author in authors %} <a href='/feed?authors={{ author.identifier|urlencode }}'> {{ author.format_name }} </a> {% if not loop.last %} , {% endif %} {% endfor %} {% endmacro %} {% macro pagination(max_pages=20, extremes=True, autohide=True) %} {% if feed_params %} {% set start = feed_params['start'] | default(0) %} {% set results = feed_params['results'] | default(10) %} {% set page = 1 if (start == 0) else (start//results + 1) %} {% set page_count = feed_params['page_count'] | default(1) %} {% if autohide and page_count > 1 %} {% set page_nums = pagination_pages(current_page=page, page_count=page_count, max_pages=max_pages, extremes=extremes) %} <div class='pagination-bar' role="group"> <a href="{{ "#" if (page == 1) else modify_query(start=(page-2)*results) }}" class="pagination-stepper{% if page == 1 %} disabled{% endif %}"{% if page == 1 %} onclick="return false;"{% endif %}> <i class="glyphicon glyphicon-chevron-left"></i> </a> <ul class="pagination"> {% for page_num in page_nums %} {% if loop.index0 > 0 and page_num - page_nums[loop.index0 - 1] > 1 %} <li class="disabled"><a>&middot;&middot;&middot;</a></li> {% endif %} <li {% if page == page_num %}class="active"{% endif %}><a href= "{{ modify_query(start=(page_num-1)*results) }}"> {{ page_num }} </a></li> {% endfor %} </ul> <a href="{{ "#" if (page == page_count) else modify_query(start=page*results) }}" class="pagination-stepper{% if page == page_count %} disabled{% endif %}"{% if page == page_count %} onclick="return false;"{% endif %}> <i class="glyphicon glyphicon-chevron-right"></i> </a> </div> {% endif %} {% endif %} {% endmacro %} {% macro page_sizer() %} {% if feed_params %} {% set results = feed_params['results'] | default(10) %} <ul class="pagination"> <li {% if results == 5 %}class="active"{% endif %}> <a href="/feed?results=5" title="Show 5 items per page" aria-current="true">5</a> </li> <li {% if results == 10 %}class="active"{% endif %}> <a href="/feed?results=10" title="Show 10 items per page" aria-current="true">10</a> </li> <li {% if results == 20 %}class="active"{% endif %}> <a href="/feed?results=20" title="Show 20 items per page" aria-current="true">20</a> </li> <span class="text" style="display:inline-flex;text-align:justify;align-items:center;line-height:35px;"> &nbsp;per page</span> </ul> {% endif %} {% endmacro %} {% block content %} <!-- Index rendering mode switch --> <div class="row"> <div class="col-md-5"> <div class="btn-group btn-group-justified index-view-btn-group" role="group"> <a href="/feed" class="btn btn-default btn-card no-underline" role="button"> <i class="glyphicon glyphicon-post-org glyphicon-align-justify"></i> <span class="index-view-name"> Card </span> </a> <a href="/table" class="btn btn-default btn-table no-underline" role="button"> <i class="glyphicon glyphicon-post-org glyphicon-th"></i> <span class="index-view-name"> Table </span> </a> <a href="/cluster" class="btn btn-default btn-cluster no-underline" role="button"> <i class="glyphicon glyphicon-post-org glyphicon-th-list"></i> <span class="index-view-name"> Cluster </span> </a> </div> </div> <div class="pull-right visible-md-block visible-lg-block"> {{ page_sizer() }} </div> </div> <!-- Container for index items --> <div class="col-12"> {% block inner_content %} {% endblock %} </div> {# Show pagination at bottom of page unless showing clusters. #} {% if request.endpoint != 'index.render_cluster' %} {{ pagination(max_pages=10) }} {% endif %} {% endblock %}

{% extends "base.html" %} {% macro format_authors(authors) %} {% for author in authors %} <a href='/feed?authors={{ author.identifier|urlencode }}'> {{ author.format_name }} </a> {% if not loop.last %} , {% endif %} {% endfor %} {% endmacro %} {% macro pagination(max_pages=20, extremes=True, autohide=True) %} {% if feed_params %} {% set start = feed_params['start'] | default(0) %} {% set results = feed_params['results'] | default(10) %} {% set page = 1 if (start == 0) else (start//results + 1) %} {% set page_count = feed_params['page_count'] | default(1) %} {% if autohide and page_count > 1 %} {% set page_nums = pagination_pages(current_page=page, page_count=page_count, max_pages=max_pages, extremes=extremes) %} <div class='pagination-bar' role="group"> <a href="{{ " #" if (page==1) else modify_query(start=(page-2)*results) }}" class="pagination-stepper{% if page == 1 %} disabled{% endif %}" {% if page==1 %} onclick="return false;" {% endif %}> <i class="glyphicon glyphicon-chevron-left"></i> </a> <ul class="pagination"> {% for page_num in page_nums %} {% if loop.index0 > 0 and page_num - page_nums[loop.index0 - 1] > 1 %} <li class="disabled"><a>&middot;&middot;&middot;</a></li> {% endif %} <li {% if page==page_num %}class="active" {% endif %}><a href="{{ modify_query(start=(page_num-1)*results) }}"> {{ page_num }} </a></li> {% endfor %} </ul> <a href="{{ " #" if (page==page_count) else modify_query(start=page*results) }}" class="pagination-stepper{% if page == page_count %} disabled{% endif %}" {% if page==page_count %} onclick="return false;" {% endif %}> <i class="glyphicon glyphicon-chevron-right"></i> </a> </div> {% endif %} {% endif %} {% endmacro %} {% macro page_sizer() %} {% if feed_params %} {% set results = feed_params['results'] | default(10) %} <ul class="pagination"> <li {% if results==5 %}class="active" {% endif %}> <a href="{{ modify_query(results=5) }}" title="Show 5 items per page" aria-current="true">5</a> </li> <li {% if results==10 %}class="active" {% endif %}> <a href="{{ modify_query(results=10) }}" title="Show 10 items per page" aria-current="true">10</a> </li> <li {% if results==20 %}class="active" {% endif %}> <a href="{{ modify_query(results=20) }}" title="Show 20 items per page" aria-current="true">20</a> </li> <span class="text" style="display:inline-flex;text-align:justify;align-items:center;line-height:35px;"> &nbsp;per page</span> </ul> {% endif %} {% endmacro %} {% macro sort_filter() %} {% if feed_params and get_current_path() == 'feed' %} {% set tab = feed_params['tab'] | default('Newest') %} <!-- Index rendering mode switch --> <div class="row"> <div class="col-md-11"> <a href="/feed?tab=Newest" class="btn btn-default btn-card no-underline {% if tab == 'Newest' %} active {% endif %}" role="button"> <span class="index-view-name"> Newest </span> </a> <a href="/feed?tab=Frequent" class="btn btn-default btn-table no-underline {% if tab == 'Frequent' %} active {% endif %} " role="button"> <span class="index-view-name"> Frequent </span> </a> <a href="/feed?tab=Vote" class="btn btn-default btn-cluster no-underline {% if tab == 'Vote' %} active {% endif %}" role="button"> <span class="index-view-name"> Vote(s) </span> </a> <!-- <a class="btn btn-default btn-card no-underline" id="toggle-button" role="button" aria-expanded="false" aria-controls="uql-form"> <svg class="justify-content: center; display: flex" width="20" height="20" viewBox="0 -5 20 20"> <path d="M2 4h14v2H2V4Zm2 4h10v2H4V8Zm8 4H6v2h6v-2Z" fill="#06c6b6"></path> </svg> Filter </a> --> <!-- </div> --> </div> </div> {% endif %} {% endmacro %} {% block panel_left %} <div class="sidebar2 homepage-side-panel"> <h2>Menu</h2> <ul> <li> <a href="/feed" role="button" class="btn btn-default btn-card no-underline"> <i class="glyphicon glyphicon-pencil"></i> <span class="index-view-name"> Home </span> </a> </li> <li> <a href="/table" role="button" class="btn btn-default btn-table no-underline"> <i class="glyphicon glyphicon-th"></i> <span class="index-view-name"> Table </span> </a> </li> <li> <a href="/cluster" role="button" class="btn btn-default btn-cluster no-underline"> <i class="glyphicon glyphicon-th-list"></i> <span class="index-view-name"> Cluster </span> </a> </li> </ul> </div> {% endblock %} {% block content %} <div data-controller="se-uql" data-se-uql-id="" data-se-uql-sanitize-tag-query="false"> <div class="row"> <div class="col-md-6"> {{ sort_filter() }} </div> <div class="pull-right visible-md-block visible-lg-block"> {{ page_sizer() }} </div> </div> <!-- <div class="row"> <div class="col-md-6"> <form class="form-group form-base" id="uql-form"> <fieldset class="fieldset"> <legend class="legend font-style">Tagged with:</legend> <div class="radio-inline radio-cluster"> <input class="radio" type="radio" name="tagModeId" value="Watched"> <label class="feed-tldr font-style">My subscribed tags</label> </div> <div class="margin-left: 0px"> <div class="radio-inline radio-cluster"> <input class="radio" type="radio" name="tagModeId" value="Watched"> <label class="feed-tldr font-style">The following tags</label> <input type="text" autocomplete="off" tabindex="0" aria-autocomplete="list" aria-haspopup="listbox" role="combobox" aria-expanded="false" placeholder="e.g. python" style="width: 100px;"> </div> </div> </fieldset> <div> </div> <button class="btn btn-default btn-card no-underline" style="justify-content: flex-end;" type="button" onclick="applySelection()">Apply</button> <button class="btn btn-default btn-card no-underline" style="justify-content: flex-end;" type="reset" id="toggle-button">Cancel</button> </form> </div> </div> --> </div> <div class="col-12"> {% block inner_content %} {% endblock %} </div> {# Show pagination at bottom of page unless showing clusters. #} {% if request.endpoint != 'index.render_cluster' %} {{ pagination(max_pages=10) }} {% endif %} <script type="text/javascript"> // const form = document.querySelector("#uql-form"); // const toggleButton = document.querySelector("#toggle-button"); // // Initially hide the form // form.style.display = "none"; // // Attach an event listener to the toggle button // toggleButton.addEventListener("click", () => { // if (form.style.display === "none") { // form.style.display = "block"; // toggleButton.setAttribute("aria-expanded", true); // } else { // form.style.display = "none"; // toggleButton.setAttribute("aria-expanded", false); // } // }); </script> {% endblock %}

mengting1010

9a3fac2c2e8cdd295b53fe38a446d535e4f2bcb5

0d7aba9ddd3336f00f42000a5c2f17ad76ef570d

The above code and some code below will be uncommented in the next PR

mengting1010

airbnb/knowledge-repo

[kp] fix md data error and ipynb upload

Description of changeset: fix md data error and ipynb upload Test Plan: [x] local test Reviewers: @csharplus @mengting1010

2023-01-29 09:14:49+00:00

2023-02-02 07:41:26+00:00

knowledge_repo/app/routes/editor.py

from .. import permissions from ..index import update_index from ..models import Comment, PageView, Post, PostAuthorAssoc from ..proxies import current_repo, current_user, db_session, s3_client, notion_client from ..utils.emails import ( send_review_email, send_reviewer_request_email, ) from ..utils.image import ( is_allowed_image_format, is_pdf, pdf_page_to_png, ) from ..utils.shared import get_blueprint from datetime import datetime from flask import ( current_app, render_template, request, send_from_directory, url_for, ) from knowledge_repo.post import KnowledgePost from sqlalchemy import or_ from urllib.parse import unquote from werkzeug.utils import secure_filename import json import logging import os from knowledge_repo.utils.s3 import put_object_to_s3 import nbformat from nbconvert import HTMLExporter import io from knowledge_repo.constants import AWS_S3_BUCKET from knowledge_repo.utils.notion import create_page logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) blueprint = get_blueprint("editor", __name__) def get_warning_msg(msg): return json.dumps({"msg": msg, "success": False}) def get_error_msg(msg): return json.dumps({"error_msg": msg, "success": False}) # TODO: These functions have not been fully married # to the KnowledgePost API # Currently, backended by Post objects but partially # implemented on KnowledgePost API # TODO: Deprecate this route in favour of integrating editing # links into primary index pages and user pages @blueprint.route("/webposts", methods=["GET"]) @PageView.logged @permissions.post_edit.require() def gitless_drafts(): """Render the gitless posts that a user has created in table form Editors can see all the posts created via Gitless_Editing """ prefixes = current_app.config.get("WEB_EDITOR_PREFIXES", []) if prefixes == []: raise Exception("Web editing is not configured") query = db_session.query(Post) if prefixes is not None: query = query.filter(or_(*[Post.path.like(p + "%") for p in prefixes])) if current_user.identifier not in current_repo.config.editors: query = query.outerjoin( PostAuthorAssoc, PostAuthorAssoc.post_id == Post.id ).filter(PostAuthorAssoc.user_id == current_user.id) return render_template("web_posts.html", posts=query.all()) @blueprint.route("/edit") @blueprint.route("/edit/<path:path>", methods=["GET", "POST"]) @PageView.logged @permissions.post_edit.require() def editor(path=None): """Render the web post editor, either with the default values or if the post already exists, with what has been saved""" prefixes = current_app.config.get("WEB_EDITOR_PREFIXES", None) if prefixes is not None: assert path is None or any( path.startswith(prefix) for prefix in prefixes ), "Editing this post online is not permitted by server configuration." # set defaults data = { "title": None, "status": current_repo.PostStatus.DRAFT.value, "markdown": request.args.get("markdown"), "thumbnail": "", "can_approve": 0, "username": current_user.identifier, "created_at": datetime.now(), "updated_at": datetime.now(), "authors": [current_user.identifier], "comments": [], "tldr": request.args.get("tldr"), } if path is not None and path in current_repo: kp = current_repo.post(path) data.update(kp.headers) data["status"] = kp.status.value data["path"] = path data["markdown"] = kp.read(images=False, headers=False) # retrieve reviews post = db_session.query(Post).filter(Post.path == path).first() if post: # post may have not been indexed yet data["comments"] = ( db_session.query(Comment) .filter(Comment.post_id == post.id) .filter(Comment.type == "review") .all() ) if ( current_user.identifier not in data["authors"] or current_user.identifier in current_repo.config.editors ): data["can_approve"] = 1 data["created_at"] = data["created_at"] data["updated_at"] = data["updated_at"] data["authors"] = json.dumps(data.get("authors")) data["tags"] = json.dumps(data.get("tags", [])) logger.info(data) if "proxy" in data or request.args.get("proxy", False): return render_template("post_editor_proxy.html", **data) if "ipynb" in data or request.args.get("ipynb", False): data["ipynb"] = True return render_template("post_editor_ipynb.html", **data) return render_template("post_editor_markdown.html", **data) @blueprint.route("/ajax/editor/save", methods=["GET", "POST"]) @PageView.logged @permissions.post_edit.require() def save_post(): """Save the post""" data = request.get_json() path = data["path"] prefixes = current_app.config["WEB_EDITOR_PREFIXES"] if prefixes == []: raise Exception("Web editing is not configured") if prefixes is not None: if not any([path.startswith(prefix) for prefix in prefixes]): return get_warning_msg(f"Your post path must begin with one of {prefixes}") # TODO better handling of overwriting kp = None if path in current_repo: kp = current_repo.post(path) if ( current_user.identifier not in kp.headers["authors"] and current_user.identifier not in current_repo.config.editors ): return get_warning_msg( f"Post with path {path} already exists and you are not " "an author!\nPlease try a different path" ) # create the knowledge post kp = kp or KnowledgePost(path=path) headers = {} headers["created_at"] = datetime.strptime(data["created_at"], "%Y-%m-%d").date() headers["updated_at"] = datetime.strptime(data["updated_at"], "%Y-%m-%d").date() headers["title"] = data["title"] headers["path"] = data["path"] # TODO: thumbnail header not working currently, as feed image set # with kp method not based on header headers["thumbnail"] = data.get("feed_image", "") headers["authors"] = [auth.strip() for auth in data["author"]] headers["tldr"] = data["tldr"] headers["tags"] = [tag.strip() for tag in data.get("tags", [])] if "proxy" in data: headers["proxy"] = data["proxy"] if "ipynb" in data: headers["ipynb"] = data["ipynb"] if ( data.get("file_name", None) is not None and data.get("file_data", None) is not None ): # save file to local env with open(data["file_name"], "w") as text_file: text_file.write(data["file_data"]) # add to repo current_repo.save(data["file_name"], path) response = s3_upload(data["file_name"], data["file_data"]) if response is None: error_msg = "ERROR during upload file to s3" logger.error(error_msg) return get_error_msg(error_msg) else: headers["display_link"] = response else: headers["display_link"] = data["display_link"] kp.write(unquote(data["markdown"]), headers=headers) # add to repo current_repo.add(kp, update=True, message=headers["title"]) # THIS IS DANGEROUS # add into notion database if "ipynb" in data: create_page(notion_client=notion_client, database_id=current_app.config.get("NOTION_DATABASE_ID", ""), params=headers) update_index() return json.dumps({"path": path}) @blueprint.route("/ajax/editor/submit", methods=["GET", "POST"]) @PageView.logged @permissions.post_edit.require() def submit_for_review(): """Submit post and if there are reviewers assigned, email them""" path = request.args.get("path", None) data = request.get_json() current_repo.submit(path) # email the reviewers reviewers = data.get("post_reviewers", None) if reviewers: for r in reviewers.split(","): send_reviewer_request_email(path=path, reviewer=r) update_index() return "OK" @blueprint.route("/ajax/editor/publish", methods=["GET", "POST"]) @PageView.logged @permissions.post_edit.require() def publish_post(): """Publish the post by changing the status""" path = request.args.get("path", None) if path not in current_repo: return get_warning_msg(f"Unable to retrieve post with path = {path}!") current_repo.publish(path) update_index(check_timeouts=False) return "OK" @blueprint.route("/ajax/editor/unpublish", methods=["GET", "POST"]) @PageView.logged @permissions.post_edit.require() def unpublish_post(): """Unpublish the post""" path = request.args.get("path", None) if path not in current_repo: return get_warning_msg(f"Unable to retrieve post with path = {path}!") current_repo.unpublish(path) update_index(check_timeouts=False) return "OK" @blueprint.route("/ajax/editor/accept", methods=["GET", "POST"]) @PageView.logged @permissions.post_edit.require() def accept(): """Accept the post""" path = request.args.get("path", None) if path not in current_repo: return get_warning_msg(f"Unable to retrieve post with path = {path}!") current_repo.accept(path) update_index() return "OK" @blueprint.route("/ajax/editor/delete", methods=["GET", "POST"]) @PageView.logged @permissions.post_edit.require() def delete_post(): """Delete a post""" path = request.args.get("path", None) if path not in current_repo: return get_warning_msg(f"Unable to retrieve post with path = {path}!") kp = current_repo.post(path) if current_user.identifier not in kp.headers["authors"]: return get_warning_msg("You can only delete a post where you are an author!") current_repo.remove(path) update_index(check_timeouts=False) return "OK" @blueprint.route("/ajax/editor/review", methods=["POST", "DELETE"]) @PageView.logged @permissions.post_edit.require() def review_comment(): """ Saves a review and sends an email that the post has been reviewed to the author of the post or deletes a submitted review """ if request.method == "POST": path = request.args.get("path", None) post_id = db_session.query(Post).filter(Post.path == path).first().id comment = Comment() comment.text = request.get_json()["text"] comment.user_id = current_user.id comment.post_id = post_id comment.type = "review" db_session.add(comment) db_session.commit() send_review_email( path=path, commenter=current_user.identifier, comment_text=comment.text ) elif request.method == "DELETE": comment = Comment.query.get(int(request.args.get("comment_id", ""))) if comment and current_user.id == comment.user_id: db_session.delete(comment) db_session.commit() return "OK" def s3_upload(file_name, file_data): """Upload file(s) to AWS s3 path and return the display link in the response""" if file_name is None or file_data is None or file_data is "": return get_warning_msg(f"File name is empty. Please re-upload!") response = put_object_to_s3(s3_client, file_data, AWS_S3_BUCKET, file_name) # create a html version of this file if ".ipynb" in file_name: with io.StringIO(file_data) as f: nb = nbformat.read(f, as_version=4) # export to html html_exporter = HTMLExporter() (html_data, resources) = html_exporter.from_notebook_node(nb) html_file_name = file_name.replace(".ipynb", ".html") response = put_object_to_s3( s3_client, html_data, AWS_S3_BUCKET, html_file_name, "text/html", ) if response: display_link = "https://s3.us-west-2.amazonaws.com/{0}/{1}".format( AWS_S3_BUCKET, html_file_name ) # todo: make s3 region name be configurable return display_link return None # DEPRECATED @blueprint.route("/file_upload", methods=["POST", "GET"]) @PageView.logged @permissions.post_edit.require() def file_upload(): """ Uploads images dropped on the web editor's markdown box to static/images and notifies editors by email """ upload_folder = "images" title = request.form["title"] files = request.files uploadedFiles = [] if files: for img_file in files.values(): filename = secure_filename(title + "_" + img_file.filename).lower() dst_folder = os.path.join(current_app.static_folder, upload_folder) if is_allowed_image_format(img_file): try: img_file.save(os.path.join(dst_folder, filename)) send_from_directory(dst_folder, filename) uploadedFiles += [ url_for( "static", filename=os.path.join(upload_folder, filename) ) ] except Exception as e: error_msg = f"ERROR during image upload: {e}" logger.error(error_msg) return get_error_msg(error_msg) elif is_pdf(filename): from PyPDF2 import PdfFileReader try: src_pdf = PdfFileReader(img_file) filename = os.path.splitext(filename)[0] num_pages = src_pdf.getNumPages() for page_num in range(num_pages): page_png = pdf_page_to_png(src_pdf, page_num) page_name = "{filename}_{page_num}.jpg".format(**locals()) page_png.save(filename=os.path.join(dst_folder, page_name)) uploadedFiles += [ url_for( "static", filename=os.path.join(upload_folder, page_name), ) ] except Exception as e: error_msg = f"ERROR during pdf upload: {e}" logger.error(error_msg) return get_error_msg(error_msg) return json.dumps({"links": uploadedFiles, "success": True})

from .. import permissions from ..index import update_index from ..models import Comment, PageView, Post, PostAuthorAssoc from ..proxies import current_repo, current_user, db_session, s3_client, notion_client from ..utils.emails import ( send_review_email, send_reviewer_request_email, ) from ..utils.image import ( is_allowed_image_format, is_pdf, pdf_page_to_png, ) from ..utils.shared import get_blueprint from datetime import datetime from flask import ( current_app, render_template, request, send_from_directory, url_for, ) from knowledge_repo.post import KnowledgePost from sqlalchemy import or_ from urllib.parse import unquote from werkzeug.utils import secure_filename import json import logging import os from knowledge_repo.utils.s3 import put_object_to_s3 import nbformat from nbconvert import HTMLExporter import io from knowledge_repo.constants import AWS_S3_BUCKET from knowledge_repo.utils.notion import create_page logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) blueprint = get_blueprint("editor", __name__) def get_warning_msg(msg): return json.dumps({"msg": msg, "success": False}) def get_error_msg(msg): return json.dumps({"error_msg": msg, "success": False}) # TODO: These functions have not been fully married # to the KnowledgePost API # Currently, backended by Post objects but partially # implemented on KnowledgePost API # TODO: Deprecate this route in favour of integrating editing # links into primary index pages and user pages @blueprint.route("/webposts", methods=["GET"]) @PageView.logged @permissions.post_edit.require() def gitless_drafts(): """Render the gitless posts that a user has created in table form Editors can see all the posts created via Gitless_Editing """ prefixes = current_app.config.get("WEB_EDITOR_PREFIXES", []) if prefixes == []: raise Exception("Web editing is not configured") query = db_session.query(Post) if prefixes is not None: query = query.filter(or_(*[Post.path.like(p + "%") for p in prefixes])) if current_user.identifier not in current_repo.config.editors: query = query.outerjoin( PostAuthorAssoc, PostAuthorAssoc.post_id == Post.id ).filter(PostAuthorAssoc.user_id == current_user.id) return render_template("web_posts.html", posts=query.all()) @blueprint.route("/edit") @blueprint.route("/edit/<path:path>", methods=["GET", "POST"]) @PageView.logged @permissions.post_edit.require() def editor(path=None): """Render the web post editor, either with the default values or if the post already exists, with what has been saved""" prefixes = current_app.config.get("WEB_EDITOR_PREFIXES", None) if prefixes is not None: assert path is None or any( path.startswith(prefix) for prefix in prefixes ), "Editing this post online is not permitted by server configuration." # set defaults data = { "title": None, "status": current_repo.PostStatus.DRAFT.value, "markdown": request.args.get("markdown"), "thumbnail": "", "can_approve": 0, "username": current_user.identifier, "created_at": datetime.now(), "updated_at": datetime.now(), "authors": [current_user.identifier], "comments": [], "tldr": request.args.get("tldr"), } if path is not None and path in current_repo: kp = current_repo.post(path) data.update(kp.headers) data["status"] = kp.status.value data["path"] = path data["markdown"] = kp.read(images=False, headers=False) # retrieve reviews post = db_session.query(Post).filter(Post.path == path).first() if post: # post may have not been indexed yet data["comments"] = ( db_session.query(Comment) .filter(Comment.post_id == post.id) .filter(Comment.type == "review") .all() ) if ( current_user.identifier not in data["authors"] or current_user.identifier in current_repo.config.editors ): data["can_approve"] = 1 data["created_at"] = data["created_at"] data["updated_at"] = data["updated_at"] data["authors"] = json.dumps(data.get("authors")) data["tags"] = json.dumps(data.get("tags", [])) logger.info(data) if "proxy" in data or request.args.get("proxy", False): return render_template("post_editor_proxy.html", **data) if "ipynb" in data or request.args.get("ipynb", False): data["ipynb"] = True return render_template("post_editor_ipynb.html", **data) return render_template("post_editor_markdown.html", **data) @blueprint.route("/ajax/editor/save", methods=["GET", "POST"]) @PageView.logged @permissions.post_edit.require() def save_post(): """Save the post""" data = request.get_json() path = data["path"] prefixes = current_app.config["WEB_EDITOR_PREFIXES"] if prefixes == []: raise Exception("Web editing is not configured") if prefixes is not None: if not any([path.startswith(prefix) for prefix in prefixes]): return get_warning_msg(f"Your post path must begin with one of {prefixes}") # TODO better handling of overwriting kp = None if path in current_repo: kp = current_repo.post(path) if ( current_user.identifier not in kp.headers["authors"] and current_user.identifier not in current_repo.config.editors ): return get_warning_msg( f"Post with path {path} already exists and you are not " "an author!\nPlease try a different path" ) # create the knowledge post kp = kp or KnowledgePost(path=path) headers = {} headers["created_at"] = datetime.strptime(data["created_at"], "%Y-%m-%d").date() headers["updated_at"] = datetime.strptime(data["updated_at"], "%Y-%m-%d").date() headers["title"] = data["title"] headers["path"] = data["path"] # TODO: thumbnail header not working currently, as feed image set # with kp method not based on header headers["thumbnail"] = data.get("feed_image", "") headers["authors"] = [auth.strip() for auth in data["author"]] headers["tldr"] = data["tldr"] headers["tags"] = [tag.strip() for tag in data.get("tags", [])] if "proxy" in data: headers["proxy"] = data["proxy"] if "ipynb" in data: headers["ipynb"] = data["ipynb"] if ( data.get("file_name", None) is not None and data.get("file_data", None) is not None ): # save file to local env with open(data["file_name"], "w") as text_file: text_file.write(data["file_data"]) # add to repo kp = current_repo.save(data["file_name"], path) # upload to s3 response = s3_upload(data["file_name"], path, data["file_data"]) if response is None: error_msg = "ERROR during upload file to s3" logger.error(error_msg) return get_error_msg(error_msg) else: headers["display_link"] = response else: headers["display_link"] = data["display_link"] # generate dummp md for post redirect if "ipynb" not in data: kp.write(unquote(data["markdown"]), headers=headers) # add to repo current_repo.add(kp, update=True, message=headers["title"]) # THIS IS DANGEROUS # add into notion database if "ipynb" in data: create_page(notion_client=notion_client, database_id=current_app.config.get("NOTION_DATABASE_ID", ""), params=headers) update_index() return json.dumps({"path": path}) @blueprint.route("/ajax/editor/submit", methods=["GET", "POST"]) @PageView.logged @permissions.post_edit.require() def submit_for_review(): """Submit post and if there are reviewers assigned, email them""" path = request.args.get("path", None) data = request.get_json() current_repo.submit(path) # email the reviewers reviewers = data.get("post_reviewers", None) if reviewers: for r in reviewers.split(","): send_reviewer_request_email(path=path, reviewer=r) update_index() return "OK" @blueprint.route("/ajax/editor/publish", methods=["GET", "POST"]) @PageView.logged @permissions.post_edit.require() def publish_post(): """Publish the post by changing the status""" path = request.args.get("path", None) if path not in current_repo: return get_warning_msg(f"Unable to retrieve post with path = {path}!") current_repo.publish(path) update_index(check_timeouts=False) return "OK" @blueprint.route("/ajax/editor/unpublish", methods=["GET", "POST"]) @PageView.logged @permissions.post_edit.require() def unpublish_post(): """Unpublish the post""" path = request.args.get("path", None) if path not in current_repo: return get_warning_msg(f"Unable to retrieve post with path = {path}!") current_repo.unpublish(path) update_index(check_timeouts=False) return "OK" @blueprint.route("/ajax/editor/accept", methods=["GET", "POST"]) @PageView.logged @permissions.post_edit.require() def accept(): """Accept the post""" path = request.args.get("path", None) if path not in current_repo: return get_warning_msg(f"Unable to retrieve post with path = {path}!") current_repo.accept(path) update_index() return "OK" @blueprint.route("/ajax/editor/delete", methods=["GET", "POST"]) @PageView.logged @permissions.post_edit.require() def delete_post(): """Delete a post""" path = request.args.get("path", None) if path not in current_repo: return get_warning_msg(f"Unable to retrieve post with path = {path}!") kp = current_repo.post(path) if current_user.identifier not in kp.headers["authors"]: return get_warning_msg("You can only delete a post where you are an author!") current_repo.remove(path) update_index(check_timeouts=False) return "OK" @blueprint.route("/ajax/editor/review", methods=["POST", "DELETE"]) @PageView.logged @permissions.post_edit.require() def review_comment(): """ Saves a review and sends an email that the post has been reviewed to the author of the post or deletes a submitted review """ if request.method == "POST": path = request.args.get("path", None) post_id = db_session.query(Post).filter(Post.path == path).first().id comment = Comment() comment.text = request.get_json()["text"] comment.user_id = current_user.id comment.post_id = post_id comment.type = "review" db_session.add(comment) db_session.commit() send_review_email( path=path, commenter=current_user.identifier, comment_text=comment.text ) elif request.method == "DELETE": comment = Comment.query.get(int(request.args.get("comment_id", ""))) if comment and current_user.id == comment.user_id: db_session.delete(comment) db_session.commit() return "OK" def s3_upload(file_name, path, file_data): """Upload file(s) to AWS s3 path and return the display link in the response""" if file_name is None or file_data is None or file_data == "": return get_warning_msg(f"File name is empty. Please re-upload!") response = put_object_to_s3(s3_client, file_data, AWS_S3_BUCKET, file_name) # create a html version of this file if ".ipynb" in file_name: with io.StringIO(file_data) as f: nb = nbformat.read(f, as_version=4) # export to html html_exporter = HTMLExporter() (html_data, resources) = html_exporter.from_notebook_node(nb) html_file_name = file_name.replace(".ipynb", ".html") response = put_object_to_s3( s3_client, html_data, AWS_S3_BUCKET, os.path.join(path + '.kp/' + html_file_name), "text/html", ) if response: display_link = "https://s3.us-west-2.amazonaws.com/{0}/{1}".format( AWS_S3_BUCKET, html_file_name ) # todo: make s3 region name be configurable return display_link return None # DEPRECATED @blueprint.route("/file_upload", methods=["POST", "GET"]) @PageView.logged @permissions.post_edit.require() def file_upload(): """ Uploads images dropped on the web editor's markdown box to static/images and notifies editors by email """ upload_folder = "images" title = request.form["title"] files = request.files uploadedFiles = [] if files: for img_file in files.values(): filename = secure_filename(title + "_" + img_file.filename).lower() dst_folder = os.path.join(current_app.static_folder, upload_folder) if is_allowed_image_format(img_file): try: img_file.save(os.path.join(dst_folder, filename)) send_from_directory(dst_folder, filename) uploadedFiles += [ url_for( "static", filename=os.path.join(upload_folder, filename) ) ] except Exception as e: error_msg = f"ERROR during image upload: {e}" logger.error(error_msg) return get_error_msg(error_msg) elif is_pdf(filename): from PyPDF2 import PdfFileReader try: src_pdf = PdfFileReader(img_file) filename = os.path.splitext(filename)[0] num_pages = src_pdf.getNumPages() for page_num in range(num_pages): page_png = pdf_page_to_png(src_pdf, page_num) page_name = "{filename}_{page_num}.jpg".format(**locals()) page_png.save(filename=os.path.join(dst_folder, page_name)) uploadedFiles += [ url_for( "static", filename=os.path.join(upload_folder, page_name), ) ] except Exception as e: error_msg = f"ERROR during pdf upload: {e}" logger.error(error_msg) return get_error_msg(error_msg) return json.dumps({"links": uploadedFiles, "success": True})

JJJ000

43635f53c93ceb0f1568c9fdda796e88a80bb2d0

1070404b926ac5f1e1c27a246d4fac95d3cd3518

Use `os.path.join()` as explained in: https://www.geeksforgeeks.org/python-os-path-join-method/ ?

csharplus

airbnb/knowledge-repo

update notion db id

Description of changeset: as title Test Plan: local dev Reviewers: @csharplus @JJJ000

2023-01-20 01:59:00+00:00

2023-01-21 19:36:20+00:00

knowledge_repo/utils/notion.py

from notion_client import Client, AsyncClient import logging from notion_client import APIResponseError from knowledge_repo.constants import KP_EDIT_PROD_LINK logger = logging.getLogger(__name__) def get_notion_client(auth): """Get a notion synchronous client for notion synchronous operations :param auth: Bearer token for authentication :return: a notion client for notion sync operations """ return Client(auth=auth) def get_notion_async_client(auth): """Get a notion asynchronous client for notion asynchronous operations :param auth: Bearer token for authentication :return: a notion async client for notion async operations """ return AsyncClient(auth=auth) def query_page(notion_client, page_id): """Retrieve a Page object using the page ID specified :param notion_client: a notion client :param pag_id: Identifier for a Notion page :return: page object if found, else False """ try: logger.info(notion_client.pages.retrieve(page_id)) except APIResponseError as error: logging.error(error) return False def create_page(notion_client, params): """Create a new page in the specified database :param notion_client: a notion client :param params: property values of this page. :return: True if page was created, else False """ name = params.get("title", None) description = params.get("tldr", "") tags = [{"name": t} for t in params.get("tags", [])] path = params.get("path", "") if len(path) > 0: post_link = "/".join([KP_EDIT_PROD_LINK, path]) logger.info(post_link) else: post_link = "" file_link = params.get("display_link", "") if name is None: logger.error("Page Name is Empty") return False try: notion_client.pages.create( parent={ "type": "database_id", "database_id": "85db4c309b6d4d00a6a1df19d56496b6", }, properties={ "Name": {"title": [{"text": {"content": name}}]}, "Description": {"rich_text": [{"text": {"content": description}}]}, "Tags": {"multi_select": tags}, "Knowledge Repo Link": { "rich_text": [ {"text": {"content": post_link, "link": {"url": post_link}}}, ] }, "Original File Link": {"rich_text": [{"text": {"content": file_link}}]}, }, ) except APIResponseError as error: logging.error(error) return False return True

from notion_client import Client, AsyncClient import logging from notion_client import APIResponseError from knowledge_repo.constants import KP_EDIT_PROD_LINK logger = logging.getLogger(__name__) def get_notion_client(auth): """Get a notion synchronous client for notion synchronous operations :param auth: Bearer token for authentication :return: a notion client for notion sync operations """ return Client(auth=auth) def get_notion_async_client(auth): """Get a notion asynchronous client for notion asynchronous operations :param auth: Bearer token for authentication :return: a notion async client for notion async operations """ return AsyncClient(auth=auth) def query_page(notion_client, page_id): """Retrieve a Page object using the page ID specified :param notion_client: a notion client :param pag_id: Identifier for a Notion page :return: page object if found, else False """ try: logger.info(notion_client.pages.retrieve(page_id)) except APIResponseError as error: logging.error(error) return False def create_page(notion_client, database_id, params): """Create a new page in the specified database :param notion_client: a notion client :param params: property values of this page. :return: True if page was created, else False """ name = params.get("title", None) description = params.get("tldr", "") tags = [{"name": t} for t in params.get("tags", [])] path = params.get("path", "") if len(path) > 0: post_link = "/".join([KP_EDIT_PROD_LINK, path]) logger.info(post_link) else: post_link = "" file_link = params.get("display_link", "") if name is None: logger.error("Page Name is Empty") return False try: notion_client.pages.create( parent={ "type": "database_id", "database_id": database_id, }, properties={ "Name": {"title": [{"text": {"content": name}}]}, "Description": {"rich_text": [{"text": {"content": description}}]}, "Tags": {"multi_select": tags}, "Knowledge Repo Link": { "rich_text": [ {"text": {"content": post_link, "link": {"url": post_link}}}, ] }, "Original File Link": {"rich_text": [{"text": {"content": file_link}}]}, }, ) except APIResponseError as error: logging.error(error) return False return True

mengting1010

582fc923aff96253440a15de493f950f8195cca6

58155530b5fe639d54f6797cd6e8b55ac4a753fe

we probably need to add it to config

JJJ000

airbnb/knowledge-repo

update notion db id

Description of changeset: as title Test Plan: local dev Reviewers: @csharplus @JJJ000

2023-01-20 01:59:00+00:00

2023-01-21 19:36:20+00:00

knowledge_repo/utils/notion.py

from notion_client import Client, AsyncClient import logging from notion_client import APIResponseError from knowledge_repo.constants import KP_EDIT_PROD_LINK logger = logging.getLogger(__name__) def get_notion_client(auth): """Get a notion synchronous client for notion synchronous operations :param auth: Bearer token for authentication :return: a notion client for notion sync operations """ return Client(auth=auth) def get_notion_async_client(auth): """Get a notion asynchronous client for notion asynchronous operations :param auth: Bearer token for authentication :return: a notion async client for notion async operations """ return AsyncClient(auth=auth) def query_page(notion_client, page_id): """Retrieve a Page object using the page ID specified :param notion_client: a notion client :param pag_id: Identifier for a Notion page :return: page object if found, else False """ try: logger.info(notion_client.pages.retrieve(page_id)) except APIResponseError as error: logging.error(error) return False def create_page(notion_client, params): """Create a new page in the specified database :param notion_client: a notion client :param params: property values of this page. :return: True if page was created, else False """ name = params.get("title", None) description = params.get("tldr", "") tags = [{"name": t} for t in params.get("tags", [])] path = params.get("path", "") if len(path) > 0: post_link = "/".join([KP_EDIT_PROD_LINK, path]) logger.info(post_link) else: post_link = "" file_link = params.get("display_link", "") if name is None: logger.error("Page Name is Empty") return False try: notion_client.pages.create( parent={ "type": "database_id", "database_id": "85db4c309b6d4d00a6a1df19d56496b6", }, properties={ "Name": {"title": [{"text": {"content": name}}]}, "Description": {"rich_text": [{"text": {"content": description}}]}, "Tags": {"multi_select": tags}, "Knowledge Repo Link": { "rich_text": [ {"text": {"content": post_link, "link": {"url": post_link}}}, ] }, "Original File Link": {"rich_text": [{"text": {"content": file_link}}]}, }, ) except APIResponseError as error: logging.error(error) return False return True

from notion_client import Client, AsyncClient import logging from notion_client import APIResponseError from knowledge_repo.constants import KP_EDIT_PROD_LINK logger = logging.getLogger(__name__) def get_notion_client(auth): """Get a notion synchronous client for notion synchronous operations :param auth: Bearer token for authentication :return: a notion client for notion sync operations """ return Client(auth=auth) def get_notion_async_client(auth): """Get a notion asynchronous client for notion asynchronous operations :param auth: Bearer token for authentication :return: a notion async client for notion async operations """ return AsyncClient(auth=auth) def query_page(notion_client, page_id): """Retrieve a Page object using the page ID specified :param notion_client: a notion client :param pag_id: Identifier for a Notion page :return: page object if found, else False """ try: logger.info(notion_client.pages.retrieve(page_id)) except APIResponseError as error: logging.error(error) return False def create_page(notion_client, database_id, params): """Create a new page in the specified database :param notion_client: a notion client :param params: property values of this page. :return: True if page was created, else False """ name = params.get("title", None) description = params.get("tldr", "") tags = [{"name": t} for t in params.get("tags", [])] path = params.get("path", "") if len(path) > 0: post_link = "/".join([KP_EDIT_PROD_LINK, path]) logger.info(post_link) else: post_link = "" file_link = params.get("display_link", "") if name is None: logger.error("Page Name is Empty") return False try: notion_client.pages.create( parent={ "type": "database_id", "database_id": database_id, }, properties={ "Name": {"title": [{"text": {"content": name}}]}, "Description": {"rich_text": [{"text": {"content": description}}]}, "Tags": {"multi_select": tags}, "Knowledge Repo Link": { "rich_text": [ {"text": {"content": post_link, "link": {"url": post_link}}}, ] }, "Original File Link": {"rich_text": [{"text": {"content": file_link}}]}, }, ) except APIResponseError as error: logging.error(error) return False return True

mengting1010

582fc923aff96253440a15de493f950f8195cca6

58155530b5fe639d54f6797cd6e8b55ac4a753fe

Updated, PTAL again. Thanks!

mengting1010

airbnb/knowledge-repo

[kp] update s3 repo

Description of changeset: update s3 repo Test Plan: [x] CI Reviewers: @csharplus @mengting1010

2023-01-02 01:35:26+00:00

2023-01-06 03:37:10+00:00

requirements.txt

boto3==1.26.37 botocore==1.29.37 cooked_input flask==2.1.2 Flask-Migrate gitdb gitpython==3.1.30 tabulate==0.8.9 pyyaml markdown==3.3.4 pygments==2.10.0 pyyaml flask_login==0.6.1 flask_principal flask_mail gunicorn inflection pillow psycopg2 nbformat nbconvert[execute] traitlets ldap3 requests requests_oauthlib weasyprint jinja2>=2.7,<=3.0.3 werkzeug>=1.0,<=2.0.3 multiprocess importlib-metadata==4.13.0 sqlalchemy==1.4.37 weasyprint==54.3

boto3==1.26.37 botocore==1.29.37 cooked_input flask==2.1.2 Flask-Migrate gitdb gitpython==3.1.30 tabulate==0.8.9 pyyaml markdown==3.3.4 pygments==2.10.0 pyyaml flask_login==0.6.1 flask_principal flask_mail gunicorn inflection pillow psycopg2 nbformat nbconvert[execute] traitlets ldap3 requests requests_oauthlib weasyprint jinja2>=2.7,<=3.0.3 werkzeug>=1.0,<=2.0.3 multiprocess importlib-metadata==4.13.0 sqlalchemy==1.4.37 weasyprint==54.3 s3path==0.3.4

JJJ000

c255ede148aef3f804a293972a21b9d7b2419326

00d51151f35a0dccf7dae17812331fdc0065f1ca

Please lock the version of the new library to avoid unexpected breaks when the library updates in the future.

csharplus

airbnb/knowledge-repo

Add Notion Integration

Description of changeset: as title. Test Plan: local dev Reviewers:

2022-12-31 20:38:03+00:00

2023-01-11 04:00:33+00:00

requirements.txt

boto3==1.26.37 botocore==1.29.37 cooked_input flask==2.1.2 Flask-Migrate gitdb gitpython==3.1.30 tabulate==0.8.9 pyyaml markdown==3.3.4 pygments==2.10.0 pyyaml flask_login==0.6.1 flask_principal flask_mail gunicorn inflection pillow psycopg2 nbformat nbconvert[execute] traitlets ldap3 requests requests_oauthlib weasyprint jinja2>=2.7,<=3.0.3 werkzeug>=1.0,<=2.0.3 multiprocess importlib-metadata==4.13.0 sqlalchemy==1.4.37 weasyprint==54.3 s3path==0.3.4

boto3==1.26.37 botocore==1.29.37 cooked_input flask==2.1.2 Flask-Migrate gitdb gitpython==3.1.30 tabulate==0.8.9 pyyaml markdown==3.3.4 pygments==2.10.0 pyyaml flask_login==0.6.1 flask_principal flask_mail gunicorn inflection pillow psycopg2 nbformat nbconvert[execute] traitlets ldap3 requests requests_oauthlib weasyprint jinja2>=2.7,<=3.0.3 werkzeug>=1.0,<=2.0.3 multiprocess importlib-metadata==4.13.0 sqlalchemy==1.4.37 weasyprint==54.3 s3path==0.3.4 notion-client==2.0.0

mengting1010

a875df6b4cc47024d8b3133776c7c6e8213f9daa

1ad529a84dcf923fdde97a3b7e804936f1d14007

Please add the current version number of `notion-client` as well to avoid future break changes from this library.

csharplus

airbnb/knowledge-repo

Update Jupyter Notebook Upload Related

Description of changeset: - Integrate with S3 client - upload Jupyter Notebook to s3 when saving the post - export a html version of Jupyter Notebook and upload to s3 Test Plan: local dev Reviewers: @csharplus @JJJ000

2022-12-28 23:02:53+00:00

2022-12-29 07:10:50+00:00

knowledge_repo/app/routes/editor.py

from .. import permissions from ..index import update_index from ..models import Comment, PageView, Post, PostAuthorAssoc from ..proxies import current_repo, current_user, db_session from ..utils.emails import ( send_review_email, send_reviewer_request_email, ) from ..utils.image import ( is_allowed_image_format, is_pdf, pdf_page_to_png, ) from ..utils.shared import get_blueprint from datetime import datetime from flask import ( current_app, render_template, request, send_from_directory, url_for, ) from knowledge_repo.post import KnowledgePost from sqlalchemy import or_ from urllib.parse import unquote from werkzeug.utils import secure_filename import json import logging import os logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) blueprint = get_blueprint("editor", __name__) def get_warning_msg(msg): return json.dumps({'msg': msg, 'success': False}) def get_error_msg(msg): return json.dumps({'error_msg': msg, 'success': False}) # TODO: These functions have not been fully married # to the KnowledgePost API # Currently, backended by Post objects but partially # implemented on KnowledgePost API # TODO: Deprecate this route in favour of integrating editing # links into primary index pages and user pages @blueprint.route('/webposts', methods=['GET']) @PageView.logged @permissions.post_edit.require() def gitless_drafts(): """ Render the gitless posts that a user has created in table form Editors can see all the posts created via Gitless_Editing """ prefixes = current_app.config.get('WEB_EDITOR_PREFIXES', []) if prefixes == []: raise Exception('Web editing is not configured') query = (db_session.query(Post)) if prefixes is not None: query = query.filter(or_(*[Post.path.like(p + '%') for p in prefixes])) if current_user.identifier not in current_repo.config.editors: query = (query.outerjoin( PostAuthorAssoc, PostAuthorAssoc.post_id == Post.id) .filter(PostAuthorAssoc.user_id == current_user.id)) return render_template('web_posts.html', posts=query.all()) @blueprint.route('/edit') @blueprint.route('/edit/<path:path>', methods=['GET', 'POST']) @PageView.logged @permissions.post_edit.require() def editor(path=None): """ Render the web post editor, either with the default values or if the post already exists, with what has been saved """ prefixes = current_app.config.get('WEB_EDITOR_PREFIXES', None) if prefixes is not None: assert ( path is None or any(path.startswith(prefix) for prefix in prefixes) ), 'Editing this post online is not permitted by server configuration.' # set defaults data = {'title': None, 'status': current_repo.PostStatus.DRAFT.value, 'markdown': request.args.get('markdown'), 'thumbnail': '', 'can_approve': 0, 'username': current_user.identifier, 'created_at': datetime.now(), 'updated_at': datetime.now(), 'authors': [current_user.identifier], 'comments': [], 'tldr': request.args.get('tldr'), } if path is not None and path in current_repo: kp = current_repo.post(path) data.update(kp.headers) data['status'] = kp.status.value data['path'] = path data['markdown'] = kp.read(images=False, headers=False) # retrieve reviews post = db_session.query(Post).filter(Post.path == path).first() if post: # post may have not been indexed yet data['comments'] = (db_session.query(Comment) .filter(Comment.post_id == post.id) .filter(Comment.type == 'review') .all()) if current_user.identifier not in data['authors'] \ or current_user.identifier in current_repo.config.editors: data['can_approve'] = 1 data['created_at'] = data['created_at'] data['updated_at'] = data['updated_at'] data['authors'] = json.dumps(data.get('authors')) data['tags'] = json.dumps(data.get('tags', [])) if "proxy" in data or request.args.get("proxy", False): return render_template("post_editor_proxy.html", **data) if "ipynb" in data or request.args.get("ipynb", False): return render_template("post_editor_ipynb.html", **data) return render_template("post_editor_markdown.html", **data) @blueprint.route('/ajax/editor/save', methods=['GET', 'POST']) @PageView.logged @permissions.post_edit.require() def save_post(): """ Save the post """ data = request.get_json() path = data['path'] prefixes = current_app.config['WEB_EDITOR_PREFIXES'] if prefixes == []: raise Exception('Web editing is not configured') if prefixes is not None: if not any([path.startswith(prefix) for prefix in prefixes]): return get_warning_msg( f'Your post path must begin with one of {prefixes}') # TODO better handling of overwriting kp = None if path in current_repo: kp = current_repo.post(path) if current_user.identifier not in kp.headers['authors'] \ and current_user.identifier not in current_repo.config.editors: return get_warning_msg( f'Post with path {path} already exists and you are not ' 'an author!\nPlease try a different path') # create the knowledge post kp = kp or KnowledgePost(path=path) headers = {} headers['created_at'] = datetime.strptime( data['created_at'], '%Y-%m-%d').date() headers['updated_at'] = datetime.strptime( data['updated_at'], '%Y-%m-%d').date() headers['title'] = data['title'] headers['path'] = data['path'] # TODO: thumbnail header not working currently, as feed image set # with kp method not based on header headers['thumbnail'] = data.get('feed_image', '') headers['authors'] = [auth.strip() for auth in data['author']] headers['tldr'] = data['tldr'] headers['tags'] = [tag.strip() for tag in data.get('tags', [])] if 'proxy' in data: headers['proxy'] = data['proxy'] if "ipynb" in data: headers["ipynb"] = data["ipynb"] kp.write(unquote(data['markdown']), headers=headers) # add to repo current_repo.add( kp, update=True, message=headers['title']) # THIS IS DANGEROUS update_index() return json.dumps({'path': path}) @blueprint.route('/ajax/editor/submit', methods=['GET', 'POST']) @PageView.logged @permissions.post_edit.require() def submit_for_review(): """ Submit post and if there are reviewers assigned, email them""" path = request.args.get('path', None) data = request.get_json() current_repo.submit(path) # email the reviewers reviewers = data.get('post_reviewers', None) if reviewers: for r in reviewers.split(','): send_reviewer_request_email(path=path, reviewer=r) update_index() return 'OK' @blueprint.route('/ajax/editor/publish', methods=['GET', 'POST']) @PageView.logged @permissions.post_edit.require() def publish_post(): """ Publish the post by changing the status """ path = request.args.get('path', None) if path not in current_repo: return get_warning_msg(f'Unable to retrieve post with path = {path}!') current_repo.publish(path) update_index(check_timeouts=False) return 'OK' @blueprint.route('/ajax/editor/unpublish', methods=['GET', 'POST']) @PageView.logged @permissions.post_edit.require() def unpublish_post(): """ Unpublish the post """ path = request.args.get('path', None) if path not in current_repo: return get_warning_msg(f'Unable to retrieve post with path = {path}!') current_repo.unpublish(path) update_index(check_timeouts=False) return 'OK' @blueprint.route('/ajax/editor/accept', methods=['GET', 'POST']) @PageView.logged @permissions.post_edit.require() def accept(): """ Accept the post """ path = request.args.get('path', None) if path not in current_repo: return get_warning_msg(f'Unable to retrieve post with path = {path}!') current_repo.accept(path) update_index() return 'OK' @blueprint.route('/ajax/editor/delete', methods=['GET', 'POST']) @PageView.logged @permissions.post_edit.require() def delete_post(): """ Delete a post """ path = request.args.get('path', None) if path not in current_repo: return get_warning_msg(f'Unable to retrieve post with path = {path}!') kp = current_repo.post(path) if current_user.identifier not in kp.headers['authors']: return get_warning_msg( 'You can only delete a post where you are an author!') current_repo.remove(path) update_index(check_timeouts=False) return 'OK' @blueprint.route('/ajax/editor/review', methods=['POST', 'DELETE']) @PageView.logged @permissions.post_edit.require() def review_comment(): """ Saves a review and sends an email that the post has been reviewed to the author of the post or deletes a submitted review """ if request.method == 'POST': path = request.args.get('path', None) post_id = db_session.query(Post).filter(Post.path == path).first().id comment = Comment() comment.text = request.get_json()['text'] comment.user_id = current_user.id comment.post_id = post_id comment.type = 'review' db_session.add(comment) db_session.commit() send_review_email(path=path, commenter=current_user.identifier, comment_text=comment.text) elif request.method == 'DELETE': comment = Comment.query.get(int(request.args.get('comment_id', ''))) if comment and current_user.id == comment.user_id: db_session.delete(comment) db_session.commit() return 'OK' @blueprint.route("/ajax/editor/s3_upload", methods=["POST", "GET"]) @PageView.logged @permissions.post_edit.require() def s3_upload(): """Upload file(s) to AWS s3 path and return the display link in the response""" if request.method == "POST": data = request.get_json() file_name = data.get("file_name", None) object_name = os.path.basename(file_name.replace("\\", "/")) logger.info("file_name: {0} & object_name: {1}".format(file_name, object_name)) if file_name is None: return get_warning_msg(f"File name is empty. Please re-upload!") bucket = data.get("bucket", "www.knowledge-repo.com") response = True # todo: replace it with real s3 upload if response: display_link = "https://s3.us-west-2.amazonaws.com/{0}/{1}".format( bucket, object_name ) # todo: make s3 region name be configurable return json.dumps({"display_link": display_link, "success": True}) error_msg = "ERROR during upload file to s3" logger.error(error_msg) return get_error_msg(error_msg) return "OK" # DEPRECATED @blueprint.route('/file_upload', methods=['POST', 'GET']) @PageView.logged @permissions.post_edit.require() def file_upload(): """ Uploads images dropped on the web editor's markdown box to static/images and notifies editors by email """ upload_folder = 'images' title = request.form['title'] files = request.files uploadedFiles = [] if files: for img_file in files.values(): filename = secure_filename(title + '_' + img_file.filename).lower() dst_folder = os.path.join(current_app.static_folder, upload_folder) if is_allowed_image_format(img_file): try: img_file.save(os.path.join(dst_folder, filename)) send_from_directory(dst_folder, filename) uploadedFiles += [url_for('static', filename=os.path.join( upload_folder, filename))] except Exception as e: error_msg = f'ERROR during image upload: {e}' logger.error(error_msg) return get_error_msg(error_msg) elif is_pdf(filename): from PyPDF2 import PdfFileReader try: src_pdf = PdfFileReader(img_file) filename = os.path.splitext(filename)[0] num_pages = src_pdf.getNumPages() for page_num in range(num_pages): page_png = pdf_page_to_png(src_pdf, page_num) page_name = '{filename}_{page_num}.jpg'.format( **locals()) page_png.save(filename=os.path.join( dst_folder, page_name)) uploadedFiles += [url_for( 'static', filename=os.path.join( upload_folder, page_name))] except Exception as e: error_msg = f'ERROR during pdf upload: {e}' logger.error(error_msg) return get_error_msg(error_msg) return json.dumps({'links': uploadedFiles, 'success': True})

from .. import permissions from ..index import update_index from ..models import Comment, PageView, Post, PostAuthorAssoc from ..proxies import current_repo, current_user, db_session from ..utils.emails import ( send_review_email, send_reviewer_request_email, ) from ..utils.image import ( is_allowed_image_format, is_pdf, pdf_page_to_png, ) from ..utils.shared import get_blueprint from datetime import datetime from flask import ( current_app, render_template, request, send_from_directory, url_for, ) from knowledge_repo.post import KnowledgePost from sqlalchemy import or_ from urllib.parse import unquote from werkzeug.utils import secure_filename import json import logging import os from knowledge_repo.utils.s3 import get_s3_client, put_object_to_s3 import nbformat from nbconvert import HTMLExporter import io from knowledge_repo.constants import AWS_S3_BUCKET logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) blueprint = get_blueprint("editor", __name__) s3_client = get_s3_client("", "", "us-west-2") def get_warning_msg(msg): return json.dumps({"msg": msg, "success": False}) def get_error_msg(msg): return json.dumps({"error_msg": msg, "success": False}) # TODO: These functions have not been fully married # to the KnowledgePost API # Currently, backended by Post objects but partially # implemented on KnowledgePost API # TODO: Deprecate this route in favour of integrating editing # links into primary index pages and user pages @blueprint.route("/webposts", methods=["GET"]) @PageView.logged @permissions.post_edit.require() def gitless_drafts(): """Render the gitless posts that a user has created in table form Editors can see all the posts created via Gitless_Editing """ prefixes = current_app.config.get("WEB_EDITOR_PREFIXES", []) if prefixes == []: raise Exception("Web editing is not configured") query = db_session.query(Post) if prefixes is not None: query = query.filter(or_(*[Post.path.like(p + "%") for p in prefixes])) if current_user.identifier not in current_repo.config.editors: query = query.outerjoin( PostAuthorAssoc, PostAuthorAssoc.post_id == Post.id ).filter(PostAuthorAssoc.user_id == current_user.id) return render_template("web_posts.html", posts=query.all()) @blueprint.route("/edit") @blueprint.route("/edit/<path:path>", methods=["GET", "POST"]) @PageView.logged @permissions.post_edit.require() def editor(path=None): """Render the web post editor, either with the default values or if the post already exists, with what has been saved""" prefixes = current_app.config.get("WEB_EDITOR_PREFIXES", None) if prefixes is not None: assert path is None or any( path.startswith(prefix) for prefix in prefixes ), "Editing this post online is not permitted by server configuration." # set defaults data = { "title": None, "status": current_repo.PostStatus.DRAFT.value, "markdown": request.args.get("markdown"), "thumbnail": "", "can_approve": 0, "username": current_user.identifier, "created_at": datetime.now(), "updated_at": datetime.now(), "authors": [current_user.identifier], "comments": [], "tldr": request.args.get("tldr"), } if path is not None and path in current_repo: kp = current_repo.post(path) data.update(kp.headers) data["status"] = kp.status.value data["path"] = path data["markdown"] = kp.read(images=False, headers=False) # retrieve reviews post = db_session.query(Post).filter(Post.path == path).first() if post: # post may have not been indexed yet data["comments"] = ( db_session.query(Comment) .filter(Comment.post_id == post.id) .filter(Comment.type == "review") .all() ) if ( current_user.identifier not in data["authors"] or current_user.identifier in current_repo.config.editors ): data["can_approve"] = 1 data["created_at"] = data["created_at"] data["updated_at"] = data["updated_at"] data["authors"] = json.dumps(data.get("authors")) data["tags"] = json.dumps(data.get("tags", [])) logger.info(data) if "proxy" in data or request.args.get("proxy", False): return render_template("post_editor_proxy.html", **data) if "ipynb" in data or request.args.get("ipynb", False): return render_template("post_editor_ipynb.html", **data) return render_template("post_editor_markdown.html", **data) @blueprint.route("/ajax/editor/save", methods=["GET", "POST"]) @PageView.logged @permissions.post_edit.require() def save_post(): """Save the post""" data = request.get_json() path = data["path"] prefixes = current_app.config["WEB_EDITOR_PREFIXES"] if prefixes == []: raise Exception("Web editing is not configured") if prefixes is not None: if not any([path.startswith(prefix) for prefix in prefixes]): return get_warning_msg(f"Your post path must begin with one of {prefixes}") # TODO better handling of overwriting kp = None if path in current_repo: kp = current_repo.post(path) if ( current_user.identifier not in kp.headers["authors"] and current_user.identifier not in current_repo.config.editors ): return get_warning_msg( f"Post with path {path} already exists and you are not " "an author!\nPlease try a different path" ) # create the knowledge post kp = kp or KnowledgePost(path=path) headers = {} headers["created_at"] = datetime.strptime(data["created_at"], "%Y-%m-%d").date() headers["updated_at"] = datetime.strptime(data["updated_at"], "%Y-%m-%d").date() headers["title"] = data["title"] headers["path"] = data["path"] # TODO: thumbnail header not working currently, as feed image set # with kp method not based on header headers["thumbnail"] = data.get("feed_image", "") headers["authors"] = [auth.strip() for auth in data["author"]] headers["tldr"] = data["tldr"] headers["tags"] = [tag.strip() for tag in data.get("tags", [])] if "proxy" in data: headers["proxy"] = data["proxy"] if "ipynb" in data: headers["ipynb"] = data["ipynb"] if ( data.get("file_name", None) is not None and data.get("file_data", None) is not None ): response = s3_upload(data["file_name"], data["file_data"]) if response is None: error_msg = "ERROR during upload file to s3" logger.error(error_msg) return get_error_msg(error_msg) else: headers["display_link"] = response else: headers["display_link"] = data["display_link"] kp.write(unquote(data["markdown"]), headers=headers) # add to repo current_repo.add(kp, update=True, message=headers["title"]) # THIS IS DANGEROUS update_index() return json.dumps({"path": path}) @blueprint.route("/ajax/editor/submit", methods=["GET", "POST"]) @PageView.logged @permissions.post_edit.require() def submit_for_review(): """Submit post and if there are reviewers assigned, email them""" path = request.args.get("path", None) data = request.get_json() current_repo.submit(path) # email the reviewers reviewers = data.get("post_reviewers", None) if reviewers: for r in reviewers.split(","): send_reviewer_request_email(path=path, reviewer=r) update_index() return "OK" @blueprint.route("/ajax/editor/publish", methods=["GET", "POST"]) @PageView.logged @permissions.post_edit.require() def publish_post(): """Publish the post by changing the status""" path = request.args.get("path", None) if path not in current_repo: return get_warning_msg(f"Unable to retrieve post with path = {path}!") current_repo.publish(path) update_index(check_timeouts=False) return "OK" @blueprint.route("/ajax/editor/unpublish", methods=["GET", "POST"]) @PageView.logged @permissions.post_edit.require() def unpublish_post(): """Unpublish the post""" path = request.args.get("path", None) if path not in current_repo: return get_warning_msg(f"Unable to retrieve post with path = {path}!") current_repo.unpublish(path) update_index(check_timeouts=False) return "OK" @blueprint.route("/ajax/editor/accept", methods=["GET", "POST"]) @PageView.logged @permissions.post_edit.require() def accept(): """Accept the post""" path = request.args.get("path", None) if path not in current_repo: return get_warning_msg(f"Unable to retrieve post with path = {path}!") current_repo.accept(path) update_index() return "OK" @blueprint.route("/ajax/editor/delete", methods=["GET", "POST"]) @PageView.logged @permissions.post_edit.require() def delete_post(): """Delete a post""" path = request.args.get("path", None) if path not in current_repo: return get_warning_msg(f"Unable to retrieve post with path = {path}!") kp = current_repo.post(path) if current_user.identifier not in kp.headers["authors"]: return get_warning_msg("You can only delete a post where you are an author!") current_repo.remove(path) update_index(check_timeouts=False) return "OK" @blueprint.route("/ajax/editor/review", methods=["POST", "DELETE"]) @PageView.logged @permissions.post_edit.require() def review_comment(): """ Saves a review and sends an email that the post has been reviewed to the author of the post or deletes a submitted review """ if request.method == "POST": path = request.args.get("path", None) post_id = db_session.query(Post).filter(Post.path == path).first().id comment = Comment() comment.text = request.get_json()["text"] comment.user_id = current_user.id comment.post_id = post_id comment.type = "review" db_session.add(comment) db_session.commit() send_review_email( path=path, commenter=current_user.identifier, comment_text=comment.text ) elif request.method == "DELETE": comment = Comment.query.get(int(request.args.get("comment_id", ""))) if comment and current_user.id == comment.user_id: db_session.delete(comment) db_session.commit() return "OK" def s3_upload(file_name, file_data): """Upload file(s) to AWS s3 path and return the display link in the response""" if file_name is None or file_data is None or file_data is "": return get_warning_msg(f"File name is empty. Please re-upload!") response = put_object_to_s3(s3_client, file_data, AWS_S3_BUCKET, file_name) # create a html version of this file if ".ipynb" in file_name: with io.StringIO(file_data) as f: nb = nbformat.read(f, as_version=4) # export to html html_exporter = HTMLExporter() (html_data, resources) = html_exporter.from_notebook_node(nb) html_file_name = file_name.replace(".ipynb", ".html") response = put_object_to_s3( s3_client, html_data, AWS_S3_BUCKET, html_file_name, "text/html", ) if response: display_link = "https://s3.us-west-2.amazonaws.com/{0}/{1}".format( AWS_S3_BUCKET, html_file_name ) # todo: make s3 region name be configurable return display_link return None # DEPRECATED @blueprint.route("/file_upload", methods=["POST", "GET"]) @PageView.logged @permissions.post_edit.require() def file_upload(): """ Uploads images dropped on the web editor's markdown box to static/images and notifies editors by email """ upload_folder = "images" title = request.form["title"] files = request.files uploadedFiles = [] if files: for img_file in files.values(): filename = secure_filename(title + "_" + img_file.filename).lower() dst_folder = os.path.join(current_app.static_folder, upload_folder) if is_allowed_image_format(img_file): try: img_file.save(os.path.join(dst_folder, filename)) send_from_directory(dst_folder, filename) uploadedFiles += [ url_for( "static", filename=os.path.join(upload_folder, filename) ) ] except Exception as e: error_msg = f"ERROR during image upload: {e}" logger.error(error_msg) return get_error_msg(error_msg) elif is_pdf(filename): from PyPDF2 import PdfFileReader try: src_pdf = PdfFileReader(img_file) filename = os.path.splitext(filename)[0] num_pages = src_pdf.getNumPages() for page_num in range(num_pages): page_png = pdf_page_to_png(src_pdf, page_num) page_name = "{filename}_{page_num}.jpg".format(**locals()) page_png.save(filename=os.path.join(dst_folder, page_name)) uploadedFiles += [ url_for( "static", filename=os.path.join(upload_folder, page_name), ) ] except Exception as e: error_msg = f"ERROR during pdf upload: {e}" logger.error(error_msg) return get_error_msg(error_msg) return json.dumps({"links": uploadedFiles, "success": True})

mengting1010

451577868d66570a463260c67dff7034214beafd

6edad5351bf3f4f0abc457b6d9532ca25c62c952

You got Lint error in CI: ``` knowledge_repo/app/routes/editor.py:198:57: E502 the backslash is redundant between brackets Error: Process completed with exit code 1. ```

csharplus

airbnb/knowledge-repo

Update Jupyter Notebook Upload Related

Description of changeset: - Integrate with S3 client - upload Jupyter Notebook to s3 when saving the post - export a html version of Jupyter Notebook and upload to s3 Test Plan: local dev Reviewers: @csharplus @JJJ000

2022-12-28 23:02:53+00:00

2022-12-29 07:10:50+00:00

knowledge_repo/app/routes/editor.py

from .. import permissions from ..index import update_index from ..models import Comment, PageView, Post, PostAuthorAssoc from ..proxies import current_repo, current_user, db_session from ..utils.emails import ( send_review_email, send_reviewer_request_email, ) from ..utils.image import ( is_allowed_image_format, is_pdf, pdf_page_to_png, ) from ..utils.shared import get_blueprint from datetime import datetime from flask import ( current_app, render_template, request, send_from_directory, url_for, ) from knowledge_repo.post import KnowledgePost from sqlalchemy import or_ from urllib.parse import unquote from werkzeug.utils import secure_filename import json import logging import os logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) blueprint = get_blueprint("editor", __name__) def get_warning_msg(msg): return json.dumps({'msg': msg, 'success': False}) def get_error_msg(msg): return json.dumps({'error_msg': msg, 'success': False}) # TODO: These functions have not been fully married # to the KnowledgePost API # Currently, backended by Post objects but partially # implemented on KnowledgePost API # TODO: Deprecate this route in favour of integrating editing # links into primary index pages and user pages @blueprint.route('/webposts', methods=['GET']) @PageView.logged @permissions.post_edit.require() def gitless_drafts(): """ Render the gitless posts that a user has created in table form Editors can see all the posts created via Gitless_Editing """ prefixes = current_app.config.get('WEB_EDITOR_PREFIXES', []) if prefixes == []: raise Exception('Web editing is not configured') query = (db_session.query(Post)) if prefixes is not None: query = query.filter(or_(*[Post.path.like(p + '%') for p in prefixes])) if current_user.identifier not in current_repo.config.editors: query = (query.outerjoin( PostAuthorAssoc, PostAuthorAssoc.post_id == Post.id) .filter(PostAuthorAssoc.user_id == current_user.id)) return render_template('web_posts.html', posts=query.all()) @blueprint.route('/edit') @blueprint.route('/edit/<path:path>', methods=['GET', 'POST']) @PageView.logged @permissions.post_edit.require() def editor(path=None): """ Render the web post editor, either with the default values or if the post already exists, with what has been saved """ prefixes = current_app.config.get('WEB_EDITOR_PREFIXES', None) if prefixes is not None: assert ( path is None or any(path.startswith(prefix) for prefix in prefixes) ), 'Editing this post online is not permitted by server configuration.' # set defaults data = {'title': None, 'status': current_repo.PostStatus.DRAFT.value, 'markdown': request.args.get('markdown'), 'thumbnail': '', 'can_approve': 0, 'username': current_user.identifier, 'created_at': datetime.now(), 'updated_at': datetime.now(), 'authors': [current_user.identifier], 'comments': [], 'tldr': request.args.get('tldr'), } if path is not None and path in current_repo: kp = current_repo.post(path) data.update(kp.headers) data['status'] = kp.status.value data['path'] = path data['markdown'] = kp.read(images=False, headers=False) # retrieve reviews post = db_session.query(Post).filter(Post.path == path).first() if post: # post may have not been indexed yet data['comments'] = (db_session.query(Comment) .filter(Comment.post_id == post.id) .filter(Comment.type == 'review') .all()) if current_user.identifier not in data['authors'] \ or current_user.identifier in current_repo.config.editors: data['can_approve'] = 1 data['created_at'] = data['created_at'] data['updated_at'] = data['updated_at'] data['authors'] = json.dumps(data.get('authors')) data['tags'] = json.dumps(data.get('tags', [])) if "proxy" in data or request.args.get("proxy", False): return render_template("post_editor_proxy.html", **data) if "ipynb" in data or request.args.get("ipynb", False): return render_template("post_editor_ipynb.html", **data) return render_template("post_editor_markdown.html", **data) @blueprint.route('/ajax/editor/save', methods=['GET', 'POST']) @PageView.logged @permissions.post_edit.require() def save_post(): """ Save the post """ data = request.get_json() path = data['path'] prefixes = current_app.config['WEB_EDITOR_PREFIXES'] if prefixes == []: raise Exception('Web editing is not configured') if prefixes is not None: if not any([path.startswith(prefix) for prefix in prefixes]): return get_warning_msg( f'Your post path must begin with one of {prefixes}') # TODO better handling of overwriting kp = None if path in current_repo: kp = current_repo.post(path) if current_user.identifier not in kp.headers['authors'] \ and current_user.identifier not in current_repo.config.editors: return get_warning_msg( f'Post with path {path} already exists and you are not ' 'an author!\nPlease try a different path') # create the knowledge post kp = kp or KnowledgePost(path=path) headers = {} headers['created_at'] = datetime.strptime( data['created_at'], '%Y-%m-%d').date() headers['updated_at'] = datetime.strptime( data['updated_at'], '%Y-%m-%d').date() headers['title'] = data['title'] headers['path'] = data['path'] # TODO: thumbnail header not working currently, as feed image set # with kp method not based on header headers['thumbnail'] = data.get('feed_image', '') headers['authors'] = [auth.strip() for auth in data['author']] headers['tldr'] = data['tldr'] headers['tags'] = [tag.strip() for tag in data.get('tags', [])] if 'proxy' in data: headers['proxy'] = data['proxy'] if "ipynb" in data: headers["ipynb"] = data["ipynb"] kp.write(unquote(data['markdown']), headers=headers) # add to repo current_repo.add( kp, update=True, message=headers['title']) # THIS IS DANGEROUS update_index() return json.dumps({'path': path}) @blueprint.route('/ajax/editor/submit', methods=['GET', 'POST']) @PageView.logged @permissions.post_edit.require() def submit_for_review(): """ Submit post and if there are reviewers assigned, email them""" path = request.args.get('path', None) data = request.get_json() current_repo.submit(path) # email the reviewers reviewers = data.get('post_reviewers', None) if reviewers: for r in reviewers.split(','): send_reviewer_request_email(path=path, reviewer=r) update_index() return 'OK' @blueprint.route('/ajax/editor/publish', methods=['GET', 'POST']) @PageView.logged @permissions.post_edit.require() def publish_post(): """ Publish the post by changing the status """ path = request.args.get('path', None) if path not in current_repo: return get_warning_msg(f'Unable to retrieve post with path = {path}!') current_repo.publish(path) update_index(check_timeouts=False) return 'OK' @blueprint.route('/ajax/editor/unpublish', methods=['GET', 'POST']) @PageView.logged @permissions.post_edit.require() def unpublish_post(): """ Unpublish the post """ path = request.args.get('path', None) if path not in current_repo: return get_warning_msg(f'Unable to retrieve post with path = {path}!') current_repo.unpublish(path) update_index(check_timeouts=False) return 'OK' @blueprint.route('/ajax/editor/accept', methods=['GET', 'POST']) @PageView.logged @permissions.post_edit.require() def accept(): """ Accept the post """ path = request.args.get('path', None) if path not in current_repo: return get_warning_msg(f'Unable to retrieve post with path = {path}!') current_repo.accept(path) update_index() return 'OK' @blueprint.route('/ajax/editor/delete', methods=['GET', 'POST']) @PageView.logged @permissions.post_edit.require() def delete_post(): """ Delete a post """ path = request.args.get('path', None) if path not in current_repo: return get_warning_msg(f'Unable to retrieve post with path = {path}!') kp = current_repo.post(path) if current_user.identifier not in kp.headers['authors']: return get_warning_msg( 'You can only delete a post where you are an author!') current_repo.remove(path) update_index(check_timeouts=False) return 'OK' @blueprint.route('/ajax/editor/review', methods=['POST', 'DELETE']) @PageView.logged @permissions.post_edit.require() def review_comment(): """ Saves a review and sends an email that the post has been reviewed to the author of the post or deletes a submitted review """ if request.method == 'POST': path = request.args.get('path', None) post_id = db_session.query(Post).filter(Post.path == path).first().id comment = Comment() comment.text = request.get_json()['text'] comment.user_id = current_user.id comment.post_id = post_id comment.type = 'review' db_session.add(comment) db_session.commit() send_review_email(path=path, commenter=current_user.identifier, comment_text=comment.text) elif request.method == 'DELETE': comment = Comment.query.get(int(request.args.get('comment_id', ''))) if comment and current_user.id == comment.user_id: db_session.delete(comment) db_session.commit() return 'OK' @blueprint.route("/ajax/editor/s3_upload", methods=["POST", "GET"]) @PageView.logged @permissions.post_edit.require() def s3_upload(): """Upload file(s) to AWS s3 path and return the display link in the response""" if request.method == "POST": data = request.get_json() file_name = data.get("file_name", None) object_name = os.path.basename(file_name.replace("\\", "/")) logger.info("file_name: {0} & object_name: {1}".format(file_name, object_name)) if file_name is None: return get_warning_msg(f"File name is empty. Please re-upload!") bucket = data.get("bucket", "www.knowledge-repo.com") response = True # todo: replace it with real s3 upload if response: display_link = "https://s3.us-west-2.amazonaws.com/{0}/{1}".format( bucket, object_name ) # todo: make s3 region name be configurable return json.dumps({"display_link": display_link, "success": True}) error_msg = "ERROR during upload file to s3" logger.error(error_msg) return get_error_msg(error_msg) return "OK" # DEPRECATED @blueprint.route('/file_upload', methods=['POST', 'GET']) @PageView.logged @permissions.post_edit.require() def file_upload(): """ Uploads images dropped on the web editor's markdown box to static/images and notifies editors by email """ upload_folder = 'images' title = request.form['title'] files = request.files uploadedFiles = [] if files: for img_file in files.values(): filename = secure_filename(title + '_' + img_file.filename).lower() dst_folder = os.path.join(current_app.static_folder, upload_folder) if is_allowed_image_format(img_file): try: img_file.save(os.path.join(dst_folder, filename)) send_from_directory(dst_folder, filename) uploadedFiles += [url_for('static', filename=os.path.join( upload_folder, filename))] except Exception as e: error_msg = f'ERROR during image upload: {e}' logger.error(error_msg) return get_error_msg(error_msg) elif is_pdf(filename): from PyPDF2 import PdfFileReader try: src_pdf = PdfFileReader(img_file) filename = os.path.splitext(filename)[0] num_pages = src_pdf.getNumPages() for page_num in range(num_pages): page_png = pdf_page_to_png(src_pdf, page_num) page_name = '{filename}_{page_num}.jpg'.format( **locals()) page_png.save(filename=os.path.join( dst_folder, page_name)) uploadedFiles += [url_for( 'static', filename=os.path.join( upload_folder, page_name))] except Exception as e: error_msg = f'ERROR during pdf upload: {e}' logger.error(error_msg) return get_error_msg(error_msg) return json.dumps({'links': uploadedFiles, 'success': True})

from .. import permissions from ..index import update_index from ..models import Comment, PageView, Post, PostAuthorAssoc from ..proxies import current_repo, current_user, db_session from ..utils.emails import ( send_review_email, send_reviewer_request_email, ) from ..utils.image import ( is_allowed_image_format, is_pdf, pdf_page_to_png, ) from ..utils.shared import get_blueprint from datetime import datetime from flask import ( current_app, render_template, request, send_from_directory, url_for, ) from knowledge_repo.post import KnowledgePost from sqlalchemy import or_ from urllib.parse import unquote from werkzeug.utils import secure_filename import json import logging import os from knowledge_repo.utils.s3 import get_s3_client, put_object_to_s3 import nbformat from nbconvert import HTMLExporter import io from knowledge_repo.constants import AWS_S3_BUCKET logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) blueprint = get_blueprint("editor", __name__) s3_client = get_s3_client("", "", "us-west-2") def get_warning_msg(msg): return json.dumps({"msg": msg, "success": False}) def get_error_msg(msg): return json.dumps({"error_msg": msg, "success": False}) # TODO: These functions have not been fully married # to the KnowledgePost API # Currently, backended by Post objects but partially # implemented on KnowledgePost API # TODO: Deprecate this route in favour of integrating editing # links into primary index pages and user pages @blueprint.route("/webposts", methods=["GET"]) @PageView.logged @permissions.post_edit.require() def gitless_drafts(): """Render the gitless posts that a user has created in table form Editors can see all the posts created via Gitless_Editing """ prefixes = current_app.config.get("WEB_EDITOR_PREFIXES", []) if prefixes == []: raise Exception("Web editing is not configured") query = db_session.query(Post) if prefixes is not None: query = query.filter(or_(*[Post.path.like(p + "%") for p in prefixes])) if current_user.identifier not in current_repo.config.editors: query = query.outerjoin( PostAuthorAssoc, PostAuthorAssoc.post_id == Post.id ).filter(PostAuthorAssoc.user_id == current_user.id) return render_template("web_posts.html", posts=query.all()) @blueprint.route("/edit") @blueprint.route("/edit/<path:path>", methods=["GET", "POST"]) @PageView.logged @permissions.post_edit.require() def editor(path=None): """Render the web post editor, either with the default values or if the post already exists, with what has been saved""" prefixes = current_app.config.get("WEB_EDITOR_PREFIXES", None) if prefixes is not None: assert path is None or any( path.startswith(prefix) for prefix in prefixes ), "Editing this post online is not permitted by server configuration." # set defaults data = { "title": None, "status": current_repo.PostStatus.DRAFT.value, "markdown": request.args.get("markdown"), "thumbnail": "", "can_approve": 0, "username": current_user.identifier, "created_at": datetime.now(), "updated_at": datetime.now(), "authors": [current_user.identifier], "comments": [], "tldr": request.args.get("tldr"), } if path is not None and path in current_repo: kp = current_repo.post(path) data.update(kp.headers) data["status"] = kp.status.value data["path"] = path data["markdown"] = kp.read(images=False, headers=False) # retrieve reviews post = db_session.query(Post).filter(Post.path == path).first() if post: # post may have not been indexed yet data["comments"] = ( db_session.query(Comment) .filter(Comment.post_id == post.id) .filter(Comment.type == "review") .all() ) if ( current_user.identifier not in data["authors"] or current_user.identifier in current_repo.config.editors ): data["can_approve"] = 1 data["created_at"] = data["created_at"] data["updated_at"] = data["updated_at"] data["authors"] = json.dumps(data.get("authors")) data["tags"] = json.dumps(data.get("tags", [])) logger.info(data) if "proxy" in data or request.args.get("proxy", False): return render_template("post_editor_proxy.html", **data) if "ipynb" in data or request.args.get("ipynb", False): return render_template("post_editor_ipynb.html", **data) return render_template("post_editor_markdown.html", **data) @blueprint.route("/ajax/editor/save", methods=["GET", "POST"]) @PageView.logged @permissions.post_edit.require() def save_post(): """Save the post""" data = request.get_json() path = data["path"] prefixes = current_app.config["WEB_EDITOR_PREFIXES"] if prefixes == []: raise Exception("Web editing is not configured") if prefixes is not None: if not any([path.startswith(prefix) for prefix in prefixes]): return get_warning_msg(f"Your post path must begin with one of {prefixes}") # TODO better handling of overwriting kp = None if path in current_repo: kp = current_repo.post(path) if ( current_user.identifier not in kp.headers["authors"] and current_user.identifier not in current_repo.config.editors ): return get_warning_msg( f"Post with path {path} already exists and you are not " "an author!\nPlease try a different path" ) # create the knowledge post kp = kp or KnowledgePost(path=path) headers = {} headers["created_at"] = datetime.strptime(data["created_at"], "%Y-%m-%d").date() headers["updated_at"] = datetime.strptime(data["updated_at"], "%Y-%m-%d").date() headers["title"] = data["title"] headers["path"] = data["path"] # TODO: thumbnail header not working currently, as feed image set # with kp method not based on header headers["thumbnail"] = data.get("feed_image", "") headers["authors"] = [auth.strip() for auth in data["author"]] headers["tldr"] = data["tldr"] headers["tags"] = [tag.strip() for tag in data.get("tags", [])] if "proxy" in data: headers["proxy"] = data["proxy"] if "ipynb" in data: headers["ipynb"] = data["ipynb"] if ( data.get("file_name", None) is not None and data.get("file_data", None) is not None ): response = s3_upload(data["file_name"], data["file_data"]) if response is None: error_msg = "ERROR during upload file to s3" logger.error(error_msg) return get_error_msg(error_msg) else: headers["display_link"] = response else: headers["display_link"] = data["display_link"] kp.write(unquote(data["markdown"]), headers=headers) # add to repo current_repo.add(kp, update=True, message=headers["title"]) # THIS IS DANGEROUS update_index() return json.dumps({"path": path}) @blueprint.route("/ajax/editor/submit", methods=["GET", "POST"]) @PageView.logged @permissions.post_edit.require() def submit_for_review(): """Submit post and if there are reviewers assigned, email them""" path = request.args.get("path", None) data = request.get_json() current_repo.submit(path) # email the reviewers reviewers = data.get("post_reviewers", None) if reviewers: for r in reviewers.split(","): send_reviewer_request_email(path=path, reviewer=r) update_index() return "OK" @blueprint.route("/ajax/editor/publish", methods=["GET", "POST"]) @PageView.logged @permissions.post_edit.require() def publish_post(): """Publish the post by changing the status""" path = request.args.get("path", None) if path not in current_repo: return get_warning_msg(f"Unable to retrieve post with path = {path}!") current_repo.publish(path) update_index(check_timeouts=False) return "OK" @blueprint.route("/ajax/editor/unpublish", methods=["GET", "POST"]) @PageView.logged @permissions.post_edit.require() def unpublish_post(): """Unpublish the post""" path = request.args.get("path", None) if path not in current_repo: return get_warning_msg(f"Unable to retrieve post with path = {path}!") current_repo.unpublish(path) update_index(check_timeouts=False) return "OK" @blueprint.route("/ajax/editor/accept", methods=["GET", "POST"]) @PageView.logged @permissions.post_edit.require() def accept(): """Accept the post""" path = request.args.get("path", None) if path not in current_repo: return get_warning_msg(f"Unable to retrieve post with path = {path}!") current_repo.accept(path) update_index() return "OK" @blueprint.route("/ajax/editor/delete", methods=["GET", "POST"]) @PageView.logged @permissions.post_edit.require() def delete_post(): """Delete a post""" path = request.args.get("path", None) if path not in current_repo: return get_warning_msg(f"Unable to retrieve post with path = {path}!") kp = current_repo.post(path) if current_user.identifier not in kp.headers["authors"]: return get_warning_msg("You can only delete a post where you are an author!") current_repo.remove(path) update_index(check_timeouts=False) return "OK" @blueprint.route("/ajax/editor/review", methods=["POST", "DELETE"]) @PageView.logged @permissions.post_edit.require() def review_comment(): """ Saves a review and sends an email that the post has been reviewed to the author of the post or deletes a submitted review """ if request.method == "POST": path = request.args.get("path", None) post_id = db_session.query(Post).filter(Post.path == path).first().id comment = Comment() comment.text = request.get_json()["text"] comment.user_id = current_user.id comment.post_id = post_id comment.type = "review" db_session.add(comment) db_session.commit() send_review_email( path=path, commenter=current_user.identifier, comment_text=comment.text ) elif request.method == "DELETE": comment = Comment.query.get(int(request.args.get("comment_id", ""))) if comment and current_user.id == comment.user_id: db_session.delete(comment) db_session.commit() return "OK" def s3_upload(file_name, file_data): """Upload file(s) to AWS s3 path and return the display link in the response""" if file_name is None or file_data is None or file_data is "": return get_warning_msg(f"File name is empty. Please re-upload!") response = put_object_to_s3(s3_client, file_data, AWS_S3_BUCKET, file_name) # create a html version of this file if ".ipynb" in file_name: with io.StringIO(file_data) as f: nb = nbformat.read(f, as_version=4) # export to html html_exporter = HTMLExporter() (html_data, resources) = html_exporter.from_notebook_node(nb) html_file_name = file_name.replace(".ipynb", ".html") response = put_object_to_s3( s3_client, html_data, AWS_S3_BUCKET, html_file_name, "text/html", ) if response: display_link = "https://s3.us-west-2.amazonaws.com/{0}/{1}".format( AWS_S3_BUCKET, html_file_name ) # todo: make s3 region name be configurable return display_link return None # DEPRECATED @blueprint.route("/file_upload", methods=["POST", "GET"]) @PageView.logged @permissions.post_edit.require() def file_upload(): """ Uploads images dropped on the web editor's markdown box to static/images and notifies editors by email """ upload_folder = "images" title = request.form["title"] files = request.files uploadedFiles = [] if files: for img_file in files.values(): filename = secure_filename(title + "_" + img_file.filename).lower() dst_folder = os.path.join(current_app.static_folder, upload_folder) if is_allowed_image_format(img_file): try: img_file.save(os.path.join(dst_folder, filename)) send_from_directory(dst_folder, filename) uploadedFiles += [ url_for( "static", filename=os.path.join(upload_folder, filename) ) ] except Exception as e: error_msg = f"ERROR during image upload: {e}" logger.error(error_msg) return get_error_msg(error_msg) elif is_pdf(filename): from PyPDF2 import PdfFileReader try: src_pdf = PdfFileReader(img_file) filename = os.path.splitext(filename)[0] num_pages = src_pdf.getNumPages() for page_num in range(num_pages): page_png = pdf_page_to_png(src_pdf, page_num) page_name = "{filename}_{page_num}.jpg".format(**locals()) page_png.save(filename=os.path.join(dst_folder, page_name)) uploadedFiles += [ url_for( "static", filename=os.path.join(upload_folder, page_name), ) ] except Exception as e: error_msg = f"ERROR during pdf upload: {e}" logger.error(error_msg) return get_error_msg(error_msg) return json.dumps({"links": uploadedFiles, "success": True})

mengting1010

451577868d66570a463260c67dff7034214beafd

6edad5351bf3f4f0abc457b6d9532ca25c62c952

Same lint issue here.

csharplus

airbnb/knowledge-repo

Update Jupyter Notebook Upload Related

Description of changeset: - Integrate with S3 client - upload Jupyter Notebook to s3 when saving the post - export a html version of Jupyter Notebook and upload to s3 Test Plan: local dev Reviewers: @csharplus @JJJ000

2022-12-28 23:02:53+00:00

2022-12-29 07:10:50+00:00

knowledge_repo/app/routes/editor.py

from .. import permissions from ..index import update_index from ..models import Comment, PageView, Post, PostAuthorAssoc from ..proxies import current_repo, current_user, db_session from ..utils.emails import ( send_review_email, send_reviewer_request_email, ) from ..utils.image import ( is_allowed_image_format, is_pdf, pdf_page_to_png, ) from ..utils.shared import get_blueprint from datetime import datetime from flask import ( current_app, render_template, request, send_from_directory, url_for, ) from knowledge_repo.post import KnowledgePost from sqlalchemy import or_ from urllib.parse import unquote from werkzeug.utils import secure_filename import json import logging import os logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) blueprint = get_blueprint("editor", __name__) def get_warning_msg(msg): return json.dumps({'msg': msg, 'success': False}) def get_error_msg(msg): return json.dumps({'error_msg': msg, 'success': False}) # TODO: These functions have not been fully married # to the KnowledgePost API # Currently, backended by Post objects but partially # implemented on KnowledgePost API # TODO: Deprecate this route in favour of integrating editing # links into primary index pages and user pages @blueprint.route('/webposts', methods=['GET']) @PageView.logged @permissions.post_edit.require() def gitless_drafts(): """ Render the gitless posts that a user has created in table form Editors can see all the posts created via Gitless_Editing """ prefixes = current_app.config.get('WEB_EDITOR_PREFIXES', []) if prefixes == []: raise Exception('Web editing is not configured') query = (db_session.query(Post)) if prefixes is not None: query = query.filter(or_(*[Post.path.like(p + '%') for p in prefixes])) if current_user.identifier not in current_repo.config.editors: query = (query.outerjoin( PostAuthorAssoc, PostAuthorAssoc.post_id == Post.id) .filter(PostAuthorAssoc.user_id == current_user.id)) return render_template('web_posts.html', posts=query.all()) @blueprint.route('/edit') @blueprint.route('/edit/<path:path>', methods=['GET', 'POST']) @PageView.logged @permissions.post_edit.require() def editor(path=None): """ Render the web post editor, either with the default values or if the post already exists, with what has been saved """ prefixes = current_app.config.get('WEB_EDITOR_PREFIXES', None) if prefixes is not None: assert ( path is None or any(path.startswith(prefix) for prefix in prefixes) ), 'Editing this post online is not permitted by server configuration.' # set defaults data = {'title': None, 'status': current_repo.PostStatus.DRAFT.value, 'markdown': request.args.get('markdown'), 'thumbnail': '', 'can_approve': 0, 'username': current_user.identifier, 'created_at': datetime.now(), 'updated_at': datetime.now(), 'authors': [current_user.identifier], 'comments': [], 'tldr': request.args.get('tldr'), } if path is not None and path in current_repo: kp = current_repo.post(path) data.update(kp.headers) data['status'] = kp.status.value data['path'] = path data['markdown'] = kp.read(images=False, headers=False) # retrieve reviews post = db_session.query(Post).filter(Post.path == path).first() if post: # post may have not been indexed yet data['comments'] = (db_session.query(Comment) .filter(Comment.post_id == post.id) .filter(Comment.type == 'review') .all()) if current_user.identifier not in data['authors'] \ or current_user.identifier in current_repo.config.editors: data['can_approve'] = 1 data['created_at'] = data['created_at'] data['updated_at'] = data['updated_at'] data['authors'] = json.dumps(data.get('authors')) data['tags'] = json.dumps(data.get('tags', [])) if "proxy" in data or request.args.get("proxy", False): return render_template("post_editor_proxy.html", **data) if "ipynb" in data or request.args.get("ipynb", False): return render_template("post_editor_ipynb.html", **data) return render_template("post_editor_markdown.html", **data) @blueprint.route('/ajax/editor/save', methods=['GET', 'POST']) @PageView.logged @permissions.post_edit.require() def save_post(): """ Save the post """ data = request.get_json() path = data['path'] prefixes = current_app.config['WEB_EDITOR_PREFIXES'] if prefixes == []: raise Exception('Web editing is not configured') if prefixes is not None: if not any([path.startswith(prefix) for prefix in prefixes]): return get_warning_msg( f'Your post path must begin with one of {prefixes}') # TODO better handling of overwriting kp = None if path in current_repo: kp = current_repo.post(path) if current_user.identifier not in kp.headers['authors'] \ and current_user.identifier not in current_repo.config.editors: return get_warning_msg( f'Post with path {path} already exists and you are not ' 'an author!\nPlease try a different path') # create the knowledge post kp = kp or KnowledgePost(path=path) headers = {} headers['created_at'] = datetime.strptime( data['created_at'], '%Y-%m-%d').date() headers['updated_at'] = datetime.strptime( data['updated_at'], '%Y-%m-%d').date() headers['title'] = data['title'] headers['path'] = data['path'] # TODO: thumbnail header not working currently, as feed image set # with kp method not based on header headers['thumbnail'] = data.get('feed_image', '') headers['authors'] = [auth.strip() for auth in data['author']] headers['tldr'] = data['tldr'] headers['tags'] = [tag.strip() for tag in data.get('tags', [])] if 'proxy' in data: headers['proxy'] = data['proxy'] if "ipynb" in data: headers["ipynb"] = data["ipynb"] kp.write(unquote(data['markdown']), headers=headers) # add to repo current_repo.add( kp, update=True, message=headers['title']) # THIS IS DANGEROUS update_index() return json.dumps({'path': path}) @blueprint.route('/ajax/editor/submit', methods=['GET', 'POST']) @PageView.logged @permissions.post_edit.require() def submit_for_review(): """ Submit post and if there are reviewers assigned, email them""" path = request.args.get('path', None) data = request.get_json() current_repo.submit(path) # email the reviewers reviewers = data.get('post_reviewers', None) if reviewers: for r in reviewers.split(','): send_reviewer_request_email(path=path, reviewer=r) update_index() return 'OK' @blueprint.route('/ajax/editor/publish', methods=['GET', 'POST']) @PageView.logged @permissions.post_edit.require() def publish_post(): """ Publish the post by changing the status """ path = request.args.get('path', None) if path not in current_repo: return get_warning_msg(f'Unable to retrieve post with path = {path}!') current_repo.publish(path) update_index(check_timeouts=False) return 'OK' @blueprint.route('/ajax/editor/unpublish', methods=['GET', 'POST']) @PageView.logged @permissions.post_edit.require() def unpublish_post(): """ Unpublish the post """ path = request.args.get('path', None) if path not in current_repo: return get_warning_msg(f'Unable to retrieve post with path = {path}!') current_repo.unpublish(path) update_index(check_timeouts=False) return 'OK' @blueprint.route('/ajax/editor/accept', methods=['GET', 'POST']) @PageView.logged @permissions.post_edit.require() def accept(): """ Accept the post """ path = request.args.get('path', None) if path not in current_repo: return get_warning_msg(f'Unable to retrieve post with path = {path}!') current_repo.accept(path) update_index() return 'OK' @blueprint.route('/ajax/editor/delete', methods=['GET', 'POST']) @PageView.logged @permissions.post_edit.require() def delete_post(): """ Delete a post """ path = request.args.get('path', None) if path not in current_repo: return get_warning_msg(f'Unable to retrieve post with path = {path}!') kp = current_repo.post(path) if current_user.identifier not in kp.headers['authors']: return get_warning_msg( 'You can only delete a post where you are an author!') current_repo.remove(path) update_index(check_timeouts=False) return 'OK' @blueprint.route('/ajax/editor/review', methods=['POST', 'DELETE']) @PageView.logged @permissions.post_edit.require() def review_comment(): """ Saves a review and sends an email that the post has been reviewed to the author of the post or deletes a submitted review """ if request.method == 'POST': path = request.args.get('path', None) post_id = db_session.query(Post).filter(Post.path == path).first().id comment = Comment() comment.text = request.get_json()['text'] comment.user_id = current_user.id comment.post_id = post_id comment.type = 'review' db_session.add(comment) db_session.commit() send_review_email(path=path, commenter=current_user.identifier, comment_text=comment.text) elif request.method == 'DELETE': comment = Comment.query.get(int(request.args.get('comment_id', ''))) if comment and current_user.id == comment.user_id: db_session.delete(comment) db_session.commit() return 'OK' @blueprint.route("/ajax/editor/s3_upload", methods=["POST", "GET"]) @PageView.logged @permissions.post_edit.require() def s3_upload(): """Upload file(s) to AWS s3 path and return the display link in the response""" if request.method == "POST": data = request.get_json() file_name = data.get("file_name", None) object_name = os.path.basename(file_name.replace("\\", "/")) logger.info("file_name: {0} & object_name: {1}".format(file_name, object_name)) if file_name is None: return get_warning_msg(f"File name is empty. Please re-upload!") bucket = data.get("bucket", "www.knowledge-repo.com") response = True # todo: replace it with real s3 upload if response: display_link = "https://s3.us-west-2.amazonaws.com/{0}/{1}".format( bucket, object_name ) # todo: make s3 region name be configurable return json.dumps({"display_link": display_link, "success": True}) error_msg = "ERROR during upload file to s3" logger.error(error_msg) return get_error_msg(error_msg) return "OK" # DEPRECATED @blueprint.route('/file_upload', methods=['POST', 'GET']) @PageView.logged @permissions.post_edit.require() def file_upload(): """ Uploads images dropped on the web editor's markdown box to static/images and notifies editors by email """ upload_folder = 'images' title = request.form['title'] files = request.files uploadedFiles = [] if files: for img_file in files.values(): filename = secure_filename(title + '_' + img_file.filename).lower() dst_folder = os.path.join(current_app.static_folder, upload_folder) if is_allowed_image_format(img_file): try: img_file.save(os.path.join(dst_folder, filename)) send_from_directory(dst_folder, filename) uploadedFiles += [url_for('static', filename=os.path.join( upload_folder, filename))] except Exception as e: error_msg = f'ERROR during image upload: {e}' logger.error(error_msg) return get_error_msg(error_msg) elif is_pdf(filename): from PyPDF2 import PdfFileReader try: src_pdf = PdfFileReader(img_file) filename = os.path.splitext(filename)[0] num_pages = src_pdf.getNumPages() for page_num in range(num_pages): page_png = pdf_page_to_png(src_pdf, page_num) page_name = '{filename}_{page_num}.jpg'.format( **locals()) page_png.save(filename=os.path.join( dst_folder, page_name)) uploadedFiles += [url_for( 'static', filename=os.path.join( upload_folder, page_name))] except Exception as e: error_msg = f'ERROR during pdf upload: {e}' logger.error(error_msg) return get_error_msg(error_msg) return json.dumps({'links': uploadedFiles, 'success': True})

from .. import permissions from ..index import update_index from ..models import Comment, PageView, Post, PostAuthorAssoc from ..proxies import current_repo, current_user, db_session from ..utils.emails import ( send_review_email, send_reviewer_request_email, ) from ..utils.image import ( is_allowed_image_format, is_pdf, pdf_page_to_png, ) from ..utils.shared import get_blueprint from datetime import datetime from flask import ( current_app, render_template, request, send_from_directory, url_for, ) from knowledge_repo.post import KnowledgePost from sqlalchemy import or_ from urllib.parse import unquote from werkzeug.utils import secure_filename import json import logging import os from knowledge_repo.utils.s3 import get_s3_client, put_object_to_s3 import nbformat from nbconvert import HTMLExporter import io from knowledge_repo.constants import AWS_S3_BUCKET logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) blueprint = get_blueprint("editor", __name__) s3_client = get_s3_client("", "", "us-west-2") def get_warning_msg(msg): return json.dumps({"msg": msg, "success": False}) def get_error_msg(msg): return json.dumps({"error_msg": msg, "success": False}) # TODO: These functions have not been fully married # to the KnowledgePost API # Currently, backended by Post objects but partially # implemented on KnowledgePost API # TODO: Deprecate this route in favour of integrating editing # links into primary index pages and user pages @blueprint.route("/webposts", methods=["GET"]) @PageView.logged @permissions.post_edit.require() def gitless_drafts(): """Render the gitless posts that a user has created in table form Editors can see all the posts created via Gitless_Editing """ prefixes = current_app.config.get("WEB_EDITOR_PREFIXES", []) if prefixes == []: raise Exception("Web editing is not configured") query = db_session.query(Post) if prefixes is not None: query = query.filter(or_(*[Post.path.like(p + "%") for p in prefixes])) if current_user.identifier not in current_repo.config.editors: query = query.outerjoin( PostAuthorAssoc, PostAuthorAssoc.post_id == Post.id ).filter(PostAuthorAssoc.user_id == current_user.id) return render_template("web_posts.html", posts=query.all()) @blueprint.route("/edit") @blueprint.route("/edit/<path:path>", methods=["GET", "POST"]) @PageView.logged @permissions.post_edit.require() def editor(path=None): """Render the web post editor, either with the default values or if the post already exists, with what has been saved""" prefixes = current_app.config.get("WEB_EDITOR_PREFIXES", None) if prefixes is not None: assert path is None or any( path.startswith(prefix) for prefix in prefixes ), "Editing this post online is not permitted by server configuration." # set defaults data = { "title": None, "status": current_repo.PostStatus.DRAFT.value, "markdown": request.args.get("markdown"), "thumbnail": "", "can_approve": 0, "username": current_user.identifier, "created_at": datetime.now(), "updated_at": datetime.now(), "authors": [current_user.identifier], "comments": [], "tldr": request.args.get("tldr"), } if path is not None and path in current_repo: kp = current_repo.post(path) data.update(kp.headers) data["status"] = kp.status.value data["path"] = path data["markdown"] = kp.read(images=False, headers=False) # retrieve reviews post = db_session.query(Post).filter(Post.path == path).first() if post: # post may have not been indexed yet data["comments"] = ( db_session.query(Comment) .filter(Comment.post_id == post.id) .filter(Comment.type == "review") .all() ) if ( current_user.identifier not in data["authors"] or current_user.identifier in current_repo.config.editors ): data["can_approve"] = 1 data["created_at"] = data["created_at"] data["updated_at"] = data["updated_at"] data["authors"] = json.dumps(data.get("authors")) data["tags"] = json.dumps(data.get("tags", [])) logger.info(data) if "proxy" in data or request.args.get("proxy", False): return render_template("post_editor_proxy.html", **data) if "ipynb" in data or request.args.get("ipynb", False): return render_template("post_editor_ipynb.html", **data) return render_template("post_editor_markdown.html", **data) @blueprint.route("/ajax/editor/save", methods=["GET", "POST"]) @PageView.logged @permissions.post_edit.require() def save_post(): """Save the post""" data = request.get_json() path = data["path"] prefixes = current_app.config["WEB_EDITOR_PREFIXES"] if prefixes == []: raise Exception("Web editing is not configured") if prefixes is not None: if not any([path.startswith(prefix) for prefix in prefixes]): return get_warning_msg(f"Your post path must begin with one of {prefixes}") # TODO better handling of overwriting kp = None if path in current_repo: kp = current_repo.post(path) if ( current_user.identifier not in kp.headers["authors"] and current_user.identifier not in current_repo.config.editors ): return get_warning_msg( f"Post with path {path} already exists and you are not " "an author!\nPlease try a different path" ) # create the knowledge post kp = kp or KnowledgePost(path=path) headers = {} headers["created_at"] = datetime.strptime(data["created_at"], "%Y-%m-%d").date() headers["updated_at"] = datetime.strptime(data["updated_at"], "%Y-%m-%d").date() headers["title"] = data["title"] headers["path"] = data["path"] # TODO: thumbnail header not working currently, as feed image set # with kp method not based on header headers["thumbnail"] = data.get("feed_image", "") headers["authors"] = [auth.strip() for auth in data["author"]] headers["tldr"] = data["tldr"] headers["tags"] = [tag.strip() for tag in data.get("tags", [])] if "proxy" in data: headers["proxy"] = data["proxy"] if "ipynb" in data: headers["ipynb"] = data["ipynb"] if ( data.get("file_name", None) is not None and data.get("file_data", None) is not None ): response = s3_upload(data["file_name"], data["file_data"]) if response is None: error_msg = "ERROR during upload file to s3" logger.error(error_msg) return get_error_msg(error_msg) else: headers["display_link"] = response else: headers["display_link"] = data["display_link"] kp.write(unquote(data["markdown"]), headers=headers) # add to repo current_repo.add(kp, update=True, message=headers["title"]) # THIS IS DANGEROUS update_index() return json.dumps({"path": path}) @blueprint.route("/ajax/editor/submit", methods=["GET", "POST"]) @PageView.logged @permissions.post_edit.require() def submit_for_review(): """Submit post and if there are reviewers assigned, email them""" path = request.args.get("path", None) data = request.get_json() current_repo.submit(path) # email the reviewers reviewers = data.get("post_reviewers", None) if reviewers: for r in reviewers.split(","): send_reviewer_request_email(path=path, reviewer=r) update_index() return "OK" @blueprint.route("/ajax/editor/publish", methods=["GET", "POST"]) @PageView.logged @permissions.post_edit.require() def publish_post(): """Publish the post by changing the status""" path = request.args.get("path", None) if path not in current_repo: return get_warning_msg(f"Unable to retrieve post with path = {path}!") current_repo.publish(path) update_index(check_timeouts=False) return "OK" @blueprint.route("/ajax/editor/unpublish", methods=["GET", "POST"]) @PageView.logged @permissions.post_edit.require() def unpublish_post(): """Unpublish the post""" path = request.args.get("path", None) if path not in current_repo: return get_warning_msg(f"Unable to retrieve post with path = {path}!") current_repo.unpublish(path) update_index(check_timeouts=False) return "OK" @blueprint.route("/ajax/editor/accept", methods=["GET", "POST"]) @PageView.logged @permissions.post_edit.require() def accept(): """Accept the post""" path = request.args.get("path", None) if path not in current_repo: return get_warning_msg(f"Unable to retrieve post with path = {path}!") current_repo.accept(path) update_index() return "OK" @blueprint.route("/ajax/editor/delete", methods=["GET", "POST"]) @PageView.logged @permissions.post_edit.require() def delete_post(): """Delete a post""" path = request.args.get("path", None) if path not in current_repo: return get_warning_msg(f"Unable to retrieve post with path = {path}!") kp = current_repo.post(path) if current_user.identifier not in kp.headers["authors"]: return get_warning_msg("You can only delete a post where you are an author!") current_repo.remove(path) update_index(check_timeouts=False) return "OK" @blueprint.route("/ajax/editor/review", methods=["POST", "DELETE"]) @PageView.logged @permissions.post_edit.require() def review_comment(): """ Saves a review and sends an email that the post has been reviewed to the author of the post or deletes a submitted review """ if request.method == "POST": path = request.args.get("path", None) post_id = db_session.query(Post).filter(Post.path == path).first().id comment = Comment() comment.text = request.get_json()["text"] comment.user_id = current_user.id comment.post_id = post_id comment.type = "review" db_session.add(comment) db_session.commit() send_review_email( path=path, commenter=current_user.identifier, comment_text=comment.text ) elif request.method == "DELETE": comment = Comment.query.get(int(request.args.get("comment_id", ""))) if comment and current_user.id == comment.user_id: db_session.delete(comment) db_session.commit() return "OK" def s3_upload(file_name, file_data): """Upload file(s) to AWS s3 path and return the display link in the response""" if file_name is None or file_data is None or file_data is "": return get_warning_msg(f"File name is empty. Please re-upload!") response = put_object_to_s3(s3_client, file_data, AWS_S3_BUCKET, file_name) # create a html version of this file if ".ipynb" in file_name: with io.StringIO(file_data) as f: nb = nbformat.read(f, as_version=4) # export to html html_exporter = HTMLExporter() (html_data, resources) = html_exporter.from_notebook_node(nb) html_file_name = file_name.replace(".ipynb", ".html") response = put_object_to_s3( s3_client, html_data, AWS_S3_BUCKET, html_file_name, "text/html", ) if response: display_link = "https://s3.us-west-2.amazonaws.com/{0}/{1}".format( AWS_S3_BUCKET, html_file_name ) # todo: make s3 region name be configurable return display_link return None # DEPRECATED @blueprint.route("/file_upload", methods=["POST", "GET"]) @PageView.logged @permissions.post_edit.require() def file_upload(): """ Uploads images dropped on the web editor's markdown box to static/images and notifies editors by email """ upload_folder = "images" title = request.form["title"] files = request.files uploadedFiles = [] if files: for img_file in files.values(): filename = secure_filename(title + "_" + img_file.filename).lower() dst_folder = os.path.join(current_app.static_folder, upload_folder) if is_allowed_image_format(img_file): try: img_file.save(os.path.join(dst_folder, filename)) send_from_directory(dst_folder, filename) uploadedFiles += [ url_for( "static", filename=os.path.join(upload_folder, filename) ) ] except Exception as e: error_msg = f"ERROR during image upload: {e}" logger.error(error_msg) return get_error_msg(error_msg) elif is_pdf(filename): from PyPDF2 import PdfFileReader try: src_pdf = PdfFileReader(img_file) filename = os.path.splitext(filename)[0] num_pages = src_pdf.getNumPages() for page_num in range(num_pages): page_png = pdf_page_to_png(src_pdf, page_num) page_name = "{filename}_{page_num}.jpg".format(**locals()) page_png.save(filename=os.path.join(dst_folder, page_name)) uploadedFiles += [ url_for( "static", filename=os.path.join(upload_folder, page_name), ) ] except Exception as e: error_msg = f"ERROR during pdf upload: {e}" logger.error(error_msg) return get_error_msg(error_msg) return json.dumps({"links": uploadedFiles, "success": True})

mengting1010

451577868d66570a463260c67dff7034214beafd

6edad5351bf3f4f0abc457b6d9532ca25c62c952

Same lint issue here.

csharplus

airbnb/knowledge-repo

Enable Jupyter Notebook Upload Post Editor

Description of changeset: - Enable jupyter notebook upload post editor  - This pr only enables the ability to upload and render a html version of jupyter notebook (if it is a ipynb file, s3 will download the file to your local instead of displaying it. todo: will try to fix it in next pr)  Test Plan: Local Dev Reviewers: @csharplus @JJJ000

2022-12-27 04:44:23+00:00

2022-12-27 20:24:27+00:00

docker/config.py

from datetime import timedelta # --------------------------------------------------- # Host configuration # --------------------------------------------------- # The server name is used by Flask to limit access to the # served content to request to a particular domain. It # is also used by some authentication providers (in particular # OAuth providers) to advertise callback providers. If # not provided, it is assumed in these contexts to be # 'localhost:7000'. Be sure to specify this before deploying # into production. SERVER_NAME = "localhost:7001" # The knowledge repository uses the secret key to sign user # sessions. If not specified, a unique secret key will be # generated every time the server starts up. If hosting # in a multi-server environment, or you want sessions # to persist accross server restarts, set this to something # static. SECRET_KEY = None # Set DEPLOY_HTTPS to True if you want to enable encrypted # communication with Flask. When enabled, you must provide # your ssl certificate, which consists of a .crt and .key # file. # Note: Even if you set DEPLOY_HTTPS to True, you still need # to set the port to 443 manually. DEPLOY_HTTPS = False SSL_CERT = {"cert": "/path/to/cert", "key": "/path/to/key"} # --------------------------------------------------- # Debug configuration # --------------------------------------------------- DEBUG = False # --------------------------------------------------- # Database configuration # --------------------------------------------------- SQLALCHEMY_DATABASE_URI = ( "postgresql+psycopg2://knowledge_repo:password@localhost:5432/knowledge_repo" ) # If you are using a MySQL database, you must specify the URI as # demonstrated below. # SQLALCHEMY_DATABASE_URI = 'mysql://username:password@hostname/database' SQLALCHEMY_ECHO = False SQLALCHEMY_TRACK_MODIFICATIONS = False # Should the database tables be automatically created DB_AUTO_CREATE = True # Should the database be automatically migrated when updates exist # Note: This is True by default if this configuration is not applied, # under the assumption that if you are concerned about this file # you are probably interested in minimising risk to stability and handling # database upgrades manually. Manual database migrations can be # performed using `knowledge_repo --repo <> db_upgrade ...`. DB_AUTO_UPGRADE = False # --------------------------------------------------- # Authentication configuration # --------------------------------------------------- # Authentication providers allow users to sign into the Knowledge Repo # in a variety of different ways. You can create your own subclass of # `KnowledgeAuthProvider` and add either the instance or identifier # used for that class below. # By default, the knowledge repo offers: # ['debug', 'oauth2', 'bitbucket', 'github', 'google', 'ldap'] AUTH_PROVIDERS = ["google"] OAUTH_GOOGLE_CLIENT_ID = "<client id>" OAUTH_GOOGLE_CLIENT_SECRET = "<client secret>" # If you are going to use a OAuth provider, you will need to specify client ids # and private tokens. This can be done by instantiating instances of # `OAuth2Provider` and adding them to the above list, or by specifying OAuth # connection properties as demonstrated below for the GitHub authenticator. # OAUTH_GITHUB_CLIENT_ID = '<client id>' # OAUTH_GITHUB_CLIENT_SECRET = '<client secret>' # To configure a generic OAuth provider that is not one of the presets # provided, you may use the provider 'oauth2' which creates an empty, # unconfigured OAuth2Provider. You must then override its configuration. # For example, for a self-managed Gitlab CE instance at gitlab.example.com: # OAUTH_OAUTH2_BASE_URL = 'https://gitlab.example.com/api/v4/' # OAUTH_OAUTH2_AUTHORIZATION_URL = 'https://gitlab.example.com/oauth/authorize' # OAUTH_OAUTH2_TOKEN_URL = 'https://gitlab.example.com/oauth/token' # OAUTH_OAUTH2_AUTO_REFRESH_URL = 'https://gitlab.example.com/oauth/token' # OAUTH_OAUTH2_SCOPES = 'api' # OAUTH_OAUTH2_USER_INFO_ENDPOINT = 'user' # OAUTH_OAUTH2_USER_INFO_MAPPING = { # 'identifier': 'username', # 'name': 'name', # 'avatar_uri': 'avatar_url' # } # OAUTH_OAUTH2_VERIFY_SSL_CERTS = '/path/to/certs/my.ca-bundle' # OAUTH_OAUTH2_CLIENT_ID = '<client id>' # OAUTH_OAUTH2_CLIENT_SECRET = '<client secret>' # The configuration OAUTH_<name>_VERIFY_SSL_CERTS is what is passed to the # 'verify' parameter in the Requests module, and can be used to disable # HTTPS verification (not recommended) or provide a custom CA bundle. See: # http://docs.python-requests.org/en/master/user/advanced/#ssl-cert-verification # You may also override the .validate() method of a KnowledgeAuthProvider # to perform an additional validation step before authenticating a user. # The following example checks whether a user has access to the git remote # of the local Knowledge Repository: # def OAUTH_OAUTH2_VALIDATE(provider, user): # # if provider.app.repository.git_has_remote: # # url_parts = ( # provider.app.repository.git_remote.url.split(':') # ) # # url_subparts = url_parts[1].split('/') # # if url_parts[0] == "git@gitlab.example.com": # git_project = ( # url_subparts[0] + "%2F" + url_subparts[1].split('.')[0]) # elif ( # url_parts[0] == "https" # and url_subparts[2] == "gitlab.example.com" # ): # git_project = ( # url_subparts[3] + "%2F" + url_subparts[4].split('.')[0]) # else: # provider.app.logger.warning( # "User validation failed: unexpected git remote url [" # + provider.app.repository.git_remote.url + "]") # return False # # user_validate_url = provider.base_url + "projects/" + git_project # # resp = provider.oauth_client.get( # user_validate_url, # verify=OAUTH_OAUTH2_VERIFY_HTTPS) # # if resp.status_code == 200: # return True # else: # provider.app.logger.warning( # "User validation failed: validation URL [" # + user_validate_url + "] returned HTTP status [" # + str(resp.status_code) + "]") # You can also forgo a fully-fledged sign in process for users by hosting the # knowledge repository behind a proxy server that pre-authenticates users, and # adds the appropriate user identifier to the http headers of the request. If # enabled below, then they take precedence over any other forms of # authentication. If the call to `AUTH_MAP_REQUEST_HEADERS` results in a null # user identifier, then the authentication flow will fall back to use any of # the providers specified above. AUTH_USE_REQUEST_HEADERS = False # If using headers to authenticate, the following function should be # implemented to transform a dictionary of headers into a dictionary of user # attributes. Currently only 'identifier', 'avatar_uri', 'name' and 'email' # are supported. If this method returns `None`, or `identifier` is not # supplied, then the authorization flow will fall back to other authentication # methods. def AUTH_MAP_REQUEST_HEADERS(headers): return { # 'identifier': None, # 'avatar_uri': None, # 'name': None, # 'email': None } # The following AUTH_USER_IDENTIFIER* configuration keys are deprecated and # will be removed in v0.9. AUTH_USER_IDENTIFIER_REQUEST_HEADER = None def AUTH_USER_IDENTIFIER_REQUEST_HEADER_MAPPING(identifier): return identifier # If the server desires to modify the attributes of the `User` object # associated with users logged in via any of the above authentication # providers, it can do so via this configuration key. This function will be # run once at user login (if using an `AuthenticationProvider`, and then at # most once during any caching lifetime period (as specified below). Note that # attributes collected via `AuthenticationProvider`s will not be updated # after initial login (user must relogin in order to reset those attributes). def AUTH_USER_ATTRIBUTE_SETTER(user): return user # The time to wait before re-checking user attributes with the above function # for users logged in via request headers. AUTH_USER_ATTRIBUTE_CACHE_LIFETIME = 24 * 60 * 60 # 1 day # Once a user is logged in via an authentication provider, they will remain # logged in via the use of cookies. By default, this cookie will last one year. # This is managed by `flask_login`, but is copied here for convenience. # For other options regarding sessions, please refer to: # https://flask-login.readthedocs.io/en/latest/#cookie-settings REMEMBER_COOKIE_DURATION = timedelta(days=365) # --------------------------------------------------- # LDAP configuration # --------------------------------------------------- # When using an LDAP server for user verification, you need to configure # the location of the server, and the directory structure used by your # organization. # Currently the port and protocol must both be included in the server address LDAP_SERVER = "ldap://127.0.0.1:389" # When entering this, note the "{0}" which denotes where the user_id # is inserted. LDAP_USERDN_SCHEMA = "cn={user_id},ou=people,dc=planetexpress,dc=com" # --------------------------------------------------- # Policy configuration # --------------------------------------------------- # This section configures various policy related to access control. # Should anonymous users be able to view the post indices POLICY_ANONYMOUS_VIEW_INDEX = True # Should anonymous users be able to view the content of posts POLICY_ANONYMOUS_VIEW_POST = True # Should anonymous users be able to view overall statistics POLICY_ANONYMOUS_VIEW_STATS = True # Should anonymous users be able to view tag pages POLICY_ANONYMOUS_VIEW_TAGS = True # Should anonymous users be able to download posts (or their source) POLICY_ANONYMOUS_DOWNLOADS = False # --------------------------------------------------- # Repository configuration # --------------------------------------------------- # You may specify a function `prepare_repo` which configures # the repository upon which this server is running. This # takes place after the repository has been instantiated # and before the server is able to serve requests. It is # possible to do anything to the repository, including # substituting the repository for another one. # By default, repositories manage their own configurations, # but this can be risky as they may run arbitrary python code, # which opens a vector for malicious users to compromise # the server. If you want to avoid this risk, pass # the '--safe' (TODO!) option to `knowledge_repo` config and # manually configure the repository here. # For example, if your server instance is sitting atop # a meta-repository, it may make sense to update the meta-repository # configuration with that of one of its children. def prepare_repo(repo): return repo # --------------------------------------------------- # Repository Indexing configuration # --------------------------------------------------- # The Knowedge Repo updates the index of available posts on a regular basis. # If the database is not thread-safe (i.e. in the case of SQLite), then the # index will be updated on the main thread before every request that is more # than `INDEX_INTERVAL` seconds after the last sync completed. Otherwise, # indexing will occur every `INDEX_INTERVAL` seconds after the previous sync. # Syncing is designed to be compatible with multiple instances of the Knowledge # Repo connected to the same database, accross multiple machines and/or # processes; and so a global indexing lock is employed. When a sync begins, # a sync lock is put in place and the responsible process is considered to be # the primary agent responsible for syncing until its last update is longer # than`INDEXING_TIMEOUT` seconds, whereby the lock is ceded to the next # requesting process. Note that `INDEXING_TIMEOUT` must be larger than # `INDEXING_INTERVAL` or strange things might begin to happen. INDEXING_INTERVAL = 5 * 60 # 5 minutes INDEXING_TIMEOUT = 10 * 60 # 10 minutes # Whether an index operation should update repositories INDEXING_UPDATES_REPOSITORIES = True # Whether repositories should be updated even without a sync lock (in which # case the repositories will be updated on the sync timers, even if the # relevant process/thread does not have a lock on updating the index). This is # useful in context of multiple Knowledge Repo servers working together to # serve the repositories across multiple machines, which each require # repository syncing. Disable this if (for some reason) you have multiple # Knowledge Repo servers running on the same machine, and you want to avoid # potential clashes. This key is ignored if `INDEXING_UPDATES_REPOSITORIES` is # False INDEXING_UPDATES_REPOSITORIES_WITHOUT_LOCK = True # In some cases you may want to disable indexing entirely, which is currently # only ever used by the Knowledge Post previewer. Disabling the index means # that posts will not be discoverable, but if know the path in the repository # you can view the post with a direct link. INDEXING_ENABLED = True # --------------------------------------------------- # Flask Mail Configuration # Refer to https://pythonhosted.org/flask-mail/ # Unless specified, upstream defaults are used as indicated # provided that MAIL_SERVER is defined. # --------------------------------------------------- # MAIL_SERVER = 'localhost' # default = 'localhost' # MAIL_PORT = 25 # default = 25 # MAIL_USE_TLS = False # default = False # MAIL_USE_SSL = False # default = False # MAIL_DEBUG = False # default = app.debug # MAIL_USERNAME = None # default = None # MAIL_PASSWORD = None # default = None # MAIL_DEFAULT_SENDER = None # default = None # MAIL_MAX_EMAILS = None # default = None # MAIL_SUPPRESS_SEND = False # default = app.testing # MAIL_ASCII_ATTACHMENTS = False # default = False # # Detailed integration procedure with SendGrid is available at: # https://sendgrid.com/blog/sending-emails-from-python-flask-applications-with-twilio-sendgrid/ # -------------------------------------------------- # Web Editor Configuration # -------------------------------------------------- # The web editor can be limited to editing posts under # a limited set of parent directories by setting # WEB_EDITOR_PREFIXES to a list of supported path prefixes. # e.g. ['webposts', 'projects'] WEB_EDITOR_PREFIXES = ["webposts"] # --------------------------------------------------- # Tag configuration # --------------------------------------------------- # Posts with certain tags can be excluded from showing up # in the app. This can be useful for security purposes EXCLUDED_TAGS = ["private"] # ------------- # Collapse Code as Default Display Option # ------------- COLLAPSE_CODE_DEFAULT = False # ------------- # AWS related settings # ------------- S3_AWS_ACCESS_KEY_ID = "" S3_AWS_SECRET_ACCESS_KEY = "" S3_AWS_REGION_NAME = ""

from datetime import timedelta # --------------------------------------------------- # Host configuration # --------------------------------------------------- # The server name is used by Flask to limit access to the # served content to request to a particular domain. It # is also used by some authentication providers (in particular # OAuth providers) to advertise callback providers. If # not provided, it is assumed in these contexts to be # 'localhost:7000'. Be sure to specify this before deploying # into production. SERVER_NAME = "localhost:7001" # The knowledge repository uses the secret key to sign user # sessions. If not specified, a unique secret key will be # generated every time the server starts up. If hosting # in a multi-server environment, or you want sessions # to persist accross server restarts, set this to something # static. SECRET_KEY = None # Set DEPLOY_HTTPS to True if you want to enable encrypted # communication with Flask. When enabled, you must provide # your ssl certificate, which consists of a .crt and .key # file. # Note: Even if you set DEPLOY_HTTPS to True, you still need # to set the port to 443 manually. DEPLOY_HTTPS = False SSL_CERT = {"cert": "/path/to/cert", "key": "/path/to/key"} # --------------------------------------------------- # Debug configuration # --------------------------------------------------- DEBUG = False # --------------------------------------------------- # Database configuration # --------------------------------------------------- SQLALCHEMY_DATABASE_URI = ( "postgresql+psycopg2://knowledge_repo:password@localhost:5432/knowledge_repo" ) # If you are using a MySQL database, you must specify the URI as # demonstrated below. # SQLALCHEMY_DATABASE_URI = 'mysql://username:password@hostname/database' SQLALCHEMY_ECHO = False SQLALCHEMY_TRACK_MODIFICATIONS = False # Should the database tables be automatically created DB_AUTO_CREATE = True # Should the database be automatically migrated when updates exist # Note: This is True by default if this configuration is not applied, # under the assumption that if you are concerned about this file # you are probably interested in minimising risk to stability and handling # database upgrades manually. Manual database migrations can be # performed using `knowledge_repo --repo <> db_upgrade ...`. DB_AUTO_UPGRADE = False # --------------------------------------------------- # Authentication configuration # --------------------------------------------------- # Authentication providers allow users to sign into the Knowledge Repo # in a variety of different ways. You can create your own subclass of # `KnowledgeAuthProvider` and add either the instance or identifier # used for that class below. # By default, the knowledge repo offers: # ['debug', 'oauth2', 'bitbucket', 'github', 'google', 'ldap'] AUTH_PROVIDERS = ["google"] OAUTH_GOOGLE_CLIENT_ID = "<client id>" OAUTH_GOOGLE_CLIENT_SECRET = "<client secret>" # If you are going to use a OAuth provider, you will need to specify client ids # and private tokens. This can be done by instantiating instances of # `OAuth2Provider` and adding them to the above list, or by specifying OAuth # connection properties as demonstrated below for the GitHub authenticator. # OAUTH_GITHUB_CLIENT_ID = '<client id>' # OAUTH_GITHUB_CLIENT_SECRET = '<client secret>' # To configure a generic OAuth provider that is not one of the presets # provided, you may use the provider 'oauth2' which creates an empty, # unconfigured OAuth2Provider. You must then override its configuration. # For example, for a self-managed Gitlab CE instance at gitlab.example.com: # OAUTH_OAUTH2_BASE_URL = 'https://gitlab.example.com/api/v4/' # OAUTH_OAUTH2_AUTHORIZATION_URL = 'https://gitlab.example.com/oauth/authorize' # OAUTH_OAUTH2_TOKEN_URL = 'https://gitlab.example.com/oauth/token' # OAUTH_OAUTH2_AUTO_REFRESH_URL = 'https://gitlab.example.com/oauth/token' # OAUTH_OAUTH2_SCOPES = 'api' # OAUTH_OAUTH2_USER_INFO_ENDPOINT = 'user' # OAUTH_OAUTH2_USER_INFO_MAPPING = { # 'identifier': 'username', # 'name': 'name', # 'avatar_uri': 'avatar_url' # } # OAUTH_OAUTH2_VERIFY_SSL_CERTS = '/path/to/certs/my.ca-bundle' # OAUTH_OAUTH2_CLIENT_ID = '<client id>' # OAUTH_OAUTH2_CLIENT_SECRET = '<client secret>' # The configuration OAUTH_<name>_VERIFY_SSL_CERTS is what is passed to the # 'verify' parameter in the Requests module, and can be used to disable # HTTPS verification (not recommended) or provide a custom CA bundle. See: # http://docs.python-requests.org/en/master/user/advanced/#ssl-cert-verification # You may also override the .validate() method of a KnowledgeAuthProvider # to perform an additional validation step before authenticating a user. # The following example checks whether a user has access to the git remote # of the local Knowledge Repository: # def OAUTH_OAUTH2_VALIDATE(provider, user): # # if provider.app.repository.git_has_remote: # # url_parts = ( # provider.app.repository.git_remote.url.split(':') # ) # # url_subparts = url_parts[1].split('/') # # if url_parts[0] == "git@gitlab.example.com": # git_project = ( # url_subparts[0] + "%2F" + url_subparts[1].split('.')[0]) # elif ( # url_parts[0] == "https" # and url_subparts[2] == "gitlab.example.com" # ): # git_project = ( # url_subparts[3] + "%2F" + url_subparts[4].split('.')[0]) # else: # provider.app.logger.warning( # "User validation failed: unexpected git remote url [" # + provider.app.repository.git_remote.url + "]") # return False # # user_validate_url = provider.base_url + "projects/" + git_project # # resp = provider.oauth_client.get( # user_validate_url, # verify=OAUTH_OAUTH2_VERIFY_HTTPS) # # if resp.status_code == 200: # return True # else: # provider.app.logger.warning( # "User validation failed: validation URL [" # + user_validate_url + "] returned HTTP status [" # + str(resp.status_code) + "]") # You can also forgo a fully-fledged sign in process for users by hosting the # knowledge repository behind a proxy server that pre-authenticates users, and # adds the appropriate user identifier to the http headers of the request. If # enabled below, then they take precedence over any other forms of # authentication. If the call to `AUTH_MAP_REQUEST_HEADERS` results in a null # user identifier, then the authentication flow will fall back to use any of # the providers specified above. AUTH_USE_REQUEST_HEADERS = False # If using headers to authenticate, the following function should be # implemented to transform a dictionary of headers into a dictionary of user # attributes. Currently only 'identifier', 'avatar_uri', 'name' and 'email' # are supported. If this method returns `None`, or `identifier` is not # supplied, then the authorization flow will fall back to other authentication # methods. def AUTH_MAP_REQUEST_HEADERS(headers): return { # 'identifier': None, # 'avatar_uri': None, # 'name': None, # 'email': None } # The following AUTH_USER_IDENTIFIER* configuration keys are deprecated and # will be removed in v0.9. AUTH_USER_IDENTIFIER_REQUEST_HEADER = None def AUTH_USER_IDENTIFIER_REQUEST_HEADER_MAPPING(identifier): return identifier # If the server desires to modify the attributes of the `User` object # associated with users logged in via any of the above authentication # providers, it can do so via this configuration key. This function will be # run once at user login (if using an `AuthenticationProvider`, and then at # most once during any caching lifetime period (as specified below). Note that # attributes collected via `AuthenticationProvider`s will not be updated # after initial login (user must relogin in order to reset those attributes). def AUTH_USER_ATTRIBUTE_SETTER(user): return user # The time to wait before re-checking user attributes with the above function # for users logged in via request headers. AUTH_USER_ATTRIBUTE_CACHE_LIFETIME = 24 * 60 * 60 # 1 day # Once a user is logged in via an authentication provider, they will remain # logged in via the use of cookies. By default, this cookie will last one year. # This is managed by `flask_login`, but is copied here for convenience. # For other options regarding sessions, please refer to: # https://flask-login.readthedocs.io/en/latest/#cookie-settings REMEMBER_COOKIE_DURATION = timedelta(days=365) # --------------------------------------------------- # LDAP configuration # --------------------------------------------------- # When using an LDAP server for user verification, you need to configure # the location of the server, and the directory structure used by your # organization. # Currently the port and protocol must both be included in the server address LDAP_SERVER = "ldap://127.0.0.1:389" # When entering this, note the "{0}" which denotes where the user_id # is inserted. LDAP_USERDN_SCHEMA = "cn={user_id},ou=people,dc=planetexpress,dc=com" # --------------------------------------------------- # Policy configuration # --------------------------------------------------- # This section configures various policy related to access control. # Should anonymous users be able to view the post indices POLICY_ANONYMOUS_VIEW_INDEX = True # Should anonymous users be able to view the content of posts POLICY_ANONYMOUS_VIEW_POST = True # Should anonymous users be able to view overall statistics POLICY_ANONYMOUS_VIEW_STATS = True # Should anonymous users be able to view tag pages POLICY_ANONYMOUS_VIEW_TAGS = True # Should anonymous users be able to download posts (or their source) POLICY_ANONYMOUS_DOWNLOADS = False # --------------------------------------------------- # Repository configuration # --------------------------------------------------- # You may specify a function `prepare_repo` which configures # the repository upon which this server is running. This # takes place after the repository has been instantiated # and before the server is able to serve requests. It is # possible to do anything to the repository, including # substituting the repository for another one. # By default, repositories manage their own configurations, # but this can be risky as they may run arbitrary python code, # which opens a vector for malicious users to compromise # the server. If you want to avoid this risk, pass # the '--safe' (TODO!) option to `knowledge_repo` config and # manually configure the repository here. # For example, if your server instance is sitting atop # a meta-repository, it may make sense to update the meta-repository # configuration with that of one of its children. def prepare_repo(repo): return repo # --------------------------------------------------- # Repository Indexing configuration # --------------------------------------------------- # The Knowedge Repo updates the index of available posts on a regular basis. # If the database is not thread-safe (i.e. in the case of SQLite), then the # index will be updated on the main thread before every request that is more # than `INDEX_INTERVAL` seconds after the last sync completed. Otherwise, # indexing will occur every `INDEX_INTERVAL` seconds after the previous sync. # Syncing is designed to be compatible with multiple instances of the Knowledge # Repo connected to the same database, accross multiple machines and/or # processes; and so a global indexing lock is employed. When a sync begins, # a sync lock is put in place and the responsible process is considered to be # the primary agent responsible for syncing until its last update is longer # than`INDEXING_TIMEOUT` seconds, whereby the lock is ceded to the next # requesting process. Note that `INDEXING_TIMEOUT` must be larger than # `INDEXING_INTERVAL` or strange things might begin to happen. INDEXING_INTERVAL = 5 * 60 # 5 minutes INDEXING_TIMEOUT = 10 * 60 # 10 minutes # Whether an index operation should update repositories INDEXING_UPDATES_REPOSITORIES = True # Whether repositories should be updated even without a sync lock (in which # case the repositories will be updated on the sync timers, even if the # relevant process/thread does not have a lock on updating the index). This is # useful in context of multiple Knowledge Repo servers working together to # serve the repositories across multiple machines, which each require # repository syncing. Disable this if (for some reason) you have multiple # Knowledge Repo servers running on the same machine, and you want to avoid # potential clashes. This key is ignored if `INDEXING_UPDATES_REPOSITORIES` is # False INDEXING_UPDATES_REPOSITORIES_WITHOUT_LOCK = True # In some cases you may want to disable indexing entirely, which is currently # only ever used by the Knowledge Post previewer. Disabling the index means # that posts will not be discoverable, but if know the path in the repository # you can view the post with a direct link. INDEXING_ENABLED = True # --------------------------------------------------- # Flask Mail Configuration # Refer to https://pythonhosted.org/flask-mail/ # Unless specified, upstream defaults are used as indicated # provided that MAIL_SERVER is defined. # --------------------------------------------------- # MAIL_SERVER = 'localhost' # default = 'localhost' # MAIL_PORT = 25 # default = 25 # MAIL_USE_TLS = False # default = False # MAIL_USE_SSL = False # default = False # MAIL_DEBUG = False # default = app.debug # MAIL_USERNAME = None # default = None # MAIL_PASSWORD = None # default = None # MAIL_DEFAULT_SENDER = None # default = None # MAIL_MAX_EMAILS = None # default = None # MAIL_SUPPRESS_SEND = False # default = app.testing # MAIL_ASCII_ATTACHMENTS = False # default = False # # Detailed integration procedure with SendGrid is available at: # https://sendgrid.com/blog/sending-emails-from-python-flask-applications-with-twilio-sendgrid/ # -------------------------------------------------- # Web Editor Configuration # -------------------------------------------------- # The web editor can be limited to editing posts under # a limited set of parent directories by setting # WEB_EDITOR_PREFIXES to a list of supported path prefixes. # e.g. ['webposts', 'projects'] WEB_EDITOR_PREFIXES = ["webposts"] # --------------------------------------------------- # Tag configuration # --------------------------------------------------- # Posts with certain tags can be excluded from showing up # in the app. This can be useful for security purposes EXCLUDED_TAGS = ["private"] # ------------- # Collapse Code as Default Display Option # ------------- COLLAPSE_CODE_DEFAULT = False # ------------- # AWS related settings # ------------- S3_AWS_ACCESS_KEY_ID = "" S3_AWS_SECRET_ACCESS_KEY = "" S3_AWS_REGION_NAME = "us-west-2"

mengting1010

b15c759e7d17b6a52c502b8bfdc174181da83789

451577868d66570a463260c67dff7034214beafd

this could cause security issue, you might need to remove it from your commit.

JJJ000

airbnb/knowledge-repo

Enable Jupyter Notebook Upload Post Editor

Description of changeset: - Enable jupyter notebook upload post editor  - This pr only enables the ability to upload and render a html version of jupyter notebook (if it is a ipynb file, s3 will download the file to your local instead of displaying it. todo: will try to fix it in next pr)  Test Plan: Local Dev Reviewers: @csharplus @JJJ000

2022-12-27 04:44:23+00:00

2022-12-27 20:24:27+00:00

docker/config.py

from datetime import timedelta # --------------------------------------------------- # Host configuration # --------------------------------------------------- # The server name is used by Flask to limit access to the # served content to request to a particular domain. It # is also used by some authentication providers (in particular # OAuth providers) to advertise callback providers. If # not provided, it is assumed in these contexts to be # 'localhost:7000'. Be sure to specify this before deploying # into production. SERVER_NAME = "localhost:7001" # The knowledge repository uses the secret key to sign user # sessions. If not specified, a unique secret key will be # generated every time the server starts up. If hosting # in a multi-server environment, or you want sessions # to persist accross server restarts, set this to something # static. SECRET_KEY = None # Set DEPLOY_HTTPS to True if you want to enable encrypted # communication with Flask. When enabled, you must provide # your ssl certificate, which consists of a .crt and .key # file. # Note: Even if you set DEPLOY_HTTPS to True, you still need # to set the port to 443 manually. DEPLOY_HTTPS = False SSL_CERT = {"cert": "/path/to/cert", "key": "/path/to/key"} # --------------------------------------------------- # Debug configuration # --------------------------------------------------- DEBUG = False # --------------------------------------------------- # Database configuration # --------------------------------------------------- SQLALCHEMY_DATABASE_URI = ( "postgresql+psycopg2://knowledge_repo:password@localhost:5432/knowledge_repo" ) # If you are using a MySQL database, you must specify the URI as # demonstrated below. # SQLALCHEMY_DATABASE_URI = 'mysql://username:password@hostname/database' SQLALCHEMY_ECHO = False SQLALCHEMY_TRACK_MODIFICATIONS = False # Should the database tables be automatically created DB_AUTO_CREATE = True # Should the database be automatically migrated when updates exist # Note: This is True by default if this configuration is not applied, # under the assumption that if you are concerned about this file # you are probably interested in minimising risk to stability and handling # database upgrades manually. Manual database migrations can be # performed using `knowledge_repo --repo <> db_upgrade ...`. DB_AUTO_UPGRADE = False # --------------------------------------------------- # Authentication configuration # --------------------------------------------------- # Authentication providers allow users to sign into the Knowledge Repo # in a variety of different ways. You can create your own subclass of # `KnowledgeAuthProvider` and add either the instance or identifier # used for that class below. # By default, the knowledge repo offers: # ['debug', 'oauth2', 'bitbucket', 'github', 'google', 'ldap'] AUTH_PROVIDERS = ["google"] OAUTH_GOOGLE_CLIENT_ID = "<client id>" OAUTH_GOOGLE_CLIENT_SECRET = "<client secret>" # If you are going to use a OAuth provider, you will need to specify client ids # and private tokens. This can be done by instantiating instances of # `OAuth2Provider` and adding them to the above list, or by specifying OAuth # connection properties as demonstrated below for the GitHub authenticator. # OAUTH_GITHUB_CLIENT_ID = '<client id>' # OAUTH_GITHUB_CLIENT_SECRET = '<client secret>' # To configure a generic OAuth provider that is not one of the presets # provided, you may use the provider 'oauth2' which creates an empty, # unconfigured OAuth2Provider. You must then override its configuration. # For example, for a self-managed Gitlab CE instance at gitlab.example.com: # OAUTH_OAUTH2_BASE_URL = 'https://gitlab.example.com/api/v4/' # OAUTH_OAUTH2_AUTHORIZATION_URL = 'https://gitlab.example.com/oauth/authorize' # OAUTH_OAUTH2_TOKEN_URL = 'https://gitlab.example.com/oauth/token' # OAUTH_OAUTH2_AUTO_REFRESH_URL = 'https://gitlab.example.com/oauth/token' # OAUTH_OAUTH2_SCOPES = 'api' # OAUTH_OAUTH2_USER_INFO_ENDPOINT = 'user' # OAUTH_OAUTH2_USER_INFO_MAPPING = { # 'identifier': 'username', # 'name': 'name', # 'avatar_uri': 'avatar_url' # } # OAUTH_OAUTH2_VERIFY_SSL_CERTS = '/path/to/certs/my.ca-bundle' # OAUTH_OAUTH2_CLIENT_ID = '<client id>' # OAUTH_OAUTH2_CLIENT_SECRET = '<client secret>' # The configuration OAUTH_<name>_VERIFY_SSL_CERTS is what is passed to the # 'verify' parameter in the Requests module, and can be used to disable # HTTPS verification (not recommended) or provide a custom CA bundle. See: # http://docs.python-requests.org/en/master/user/advanced/#ssl-cert-verification # You may also override the .validate() method of a KnowledgeAuthProvider # to perform an additional validation step before authenticating a user. # The following example checks whether a user has access to the git remote # of the local Knowledge Repository: # def OAUTH_OAUTH2_VALIDATE(provider, user): # # if provider.app.repository.git_has_remote: # # url_parts = ( # provider.app.repository.git_remote.url.split(':') # ) # # url_subparts = url_parts[1].split('/') # # if url_parts[0] == "git@gitlab.example.com": # git_project = ( # url_subparts[0] + "%2F" + url_subparts[1].split('.')[0]) # elif ( # url_parts[0] == "https" # and url_subparts[2] == "gitlab.example.com" # ): # git_project = ( # url_subparts[3] + "%2F" + url_subparts[4].split('.')[0]) # else: # provider.app.logger.warning( # "User validation failed: unexpected git remote url [" # + provider.app.repository.git_remote.url + "]") # return False # # user_validate_url = provider.base_url + "projects/" + git_project # # resp = provider.oauth_client.get( # user_validate_url, # verify=OAUTH_OAUTH2_VERIFY_HTTPS) # # if resp.status_code == 200: # return True # else: # provider.app.logger.warning( # "User validation failed: validation URL [" # + user_validate_url + "] returned HTTP status [" # + str(resp.status_code) + "]") # You can also forgo a fully-fledged sign in process for users by hosting the # knowledge repository behind a proxy server that pre-authenticates users, and # adds the appropriate user identifier to the http headers of the request. If # enabled below, then they take precedence over any other forms of # authentication. If the call to `AUTH_MAP_REQUEST_HEADERS` results in a null # user identifier, then the authentication flow will fall back to use any of # the providers specified above. AUTH_USE_REQUEST_HEADERS = False # If using headers to authenticate, the following function should be # implemented to transform a dictionary of headers into a dictionary of user # attributes. Currently only 'identifier', 'avatar_uri', 'name' and 'email' # are supported. If this method returns `None`, or `identifier` is not # supplied, then the authorization flow will fall back to other authentication # methods. def AUTH_MAP_REQUEST_HEADERS(headers): return { # 'identifier': None, # 'avatar_uri': None, # 'name': None, # 'email': None } # The following AUTH_USER_IDENTIFIER* configuration keys are deprecated and # will be removed in v0.9. AUTH_USER_IDENTIFIER_REQUEST_HEADER = None def AUTH_USER_IDENTIFIER_REQUEST_HEADER_MAPPING(identifier): return identifier # If the server desires to modify the attributes of the `User` object # associated with users logged in via any of the above authentication # providers, it can do so via this configuration key. This function will be # run once at user login (if using an `AuthenticationProvider`, and then at # most once during any caching lifetime period (as specified below). Note that # attributes collected via `AuthenticationProvider`s will not be updated # after initial login (user must relogin in order to reset those attributes). def AUTH_USER_ATTRIBUTE_SETTER(user): return user # The time to wait before re-checking user attributes with the above function # for users logged in via request headers. AUTH_USER_ATTRIBUTE_CACHE_LIFETIME = 24 * 60 * 60 # 1 day # Once a user is logged in via an authentication provider, they will remain # logged in via the use of cookies. By default, this cookie will last one year. # This is managed by `flask_login`, but is copied here for convenience. # For other options regarding sessions, please refer to: # https://flask-login.readthedocs.io/en/latest/#cookie-settings REMEMBER_COOKIE_DURATION = timedelta(days=365) # --------------------------------------------------- # LDAP configuration # --------------------------------------------------- # When using an LDAP server for user verification, you need to configure # the location of the server, and the directory structure used by your # organization. # Currently the port and protocol must both be included in the server address LDAP_SERVER = "ldap://127.0.0.1:389" # When entering this, note the "{0}" which denotes where the user_id # is inserted. LDAP_USERDN_SCHEMA = "cn={user_id},ou=people,dc=planetexpress,dc=com" # --------------------------------------------------- # Policy configuration # --------------------------------------------------- # This section configures various policy related to access control. # Should anonymous users be able to view the post indices POLICY_ANONYMOUS_VIEW_INDEX = True # Should anonymous users be able to view the content of posts POLICY_ANONYMOUS_VIEW_POST = True # Should anonymous users be able to view overall statistics POLICY_ANONYMOUS_VIEW_STATS = True # Should anonymous users be able to view tag pages POLICY_ANONYMOUS_VIEW_TAGS = True # Should anonymous users be able to download posts (or their source) POLICY_ANONYMOUS_DOWNLOADS = False # --------------------------------------------------- # Repository configuration # --------------------------------------------------- # You may specify a function `prepare_repo` which configures # the repository upon which this server is running. This # takes place after the repository has been instantiated # and before the server is able to serve requests. It is # possible to do anything to the repository, including # substituting the repository for another one. # By default, repositories manage their own configurations, # but this can be risky as they may run arbitrary python code, # which opens a vector for malicious users to compromise # the server. If you want to avoid this risk, pass # the '--safe' (TODO!) option to `knowledge_repo` config and # manually configure the repository here. # For example, if your server instance is sitting atop # a meta-repository, it may make sense to update the meta-repository # configuration with that of one of its children. def prepare_repo(repo): return repo # --------------------------------------------------- # Repository Indexing configuration # --------------------------------------------------- # The Knowedge Repo updates the index of available posts on a regular basis. # If the database is not thread-safe (i.e. in the case of SQLite), then the # index will be updated on the main thread before every request that is more # than `INDEX_INTERVAL` seconds after the last sync completed. Otherwise, # indexing will occur every `INDEX_INTERVAL` seconds after the previous sync. # Syncing is designed to be compatible with multiple instances of the Knowledge # Repo connected to the same database, accross multiple machines and/or # processes; and so a global indexing lock is employed. When a sync begins, # a sync lock is put in place and the responsible process is considered to be # the primary agent responsible for syncing until its last update is longer # than`INDEXING_TIMEOUT` seconds, whereby the lock is ceded to the next # requesting process. Note that `INDEXING_TIMEOUT` must be larger than # `INDEXING_INTERVAL` or strange things might begin to happen. INDEXING_INTERVAL = 5 * 60 # 5 minutes INDEXING_TIMEOUT = 10 * 60 # 10 minutes # Whether an index operation should update repositories INDEXING_UPDATES_REPOSITORIES = True # Whether repositories should be updated even without a sync lock (in which # case the repositories will be updated on the sync timers, even if the # relevant process/thread does not have a lock on updating the index). This is # useful in context of multiple Knowledge Repo servers working together to # serve the repositories across multiple machines, which each require # repository syncing. Disable this if (for some reason) you have multiple # Knowledge Repo servers running on the same machine, and you want to avoid # potential clashes. This key is ignored if `INDEXING_UPDATES_REPOSITORIES` is # False INDEXING_UPDATES_REPOSITORIES_WITHOUT_LOCK = True # In some cases you may want to disable indexing entirely, which is currently # only ever used by the Knowledge Post previewer. Disabling the index means # that posts will not be discoverable, but if know the path in the repository # you can view the post with a direct link. INDEXING_ENABLED = True # --------------------------------------------------- # Flask Mail Configuration # Refer to https://pythonhosted.org/flask-mail/ # Unless specified, upstream defaults are used as indicated # provided that MAIL_SERVER is defined. # --------------------------------------------------- # MAIL_SERVER = 'localhost' # default = 'localhost' # MAIL_PORT = 25 # default = 25 # MAIL_USE_TLS = False # default = False # MAIL_USE_SSL = False # default = False # MAIL_DEBUG = False # default = app.debug # MAIL_USERNAME = None # default = None # MAIL_PASSWORD = None # default = None # MAIL_DEFAULT_SENDER = None # default = None # MAIL_MAX_EMAILS = None # default = None # MAIL_SUPPRESS_SEND = False # default = app.testing # MAIL_ASCII_ATTACHMENTS = False # default = False # # Detailed integration procedure with SendGrid is available at: # https://sendgrid.com/blog/sending-emails-from-python-flask-applications-with-twilio-sendgrid/ # -------------------------------------------------- # Web Editor Configuration # -------------------------------------------------- # The web editor can be limited to editing posts under # a limited set of parent directories by setting # WEB_EDITOR_PREFIXES to a list of supported path prefixes. # e.g. ['webposts', 'projects'] WEB_EDITOR_PREFIXES = ["webposts"] # --------------------------------------------------- # Tag configuration # --------------------------------------------------- # Posts with certain tags can be excluded from showing up # in the app. This can be useful for security purposes EXCLUDED_TAGS = ["private"] # ------------- # Collapse Code as Default Display Option # ------------- COLLAPSE_CODE_DEFAULT = False # ------------- # AWS related settings # ------------- S3_AWS_ACCESS_KEY_ID = "" S3_AWS_SECRET_ACCESS_KEY = "" S3_AWS_REGION_NAME = ""

from datetime import timedelta # --------------------------------------------------- # Host configuration # --------------------------------------------------- # The server name is used by Flask to limit access to the # served content to request to a particular domain. It # is also used by some authentication providers (in particular # OAuth providers) to advertise callback providers. If # not provided, it is assumed in these contexts to be # 'localhost:7000'. Be sure to specify this before deploying # into production. SERVER_NAME = "localhost:7001" # The knowledge repository uses the secret key to sign user # sessions. If not specified, a unique secret key will be # generated every time the server starts up. If hosting # in a multi-server environment, or you want sessions # to persist accross server restarts, set this to something # static. SECRET_KEY = None # Set DEPLOY_HTTPS to True if you want to enable encrypted # communication with Flask. When enabled, you must provide # your ssl certificate, which consists of a .crt and .key # file. # Note: Even if you set DEPLOY_HTTPS to True, you still need # to set the port to 443 manually. DEPLOY_HTTPS = False SSL_CERT = {"cert": "/path/to/cert", "key": "/path/to/key"} # --------------------------------------------------- # Debug configuration # --------------------------------------------------- DEBUG = False # --------------------------------------------------- # Database configuration # --------------------------------------------------- SQLALCHEMY_DATABASE_URI = ( "postgresql+psycopg2://knowledge_repo:password@localhost:5432/knowledge_repo" ) # If you are using a MySQL database, you must specify the URI as # demonstrated below. # SQLALCHEMY_DATABASE_URI = 'mysql://username:password@hostname/database' SQLALCHEMY_ECHO = False SQLALCHEMY_TRACK_MODIFICATIONS = False # Should the database tables be automatically created DB_AUTO_CREATE = True # Should the database be automatically migrated when updates exist # Note: This is True by default if this configuration is not applied, # under the assumption that if you are concerned about this file # you are probably interested in minimising risk to stability and handling # database upgrades manually. Manual database migrations can be # performed using `knowledge_repo --repo <> db_upgrade ...`. DB_AUTO_UPGRADE = False # --------------------------------------------------- # Authentication configuration # --------------------------------------------------- # Authentication providers allow users to sign into the Knowledge Repo # in a variety of different ways. You can create your own subclass of # `KnowledgeAuthProvider` and add either the instance or identifier # used for that class below. # By default, the knowledge repo offers: # ['debug', 'oauth2', 'bitbucket', 'github', 'google', 'ldap'] AUTH_PROVIDERS = ["google"] OAUTH_GOOGLE_CLIENT_ID = "<client id>" OAUTH_GOOGLE_CLIENT_SECRET = "<client secret>" # If you are going to use a OAuth provider, you will need to specify client ids # and private tokens. This can be done by instantiating instances of # `OAuth2Provider` and adding them to the above list, or by specifying OAuth # connection properties as demonstrated below for the GitHub authenticator. # OAUTH_GITHUB_CLIENT_ID = '<client id>' # OAUTH_GITHUB_CLIENT_SECRET = '<client secret>' # To configure a generic OAuth provider that is not one of the presets # provided, you may use the provider 'oauth2' which creates an empty, # unconfigured OAuth2Provider. You must then override its configuration. # For example, for a self-managed Gitlab CE instance at gitlab.example.com: # OAUTH_OAUTH2_BASE_URL = 'https://gitlab.example.com/api/v4/' # OAUTH_OAUTH2_AUTHORIZATION_URL = 'https://gitlab.example.com/oauth/authorize' # OAUTH_OAUTH2_TOKEN_URL = 'https://gitlab.example.com/oauth/token' # OAUTH_OAUTH2_AUTO_REFRESH_URL = 'https://gitlab.example.com/oauth/token' # OAUTH_OAUTH2_SCOPES = 'api' # OAUTH_OAUTH2_USER_INFO_ENDPOINT = 'user' # OAUTH_OAUTH2_USER_INFO_MAPPING = { # 'identifier': 'username', # 'name': 'name', # 'avatar_uri': 'avatar_url' # } # OAUTH_OAUTH2_VERIFY_SSL_CERTS = '/path/to/certs/my.ca-bundle' # OAUTH_OAUTH2_CLIENT_ID = '<client id>' # OAUTH_OAUTH2_CLIENT_SECRET = '<client secret>' # The configuration OAUTH_<name>_VERIFY_SSL_CERTS is what is passed to the # 'verify' parameter in the Requests module, and can be used to disable # HTTPS verification (not recommended) or provide a custom CA bundle. See: # http://docs.python-requests.org/en/master/user/advanced/#ssl-cert-verification # You may also override the .validate() method of a KnowledgeAuthProvider # to perform an additional validation step before authenticating a user. # The following example checks whether a user has access to the git remote # of the local Knowledge Repository: # def OAUTH_OAUTH2_VALIDATE(provider, user): # # if provider.app.repository.git_has_remote: # # url_parts = ( # provider.app.repository.git_remote.url.split(':') # ) # # url_subparts = url_parts[1].split('/') # # if url_parts[0] == "git@gitlab.example.com": # git_project = ( # url_subparts[0] + "%2F" + url_subparts[1].split('.')[0]) # elif ( # url_parts[0] == "https" # and url_subparts[2] == "gitlab.example.com" # ): # git_project = ( # url_subparts[3] + "%2F" + url_subparts[4].split('.')[0]) # else: # provider.app.logger.warning( # "User validation failed: unexpected git remote url [" # + provider.app.repository.git_remote.url + "]") # return False # # user_validate_url = provider.base_url + "projects/" + git_project # # resp = provider.oauth_client.get( # user_validate_url, # verify=OAUTH_OAUTH2_VERIFY_HTTPS) # # if resp.status_code == 200: # return True # else: # provider.app.logger.warning( # "User validation failed: validation URL [" # + user_validate_url + "] returned HTTP status [" # + str(resp.status_code) + "]") # You can also forgo a fully-fledged sign in process for users by hosting the # knowledge repository behind a proxy server that pre-authenticates users, and # adds the appropriate user identifier to the http headers of the request. If # enabled below, then they take precedence over any other forms of # authentication. If the call to `AUTH_MAP_REQUEST_HEADERS` results in a null # user identifier, then the authentication flow will fall back to use any of # the providers specified above. AUTH_USE_REQUEST_HEADERS = False # If using headers to authenticate, the following function should be # implemented to transform a dictionary of headers into a dictionary of user # attributes. Currently only 'identifier', 'avatar_uri', 'name' and 'email' # are supported. If this method returns `None`, or `identifier` is not # supplied, then the authorization flow will fall back to other authentication # methods. def AUTH_MAP_REQUEST_HEADERS(headers): return { # 'identifier': None, # 'avatar_uri': None, # 'name': None, # 'email': None } # The following AUTH_USER_IDENTIFIER* configuration keys are deprecated and # will be removed in v0.9. AUTH_USER_IDENTIFIER_REQUEST_HEADER = None def AUTH_USER_IDENTIFIER_REQUEST_HEADER_MAPPING(identifier): return identifier # If the server desires to modify the attributes of the `User` object # associated with users logged in via any of the above authentication # providers, it can do so via this configuration key. This function will be # run once at user login (if using an `AuthenticationProvider`, and then at # most once during any caching lifetime period (as specified below). Note that # attributes collected via `AuthenticationProvider`s will not be updated # after initial login (user must relogin in order to reset those attributes). def AUTH_USER_ATTRIBUTE_SETTER(user): return user # The time to wait before re-checking user attributes with the above function # for users logged in via request headers. AUTH_USER_ATTRIBUTE_CACHE_LIFETIME = 24 * 60 * 60 # 1 day # Once a user is logged in via an authentication provider, they will remain # logged in via the use of cookies. By default, this cookie will last one year. # This is managed by `flask_login`, but is copied here for convenience. # For other options regarding sessions, please refer to: # https://flask-login.readthedocs.io/en/latest/#cookie-settings REMEMBER_COOKIE_DURATION = timedelta(days=365) # --------------------------------------------------- # LDAP configuration # --------------------------------------------------- # When using an LDAP server for user verification, you need to configure # the location of the server, and the directory structure used by your # organization. # Currently the port and protocol must both be included in the server address LDAP_SERVER = "ldap://127.0.0.1:389" # When entering this, note the "{0}" which denotes where the user_id # is inserted. LDAP_USERDN_SCHEMA = "cn={user_id},ou=people,dc=planetexpress,dc=com" # --------------------------------------------------- # Policy configuration # --------------------------------------------------- # This section configures various policy related to access control. # Should anonymous users be able to view the post indices POLICY_ANONYMOUS_VIEW_INDEX = True # Should anonymous users be able to view the content of posts POLICY_ANONYMOUS_VIEW_POST = True # Should anonymous users be able to view overall statistics POLICY_ANONYMOUS_VIEW_STATS = True # Should anonymous users be able to view tag pages POLICY_ANONYMOUS_VIEW_TAGS = True # Should anonymous users be able to download posts (or their source) POLICY_ANONYMOUS_DOWNLOADS = False # --------------------------------------------------- # Repository configuration # --------------------------------------------------- # You may specify a function `prepare_repo` which configures # the repository upon which this server is running. This # takes place after the repository has been instantiated # and before the server is able to serve requests. It is # possible to do anything to the repository, including # substituting the repository for another one. # By default, repositories manage their own configurations, # but this can be risky as they may run arbitrary python code, # which opens a vector for malicious users to compromise # the server. If you want to avoid this risk, pass # the '--safe' (TODO!) option to `knowledge_repo` config and # manually configure the repository here. # For example, if your server instance is sitting atop # a meta-repository, it may make sense to update the meta-repository # configuration with that of one of its children. def prepare_repo(repo): return repo # --------------------------------------------------- # Repository Indexing configuration # --------------------------------------------------- # The Knowedge Repo updates the index of available posts on a regular basis. # If the database is not thread-safe (i.e. in the case of SQLite), then the # index will be updated on the main thread before every request that is more # than `INDEX_INTERVAL` seconds after the last sync completed. Otherwise, # indexing will occur every `INDEX_INTERVAL` seconds after the previous sync. # Syncing is designed to be compatible with multiple instances of the Knowledge # Repo connected to the same database, accross multiple machines and/or # processes; and so a global indexing lock is employed. When a sync begins, # a sync lock is put in place and the responsible process is considered to be # the primary agent responsible for syncing until its last update is longer # than`INDEXING_TIMEOUT` seconds, whereby the lock is ceded to the next # requesting process. Note that `INDEXING_TIMEOUT` must be larger than # `INDEXING_INTERVAL` or strange things might begin to happen. INDEXING_INTERVAL = 5 * 60 # 5 minutes INDEXING_TIMEOUT = 10 * 60 # 10 minutes # Whether an index operation should update repositories INDEXING_UPDATES_REPOSITORIES = True # Whether repositories should be updated even without a sync lock (in which # case the repositories will be updated on the sync timers, even if the # relevant process/thread does not have a lock on updating the index). This is # useful in context of multiple Knowledge Repo servers working together to # serve the repositories across multiple machines, which each require # repository syncing. Disable this if (for some reason) you have multiple # Knowledge Repo servers running on the same machine, and you want to avoid # potential clashes. This key is ignored if `INDEXING_UPDATES_REPOSITORIES` is # False INDEXING_UPDATES_REPOSITORIES_WITHOUT_LOCK = True # In some cases you may want to disable indexing entirely, which is currently # only ever used by the Knowledge Post previewer. Disabling the index means # that posts will not be discoverable, but if know the path in the repository # you can view the post with a direct link. INDEXING_ENABLED = True # --------------------------------------------------- # Flask Mail Configuration # Refer to https://pythonhosted.org/flask-mail/ # Unless specified, upstream defaults are used as indicated # provided that MAIL_SERVER is defined. # --------------------------------------------------- # MAIL_SERVER = 'localhost' # default = 'localhost' # MAIL_PORT = 25 # default = 25 # MAIL_USE_TLS = False # default = False # MAIL_USE_SSL = False # default = False # MAIL_DEBUG = False # default = app.debug # MAIL_USERNAME = None # default = None # MAIL_PASSWORD = None # default = None # MAIL_DEFAULT_SENDER = None # default = None # MAIL_MAX_EMAILS = None # default = None # MAIL_SUPPRESS_SEND = False # default = app.testing # MAIL_ASCII_ATTACHMENTS = False # default = False # # Detailed integration procedure with SendGrid is available at: # https://sendgrid.com/blog/sending-emails-from-python-flask-applications-with-twilio-sendgrid/ # -------------------------------------------------- # Web Editor Configuration # -------------------------------------------------- # The web editor can be limited to editing posts under # a limited set of parent directories by setting # WEB_EDITOR_PREFIXES to a list of supported path prefixes. # e.g. ['webposts', 'projects'] WEB_EDITOR_PREFIXES = ["webposts"] # --------------------------------------------------- # Tag configuration # --------------------------------------------------- # Posts with certain tags can be excluded from showing up # in the app. This can be useful for security purposes EXCLUDED_TAGS = ["private"] # ------------- # Collapse Code as Default Display Option # ------------- COLLAPSE_CODE_DEFAULT = False # ------------- # AWS related settings # ------------- S3_AWS_ACCESS_KEY_ID = "" S3_AWS_SECRET_ACCESS_KEY = "" S3_AWS_REGION_NAME = "us-west-2"

mengting1010

b15c759e7d17b6a52c502b8bfdc174181da83789

451577868d66570a463260c67dff7034214beafd

I add this file in gitignore, somehow it does not works looks like.

JJJ000

airbnb/knowledge-repo

Enable Jupyter Notebook Upload Post Editor

Description of changeset: - Enable jupyter notebook upload post editor  - This pr only enables the ability to upload and render a html version of jupyter notebook (if it is a ipynb file, s3 will download the file to your local instead of displaying it. todo: will try to fix it in next pr)  Test Plan: Local Dev Reviewers: @csharplus @JJJ000

2022-12-27 04:44:23+00:00

2022-12-27 20:24:27+00:00

docker/config.py

from datetime import timedelta # --------------------------------------------------- # Host configuration # --------------------------------------------------- # The server name is used by Flask to limit access to the # served content to request to a particular domain. It # is also used by some authentication providers (in particular # OAuth providers) to advertise callback providers. If # not provided, it is assumed in these contexts to be # 'localhost:7000'. Be sure to specify this before deploying # into production. SERVER_NAME = "localhost:7001" # The knowledge repository uses the secret key to sign user # sessions. If not specified, a unique secret key will be # generated every time the server starts up. If hosting # in a multi-server environment, or you want sessions # to persist accross server restarts, set this to something # static. SECRET_KEY = None # Set DEPLOY_HTTPS to True if you want to enable encrypted # communication with Flask. When enabled, you must provide # your ssl certificate, which consists of a .crt and .key # file. # Note: Even if you set DEPLOY_HTTPS to True, you still need # to set the port to 443 manually. DEPLOY_HTTPS = False SSL_CERT = {"cert": "/path/to/cert", "key": "/path/to/key"} # --------------------------------------------------- # Debug configuration # --------------------------------------------------- DEBUG = False # --------------------------------------------------- # Database configuration # --------------------------------------------------- SQLALCHEMY_DATABASE_URI = ( "postgresql+psycopg2://knowledge_repo:password@localhost:5432/knowledge_repo" ) # If you are using a MySQL database, you must specify the URI as # demonstrated below. # SQLALCHEMY_DATABASE_URI = 'mysql://username:password@hostname/database' SQLALCHEMY_ECHO = False SQLALCHEMY_TRACK_MODIFICATIONS = False # Should the database tables be automatically created DB_AUTO_CREATE = True # Should the database be automatically migrated when updates exist # Note: This is True by default if this configuration is not applied, # under the assumption that if you are concerned about this file # you are probably interested in minimising risk to stability and handling # database upgrades manually. Manual database migrations can be # performed using `knowledge_repo --repo <> db_upgrade ...`. DB_AUTO_UPGRADE = False # --------------------------------------------------- # Authentication configuration # --------------------------------------------------- # Authentication providers allow users to sign into the Knowledge Repo # in a variety of different ways. You can create your own subclass of # `KnowledgeAuthProvider` and add either the instance or identifier # used for that class below. # By default, the knowledge repo offers: # ['debug', 'oauth2', 'bitbucket', 'github', 'google', 'ldap'] AUTH_PROVIDERS = ["google"] OAUTH_GOOGLE_CLIENT_ID = "<client id>" OAUTH_GOOGLE_CLIENT_SECRET = "<client secret>" # If you are going to use a OAuth provider, you will need to specify client ids # and private tokens. This can be done by instantiating instances of # `OAuth2Provider` and adding them to the above list, or by specifying OAuth # connection properties as demonstrated below for the GitHub authenticator. # OAUTH_GITHUB_CLIENT_ID = '<client id>' # OAUTH_GITHUB_CLIENT_SECRET = '<client secret>' # To configure a generic OAuth provider that is not one of the presets # provided, you may use the provider 'oauth2' which creates an empty, # unconfigured OAuth2Provider. You must then override its configuration. # For example, for a self-managed Gitlab CE instance at gitlab.example.com: # OAUTH_OAUTH2_BASE_URL = 'https://gitlab.example.com/api/v4/' # OAUTH_OAUTH2_AUTHORIZATION_URL = 'https://gitlab.example.com/oauth/authorize' # OAUTH_OAUTH2_TOKEN_URL = 'https://gitlab.example.com/oauth/token' # OAUTH_OAUTH2_AUTO_REFRESH_URL = 'https://gitlab.example.com/oauth/token' # OAUTH_OAUTH2_SCOPES = 'api' # OAUTH_OAUTH2_USER_INFO_ENDPOINT = 'user' # OAUTH_OAUTH2_USER_INFO_MAPPING = { # 'identifier': 'username', # 'name': 'name', # 'avatar_uri': 'avatar_url' # } # OAUTH_OAUTH2_VERIFY_SSL_CERTS = '/path/to/certs/my.ca-bundle' # OAUTH_OAUTH2_CLIENT_ID = '<client id>' # OAUTH_OAUTH2_CLIENT_SECRET = '<client secret>' # The configuration OAUTH_<name>_VERIFY_SSL_CERTS is what is passed to the # 'verify' parameter in the Requests module, and can be used to disable # HTTPS verification (not recommended) or provide a custom CA bundle. See: # http://docs.python-requests.org/en/master/user/advanced/#ssl-cert-verification # You may also override the .validate() method of a KnowledgeAuthProvider # to perform an additional validation step before authenticating a user. # The following example checks whether a user has access to the git remote # of the local Knowledge Repository: # def OAUTH_OAUTH2_VALIDATE(provider, user): # # if provider.app.repository.git_has_remote: # # url_parts = ( # provider.app.repository.git_remote.url.split(':') # ) # # url_subparts = url_parts[1].split('/') # # if url_parts[0] == "git@gitlab.example.com": # git_project = ( # url_subparts[0] + "%2F" + url_subparts[1].split('.')[0]) # elif ( # url_parts[0] == "https" # and url_subparts[2] == "gitlab.example.com" # ): # git_project = ( # url_subparts[3] + "%2F" + url_subparts[4].split('.')[0]) # else: # provider.app.logger.warning( # "User validation failed: unexpected git remote url [" # + provider.app.repository.git_remote.url + "]") # return False # # user_validate_url = provider.base_url + "projects/" + git_project # # resp = provider.oauth_client.get( # user_validate_url, # verify=OAUTH_OAUTH2_VERIFY_HTTPS) # # if resp.status_code == 200: # return True # else: # provider.app.logger.warning( # "User validation failed: validation URL [" # + user_validate_url + "] returned HTTP status [" # + str(resp.status_code) + "]") # You can also forgo a fully-fledged sign in process for users by hosting the # knowledge repository behind a proxy server that pre-authenticates users, and # adds the appropriate user identifier to the http headers of the request. If # enabled below, then they take precedence over any other forms of # authentication. If the call to `AUTH_MAP_REQUEST_HEADERS` results in a null # user identifier, then the authentication flow will fall back to use any of # the providers specified above. AUTH_USE_REQUEST_HEADERS = False # If using headers to authenticate, the following function should be # implemented to transform a dictionary of headers into a dictionary of user # attributes. Currently only 'identifier', 'avatar_uri', 'name' and 'email' # are supported. If this method returns `None`, or `identifier` is not # supplied, then the authorization flow will fall back to other authentication # methods. def AUTH_MAP_REQUEST_HEADERS(headers): return { # 'identifier': None, # 'avatar_uri': None, # 'name': None, # 'email': None } # The following AUTH_USER_IDENTIFIER* configuration keys are deprecated and # will be removed in v0.9. AUTH_USER_IDENTIFIER_REQUEST_HEADER = None def AUTH_USER_IDENTIFIER_REQUEST_HEADER_MAPPING(identifier): return identifier # If the server desires to modify the attributes of the `User` object # associated with users logged in via any of the above authentication # providers, it can do so via this configuration key. This function will be # run once at user login (if using an `AuthenticationProvider`, and then at # most once during any caching lifetime period (as specified below). Note that # attributes collected via `AuthenticationProvider`s will not be updated # after initial login (user must relogin in order to reset those attributes). def AUTH_USER_ATTRIBUTE_SETTER(user): return user # The time to wait before re-checking user attributes with the above function # for users logged in via request headers. AUTH_USER_ATTRIBUTE_CACHE_LIFETIME = 24 * 60 * 60 # 1 day # Once a user is logged in via an authentication provider, they will remain # logged in via the use of cookies. By default, this cookie will last one year. # This is managed by `flask_login`, but is copied here for convenience. # For other options regarding sessions, please refer to: # https://flask-login.readthedocs.io/en/latest/#cookie-settings REMEMBER_COOKIE_DURATION = timedelta(days=365) # --------------------------------------------------- # LDAP configuration # --------------------------------------------------- # When using an LDAP server for user verification, you need to configure # the location of the server, and the directory structure used by your # organization. # Currently the port and protocol must both be included in the server address LDAP_SERVER = "ldap://127.0.0.1:389" # When entering this, note the "{0}" which denotes where the user_id # is inserted. LDAP_USERDN_SCHEMA = "cn={user_id},ou=people,dc=planetexpress,dc=com" # --------------------------------------------------- # Policy configuration # --------------------------------------------------- # This section configures various policy related to access control. # Should anonymous users be able to view the post indices POLICY_ANONYMOUS_VIEW_INDEX = True # Should anonymous users be able to view the content of posts POLICY_ANONYMOUS_VIEW_POST = True # Should anonymous users be able to view overall statistics POLICY_ANONYMOUS_VIEW_STATS = True # Should anonymous users be able to view tag pages POLICY_ANONYMOUS_VIEW_TAGS = True # Should anonymous users be able to download posts (or their source) POLICY_ANONYMOUS_DOWNLOADS = False # --------------------------------------------------- # Repository configuration # --------------------------------------------------- # You may specify a function `prepare_repo` which configures # the repository upon which this server is running. This # takes place after the repository has been instantiated # and before the server is able to serve requests. It is # possible to do anything to the repository, including # substituting the repository for another one. # By default, repositories manage their own configurations, # but this can be risky as they may run arbitrary python code, # which opens a vector for malicious users to compromise # the server. If you want to avoid this risk, pass # the '--safe' (TODO!) option to `knowledge_repo` config and # manually configure the repository here. # For example, if your server instance is sitting atop # a meta-repository, it may make sense to update the meta-repository # configuration with that of one of its children. def prepare_repo(repo): return repo # --------------------------------------------------- # Repository Indexing configuration # --------------------------------------------------- # The Knowedge Repo updates the index of available posts on a regular basis. # If the database is not thread-safe (i.e. in the case of SQLite), then the # index will be updated on the main thread before every request that is more # than `INDEX_INTERVAL` seconds after the last sync completed. Otherwise, # indexing will occur every `INDEX_INTERVAL` seconds after the previous sync. # Syncing is designed to be compatible with multiple instances of the Knowledge # Repo connected to the same database, accross multiple machines and/or # processes; and so a global indexing lock is employed. When a sync begins, # a sync lock is put in place and the responsible process is considered to be # the primary agent responsible for syncing until its last update is longer # than`INDEXING_TIMEOUT` seconds, whereby the lock is ceded to the next # requesting process. Note that `INDEXING_TIMEOUT` must be larger than # `INDEXING_INTERVAL` or strange things might begin to happen. INDEXING_INTERVAL = 5 * 60 # 5 minutes INDEXING_TIMEOUT = 10 * 60 # 10 minutes # Whether an index operation should update repositories INDEXING_UPDATES_REPOSITORIES = True # Whether repositories should be updated even without a sync lock (in which # case the repositories will be updated on the sync timers, even if the # relevant process/thread does not have a lock on updating the index). This is # useful in context of multiple Knowledge Repo servers working together to # serve the repositories across multiple machines, which each require # repository syncing. Disable this if (for some reason) you have multiple # Knowledge Repo servers running on the same machine, and you want to avoid # potential clashes. This key is ignored if `INDEXING_UPDATES_REPOSITORIES` is # False INDEXING_UPDATES_REPOSITORIES_WITHOUT_LOCK = True # In some cases you may want to disable indexing entirely, which is currently # only ever used by the Knowledge Post previewer. Disabling the index means # that posts will not be discoverable, but if know the path in the repository # you can view the post with a direct link. INDEXING_ENABLED = True # --------------------------------------------------- # Flask Mail Configuration # Refer to https://pythonhosted.org/flask-mail/ # Unless specified, upstream defaults are used as indicated # provided that MAIL_SERVER is defined. # --------------------------------------------------- # MAIL_SERVER = 'localhost' # default = 'localhost' # MAIL_PORT = 25 # default = 25 # MAIL_USE_TLS = False # default = False # MAIL_USE_SSL = False # default = False # MAIL_DEBUG = False # default = app.debug # MAIL_USERNAME = None # default = None # MAIL_PASSWORD = None # default = None # MAIL_DEFAULT_SENDER = None # default = None # MAIL_MAX_EMAILS = None # default = None # MAIL_SUPPRESS_SEND = False # default = app.testing # MAIL_ASCII_ATTACHMENTS = False # default = False # # Detailed integration procedure with SendGrid is available at: # https://sendgrid.com/blog/sending-emails-from-python-flask-applications-with-twilio-sendgrid/ # -------------------------------------------------- # Web Editor Configuration # -------------------------------------------------- # The web editor can be limited to editing posts under # a limited set of parent directories by setting # WEB_EDITOR_PREFIXES to a list of supported path prefixes. # e.g. ['webposts', 'projects'] WEB_EDITOR_PREFIXES = ["webposts"] # --------------------------------------------------- # Tag configuration # --------------------------------------------------- # Posts with certain tags can be excluded from showing up # in the app. This can be useful for security purposes EXCLUDED_TAGS = ["private"] # ------------- # Collapse Code as Default Display Option # ------------- COLLAPSE_CODE_DEFAULT = False # ------------- # AWS related settings # ------------- S3_AWS_ACCESS_KEY_ID = "" S3_AWS_SECRET_ACCESS_KEY = "" S3_AWS_REGION_NAME = ""

from datetime import timedelta # --------------------------------------------------- # Host configuration # --------------------------------------------------- # The server name is used by Flask to limit access to the # served content to request to a particular domain. It # is also used by some authentication providers (in particular # OAuth providers) to advertise callback providers. If # not provided, it is assumed in these contexts to be # 'localhost:7000'. Be sure to specify this before deploying # into production. SERVER_NAME = "localhost:7001" # The knowledge repository uses the secret key to sign user # sessions. If not specified, a unique secret key will be # generated every time the server starts up. If hosting # in a multi-server environment, or you want sessions # to persist accross server restarts, set this to something # static. SECRET_KEY = None # Set DEPLOY_HTTPS to True if you want to enable encrypted # communication with Flask. When enabled, you must provide # your ssl certificate, which consists of a .crt and .key # file. # Note: Even if you set DEPLOY_HTTPS to True, you still need # to set the port to 443 manually. DEPLOY_HTTPS = False SSL_CERT = {"cert": "/path/to/cert", "key": "/path/to/key"} # --------------------------------------------------- # Debug configuration # --------------------------------------------------- DEBUG = False # --------------------------------------------------- # Database configuration # --------------------------------------------------- SQLALCHEMY_DATABASE_URI = ( "postgresql+psycopg2://knowledge_repo:password@localhost:5432/knowledge_repo" ) # If you are using a MySQL database, you must specify the URI as # demonstrated below. # SQLALCHEMY_DATABASE_URI = 'mysql://username:password@hostname/database' SQLALCHEMY_ECHO = False SQLALCHEMY_TRACK_MODIFICATIONS = False # Should the database tables be automatically created DB_AUTO_CREATE = True # Should the database be automatically migrated when updates exist # Note: This is True by default if this configuration is not applied, # under the assumption that if you are concerned about this file # you are probably interested in minimising risk to stability and handling # database upgrades manually. Manual database migrations can be # performed using `knowledge_repo --repo <> db_upgrade ...`. DB_AUTO_UPGRADE = False # --------------------------------------------------- # Authentication configuration # --------------------------------------------------- # Authentication providers allow users to sign into the Knowledge Repo # in a variety of different ways. You can create your own subclass of # `KnowledgeAuthProvider` and add either the instance or identifier # used for that class below. # By default, the knowledge repo offers: # ['debug', 'oauth2', 'bitbucket', 'github', 'google', 'ldap'] AUTH_PROVIDERS = ["google"] OAUTH_GOOGLE_CLIENT_ID = "<client id>" OAUTH_GOOGLE_CLIENT_SECRET = "<client secret>" # If you are going to use a OAuth provider, you will need to specify client ids # and private tokens. This can be done by instantiating instances of # `OAuth2Provider` and adding them to the above list, or by specifying OAuth # connection properties as demonstrated below for the GitHub authenticator. # OAUTH_GITHUB_CLIENT_ID = '<client id>' # OAUTH_GITHUB_CLIENT_SECRET = '<client secret>' # To configure a generic OAuth provider that is not one of the presets # provided, you may use the provider 'oauth2' which creates an empty, # unconfigured OAuth2Provider. You must then override its configuration. # For example, for a self-managed Gitlab CE instance at gitlab.example.com: # OAUTH_OAUTH2_BASE_URL = 'https://gitlab.example.com/api/v4/' # OAUTH_OAUTH2_AUTHORIZATION_URL = 'https://gitlab.example.com/oauth/authorize' # OAUTH_OAUTH2_TOKEN_URL = 'https://gitlab.example.com/oauth/token' # OAUTH_OAUTH2_AUTO_REFRESH_URL = 'https://gitlab.example.com/oauth/token' # OAUTH_OAUTH2_SCOPES = 'api' # OAUTH_OAUTH2_USER_INFO_ENDPOINT = 'user' # OAUTH_OAUTH2_USER_INFO_MAPPING = { # 'identifier': 'username', # 'name': 'name', # 'avatar_uri': 'avatar_url' # } # OAUTH_OAUTH2_VERIFY_SSL_CERTS = '/path/to/certs/my.ca-bundle' # OAUTH_OAUTH2_CLIENT_ID = '<client id>' # OAUTH_OAUTH2_CLIENT_SECRET = '<client secret>' # The configuration OAUTH_<name>_VERIFY_SSL_CERTS is what is passed to the # 'verify' parameter in the Requests module, and can be used to disable # HTTPS verification (not recommended) or provide a custom CA bundle. See: # http://docs.python-requests.org/en/master/user/advanced/#ssl-cert-verification # You may also override the .validate() method of a KnowledgeAuthProvider # to perform an additional validation step before authenticating a user. # The following example checks whether a user has access to the git remote # of the local Knowledge Repository: # def OAUTH_OAUTH2_VALIDATE(provider, user): # # if provider.app.repository.git_has_remote: # # url_parts = ( # provider.app.repository.git_remote.url.split(':') # ) # # url_subparts = url_parts[1].split('/') # # if url_parts[0] == "git@gitlab.example.com": # git_project = ( # url_subparts[0] + "%2F" + url_subparts[1].split('.')[0]) # elif ( # url_parts[0] == "https" # and url_subparts[2] == "gitlab.example.com" # ): # git_project = ( # url_subparts[3] + "%2F" + url_subparts[4].split('.')[0]) # else: # provider.app.logger.warning( # "User validation failed: unexpected git remote url [" # + provider.app.repository.git_remote.url + "]") # return False # # user_validate_url = provider.base_url + "projects/" + git_project # # resp = provider.oauth_client.get( # user_validate_url, # verify=OAUTH_OAUTH2_VERIFY_HTTPS) # # if resp.status_code == 200: # return True # else: # provider.app.logger.warning( # "User validation failed: validation URL [" # + user_validate_url + "] returned HTTP status [" # + str(resp.status_code) + "]") # You can also forgo a fully-fledged sign in process for users by hosting the # knowledge repository behind a proxy server that pre-authenticates users, and # adds the appropriate user identifier to the http headers of the request. If # enabled below, then they take precedence over any other forms of # authentication. If the call to `AUTH_MAP_REQUEST_HEADERS` results in a null # user identifier, then the authentication flow will fall back to use any of # the providers specified above. AUTH_USE_REQUEST_HEADERS = False # If using headers to authenticate, the following function should be # implemented to transform a dictionary of headers into a dictionary of user # attributes. Currently only 'identifier', 'avatar_uri', 'name' and 'email' # are supported. If this method returns `None`, or `identifier` is not # supplied, then the authorization flow will fall back to other authentication # methods. def AUTH_MAP_REQUEST_HEADERS(headers): return { # 'identifier': None, # 'avatar_uri': None, # 'name': None, # 'email': None } # The following AUTH_USER_IDENTIFIER* configuration keys are deprecated and # will be removed in v0.9. AUTH_USER_IDENTIFIER_REQUEST_HEADER = None def AUTH_USER_IDENTIFIER_REQUEST_HEADER_MAPPING(identifier): return identifier # If the server desires to modify the attributes of the `User` object # associated with users logged in via any of the above authentication # providers, it can do so via this configuration key. This function will be # run once at user login (if using an `AuthenticationProvider`, and then at # most once during any caching lifetime period (as specified below). Note that # attributes collected via `AuthenticationProvider`s will not be updated # after initial login (user must relogin in order to reset those attributes). def AUTH_USER_ATTRIBUTE_SETTER(user): return user # The time to wait before re-checking user attributes with the above function # for users logged in via request headers. AUTH_USER_ATTRIBUTE_CACHE_LIFETIME = 24 * 60 * 60 # 1 day # Once a user is logged in via an authentication provider, they will remain # logged in via the use of cookies. By default, this cookie will last one year. # This is managed by `flask_login`, but is copied here for convenience. # For other options regarding sessions, please refer to: # https://flask-login.readthedocs.io/en/latest/#cookie-settings REMEMBER_COOKIE_DURATION = timedelta(days=365) # --------------------------------------------------- # LDAP configuration # --------------------------------------------------- # When using an LDAP server for user verification, you need to configure # the location of the server, and the directory structure used by your # organization. # Currently the port and protocol must both be included in the server address LDAP_SERVER = "ldap://127.0.0.1:389" # When entering this, note the "{0}" which denotes where the user_id # is inserted. LDAP_USERDN_SCHEMA = "cn={user_id},ou=people,dc=planetexpress,dc=com" # --------------------------------------------------- # Policy configuration # --------------------------------------------------- # This section configures various policy related to access control. # Should anonymous users be able to view the post indices POLICY_ANONYMOUS_VIEW_INDEX = True # Should anonymous users be able to view the content of posts POLICY_ANONYMOUS_VIEW_POST = True # Should anonymous users be able to view overall statistics POLICY_ANONYMOUS_VIEW_STATS = True # Should anonymous users be able to view tag pages POLICY_ANONYMOUS_VIEW_TAGS = True # Should anonymous users be able to download posts (or their source) POLICY_ANONYMOUS_DOWNLOADS = False # --------------------------------------------------- # Repository configuration # --------------------------------------------------- # You may specify a function `prepare_repo` which configures # the repository upon which this server is running. This # takes place after the repository has been instantiated # and before the server is able to serve requests. It is # possible to do anything to the repository, including # substituting the repository for another one. # By default, repositories manage their own configurations, # but this can be risky as they may run arbitrary python code, # which opens a vector for malicious users to compromise # the server. If you want to avoid this risk, pass # the '--safe' (TODO!) option to `knowledge_repo` config and # manually configure the repository here. # For example, if your server instance is sitting atop # a meta-repository, it may make sense to update the meta-repository # configuration with that of one of its children. def prepare_repo(repo): return repo # --------------------------------------------------- # Repository Indexing configuration # --------------------------------------------------- # The Knowedge Repo updates the index of available posts on a regular basis. # If the database is not thread-safe (i.e. in the case of SQLite), then the # index will be updated on the main thread before every request that is more # than `INDEX_INTERVAL` seconds after the last sync completed. Otherwise, # indexing will occur every `INDEX_INTERVAL` seconds after the previous sync. # Syncing is designed to be compatible with multiple instances of the Knowledge # Repo connected to the same database, accross multiple machines and/or # processes; and so a global indexing lock is employed. When a sync begins, # a sync lock is put in place and the responsible process is considered to be # the primary agent responsible for syncing until its last update is longer # than`INDEXING_TIMEOUT` seconds, whereby the lock is ceded to the next # requesting process. Note that `INDEXING_TIMEOUT` must be larger than # `INDEXING_INTERVAL` or strange things might begin to happen. INDEXING_INTERVAL = 5 * 60 # 5 minutes INDEXING_TIMEOUT = 10 * 60 # 10 minutes # Whether an index operation should update repositories INDEXING_UPDATES_REPOSITORIES = True # Whether repositories should be updated even without a sync lock (in which # case the repositories will be updated on the sync timers, even if the # relevant process/thread does not have a lock on updating the index). This is # useful in context of multiple Knowledge Repo servers working together to # serve the repositories across multiple machines, which each require # repository syncing. Disable this if (for some reason) you have multiple # Knowledge Repo servers running on the same machine, and you want to avoid # potential clashes. This key is ignored if `INDEXING_UPDATES_REPOSITORIES` is # False INDEXING_UPDATES_REPOSITORIES_WITHOUT_LOCK = True # In some cases you may want to disable indexing entirely, which is currently # only ever used by the Knowledge Post previewer. Disabling the index means # that posts will not be discoverable, but if know the path in the repository # you can view the post with a direct link. INDEXING_ENABLED = True # --------------------------------------------------- # Flask Mail Configuration # Refer to https://pythonhosted.org/flask-mail/ # Unless specified, upstream defaults are used as indicated # provided that MAIL_SERVER is defined. # --------------------------------------------------- # MAIL_SERVER = 'localhost' # default = 'localhost' # MAIL_PORT = 25 # default = 25 # MAIL_USE_TLS = False # default = False # MAIL_USE_SSL = False # default = False # MAIL_DEBUG = False # default = app.debug # MAIL_USERNAME = None # default = None # MAIL_PASSWORD = None # default = None # MAIL_DEFAULT_SENDER = None # default = None # MAIL_MAX_EMAILS = None # default = None # MAIL_SUPPRESS_SEND = False # default = app.testing # MAIL_ASCII_ATTACHMENTS = False # default = False # # Detailed integration procedure with SendGrid is available at: # https://sendgrid.com/blog/sending-emails-from-python-flask-applications-with-twilio-sendgrid/ # -------------------------------------------------- # Web Editor Configuration # -------------------------------------------------- # The web editor can be limited to editing posts under # a limited set of parent directories by setting # WEB_EDITOR_PREFIXES to a list of supported path prefixes. # e.g. ['webposts', 'projects'] WEB_EDITOR_PREFIXES = ["webposts"] # --------------------------------------------------- # Tag configuration # --------------------------------------------------- # Posts with certain tags can be excluded from showing up # in the app. This can be useful for security purposes EXCLUDED_TAGS = ["private"] # ------------- # Collapse Code as Default Display Option # ------------- COLLAPSE_CODE_DEFAULT = False # ------------- # AWS related settings # ------------- S3_AWS_ACCESS_KEY_ID = "" S3_AWS_SECRET_ACCESS_KEY = "" S3_AWS_REGION_NAME = "us-west-2"

mengting1010

b15c759e7d17b6a52c502b8bfdc174181da83789

451577868d66570a463260c67dff7034214beafd

removed

mengting1010

airbnb/knowledge-repo

fix index and db bugs

Description of changeset: 1. index_failure when service start 2. PostgreSQL close unexpectedly the connection issue Test Plan: local build @csharplus

2022-06-12 16:39:05+00:00

2022-06-13 05:23:21+00:00

requirements.txt

cooked_input flask Flask-Migrate gitdb gitpython==3.1.18 tabulate==0.8.9 pyyaml markdown==3.3.4 pygments==2.10.0 pyyaml flask_login flask_principal flask_mail gunicorn inflection pillow nbformat nbconvert[execute] traitlets ldap3 requests weasyprint jinja2>=2.7,<=3.0.3 werkzeug>=1.0,<=2.0.3

cooked_input flask Flask-Migrate gitdb gitpython==3.1.18 tabulate==0.8.9 pyyaml markdown==3.3.4 pygments==2.10.0 pyyaml flask_login flask_principal flask_mail gunicorn inflection pillow nbformat nbconvert[execute] traitlets ldap3 requests weasyprint jinja2>=2.7,<=3.0.3 werkzeug>=1.0,<=2.0.3 multiprocess

JJJ000

5dfd384612a54adf846cc1e81cc09690f1f942a6

f08ced24fda6cee63874b0921869d8405e894358

Is this the correct library name? I searched for it and could not find it. I found the following instead: https://pypi.org/project/multiprocess/

csharplus

airbnb/knowledge-repo

fix index and db bugs

Description of changeset: 1. index_failure when service start 2. PostgreSQL close unexpectedly the connection issue Test Plan: local build @csharplus

2022-06-12 16:39:05+00:00

2022-06-13 05:23:21+00:00

requirements.txt

cooked_input flask Flask-Migrate gitdb gitpython==3.1.18 tabulate==0.8.9 pyyaml markdown==3.3.4 pygments==2.10.0 pyyaml flask_login flask_principal flask_mail gunicorn inflection pillow nbformat nbconvert[execute] traitlets ldap3 requests weasyprint jinja2>=2.7,<=3.0.3 werkzeug>=1.0,<=2.0.3

cooked_input flask Flask-Migrate gitdb gitpython==3.1.18 tabulate==0.8.9 pyyaml markdown==3.3.4 pygments==2.10.0 pyyaml flask_login flask_principal flask_mail gunicorn inflection pillow nbformat nbconvert[execute] traitlets ldap3 requests weasyprint jinja2>=2.7,<=3.0.3 werkzeug>=1.0,<=2.0.3 multiprocess

JJJ000

5dfd384612a54adf846cc1e81cc09690f1f942a6

f08ced24fda6cee63874b0921869d8405e894358

@csharplus yeah, you are right, I past the wrong package name

JJJ000

airbnb/knowledge-repo

fix package dependency to support local server

Description of changeset: fix package dependency to support local server Test Plan: [x]ci @csharplus

2022-05-23 06:36:29+00:00

2022-05-25 02:29:21+00:00

docker/Dockerfile

FROM ubuntu:18.04 LABEL description="knowledge-repo service" # Define some build time variables ARG PIP=pip3 ARG VERSION=0.9.1 # Install required tools and libraries RUN apt-get update && \ apt-get -y install \ wget \ zip \ python3-pip \ python3-dev \ git \ && cd /usr/local/bin \ && ln -s /usr/bin/python3 python \ && ${PIP} install --upgrade pip \ && rm -rf /var/lib/apt/lists/* # Install the latest version of knowledge-repo RUN wget https://github.com/airbnb/knowledge-repo/archive/v$VERSION.zip && \ unzip v$VERSION.zip -d /app COPY . /app/knowledge-repo-$VERSION COPY docker/entrypoint.sh /app/knowledge-repo-$VERSION RUN ${PIP} install -r /app/knowledge-repo-$VERSION/requirements.txt WORKDIR /app/knowledge-repo-$VERSION VOLUME /data EXPOSE 7000 CMD ["bash", "./entrypoint.sh"]

FROM ubuntu:18.04 LABEL description="knowledge-repo service" # Define some build time variables ARG PIP=pip3 ARG VERSION=0.9.0 # Install required tools and libraries RUN apt-get update && \ apt-get -y install \ wget \ zip \ python3-pip \ python3-dev \ git \ && cd /usr/local/bin \ && ln -s /usr/bin/python3 python \ && ${PIP} install --upgrade pip \ && rm -rf /var/lib/apt/lists/* # Install the latest version of knowledge-repo RUN wget https://github.com/airbnb/knowledge-repo/archive/v$VERSION.zip && \ unzip v$VERSION.zip -d /app COPY . /app/knowledge-repo-$VERSION COPY docker/entrypoint.sh /app/knowledge-repo-$VERSION COPY docker/config.py /app/knowledge-repo-$VERSION RUN ${PIP} install -r /app/knowledge-repo-$VERSION/requirements.txt WORKDIR /app/knowledge-repo-$VERSION VOLUME /data EXPOSE 7000 CMD ["bash", "./entrypoint.sh"]

JJJ000

09fe061460e3463c33b7eb811899ae6ee4a959c9

2eafc06a4c57ff710283e2fdc3abf0cb17d15e1d

Avoid reducing the version number?

csharplus

airbnb/knowledge-repo

reformat unit test code

Description of changeset: reformat code Test Plan: [x]CI @csharplus

2022-03-23 02:45:22+00:00

2022-03-26 21:37:54+00:00

knowledge_repo/config.py

from .constants import PY_EXTENSION, YML_EXTENSION from .utils.files import read_yaml import functools import importlib import logging import os import sys import time import types logger = logging.getLogger(__name__) class KnowledgeRepositoryConfig(dict): def __init__(self, repo, *args, **kwargs): self._repo = repo super(KnowledgeRepositoryConfig, self).__init__(*args, **kwargs) self.DEFAULT_CONFIGURATION = {} def __getitem__(self, key): try: value = super(KnowledgeRepositoryConfig, self).__getitem__(key) except KeyError: value = self.DEFAULT_CONFIGURATION[key] if isinstance(value, types.FunctionType): value = functools.partial(value, self._repo) return value def __getattr__(self, attr): return self[attr] def __setattr__(self, attr, value): self[attr] = value def __dir__(self): return list(set(list(self.DEFAULT_CONFIGURATION.keys()) + list(self.keys()))) def update(self, *values, **kwargs): for value in values: if isinstance(value, dict): value = value.copy() value.pop('DEFAULT_CONFIGURATION', None) value.pop('_repo', None) dict.update(self, value) elif isinstance(value, types.ModuleType): self.__update_from_module(value) elif isinstance(value, str): if os.path.exists(value): self.__update_from_file(value) else: logger.warning( f'Configuration file {value} does not exist.') elif isinstance(value, type(None)): pass else: raise ValueError(f'Cannot interpret {value}') dict.update(self, kwargs) def update_defaults(self, *values, **kwargs): for value in values: if type(value) == dict: self.DEFAULT_CONFIGURATION.update(value) elif isinstance(value, types.ModuleType): self.__defaults_from_module(value) elif isinstance(value, str): if os.path.exists(value): self.__defaults_from_file(value) else: logger.warning(f'Configuration file {value} does not exist.') elif isinstance(value, type(None)): pass else: raise ValueError(f'Cannot interpret {value}') self.DEFAULT_CONFIGURATION.update(kwargs) def __defaults_from_file(self, filename): self.__set_from_file(self.DEFAULT_CONFIGURATION, filename, force=True) def __update_from_file(self, filename): self.__set_from_file(self, filename) def __defaults_from_module(self, module): self.__set_from_module(self.DEFAULT_CONFIGURATION, module, force=True) def __update_from_module(self, module): self.__set_from_module(self, module) def __set_from_file(self, d, filename, force=False): if filename.endswith(PY_EXTENSION): time_str = str(time.time()).replace('.', '') module_name = f'knowledge_repo.config_{time_str}' spec = importlib.util.spec_from_file_location(module_name, filename) config = importlib.util.module_from_spec(spec) sys.modules[module_name] = module spec.loader.exec_module(module) self.__set_from_module(d, config, force) elif filename.endswith(YML_EXTENSION): config = read_yaml(filename) self.update(config) def __set_from_module(self, d, module, force=False): for key in dir(module): if not key.startswith('_'): if not force and key not in self.DEFAULT_CONFIGURATION: logger.debug( f'Ignoring configuration key `{key}` which is not a valid configuration key.') else: d[key] = getattr(module, key)

from .constants import PY_EXTENSION, YML_EXTENSION from .utils.files import read_yaml import functools import importlib import logging import os import sys import time import types logger = logging.getLogger(__name__) class KnowledgeRepositoryConfig(dict): def __init__(self, repo, *args, **kwargs): self._repo = repo super(KnowledgeRepositoryConfig, self).__init__(*args, **kwargs) self.DEFAULT_CONFIGURATION = {} def __getitem__(self, key): try: value = super(KnowledgeRepositoryConfig, self).__getitem__(key) except KeyError: value = self.DEFAULT_CONFIGURATION[key] if isinstance(value, types.FunctionType): value = functools.partial(value, self._repo) return value def __getattr__(self, attr): return self[attr] def __setattr__(self, attr, value): self[attr] = value def __dir__(self): return list(set(list(self.DEFAULT_CONFIGURATION.keys()) + list(self.keys()))) def update(self, *values, **kwargs): for value in values: if isinstance(value, dict): value = value.copy() value.pop('DEFAULT_CONFIGURATION', None) value.pop('_repo', None) dict.update(self, value) elif isinstance(value, types.ModuleType): self.__update_from_module(value) elif isinstance(value, str): if os.path.exists(value): self.__update_from_file(value) else: logger.warning( f'Configuration file {value} does not exist.') elif isinstance(value, type(None)): pass else: raise ValueError(f'Cannot interpret {value}') dict.update(self, kwargs) def update_defaults(self, *values, **kwargs): for value in values: if type(value) == dict: self.DEFAULT_CONFIGURATION.update(value) elif isinstance(value, types.ModuleType): self.__defaults_from_module(value) elif isinstance(value, str): if os.path.exists(value): self.__defaults_from_file(value) else: logger.warning(f'Configuration file {value} does not exist.') elif isinstance(value, type(None)): pass else: raise ValueError(f'Cannot interpret {value}') self.DEFAULT_CONFIGURATION.update(kwargs) def __defaults_from_file(self, filename): self.__set_from_file(self.DEFAULT_CONFIGURATION, filename, force=True) def __update_from_file(self, filename): self.__set_from_file(self, filename) def __defaults_from_module(self, module): self.__set_from_module(self.DEFAULT_CONFIGURATION, module, force=True) def __update_from_module(self, module): self.__set_from_module(self, module) def __set_from_file(self, d, filename, force=False): if filename.endswith(PY_EXTENSION): time_str = str(time.time()).replace('.', '') module_name = f'knowledge_repo.config_{time_str}' spec = importlib.util.spec_from_file_location(module_name, filename) config = importlib.util.module_from_spec(spec) sys.modules[module_name] = module spec.loader.exec_module(module) self.__set_from_module(d, config, force) elif filename.endswith(YML_EXTENSION): config = read_yaml(filename) self.update(config) def __set_from_module(self, d, module, force=False): for key in dir(module): if not key.startswith('_'): if not force and key not in self.DEFAULT_CONFIGURATION: logger.debug( f'Ignoring configuration key `{key}` which is not a valid configuration key.') else: d[key] = getattr(module, key)

JJJ000

48416e89dbdc485efd7d95fa99f9d28adcd79d12

ce89b54b9c18b8f639facb122b7427d0b4d7c369

Let's keep all import statements sorted in alphabetical order for easy maintenance.

csharplus

py-why/dowhy

Rename classes in test folder

The TestEstimator and TestRefuter were falsely interpreted as unit test classes due to their "Test" prefix.

2023-12-11 15:43:46+00:00

2023-12-11 18:52:07+00:00

tests/gcm/test_stochastic_models.py

import numpy as np import pytest from flaky import flaky from pytest import approx from scipy import stats from dowhy.gcm import BayesianGaussianMixtureDistribution, EmpiricalDistribution, ScipyDistribution def test_when_fitting_bayesian_gaussian_mixture_distribution_then_samples_should_be_drawn_correctly(): test_data = np.array([[0, 0], [0, 0], [1, 2], [1, 2]]) approximated_data_distribution_model = BayesianGaussianMixtureDistribution() approximated_data_distribution_model.fit(test_data) assert approximated_data_distribution_model.draw_samples(5).shape == (5, 2) def test_when_drawing_samples_from_unfitted_bayesian_gaussian_mixture_distribution_then_runtime_error_should_occur(): approximated_data_distribution_model = BayesianGaussianMixtureDistribution() with pytest.raises(RuntimeError): approximated_data_distribution_model.draw_samples(5) def test_when_creating_scipy_distribution_with_fixed_parameters_then_it_should_return_the_correct_parameter_values(): distribution = ScipyDistribution(stats.norm, loc=0, scale=1) assert distribution.parameters["loc"] == 0 assert distribution.parameters["scale"] == 1 @flaky(max_runs=5) def test_when_fitting_normal_scipy_distribution_then_it_should_return_correctly_fitted_parameter_values(): distribution = ScipyDistribution(stats.norm) X = np.random.normal(0, 1, 1000) distribution.fit(X) assert distribution.parameters["loc"] == approx(0, abs=0.1) assert distribution.parameters["scale"] == approx(1, abs=0.1) @flaky(max_runs=5) def test_given_gaussian_data_when_fitting_scipy_distribution_automatically_then_it_should_return_correctly_fitted_parameter_values(): distribution = ScipyDistribution() X = np.random.normal(0, 1, 1000) distribution.fit(X) assert np.mean(distribution.draw_samples(1000)) == approx(0, abs=0.1) assert np.std(distribution.draw_samples(1000)) == approx(1, abs=0.1) def test_when_drawing_samples_from_empirical_distribution_then_all_samples_should_be_present_in_the_data(): X = np.random.normal(0, 1, 1000) distribution = EmpiricalDistribution() distribution.fit(X) X = list(X) for val in distribution.draw_samples(1000): assert val in X @flaky(max_runs=5) def test_when_fitting_scipy_distribution_with_normal_distribution_then_it_should_return_correctly_fitted_parameter_values(): distribution = ScipyDistribution(stats.norm) distribution.fit(ScipyDistribution(stats.norm, loc=3, scale=2).draw_samples(10000)) assert distribution.parameters["loc"] == approx(3, abs=0.3) assert distribution.parameters["scale"] == approx(2, abs=0.3) @flaky(max_runs=5) def test_when_fitting_scipy_distribution_with_beta_distribution_then_it_should_return_correctly_fitted_parameter_values(): distribution = ScipyDistribution(stats.beta) distribution.fit(ScipyDistribution(stats.beta, a=2, b=0.5).draw_samples(10000)) assert distribution.parameters["loc"] == approx(0, abs=0.1) assert distribution.parameters["scale"] == approx(1, abs=0.1) assert distribution.parameters["a"] == approx(2, abs=0.5) assert distribution.parameters["b"] == approx(0.5, abs=0.5)

import numpy as np import pytest from flaky import flaky from pytest import approx from scipy import stats from dowhy.gcm import BayesianGaussianMixtureDistribution, EmpiricalDistribution, ScipyDistribution def test_when_fitting_bayesian_gaussian_mixture_distribution_then_samples_should_be_drawn_correctly(): test_data = np.array([[0, 0], [0, 0], [1, 2], [1, 2]]) approximated_data_distribution_model = BayesianGaussianMixtureDistribution() approximated_data_distribution_model.fit(test_data) assert approximated_data_distribution_model.draw_samples(5).shape == (5, 2) def test_when_drawing_samples_from_unfitted_bayesian_gaussian_mixture_distribution_then_runtime_error_should_occur(): approximated_data_distribution_model = BayesianGaussianMixtureDistribution() with pytest.raises(RuntimeError): approximated_data_distribution_model.draw_samples(5) def test_when_creating_scipy_distribution_with_fixed_parameters_then_it_should_return_the_correct_parameter_values(): distribution = ScipyDistribution(stats.norm, loc=0, scale=1) assert distribution.parameters["loc"] == 0 assert distribution.parameters["scale"] == 1 @flaky(max_runs=5) def test_when_fitting_normal_scipy_distribution_then_it_should_return_correctly_fitted_parameter_values(): distribution = ScipyDistribution(stats.norm) X = np.random.normal(0, 1, 1000) distribution.fit(X) assert distribution.parameters["loc"] == approx(0, abs=0.1) assert distribution.parameters["scale"] == approx(1, abs=0.1) @flaky(max_runs=5) def test_given_gaussian_data_when_fitting_scipy_distribution_automatically_then_it_should_return_correctly_fitted_parameter_values(): distribution = ScipyDistribution() X = np.random.normal(0, 1, 5000) distribution.fit(X) assert np.mean(distribution.draw_samples(1000)) == approx(0, abs=0.2) assert np.std(distribution.draw_samples(1000)) == approx(1, abs=0.2) def test_when_drawing_samples_from_empirical_distribution_then_all_samples_should_be_present_in_the_data(): X = np.random.normal(0, 1, 1000) distribution = EmpiricalDistribution() distribution.fit(X) X = list(X) for val in distribution.draw_samples(1000): assert val in X @flaky(max_runs=5) def test_when_fitting_scipy_distribution_with_normal_distribution_then_it_should_return_correctly_fitted_parameter_values(): distribution = ScipyDistribution(stats.norm) distribution.fit(ScipyDistribution(stats.norm, loc=3, scale=2).draw_samples(10000)) assert distribution.parameters["loc"] == approx(3, abs=0.3) assert distribution.parameters["scale"] == approx(2, abs=0.3) @flaky(max_runs=5) def test_when_fitting_scipy_distribution_with_beta_distribution_then_it_should_return_correctly_fitted_parameter_values(): distribution = ScipyDistribution(stats.beta) distribution.fit(ScipyDistribution(stats.beta, a=2, b=2).draw_samples(10000)) assert distribution.parameters["loc"] == approx(0, abs=0.1) assert distribution.parameters["scale"] == approx(1, abs=0.1) assert distribution.parameters["a"] == approx(2, abs=0.5) assert distribution.parameters["b"] == approx(2, abs=0.5)

bloebp

711e6ba9025f42b20c6f7b423bafae2913f94d2d

1f9ee6be46ad35ce5a710acc3e259fc8360f8a38

Recommend not to introduce many changes in one PR (specially as the change here should only concern class renaming).

kailashbuki

py-why/dowhy

Fix bug in networkx plot function with 0 error strenghts

2023-12-08 20:17:35+00:00

2023-12-11 18:56:00+00:00

dowhy/utils/plotting.py

import logging import os import tempfile from typing import Any, Dict, List, Optional, Tuple, Union import matplotlib.pyplot as plt import networkx as nx import numpy as np import pandas as pd from matplotlib import image _logger = logging.getLogger(__name__) def plot( causal_graph: nx.Graph, layout_prog: Optional[str] = None, causal_strengths: Optional[Dict[Tuple[Any, Any], float]] = None, colors: Optional[Dict[Union[Any, Tuple[Any, Any]], str]] = None, filename: Optional[str] = None, display_plot: bool = True, figure_size: Optional[Tuple[int, int]] = None, **kwargs, ) -> None: """Convenience function to plot causal graphs. This function uses different backends based on what's available on the system. The best result is achieved when using Graphviz as the backend. This requires both the shared system library (e.g. ``brew install graphviz`` or ``apt-get install graphviz``) and the Python pygraphviz package (``pip install pygraphviz``). When graphviz is not available, it will fall back to the networkx backend. :param causal_graph: The graph to be plotted :param layout_prog: Defines the layout type. If None is given, the 'dot' layout is used for graphviz plots and a customized layout for networkx plots. :param causal_strengths: An optional dictionary with Edge -> float entries. :param colors: An optional dictionary with color specifications for edges or nodes. :param filename: An optional filename if the output should be plotted into a file. :param display_plot: Optionally specify if the plot should be displayed or not (default to True). :param figure_size: A tuple to define the width and height (as a tuple) of the pyplot. This is used to parameter to modify pyplot's 'figure.figsize' parameter. If None is given, the current/default value is used. :param kwargs: Remaining parameters will be passed through to the backend verbatim. **Example usage**:: >>> plot(nx.DiGraph([('X', 'Y')])) # plots X -> Y >>> plot(nx.DiGraph([('X', 'Y')]), causal_strengths={('X', 'Y'): 0.43}) # annotates arrow with 0.43 >>> plot(nx.DiGraph([('X', 'Y')]), colors={('X', 'Y'): 'red', 'X': 'green'}) # colors X -> Y red and X green """ try: from dowhy.utils.graphviz_plotting import plot_causal_graph_graphviz try: plot_causal_graph_graphviz( causal_graph, layout_prog=layout_prog, causal_strengths=causal_strengths, colors=colors, filename=filename, display_plot=display_plot, figure_size=figure_size, **kwargs, ) except Exception as error: from dowhy.utils.networkx_plotting import plot_causal_graph_networkx _logger.info( "There was an error when trying to plot the graph via graphviz, falling back to networkx " "plotting. If graphviz is not installed, consider installing it for better looking plots. The" " error is:" + str(error) ) plot_causal_graph_networkx( causal_graph, layout_prog=layout_prog, causal_strengths=causal_strengths, colors=colors, filename=filename, display_plot=display_plot, figure_size=figure_size, **kwargs, ) except (ImportError, ModuleNotFoundError): from dowhy.utils.networkx_plotting import plot_causal_graph_networkx _logger.info( "Pygraphviz installation not found, falling back to networkx plotting. " "For better looking plots, consider installing pygraphviz. Note This requires both the Python " "pygraphviz package (``pip install pygraphviz``) and the shared system library (e.g. " "``brew install graphviz`` or ``apt-get install graphviz``)" ) plot_causal_graph_networkx( causal_graph, layout_prog=layout_prog, causal_strengths=causal_strengths, colors=colors, filename=filename, display_plot=display_plot, figure_size=figure_size, **kwargs, ) def plot_adjacency_matrix( adjacency_matrix: pd.DataFrame, is_directed: bool, filename: Optional[str] = None, display_plot: bool = True ) -> None: plot( nx.from_pandas_adjacency(adjacency_matrix, nx.DiGraph() if is_directed else nx.Graph()), display_plot=display_plot, filename=filename, ) def bar_plot( values: Dict[str, float], uncertainties: Optional[Dict[str, Tuple[float, float]]] = None, ylabel: str = "", filename: Optional[str] = None, display_plot: bool = True, figure_size: Optional[List[int]] = None, bar_width: float = 0.8, xticks: List[str] = None, xticks_rotation: int = 90, sort_names: bool = False, ) -> None: """Convenience function to make a bar plot of the given values with uncertainty bars, if provided. Useful for all kinds of attribution results (including confidence intervals). :param values: A dictionary where the keys are the labels and the values are the values to be plotted. :param uncertainties: A dictionary of attributes to be added to the error bars. :param ylabel: The label for the y-axis. :param filename: An optional filename if the output should be plotted into a file. :param display_plot: Optionally specify if the plot should be displayed or not (default to True). :param figure_size: The size of the figure to be plotted. :param bar_width: The width of the bars. :param xticks: Explicitly specify the labels for the bars on the x-axis. :param xticks_rotation: Specify the rotation of the labels on the x-axis. :param sort_names: If True, the names in the plot are sorted alphabetically. If False, the order as given in values are used. """ if sort_names: values = {k: values[k] for k in sorted(values)} if xticks is not None: xticks = sorted(xticks) if uncertainties is None: uncertainties = {node: [values[node], values[node]] for node in values} else: for node in values: if node not in uncertainties: uncertainties[node] = [values[node], values[node]] figure, ax = plt.subplots(figsize=figure_size) ci_plus = np.array([uncertainties[node][1] - values[node] for node in values.keys()]) ci_minus = np.array([values[node] - uncertainties[node][0] for node in values.keys()]) is_negative_yerr = np.logical_or(ci_plus < 0, ci_minus < 0) ci_plus[is_negative_yerr] = 0 ci_minus[is_negative_yerr] = 0 yerr = np.array([ci_minus, ci_plus]) plt.bar(values.keys(), values.values(), yerr=yerr, ecolor="#1E88E5", color="#ff0d57", width=bar_width) plt.ylabel(ylabel) plt.xticks(rotation=xticks_rotation) ax.spines["right"].set_visible(False) ax.spines["top"].set_visible(False) if xticks: plt.xticks(list(uncertainties.keys()), xticks) if display_plot: plt.show() if filename is not None: figure.savefig(filename) def _calc_arrow_width(strength: float, max_strength: float): return 0.1 + 4.0 * float(abs(strength)) / float(max_strength) def _plot_as_pyplot_figure(pygraphviz_graph: Any, figure_size: Optional[Tuple[int, int]] = None) -> None: with tempfile.TemporaryDirectory() as tmp_dir_name: pygraphviz_graph.draw(tmp_dir_name + os.sep + "Graph.png") img = image.imread(tmp_dir_name + os.sep + "Graph.png") if figure_size is not None: org_fig_size = plt.rcParams["figure.figsize"] plt.rcParams["figure.figsize"] = figure_size plt.imshow(img) plt.axis("off") plt.show() if figure_size is not None: plt.rcParams["figure.figsize"] = org_fig_size

import logging import os import tempfile from typing import Any, Dict, List, Optional, Tuple, Union import matplotlib.pyplot as plt import networkx as nx import numpy as np import pandas as pd from matplotlib import image _logger = logging.getLogger(__name__) def plot( causal_graph: nx.Graph, layout_prog: Optional[str] = None, causal_strengths: Optional[Dict[Tuple[Any, Any], float]] = None, colors: Optional[Dict[Union[Any, Tuple[Any, Any]], str]] = None, filename: Optional[str] = None, display_plot: bool = True, figure_size: Optional[Tuple[int, int]] = None, **kwargs, ) -> None: """Convenience function to plot causal graphs. This function uses different backends based on what's available on the system. The best result is achieved when using Graphviz as the backend. This requires both the shared system library (e.g. ``brew install graphviz`` or ``apt-get install graphviz``) and the Python pygraphviz package (``pip install pygraphviz``). When graphviz is not available, it will fall back to the networkx backend. :param causal_graph: The graph to be plotted :param layout_prog: Defines the layout type. If None is given, the 'dot' layout is used for graphviz plots and a customized layout for networkx plots. :param causal_strengths: An optional dictionary with Edge -> float entries. :param colors: An optional dictionary with color specifications for edges or nodes. :param filename: An optional filename if the output should be plotted into a file. :param display_plot: Optionally specify if the plot should be displayed or not (default to True). :param figure_size: A tuple to define the width and height (as a tuple) of the pyplot. This is used to parameter to modify pyplot's 'figure.figsize' parameter. If None is given, the current/default value is used. :param kwargs: Remaining parameters will be passed through to the backend verbatim. **Example usage**:: >>> plot(nx.DiGraph([('X', 'Y')])) # plots X -> Y >>> plot(nx.DiGraph([('X', 'Y')]), causal_strengths={('X', 'Y'): 0.43}) # annotates arrow with 0.43 >>> plot(nx.DiGraph([('X', 'Y')]), colors={('X', 'Y'): 'red', 'X': 'green'}) # colors X -> Y red and X green """ try: from dowhy.utils.graphviz_plotting import plot_causal_graph_graphviz try: plot_causal_graph_graphviz( causal_graph, layout_prog=layout_prog, causal_strengths=causal_strengths, colors=colors, filename=filename, display_plot=display_plot, figure_size=figure_size, **kwargs, ) except Exception as error: from dowhy.utils.networkx_plotting import plot_causal_graph_networkx _logger.info( "There was an error when trying to plot the graph via graphviz, falling back to networkx " "plotting. If graphviz is not installed, consider installing it for better looking plots. The" " error is:" + str(error) ) plot_causal_graph_networkx( causal_graph, layout_prog=layout_prog, causal_strengths=causal_strengths, colors=colors, filename=filename, display_plot=display_plot, figure_size=figure_size, **kwargs, ) except (ImportError, ModuleNotFoundError): from dowhy.utils.networkx_plotting import plot_causal_graph_networkx _logger.info( "Pygraphviz installation not found, falling back to networkx plotting. " "For better looking plots, consider installing pygraphviz. Note This requires both the Python " "pygraphviz package (``pip install pygraphviz``) and the shared system library (e.g. " "``brew install graphviz`` or ``apt-get install graphviz``)" ) plot_causal_graph_networkx( causal_graph, layout_prog=layout_prog, causal_strengths=causal_strengths, colors=colors, filename=filename, display_plot=display_plot, figure_size=figure_size, **kwargs, ) def plot_adjacency_matrix( adjacency_matrix: pd.DataFrame, is_directed: bool, filename: Optional[str] = None, display_plot: bool = True ) -> None: plot( nx.from_pandas_adjacency(adjacency_matrix, nx.DiGraph() if is_directed else nx.Graph()), display_plot=display_plot, filename=filename, ) def bar_plot( values: Dict[str, float], uncertainties: Optional[Dict[str, Tuple[float, float]]] = None, ylabel: str = "", filename: Optional[str] = None, display_plot: bool = True, figure_size: Optional[List[int]] = None, bar_width: float = 0.8, xticks: List[str] = None, xticks_rotation: int = 90, sort_names: bool = False, ) -> None: """Convenience function to make a bar plot of the given values with uncertainty bars, if provided. Useful for all kinds of attribution results (including confidence intervals). :param values: A dictionary where the keys are the labels and the values are the values to be plotted. :param uncertainties: A dictionary of attributes to be added to the error bars. :param ylabel: The label for the y-axis. :param filename: An optional filename if the output should be plotted into a file. :param display_plot: Optionally specify if the plot should be displayed or not (default to True). :param figure_size: The size of the figure to be plotted. :param bar_width: The width of the bars. :param xticks: Explicitly specify the labels for the bars on the x-axis. :param xticks_rotation: Specify the rotation of the labels on the x-axis. :param sort_names: If True, the names in the plot are sorted alphabetically. If False, the order as given in values are used. """ if sort_names: values = {k: values[k] for k in sorted(values)} if xticks is not None: xticks = sorted(xticks) if uncertainties is None: uncertainties = {node: [values[node], values[node]] for node in values} else: for node in values: if node not in uncertainties: uncertainties[node] = [values[node], values[node]] figure, ax = plt.subplots(figsize=figure_size) ci_plus = np.array([uncertainties[node][1] - values[node] for node in values.keys()]) ci_minus = np.array([values[node] - uncertainties[node][0] for node in values.keys()]) is_negative_yerr = np.logical_or(ci_plus < 0, ci_minus < 0) ci_plus[is_negative_yerr] = 0 ci_minus[is_negative_yerr] = 0 yerr = np.array([ci_minus, ci_plus]) plt.bar(values.keys(), values.values(), yerr=yerr, ecolor="#1E88E5", color="#ff0d57", width=bar_width) plt.ylabel(ylabel) plt.xticks(rotation=xticks_rotation) ax.spines["right"].set_visible(False) ax.spines["top"].set_visible(False) if xticks: plt.xticks(list(uncertainties.keys()), xticks) if display_plot: plt.show() if filename is not None: figure.savefig(filename) def _calc_arrow_width(strength: float, max_strength: float): if max_strength == 0: return 4.1 elif max_strength < 0: raise ValueError("Got a negative strength! The strength needs to be positive.") return 0.1 + 4.0 * float(abs(strength)) / float(max_strength) def _plot_as_pyplot_figure(pygraphviz_graph: Any, figure_size: Optional[Tuple[int, int]] = None) -> None: with tempfile.TemporaryDirectory() as tmp_dir_name: pygraphviz_graph.draw(tmp_dir_name + os.sep + "Graph.png") img = image.imread(tmp_dir_name + os.sep + "Graph.png") if figure_size is not None: org_fig_size = plt.rcParams["figure.figsize"] plt.rcParams["figure.figsize"] = figure_size plt.imshow(img) plt.axis("off") plt.show() if figure_size is not None: plt.rcParams["figure.figsize"] = org_fig_size

bloebp

4f317345dcc5fc8763b13105bd5906e3434ace2f

72986a859db309b1afef92a542955569669bd8ea

Recommend a check beforehand to ensure that the `strength` is less than or equal to `max_strength`.

kailashbuki

py-why/dowhy

Overhauled readme

The current readme is too overloaded for users. Changes here are: - Reduce it only the essential points, since most of the content is part of the documentation - Add image as overview of the offered features - Made connection between GCM and PO framework more consistent - Revise the GCM example to an executable code snipped - Extended some references to include GCM related work - Fix build status icon - Changed github references to py-why (was still pointing to microsoft) Here the new readme: https://github.com/py-why/dowhy/blob/c0ac8433625b5693175629076ad32a92b0d1eb71/README.rst The image in the key features section is not show. It is the following: <img width="2887" alt="Screenshot 2023-11-28 at 08 52 50" src="https://github.com/py-why/dowhy/assets/51325689/98f5099f-6cc6-4969-b804-609d8006984d">

2023-11-26 05:56:29+00:00

2023-12-01 14:58:26+00:00

README.rst

|BuildStatus|_ |PyPiVersion|_ |PythonSupport|_ |Downloads|_ |discord|_ .. |PyPiVersion| image:: https://img.shields.io/pypi/v/dowhy.svg .. _PyPiVersion: https://pypi.org/project/dowhy/ .. |PythonSupport| image:: https://img.shields.io/pypi/pyversions/dowhy.svg .. _PythonSupport: https://pypi.org/project/dowhy/ .. |BuildStatus| image:: https://github.com/py-why/dowhy/actions/workflows/ci.yml/badge.svg .. _BuildStatus: https://github.com/py-why/dowhy/actions .. |Downloads| image:: https://pepy.tech/badge/dowhy .. _Downloads: https://pepy.tech/project/dowhy .. |discord| image:: https://img.shields.io/discord/818456847551168542 .. _discord: https://discord.gg/cSBGb3vsZb .. image:: dowhy-logo-large.png :width: 50% :align: center `Checkout the documentation <https://py-why.github.io/dowhy/>`_ =============================================================== - The documentation, user guide, sample notebooks and other information are available at `https://py-why.github.io/dowhy <https://py-why.github.io/dowhy/>`_ - DoWhy is part of the `PyWhy Ecosystem <https://www.pywhy.org/>`_. For more tools and libraries related to causality, checkout the `PyWhy GitHub organization <https://github.com/py-why/>`_! - For any questions, comments, or discussions about specific use cases, join our community on `Discord <https://discord.gg/cSBGb3vsZb>`_ (|discord|_) - Jump right into some case studies: - Effect estimation: `Hotel booking cancellations <https://towardsdatascience.com/beyond-predictive-models-the-causal-story-behind-hotel-booking-cancellations-d29e8558cbaf>`_ | `Effect of customer loyalty programs <https://github.com/microsoft/dowhy/blob/main/docs/source/example_notebooks/dowhy_example_effect_of_memberrewards_program.ipynb>`_ | `Optimizing article headlines <https://medium.com/@akelleh/introducing-the-do-sampler-for-causal-inference-a3296ea9e78d>`_ | `Effect of home visits on infant health (IHDP) <https://towardsdatascience.com/implementing-causal-inference-a-key-step-towards-agi-de2cde8ea599>`_ | `Causes of customer churn/attrition <https://medium.com/geekculture/a-quickstart-for-causal-analysis-decision-making-with-dowhy-2ce2d4d1efa9>`_ - Root cause analysis and explanations: `Root Cause Analysis with DoWhy, an Open Source Python Library for Causal Machine Learning <https://aws.amazon.com/blogs/opensource/root-cause-analysis-with-dowhy-an-open-source-python-library-for-causal-machine-learning/>`_ | `Finding the Root Cause of Elevated Latencies in a Microservice Architecture <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_rca_microservice_architecture.ipynb>`_ | `Finding Root Causes of Changes in a Supply Chain <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_supply_chain_dist_change.ipynb>`_ For more example notebooks, see `here! <https://www.pywhy.org/dowhy/main/example_notebooks/nb_index.html>`_ Introduction & Key Features =========================== Decision-making involves understanding how different variables affect each other and predicting the outcome when some of them are changed to new values. For instance, given an outcome variable, one may be interested in determining how a potential action(s) may affect it, understanding what led to its current value, or simulate what would happen if some variables are changed. Answering such questions requires causal reasoning. DoWhy is a Python library that guides you through the various steps of causal reasoning and provides a unified interface for answering causal questions. DoWhy provides a wide variety of algorithms for effect estimation, prediction, quantification of causal influences, diagnosis of causal structures, root cause analysis, interventions and counterfactuals. A key feature of DoWhy is its refutation and falsification API that can test causal assumptions for any estimation method, thus making inference more robust and accessible to non-experts. **Graphical Causal Models and Potential Outcomes: Best of both worlds** DoWhy builds on two of the most powerful frameworks for causal inference: graphical causal models and potential outcomes. For effect estimation, it uses graph-based criteria and do-calculus for modeling assumptions and identifying a non-parametric causal effect. For estimation, it switches to methods based primarily on potential outcomes. For causal questions beyond effect estimation, it uses the power of graphical causal models by modeling the data generation process via explicit causal mechanisms at each node, which, for instance, unlocks capabilities to attribute observed effects to particular variables or estimate point-wise counterfactuals. For a quick introduction to causal inference, check out `amit-sharma/causal-inference-tutorial <https://github.com/amit-sharma/causal-inference-tutorial/>`_ We also gave a more comprehensive tutorial at the ACM Knowledge Discovery and Data Mining (`KDD 2018 <http://www.kdd.org/kdd2018/>`_) conference: `causalinference.gitlab.io/kdd-tutorial <http://causalinference.gitlab.io/kdd-tutorial/>`_. For an introduction to the four steps of causal inference and its implications for machine learning, you can access this video tutorial from Microsoft Research `DoWhy Webinar <https://www.microsoft.com/en-us/research/video/foundations-of-causal-inference-and-its-impacts-on-machine-learning/>`_ and for an introduction to the graphical causal model API, see the `PyCon presentation on Root Cause Analysis with DoWhy <https://www.youtube.com/watch?v=icpHrbDlGaw>`_. Key Features ~~~~~~~~~~~~ .. image:: https://raw.githubusercontent.com/py-why/dowhy/main/docs/images/dowhy-features.png DoWhy supports the following causal tasks: - Effect estimation (identification, average causal effect, conditional average causal effect, instrumental variables and more) - Quantify causal influences (mediation analysis, direct arrow strength, intrinsic causal influence) - What-if analysis (generate samples from interventional distribution, estimate counterfactuals) - Root cause analysis and explanations (attribute anomalies to their causes, find causes for changes in distributions, estimate feature relevance and more) For more details and how to use these methods in practice, checkout the documentation at `https://py-why.github.io/dowhy <https://py-why.github.io/dowhy/>`_ Quick Start =========== DoWhy support Python 3.8+. To install, you can use pip, poetry, or conda. **Latest Release** Install the latest `release <https://pypi.org/project/dowhy/>`__ using pip. .. code:: shell pip install dowhy Install the latest `release <https://pypi.org/project/dowhy/>`__ using poetry. .. code:: shell poetry add dowhy Install the latest `release <https://anaconda.org/conda-forge/dowhy>`__ using conda. .. code:: shell conda install -c conda-forge dowhy If you face "Solving environment" problems with conda, then try :code:`conda update --all` and then install dowhy. If that does not work, then use :code:`conda config --set channel_priority false` and try to install again. If the problem persists, please `add your issue here <https://github.com/microsoft/dowhy/issues/197>`_. **Development Version** If you prefer to use the latest dev version, your dependency management tool will need to point at our GitHub repository. .. code:: shell pip install git+https://github.com/py-why/dowhy@main **Requirements** DoWhy requires a few dependencies. Details on specific versions can be found in `pyproject.toml <./pyproject.toml>`_, under the `tool.poetry.dependencies` section. If you face any problems, try installing dependencies manually. .. code:: shell pip install '<dependency-name>==<version>' Optionally, if you wish to input graphs in the dot format, then install pydot (or pygraphviz). For better-looking graphs, you can optionally install pygraphviz. To proceed, first install graphviz and then pygraphviz (on Ubuntu and Ubuntu WSL). .. code:: shell sudo apt install graphviz libgraphviz-dev graphviz-dev pkg-config ## from https://github.com/pygraphviz/pygraphviz/issues/71 pip install pygraphviz --install-option="--include-path=/usr/include/graphviz" \ --install-option="--library-path=/usr/lib/graphviz/" Example usage ~~~~~~~~~~~~~ Two examples demonstrating the effect estimation and graphical causal models API. Effect identification and estimation ++++++++++++++++++++++++++++++++++++ Most causal tasks in DoWhy only require a few lines of code to write. Here, we exemplarily estimate the causal effect of a treatment on an outcome variable: .. code:: python from dowhy import CausalModel import dowhy.datasets # Load some sample data data = dowhy.datasets.linear_dataset( beta=10, num_common_causes=5, num_instruments=2, num_samples=10000, treatment_is_binary=True) A causal graph can be defined in different way, but the most common way is via `NetworkX <https://networkx.org/>`_. After loading in the data, we use the four main operations for effect estimation in DoWhy: *model*, *identify*, *estimate* and *refute*: .. code:: python # I. Create a causal model from the data and given graph. model = CausalModel( data=data["df"], treatment=data["treatment_name"], outcome=data["outcome_name"], graph=data["gml_graph"]) # Or alternatively, as nx.DiGraph # II. Identify causal effect and return target estimands identified_estimand = model.identify_effect() # III. Estimate the target estimand using a statistical method. estimate = model.estimate_effect(identified_estimand, method_name="backdoor.propensity_score_matching") # IV. Refute the obtained estimate using multiple robustness checks. refute_results = model.refute_estimate(identified_estimand, estimate, method_name="random_common_cause") DoWhy stresses on the interpretability of its output. At any point in the analysis, you can inspect the untested assumptions, identified estimands (if any), and the estimate (if any). Here's a sample output of the linear regression estimator: .. image:: https://raw.githubusercontent.com/py-why/dowhy/main/docs/images/regression_output.png :width: 80% For a full code example, check out the `Getting Started with DoWhy <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/dowhy_simple_example.ipynb>`_ notebook. You can also use Conditional Average Treatment Effect (CATE) estimation methods from `EconML <https://github.com/py-why/econml>`_, as shown in the `Conditional Treatment Effects <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/dowhy-conditional-treatment-effects.ipynb>`_ notebook. Here's a code snippet. .. code:: python from sklearn.preprocessing import PolynomialFeatures from sklearn.linear_model import LassoCV from sklearn.ensemble import GradientBoostingRegressor dml_estimate = model.estimate_effect(identified_estimand, method_name="backdoor.econml.dml.DML", control_value = 0, treatment_value = 1, target_units = lambda df: df["X0"]>1, confidence_intervals=False, method_params={ "init_params":{'model_y':GradientBoostingRegressor(), 'model_t': GradientBoostingRegressor(), 'model_final':LassoCV(), 'featurizer':PolynomialFeatures(degree=1, include_bias=True)}, "fit_params":{}}) Graphical causal model (GCM) based inference ++++++++++++++++++++++++++++++++++++++++++++ DoWhy's graphical causal model framework offers powerful tools to address causal questions beyond effect estimation. It is based on Pearl's graphical causal model framework and models the causal data generation process of each variable explicitly via *causal mechanisms* to support a wide range of causal algorithms. For more details, see the book `Elements of Causal Inference <https://mitpress.mit.edu/9780262037310/elements-of-causal-inference/>`_. Complex causal queries, such as attributing observed anomalies to nodes in the system, can be performed with just a few lines of code: .. code:: python import networkx as nx, numpy as np, pandas as pd from dowhy import gcm # Let's generate some "normal" data we assume we're given from our problem domain: X = np.random.normal(loc=0, scale=1, size=1000) Y = 2 * X + np.random.normal(loc=0, scale=1, size=1000) Z = 3 * Y + np.random.normal(loc=0, scale=1, size=1000) data = pd.DataFrame(dict(X=X, Y=Y, Z=Z)) # 1. Modeling cause-effect relationships as a structural causal model # (causal graph + functional causal models): causal_model = gcm.StructuralCausalModel(nx.DiGraph([('X', 'Y'), ('Y', 'Z')])) # X -> Y -> Z gcm.auto.assign_causal_mechanisms(causal_model, data) # 2. Fitting the SCM to the data: gcm.fit(causal_model, data) # Optional: Evaluate causal model print(gcm.evaluate_causal_model(causal_model, data)) # Step 3: Perform a causal analysis. # results = gcm.<causal_query>(causal_model, ...) # For instance, root cause analysis: anomalous_sample = pd.DataFrame(dict(X=[0.1], Y=[6.2], Z=[19])) # Here, Y is the root cause. # "Which node is the root cause of the anomaly in Z?": anomaly_attribution = gcm.attribute_anomalies(causal_model, "Z", anomalous_sample) # Or sampling from an interventional distribution. Here, under the intervention do(Y := 2). samples = gcm.interventional_samples(causal_model, interventions={'Y': lambda y: 2}, num_samples_to_draw=100) The GCM framework offers many more features beyond these examples. For a full code example, check out the `Online Shop example notebook <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_online_shop.ipynb>`_. For more functionalities, example applications of DoWhy and details about the outputs, see the `User Guide <https://www.pywhy.org/dowhy/main/user_guide/intro.html>`_ or checkout `Jupyter notebooks <https://www.pywhy.org/dowhy/main/example_notebooks/nb_index.html>`_. More Information & Resources ============================ `Microsoft Research Blog <https://www.microsoft.com/en-us/research/blog/dowhy-a-library-for-causal-inference/>`_ | `Video Tutorial for Effect Estimation <https://www.youtube.com/watch?v=LALfQStONEc&t=114s>`_ | `Video Tutorial for Root Cause Analysis <https://www.youtube.com/watch?v=icpHrbDlGaw>`_ | `Arxiv Paper <https://arxiv.org/abs/2011.04216>`_ | `Arxiv Paper (Graphical Causal Model extension) <https://arxiv.org/abs/2206.06821>`_ | `Slides <https://www2.slideshare.net/AmitSharma315/dowhy-an-endtoend-library-for-causal-inference>`_ Citing this package ~~~~~~~~~~~~~~~~~~~ If you find DoWhy useful for your work, please cite **both** of the following two references: - Amit Sharma, Emre Kiciman. DoWhy: An End-to-End Library for Causal Inference. 2020. https://arxiv.org/abs/2011.04216 - Patrick Blöbaum, Peter Götz, Kailash Budhathoki, Atalanti A. Mastakouri, Dominik Janzing. DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models. 2022. https://arxiv.org/abs/2206.06821 Bibtex:: @article{dowhy, title={DoWhy: An End-to-End Library for Causal Inference}, author={Sharma, Amit and Kiciman, Emre}, journal={arXiv preprint arXiv:2011.04216}, year={2020} } @article{dowhy_gcm, author = {Bl{\"o}baum, Patrick and G{\"o}tz, Peter and Budhathoki, Kailash and Mastakouri, Atalanti A. and Janzing, Dominik}, title = {DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models}, journal={arXiv preprint arXiv:2206.06821}, year={2022} } Issues ~~~~~~ If you encounter an issue or have a specific request for DoWhy, please `raise an issue <https://github.com/py-why/dowhy/issues>`_. Contributing ~~~~~~~~~~~~ This project welcomes contributions and suggestions. For a guide to contributing and a list of all contributors, check out `CONTRIBUTING.md <https://github.com/py-why/dowhy/blob/main/CONTRIBUTING.md>`_ and our `docs for contributing code <https://github.com/py-why/dowhy/blob/main/docs/source/contributing/contributing-code.rst>`_. Our `contributor code of conduct is available here <https://github.com/py-why/governance/blob/main/CODE-OF-CONDUCT.md>`_.

|BuildStatus|_ |PyPiVersion|_ |PythonSupport|_ |Downloads|_ |discord|_ .. |PyPiVersion| image:: https://img.shields.io/pypi/v/dowhy.svg .. _PyPiVersion: https://pypi.org/project/dowhy/ .. |PythonSupport| image:: https://img.shields.io/pypi/pyversions/dowhy.svg .. _PythonSupport: https://pypi.org/project/dowhy/ .. |BuildStatus| image:: https://github.com/py-why/dowhy/actions/workflows/ci.yml/badge.svg .. _BuildStatus: https://github.com/py-why/dowhy/actions .. |Downloads| image:: https://pepy.tech/badge/dowhy .. _Downloads: https://pepy.tech/project/dowhy .. |discord| image:: https://img.shields.io/discord/818456847551168542 .. _discord: https://discord.gg/cSBGb3vsZb .. image:: dowhy-logo-large.png :width: 50% :align: center `Checkout the documentation <https://py-why.github.io/dowhy/>`_ =============================================================== - The documentation, user guide, sample notebooks and other information are available at `https://py-why.github.io/dowhy <https://py-why.github.io/dowhy/>`_ - DoWhy is part of the `PyWhy Ecosystem <https://www.pywhy.org/>`_. For more tools and libraries related to causality, checkout the `PyWhy GitHub organization <https://github.com/py-why/>`_! - For any questions, comments, or discussions about specific use cases, join our community on `Discord <https://discord.gg/cSBGb3vsZb>`_ (|discord|_) - Jump right into some case studies: - Effect estimation: `Hotel booking cancellations <https://towardsdatascience.com/beyond-predictive-models-the-causal-story-behind-hotel-booking-cancellations-d29e8558cbaf>`_ | `Effect of customer loyalty programs <https://github.com/microsoft/dowhy/blob/main/docs/source/example_notebooks/dowhy_example_effect_of_memberrewards_program.ipynb>`_ | `Optimizing article headlines <https://medium.com/@akelleh/introducing-the-do-sampler-for-causal-inference-a3296ea9e78d>`_ | `Effect of home visits on infant health (IHDP) <https://towardsdatascience.com/implementing-causal-inference-a-key-step-towards-agi-de2cde8ea599>`_ | `Causes of customer churn/attrition <https://medium.com/geekculture/a-quickstart-for-causal-analysis-decision-making-with-dowhy-2ce2d4d1efa9>`_ - Root cause analysis and explanations: `Root Cause Analysis with DoWhy, an Open Source Python Library for Causal Machine Learning <https://aws.amazon.com/blogs/opensource/root-cause-analysis-with-dowhy-an-open-source-python-library-for-causal-machine-learning/>`_ | `Finding the Root Cause of Elevated Latencies in a Microservice Architecture <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_rca_microservice_architecture.ipynb>`_ | `Finding Root Causes of Changes in a Supply Chain <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_supply_chain_dist_change.ipynb>`_ For more example notebooks, see `here! <https://www.pywhy.org/dowhy/main/example_notebooks/nb_index.html>`_ Introduction & Key Features =========================== Decision-making involves understanding how different variables affect each other and predicting the outcome when some of them are changed to new values. For instance, given an outcome variable, one may be interested in determining how a potential action(s) may affect it, understanding what led to its current value, or simulate what would happen if some variables are changed. Answering such questions requires causal reasoning. DoWhy is a Python library that guides you through the various steps of causal reasoning and provides a unified interface for answering causal questions. DoWhy provides a wide variety of algorithms for effect estimation, prediction, quantification of causal influences, diagnosis of causal structures, root cause analysis, interventions and counterfactuals. A key feature of DoWhy is its refutation and falsification API that can test causal assumptions for any estimation method, thus making inference more robust and accessible to non-experts. **Graphical Causal Models and Potential Outcomes: Best of both worlds** DoWhy builds on two of the most powerful frameworks for causal inference: graphical causal models and potential outcomes. For effect estimation, it uses graph-based criteria and do-calculus for modeling assumptions and identifying a non-parametric causal effect. For estimation, it switches to methods based primarily on potential outcomes. For causal questions beyond effect estimation, it uses the power of graphical causal models by modeling the data generation process via explicit causal mechanisms at each node, which, for instance, unlocks capabilities to attribute observed effects to particular variables or estimate point-wise counterfactuals. For a quick introduction to causal inference, check out `amit-sharma/causal-inference-tutorial <https://github.com/amit-sharma/causal-inference-tutorial/>`_ We also gave a more comprehensive tutorial at the ACM Knowledge Discovery and Data Mining (`KDD 2018 <http://www.kdd.org/kdd2018/>`_) conference: `causalinference.gitlab.io/kdd-tutorial <http://causalinference.gitlab.io/kdd-tutorial/>`_. For an introduction to the four steps of causal inference and its implications for machine learning, you can access this video tutorial from Microsoft Research `DoWhy Webinar <https://www.microsoft.com/en-us/research/video/foundations-of-causal-inference-and-its-impacts-on-machine-learning/>`_ and for an introduction to the graphical causal model API, see the `PyCon presentation on Root Cause Analysis with DoWhy <https://www.youtube.com/watch?v=icpHrbDlGaw>`_. Key Features ~~~~~~~~~~~~ .. image:: https://raw.githubusercontent.com/py-why/dowhy/main/docs/images/dowhy-features.png DoWhy supports the following causal tasks: - Effect estimation (identification, average causal effect, conditional average causal effect, instrumental variables and more) - Quantify causal influences (mediation analysis, direct arrow strength, intrinsic causal influence) - What-if analysis (generate samples from interventional distribution, estimate counterfactuals) - Root cause analysis and explanations (attribute anomalies to their causes, find causes for changes in distributions, estimate feature relevance and more) For more details and how to use these methods in practice, checkout the documentation at `https://py-why.github.io/dowhy <https://py-why.github.io/dowhy/>`_ Quick Start =========== DoWhy support Python 3.8+. To install, you can use pip, poetry, or conda. **Latest Release** Install the latest `release <https://pypi.org/project/dowhy/>`__ using pip. .. code:: shell pip install dowhy Install the latest `release <https://pypi.org/project/dowhy/>`__ using poetry. .. code:: shell poetry add dowhy Install the latest `release <https://anaconda.org/conda-forge/dowhy>`__ using conda. .. code:: shell conda install -c conda-forge dowhy If you face "Solving environment" problems with conda, then try :code:`conda update --all` and then install dowhy. If that does not work, then use :code:`conda config --set channel_priority false` and try to install again. If the problem persists, please `add your issue here <https://github.com/microsoft/dowhy/issues/197>`_. **Development Version** If you prefer to use the latest dev version, your dependency management tool will need to point at our GitHub repository. .. code:: shell pip install git+https://github.com/py-why/dowhy@main **Requirements** DoWhy requires a few dependencies. Details on specific versions can be found in `pyproject.toml <./pyproject.toml>`_, under the `tool.poetry.dependencies` section. If you face any problems, try installing dependencies manually. .. code:: shell pip install '<dependency-name>==<version>' Optionally, if you wish to input graphs in the dot format, then install pydot (or pygraphviz). For better-looking graphs, you can optionally install pygraphviz. To proceed, first install graphviz and then pygraphviz (on Ubuntu and Ubuntu WSL). .. code:: shell sudo apt install graphviz libgraphviz-dev graphviz-dev pkg-config ## from https://github.com/pygraphviz/pygraphviz/issues/71 pip install pygraphviz --install-option="--include-path=/usr/include/graphviz" \ --install-option="--library-path=/usr/lib/graphviz/" Example: Effect identification and estimation ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Most causal tasks in DoWhy only require a few lines of code to write. Here, we exemplarily estimate the causal effect of a treatment on an outcome variable: .. code:: python from dowhy import CausalModel import dowhy.datasets # Load some sample data data = dowhy.datasets.linear_dataset( beta=10, num_common_causes=5, num_instruments=2, num_samples=10000, treatment_is_binary=True) A causal graph can be defined in different way, but the most common way is via `NetworkX <https://networkx.org/>`_. After loading in the data, we use the four main operations for effect estimation in DoWhy: *model*, *identify*, *estimate* and *refute*: .. code:: python # I. Create a causal model from the data and given graph. model = CausalModel( data=data["df"], treatment=data["treatment_name"], outcome=data["outcome_name"], graph=data["gml_graph"]) # Or alternatively, as nx.DiGraph # II. Identify causal effect and return target estimands identified_estimand = model.identify_effect() # III. Estimate the target estimand using a statistical method. estimate = model.estimate_effect(identified_estimand, method_name="backdoor.propensity_score_matching") # IV. Refute the obtained estimate using multiple robustness checks. refute_results = model.refute_estimate(identified_estimand, estimate, method_name="random_common_cause") DoWhy stresses on the interpretability of its output. At any point in the analysis, you can inspect the untested assumptions, identified estimands (if any), and the estimate (if any). Here's a sample output of the linear regression estimator: .. image:: https://raw.githubusercontent.com/py-why/dowhy/main/docs/images/regression_output.png :width: 80% For a full code example, check out the `Getting Started with DoWhy <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/dowhy_simple_example.ipynb>`_ notebook. You can also use Conditional Average Treatment Effect (CATE) estimation methods from `EconML <https://github.com/py-why/econml>`_, as shown in the `Conditional Treatment Effects <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/dowhy-conditional-treatment-effects.ipynb>`_ notebook. Here's a code snippet. .. code:: python from sklearn.preprocessing import PolynomialFeatures from sklearn.linear_model import LassoCV from sklearn.ensemble import GradientBoostingRegressor dml_estimate = model.estimate_effect(identified_estimand, method_name="backdoor.econml.dml.DML", control_value = 0, treatment_value = 1, target_units = lambda df: df["X0"]>1, confidence_intervals=False, method_params={ "init_params":{'model_y':GradientBoostingRegressor(), 'model_t': GradientBoostingRegressor(), 'model_final':LassoCV(), 'featurizer':PolynomialFeatures(degree=1, include_bias=True)}, "fit_params":{}}) Example: Graphical causal model (GCM) based inference ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DoWhy's graphical causal model framework offers powerful tools to address causal questions beyond effect estimation. It is based on Pearl's graphical causal model framework and models the causal data generation process of each variable explicitly via *causal mechanisms* to support a wide range of causal algorithms. For more details, see the book `Elements of Causal Inference <https://mitpress.mit.edu/9780262037310/elements-of-causal-inference/>`_. Complex causal queries, such as attributing observed anomalies to nodes in the system, can be performed with just a few lines of code: .. code:: python import networkx as nx, numpy as np, pandas as pd from dowhy import gcm # Let's generate some "normal" data we assume we're given from our problem domain: X = np.random.normal(loc=0, scale=1, size=1000) Y = 2 * X + np.random.normal(loc=0, scale=1, size=1000) Z = 3 * Y + np.random.normal(loc=0, scale=1, size=1000) data = pd.DataFrame(dict(X=X, Y=Y, Z=Z)) # 1. Modeling cause-effect relationships as a structural causal model # (causal graph + functional causal models): causal_model = gcm.StructuralCausalModel(nx.DiGraph([('X', 'Y'), ('Y', 'Z')])) # X -> Y -> Z gcm.auto.assign_causal_mechanisms(causal_model, data) # 2. Fitting the SCM to the data: gcm.fit(causal_model, data) # Optional: Evaluate causal model print(gcm.evaluate_causal_model(causal_model, data)) # Step 3: Perform a causal analysis. # results = gcm.<causal_query>(causal_model, ...) # For instance, root cause analysis: anomalous_sample = pd.DataFrame(dict(X=[0.1], Y=[6.2], Z=[19])) # Here, Y is the root cause. # "Which node is the root cause of the anomaly in Z?": anomaly_attribution = gcm.attribute_anomalies(causal_model, "Z", anomalous_sample) # Or sampling from an interventional distribution. Here, under the intervention do(Y := 2). samples = gcm.interventional_samples(causal_model, interventions={'Y': lambda y: 2}, num_samples_to_draw=100) The GCM framework offers many more features beyond these examples. For a full code example, check out the `Online Shop example notebook <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_online_shop.ipynb>`_. For more functionalities, example applications of DoWhy and details about the outputs, see the `User Guide <https://www.pywhy.org/dowhy/main/user_guide/intro.html>`_ or checkout `Jupyter notebooks <https://www.pywhy.org/dowhy/main/example_notebooks/nb_index.html>`_. More Information & Resources ============================ `Microsoft Research Blog <https://www.microsoft.com/en-us/research/blog/dowhy-a-library-for-causal-inference/>`_ | `Video Tutorial for Effect Estimation <https://www.youtube.com/watch?v=LALfQStONEc&t=114s>`_ | `Video Tutorial for Root Cause Analysis <https://www.youtube.com/watch?v=icpHrbDlGaw>`_ | `Arxiv Paper <https://arxiv.org/abs/2011.04216>`_ | `Arxiv Paper (Graphical Causal Model extension) <https://arxiv.org/abs/2206.06821>`_ | `Slides <https://www2.slideshare.net/AmitSharma315/dowhy-an-endtoend-library-for-causal-inference>`_ Citing this package ~~~~~~~~~~~~~~~~~~~ If you find DoWhy useful for your work, please cite **both** of the following two references: - Amit Sharma, Emre Kiciman. DoWhy: An End-to-End Library for Causal Inference. 2020. https://arxiv.org/abs/2011.04216 - Patrick Blöbaum, Peter Götz, Kailash Budhathoki, Atalanti A. Mastakouri, Dominik Janzing. DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models. 2022. https://arxiv.org/abs/2206.06821 Bibtex:: @article{dowhy, title={DoWhy: An End-to-End Library for Causal Inference}, author={Sharma, Amit and Kiciman, Emre}, journal={arXiv preprint arXiv:2011.04216}, year={2020} } @article{dowhy_gcm, author = {Bl{\"o}baum, Patrick and G{\"o}tz, Peter and Budhathoki, Kailash and Mastakouri, Atalanti A. and Janzing, Dominik}, title = {DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models}, journal={arXiv preprint arXiv:2206.06821}, year={2022} } Issues ~~~~~~ If you encounter an issue or have a specific request for DoWhy, please `raise an issue <https://github.com/py-why/dowhy/issues>`_. Contributing ~~~~~~~~~~~~ This project welcomes contributions and suggestions. For a guide to contributing and a list of all contributors, check out `CONTRIBUTING.md <https://github.com/py-why/dowhy/blob/main/CONTRIBUTING.md>`_ and our `docs for contributing code <https://github.com/py-why/dowhy/blob/main/docs/source/contributing/contributing-code.rst>`_. Our `contributor code of conduct is available here <https://github.com/py-why/governance/blob/main/CODE-OF-CONDUCT.md>`_.

bloebp

591e9d916e19bcad07e28687aeb8c81e1db26e8d

f455c569db0e7d0c1e1e7b54d1f8c85f026ec611

is there a specific reason for removing the conda installation guide? There is a currently an issue with conda build that we can fix. It is also nice to share how to get the development version.

amit-sharma

py-why/dowhy

Overhauled readme

The current readme is too overloaded for users. Changes here are: - Reduce it only the essential points, since most of the content is part of the documentation - Add image as overview of the offered features - Made connection between GCM and PO framework more consistent - Revise the GCM example to an executable code snipped - Extended some references to include GCM related work - Fix build status icon - Changed github references to py-why (was still pointing to microsoft) Here the new readme: https://github.com/py-why/dowhy/blob/c0ac8433625b5693175629076ad32a92b0d1eb71/README.rst The image in the key features section is not show. It is the following: <img width="2887" alt="Screenshot 2023-11-28 at 08 52 50" src="https://github.com/py-why/dowhy/assets/51325689/98f5099f-6cc6-4969-b804-609d8006984d">

2023-11-26 05:56:29+00:00

2023-12-01 14:58:26+00:00

README.rst

|BuildStatus|_ |PyPiVersion|_ |PythonSupport|_ |Downloads|_ |discord|_ .. |PyPiVersion| image:: https://img.shields.io/pypi/v/dowhy.svg .. _PyPiVersion: https://pypi.org/project/dowhy/ .. |PythonSupport| image:: https://img.shields.io/pypi/pyversions/dowhy.svg .. _PythonSupport: https://pypi.org/project/dowhy/ .. |BuildStatus| image:: https://github.com/py-why/dowhy/actions/workflows/ci.yml/badge.svg .. _BuildStatus: https://github.com/py-why/dowhy/actions .. |Downloads| image:: https://pepy.tech/badge/dowhy .. _Downloads: https://pepy.tech/project/dowhy .. |discord| image:: https://img.shields.io/discord/818456847551168542 .. _discord: https://discord.gg/cSBGb3vsZb .. image:: dowhy-logo-large.png :width: 50% :align: center `Checkout the documentation <https://py-why.github.io/dowhy/>`_ =============================================================== - The documentation, user guide, sample notebooks and other information are available at `https://py-why.github.io/dowhy <https://py-why.github.io/dowhy/>`_ - DoWhy is part of the `PyWhy Ecosystem <https://www.pywhy.org/>`_. For more tools and libraries related to causality, checkout the `PyWhy GitHub organization <https://github.com/py-why/>`_! - For any questions, comments, or discussions about specific use cases, join our community on `Discord <https://discord.gg/cSBGb3vsZb>`_ (|discord|_) - Jump right into some case studies: - Effect estimation: `Hotel booking cancellations <https://towardsdatascience.com/beyond-predictive-models-the-causal-story-behind-hotel-booking-cancellations-d29e8558cbaf>`_ | `Effect of customer loyalty programs <https://github.com/microsoft/dowhy/blob/main/docs/source/example_notebooks/dowhy_example_effect_of_memberrewards_program.ipynb>`_ | `Optimizing article headlines <https://medium.com/@akelleh/introducing-the-do-sampler-for-causal-inference-a3296ea9e78d>`_ | `Effect of home visits on infant health (IHDP) <https://towardsdatascience.com/implementing-causal-inference-a-key-step-towards-agi-de2cde8ea599>`_ | `Causes of customer churn/attrition <https://medium.com/geekculture/a-quickstart-for-causal-analysis-decision-making-with-dowhy-2ce2d4d1efa9>`_ - Root cause analysis and explanations: `Root Cause Analysis with DoWhy, an Open Source Python Library for Causal Machine Learning <https://aws.amazon.com/blogs/opensource/root-cause-analysis-with-dowhy-an-open-source-python-library-for-causal-machine-learning/>`_ | `Finding the Root Cause of Elevated Latencies in a Microservice Architecture <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_rca_microservice_architecture.ipynb>`_ | `Finding Root Causes of Changes in a Supply Chain <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_supply_chain_dist_change.ipynb>`_ For more example notebooks, see `here! <https://www.pywhy.org/dowhy/main/example_notebooks/nb_index.html>`_ Introduction & Key Features =========================== Decision-making involves understanding how different variables affect each other and predicting the outcome when some of them are changed to new values. For instance, given an outcome variable, one may be interested in determining how a potential action(s) may affect it, understanding what led to its current value, or simulate what would happen if some variables are changed. Answering such questions requires causal reasoning. DoWhy is a Python library that guides you through the various steps of causal reasoning and provides a unified interface for answering causal questions. DoWhy provides a wide variety of algorithms for effect estimation, prediction, quantification of causal influences, diagnosis of causal structures, root cause analysis, interventions and counterfactuals. A key feature of DoWhy is its refutation and falsification API that can test causal assumptions for any estimation method, thus making inference more robust and accessible to non-experts. **Graphical Causal Models and Potential Outcomes: Best of both worlds** DoWhy builds on two of the most powerful frameworks for causal inference: graphical causal models and potential outcomes. For effect estimation, it uses graph-based criteria and do-calculus for modeling assumptions and identifying a non-parametric causal effect. For estimation, it switches to methods based primarily on potential outcomes. For causal questions beyond effect estimation, it uses the power of graphical causal models by modeling the data generation process via explicit causal mechanisms at each node, which, for instance, unlocks capabilities to attribute observed effects to particular variables or estimate point-wise counterfactuals. For a quick introduction to causal inference, check out `amit-sharma/causal-inference-tutorial <https://github.com/amit-sharma/causal-inference-tutorial/>`_ We also gave a more comprehensive tutorial at the ACM Knowledge Discovery and Data Mining (`KDD 2018 <http://www.kdd.org/kdd2018/>`_) conference: `causalinference.gitlab.io/kdd-tutorial <http://causalinference.gitlab.io/kdd-tutorial/>`_. For an introduction to the four steps of causal inference and its implications for machine learning, you can access this video tutorial from Microsoft Research `DoWhy Webinar <https://www.microsoft.com/en-us/research/video/foundations-of-causal-inference-and-its-impacts-on-machine-learning/>`_ and for an introduction to the graphical causal model API, see the `PyCon presentation on Root Cause Analysis with DoWhy <https://www.youtube.com/watch?v=icpHrbDlGaw>`_. Key Features ~~~~~~~~~~~~ .. image:: https://raw.githubusercontent.com/py-why/dowhy/main/docs/images/dowhy-features.png DoWhy supports the following causal tasks: - Effect estimation (identification, average causal effect, conditional average causal effect, instrumental variables and more) - Quantify causal influences (mediation analysis, direct arrow strength, intrinsic causal influence) - What-if analysis (generate samples from interventional distribution, estimate counterfactuals) - Root cause analysis and explanations (attribute anomalies to their causes, find causes for changes in distributions, estimate feature relevance and more) For more details and how to use these methods in practice, checkout the documentation at `https://py-why.github.io/dowhy <https://py-why.github.io/dowhy/>`_ Quick Start =========== DoWhy support Python 3.8+. To install, you can use pip, poetry, or conda. **Latest Release** Install the latest `release <https://pypi.org/project/dowhy/>`__ using pip. .. code:: shell pip install dowhy Install the latest `release <https://pypi.org/project/dowhy/>`__ using poetry. .. code:: shell poetry add dowhy Install the latest `release <https://anaconda.org/conda-forge/dowhy>`__ using conda. .. code:: shell conda install -c conda-forge dowhy If you face "Solving environment" problems with conda, then try :code:`conda update --all` and then install dowhy. If that does not work, then use :code:`conda config --set channel_priority false` and try to install again. If the problem persists, please `add your issue here <https://github.com/microsoft/dowhy/issues/197>`_. **Development Version** If you prefer to use the latest dev version, your dependency management tool will need to point at our GitHub repository. .. code:: shell pip install git+https://github.com/py-why/dowhy@main **Requirements** DoWhy requires a few dependencies. Details on specific versions can be found in `pyproject.toml <./pyproject.toml>`_, under the `tool.poetry.dependencies` section. If you face any problems, try installing dependencies manually. .. code:: shell pip install '<dependency-name>==<version>' Optionally, if you wish to input graphs in the dot format, then install pydot (or pygraphviz). For better-looking graphs, you can optionally install pygraphviz. To proceed, first install graphviz and then pygraphviz (on Ubuntu and Ubuntu WSL). .. code:: shell sudo apt install graphviz libgraphviz-dev graphviz-dev pkg-config ## from https://github.com/pygraphviz/pygraphviz/issues/71 pip install pygraphviz --install-option="--include-path=/usr/include/graphviz" \ --install-option="--library-path=/usr/lib/graphviz/" Example usage ~~~~~~~~~~~~~ Two examples demonstrating the effect estimation and graphical causal models API. Effect identification and estimation ++++++++++++++++++++++++++++++++++++ Most causal tasks in DoWhy only require a few lines of code to write. Here, we exemplarily estimate the causal effect of a treatment on an outcome variable: .. code:: python from dowhy import CausalModel import dowhy.datasets # Load some sample data data = dowhy.datasets.linear_dataset( beta=10, num_common_causes=5, num_instruments=2, num_samples=10000, treatment_is_binary=True) A causal graph can be defined in different way, but the most common way is via `NetworkX <https://networkx.org/>`_. After loading in the data, we use the four main operations for effect estimation in DoWhy: *model*, *identify*, *estimate* and *refute*: .. code:: python # I. Create a causal model from the data and given graph. model = CausalModel( data=data["df"], treatment=data["treatment_name"], outcome=data["outcome_name"], graph=data["gml_graph"]) # Or alternatively, as nx.DiGraph # II. Identify causal effect and return target estimands identified_estimand = model.identify_effect() # III. Estimate the target estimand using a statistical method. estimate = model.estimate_effect(identified_estimand, method_name="backdoor.propensity_score_matching") # IV. Refute the obtained estimate using multiple robustness checks. refute_results = model.refute_estimate(identified_estimand, estimate, method_name="random_common_cause") DoWhy stresses on the interpretability of its output. At any point in the analysis, you can inspect the untested assumptions, identified estimands (if any), and the estimate (if any). Here's a sample output of the linear regression estimator: .. image:: https://raw.githubusercontent.com/py-why/dowhy/main/docs/images/regression_output.png :width: 80% For a full code example, check out the `Getting Started with DoWhy <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/dowhy_simple_example.ipynb>`_ notebook. You can also use Conditional Average Treatment Effect (CATE) estimation methods from `EconML <https://github.com/py-why/econml>`_, as shown in the `Conditional Treatment Effects <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/dowhy-conditional-treatment-effects.ipynb>`_ notebook. Here's a code snippet. .. code:: python from sklearn.preprocessing import PolynomialFeatures from sklearn.linear_model import LassoCV from sklearn.ensemble import GradientBoostingRegressor dml_estimate = model.estimate_effect(identified_estimand, method_name="backdoor.econml.dml.DML", control_value = 0, treatment_value = 1, target_units = lambda df: df["X0"]>1, confidence_intervals=False, method_params={ "init_params":{'model_y':GradientBoostingRegressor(), 'model_t': GradientBoostingRegressor(), 'model_final':LassoCV(), 'featurizer':PolynomialFeatures(degree=1, include_bias=True)}, "fit_params":{}}) Graphical causal model (GCM) based inference ++++++++++++++++++++++++++++++++++++++++++++ DoWhy's graphical causal model framework offers powerful tools to address causal questions beyond effect estimation. It is based on Pearl's graphical causal model framework and models the causal data generation process of each variable explicitly via *causal mechanisms* to support a wide range of causal algorithms. For more details, see the book `Elements of Causal Inference <https://mitpress.mit.edu/9780262037310/elements-of-causal-inference/>`_. Complex causal queries, such as attributing observed anomalies to nodes in the system, can be performed with just a few lines of code: .. code:: python import networkx as nx, numpy as np, pandas as pd from dowhy import gcm # Let's generate some "normal" data we assume we're given from our problem domain: X = np.random.normal(loc=0, scale=1, size=1000) Y = 2 * X + np.random.normal(loc=0, scale=1, size=1000) Z = 3 * Y + np.random.normal(loc=0, scale=1, size=1000) data = pd.DataFrame(dict(X=X, Y=Y, Z=Z)) # 1. Modeling cause-effect relationships as a structural causal model # (causal graph + functional causal models): causal_model = gcm.StructuralCausalModel(nx.DiGraph([('X', 'Y'), ('Y', 'Z')])) # X -> Y -> Z gcm.auto.assign_causal_mechanisms(causal_model, data) # 2. Fitting the SCM to the data: gcm.fit(causal_model, data) # Optional: Evaluate causal model print(gcm.evaluate_causal_model(causal_model, data)) # Step 3: Perform a causal analysis. # results = gcm.<causal_query>(causal_model, ...) # For instance, root cause analysis: anomalous_sample = pd.DataFrame(dict(X=[0.1], Y=[6.2], Z=[19])) # Here, Y is the root cause. # "Which node is the root cause of the anomaly in Z?": anomaly_attribution = gcm.attribute_anomalies(causal_model, "Z", anomalous_sample) # Or sampling from an interventional distribution. Here, under the intervention do(Y := 2). samples = gcm.interventional_samples(causal_model, interventions={'Y': lambda y: 2}, num_samples_to_draw=100) The GCM framework offers many more features beyond these examples. For a full code example, check out the `Online Shop example notebook <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_online_shop.ipynb>`_. For more functionalities, example applications of DoWhy and details about the outputs, see the `User Guide <https://www.pywhy.org/dowhy/main/user_guide/intro.html>`_ or checkout `Jupyter notebooks <https://www.pywhy.org/dowhy/main/example_notebooks/nb_index.html>`_. More Information & Resources ============================ `Microsoft Research Blog <https://www.microsoft.com/en-us/research/blog/dowhy-a-library-for-causal-inference/>`_ | `Video Tutorial for Effect Estimation <https://www.youtube.com/watch?v=LALfQStONEc&t=114s>`_ | `Video Tutorial for Root Cause Analysis <https://www.youtube.com/watch?v=icpHrbDlGaw>`_ | `Arxiv Paper <https://arxiv.org/abs/2011.04216>`_ | `Arxiv Paper (Graphical Causal Model extension) <https://arxiv.org/abs/2206.06821>`_ | `Slides <https://www2.slideshare.net/AmitSharma315/dowhy-an-endtoend-library-for-causal-inference>`_ Citing this package ~~~~~~~~~~~~~~~~~~~ If you find DoWhy useful for your work, please cite **both** of the following two references: - Amit Sharma, Emre Kiciman. DoWhy: An End-to-End Library for Causal Inference. 2020. https://arxiv.org/abs/2011.04216 - Patrick Blöbaum, Peter Götz, Kailash Budhathoki, Atalanti A. Mastakouri, Dominik Janzing. DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models. 2022. https://arxiv.org/abs/2206.06821 Bibtex:: @article{dowhy, title={DoWhy: An End-to-End Library for Causal Inference}, author={Sharma, Amit and Kiciman, Emre}, journal={arXiv preprint arXiv:2011.04216}, year={2020} } @article{dowhy_gcm, author = {Bl{\"o}baum, Patrick and G{\"o}tz, Peter and Budhathoki, Kailash and Mastakouri, Atalanti A. and Janzing, Dominik}, title = {DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models}, journal={arXiv preprint arXiv:2206.06821}, year={2022} } Issues ~~~~~~ If you encounter an issue or have a specific request for DoWhy, please `raise an issue <https://github.com/py-why/dowhy/issues>`_. Contributing ~~~~~~~~~~~~ This project welcomes contributions and suggestions. For a guide to contributing and a list of all contributors, check out `CONTRIBUTING.md <https://github.com/py-why/dowhy/blob/main/CONTRIBUTING.md>`_ and our `docs for contributing code <https://github.com/py-why/dowhy/blob/main/docs/source/contributing/contributing-code.rst>`_. Our `contributor code of conduct is available here <https://github.com/py-why/governance/blob/main/CODE-OF-CONDUCT.md>`_.

|BuildStatus|_ |PyPiVersion|_ |PythonSupport|_ |Downloads|_ |discord|_ .. |PyPiVersion| image:: https://img.shields.io/pypi/v/dowhy.svg .. _PyPiVersion: https://pypi.org/project/dowhy/ .. |PythonSupport| image:: https://img.shields.io/pypi/pyversions/dowhy.svg .. _PythonSupport: https://pypi.org/project/dowhy/ .. |BuildStatus| image:: https://github.com/py-why/dowhy/actions/workflows/ci.yml/badge.svg .. _BuildStatus: https://github.com/py-why/dowhy/actions .. |Downloads| image:: https://pepy.tech/badge/dowhy .. _Downloads: https://pepy.tech/project/dowhy .. |discord| image:: https://img.shields.io/discord/818456847551168542 .. _discord: https://discord.gg/cSBGb3vsZb .. image:: dowhy-logo-large.png :width: 50% :align: center `Checkout the documentation <https://py-why.github.io/dowhy/>`_ =============================================================== - The documentation, user guide, sample notebooks and other information are available at `https://py-why.github.io/dowhy <https://py-why.github.io/dowhy/>`_ - DoWhy is part of the `PyWhy Ecosystem <https://www.pywhy.org/>`_. For more tools and libraries related to causality, checkout the `PyWhy GitHub organization <https://github.com/py-why/>`_! - For any questions, comments, or discussions about specific use cases, join our community on `Discord <https://discord.gg/cSBGb3vsZb>`_ (|discord|_) - Jump right into some case studies: - Effect estimation: `Hotel booking cancellations <https://towardsdatascience.com/beyond-predictive-models-the-causal-story-behind-hotel-booking-cancellations-d29e8558cbaf>`_ | `Effect of customer loyalty programs <https://github.com/microsoft/dowhy/blob/main/docs/source/example_notebooks/dowhy_example_effect_of_memberrewards_program.ipynb>`_ | `Optimizing article headlines <https://medium.com/@akelleh/introducing-the-do-sampler-for-causal-inference-a3296ea9e78d>`_ | `Effect of home visits on infant health (IHDP) <https://towardsdatascience.com/implementing-causal-inference-a-key-step-towards-agi-de2cde8ea599>`_ | `Causes of customer churn/attrition <https://medium.com/geekculture/a-quickstart-for-causal-analysis-decision-making-with-dowhy-2ce2d4d1efa9>`_ - Root cause analysis and explanations: `Root Cause Analysis with DoWhy, an Open Source Python Library for Causal Machine Learning <https://aws.amazon.com/blogs/opensource/root-cause-analysis-with-dowhy-an-open-source-python-library-for-causal-machine-learning/>`_ | `Finding the Root Cause of Elevated Latencies in a Microservice Architecture <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_rca_microservice_architecture.ipynb>`_ | `Finding Root Causes of Changes in a Supply Chain <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_supply_chain_dist_change.ipynb>`_ For more example notebooks, see `here! <https://www.pywhy.org/dowhy/main/example_notebooks/nb_index.html>`_ Introduction & Key Features =========================== Decision-making involves understanding how different variables affect each other and predicting the outcome when some of them are changed to new values. For instance, given an outcome variable, one may be interested in determining how a potential action(s) may affect it, understanding what led to its current value, or simulate what would happen if some variables are changed. Answering such questions requires causal reasoning. DoWhy is a Python library that guides you through the various steps of causal reasoning and provides a unified interface for answering causal questions. DoWhy provides a wide variety of algorithms for effect estimation, prediction, quantification of causal influences, diagnosis of causal structures, root cause analysis, interventions and counterfactuals. A key feature of DoWhy is its refutation and falsification API that can test causal assumptions for any estimation method, thus making inference more robust and accessible to non-experts. **Graphical Causal Models and Potential Outcomes: Best of both worlds** DoWhy builds on two of the most powerful frameworks for causal inference: graphical causal models and potential outcomes. For effect estimation, it uses graph-based criteria and do-calculus for modeling assumptions and identifying a non-parametric causal effect. For estimation, it switches to methods based primarily on potential outcomes. For causal questions beyond effect estimation, it uses the power of graphical causal models by modeling the data generation process via explicit causal mechanisms at each node, which, for instance, unlocks capabilities to attribute observed effects to particular variables or estimate point-wise counterfactuals. For a quick introduction to causal inference, check out `amit-sharma/causal-inference-tutorial <https://github.com/amit-sharma/causal-inference-tutorial/>`_ We also gave a more comprehensive tutorial at the ACM Knowledge Discovery and Data Mining (`KDD 2018 <http://www.kdd.org/kdd2018/>`_) conference: `causalinference.gitlab.io/kdd-tutorial <http://causalinference.gitlab.io/kdd-tutorial/>`_. For an introduction to the four steps of causal inference and its implications for machine learning, you can access this video tutorial from Microsoft Research `DoWhy Webinar <https://www.microsoft.com/en-us/research/video/foundations-of-causal-inference-and-its-impacts-on-machine-learning/>`_ and for an introduction to the graphical causal model API, see the `PyCon presentation on Root Cause Analysis with DoWhy <https://www.youtube.com/watch?v=icpHrbDlGaw>`_. Key Features ~~~~~~~~~~~~ .. image:: https://raw.githubusercontent.com/py-why/dowhy/main/docs/images/dowhy-features.png DoWhy supports the following causal tasks: - Effect estimation (identification, average causal effect, conditional average causal effect, instrumental variables and more) - Quantify causal influences (mediation analysis, direct arrow strength, intrinsic causal influence) - What-if analysis (generate samples from interventional distribution, estimate counterfactuals) - Root cause analysis and explanations (attribute anomalies to their causes, find causes for changes in distributions, estimate feature relevance and more) For more details and how to use these methods in practice, checkout the documentation at `https://py-why.github.io/dowhy <https://py-why.github.io/dowhy/>`_ Quick Start =========== DoWhy support Python 3.8+. To install, you can use pip, poetry, or conda. **Latest Release** Install the latest `release <https://pypi.org/project/dowhy/>`__ using pip. .. code:: shell pip install dowhy Install the latest `release <https://pypi.org/project/dowhy/>`__ using poetry. .. code:: shell poetry add dowhy Install the latest `release <https://anaconda.org/conda-forge/dowhy>`__ using conda. .. code:: shell conda install -c conda-forge dowhy If you face "Solving environment" problems with conda, then try :code:`conda update --all` and then install dowhy. If that does not work, then use :code:`conda config --set channel_priority false` and try to install again. If the problem persists, please `add your issue here <https://github.com/microsoft/dowhy/issues/197>`_. **Development Version** If you prefer to use the latest dev version, your dependency management tool will need to point at our GitHub repository. .. code:: shell pip install git+https://github.com/py-why/dowhy@main **Requirements** DoWhy requires a few dependencies. Details on specific versions can be found in `pyproject.toml <./pyproject.toml>`_, under the `tool.poetry.dependencies` section. If you face any problems, try installing dependencies manually. .. code:: shell pip install '<dependency-name>==<version>' Optionally, if you wish to input graphs in the dot format, then install pydot (or pygraphviz). For better-looking graphs, you can optionally install pygraphviz. To proceed, first install graphviz and then pygraphviz (on Ubuntu and Ubuntu WSL). .. code:: shell sudo apt install graphviz libgraphviz-dev graphviz-dev pkg-config ## from https://github.com/pygraphviz/pygraphviz/issues/71 pip install pygraphviz --install-option="--include-path=/usr/include/graphviz" \ --install-option="--library-path=/usr/lib/graphviz/" Example: Effect identification and estimation ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Most causal tasks in DoWhy only require a few lines of code to write. Here, we exemplarily estimate the causal effect of a treatment on an outcome variable: .. code:: python from dowhy import CausalModel import dowhy.datasets # Load some sample data data = dowhy.datasets.linear_dataset( beta=10, num_common_causes=5, num_instruments=2, num_samples=10000, treatment_is_binary=True) A causal graph can be defined in different way, but the most common way is via `NetworkX <https://networkx.org/>`_. After loading in the data, we use the four main operations for effect estimation in DoWhy: *model*, *identify*, *estimate* and *refute*: .. code:: python # I. Create a causal model from the data and given graph. model = CausalModel( data=data["df"], treatment=data["treatment_name"], outcome=data["outcome_name"], graph=data["gml_graph"]) # Or alternatively, as nx.DiGraph # II. Identify causal effect and return target estimands identified_estimand = model.identify_effect() # III. Estimate the target estimand using a statistical method. estimate = model.estimate_effect(identified_estimand, method_name="backdoor.propensity_score_matching") # IV. Refute the obtained estimate using multiple robustness checks. refute_results = model.refute_estimate(identified_estimand, estimate, method_name="random_common_cause") DoWhy stresses on the interpretability of its output. At any point in the analysis, you can inspect the untested assumptions, identified estimands (if any), and the estimate (if any). Here's a sample output of the linear regression estimator: .. image:: https://raw.githubusercontent.com/py-why/dowhy/main/docs/images/regression_output.png :width: 80% For a full code example, check out the `Getting Started with DoWhy <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/dowhy_simple_example.ipynb>`_ notebook. You can also use Conditional Average Treatment Effect (CATE) estimation methods from `EconML <https://github.com/py-why/econml>`_, as shown in the `Conditional Treatment Effects <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/dowhy-conditional-treatment-effects.ipynb>`_ notebook. Here's a code snippet. .. code:: python from sklearn.preprocessing import PolynomialFeatures from sklearn.linear_model import LassoCV from sklearn.ensemble import GradientBoostingRegressor dml_estimate = model.estimate_effect(identified_estimand, method_name="backdoor.econml.dml.DML", control_value = 0, treatment_value = 1, target_units = lambda df: df["X0"]>1, confidence_intervals=False, method_params={ "init_params":{'model_y':GradientBoostingRegressor(), 'model_t': GradientBoostingRegressor(), 'model_final':LassoCV(), 'featurizer':PolynomialFeatures(degree=1, include_bias=True)}, "fit_params":{}}) Example: Graphical causal model (GCM) based inference ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DoWhy's graphical causal model framework offers powerful tools to address causal questions beyond effect estimation. It is based on Pearl's graphical causal model framework and models the causal data generation process of each variable explicitly via *causal mechanisms* to support a wide range of causal algorithms. For more details, see the book `Elements of Causal Inference <https://mitpress.mit.edu/9780262037310/elements-of-causal-inference/>`_. Complex causal queries, such as attributing observed anomalies to nodes in the system, can be performed with just a few lines of code: .. code:: python import networkx as nx, numpy as np, pandas as pd from dowhy import gcm # Let's generate some "normal" data we assume we're given from our problem domain: X = np.random.normal(loc=0, scale=1, size=1000) Y = 2 * X + np.random.normal(loc=0, scale=1, size=1000) Z = 3 * Y + np.random.normal(loc=0, scale=1, size=1000) data = pd.DataFrame(dict(X=X, Y=Y, Z=Z)) # 1. Modeling cause-effect relationships as a structural causal model # (causal graph + functional causal models): causal_model = gcm.StructuralCausalModel(nx.DiGraph([('X', 'Y'), ('Y', 'Z')])) # X -> Y -> Z gcm.auto.assign_causal_mechanisms(causal_model, data) # 2. Fitting the SCM to the data: gcm.fit(causal_model, data) # Optional: Evaluate causal model print(gcm.evaluate_causal_model(causal_model, data)) # Step 3: Perform a causal analysis. # results = gcm.<causal_query>(causal_model, ...) # For instance, root cause analysis: anomalous_sample = pd.DataFrame(dict(X=[0.1], Y=[6.2], Z=[19])) # Here, Y is the root cause. # "Which node is the root cause of the anomaly in Z?": anomaly_attribution = gcm.attribute_anomalies(causal_model, "Z", anomalous_sample) # Or sampling from an interventional distribution. Here, under the intervention do(Y := 2). samples = gcm.interventional_samples(causal_model, interventions={'Y': lambda y: 2}, num_samples_to_draw=100) The GCM framework offers many more features beyond these examples. For a full code example, check out the `Online Shop example notebook <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_online_shop.ipynb>`_. For more functionalities, example applications of DoWhy and details about the outputs, see the `User Guide <https://www.pywhy.org/dowhy/main/user_guide/intro.html>`_ or checkout `Jupyter notebooks <https://www.pywhy.org/dowhy/main/example_notebooks/nb_index.html>`_. More Information & Resources ============================ `Microsoft Research Blog <https://www.microsoft.com/en-us/research/blog/dowhy-a-library-for-causal-inference/>`_ | `Video Tutorial for Effect Estimation <https://www.youtube.com/watch?v=LALfQStONEc&t=114s>`_ | `Video Tutorial for Root Cause Analysis <https://www.youtube.com/watch?v=icpHrbDlGaw>`_ | `Arxiv Paper <https://arxiv.org/abs/2011.04216>`_ | `Arxiv Paper (Graphical Causal Model extension) <https://arxiv.org/abs/2206.06821>`_ | `Slides <https://www2.slideshare.net/AmitSharma315/dowhy-an-endtoend-library-for-causal-inference>`_ Citing this package ~~~~~~~~~~~~~~~~~~~ If you find DoWhy useful for your work, please cite **both** of the following two references: - Amit Sharma, Emre Kiciman. DoWhy: An End-to-End Library for Causal Inference. 2020. https://arxiv.org/abs/2011.04216 - Patrick Blöbaum, Peter Götz, Kailash Budhathoki, Atalanti A. Mastakouri, Dominik Janzing. DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models. 2022. https://arxiv.org/abs/2206.06821 Bibtex:: @article{dowhy, title={DoWhy: An End-to-End Library for Causal Inference}, author={Sharma, Amit and Kiciman, Emre}, journal={arXiv preprint arXiv:2011.04216}, year={2020} } @article{dowhy_gcm, author = {Bl{\"o}baum, Patrick and G{\"o}tz, Peter and Budhathoki, Kailash and Mastakouri, Atalanti A. and Janzing, Dominik}, title = {DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models}, journal={arXiv preprint arXiv:2206.06821}, year={2022} } Issues ~~~~~~ If you encounter an issue or have a specific request for DoWhy, please `raise an issue <https://github.com/py-why/dowhy/issues>`_. Contributing ~~~~~~~~~~~~ This project welcomes contributions and suggestions. For a guide to contributing and a list of all contributors, check out `CONTRIBUTING.md <https://github.com/py-why/dowhy/blob/main/CONTRIBUTING.md>`_ and our `docs for contributing code <https://github.com/py-why/dowhy/blob/main/docs/source/contributing/contributing-code.rst>`_. Our `contributor code of conduct is available here <https://github.com/py-why/governance/blob/main/CODE-OF-CONDUCT.md>`_.

bloebp

591e9d916e19bcad07e28687aeb8c81e1db26e8d

f455c569db0e7d0c1e1e7b54d1f8c85f026ec611

is there a specific reason for removing the conda installation guide? There is a currently an issue with conda build that we can fix.

amit-sharma

py-why/dowhy

Overhauled readme

The current readme is too overloaded for users. Changes here are: - Reduce it only the essential points, since most of the content is part of the documentation - Add image as overview of the offered features - Made connection between GCM and PO framework more consistent - Revise the GCM example to an executable code snipped - Extended some references to include GCM related work - Fix build status icon - Changed github references to py-why (was still pointing to microsoft) Here the new readme: https://github.com/py-why/dowhy/blob/c0ac8433625b5693175629076ad32a92b0d1eb71/README.rst The image in the key features section is not show. It is the following: <img width="2887" alt="Screenshot 2023-11-28 at 08 52 50" src="https://github.com/py-why/dowhy/assets/51325689/98f5099f-6cc6-4969-b804-609d8006984d">

2023-11-26 05:56:29+00:00

2023-12-01 14:58:26+00:00

README.rst

|BuildStatus|_ |PyPiVersion|_ |PythonSupport|_ |Downloads|_ |discord|_ .. |PyPiVersion| image:: https://img.shields.io/pypi/v/dowhy.svg .. _PyPiVersion: https://pypi.org/project/dowhy/ .. |PythonSupport| image:: https://img.shields.io/pypi/pyversions/dowhy.svg .. _PythonSupport: https://pypi.org/project/dowhy/ .. |BuildStatus| image:: https://github.com/py-why/dowhy/actions/workflows/ci.yml/badge.svg .. _BuildStatus: https://github.com/py-why/dowhy/actions .. |Downloads| image:: https://pepy.tech/badge/dowhy .. _Downloads: https://pepy.tech/project/dowhy .. |discord| image:: https://img.shields.io/discord/818456847551168542 .. _discord: https://discord.gg/cSBGb3vsZb .. image:: dowhy-logo-large.png :width: 50% :align: center `Checkout the documentation <https://py-why.github.io/dowhy/>`_ =============================================================== - The documentation, user guide, sample notebooks and other information are available at `https://py-why.github.io/dowhy <https://py-why.github.io/dowhy/>`_ - DoWhy is part of the `PyWhy Ecosystem <https://www.pywhy.org/>`_. For more tools and libraries related to causality, checkout the `PyWhy GitHub organization <https://github.com/py-why/>`_! - For any questions, comments, or discussions about specific use cases, join our community on `Discord <https://discord.gg/cSBGb3vsZb>`_ (|discord|_) - Jump right into some case studies: - Effect estimation: `Hotel booking cancellations <https://towardsdatascience.com/beyond-predictive-models-the-causal-story-behind-hotel-booking-cancellations-d29e8558cbaf>`_ | `Effect of customer loyalty programs <https://github.com/microsoft/dowhy/blob/main/docs/source/example_notebooks/dowhy_example_effect_of_memberrewards_program.ipynb>`_ | `Optimizing article headlines <https://medium.com/@akelleh/introducing-the-do-sampler-for-causal-inference-a3296ea9e78d>`_ | `Effect of home visits on infant health (IHDP) <https://towardsdatascience.com/implementing-causal-inference-a-key-step-towards-agi-de2cde8ea599>`_ | `Causes of customer churn/attrition <https://medium.com/geekculture/a-quickstart-for-causal-analysis-decision-making-with-dowhy-2ce2d4d1efa9>`_ - Root cause analysis and explanations: `Root Cause Analysis with DoWhy, an Open Source Python Library for Causal Machine Learning <https://aws.amazon.com/blogs/opensource/root-cause-analysis-with-dowhy-an-open-source-python-library-for-causal-machine-learning/>`_ | `Finding the Root Cause of Elevated Latencies in a Microservice Architecture <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_rca_microservice_architecture.ipynb>`_ | `Finding Root Causes of Changes in a Supply Chain <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_supply_chain_dist_change.ipynb>`_ For more example notebooks, see `here! <https://www.pywhy.org/dowhy/main/example_notebooks/nb_index.html>`_ Introduction & Key Features =========================== Decision-making involves understanding how different variables affect each other and predicting the outcome when some of them are changed to new values. For instance, given an outcome variable, one may be interested in determining how a potential action(s) may affect it, understanding what led to its current value, or simulate what would happen if some variables are changed. Answering such questions requires causal reasoning. DoWhy is a Python library that guides you through the various steps of causal reasoning and provides a unified interface for answering causal questions. DoWhy provides a wide variety of algorithms for effect estimation, prediction, quantification of causal influences, diagnosis of causal structures, root cause analysis, interventions and counterfactuals. A key feature of DoWhy is its refutation and falsification API that can test causal assumptions for any estimation method, thus making inference more robust and accessible to non-experts. **Graphical Causal Models and Potential Outcomes: Best of both worlds** DoWhy builds on two of the most powerful frameworks for causal inference: graphical causal models and potential outcomes. For effect estimation, it uses graph-based criteria and do-calculus for modeling assumptions and identifying a non-parametric causal effect. For estimation, it switches to methods based primarily on potential outcomes. For causal questions beyond effect estimation, it uses the power of graphical causal models by modeling the data generation process via explicit causal mechanisms at each node, which, for instance, unlocks capabilities to attribute observed effects to particular variables or estimate point-wise counterfactuals. For a quick introduction to causal inference, check out `amit-sharma/causal-inference-tutorial <https://github.com/amit-sharma/causal-inference-tutorial/>`_ We also gave a more comprehensive tutorial at the ACM Knowledge Discovery and Data Mining (`KDD 2018 <http://www.kdd.org/kdd2018/>`_) conference: `causalinference.gitlab.io/kdd-tutorial <http://causalinference.gitlab.io/kdd-tutorial/>`_. For an introduction to the four steps of causal inference and its implications for machine learning, you can access this video tutorial from Microsoft Research `DoWhy Webinar <https://www.microsoft.com/en-us/research/video/foundations-of-causal-inference-and-its-impacts-on-machine-learning/>`_ and for an introduction to the graphical causal model API, see the `PyCon presentation on Root Cause Analysis with DoWhy <https://www.youtube.com/watch?v=icpHrbDlGaw>`_. Key Features ~~~~~~~~~~~~ .. image:: https://raw.githubusercontent.com/py-why/dowhy/main/docs/images/dowhy-features.png DoWhy supports the following causal tasks: - Effect estimation (identification, average causal effect, conditional average causal effect, instrumental variables and more) - Quantify causal influences (mediation analysis, direct arrow strength, intrinsic causal influence) - What-if analysis (generate samples from interventional distribution, estimate counterfactuals) - Root cause analysis and explanations (attribute anomalies to their causes, find causes for changes in distributions, estimate feature relevance and more) For more details and how to use these methods in practice, checkout the documentation at `https://py-why.github.io/dowhy <https://py-why.github.io/dowhy/>`_ Quick Start =========== DoWhy support Python 3.8+. To install, you can use pip, poetry, or conda. **Latest Release** Install the latest `release <https://pypi.org/project/dowhy/>`__ using pip. .. code:: shell pip install dowhy Install the latest `release <https://pypi.org/project/dowhy/>`__ using poetry. .. code:: shell poetry add dowhy Install the latest `release <https://anaconda.org/conda-forge/dowhy>`__ using conda. .. code:: shell conda install -c conda-forge dowhy If you face "Solving environment" problems with conda, then try :code:`conda update --all` and then install dowhy. If that does not work, then use :code:`conda config --set channel_priority false` and try to install again. If the problem persists, please `add your issue here <https://github.com/microsoft/dowhy/issues/197>`_. **Development Version** If you prefer to use the latest dev version, your dependency management tool will need to point at our GitHub repository. .. code:: shell pip install git+https://github.com/py-why/dowhy@main **Requirements** DoWhy requires a few dependencies. Details on specific versions can be found in `pyproject.toml <./pyproject.toml>`_, under the `tool.poetry.dependencies` section. If you face any problems, try installing dependencies manually. .. code:: shell pip install '<dependency-name>==<version>' Optionally, if you wish to input graphs in the dot format, then install pydot (or pygraphviz). For better-looking graphs, you can optionally install pygraphviz. To proceed, first install graphviz and then pygraphviz (on Ubuntu and Ubuntu WSL). .. code:: shell sudo apt install graphviz libgraphviz-dev graphviz-dev pkg-config ## from https://github.com/pygraphviz/pygraphviz/issues/71 pip install pygraphviz --install-option="--include-path=/usr/include/graphviz" \ --install-option="--library-path=/usr/lib/graphviz/" Example usage ~~~~~~~~~~~~~ Two examples demonstrating the effect estimation and graphical causal models API. Effect identification and estimation ++++++++++++++++++++++++++++++++++++ Most causal tasks in DoWhy only require a few lines of code to write. Here, we exemplarily estimate the causal effect of a treatment on an outcome variable: .. code:: python from dowhy import CausalModel import dowhy.datasets # Load some sample data data = dowhy.datasets.linear_dataset( beta=10, num_common_causes=5, num_instruments=2, num_samples=10000, treatment_is_binary=True) A causal graph can be defined in different way, but the most common way is via `NetworkX <https://networkx.org/>`_. After loading in the data, we use the four main operations for effect estimation in DoWhy: *model*, *identify*, *estimate* and *refute*: .. code:: python # I. Create a causal model from the data and given graph. model = CausalModel( data=data["df"], treatment=data["treatment_name"], outcome=data["outcome_name"], graph=data["gml_graph"]) # Or alternatively, as nx.DiGraph # II. Identify causal effect and return target estimands identified_estimand = model.identify_effect() # III. Estimate the target estimand using a statistical method. estimate = model.estimate_effect(identified_estimand, method_name="backdoor.propensity_score_matching") # IV. Refute the obtained estimate using multiple robustness checks. refute_results = model.refute_estimate(identified_estimand, estimate, method_name="random_common_cause") DoWhy stresses on the interpretability of its output. At any point in the analysis, you can inspect the untested assumptions, identified estimands (if any), and the estimate (if any). Here's a sample output of the linear regression estimator: .. image:: https://raw.githubusercontent.com/py-why/dowhy/main/docs/images/regression_output.png :width: 80% For a full code example, check out the `Getting Started with DoWhy <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/dowhy_simple_example.ipynb>`_ notebook. You can also use Conditional Average Treatment Effect (CATE) estimation methods from `EconML <https://github.com/py-why/econml>`_, as shown in the `Conditional Treatment Effects <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/dowhy-conditional-treatment-effects.ipynb>`_ notebook. Here's a code snippet. .. code:: python from sklearn.preprocessing import PolynomialFeatures from sklearn.linear_model import LassoCV from sklearn.ensemble import GradientBoostingRegressor dml_estimate = model.estimate_effect(identified_estimand, method_name="backdoor.econml.dml.DML", control_value = 0, treatment_value = 1, target_units = lambda df: df["X0"]>1, confidence_intervals=False, method_params={ "init_params":{'model_y':GradientBoostingRegressor(), 'model_t': GradientBoostingRegressor(), 'model_final':LassoCV(), 'featurizer':PolynomialFeatures(degree=1, include_bias=True)}, "fit_params":{}}) Graphical causal model (GCM) based inference ++++++++++++++++++++++++++++++++++++++++++++ DoWhy's graphical causal model framework offers powerful tools to address causal questions beyond effect estimation. It is based on Pearl's graphical causal model framework and models the causal data generation process of each variable explicitly via *causal mechanisms* to support a wide range of causal algorithms. For more details, see the book `Elements of Causal Inference <https://mitpress.mit.edu/9780262037310/elements-of-causal-inference/>`_. Complex causal queries, such as attributing observed anomalies to nodes in the system, can be performed with just a few lines of code: .. code:: python import networkx as nx, numpy as np, pandas as pd from dowhy import gcm # Let's generate some "normal" data we assume we're given from our problem domain: X = np.random.normal(loc=0, scale=1, size=1000) Y = 2 * X + np.random.normal(loc=0, scale=1, size=1000) Z = 3 * Y + np.random.normal(loc=0, scale=1, size=1000) data = pd.DataFrame(dict(X=X, Y=Y, Z=Z)) # 1. Modeling cause-effect relationships as a structural causal model # (causal graph + functional causal models): causal_model = gcm.StructuralCausalModel(nx.DiGraph([('X', 'Y'), ('Y', 'Z')])) # X -> Y -> Z gcm.auto.assign_causal_mechanisms(causal_model, data) # 2. Fitting the SCM to the data: gcm.fit(causal_model, data) # Optional: Evaluate causal model print(gcm.evaluate_causal_model(causal_model, data)) # Step 3: Perform a causal analysis. # results = gcm.<causal_query>(causal_model, ...) # For instance, root cause analysis: anomalous_sample = pd.DataFrame(dict(X=[0.1], Y=[6.2], Z=[19])) # Here, Y is the root cause. # "Which node is the root cause of the anomaly in Z?": anomaly_attribution = gcm.attribute_anomalies(causal_model, "Z", anomalous_sample) # Or sampling from an interventional distribution. Here, under the intervention do(Y := 2). samples = gcm.interventional_samples(causal_model, interventions={'Y': lambda y: 2}, num_samples_to_draw=100) The GCM framework offers many more features beyond these examples. For a full code example, check out the `Online Shop example notebook <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_online_shop.ipynb>`_. For more functionalities, example applications of DoWhy and details about the outputs, see the `User Guide <https://www.pywhy.org/dowhy/main/user_guide/intro.html>`_ or checkout `Jupyter notebooks <https://www.pywhy.org/dowhy/main/example_notebooks/nb_index.html>`_. More Information & Resources ============================ `Microsoft Research Blog <https://www.microsoft.com/en-us/research/blog/dowhy-a-library-for-causal-inference/>`_ | `Video Tutorial for Effect Estimation <https://www.youtube.com/watch?v=LALfQStONEc&t=114s>`_ | `Video Tutorial for Root Cause Analysis <https://www.youtube.com/watch?v=icpHrbDlGaw>`_ | `Arxiv Paper <https://arxiv.org/abs/2011.04216>`_ | `Arxiv Paper (Graphical Causal Model extension) <https://arxiv.org/abs/2206.06821>`_ | `Slides <https://www2.slideshare.net/AmitSharma315/dowhy-an-endtoend-library-for-causal-inference>`_ Citing this package ~~~~~~~~~~~~~~~~~~~ If you find DoWhy useful for your work, please cite **both** of the following two references: - Amit Sharma, Emre Kiciman. DoWhy: An End-to-End Library for Causal Inference. 2020. https://arxiv.org/abs/2011.04216 - Patrick Blöbaum, Peter Götz, Kailash Budhathoki, Atalanti A. Mastakouri, Dominik Janzing. DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models. 2022. https://arxiv.org/abs/2206.06821 Bibtex:: @article{dowhy, title={DoWhy: An End-to-End Library for Causal Inference}, author={Sharma, Amit and Kiciman, Emre}, journal={arXiv preprint arXiv:2011.04216}, year={2020} } @article{dowhy_gcm, author = {Bl{\"o}baum, Patrick and G{\"o}tz, Peter and Budhathoki, Kailash and Mastakouri, Atalanti A. and Janzing, Dominik}, title = {DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models}, journal={arXiv preprint arXiv:2206.06821}, year={2022} } Issues ~~~~~~ If you encounter an issue or have a specific request for DoWhy, please `raise an issue <https://github.com/py-why/dowhy/issues>`_. Contributing ~~~~~~~~~~~~ This project welcomes contributions and suggestions. For a guide to contributing and a list of all contributors, check out `CONTRIBUTING.md <https://github.com/py-why/dowhy/blob/main/CONTRIBUTING.md>`_ and our `docs for contributing code <https://github.com/py-why/dowhy/blob/main/docs/source/contributing/contributing-code.rst>`_. Our `contributor code of conduct is available here <https://github.com/py-why/governance/blob/main/CODE-OF-CONDUCT.md>`_.

|BuildStatus|_ |PyPiVersion|_ |PythonSupport|_ |Downloads|_ |discord|_ .. |PyPiVersion| image:: https://img.shields.io/pypi/v/dowhy.svg .. _PyPiVersion: https://pypi.org/project/dowhy/ .. |PythonSupport| image:: https://img.shields.io/pypi/pyversions/dowhy.svg .. _PythonSupport: https://pypi.org/project/dowhy/ .. |BuildStatus| image:: https://github.com/py-why/dowhy/actions/workflows/ci.yml/badge.svg .. _BuildStatus: https://github.com/py-why/dowhy/actions .. |Downloads| image:: https://pepy.tech/badge/dowhy .. _Downloads: https://pepy.tech/project/dowhy .. |discord| image:: https://img.shields.io/discord/818456847551168542 .. _discord: https://discord.gg/cSBGb3vsZb .. image:: dowhy-logo-large.png :width: 50% :align: center `Checkout the documentation <https://py-why.github.io/dowhy/>`_ =============================================================== - The documentation, user guide, sample notebooks and other information are available at `https://py-why.github.io/dowhy <https://py-why.github.io/dowhy/>`_ - DoWhy is part of the `PyWhy Ecosystem <https://www.pywhy.org/>`_. For more tools and libraries related to causality, checkout the `PyWhy GitHub organization <https://github.com/py-why/>`_! - For any questions, comments, or discussions about specific use cases, join our community on `Discord <https://discord.gg/cSBGb3vsZb>`_ (|discord|_) - Jump right into some case studies: - Effect estimation: `Hotel booking cancellations <https://towardsdatascience.com/beyond-predictive-models-the-causal-story-behind-hotel-booking-cancellations-d29e8558cbaf>`_ | `Effect of customer loyalty programs <https://github.com/microsoft/dowhy/blob/main/docs/source/example_notebooks/dowhy_example_effect_of_memberrewards_program.ipynb>`_ | `Optimizing article headlines <https://medium.com/@akelleh/introducing-the-do-sampler-for-causal-inference-a3296ea9e78d>`_ | `Effect of home visits on infant health (IHDP) <https://towardsdatascience.com/implementing-causal-inference-a-key-step-towards-agi-de2cde8ea599>`_ | `Causes of customer churn/attrition <https://medium.com/geekculture/a-quickstart-for-causal-analysis-decision-making-with-dowhy-2ce2d4d1efa9>`_ - Root cause analysis and explanations: `Root Cause Analysis with DoWhy, an Open Source Python Library for Causal Machine Learning <https://aws.amazon.com/blogs/opensource/root-cause-analysis-with-dowhy-an-open-source-python-library-for-causal-machine-learning/>`_ | `Finding the Root Cause of Elevated Latencies in a Microservice Architecture <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_rca_microservice_architecture.ipynb>`_ | `Finding Root Causes of Changes in a Supply Chain <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_supply_chain_dist_change.ipynb>`_ For more example notebooks, see `here! <https://www.pywhy.org/dowhy/main/example_notebooks/nb_index.html>`_ Introduction & Key Features =========================== Decision-making involves understanding how different variables affect each other and predicting the outcome when some of them are changed to new values. For instance, given an outcome variable, one may be interested in determining how a potential action(s) may affect it, understanding what led to its current value, or simulate what would happen if some variables are changed. Answering such questions requires causal reasoning. DoWhy is a Python library that guides you through the various steps of causal reasoning and provides a unified interface for answering causal questions. DoWhy provides a wide variety of algorithms for effect estimation, prediction, quantification of causal influences, diagnosis of causal structures, root cause analysis, interventions and counterfactuals. A key feature of DoWhy is its refutation and falsification API that can test causal assumptions for any estimation method, thus making inference more robust and accessible to non-experts. **Graphical Causal Models and Potential Outcomes: Best of both worlds** DoWhy builds on two of the most powerful frameworks for causal inference: graphical causal models and potential outcomes. For effect estimation, it uses graph-based criteria and do-calculus for modeling assumptions and identifying a non-parametric causal effect. For estimation, it switches to methods based primarily on potential outcomes. For causal questions beyond effect estimation, it uses the power of graphical causal models by modeling the data generation process via explicit causal mechanisms at each node, which, for instance, unlocks capabilities to attribute observed effects to particular variables or estimate point-wise counterfactuals. For a quick introduction to causal inference, check out `amit-sharma/causal-inference-tutorial <https://github.com/amit-sharma/causal-inference-tutorial/>`_ We also gave a more comprehensive tutorial at the ACM Knowledge Discovery and Data Mining (`KDD 2018 <http://www.kdd.org/kdd2018/>`_) conference: `causalinference.gitlab.io/kdd-tutorial <http://causalinference.gitlab.io/kdd-tutorial/>`_. For an introduction to the four steps of causal inference and its implications for machine learning, you can access this video tutorial from Microsoft Research `DoWhy Webinar <https://www.microsoft.com/en-us/research/video/foundations-of-causal-inference-and-its-impacts-on-machine-learning/>`_ and for an introduction to the graphical causal model API, see the `PyCon presentation on Root Cause Analysis with DoWhy <https://www.youtube.com/watch?v=icpHrbDlGaw>`_. Key Features ~~~~~~~~~~~~ .. image:: https://raw.githubusercontent.com/py-why/dowhy/main/docs/images/dowhy-features.png DoWhy supports the following causal tasks: - Effect estimation (identification, average causal effect, conditional average causal effect, instrumental variables and more) - Quantify causal influences (mediation analysis, direct arrow strength, intrinsic causal influence) - What-if analysis (generate samples from interventional distribution, estimate counterfactuals) - Root cause analysis and explanations (attribute anomalies to their causes, find causes for changes in distributions, estimate feature relevance and more) For more details and how to use these methods in practice, checkout the documentation at `https://py-why.github.io/dowhy <https://py-why.github.io/dowhy/>`_ Quick Start =========== DoWhy support Python 3.8+. To install, you can use pip, poetry, or conda. **Latest Release** Install the latest `release <https://pypi.org/project/dowhy/>`__ using pip. .. code:: shell pip install dowhy Install the latest `release <https://pypi.org/project/dowhy/>`__ using poetry. .. code:: shell poetry add dowhy Install the latest `release <https://anaconda.org/conda-forge/dowhy>`__ using conda. .. code:: shell conda install -c conda-forge dowhy If you face "Solving environment" problems with conda, then try :code:`conda update --all` and then install dowhy. If that does not work, then use :code:`conda config --set channel_priority false` and try to install again. If the problem persists, please `add your issue here <https://github.com/microsoft/dowhy/issues/197>`_. **Development Version** If you prefer to use the latest dev version, your dependency management tool will need to point at our GitHub repository. .. code:: shell pip install git+https://github.com/py-why/dowhy@main **Requirements** DoWhy requires a few dependencies. Details on specific versions can be found in `pyproject.toml <./pyproject.toml>`_, under the `tool.poetry.dependencies` section. If you face any problems, try installing dependencies manually. .. code:: shell pip install '<dependency-name>==<version>' Optionally, if you wish to input graphs in the dot format, then install pydot (or pygraphviz). For better-looking graphs, you can optionally install pygraphviz. To proceed, first install graphviz and then pygraphviz (on Ubuntu and Ubuntu WSL). .. code:: shell sudo apt install graphviz libgraphviz-dev graphviz-dev pkg-config ## from https://github.com/pygraphviz/pygraphviz/issues/71 pip install pygraphviz --install-option="--include-path=/usr/include/graphviz" \ --install-option="--library-path=/usr/lib/graphviz/" Example: Effect identification and estimation ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Most causal tasks in DoWhy only require a few lines of code to write. Here, we exemplarily estimate the causal effect of a treatment on an outcome variable: .. code:: python from dowhy import CausalModel import dowhy.datasets # Load some sample data data = dowhy.datasets.linear_dataset( beta=10, num_common_causes=5, num_instruments=2, num_samples=10000, treatment_is_binary=True) A causal graph can be defined in different way, but the most common way is via `NetworkX <https://networkx.org/>`_. After loading in the data, we use the four main operations for effect estimation in DoWhy: *model*, *identify*, *estimate* and *refute*: .. code:: python # I. Create a causal model from the data and given graph. model = CausalModel( data=data["df"], treatment=data["treatment_name"], outcome=data["outcome_name"], graph=data["gml_graph"]) # Or alternatively, as nx.DiGraph # II. Identify causal effect and return target estimands identified_estimand = model.identify_effect() # III. Estimate the target estimand using a statistical method. estimate = model.estimate_effect(identified_estimand, method_name="backdoor.propensity_score_matching") # IV. Refute the obtained estimate using multiple robustness checks. refute_results = model.refute_estimate(identified_estimand, estimate, method_name="random_common_cause") DoWhy stresses on the interpretability of its output. At any point in the analysis, you can inspect the untested assumptions, identified estimands (if any), and the estimate (if any). Here's a sample output of the linear regression estimator: .. image:: https://raw.githubusercontent.com/py-why/dowhy/main/docs/images/regression_output.png :width: 80% For a full code example, check out the `Getting Started with DoWhy <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/dowhy_simple_example.ipynb>`_ notebook. You can also use Conditional Average Treatment Effect (CATE) estimation methods from `EconML <https://github.com/py-why/econml>`_, as shown in the `Conditional Treatment Effects <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/dowhy-conditional-treatment-effects.ipynb>`_ notebook. Here's a code snippet. .. code:: python from sklearn.preprocessing import PolynomialFeatures from sklearn.linear_model import LassoCV from sklearn.ensemble import GradientBoostingRegressor dml_estimate = model.estimate_effect(identified_estimand, method_name="backdoor.econml.dml.DML", control_value = 0, treatment_value = 1, target_units = lambda df: df["X0"]>1, confidence_intervals=False, method_params={ "init_params":{'model_y':GradientBoostingRegressor(), 'model_t': GradientBoostingRegressor(), 'model_final':LassoCV(), 'featurizer':PolynomialFeatures(degree=1, include_bias=True)}, "fit_params":{}}) Example: Graphical causal model (GCM) based inference ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DoWhy's graphical causal model framework offers powerful tools to address causal questions beyond effect estimation. It is based on Pearl's graphical causal model framework and models the causal data generation process of each variable explicitly via *causal mechanisms* to support a wide range of causal algorithms. For more details, see the book `Elements of Causal Inference <https://mitpress.mit.edu/9780262037310/elements-of-causal-inference/>`_. Complex causal queries, such as attributing observed anomalies to nodes in the system, can be performed with just a few lines of code: .. code:: python import networkx as nx, numpy as np, pandas as pd from dowhy import gcm # Let's generate some "normal" data we assume we're given from our problem domain: X = np.random.normal(loc=0, scale=1, size=1000) Y = 2 * X + np.random.normal(loc=0, scale=1, size=1000) Z = 3 * Y + np.random.normal(loc=0, scale=1, size=1000) data = pd.DataFrame(dict(X=X, Y=Y, Z=Z)) # 1. Modeling cause-effect relationships as a structural causal model # (causal graph + functional causal models): causal_model = gcm.StructuralCausalModel(nx.DiGraph([('X', 'Y'), ('Y', 'Z')])) # X -> Y -> Z gcm.auto.assign_causal_mechanisms(causal_model, data) # 2. Fitting the SCM to the data: gcm.fit(causal_model, data) # Optional: Evaluate causal model print(gcm.evaluate_causal_model(causal_model, data)) # Step 3: Perform a causal analysis. # results = gcm.<causal_query>(causal_model, ...) # For instance, root cause analysis: anomalous_sample = pd.DataFrame(dict(X=[0.1], Y=[6.2], Z=[19])) # Here, Y is the root cause. # "Which node is the root cause of the anomaly in Z?": anomaly_attribution = gcm.attribute_anomalies(causal_model, "Z", anomalous_sample) # Or sampling from an interventional distribution. Here, under the intervention do(Y := 2). samples = gcm.interventional_samples(causal_model, interventions={'Y': lambda y: 2}, num_samples_to_draw=100) The GCM framework offers many more features beyond these examples. For a full code example, check out the `Online Shop example notebook <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_online_shop.ipynb>`_. For more functionalities, example applications of DoWhy and details about the outputs, see the `User Guide <https://www.pywhy.org/dowhy/main/user_guide/intro.html>`_ or checkout `Jupyter notebooks <https://www.pywhy.org/dowhy/main/example_notebooks/nb_index.html>`_. More Information & Resources ============================ `Microsoft Research Blog <https://www.microsoft.com/en-us/research/blog/dowhy-a-library-for-causal-inference/>`_ | `Video Tutorial for Effect Estimation <https://www.youtube.com/watch?v=LALfQStONEc&t=114s>`_ | `Video Tutorial for Root Cause Analysis <https://www.youtube.com/watch?v=icpHrbDlGaw>`_ | `Arxiv Paper <https://arxiv.org/abs/2011.04216>`_ | `Arxiv Paper (Graphical Causal Model extension) <https://arxiv.org/abs/2206.06821>`_ | `Slides <https://www2.slideshare.net/AmitSharma315/dowhy-an-endtoend-library-for-causal-inference>`_ Citing this package ~~~~~~~~~~~~~~~~~~~ If you find DoWhy useful for your work, please cite **both** of the following two references: - Amit Sharma, Emre Kiciman. DoWhy: An End-to-End Library for Causal Inference. 2020. https://arxiv.org/abs/2011.04216 - Patrick Blöbaum, Peter Götz, Kailash Budhathoki, Atalanti A. Mastakouri, Dominik Janzing. DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models. 2022. https://arxiv.org/abs/2206.06821 Bibtex:: @article{dowhy, title={DoWhy: An End-to-End Library for Causal Inference}, author={Sharma, Amit and Kiciman, Emre}, journal={arXiv preprint arXiv:2011.04216}, year={2020} } @article{dowhy_gcm, author = {Bl{\"o}baum, Patrick and G{\"o}tz, Peter and Budhathoki, Kailash and Mastakouri, Atalanti A. and Janzing, Dominik}, title = {DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models}, journal={arXiv preprint arXiv:2206.06821}, year={2022} } Issues ~~~~~~ If you encounter an issue or have a specific request for DoWhy, please `raise an issue <https://github.com/py-why/dowhy/issues>`_. Contributing ~~~~~~~~~~~~ This project welcomes contributions and suggestions. For a guide to contributing and a list of all contributors, check out `CONTRIBUTING.md <https://github.com/py-why/dowhy/blob/main/CONTRIBUTING.md>`_ and our `docs for contributing code <https://github.com/py-why/dowhy/blob/main/docs/source/contributing/contributing-code.rst>`_. Our `contributor code of conduct is available here <https://github.com/py-why/governance/blob/main/CODE-OF-CONDUCT.md>`_.

bloebp

591e9d916e19bcad07e28687aeb8c81e1db26e8d

f455c569db0e7d0c1e1e7b54d1f8c85f026ec611

Let's make it: 'Effect identification and estimation.' since identification happens first.

amit-sharma

py-why/dowhy

Overhauled readme

The current readme is too overloaded for users. Changes here are: - Reduce it only the essential points, since most of the content is part of the documentation - Add image as overview of the offered features - Made connection between GCM and PO framework more consistent - Revise the GCM example to an executable code snipped - Extended some references to include GCM related work - Fix build status icon - Changed github references to py-why (was still pointing to microsoft) Here the new readme: https://github.com/py-why/dowhy/blob/c0ac8433625b5693175629076ad32a92b0d1eb71/README.rst The image in the key features section is not show. It is the following: <img width="2887" alt="Screenshot 2023-11-28 at 08 52 50" src="https://github.com/py-why/dowhy/assets/51325689/98f5099f-6cc6-4969-b804-609d8006984d">

2023-11-26 05:56:29+00:00

2023-12-01 14:58:26+00:00

README.rst

|BuildStatus|_ |PyPiVersion|_ |PythonSupport|_ |Downloads|_ |discord|_ .. |PyPiVersion| image:: https://img.shields.io/pypi/v/dowhy.svg .. _PyPiVersion: https://pypi.org/project/dowhy/ .. |PythonSupport| image:: https://img.shields.io/pypi/pyversions/dowhy.svg .. _PythonSupport: https://pypi.org/project/dowhy/ .. |BuildStatus| image:: https://github.com/py-why/dowhy/actions/workflows/ci.yml/badge.svg .. _BuildStatus: https://github.com/py-why/dowhy/actions .. |Downloads| image:: https://pepy.tech/badge/dowhy .. _Downloads: https://pepy.tech/project/dowhy .. |discord| image:: https://img.shields.io/discord/818456847551168542 .. _discord: https://discord.gg/cSBGb3vsZb .. image:: dowhy-logo-large.png :width: 50% :align: center `Checkout the documentation <https://py-why.github.io/dowhy/>`_ =============================================================== - The documentation, user guide, sample notebooks and other information are available at `https://py-why.github.io/dowhy <https://py-why.github.io/dowhy/>`_ - DoWhy is part of the `PyWhy Ecosystem <https://www.pywhy.org/>`_. For more tools and libraries related to causality, checkout the `PyWhy GitHub organization <https://github.com/py-why/>`_! - For any questions, comments, or discussions about specific use cases, join our community on `Discord <https://discord.gg/cSBGb3vsZb>`_ (|discord|_) - Jump right into some case studies: - Effect estimation: `Hotel booking cancellations <https://towardsdatascience.com/beyond-predictive-models-the-causal-story-behind-hotel-booking-cancellations-d29e8558cbaf>`_ | `Effect of customer loyalty programs <https://github.com/microsoft/dowhy/blob/main/docs/source/example_notebooks/dowhy_example_effect_of_memberrewards_program.ipynb>`_ | `Optimizing article headlines <https://medium.com/@akelleh/introducing-the-do-sampler-for-causal-inference-a3296ea9e78d>`_ | `Effect of home visits on infant health (IHDP) <https://towardsdatascience.com/implementing-causal-inference-a-key-step-towards-agi-de2cde8ea599>`_ | `Causes of customer churn/attrition <https://medium.com/geekculture/a-quickstart-for-causal-analysis-decision-making-with-dowhy-2ce2d4d1efa9>`_ - Root cause analysis and explanations: `Root Cause Analysis with DoWhy, an Open Source Python Library for Causal Machine Learning <https://aws.amazon.com/blogs/opensource/root-cause-analysis-with-dowhy-an-open-source-python-library-for-causal-machine-learning/>`_ | `Finding the Root Cause of Elevated Latencies in a Microservice Architecture <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_rca_microservice_architecture.ipynb>`_ | `Finding Root Causes of Changes in a Supply Chain <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_supply_chain_dist_change.ipynb>`_ For more example notebooks, see `here! <https://www.pywhy.org/dowhy/main/example_notebooks/nb_index.html>`_ Introduction & Key Features =========================== Decision-making involves understanding how different variables affect each other and predicting the outcome when some of them are changed to new values. For instance, given an outcome variable, one may be interested in determining how a potential action(s) may affect it, understanding what led to its current value, or simulate what would happen if some variables are changed. Answering such questions requires causal reasoning. DoWhy is a Python library that guides you through the various steps of causal reasoning and provides a unified interface for answering causal questions. DoWhy provides a wide variety of algorithms for effect estimation, prediction, quantification of causal influences, diagnosis of causal structures, root cause analysis, interventions and counterfactuals. A key feature of DoWhy is its refutation and falsification API that can test causal assumptions for any estimation method, thus making inference more robust and accessible to non-experts. **Graphical Causal Models and Potential Outcomes: Best of both worlds** DoWhy builds on two of the most powerful frameworks for causal inference: graphical causal models and potential outcomes. For effect estimation, it uses graph-based criteria and do-calculus for modeling assumptions and identifying a non-parametric causal effect. For estimation, it switches to methods based primarily on potential outcomes. For causal questions beyond effect estimation, it uses the power of graphical causal models by modeling the data generation process via explicit causal mechanisms at each node, which, for instance, unlocks capabilities to attribute observed effects to particular variables or estimate point-wise counterfactuals. For a quick introduction to causal inference, check out `amit-sharma/causal-inference-tutorial <https://github.com/amit-sharma/causal-inference-tutorial/>`_ We also gave a more comprehensive tutorial at the ACM Knowledge Discovery and Data Mining (`KDD 2018 <http://www.kdd.org/kdd2018/>`_) conference: `causalinference.gitlab.io/kdd-tutorial <http://causalinference.gitlab.io/kdd-tutorial/>`_. For an introduction to the four steps of causal inference and its implications for machine learning, you can access this video tutorial from Microsoft Research `DoWhy Webinar <https://www.microsoft.com/en-us/research/video/foundations-of-causal-inference-and-its-impacts-on-machine-learning/>`_ and for an introduction to the graphical causal model API, see the `PyCon presentation on Root Cause Analysis with DoWhy <https://www.youtube.com/watch?v=icpHrbDlGaw>`_. Key Features ~~~~~~~~~~~~ .. image:: https://raw.githubusercontent.com/py-why/dowhy/main/docs/images/dowhy-features.png DoWhy supports the following causal tasks: - Effect estimation (identification, average causal effect, conditional average causal effect, instrumental variables and more) - Quantify causal influences (mediation analysis, direct arrow strength, intrinsic causal influence) - What-if analysis (generate samples from interventional distribution, estimate counterfactuals) - Root cause analysis and explanations (attribute anomalies to their causes, find causes for changes in distributions, estimate feature relevance and more) For more details and how to use these methods in practice, checkout the documentation at `https://py-why.github.io/dowhy <https://py-why.github.io/dowhy/>`_ Quick Start =========== DoWhy support Python 3.8+. To install, you can use pip, poetry, or conda. **Latest Release** Install the latest `release <https://pypi.org/project/dowhy/>`__ using pip. .. code:: shell pip install dowhy Install the latest `release <https://pypi.org/project/dowhy/>`__ using poetry. .. code:: shell poetry add dowhy Install the latest `release <https://anaconda.org/conda-forge/dowhy>`__ using conda. .. code:: shell conda install -c conda-forge dowhy If you face "Solving environment" problems with conda, then try :code:`conda update --all` and then install dowhy. If that does not work, then use :code:`conda config --set channel_priority false` and try to install again. If the problem persists, please `add your issue here <https://github.com/microsoft/dowhy/issues/197>`_. **Development Version** If you prefer to use the latest dev version, your dependency management tool will need to point at our GitHub repository. .. code:: shell pip install git+https://github.com/py-why/dowhy@main **Requirements** DoWhy requires a few dependencies. Details on specific versions can be found in `pyproject.toml <./pyproject.toml>`_, under the `tool.poetry.dependencies` section. If you face any problems, try installing dependencies manually. .. code:: shell pip install '<dependency-name>==<version>' Optionally, if you wish to input graphs in the dot format, then install pydot (or pygraphviz). For better-looking graphs, you can optionally install pygraphviz. To proceed, first install graphviz and then pygraphviz (on Ubuntu and Ubuntu WSL). .. code:: shell sudo apt install graphviz libgraphviz-dev graphviz-dev pkg-config ## from https://github.com/pygraphviz/pygraphviz/issues/71 pip install pygraphviz --install-option="--include-path=/usr/include/graphviz" \ --install-option="--library-path=/usr/lib/graphviz/" Example usage ~~~~~~~~~~~~~ Two examples demonstrating the effect estimation and graphical causal models API. Effect identification and estimation ++++++++++++++++++++++++++++++++++++ Most causal tasks in DoWhy only require a few lines of code to write. Here, we exemplarily estimate the causal effect of a treatment on an outcome variable: .. code:: python from dowhy import CausalModel import dowhy.datasets # Load some sample data data = dowhy.datasets.linear_dataset( beta=10, num_common_causes=5, num_instruments=2, num_samples=10000, treatment_is_binary=True) A causal graph can be defined in different way, but the most common way is via `NetworkX <https://networkx.org/>`_. After loading in the data, we use the four main operations for effect estimation in DoWhy: *model*, *identify*, *estimate* and *refute*: .. code:: python # I. Create a causal model from the data and given graph. model = CausalModel( data=data["df"], treatment=data["treatment_name"], outcome=data["outcome_name"], graph=data["gml_graph"]) # Or alternatively, as nx.DiGraph # II. Identify causal effect and return target estimands identified_estimand = model.identify_effect() # III. Estimate the target estimand using a statistical method. estimate = model.estimate_effect(identified_estimand, method_name="backdoor.propensity_score_matching") # IV. Refute the obtained estimate using multiple robustness checks. refute_results = model.refute_estimate(identified_estimand, estimate, method_name="random_common_cause") DoWhy stresses on the interpretability of its output. At any point in the analysis, you can inspect the untested assumptions, identified estimands (if any), and the estimate (if any). Here's a sample output of the linear regression estimator: .. image:: https://raw.githubusercontent.com/py-why/dowhy/main/docs/images/regression_output.png :width: 80% For a full code example, check out the `Getting Started with DoWhy <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/dowhy_simple_example.ipynb>`_ notebook. You can also use Conditional Average Treatment Effect (CATE) estimation methods from `EconML <https://github.com/py-why/econml>`_, as shown in the `Conditional Treatment Effects <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/dowhy-conditional-treatment-effects.ipynb>`_ notebook. Here's a code snippet. .. code:: python from sklearn.preprocessing import PolynomialFeatures from sklearn.linear_model import LassoCV from sklearn.ensemble import GradientBoostingRegressor dml_estimate = model.estimate_effect(identified_estimand, method_name="backdoor.econml.dml.DML", control_value = 0, treatment_value = 1, target_units = lambda df: df["X0"]>1, confidence_intervals=False, method_params={ "init_params":{'model_y':GradientBoostingRegressor(), 'model_t': GradientBoostingRegressor(), 'model_final':LassoCV(), 'featurizer':PolynomialFeatures(degree=1, include_bias=True)}, "fit_params":{}}) Graphical causal model (GCM) based inference ++++++++++++++++++++++++++++++++++++++++++++ DoWhy's graphical causal model framework offers powerful tools to address causal questions beyond effect estimation. It is based on Pearl's graphical causal model framework and models the causal data generation process of each variable explicitly via *causal mechanisms* to support a wide range of causal algorithms. For more details, see the book `Elements of Causal Inference <https://mitpress.mit.edu/9780262037310/elements-of-causal-inference/>`_. Complex causal queries, such as attributing observed anomalies to nodes in the system, can be performed with just a few lines of code: .. code:: python import networkx as nx, numpy as np, pandas as pd from dowhy import gcm # Let's generate some "normal" data we assume we're given from our problem domain: X = np.random.normal(loc=0, scale=1, size=1000) Y = 2 * X + np.random.normal(loc=0, scale=1, size=1000) Z = 3 * Y + np.random.normal(loc=0, scale=1, size=1000) data = pd.DataFrame(dict(X=X, Y=Y, Z=Z)) # 1. Modeling cause-effect relationships as a structural causal model # (causal graph + functional causal models): causal_model = gcm.StructuralCausalModel(nx.DiGraph([('X', 'Y'), ('Y', 'Z')])) # X -> Y -> Z gcm.auto.assign_causal_mechanisms(causal_model, data) # 2. Fitting the SCM to the data: gcm.fit(causal_model, data) # Optional: Evaluate causal model print(gcm.evaluate_causal_model(causal_model, data)) # Step 3: Perform a causal analysis. # results = gcm.<causal_query>(causal_model, ...) # For instance, root cause analysis: anomalous_sample = pd.DataFrame(dict(X=[0.1], Y=[6.2], Z=[19])) # Here, Y is the root cause. # "Which node is the root cause of the anomaly in Z?": anomaly_attribution = gcm.attribute_anomalies(causal_model, "Z", anomalous_sample) # Or sampling from an interventional distribution. Here, under the intervention do(Y := 2). samples = gcm.interventional_samples(causal_model, interventions={'Y': lambda y: 2}, num_samples_to_draw=100) The GCM framework offers many more features beyond these examples. For a full code example, check out the `Online Shop example notebook <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_online_shop.ipynb>`_. For more functionalities, example applications of DoWhy and details about the outputs, see the `User Guide <https://www.pywhy.org/dowhy/main/user_guide/intro.html>`_ or checkout `Jupyter notebooks <https://www.pywhy.org/dowhy/main/example_notebooks/nb_index.html>`_. More Information & Resources ============================ `Microsoft Research Blog <https://www.microsoft.com/en-us/research/blog/dowhy-a-library-for-causal-inference/>`_ | `Video Tutorial for Effect Estimation <https://www.youtube.com/watch?v=LALfQStONEc&t=114s>`_ | `Video Tutorial for Root Cause Analysis <https://www.youtube.com/watch?v=icpHrbDlGaw>`_ | `Arxiv Paper <https://arxiv.org/abs/2011.04216>`_ | `Arxiv Paper (Graphical Causal Model extension) <https://arxiv.org/abs/2206.06821>`_ | `Slides <https://www2.slideshare.net/AmitSharma315/dowhy-an-endtoend-library-for-causal-inference>`_ Citing this package ~~~~~~~~~~~~~~~~~~~ If you find DoWhy useful for your work, please cite **both** of the following two references: - Amit Sharma, Emre Kiciman. DoWhy: An End-to-End Library for Causal Inference. 2020. https://arxiv.org/abs/2011.04216 - Patrick Blöbaum, Peter Götz, Kailash Budhathoki, Atalanti A. Mastakouri, Dominik Janzing. DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models. 2022. https://arxiv.org/abs/2206.06821 Bibtex:: @article{dowhy, title={DoWhy: An End-to-End Library for Causal Inference}, author={Sharma, Amit and Kiciman, Emre}, journal={arXiv preprint arXiv:2011.04216}, year={2020} } @article{dowhy_gcm, author = {Bl{\"o}baum, Patrick and G{\"o}tz, Peter and Budhathoki, Kailash and Mastakouri, Atalanti A. and Janzing, Dominik}, title = {DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models}, journal={arXiv preprint arXiv:2206.06821}, year={2022} } Issues ~~~~~~ If you encounter an issue or have a specific request for DoWhy, please `raise an issue <https://github.com/py-why/dowhy/issues>`_. Contributing ~~~~~~~~~~~~ This project welcomes contributions and suggestions. For a guide to contributing and a list of all contributors, check out `CONTRIBUTING.md <https://github.com/py-why/dowhy/blob/main/CONTRIBUTING.md>`_ and our `docs for contributing code <https://github.com/py-why/dowhy/blob/main/docs/source/contributing/contributing-code.rst>`_. Our `contributor code of conduct is available here <https://github.com/py-why/governance/blob/main/CODE-OF-CONDUCT.md>`_.

|BuildStatus|_ |PyPiVersion|_ |PythonSupport|_ |Downloads|_ |discord|_ .. |PyPiVersion| image:: https://img.shields.io/pypi/v/dowhy.svg .. _PyPiVersion: https://pypi.org/project/dowhy/ .. |PythonSupport| image:: https://img.shields.io/pypi/pyversions/dowhy.svg .. _PythonSupport: https://pypi.org/project/dowhy/ .. |BuildStatus| image:: https://github.com/py-why/dowhy/actions/workflows/ci.yml/badge.svg .. _BuildStatus: https://github.com/py-why/dowhy/actions .. |Downloads| image:: https://pepy.tech/badge/dowhy .. _Downloads: https://pepy.tech/project/dowhy .. |discord| image:: https://img.shields.io/discord/818456847551168542 .. _discord: https://discord.gg/cSBGb3vsZb .. image:: dowhy-logo-large.png :width: 50% :align: center `Checkout the documentation <https://py-why.github.io/dowhy/>`_ =============================================================== - The documentation, user guide, sample notebooks and other information are available at `https://py-why.github.io/dowhy <https://py-why.github.io/dowhy/>`_ - DoWhy is part of the `PyWhy Ecosystem <https://www.pywhy.org/>`_. For more tools and libraries related to causality, checkout the `PyWhy GitHub organization <https://github.com/py-why/>`_! - For any questions, comments, or discussions about specific use cases, join our community on `Discord <https://discord.gg/cSBGb3vsZb>`_ (|discord|_) - Jump right into some case studies: - Effect estimation: `Hotel booking cancellations <https://towardsdatascience.com/beyond-predictive-models-the-causal-story-behind-hotel-booking-cancellations-d29e8558cbaf>`_ | `Effect of customer loyalty programs <https://github.com/microsoft/dowhy/blob/main/docs/source/example_notebooks/dowhy_example_effect_of_memberrewards_program.ipynb>`_ | `Optimizing article headlines <https://medium.com/@akelleh/introducing-the-do-sampler-for-causal-inference-a3296ea9e78d>`_ | `Effect of home visits on infant health (IHDP) <https://towardsdatascience.com/implementing-causal-inference-a-key-step-towards-agi-de2cde8ea599>`_ | `Causes of customer churn/attrition <https://medium.com/geekculture/a-quickstart-for-causal-analysis-decision-making-with-dowhy-2ce2d4d1efa9>`_ - Root cause analysis and explanations: `Root Cause Analysis with DoWhy, an Open Source Python Library for Causal Machine Learning <https://aws.amazon.com/blogs/opensource/root-cause-analysis-with-dowhy-an-open-source-python-library-for-causal-machine-learning/>`_ | `Finding the Root Cause of Elevated Latencies in a Microservice Architecture <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_rca_microservice_architecture.ipynb>`_ | `Finding Root Causes of Changes in a Supply Chain <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_supply_chain_dist_change.ipynb>`_ For more example notebooks, see `here! <https://www.pywhy.org/dowhy/main/example_notebooks/nb_index.html>`_ Introduction & Key Features =========================== Decision-making involves understanding how different variables affect each other and predicting the outcome when some of them are changed to new values. For instance, given an outcome variable, one may be interested in determining how a potential action(s) may affect it, understanding what led to its current value, or simulate what would happen if some variables are changed. Answering such questions requires causal reasoning. DoWhy is a Python library that guides you through the various steps of causal reasoning and provides a unified interface for answering causal questions. DoWhy provides a wide variety of algorithms for effect estimation, prediction, quantification of causal influences, diagnosis of causal structures, root cause analysis, interventions and counterfactuals. A key feature of DoWhy is its refutation and falsification API that can test causal assumptions for any estimation method, thus making inference more robust and accessible to non-experts. **Graphical Causal Models and Potential Outcomes: Best of both worlds** DoWhy builds on two of the most powerful frameworks for causal inference: graphical causal models and potential outcomes. For effect estimation, it uses graph-based criteria and do-calculus for modeling assumptions and identifying a non-parametric causal effect. For estimation, it switches to methods based primarily on potential outcomes. For causal questions beyond effect estimation, it uses the power of graphical causal models by modeling the data generation process via explicit causal mechanisms at each node, which, for instance, unlocks capabilities to attribute observed effects to particular variables or estimate point-wise counterfactuals. For a quick introduction to causal inference, check out `amit-sharma/causal-inference-tutorial <https://github.com/amit-sharma/causal-inference-tutorial/>`_ We also gave a more comprehensive tutorial at the ACM Knowledge Discovery and Data Mining (`KDD 2018 <http://www.kdd.org/kdd2018/>`_) conference: `causalinference.gitlab.io/kdd-tutorial <http://causalinference.gitlab.io/kdd-tutorial/>`_. For an introduction to the four steps of causal inference and its implications for machine learning, you can access this video tutorial from Microsoft Research `DoWhy Webinar <https://www.microsoft.com/en-us/research/video/foundations-of-causal-inference-and-its-impacts-on-machine-learning/>`_ and for an introduction to the graphical causal model API, see the `PyCon presentation on Root Cause Analysis with DoWhy <https://www.youtube.com/watch?v=icpHrbDlGaw>`_. Key Features ~~~~~~~~~~~~ .. image:: https://raw.githubusercontent.com/py-why/dowhy/main/docs/images/dowhy-features.png DoWhy supports the following causal tasks: - Effect estimation (identification, average causal effect, conditional average causal effect, instrumental variables and more) - Quantify causal influences (mediation analysis, direct arrow strength, intrinsic causal influence) - What-if analysis (generate samples from interventional distribution, estimate counterfactuals) - Root cause analysis and explanations (attribute anomalies to their causes, find causes for changes in distributions, estimate feature relevance and more) For more details and how to use these methods in practice, checkout the documentation at `https://py-why.github.io/dowhy <https://py-why.github.io/dowhy/>`_ Quick Start =========== DoWhy support Python 3.8+. To install, you can use pip, poetry, or conda. **Latest Release** Install the latest `release <https://pypi.org/project/dowhy/>`__ using pip. .. code:: shell pip install dowhy Install the latest `release <https://pypi.org/project/dowhy/>`__ using poetry. .. code:: shell poetry add dowhy Install the latest `release <https://anaconda.org/conda-forge/dowhy>`__ using conda. .. code:: shell conda install -c conda-forge dowhy If you face "Solving environment" problems with conda, then try :code:`conda update --all` and then install dowhy. If that does not work, then use :code:`conda config --set channel_priority false` and try to install again. If the problem persists, please `add your issue here <https://github.com/microsoft/dowhy/issues/197>`_. **Development Version** If you prefer to use the latest dev version, your dependency management tool will need to point at our GitHub repository. .. code:: shell pip install git+https://github.com/py-why/dowhy@main **Requirements** DoWhy requires a few dependencies. Details on specific versions can be found in `pyproject.toml <./pyproject.toml>`_, under the `tool.poetry.dependencies` section. If you face any problems, try installing dependencies manually. .. code:: shell pip install '<dependency-name>==<version>' Optionally, if you wish to input graphs in the dot format, then install pydot (or pygraphviz). For better-looking graphs, you can optionally install pygraphviz. To proceed, first install graphviz and then pygraphviz (on Ubuntu and Ubuntu WSL). .. code:: shell sudo apt install graphviz libgraphviz-dev graphviz-dev pkg-config ## from https://github.com/pygraphviz/pygraphviz/issues/71 pip install pygraphviz --install-option="--include-path=/usr/include/graphviz" \ --install-option="--library-path=/usr/lib/graphviz/" Example: Effect identification and estimation ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Most causal tasks in DoWhy only require a few lines of code to write. Here, we exemplarily estimate the causal effect of a treatment on an outcome variable: .. code:: python from dowhy import CausalModel import dowhy.datasets # Load some sample data data = dowhy.datasets.linear_dataset( beta=10, num_common_causes=5, num_instruments=2, num_samples=10000, treatment_is_binary=True) A causal graph can be defined in different way, but the most common way is via `NetworkX <https://networkx.org/>`_. After loading in the data, we use the four main operations for effect estimation in DoWhy: *model*, *identify*, *estimate* and *refute*: .. code:: python # I. Create a causal model from the data and given graph. model = CausalModel( data=data["df"], treatment=data["treatment_name"], outcome=data["outcome_name"], graph=data["gml_graph"]) # Or alternatively, as nx.DiGraph # II. Identify causal effect and return target estimands identified_estimand = model.identify_effect() # III. Estimate the target estimand using a statistical method. estimate = model.estimate_effect(identified_estimand, method_name="backdoor.propensity_score_matching") # IV. Refute the obtained estimate using multiple robustness checks. refute_results = model.refute_estimate(identified_estimand, estimate, method_name="random_common_cause") DoWhy stresses on the interpretability of its output. At any point in the analysis, you can inspect the untested assumptions, identified estimands (if any), and the estimate (if any). Here's a sample output of the linear regression estimator: .. image:: https://raw.githubusercontent.com/py-why/dowhy/main/docs/images/regression_output.png :width: 80% For a full code example, check out the `Getting Started with DoWhy <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/dowhy_simple_example.ipynb>`_ notebook. You can also use Conditional Average Treatment Effect (CATE) estimation methods from `EconML <https://github.com/py-why/econml>`_, as shown in the `Conditional Treatment Effects <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/dowhy-conditional-treatment-effects.ipynb>`_ notebook. Here's a code snippet. .. code:: python from sklearn.preprocessing import PolynomialFeatures from sklearn.linear_model import LassoCV from sklearn.ensemble import GradientBoostingRegressor dml_estimate = model.estimate_effect(identified_estimand, method_name="backdoor.econml.dml.DML", control_value = 0, treatment_value = 1, target_units = lambda df: df["X0"]>1, confidence_intervals=False, method_params={ "init_params":{'model_y':GradientBoostingRegressor(), 'model_t': GradientBoostingRegressor(), 'model_final':LassoCV(), 'featurizer':PolynomialFeatures(degree=1, include_bias=True)}, "fit_params":{}}) Example: Graphical causal model (GCM) based inference ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DoWhy's graphical causal model framework offers powerful tools to address causal questions beyond effect estimation. It is based on Pearl's graphical causal model framework and models the causal data generation process of each variable explicitly via *causal mechanisms* to support a wide range of causal algorithms. For more details, see the book `Elements of Causal Inference <https://mitpress.mit.edu/9780262037310/elements-of-causal-inference/>`_. Complex causal queries, such as attributing observed anomalies to nodes in the system, can be performed with just a few lines of code: .. code:: python import networkx as nx, numpy as np, pandas as pd from dowhy import gcm # Let's generate some "normal" data we assume we're given from our problem domain: X = np.random.normal(loc=0, scale=1, size=1000) Y = 2 * X + np.random.normal(loc=0, scale=1, size=1000) Z = 3 * Y + np.random.normal(loc=0, scale=1, size=1000) data = pd.DataFrame(dict(X=X, Y=Y, Z=Z)) # 1. Modeling cause-effect relationships as a structural causal model # (causal graph + functional causal models): causal_model = gcm.StructuralCausalModel(nx.DiGraph([('X', 'Y'), ('Y', 'Z')])) # X -> Y -> Z gcm.auto.assign_causal_mechanisms(causal_model, data) # 2. Fitting the SCM to the data: gcm.fit(causal_model, data) # Optional: Evaluate causal model print(gcm.evaluate_causal_model(causal_model, data)) # Step 3: Perform a causal analysis. # results = gcm.<causal_query>(causal_model, ...) # For instance, root cause analysis: anomalous_sample = pd.DataFrame(dict(X=[0.1], Y=[6.2], Z=[19])) # Here, Y is the root cause. # "Which node is the root cause of the anomaly in Z?": anomaly_attribution = gcm.attribute_anomalies(causal_model, "Z", anomalous_sample) # Or sampling from an interventional distribution. Here, under the intervention do(Y := 2). samples = gcm.interventional_samples(causal_model, interventions={'Y': lambda y: 2}, num_samples_to_draw=100) The GCM framework offers many more features beyond these examples. For a full code example, check out the `Online Shop example notebook <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_online_shop.ipynb>`_. For more functionalities, example applications of DoWhy and details about the outputs, see the `User Guide <https://www.pywhy.org/dowhy/main/user_guide/intro.html>`_ or checkout `Jupyter notebooks <https://www.pywhy.org/dowhy/main/example_notebooks/nb_index.html>`_. More Information & Resources ============================ `Microsoft Research Blog <https://www.microsoft.com/en-us/research/blog/dowhy-a-library-for-causal-inference/>`_ | `Video Tutorial for Effect Estimation <https://www.youtube.com/watch?v=LALfQStONEc&t=114s>`_ | `Video Tutorial for Root Cause Analysis <https://www.youtube.com/watch?v=icpHrbDlGaw>`_ | `Arxiv Paper <https://arxiv.org/abs/2011.04216>`_ | `Arxiv Paper (Graphical Causal Model extension) <https://arxiv.org/abs/2206.06821>`_ | `Slides <https://www2.slideshare.net/AmitSharma315/dowhy-an-endtoend-library-for-causal-inference>`_ Citing this package ~~~~~~~~~~~~~~~~~~~ If you find DoWhy useful for your work, please cite **both** of the following two references: - Amit Sharma, Emre Kiciman. DoWhy: An End-to-End Library for Causal Inference. 2020. https://arxiv.org/abs/2011.04216 - Patrick Blöbaum, Peter Götz, Kailash Budhathoki, Atalanti A. Mastakouri, Dominik Janzing. DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models. 2022. https://arxiv.org/abs/2206.06821 Bibtex:: @article{dowhy, title={DoWhy: An End-to-End Library for Causal Inference}, author={Sharma, Amit and Kiciman, Emre}, journal={arXiv preprint arXiv:2011.04216}, year={2020} } @article{dowhy_gcm, author = {Bl{\"o}baum, Patrick and G{\"o}tz, Peter and Budhathoki, Kailash and Mastakouri, Atalanti A. and Janzing, Dominik}, title = {DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models}, journal={arXiv preprint arXiv:2206.06821}, year={2022} } Issues ~~~~~~ If you encounter an issue or have a specific request for DoWhy, please `raise an issue <https://github.com/py-why/dowhy/issues>`_. Contributing ~~~~~~~~~~~~ This project welcomes contributions and suggestions. For a guide to contributing and a list of all contributors, check out `CONTRIBUTING.md <https://github.com/py-why/dowhy/blob/main/CONTRIBUTING.md>`_ and our `docs for contributing code <https://github.com/py-why/dowhy/blob/main/docs/source/contributing/contributing-code.rst>`_. Our `contributor code of conduct is available here <https://github.com/py-why/governance/blob/main/CODE-OF-CONDUCT.md>`_.

bloebp

591e9d916e19bcad07e28687aeb8c81e1db26e8d

f455c569db0e7d0c1e1e7b54d1f8c85f026ec611

"follows modern concepts" -- feels awkward. Modern can be vague. how about, "follows best practices"?

amit-sharma

py-why/dowhy

Overhauled readme

The current readme is too overloaded for users. Changes here are: - Reduce it only the essential points, since most of the content is part of the documentation - Add image as overview of the offered features - Made connection between GCM and PO framework more consistent - Revise the GCM example to an executable code snipped - Extended some references to include GCM related work - Fix build status icon - Changed github references to py-why (was still pointing to microsoft) Here the new readme: https://github.com/py-why/dowhy/blob/c0ac8433625b5693175629076ad32a92b0d1eb71/README.rst The image in the key features section is not show. It is the following: <img width="2887" alt="Screenshot 2023-11-28 at 08 52 50" src="https://github.com/py-why/dowhy/assets/51325689/98f5099f-6cc6-4969-b804-609d8006984d">

2023-11-26 05:56:29+00:00

2023-12-01 14:58:26+00:00

README.rst

|BuildStatus|_ |PyPiVersion|_ |PythonSupport|_ |Downloads|_ |discord|_ .. |PyPiVersion| image:: https://img.shields.io/pypi/v/dowhy.svg .. _PyPiVersion: https://pypi.org/project/dowhy/ .. |PythonSupport| image:: https://img.shields.io/pypi/pyversions/dowhy.svg .. _PythonSupport: https://pypi.org/project/dowhy/ .. |BuildStatus| image:: https://github.com/py-why/dowhy/actions/workflows/ci.yml/badge.svg .. _BuildStatus: https://github.com/py-why/dowhy/actions .. |Downloads| image:: https://pepy.tech/badge/dowhy .. _Downloads: https://pepy.tech/project/dowhy .. |discord| image:: https://img.shields.io/discord/818456847551168542 .. _discord: https://discord.gg/cSBGb3vsZb .. image:: dowhy-logo-large.png :width: 50% :align: center `Checkout the documentation <https://py-why.github.io/dowhy/>`_ =============================================================== - The documentation, user guide, sample notebooks and other information are available at `https://py-why.github.io/dowhy <https://py-why.github.io/dowhy/>`_ - DoWhy is part of the `PyWhy Ecosystem <https://www.pywhy.org/>`_. For more tools and libraries related to causality, checkout the `PyWhy GitHub organization <https://github.com/py-why/>`_! - For any questions, comments, or discussions about specific use cases, join our community on `Discord <https://discord.gg/cSBGb3vsZb>`_ (|discord|_) - Jump right into some case studies: - Effect estimation: `Hotel booking cancellations <https://towardsdatascience.com/beyond-predictive-models-the-causal-story-behind-hotel-booking-cancellations-d29e8558cbaf>`_ | `Effect of customer loyalty programs <https://github.com/microsoft/dowhy/blob/main/docs/source/example_notebooks/dowhy_example_effect_of_memberrewards_program.ipynb>`_ | `Optimizing article headlines <https://medium.com/@akelleh/introducing-the-do-sampler-for-causal-inference-a3296ea9e78d>`_ | `Effect of home visits on infant health (IHDP) <https://towardsdatascience.com/implementing-causal-inference-a-key-step-towards-agi-de2cde8ea599>`_ | `Causes of customer churn/attrition <https://medium.com/geekculture/a-quickstart-for-causal-analysis-decision-making-with-dowhy-2ce2d4d1efa9>`_ - Root cause analysis and explanations: `Root Cause Analysis with DoWhy, an Open Source Python Library for Causal Machine Learning <https://aws.amazon.com/blogs/opensource/root-cause-analysis-with-dowhy-an-open-source-python-library-for-causal-machine-learning/>`_ | `Finding the Root Cause of Elevated Latencies in a Microservice Architecture <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_rca_microservice_architecture.ipynb>`_ | `Finding Root Causes of Changes in a Supply Chain <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_supply_chain_dist_change.ipynb>`_ For more example notebooks, see `here! <https://www.pywhy.org/dowhy/main/example_notebooks/nb_index.html>`_ Introduction & Key Features =========================== Decision-making involves understanding how different variables affect each other and predicting the outcome when some of them are changed to new values. For instance, given an outcome variable, one may be interested in determining how a potential action(s) may affect it, understanding what led to its current value, or simulate what would happen if some variables are changed. Answering such questions requires causal reasoning. DoWhy is a Python library that guides you through the various steps of causal reasoning and provides a unified interface for answering causal questions. DoWhy provides a wide variety of algorithms for effect estimation, prediction, quantification of causal influences, diagnosis of causal structures, root cause analysis, interventions and counterfactuals. A key feature of DoWhy is its refutation and falsification API that can test causal assumptions for any estimation method, thus making inference more robust and accessible to non-experts. **Graphical Causal Models and Potential Outcomes: Best of both worlds** DoWhy builds on two of the most powerful frameworks for causal inference: graphical causal models and potential outcomes. For effect estimation, it uses graph-based criteria and do-calculus for modeling assumptions and identifying a non-parametric causal effect. For estimation, it switches to methods based primarily on potential outcomes. For causal questions beyond effect estimation, it uses the power of graphical causal models by modeling the data generation process via explicit causal mechanisms at each node, which, for instance, unlocks capabilities to attribute observed effects to particular variables or estimate point-wise counterfactuals. For a quick introduction to causal inference, check out `amit-sharma/causal-inference-tutorial <https://github.com/amit-sharma/causal-inference-tutorial/>`_ We also gave a more comprehensive tutorial at the ACM Knowledge Discovery and Data Mining (`KDD 2018 <http://www.kdd.org/kdd2018/>`_) conference: `causalinference.gitlab.io/kdd-tutorial <http://causalinference.gitlab.io/kdd-tutorial/>`_. For an introduction to the four steps of causal inference and its implications for machine learning, you can access this video tutorial from Microsoft Research `DoWhy Webinar <https://www.microsoft.com/en-us/research/video/foundations-of-causal-inference-and-its-impacts-on-machine-learning/>`_ and for an introduction to the graphical causal model API, see the `PyCon presentation on Root Cause Analysis with DoWhy <https://www.youtube.com/watch?v=icpHrbDlGaw>`_. Key Features ~~~~~~~~~~~~ .. image:: https://raw.githubusercontent.com/py-why/dowhy/main/docs/images/dowhy-features.png DoWhy supports the following causal tasks: - Effect estimation (identification, average causal effect, conditional average causal effect, instrumental variables and more) - Quantify causal influences (mediation analysis, direct arrow strength, intrinsic causal influence) - What-if analysis (generate samples from interventional distribution, estimate counterfactuals) - Root cause analysis and explanations (attribute anomalies to their causes, find causes for changes in distributions, estimate feature relevance and more) For more details and how to use these methods in practice, checkout the documentation at `https://py-why.github.io/dowhy <https://py-why.github.io/dowhy/>`_ Quick Start =========== DoWhy support Python 3.8+. To install, you can use pip, poetry, or conda. **Latest Release** Install the latest `release <https://pypi.org/project/dowhy/>`__ using pip. .. code:: shell pip install dowhy Install the latest `release <https://pypi.org/project/dowhy/>`__ using poetry. .. code:: shell poetry add dowhy Install the latest `release <https://anaconda.org/conda-forge/dowhy>`__ using conda. .. code:: shell conda install -c conda-forge dowhy If you face "Solving environment" problems with conda, then try :code:`conda update --all` and then install dowhy. If that does not work, then use :code:`conda config --set channel_priority false` and try to install again. If the problem persists, please `add your issue here <https://github.com/microsoft/dowhy/issues/197>`_. **Development Version** If you prefer to use the latest dev version, your dependency management tool will need to point at our GitHub repository. .. code:: shell pip install git+https://github.com/py-why/dowhy@main **Requirements** DoWhy requires a few dependencies. Details on specific versions can be found in `pyproject.toml <./pyproject.toml>`_, under the `tool.poetry.dependencies` section. If you face any problems, try installing dependencies manually. .. code:: shell pip install '<dependency-name>==<version>' Optionally, if you wish to input graphs in the dot format, then install pydot (or pygraphviz). For better-looking graphs, you can optionally install pygraphviz. To proceed, first install graphviz and then pygraphviz (on Ubuntu and Ubuntu WSL). .. code:: shell sudo apt install graphviz libgraphviz-dev graphviz-dev pkg-config ## from https://github.com/pygraphviz/pygraphviz/issues/71 pip install pygraphviz --install-option="--include-path=/usr/include/graphviz" \ --install-option="--library-path=/usr/lib/graphviz/" Example usage ~~~~~~~~~~~~~ Two examples demonstrating the effect estimation and graphical causal models API. Effect identification and estimation ++++++++++++++++++++++++++++++++++++ Most causal tasks in DoWhy only require a few lines of code to write. Here, we exemplarily estimate the causal effect of a treatment on an outcome variable: .. code:: python from dowhy import CausalModel import dowhy.datasets # Load some sample data data = dowhy.datasets.linear_dataset( beta=10, num_common_causes=5, num_instruments=2, num_samples=10000, treatment_is_binary=True) A causal graph can be defined in different way, but the most common way is via `NetworkX <https://networkx.org/>`_. After loading in the data, we use the four main operations for effect estimation in DoWhy: *model*, *identify*, *estimate* and *refute*: .. code:: python # I. Create a causal model from the data and given graph. model = CausalModel( data=data["df"], treatment=data["treatment_name"], outcome=data["outcome_name"], graph=data["gml_graph"]) # Or alternatively, as nx.DiGraph # II. Identify causal effect and return target estimands identified_estimand = model.identify_effect() # III. Estimate the target estimand using a statistical method. estimate = model.estimate_effect(identified_estimand, method_name="backdoor.propensity_score_matching") # IV. Refute the obtained estimate using multiple robustness checks. refute_results = model.refute_estimate(identified_estimand, estimate, method_name="random_common_cause") DoWhy stresses on the interpretability of its output. At any point in the analysis, you can inspect the untested assumptions, identified estimands (if any), and the estimate (if any). Here's a sample output of the linear regression estimator: .. image:: https://raw.githubusercontent.com/py-why/dowhy/main/docs/images/regression_output.png :width: 80% For a full code example, check out the `Getting Started with DoWhy <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/dowhy_simple_example.ipynb>`_ notebook. You can also use Conditional Average Treatment Effect (CATE) estimation methods from `EconML <https://github.com/py-why/econml>`_, as shown in the `Conditional Treatment Effects <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/dowhy-conditional-treatment-effects.ipynb>`_ notebook. Here's a code snippet. .. code:: python from sklearn.preprocessing import PolynomialFeatures from sklearn.linear_model import LassoCV from sklearn.ensemble import GradientBoostingRegressor dml_estimate = model.estimate_effect(identified_estimand, method_name="backdoor.econml.dml.DML", control_value = 0, treatment_value = 1, target_units = lambda df: df["X0"]>1, confidence_intervals=False, method_params={ "init_params":{'model_y':GradientBoostingRegressor(), 'model_t': GradientBoostingRegressor(), 'model_final':LassoCV(), 'featurizer':PolynomialFeatures(degree=1, include_bias=True)}, "fit_params":{}}) Graphical causal model (GCM) based inference ++++++++++++++++++++++++++++++++++++++++++++ DoWhy's graphical causal model framework offers powerful tools to address causal questions beyond effect estimation. It is based on Pearl's graphical causal model framework and models the causal data generation process of each variable explicitly via *causal mechanisms* to support a wide range of causal algorithms. For more details, see the book `Elements of Causal Inference <https://mitpress.mit.edu/9780262037310/elements-of-causal-inference/>`_. Complex causal queries, such as attributing observed anomalies to nodes in the system, can be performed with just a few lines of code: .. code:: python import networkx as nx, numpy as np, pandas as pd from dowhy import gcm # Let's generate some "normal" data we assume we're given from our problem domain: X = np.random.normal(loc=0, scale=1, size=1000) Y = 2 * X + np.random.normal(loc=0, scale=1, size=1000) Z = 3 * Y + np.random.normal(loc=0, scale=1, size=1000) data = pd.DataFrame(dict(X=X, Y=Y, Z=Z)) # 1. Modeling cause-effect relationships as a structural causal model # (causal graph + functional causal models): causal_model = gcm.StructuralCausalModel(nx.DiGraph([('X', 'Y'), ('Y', 'Z')])) # X -> Y -> Z gcm.auto.assign_causal_mechanisms(causal_model, data) # 2. Fitting the SCM to the data: gcm.fit(causal_model, data) # Optional: Evaluate causal model print(gcm.evaluate_causal_model(causal_model, data)) # Step 3: Perform a causal analysis. # results = gcm.<causal_query>(causal_model, ...) # For instance, root cause analysis: anomalous_sample = pd.DataFrame(dict(X=[0.1], Y=[6.2], Z=[19])) # Here, Y is the root cause. # "Which node is the root cause of the anomaly in Z?": anomaly_attribution = gcm.attribute_anomalies(causal_model, "Z", anomalous_sample) # Or sampling from an interventional distribution. Here, under the intervention do(Y := 2). samples = gcm.interventional_samples(causal_model, interventions={'Y': lambda y: 2}, num_samples_to_draw=100) The GCM framework offers many more features beyond these examples. For a full code example, check out the `Online Shop example notebook <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_online_shop.ipynb>`_. For more functionalities, example applications of DoWhy and details about the outputs, see the `User Guide <https://www.pywhy.org/dowhy/main/user_guide/intro.html>`_ or checkout `Jupyter notebooks <https://www.pywhy.org/dowhy/main/example_notebooks/nb_index.html>`_. More Information & Resources ============================ `Microsoft Research Blog <https://www.microsoft.com/en-us/research/blog/dowhy-a-library-for-causal-inference/>`_ | `Video Tutorial for Effect Estimation <https://www.youtube.com/watch?v=LALfQStONEc&t=114s>`_ | `Video Tutorial for Root Cause Analysis <https://www.youtube.com/watch?v=icpHrbDlGaw>`_ | `Arxiv Paper <https://arxiv.org/abs/2011.04216>`_ | `Arxiv Paper (Graphical Causal Model extension) <https://arxiv.org/abs/2206.06821>`_ | `Slides <https://www2.slideshare.net/AmitSharma315/dowhy-an-endtoend-library-for-causal-inference>`_ Citing this package ~~~~~~~~~~~~~~~~~~~ If you find DoWhy useful for your work, please cite **both** of the following two references: - Amit Sharma, Emre Kiciman. DoWhy: An End-to-End Library for Causal Inference. 2020. https://arxiv.org/abs/2011.04216 - Patrick Blöbaum, Peter Götz, Kailash Budhathoki, Atalanti A. Mastakouri, Dominik Janzing. DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models. 2022. https://arxiv.org/abs/2206.06821 Bibtex:: @article{dowhy, title={DoWhy: An End-to-End Library for Causal Inference}, author={Sharma, Amit and Kiciman, Emre}, journal={arXiv preprint arXiv:2011.04216}, year={2020} } @article{dowhy_gcm, author = {Bl{\"o}baum, Patrick and G{\"o}tz, Peter and Budhathoki, Kailash and Mastakouri, Atalanti A. and Janzing, Dominik}, title = {DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models}, journal={arXiv preprint arXiv:2206.06821}, year={2022} } Issues ~~~~~~ If you encounter an issue or have a specific request for DoWhy, please `raise an issue <https://github.com/py-why/dowhy/issues>`_. Contributing ~~~~~~~~~~~~ This project welcomes contributions and suggestions. For a guide to contributing and a list of all contributors, check out `CONTRIBUTING.md <https://github.com/py-why/dowhy/blob/main/CONTRIBUTING.md>`_ and our `docs for contributing code <https://github.com/py-why/dowhy/blob/main/docs/source/contributing/contributing-code.rst>`_. Our `contributor code of conduct is available here <https://github.com/py-why/governance/blob/main/CODE-OF-CONDUCT.md>`_.

|BuildStatus|_ |PyPiVersion|_ |PythonSupport|_ |Downloads|_ |discord|_ .. |PyPiVersion| image:: https://img.shields.io/pypi/v/dowhy.svg .. _PyPiVersion: https://pypi.org/project/dowhy/ .. |PythonSupport| image:: https://img.shields.io/pypi/pyversions/dowhy.svg .. _PythonSupport: https://pypi.org/project/dowhy/ .. |BuildStatus| image:: https://github.com/py-why/dowhy/actions/workflows/ci.yml/badge.svg .. _BuildStatus: https://github.com/py-why/dowhy/actions .. |Downloads| image:: https://pepy.tech/badge/dowhy .. _Downloads: https://pepy.tech/project/dowhy .. |discord| image:: https://img.shields.io/discord/818456847551168542 .. _discord: https://discord.gg/cSBGb3vsZb .. image:: dowhy-logo-large.png :width: 50% :align: center `Checkout the documentation <https://py-why.github.io/dowhy/>`_ =============================================================== - The documentation, user guide, sample notebooks and other information are available at `https://py-why.github.io/dowhy <https://py-why.github.io/dowhy/>`_ - DoWhy is part of the `PyWhy Ecosystem <https://www.pywhy.org/>`_. For more tools and libraries related to causality, checkout the `PyWhy GitHub organization <https://github.com/py-why/>`_! - For any questions, comments, or discussions about specific use cases, join our community on `Discord <https://discord.gg/cSBGb3vsZb>`_ (|discord|_) - Jump right into some case studies: - Effect estimation: `Hotel booking cancellations <https://towardsdatascience.com/beyond-predictive-models-the-causal-story-behind-hotel-booking-cancellations-d29e8558cbaf>`_ | `Effect of customer loyalty programs <https://github.com/microsoft/dowhy/blob/main/docs/source/example_notebooks/dowhy_example_effect_of_memberrewards_program.ipynb>`_ | `Optimizing article headlines <https://medium.com/@akelleh/introducing-the-do-sampler-for-causal-inference-a3296ea9e78d>`_ | `Effect of home visits on infant health (IHDP) <https://towardsdatascience.com/implementing-causal-inference-a-key-step-towards-agi-de2cde8ea599>`_ | `Causes of customer churn/attrition <https://medium.com/geekculture/a-quickstart-for-causal-analysis-decision-making-with-dowhy-2ce2d4d1efa9>`_ - Root cause analysis and explanations: `Root Cause Analysis with DoWhy, an Open Source Python Library for Causal Machine Learning <https://aws.amazon.com/blogs/opensource/root-cause-analysis-with-dowhy-an-open-source-python-library-for-causal-machine-learning/>`_ | `Finding the Root Cause of Elevated Latencies in a Microservice Architecture <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_rca_microservice_architecture.ipynb>`_ | `Finding Root Causes of Changes in a Supply Chain <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_supply_chain_dist_change.ipynb>`_ For more example notebooks, see `here! <https://www.pywhy.org/dowhy/main/example_notebooks/nb_index.html>`_ Introduction & Key Features =========================== Decision-making involves understanding how different variables affect each other and predicting the outcome when some of them are changed to new values. For instance, given an outcome variable, one may be interested in determining how a potential action(s) may affect it, understanding what led to its current value, or simulate what would happen if some variables are changed. Answering such questions requires causal reasoning. DoWhy is a Python library that guides you through the various steps of causal reasoning and provides a unified interface for answering causal questions. DoWhy provides a wide variety of algorithms for effect estimation, prediction, quantification of causal influences, diagnosis of causal structures, root cause analysis, interventions and counterfactuals. A key feature of DoWhy is its refutation and falsification API that can test causal assumptions for any estimation method, thus making inference more robust and accessible to non-experts. **Graphical Causal Models and Potential Outcomes: Best of both worlds** DoWhy builds on two of the most powerful frameworks for causal inference: graphical causal models and potential outcomes. For effect estimation, it uses graph-based criteria and do-calculus for modeling assumptions and identifying a non-parametric causal effect. For estimation, it switches to methods based primarily on potential outcomes. For causal questions beyond effect estimation, it uses the power of graphical causal models by modeling the data generation process via explicit causal mechanisms at each node, which, for instance, unlocks capabilities to attribute observed effects to particular variables or estimate point-wise counterfactuals. For a quick introduction to causal inference, check out `amit-sharma/causal-inference-tutorial <https://github.com/amit-sharma/causal-inference-tutorial/>`_ We also gave a more comprehensive tutorial at the ACM Knowledge Discovery and Data Mining (`KDD 2018 <http://www.kdd.org/kdd2018/>`_) conference: `causalinference.gitlab.io/kdd-tutorial <http://causalinference.gitlab.io/kdd-tutorial/>`_. For an introduction to the four steps of causal inference and its implications for machine learning, you can access this video tutorial from Microsoft Research `DoWhy Webinar <https://www.microsoft.com/en-us/research/video/foundations-of-causal-inference-and-its-impacts-on-machine-learning/>`_ and for an introduction to the graphical causal model API, see the `PyCon presentation on Root Cause Analysis with DoWhy <https://www.youtube.com/watch?v=icpHrbDlGaw>`_. Key Features ~~~~~~~~~~~~ .. image:: https://raw.githubusercontent.com/py-why/dowhy/main/docs/images/dowhy-features.png DoWhy supports the following causal tasks: - Effect estimation (identification, average causal effect, conditional average causal effect, instrumental variables and more) - Quantify causal influences (mediation analysis, direct arrow strength, intrinsic causal influence) - What-if analysis (generate samples from interventional distribution, estimate counterfactuals) - Root cause analysis and explanations (attribute anomalies to their causes, find causes for changes in distributions, estimate feature relevance and more) For more details and how to use these methods in practice, checkout the documentation at `https://py-why.github.io/dowhy <https://py-why.github.io/dowhy/>`_ Quick Start =========== DoWhy support Python 3.8+. To install, you can use pip, poetry, or conda. **Latest Release** Install the latest `release <https://pypi.org/project/dowhy/>`__ using pip. .. code:: shell pip install dowhy Install the latest `release <https://pypi.org/project/dowhy/>`__ using poetry. .. code:: shell poetry add dowhy Install the latest `release <https://anaconda.org/conda-forge/dowhy>`__ using conda. .. code:: shell conda install -c conda-forge dowhy If you face "Solving environment" problems with conda, then try :code:`conda update --all` and then install dowhy. If that does not work, then use :code:`conda config --set channel_priority false` and try to install again. If the problem persists, please `add your issue here <https://github.com/microsoft/dowhy/issues/197>`_. **Development Version** If you prefer to use the latest dev version, your dependency management tool will need to point at our GitHub repository. .. code:: shell pip install git+https://github.com/py-why/dowhy@main **Requirements** DoWhy requires a few dependencies. Details on specific versions can be found in `pyproject.toml <./pyproject.toml>`_, under the `tool.poetry.dependencies` section. If you face any problems, try installing dependencies manually. .. code:: shell pip install '<dependency-name>==<version>' Optionally, if you wish to input graphs in the dot format, then install pydot (or pygraphviz). For better-looking graphs, you can optionally install pygraphviz. To proceed, first install graphviz and then pygraphviz (on Ubuntu and Ubuntu WSL). .. code:: shell sudo apt install graphviz libgraphviz-dev graphviz-dev pkg-config ## from https://github.com/pygraphviz/pygraphviz/issues/71 pip install pygraphviz --install-option="--include-path=/usr/include/graphviz" \ --install-option="--library-path=/usr/lib/graphviz/" Example: Effect identification and estimation ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Most causal tasks in DoWhy only require a few lines of code to write. Here, we exemplarily estimate the causal effect of a treatment on an outcome variable: .. code:: python from dowhy import CausalModel import dowhy.datasets # Load some sample data data = dowhy.datasets.linear_dataset( beta=10, num_common_causes=5, num_instruments=2, num_samples=10000, treatment_is_binary=True) A causal graph can be defined in different way, but the most common way is via `NetworkX <https://networkx.org/>`_. After loading in the data, we use the four main operations for effect estimation in DoWhy: *model*, *identify*, *estimate* and *refute*: .. code:: python # I. Create a causal model from the data and given graph. model = CausalModel( data=data["df"], treatment=data["treatment_name"], outcome=data["outcome_name"], graph=data["gml_graph"]) # Or alternatively, as nx.DiGraph # II. Identify causal effect and return target estimands identified_estimand = model.identify_effect() # III. Estimate the target estimand using a statistical method. estimate = model.estimate_effect(identified_estimand, method_name="backdoor.propensity_score_matching") # IV. Refute the obtained estimate using multiple robustness checks. refute_results = model.refute_estimate(identified_estimand, estimate, method_name="random_common_cause") DoWhy stresses on the interpretability of its output. At any point in the analysis, you can inspect the untested assumptions, identified estimands (if any), and the estimate (if any). Here's a sample output of the linear regression estimator: .. image:: https://raw.githubusercontent.com/py-why/dowhy/main/docs/images/regression_output.png :width: 80% For a full code example, check out the `Getting Started with DoWhy <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/dowhy_simple_example.ipynb>`_ notebook. You can also use Conditional Average Treatment Effect (CATE) estimation methods from `EconML <https://github.com/py-why/econml>`_, as shown in the `Conditional Treatment Effects <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/dowhy-conditional-treatment-effects.ipynb>`_ notebook. Here's a code snippet. .. code:: python from sklearn.preprocessing import PolynomialFeatures from sklearn.linear_model import LassoCV from sklearn.ensemble import GradientBoostingRegressor dml_estimate = model.estimate_effect(identified_estimand, method_name="backdoor.econml.dml.DML", control_value = 0, treatment_value = 1, target_units = lambda df: df["X0"]>1, confidence_intervals=False, method_params={ "init_params":{'model_y':GradientBoostingRegressor(), 'model_t': GradientBoostingRegressor(), 'model_final':LassoCV(), 'featurizer':PolynomialFeatures(degree=1, include_bias=True)}, "fit_params":{}}) Example: Graphical causal model (GCM) based inference ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DoWhy's graphical causal model framework offers powerful tools to address causal questions beyond effect estimation. It is based on Pearl's graphical causal model framework and models the causal data generation process of each variable explicitly via *causal mechanisms* to support a wide range of causal algorithms. For more details, see the book `Elements of Causal Inference <https://mitpress.mit.edu/9780262037310/elements-of-causal-inference/>`_. Complex causal queries, such as attributing observed anomalies to nodes in the system, can be performed with just a few lines of code: .. code:: python import networkx as nx, numpy as np, pandas as pd from dowhy import gcm # Let's generate some "normal" data we assume we're given from our problem domain: X = np.random.normal(loc=0, scale=1, size=1000) Y = 2 * X + np.random.normal(loc=0, scale=1, size=1000) Z = 3 * Y + np.random.normal(loc=0, scale=1, size=1000) data = pd.DataFrame(dict(X=X, Y=Y, Z=Z)) # 1. Modeling cause-effect relationships as a structural causal model # (causal graph + functional causal models): causal_model = gcm.StructuralCausalModel(nx.DiGraph([('X', 'Y'), ('Y', 'Z')])) # X -> Y -> Z gcm.auto.assign_causal_mechanisms(causal_model, data) # 2. Fitting the SCM to the data: gcm.fit(causal_model, data) # Optional: Evaluate causal model print(gcm.evaluate_causal_model(causal_model, data)) # Step 3: Perform a causal analysis. # results = gcm.<causal_query>(causal_model, ...) # For instance, root cause analysis: anomalous_sample = pd.DataFrame(dict(X=[0.1], Y=[6.2], Z=[19])) # Here, Y is the root cause. # "Which node is the root cause of the anomaly in Z?": anomaly_attribution = gcm.attribute_anomalies(causal_model, "Z", anomalous_sample) # Or sampling from an interventional distribution. Here, under the intervention do(Y := 2). samples = gcm.interventional_samples(causal_model, interventions={'Y': lambda y: 2}, num_samples_to_draw=100) The GCM framework offers many more features beyond these examples. For a full code example, check out the `Online Shop example notebook <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_online_shop.ipynb>`_. For more functionalities, example applications of DoWhy and details about the outputs, see the `User Guide <https://www.pywhy.org/dowhy/main/user_guide/intro.html>`_ or checkout `Jupyter notebooks <https://www.pywhy.org/dowhy/main/example_notebooks/nb_index.html>`_. More Information & Resources ============================ `Microsoft Research Blog <https://www.microsoft.com/en-us/research/blog/dowhy-a-library-for-causal-inference/>`_ | `Video Tutorial for Effect Estimation <https://www.youtube.com/watch?v=LALfQStONEc&t=114s>`_ | `Video Tutorial for Root Cause Analysis <https://www.youtube.com/watch?v=icpHrbDlGaw>`_ | `Arxiv Paper <https://arxiv.org/abs/2011.04216>`_ | `Arxiv Paper (Graphical Causal Model extension) <https://arxiv.org/abs/2206.06821>`_ | `Slides <https://www2.slideshare.net/AmitSharma315/dowhy-an-endtoend-library-for-causal-inference>`_ Citing this package ~~~~~~~~~~~~~~~~~~~ If you find DoWhy useful for your work, please cite **both** of the following two references: - Amit Sharma, Emre Kiciman. DoWhy: An End-to-End Library for Causal Inference. 2020. https://arxiv.org/abs/2011.04216 - Patrick Blöbaum, Peter Götz, Kailash Budhathoki, Atalanti A. Mastakouri, Dominik Janzing. DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models. 2022. https://arxiv.org/abs/2206.06821 Bibtex:: @article{dowhy, title={DoWhy: An End-to-End Library for Causal Inference}, author={Sharma, Amit and Kiciman, Emre}, journal={arXiv preprint arXiv:2011.04216}, year={2020} } @article{dowhy_gcm, author = {Bl{\"o}baum, Patrick and G{\"o}tz, Peter and Budhathoki, Kailash and Mastakouri, Atalanti A. and Janzing, Dominik}, title = {DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models}, journal={arXiv preprint arXiv:2206.06821}, year={2022} } Issues ~~~~~~ If you encounter an issue or have a specific request for DoWhy, please `raise an issue <https://github.com/py-why/dowhy/issues>`_. Contributing ~~~~~~~~~~~~ This project welcomes contributions and suggestions. For a guide to contributing and a list of all contributors, check out `CONTRIBUTING.md <https://github.com/py-why/dowhy/blob/main/CONTRIBUTING.md>`_ and our `docs for contributing code <https://github.com/py-why/dowhy/blob/main/docs/source/contributing/contributing-code.rst>`_. Our `contributor code of conduct is available here <https://github.com/py-why/governance/blob/main/CODE-OF-CONDUCT.md>`_.

bloebp

591e9d916e19bcad07e28687aeb8c81e1db26e8d

f455c569db0e7d0c1e1e7b54d1f8c85f026ec611

"or checkout `Jupyter...." "the other" can be removed?

amit-sharma

py-why/dowhy

Overhauled readme

The current readme is too overloaded for users. Changes here are: - Reduce it only the essential points, since most of the content is part of the documentation - Add image as overview of the offered features - Made connection between GCM and PO framework more consistent - Revise the GCM example to an executable code snipped - Extended some references to include GCM related work - Fix build status icon - Changed github references to py-why (was still pointing to microsoft) Here the new readme: https://github.com/py-why/dowhy/blob/c0ac8433625b5693175629076ad32a92b0d1eb71/README.rst The image in the key features section is not show. It is the following: <img width="2887" alt="Screenshot 2023-11-28 at 08 52 50" src="https://github.com/py-why/dowhy/assets/51325689/98f5099f-6cc6-4969-b804-609d8006984d">

2023-11-26 05:56:29+00:00

2023-12-01 14:58:26+00:00

README.rst

|BuildStatus|_ |PyPiVersion|_ |PythonSupport|_ |Downloads|_ |discord|_ .. |PyPiVersion| image:: https://img.shields.io/pypi/v/dowhy.svg .. _PyPiVersion: https://pypi.org/project/dowhy/ .. |PythonSupport| image:: https://img.shields.io/pypi/pyversions/dowhy.svg .. _PythonSupport: https://pypi.org/project/dowhy/ .. |BuildStatus| image:: https://github.com/py-why/dowhy/actions/workflows/ci.yml/badge.svg .. _BuildStatus: https://github.com/py-why/dowhy/actions .. |Downloads| image:: https://pepy.tech/badge/dowhy .. _Downloads: https://pepy.tech/project/dowhy .. |discord| image:: https://img.shields.io/discord/818456847551168542 .. _discord: https://discord.gg/cSBGb3vsZb .. image:: dowhy-logo-large.png :width: 50% :align: center `Checkout the documentation <https://py-why.github.io/dowhy/>`_ =============================================================== - The documentation, user guide, sample notebooks and other information are available at `https://py-why.github.io/dowhy <https://py-why.github.io/dowhy/>`_ - DoWhy is part of the `PyWhy Ecosystem <https://www.pywhy.org/>`_. For more tools and libraries related to causality, checkout the `PyWhy GitHub organization <https://github.com/py-why/>`_! - For any questions, comments, or discussions about specific use cases, join our community on `Discord <https://discord.gg/cSBGb3vsZb>`_ (|discord|_) - Jump right into some case studies: - Effect estimation: `Hotel booking cancellations <https://towardsdatascience.com/beyond-predictive-models-the-causal-story-behind-hotel-booking-cancellations-d29e8558cbaf>`_ | `Effect of customer loyalty programs <https://github.com/microsoft/dowhy/blob/main/docs/source/example_notebooks/dowhy_example_effect_of_memberrewards_program.ipynb>`_ | `Optimizing article headlines <https://medium.com/@akelleh/introducing-the-do-sampler-for-causal-inference-a3296ea9e78d>`_ | `Effect of home visits on infant health (IHDP) <https://towardsdatascience.com/implementing-causal-inference-a-key-step-towards-agi-de2cde8ea599>`_ | `Causes of customer churn/attrition <https://medium.com/geekculture/a-quickstart-for-causal-analysis-decision-making-with-dowhy-2ce2d4d1efa9>`_ - Root cause analysis and explanations: `Root Cause Analysis with DoWhy, an Open Source Python Library for Causal Machine Learning <https://aws.amazon.com/blogs/opensource/root-cause-analysis-with-dowhy-an-open-source-python-library-for-causal-machine-learning/>`_ | `Finding the Root Cause of Elevated Latencies in a Microservice Architecture <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_rca_microservice_architecture.ipynb>`_ | `Finding Root Causes of Changes in a Supply Chain <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_supply_chain_dist_change.ipynb>`_ For more example notebooks, see `here! <https://www.pywhy.org/dowhy/main/example_notebooks/nb_index.html>`_ Introduction & Key Features =========================== Decision-making involves understanding how different variables affect each other and predicting the outcome when some of them are changed to new values. For instance, given an outcome variable, one may be interested in determining how a potential action(s) may affect it, understanding what led to its current value, or simulate what would happen if some variables are changed. Answering such questions requires causal reasoning. DoWhy is a Python library that guides you through the various steps of causal reasoning and provides a unified interface for answering causal questions. DoWhy provides a wide variety of algorithms for effect estimation, prediction, quantification of causal influences, diagnosis of causal structures, root cause analysis, interventions and counterfactuals. A key feature of DoWhy is its refutation and falsification API that can test causal assumptions for any estimation method, thus making inference more robust and accessible to non-experts. **Graphical Causal Models and Potential Outcomes: Best of both worlds** DoWhy builds on two of the most powerful frameworks for causal inference: graphical causal models and potential outcomes. For effect estimation, it uses graph-based criteria and do-calculus for modeling assumptions and identifying a non-parametric causal effect. For estimation, it switches to methods based primarily on potential outcomes. For causal questions beyond effect estimation, it uses the power of graphical causal models by modeling the data generation process via explicit causal mechanisms at each node, which, for instance, unlocks capabilities to attribute observed effects to particular variables or estimate point-wise counterfactuals. For a quick introduction to causal inference, check out `amit-sharma/causal-inference-tutorial <https://github.com/amit-sharma/causal-inference-tutorial/>`_ We also gave a more comprehensive tutorial at the ACM Knowledge Discovery and Data Mining (`KDD 2018 <http://www.kdd.org/kdd2018/>`_) conference: `causalinference.gitlab.io/kdd-tutorial <http://causalinference.gitlab.io/kdd-tutorial/>`_. For an introduction to the four steps of causal inference and its implications for machine learning, you can access this video tutorial from Microsoft Research `DoWhy Webinar <https://www.microsoft.com/en-us/research/video/foundations-of-causal-inference-and-its-impacts-on-machine-learning/>`_ and for an introduction to the graphical causal model API, see the `PyCon presentation on Root Cause Analysis with DoWhy <https://www.youtube.com/watch?v=icpHrbDlGaw>`_. Key Features ~~~~~~~~~~~~ .. image:: https://raw.githubusercontent.com/py-why/dowhy/main/docs/images/dowhy-features.png DoWhy supports the following causal tasks: - Effect estimation (identification, average causal effect, conditional average causal effect, instrumental variables and more) - Quantify causal influences (mediation analysis, direct arrow strength, intrinsic causal influence) - What-if analysis (generate samples from interventional distribution, estimate counterfactuals) - Root cause analysis and explanations (attribute anomalies to their causes, find causes for changes in distributions, estimate feature relevance and more) For more details and how to use these methods in practice, checkout the documentation at `https://py-why.github.io/dowhy <https://py-why.github.io/dowhy/>`_ Quick Start =========== DoWhy support Python 3.8+. To install, you can use pip, poetry, or conda. **Latest Release** Install the latest `release <https://pypi.org/project/dowhy/>`__ using pip. .. code:: shell pip install dowhy Install the latest `release <https://pypi.org/project/dowhy/>`__ using poetry. .. code:: shell poetry add dowhy Install the latest `release <https://anaconda.org/conda-forge/dowhy>`__ using conda. .. code:: shell conda install -c conda-forge dowhy If you face "Solving environment" problems with conda, then try :code:`conda update --all` and then install dowhy. If that does not work, then use :code:`conda config --set channel_priority false` and try to install again. If the problem persists, please `add your issue here <https://github.com/microsoft/dowhy/issues/197>`_. **Development Version** If you prefer to use the latest dev version, your dependency management tool will need to point at our GitHub repository. .. code:: shell pip install git+https://github.com/py-why/dowhy@main **Requirements** DoWhy requires a few dependencies. Details on specific versions can be found in `pyproject.toml <./pyproject.toml>`_, under the `tool.poetry.dependencies` section. If you face any problems, try installing dependencies manually. .. code:: shell pip install '<dependency-name>==<version>' Optionally, if you wish to input graphs in the dot format, then install pydot (or pygraphviz). For better-looking graphs, you can optionally install pygraphviz. To proceed, first install graphviz and then pygraphviz (on Ubuntu and Ubuntu WSL). .. code:: shell sudo apt install graphviz libgraphviz-dev graphviz-dev pkg-config ## from https://github.com/pygraphviz/pygraphviz/issues/71 pip install pygraphviz --install-option="--include-path=/usr/include/graphviz" \ --install-option="--library-path=/usr/lib/graphviz/" Example usage ~~~~~~~~~~~~~ Two examples demonstrating the effect estimation and graphical causal models API. Effect identification and estimation ++++++++++++++++++++++++++++++++++++ Most causal tasks in DoWhy only require a few lines of code to write. Here, we exemplarily estimate the causal effect of a treatment on an outcome variable: .. code:: python from dowhy import CausalModel import dowhy.datasets # Load some sample data data = dowhy.datasets.linear_dataset( beta=10, num_common_causes=5, num_instruments=2, num_samples=10000, treatment_is_binary=True) A causal graph can be defined in different way, but the most common way is via `NetworkX <https://networkx.org/>`_. After loading in the data, we use the four main operations for effect estimation in DoWhy: *model*, *identify*, *estimate* and *refute*: .. code:: python # I. Create a causal model from the data and given graph. model = CausalModel( data=data["df"], treatment=data["treatment_name"], outcome=data["outcome_name"], graph=data["gml_graph"]) # Or alternatively, as nx.DiGraph # II. Identify causal effect and return target estimands identified_estimand = model.identify_effect() # III. Estimate the target estimand using a statistical method. estimate = model.estimate_effect(identified_estimand, method_name="backdoor.propensity_score_matching") # IV. Refute the obtained estimate using multiple robustness checks. refute_results = model.refute_estimate(identified_estimand, estimate, method_name="random_common_cause") DoWhy stresses on the interpretability of its output. At any point in the analysis, you can inspect the untested assumptions, identified estimands (if any), and the estimate (if any). Here's a sample output of the linear regression estimator: .. image:: https://raw.githubusercontent.com/py-why/dowhy/main/docs/images/regression_output.png :width: 80% For a full code example, check out the `Getting Started with DoWhy <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/dowhy_simple_example.ipynb>`_ notebook. You can also use Conditional Average Treatment Effect (CATE) estimation methods from `EconML <https://github.com/py-why/econml>`_, as shown in the `Conditional Treatment Effects <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/dowhy-conditional-treatment-effects.ipynb>`_ notebook. Here's a code snippet. .. code:: python from sklearn.preprocessing import PolynomialFeatures from sklearn.linear_model import LassoCV from sklearn.ensemble import GradientBoostingRegressor dml_estimate = model.estimate_effect(identified_estimand, method_name="backdoor.econml.dml.DML", control_value = 0, treatment_value = 1, target_units = lambda df: df["X0"]>1, confidence_intervals=False, method_params={ "init_params":{'model_y':GradientBoostingRegressor(), 'model_t': GradientBoostingRegressor(), 'model_final':LassoCV(), 'featurizer':PolynomialFeatures(degree=1, include_bias=True)}, "fit_params":{}}) Graphical causal model (GCM) based inference ++++++++++++++++++++++++++++++++++++++++++++ DoWhy's graphical causal model framework offers powerful tools to address causal questions beyond effect estimation. It is based on Pearl's graphical causal model framework and models the causal data generation process of each variable explicitly via *causal mechanisms* to support a wide range of causal algorithms. For more details, see the book `Elements of Causal Inference <https://mitpress.mit.edu/9780262037310/elements-of-causal-inference/>`_. Complex causal queries, such as attributing observed anomalies to nodes in the system, can be performed with just a few lines of code: .. code:: python import networkx as nx, numpy as np, pandas as pd from dowhy import gcm # Let's generate some "normal" data we assume we're given from our problem domain: X = np.random.normal(loc=0, scale=1, size=1000) Y = 2 * X + np.random.normal(loc=0, scale=1, size=1000) Z = 3 * Y + np.random.normal(loc=0, scale=1, size=1000) data = pd.DataFrame(dict(X=X, Y=Y, Z=Z)) # 1. Modeling cause-effect relationships as a structural causal model # (causal graph + functional causal models): causal_model = gcm.StructuralCausalModel(nx.DiGraph([('X', 'Y'), ('Y', 'Z')])) # X -> Y -> Z gcm.auto.assign_causal_mechanisms(causal_model, data) # 2. Fitting the SCM to the data: gcm.fit(causal_model, data) # Optional: Evaluate causal model print(gcm.evaluate_causal_model(causal_model, data)) # Step 3: Perform a causal analysis. # results = gcm.<causal_query>(causal_model, ...) # For instance, root cause analysis: anomalous_sample = pd.DataFrame(dict(X=[0.1], Y=[6.2], Z=[19])) # Here, Y is the root cause. # "Which node is the root cause of the anomaly in Z?": anomaly_attribution = gcm.attribute_anomalies(causal_model, "Z", anomalous_sample) # Or sampling from an interventional distribution. Here, under the intervention do(Y := 2). samples = gcm.interventional_samples(causal_model, interventions={'Y': lambda y: 2}, num_samples_to_draw=100) The GCM framework offers many more features beyond these examples. For a full code example, check out the `Online Shop example notebook <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_online_shop.ipynb>`_. For more functionalities, example applications of DoWhy and details about the outputs, see the `User Guide <https://www.pywhy.org/dowhy/main/user_guide/intro.html>`_ or checkout `Jupyter notebooks <https://www.pywhy.org/dowhy/main/example_notebooks/nb_index.html>`_. More Information & Resources ============================ `Microsoft Research Blog <https://www.microsoft.com/en-us/research/blog/dowhy-a-library-for-causal-inference/>`_ | `Video Tutorial for Effect Estimation <https://www.youtube.com/watch?v=LALfQStONEc&t=114s>`_ | `Video Tutorial for Root Cause Analysis <https://www.youtube.com/watch?v=icpHrbDlGaw>`_ | `Arxiv Paper <https://arxiv.org/abs/2011.04216>`_ | `Arxiv Paper (Graphical Causal Model extension) <https://arxiv.org/abs/2206.06821>`_ | `Slides <https://www2.slideshare.net/AmitSharma315/dowhy-an-endtoend-library-for-causal-inference>`_ Citing this package ~~~~~~~~~~~~~~~~~~~ If you find DoWhy useful for your work, please cite **both** of the following two references: - Amit Sharma, Emre Kiciman. DoWhy: An End-to-End Library for Causal Inference. 2020. https://arxiv.org/abs/2011.04216 - Patrick Blöbaum, Peter Götz, Kailash Budhathoki, Atalanti A. Mastakouri, Dominik Janzing. DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models. 2022. https://arxiv.org/abs/2206.06821 Bibtex:: @article{dowhy, title={DoWhy: An End-to-End Library for Causal Inference}, author={Sharma, Amit and Kiciman, Emre}, journal={arXiv preprint arXiv:2011.04216}, year={2020} } @article{dowhy_gcm, author = {Bl{\"o}baum, Patrick and G{\"o}tz, Peter and Budhathoki, Kailash and Mastakouri, Atalanti A. and Janzing, Dominik}, title = {DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models}, journal={arXiv preprint arXiv:2206.06821}, year={2022} } Issues ~~~~~~ If you encounter an issue or have a specific request for DoWhy, please `raise an issue <https://github.com/py-why/dowhy/issues>`_. Contributing ~~~~~~~~~~~~ This project welcomes contributions and suggestions. For a guide to contributing and a list of all contributors, check out `CONTRIBUTING.md <https://github.com/py-why/dowhy/blob/main/CONTRIBUTING.md>`_ and our `docs for contributing code <https://github.com/py-why/dowhy/blob/main/docs/source/contributing/contributing-code.rst>`_. Our `contributor code of conduct is available here <https://github.com/py-why/governance/blob/main/CODE-OF-CONDUCT.md>`_.

|BuildStatus|_ |PyPiVersion|_ |PythonSupport|_ |Downloads|_ |discord|_ .. |PyPiVersion| image:: https://img.shields.io/pypi/v/dowhy.svg .. _PyPiVersion: https://pypi.org/project/dowhy/ .. |PythonSupport| image:: https://img.shields.io/pypi/pyversions/dowhy.svg .. _PythonSupport: https://pypi.org/project/dowhy/ .. |BuildStatus| image:: https://github.com/py-why/dowhy/actions/workflows/ci.yml/badge.svg .. _BuildStatus: https://github.com/py-why/dowhy/actions .. |Downloads| image:: https://pepy.tech/badge/dowhy .. _Downloads: https://pepy.tech/project/dowhy .. |discord| image:: https://img.shields.io/discord/818456847551168542 .. _discord: https://discord.gg/cSBGb3vsZb .. image:: dowhy-logo-large.png :width: 50% :align: center `Checkout the documentation <https://py-why.github.io/dowhy/>`_ =============================================================== - The documentation, user guide, sample notebooks and other information are available at `https://py-why.github.io/dowhy <https://py-why.github.io/dowhy/>`_ - DoWhy is part of the `PyWhy Ecosystem <https://www.pywhy.org/>`_. For more tools and libraries related to causality, checkout the `PyWhy GitHub organization <https://github.com/py-why/>`_! - For any questions, comments, or discussions about specific use cases, join our community on `Discord <https://discord.gg/cSBGb3vsZb>`_ (|discord|_) - Jump right into some case studies: - Effect estimation: `Hotel booking cancellations <https://towardsdatascience.com/beyond-predictive-models-the-causal-story-behind-hotel-booking-cancellations-d29e8558cbaf>`_ | `Effect of customer loyalty programs <https://github.com/microsoft/dowhy/blob/main/docs/source/example_notebooks/dowhy_example_effect_of_memberrewards_program.ipynb>`_ | `Optimizing article headlines <https://medium.com/@akelleh/introducing-the-do-sampler-for-causal-inference-a3296ea9e78d>`_ | `Effect of home visits on infant health (IHDP) <https://towardsdatascience.com/implementing-causal-inference-a-key-step-towards-agi-de2cde8ea599>`_ | `Causes of customer churn/attrition <https://medium.com/geekculture/a-quickstart-for-causal-analysis-decision-making-with-dowhy-2ce2d4d1efa9>`_ - Root cause analysis and explanations: `Root Cause Analysis with DoWhy, an Open Source Python Library for Causal Machine Learning <https://aws.amazon.com/blogs/opensource/root-cause-analysis-with-dowhy-an-open-source-python-library-for-causal-machine-learning/>`_ | `Finding the Root Cause of Elevated Latencies in a Microservice Architecture <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_rca_microservice_architecture.ipynb>`_ | `Finding Root Causes of Changes in a Supply Chain <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_supply_chain_dist_change.ipynb>`_ For more example notebooks, see `here! <https://www.pywhy.org/dowhy/main/example_notebooks/nb_index.html>`_ Introduction & Key Features =========================== Decision-making involves understanding how different variables affect each other and predicting the outcome when some of them are changed to new values. For instance, given an outcome variable, one may be interested in determining how a potential action(s) may affect it, understanding what led to its current value, or simulate what would happen if some variables are changed. Answering such questions requires causal reasoning. DoWhy is a Python library that guides you through the various steps of causal reasoning and provides a unified interface for answering causal questions. DoWhy provides a wide variety of algorithms for effect estimation, prediction, quantification of causal influences, diagnosis of causal structures, root cause analysis, interventions and counterfactuals. A key feature of DoWhy is its refutation and falsification API that can test causal assumptions for any estimation method, thus making inference more robust and accessible to non-experts. **Graphical Causal Models and Potential Outcomes: Best of both worlds** DoWhy builds on two of the most powerful frameworks for causal inference: graphical causal models and potential outcomes. For effect estimation, it uses graph-based criteria and do-calculus for modeling assumptions and identifying a non-parametric causal effect. For estimation, it switches to methods based primarily on potential outcomes. For causal questions beyond effect estimation, it uses the power of graphical causal models by modeling the data generation process via explicit causal mechanisms at each node, which, for instance, unlocks capabilities to attribute observed effects to particular variables or estimate point-wise counterfactuals. For a quick introduction to causal inference, check out `amit-sharma/causal-inference-tutorial <https://github.com/amit-sharma/causal-inference-tutorial/>`_ We also gave a more comprehensive tutorial at the ACM Knowledge Discovery and Data Mining (`KDD 2018 <http://www.kdd.org/kdd2018/>`_) conference: `causalinference.gitlab.io/kdd-tutorial <http://causalinference.gitlab.io/kdd-tutorial/>`_. For an introduction to the four steps of causal inference and its implications for machine learning, you can access this video tutorial from Microsoft Research `DoWhy Webinar <https://www.microsoft.com/en-us/research/video/foundations-of-causal-inference-and-its-impacts-on-machine-learning/>`_ and for an introduction to the graphical causal model API, see the `PyCon presentation on Root Cause Analysis with DoWhy <https://www.youtube.com/watch?v=icpHrbDlGaw>`_. Key Features ~~~~~~~~~~~~ .. image:: https://raw.githubusercontent.com/py-why/dowhy/main/docs/images/dowhy-features.png DoWhy supports the following causal tasks: - Effect estimation (identification, average causal effect, conditional average causal effect, instrumental variables and more) - Quantify causal influences (mediation analysis, direct arrow strength, intrinsic causal influence) - What-if analysis (generate samples from interventional distribution, estimate counterfactuals) - Root cause analysis and explanations (attribute anomalies to their causes, find causes for changes in distributions, estimate feature relevance and more) For more details and how to use these methods in practice, checkout the documentation at `https://py-why.github.io/dowhy <https://py-why.github.io/dowhy/>`_ Quick Start =========== DoWhy support Python 3.8+. To install, you can use pip, poetry, or conda. **Latest Release** Install the latest `release <https://pypi.org/project/dowhy/>`__ using pip. .. code:: shell pip install dowhy Install the latest `release <https://pypi.org/project/dowhy/>`__ using poetry. .. code:: shell poetry add dowhy Install the latest `release <https://anaconda.org/conda-forge/dowhy>`__ using conda. .. code:: shell conda install -c conda-forge dowhy If you face "Solving environment" problems with conda, then try :code:`conda update --all` and then install dowhy. If that does not work, then use :code:`conda config --set channel_priority false` and try to install again. If the problem persists, please `add your issue here <https://github.com/microsoft/dowhy/issues/197>`_. **Development Version** If you prefer to use the latest dev version, your dependency management tool will need to point at our GitHub repository. .. code:: shell pip install git+https://github.com/py-why/dowhy@main **Requirements** DoWhy requires a few dependencies. Details on specific versions can be found in `pyproject.toml <./pyproject.toml>`_, under the `tool.poetry.dependencies` section. If you face any problems, try installing dependencies manually. .. code:: shell pip install '<dependency-name>==<version>' Optionally, if you wish to input graphs in the dot format, then install pydot (or pygraphviz). For better-looking graphs, you can optionally install pygraphviz. To proceed, first install graphviz and then pygraphviz (on Ubuntu and Ubuntu WSL). .. code:: shell sudo apt install graphviz libgraphviz-dev graphviz-dev pkg-config ## from https://github.com/pygraphviz/pygraphviz/issues/71 pip install pygraphviz --install-option="--include-path=/usr/include/graphviz" \ --install-option="--library-path=/usr/lib/graphviz/" Example: Effect identification and estimation ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Most causal tasks in DoWhy only require a few lines of code to write. Here, we exemplarily estimate the causal effect of a treatment on an outcome variable: .. code:: python from dowhy import CausalModel import dowhy.datasets # Load some sample data data = dowhy.datasets.linear_dataset( beta=10, num_common_causes=5, num_instruments=2, num_samples=10000, treatment_is_binary=True) A causal graph can be defined in different way, but the most common way is via `NetworkX <https://networkx.org/>`_. After loading in the data, we use the four main operations for effect estimation in DoWhy: *model*, *identify*, *estimate* and *refute*: .. code:: python # I. Create a causal model from the data and given graph. model = CausalModel( data=data["df"], treatment=data["treatment_name"], outcome=data["outcome_name"], graph=data["gml_graph"]) # Or alternatively, as nx.DiGraph # II. Identify causal effect and return target estimands identified_estimand = model.identify_effect() # III. Estimate the target estimand using a statistical method. estimate = model.estimate_effect(identified_estimand, method_name="backdoor.propensity_score_matching") # IV. Refute the obtained estimate using multiple robustness checks. refute_results = model.refute_estimate(identified_estimand, estimate, method_name="random_common_cause") DoWhy stresses on the interpretability of its output. At any point in the analysis, you can inspect the untested assumptions, identified estimands (if any), and the estimate (if any). Here's a sample output of the linear regression estimator: .. image:: https://raw.githubusercontent.com/py-why/dowhy/main/docs/images/regression_output.png :width: 80% For a full code example, check out the `Getting Started with DoWhy <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/dowhy_simple_example.ipynb>`_ notebook. You can also use Conditional Average Treatment Effect (CATE) estimation methods from `EconML <https://github.com/py-why/econml>`_, as shown in the `Conditional Treatment Effects <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/dowhy-conditional-treatment-effects.ipynb>`_ notebook. Here's a code snippet. .. code:: python from sklearn.preprocessing import PolynomialFeatures from sklearn.linear_model import LassoCV from sklearn.ensemble import GradientBoostingRegressor dml_estimate = model.estimate_effect(identified_estimand, method_name="backdoor.econml.dml.DML", control_value = 0, treatment_value = 1, target_units = lambda df: df["X0"]>1, confidence_intervals=False, method_params={ "init_params":{'model_y':GradientBoostingRegressor(), 'model_t': GradientBoostingRegressor(), 'model_final':LassoCV(), 'featurizer':PolynomialFeatures(degree=1, include_bias=True)}, "fit_params":{}}) Example: Graphical causal model (GCM) based inference ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DoWhy's graphical causal model framework offers powerful tools to address causal questions beyond effect estimation. It is based on Pearl's graphical causal model framework and models the causal data generation process of each variable explicitly via *causal mechanisms* to support a wide range of causal algorithms. For more details, see the book `Elements of Causal Inference <https://mitpress.mit.edu/9780262037310/elements-of-causal-inference/>`_. Complex causal queries, such as attributing observed anomalies to nodes in the system, can be performed with just a few lines of code: .. code:: python import networkx as nx, numpy as np, pandas as pd from dowhy import gcm # Let's generate some "normal" data we assume we're given from our problem domain: X = np.random.normal(loc=0, scale=1, size=1000) Y = 2 * X + np.random.normal(loc=0, scale=1, size=1000) Z = 3 * Y + np.random.normal(loc=0, scale=1, size=1000) data = pd.DataFrame(dict(X=X, Y=Y, Z=Z)) # 1. Modeling cause-effect relationships as a structural causal model # (causal graph + functional causal models): causal_model = gcm.StructuralCausalModel(nx.DiGraph([('X', 'Y'), ('Y', 'Z')])) # X -> Y -> Z gcm.auto.assign_causal_mechanisms(causal_model, data) # 2. Fitting the SCM to the data: gcm.fit(causal_model, data) # Optional: Evaluate causal model print(gcm.evaluate_causal_model(causal_model, data)) # Step 3: Perform a causal analysis. # results = gcm.<causal_query>(causal_model, ...) # For instance, root cause analysis: anomalous_sample = pd.DataFrame(dict(X=[0.1], Y=[6.2], Z=[19])) # Here, Y is the root cause. # "Which node is the root cause of the anomaly in Z?": anomaly_attribution = gcm.attribute_anomalies(causal_model, "Z", anomalous_sample) # Or sampling from an interventional distribution. Here, under the intervention do(Y := 2). samples = gcm.interventional_samples(causal_model, interventions={'Y': lambda y: 2}, num_samples_to_draw=100) The GCM framework offers many more features beyond these examples. For a full code example, check out the `Online Shop example notebook <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_online_shop.ipynb>`_. For more functionalities, example applications of DoWhy and details about the outputs, see the `User Guide <https://www.pywhy.org/dowhy/main/user_guide/intro.html>`_ or checkout `Jupyter notebooks <https://www.pywhy.org/dowhy/main/example_notebooks/nb_index.html>`_. More Information & Resources ============================ `Microsoft Research Blog <https://www.microsoft.com/en-us/research/blog/dowhy-a-library-for-causal-inference/>`_ | `Video Tutorial for Effect Estimation <https://www.youtube.com/watch?v=LALfQStONEc&t=114s>`_ | `Video Tutorial for Root Cause Analysis <https://www.youtube.com/watch?v=icpHrbDlGaw>`_ | `Arxiv Paper <https://arxiv.org/abs/2011.04216>`_ | `Arxiv Paper (Graphical Causal Model extension) <https://arxiv.org/abs/2206.06821>`_ | `Slides <https://www2.slideshare.net/AmitSharma315/dowhy-an-endtoend-library-for-causal-inference>`_ Citing this package ~~~~~~~~~~~~~~~~~~~ If you find DoWhy useful for your work, please cite **both** of the following two references: - Amit Sharma, Emre Kiciman. DoWhy: An End-to-End Library for Causal Inference. 2020. https://arxiv.org/abs/2011.04216 - Patrick Blöbaum, Peter Götz, Kailash Budhathoki, Atalanti A. Mastakouri, Dominik Janzing. DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models. 2022. https://arxiv.org/abs/2206.06821 Bibtex:: @article{dowhy, title={DoWhy: An End-to-End Library for Causal Inference}, author={Sharma, Amit and Kiciman, Emre}, journal={arXiv preprint arXiv:2011.04216}, year={2020} } @article{dowhy_gcm, author = {Bl{\"o}baum, Patrick and G{\"o}tz, Peter and Budhathoki, Kailash and Mastakouri, Atalanti A. and Janzing, Dominik}, title = {DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models}, journal={arXiv preprint arXiv:2206.06821}, year={2022} } Issues ~~~~~~ If you encounter an issue or have a specific request for DoWhy, please `raise an issue <https://github.com/py-why/dowhy/issues>`_. Contributing ~~~~~~~~~~~~ This project welcomes contributions and suggestions. For a guide to contributing and a list of all contributors, check out `CONTRIBUTING.md <https://github.com/py-why/dowhy/blob/main/CONTRIBUTING.md>`_ and our `docs for contributing code <https://github.com/py-why/dowhy/blob/main/docs/source/contributing/contributing-code.rst>`_. Our `contributor code of conduct is available here <https://github.com/py-why/governance/blob/main/CODE-OF-CONDUCT.md>`_.

bloebp

591e9d916e19bcad07e28687aeb8c81e1db26e8d

f455c569db0e7d0c1e1e7b54d1f8c85f026ec611

The reason was due to the very outdated conda version (still on 0.8). I will put it back in, but let's try to fix the build issues there.

bloebp

py-why/dowhy

Overhauled readme

The current readme is too overloaded for users. Changes here are: - Reduce it only the essential points, since most of the content is part of the documentation - Add image as overview of the offered features - Made connection between GCM and PO framework more consistent - Revise the GCM example to an executable code snipped - Extended some references to include GCM related work - Fix build status icon - Changed github references to py-why (was still pointing to microsoft) Here the new readme: https://github.com/py-why/dowhy/blob/c0ac8433625b5693175629076ad32a92b0d1eb71/README.rst The image in the key features section is not show. It is the following: <img width="2887" alt="Screenshot 2023-11-28 at 08 52 50" src="https://github.com/py-why/dowhy/assets/51325689/98f5099f-6cc6-4969-b804-609d8006984d">

2023-11-26 05:56:29+00:00

2023-12-01 14:58:26+00:00

README.rst

|BuildStatus|_ |PyPiVersion|_ |PythonSupport|_ |Downloads|_ |discord|_ .. |PyPiVersion| image:: https://img.shields.io/pypi/v/dowhy.svg .. _PyPiVersion: https://pypi.org/project/dowhy/ .. |PythonSupport| image:: https://img.shields.io/pypi/pyversions/dowhy.svg .. _PythonSupport: https://pypi.org/project/dowhy/ .. |BuildStatus| image:: https://github.com/py-why/dowhy/actions/workflows/ci.yml/badge.svg .. _BuildStatus: https://github.com/py-why/dowhy/actions .. |Downloads| image:: https://pepy.tech/badge/dowhy .. _Downloads: https://pepy.tech/project/dowhy .. |discord| image:: https://img.shields.io/discord/818456847551168542 .. _discord: https://discord.gg/cSBGb3vsZb .. image:: dowhy-logo-large.png :width: 50% :align: center `Checkout the documentation <https://py-why.github.io/dowhy/>`_ =============================================================== - The documentation, user guide, sample notebooks and other information are available at `https://py-why.github.io/dowhy <https://py-why.github.io/dowhy/>`_ - DoWhy is part of the `PyWhy Ecosystem <https://www.pywhy.org/>`_. For more tools and libraries related to causality, checkout the `PyWhy GitHub organization <https://github.com/py-why/>`_! - For any questions, comments, or discussions about specific use cases, join our community on `Discord <https://discord.gg/cSBGb3vsZb>`_ (|discord|_) - Jump right into some case studies: - Effect estimation: `Hotel booking cancellations <https://towardsdatascience.com/beyond-predictive-models-the-causal-story-behind-hotel-booking-cancellations-d29e8558cbaf>`_ | `Effect of customer loyalty programs <https://github.com/microsoft/dowhy/blob/main/docs/source/example_notebooks/dowhy_example_effect_of_memberrewards_program.ipynb>`_ | `Optimizing article headlines <https://medium.com/@akelleh/introducing-the-do-sampler-for-causal-inference-a3296ea9e78d>`_ | `Effect of home visits on infant health (IHDP) <https://towardsdatascience.com/implementing-causal-inference-a-key-step-towards-agi-de2cde8ea599>`_ | `Causes of customer churn/attrition <https://medium.com/geekculture/a-quickstart-for-causal-analysis-decision-making-with-dowhy-2ce2d4d1efa9>`_ - Root cause analysis and explanations: `Root Cause Analysis with DoWhy, an Open Source Python Library for Causal Machine Learning <https://aws.amazon.com/blogs/opensource/root-cause-analysis-with-dowhy-an-open-source-python-library-for-causal-machine-learning/>`_ | `Finding the Root Cause of Elevated Latencies in a Microservice Architecture <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_rca_microservice_architecture.ipynb>`_ | `Finding Root Causes of Changes in a Supply Chain <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_supply_chain_dist_change.ipynb>`_ For more example notebooks, see `here! <https://www.pywhy.org/dowhy/main/example_notebooks/nb_index.html>`_ Introduction & Key Features =========================== Decision-making involves understanding how different variables affect each other and predicting the outcome when some of them are changed to new values. For instance, given an outcome variable, one may be interested in determining how a potential action(s) may affect it, understanding what led to its current value, or simulate what would happen if some variables are changed. Answering such questions requires causal reasoning. DoWhy is a Python library that guides you through the various steps of causal reasoning and provides a unified interface for answering causal questions. DoWhy provides a wide variety of algorithms for effect estimation, prediction, quantification of causal influences, diagnosis of causal structures, root cause analysis, interventions and counterfactuals. A key feature of DoWhy is its refutation and falsification API that can test causal assumptions for any estimation method, thus making inference more robust and accessible to non-experts. **Graphical Causal Models and Potential Outcomes: Best of both worlds** DoWhy builds on two of the most powerful frameworks for causal inference: graphical causal models and potential outcomes. For effect estimation, it uses graph-based criteria and do-calculus for modeling assumptions and identifying a non-parametric causal effect. For estimation, it switches to methods based primarily on potential outcomes. For causal questions beyond effect estimation, it uses the power of graphical causal models by modeling the data generation process via explicit causal mechanisms at each node, which, for instance, unlocks capabilities to attribute observed effects to particular variables or estimate point-wise counterfactuals. For a quick introduction to causal inference, check out `amit-sharma/causal-inference-tutorial <https://github.com/amit-sharma/causal-inference-tutorial/>`_ We also gave a more comprehensive tutorial at the ACM Knowledge Discovery and Data Mining (`KDD 2018 <http://www.kdd.org/kdd2018/>`_) conference: `causalinference.gitlab.io/kdd-tutorial <http://causalinference.gitlab.io/kdd-tutorial/>`_. For an introduction to the four steps of causal inference and its implications for machine learning, you can access this video tutorial from Microsoft Research `DoWhy Webinar <https://www.microsoft.com/en-us/research/video/foundations-of-causal-inference-and-its-impacts-on-machine-learning/>`_ and for an introduction to the graphical causal model API, see the `PyCon presentation on Root Cause Analysis with DoWhy <https://www.youtube.com/watch?v=icpHrbDlGaw>`_. Key Features ~~~~~~~~~~~~ .. image:: https://raw.githubusercontent.com/py-why/dowhy/main/docs/images/dowhy-features.png DoWhy supports the following causal tasks: - Effect estimation (identification, average causal effect, conditional average causal effect, instrumental variables and more) - Quantify causal influences (mediation analysis, direct arrow strength, intrinsic causal influence) - What-if analysis (generate samples from interventional distribution, estimate counterfactuals) - Root cause analysis and explanations (attribute anomalies to their causes, find causes for changes in distributions, estimate feature relevance and more) For more details and how to use these methods in practice, checkout the documentation at `https://py-why.github.io/dowhy <https://py-why.github.io/dowhy/>`_ Quick Start =========== DoWhy support Python 3.8+. To install, you can use pip, poetry, or conda. **Latest Release** Install the latest `release <https://pypi.org/project/dowhy/>`__ using pip. .. code:: shell pip install dowhy Install the latest `release <https://pypi.org/project/dowhy/>`__ using poetry. .. code:: shell poetry add dowhy Install the latest `release <https://anaconda.org/conda-forge/dowhy>`__ using conda. .. code:: shell conda install -c conda-forge dowhy If you face "Solving environment" problems with conda, then try :code:`conda update --all` and then install dowhy. If that does not work, then use :code:`conda config --set channel_priority false` and try to install again. If the problem persists, please `add your issue here <https://github.com/microsoft/dowhy/issues/197>`_. **Development Version** If you prefer to use the latest dev version, your dependency management tool will need to point at our GitHub repository. .. code:: shell pip install git+https://github.com/py-why/dowhy@main **Requirements** DoWhy requires a few dependencies. Details on specific versions can be found in `pyproject.toml <./pyproject.toml>`_, under the `tool.poetry.dependencies` section. If you face any problems, try installing dependencies manually. .. code:: shell pip install '<dependency-name>==<version>' Optionally, if you wish to input graphs in the dot format, then install pydot (or pygraphviz). For better-looking graphs, you can optionally install pygraphviz. To proceed, first install graphviz and then pygraphviz (on Ubuntu and Ubuntu WSL). .. code:: shell sudo apt install graphviz libgraphviz-dev graphviz-dev pkg-config ## from https://github.com/pygraphviz/pygraphviz/issues/71 pip install pygraphviz --install-option="--include-path=/usr/include/graphviz" \ --install-option="--library-path=/usr/lib/graphviz/" Example usage ~~~~~~~~~~~~~ Two examples demonstrating the effect estimation and graphical causal models API. Effect identification and estimation ++++++++++++++++++++++++++++++++++++ Most causal tasks in DoWhy only require a few lines of code to write. Here, we exemplarily estimate the causal effect of a treatment on an outcome variable: .. code:: python from dowhy import CausalModel import dowhy.datasets # Load some sample data data = dowhy.datasets.linear_dataset( beta=10, num_common_causes=5, num_instruments=2, num_samples=10000, treatment_is_binary=True) A causal graph can be defined in different way, but the most common way is via `NetworkX <https://networkx.org/>`_. After loading in the data, we use the four main operations for effect estimation in DoWhy: *model*, *identify*, *estimate* and *refute*: .. code:: python # I. Create a causal model from the data and given graph. model = CausalModel( data=data["df"], treatment=data["treatment_name"], outcome=data["outcome_name"], graph=data["gml_graph"]) # Or alternatively, as nx.DiGraph # II. Identify causal effect and return target estimands identified_estimand = model.identify_effect() # III. Estimate the target estimand using a statistical method. estimate = model.estimate_effect(identified_estimand, method_name="backdoor.propensity_score_matching") # IV. Refute the obtained estimate using multiple robustness checks. refute_results = model.refute_estimate(identified_estimand, estimate, method_name="random_common_cause") DoWhy stresses on the interpretability of its output. At any point in the analysis, you can inspect the untested assumptions, identified estimands (if any), and the estimate (if any). Here's a sample output of the linear regression estimator: .. image:: https://raw.githubusercontent.com/py-why/dowhy/main/docs/images/regression_output.png :width: 80% For a full code example, check out the `Getting Started with DoWhy <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/dowhy_simple_example.ipynb>`_ notebook. You can also use Conditional Average Treatment Effect (CATE) estimation methods from `EconML <https://github.com/py-why/econml>`_, as shown in the `Conditional Treatment Effects <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/dowhy-conditional-treatment-effects.ipynb>`_ notebook. Here's a code snippet. .. code:: python from sklearn.preprocessing import PolynomialFeatures from sklearn.linear_model import LassoCV from sklearn.ensemble import GradientBoostingRegressor dml_estimate = model.estimate_effect(identified_estimand, method_name="backdoor.econml.dml.DML", control_value = 0, treatment_value = 1, target_units = lambda df: df["X0"]>1, confidence_intervals=False, method_params={ "init_params":{'model_y':GradientBoostingRegressor(), 'model_t': GradientBoostingRegressor(), 'model_final':LassoCV(), 'featurizer':PolynomialFeatures(degree=1, include_bias=True)}, "fit_params":{}}) Graphical causal model (GCM) based inference ++++++++++++++++++++++++++++++++++++++++++++ DoWhy's graphical causal model framework offers powerful tools to address causal questions beyond effect estimation. It is based on Pearl's graphical causal model framework and models the causal data generation process of each variable explicitly via *causal mechanisms* to support a wide range of causal algorithms. For more details, see the book `Elements of Causal Inference <https://mitpress.mit.edu/9780262037310/elements-of-causal-inference/>`_. Complex causal queries, such as attributing observed anomalies to nodes in the system, can be performed with just a few lines of code: .. code:: python import networkx as nx, numpy as np, pandas as pd from dowhy import gcm # Let's generate some "normal" data we assume we're given from our problem domain: X = np.random.normal(loc=0, scale=1, size=1000) Y = 2 * X + np.random.normal(loc=0, scale=1, size=1000) Z = 3 * Y + np.random.normal(loc=0, scale=1, size=1000) data = pd.DataFrame(dict(X=X, Y=Y, Z=Z)) # 1. Modeling cause-effect relationships as a structural causal model # (causal graph + functional causal models): causal_model = gcm.StructuralCausalModel(nx.DiGraph([('X', 'Y'), ('Y', 'Z')])) # X -> Y -> Z gcm.auto.assign_causal_mechanisms(causal_model, data) # 2. Fitting the SCM to the data: gcm.fit(causal_model, data) # Optional: Evaluate causal model print(gcm.evaluate_causal_model(causal_model, data)) # Step 3: Perform a causal analysis. # results = gcm.<causal_query>(causal_model, ...) # For instance, root cause analysis: anomalous_sample = pd.DataFrame(dict(X=[0.1], Y=[6.2], Z=[19])) # Here, Y is the root cause. # "Which node is the root cause of the anomaly in Z?": anomaly_attribution = gcm.attribute_anomalies(causal_model, "Z", anomalous_sample) # Or sampling from an interventional distribution. Here, under the intervention do(Y := 2). samples = gcm.interventional_samples(causal_model, interventions={'Y': lambda y: 2}, num_samples_to_draw=100) The GCM framework offers many more features beyond these examples. For a full code example, check out the `Online Shop example notebook <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_online_shop.ipynb>`_. For more functionalities, example applications of DoWhy and details about the outputs, see the `User Guide <https://www.pywhy.org/dowhy/main/user_guide/intro.html>`_ or checkout `Jupyter notebooks <https://www.pywhy.org/dowhy/main/example_notebooks/nb_index.html>`_. More Information & Resources ============================ `Microsoft Research Blog <https://www.microsoft.com/en-us/research/blog/dowhy-a-library-for-causal-inference/>`_ | `Video Tutorial for Effect Estimation <https://www.youtube.com/watch?v=LALfQStONEc&t=114s>`_ | `Video Tutorial for Root Cause Analysis <https://www.youtube.com/watch?v=icpHrbDlGaw>`_ | `Arxiv Paper <https://arxiv.org/abs/2011.04216>`_ | `Arxiv Paper (Graphical Causal Model extension) <https://arxiv.org/abs/2206.06821>`_ | `Slides <https://www2.slideshare.net/AmitSharma315/dowhy-an-endtoend-library-for-causal-inference>`_ Citing this package ~~~~~~~~~~~~~~~~~~~ If you find DoWhy useful for your work, please cite **both** of the following two references: - Amit Sharma, Emre Kiciman. DoWhy: An End-to-End Library for Causal Inference. 2020. https://arxiv.org/abs/2011.04216 - Patrick Blöbaum, Peter Götz, Kailash Budhathoki, Atalanti A. Mastakouri, Dominik Janzing. DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models. 2022. https://arxiv.org/abs/2206.06821 Bibtex:: @article{dowhy, title={DoWhy: An End-to-End Library for Causal Inference}, author={Sharma, Amit and Kiciman, Emre}, journal={arXiv preprint arXiv:2011.04216}, year={2020} } @article{dowhy_gcm, author = {Bl{\"o}baum, Patrick and G{\"o}tz, Peter and Budhathoki, Kailash and Mastakouri, Atalanti A. and Janzing, Dominik}, title = {DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models}, journal={arXiv preprint arXiv:2206.06821}, year={2022} } Issues ~~~~~~ If you encounter an issue or have a specific request for DoWhy, please `raise an issue <https://github.com/py-why/dowhy/issues>`_. Contributing ~~~~~~~~~~~~ This project welcomes contributions and suggestions. For a guide to contributing and a list of all contributors, check out `CONTRIBUTING.md <https://github.com/py-why/dowhy/blob/main/CONTRIBUTING.md>`_ and our `docs for contributing code <https://github.com/py-why/dowhy/blob/main/docs/source/contributing/contributing-code.rst>`_. Our `contributor code of conduct is available here <https://github.com/py-why/governance/blob/main/CODE-OF-CONDUCT.md>`_.

|BuildStatus|_ |PyPiVersion|_ |PythonSupport|_ |Downloads|_ |discord|_ .. |PyPiVersion| image:: https://img.shields.io/pypi/v/dowhy.svg .. _PyPiVersion: https://pypi.org/project/dowhy/ .. |PythonSupport| image:: https://img.shields.io/pypi/pyversions/dowhy.svg .. _PythonSupport: https://pypi.org/project/dowhy/ .. |BuildStatus| image:: https://github.com/py-why/dowhy/actions/workflows/ci.yml/badge.svg .. _BuildStatus: https://github.com/py-why/dowhy/actions .. |Downloads| image:: https://pepy.tech/badge/dowhy .. _Downloads: https://pepy.tech/project/dowhy .. |discord| image:: https://img.shields.io/discord/818456847551168542 .. _discord: https://discord.gg/cSBGb3vsZb .. image:: dowhy-logo-large.png :width: 50% :align: center `Checkout the documentation <https://py-why.github.io/dowhy/>`_ =============================================================== - The documentation, user guide, sample notebooks and other information are available at `https://py-why.github.io/dowhy <https://py-why.github.io/dowhy/>`_ - DoWhy is part of the `PyWhy Ecosystem <https://www.pywhy.org/>`_. For more tools and libraries related to causality, checkout the `PyWhy GitHub organization <https://github.com/py-why/>`_! - For any questions, comments, or discussions about specific use cases, join our community on `Discord <https://discord.gg/cSBGb3vsZb>`_ (|discord|_) - Jump right into some case studies: - Effect estimation: `Hotel booking cancellations <https://towardsdatascience.com/beyond-predictive-models-the-causal-story-behind-hotel-booking-cancellations-d29e8558cbaf>`_ | `Effect of customer loyalty programs <https://github.com/microsoft/dowhy/blob/main/docs/source/example_notebooks/dowhy_example_effect_of_memberrewards_program.ipynb>`_ | `Optimizing article headlines <https://medium.com/@akelleh/introducing-the-do-sampler-for-causal-inference-a3296ea9e78d>`_ | `Effect of home visits on infant health (IHDP) <https://towardsdatascience.com/implementing-causal-inference-a-key-step-towards-agi-de2cde8ea599>`_ | `Causes of customer churn/attrition <https://medium.com/geekculture/a-quickstart-for-causal-analysis-decision-making-with-dowhy-2ce2d4d1efa9>`_ - Root cause analysis and explanations: `Root Cause Analysis with DoWhy, an Open Source Python Library for Causal Machine Learning <https://aws.amazon.com/blogs/opensource/root-cause-analysis-with-dowhy-an-open-source-python-library-for-causal-machine-learning/>`_ | `Finding the Root Cause of Elevated Latencies in a Microservice Architecture <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_rca_microservice_architecture.ipynb>`_ | `Finding Root Causes of Changes in a Supply Chain <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_supply_chain_dist_change.ipynb>`_ For more example notebooks, see `here! <https://www.pywhy.org/dowhy/main/example_notebooks/nb_index.html>`_ Introduction & Key Features =========================== Decision-making involves understanding how different variables affect each other and predicting the outcome when some of them are changed to new values. For instance, given an outcome variable, one may be interested in determining how a potential action(s) may affect it, understanding what led to its current value, or simulate what would happen if some variables are changed. Answering such questions requires causal reasoning. DoWhy is a Python library that guides you through the various steps of causal reasoning and provides a unified interface for answering causal questions. DoWhy provides a wide variety of algorithms for effect estimation, prediction, quantification of causal influences, diagnosis of causal structures, root cause analysis, interventions and counterfactuals. A key feature of DoWhy is its refutation and falsification API that can test causal assumptions for any estimation method, thus making inference more robust and accessible to non-experts. **Graphical Causal Models and Potential Outcomes: Best of both worlds** DoWhy builds on two of the most powerful frameworks for causal inference: graphical causal models and potential outcomes. For effect estimation, it uses graph-based criteria and do-calculus for modeling assumptions and identifying a non-parametric causal effect. For estimation, it switches to methods based primarily on potential outcomes. For causal questions beyond effect estimation, it uses the power of graphical causal models by modeling the data generation process via explicit causal mechanisms at each node, which, for instance, unlocks capabilities to attribute observed effects to particular variables or estimate point-wise counterfactuals. For a quick introduction to causal inference, check out `amit-sharma/causal-inference-tutorial <https://github.com/amit-sharma/causal-inference-tutorial/>`_ We also gave a more comprehensive tutorial at the ACM Knowledge Discovery and Data Mining (`KDD 2018 <http://www.kdd.org/kdd2018/>`_) conference: `causalinference.gitlab.io/kdd-tutorial <http://causalinference.gitlab.io/kdd-tutorial/>`_. For an introduction to the four steps of causal inference and its implications for machine learning, you can access this video tutorial from Microsoft Research `DoWhy Webinar <https://www.microsoft.com/en-us/research/video/foundations-of-causal-inference-and-its-impacts-on-machine-learning/>`_ and for an introduction to the graphical causal model API, see the `PyCon presentation on Root Cause Analysis with DoWhy <https://www.youtube.com/watch?v=icpHrbDlGaw>`_. Key Features ~~~~~~~~~~~~ .. image:: https://raw.githubusercontent.com/py-why/dowhy/main/docs/images/dowhy-features.png DoWhy supports the following causal tasks: - Effect estimation (identification, average causal effect, conditional average causal effect, instrumental variables and more) - Quantify causal influences (mediation analysis, direct arrow strength, intrinsic causal influence) - What-if analysis (generate samples from interventional distribution, estimate counterfactuals) - Root cause analysis and explanations (attribute anomalies to their causes, find causes for changes in distributions, estimate feature relevance and more) For more details and how to use these methods in practice, checkout the documentation at `https://py-why.github.io/dowhy <https://py-why.github.io/dowhy/>`_ Quick Start =========== DoWhy support Python 3.8+. To install, you can use pip, poetry, or conda. **Latest Release** Install the latest `release <https://pypi.org/project/dowhy/>`__ using pip. .. code:: shell pip install dowhy Install the latest `release <https://pypi.org/project/dowhy/>`__ using poetry. .. code:: shell poetry add dowhy Install the latest `release <https://anaconda.org/conda-forge/dowhy>`__ using conda. .. code:: shell conda install -c conda-forge dowhy If you face "Solving environment" problems with conda, then try :code:`conda update --all` and then install dowhy. If that does not work, then use :code:`conda config --set channel_priority false` and try to install again. If the problem persists, please `add your issue here <https://github.com/microsoft/dowhy/issues/197>`_. **Development Version** If you prefer to use the latest dev version, your dependency management tool will need to point at our GitHub repository. .. code:: shell pip install git+https://github.com/py-why/dowhy@main **Requirements** DoWhy requires a few dependencies. Details on specific versions can be found in `pyproject.toml <./pyproject.toml>`_, under the `tool.poetry.dependencies` section. If you face any problems, try installing dependencies manually. .. code:: shell pip install '<dependency-name>==<version>' Optionally, if you wish to input graphs in the dot format, then install pydot (or pygraphviz). For better-looking graphs, you can optionally install pygraphviz. To proceed, first install graphviz and then pygraphviz (on Ubuntu and Ubuntu WSL). .. code:: shell sudo apt install graphviz libgraphviz-dev graphviz-dev pkg-config ## from https://github.com/pygraphviz/pygraphviz/issues/71 pip install pygraphviz --install-option="--include-path=/usr/include/graphviz" \ --install-option="--library-path=/usr/lib/graphviz/" Example: Effect identification and estimation ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Most causal tasks in DoWhy only require a few lines of code to write. Here, we exemplarily estimate the causal effect of a treatment on an outcome variable: .. code:: python from dowhy import CausalModel import dowhy.datasets # Load some sample data data = dowhy.datasets.linear_dataset( beta=10, num_common_causes=5, num_instruments=2, num_samples=10000, treatment_is_binary=True) A causal graph can be defined in different way, but the most common way is via `NetworkX <https://networkx.org/>`_. After loading in the data, we use the four main operations for effect estimation in DoWhy: *model*, *identify*, *estimate* and *refute*: .. code:: python # I. Create a causal model from the data and given graph. model = CausalModel( data=data["df"], treatment=data["treatment_name"], outcome=data["outcome_name"], graph=data["gml_graph"]) # Or alternatively, as nx.DiGraph # II. Identify causal effect and return target estimands identified_estimand = model.identify_effect() # III. Estimate the target estimand using a statistical method. estimate = model.estimate_effect(identified_estimand, method_name="backdoor.propensity_score_matching") # IV. Refute the obtained estimate using multiple robustness checks. refute_results = model.refute_estimate(identified_estimand, estimate, method_name="random_common_cause") DoWhy stresses on the interpretability of its output. At any point in the analysis, you can inspect the untested assumptions, identified estimands (if any), and the estimate (if any). Here's a sample output of the linear regression estimator: .. image:: https://raw.githubusercontent.com/py-why/dowhy/main/docs/images/regression_output.png :width: 80% For a full code example, check out the `Getting Started with DoWhy <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/dowhy_simple_example.ipynb>`_ notebook. You can also use Conditional Average Treatment Effect (CATE) estimation methods from `EconML <https://github.com/py-why/econml>`_, as shown in the `Conditional Treatment Effects <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/dowhy-conditional-treatment-effects.ipynb>`_ notebook. Here's a code snippet. .. code:: python from sklearn.preprocessing import PolynomialFeatures from sklearn.linear_model import LassoCV from sklearn.ensemble import GradientBoostingRegressor dml_estimate = model.estimate_effect(identified_estimand, method_name="backdoor.econml.dml.DML", control_value = 0, treatment_value = 1, target_units = lambda df: df["X0"]>1, confidence_intervals=False, method_params={ "init_params":{'model_y':GradientBoostingRegressor(), 'model_t': GradientBoostingRegressor(), 'model_final':LassoCV(), 'featurizer':PolynomialFeatures(degree=1, include_bias=True)}, "fit_params":{}}) Example: Graphical causal model (GCM) based inference ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DoWhy's graphical causal model framework offers powerful tools to address causal questions beyond effect estimation. It is based on Pearl's graphical causal model framework and models the causal data generation process of each variable explicitly via *causal mechanisms* to support a wide range of causal algorithms. For more details, see the book `Elements of Causal Inference <https://mitpress.mit.edu/9780262037310/elements-of-causal-inference/>`_. Complex causal queries, such as attributing observed anomalies to nodes in the system, can be performed with just a few lines of code: .. code:: python import networkx as nx, numpy as np, pandas as pd from dowhy import gcm # Let's generate some "normal" data we assume we're given from our problem domain: X = np.random.normal(loc=0, scale=1, size=1000) Y = 2 * X + np.random.normal(loc=0, scale=1, size=1000) Z = 3 * Y + np.random.normal(loc=0, scale=1, size=1000) data = pd.DataFrame(dict(X=X, Y=Y, Z=Z)) # 1. Modeling cause-effect relationships as a structural causal model # (causal graph + functional causal models): causal_model = gcm.StructuralCausalModel(nx.DiGraph([('X', 'Y'), ('Y', 'Z')])) # X -> Y -> Z gcm.auto.assign_causal_mechanisms(causal_model, data) # 2. Fitting the SCM to the data: gcm.fit(causal_model, data) # Optional: Evaluate causal model print(gcm.evaluate_causal_model(causal_model, data)) # Step 3: Perform a causal analysis. # results = gcm.<causal_query>(causal_model, ...) # For instance, root cause analysis: anomalous_sample = pd.DataFrame(dict(X=[0.1], Y=[6.2], Z=[19])) # Here, Y is the root cause. # "Which node is the root cause of the anomaly in Z?": anomaly_attribution = gcm.attribute_anomalies(causal_model, "Z", anomalous_sample) # Or sampling from an interventional distribution. Here, under the intervention do(Y := 2). samples = gcm.interventional_samples(causal_model, interventions={'Y': lambda y: 2}, num_samples_to_draw=100) The GCM framework offers many more features beyond these examples. For a full code example, check out the `Online Shop example notebook <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_online_shop.ipynb>`_. For more functionalities, example applications of DoWhy and details about the outputs, see the `User Guide <https://www.pywhy.org/dowhy/main/user_guide/intro.html>`_ or checkout `Jupyter notebooks <https://www.pywhy.org/dowhy/main/example_notebooks/nb_index.html>`_. More Information & Resources ============================ `Microsoft Research Blog <https://www.microsoft.com/en-us/research/blog/dowhy-a-library-for-causal-inference/>`_ | `Video Tutorial for Effect Estimation <https://www.youtube.com/watch?v=LALfQStONEc&t=114s>`_ | `Video Tutorial for Root Cause Analysis <https://www.youtube.com/watch?v=icpHrbDlGaw>`_ | `Arxiv Paper <https://arxiv.org/abs/2011.04216>`_ | `Arxiv Paper (Graphical Causal Model extension) <https://arxiv.org/abs/2206.06821>`_ | `Slides <https://www2.slideshare.net/AmitSharma315/dowhy-an-endtoend-library-for-causal-inference>`_ Citing this package ~~~~~~~~~~~~~~~~~~~ If you find DoWhy useful for your work, please cite **both** of the following two references: - Amit Sharma, Emre Kiciman. DoWhy: An End-to-End Library for Causal Inference. 2020. https://arxiv.org/abs/2011.04216 - Patrick Blöbaum, Peter Götz, Kailash Budhathoki, Atalanti A. Mastakouri, Dominik Janzing. DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models. 2022. https://arxiv.org/abs/2206.06821 Bibtex:: @article{dowhy, title={DoWhy: An End-to-End Library for Causal Inference}, author={Sharma, Amit and Kiciman, Emre}, journal={arXiv preprint arXiv:2011.04216}, year={2020} } @article{dowhy_gcm, author = {Bl{\"o}baum, Patrick and G{\"o}tz, Peter and Budhathoki, Kailash and Mastakouri, Atalanti A. and Janzing, Dominik}, title = {DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models}, journal={arXiv preprint arXiv:2206.06821}, year={2022} } Issues ~~~~~~ If you encounter an issue or have a specific request for DoWhy, please `raise an issue <https://github.com/py-why/dowhy/issues>`_. Contributing ~~~~~~~~~~~~ This project welcomes contributions and suggestions. For a guide to contributing and a list of all contributors, check out `CONTRIBUTING.md <https://github.com/py-why/dowhy/blob/main/CONTRIBUTING.md>`_ and our `docs for contributing code <https://github.com/py-why/dowhy/blob/main/docs/source/contributing/contributing-code.rst>`_. Our `contributor code of conduct is available here <https://github.com/py-why/governance/blob/main/CODE-OF-CONDUCT.md>`_.

bloebp

591e9d916e19bcad07e28687aeb8c81e1db26e8d

f455c569db0e7d0c1e1e7b54d1f8c85f026ec611

Agree, what does "modern" even mean and is also subjective to the application. I changed it to "It is based on Pearl's graphical causal model framework and models the causal data generation process of each variable explicitly via *causal mechanisms* to support a wide range of causal algorithms."

bloebp

py-why/dowhy

Overhauled readme

The current readme is too overloaded for users. Changes here are: - Reduce it only the essential points, since most of the content is part of the documentation - Add image as overview of the offered features - Made connection between GCM and PO framework more consistent - Revise the GCM example to an executable code snipped - Extended some references to include GCM related work - Fix build status icon - Changed github references to py-why (was still pointing to microsoft) Here the new readme: https://github.com/py-why/dowhy/blob/c0ac8433625b5693175629076ad32a92b0d1eb71/README.rst The image in the key features section is not show. It is the following: <img width="2887" alt="Screenshot 2023-11-28 at 08 52 50" src="https://github.com/py-why/dowhy/assets/51325689/98f5099f-6cc6-4969-b804-609d8006984d">

2023-11-26 05:56:29+00:00

2023-12-01 14:58:26+00:00

README.rst

|BuildStatus|_ |PyPiVersion|_ |PythonSupport|_ |Downloads|_ |discord|_ .. |PyPiVersion| image:: https://img.shields.io/pypi/v/dowhy.svg .. _PyPiVersion: https://pypi.org/project/dowhy/ .. |PythonSupport| image:: https://img.shields.io/pypi/pyversions/dowhy.svg .. _PythonSupport: https://pypi.org/project/dowhy/ .. |BuildStatus| image:: https://github.com/py-why/dowhy/actions/workflows/ci.yml/badge.svg .. _BuildStatus: https://github.com/py-why/dowhy/actions .. |Downloads| image:: https://pepy.tech/badge/dowhy .. _Downloads: https://pepy.tech/project/dowhy .. |discord| image:: https://img.shields.io/discord/818456847551168542 .. _discord: https://discord.gg/cSBGb3vsZb .. image:: dowhy-logo-large.png :width: 50% :align: center `Checkout the documentation <https://py-why.github.io/dowhy/>`_ =============================================================== - The documentation, user guide, sample notebooks and other information are available at `https://py-why.github.io/dowhy <https://py-why.github.io/dowhy/>`_ - DoWhy is part of the `PyWhy Ecosystem <https://www.pywhy.org/>`_. For more tools and libraries related to causality, checkout the `PyWhy GitHub organization <https://github.com/py-why/>`_! - For any questions, comments, or discussions about specific use cases, join our community on `Discord <https://discord.gg/cSBGb3vsZb>`_ (|discord|_) - Jump right into some case studies: - Effect estimation: `Hotel booking cancellations <https://towardsdatascience.com/beyond-predictive-models-the-causal-story-behind-hotel-booking-cancellations-d29e8558cbaf>`_ | `Effect of customer loyalty programs <https://github.com/microsoft/dowhy/blob/main/docs/source/example_notebooks/dowhy_example_effect_of_memberrewards_program.ipynb>`_ | `Optimizing article headlines <https://medium.com/@akelleh/introducing-the-do-sampler-for-causal-inference-a3296ea9e78d>`_ | `Effect of home visits on infant health (IHDP) <https://towardsdatascience.com/implementing-causal-inference-a-key-step-towards-agi-de2cde8ea599>`_ | `Causes of customer churn/attrition <https://medium.com/geekculture/a-quickstart-for-causal-analysis-decision-making-with-dowhy-2ce2d4d1efa9>`_ - Root cause analysis and explanations: `Root Cause Analysis with DoWhy, an Open Source Python Library for Causal Machine Learning <https://aws.amazon.com/blogs/opensource/root-cause-analysis-with-dowhy-an-open-source-python-library-for-causal-machine-learning/>`_ | `Finding the Root Cause of Elevated Latencies in a Microservice Architecture <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_rca_microservice_architecture.ipynb>`_ | `Finding Root Causes of Changes in a Supply Chain <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_supply_chain_dist_change.ipynb>`_ For more example notebooks, see `here! <https://www.pywhy.org/dowhy/main/example_notebooks/nb_index.html>`_ Introduction & Key Features =========================== Decision-making involves understanding how different variables affect each other and predicting the outcome when some of them are changed to new values. For instance, given an outcome variable, one may be interested in determining how a potential action(s) may affect it, understanding what led to its current value, or simulate what would happen if some variables are changed. Answering such questions requires causal reasoning. DoWhy is a Python library that guides you through the various steps of causal reasoning and provides a unified interface for answering causal questions. DoWhy provides a wide variety of algorithms for effect estimation, prediction, quantification of causal influences, diagnosis of causal structures, root cause analysis, interventions and counterfactuals. A key feature of DoWhy is its refutation and falsification API that can test causal assumptions for any estimation method, thus making inference more robust and accessible to non-experts. **Graphical Causal Models and Potential Outcomes: Best of both worlds** DoWhy builds on two of the most powerful frameworks for causal inference: graphical causal models and potential outcomes. For effect estimation, it uses graph-based criteria and do-calculus for modeling assumptions and identifying a non-parametric causal effect. For estimation, it switches to methods based primarily on potential outcomes. For causal questions beyond effect estimation, it uses the power of graphical causal models by modeling the data generation process via explicit causal mechanisms at each node, which, for instance, unlocks capabilities to attribute observed effects to particular variables or estimate point-wise counterfactuals. For a quick introduction to causal inference, check out `amit-sharma/causal-inference-tutorial <https://github.com/amit-sharma/causal-inference-tutorial/>`_ We also gave a more comprehensive tutorial at the ACM Knowledge Discovery and Data Mining (`KDD 2018 <http://www.kdd.org/kdd2018/>`_) conference: `causalinference.gitlab.io/kdd-tutorial <http://causalinference.gitlab.io/kdd-tutorial/>`_. For an introduction to the four steps of causal inference and its implications for machine learning, you can access this video tutorial from Microsoft Research `DoWhy Webinar <https://www.microsoft.com/en-us/research/video/foundations-of-causal-inference-and-its-impacts-on-machine-learning/>`_ and for an introduction to the graphical causal model API, see the `PyCon presentation on Root Cause Analysis with DoWhy <https://www.youtube.com/watch?v=icpHrbDlGaw>`_. Key Features ~~~~~~~~~~~~ .. image:: https://raw.githubusercontent.com/py-why/dowhy/main/docs/images/dowhy-features.png DoWhy supports the following causal tasks: - Effect estimation (identification, average causal effect, conditional average causal effect, instrumental variables and more) - Quantify causal influences (mediation analysis, direct arrow strength, intrinsic causal influence) - What-if analysis (generate samples from interventional distribution, estimate counterfactuals) - Root cause analysis and explanations (attribute anomalies to their causes, find causes for changes in distributions, estimate feature relevance and more) For more details and how to use these methods in practice, checkout the documentation at `https://py-why.github.io/dowhy <https://py-why.github.io/dowhy/>`_ Quick Start =========== DoWhy support Python 3.8+. To install, you can use pip, poetry, or conda. **Latest Release** Install the latest `release <https://pypi.org/project/dowhy/>`__ using pip. .. code:: shell pip install dowhy Install the latest `release <https://pypi.org/project/dowhy/>`__ using poetry. .. code:: shell poetry add dowhy Install the latest `release <https://anaconda.org/conda-forge/dowhy>`__ using conda. .. code:: shell conda install -c conda-forge dowhy If you face "Solving environment" problems with conda, then try :code:`conda update --all` and then install dowhy. If that does not work, then use :code:`conda config --set channel_priority false` and try to install again. If the problem persists, please `add your issue here <https://github.com/microsoft/dowhy/issues/197>`_. **Development Version** If you prefer to use the latest dev version, your dependency management tool will need to point at our GitHub repository. .. code:: shell pip install git+https://github.com/py-why/dowhy@main **Requirements** DoWhy requires a few dependencies. Details on specific versions can be found in `pyproject.toml <./pyproject.toml>`_, under the `tool.poetry.dependencies` section. If you face any problems, try installing dependencies manually. .. code:: shell pip install '<dependency-name>==<version>' Optionally, if you wish to input graphs in the dot format, then install pydot (or pygraphviz). For better-looking graphs, you can optionally install pygraphviz. To proceed, first install graphviz and then pygraphviz (on Ubuntu and Ubuntu WSL). .. code:: shell sudo apt install graphviz libgraphviz-dev graphviz-dev pkg-config ## from https://github.com/pygraphviz/pygraphviz/issues/71 pip install pygraphviz --install-option="--include-path=/usr/include/graphviz" \ --install-option="--library-path=/usr/lib/graphviz/" Example usage ~~~~~~~~~~~~~ Two examples demonstrating the effect estimation and graphical causal models API. Effect identification and estimation ++++++++++++++++++++++++++++++++++++ Most causal tasks in DoWhy only require a few lines of code to write. Here, we exemplarily estimate the causal effect of a treatment on an outcome variable: .. code:: python from dowhy import CausalModel import dowhy.datasets # Load some sample data data = dowhy.datasets.linear_dataset( beta=10, num_common_causes=5, num_instruments=2, num_samples=10000, treatment_is_binary=True) A causal graph can be defined in different way, but the most common way is via `NetworkX <https://networkx.org/>`_. After loading in the data, we use the four main operations for effect estimation in DoWhy: *model*, *identify*, *estimate* and *refute*: .. code:: python # I. Create a causal model from the data and given graph. model = CausalModel( data=data["df"], treatment=data["treatment_name"], outcome=data["outcome_name"], graph=data["gml_graph"]) # Or alternatively, as nx.DiGraph # II. Identify causal effect and return target estimands identified_estimand = model.identify_effect() # III. Estimate the target estimand using a statistical method. estimate = model.estimate_effect(identified_estimand, method_name="backdoor.propensity_score_matching") # IV. Refute the obtained estimate using multiple robustness checks. refute_results = model.refute_estimate(identified_estimand, estimate, method_name="random_common_cause") DoWhy stresses on the interpretability of its output. At any point in the analysis, you can inspect the untested assumptions, identified estimands (if any), and the estimate (if any). Here's a sample output of the linear regression estimator: .. image:: https://raw.githubusercontent.com/py-why/dowhy/main/docs/images/regression_output.png :width: 80% For a full code example, check out the `Getting Started with DoWhy <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/dowhy_simple_example.ipynb>`_ notebook. You can also use Conditional Average Treatment Effect (CATE) estimation methods from `EconML <https://github.com/py-why/econml>`_, as shown in the `Conditional Treatment Effects <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/dowhy-conditional-treatment-effects.ipynb>`_ notebook. Here's a code snippet. .. code:: python from sklearn.preprocessing import PolynomialFeatures from sklearn.linear_model import LassoCV from sklearn.ensemble import GradientBoostingRegressor dml_estimate = model.estimate_effect(identified_estimand, method_name="backdoor.econml.dml.DML", control_value = 0, treatment_value = 1, target_units = lambda df: df["X0"]>1, confidence_intervals=False, method_params={ "init_params":{'model_y':GradientBoostingRegressor(), 'model_t': GradientBoostingRegressor(), 'model_final':LassoCV(), 'featurizer':PolynomialFeatures(degree=1, include_bias=True)}, "fit_params":{}}) Graphical causal model (GCM) based inference ++++++++++++++++++++++++++++++++++++++++++++ DoWhy's graphical causal model framework offers powerful tools to address causal questions beyond effect estimation. It is based on Pearl's graphical causal model framework and models the causal data generation process of each variable explicitly via *causal mechanisms* to support a wide range of causal algorithms. For more details, see the book `Elements of Causal Inference <https://mitpress.mit.edu/9780262037310/elements-of-causal-inference/>`_. Complex causal queries, such as attributing observed anomalies to nodes in the system, can be performed with just a few lines of code: .. code:: python import networkx as nx, numpy as np, pandas as pd from dowhy import gcm # Let's generate some "normal" data we assume we're given from our problem domain: X = np.random.normal(loc=0, scale=1, size=1000) Y = 2 * X + np.random.normal(loc=0, scale=1, size=1000) Z = 3 * Y + np.random.normal(loc=0, scale=1, size=1000) data = pd.DataFrame(dict(X=X, Y=Y, Z=Z)) # 1. Modeling cause-effect relationships as a structural causal model # (causal graph + functional causal models): causal_model = gcm.StructuralCausalModel(nx.DiGraph([('X', 'Y'), ('Y', 'Z')])) # X -> Y -> Z gcm.auto.assign_causal_mechanisms(causal_model, data) # 2. Fitting the SCM to the data: gcm.fit(causal_model, data) # Optional: Evaluate causal model print(gcm.evaluate_causal_model(causal_model, data)) # Step 3: Perform a causal analysis. # results = gcm.<causal_query>(causal_model, ...) # For instance, root cause analysis: anomalous_sample = pd.DataFrame(dict(X=[0.1], Y=[6.2], Z=[19])) # Here, Y is the root cause. # "Which node is the root cause of the anomaly in Z?": anomaly_attribution = gcm.attribute_anomalies(causal_model, "Z", anomalous_sample) # Or sampling from an interventional distribution. Here, under the intervention do(Y := 2). samples = gcm.interventional_samples(causal_model, interventions={'Y': lambda y: 2}, num_samples_to_draw=100) The GCM framework offers many more features beyond these examples. For a full code example, check out the `Online Shop example notebook <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_online_shop.ipynb>`_. For more functionalities, example applications of DoWhy and details about the outputs, see the `User Guide <https://www.pywhy.org/dowhy/main/user_guide/intro.html>`_ or checkout `Jupyter notebooks <https://www.pywhy.org/dowhy/main/example_notebooks/nb_index.html>`_. More Information & Resources ============================ `Microsoft Research Blog <https://www.microsoft.com/en-us/research/blog/dowhy-a-library-for-causal-inference/>`_ | `Video Tutorial for Effect Estimation <https://www.youtube.com/watch?v=LALfQStONEc&t=114s>`_ | `Video Tutorial for Root Cause Analysis <https://www.youtube.com/watch?v=icpHrbDlGaw>`_ | `Arxiv Paper <https://arxiv.org/abs/2011.04216>`_ | `Arxiv Paper (Graphical Causal Model extension) <https://arxiv.org/abs/2206.06821>`_ | `Slides <https://www2.slideshare.net/AmitSharma315/dowhy-an-endtoend-library-for-causal-inference>`_ Citing this package ~~~~~~~~~~~~~~~~~~~ If you find DoWhy useful for your work, please cite **both** of the following two references: - Amit Sharma, Emre Kiciman. DoWhy: An End-to-End Library for Causal Inference. 2020. https://arxiv.org/abs/2011.04216 - Patrick Blöbaum, Peter Götz, Kailash Budhathoki, Atalanti A. Mastakouri, Dominik Janzing. DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models. 2022. https://arxiv.org/abs/2206.06821 Bibtex:: @article{dowhy, title={DoWhy: An End-to-End Library for Causal Inference}, author={Sharma, Amit and Kiciman, Emre}, journal={arXiv preprint arXiv:2011.04216}, year={2020} } @article{dowhy_gcm, author = {Bl{\"o}baum, Patrick and G{\"o}tz, Peter and Budhathoki, Kailash and Mastakouri, Atalanti A. and Janzing, Dominik}, title = {DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models}, journal={arXiv preprint arXiv:2206.06821}, year={2022} } Issues ~~~~~~ If you encounter an issue or have a specific request for DoWhy, please `raise an issue <https://github.com/py-why/dowhy/issues>`_. Contributing ~~~~~~~~~~~~ This project welcomes contributions and suggestions. For a guide to contributing and a list of all contributors, check out `CONTRIBUTING.md <https://github.com/py-why/dowhy/blob/main/CONTRIBUTING.md>`_ and our `docs for contributing code <https://github.com/py-why/dowhy/blob/main/docs/source/contributing/contributing-code.rst>`_. Our `contributor code of conduct is available here <https://github.com/py-why/governance/blob/main/CODE-OF-CONDUCT.md>`_.

|BuildStatus|_ |PyPiVersion|_ |PythonSupport|_ |Downloads|_ |discord|_ .. |PyPiVersion| image:: https://img.shields.io/pypi/v/dowhy.svg .. _PyPiVersion: https://pypi.org/project/dowhy/ .. |PythonSupport| image:: https://img.shields.io/pypi/pyversions/dowhy.svg .. _PythonSupport: https://pypi.org/project/dowhy/ .. |BuildStatus| image:: https://github.com/py-why/dowhy/actions/workflows/ci.yml/badge.svg .. _BuildStatus: https://github.com/py-why/dowhy/actions .. |Downloads| image:: https://pepy.tech/badge/dowhy .. _Downloads: https://pepy.tech/project/dowhy .. |discord| image:: https://img.shields.io/discord/818456847551168542 .. _discord: https://discord.gg/cSBGb3vsZb .. image:: dowhy-logo-large.png :width: 50% :align: center `Checkout the documentation <https://py-why.github.io/dowhy/>`_ =============================================================== - The documentation, user guide, sample notebooks and other information are available at `https://py-why.github.io/dowhy <https://py-why.github.io/dowhy/>`_ - DoWhy is part of the `PyWhy Ecosystem <https://www.pywhy.org/>`_. For more tools and libraries related to causality, checkout the `PyWhy GitHub organization <https://github.com/py-why/>`_! - For any questions, comments, or discussions about specific use cases, join our community on `Discord <https://discord.gg/cSBGb3vsZb>`_ (|discord|_) - Jump right into some case studies: - Effect estimation: `Hotel booking cancellations <https://towardsdatascience.com/beyond-predictive-models-the-causal-story-behind-hotel-booking-cancellations-d29e8558cbaf>`_ | `Effect of customer loyalty programs <https://github.com/microsoft/dowhy/blob/main/docs/source/example_notebooks/dowhy_example_effect_of_memberrewards_program.ipynb>`_ | `Optimizing article headlines <https://medium.com/@akelleh/introducing-the-do-sampler-for-causal-inference-a3296ea9e78d>`_ | `Effect of home visits on infant health (IHDP) <https://towardsdatascience.com/implementing-causal-inference-a-key-step-towards-agi-de2cde8ea599>`_ | `Causes of customer churn/attrition <https://medium.com/geekculture/a-quickstart-for-causal-analysis-decision-making-with-dowhy-2ce2d4d1efa9>`_ - Root cause analysis and explanations: `Root Cause Analysis with DoWhy, an Open Source Python Library for Causal Machine Learning <https://aws.amazon.com/blogs/opensource/root-cause-analysis-with-dowhy-an-open-source-python-library-for-causal-machine-learning/>`_ | `Finding the Root Cause of Elevated Latencies in a Microservice Architecture <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_rca_microservice_architecture.ipynb>`_ | `Finding Root Causes of Changes in a Supply Chain <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_supply_chain_dist_change.ipynb>`_ For more example notebooks, see `here! <https://www.pywhy.org/dowhy/main/example_notebooks/nb_index.html>`_ Introduction & Key Features =========================== Decision-making involves understanding how different variables affect each other and predicting the outcome when some of them are changed to new values. For instance, given an outcome variable, one may be interested in determining how a potential action(s) may affect it, understanding what led to its current value, or simulate what would happen if some variables are changed. Answering such questions requires causal reasoning. DoWhy is a Python library that guides you through the various steps of causal reasoning and provides a unified interface for answering causal questions. DoWhy provides a wide variety of algorithms for effect estimation, prediction, quantification of causal influences, diagnosis of causal structures, root cause analysis, interventions and counterfactuals. A key feature of DoWhy is its refutation and falsification API that can test causal assumptions for any estimation method, thus making inference more robust and accessible to non-experts. **Graphical Causal Models and Potential Outcomes: Best of both worlds** DoWhy builds on two of the most powerful frameworks for causal inference: graphical causal models and potential outcomes. For effect estimation, it uses graph-based criteria and do-calculus for modeling assumptions and identifying a non-parametric causal effect. For estimation, it switches to methods based primarily on potential outcomes. For causal questions beyond effect estimation, it uses the power of graphical causal models by modeling the data generation process via explicit causal mechanisms at each node, which, for instance, unlocks capabilities to attribute observed effects to particular variables or estimate point-wise counterfactuals. For a quick introduction to causal inference, check out `amit-sharma/causal-inference-tutorial <https://github.com/amit-sharma/causal-inference-tutorial/>`_ We also gave a more comprehensive tutorial at the ACM Knowledge Discovery and Data Mining (`KDD 2018 <http://www.kdd.org/kdd2018/>`_) conference: `causalinference.gitlab.io/kdd-tutorial <http://causalinference.gitlab.io/kdd-tutorial/>`_. For an introduction to the four steps of causal inference and its implications for machine learning, you can access this video tutorial from Microsoft Research `DoWhy Webinar <https://www.microsoft.com/en-us/research/video/foundations-of-causal-inference-and-its-impacts-on-machine-learning/>`_ and for an introduction to the graphical causal model API, see the `PyCon presentation on Root Cause Analysis with DoWhy <https://www.youtube.com/watch?v=icpHrbDlGaw>`_. Key Features ~~~~~~~~~~~~ .. image:: https://raw.githubusercontent.com/py-why/dowhy/main/docs/images/dowhy-features.png DoWhy supports the following causal tasks: - Effect estimation (identification, average causal effect, conditional average causal effect, instrumental variables and more) - Quantify causal influences (mediation analysis, direct arrow strength, intrinsic causal influence) - What-if analysis (generate samples from interventional distribution, estimate counterfactuals) - Root cause analysis and explanations (attribute anomalies to their causes, find causes for changes in distributions, estimate feature relevance and more) For more details and how to use these methods in practice, checkout the documentation at `https://py-why.github.io/dowhy <https://py-why.github.io/dowhy/>`_ Quick Start =========== DoWhy support Python 3.8+. To install, you can use pip, poetry, or conda. **Latest Release** Install the latest `release <https://pypi.org/project/dowhy/>`__ using pip. .. code:: shell pip install dowhy Install the latest `release <https://pypi.org/project/dowhy/>`__ using poetry. .. code:: shell poetry add dowhy Install the latest `release <https://anaconda.org/conda-forge/dowhy>`__ using conda. .. code:: shell conda install -c conda-forge dowhy If you face "Solving environment" problems with conda, then try :code:`conda update --all` and then install dowhy. If that does not work, then use :code:`conda config --set channel_priority false` and try to install again. If the problem persists, please `add your issue here <https://github.com/microsoft/dowhy/issues/197>`_. **Development Version** If you prefer to use the latest dev version, your dependency management tool will need to point at our GitHub repository. .. code:: shell pip install git+https://github.com/py-why/dowhy@main **Requirements** DoWhy requires a few dependencies. Details on specific versions can be found in `pyproject.toml <./pyproject.toml>`_, under the `tool.poetry.dependencies` section. If you face any problems, try installing dependencies manually. .. code:: shell pip install '<dependency-name>==<version>' Optionally, if you wish to input graphs in the dot format, then install pydot (or pygraphviz). For better-looking graphs, you can optionally install pygraphviz. To proceed, first install graphviz and then pygraphviz (on Ubuntu and Ubuntu WSL). .. code:: shell sudo apt install graphviz libgraphviz-dev graphviz-dev pkg-config ## from https://github.com/pygraphviz/pygraphviz/issues/71 pip install pygraphviz --install-option="--include-path=/usr/include/graphviz" \ --install-option="--library-path=/usr/lib/graphviz/" Example: Effect identification and estimation ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Most causal tasks in DoWhy only require a few lines of code to write. Here, we exemplarily estimate the causal effect of a treatment on an outcome variable: .. code:: python from dowhy import CausalModel import dowhy.datasets # Load some sample data data = dowhy.datasets.linear_dataset( beta=10, num_common_causes=5, num_instruments=2, num_samples=10000, treatment_is_binary=True) A causal graph can be defined in different way, but the most common way is via `NetworkX <https://networkx.org/>`_. After loading in the data, we use the four main operations for effect estimation in DoWhy: *model*, *identify*, *estimate* and *refute*: .. code:: python # I. Create a causal model from the data and given graph. model = CausalModel( data=data["df"], treatment=data["treatment_name"], outcome=data["outcome_name"], graph=data["gml_graph"]) # Or alternatively, as nx.DiGraph # II. Identify causal effect and return target estimands identified_estimand = model.identify_effect() # III. Estimate the target estimand using a statistical method. estimate = model.estimate_effect(identified_estimand, method_name="backdoor.propensity_score_matching") # IV. Refute the obtained estimate using multiple robustness checks. refute_results = model.refute_estimate(identified_estimand, estimate, method_name="random_common_cause") DoWhy stresses on the interpretability of its output. At any point in the analysis, you can inspect the untested assumptions, identified estimands (if any), and the estimate (if any). Here's a sample output of the linear regression estimator: .. image:: https://raw.githubusercontent.com/py-why/dowhy/main/docs/images/regression_output.png :width: 80% For a full code example, check out the `Getting Started with DoWhy <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/dowhy_simple_example.ipynb>`_ notebook. You can also use Conditional Average Treatment Effect (CATE) estimation methods from `EconML <https://github.com/py-why/econml>`_, as shown in the `Conditional Treatment Effects <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/dowhy-conditional-treatment-effects.ipynb>`_ notebook. Here's a code snippet. .. code:: python from sklearn.preprocessing import PolynomialFeatures from sklearn.linear_model import LassoCV from sklearn.ensemble import GradientBoostingRegressor dml_estimate = model.estimate_effect(identified_estimand, method_name="backdoor.econml.dml.DML", control_value = 0, treatment_value = 1, target_units = lambda df: df["X0"]>1, confidence_intervals=False, method_params={ "init_params":{'model_y':GradientBoostingRegressor(), 'model_t': GradientBoostingRegressor(), 'model_final':LassoCV(), 'featurizer':PolynomialFeatures(degree=1, include_bias=True)}, "fit_params":{}}) Example: Graphical causal model (GCM) based inference ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DoWhy's graphical causal model framework offers powerful tools to address causal questions beyond effect estimation. It is based on Pearl's graphical causal model framework and models the causal data generation process of each variable explicitly via *causal mechanisms* to support a wide range of causal algorithms. For more details, see the book `Elements of Causal Inference <https://mitpress.mit.edu/9780262037310/elements-of-causal-inference/>`_. Complex causal queries, such as attributing observed anomalies to nodes in the system, can be performed with just a few lines of code: .. code:: python import networkx as nx, numpy as np, pandas as pd from dowhy import gcm # Let's generate some "normal" data we assume we're given from our problem domain: X = np.random.normal(loc=0, scale=1, size=1000) Y = 2 * X + np.random.normal(loc=0, scale=1, size=1000) Z = 3 * Y + np.random.normal(loc=0, scale=1, size=1000) data = pd.DataFrame(dict(X=X, Y=Y, Z=Z)) # 1. Modeling cause-effect relationships as a structural causal model # (causal graph + functional causal models): causal_model = gcm.StructuralCausalModel(nx.DiGraph([('X', 'Y'), ('Y', 'Z')])) # X -> Y -> Z gcm.auto.assign_causal_mechanisms(causal_model, data) # 2. Fitting the SCM to the data: gcm.fit(causal_model, data) # Optional: Evaluate causal model print(gcm.evaluate_causal_model(causal_model, data)) # Step 3: Perform a causal analysis. # results = gcm.<causal_query>(causal_model, ...) # For instance, root cause analysis: anomalous_sample = pd.DataFrame(dict(X=[0.1], Y=[6.2], Z=[19])) # Here, Y is the root cause. # "Which node is the root cause of the anomaly in Z?": anomaly_attribution = gcm.attribute_anomalies(causal_model, "Z", anomalous_sample) # Or sampling from an interventional distribution. Here, under the intervention do(Y := 2). samples = gcm.interventional_samples(causal_model, interventions={'Y': lambda y: 2}, num_samples_to_draw=100) The GCM framework offers many more features beyond these examples. For a full code example, check out the `Online Shop example notebook <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_online_shop.ipynb>`_. For more functionalities, example applications of DoWhy and details about the outputs, see the `User Guide <https://www.pywhy.org/dowhy/main/user_guide/intro.html>`_ or checkout `Jupyter notebooks <https://www.pywhy.org/dowhy/main/example_notebooks/nb_index.html>`_. More Information & Resources ============================ `Microsoft Research Blog <https://www.microsoft.com/en-us/research/blog/dowhy-a-library-for-causal-inference/>`_ | `Video Tutorial for Effect Estimation <https://www.youtube.com/watch?v=LALfQStONEc&t=114s>`_ | `Video Tutorial for Root Cause Analysis <https://www.youtube.com/watch?v=icpHrbDlGaw>`_ | `Arxiv Paper <https://arxiv.org/abs/2011.04216>`_ | `Arxiv Paper (Graphical Causal Model extension) <https://arxiv.org/abs/2206.06821>`_ | `Slides <https://www2.slideshare.net/AmitSharma315/dowhy-an-endtoend-library-for-causal-inference>`_ Citing this package ~~~~~~~~~~~~~~~~~~~ If you find DoWhy useful for your work, please cite **both** of the following two references: - Amit Sharma, Emre Kiciman. DoWhy: An End-to-End Library for Causal Inference. 2020. https://arxiv.org/abs/2011.04216 - Patrick Blöbaum, Peter Götz, Kailash Budhathoki, Atalanti A. Mastakouri, Dominik Janzing. DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models. 2022. https://arxiv.org/abs/2206.06821 Bibtex:: @article{dowhy, title={DoWhy: An End-to-End Library for Causal Inference}, author={Sharma, Amit and Kiciman, Emre}, journal={arXiv preprint arXiv:2011.04216}, year={2020} } @article{dowhy_gcm, author = {Bl{\"o}baum, Patrick and G{\"o}tz, Peter and Budhathoki, Kailash and Mastakouri, Atalanti A. and Janzing, Dominik}, title = {DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models}, journal={arXiv preprint arXiv:2206.06821}, year={2022} } Issues ~~~~~~ If you encounter an issue or have a specific request for DoWhy, please `raise an issue <https://github.com/py-why/dowhy/issues>`_. Contributing ~~~~~~~~~~~~ This project welcomes contributions and suggestions. For a guide to contributing and a list of all contributors, check out `CONTRIBUTING.md <https://github.com/py-why/dowhy/blob/main/CONTRIBUTING.md>`_ and our `docs for contributing code <https://github.com/py-why/dowhy/blob/main/docs/source/contributing/contributing-code.rst>`_. Our `contributor code of conduct is available here <https://github.com/py-why/governance/blob/main/CODE-OF-CONDUCT.md>`_.

bloebp

591e9d916e19bcad07e28687aeb8c81e1db26e8d

f455c569db0e7d0c1e1e7b54d1f8c85f026ec611

conda issue is now resolved: https://github.com/conda-forge/dowhy-feedstock/pull/10

amit-sharma

py-why/dowhy

Overhauled readme

The current readme is too overloaded for users. Changes here are: - Reduce it only the essential points, since most of the content is part of the documentation - Add image as overview of the offered features - Made connection between GCM and PO framework more consistent - Revise the GCM example to an executable code snipped - Extended some references to include GCM related work - Fix build status icon - Changed github references to py-why (was still pointing to microsoft) Here the new readme: https://github.com/py-why/dowhy/blob/c0ac8433625b5693175629076ad32a92b0d1eb71/README.rst The image in the key features section is not show. It is the following: <img width="2887" alt="Screenshot 2023-11-28 at 08 52 50" src="https://github.com/py-why/dowhy/assets/51325689/98f5099f-6cc6-4969-b804-609d8006984d">

2023-11-26 05:56:29+00:00

2023-12-01 14:58:26+00:00

README.rst

|BuildStatus|_ |PyPiVersion|_ |PythonSupport|_ |Downloads|_ |discord|_ .. |PyPiVersion| image:: https://img.shields.io/pypi/v/dowhy.svg .. _PyPiVersion: https://pypi.org/project/dowhy/ .. |PythonSupport| image:: https://img.shields.io/pypi/pyversions/dowhy.svg .. _PythonSupport: https://pypi.org/project/dowhy/ .. |BuildStatus| image:: https://github.com/py-why/dowhy/actions/workflows/ci.yml/badge.svg .. _BuildStatus: https://github.com/py-why/dowhy/actions .. |Downloads| image:: https://pepy.tech/badge/dowhy .. _Downloads: https://pepy.tech/project/dowhy .. |discord| image:: https://img.shields.io/discord/818456847551168542 .. _discord: https://discord.gg/cSBGb3vsZb .. image:: dowhy-logo-large.png :width: 50% :align: center `Checkout the documentation <https://py-why.github.io/dowhy/>`_ =============================================================== - The documentation, user guide, sample notebooks and other information are available at `https://py-why.github.io/dowhy <https://py-why.github.io/dowhy/>`_ - DoWhy is part of the `PyWhy Ecosystem <https://www.pywhy.org/>`_. For more tools and libraries related to causality, checkout the `PyWhy GitHub organization <https://github.com/py-why/>`_! - For any questions, comments, or discussions about specific use cases, join our community on `Discord <https://discord.gg/cSBGb3vsZb>`_ (|discord|_) - Jump right into some case studies: - Effect estimation: `Hotel booking cancellations <https://towardsdatascience.com/beyond-predictive-models-the-causal-story-behind-hotel-booking-cancellations-d29e8558cbaf>`_ | `Effect of customer loyalty programs <https://github.com/microsoft/dowhy/blob/main/docs/source/example_notebooks/dowhy_example_effect_of_memberrewards_program.ipynb>`_ | `Optimizing article headlines <https://medium.com/@akelleh/introducing-the-do-sampler-for-causal-inference-a3296ea9e78d>`_ | `Effect of home visits on infant health (IHDP) <https://towardsdatascience.com/implementing-causal-inference-a-key-step-towards-agi-de2cde8ea599>`_ | `Causes of customer churn/attrition <https://medium.com/geekculture/a-quickstart-for-causal-analysis-decision-making-with-dowhy-2ce2d4d1efa9>`_ - Root cause analysis and explanations: `Root Cause Analysis with DoWhy, an Open Source Python Library for Causal Machine Learning <https://aws.amazon.com/blogs/opensource/root-cause-analysis-with-dowhy-an-open-source-python-library-for-causal-machine-learning/>`_ | `Finding the Root Cause of Elevated Latencies in a Microservice Architecture <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_rca_microservice_architecture.ipynb>`_ | `Finding Root Causes of Changes in a Supply Chain <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_supply_chain_dist_change.ipynb>`_ For more example notebooks, see `here! <https://www.pywhy.org/dowhy/main/example_notebooks/nb_index.html>`_ Introduction & Key Features =========================== Decision-making involves understanding how different variables affect each other and predicting the outcome when some of them are changed to new values. For instance, given an outcome variable, one may be interested in determining how a potential action(s) may affect it, understanding what led to its current value, or simulate what would happen if some variables are changed. Answering such questions requires causal reasoning. DoWhy is a Python library that guides you through the various steps of causal reasoning and provides a unified interface for answering causal questions. DoWhy provides a wide variety of algorithms for effect estimation, prediction, quantification of causal influences, diagnosis of causal structures, root cause analysis, interventions and counterfactuals. A key feature of DoWhy is its refutation and falsification API that can test causal assumptions for any estimation method, thus making inference more robust and accessible to non-experts. **Graphical Causal Models and Potential Outcomes: Best of both worlds** DoWhy builds on two of the most powerful frameworks for causal inference: graphical causal models and potential outcomes. For effect estimation, it uses graph-based criteria and do-calculus for modeling assumptions and identifying a non-parametric causal effect. For estimation, it switches to methods based primarily on potential outcomes. For causal questions beyond effect estimation, it uses the power of graphical causal models by modeling the data generation process via explicit causal mechanisms at each node, which, for instance, unlocks capabilities to attribute observed effects to particular variables or estimate point-wise counterfactuals. For a quick introduction to causal inference, check out `amit-sharma/causal-inference-tutorial <https://github.com/amit-sharma/causal-inference-tutorial/>`_ We also gave a more comprehensive tutorial at the ACM Knowledge Discovery and Data Mining (`KDD 2018 <http://www.kdd.org/kdd2018/>`_) conference: `causalinference.gitlab.io/kdd-tutorial <http://causalinference.gitlab.io/kdd-tutorial/>`_. For an introduction to the four steps of causal inference and its implications for machine learning, you can access this video tutorial from Microsoft Research `DoWhy Webinar <https://www.microsoft.com/en-us/research/video/foundations-of-causal-inference-and-its-impacts-on-machine-learning/>`_ and for an introduction to the graphical causal model API, see the `PyCon presentation on Root Cause Analysis with DoWhy <https://www.youtube.com/watch?v=icpHrbDlGaw>`_. Key Features ~~~~~~~~~~~~ .. image:: https://raw.githubusercontent.com/py-why/dowhy/main/docs/images/dowhy-features.png DoWhy supports the following causal tasks: - Effect estimation (identification, average causal effect, conditional average causal effect, instrumental variables and more) - Quantify causal influences (mediation analysis, direct arrow strength, intrinsic causal influence) - What-if analysis (generate samples from interventional distribution, estimate counterfactuals) - Root cause analysis and explanations (attribute anomalies to their causes, find causes for changes in distributions, estimate feature relevance and more) For more details and how to use these methods in practice, checkout the documentation at `https://py-why.github.io/dowhy <https://py-why.github.io/dowhy/>`_ Quick Start =========== DoWhy support Python 3.8+. To install, you can use pip, poetry, or conda. **Latest Release** Install the latest `release <https://pypi.org/project/dowhy/>`__ using pip. .. code:: shell pip install dowhy Install the latest `release <https://pypi.org/project/dowhy/>`__ using poetry. .. code:: shell poetry add dowhy Install the latest `release <https://anaconda.org/conda-forge/dowhy>`__ using conda. .. code:: shell conda install -c conda-forge dowhy If you face "Solving environment" problems with conda, then try :code:`conda update --all` and then install dowhy. If that does not work, then use :code:`conda config --set channel_priority false` and try to install again. If the problem persists, please `add your issue here <https://github.com/microsoft/dowhy/issues/197>`_. **Development Version** If you prefer to use the latest dev version, your dependency management tool will need to point at our GitHub repository. .. code:: shell pip install git+https://github.com/py-why/dowhy@main **Requirements** DoWhy requires a few dependencies. Details on specific versions can be found in `pyproject.toml <./pyproject.toml>`_, under the `tool.poetry.dependencies` section. If you face any problems, try installing dependencies manually. .. code:: shell pip install '<dependency-name>==<version>' Optionally, if you wish to input graphs in the dot format, then install pydot (or pygraphviz). For better-looking graphs, you can optionally install pygraphviz. To proceed, first install graphviz and then pygraphviz (on Ubuntu and Ubuntu WSL). .. code:: shell sudo apt install graphviz libgraphviz-dev graphviz-dev pkg-config ## from https://github.com/pygraphviz/pygraphviz/issues/71 pip install pygraphviz --install-option="--include-path=/usr/include/graphviz" \ --install-option="--library-path=/usr/lib/graphviz/" Example usage ~~~~~~~~~~~~~ Two examples demonstrating the effect estimation and graphical causal models API. Effect identification and estimation ++++++++++++++++++++++++++++++++++++ Most causal tasks in DoWhy only require a few lines of code to write. Here, we exemplarily estimate the causal effect of a treatment on an outcome variable: .. code:: python from dowhy import CausalModel import dowhy.datasets # Load some sample data data = dowhy.datasets.linear_dataset( beta=10, num_common_causes=5, num_instruments=2, num_samples=10000, treatment_is_binary=True) A causal graph can be defined in different way, but the most common way is via `NetworkX <https://networkx.org/>`_. After loading in the data, we use the four main operations for effect estimation in DoWhy: *model*, *identify*, *estimate* and *refute*: .. code:: python # I. Create a causal model from the data and given graph. model = CausalModel( data=data["df"], treatment=data["treatment_name"], outcome=data["outcome_name"], graph=data["gml_graph"]) # Or alternatively, as nx.DiGraph # II. Identify causal effect and return target estimands identified_estimand = model.identify_effect() # III. Estimate the target estimand using a statistical method. estimate = model.estimate_effect(identified_estimand, method_name="backdoor.propensity_score_matching") # IV. Refute the obtained estimate using multiple robustness checks. refute_results = model.refute_estimate(identified_estimand, estimate, method_name="random_common_cause") DoWhy stresses on the interpretability of its output. At any point in the analysis, you can inspect the untested assumptions, identified estimands (if any), and the estimate (if any). Here's a sample output of the linear regression estimator: .. image:: https://raw.githubusercontent.com/py-why/dowhy/main/docs/images/regression_output.png :width: 80% For a full code example, check out the `Getting Started with DoWhy <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/dowhy_simple_example.ipynb>`_ notebook. You can also use Conditional Average Treatment Effect (CATE) estimation methods from `EconML <https://github.com/py-why/econml>`_, as shown in the `Conditional Treatment Effects <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/dowhy-conditional-treatment-effects.ipynb>`_ notebook. Here's a code snippet. .. code:: python from sklearn.preprocessing import PolynomialFeatures from sklearn.linear_model import LassoCV from sklearn.ensemble import GradientBoostingRegressor dml_estimate = model.estimate_effect(identified_estimand, method_name="backdoor.econml.dml.DML", control_value = 0, treatment_value = 1, target_units = lambda df: df["X0"]>1, confidence_intervals=False, method_params={ "init_params":{'model_y':GradientBoostingRegressor(), 'model_t': GradientBoostingRegressor(), 'model_final':LassoCV(), 'featurizer':PolynomialFeatures(degree=1, include_bias=True)}, "fit_params":{}}) Graphical causal model (GCM) based inference ++++++++++++++++++++++++++++++++++++++++++++ DoWhy's graphical causal model framework offers powerful tools to address causal questions beyond effect estimation. It is based on Pearl's graphical causal model framework and models the causal data generation process of each variable explicitly via *causal mechanisms* to support a wide range of causal algorithms. For more details, see the book `Elements of Causal Inference <https://mitpress.mit.edu/9780262037310/elements-of-causal-inference/>`_. Complex causal queries, such as attributing observed anomalies to nodes in the system, can be performed with just a few lines of code: .. code:: python import networkx as nx, numpy as np, pandas as pd from dowhy import gcm # Let's generate some "normal" data we assume we're given from our problem domain: X = np.random.normal(loc=0, scale=1, size=1000) Y = 2 * X + np.random.normal(loc=0, scale=1, size=1000) Z = 3 * Y + np.random.normal(loc=0, scale=1, size=1000) data = pd.DataFrame(dict(X=X, Y=Y, Z=Z)) # 1. Modeling cause-effect relationships as a structural causal model # (causal graph + functional causal models): causal_model = gcm.StructuralCausalModel(nx.DiGraph([('X', 'Y'), ('Y', 'Z')])) # X -> Y -> Z gcm.auto.assign_causal_mechanisms(causal_model, data) # 2. Fitting the SCM to the data: gcm.fit(causal_model, data) # Optional: Evaluate causal model print(gcm.evaluate_causal_model(causal_model, data)) # Step 3: Perform a causal analysis. # results = gcm.<causal_query>(causal_model, ...) # For instance, root cause analysis: anomalous_sample = pd.DataFrame(dict(X=[0.1], Y=[6.2], Z=[19])) # Here, Y is the root cause. # "Which node is the root cause of the anomaly in Z?": anomaly_attribution = gcm.attribute_anomalies(causal_model, "Z", anomalous_sample) # Or sampling from an interventional distribution. Here, under the intervention do(Y := 2). samples = gcm.interventional_samples(causal_model, interventions={'Y': lambda y: 2}, num_samples_to_draw=100) The GCM framework offers many more features beyond these examples. For a full code example, check out the `Online Shop example notebook <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_online_shop.ipynb>`_. For more functionalities, example applications of DoWhy and details about the outputs, see the `User Guide <https://www.pywhy.org/dowhy/main/user_guide/intro.html>`_ or checkout `Jupyter notebooks <https://www.pywhy.org/dowhy/main/example_notebooks/nb_index.html>`_. More Information & Resources ============================ `Microsoft Research Blog <https://www.microsoft.com/en-us/research/blog/dowhy-a-library-for-causal-inference/>`_ | `Video Tutorial for Effect Estimation <https://www.youtube.com/watch?v=LALfQStONEc&t=114s>`_ | `Video Tutorial for Root Cause Analysis <https://www.youtube.com/watch?v=icpHrbDlGaw>`_ | `Arxiv Paper <https://arxiv.org/abs/2011.04216>`_ | `Arxiv Paper (Graphical Causal Model extension) <https://arxiv.org/abs/2206.06821>`_ | `Slides <https://www2.slideshare.net/AmitSharma315/dowhy-an-endtoend-library-for-causal-inference>`_ Citing this package ~~~~~~~~~~~~~~~~~~~ If you find DoWhy useful for your work, please cite **both** of the following two references: - Amit Sharma, Emre Kiciman. DoWhy: An End-to-End Library for Causal Inference. 2020. https://arxiv.org/abs/2011.04216 - Patrick Blöbaum, Peter Götz, Kailash Budhathoki, Atalanti A. Mastakouri, Dominik Janzing. DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models. 2022. https://arxiv.org/abs/2206.06821 Bibtex:: @article{dowhy, title={DoWhy: An End-to-End Library for Causal Inference}, author={Sharma, Amit and Kiciman, Emre}, journal={arXiv preprint arXiv:2011.04216}, year={2020} } @article{dowhy_gcm, author = {Bl{\"o}baum, Patrick and G{\"o}tz, Peter and Budhathoki, Kailash and Mastakouri, Atalanti A. and Janzing, Dominik}, title = {DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models}, journal={arXiv preprint arXiv:2206.06821}, year={2022} } Issues ~~~~~~ If you encounter an issue or have a specific request for DoWhy, please `raise an issue <https://github.com/py-why/dowhy/issues>`_. Contributing ~~~~~~~~~~~~ This project welcomes contributions and suggestions. For a guide to contributing and a list of all contributors, check out `CONTRIBUTING.md <https://github.com/py-why/dowhy/blob/main/CONTRIBUTING.md>`_ and our `docs for contributing code <https://github.com/py-why/dowhy/blob/main/docs/source/contributing/contributing-code.rst>`_. Our `contributor code of conduct is available here <https://github.com/py-why/governance/blob/main/CODE-OF-CONDUCT.md>`_.

|BuildStatus|_ |PyPiVersion|_ |PythonSupport|_ |Downloads|_ |discord|_ .. |PyPiVersion| image:: https://img.shields.io/pypi/v/dowhy.svg .. _PyPiVersion: https://pypi.org/project/dowhy/ .. |PythonSupport| image:: https://img.shields.io/pypi/pyversions/dowhy.svg .. _PythonSupport: https://pypi.org/project/dowhy/ .. |BuildStatus| image:: https://github.com/py-why/dowhy/actions/workflows/ci.yml/badge.svg .. _BuildStatus: https://github.com/py-why/dowhy/actions .. |Downloads| image:: https://pepy.tech/badge/dowhy .. _Downloads: https://pepy.tech/project/dowhy .. |discord| image:: https://img.shields.io/discord/818456847551168542 .. _discord: https://discord.gg/cSBGb3vsZb .. image:: dowhy-logo-large.png :width: 50% :align: center `Checkout the documentation <https://py-why.github.io/dowhy/>`_ =============================================================== - The documentation, user guide, sample notebooks and other information are available at `https://py-why.github.io/dowhy <https://py-why.github.io/dowhy/>`_ - DoWhy is part of the `PyWhy Ecosystem <https://www.pywhy.org/>`_. For more tools and libraries related to causality, checkout the `PyWhy GitHub organization <https://github.com/py-why/>`_! - For any questions, comments, or discussions about specific use cases, join our community on `Discord <https://discord.gg/cSBGb3vsZb>`_ (|discord|_) - Jump right into some case studies: - Effect estimation: `Hotel booking cancellations <https://towardsdatascience.com/beyond-predictive-models-the-causal-story-behind-hotel-booking-cancellations-d29e8558cbaf>`_ | `Effect of customer loyalty programs <https://github.com/microsoft/dowhy/blob/main/docs/source/example_notebooks/dowhy_example_effect_of_memberrewards_program.ipynb>`_ | `Optimizing article headlines <https://medium.com/@akelleh/introducing-the-do-sampler-for-causal-inference-a3296ea9e78d>`_ | `Effect of home visits on infant health (IHDP) <https://towardsdatascience.com/implementing-causal-inference-a-key-step-towards-agi-de2cde8ea599>`_ | `Causes of customer churn/attrition <https://medium.com/geekculture/a-quickstart-for-causal-analysis-decision-making-with-dowhy-2ce2d4d1efa9>`_ - Root cause analysis and explanations: `Root Cause Analysis with DoWhy, an Open Source Python Library for Causal Machine Learning <https://aws.amazon.com/blogs/opensource/root-cause-analysis-with-dowhy-an-open-source-python-library-for-causal-machine-learning/>`_ | `Finding the Root Cause of Elevated Latencies in a Microservice Architecture <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_rca_microservice_architecture.ipynb>`_ | `Finding Root Causes of Changes in a Supply Chain <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_supply_chain_dist_change.ipynb>`_ For more example notebooks, see `here! <https://www.pywhy.org/dowhy/main/example_notebooks/nb_index.html>`_ Introduction & Key Features =========================== Decision-making involves understanding how different variables affect each other and predicting the outcome when some of them are changed to new values. For instance, given an outcome variable, one may be interested in determining how a potential action(s) may affect it, understanding what led to its current value, or simulate what would happen if some variables are changed. Answering such questions requires causal reasoning. DoWhy is a Python library that guides you through the various steps of causal reasoning and provides a unified interface for answering causal questions. DoWhy provides a wide variety of algorithms for effect estimation, prediction, quantification of causal influences, diagnosis of causal structures, root cause analysis, interventions and counterfactuals. A key feature of DoWhy is its refutation and falsification API that can test causal assumptions for any estimation method, thus making inference more robust and accessible to non-experts. **Graphical Causal Models and Potential Outcomes: Best of both worlds** DoWhy builds on two of the most powerful frameworks for causal inference: graphical causal models and potential outcomes. For effect estimation, it uses graph-based criteria and do-calculus for modeling assumptions and identifying a non-parametric causal effect. For estimation, it switches to methods based primarily on potential outcomes. For causal questions beyond effect estimation, it uses the power of graphical causal models by modeling the data generation process via explicit causal mechanisms at each node, which, for instance, unlocks capabilities to attribute observed effects to particular variables or estimate point-wise counterfactuals. For a quick introduction to causal inference, check out `amit-sharma/causal-inference-tutorial <https://github.com/amit-sharma/causal-inference-tutorial/>`_ We also gave a more comprehensive tutorial at the ACM Knowledge Discovery and Data Mining (`KDD 2018 <http://www.kdd.org/kdd2018/>`_) conference: `causalinference.gitlab.io/kdd-tutorial <http://causalinference.gitlab.io/kdd-tutorial/>`_. For an introduction to the four steps of causal inference and its implications for machine learning, you can access this video tutorial from Microsoft Research `DoWhy Webinar <https://www.microsoft.com/en-us/research/video/foundations-of-causal-inference-and-its-impacts-on-machine-learning/>`_ and for an introduction to the graphical causal model API, see the `PyCon presentation on Root Cause Analysis with DoWhy <https://www.youtube.com/watch?v=icpHrbDlGaw>`_. Key Features ~~~~~~~~~~~~ .. image:: https://raw.githubusercontent.com/py-why/dowhy/main/docs/images/dowhy-features.png DoWhy supports the following causal tasks: - Effect estimation (identification, average causal effect, conditional average causal effect, instrumental variables and more) - Quantify causal influences (mediation analysis, direct arrow strength, intrinsic causal influence) - What-if analysis (generate samples from interventional distribution, estimate counterfactuals) - Root cause analysis and explanations (attribute anomalies to their causes, find causes for changes in distributions, estimate feature relevance and more) For more details and how to use these methods in practice, checkout the documentation at `https://py-why.github.io/dowhy <https://py-why.github.io/dowhy/>`_ Quick Start =========== DoWhy support Python 3.8+. To install, you can use pip, poetry, or conda. **Latest Release** Install the latest `release <https://pypi.org/project/dowhy/>`__ using pip. .. code:: shell pip install dowhy Install the latest `release <https://pypi.org/project/dowhy/>`__ using poetry. .. code:: shell poetry add dowhy Install the latest `release <https://anaconda.org/conda-forge/dowhy>`__ using conda. .. code:: shell conda install -c conda-forge dowhy If you face "Solving environment" problems with conda, then try :code:`conda update --all` and then install dowhy. If that does not work, then use :code:`conda config --set channel_priority false` and try to install again. If the problem persists, please `add your issue here <https://github.com/microsoft/dowhy/issues/197>`_. **Development Version** If you prefer to use the latest dev version, your dependency management tool will need to point at our GitHub repository. .. code:: shell pip install git+https://github.com/py-why/dowhy@main **Requirements** DoWhy requires a few dependencies. Details on specific versions can be found in `pyproject.toml <./pyproject.toml>`_, under the `tool.poetry.dependencies` section. If you face any problems, try installing dependencies manually. .. code:: shell pip install '<dependency-name>==<version>' Optionally, if you wish to input graphs in the dot format, then install pydot (or pygraphviz). For better-looking graphs, you can optionally install pygraphviz. To proceed, first install graphviz and then pygraphviz (on Ubuntu and Ubuntu WSL). .. code:: shell sudo apt install graphviz libgraphviz-dev graphviz-dev pkg-config ## from https://github.com/pygraphviz/pygraphviz/issues/71 pip install pygraphviz --install-option="--include-path=/usr/include/graphviz" \ --install-option="--library-path=/usr/lib/graphviz/" Example: Effect identification and estimation ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Most causal tasks in DoWhy only require a few lines of code to write. Here, we exemplarily estimate the causal effect of a treatment on an outcome variable: .. code:: python from dowhy import CausalModel import dowhy.datasets # Load some sample data data = dowhy.datasets.linear_dataset( beta=10, num_common_causes=5, num_instruments=2, num_samples=10000, treatment_is_binary=True) A causal graph can be defined in different way, but the most common way is via `NetworkX <https://networkx.org/>`_. After loading in the data, we use the four main operations for effect estimation in DoWhy: *model*, *identify*, *estimate* and *refute*: .. code:: python # I. Create a causal model from the data and given graph. model = CausalModel( data=data["df"], treatment=data["treatment_name"], outcome=data["outcome_name"], graph=data["gml_graph"]) # Or alternatively, as nx.DiGraph # II. Identify causal effect and return target estimands identified_estimand = model.identify_effect() # III. Estimate the target estimand using a statistical method. estimate = model.estimate_effect(identified_estimand, method_name="backdoor.propensity_score_matching") # IV. Refute the obtained estimate using multiple robustness checks. refute_results = model.refute_estimate(identified_estimand, estimate, method_name="random_common_cause") DoWhy stresses on the interpretability of its output. At any point in the analysis, you can inspect the untested assumptions, identified estimands (if any), and the estimate (if any). Here's a sample output of the linear regression estimator: .. image:: https://raw.githubusercontent.com/py-why/dowhy/main/docs/images/regression_output.png :width: 80% For a full code example, check out the `Getting Started with DoWhy <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/dowhy_simple_example.ipynb>`_ notebook. You can also use Conditional Average Treatment Effect (CATE) estimation methods from `EconML <https://github.com/py-why/econml>`_, as shown in the `Conditional Treatment Effects <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/dowhy-conditional-treatment-effects.ipynb>`_ notebook. Here's a code snippet. .. code:: python from sklearn.preprocessing import PolynomialFeatures from sklearn.linear_model import LassoCV from sklearn.ensemble import GradientBoostingRegressor dml_estimate = model.estimate_effect(identified_estimand, method_name="backdoor.econml.dml.DML", control_value = 0, treatment_value = 1, target_units = lambda df: df["X0"]>1, confidence_intervals=False, method_params={ "init_params":{'model_y':GradientBoostingRegressor(), 'model_t': GradientBoostingRegressor(), 'model_final':LassoCV(), 'featurizer':PolynomialFeatures(degree=1, include_bias=True)}, "fit_params":{}}) Example: Graphical causal model (GCM) based inference ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DoWhy's graphical causal model framework offers powerful tools to address causal questions beyond effect estimation. It is based on Pearl's graphical causal model framework and models the causal data generation process of each variable explicitly via *causal mechanisms* to support a wide range of causal algorithms. For more details, see the book `Elements of Causal Inference <https://mitpress.mit.edu/9780262037310/elements-of-causal-inference/>`_. Complex causal queries, such as attributing observed anomalies to nodes in the system, can be performed with just a few lines of code: .. code:: python import networkx as nx, numpy as np, pandas as pd from dowhy import gcm # Let's generate some "normal" data we assume we're given from our problem domain: X = np.random.normal(loc=0, scale=1, size=1000) Y = 2 * X + np.random.normal(loc=0, scale=1, size=1000) Z = 3 * Y + np.random.normal(loc=0, scale=1, size=1000) data = pd.DataFrame(dict(X=X, Y=Y, Z=Z)) # 1. Modeling cause-effect relationships as a structural causal model # (causal graph + functional causal models): causal_model = gcm.StructuralCausalModel(nx.DiGraph([('X', 'Y'), ('Y', 'Z')])) # X -> Y -> Z gcm.auto.assign_causal_mechanisms(causal_model, data) # 2. Fitting the SCM to the data: gcm.fit(causal_model, data) # Optional: Evaluate causal model print(gcm.evaluate_causal_model(causal_model, data)) # Step 3: Perform a causal analysis. # results = gcm.<causal_query>(causal_model, ...) # For instance, root cause analysis: anomalous_sample = pd.DataFrame(dict(X=[0.1], Y=[6.2], Z=[19])) # Here, Y is the root cause. # "Which node is the root cause of the anomaly in Z?": anomaly_attribution = gcm.attribute_anomalies(causal_model, "Z", anomalous_sample) # Or sampling from an interventional distribution. Here, under the intervention do(Y := 2). samples = gcm.interventional_samples(causal_model, interventions={'Y': lambda y: 2}, num_samples_to_draw=100) The GCM framework offers many more features beyond these examples. For a full code example, check out the `Online Shop example notebook <https://github.com/py-why/dowhy/blob/main/docs/source/example_notebooks/gcm_online_shop.ipynb>`_. For more functionalities, example applications of DoWhy and details about the outputs, see the `User Guide <https://www.pywhy.org/dowhy/main/user_guide/intro.html>`_ or checkout `Jupyter notebooks <https://www.pywhy.org/dowhy/main/example_notebooks/nb_index.html>`_. More Information & Resources ============================ `Microsoft Research Blog <https://www.microsoft.com/en-us/research/blog/dowhy-a-library-for-causal-inference/>`_ | `Video Tutorial for Effect Estimation <https://www.youtube.com/watch?v=LALfQStONEc&t=114s>`_ | `Video Tutorial for Root Cause Analysis <https://www.youtube.com/watch?v=icpHrbDlGaw>`_ | `Arxiv Paper <https://arxiv.org/abs/2011.04216>`_ | `Arxiv Paper (Graphical Causal Model extension) <https://arxiv.org/abs/2206.06821>`_ | `Slides <https://www2.slideshare.net/AmitSharma315/dowhy-an-endtoend-library-for-causal-inference>`_ Citing this package ~~~~~~~~~~~~~~~~~~~ If you find DoWhy useful for your work, please cite **both** of the following two references: - Amit Sharma, Emre Kiciman. DoWhy: An End-to-End Library for Causal Inference. 2020. https://arxiv.org/abs/2011.04216 - Patrick Blöbaum, Peter Götz, Kailash Budhathoki, Atalanti A. Mastakouri, Dominik Janzing. DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models. 2022. https://arxiv.org/abs/2206.06821 Bibtex:: @article{dowhy, title={DoWhy: An End-to-End Library for Causal Inference}, author={Sharma, Amit and Kiciman, Emre}, journal={arXiv preprint arXiv:2011.04216}, year={2020} } @article{dowhy_gcm, author = {Bl{\"o}baum, Patrick and G{\"o}tz, Peter and Budhathoki, Kailash and Mastakouri, Atalanti A. and Janzing, Dominik}, title = {DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models}, journal={arXiv preprint arXiv:2206.06821}, year={2022} } Issues ~~~~~~ If you encounter an issue or have a specific request for DoWhy, please `raise an issue <https://github.com/py-why/dowhy/issues>`_. Contributing ~~~~~~~~~~~~ This project welcomes contributions and suggestions. For a guide to contributing and a list of all contributors, check out `CONTRIBUTING.md <https://github.com/py-why/dowhy/blob/main/CONTRIBUTING.md>`_ and our `docs for contributing code <https://github.com/py-why/dowhy/blob/main/docs/source/contributing/contributing-code.rst>`_. Our `contributor code of conduct is available here <https://github.com/py-why/governance/blob/main/CODE-OF-CONDUCT.md>`_.

bloebp

591e9d916e19bcad07e28687aeb8c81e1db26e8d

f455c569db0e7d0c1e1e7b54d1f8c85f026ec611

Awesome! I already put the conda installation guide back in.

bloebp

py-why/dowhy

Add new method to estimate KL divergence using classifier

This should work better with multivariate data and mixed data types. However, it is slower than the knn approach.

2023-10-20 02:13:28+00:00

2023-11-15 15:00:17+00:00

dowhy/gcm/stochastic_models.py

"""This module defines multiple implementations of the abstract class :class:`~dowhy.gcm.graph.StochasticModel`. Classes in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from typing import Dict, Optional, Tuple, Union import numpy as np import scipy from scipy.stats import norm, rv_continuous, rv_discrete from sklearn.cluster import KMeans from sklearn.metrics import silhouette_score from sklearn.mixture import BayesianGaussianMixture from dowhy.gcm.causal_mechanisms import StochasticModel from dowhy.gcm.divergence import estimate_kl_divergence_continuous from dowhy.gcm.util.general import shape_into_2d _CONTINUOUS_DISTRIBUTIONS = [ scipy.stats.norm, scipy.stats.laplace, scipy.stats.t, scipy.stats.uniform, scipy.stats.rayleigh, ] _CONTINUOUS_DISTRIBUTIONS.extend( [ getattr(scipy.stats, d) for d in dir(scipy.stats) if isinstance(getattr(scipy.stats, d), scipy.stats.rv_continuous) and d not in _CONTINUOUS_DISTRIBUTIONS ] ) _DISCRETE_DISTRIBUTIONS = [ getattr(scipy.stats, d) for d in dir(scipy.stats) if isinstance(getattr(scipy.stats, d), scipy.stats.rv_discrete) ] _CONTINUOUS_DISTRIBUTIONS = {x.name: x for x in _CONTINUOUS_DISTRIBUTIONS} _DISCRETE_DISTRIBUTIONS = {x.name: x for x in _DISCRETE_DISTRIBUTIONS} class ScipyDistribution(StochasticModel): """Represents any parametric distribution that can be modeled by scipy.""" def __init__(self, scipy_distribution: Optional[Union[rv_continuous, rv_discrete]] = None, **parameters) -> None: """Initializes a stochastic model that allows to sample from a parametric distribution implemented in Scipy. For instance, to use a beta distribution with parameters a=2 and b=0.5: ScipyDistribution(stats.beta, a=2, b=0.5) Or a Gaussian distribution with mean=0 and standard deviation 2: ScipyDistribution(stats.norm, loc=2, scale=0.5) Note that the parameter names need to coincide with the parameter names in the corresponding Scipy implementations. See https://docs.scipy.org/doc/scipy/tutorial/stats.html for more information. :param scipy_distribution: A continuous or discrete distribution parametric distribution implemented in Scipy. :param parameters: Set of parameters of the parametric distribution. """ self._distribution = scipy_distribution self._parameters = parameters self._fixed_parameters = len(parameters) > 0 def draw_samples(self, num_samples: int) -> np.ndarray: if len(self._parameters) == 0 or self._distribution is None: raise ValueError("Cannot draw samples. Model has not been fit!") return shape_into_2d(self._distribution.rvs(size=num_samples, **self.parameters)) def fit(self, X: np.ndarray) -> None: if self._distribution is None: # Currently only support continuous distributions for auto selection. best_model, best_parameters = self.find_suitable_continuous_distribution(X) self._distribution = best_model self._parameters = best_parameters elif not self._fixed_parameters: self._parameters = self.map_scipy_distribution_parameters_to_names( self._distribution, self._distribution.fit(shape_into_2d(X)) ) @property def parameters(self) -> Dict[str, float]: return self._parameters @property def scipy_distribution(self) -> Optional[Union[rv_continuous, rv_discrete]]: return self._distribution def clone(self): if self._fixed_parameters: return ScipyDistribution(scipy_distribution=self._distribution, **self._parameters) else: return ScipyDistribution(scipy_distribution=self._distribution) @staticmethod def find_suitable_continuous_distribution( distribution_samples: np.ndarray, divergence_threshold: float = 10**-2 ) -> Tuple[rv_continuous, Dict[str, float]]: """Tries to find the best fitting continuous parametric distribution of given samples. This is done by fitting different parametric models and selecting the one with the smallest KL divergence between observed and generated samples. """ distribution_samples = shape_into_2d(distribution_samples) currently_best_distribution = norm currently_best_parameters = (0.0, 1.0) currently_smallest_divergence = np.inf # Estimate distribution parameters from data. for distribution in _CONTINUOUS_DISTRIBUTIONS.values(): # Ignore warnings from fitting process. with warnings.catch_warnings(): warnings.filterwarnings("ignore") try: # Fit distribution to data. params = distribution.fit(distribution_samples) except ValueError: # Some distributions might not be compatible with the data. continue # Separate parts of parameters. arg = params[:-2] loc = params[-2] scale = params[-1] generated_samples = distribution.rvs(size=distribution_samples.shape[0], loc=loc, scale=scale, *arg) # Check the KL divergence between the distribution of the given and fitted distribution. divergence = estimate_kl_divergence_continuous(distribution_samples, generated_samples) if divergence < divergence_threshold: currently_best_distribution = distribution currently_best_parameters = params break # Identify if this distribution is better. if currently_smallest_divergence > divergence: currently_best_distribution = distribution currently_best_parameters = params currently_smallest_divergence = divergence return currently_best_distribution, ScipyDistribution.map_scipy_distribution_parameters_to_names( currently_best_distribution, currently_best_parameters ) @staticmethod def map_scipy_distribution_parameters_to_names( scipy_distribution: Union[rv_continuous, rv_discrete], parameters: Tuple[float] ) -> Dict[str, float]: """Helper function to obtain a mapping from parameter name to parameter value. Depending whether the distribution is discrete or continuous, there are slightly different parameter names. The given parameters are assumed to follow the order as provided by the scipy fit function. :param scipy_distribution: The scipy distribution. :param parameters: The values of the corresponding parameters of the distribution. Here, it is expected to follow the same order as defined by the scipy fit function. :return: A dictionary that maps a parameter name to its value. """ if scipy_distribution.shapes: parameter_list = [name.strip() for name in scipy_distribution.shapes.split(",")] else: parameter_list = [] if scipy_distribution.name in _DISCRETE_DISTRIBUTIONS: parameter_list += ["loc"] elif scipy_distribution.name in _CONTINUOUS_DISTRIBUTIONS: parameter_list += ["loc", "scale"] else: raise ValueError( "Distribution %s not found in the list of continuous and discrete distributions!" % scipy_distribution.name ) parameters_dictionary = {} for i, parameter_name in enumerate(parameter_list): parameters_dictionary[parameter_name] = parameters[i] return parameters_dictionary def __str__(self) -> str: return str(self._distribution.name) + " distribution" class EmpiricalDistribution(StochasticModel): """An implementation of a stochastic model that uniformly samples from data samples. By randomly returning a sample from the training data set, this model represents a parameter free representation of the marginal distribution of the training data. However, it will not generate unseen data points. For this, consider :py:class:`BayesianGaussianMixtureDistribution <dowhy.gcm.BayesianGaussianMixtureDistribution>`. """ def __init__(self) -> None: self._data = None @property def data(self) -> np.ndarray: return self._data def fit(self, X: np.ndarray) -> None: self._data = shape_into_2d(X) def draw_samples(self, num_samples: int) -> np.ndarray: if self.data is None: raise RuntimeError("%s has not been fitted!" % self.__class__.__name__) return self.data[np.random.choice(self.data.shape[0], size=num_samples, replace=True), :] def clone(self): return EmpiricalDistribution() def __str__(self): return "Empirical Distribution" class BayesianGaussianMixtureDistribution(StochasticModel): def __init__(self) -> None: self._gmm_model = None def fit(self, X: np.ndarray) -> None: X = shape_into_2d(X) self._gmm_model = BayesianGaussianMixture( n_components=BayesianGaussianMixtureDistribution._get_optimal_number_of_components(X), max_iter=1000 ).fit(X) @staticmethod def _get_optimal_number_of_components(X: np.ndarray) -> int: current_best = 0 current_best_num_components = 1 num_best_in_succession = 0 try: for i in range(2, int(np.sqrt(X.shape[0] / 2))): kmeans = KMeans(n_clusters=i).fit(X) coefficient = silhouette_score(X, kmeans.labels_, sample_size=5000) if coefficient > current_best: current_best = coefficient current_best_num_components = i num_best_in_succession = 0 else: num_best_in_succession += 1 if num_best_in_succession >= 3: break except ValueError: # This error is typically raised when the data is discrete and all points are assigned to less cluster than # specified. It can also happen due to duplicated points. In these cases, the current best solution should # be sufficient. return current_best_num_components return current_best_num_components def draw_samples(self, num_samples: int) -> np.ndarray: if self._gmm_model is None: raise RuntimeError("%s has not been fitted!" % self.__class__.__name__) return shape_into_2d(self._gmm_model.sample(num_samples)[0]) def __str__(self) -> str: return "Gaussian Mixture Distribution" def clone(self): return BayesianGaussianMixtureDistribution()

"""This module defines multiple implementations of the abstract class :class:`~dowhy.gcm.graph.StochasticModel`. Classes in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from typing import Dict, Optional, Tuple, Union import numpy as np import scipy from scipy.stats import norm, rv_continuous, rv_discrete from sklearn.cluster import KMeans from sklearn.metrics import silhouette_score from sklearn.mixture import BayesianGaussianMixture from dowhy.gcm.causal_mechanisms import StochasticModel from dowhy.gcm.util.general import shape_into_2d _CONTINUOUS_DISTRIBUTIONS = [ scipy.stats.norm, scipy.stats.laplace, scipy.stats.t, scipy.stats.uniform, scipy.stats.rayleigh, ] _CONTINUOUS_DISTRIBUTIONS.extend( [ getattr(scipy.stats, d) for d in dir(scipy.stats) if isinstance(getattr(scipy.stats, d), scipy.stats.rv_continuous) and d not in _CONTINUOUS_DISTRIBUTIONS ] ) _DISCRETE_DISTRIBUTIONS = [ getattr(scipy.stats, d) for d in dir(scipy.stats) if isinstance(getattr(scipy.stats, d), scipy.stats.rv_discrete) ] _CONTINUOUS_DISTRIBUTIONS = {x.name: x for x in _CONTINUOUS_DISTRIBUTIONS} _DISCRETE_DISTRIBUTIONS = {x.name: x for x in _DISCRETE_DISTRIBUTIONS} class ScipyDistribution(StochasticModel): """Represents any parametric distribution that can be modeled by scipy.""" def __init__(self, scipy_distribution: Optional[Union[rv_continuous, rv_discrete]] = None, **parameters) -> None: """Initializes a stochastic model that allows to sample from a parametric distribution implemented in Scipy. For instance, to use a beta distribution with parameters a=2 and b=0.5: ScipyDistribution(stats.beta, a=2, b=0.5) Or a Gaussian distribution with mean=0 and standard deviation 2: ScipyDistribution(stats.norm, loc=2, scale=0.5) Note that the parameter names need to coincide with the parameter names in the corresponding Scipy implementations. See https://docs.scipy.org/doc/scipy/tutorial/stats.html for more information. :param scipy_distribution: A continuous or discrete distribution parametric distribution implemented in Scipy. :param parameters: Set of parameters of the parametric distribution. """ self._distribution = scipy_distribution self._parameters = parameters self._fixed_parameters = len(parameters) > 0 def draw_samples(self, num_samples: int) -> np.ndarray: if len(self._parameters) == 0 or self._distribution is None: raise ValueError("Cannot draw samples. Model has not been fit!") return shape_into_2d(self._distribution.rvs(size=num_samples, **self.parameters)) def fit(self, X: np.ndarray) -> None: if self._distribution is None: # Currently only support continuous distributions for auto selection. best_model, best_parameters = self.find_suitable_continuous_distribution(X) self._distribution = best_model self._parameters = best_parameters elif not self._fixed_parameters: self._parameters = self.map_scipy_distribution_parameters_to_names( self._distribution, self._distribution.fit(shape_into_2d(X)) ) @property def parameters(self) -> Dict[str, float]: return self._parameters @property def scipy_distribution(self) -> Optional[Union[rv_continuous, rv_discrete]]: return self._distribution def clone(self): if self._fixed_parameters: return ScipyDistribution(scipy_distribution=self._distribution, **self._parameters) else: return ScipyDistribution(scipy_distribution=self._distribution) @staticmethod def find_suitable_continuous_distribution( distribution_samples: np.ndarray, divergence_threshold: float = 10**-2 ) -> Tuple[rv_continuous, Dict[str, float]]: """Tries to find the best fitting continuous parametric distribution of given samples. This is done by fitting different parametric models and selecting the one with the smallest KL divergence between observed and generated samples. """ distribution_samples = shape_into_2d(distribution_samples) currently_best_distribution = norm currently_best_parameters = (0.0, 1.0) currently_smallest_divergence = np.inf # Estimate distribution parameters from data. for distribution in _CONTINUOUS_DISTRIBUTIONS.values(): # Ignore warnings from fitting process. with warnings.catch_warnings(): warnings.filterwarnings("ignore") try: # Fit distribution to data. params = distribution.fit(distribution_samples) except ValueError: # Some distributions might not be compatible with the data. continue # Separate parts of parameters. arg = params[:-2] loc = params[-2] scale = params[-1] generated_samples = distribution.rvs(size=distribution_samples.shape[0], loc=loc, scale=scale, *arg) # Check the KL divergence between the distribution of the given and fitted distribution. from dowhy.gcm.divergence import estimate_kl_divergence_continuous_knn divergence = estimate_kl_divergence_continuous_knn(distribution_samples, generated_samples) if divergence < divergence_threshold: currently_best_distribution = distribution currently_best_parameters = params break # Identify if this distribution is better. if currently_smallest_divergence > divergence: currently_best_distribution = distribution currently_best_parameters = params currently_smallest_divergence = divergence return currently_best_distribution, ScipyDistribution.map_scipy_distribution_parameters_to_names( currently_best_distribution, currently_best_parameters ) @staticmethod def map_scipy_distribution_parameters_to_names( scipy_distribution: Union[rv_continuous, rv_discrete], parameters: Tuple[float] ) -> Dict[str, float]: """Helper function to obtain a mapping from parameter name to parameter value. Depending whether the distribution is discrete or continuous, there are slightly different parameter names. The given parameters are assumed to follow the order as provided by the scipy fit function. :param scipy_distribution: The scipy distribution. :param parameters: The values of the corresponding parameters of the distribution. Here, it is expected to follow the same order as defined by the scipy fit function. :return: A dictionary that maps a parameter name to its value. """ if scipy_distribution.shapes: parameter_list = [name.strip() for name in scipy_distribution.shapes.split(",")] else: parameter_list = [] if scipy_distribution.name in _DISCRETE_DISTRIBUTIONS: parameter_list += ["loc"] elif scipy_distribution.name in _CONTINUOUS_DISTRIBUTIONS: parameter_list += ["loc", "scale"] else: raise ValueError( "Distribution %s not found in the list of continuous and discrete distributions!" % scipy_distribution.name ) parameters_dictionary = {} for i, parameter_name in enumerate(parameter_list): parameters_dictionary[parameter_name] = parameters[i] return parameters_dictionary def __str__(self) -> str: return str(self._distribution.name) + " distribution" class EmpiricalDistribution(StochasticModel): """An implementation of a stochastic model that uniformly samples from data samples. By randomly returning a sample from the training data set, this model represents a parameter free representation of the marginal distribution of the training data. However, it will not generate unseen data points. For this, consider :py:class:`BayesianGaussianMixtureDistribution <dowhy.gcm.BayesianGaussianMixtureDistribution>`. """ def __init__(self) -> None: self._data = None @property def data(self) -> np.ndarray: return self._data def fit(self, X: np.ndarray) -> None: self._data = shape_into_2d(X) def draw_samples(self, num_samples: int) -> np.ndarray: if self.data is None: raise RuntimeError("%s has not been fitted!" % self.__class__.__name__) return self.data[np.random.choice(self.data.shape[0], size=num_samples, replace=True), :] def clone(self): return EmpiricalDistribution() def __str__(self): return "Empirical Distribution" class BayesianGaussianMixtureDistribution(StochasticModel): def __init__(self) -> None: self._gmm_model = None def fit(self, X: np.ndarray) -> None: X = shape_into_2d(X) self._gmm_model = BayesianGaussianMixture( n_components=BayesianGaussianMixtureDistribution._get_optimal_number_of_components(X), max_iter=1000 ).fit(X) @staticmethod def _get_optimal_number_of_components(X: np.ndarray) -> int: current_best = 0 current_best_num_components = 1 num_best_in_succession = 0 try: for i in range(2, int(np.sqrt(X.shape[0] / 2))): kmeans = KMeans(n_clusters=i).fit(X) coefficient = silhouette_score(X, kmeans.labels_, sample_size=5000) if coefficient > current_best: current_best = coefficient current_best_num_components = i num_best_in_succession = 0 else: num_best_in_succession += 1 if num_best_in_succession >= 3: break except ValueError: # This error is typically raised when the data is discrete and all points are assigned to less cluster than # specified. It can also happen due to duplicated points. In these cases, the current best solution should # be sufficient. return current_best_num_components return current_best_num_components def draw_samples(self, num_samples: int) -> np.ndarray: if self._gmm_model is None: raise RuntimeError("%s has not been fitted!" % self.__class__.__name__) return shape_into_2d(self._gmm_model.sample(num_samples)[0]) def __str__(self) -> str: return "Gaussian Mixture Distribution" def clone(self): return BayesianGaussianMixtureDistribution()

bloebp

395d1fa46743234fdbaf765e548b85e8089d0c7e

eb887358064d72611387b41510a77223952bfdb8

I assume you've run into import cycles and that's why you had to do a local import. This is likely a smell. I'm sure this problem is related to [this change in PR 932](https://github.com/py-why/dowhy/pull/932/files#diff-62eae184e3560bffa3e7cbc57e06f30910651732d961cf7e34c1a032e3c541fd). `PredictionModel` is an interface and you should not tie it to ML implementations in the `ml` sub-package (which automatically happens as soon as it's in that folder). I think moving `prediction_model.py` one folder up will relax the situation.

petergtz

py-why/dowhy

Add new method to estimate KL divergence using classifier

This should work better with multivariate data and mixed data types. However, it is slower than the knn approach.

2023-10-20 02:13:28+00:00

2023-11-15 15:00:17+00:00

dowhy/gcm/stochastic_models.py

"""This module defines multiple implementations of the abstract class :class:`~dowhy.gcm.graph.StochasticModel`. Classes in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from typing import Dict, Optional, Tuple, Union import numpy as np import scipy from scipy.stats import norm, rv_continuous, rv_discrete from sklearn.cluster import KMeans from sklearn.metrics import silhouette_score from sklearn.mixture import BayesianGaussianMixture from dowhy.gcm.causal_mechanisms import StochasticModel from dowhy.gcm.divergence import estimate_kl_divergence_continuous from dowhy.gcm.util.general import shape_into_2d _CONTINUOUS_DISTRIBUTIONS = [ scipy.stats.norm, scipy.stats.laplace, scipy.stats.t, scipy.stats.uniform, scipy.stats.rayleigh, ] _CONTINUOUS_DISTRIBUTIONS.extend( [ getattr(scipy.stats, d) for d in dir(scipy.stats) if isinstance(getattr(scipy.stats, d), scipy.stats.rv_continuous) and d not in _CONTINUOUS_DISTRIBUTIONS ] ) _DISCRETE_DISTRIBUTIONS = [ getattr(scipy.stats, d) for d in dir(scipy.stats) if isinstance(getattr(scipy.stats, d), scipy.stats.rv_discrete) ] _CONTINUOUS_DISTRIBUTIONS = {x.name: x for x in _CONTINUOUS_DISTRIBUTIONS} _DISCRETE_DISTRIBUTIONS = {x.name: x for x in _DISCRETE_DISTRIBUTIONS} class ScipyDistribution(StochasticModel): """Represents any parametric distribution that can be modeled by scipy.""" def __init__(self, scipy_distribution: Optional[Union[rv_continuous, rv_discrete]] = None, **parameters) -> None: """Initializes a stochastic model that allows to sample from a parametric distribution implemented in Scipy. For instance, to use a beta distribution with parameters a=2 and b=0.5: ScipyDistribution(stats.beta, a=2, b=0.5) Or a Gaussian distribution with mean=0 and standard deviation 2: ScipyDistribution(stats.norm, loc=2, scale=0.5) Note that the parameter names need to coincide with the parameter names in the corresponding Scipy implementations. See https://docs.scipy.org/doc/scipy/tutorial/stats.html for more information. :param scipy_distribution: A continuous or discrete distribution parametric distribution implemented in Scipy. :param parameters: Set of parameters of the parametric distribution. """ self._distribution = scipy_distribution self._parameters = parameters self._fixed_parameters = len(parameters) > 0 def draw_samples(self, num_samples: int) -> np.ndarray: if len(self._parameters) == 0 or self._distribution is None: raise ValueError("Cannot draw samples. Model has not been fit!") return shape_into_2d(self._distribution.rvs(size=num_samples, **self.parameters)) def fit(self, X: np.ndarray) -> None: if self._distribution is None: # Currently only support continuous distributions for auto selection. best_model, best_parameters = self.find_suitable_continuous_distribution(X) self._distribution = best_model self._parameters = best_parameters elif not self._fixed_parameters: self._parameters = self.map_scipy_distribution_parameters_to_names( self._distribution, self._distribution.fit(shape_into_2d(X)) ) @property def parameters(self) -> Dict[str, float]: return self._parameters @property def scipy_distribution(self) -> Optional[Union[rv_continuous, rv_discrete]]: return self._distribution def clone(self): if self._fixed_parameters: return ScipyDistribution(scipy_distribution=self._distribution, **self._parameters) else: return ScipyDistribution(scipy_distribution=self._distribution) @staticmethod def find_suitable_continuous_distribution( distribution_samples: np.ndarray, divergence_threshold: float = 10**-2 ) -> Tuple[rv_continuous, Dict[str, float]]: """Tries to find the best fitting continuous parametric distribution of given samples. This is done by fitting different parametric models and selecting the one with the smallest KL divergence between observed and generated samples. """ distribution_samples = shape_into_2d(distribution_samples) currently_best_distribution = norm currently_best_parameters = (0.0, 1.0) currently_smallest_divergence = np.inf # Estimate distribution parameters from data. for distribution in _CONTINUOUS_DISTRIBUTIONS.values(): # Ignore warnings from fitting process. with warnings.catch_warnings(): warnings.filterwarnings("ignore") try: # Fit distribution to data. params = distribution.fit(distribution_samples) except ValueError: # Some distributions might not be compatible with the data. continue # Separate parts of parameters. arg = params[:-2] loc = params[-2] scale = params[-1] generated_samples = distribution.rvs(size=distribution_samples.shape[0], loc=loc, scale=scale, *arg) # Check the KL divergence between the distribution of the given and fitted distribution. divergence = estimate_kl_divergence_continuous(distribution_samples, generated_samples) if divergence < divergence_threshold: currently_best_distribution = distribution currently_best_parameters = params break # Identify if this distribution is better. if currently_smallest_divergence > divergence: currently_best_distribution = distribution currently_best_parameters = params currently_smallest_divergence = divergence return currently_best_distribution, ScipyDistribution.map_scipy_distribution_parameters_to_names( currently_best_distribution, currently_best_parameters ) @staticmethod def map_scipy_distribution_parameters_to_names( scipy_distribution: Union[rv_continuous, rv_discrete], parameters: Tuple[float] ) -> Dict[str, float]: """Helper function to obtain a mapping from parameter name to parameter value. Depending whether the distribution is discrete or continuous, there are slightly different parameter names. The given parameters are assumed to follow the order as provided by the scipy fit function. :param scipy_distribution: The scipy distribution. :param parameters: The values of the corresponding parameters of the distribution. Here, it is expected to follow the same order as defined by the scipy fit function. :return: A dictionary that maps a parameter name to its value. """ if scipy_distribution.shapes: parameter_list = [name.strip() for name in scipy_distribution.shapes.split(",")] else: parameter_list = [] if scipy_distribution.name in _DISCRETE_DISTRIBUTIONS: parameter_list += ["loc"] elif scipy_distribution.name in _CONTINUOUS_DISTRIBUTIONS: parameter_list += ["loc", "scale"] else: raise ValueError( "Distribution %s not found in the list of continuous and discrete distributions!" % scipy_distribution.name ) parameters_dictionary = {} for i, parameter_name in enumerate(parameter_list): parameters_dictionary[parameter_name] = parameters[i] return parameters_dictionary def __str__(self) -> str: return str(self._distribution.name) + " distribution" class EmpiricalDistribution(StochasticModel): """An implementation of a stochastic model that uniformly samples from data samples. By randomly returning a sample from the training data set, this model represents a parameter free representation of the marginal distribution of the training data. However, it will not generate unseen data points. For this, consider :py:class:`BayesianGaussianMixtureDistribution <dowhy.gcm.BayesianGaussianMixtureDistribution>`. """ def __init__(self) -> None: self._data = None @property def data(self) -> np.ndarray: return self._data def fit(self, X: np.ndarray) -> None: self._data = shape_into_2d(X) def draw_samples(self, num_samples: int) -> np.ndarray: if self.data is None: raise RuntimeError("%s has not been fitted!" % self.__class__.__name__) return self.data[np.random.choice(self.data.shape[0], size=num_samples, replace=True), :] def clone(self): return EmpiricalDistribution() def __str__(self): return "Empirical Distribution" class BayesianGaussianMixtureDistribution(StochasticModel): def __init__(self) -> None: self._gmm_model = None def fit(self, X: np.ndarray) -> None: X = shape_into_2d(X) self._gmm_model = BayesianGaussianMixture( n_components=BayesianGaussianMixtureDistribution._get_optimal_number_of_components(X), max_iter=1000 ).fit(X) @staticmethod def _get_optimal_number_of_components(X: np.ndarray) -> int: current_best = 0 current_best_num_components = 1 num_best_in_succession = 0 try: for i in range(2, int(np.sqrt(X.shape[0] / 2))): kmeans = KMeans(n_clusters=i).fit(X) coefficient = silhouette_score(X, kmeans.labels_, sample_size=5000) if coefficient > current_best: current_best = coefficient current_best_num_components = i num_best_in_succession = 0 else: num_best_in_succession += 1 if num_best_in_succession >= 3: break except ValueError: # This error is typically raised when the data is discrete and all points are assigned to less cluster than # specified. It can also happen due to duplicated points. In these cases, the current best solution should # be sufficient. return current_best_num_components return current_best_num_components def draw_samples(self, num_samples: int) -> np.ndarray: if self._gmm_model is None: raise RuntimeError("%s has not been fitted!" % self.__class__.__name__) return shape_into_2d(self._gmm_model.sample(num_samples)[0]) def __str__(self) -> str: return "Gaussian Mixture Distribution" def clone(self): return BayesianGaussianMixtureDistribution()

"""This module defines multiple implementations of the abstract class :class:`~dowhy.gcm.graph.StochasticModel`. Classes in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from typing import Dict, Optional, Tuple, Union import numpy as np import scipy from scipy.stats import norm, rv_continuous, rv_discrete from sklearn.cluster import KMeans from sklearn.metrics import silhouette_score from sklearn.mixture import BayesianGaussianMixture from dowhy.gcm.causal_mechanisms import StochasticModel from dowhy.gcm.util.general import shape_into_2d _CONTINUOUS_DISTRIBUTIONS = [ scipy.stats.norm, scipy.stats.laplace, scipy.stats.t, scipy.stats.uniform, scipy.stats.rayleigh, ] _CONTINUOUS_DISTRIBUTIONS.extend( [ getattr(scipy.stats, d) for d in dir(scipy.stats) if isinstance(getattr(scipy.stats, d), scipy.stats.rv_continuous) and d not in _CONTINUOUS_DISTRIBUTIONS ] ) _DISCRETE_DISTRIBUTIONS = [ getattr(scipy.stats, d) for d in dir(scipy.stats) if isinstance(getattr(scipy.stats, d), scipy.stats.rv_discrete) ] _CONTINUOUS_DISTRIBUTIONS = {x.name: x for x in _CONTINUOUS_DISTRIBUTIONS} _DISCRETE_DISTRIBUTIONS = {x.name: x for x in _DISCRETE_DISTRIBUTIONS} class ScipyDistribution(StochasticModel): """Represents any parametric distribution that can be modeled by scipy.""" def __init__(self, scipy_distribution: Optional[Union[rv_continuous, rv_discrete]] = None, **parameters) -> None: """Initializes a stochastic model that allows to sample from a parametric distribution implemented in Scipy. For instance, to use a beta distribution with parameters a=2 and b=0.5: ScipyDistribution(stats.beta, a=2, b=0.5) Or a Gaussian distribution with mean=0 and standard deviation 2: ScipyDistribution(stats.norm, loc=2, scale=0.5) Note that the parameter names need to coincide with the parameter names in the corresponding Scipy implementations. See https://docs.scipy.org/doc/scipy/tutorial/stats.html for more information. :param scipy_distribution: A continuous or discrete distribution parametric distribution implemented in Scipy. :param parameters: Set of parameters of the parametric distribution. """ self._distribution = scipy_distribution self._parameters = parameters self._fixed_parameters = len(parameters) > 0 def draw_samples(self, num_samples: int) -> np.ndarray: if len(self._parameters) == 0 or self._distribution is None: raise ValueError("Cannot draw samples. Model has not been fit!") return shape_into_2d(self._distribution.rvs(size=num_samples, **self.parameters)) def fit(self, X: np.ndarray) -> None: if self._distribution is None: # Currently only support continuous distributions for auto selection. best_model, best_parameters = self.find_suitable_continuous_distribution(X) self._distribution = best_model self._parameters = best_parameters elif not self._fixed_parameters: self._parameters = self.map_scipy_distribution_parameters_to_names( self._distribution, self._distribution.fit(shape_into_2d(X)) ) @property def parameters(self) -> Dict[str, float]: return self._parameters @property def scipy_distribution(self) -> Optional[Union[rv_continuous, rv_discrete]]: return self._distribution def clone(self): if self._fixed_parameters: return ScipyDistribution(scipy_distribution=self._distribution, **self._parameters) else: return ScipyDistribution(scipy_distribution=self._distribution) @staticmethod def find_suitable_continuous_distribution( distribution_samples: np.ndarray, divergence_threshold: float = 10**-2 ) -> Tuple[rv_continuous, Dict[str, float]]: """Tries to find the best fitting continuous parametric distribution of given samples. This is done by fitting different parametric models and selecting the one with the smallest KL divergence between observed and generated samples. """ distribution_samples = shape_into_2d(distribution_samples) currently_best_distribution = norm currently_best_parameters = (0.0, 1.0) currently_smallest_divergence = np.inf # Estimate distribution parameters from data. for distribution in _CONTINUOUS_DISTRIBUTIONS.values(): # Ignore warnings from fitting process. with warnings.catch_warnings(): warnings.filterwarnings("ignore") try: # Fit distribution to data. params = distribution.fit(distribution_samples) except ValueError: # Some distributions might not be compatible with the data. continue # Separate parts of parameters. arg = params[:-2] loc = params[-2] scale = params[-1] generated_samples = distribution.rvs(size=distribution_samples.shape[0], loc=loc, scale=scale, *arg) # Check the KL divergence between the distribution of the given and fitted distribution. from dowhy.gcm.divergence import estimate_kl_divergence_continuous_knn divergence = estimate_kl_divergence_continuous_knn(distribution_samples, generated_samples) if divergence < divergence_threshold: currently_best_distribution = distribution currently_best_parameters = params break # Identify if this distribution is better. if currently_smallest_divergence > divergence: currently_best_distribution = distribution currently_best_parameters = params currently_smallest_divergence = divergence return currently_best_distribution, ScipyDistribution.map_scipy_distribution_parameters_to_names( currently_best_distribution, currently_best_parameters ) @staticmethod def map_scipy_distribution_parameters_to_names( scipy_distribution: Union[rv_continuous, rv_discrete], parameters: Tuple[float] ) -> Dict[str, float]: """Helper function to obtain a mapping from parameter name to parameter value. Depending whether the distribution is discrete or continuous, there are slightly different parameter names. The given parameters are assumed to follow the order as provided by the scipy fit function. :param scipy_distribution: The scipy distribution. :param parameters: The values of the corresponding parameters of the distribution. Here, it is expected to follow the same order as defined by the scipy fit function. :return: A dictionary that maps a parameter name to its value. """ if scipy_distribution.shapes: parameter_list = [name.strip() for name in scipy_distribution.shapes.split(",")] else: parameter_list = [] if scipy_distribution.name in _DISCRETE_DISTRIBUTIONS: parameter_list += ["loc"] elif scipy_distribution.name in _CONTINUOUS_DISTRIBUTIONS: parameter_list += ["loc", "scale"] else: raise ValueError( "Distribution %s not found in the list of continuous and discrete distributions!" % scipy_distribution.name ) parameters_dictionary = {} for i, parameter_name in enumerate(parameter_list): parameters_dictionary[parameter_name] = parameters[i] return parameters_dictionary def __str__(self) -> str: return str(self._distribution.name) + " distribution" class EmpiricalDistribution(StochasticModel): """An implementation of a stochastic model that uniformly samples from data samples. By randomly returning a sample from the training data set, this model represents a parameter free representation of the marginal distribution of the training data. However, it will not generate unseen data points. For this, consider :py:class:`BayesianGaussianMixtureDistribution <dowhy.gcm.BayesianGaussianMixtureDistribution>`. """ def __init__(self) -> None: self._data = None @property def data(self) -> np.ndarray: return self._data def fit(self, X: np.ndarray) -> None: self._data = shape_into_2d(X) def draw_samples(self, num_samples: int) -> np.ndarray: if self.data is None: raise RuntimeError("%s has not been fitted!" % self.__class__.__name__) return self.data[np.random.choice(self.data.shape[0], size=num_samples, replace=True), :] def clone(self): return EmpiricalDistribution() def __str__(self): return "Empirical Distribution" class BayesianGaussianMixtureDistribution(StochasticModel): def __init__(self) -> None: self._gmm_model = None def fit(self, X: np.ndarray) -> None: X = shape_into_2d(X) self._gmm_model = BayesianGaussianMixture( n_components=BayesianGaussianMixtureDistribution._get_optimal_number_of_components(X), max_iter=1000 ).fit(X) @staticmethod def _get_optimal_number_of_components(X: np.ndarray) -> int: current_best = 0 current_best_num_components = 1 num_best_in_succession = 0 try: for i in range(2, int(np.sqrt(X.shape[0] / 2))): kmeans = KMeans(n_clusters=i).fit(X) coefficient = silhouette_score(X, kmeans.labels_, sample_size=5000) if coefficient > current_best: current_best = coefficient current_best_num_components = i num_best_in_succession = 0 else: num_best_in_succession += 1 if num_best_in_succession >= 3: break except ValueError: # This error is typically raised when the data is discrete and all points are assigned to less cluster than # specified. It can also happen due to duplicated points. In these cases, the current best solution should # be sufficient. return current_best_num_components return current_best_num_components def draw_samples(self, num_samples: int) -> np.ndarray: if self._gmm_model is None: raise RuntimeError("%s has not been fitted!" % self.__class__.__name__) return shape_into_2d(self._gmm_model.sample(num_samples)[0]) def __str__(self) -> str: return "Gaussian Mixture Distribution" def clone(self): return BayesianGaussianMixtureDistribution()

bloebp

395d1fa46743234fdbaf765e548b85e8089d0c7e

eb887358064d72611387b41510a77223952bfdb8

It is unfortunately a bit more complicated because the new KL divergence method now also relies on the auto module (for optional automatic selection of the classifier model), which in turn imports the `EmpiricalDistribution` from the `stochastic_models` module. We might need to move these implementations into separate files. However, I will take a look this in a follow-up.

bloebp

py-why/dowhy

GCM auto assignment now returns a summary object

The summary object contains information about the evaluated models and model choices. This object is printable to provide quick summary.

2023-10-16 19:40:03+00:00

2023-11-02 18:42:28+00:00

dowhy/gcm/auto.py

import warnings from enum import Enum, auto from functools import partial from typing import Callable, List, Optional, Union import numpy as np import pandas as pd from joblib import Parallel, delayed from sklearn import metrics from sklearn.exceptions import ConvergenceWarning from sklearn.linear_model import LinearRegression, LogisticRegression from sklearn.model_selection import KFold, train_test_split from sklearn.preprocessing import MultiLabelBinarizer from dowhy.gcm import config from dowhy.gcm.causal_mechanisms import AdditiveNoiseModel, ClassifierFCM from dowhy.gcm.causal_models import CAUSAL_MECHANISM, ProbabilisticCausalModel, validate_causal_model_assignment from dowhy.gcm.ml import ( ClassificationModel, PredictionModel, create_hist_gradient_boost_classifier, create_hist_gradient_boost_regressor, create_lasso_regressor, create_linear_regressor, create_logistic_regression_classifier, create_random_forest_regressor, create_ridge_regressor, create_support_vector_regressor, ) from dowhy.gcm.ml.classification import ( create_ada_boost_classifier, create_extra_trees_classifier, create_gaussian_nb_classifier, create_knn_classifier, create_polynom_logistic_regression_classifier, create_random_forest_classifier, create_support_vector_classifier, ) from dowhy.gcm.ml.regression import ( create_ada_boost_regressor, create_extra_trees_regressor, create_knn_regressor, create_polynom_regressor, ) from dowhy.gcm.stochastic_models import EmpiricalDistribution from dowhy.gcm.util.general import ( auto_apply_encoders, auto_fit_encoders, is_categorical, set_random_seed, shape_into_2d, ) from dowhy.graph import get_ordered_predecessors, is_root_node _LIST_OF_POTENTIAL_CLASSIFIERS_GOOD = [ partial(create_logistic_regression_classifier, max_iter=1000), create_hist_gradient_boost_classifier, ] _LIST_OF_POTENTIAL_REGRESSORS_GOOD = [ create_linear_regressor, create_hist_gradient_boost_regressor, ] _LIST_OF_POTENTIAL_CLASSIFIERS_BETTER = _LIST_OF_POTENTIAL_CLASSIFIERS_GOOD + [ create_random_forest_classifier, create_extra_trees_classifier, create_support_vector_classifier, create_knn_classifier, create_gaussian_nb_classifier, create_ada_boost_classifier, ] _LIST_OF_POTENTIAL_REGRESSORS_BETTER = _LIST_OF_POTENTIAL_REGRESSORS_GOOD + [ create_ridge_regressor, create_polynom_regressor, partial(create_lasso_regressor, max_iter=5000), create_random_forest_regressor, create_support_vector_regressor, create_extra_trees_regressor, create_knn_regressor, create_ada_boost_regressor, ] class AssignmentQuality(Enum): GOOD = auto() BETTER = auto() BEST = auto() def assign_causal_mechanisms( causal_model: ProbabilisticCausalModel, based_on: pd.DataFrame, quality: AssignmentQuality = AssignmentQuality.GOOD, override_models: bool = False, ) -> None: """Automatically assigns appropriate causal models. If causal models are already assigned to nodes and override_models is set to False, this function only validates the assignments with respect to the graph structure. Here, the validation checks whether root nodes have StochasticModels and non-root ConditionalStochasticModels assigned. :param causal_model: The causal model to whose nodes to assign causal models. :param based_on: Jointly sampled data corresponding to the nodes of the given graph. :param quality: AssignmentQuality for the automatic model selection and model accuracy. This changes the type of prediction model and time spent on the selection. Options are: - AssignmentQuality.GOOD: Compares a linear, polynomial and gradient boost model on small test-training split of the data. The best performing model is then selected. Model selection speed: Fast Model training speed: Fast Model inference speed: Fast Model accuracy: Medium - AssignmentQuality.BETTER: Compares multiple model types and uses the one with the best performance averaged over multiple splits of the training data. By default, the model with the smallest root mean squared error is selected for regression problems and the model with the highest F1 score is selected for classification problems. For a list of possible models, see _LIST_OF_POTENTIAL_REGRESSORS_BETTER and _LIST_OF_POTENTIAL_CLASSIFIERS_BETTER, respectively. Model selection speed: Medium Model training speed: Fast Model inference speed: Fast Model accuracy: Good - AssignmentQuality.BEST: Uses an AutoGluon (auto ML) model with default settings defined by the AutoGluon wrapper. While the model selection itself is fast, the training and inference speed can be significantly slower than in the other options. NOTE: This requires the optional autogluon.tabular dependency. Model selection speed: Instant Model training speed: Slow Model inference speed: Slow-Medium Model accuracy: Best :param override_models: If set to True, existing model assignments are replaced with automatically selected ones. If set to False, the assigned models are only validated with respect to the graph structure. :return: None """ for node in causal_model.graph.nodes: if not override_models and CAUSAL_MECHANISM in causal_model.graph.nodes[node]: validate_causal_model_assignment(causal_model.graph, node) continue assign_causal_mechanism_node(causal_model, node, based_on, quality) def assign_causal_mechanism_node( causal_model: ProbabilisticCausalModel, node: str, based_on: pd.DataFrame, quality: AssignmentQuality = AssignmentQuality.GOOD, ) -> None: if is_root_node(causal_model.graph, node): causal_model.set_causal_mechanism(node, EmpiricalDistribution()) else: prediction_model = select_model( based_on[get_ordered_predecessors(causal_model.graph, node)].to_numpy(), based_on[node].to_numpy(), quality, ) if isinstance(prediction_model, ClassificationModel): causal_model.set_causal_mechanism(node, ClassifierFCM(prediction_model)) else: causal_model.set_causal_mechanism(node, AdditiveNoiseModel(prediction_model)) def select_model( X: np.ndarray, Y: np.ndarray, model_selection_quality: AssignmentQuality ) -> Union[PredictionModel, ClassificationModel]: if model_selection_quality == AssignmentQuality.BEST: try: from dowhy.gcm.ml.autogluon import AutoGluonClassifier, AutoGluonRegressor if is_categorical(Y): return AutoGluonClassifier() else: return AutoGluonRegressor() except ImportError: raise RuntimeError( "AutoGluon module not found! For the BEST auto assign quality, consider installing the " "optional AutoGluon dependency." ) elif model_selection_quality == AssignmentQuality.GOOD: list_of_regressor = list(_LIST_OF_POTENTIAL_REGRESSORS_GOOD) list_of_classifier = list(_LIST_OF_POTENTIAL_CLASSIFIERS_GOOD) model_selection_splits = 2 elif model_selection_quality == AssignmentQuality.BETTER: list_of_regressor = list(_LIST_OF_POTENTIAL_REGRESSORS_BETTER) list_of_classifier = list(_LIST_OF_POTENTIAL_CLASSIFIERS_BETTER) model_selection_splits = 5 else: raise ValueError("Invalid model selection quality.") if auto_apply_encoders(X, auto_fit_encoders(X)).shape[1] <= 5: # Avoid too many features list_of_regressor += [create_polynom_regressor] list_of_classifier += [partial(create_polynom_logistic_regression_classifier, max_iter=1000)] if is_categorical(Y): return find_best_model(list_of_classifier, X, Y, model_selection_splits=model_selection_splits)() else: return find_best_model(list_of_regressor, X, Y, model_selection_splits=model_selection_splits)() def has_linear_relationship(X: np.ndarray, Y: np.ndarray, max_num_samples: int = 3000) -> bool: X, Y = shape_into_2d(X, Y) target_is_categorical = is_categorical(Y) # Making sure there are at least 30% test samples. num_trainings_samples = min(max_num_samples, round(X.shape[0] * 0.7)) num_test_samples = min(X.shape[0] - num_trainings_samples, max_num_samples) if target_is_categorical: all_classes, indices, counts = np.unique(Y, return_counts=True, return_index=True) for i in range(all_classes.size): # Making sure that there are at least 2 samples from one class (here, simply duplicate the point). if counts[i] == 1: X = np.row_stack([X, X[indices[i], :]]) Y = np.row_stack([Y, Y[indices[i], :]]) x_train, x_test, y_train, y_test = train_test_split( X, Y, train_size=num_trainings_samples, test_size=num_test_samples, stratify=Y ) else: x_train, x_test, y_train, y_test = train_test_split( X, Y, train_size=num_trainings_samples, test_size=num_test_samples ) encoders = auto_fit_encoders(x_train, y_train) x_train = auto_apply_encoders(x_train, encoders) x_test = auto_apply_encoders(x_test, encoders) if target_is_categorical: linear_mdl = LogisticRegression(max_iter=1000) nonlinear_mdl = create_hist_gradient_boost_classifier() linear_mdl.fit(x_train, y_train.squeeze()) nonlinear_mdl.fit(x_train, y_train.squeeze()) # Compare number of correct classifications. return np.sum(shape_into_2d(linear_mdl.predict(x_test)) == y_test) >= np.sum( shape_into_2d(nonlinear_mdl.predict(x_test)) == y_test ) else: linear_mdl = LinearRegression() nonlinear_mdl = create_hist_gradient_boost_regressor() linear_mdl.fit(x_train, y_train.squeeze()) nonlinear_mdl.fit(x_train, y_train.squeeze()) return np.mean((y_test - shape_into_2d(linear_mdl.predict(x_test))) ** 2) <= np.mean( (y_test - shape_into_2d(nonlinear_mdl.predict(x_test))) ** 2 ) def find_best_model( prediction_model_factories: List[Callable[[], PredictionModel]], X: np.ndarray, Y: np.ndarray, metric: Optional[Callable[[np.ndarray, np.ndarray], float]] = None, max_samples_per_split: int = 10000, model_selection_splits: int = 5, n_jobs: Optional[int] = None, ) -> Callable[[], PredictionModel]: n_jobs = config.default_n_jobs if n_jobs is None else n_jobs X, Y = shape_into_2d(X, Y) is_classification_problem = isinstance(prediction_model_factories[0](), ClassificationModel) if metric is None: if is_classification_problem: metric = lambda y_true, y_preds: -metrics.f1_score( y_true, y_preds, average="macro", zero_division=0 ) # Higher score is better else: metric = metrics.mean_squared_error labelBinarizer = None if is_classification_problem: labelBinarizer = MultiLabelBinarizer() labelBinarizer.fit(Y) kfolds = list(KFold(n_splits=model_selection_splits).split(range(X.shape[0]))) def estimate_average_score(prediction_model_factory: Callable[[], PredictionModel], random_seed: int) -> float: set_random_seed(random_seed) average_result = 0 with warnings.catch_warnings(): warnings.filterwarnings("ignore", category=ConvergenceWarning) for train_indices, test_indices in kfolds: model_instance = prediction_model_factory() model_instance.fit(X[train_indices[:max_samples_per_split]], Y[train_indices[:max_samples_per_split]]) y_true = Y[test_indices[:max_samples_per_split]] y_pred = model_instance.predict(X[test_indices[:max_samples_per_split]]) if labelBinarizer is not None: y_true = labelBinarizer.transform(y_true) y_pred = labelBinarizer.transform(y_pred) average_result += metric(y_true, y_pred) return average_result / model_selection_splits random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(prediction_model_factories)) average_metric_scores = Parallel(n_jobs=n_jobs)( delayed(estimate_average_score)(prediction_model_factory, int(random_seed)) for prediction_model_factory, random_seed in zip(prediction_model_factories, random_seeds) ) return sorted(zip(prediction_model_factories, average_metric_scores), key=lambda x: x[1])[0][0]

import warnings from enum import Enum, auto from functools import partial from typing import Any, Callable, Dict, List, Optional, Tuple, Union import networkx as nx import numpy as np import pandas as pd from joblib import Parallel, delayed from sklearn import metrics from sklearn.exceptions import ConvergenceWarning from sklearn.linear_model import LinearRegression, LogisticRegression from sklearn.model_selection import KFold, train_test_split from sklearn.preprocessing import MultiLabelBinarizer from dowhy.gcm import config from dowhy.gcm.causal_mechanisms import AdditiveNoiseModel, ClassifierFCM from dowhy.gcm.causal_models import CAUSAL_MECHANISM, ProbabilisticCausalModel, validate_causal_model_assignment from dowhy.gcm.ml import ( ClassificationModel, PredictionModel, create_hist_gradient_boost_classifier, create_hist_gradient_boost_regressor, create_lasso_regressor, create_linear_regressor, create_logistic_regression_classifier, create_random_forest_regressor, create_ridge_regressor, create_support_vector_regressor, ) from dowhy.gcm.ml.classification import ( create_ada_boost_classifier, create_extra_trees_classifier, create_gaussian_nb_classifier, create_knn_classifier, create_polynom_logistic_regression_classifier, create_random_forest_classifier, create_support_vector_classifier, ) from dowhy.gcm.ml.regression import ( create_ada_boost_regressor, create_extra_trees_regressor, create_knn_regressor, create_polynom_regressor, ) from dowhy.gcm.stochastic_models import EmpiricalDistribution from dowhy.gcm.util.general import ( auto_apply_encoders, auto_fit_encoders, is_categorical, set_random_seed, shape_into_2d, ) from dowhy.graph import get_ordered_predecessors, is_root_node _LIST_OF_POTENTIAL_CLASSIFIERS_GOOD = [ partial(create_logistic_regression_classifier, max_iter=10000), create_hist_gradient_boost_classifier, ] _LIST_OF_POTENTIAL_REGRESSORS_GOOD = [ create_linear_regressor, create_hist_gradient_boost_regressor, ] _LIST_OF_POTENTIAL_CLASSIFIERS_BETTER = _LIST_OF_POTENTIAL_CLASSIFIERS_GOOD + [ create_random_forest_classifier, create_extra_trees_classifier, create_support_vector_classifier, create_knn_classifier, create_gaussian_nb_classifier, create_ada_boost_classifier, ] _LIST_OF_POTENTIAL_REGRESSORS_BETTER = _LIST_OF_POTENTIAL_REGRESSORS_GOOD + [ create_ridge_regressor, partial(create_lasso_regressor, max_iter=5000), create_random_forest_regressor, create_support_vector_regressor, create_extra_trees_regressor, create_knn_regressor, create_ada_boost_regressor, ] class AssignmentQuality(Enum): GOOD = auto() BETTER = auto() BEST = auto() class AutoAssignmentSummary: """Summary class for logging and storing information of the auto assignment process.""" def __init__(self): self._nodes: Dict[Dict[Any, Any]] = {} def _init_node_entry(self, node: Any): if node not in self._nodes: self._nodes[node] = {"messages": [], "model_performances": []} def add_node_log_message(self, node: Any, message: str): self._init_node_entry(node) self._nodes[node]["messages"].append(message) def add_model_performance(self, node, model: str, performance: str, metric_name: str): self._nodes[node]["model_performances"].append((model, performance, metric_name)) def __str__(self): summary_strings = [] summary_strings.append("Analyzed %d nodes." % len(list(self._nodes))) for node in self._nodes: summary_strings.append("--- Node: %s" % node) summary_strings.extend(self._nodes[node]["messages"]) if len(self._nodes[node]["model_performances"]) > 0: summary_strings.append( "For the model selection, the following models were evaluated on the %s metric:" % self._nodes[node]["model_performances"][0][2] ) for (model, performance, metric_name) in self._nodes[node]["model_performances"]: summary_strings.append("%s: %s" % (str(model()).replace("()", ""), str(performance))) summary_strings.append( "Based on the type of causal mechanism, the model with the lowest metric value " "represents the best choice." ) return "\n".join(summary_strings) def assign_causal_mechanisms( causal_model: ProbabilisticCausalModel, based_on: pd.DataFrame, quality: AssignmentQuality = AssignmentQuality.GOOD, override_models: bool = False, ) -> AutoAssignmentSummary: """Automatically assigns appropriate causal models. If causal models are already assigned to nodes and override_models is set to False, this function only validates the assignments with respect to the graph structure. Here, the validation checks whether root nodes have StochasticModels and non-root ConditionalStochasticModels assigned. :param causal_model: The causal model to whose nodes to assign causal models. :param based_on: Jointly sampled data corresponding to the nodes of the given graph. :param quality: AssignmentQuality for the automatic model selection and model accuracy. This changes the type of prediction model and time spent on the selection. Options are: - AssignmentQuality.GOOD: Compares a linear, polynomial and gradient boost model on small test-training split of the data. The best performing model is then selected. Model selection speed: Fast Model training speed: Fast Model inference speed: Fast Model accuracy: Medium - AssignmentQuality.BETTER: Compares multiple model types and uses the one with the best performance averaged over multiple splits of the training data. By default, the model with the smallest root mean squared error is selected for regression problems and the model with the highest F1 score is selected for classification problems. For a list of possible models, see _LIST_OF_POTENTIAL_REGRESSORS_BETTER and _LIST_OF_POTENTIAL_CLASSIFIERS_BETTER, respectively. Model selection speed: Medium Model training speed: Fast Model inference speed: Fast Model accuracy: Good - AssignmentQuality.BEST: Uses an AutoGluon (auto ML) model with default settings defined by the AutoGluon wrapper. While the model selection itself is fast, the training and inference speed can be significantly slower than in the other options. NOTE: This requires the optional autogluon.tabular dependency. Model selection speed: Instant Model training speed: Slow Model inference speed: Slow-Medium Model accuracy: Best :param override_models: If set to True, existing model assignments are replaced with automatically selected ones. If set to False, the assigned models are only validated with respect to the graph structure. :return: A summary object containing details about the model selection process. """ auto_assignment_summary = AutoAssignmentSummary() for node in nx.topological_sort(causal_model.graph): if not override_models and CAUSAL_MECHANISM in causal_model.graph.nodes[node]: auto_assignment_summary.add_node_log_message( node, "Node %s already has a model assigned and the override parameter is False. Skipping this node." % node, ) validate_causal_model_assignment(causal_model.graph, node) continue model_performances = assign_causal_mechanism_node(causal_model, node, based_on, quality) if is_root_node(causal_model.graph, node): auto_assignment_summary.add_node_log_message( node, "Node %s is a root node. Assigning '%s' to the node representing the marginal distribution." % (node, causal_model.causal_mechanism(node)), ) else: auto_assignment_summary.add_node_log_message( node, "Node %s is a non-root node. Assigning '%s' to the node." % (node, causal_model.causal_mechanism(node)), ) if isinstance(causal_model.causal_mechanism(node), AdditiveNoiseModel): auto_assignment_summary.add_node_log_message( node, "This represents the causal relationship as " + str(node) + " := f(" + ",".join([str(parent) for parent in get_ordered_predecessors(causal_model.graph, node)]) + ") + N.", ) elif isinstance(causal_model.causal_mechanism(node), ClassifierFCM): auto_assignment_summary.add_node_log_message( node, "This represents the causal relationship as " + str(node) + " := f(" + ",".join([str(parent) for parent in get_ordered_predecessors(causal_model.graph, node)]) + ",N).", ) for (model, performance, metric_name) in model_performances: auto_assignment_summary.add_model_performance(node, model, performance, metric_name) return auto_assignment_summary def assign_causal_mechanism_node( causal_model: ProbabilisticCausalModel, node: str, based_on: pd.DataFrame, quality: AssignmentQuality ) -> List[Tuple[Callable[[], PredictionModel], float, str]]: if is_root_node(causal_model.graph, node): causal_model.set_causal_mechanism(node, EmpiricalDistribution()) model_performances = [] else: best_model, model_performances = select_model( based_on[get_ordered_predecessors(causal_model.graph, node)].to_numpy(), based_on[node].to_numpy(), quality, ) if isinstance(best_model, ClassificationModel): causal_model.set_causal_mechanism(node, ClassifierFCM(best_model)) else: causal_model.set_causal_mechanism(node, AdditiveNoiseModel(best_model)) return model_performances def select_model( X: np.ndarray, Y: np.ndarray, model_selection_quality: AssignmentQuality ) -> Tuple[Union[PredictionModel, ClassificationModel], List[Tuple[Callable[[], PredictionModel], float, str]]]: if model_selection_quality == AssignmentQuality.BEST: try: from dowhy.gcm.ml.autogluon import AutoGluonClassifier, AutoGluonRegressor if is_categorical(Y): return AutoGluonClassifier(), [] else: return AutoGluonRegressor(), [] except ImportError: raise RuntimeError( "AutoGluon module not found! For the BEST auto assign quality, consider installing the " "optional AutoGluon dependency." ) elif model_selection_quality == AssignmentQuality.GOOD: list_of_regressor = list(_LIST_OF_POTENTIAL_REGRESSORS_GOOD) list_of_classifier = list(_LIST_OF_POTENTIAL_CLASSIFIERS_GOOD) model_selection_splits = 2 elif model_selection_quality == AssignmentQuality.BETTER: list_of_regressor = list(_LIST_OF_POTENTIAL_REGRESSORS_BETTER) list_of_classifier = list(_LIST_OF_POTENTIAL_CLASSIFIERS_BETTER) model_selection_splits = 5 else: raise ValueError("Invalid model selection quality.") if auto_apply_encoders(X, auto_fit_encoders(X)).shape[1] <= 5: # Avoid too many features list_of_regressor += [create_polynom_regressor] list_of_classifier += [partial(create_polynom_logistic_regression_classifier, max_iter=10000)] if is_categorical(Y): best_model, model_performances = find_best_model( list_of_classifier, X, Y, model_selection_splits=model_selection_splits ) return best_model(), model_performances else: best_model, model_performances = find_best_model( list_of_regressor, X, Y, model_selection_splits=model_selection_splits ) return best_model(), model_performances def has_linear_relationship(X: np.ndarray, Y: np.ndarray, max_num_samples: int = 3000) -> bool: X, Y = shape_into_2d(X, Y) target_is_categorical = is_categorical(Y) # Making sure there are at least 30% test samples. num_trainings_samples = min(max_num_samples, round(X.shape[0] * 0.7)) num_test_samples = min(X.shape[0] - num_trainings_samples, max_num_samples) if target_is_categorical: all_classes, indices, counts = np.unique(Y, return_counts=True, return_index=True) for i in range(all_classes.size): # Making sure that there are at least 2 samples from one class (here, simply duplicate the point). if counts[i] == 1: X = np.row_stack([X, X[indices[i], :]]) Y = np.row_stack([Y, Y[indices[i], :]]) x_train, x_test, y_train, y_test = train_test_split( X, Y, train_size=num_trainings_samples, test_size=num_test_samples, stratify=Y ) else: x_train, x_test, y_train, y_test = train_test_split( X, Y, train_size=num_trainings_samples, test_size=num_test_samples ) encoders = auto_fit_encoders(x_train, y_train) x_train = auto_apply_encoders(x_train, encoders) x_test = auto_apply_encoders(x_test, encoders) if target_is_categorical: linear_mdl = LogisticRegression(max_iter=1000) nonlinear_mdl = create_hist_gradient_boost_classifier() linear_mdl.fit(x_train, y_train.squeeze()) nonlinear_mdl.fit(x_train, y_train.squeeze()) # Compare number of correct classifications. return np.sum(shape_into_2d(linear_mdl.predict(x_test)) == y_test) >= np.sum( shape_into_2d(nonlinear_mdl.predict(x_test)) == y_test ) else: linear_mdl = LinearRegression() nonlinear_mdl = create_hist_gradient_boost_regressor() linear_mdl.fit(x_train, y_train.squeeze()) nonlinear_mdl.fit(x_train, y_train.squeeze()) return np.mean((y_test - shape_into_2d(linear_mdl.predict(x_test))) ** 2) <= np.mean( (y_test - shape_into_2d(nonlinear_mdl.predict(x_test))) ** 2 ) def find_best_model( prediction_model_factories: List[Callable[[], PredictionModel]], X: np.ndarray, Y: np.ndarray, metric: Optional[Callable[[np.ndarray, np.ndarray], float]] = None, max_samples_per_split: int = 10000, model_selection_splits: int = 5, n_jobs: Optional[int] = None, ) -> Tuple[Callable[[], PredictionModel], List[Tuple[Callable[[], PredictionModel], float, str]]]: n_jobs = config.default_n_jobs if n_jobs is None else n_jobs X, Y = shape_into_2d(X, Y) is_classification_problem = isinstance(prediction_model_factories[0](), ClassificationModel) metric_name = "given" if metric is None: metric_name = "(negative) F1" if is_classification_problem: metric = lambda y_true, y_preds: -metrics.f1_score( y_true, y_preds, average="macro", zero_division=0 ) # Higher score is better else: metric_name = "mean squared error (MSE)" metric = metrics.mean_squared_error labelBinarizer = None if is_classification_problem: labelBinarizer = MultiLabelBinarizer() labelBinarizer.fit(Y) kfolds = list(KFold(n_splits=model_selection_splits, shuffle=True).split(range(X.shape[0]))) def estimate_average_score(prediction_model_factory: Callable[[], PredictionModel], random_seed: int) -> float: set_random_seed(random_seed) average_result = 0 with warnings.catch_warnings(): warnings.filterwarnings("ignore", category=ConvergenceWarning) for train_indices, test_indices in kfolds: model_instance = prediction_model_factory() model_instance.fit(X[train_indices[:max_samples_per_split]], Y[train_indices[:max_samples_per_split]]) y_true = Y[test_indices[:max_samples_per_split]] y_pred = model_instance.predict(X[test_indices[:max_samples_per_split]]) if labelBinarizer is not None: y_true = labelBinarizer.transform(y_true) y_pred = labelBinarizer.transform(y_pred) average_result += metric(y_true, y_pred) return average_result / model_selection_splits random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(prediction_model_factories)) average_metric_scores = Parallel(n_jobs=n_jobs)( delayed(estimate_average_score)(prediction_model_factory, int(random_seed)) for prediction_model_factory, random_seed in zip(prediction_model_factories, random_seeds) ) sorted_results = sorted( zip(prediction_model_factories, average_metric_scores, [metric_name] * len(prediction_model_factories)), key=lambda x: x[1], ) return sorted_results[0][0], sorted_results

bloebp

86fcb28ba23269922dad2c2b93a00fca3c611e0f

d83fc3f022c9ce4bc8f3ebfc7eee099a3c5db74b

`.keys()` is redundant here

kailashbuki

py-why/dowhy

Update the causal discovery notebook with examples using causal-learn

Updating the old notebook as mentioned in #1021.

2023-08-30 21:25:09+00:00

2023-10-05 21:26:19+00:00

docs/source/example_notebooks/dowhy_causal_discovery_example.ipynb

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery example\n", "\n", "The goal of this notebook is to show how causal discovery methods can work with DoWhy. We use discovery methods from [Causal Discovery Tool (CDT)](https://github.com/FenTechSolutions/CausalDiscoveryToolbox) repo. As we will see, causal discovery methods are not fool-proof and there is no guarantee that they will recover the correct causal graph. Even for the simple examples below, there is a large variance in results. These methods, however, may be combined usefully with domain knowledge to construct the final causal graph." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import dowhy\n", "from dowhy import CausalModel\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import graphviz\n", "import networkx as nx \n", "\n", "np.set_printoptions(precision=3, suppress=True)\n", "np.random.seed(0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Utility function\n", "We define a utility function to draw the directed acyclic graph." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def make_graph(adjacency_matrix, labels=None):\n", " idx = np.abs(adjacency_matrix) > 0.01\n", " dirs = np.where(idx)\n", " d = graphviz.Digraph(engine='dot')\n", " names = labels if labels else [f'x{i}' for i in range(len(adjacency_matrix))]\n", " for name in names:\n", " d.node(name)\n", " for to, from_, coef in zip(dirs[0], dirs[1], adjacency_matrix[idx]):\n", " d.edge(names[from_], names[to], label=str(coef))\n", " return d\n", "\n", "def str_to_dot(string):\n", " '''\n", " Converts input string from graphviz library to valid DOT graph format.\n", " '''\n", " graph = string.strip().replace('\\n', ';').replace('\\t','')\n", " graph = graph[:9] + graph[10:-2] + graph[-1] # Removing unnecessary characters from string\n", " return graph" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Experiments on the Auto-MPG dataset\n", "\n", "In this section, we will use a dataset on the technical specification of cars. The dataset is downloaded from UCI Machine Learning Repository. The dataset contains 9 attributes and 398 instances. We do not know the true causal graph for the dataset and will use CDT to discover it. The causal graph obtained will then be used to estimate the causal effect.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Load the data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data_mpg = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data-original',\n", " delim_whitespace=True, header=None,\n", " names = ['mpg', 'cylinders', 'displacement',\n", " 'horsepower', 'weight', 'acceleration',\n", " 'model year', 'origin', 'car name'])\n", "data_mpg.dropna(inplace=True)\n", "data_mpg.drop(['model year', 'origin', 'car name'], axis=1, inplace=True)\n", "print(data_mpg.shape)\n", "data_mpg.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery with Causal Discovery Tool (CDT)\n", "\n", "We use the CDT library to perform causal discovery on the Auto-MPG dataset. We use three methods for causal discovery here -LiNGAM, PC and GES. These methods are widely used and do not take much time to run. Hence, these are ideal for an introduction to the topic. Other neural network based methods are also available in CDT and the users are encouraged to try them out by themselves. \n", "\n", "The documentation for the methods used are as follows:\n", "- LiNGAM [[link]](https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/_modules/cdt/causality/graph/LiNGAM.html)\n", "- PC [[link]](https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/_modules/cdt/causality/graph/PC.html)\n", "- GES [[link]](https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/_modules/cdt/causality/graph/GES.html)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from cdt.causality.graph import LiNGAM, PC, GES\n", "\n", "graphs = {}\n", "labels = [f'{col}' for i, col in enumerate(data_mpg.columns)]\n", "functions = {\n", " 'LiNGAM' : LiNGAM,\n", " 'PC' : PC,\n", " 'GES' : GES,\n", "}\n", "\n", "for method, lib in functions.items():\n", " obj = lib()\n", " output = obj.predict(data_mpg)\n", " adj_matrix = nx.to_numpy_array(output)\n", " adj_matrix = np.asarray(adj_matrix)\n", " graph_dot = make_graph(adj_matrix, labels)\n", " graphs[method] = graph_dot\n", "\n", "# Visualize graphs\n", "for method, graph in graphs.items():\n", " print(\"Method : %s\"%(method))\n", " display(graph)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, no two methods agree on the graphs. PC and GES effectively produce an undirected graph whereas LiNGAM produces a directed graph. We use only the LiNGAM method in the next section." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimate causal effects using Linear Regression\n", "\n", "Now let us see whether these differences in the graphs also lead to significant differences in the causal estimate of effect of *mpg* on *weight*." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for method, graph in graphs.items():\n", " if method != \"LiNGAM\":\n", " continue\n", " print('\\n*****************************************************************************\\n')\n", " print(\"Causal Discovery Method : %s\"%(method))\n", " \n", " # Obtain valid dot format\n", " graph_dot = str_to_dot(graph.source)\n", "\n", " # Define Causal Model\n", " model=CausalModel(\n", " data = data_mpg,\n", " treatment='mpg',\n", " outcome='weight',\n", " graph=graph_dot)\n", "\n", " # Identification\n", " identified_estimand = model.identify_effect(proceed_when_unidentifiable=True)\n", " print(identified_estimand)\n", " \n", " # Estimation\n", " estimate = model.estimate_effect(identified_estimand,\n", " method_name=\"backdoor.linear_regression\",\n", " control_value=0,\n", " treatment_value=1,\n", " confidence_intervals=True,\n", " test_significance=True)\n", " print(\"Causal Estimate is \" + str(estimate.value))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As mentioned earlier, due to the absence of directed edges, no backdoor, instrmental or frontdoor variables can be found out for PC and GES. Thus, causal effect estimation is not possible for these methods. However, LiNGAM does discover a DAG and hence, its possible to output a causal estimate for LiNGAM. The estimate is still pretty far from the original estimate of -70.466 (which can be calculated from the graph)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Experiments on the Sachs dataset\n", "\n", "The dataset consists of the simultaneous measurements of 11 phosphorylated proteins and phospholipids derived from thousands of individual primary immune system cells, subjected to both general and specific molecular interventions (Sachs et al., 2005).\n", "\n", "The specifications of the dataset are as follows - \n", "- Number of nodes: 11\n", "- Number of arcs: 17\n", "- Number of parameters: 178\n", "- Average Markov blanket size: 3.09\n", "- Average degree: 3.09\n", "- Maximum in-degree: 3\n", "- Number of instances: 7466\n", "\n", "The original causal graph is known for the Sachs dataset and we compare the original graph with the ones discovered using CDT in this section." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Load the data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from cdt.data import load_dataset\n", "data_sachs, graph_sachs = load_dataset(\"sachs\")\n", "\n", "data_sachs.dropna(inplace=True)\n", "print(data_sachs.shape)\n", "data_sachs.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Ground truth of the causal graph" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "labels = [f'{col}' for i, col in enumerate(data_sachs.columns)]\n", "adj_matrix = nx.to_numpy_array(graph_sachs)\n", "adj_matrix = np.asarray(adj_matrix)\n", "graph_dot = make_graph(adj_matrix, labels)\n", "display(graph_dot)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery with Causal Discovery Tool (CDT)\n", "\n", "We use the CDT library to perform causal discovery on the Auto-MPG dataset. We use three methods for causal discovery here -LiNGAM, PC and GES. These methods are widely used and do not take much time to run. Hence, these are ideal for an introduction to the topic. Other neural network based methods are also available in CDT and the users the encourages to try them out by themselves. \n", "\n", "The documentation for the methods used in as follows:\n", "- LiNGAM [[link]](https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/_modules/cdt/causality/graph/LiNGAM.html)\n", "- PC [[link]](https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/_modules/cdt/causality/graph/PC.html)\n", "- GES [[link]](https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/_modules/cdt/causality/graph/GES.html)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from cdt.causality.graph import LiNGAM, PC, GES\n", "\n", "graphs = {}\n", "graphs_nx = {}\n", "labels = [f'{col}' for i, col in enumerate(data_sachs.columns)]\n", "functions = {\n", " 'LiNGAM' : LiNGAM,\n", " 'PC' : PC,\n", " 'GES' : GES,\n", "}\n", "\n", "for method, lib in functions.items():\n", " obj = lib()\n", " output = obj.predict(data_sachs)\n", " graphs_nx[method] = output\n", " adj_matrix = nx.to_numpy_array(output)\n", " adj_matrix = np.asarray(adj_matrix)\n", " graph_dot = make_graph(adj_matrix, labels)\n", " graphs[method] = graph_dot\n", "\n", "# Visualize graphs\n", "for method, graph in graphs.items():\n", " print(\"Method : %s\"%(method))\n", " display(graph)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, no two methods agree on the graphs. Next we study the causal effects of these different graphs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimate effects using Linear Regression\n", "\n", "Now let us see whether these differences in the graphs also lead to significant differences in the causal estimate of effect of *PIP2* on *PKC*." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for method, graph in graphs.items():\n", " if method != \"LiNGAM\":\n", " continue\n", " print('\\n*****************************************************************************\\n')\n", " print(\"Causal Discovery Method : %s\"%(method))\n", "\n", " # Obtain valid dot format\n", " graph_dot = str_to_dot(graph.source)\n", "\n", " # Define Causal Model\n", " model=CausalModel(\n", " data = data_sachs,\n", " treatment='PIP2',\n", " outcome='PKC',\n", " graph=graph_dot)\n", "\n", " # Identification\n", " identified_estimand = model.identify_effect(proceed_when_unidentifiable=True)\n", " print(identified_estimand)\n", "\n", " # Estimation\n", " estimate = model.estimate_effect(identified_estimand,\n", " method_name=\"backdoor.linear_regression\",\n", " control_value=0,\n", " treatment_value=1,\n", " confidence_intervals=True,\n", " test_significance=True)\n", " print(\"Causal Estimate is \" + str(estimate.value))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the causal estimates obtained, it can be seen that the three estimates differ in different aspects. The graph obtained using LiNGAM contains a backdoor path and instrumental variables. On the other hand, the graph obtained using PC contains a backdoor path and a frontdoor path. However, despite these differences, both obtain the same mean causal estimate.\n", "\n", "The graph obtained using GES contains only a backdoor path with different backdoor variables and obtains a different causal estimate than the first two cases. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Graph Validation\n", "\n", "We compare the graphs obtained with the true causal graph using the causal discovery methods using 2 graph distance metrics - Structural Hamming Distance (SHD) and Structural Intervention Distance (SID). SHD between two graphs is, in simple terms, the number of edge insertions, deletions or flips in order to transform one graph to another graph. SID, on the other hand, is based on a graphical criterion only and quantifies the closeness between two DAGs in terms of their corresponding causal inference statements." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from cdt.metrics import SHD, SHD_CPDAG, SID, SID_CPDAG\n", "from numpy.random import randint\n", "\n", "for method, graph in graphs_nx.items():\n", " print(\"***********************************************************\")\n", " print(\"Method: %s\"%(method))\n", " tar, pred = graph_sachs, graph\n", " print(\"SHD_CPDAG = %f\"%(SHD_CPDAG(tar, pred)))\n", " print(\"SHD = %f\"%(SHD(tar, pred, double_for_anticausal=False)))\n", " print(\"SID_CPDAG = [%f, %f]\"%(SID_CPDAG(tar, pred)))\n", " print(\"SID = %f\"%(SID(tar, pred)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The graph similarity metrics show that the scores are the lowest for the LiNGAM method of graph extraction. Hence, of the three methods used, LiNGAM provides the graph that is most similar to the original graph." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Graph Refutation\n", "\n", "Here, we use the same SHD and SID metric to find out how different the discovered graph are from each other." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import itertools\n", "from numpy.random import randint\n", "from cdt.metrics import SHD, SHD_CPDAG, SID, SID_CPDAG\n", "\n", "# Find combinations of pair of methods to compare\n", "combinations = list(itertools.combinations(graphs_nx, 2))\n", "\n", "for pair in combinations:\n", " print(\"***********************************************************\")\n", " graph1 = graphs_nx[pair[0]]\n", " graph2 = graphs_nx[pair[1]]\n", " print(\"Methods: %s and %s\"%(pair[0], pair[1]))\n", " print(\"SHD_CPDAG = %f\"%(SHD_CPDAG(graph1, graph2)))\n", " print(\"SHD = %f\"%(SHD(graph1, graph2, double_for_anticausal=False)))\n", " print(\"SID_CPDAG = [%f, %f]\"%(SID_CPDAG(graph1, graph2)))\n", " print(\"SID = %f\"%(SID(graph1, graph2)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The values for the metrics show how different the graphs are from each other. A higher distance value implies that the difference between the graphs is more." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.12" }, "metadata": { "interpreter": { "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" } }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": false, "sideBar": true, "skip_h1_title": true, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery example\n", "\n", "The goal of this notebook is to show how causal discovery methods can work with DoWhy. We use discovery methods from [causal-learn](https://github.com/py-why/causal-learn) repo. As we will see, causal discovery methods require appropriate assumptions for the correctness guarantees, adn thus there will be variance across results returned by different methods in practice. These methods, however, may be combined usefully with domain knowledge to construct the final causal graph." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import dowhy\n", "from dowhy import CausalModel\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import graphviz\n", "import networkx as nx \n", "\n", "np.set_printoptions(precision=3, suppress=True)\n", "np.random.seed(0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Utility function\n", "We define a utility function to draw the directed acyclic graph." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def make_graph(adjacency_matrix, labels=None):\n", " idx = np.abs(adjacency_matrix) > 0.01\n", " dirs = np.where(idx)\n", " d = graphviz.Digraph(engine='dot')\n", " names = labels if labels else [f'x{i}' for i in range(len(adjacency_matrix))]\n", " for name in names:\n", " d.node(name)\n", " for to, from_, coef in zip(dirs[0], dirs[1], adjacency_matrix[idx]):\n", " d.edge(names[from_], names[to], label=str(coef))\n", " return d\n", "\n", "def str_to_dot(string):\n", " '''\n", " Converts input string from graphviz library to valid DOT graph format.\n", " '''\n", " graph = string.strip().replace('\\n', ';').replace('\\t','')\n", " graph = graph[:9] + graph[10:-2] + graph[-1] # Removing unnecessary characters from string\n", " return graph" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Experiments on the Auto-MPG dataset\n", "\n", "In this section, we will use a dataset on the technical specification of cars. The dataset is downloaded from UCI Machine Learning Repository. The dataset contains 9 attributes and 398 instances. We do not know the true causal graph for the dataset and will use causal-learn to discover it. The causal graph obtained will then be used to estimate the causal effect.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Load the data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(392, 6)\n" ] }, { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>mpg</th>\n", " <th>cylinders</th>\n", " <th>displacement</th>\n", " <th>horsepower</th>\n", " <th>weight</th>\n", " <th>acceleration</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>18.0</td>\n", " <td>8.0</td>\n", " <td>307.0</td>\n", " <td>130.0</td>\n", " <td>3504.0</td>\n", " <td>12.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>15.0</td>\n", " <td>8.0</td>\n", " <td>350.0</td>\n", " <td>165.0</td>\n", " <td>3693.0</td>\n", " <td>11.5</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>18.0</td>\n", " <td>8.0</td>\n", " <td>318.0</td>\n", " <td>150.0</td>\n", " <td>3436.0</td>\n", " <td>11.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>16.0</td>\n", " <td>8.0</td>\n", " <td>304.0</td>\n", " <td>150.0</td>\n", " <td>3433.0</td>\n", " <td>12.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>17.0</td>\n", " <td>8.0</td>\n", " <td>302.0</td>\n", " <td>140.0</td>\n", " <td>3449.0</td>\n", " <td>10.5</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " mpg cylinders displacement horsepower weight acceleration\n", "0 18.0 8.0 307.0 130.0 3504.0 12.0\n", "1 15.0 8.0 350.0 165.0 3693.0 11.5\n", "2 18.0 8.0 318.0 150.0 3436.0 11.0\n", "3 16.0 8.0 304.0 150.0 3433.0 12.0\n", "4 17.0 8.0 302.0 140.0 3449.0 10.5" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_mpg = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data-original',\n", " delim_whitespace=True, header=None,\n", " names = ['mpg', 'cylinders', 'displacement',\n", " 'horsepower', 'weight', 'acceleration',\n", " 'model year', 'origin', 'car name'])\n", "data_mpg.dropna(inplace=True)\n", "data_mpg.drop(['model year', 'origin', 'car name'], axis=1, inplace=True)\n", "print(data_mpg.shape)\n", "data_mpg.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery with causal-learn\n", "\n", "We use the causal-learn library to perform causal discovery on the Auto-MPG dataset. We use three methods for causal discovery here: PC, FCI and GES. These methods are widely used and do not take much time to run. Hence, these are ideal for an introduction to the topic. Causal-learn provides a comprehensive list of well-tested causal-discovery methods, and readers are welcome to explore.\n", "\n", "The documentation for the methods used are as follows:\n", "- PC [[link]](https://causal-learn.readthedocs.io/en/latest/search_methods_index/Constraint-based%20causal%20discovery%20methods/PC.html)\n", "- GES [[link]](https://causal-learn.readthedocs.io/en/latest/search_methods_index/Score-based%20causal%20discovery%20methods/GES.html)\n", "- LiNGAM [[link]](https://causal-learn.readthedocs.io/en/latest/search_methods_index/Causal%20discovery%20methods%20based%20on%20constrained%20functional%20causal%20models/lingam.html#ica-based-lingam)\n", "\n", "More methods could be found in the causal-learn documentation [[link]](https://causal-learn.readthedocs.io/en/latest/)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We first try the PC algorithm with default parameters." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1ed197e9f5ec42c8bf7fc51c5ece4485", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/6 [00:00<?, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from causallearn.search.ConstraintBased.PC import pc\n", "\n", "labels = [f'{col}' for i, col in enumerate(data_mpg.columns)]\n", "data = data_mpg.to_numpy()\n", "\n", "cg = pc(data)\n", "\n", "# Visualization using pydot\n", "from causallearn.utils.GraphUtils import GraphUtils\n", "import matplotlib.image as mpimg\n", "import matplotlib.pyplot as plt\n", "import io\n", "\n", "pyd = GraphUtils.to_pydot(cg.G, labels=labels)\n", "tmp_png = pyd.create_png(f=\"png\")\n", "fp = io.BytesIO(tmp_png)\n", "img = mpimg.imread(fp, format='png')\n", "plt.axis('off')\n", "plt.imshow(img)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we have a causal graph discovered by PC. Let us also try GES to see its result." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from causallearn.search.ScoreBased.GES import ges\n", "\n", "# default parameters\n", "Record = ges(data)\n", "\n", "# Visualization using pydot\n", "from causallearn.utils.GraphUtils import GraphUtils\n", "import matplotlib.image as mpimg\n", "import matplotlib.pyplot as plt\n", "import io\n", "\n", "pyd = GraphUtils.to_pydot(Record['G'], labels=labels)\n", "tmp_png = pyd.create_png(f=\"png\")\n", "fp = io.BytesIO(tmp_png)\n", "img = mpimg.imread(fp, format='png')\n", "plt.axis('off')\n", "plt.imshow(img)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Well, these two results are different, which is not rare when applying causal discovery on real-world dataset, since the required assumptions on the data-generating process are hard to verify.\n", "\n", "In addition, the graphs returned by PC and GES are CPDAGs instead of DAGs, so it is possible to have undirected edges (e.g., the result returned by GES). Thus, causal effect estimataion is difficult for those methods, since there may be absence of backdoor, instrumental or frontdoor variables. In order to get a DAG, we decide to try LiNGAM on our dataset." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n", "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n", " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n", "<!-- Generated by graphviz version 2.43.0 (0)\n", " -->\n", "<!-- Title: %3 Pages: 1 -->\n", "<svg width=\"369pt\" height=\"392pt\"\n", " viewBox=\"0.00 0.00 369.40 392.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n", "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 388)\">\n", "<title>%3</title>\n", "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-388 365.4,-388 365.4,4 -4,4\"/>\n", "<!-- mpg -->\n", "<g id=\"node1\" class=\"node\">\n", "<title>mpg</title>\n", "<ellipse fill=\"none\" stroke=\"black\" cx=\"34.8\" cy=\"-279\" rx=\"31.4\" 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"text/plain": [ "<graphviz.graphs.Digraph at 0x7f957464c040>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from causallearn.search.FCMBased import lingam\n", "model = lingam.ICALiNGAM()\n", "model.fit(data)\n", "\n", "from causallearn.search.FCMBased.lingam.utils import make_dot\n", "make_dot(model.adjacency_matrix_, labels=labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we have a DAG and are ready to estimate the causal effects based on that." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimate causal effects using Linear Regression\n", "\n", "Now let us see the estimate of causal effect of *mpg* on *weight*." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Estimand type: EstimandType.NONPARAMETRIC_ATE\n", "\n", "### Estimand : 1\n", "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", "──────(E[weight|cylinders])\n", "d[mpg] \n", "Estimand assumption 1, Unconfoundedness: If U→{mpg} and U→weight then P(weight|mpg,cylinders,U) = P(weight|mpg,cylinders)\n", "\n", "### Estimand : 2\n", "Estimand name: iv\n", "No such variable(s) found!\n", "\n", "### Estimand : 3\n", "Estimand name: frontdoor\n", "No such variable(s) found!\n", "\n", "Causal Estimate is -38.940973656209735\n" ] } ], "source": [ "# Obtain valid dot format\n", "graph_dot = make_graph(model.adjacency_matrix_, labels=labels)\n", "\n", "# Define Causal Model\n", "model=CausalModel(\n", " data = data_mpg,\n", " treatment='mpg',\n", " outcome='weight',\n", " graph=str_to_dot(graph_dot.source))\n", "\n", "# Identification\n", "identified_estimand = model.identify_effect(proceed_when_unidentifiable=True)\n", "print(identified_estimand)\n", "\n", "# Estimation\n", "estimate = model.estimate_effect(identified_estimand,\n", " method_name=\"backdoor.linear_regression\",\n", " control_value=0,\n", " treatment_value=1,\n", " confidence_intervals=True,\n", " test_significance=True)\n", "print(\"Causal Estimate is \" + str(estimate.value))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Experiments on the Sachs dataset\n", "\n", "The dataset consists of the simultaneous measurements of 11 phosphorylated proteins and phospholipids derived from thousands of individual primary immune system cells, subjected to both general and specific molecular interventions (Sachs et al., 2005).\n", "\n", "The specifications of the dataset are as follows - \n", "- Number of nodes: 11\n", "- Number of arcs: 17\n", "- Number of parameters: 178\n", "- Average Markov blanket size: 3.09\n", "- Average degree: 3.09\n", "- Maximum in-degree: 3\n", "- Number of instances: 7466" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Load the data" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(7466, 11)\n", "['raf', 'mek', 'plc', 'pip2', 'pip3', 'erk', 'akt', 'pka', 'pkc', 'p38', 'jnk']\n" ] } ], "source": [ "from causallearn.utils.Dataset import load_dataset\n", "\n", "data_sachs, labels = load_dataset(\"sachs\")\n", "\n", "print(data.shape)\n", "print(labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery with causal-learn\n", "\n", "We use the three causal discovery methods mentioned above (PC, GES, and LiNGAM) to find the causal graphs." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, let us take a look at how PC works." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "bc0f31d1492e4934994a6d4ba68f1ad3", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/11 [00:00<?, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "graphs = {}\n", "graphs_nx = {}\n", "labels = [f'{col}' for i, col in enumerate(labels)]\n", "data = data_sachs\n", "\n", "from causallearn.search.ConstraintBased.PC import pc\n", "\n", "cg = pc(data)\n", "\n", "# Visualization using pydot\n", "from causallearn.utils.GraphUtils import GraphUtils\n", "import matplotlib.image as mpimg\n", "import matplotlib.pyplot as plt\n", "import io\n", "\n", "pyd = GraphUtils.to_pydot(cg.G, labels=labels)\n", "tmp_png = pyd.create_png(f=\"png\")\n", "fp = io.BytesIO(tmp_png)\n", "img = mpimg.imread(fp, format='png')\n", "plt.axis('off')\n", "plt.imshow(img)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then, let us try GES." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from causallearn.search.ScoreBased.GES import ges\n", "\n", "# default parameters\n", "Record = ges(data)\n", "\n", "# Visualization using pydot\n", "from causallearn.utils.GraphUtils import GraphUtils\n", "import matplotlib.image as mpimg\n", "import matplotlib.pyplot as plt\n", "import io\n", "\n", "pyd = GraphUtils.to_pydot(Record['G'], labels=labels)\n", "tmp_png = pyd.create_png(f=\"png\")\n", "fp = io.BytesIO(tmp_png)\n", "img = mpimg.imread(fp, format='png')\n", "plt.axis('off')\n", "plt.imshow(img)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And also LiNGAM." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n", "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n", " 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points=\"674.5,-46.18 671,-36.18 667.5,-46.18 674.5,-46.18\"/>\n", "<text text-anchor=\"middle\" x=\"687\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">0.04</text>\n", "</g>\n", "</g>\n", "</svg>\n" ], "text/plain": [ "<graphviz.graphs.Digraph at 0x7f96cd974ca0>" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from causallearn.search.FCMBased import lingam\n", "model = lingam.ICALiNGAM()\n", "model.fit(data)\n", "\n", "from causallearn.search.FCMBased.lingam.utils import make_dot\n", "make_dot(model.adjacency_matrix_, labels=labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimate effects using Linear Regression\n", "\n", "Similarly, let us use the DAG returned by LiNGAM to estimate the causal effect of *PIP2* on *PKC*." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Estimand type: EstimandType.NONPARAMETRIC_ATE\n", "\n", "### Estimand : 1\n", "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", "───────(E[pkc|plc,pip3])\n", "d[pip₂] \n", "Estimand assumption 1, Unconfoundedness: If U→{pip2} and U→pkc then P(pkc|pip2,plc,pip3,U) = P(pkc|pip2,plc,pip3)\n", "\n", "### Estimand : 2\n", "Estimand name: iv\n", "No such variable(s) found!\n", "\n", "### Estimand : 3\n", "Estimand name: frontdoor\n", "No such variable(s) found!\n", "\n", "Causal Estimate is 0.03397189228452291\n" ] } ], "source": [ "# Obtain valid dot format\n", "graph_dot = make_graph(model.adjacency_matrix_, labels=labels)\n", "\n", "data_df = pd.DataFrame(data=data, columns=labels)\n", "\n", "# Define Causal Model\n", "model_est=CausalModel(\n", " data = data_df,\n", " treatment='pip2',\n", " outcome='pkc',\n", " graph=str_to_dot(graph_dot.source))\n", "\n", "# Identification\n", "identified_estimand = model_est.identify_effect(proceed_when_unidentifiable=False)\n", "print(identified_estimand)\n", "\n", "# Estimation\n", "estimate = model_est.estimate_effect(identified_estimand,\n", " method_name=\"backdoor.linear_regression\",\n", " control_value=0,\n", " treatment_value=1,\n", " confidence_intervals=True,\n", " test_significance=True)\n", "print(\"Causal Estimate is \" + str(estimate.value))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.17" }, "metadata": { "interpreter": { "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" } }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": false, "sideBar": true, "skip_h1_title": true, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }

kunwuz

67b305db5224bf718067a21acfe4baa92a7d2c8c

7eb4a0c253514a920588d1ab222e1aeb5e07cb51

"We use the CDT library" should be "we use the causal-learn library"

emrekiciman

py-why/dowhy

Update the causal discovery notebook with examples using causal-learn

Updating the old notebook as mentioned in #1021.

2023-08-30 21:25:09+00:00

2023-10-05 21:26:19+00:00

docs/source/example_notebooks/dowhy_causal_discovery_example.ipynb

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery example\n", "\n", "The goal of this notebook is to show how causal discovery methods can work with DoWhy. We use discovery methods from [Causal Discovery Tool (CDT)](https://github.com/FenTechSolutions/CausalDiscoveryToolbox) repo. As we will see, causal discovery methods are not fool-proof and there is no guarantee that they will recover the correct causal graph. Even for the simple examples below, there is a large variance in results. These methods, however, may be combined usefully with domain knowledge to construct the final causal graph." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import dowhy\n", "from dowhy import CausalModel\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import graphviz\n", "import networkx as nx \n", "\n", "np.set_printoptions(precision=3, suppress=True)\n", "np.random.seed(0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Utility function\n", "We define a utility function to draw the directed acyclic graph." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def make_graph(adjacency_matrix, labels=None):\n", " idx = np.abs(adjacency_matrix) > 0.01\n", " dirs = np.where(idx)\n", " d = graphviz.Digraph(engine='dot')\n", " names = labels if labels else [f'x{i}' for i in range(len(adjacency_matrix))]\n", " for name in names:\n", " d.node(name)\n", " for to, from_, coef in zip(dirs[0], dirs[1], adjacency_matrix[idx]):\n", " d.edge(names[from_], names[to], label=str(coef))\n", " return d\n", "\n", "def str_to_dot(string):\n", " '''\n", " Converts input string from graphviz library to valid DOT graph format.\n", " '''\n", " graph = string.strip().replace('\\n', ';').replace('\\t','')\n", " graph = graph[:9] + graph[10:-2] + graph[-1] # Removing unnecessary characters from string\n", " return graph" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Experiments on the Auto-MPG dataset\n", "\n", "In this section, we will use a dataset on the technical specification of cars. The dataset is downloaded from UCI Machine Learning Repository. The dataset contains 9 attributes and 398 instances. We do not know the true causal graph for the dataset and will use CDT to discover it. The causal graph obtained will then be used to estimate the causal effect.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Load the data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data_mpg = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data-original',\n", " delim_whitespace=True, header=None,\n", " names = ['mpg', 'cylinders', 'displacement',\n", " 'horsepower', 'weight', 'acceleration',\n", " 'model year', 'origin', 'car name'])\n", "data_mpg.dropna(inplace=True)\n", "data_mpg.drop(['model year', 'origin', 'car name'], axis=1, inplace=True)\n", "print(data_mpg.shape)\n", "data_mpg.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery with Causal Discovery Tool (CDT)\n", "\n", "We use the CDT library to perform causal discovery on the Auto-MPG dataset. We use three methods for causal discovery here -LiNGAM, PC and GES. These methods are widely used and do not take much time to run. Hence, these are ideal for an introduction to the topic. Other neural network based methods are also available in CDT and the users are encouraged to try them out by themselves. \n", "\n", "The documentation for the methods used are as follows:\n", "- LiNGAM [[link]](https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/_modules/cdt/causality/graph/LiNGAM.html)\n", "- PC [[link]](https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/_modules/cdt/causality/graph/PC.html)\n", "- GES [[link]](https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/_modules/cdt/causality/graph/GES.html)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from cdt.causality.graph import LiNGAM, PC, GES\n", "\n", "graphs = {}\n", "labels = [f'{col}' for i, col in enumerate(data_mpg.columns)]\n", "functions = {\n", " 'LiNGAM' : LiNGAM,\n", " 'PC' : PC,\n", " 'GES' : GES,\n", "}\n", "\n", "for method, lib in functions.items():\n", " obj = lib()\n", " output = obj.predict(data_mpg)\n", " adj_matrix = nx.to_numpy_array(output)\n", " adj_matrix = np.asarray(adj_matrix)\n", " graph_dot = make_graph(adj_matrix, labels)\n", " graphs[method] = graph_dot\n", "\n", "# Visualize graphs\n", "for method, graph in graphs.items():\n", " print(\"Method : %s\"%(method))\n", " display(graph)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, no two methods agree on the graphs. PC and GES effectively produce an undirected graph whereas LiNGAM produces a directed graph. We use only the LiNGAM method in the next section." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimate causal effects using Linear Regression\n", "\n", "Now let us see whether these differences in the graphs also lead to significant differences in the causal estimate of effect of *mpg* on *weight*." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for method, graph in graphs.items():\n", " if method != \"LiNGAM\":\n", " continue\n", " print('\\n*****************************************************************************\\n')\n", " print(\"Causal Discovery Method : %s\"%(method))\n", " \n", " # Obtain valid dot format\n", " graph_dot = str_to_dot(graph.source)\n", "\n", " # Define Causal Model\n", " model=CausalModel(\n", " data = data_mpg,\n", " treatment='mpg',\n", " outcome='weight',\n", " graph=graph_dot)\n", "\n", " # Identification\n", " identified_estimand = model.identify_effect(proceed_when_unidentifiable=True)\n", " print(identified_estimand)\n", " \n", " # Estimation\n", " estimate = model.estimate_effect(identified_estimand,\n", " method_name=\"backdoor.linear_regression\",\n", " control_value=0,\n", " treatment_value=1,\n", " confidence_intervals=True,\n", " test_significance=True)\n", " print(\"Causal Estimate is \" + str(estimate.value))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As mentioned earlier, due to the absence of directed edges, no backdoor, instrmental or frontdoor variables can be found out for PC and GES. Thus, causal effect estimation is not possible for these methods. However, LiNGAM does discover a DAG and hence, its possible to output a causal estimate for LiNGAM. The estimate is still pretty far from the original estimate of -70.466 (which can be calculated from the graph)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Experiments on the Sachs dataset\n", "\n", "The dataset consists of the simultaneous measurements of 11 phosphorylated proteins and phospholipids derived from thousands of individual primary immune system cells, subjected to both general and specific molecular interventions (Sachs et al., 2005).\n", "\n", "The specifications of the dataset are as follows - \n", "- Number of nodes: 11\n", "- Number of arcs: 17\n", "- Number of parameters: 178\n", "- Average Markov blanket size: 3.09\n", "- Average degree: 3.09\n", "- Maximum in-degree: 3\n", "- Number of instances: 7466\n", "\n", "The original causal graph is known for the Sachs dataset and we compare the original graph with the ones discovered using CDT in this section." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Load the data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from cdt.data import load_dataset\n", "data_sachs, graph_sachs = load_dataset(\"sachs\")\n", "\n", "data_sachs.dropna(inplace=True)\n", "print(data_sachs.shape)\n", "data_sachs.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Ground truth of the causal graph" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "labels = [f'{col}' for i, col in enumerate(data_sachs.columns)]\n", "adj_matrix = nx.to_numpy_array(graph_sachs)\n", "adj_matrix = np.asarray(adj_matrix)\n", "graph_dot = make_graph(adj_matrix, labels)\n", "display(graph_dot)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery with Causal Discovery Tool (CDT)\n", "\n", "We use the CDT library to perform causal discovery on the Auto-MPG dataset. We use three methods for causal discovery here -LiNGAM, PC and GES. These methods are widely used and do not take much time to run. Hence, these are ideal for an introduction to the topic. Other neural network based methods are also available in CDT and the users the encourages to try them out by themselves. \n", "\n", "The documentation for the methods used in as follows:\n", "- LiNGAM [[link]](https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/_modules/cdt/causality/graph/LiNGAM.html)\n", "- PC [[link]](https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/_modules/cdt/causality/graph/PC.html)\n", "- GES [[link]](https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/_modules/cdt/causality/graph/GES.html)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from cdt.causality.graph import LiNGAM, PC, GES\n", "\n", "graphs = {}\n", "graphs_nx = {}\n", "labels = [f'{col}' for i, col in enumerate(data_sachs.columns)]\n", "functions = {\n", " 'LiNGAM' : LiNGAM,\n", " 'PC' : PC,\n", " 'GES' : GES,\n", "}\n", "\n", "for method, lib in functions.items():\n", " obj = lib()\n", " output = obj.predict(data_sachs)\n", " graphs_nx[method] = output\n", " adj_matrix = nx.to_numpy_array(output)\n", " adj_matrix = np.asarray(adj_matrix)\n", " graph_dot = make_graph(adj_matrix, labels)\n", " graphs[method] = graph_dot\n", "\n", "# Visualize graphs\n", "for method, graph in graphs.items():\n", " print(\"Method : %s\"%(method))\n", " display(graph)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, no two methods agree on the graphs. Next we study the causal effects of these different graphs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimate effects using Linear Regression\n", "\n", "Now let us see whether these differences in the graphs also lead to significant differences in the causal estimate of effect of *PIP2* on *PKC*." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for method, graph in graphs.items():\n", " if method != \"LiNGAM\":\n", " continue\n", " print('\\n*****************************************************************************\\n')\n", " print(\"Causal Discovery Method : %s\"%(method))\n", "\n", " # Obtain valid dot format\n", " graph_dot = str_to_dot(graph.source)\n", "\n", " # Define Causal Model\n", " model=CausalModel(\n", " data = data_sachs,\n", " treatment='PIP2',\n", " outcome='PKC',\n", " graph=graph_dot)\n", "\n", " # Identification\n", " identified_estimand = model.identify_effect(proceed_when_unidentifiable=True)\n", " print(identified_estimand)\n", "\n", " # Estimation\n", " estimate = model.estimate_effect(identified_estimand,\n", " method_name=\"backdoor.linear_regression\",\n", " control_value=0,\n", " treatment_value=1,\n", " confidence_intervals=True,\n", " test_significance=True)\n", " print(\"Causal Estimate is \" + str(estimate.value))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the causal estimates obtained, it can be seen that the three estimates differ in different aspects. The graph obtained using LiNGAM contains a backdoor path and instrumental variables. On the other hand, the graph obtained using PC contains a backdoor path and a frontdoor path. However, despite these differences, both obtain the same mean causal estimate.\n", "\n", "The graph obtained using GES contains only a backdoor path with different backdoor variables and obtains a different causal estimate than the first two cases. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Graph Validation\n", "\n", "We compare the graphs obtained with the true causal graph using the causal discovery methods using 2 graph distance metrics - Structural Hamming Distance (SHD) and Structural Intervention Distance (SID). SHD between two graphs is, in simple terms, the number of edge insertions, deletions or flips in order to transform one graph to another graph. SID, on the other hand, is based on a graphical criterion only and quantifies the closeness between two DAGs in terms of their corresponding causal inference statements." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from cdt.metrics import SHD, SHD_CPDAG, SID, SID_CPDAG\n", "from numpy.random import randint\n", "\n", "for method, graph in graphs_nx.items():\n", " print(\"***********************************************************\")\n", " print(\"Method: %s\"%(method))\n", " tar, pred = graph_sachs, graph\n", " print(\"SHD_CPDAG = %f\"%(SHD_CPDAG(tar, pred)))\n", " print(\"SHD = %f\"%(SHD(tar, pred, double_for_anticausal=False)))\n", " print(\"SID_CPDAG = [%f, %f]\"%(SID_CPDAG(tar, pred)))\n", " print(\"SID = %f\"%(SID(tar, pred)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The graph similarity metrics show that the scores are the lowest for the LiNGAM method of graph extraction. Hence, of the three methods used, LiNGAM provides the graph that is most similar to the original graph." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Graph Refutation\n", "\n", "Here, we use the same SHD and SID metric to find out how different the discovered graph are from each other." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import itertools\n", "from numpy.random import randint\n", "from cdt.metrics import SHD, SHD_CPDAG, SID, SID_CPDAG\n", "\n", "# Find combinations of pair of methods to compare\n", "combinations = list(itertools.combinations(graphs_nx, 2))\n", "\n", "for pair in combinations:\n", " print(\"***********************************************************\")\n", " graph1 = graphs_nx[pair[0]]\n", " graph2 = graphs_nx[pair[1]]\n", " print(\"Methods: %s and %s\"%(pair[0], pair[1]))\n", " print(\"SHD_CPDAG = %f\"%(SHD_CPDAG(graph1, graph2)))\n", " print(\"SHD = %f\"%(SHD(graph1, graph2, double_for_anticausal=False)))\n", " print(\"SID_CPDAG = [%f, %f]\"%(SID_CPDAG(graph1, graph2)))\n", " print(\"SID = %f\"%(SID(graph1, graph2)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The values for the metrics show how different the graphs are from each other. A higher distance value implies that the difference between the graphs is more." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.12" }, "metadata": { "interpreter": { "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" } }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": false, "sideBar": true, "skip_h1_title": true, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery example\n", "\n", "The goal of this notebook is to show how causal discovery methods can work with DoWhy. We use discovery methods from [causal-learn](https://github.com/py-why/causal-learn) repo. As we will see, causal discovery methods require appropriate assumptions for the correctness guarantees, adn thus there will be variance across results returned by different methods in practice. These methods, however, may be combined usefully with domain knowledge to construct the final causal graph." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import dowhy\n", "from dowhy import CausalModel\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import graphviz\n", "import networkx as nx \n", "\n", "np.set_printoptions(precision=3, suppress=True)\n", "np.random.seed(0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Utility function\n", "We define a utility function to draw the directed acyclic graph." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def make_graph(adjacency_matrix, labels=None):\n", " idx = np.abs(adjacency_matrix) > 0.01\n", " dirs = np.where(idx)\n", " d = graphviz.Digraph(engine='dot')\n", " names = labels if labels else [f'x{i}' for i in range(len(adjacency_matrix))]\n", " for name in names:\n", " d.node(name)\n", " for to, from_, coef in zip(dirs[0], dirs[1], adjacency_matrix[idx]):\n", " d.edge(names[from_], names[to], label=str(coef))\n", " return d\n", "\n", "def str_to_dot(string):\n", " '''\n", " Converts input string from graphviz library to valid DOT graph format.\n", " '''\n", " graph = string.strip().replace('\\n', ';').replace('\\t','')\n", " graph = graph[:9] + graph[10:-2] + graph[-1] # Removing unnecessary characters from string\n", " return graph" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Experiments on the Auto-MPG dataset\n", "\n", "In this section, we will use a dataset on the technical specification of cars. The dataset is downloaded from UCI Machine Learning Repository. The dataset contains 9 attributes and 398 instances. We do not know the true causal graph for the dataset and will use causal-learn to discover it. The causal graph obtained will then be used to estimate the causal effect.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Load the data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(392, 6)\n" ] }, { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>mpg</th>\n", " <th>cylinders</th>\n", " <th>displacement</th>\n", " <th>horsepower</th>\n", " <th>weight</th>\n", " <th>acceleration</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>18.0</td>\n", " <td>8.0</td>\n", " <td>307.0</td>\n", " <td>130.0</td>\n", " <td>3504.0</td>\n", " <td>12.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>15.0</td>\n", " <td>8.0</td>\n", " <td>350.0</td>\n", " <td>165.0</td>\n", " <td>3693.0</td>\n", " <td>11.5</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>18.0</td>\n", " <td>8.0</td>\n", " <td>318.0</td>\n", " <td>150.0</td>\n", " <td>3436.0</td>\n", " <td>11.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>16.0</td>\n", " <td>8.0</td>\n", " <td>304.0</td>\n", " <td>150.0</td>\n", " <td>3433.0</td>\n", " <td>12.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>17.0</td>\n", " <td>8.0</td>\n", " <td>302.0</td>\n", " <td>140.0</td>\n", " <td>3449.0</td>\n", " <td>10.5</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " mpg cylinders displacement horsepower weight acceleration\n", "0 18.0 8.0 307.0 130.0 3504.0 12.0\n", "1 15.0 8.0 350.0 165.0 3693.0 11.5\n", "2 18.0 8.0 318.0 150.0 3436.0 11.0\n", "3 16.0 8.0 304.0 150.0 3433.0 12.0\n", "4 17.0 8.0 302.0 140.0 3449.0 10.5" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_mpg = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data-original',\n", " delim_whitespace=True, header=None,\n", " names = ['mpg', 'cylinders', 'displacement',\n", " 'horsepower', 'weight', 'acceleration',\n", " 'model year', 'origin', 'car name'])\n", "data_mpg.dropna(inplace=True)\n", "data_mpg.drop(['model year', 'origin', 'car name'], axis=1, inplace=True)\n", "print(data_mpg.shape)\n", "data_mpg.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery with causal-learn\n", "\n", "We use the causal-learn library to perform causal discovery on the Auto-MPG dataset. We use three methods for causal discovery here: PC, FCI and GES. These methods are widely used and do not take much time to run. Hence, these are ideal for an introduction to the topic. Causal-learn provides a comprehensive list of well-tested causal-discovery methods, and readers are welcome to explore.\n", "\n", "The documentation for the methods used are as follows:\n", "- PC [[link]](https://causal-learn.readthedocs.io/en/latest/search_methods_index/Constraint-based%20causal%20discovery%20methods/PC.html)\n", "- GES [[link]](https://causal-learn.readthedocs.io/en/latest/search_methods_index/Score-based%20causal%20discovery%20methods/GES.html)\n", "- LiNGAM [[link]](https://causal-learn.readthedocs.io/en/latest/search_methods_index/Causal%20discovery%20methods%20based%20on%20constrained%20functional%20causal%20models/lingam.html#ica-based-lingam)\n", "\n", "More methods could be found in the causal-learn documentation [[link]](https://causal-learn.readthedocs.io/en/latest/)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We first try the PC algorithm with default parameters." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1ed197e9f5ec42c8bf7fc51c5ece4485", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/6 [00:00<?, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from causallearn.search.ConstraintBased.PC import pc\n", "\n", "labels = [f'{col}' for i, col in enumerate(data_mpg.columns)]\n", "data = data_mpg.to_numpy()\n", "\n", "cg = pc(data)\n", "\n", "# Visualization using pydot\n", "from causallearn.utils.GraphUtils import GraphUtils\n", "import matplotlib.image as mpimg\n", "import matplotlib.pyplot as plt\n", "import io\n", "\n", "pyd = GraphUtils.to_pydot(cg.G, labels=labels)\n", "tmp_png = pyd.create_png(f=\"png\")\n", "fp = io.BytesIO(tmp_png)\n", "img = mpimg.imread(fp, format='png')\n", "plt.axis('off')\n", "plt.imshow(img)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we have a causal graph discovered by PC. Let us also try GES to see its result." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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OHDkCoVCILl26QCAQ8DYngUCAqKgo6HQ6HD169LE02draonfv3rx4mpub4/PPP0f37t0RFBQER0dHrF27Fl988QWuXLmCkpISWFtbY8mSJQgJCamUfPk3CAQCtGvXDmKxGFeuXGGGfQajkqgzNpfS0lLk5+ejU6dO1T4vnpmZCbVaDYlEwrvoNyAQCBAQEMD/36hRI/Tq1QsHDhzApUuX0KVLFyiVSuzatQsjRoyAra1tpaXLwcHBSBgEAgEsLCyg0+l42w8RIS0tDQDg7u7+WBhP8rum1WqRnp4OrVaLOXPm8AenGZDJZACAlJQUcBxnlAZzc/On2pJCQkKwevVqLFq0CEuWLMHy5cvRsGFDdO/eHePHj0doaGiNrIqzs7ODq6sr0tLSeAFlMBgvRp0RF51OB61WCwsLi5pOyjMRi8WYNGkS9uzZg+3bt6Njx46Ij4/H/fv3sXjx4koVxieF9aSG8VmN5bPSIxKJ8NZbb8Hb2/uJn9vY2DwWtmG12JMwMTFBv3790LFjR8TFxeHkyZP4448/sHjxYuzatQubNm1C+/btq71xN4iyWq0Gx3HMqM9gVAJ1RlzMzc1hZWWF/Pz8au9denl5wczMDIWFhVCpVLCysuI/IyIkJiaCiBAcHAyBQIBWrVqhXbt2OHLkCJKTk/Hbb7+hV69e1TqdZ8CwfPj8+fPIzs5+7PPi4uLHpoJMTU3h4+ODgoICNGzYEO3btzf6XK/XIz4+HtbW1v/qORQUFKCoqAj+/v5o06YN2rRpg9dffx2LFi3C/PnzsXr16sfiqg70ej1kMhns7OyYvzEGo5KoM100e3t7+Pr64ubNm7yzxepAIBAgMjISXl5eiI2NRW5urtHnxcXFeOWVV7Bq1Sr+PRsbG4wfPx75+flYs2YN/vrrL4waNarGGq6ePXvCxMQEZ8+eNdqLQw/3DT26P8fc3Bx9+vQBEeHMmTNGvs2ICMnJyRgwYAAOHDjwr9IRHR2NN954A3K5HAKBAAKBAA4ODhg+fDjs7e1RVFT0Yjf6H0lPT0dhYSFCQ0NrJH4Goz5SZ8TFxMQE3bp14zcMVqfh1cPDA3PnzkVxcTGWLFmCvLw86PV6lJSUYP369ZDJZBg/fjz/fYFAgB49eiAoKAjr169HYGAgGjVqVCNz+QKBAH379uVHUgcOHIBSqYRGo8GtW7ewY8eOxxxjCoVCTJo0CREREdiwYQNOnz4NpVIJnU6H7OxsfPfdd5BIJE/dI/Msrl+/joMHD/IOOeVyOc6ePYvy8nL06NGjsm77ueE4DtHR0TA1NUXbtm2ZvYXBqCyqe+3zi5CUlEQ+Pj40d+5c0mq11RYvx3GkVCppxYoVFBISQu3bt6cRI0ZQjx49KDw8nH777TfS6/VGv9Hr9fTFF1+Qra0tHTly5LF9OWq1mt555x3q2bMnicVi8vLyotGjR9OZM2coKyuLXn/9dYqKiiIAFB4eTlOmTKFbt27R559/Tt27dyehUEj+/v40fvx4SkhIoOvXr9Orr75KXl5eJBKJqG/fvrRgwQLS6/XEcRzduXOHunbtSl5eXtS3b18aNWoU9erVixYvXkympqbk4+NDo0ePpqNHj/L3HBsbSwMHDqTAwEDq168fjRgxgqKioqhr1650+fJl/p6OHz9OI0aMIFdXV7KwsKABAwbQ1KlTKS0tzeieY2NjacCAARQYGEh9+vShkSNHUs+ePcnf35/efPNNKioqqrqH+BSys7MpLCyMxo0bR0qlstrjZzDqK3XqPBe9Xo/PPvsMGzZswM6dO6vV+EtPcP9ia2uLiIgIeHp6PtHdyg8//IDo6Gjs27fvsbNa9Ho9Dh069NhUULt27eDs7Ixjx44ZOawUi8Xo2LEjYmJikJeXx78vEonQo0cPqNVq/PXXX0ZTht7e3kZLiQ3uX6RSKZ92a2trHDhwAHq9HgKBAK1bt0ajRo34e1CpVIiJiUFqair0ej08PT0RHh5uZMxPSEjApUuXjO7DzMwMvXr1Mtq7Qg/Pu09KSkJycjLkcjksLS0RHByMoKCganX/Avx9pMHXX3+NtWvXYs+ePYiKimIjFwajkqhT4gIAWVlZGDp0KExNTbF9+/Yn+tGqKQoLC2FmZgYrKysUFRVh6NChmDBhAiZOnFhr0sj4G47jcODAAbz22muYMmUKPv/883rpAJXBqCnqjM3FgIeHB+bPn4+0tDS8/fbbKCgoqDUb3z7//HOsXbsWOp0O586dQ0lJSbV7E2D8MxzH4cKFC5g3bx5atmyJOXPmVJsDVAbjZaHOiYtAIECHDh0wf/58nD59Gm+99RZyc3NrhcCYmppi9erVmDFjBr766iu89tprT91QyKgZOI7DuXPnMG3aNDg7O2Px4sVwcnJiHQAGo5Kpc9NiBrRaLXbs2IH33nsPTZs2xZIlSxAcHFyjG+Cys7Px559/oqCgAE2bNkXHjh1hamrKGq5agkajwf79+/HOO+/A09MT69atQ0hICHs+DEYVUGfFBQDv38owrfHtt9+id+/eEIvFrMFg8NDDY7GXLVuG1atXIyoqCj/++CP8/PxYOWEwqog6LS7A39McMTEx/ImCY8eO5XumzI0HQ6fT4fLly/jss89w69YtTJ8+HfPmzYODgwMTFgajCqnz4gL83TMtKirC2rVrsXz5cjg6OuKdd97B0KFD/7WLEkbdx7BsPCsrC6tWrcKmTZvg4eGBTz/9FH369GFTlQxGNVAvxMWAVqvF9evXMX/+fJw5cwatWrXCm2++iS5dusDCwoI1KPUcg6gUFBRg+/btWLt2LYqLizFp0iS8/vrrT9yPxGAwqoZ6JS7A3w2MQqFAdHQ0li1bhvj4eLRt2xbTpk1Dt27dYGVlxRqYeoZBVHJycrBr1y78/PPPyMrKQr9+/fDmm28iLCys2jdoMhgvO/VOXAwYjLj79+/Hhg0bEB8fjyZNmmDChAno27cvXF1deeeJjLoJEUGn0yEuLg67du3C77//jsLCQvTo0QP/+9//0Lp161p/RAODUV+pt+JiwGCP+fPPP/HLL7/gwoULcHZ2Rv/+/TF06FBERETwDRATmtqPYZSSn5+Pv/76C7/99hvOnz8PU1NT9OvXDxMnTkRYWBgTFQajhqn34mKAiFBeXo7bt29jx44dOHLkCAoLCxESEoLBgweja9euCAkJ4U+aZEJTezAISmFhIW7evImDBw/i+PHjyM3NRWhoKIYOHYqBAwfCz88PIlGdOaKIwajXvDTiUhG9Xo+srCycP38eu3btwqVLl6DVahEQEIDu3buje/fuaNKkiZHTRSY21YehSHIch4yMDNy4cQPHjh3D2bNnkZWVBU9PT3Tu3BlDhgxBixYtYG9vz54Pg1HLeCnFpSJqtRoPHjzAxYsXcfjwYdy6dQsFBQXw8fFBu3bt0KlTJzRq1Ah+fn6wsbEBwISmsqlYBGUyGZKTk3H16lWcPHkSN2/ehEwmQ8OGDdGhQwf07dsXYWFhcHNzY/uYGIxazEsvLhUxHIYVExOD6OhonD9/Hunp6TAxMYGvry9atmyJDh06ICwsDB4eHrC3t2cN3H/EYIyXSqXIysrC7du3cerUKcTGxiIrKwsWFhbw9/dH586d0aNHDzRq1AgSiYQJO4NRR2Di8gQMWVJUVIQHDx7g7t27OH36NK5fv47MzEyIRCJ4eHjA398fzZs3R4sWLdCgQQO4uLjAzs6OCc4jGM5xkclkyMvLw4MHD3D9+nVcvXoVqampyM3NhZWVFS/gXbp0QePGjeHj4wMzMzMAbLTIYNQ1mLj8AxWzp6ioCPfv38e9e/dw8+ZNXL9+Henp6ZDJZLCwsICXlxc8PT0RGBiI0NBQBAcHw8XFBfb29rCzs6v3ey2ICEqlEsXFxSgqKoJUKkVCQgLi4uKQkJCAzMxM5OTkwMTEBM7OzvDx8UFERARatWqF0NBQNGjQgIkJg1FPYOLyL6mYXTqdDjk5OUhLS0N8fDxu376N+Ph4ZGdno6SkBGVlZbCxsYGbmxtcXFzg6ekJX19f+Pr6wsfHB87OzrC2toalpSXMzc1hbm5eawWIiKBWq6FSqaBSqVBWVga5XI7s7GykpaXxV15eHnJzcyGVSqHT6WBrawsHBwf4+fmhcePGaNasGfz9/dGgQQMj/1618Z4ZDMZ/h4lLJVAxCzmOQ0lJCbKzs5GdnY0HDx4gISEB9+/fR0ZGBkpKSlBeXo7y8nIIBALY2dnBwcEBtra2sLW1haOjI5ydneHq6goXFxc4OjrC1tYW1tbWsLCwgKmpKUQiEX8JhUIIhUIIBILHXgUCATiOA/D3CrlHXzmOg06ng1ar5V8VCgXkcjlKS0tRUFAAqVSK/Px85Ofno7i4GKWlpSgpKYFMJoNCoYCpqSksLS1hZWUFJycn+Pr6IjAwEEFBQfD29oanpyfc3Nxgbm7OhITBeIlg4lKFGLLWsE9Dr9ejuLiYb6ylUimys7ORk5PD/29oxNVqNTQaDbRaLfR6PXQ6HQBALBZDLBbDwsIC5ubmMDEx4UXm0VcTExPodDpeRAy2D8OrVquFUqmESqWCRqOBXq+HQCDghcvU1JSPSyKRwMXFBc7OznBxcYGHhwfc3d35/52dnWFlZQUARp4PmJAwGC8nTFxqkEfFx3BptVqUlJSguLgYcrkcSqUSSqUS5eXlUCqVKCsr46elDKOOiiMQnU6H+Ph4XL16Ff3794eTkxNMTEwgFoshFAohFothYmICc3Nz2NjYwMrKCpaWlvz0nOFvW1tb2Nvbw8bGxmg0VPFiMBiMJ8HEpQ5SUZQqvlb8fMuWLfjggw9w+vRpBAYGGn1eURTYCIPBYFQFzFdGHeSfBIGI+JGGiYkJTExMqjN5DAaDAbYhg8FgMBiVDhMXBoPBYFQ6TFwYDAaDUekwcWEwGAxGpcPEhcFgMBiVDhMXBoPBYFQ6TFwYDAaDUekwcWEwGAxGpcPEhcFgMBiVDhMXBoPBYFQ6TFwYDAaDUekwcWEwGAxGpcPEhcFgMBiVDhMXBoPBYFQ6TFwYDAaDUekwcWEwGAxGpcPEhcFgMBiVDhMXBoPBYFQ6TFwYDAaDUekwcWEwGAxGpcPEhcFgMBiVDhMXBoPBYFQ6TFwYDAaDUekwcWEwGAxGpcPEhcFgMBiVDhMXBoPBYFQ6TFwYDAaDUekwcWEwGAxGpcPEhcFgMBiVDhMXBoPBYFQ6TFwYDAaDUekwcWEwGAxGpcPEhcFgMBiVDhMXBoPBYFQ6TFwYDAaDUekwcWEwGAxGpcPEhcFgMBiVDhMXBoPBYFQ6TFwYDAaDUekwcWEwGAxGpcPEhcFgMBiVDhMXBoPBYFQ6TFwYDAaDUekwcWEwGAxGpcPEhcFgMBiVDhMXBoPBYFQ6oppOAOPfQUTP9T4RgYjAcRw4juPfFwgETw37WZ8xGFVNxTL8aHmuWDZZOa0bMHGpYgyV5Gmver0eZWVlKCsrg1qthlqthkaj4f9+9H+NRgONRsOLhkajARFBr9dDq9UCALRaLW7cuAGFQoEVK1bAzc0NQqEQAoEAYrEYQqEQQqEQYrEYACASiSAWi2FmZsa/Pvq34X8LCwtYWVnB3NwcQuHfA19DZX/aK+PlpmKZN3R4iouLUVhYiJKSEhQXF6OkpASlpaVQqVRG5d1QTs3MzGBubg5zc3PY2dnBzs4O9vb2sLe3h5OTE6ysrCAQCIwuRs0ioKd1hRlP5UlCQURQKBQoKSnhr+LiYqOrYkUyvMrlcmg0Guh0Ouj1euj1enAcZ/Q3x3EwMTGBSCSCiYkJTExMIBAIYGpqCgAQCoUQif7uJxg+fzS9Op2OT2fFvw2CpNPp+DRwHAehUMiHZWJiYvS/qakpzMzMHqvk9vb2T/zf8J6dnR3MzMwAMCGqzxjKFhGhsLAQKSkpSE5ORnJyMpKSkpCSkoL8/HxeQLRaLYRCIczNzWFqaspfIpGIL69arRZarRYajQYqlYrvKInFYlhaWsLDwwMBAQFGV8OGDWFlZfVYJ4hRPTBxeYQnTS9pNBrIZDIUFhaisLAQ+fn5yMrKQlZWFqRSKfLy8pCfnw+FQgGVSgW1Wg2VSgUigpmZGaysrGBpaWl0WVlZwdbW1qgRtrKygoWFBSwsLPjvWVhYwNzcnBcWkUjE98wqVpon/V0RjuOMRkuGezNMmXEcx4uLVquFUqlEeXk5lEol/3d5eTnKysqMBFMul6OsrIz/vOKl1WphamoKc3Nzvudpb28PFxcXuLi4wM3NDZ6envDw8IBEIoGTkxMkEgns7Oz4kZYB1jDUXgzliuM4ZGZmIiYmBhcvXsTVq1dx//59FBUVQSAQwM3NDa6urvDy8kJAQAC8vb3h4uICZ2dn2NjYwMzMjO/EGEbXAIw6WTqdDmq1GsXFxZBKpZBKpUhNTUVKSgqys7ORm5uL/Px8mJmZQSKRICQkBG3atEGbNm3QuHFjODo6sg5NNfHSisuj87vFxcXIy8uDVCpFZmYmHjx4gPT0dKSnpyMnJwcKhQJlZWVQKpUwMTHhRcHW1hZ2dnZwdnbmK4+LiwtcXV3h6OjIN6yGXpbhMjU1faII1DX0ej0/VVfxUqvVKCsrQ35+Pp+vubm5kEqlKCwsRGlpKUpKSlBUVAS5XA6hUAgrKytYWVnBzs4OXl5e8PHxga+vL/z8/ODu7g5XV1c4OzvD0tLSKA2skah+DPVHo9EgLi4OZ86cwYkTJ3D37l3IZDJ4enoiICAA4eHhaNWqFQICAuDg4ABbW1tYWFhU+jMzTC+XlpaisLAQsbGxuHbtGmJiYpCcnIySkhK4ubmhZcuW6NGjB9q1a4cGDRo81olhVB71Xlwq3p5Wq4VUKkVOTg5yc3P54Xp8fDxyc3P5nri5uTkkEgkcHBzg4OAALy8veHp6ws/PD97e3nBycoK1tTWsra1haWlpZH9gPBuDfcgwulEoFFAoFCgqKkJqaiqysrLw4MEDZGRkQCaTQSaT8T1fw/Sam5sbgoKCEBgYiMDAQHh4eMDd3d2oVwow0akKDKPdpKQknDlzBrt378bt27chEokQEhKCDh06oEuXLggICICzszM/dVtTaVWpVMjJyUFSUhJOnTqFv/76CwkJCbCxsUGrVq0wZMgQdO7cGe7u7gBYmalM6qW4GOwfubm5yM7Oxr1793Dr1i3cvXsX2dnZKCwsBMdxcHR0hJOTE7y8vODv74/g4GAEBgbCxcWFH5VYWloy4agBtFotb8MqLi7mOwP37t1DSkoKcnNzUVhYiOLiYpibm8PJyQne3t4IDQ1Fs2bNEBgYCDc3N7i7u8Pc3Jw1GpVAWVkZrl69iq1bt+Lo0aPQarWIiIjAgAED0LVrVzRo0ABmZma1Nq+JCHK5HElJSTh69CgOHjyIe/fuwcXFBYMHD8aYMWMQGhpao4JYn6jz4kJEKCsrQ3Z2NtLS0nDjxg1cu3YNqampyM3NhVwuh62tLVxcXBAQEIBmzZqhSZMmfE/XyckJNjY2tbZCMB5Hr9ejtLQUMpkM+fn5SElJwZ07dxATE4P09HTk5+dDqVRCIpHAw8MDjRs3RuvWrdG0aVN4enrC1dWVt10x/hmFQoHjx49j1apVuHbtGjw9PTFy5EgMHjwYQUFBtVpQngYRobS0FLdv38b27dtx5MgRqFQqdO/eHa+//jpatmzJr6Zk/DfqnLgYljEaDIfnz5/HrVu3kJ6eDrlcDolEAm9vbzRt2hTNmjVDcHAw3N3d4e7uDisrKz6culYZGE/mUduZTCZDTk4OMjMzERsbi9u3byM+Ph45OTkoKyuDq6sr/P390bp1a0RFRcHf3x+enp5sdPMIhiml06dPY9myZbh06RIiIiIwefJkdOvWDW5ubgDqfj0yrGp78OAB9u7di19++QW5ubkYMGAAZs6cibCwMH4lJuPfUevFxTDFlZGRgZs3b+L06dO4efMmHjx4ABMTE7i7uyM8PByRkZFo0qQJfH194e7ublQg6noFYPx7KhbrsrIyZGRk4P79+7hx4wYuXryIpKQk5Ofnw8bGBkFBQYiMjESnTp34zsjL3GvlOA5xcXGYP38+Dh48iNDQUMycORP9+/eHtbU1gPpXpwzlJScnB7/99hvWrVuH4uJiTJw4EbNmzYK7u3u9u+eqplaKi16vR0FBAe7du4fjx4/j3LlziIuLAxHB09MTbdq0QceOHREaGgp/f3/Y2Njwv2UFgPE0Ki6ZzcvLQ1JSEm7evIkzZ84gNjYW+fn5kEgkaNasGbp27YqOHTuiQYMGL820qWGqaMuWLfjhhx9gamqKuXPnYtSoUbC3t38p8gD4Ox/S09Oxbt06bNiwAb6+vvj444/Rq1cvfp8W45+pNeKi1+uRl5eH69ev49ChQzh//jwyMjIgkUjQvHlz9OjRAy1atEBwcDAsLS3ZWnXGC1NRbPLz83H37l1cuHABJ06cQFJSEtRqNZo0aYKuXbuid+/eCA4Ohp2dXb0sc0SEe/fu4cMPP8SZM2cwaNAgfPDBBwgMDKyX9/tPGFY1Xrx4EV999RWuX7+OSZMm4b333oOTk9NLmSf/lhoVF47jUFRUhBs3buCPP/7AyZMnkZeXB3d3d3Tt2hU9evRAy5Yt4erqynbZMqqcil4LUlJScPHiRURHR+Pq1asoLS1FkyZN0KdPH/Tv3x8BAQFVsl+jJtDr9Thx4gTmzp0LjUaDL7/8EkOHDoVYLK4X9/ciGPbArVq1CkuXLkWLFi3w448/olGjRi993vwT1S4uhh3vKSkp2LNnDw4cOIDExES4ubmhd+/e6NOnD1q1agVbW1u2wYlRY1T0ZpCVlYWzZ89i3759uHjxItRqNdq1a4cRI0agW7ducHZ2fszlTl1Bo9Fgx44deP/99xEaGooff/wRTZo0YcvvH0Gn0yE6OhrvvPMOLCwssHLlSkRGRrJ8egbVJi4Gw/y1a9ewadMmHDt2DEKhEB07dsSIESPQsWNHODg4MEFh1DoMVUSn0yE1NRVHjx7F3r17cevWLbi7u2PIkCEYM2YMAgMD69QSZ41Gg02bNuGjjz5Cz549sWjRIri5udWZ9Fc3RISbN2/ijTfeQFFREdatW4cOHTowgXkKVS4uho1LZ86cwdq1a3Hx4kW4u7tjzJgxGD58OPz8/GBqasoKNKPOwHEclEolrly5gu3bt+Pw4cMAgGHDhmHSpEn8RrzaXKZ1Oh22b9+OuXPnYuDAgVi4cCEcHBxqdZprA0SEhIQETJs2DQUFBdiyZQtatGjB8u0JVKm4qFQqnD9/Hj/++CMuXryIRo0aYdq0aejfvz8cHR3ZKIVRpzF47E1OTsbWrVuxY8cOKJVKjBo1CtOnT0dgYGCtnC4jIvz555+YMGECOnXqhBUrVjBh+RcQEeLj4/Hqq69CJBJhx44daNCgAcu/R6gScTH4HlqwYAH27dsHf39/vPnmm+jXrx9vS6lOiouLsXHjRhQXFwMAv8y0pgqDRqPBunXrkJ+fDwBo2rQphg8fXiNpYVQOOp0O6enp2LRpE3755ReIRCLMmDEDkyZNqlXLeA0bBocOHQoHBwf8+uuvbA/Hf4CIcPHiRYwdOxbt2rXD2rVr+T1AjIdQJVNeXk6bNm2igIAA8vX1pR9//JGkUilxHFfZUT03RUVFNH/+fBo+fDgBoC+//NIoPWVlZfTrr7/SyZMnSa/XV3l61Go1/fDDDzR+/HgyNzenMWPGVHmcjOpBq9VSTEwMTZkyhRwdHalXr1505coV0ul0NZ00Ivq7rE+dOpV8fHzo6tWrNVov6zo6nY5++ukncnBwoLVr11ZL21GXqDRx4TiOpFIpzZgxgxwcHGjUqFEUExNTayoVx3G0f//+J4rLjRs3yMbGhsLDw6m8vLza0nTp0iVycHBg4lJJcBxHu3fvplWrVlFBQUGNpqO8vJz27t1LzZo1I19fX9q4cSOpVKoaS5MhXQcPHiRHR0dasmRJrambdRmFQkFjx46l4OBgSklJqenk1CoqZX6KHu5onTJlCnbu3ImPP/4Y69evR+PGjWvlnPOj+Pv747PPPsMHH3zwUrv9qOsQEdatW4dPP/0UUqm0xtIhEAhgYWGBQYMGYc+ePWjfvj3mzZuHBQsWoLy8vMbSJZfLsWzZMoSGhuLVV1+tE3WztmNpaYl33nkHcrkcGzduhE6nq+kk1Rpe2CMbESE3Nxf/+9//EBcXhzVr1mDAgAE16uyNHm6G0+v1ICL+dLunYWtri3nz5j01LMNJeAD4sJ41R/1o/IaT9f7NvPajYRhOmHxWOE9Ka8UT/SpiONmP47jnDhsAf0RyxXyoGK9AIOCf/ZPCetJ9GcL5pzw1HP/8vL+paQQCAfz8/LBy5Up8//33WLx4MQDgnXfegbm5ebWmhYhw+fJlXL16FcuWLYODg0OVxvWs+vfoUdrAszdH/5tn/2/KYmUgEAjQuHFjDBkyBDt27MD06dPh5eVVJXHVNV5IAYgI5eXlePfdd3Hnzh1s2LABvXr1qtF130SEkpISbNmyBX/88QdkMhkaNGiAV1555YkF7M6dO7h16xYAwMLCAr179+Z9lanVauzfvx87d+5EUlIShEIhgoOD0atXL/Tp0wcuLi4gIpw6dQqZmZkAAEdHR7Ro0QIbNmzA8ePHUVZWhmbNmmH69Olo1arVc+UNx3G4c+cO/vjjD5w7dw5SqRS2traIjIzExIkTERoaahQOPdxVfvToUfz++++Ij48Hx3Hw9vZGx44d0a9fPwQFBUEoFPI7jnfs2IGDBw8iKysLVlZWaN26NSZMmICwsDAIhUJkZWXhzJkz0Ol0EAgEaN++PS5duoTNmzcjOzsbQUFBmDFjBjp06ICzZ89i5cqVSExMhKurKyZMmICRI0c+di4GESEtLQ2bN2/GqVOnIJPJ4OTkhK5du2L8+PHw8vKCQCCAQqHAsWPHoFAoAPy94EEsFmPJkiW4evUq7O3tMXz4cEyePJk/lTItLQ1//fUXsrOzoVKpsG/fPly9ehUA0LJlS4SEhNSIEBkOOfvkk08gFAqxePFieHt7Y/z48dVaT3Q6HXbu3AlPT0/06dOnUvMiOTkZly5dAsdxEIlEaN++PY4ePYrffvsNBQUFaNq0KWbNmoUWLVrg6NGjWLt2LR48eABvb29Mnz4d/fr140WmvLwcBw8ehEqlAvD3sycirF27Fjdu3ICpqSm6deuG6dOnw9PT0+g+VCoVDhw4gM2bNyMzMxPu7u4YNWoU+vbti+PHj/NluXnz5mjcuHGl3b9IJMLYsWOxY8cOnDhxAhMmTKjVnZ5q40Xm1DiOo7Vr15K9vT1t2LChVszhyuVymjJlCtnY2NDMmTPp/Pnz9Oeff9LkyZNpxIgRj9lcdu/eTcOGDSM3NzdydXXl5011Oh2tWLGCXFxc6MMPP6SLFy/SlStX6OuvvyYXFxeaMWMGEf2dB/Pnz6f+/fuTtbU1+fv706xZs2jFihV09epV2rZtGzVp0oR8fX3pzz//NLL1PM3mkp6eTuHh4dSmTRvas2cP3bhxg7Zv306tW7em4OBgun79ulE4KpWKPv30U3JycqLXXnuNzpw5Q5cuXaIvv/ySJBIJtWvXjqRSKRERSaVSGjZsGLm7u9OCBQvo6tWrtGfPHmrXrh01bNiQjh8/ThzH0Y0bN2jcuHEUGhpKQqGQxo4dS1988QVdunSJfv/9d2rYsCH5+fnR6tWr6fXXX6eTJ0/Sn3/+SV27diUHBwc6ePCgURo5jqPbt29Ty5YtKTQ0lDZv3kzXrl2jdevWUUBAAEVFRVFycrKR7a5Dhw4EgCZMmEDvvfceHT16lP766y8aMWIEWVlZ0erVq3kj6qVLl2j06NHk5uZGYrGYevfuTSNHjqSRI0fSgQMHatxwzXEcyeVyGjt2LPn5+VFsbGy1xp+Tk0ONGjWit99+u9Lr6alTp2j06NHk5+dHZmZmNH78eFq4cCFduXKFNm3aRO7u7hQaGkorVqyg2bNn09mzZyk6OppatWpFbm5udOHCBf75FBQU0JQpUygqKoqEQiENGzaMJk6cSHv37qUrV67Qd999RxKJhLp27UqZmZl8GrRaLS1cuJBsbW1p8ODBdPz4cTp//jx98MEHNHfuXDIzMyNPT08aMWIE7d+/v1Lvn+hv20vbtm1pzJgxNW5bqy28kLjk5uZS06ZNadSoUVRWVlZZafrPcBxHmzdvJgsLCxo5ciTJ5XL+/fz8fIqKinrqarFevXoZiUtpaSm1a9eOwsPDqbCwkP+uRqOh2bNn09ixY43iTktLI39/fzI3N6effvqJb/Q4jqOTJ0+SjY0NtW3blmQyGf+bp4nL/fv3KSIigs6cOcOnk+M4unr1Kjk6OtLEiRNJo9Hw7+/du5dsbW1p+PDhVFpayoej1Wrps88+o9DQUMrOzia9Xk8ffvghmZqa0vz58/lGhuM4unPnDnl4eFCrVq14Y7her6ePP/6YANDQoUNJoVDw3//2228JAIWFhdH9+/f5dJ45c4bEYjFNnjyZTyPR35Wvb9++ZG1tTUeOHDG6r99++40sLCxo2rRppNVq+d8cPnyYhEIhNWnShBISEvjf3Lt3jyQSCXXp0oVKSkr47+v1eurVqxc5OTlRXFzc0wtKDcFxHN27d4/8/Pxo1qxZRvda1Vy+fJns7e1p7969VSK0Go2GpkyZQgKBgKZPn843sDqdjmbPnk0AqF27dpSdnc3/Zvfu3SQQCOiDDz54bKXV7t27yczMjHx9fSkmJoZPs1arpc8//5xMTEzo/fff53937do1cnZ2pmbNmlF2djb/fcPqOIFAQIMHDyalUlnp9264z3fffZdCQ0MpLy+vSuKoa/zncTkR4a+//kJGRgbeeOMNfnqiJlGr1di1axe0Wi2GDh1qdPaERCJBv379/lV4AoEAaWlpOH36NJRKJYgIIpEIH3zwAb766qsn/sbFxQVdu3Y1crTZokULNGvWDLdv3+anap6Fh4cHtm/fjjZt2gD4f3tHgwYN4Ovri0uXLkGr1fL3vHXrViiVSgwfPtzo+AETExMMGzYM3bt3h5mZGXJycrB79244OTlhwIABRvPdAQEBiIqKQmxsLC5duvRYPnTt2tXosLWwsDAAQFBQEHx9fflpAF9fX9jZ2eH+/fvQ6/X8969fv45z584hLCwMbdq0MfJq3bFjR/j4+ODYsWNIT09/LD+CgoLg5+fH/8bd3R0eHh64f/8+lErlP+ZnbUEgECAwMBCjR4/G4cOHkZ2dXW1xx8XFwdzcHEFBQVU6ZWOYtjK4phcKhWjatCmAv8uM4ZAxAAgICICVlRWSkpKeGl7r1q0REBDAp1kkEqF///6wt7fHgQMH+IUb+/fvR35+Pvr06WPkwsbS0hL9+/evchuwUChEkyZNkJ2dze+ne9n5zznOcRwuXLiAhg0bIiQkpDLT9J9Rq9W4c+cOTExMEBwc/NjnAQEBzx2WlZUVXn/9dbz99tuYNGkSwsPD0b17d3Tq1AkRERFwdnZ+4u9sbW2NKhAAmJubo2HDhrhw4QJiY2PRs2fPZ8YtFoshEomwdOlSXLhwAQUFBdDr9dDpdEhKSoKdnR2/KkWtVuPGjRswMTFBo0aNjMIRCARo2rQpli5dCgC4du0a0tLS4OzsjMTERGRkZPDfpYe7zVUqFW7duoW+ffsahfOokdLCwgIAeDuJARMTE5ibm0OlUvGiSESIiYmBQqGAubk5Ll68+JiBFwCys7ORkZFhJCQA4OfnZ2SfEAqFsLOzQ25uLr94oa4gEAjQvXt3rFy5EikpKfDx8amWeDMyMiAWi+Hu7l6l8ZiYmMDDw8PovaeVFZFIBLFYzNtXnkSDBg0eW8Hp7e0NOzs7/khrV1dXXL9+HQAQGhr6WBgBAQFVLi4CgQAeHh4QCoXIzc1FUFBQlcZXF/jPOU5EyMzMhIuLi1FvuSbR6XQoKyuDQCAw6mUbMBTy50EoFGLUqFEICQnBrl278Oeff2LRokWYP38+2rRpg6+++gpt27Z9rBdoqDCPhmVpaQmO4yCXy58ZLxHh9u3bGD9+PGQyGd544w20atUK5ubmKC0txTvvvIOSkhKj1VsKheKp91wRpVIJlUoFmUyGH3/88YkG5U6dOsHBwcHoJEcARsb5iit1HjXaP43S0lJwHIf4+HjMnz//sXwzHEX9pEbg0SOIDSuG6OGqpLqEQCCAq6srLC0tkZOTU23xKhQKiMXiKl+lVnGFluF/A08qK/80inrS8dPm5uYwMTFBeXk5NBoN778QwAvX+xfBysoKQqHwH+v4y8ILybm5uTlkMpnR9EdNYmpqCmtra5SWlqKsrOyxzzUazb8Kz8TEBBEREQgPD8d7772HmJgY/PTTT9i2bRtmzJiBw4cPP9YT5DgOOp3OSGA4jkN5eTmEQuFzCfGKFSsQExODFStW4I033uArl0wmg42NDUpKSvjvikQi2NjYQC6XP/GeK2JhYQFzc3P4+vri0KFD/+iuojJHBXZ2dhAKhejevTt+/vnnl/pcco1GA71eX63LkU1MTMBxXJ0T4yftG1EqldDpdLC0tOTPnLG1tQUAfoVhRQxTyFWNoR1k+4f+5j/bXIRCIRo1aoTU1FQUFBRUZpr+M2KxGGFhYdDr9bh3795jnz948OC5w1IoFJgxYwbu3bvHF96oqCgsX74cUVFRuHv3LnJzc5/4u0fzQ6lUIiUlBebm5vz889Ogh15XxWLxY8slDWv9K2JmZobmzZtDr9cjPj7+sbAuX76M4cOHIykpCe7u7mjQoAGKi4ufON+fkJCAkSNH4vz580bvv+gcvUAgQFhYGKytrZGWlvaYCBIRDh06hFdffRVZWVkvFFdth4iQlJQErVYLX1/faotXIpFAo9EYdUzqApmZmY91CjMyMlBaWgpfX184Ozvzdk0AuHv37mNhpKenV0sHuKioCDqdDhKJpMrjqgv8Z3ERCATo1KkTiouLceLEiVrRIzIzM+P3V+zevZsfntLDvR379u177rC0Wi1OnTqF48eP8z14w3SMmZkZ7O3tn7iIITs7G6dOnTKyN1y7dg23b99GREQEWrVq9cx4BQIBfHx8oNVqERsbaxROZmYm0tLSHrvnV199FZaWlti5cydKS0v5zzQaDX766Sfk5ORAIpHA3d0dI0eOREFBAfbu3WvUK1Sr1Vi/fj3u3r2LBg0aGMVRGc82IiICnTp1ws2bN3HhwgU+Tw3PZsmSJVAqlXBycnqheCwsLKDT6fjGZN++fXj99ddrTaOq0Wjw22+/oVGjRmjYsGG1xevv7w+VSvXEBRO1mXPnziE1NdVoGnj//v0oLi7GwIED4eLiAgAYNGgQXFxcEB0djZycHP77KpUKe/furfLRCxEhNTUVYrGYT9PLzn+emxAIBGjZsiU6duyI5cuXo3v37tXaE3tamgYNGoQzZ85g+/btePfddzF27FhoNBr8+uuv/DREXl4e7t69Cz8/P5SVlSEtLQ1yuRw6nQ4JCQkQCoWwt7eHvb09Fi5cCK1Wi/bt20Or1eLAgQO4evUqpkyZ8lgjDPzdQzxy5AhKSkrQsmVLJCYm4rvvvoOTkxO++uor2NnZgeM4JCcnIzk5GXq9HsXFxYiJieENldOmTcPp06fx/fffAwAiIyORlpaGtWvXoqysDEKhEHFxcWjQoAF/gue8efOwfPlyzJ07F+PGjYNIJMKePXtw6tQpLF26lHep/uabbyIpKQmLFy+GQqFA7969odfrsXfvXhw4cADffvst3N3dUV5ejvv37/OrcVJTU5GSkgJfX1+kpqbi/v37AID8/HzEx8fDz88PxcXFSEhIgFarRXl5Oe7evcv3Li0tLfH9999j6tSpePPNNzF79my0atUKhYWF+Omnn1BYWIiFCxdCLBZDp9PhwYMHSE1NBQBIpVLEx8fD398fSqUSycnJKCsrg06nw71798BxHL+hrnXr1jh06BD27duHwsJCLF68GC4uLv9oj6oOiAiHDx/GqVOn8P3338Pe3r7a4g4NDQUR4c6dO2jdunWlrhiTy+VISUlBUVEROI5DSkoKXF1d4eXlhZSUFH7hSE5ODu7du4eGDRsiLy8PSUlJ0Ol0KC0tRWxsLHx8fB7LkwYNGuCLL77AyJEj4e7ujuPHj2PlypXo3LkzZsyYwdsNw8LC8P777+OLL77A9OnTMXPmTFhbW+PQoUMoLS2t8mlYnU6Ha9euITAwEHZ2dlUaV13hhVzuG6ZdRowYgR49emDx4sU1nrFEhNLSUmzbtg179uxBYWEhvLy8MHz4cDg5OWHu3LkQCASwtLTE2rVrce3aNSxfvpyfjzacjrlkyRKkp6fj0KFD+Ouvv/geX8OGDTFo0CAMGTLEqMFKT09H165dYWNjg927d2P9+vU4e/YsVCoVwsPDMX36dLRo0QJCoRBKpRIjR45ESkoK76bCxMQE8+fPR79+/cBxHM6fP4+ffvoJMTExMDExQePGjdGvXz9s374dd+/ehYmJCSZPnow5c+ZAIBBAq9XixIkT+P333xEXFwciQlhYGCZNmoS2bdvy88CG/Nm9ezcOHDiAtLQ0mJmZITw8HOPGjUNkZCRMTEwQExODadOm8YsHhEIhwsPDsXLlSkyfPh2xsbF82p2cnLBhwwbs27cPv/zyi5GbjrfffhsTJ07k487KysK2bdtw4sQJSKVS2NnZoX379pgwYQICAwMhEAggk8kwbdo03tOAQCCAo6MjfvnlF8TGxuKzzz7jDbkmJiYYMmQIvzQ8Pz8fCxcuxJkzZ6DX69G8eXO899578Pf3r9Fd00SEuLg4jBo1Ct7e3ti2bVuVumB5lJKSEgwYMADu7u7YvHkzv1S4Mvjrr7/w1ltv8SsEhUIhunTpgq+++gpjxoxBRkYG7+7Fw8MDGzduxIoVK3DgwAEjNy3z58/nVynu2bMHY8eOxdtvv42ePXvi559/xt27d2FmZobu3btj2rRp8PDwMHqmarUahw8fxi+//IL09HQ4Oztj4MCBaNu2Ldq3b49+/fphy5YtVWLrkkqlaNeuHQYOHIgFCxYwuwvw4i739Xo9rVu3jhwdHWnOnDlGm/hqCo7jiOM40mq1pNFoSKvVEsdxpNfrSaPR8JderyedTmf0XsXvPxqORqMhnU73xE1ohk2U4eHhpNfrSa/X87979DePhlkxPRW/UzFthjCelRbDb54W76N5ZAhfq9U+Mayn5cuT0v5oep90T4ZwK+aNVqslvV7/XPnzpGf46H0+Gv6z8qC64DiOEhISqH379hQSEkK3bt2q9jRxHEdff/01eXh4UFxcXKXG/6Rn8rSyYnj/n8qKYRPlJ5988lhZfbS8PHqfj5at8+fPk5mZGc2YMaNKNq5yHEe//vorOTg40MmTJys9/LrKC48VBQIBJk6ciKKiIsyfPx8KhQJffvklXF1da6ynWHHD1aPvP2n57T/1Mv7LkPppDiMN6finMA09/0fT9qzfGX7zPDwt/IqfP22Z8dPS8KzwKob7tOdQ8TtPi+N5fvtP36lO9Ho9rl+/jlmzZkEmk2HDhg0ICwur9rohEAgwdOhQrFq1Clu3bsUXX3xRaVNFzyrrL1JWDPxTWQX+npZatmwZOnXqhObNm/P5q9PpcPToUZiamqJTp05VMqIweESOiIhA8+bNKz38usoL10BDQzB79mx88803OHDgAEaPHo2rV6/WmiXKVU1hYSHy8vKg0+mg1WqRnZ2N0tLSWrHIgVEzEBHKysqwdetWjB49GjqdDr/88gs6dOhQY52ugIAAjBgxAlu3bsW9e/dqZfnU6/XIycmBTCbj969kZ2dDrVY/83ccx+GPP/7AW2+9haNHjyI1NRVxcXFYsmQJNmzYgMGDB6N3796Vnvccx+HgwYO4fv06XnvtNX5JNAOVdxKlYegaHR1N4eHh5O3tTQsXLqSCgoIan5aoSjiOo6lTp1KDBg3I1dWV3NzcyM/Pj3788Ud2Mt1Lik6no7i4OJowYQJ/cJ7BKWdNYvBt5u/vT6+88grvK642kZOTQy1atCAfHx9ycXEhT09PCgkJocuXLz/zdzqdjlauXEkDBgygFi1aUKNGjSgkJIR69OhBS5YsIZlMVun5z3EcpaamUrNmzah///5Gfu4YRC9k0H+KWCE1NRULFizAzp070aRJE8yZMwfdu3eHpaVlvXRFbVi5VBEzMzOYmZnVy/tlPBmO4yCVSrFlyxasWbMGAoEA8+bNw7hx42BtbV0ryoJer8fPP/+M9957D99++y2mTp1aqza0chwHhULx2KjKysrqH9Op1+uh1Wqh1Wr534tEIpibm1fJNKlCocDs2bNx7Ngx7Nq1C61ataoVz7jWUFWqpVQq6eDBg9SlSxdydHSkQYMG0aFDh0ihUNR4D47BqEx0Oh3l5OTQ8uXLKSwsjJydnWnq1KkUHx9fK46heBS5XE6TJ08mT09POnLkCBth/wfUajUtWrSIHBwcaOXKldXq4bquUOkjl0eEC8XFxdi7dy/Wrl2L5ORkREZGYvz48ejRowfs7e1rjeGVwfg30ENHn5mZmdi1axc2b96MnJwcdOnSBW+99RZatmxZa0euRITs7GyMHz8eWVlZ2LhxIyIjI2tlWmsjWq0W27Ztw9tvv42xY8fiu+++qxVe4WsbVSouBjiOg0wmw8GDB7Fx40bcuXMHDRs2xOjRo9G/f3/4+/vD1NSUFW5GrcfgJ+7KlSvYtWsXDh06BLVajV69emHq1Klo0aIFLCwsan1ZJiLcu3cPEydOhEKhwNq1axEVFcU6e/+ARqPB1q1b8cEHH6Bnz55YtmwZ7O3ta/3zrgmqRVwM0MMjiC9evIgtW7bg1KlT/HkegwcPRseOHeHi4vKPZ9QzGNWFoXpoNBokJSXh+PHj2L17N+Lj4+Hq6oohQ4Zg9OjRCAwMrLUjladBDzd2Tp06FVKpFIsXL0bfvn1rlQ2mtkAPj3RftWoV5s+fj/79+2PRokWQSCR16plXJ9UqLgaICGq1Gvfv38e+fftw4MABxMfHQyKRoEuXLujduzciIyPh6urKhIZR7dBDV/5arRZJSUk4d+4cDhw4gBs3boCIEBUVhREjRqBz5858Z6iuQkRITk7GW2+9hRs3buC9997DtGnTYGVlxerdQ4gIOTk5+Pzzz7Fr1y5MnDgRn376Kezs7FgePYMaEZeKEBGKiooQFxeHffv24eTJk0hJSYGjoyMiIyPRs2dPtGzZEoGBgfzUGXugjMrEUAXooWucmJgYXLx4EUeOHEF8fDx0Oh0iIiLQr18/9OrVC76+vk88Z6SuQkTIz8/Hl19+iW3btqFXr1749NNPERwc/FJPkxnsamfOnMEnn3yCtLQ0fPTRR5g0aVKdmPqsaWpcXCrCcRwKCwuRmJiI6OhonD59GvHx8RCLxQgICECnTp0QFRWFxo0bw9PT0+iYXgbjealY5A2OMG/fvo2//voLFy9eRHZ2NqytrREeHo5evXqhS5cu8PHxqde9eSKCUqnEzp078fXXX4PjOMyZMwfjx4+HjY1Nvb3vp0EP/eAtXboUmzZtQmBgIL755ht06NCBTRs+J7VKXCpisM+kp6fjwoULOHHiBGJiYpCbmwtHR0cEBgYiMjISLVu2REBAAHx9fY1WbLxslYHxdCqOTAoLC/HgwQPEx8fj0qVLuHbtGjIyMqDVatGwYUO0atUKPXr0QLNmzeDh4VGtB3rVBjiOQ0JCAhYsWIB9+/ahdevWmDNnDjp16lTnbEr/FkM5KS0txZ49e7BkyRLk5eXhjTfewPTp0+Hi4lKv77+yqbXi8iharRZSqRT379/HuXPncOHCBSQnJyMnJweWlpbw8PBAs2bNEBkZicaNG8Pb2xuurq6PHc/LqN9ULM4KhQLZ2dlIS0vD9evXcenSJSQmJkIqlcLExAS+vr4IDQ1Fp06d0KJFC3h7e7Pl8fj/qeoPP/wQGzZsgKWlJXr06IFZs2YhMjKS96hcX+qTocyUlJQgOjoaK1euRFxcHLp06YJ33nkHLVu2ZKOV/0CdEZdH0Wq1yM/PR2ZmJu7evYtLly7hzp07yMjIQFlZGSQSCVxcXBAYGIjmzZujcePG8PDwgLu7O3/kLqN+oNfrIZPJkJubi7S0NMTFxeHGjRtISkpCXl4eSktLIZFI4Ofnh+bNmyMyMhIhISHw8PBgy0gfQavV4vLly1i0aBHOnDmDNm3aoGPHjti9ezdSU1MRGRmJCRMmoGvXrvVipRTHcUhLS0N0dDQ2b96MhIQEtGrVCm+99Ra6dOnCbCsvQJ0Vl0cxLBXMzMzE/fv3cefOHdy+fRspKSmQSqUoLCyElZUVXF1d4efnh0aNGiE4OBh+fn6QSCRwcHCARCKp90P/uorBJlBYWIiioiJkZ2cjMTERiYmJSEhIQE5ODqRSKdRqNVxcXODh4YHGjRujWbNmCA0NhZeXFzw8PPgz1xnGcByHe/fuYcWKFdi5cye8vb0xZ84cDBo0CDY2NpBKpTh48CA2b96MmJgY+Pn5YciQIejVqxcaNWpUa9zbPA+GkdnNmzexd+9eREdHQy6Xo0OHDpg8eTI6deoEa2vrmk5mnafeiEtFKt5SeXk5pFIpMjMzkZCQgNu3b+Pu3buQSqUoKiqCXC6HmZkZJBIJXF1d0bBhQwQFBcHf3x8eHh6wtbWFnZ0drK2tYWNjwzZ7VhGG5ellZWUoLS2FQqGAVCpFWloakpKSkJSUhJycHOTn50Mmk0Gn08HW1hYODg7w9fVFWFgYwsLC4OPjA09PTzYl+pwYdutv3LgRGzZsgFAoxNSpUzFp0iS4ubnx+WaoU2VlZbhw4QJ++eUXXLhwAaWlpWjatCkGDBiAjh07ws/PD46OjrVuZkCv1yM/Px8JCQmIjo7GiRMnkJycDDc3N/Tv3x+jRo1CWFgY63xUIvVSXJ5Exds0uKXJz8+HVCpFamoqEhMTkZSUhPv370Mmk0Eul6O8vBwmJiZwdHSEvb09JBIJPD094ePjA19fX3h6esLJyQmWlpawtLSEhYUFf4lEIlZI8f+ioVQqoVQqUV5eDqVSCblcDqlUivT0dGRkZCA9PR1SqRQlJSV8/nMcBysrK9ja2sLV1RWBgYEIDAxEQEAA3Nzc4OzsDBcXF9jY2BjFyfL9nzHUgT/++ANLlixBbm4uRo4ciZkzZ/Ingj4tH4kIHMchPT0dZ86cwZ49e3Dz5k3I5XI0bNgQLVu2RLdu3dC4cWO4uLjA0dGxWm0WhjJXUFCA3NxcXLt2DadOncLdu3eRlZUFJycndOzYEQMHDkSrVq14EWXlpnJ5acTlaTx6+1qtFgUFBSgoKEBhYSHy8/ORkZHBN4CZmZkoKSlBWVkZysvLodVqIRaLYW1tDVtbW/5ydHSEq6srX7ns7e35y9LSEmKxGGKxGKampvzfIpEIJiYm/GtNF3bDZkK9Xg+dTsd7ndVoNPyrRqOBWq2GXC5HcXExfxnOuJFKpSguLoZcLkdpaSlKS0tRVlYGjuOgVquh0Whgbm6OZs2aISAgAD4+PvzoQyKR8LazRzes1XTe1GXKy8tx5swZLFq0CDdu3EDXrl0xb948tG7d+l+VO0Pd0ev1uH//Pm7duoVTp07hwoULyM7OBsdx8PLygr+/P8LDwxEaGgo/Pz84ODjA1tYWlpaW/H6hf/s8DeVSpVJBoVBALpcjPz8fycnJiImJQUxMDB48eIDc3FxYWVnBz88PnTt3RocOHdC0aVO4u7szQaliXnpxeRaPZo2hx1ZSUoKCggIUFRWhpKSEHwUZGtOUlBRcuHABHMfBzc3NyBW4VqvlT3m0sLCAlZUVP/IxMzPjhcbMzIwfBRlGRYZXExMTmJqa8r1Bg51IKBTyw3rDd9RqNZ9uw4FLOp0OOp2O34Wu1+ufOLpQKpVQqVS8CGg0GqhUKpSXl/PiqlKpoNPpIBQKYWpqyl9isRiWlpZwcnKCi4sLXFxceLGVSCSws7NDRkYGli9fjgcPHmDIkCGYNWsWQkND+ftiFb9y0Wq1uH37NpYsWYLDhw8jNDQU8+bNQ+/evStlU6ihM6JQKHh72M2bN3H9+nWkpaVBLpdDpVLBxsYGbm5ukEgksLW1hbOzM5ydneHo6AgLCwv+uAozMzN+FGIoh2VlZSgsLIRUKoVMJkNxcTEKCgqQl5cHtVoNS0tL2NnZITg4GC1atECzZs0QGBiIhg0b1rtVbrUdJi6VQMV9FHl5eXj//fdx4MABzJs3D6+++io0Gg3KysqMLoVCgdLSUhQXF6OkpAQlJSV8o16xcTc08DqdDhzHQa/X8/FxHGf0tyENFe0XdnZ2MDEx4efAK/bWhEKh0d+G7xmEr+JlEDYrKyt+BGZnZwdbW1tYWVk9dtnY2MDGxuaxja6PVuz8/Hxs3rwZa9asgVqtxqRJkzB16lR4eXnVunn7ugrHcUhNTcW6deuwefNm2NnZYebMmRg7dmyVrZarWCcMm6OzsrKQnZ2N9PR0pKSkIDs7G1KpFAUFBVAoFNBqteA4jr+0Wi1KS0thY2MDCwsL/jhlsVgMOzs7flrU29sb/v7+8PLygru7O7y8vPiNn2x0UnMwcakkiAhpaWmYPXs2Ll26hC+//BKTJk16qu2lYuV71qvhb8P0k1qthlarBQBecIC/HSsCfzckOp0OBw4cwLJly/DTTz/B19eXT4dQKORHBiKRiBcYw6jJzMzMKM1PmoqqWGErq7f74MEDrFq1Ctu2bYODgwNmzJiBUaNG1YvlrjWFwa3Ltm3bsGrVKpSXl2PChAmYNm0afHx8aqzhNQiO4dkbVnrK5XKjadeEhARMmzYNH3/8MQYMGABTU1OYmZnB1taWH60/ejFqEU8+5oXxb+A4jmJjY6l9+/bk6+tLu3fvJp1OV2OHonEcR5s2bSI3NzdKTEyskTT8WziOI61WS1evXqVXXnmFHB0dqV27drRz506Sy+XsgLl/AcdxJJfLadeuXdS2bVuSSCQ0fvx4unXrVo2Wy39LQkICOTk50ZYtW2o6KYz/AJt3eEE4jsPFixcxduxYyGQy/PLLLxg8eHCtMMjXJQQCAUQiEVq0aIH169dj69atsLCwwGuvvYZXX30V586dg0qleswOxvh/6OEI9+LFi/wIxcrKCr/++ivWrVuHsLCwOlUuxWIxAPC2QkbdgonLC8BxHE6ePInx48fD0tIS27ZtQ8eOHZmt4AUQCASwsLBA7969sWvXLixevBipqakYNmwY3nrrLdy9exc6na6mk1mroIcrp5KTk/HOO+9gyJAhSE5OxqJFi7Br1y707NmzTm4ONoiLYcqXUbdgDnP+I3q9HocPH8bMmTMREBCAtWvXwt/fv85V4NqKQCCAnZ0dXn31VfTq1QubNm3C+vXrER0dzRv9PT09X3oh5zgOBQUF+OWXX7BmzRpotVq88cYbmDp1Ktzd3et0/hhWPrKRS92k7pa8GkSv12Pnzp343//+h6ZNm+Lnn39mwlJFCIVCuLm54Z133sGRI0cwbNgwrFu3Dr169cKaNWtQWFj4Uk6V0cMlvzt27EC/fv2wYMECdO3aFdHR0fjoo4/g4eFRp4UFAL9kn41c6iZ1u/TVAHq9Hjt27MCcOXMQFRWFtWvX8itvGFWHiYkJAgICMH/+fOzbtw/h4eH45JNPMGjQIOzZswdlZWUvhcgY7CpnzpzB6NGjMXPmTDg7O+O3337D8uXL0bhx43rjHcIgLmzkUjdh4vKc0MNT6bZu3Yq5c+eiS5cuWLFiBTw9PetFRa4LCAQCiMVitGrVChs2bMCWLVtgYWGBadOm4ZVXXsHZs2eh0WjqpcgY7CpxcXGYMWMGRowYgby8PCxfvhw7duxAly5d6t3ZM4Y9LWzkUjdhNpfnwFCxf/nlF3zwwQfo1asXfvjhB7i4uNR00l5KBAIBLC0t0adPH0RFRWHfvn1YunQphg8fjsGDB+PNN99ESEhInT7bviIcxyE7Oxs///wzfv75ZwiFQrzzzjsYP348XF1d623npuK0GBHV2/usrzBxeQ70ej02b96MDz74gJ/fdnZ2rulkvfQIBALY29tj/Pjx6NmzJ7Zs2YI1a9bgyJEjmDRpEqZNm1anR5b08DTWnTt3YtmyZZBKpbxzyYCAgHojnk/DMFJl4lI3YdNi/4Ber8e2bdvw/vvvo3fv3li4cCETllqGQCCAu7s75s2bhyNHjmD48OFYs2YNevXqhbVr16KoqIjfCV4XoIfuew4fPoyhQ4fivffeQ0hICPbt24cffvgBwcHB9V5YAPCeI+rrVGd9h4nLMzAY79955x307NkTixYtgpOTU00ni/EUTExMEBgYiPnz5+PgwYMICwvDhx9+iIEDB+KPP/6o9ZswDdOv169fx6RJk/Dqq69Cp9Nh48aN2LhxI9q0acPv/XgZqDhyMbg5YtQdmLg8Bb1ej/379+Odd95B586d8eOPPzIbSx3AME/fsmVL/Pzzz9i6dSvEYjEmT56MV155BefPn+c9QtcWDKOq1NRUXgxv3ryJr7/+Gnv27MHAgQNhZWX10k0LPTotxqhbMJvLE+A4Dn/++SfeeusttG7dGkuXLmXCUscw7PTv27cvoqKi8Mcff2Dp0qUYMmQIhg4dyrv3r2mHh0SEwsJCbN26FatXr0ZZWRkmTpyI1157Db6+vi+doFSEjVzqNkxcHoHjOJw7dw6vv/46GjVqhOXLl8PNza2mk8V4Aezt7TFhwgT07NkTW7duxdq1axEdHY2JEydi6tSp8Pb2rvZGnB56Ao6OjsaiRYuQlJSEgQMHYvbs2WjSpMlLYVP5J9jIpW7DpsUqQES4ffs2Xn/9dbi7u2P16tXw8vKq6WQxKgGBQAAPDw+8/fbbiI6OxvDhw7F+/Xr06dMHq1evRn5+frU1YBqNBufPn8crr7yCadOmwc7ODjt27MCaNWvQrFkzJiwPYQb9ug0Tl4cQEZKTkzF9+nSIRCKsXbsWDRs2fKmnJeojQqEQgYGBWLhwIfbv38/v9B88eDB2794NuVxeZXFzHIf4+HjMmTMHQ4YMQVpaGpYuXYpdu3ahe/fu/EmJjL9h02J1GyYuD8nKysLMmTNRVFSENWvW8PPxjPqH4Rhog9H/119/hZWVFb/T//Tp05XqcoSIkJ2djQULFqBfv344dOgQZs+ejejoaLz66qv8qYkMY9i0WN2G2VwAFBUV4b333kNsbCw2bNiANm3asMr+EmCYdunVqxfatWuHffv2YcmSJRg+fDgGDRqEN998k/fV9V8wbILcu3cvli5dipycHIwYMQIzZsxAcHBwjS8mqO1UnBZjI5e6x0svLiqVCgsXLsSRI0ewaNEi9OzZs857k2X8OwQCAaytrfHKK68Y7fQ/duwYxo8fzx8L/LzlgoigUqlw7tw5LFq0CFevXkXHjh2xYsUKtGnTpt44lqxqDCMXrVbLRi51kJe6FdXpdNi0aRPWrFmD2bNnY9y4cUxYXlIMowhnZ2fMmTMHR44cwejRo7Fx40b06dMHq1atei6jv1ar5ReFjB49GqWlpVi7di22bduGdu3awdTUlAnLc8Kmxeo2L21LSkQ4fvw4Pv/8cwwbNgxz585lPUoGBAIBhEIhGjZsiO+//x4HDhxAy5Yt8fnnn2PgwIG80f/Rxo7jOKSmpuLzzz9H//79cfHiRXz66ac4ePAghg8fDktLS1a2/iUVvSKzabG6x0spLkSEu3fvYu7cuWjWrBm++eYbVvkZRhiM/s2bN8eGDRuwbds22Nra4rXXXuPd+6vVahARCgoKsGrVKvTu3RsbN27E2LFjER0djVmzZkEikbBy9QKwkUvd5aW0uUilUrz99tsAgB9++AHOzs6sAWA8EYNRuUePHoiMjMQff/yBJUuWYNiwYRgyZAhatmyJLVu2IC4uDr1798bcuXMRFhbGRsGVQEWbCxu51D1eKnExGFq//vpr3Lp1Cz/99BMaN27MGgHGPyIQCGBra4tx48aha9eu+PDDD7Fx40Zs2rQJ1tbW+P777zFu3DhYWFiw8lSJiMXiWucLjvF8vFTTYkSEX3/9Fb/++iveffdd9O7dmzUEjOfC4LH4/v37WLRoEY4ePYqQkBCMGjUKYrEYS5cuxaZNmyCTyVhDWImYmZmx1WJ1lJdGXIgIV65cwZdffon+/fvjtddeYyvDGM+Fwa6yZMkS9OnTB7t378b//vc/HD58GBs2bMD+/fvRrFkzfPLJJxg4cCD27NmDsrIy1iBWAoaRi16vr+mkMP4lL0XrSkTIy8vDu+++C2dnZ3zxxRewtrZmoxbGMyEilJWV4bfffkO/fv3w3XffoVOnTjh06BA++eQTeHp6wszMDK1atcKGDRvw66+/wtLSkt/pf/bsWWaMfkEM59doNJoaTgnj31LvxYWIoNVq8d133yExMRHz589HgwYNajpZjFqMocycPXsWY8eOxRtvvAGJRILff/8dK1aseMxgLxAIYGlpiV69euH333/H4sWLkZaWhuHDh2PWrFmIjY2FXq9nIvMfEIvFEAgETFzqIPVeXABg37592Lp1K+bMmYPOnTuzEQvjiRjsKvfu3cNbb72F4cOHIyMjA0uXLsWOHTvQpUsXmJubP/X3AoEADg4OGD9+PA4fPox58+bhzz//RL9+/fDFF18gIyODrXr6lxhGLpXp641RPdTr1WJEhJSUFHz22Wdo164dXnvttf/sJ4pRfzGMKPLy8vDzzz9j3bp1AIA5c+ZgwoQJcHd3/1cdEoFAAHd3d7z99tsYMmQI1qxZg/Xr12PXrl2YMWMGxowZAwcHB9bJeQ7YyKXuUm9HLkQEpVKJzz77DEqlEl999RUcHBxqOlmMWgYRQS6XY9OmTejTpw+WLFmCvn37Ijo6Gu+++y48PDz+swiYmJggKCgI8+fPx/79+xEREYFPPvkEAwYMwJ49e1BeXs6myv4BNnKpu9Rrcfn9999x+PBhfPzxx2jatGlNJ4lRiyAiqNVqHD16FEOHDsXcuXPh5+eHvXv3YsmSJQgJCam0Q7tMTU3RsmVLbNiwAdu3b4elpSWmTp2KMWPG4OzZs2yp7TMwMzODQCBg4lIHqZfiQkS4f/8+vv/+e/To0QMjR45ky44ZAP7frnLjxg1MnToVr7zyCpRKJX766Sds3rwZ7dq143vLlYlAIICFhQV69uyJnTt3YunSpcjIyMCwYcMwc+ZM3L17FxzHMZF5BLZarO5SLw0QarUa33zzDVQqFT755BPY2NjUdJIYtQAiQmpqKtauXYvNmzfDzs4OX3zxBUaPHl1tPsAEAgHs7e3x6quvokePHtiyZQvWrVuH6OhoTJo0CVOmTIG3tzezxzzEYHNhI5e6R70TFyLCoUOHsH//fnz11VcIDQ2t6SQxagH5+fnYsWMHVq5cCblcjvHjx+P111+Hj49PjTTkjxr9V69ejXXr1mHXrl2YOXMmRo4cCUdHx5dKZPLz83H79m0olUqo1Wqo1WqkpKRApVJh7969iI+P599Xq9Xo2bMnevXqVdPJZjwNqmfk5uZSy5YtqXfv3lRUVFTTyakROI6jTZs2kZubGyUmJtZ0cmoUhUJBO3fupMjISHJ0dKQJEybQjRs3SKfTEcdxNZ08Hq1WS1euXKExY8aQg4MDtWvXjnbu3ElyubxWpbMquX37Njk4OBCAf7xMTU3p+PHjNZ1kxjOoV4YInU6HtWvXIi0tDR988AHs7OxqOkmMGoAeboI8f/48xo0bh2nTpsHW1ha//fYb1q1bh/DwcJiYmNSqUYFIJELLli2xceNGbNu2zWin/5kzZ16KaaGAgAB07Njxub4bFBSEJk2aVHGKGC9CvREXIsKdO3ewbt06vPLKK4iMjKxVjQejeuA4DomJiZgzZw6GDBmC1NRULFmyBLt27UK3bt34OfzaiMG9f69evbBr1y4sW7YMWVlZGD58ON544w3cvn0bOp2uppNZZVhYWGD48OHPtRetXbt2cHZ2roZUMf4r9cbmolKpsGTJElhbW+PNN9+EqalpTSeJUY0QEXJzc7FlyxasWbMGer0es2bNwtSpU+Hm5gYAtVZUHqWie/+ePXti69atWLNmDY4ePYoJEyZg2rRp8PHxqXcrIAUCAbp06YIGDRogOTn5qd8zMTFBv379Km2pOKNqqBelk4hw/vx5HD58GG+88QZ8fX3rTEPCeDGICCUlJdi6dSv69euHRYsWoUePHjh8+DA++ugjfnd9XSwPAoEALi4umDNnDo4ePYrRo0dj06ZN6N27N1auXAmpVPqPS5ezs7Nx69atOrPE2dXVFX379n3m82rQoAGaN29ejali/BfqhbgoFAosXrwY/v7+GD16dL3r0TEehx56YDh58iRGjRqFN998E15eXti1axdWrFiB0NDQelEOBAIBhEIh/Pz88P333+PAgQNo1aoVvvjiCwwcOBC///47SktLnygeOp0Oixcvxvjx4xEbG1snBMbExATDhw+HpaXlU7/Ttm1buLq6VmOqGP+FOl/7iAjHjh3DxYsXMWvWLDYP+xKg1WoRExODGTNmYNSoUZDJZFi9ejW2b9+ODh06wNTUtE6OVJ6FQCCASCRCREQE797fwcEBr7/+Ol555RWcPn0aKpXK6De3b9/G5s2bERMTgzfeeANpaWm1XmAEAgHCwsLQokWLJ35uYmKCPn36MB+BdYA6Ly4lJSVYtWoVIiIiMGDAgHrXqDD+H47jkJ6eji+//BJ9+/bFuXPn8OGHH+LQoUMYNWoUrKys6v3zNxj9e/bsid9//x3Lli1Dbm4uRowYgRkzZuDOnTvQarVQqVRYunQppFIpAOD8+fN48803kZ+fX+sFxtbWFkOGDHmiTcXNzQ1t27at98+5XlAjC6ArCY7jaPfu3eTg4EC//fbbS7Mf4J+ob/tcOI6jwsJCWr16NTVp0oTc3Nxo9uzZlJSURDqdrqaTV6Po9XrKy8ujhQsXkr+/P3l6etLHH39M27dvJxsbG6O9ISYmJjRp0iQqLi6u9XUlKSmJ3NzcHtvfMmzYMFIqlTWdPMZzUKfHlnK5HGvXrkXTpk3Rq1cv1pupZ9BDu8qJEyewaNEi3L17Fz179sTs2bMRERFRL6e//i1CoRDOzs6YM2cOBg8ejFWrVmH9+vVQKBQoKysz+q5er+en07766qtn2jVqGm9vb3Tv3h1bt27l3zMxMUGvXr1gZmZWgyljPC91dlqMiHD27FlcvXoV06dPh62tbU0niVGJaLVaXL16FZMnT8akSZMgFAqxefNm/PTTT2jdunWt3q9S3QgEApiYmMDf3x/ff/893nzzzceExYBGo8Hq1auxYsWKWr0xUywWY+jQoUaHszk4OKBTp07sudcR6uzIRaVS4ZdffkFAQAD69OnDClw9geM4pKamYvXq1di6dSscHR3xzTffYOTIkeyAreegpKQE+/fvf+Z3lEolvvnmG7i4uGDcuHG10jguEAjQoUMHBAQEIDY2FgDQpk0beHp61nDKGM9L7StVz0l8fDxOnz6Njz76iLl5qePQQwOzTCbD1q1bsXLlSpSXl2PKlCl47bXX4OXlVS+WFVcH27dvx7Vr1/7xe6WlpXjvvfcgkUjQr1+/Wpm/Dg4OGDRoEOLi4iAQCNC9e/daPZXHMKZOiotOp8Ovv/4Ka2trDB48uFZWjMrE0PgS0RMvnU4HvV4PrVYLvV4PnU6HwsJC6PV65ObmwtraGiKRyOiq6FvLsMmw4mbD6hohGOwqhw4dwg8//IDk5GQMGDAAs2fPRmhoKEQiERutPAdEhLS0NKxatQp6vf65fiOVSjFr1izY29ujffv2lZbPj5bXR9979FWv10Ov1/M+4Sq+17ZtW1hZWYGIEBwcjMzMTL7sGrxwVCzPFe1wz3qt7nL+MlInxSU7Oxv79u3D4MGD68Uw2VDRDIdFqVQqFBYW8ldBQQHy8vIglUpRXFyM4uJilJSU8K8qlQpExP+eiKBQKFBaWopRo0bB1NQUQqGQr1QmJiawsrKCvb097OzsYG9vD3t7ezg4OMDNzQ3Ozs6QSCSQSCRwcnKCvb09X2kra7e7oQG5dOkSfvjhB5w+fRqtW7fm96oYTiBkPD8HDx5EZmYmhEKhUcP+LNLS0jBjxgxs3boVTZs2fWKeP6lzw3EcysvLIZfLoVAooFAojP4uKSlBUVER5HI5ysvL+ausrMzof6VSaXQSJ8dxRnHp9XqUl5cDAKZMmWLU2TB0KiuWS6FQCHNzc1haWhpdVlZWRv8byrytrS2sra35y8bGBjY2NrC2tjYq84Z4Kr4yno2AnqcE1iKICD/99BM++OADHDhwAG3atKkzD7tiJeU4DlKpFFlZWcjKysKDBw+QmJiIhIQEZGdno7y8HCqVCmq1GiYmJrwIWFlZ8RXFxsYG9vb2fEUwNTWFWCyGSCTiT/Cjh8f56nQ6aLVaaDQaaDQalJSUoKSkBAqFgq/0CoUCRUVFUCgUfCU1NzeHjY0N/Pz8EBwcjMDAQPj4+MDDwwPe3t6wtbX9V5XO0GgkJiZixYoV+P333+Hh4cGvdmJTnP8NIkJhYSGkUimSk5MRFxeHuLg4PHjwABkZGcjNzYVGo3mq4ERFRWHLli3w8fFBaWkpCgsLUVRUhKKiIshkMuTm5iInJ4d/zcvLQ3l5ObRardHFcRxMTU1haWkJa2trWFhYQCwWw8zMDObm5jAzM4NYLIaFhQVfjs3Nzfn9O4bOj6FhF4vFuHv3LsrKyhAZGQng70UJer0eHMfxJ1QaRu1arRZlZWVQKpVGdUij0fDnwKhUKpSVlaGsrAxqtZpPc8XLzMwMjo6OcHNzg7u7O9zd3eHm5gYnJyc4ODjAwcEBjo6OkEgkfF1j4mNMnRMXuVyO4cOHg4iwf/9+o9UktY2KvTGpVIrU1FQkJSXhxo0buHnzJjIyMlBaWgqtVssXZE9PTzRs2BDe3t7w9PSEp6cn7O3tYWFhAQsLC15AxGLxC7uN5zgOOp2OFxyNRsNXyvz8fF740tLSkJqaiuzsbOTm5qKsrIwf+QQFBaFFixZo1qwZ/Pz80KBBA1hbWz9W0Qx5kZeXh02bNmHt2rUgIkybNg0TJ06Eh4cHq5SVjF6v50cRhYWFSEhIQGxsLG7fvo24uDjk5ubyowKBQICQkBDY2NhAJpPxDbNGo4FYLIatra3RCNfV1RXOzs5wdHQ0amxtbW15ARGLxTA1NeWnrSpOyf4bp5OFhYUoKyuDj4/Pv7r/ilPGOp3O6O+KZb6srMxISIuKilBYWIi8vDwUFBTwswXFxcUoLy+HQCCAubk5LCwsYGtrCy8vL/j5+cHPzw8+Pj583jg7O8PBwcFo2v5lKuN1TlwuX76Mvn37YsGCBZg8eXKtelgVRyb5+flITEzE5cuXcebMGdy7dw8FBQUQCATw9PREUFAQIiIi0KRJEzRo0AAODg6ws7ODjY1NrfP2SkTQaDQoLS1FSUkJCgoKkJiYiNu3byM2NhZpaWnIzc2Fubk53Nzc0LJlS3Ts2BFNmzZFQEAArK2tIZfLsW/fPixZsgTp6ekYNmwY3nzzTQQHB9e6+63LVKzO5eXlyMvLQ05ODlJTU/nRTEpKCnJzc1FaWgrgb8O5RCLhpz/btGkDf39/eHh4wNXVFdbW1rC0tISFhQX/Wledgf4bDOXeMH1nmMqTyWTIyclBRkYGHjx4gMzMTBQUFKCwsBAymQw6nQ5WVlawsbGBm5sbQkJCEBoaioCAALi5ufFTzxXLfX3MyzolLhzH4b333sPevXtx+vRpeHl51XSS+Mqs0+lw//59nDt3DtHR0YiNjeWN6Y0aNUJUVBRat24NX19fuLi4QCKR1MoloP8WtVqNgoIC5ObmIjY2FhcvXsTNmzeRmpoKjuPg4+ODsLAwPHjwALdu3ULnzp0xb948tGnThp9OYPx7Hq22paWlyMrKQkpKCq5fv45bt24hLS0NMpkMxcXFMDU1hYuLC9zd3REcHIzg4GD4+fnBw8MDtra2vO1Bp9PVyg5ObUav16OsrAwlJSUoLS1Ffn4+UlNTkZycjHv37iEzMxN5eXmQyWQwNTWFo6Mj3N3d0aRJE7Ro0QLBwcHw8vKCm5ubUZ2o64JTp8QlLy8PPXr0QFRUFFauXFmjFcDQq0lISMDZs2dx8OBBxMTEQKPRIDg4GFFRUejYsSMCAwPh7e3NzyvXd4gIpaWlyMjIwK1bt3Dq1ClcuXIFSUlJAIC+ffti1KhRaNu2LVti/B8gIpSVlSEzMxOpqam4ffs2rl+/jqSkJOTl5UGlUsHFxQUeHh4ICAhAaGgoQkND4e7uzi/SYBtQqw+O46BQKFBYWMiLTsURpMG+KpFI4OXlhcaNG6NFixYICwuDj4/PYyOcukSdEpdDhw5h3Lhx2L59e425e+E4DtnZ2Th37hx27NiBixcvQiQSoWnTpujTpw86deqEoKAgfurgZccwRRgbG4uTJ0/i2LFjSE5OhkQiQdeuXTF69Gg0b96cGfKfAhFBLpcjPT0dd+7cwblz53Dz5k2kpaWhvLwczs7O8PLyQrNmzdCyZUsEBwfD09MTzs7ObBl3LYYenkNkmLK8desWrl+/juTkZOTm5kKtVsPDwwNBQUFo164dP1Xp6upaZ8SmzoiLTqfD1KlTcfv2bZw6dQr29vbVFrdhlBITE4Nff/0VR44cQW5uLpo2bYqRI0eiW7du8Pf359fdswr9OIZiVlpaitjYWPzxxx84cuQIsrKyEBoaihEjRmDAgAHw9fWtM5WnqtBoNMjKykJsbCxOnDiBq1evIiUlBRzHwcPDA82aNUO7du3QtGlTeHt7w83NzWiKlZW/ukXFJri4uBhZWVlITk7GpUuXcPnyZTx48ABFRUXw8PBA48aN0bVrV0RFRaFBgwaws7Ortc+7zohLWloaunXrhtGjR+PLL7+slukUIkJ5eTkuXLiAdevW4cyZM7CxscGAAQMwZMgQtGzZkt8xXFsfcG3EUOQKCgpw5swZbNu2DefPn4eVlRVGjhyJ8ePHIygoqF7YpJ4Hw1RXamoqTp8+jWPHjuHOnTsoKSmBh4cHWrRogc6dOyM8PBz+/v5GfvRYuat/VGySdTodMjMzER8fj/Pnz+Ps2bNITk6GWq1GUFAQ2rdvjz59+qBx48ZwdnauVdPMdUZc9uzZgylTpmDfvn3o0KFDlVcqpVKJCxcuYPny5fjrr7/QsGFDTJgwAQMGDICPj89LsVqmqqm4GCImJgZbtmzBnj17wHEcRo8ejalTpyIgIKBejmQMngnu37+Po0eP4vDhw7hz5w5EIhFCQ0N522KTJk1gb2/P9lC85Bjqilar5RdtHD9+HJcuXUJeXh68vb3RsWNHDBkyBGFhYXBycqpxoakT4qLX6zFlyhTcuXMHp0+frlIPyHq9HrGxsfjhhx9w6NAhNGzYENOnT8egQYPg5OQEgFXwqsCwuTIpKQk///wztm/fDqFQiDfeeAMTJ06Es7Nzvch3nU6HnJwcnDx5Ejt37sSVK1cgEonQokUL9O/fH+3atUNQUBCbYmU8lYpbHqRSKa5du4ajR4/izz//RHZ2Nvz8/NC7d28MGzYMjRo1qrFD9OqEuGRnZ6Nbt24YMGAA5s+fXyUZRUSQyWTYsGEDVq5cCQsLC8yaNQujR4+GRCJhlbyaMHgvSEhIwLJly7B7924EBgbi008/RefOnWv1ptmnYZhevXv3LrZu3YrDhw+juLgYYWFhGDp0KLp164aAgAB+GpCVNca/wdAxk8lkuHDhAg4cOIATJ06gtLQUbdq0wdixY9G9e3e4uLhU72jm6eeI1R5OnjxJtra2FB0dXSUn6Ol0Orp8+TL16tWLJBIJzZgxg5KSkkiv19f6E/vqKxzHkVqtpj///JM6d+5MEomE5s2bR7m5uXXmmXAcRyUlJXTgwAEaNGgQOTo6kr+/P82bN4/OnTtH5eXldeZeGHUDjuNIq9VSamoqrVu3jrp06UISiYSaNm1K33zzDaWkpJBWq62WtNR6ceE4jj799FMKCAigjIyMSg9fpVLRxo0bycfHh8LDw+mPP/4gtVrNKn0tgeM4KigooC+//JJcXV2pS5cudOPGDdLr9TWdtKfCcRzJ5XLau3cv9e7dmxwcHCgyMpJWrFhBaWlppNPpWPliVDkcx1FZWRmdOnWKJk2aRO7u7uTr60sfffQR3b9/v8qPCK/14qJUKqlLly40ZMiQSs+M0tJS+uyzz8jR0ZHGjBlDycnJrNLXUjQaDR05coSaNm1KgYGBdPjw4SqvHP8FtVpNp0+fpsGDB5OjoyN16tSJNm/eTDKZrFYLIqP+YpgFuHXrFs2aNYs8PDwoICCAFi1aRFKptMravFovLsnJyeTr60s//vhjpWZCcXExzZ49mxwcHOijjz6ikpISJiz0d0EsLi6m/Px8ys/Pr1X5wnEcxcXFUY8ePcjLy4t27txZbUP8f4LjOEpPT6e5c+eSi4sLtWjRgjZu3EhFRUVVkn8ajYYKCgr456RSqSo9Dkb9guM40mg0dOvWLZo8eTI5OTlRVFQURUdHk1qtrvT4arW4cBxHx48fJ1tbWzp16lSlVdKysjJ6++23ydHRkRYuXEgqlarWNKAvgk6no5KSEtJoNP85jLKyMhozZgz5+vqSo6MjjR49msrLyysxlS8Gx3GUmZlJQ4YMIU9PT9q/f3+Njwg0Gg1FR0dTy5YtycPDgz777DPKysqq0nRdvHiRmjZtSu7u7uTo6Ej79u2rsrgY9QuO40ipVNLRo0epQ4cO5OzsTB9++CHl5+dXajtY68Vl/vz55OfnR6mpqZUSpk6nox9//JEcHBzou+++qzf2FY7j6ODBgxQSEkLLly//z/fEcRwVFRXR5cuXSSKRUP/+/amsrOy5fqtUKqm0tLTKRxMcx1F2djb17duXGjZsSJcvX66xZ6hQKOiHH34gFxcX6tq1K509e/aFxP15UavVJJVK6eOPPyaBQEA7d+6s8jgZ9QuO40gqldKnn35KLi4u1KdPH4qPj6+0ulR7tnM+hZiYGN5N9YtCRDhz5gzmz5+PsWPH4q233jI6FrWuU15ejpKSEsjl8v8chkAggL29PRwdHf91vnz//fcIDw/HyZMn/3P8z4NAIICbmxuWLFkCe3t7vPPOO5BKpVUa55OQy+X49NNP8fXXX2PkyJHYvn072rVrx+9RqUrEYjEkEgmsra2rPC5G/UQgEMDJyQkff/wx1q9fj+TkZIwdOxY3btx4rlNM/4laLS5qtRrJyckIDAx8Yffs9PCkvs8//xwBAQH4+OOP65WnYoFAgEGDBuHatWuYPXt2jdxXaWkp73SvqhEIBAgICMD8+fNx7949rFq1CjqdrsrjNVBWVoZPP/0UmzZtwgcffID58+fXm42ejJcHgUAAU1NT9O/fH9u3bwcATJo0CTExMS8sMLXaeVNBQQEKCgowaNCgSgnv999/x927d7Ft2za4urpWSUOg1+uh1+uN3hOJRPy55hzHgeM43mMtPTwn3HB2eMXvA/+/C/fGjRvIz8+HpaUlQkJC+EO2KoYBABKJhI+r4v3pdDrcu3cPsbGxAIDGjRsjJCQEwP+fWy4QCJ7oSVev1/OnFwoEAjRr1gzBwcFGaTQccWuIy3D8rFAofOETM5+GQCBAp06d8Oqrr+Lnn3/GyJEj0bhx40qP51H0ej3WrFmDX375BR999BFmzpwJMzOzKo/3nygpKeHdgTg7OyMyMtLIdYwBengsws2bN5GVlQWhUAh/f3+EhYXxRw0/Wi4NZSMnJweXL19GWVkZgoOD0axZM5iamkKv1yM5ORl3796FQqGAg4MDmjRpAh8fn8eePz08evvu3btISkqCTqeDp6cnIiIijBwxPlqXRCIRZDIZrly5gsLCQri5uaFNmzawsbF54j3KZDLcuHEDubm5EIvFCA4ORmhoKD9bQQ9PqqzYiAqFQn4zq+Gk1or10fCeAcOpmoa6nZaWhjt37qC0tBT29vYIDw83Olri0ThNTEzAcRzu3LmD+Ph4WFpaolWrVvDy8qrWjopQKETz5s2xceNGjBs3Dm+++Sa2b98ONze3/56OSplcqyJiYmLIxcWFtm3b9sLzgDKZjFq2bEnDhw8npVJZSSl8nN27d1NISAgFBQVRcHAwhYWF0cGDB4mISK/X05IlS6h169a8DUmv19OXX35JjRo1oqCgIAoPD6eYmBgiItJqtbRr1y5q1qwZtW7dmkaNGkU9e/akhg0b0pw5c6iwsJA4jiOVSkXTpk3jw5g4cSJvJ+E4jsrLy+nrr78mLy8vatWqFY0ZM4aGDRtGCxYsoDlz5lBQUBA1atSI+vXrRwqFgoiIkpKSyMnJiXr16kVff/01jRo1ikaPHk2hoaHk7e1t9EwSExOpefPm5OjoSAKBgLy8vCgkJIRCQkLogw8+qFIbBMdxlJiYSL6+vvT+++9XuXGf4zi6cOECubu708yZM2t0sYNer6fvv/+eBAIBLVy4kCZPnkzjxo2jvn37kqurK/Xs2ZMyMjKM6o5er6ezZ89Sx44dqUmTJjRs2DAaOHAgBQUF0bBhw+jevXvEcRxxHEdffPEFX5b79u1Lhw4dooiICLK0tCSRSETe3t70119/kVKppC+//JKCgoKoT58+NGbMGOrbty8FBQXR1KlTqbCwkI+f4zhKS0ujcePGUUBAAA0cOJCGDRtGTZo0ofbt29PZs2f5Z7h+/Xpq3LgxBQcHU9OmTengwYM0YsQIGjt2LPXr1488PT2pZ8+eFBcXZ3SPOp2ODh48SK1ataKIiAgaMWIE9evXjwICAmjy5Ml8nty7d4/atm1LQUFBFBQURCEhIfTZZ5/x4Zw9e5aaNGlitHl737591KRJEwoKCqLQ0FDavHkzv5/k+++/p8DAQOratSuNGjWK2rVrR8HBwbRy5Up+NV96ejp17dqVr6uLFi2ir776itzc3MjU1JTMzMyoX79+JJfLq7LoPBWO4+jEiRPk5uZG8+bNeyH7aa0VF47j6Pz582RjY0MnT558IXHhOI6OHj1KdnZ2fENfVchkMjp8+DA5ODhQREQE3bx5k4qLi4no73017du3J5FIRBs2bOArcXZ2Nq1Zs4ZatGhBFy9e5HduHzlyhJycnKhfv36UlpZGWq2WSkpKaPHixWRlZUVvv/02qdVq0uv1lJqaSseOHSNnZ2fq3r07Xzg5jqP169eTubk5jRkzhnJyckin01FhYSF9++235OvrSzY2NrR161ZKSkri944YxMXJyYm+/fZbKi4uJq1WSzdv3iQfHx9q3rw5SaVSIvrbkB8XF0fjx48nc3NzWrFiBcXGxlJsbCxlZmZWubFdq9XS//73PwoLC6Pc3NwqjUupVNLw4cOpWbNmjzXc1Y1BXABQ69at6dy5c6RWq6msrIy+/fZbEgqF9P333/Np5DiObt26RQEBARQeHk63b98mtVpN5eXldOzYMfLw8KBOnTpRTk4OcRxHWVlZdPHiRQoODiY3Nzfq3r07LV26lK5cuUKLFi0iFxcXOnr0KB07dozs7e3piy++oNLSUtLr9VRaWkpLliwhiURCCQkJfPxFRUU0cOBAcnJyoj179lB5eTmp1Wq6efMmNW3alEJCQigxMZE4jqP8/Hy6ceMGdenShUxMTGjYsGF08+ZN/h63b99OdnZ21KNHD17AOI6jU6dOkZubG3Xu3JmSkpJIo9GQQqGgHTt2kIODAw0ZMoSKi4tJqVRSTEwMtWjRguzt7enAgQOUlZXFhzNnzhwCQFOnTuUFr6ioiP78808KCgqiPXv2UEFBAWm1Wpo/fz5ZWVnRu+++S4WFhaTT6SgvL48mTZpE1tbWtH37dn6/SUJCAq1YsYIEAgG1bduWBg8eTIcOHaKzZ8/SgAEDqG3btlRSUlIDJepvtFotv2n5ypUr/7mM12pxOXLkCNna2tLVq1dfOKwPP/yQGjVqxBeeqkQul1PPnj3J2dmZ7ty5w4vIlStXyN3dnYRCIQ0fPpzv9er1enr33Xfprbfe4guxXC6n7t27k42NDf35559GD7igoIBat25Nzs7OdPfuXf791NRUcnd3NxKXgoICioiIIAcHh8dWVaWlpZG/vz/Z2dnR+fPnje7BIC4+Pj704MED/n29Xk99+/Yle3t7unHjhtFv5syZQ5aWlrR///7KycjnhOM4+uOPP8jOzo4uXLhQpXFdu3aNJBIJrVq1qsZXGVYUlxkzZhiN2m7cuEHW1tY0YMAA/n2VSkVTpkwhMzMzWr9+/WO9/Tlz5pBQKDTaUyaTyahZs2ZkampKa9eu5cMqLi6m9957j27evEnLli0jCwsLWrVqlZHLpPz8fHr99deNGuxt27aRqakpvfLKK0a9Yo7jaNmyZSQSieiTTz7hOzlKpZIGDhxIJiYmtGzZMqM0q9VqGjRoEIlEIvr999/5EUT//v3JysqK9u/fb/R9pVJJY8aMITMzM37krdfr6auvviKhUEg///yzUdpbt25NZmZmFBQURNnZ2Xw6d+/eTZ07d+brWFJSEnl5eZG/vz+lp6cb3dPNmzfJxcWF2rVrx3cyiYj2799PAoGAAgMD6cGDB3y8Fy5coI8++ui5V2hWFQ8ePKCAgACaO3fuf54NqNUG/fLycpiamlaKMf/mzZto2LAhXFxcKil1T8fKygq9e/dGUVERTp06xTuWO3r0KLp164bGjRvj3LlzyM3NBfD3qqNTp05hwIAB/NzsgwcPcPPmTf6sbcN9EBFsbW3RtGlTFBcX48KFC880vCUlJSE5ORne3t7w9/c3mj91dnZGaGjoM+/F398fjo6O/P+GlVpKpRJKpfI/51FlIhAIeO+vBptSVXHhwgWYm5ujW7dutcp4HxERYeSU0NraGjY2NpDJZHz5KCoqwunTp2FhYYHIyEij9AuFQkRFRUEkEuHYsWNQqVRG4bu6uqJz5858HHZ2dvzqwLCwMJibm+O7777DggULcOPGDcjlckgkEqxcuRIeHh58OIcOHYJWq0XXrl15W4UhfW3atIFAIMCff/7J2+wMiEQitG3b1ijNpqamaNeuHXQ6HU6cOAEAyMzMxJUrVyCRSBAeHm70fTMzM0RGRkKn0+HYsWPQarUQCATo1q0b7OzscODAAWg0GhARf/zBwIEDkZaWhitXrvD2ksOHD6NHjx6wtLQEEeH8+fOQSqVo3rw5nJ2dje7J398fbm5uiIuLw4MHDx57bu3bt4e3tzefzrZt2+Lrr7/mz4mqKQwu/E+dOvVYWXhearW4KJVKmJqavvChUXq9Hjk5OfD29q6WA6gEAgF69uwJGxsbvsAqFAqcOXMGQ4cOxcCBA1FYWIiTJ0+CiHDt2jWYmZmhadOmfBhFRUWQyWTIzMzEwIED0a5dO/7q2LEjX0nT0tKemg56aNRUKBR8Y1MRsVj8j8cLW1paPpZnYrGYX5hQW3B1dYWlpSUyMzOrNJ64uDi4uLjA29u7SuP5NwgEgseOoTAYpis+I6VSiZycHJiamsLZ2fmxMCQSCczNzZGbmwuFQmH0ubW19VNPf42KisLKlSvh6emJr7/+Gr169ULXrl3x+eefIyUlxchFvKGBXbBggVGZbteuHV577TXodDqkpaU9tuJQKBRCIpE8Fre7uzsAIDU1FcDfixoKCgpgYWFh1Cky3KOLiwtEIhEyMzOhVquNFqhcvnwZ6enpAICDBw+iY8eOmDBhAvR6PQ4ePMgvrrl16xa6d+/OC21GRgY0Gg3OnDmDLl26GN1Tz549kZKSguLiYuTn5z/WEfT09KxVnRQDJiYmiIiIQEFBwX9e5l+rV4vpdDoIBIIXdhNtWHVSnSt6fH190bp1a1y/fh2JiYlQqVQoKipC+/btIZFIsHz5ckRHR2P06NE4evQoWrZsyZ8XA4A/jMzDwwPffPPNU/dOeHl5/ec0Ps+BZ0/6vDZWBlNTUwiFwipfBl1eXg6xWPzCo+nK5tFn8rRn9E/Pjh5ZZWhAKBQ+tR6amppi9OjR6N27N+7evYvjx49j//79+Pbbb7F79278+uuvaNasmdFvJk+ejMjIyCeGJxaLn9hzf1Y7UPEwtX9bPs3NzdG/f398+umnOHfuHBwdHXHlyhV899138Pf3R0BAAM6cOQOpVIpLly7BxsaGn02oGHdkZCTmzJnz1HQ+aSVjbT5t1crKCnq9/j/Xqdp7Z/j7oev1+hfev2BqagoLCwsUFRU9tfJUNoapsZMnT+LEiROQy+WIiorilycGBATg/PnzSEhIwMmTJ7Fw4UKjQung4ABHR0cIhUI0a9bMSHiAvxu52NhY2NraPrMhkUgksLGxgVwu56cqDGi1WpSVlVVNBlQzcrkcWq32qb3rysLV1RXXr19HSUnJYz3j2o6lpSU8PDyQn5+PvLw8uLq68p8REQoKCqBWq+Hm5vavNmdmZmZCo9HAz8+P77G/8cYb+Oyzz7B+/Xr88ssv+PHHHyEQCODv748LFy7AxcXlsRNlOY5DUlIS9Hr9Y40uEaGkpOSxuLOzswEADRo0APD3dJ2zszOUSuX/tXfe8VEU7x//3OVKern03ggJCaGEXqSX0EJVIBRRqSqCoIjtK9gVlSZFEKRIFRQpoUMgEAIhECAEQghppJdLv1zb5/cH3v5yhKaQPu/Xa193t7e3O7s3M5+ZZ2aeBwUFBTAxMdE7R05ODjQaDVxdXfnYQDrT2I8//oj9+/fD0dERBgYGaNmyJczNzfHSSy9h69atOHfuHMLCwhAcHKz3Wzc3N0gkEhgaGqJz5856MYeICHl5eUhNTYWRkVG9bJg9joyMDEil0qdaNx5HvTaLGRkZQa1WP7e4CAQC+Pj4IDk5GRUVFS8odU+/5oABA2BqaoqdO3fixIkTGDBgAEQiEUxMTDB48GDk5+djyZIlMDY21msJAYCnpyfatWuH7OzsaguaiAgHDx5ESEgIkpKSnpiO5s2bo3nz5khPT0diYqLeeQoKCnD79u0Xds9V5/ID4NdS1IaApaeno7S0FD4+PjV6nU6dOiEzMxO3bt16IauYaxMrKyv07t0bFRUVuHDhgp7JjOM4nD9/HhqNRq/yfBa2bNmCjz/+GCqViu852NnZYeTIkTA2NtYThWHDhkEikeD06dPV1oMVFBRg0qRJWLNmTTWTq0ajQXR0tN4zV6vViIiIgFgsRv/+/QE86Ml36tQJ+fn5uHr1qt7xSqUSkZGREIlECA4O1gvO1qpVK/j5+eHSpUtYvXo1unfvDktLSxgYGGD48OHQaDTYuHEjYmNj0adPHz2R6N69OxwcHHD16lXk5eXppZvjOCxZsgTTpk2rZmqszygUCpw/fx4+Pj6PNEc+C/VaXExMTKDRaKBQKJ6rIAsEAnTp0gVJSUlPHKN40bi5uaFTp06IiYlBYWEhP4gqFAr5AcG9e/eiffv21f5AY2NjLFiwAEZGRvjmm2+QmJgItVoNpVKJK1euYMmSJRgyZEg1c8PDWFpa4u2334ZSqcSPP/6IzMxMaDQaFBYWYvPmzcjJyXlh9+vk5AS1Wo2srCyoVCrs3bsXw4YN4ycu1BREhJiYGAgEgqdOUHheOnToABsbG+zYsaNa5VjfEYvFmD17Njw9PbFq1SrExsZCpVJBoVDg6NGj2LVrF3r16oVx48b96xb26dOncerUKVRUVIDjOH7yABGhT58+AB6Uw4EDByIkJAQHDx7Erl27UFpaCq1Wi7y8PKxYsQIFBQV4/fXXYWBgoHd+rVaLPXv24MaNG1CpVCgvL8euXbtw9uxZ9O3bl59gYWhoiPfeew9WVlb48ccfcefOHajVapSVlWHv3r04duwYQkJCEBwcrHePRkZGGDJkCLKysnD27FkMGjSI/75t27Zo1qwZjh8/DhMTE7Rs2VLvt25ubnj33XeRnZ2NH374AVlZWdBoNCgvL8fBgwexe/duTJ8+vdo4V33mypUriI6OxsiRI/+76e4/zTGrJaKjo8nS0pIOHTr03OtcYmNjyd7enpYuXVprU0g5jqOlS5eSgYEBzZ49W28xYUlJCQUFBZFUKqXTp08/8vdVF4O1atWKQkJCKCQkhNq2bUtvvPEGZWZm8nPnv/zySxo8eDAZGhqSnZ0djRw5kvbu3UtED6Zgfv/99+Tu7k5t27blF8599NFHNHr0aL0pvCqVin766ScKDg4miURCjo6ONHr0aLp48SIlJibSlClTyMvLiwQCAfXq1Ys++OADfqpifHw8tWrVinx8fGjo0KEUEBBA8+bNq3F38BUVFdS/f38aOHBgjS8+0y0utLOze+71V89DSkoKvfbaaxQYGEgAqHPnzvTee+9RSUkJbdy4kQYNGkRGRkYkk8lo9OjR/HR2rVZLUVFR1LdvXwoICKCQkBAaNGgQ+fr60rhx4ygxMZGfOr97924aPnw4WVhYkLm5OQ0bNoxmzZpFeXl5emk5f/489evXj/z8/GjQoEE0atQo6tmzJ/n4+NDnn3+uN61Wt35m2rRp5O3tTX379uWP79y5M4WFhelNfdVNRTY0NKRNmzZRSEgIvfzyy9S/f39ydnamwYMHU0JCQrVp1UePHqUuXbpQmzZtaPjw4TRgwADy8fGhGTNmUEZGRrX/jeM4ioqKIisrK+rQoQMVFhbqnW/69On8uqGHp+bqFiovX76c/Pz8qHPnzjRixAgaNGgQBQYG0tdff80/A7lcTvPmzaNu3boRAPL396fRo0fThg0b6k18IrlcTkOHDqU2bdrwdcx/QUBUf/v2d+7cQd++ffHZZ5/hjTfeeC57pVqtxoQJE3D37l2EhYXVmPuXh8nOzkZ4eDjatWuHZs2a8dfkOA4XLlxAdnY2Bg4c+FgbNxFBLpfj2rVryM3NhVgshre3N1q0aMG7sdBqtThz5gxvf9bRpk0b3tym1WqRmJiI+Ph4EBG8vb3RvHlzvP766zh+/DhOnTqF1q1bQ6vVIjIyUq+HJxAI0KNHDxgZGeH06dN6A3zW1tYYOHAg73ImLS0NsbGxUCgUcHZ2Rvv27WvUhxsR4ciRI5g4cSJ++uknTJ48ucb/15ycHIwaNQoajQY7duyAp6dnrdvS5XI5Tp48qTdNVCaToW/fvrhx40Y1c2fnzp35qehEhPLycly/fp13/+Lp6YmAgABIJBL+mBs3buD69et65zE1NUX//v2rjWUolUokJCQgJSUFFRUVMDMzQ4sWLeDh4QGhUFjN/YtGo0FCQgISExOhUqlga2uL1q1bV3OYWllZibFjx+L48eO4desWpFIpYmNjUVxcDAcHBwQFBcHU1PSR7l+Ki4tx7do15OTkQCKRoFmzZvD19X2kiyPggSno2LFjkMlk6N69u94xCQkJuHr1Krp37/7IGV70z/TjzMxM3LhxA8XFxTA1NYW/vz/c3d35nphCocDJkydRVFSk93tfX1+0a9eudmPcPwKVSoVvv/0Wy5cvx9q1azFmzJjG6f4lLy+PAgIC6IMPPnjuFiLHcRQeHk42Njb0zTff1JtWQk3DcRyVlZU9sgWiW4zp4+ND+fn5dZTC56OgoID69OlDXbt2rdairil0LmA8PT0pODiYUlJS6nxBZWNF13MxMjKi1NTUuk5Oo0VnAVm+fDlZWVnRRx999NzhSOr1mIuVlRWcnJwQHx//3OcSCATo2rUrQkNDsWLFCpw/f77BDcj+V06cOIEhQ4bwTgI5juNt0PHx8Zg4cWKNz7KqCVQqFX7++Wdcu3YNH3744X8eePy3CAQCdOrUCStXrsStW7demBdZBqMuoH96sj/++CMWL16M0NBQfPDBB88fjuRFKV9NwHEczZ49m1q3bq3nOuF5zpeenk5dunSh9u3b8/blxgzHcXTo0CGysrKiNm3a0NSpU+mtt96ikJAQ8vDwoHfeeYcKCwsb3HPQaDS0c+dOsrGxoQ8++KBOwvxqtVo6duwY+fn5UYsWLejAgQONJqppfeDEiRM0e/Zs8vLyIpFIRK+//rqeE0jG88NxHKWkpNDrr79OMpmMFixY8MJCm9frMRciwubNm7Fw4UKcOHECAQEBz23bJiJER0cjNDQU3t7eWL9+vZ77hcYG/WMPv3XrFhISElBYWIjKykpYWloiKChIzwV5Q0Gr1eLYsWOYMWMGOnTogHXr1tVar+VhOI5DXFwc3nvvPVy9ehWvvfYa5s6dC0dHxwb1TOsjFy9exJkzZ/R6hB4eHhg5cmS9W8Ta0NDVC8ePH8dnn32GvLw8fPLJJ3j11Vf5sAsv4iL1Fo7jKDo6mqysrGjv3r0vrEWo1WrpyJEj5ObmRn379m0SPZjGgkqloj///JPc3Nxo4MCBde6ZmOhBPs3Pz6dFixaRg4MDBQUF0c6dO6mkpKTO08ZgVIXjOFKr1XTz5k164403SCaTUb9+/SgqKuqFj0PXa3EhIsrOzqaAgAB6//33X2hB1Wq1dPToUfL29qZ27dpRZGRkkxnkb4joPN6uXLmS7O3tadiwYfVuIF2lUtH58+dpyJAhJJPJKDg4mI4cOUJlZWX1Kp2MpodOVJKSkujTTz8lNzc38vLyoh9++IHy8/NrJH/We3HRarX0yiuvUNeuXV94YCbdnP8OHTqQm5sbrV+/no+lwqg/6MbKZs2aRVZWVjR9+nTKzs6ul/8Tx3FUXFxM27dvp06dOvEi8/fff/OxThiM2oLjOFKpVHTr1i369NNPydvbmxwdHWnu3Ll0+/btGm1Q13txIXoQkc7e3p6PjfIi4TiO7t27RxMmTCArKyt69dVXKSEhgVUC9QBdlM2DBw9S+/btycnJiVasWEGlpaX1Uliqogt2tX79eurSpQvJZDJ66aWXaO3atZSRkUFqtbre3wOj4aLVaqmsrIxOnTpFU6dOJWdnZ3JxcaF33nmHYmNjazQ6rI4GIS6xsbFkY2NDv/32W40VyOLiYlq9ejV5eHiQj48PrVq1iuRyOasA6gitVku3b9+mGTNmkLW1NfXp04ciIiKeK+xqXaAbj9m1axcNHTqUZDIZNWvWjObMmUPh4eFUVlamF2CLwfiv6Hopd+/epZUrV1LPnj3J2tqaAgIC6H//+x/dunWrVkRFR72eLaajpKQEgwcPhpOTE3bs2FHN79CLQqvVIj4+HkuWLMHBgwfRsmVLzJkzB8HBwTA2Nmazf2oBjuOQlZWFTZs2YcOGDSAivPnmm3j99derrd5uSNA/awni4uKwa9cuHD58GHl5efDx8UFISAgGDBiAgIAAfqZOQ71PRu1CRNBqtcjMzMS5c+fw999/IzIyEpWVlejSpQtefvll9OvXD3Z2djVWbz6OBiEuRITFixdj8+bNOH36NNzd3Wu08FVUVODUqVNYunQprl69ivbt22PWrFno27cvzMzMWMF/wdA/rjPu37+PXbt24bfffkN+fj7GjBmDt99+G76+vrVeMGoSrVaL7OxsnD17Fvv27cP58+ehVCrh6+uL4OBg9OrVC4GBgXxeY/mNoUNXXWs0Gty7dw8XL15EWFgYLl68iKKiIvj7+yM4OBjDhg2Dj49PnTaKG4S4AEBUVBQGDRqENWvWYOzYsTX+wIgIRUVFOHr0KNauXYtr167B398fU6ZMQXBwMO9fiBX8/0bVQnLz5k3s2rULe/bsQXFxMYYMGYKZM2eibdu2DW4Nzr+BiKBSqZCeno6IiAiEhYUhOjoaJSUlcHFxQbdu3dCnTx8EBgbCw8ODD3bXWJ8Hozq6ckL/RMFMSEjAhQsXcOLECdy6dQsKhQIBAQHo3bs3Bg0aBD8/P1haWtaLPNJgxEUul2Pw4MHw9PTE1q1ba60lqxOZs2fPYuPGjTh37hwsLS3Rt29fjB07Fh07duSdTtaHP7S+o8tuum78zp07+bj0Q4YMwWuvvYbWrVs3alF5HCqVChkZGbh69SqOHTuGS5cuITk5GcbGxnBzc0OXLl3QtWtX+Pv7w9PTUy/mSlN7Vo2VqmKSnZ2NxMREXL58GREREYiPj0dubi4sLS0RGBiIfv36oUePHvDy8npi0MC6osGICxHhiy++wIYNG3Dq1Cl4e3vX+vUVCgWuX7+OPXv24MiRI8jIyIC/vz9CQkLQs2dPBAQEMKF5iKrZKzc3F1evXsWhQ4dw/PhxZGdno2XLlhg7diyCg4Ph5eXVqMxfzwPHcSgsLERKSgqio6P5yiU9PR1SqRRubm5o37492rdvj+bNm8PFxQWOjo56sTdYHqzfVC0bZWVlyMjIQFJSEq5cuYJLly4hISEBOTk5MDc3R7NmzdC1a1d0794dfn5+cHJyqtWw7f+FBiMuAHD16lX0798fX331FaZPn15nhUfXqoiKisKOHTsQFRWFsrIyNGvWDH369MGgQYPg6+sLOzu7OnehXZcolUqkp6fjxo0bOHjwIKKiopCRkQEXFxf06NEDY8eORevWrRuk08zaRic2aWlpuHr1Ks6ePYu4uDhkZGRAoVDAxsYGLi4uCAwMRPv27eHv7w97e3vY2NiwySj1CF2gvtzcXGRkZOD69eu4cuUKLyQVFRWwtbWFu7s7OnbsiO7du8PHxwdOTk4NLkxygxKX8vJyjBo1CgCwf//+Oldu+icuRWpqKmJiYhAWFobIyEjk5eXByckJ/v7+6NatG7p27QoPDw/Y2Ng06pZ5ZWUlsrKycPfuXURGRuLs2bNITExEaWkpvLy80L17dwwdOhStWrWCra1tkxbe50Vnrs3KysL9+/dx48YNXLx4EQkJCXwkUGtra9ja2sLZ2RktWrRAQEAAmjVrBltbW8hkMpiamrL/oAbQjaXJ5XIUFBTw5q3bt28jPj4eGRkZyM/Ph1Kp1GsUBAUFISAgAE5OTrC3t4dYLK7rW3kuGpS4EBHWrVuHTz75BIcOHUKHDh3qjZITETiOQ2ZmJuLi4nD+/HmcPXsW9+7dQ3l5OVxcXODu7o7AwEB07twZPj4+sLOzg6Wl5X8PI1pH0D9O7/Lz85GTk4Nbt24hJiYG165dQ3JyMuRyOezs7NCiRQt07doVL730Evz9/WFhYQGAmWteNFWLsFwux/3793H//n3cuXMH8fHxuHXrFnJyclBYWAi1Ws2LjrW1Ndzc3ODl5QVPT0+4ubnBysoK5ubmMDMzg5GREROfR0BEfOjkkpISlJSUoKCgACkpKUhOTkZSUhIvILm5uVAqlbCwsIC1tTVcXFzg7+8Pf39/+Pj4wMXFBQ4ODnpC0ljKR4MSFwBITk5Gnz59MH78eHzxxRf1siege6QqlQopKSmIj4/HhQsXEBMTg7S0NBQUFMDAwAAuLi5wcXHhI+T5+PjA0dERFhYWMDExgYmJyWOj5tXGPSiVSpSXl6OsrAxyuRxpaWlISEhAQkICkpOTcf/+feTk5MDQ0BC2trbw9fVFp06d0KFDBzRv3hwODg582htLgWkoVC3WGo0G+fn5yM7ORkZGBhITE3Hnzh0kJycjNTUV9+7dg0qlglQqhZWVFaytrSGTyWBlZQVHR0e9zd7enhceIyMjSKVSSKVSSCSSahEnGxI6wVAqlVAqlaisrIRCoUB5eTkKCwuRlZWFrKwsZGZmIisrC/n5+XzPpKioCEQEU1NTmJmZQSaTwd3dHd7e3vDz84O7uzv/7KpG8AQad7locOKi0Wjw1ltvISIiAuHh4bCzs6vrJD0V3SPmOA65ublITU1FYmIiYmNj+UHa4uJilJaWwsDAADY2NpDJZJDJZLC3t4eLiwucnZ1hb28PKysrWFhY8AVaIpFAJBJBKBRW2wQCATiOA8dx/GIr3We1Wg2VSgWVSoWKigoUFRWhsLAQmZmZyMjI4FteugJUWFgIsVjMFx5vb28EBgaidevW8PT0hLu7O8zMzPh7bsyFpiFTdQp4XFwcfvjhBxw8eBA+Pj6YNm0aJBIJkpOTkZaWhqysLOTm5qK8vBwKhQIKhQIqlYrPB2ZmZnwjyMTEBJaWlrwo6V6rHmNkZASxWAyRSASRSAQDAwP+VSAQ8L0kXYPx4X26fKyzEgCotk9nqtZoNNBqtVCr1dBqtaisrERFRQXKy8tRXl7O5/fCwkLI5XIUFhairKyM/17XK1EoFBAKhTA0NOQFVSaTwcHBAc7OznyPz87ODjY2NrC1tYWFhUW1Hl9TLA8NTlyICOHh4Rg1alStrXl50VSdbgg8WLSpaxndv3+f7xVkZGQgMzMTxcXFfMFWq9XQaDQQiUSQSqUwMTGBVCqFWCzmC6uuwAqFQmi1Wr6g6QqdRqNBRUUFKioqoFKpQEQQiUSQSCSQSqUwNjaGjY0NnJyc4OLiAldXV3h4eMDJyQlOTk6wtbXVmyrc0J5/U4bjOKSmpmL9+vXYunUrDA0NMXPmTEycOFGvoVZVhEpKSiCXy1FUVAS5XA65XI6cnBzk5+fzlbNuKy4u5hsturyq1Wr5ho1QKIRIJIJYLOYbSLq8qxMZXW9dKBTy+8RiMX8enYDo0ldVVLRaLZRKJZ8GXX4XCAT8NQwMDCAWi/lrGxsbw8rKihfFqg07a2trWFlZwdLSkn81NDSslvdZGahOgxMXACgqKsKIESNgZmaGP/74Q2++f0PmYdHRtcbKyspQUFCA4uJivdZVaWkpioqKUFZWBo1Go1eg4uLiEBUVhREjRsDW1pbv4eiEyMLCAhYWFjA1NeVblqampnzBquqGhBWghg8RIS8vDzt37sTq1atRWlqKCRMmYMaMGfD09PxXC4IflU91r1qtls+bZWVlUCgUeqYm3XuFQsE3cBQKhV6Pg+M4KJVKAOB7HlXR5U1dQ0ooFEIikUAgEEAikcDY2BjGxsYwMjKCoaEhb7qr+t7U1JTfdPVH1XzO8vzz0yDFperA/v79+9G5c+cmmQkeLtxV32/ZsgUffvghwsPD4ePjA0C/oLDC0zQgIpSWluLo0aP48ccfcffuXQwaNAjvvvsuWrVqxfcM6iJdVV8f9d2z8Ki0PyqfM2qfBjkVRCAQYMiQIbCyssL27dv5LnJTQ9fCqjrOojOH6cZcqpoCHh6PYQWvcVNZWYnw8HCEhoZixowZsLCwwI4dO/Drr7+ibdu2dTZZBHh03q2ah591e9xYI8vfdU+DFBcAcHBwwOjRo/H333/j3r17/6q1w2A0ZjQaDa5fv4633noLr7zyCnJycrBixQrs2bMH/fr1e3Ex0hmMJ9BgxUUkEmHy5MngOA5bt26tZpdlMJoSuvG5tLQ0LFq0CEOGDMHZs2excOFCHDp0CKGhocyjN6NWaVir9x7C29sbI0aMwI4dOzB16tQad8XPYNRHiAiFhYXYuXMnfv75ZxQVFWHChAmYNWsWPDw8GvT6E0bDpcH2XABAIpHgjTfegEKhwJYtW1jvhdGk0AUg++uvvxASEoL//e9/CAoKwsGDB/HNN9/wjkCZsDDqggYtLgAQEBCAUaNGYfPmzUhNTWVjL4xGj853VUREBCZMmICpU6fC2NgY27Ztw6+//op27do1yZAFjPpFgxcXiUSCGTNmQKlUYsOGDU125hij8aNbRxIXF4e3334bY8aMQXp6OpYuXYo//vgDAwcOhJGRUV0nk8EA0MDHXHT4+flh/Pjx2LJlC8aOHYtWrVqxVhuj0VA1DPSGDRvw22+/QSKRYP78+Zg8eTLs7e2Zg0lGvaNR5EixWIxZs2bB2NgYy5Yt41f3MhgNHSKCXC7H2rVrERwcjF9++QWjR49GWFgY3nvvPTg6OjJhYdRLGk2udHd3x8yZM3HgwAGcO3eOjb0wGjREhIqKCuzZswdDhw7FJ598grZt2+LAgQP4/vvv0bx583rpEZzB0NFoxMXAwACTJk2Cr68vvvnmGxQVFdV1khiMf43O9fuZM2cwbtw4zJgxA6ampti+fTvWr1+PDh06NPggUoymQaMRFwCwsbHBBx98gNjYWGzfvp13y81g1Hd0g/XXr1/HrFmzMGbMGGRmZmLFihXYvXs3Bg4cCGNj47pOJoPxzDSKAX0dAoEA/fv3x9ChQ7F06VL07dsXfn5+dZ0sBuOJEBHS0tLw66+/YtOmTTA0NMTChQsxceJE2Nvbs8kpjAZJo+q5AOALJhHh66+/RkVFRV0nicF4JESE/Px8/PzzzwgODsaGDRswduxYHD58GPPnz9eL5MlgNDQanbgIBAL4+vrivffew8GDB7F//342uM+oVxARysrK8Mcff2DYsGFYtGgROnTogAMHDuC7775Ds2bNmKgwGjyNyiymQyAQYOLEiThy5Ai+/PJLdOjQAd7e3nWdLEYTR7eyPioqCj/++CMiIiLQsWNH7Ny5Ez169OADXjEYjYFG13MBHoiLqakpFi9ejLKyMnz11VfMPMaoUzQaDW7cuIG33noLo0ePRmZmJn7++WfmBp/RaGmUPRfggcC0atUKCxYswMcff4zevXtjwoQJbMEZo1bRucFfv349Nm/eDCMjI3z44YeYNGkSbG1tmaAwGi2NVlyABwIzefJkhIeHY/HixWjbti1atmxZ18liNAF0g/U7duzAmjVrUFxcjNDQUMyaNQuenp6skcNo9DTqHC4QCGBiYoIvvvgCYrEYn3zyCeRyeV0ni9GIISKUlJRg9+7dCAkJweeff84P1n/77bfw8vJiwsJoEjT6XC4QCNC8eXN8/vnniIiIwOrVq5nnZMYLh4igUCgQHh7OB+qysLDgV9YHBQXVacx6BqO2adRmsaoMHz4cly5dwvLly9GmTRsMHjyYFXTGC0GtViM+Ph7Lly/H/v374enpiRUrVmD48OEwNTVl+YzRJGkS4iIQCCAWi7FgwQJcv34dH3zwAZo1a4bmzZuzgs/4z2i1WqSnp2PdunXYsmULjI2N8dFHH2HixIlssJ7R5Gn0ZjEdAoEANjY2+O6771BZWYmFCxdCLpezBZaMf41usH7VqlUIDg7Gpk2bMH78eBw5cgRz5syBnZ0dExZGk6fJiAvw/9OTv/76a0REROD777+HWq1mAsN4Jh5eWf/555+jY8eOOHjwIL7++mt4enoyN/gMxj80CbNYVYRCIUaOHIk7d+7gp59+QosWLTBp0iTW0mQ8Fp0b/MjISCxdupRfWb99+3b06NGDLYBkMB5BkxMX4EHkytmzZ+P27dv45JNP4Orqit69e7MKgqEHEYHjONy6dQvLly/Hvn374ObmhmXLlmH48OEwNzdneYbBeAxNyixWFQsLC3zzzTfw9vbG7NmzERcXx8xjDAD/Lyr379/H4sWLMXjwYJw6dQoLFizAoUOHMGnSJFhYWDBhYTCeQJMVFwBwcXHBypUrAQCzZ89GRkYGE5gmji5m/S+//ILg4GCsW7cOY8aMwZEjRzBv3jzmBp/BeEaatLgIBAK0bNkSK1euxN27d/H++++juLiYCUwTRBez/s8//8SwYcPw8ccfo3Xr1vzKeh8fHzZYz2D8C5q0uAAPBKZnz5745ptvcPz4cXz55ZeorKxkAtNEqBqzPjQ0FNOmTYOJiQm2b9+ODRs2oEOHDpBIJHWdTAajwdEkB/QfxsDAAGPHjkVGRga+//57WFlZ4f3334dYLGYmkEaKblzl5s2b/Mp6d3d3LFu2DCEhIWxMhcF4Tpi4/INEIsE777wDuVyOn376CRYWFpg5cyZEIvaIGhO6HmlaWhq/sl4qleKDDz7AhAkT2JgKg/GCYDVnFYyNjfHxxx+jtLQUixcvhoWFBUJDQ+u1rZ2IUFpaCoVCobe/pKQEHMehoKAA5ubm/H6BQAALCwtIpdLaTmqdo1tZv3PnTqxevRrFxcWYMGECZsyYwbwVMxgvGAGxwYVqFBYWYs6cOTh58iRWrVqFkJCQeiswHMdh48aNWL58OTiO4/cXFRUhNzcXHh4eemMGVlZWWLduHfz9/esiuXVGaWkpwsLCsHTpUiQmJmLw4MGYO3cu2rRpU2//WwajIcN6Lo9AJpNhyZIlmDFjBubMmQMTExP069fvkS1brVYLAHVWQQmFQgQGBiI9PR3FxcXVvr97967e527dusHd3b22klfnKJVKREZG4ocffkBkZCQ6d+6MXbt24aWXXmIx6xmMGoTZAR6Dvb09Vq5cCV9fX8yYMQOnT5/W6xkAD+Kib9myBWFhYXWUyge0bNkSrVq1eqZjhw8fDmNj4xpOUd2j0WgQGxuLmTNnYsyYMcjLy8Pq1avxxx9/oG/fvsxlC4NRwzBxeQwCgQCurq5Yt24dvLy8MH36dISHh/MCw3Ec9u7di/nz5+O7775DQUFBnU1fNjIywrBhw546ZmBjY4P+/fvXUqrqBo7jkJycjP/9738YMmQIzp8/j08++QSHDh3CuHHjYGJiwkSFwagNiPFEOI6je/fuUa9evcjT05NOnjxJGo2GwsLCyNHRkQCQSCSidevWEcdxdZbGa9eukY2NDQF47NavXz+qrKyskzT+V5RKJeXk5Dz12XIcR7m5ubR8+XLy8/MjR0dHWrBgASUlJRHHcXX23zAYTRUmLs8Ax3F09+5d6tGjB3l7e9PPP/9MXl5eehV369atKTMzs84qMYVCQQMHDnyssAgEAlq1alWDqmTVajVt3ryZhg4dSnK5/JHHcBxHxcXFtHv3burcuTPJZDKaNGkSXb58mTQaTYO6XwajMcHE5RnhOI7u3LlDQUFBZGBgUK3yFgqFtGTJkjrtvaxevZpEItEjxcXR0ZHi4+MbTGWr1Wpp//79ZGtrS1KplH755RfSarX89xzHkUKhoNOnT9OQIUPIysqKBgwYQMeOHaPKysoGc58MRmOFjbn8C+iBGPMzxKrCcRzWrVuHlJSUOhl7EQgE6Nu3L2xtbR/5fYcOHdCsWbMGMd5ARLhw4QJmz56NvLw8KJVKrFy5EllZWQAexKy/fv063nrrLYwZMwbZ2dlYsWIF9uzZg379+rHBegajHsDE5RkgIty/fx9vv/02YmNjH3vc3bt3sWHDhmqzymoLDw8PdOnSpdp+gUCAUaNGNQhvA0SE+Ph4vPnmm0hNTeX337p1C7/++ivu3r2Lzz77DEOGDEFERAQWLlyIsLAwhIaGwszMjIkKg1FPYIsonwIRIS8vDzNmzMD+/fufKhwuLi44cuQI/P39a72iIyJs3boVU6dOhVqt5ve7ubnh1KlT8Pb2rtX0/FuICGlpaZg8eTLOnj1b7XsrKyvIZDIoFAp+Zb2HhwdbBMlg1ENYz+UpqNVqfPXVVzh06NAz9Uju37+PNWvWPNJ0VtPoPDw7Ojrq7e/atWuDWDiZl5eHOXPmICIi4pHfy+VyiEQi7N27F1999RW8vLyYsDAY9RQmLk9BJBJh2rRpeO+99+Dj4wOxWPzU3/zxxx+IiYmpk7EXZ2dn9OjRg/8sFAoxcuTIem0SIyKUlJTg448/xoEDB5743PLy8sBxHEQiETOBMRj1GCYuT0EoFCIgIACff/45wsPDsXr1avTq1QtGRkaP/U1ubi5WrlypZ5qqLQwMDDB8+HDeMaWXlxc6d+5c6+l4VogISqUS3333HTZv3vzU3mFhYSF++uknVFRU1FIKGQzGf4GJyzMgEAggEong5OSEN954A/v27cPff/+NSZMmwcbG5pEt6EOHDiEiIqJGei+6WWu6jeM4fiMidO3aFa6urgCA7t27w9HRUe8Y3XFVt7pAN/Nu7dq1WLZs2TOL8ZEjR3DixAkW0I3BqMewAf3/iK7FnZiYiO3bt2PPnj24d++eXst72LBh2LlzJ4yNjfmK8OEKXfe+srISZWVlqKiogFKpRGVlpd6r7n1lZSVUKhVUKhWAB2NCWq0WHMdBpVLxkRV37dqFO3fuYOjQoejQoQMEAgEMDAz4AGhisRgGBgYQCASQSqUwNDSEVCrVe//wPhMTE5iamvImKd0GoNr7Z4HjOGzbtg1z5syBXC5/5mcvFArRq1cv7N27F5aWls/8OwaDUXswcXkOdI9Oq9UiIyMDhw8fxpYtWxAdHQ2NRgMTExN88cUX8Pb2RmFhIeRyOYqKivRede8VCgU0Gg20Wi2/cRzHvxIRRCIRvwmFQhgYGPAD2jrREAqFEIlEqKysRG5uLlxcXCAQCKDRaPiegm6ygUajAcdx0Gg0/KbVamFgYMCfv+p1dOJkZmYGS0tLWFlZ8a+69zKZjP+s28zNzXkhqypCR44cwRtvvMGvX3kY3bEGBgawtbWFu7s7PDw80KJFCwQGBmLAgAFNwgkng9EQYeLyFKr2MABApVKhsLAQeXl5yM3NRU5ODlJTU5GZmYmsrCzcvn0bd+7c4XswEokEEokEhoaGMDU1hYmJid5mZmamVzmbm5vD2Ni42mZkZASRSMRX8rqKV+esUveq21dSUoKwsDCMHTsWAoHgkaawqmY1rVYLjUYDtVoNhUKBiooKVFRUoLy8HBUVFVAoFNUEsaysDOXl5fym+6zRaPh7lkqlMDExga2tLRwcHODo6AgXFxcYGRlhyZIlSEpK4p+1QCCAtbU13N3d4e7uDj8/PwQEBMDX1xfW1tawsLCAmZkZf/8MBqP+wsTlH6o+BpVKhZycHGRlZSEjIwNJSUlITk5GSkoKMjIyUFpaivLycigUCkilUlhbW/PiYG1tzVeiVlZWiI+Ph5+fH3r27AmpVAqJRMK/SiSSGpv1pDO1PWniwfOcW2d+U6lUUCqVvKmusrISpaWlyMnJQXZ2NrKzs5GVlYWcnBzI5XIUFxcjIyMD+fn50Gq1/HOXSCRwc3NDYGAggoKC0KZNG7i5ucHR0REymUzP4zMTFgaj/tPkxKXq7arVal5AMjIycPv2bSQkJODOnTvIz89HUVERiAjW1tawtraGjY0NXFxc4OzsDE9PT3h5ecHa2hpmZmYwMTGBsbExb57SwXEclEolDA0Nm3SlqHsO5eXlyM/PR3l5OUpKSnD//n2kp6cjKSkJqampyM/PR35+PkpKSmBsbMwLdvPmzeHv7w8/Pz+4uLjAxcUF1tbWes+0KT9fBqO+0ajF5eHeSFZWFu7fv4/bt28jJiYGN27cQGZmJgoKCiASiWBraws7Ozt4eXmhRYsW8PX1hYeHB2QyGSwsLGBqasoW7dUQRASVSoXS0lIUFRWhsLAQycnJuHPnDuLj45GSkoK8vDwUFhbqjcEEBASgbdu2aNasGZydneHg4MD/R0xsGIy6o1GKC8dxKCgoQHp6Om7evInIyEhcv34dWVlZKCwshImJCRwdHdG8eXMEBQWhVatWcHBw4HsozPFh/YKIUF5ejsLCQuTn5yMpKQlXr15FbGwsUlNTkZ2dDY1GAzs7O3h6eqJjx47o3LkzmjVrBhcXFxYgjMGoAxqFuBARCgoKkJKSgsuXL+PMmTO4efMm7t+/D7FYDGdnZ/j7+/NC4urqCkdHR+bosIGj1WpRUFCAzMxMJCcn4+rVq7hy5Qru3r2LnJwcGBkZwcPDAx06dECPHj0QEBAANzc3GBkZsf+dwahhGqy4VFZWIi0tDVevXsWJEydw+fJlpKSkwMjICO7u7ujcuTO6du0KX19fuLu7w9zcnP8tq1gaF1WzMMdxyMvLQ3JyMq5fv46IiAjExsYiMzMThoaG8PX1xUsvvYTevXvD19cX9vb2zNTJYNQADUZcdKaRpKQknD59GseOHcO1a9egVCrh4uKCbt26oVevXggICICnpycMDQ0BMCFpqlTN1nK5HImJiYiJicGJEydw/fp15OXlwdnZGZ06dcLgwYPRoUMHODs7P5PvOAaD8XTqtbjoptMmJSXhyJEjOHz4MK5duwapVIqWLVti0KBB6Nq1K/z8/GBmZgaAiQnj0eiyOcdxyMnJwfXr13Hy5EmcPHkSqampMDc3R5cuXTB8+HB07doVTk5OrEfDYDwH9VJctFotcnJycPr0aezZsweRkZEQiUQICgrC8OHD0b17d3h7e/OefpmgMP4turU65eXluH79Oo4fP46wsDDcvXsXtra26NevH15++WW0bdsW5ubmLI8xGP+SeiMuuqmoCQkJ2LlzJ/bt24ecnBz4+/tjzJgxGDhwIBMURo2gE5qKigrExMRg//79CAsLQ05ODlq3bo3x48dj8ODBcHR0ZL0ZBuMZqXNx0Zm+rly5gg0bNiAsLAxSqRQDBw5EaGgoOnTowM/uYYLCqGl07nAKCgpw4sQJ7Nq1C5GRkbC1tcXYsWMxYcIEeHp6QigUsvzIYDyBOhMXXU8lNjYWP//8Mw4fPgw7OztMmjQJr7zyCtzd3ZkPKUadovN8HRMTg61bt2L//v0wMjLC5MmTMWXKFLi6uuq5pWEwGP9PnYgLx3FITk7GihUrsHPnTlhbW2PatGkYN24c7OzsmOmBUa8gImg0GsTHx2PdunXYu3cvZDIZ3nnnHYwbNw4WFhasEcRgPEStiovOrv3HH3/gm2++QWVlJaZOnYrXX39dz20HA9i2bRtu3boFAHBwcMCbb77JWsn1ALVajdjYWCxduhRHjhxBp06dsGjRIrRr165eh5JmMGqbWqutiAhpaWl48803MW/ePLRp0wYHDhzAhx9+CGdnZyYsDyEQCFBWVoY1a9Zg/fr1DS7qIhHhzp07WL16NdLS0hpc+h+HWCxG+/bt8euvv2L9+vXIzs7GmDFjsHr1alRUVDSa+2QwnhuqBbRaLUVFRVGnTp3Izc2N1qxZQyUlJcRxXG1cvsGSnp5OPj4+1KpVK9JoNHWdnGocOHCAli1bRhkZGdW+02g09P7775NEIqHvvvuuUf7XWq2WUlNTafr06WRlZUUzZ86kvLy8RnmvDMa/pcb78USEM2fOYNasWTAzM8O2bdvQpUsX1lNpBOzYsQMHDx5Ex44d4eTkpPedUChEaGgoTE1NMXz48DpKYc0iFArh5uaGn376CYGBgfjiiy+Qn5+PFStWwMHBgY3DMJo0NSouRISYmBjMnDkTDg4OWLt2Lfz8/Oqk0NE/5gpdaF8AfEhg4NHrZqhKlEYi4kPuPmoaKj0UQvhpx1ZNR9Xokv/2nv7LeXShjYlI7zf0z3oPXWjl50mbQCBAmzZt0KZNmyemv+oze9r/8aj0635TlxW5iYkJZsyYARsbG8ybNw/z58/H6tWr2UA/o0lTo+KSk5ODd999FyYmJvjll1/g6+tbZ8JSUFCAQ4cO4fDhw3wYYm9vb4wePRojR46sFsyL/hkz2LJlCyIiIiCXy2FpaYnWrVsjODgYvXv3homJCYAHHgUuXLiArVu3IjY2FgqFAg4ODujUqROGDBmC9u3bQyQS8Wt69u/fjz179uDu3bswMDBAQEAAJkyYgF69ekEikTzT/ZSXl+PPP//EX3/9heTkZEgkEgQGBmLSpEno3r07f72oqCjcvn0bAGBpaYmXXnoJv//+O3bv3o2SkhJ06dIFH3/8Mdzd3ZGcnIx9+/bh1KlTSE9Ph1gsRsuWLfm06fxuZWdn49ixY7h37x5UKhUOHTrEXyMwMBAtW7bE0aNHUVhYCADw8fHR663qRPHUqVPYsWMH4uLioNVq0axZM7z88ssYOnQov7bp5s2biI6OBhFBKpWiX79+OHLkCDZv3oy8vDz4+/tj7ty56NSpU51W5GKxGGPGjIFSqcT8+fOxfPlyfPTRR8xXGaPpUlP2No1GQ59++inZ2dnRyZMn69QOzXEcffbZZ2RjY0NffPEFxcTE0Pnz52nOnDlkbW1Nn3zyCanVar3jz507R/7+/tSuXTvatWsXXbt2jf766y966aWXyNTUlHbt2kVED+zuGzduJEdHRxoxYgSdOHGCrl69Sr/88gt5eXmRp6cnxcXFERFReXk5zZs3j2xsbGjevHl08eJFCg8Pp3HjxpGtrS39/PPPemMrjxtzKSkpoRkzZpCtrS19/PHHFB0dTSdOnKARI0aQvb09bdq0iTQaDXEcRxs2bKAxY8aQpaUlNW/enN58801q164djR49mlq3bk1mZmZ09OhRKisro5EjR5K3tzdt2LCBrl27RseOHaMxY8aQra0t/f777/x/GB8fT6+++ip5eHiQWCymAQMGUGhoKIWGhtKOHTuouLiYPvzwQwoODiYDAwOaMmUKKZVKPv1qtZq+//57srW1pcmTJ9OZM2coKiqK5s+fTzY2NjR37lwqLy8njuMoLCyMxo0bR66urmRpaUmLFi2iZcuWUUxMDO3YsYM8PDzI39+fUlJSajQPPSuVlZW0YMECsre3p3PnzrHxF0aTpcbE5e7du+Th4UHz5s3Tq7jrAo7j6N1336WZM2eSSqXi91dUVNArr7xCjo6OdOPGDX5/fn4+denShWxsbCgqKoqvIDiOo+vXr5OzszNt3LiROI6j2NhYcnZ2pqCgIMrKyuKP1Wq1tGPHDrK3t6crV64Qx3G0detWMjExoUmTJlF5eTl/zvz8fOrcuXO1dDxKXLRaLa1evZoMDQ3p7bffJoVCwZ8nMzOTWrVqRe7u7pSYmMifp7CwkFq3bk1SqZSmTJlCGRkZpFar6ebNm9SyZUs6duwYlZSUUHBwMK1du5a0Wi1/ztzcXGrTpg21a9eOcnNz9Z5raGgomZubU2Rk5COf++XLl8nY2FhPXDiOo5MnT5JMJqN+/fpRQUEBf3xlZSVNnTqVDA0NadOmTfyzVCgUNHLkSBKJRPTBBx9QZWUlET1owCxatIhEIhFt3Ljx2TJDLZCRkUGBgYE0adIk/v9hMJoaNTYVOTw8HBUVFZgwYUK9GLyfN28eFi9ezJuLiAiGhobo0KED8vPzcfv2bX5cJiIiAleuXEGnTp0QGBjIm1sEAgFatGiBV199Fa6urgCAvXv3IiMjA0OHDoW9vT1/rFAoRJ8+fTB69GiYm5ujoqIC27Ztg1arxcsvvwxjY2P+nDKZDEOGDEFWVhYOHTr0xPsoKSnB9u3bIRKJMHr0aL3QAnZ2dhg4cCDS0tJw9OjRar81NDTE1KlT4ejoCJFIhObNm+PPP/9Ep06dYGxsjFWrViE0NJQffwEemNICAwNx+/Zt5OfnP/f/oNVqsX37dsjlcowaNQoymYz/TiqVYvz48dBoNNi4cSPUarXeb8ViMbp16wapVArgwXiQv78/RCIRb5arDzg4OGDcuHE4efIkcnJy6jo5DEadUCNjLkSEiIgIuLu7w9/fv14MalpZWeHIkSM4ePAg7t27B4VCAeDBuJBarUZZWRmAB2m/cuUKVCoVmjVrxldkOkQiEb766iv+2KioKABAQEBAtWva2dlh1apVAID8/Hxcv34dAoEA2dnZOHz4sN6xxcXFAIBLly7xkwceRWlpKeLj4yEUCpGWllbtPGVlZSAiXL58udp5bG1t4eHhwe8TiUTw8fHh78XExATbt2/HyZMnkZmZCZVKBSJCSkoKFAoFlErl0x7zU9FqtYiOjoZAIHjkM3NycoJMJkNKSgqysrLg7u7OfyeVSuHi4qJ3vJmZGUQiEf//1QeEQiF69uyJpUuX4vr163r3wGA0FWpEXHQxM5ycnJ5pgLqmqaysxMKFC7Fp0yYMHjwYc+fOha2tLQQCAfbs2YNly5bpLX4rKSkBEcHY2Pips5ZKSkoAAKampk9Mg1qtRnl5OVQqFTZv3lxNtACgT58+8PT0hFarfexqb5VKhYqKCmi1Wvz666+PHDDu06cPXFxcwHGcXq9RIpE88rrAg0H6KVOm4NKlS5gyZQpee+01mJubg+M4LFmyBIcOHXohCwS1Wi1KS0shEAj4CRFVEYvFkEgkUKlUKC8v1/tOIBBUS7/u/nSz2+oLLi4uEAqFyM7OruukMBh1Qo2Ii1AohEQiQWVlZU2c/l8TExODLVu2wN/fH2vWrNEzxcTExFQTEEtLSwgEApSXl4PjuMe6XREKhbC0tATwoEfxJMRiMUxNTcFxHH777Te+x/BvkUgkMDExgUAgwI4dO+Ds7PyfzvMwe/fuxalTpzBlyhR8++23fCWu0WhgZ2f3Qq4BPBADCwsLENEjn5lKpYJSqYSxsfFTBbs+U1lZyc9wYzCaIjUy5iIQCODr64uUlBQUFBTUxCWeGSJCZmYmSkpK4O7uDisrK73vNRqN3meBQID27dtDKpXizp071UxBSqUSs2bNwooVKwAAXbp0AQDcuHGj2rXT09MxevRonDx5EsbGxmjbti3UajXu3LlT7djCwkJMmTIF27Zte+L9mJubIzAwEEqlEklJSdW+z8nJQWhoKP78888nnqcqRIS7d++C4zi0aNGiWm/z4Wek47+YOw0MDNCpUycQEeLi4qp9f//+fcjlcnh5ecHBweFfn78+QESIj4/np1czGE2RGhvQ79evH7KzsxEREVHn/pZsbW1hYmKC5ORkPbGrrKzk11BUpVu3bujUqRMuXbqE2NhY/nvdWNK+ffvg4eEBABgzZgzc3d1x8OBBZGZm6i3W3LlzJ65evQo3NzcYGRlh0qRJEIvF2L59u16rneM47Nu3D8eOHXtqZWRubo5JkyZBq9Vix44d/NhR1WtGRET860rN1dUVAoEAcXFxvKASEYqKih4pnABgZGQErVbLi8+pU6fw6quvIjc397HXMTAwwIQJE2BjY4M9e/boTRKorKzEtm3bIBaLMXXq1Aa7RkSlUmH//v1wdnaGr69vXSeHwagbamoamlwup549e1Lv3r2psLCwpi7zTJSWltIrr7xCUqmUxo8fT8ePH6ezZ8/SvHnzyMXFhQDQ4sWLKS4ujtRqNXEcR5cuXaI2bdpQq1ataPPmzRQVFUUbN26k1q1b0/Tp06msrIyIHkyt3b59O7m4uNCgQYPowIEDFBkZSV9++SV5e3vTqlWr+GnECoWCPv30U7K1taUpU6bQ0aNHKTIykr7++mvy9vamr7/+mp8qfe/ePTp69Ci5ubmRj48PXblyhTIzM4njOCorK+PXy8ycOZNOnDhB586do88++4y8vLxo+fLl/H1kZWXR2bNnqXnz5uTl5UXh4eF08+ZNvXUnuusFBQWRmZkZLViwgCIiIujo0aM0ceJEsra2JqFQSDt37qS7d+/yU5XXr19PYrGY5s+fT+Hh4TR06FDq378/lZaW0u3bt2nHjh1kaGhIISEhdPnyZX7asUajoZUrV5K9vT298sordPToUQoPD6e33nqLbGxsaOHChVRRUUEcx1FhYSFdunSJ+vTpQ+bm5vTnn39SamoqaTQaSkxMpDVr1pCxsTG9/PLLFBsby0/xris4jqOzZ8+SnZ0dffvtt/XSJxyDURvUmMt9IsLBgwcxZcoUzJkzBwsXLqyzwX0iQn5+PjZt2oTDhw+jqKgIDg4OGDRoEIgI69atA/BgCunu3bshk8n4WVLbt29HeHg4CgoK4OjoiOHDh2PcuHEwMzPjzUIcxyEmJgbbtm3D5cuXUVlZiebNm2PChAkYMGAA756EiKBWq3HixAn88ccfuHXrFjiOg5+fH15++WUMGDCAt9HPmTMHp06d0nNVM23aNLz99tsQCARQKpU4fPgw9u7dizt37vCzr8aOHYvevXtDIpGAiLB27VqsXr1a7zxOTk7YvHmzntmJiJCYmIj169cjMjISSqUSnp6eGD58OK5du4awsDAIhUJ07NgRq1evhlQqRXFxMZYvX44jR45ApVIhMDAQ8+fPh4uLC6ZNm4bbt2+D4zgIBAKIxWIsXrwYISEhvNuX8+fPY+fOnbh27Ro0Gg18fX31noNAIEBYWBg+/PBD3gWPUChESEgIFi5ciLlz5+LixYt6rnl27tyJFi1a1Gb20iMzMxPjx49HZWUl/vrrLzg6OtaL2ZIMRm1To/FclEolFi1ahHXr1uH777/H5MmT68zUQf+sbdFtAoEAQqGQ9x+mo6ofrcf95nF+yKqe62nHchzHm9AedayuMq2K7piq6XvSeR5OU1Ue5S/scffwuGs87ngAer+pmv6qkyOe9Puqwv1w+h+VrifdV22Rn5+PuXPn4vTp09i8eTP69u3LhIXRZKlR32JSqRQLFy5EVlYWFi5cCLVajVdffRVGRkY1edlHUrVSfnj/42aDPe43Tzr2WQJ66VrZT+JZFp4+7Tz/Jk1POv5x13jS+Z81/U9L38OC9G+vURsQEbKysrBgwQIcO3YMS5YsQa9evZiwMJo0Ne5y39zcHD/99BOkUik++ugjJCYm4sMPP4S1tTUrfIwGj1arRVxcHObPn4/4+Hj88MMPGD9+PItKyWjy1EqYYyJCWVkZVqxYgeXLl6N169ZYvHgxOnbsyAoho0FC/3im3rVrF77++msYGxvjhx9+QN++fVmeZjBQS+KiQ6VS4dixY1i0aBGys7MxdepUTJs2DY6Ojiw+PKPBoFarERsbiyVLluDEiRMIDg7GokWL0KxZM5aPGYx/qFVxAR60+NLT07F8+XJs27YNjo6OePPNNzFq1ChYWVmxwsmol+hmuCUnJ2P9+vX4/fffYWVlhfnz52Ps2LFPdRXEYDQ1al1cAPBTci9duoRly5bh9OnTaN68OaZOnYqQkBBYW1szkWHUC+ifwGbJycnYunUrfv/9d6jVakyaNAmzZs3ifYgxGAx96kRcdBARKioqcObMGaxevRoXLlyAq6srJk6ciBEjRsDd3b3OQ9gymib0T9TQK1euYMeOHdi/fz84jsPo0aMxffp0NG/enOVNBuMJ1Km46NANjl68eBG//fYbjh8/DqlUiuDgYIwePRpdunSBqanpv5oazGD8W3Smr+zsbBw/fhy7du1CTEwMZDIZxowZg0mTJsHLy6teePpmMOo79UJcdBARlEolEhMTsXv3buzbtw8ZGRnw8vLCiBEj0L9/fwQGBvLx7pnQMJ4X3SLOwsJCXLhwAQcPHsSJEydQXFyMoKAgjB07FsHBwXB0dKw362oYjIZAvRKXqmi1WuTn5yMyMhJ79uxBREQEFAoFfH19MXjwYPTs2ROtWrXi3c8zoWE8K7oeSk5ODqKjo3H8+HGcOnUK2dnZcHd3x8CBAzFq1Ci0aNFCz80Pg8F4duqtuFRFqVQiIyMD586dw/79+xEdHY2ysjK4ubmhV69e6NGjB9q0aQNXV1e+dckqBIYOnZuZyspKJCQkIDo6GidPnkR0dDQKCwvh4eGBHj16ICQkBK1atYKNjQ0bpGcwnpMGIS5VUSqVyMzMRExMDA4fPozo6GikpaXBwsICvr6+6NatGzp27AhfX1+4urryC9qY2DQNqmbn8vJyJCUl4ebNm7hw4QKioqKQmpoKAGjRogV69OiBAQMGwNfXlwkKg/GCaXDiUhWd6SwpKQlnzpzB+fPnkZCQgNzcXFhaWsLDwwNdu3ZF586d4ePjA1dXV73ohkxwGjZVsy7HcSgoKEBaWhri4uJw7tw5XLlyBRkZGVCr1fDw8EDLli3Rp08fdOzYES4uLszkxWDUIA1aXKqiWzuTk5OD9PR0REdH49y5c0hISMD9+/dhaGgIW1tbeHp6om3btggKCoK7uzucnZ31wh4DTHTqIw8LSX5+Pu7fv4+kpCTExsbiypUrSElJQV5eHgQCATw9PREYGIhu3bqhXbt2cHFxgUwmY70TBqOWaDTi8ii0Wi0KCgqQmpqKuLg4xMTEIC4uDhkZGcjNzYVUKoWDgwN8fHzg7++PFi1awM3NDba2trC1tYWlpSWrjOoQtVoNuVyOvLw8ZGdn486dO7h16xbi4+P5/5DjODg4OMDLywtBQUEICgpCs2bN4OrqCgsLC9ZQYDDqiEYtLg9TtXeTkZGB27dv84KTl5eHgoIClJeXw8zMDLa2tnBxcYGfnx98fX3h6ekJmUwGS0tLmJubw8zMDBKJhFVez4Hu/yguLkZxcTHkcjmysrJw9+5d3Lp1C0lJScjLy0NeXh4qKipgbm4OmUwGDw8PtG3bFm3atIGbmxucnJxga2vLHEYyGPWIJiUuVXnYzFJUVIScnBxkZWUhMTERt27dwu3bt5GZmQm5XM7HvLe0tIRMJoOVlRXc3Nzg6ekJT09PODs7w8rKCiYmJjA2NoaxsTFMTEz4iIpNDa1WC4VCgYqKCpSXl/OveXl5SE1NRUpKCm/GKiwsREFBAcrKymBgYAALCwvIZDJ4enqiRYsWaNGiBVxdXWFnZwdbW9tqPZKm+HwZjPpOkxWXx1H1cRARSkpKkJOTg4KCAuTl5SEtLY2vGNPS0lBcXIyysjJUVFSA4zgYGxvD3Nwc5ubmsLCwgJWVFRwcHGBvbw8bGxtemCwtLWFsbAyJRAKpVMq/isViiEQifqurilO3uFCr1UKj0UCj0UClUkGpVEKpVPLvy8rKUFhYCLlcDrlcjtzcXGRnZyM3NxdFRUUoKSnhN7VaDbFYDBMTE5iamsLGxgbu7u7w8PDgBdrGxgY2Njaws7ODoaGhXpqYiDAYDQcmLv+Ch4VHq9VCLpcjPz8fcrkcxcXFKCgoQHZ2NrKyspCdnY3s7Gzk5+ejoqICKpUKKpUKarUaarUaQqEQEomE7+WYmJjA0NBQT2yMjIz473U9IiMjIwiFQr5XJBKJ+PDRun0GBgYQi8VQKpV8WtVqNYgIKpWKFw5dWioqKqptlZWVekKi26/riajVaggEAkgkEojFYj7NZmZmsLe3h4ODA7/Z29vzoiqTyWBjYwNTU9NqY1pMQBiMxgETlxfIw+Kje9VoNCgpKUFpaSnKyspQXl7Ov5aWlvKt/qKiIpSWlj62otdoNNBqtdBqtfz5dXHkdZtuX9V0VA0lXDVGfdV9BgYGvCDpBEwnZrpN1xPTiYSpqSnfC9G9Nzc3r+YH7uFXBoPR+GHiUoc8TowetU/X49D1JnSiolarAQAajQYcx4HjOGg0Gr3r6Ho3ulehUAiBQMD3dsRiMaRSKQwNDauZ4h71nokFg8F4GkxcGAwGg/HCYYs4GAwGg/HCYeLCYDAYjBcOExcGg8FgvHCYuDAYDAbjhcPEhcFgMBgvHCYuDAaDwXjhMHFhMBgMxguHiQuDwWAwXjhMXBgMBoPxwmHiwmAwGIwXDhMXBoPBYLxwmLgwGAwG44XDxIXBYDAYL5z/AxyeTbe927M1AAAAAElFTkSuQmCC", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from causallearn.search.ScoreBased.GES import ges\n", "\n", "# default parameters\n", "Record = ges(data)\n", "\n", "# Visualization using pydot\n", "from causallearn.utils.GraphUtils import GraphUtils\n", "import matplotlib.image as mpimg\n", "import matplotlib.pyplot as plt\n", "import io\n", "\n", "pyd = GraphUtils.to_pydot(Record['G'], labels=labels)\n", "tmp_png = pyd.create_png(f=\"png\")\n", "fp = io.BytesIO(tmp_png)\n", "img = mpimg.imread(fp, format='png')\n", "plt.axis('off')\n", "plt.imshow(img)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Well, these two results are different, which is not rare when applying causal discovery on real-world dataset, since the required assumptions on the data-generating process are hard to verify.\n", "\n", "In addition, the graphs returned by PC and GES are CPDAGs instead of DAGs, so it is possible to have undirected edges (e.g., the result returned by GES). Thus, causal effect estimataion is difficult for those methods, since there may be absence of backdoor, instrumental or frontdoor variables. In order to get a DAG, we decide to try LiNGAM on our dataset." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n", "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n", " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n", "<!-- Generated by graphviz version 2.43.0 (0)\n", " -->\n", "<!-- Title: %3 Pages: 1 -->\n", "<svg width=\"369pt\" height=\"392pt\"\n", " viewBox=\"0.00 0.00 369.40 392.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n", "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 388)\">\n", "<title>%3</title>\n", "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-388 365.4,-388 365.4,4 -4,4\"/>\n", "<!-- mpg -->\n", "<g id=\"node1\" class=\"node\">\n", "<title>mpg</title>\n", "<ellipse fill=\"none\" stroke=\"black\" cx=\"34.8\" cy=\"-279\" rx=\"31.4\" 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"text/plain": [ "<graphviz.graphs.Digraph at 0x7f957464c040>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from causallearn.search.FCMBased import lingam\n", "model = lingam.ICALiNGAM()\n", "model.fit(data)\n", "\n", "from causallearn.search.FCMBased.lingam.utils import make_dot\n", "make_dot(model.adjacency_matrix_, labels=labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we have a DAG and are ready to estimate the causal effects based on that." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimate causal effects using Linear Regression\n", "\n", "Now let us see the estimate of causal effect of *mpg* on *weight*." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Estimand type: EstimandType.NONPARAMETRIC_ATE\n", "\n", "### Estimand : 1\n", "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", "──────(E[weight|cylinders])\n", "d[mpg] \n", "Estimand assumption 1, Unconfoundedness: If U→{mpg} and U→weight then P(weight|mpg,cylinders,U) = P(weight|mpg,cylinders)\n", "\n", "### Estimand : 2\n", "Estimand name: iv\n", "No such variable(s) found!\n", "\n", "### Estimand : 3\n", "Estimand name: frontdoor\n", "No such variable(s) found!\n", "\n", "Causal Estimate is -38.940973656209735\n" ] } ], "source": [ "# Obtain valid dot format\n", "graph_dot = make_graph(model.adjacency_matrix_, labels=labels)\n", "\n", "# Define Causal Model\n", "model=CausalModel(\n", " data = data_mpg,\n", " treatment='mpg',\n", " outcome='weight',\n", " graph=str_to_dot(graph_dot.source))\n", "\n", "# Identification\n", "identified_estimand = model.identify_effect(proceed_when_unidentifiable=True)\n", "print(identified_estimand)\n", "\n", "# Estimation\n", "estimate = model.estimate_effect(identified_estimand,\n", " method_name=\"backdoor.linear_regression\",\n", " control_value=0,\n", " treatment_value=1,\n", " confidence_intervals=True,\n", " test_significance=True)\n", "print(\"Causal Estimate is \" + str(estimate.value))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Experiments on the Sachs dataset\n", "\n", "The dataset consists of the simultaneous measurements of 11 phosphorylated proteins and phospholipids derived from thousands of individual primary immune system cells, subjected to both general and specific molecular interventions (Sachs et al., 2005).\n", "\n", "The specifications of the dataset are as follows - \n", "- Number of nodes: 11\n", "- Number of arcs: 17\n", "- Number of parameters: 178\n", "- Average Markov blanket size: 3.09\n", "- Average degree: 3.09\n", "- Maximum in-degree: 3\n", "- Number of instances: 7466" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Load the data" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(7466, 11)\n", "['raf', 'mek', 'plc', 'pip2', 'pip3', 'erk', 'akt', 'pka', 'pkc', 'p38', 'jnk']\n" ] } ], "source": [ "from causallearn.utils.Dataset import load_dataset\n", "\n", "data_sachs, labels = load_dataset(\"sachs\")\n", "\n", "print(data.shape)\n", "print(labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery with causal-learn\n", "\n", "We use the three causal discovery methods mentioned above (PC, GES, and LiNGAM) to find the causal graphs." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, let us take a look at how PC works." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "bc0f31d1492e4934994a6d4ba68f1ad3", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/11 [00:00<?, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "graphs = {}\n", "graphs_nx = {}\n", "labels = [f'{col}' for i, col in enumerate(labels)]\n", "data = data_sachs\n", "\n", "from causallearn.search.ConstraintBased.PC import pc\n", "\n", "cg = pc(data)\n", "\n", "# Visualization using pydot\n", "from causallearn.utils.GraphUtils import GraphUtils\n", "import matplotlib.image as mpimg\n", "import matplotlib.pyplot as plt\n", "import io\n", "\n", "pyd = GraphUtils.to_pydot(cg.G, labels=labels)\n", "tmp_png = pyd.create_png(f=\"png\")\n", "fp = io.BytesIO(tmp_png)\n", "img = mpimg.imread(fp, format='png')\n", "plt.axis('off')\n", "plt.imshow(img)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then, let us try GES." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from causallearn.search.ScoreBased.GES import ges\n", "\n", "# default parameters\n", "Record = ges(data)\n", "\n", "# Visualization using pydot\n", "from causallearn.utils.GraphUtils import GraphUtils\n", "import matplotlib.image as mpimg\n", "import matplotlib.pyplot as plt\n", "import io\n", "\n", "pyd = GraphUtils.to_pydot(Record['G'], labels=labels)\n", "tmp_png = pyd.create_png(f=\"png\")\n", "fp = io.BytesIO(tmp_png)\n", "img = mpimg.imread(fp, format='png')\n", "plt.axis('off')\n", "plt.imshow(img)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And also LiNGAM." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n", "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n", " 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points=\"674.5,-46.18 671,-36.18 667.5,-46.18 674.5,-46.18\"/>\n", "<text text-anchor=\"middle\" x=\"687\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">0.04</text>\n", "</g>\n", "</g>\n", "</svg>\n" ], "text/plain": [ "<graphviz.graphs.Digraph at 0x7f96cd974ca0>" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from causallearn.search.FCMBased import lingam\n", "model = lingam.ICALiNGAM()\n", "model.fit(data)\n", "\n", "from causallearn.search.FCMBased.lingam.utils import make_dot\n", "make_dot(model.adjacency_matrix_, labels=labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimate effects using Linear Regression\n", "\n", "Similarly, let us use the DAG returned by LiNGAM to estimate the causal effect of *PIP2* on *PKC*." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Estimand type: EstimandType.NONPARAMETRIC_ATE\n", "\n", "### Estimand : 1\n", "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", "───────(E[pkc|plc,pip3])\n", "d[pip₂] \n", "Estimand assumption 1, Unconfoundedness: If U→{pip2} and U→pkc then P(pkc|pip2,plc,pip3,U) = P(pkc|pip2,plc,pip3)\n", "\n", "### Estimand : 2\n", "Estimand name: iv\n", "No such variable(s) found!\n", "\n", "### Estimand : 3\n", "Estimand name: frontdoor\n", "No such variable(s) found!\n", "\n", "Causal Estimate is 0.03397189228452291\n" ] } ], "source": [ "# Obtain valid dot format\n", "graph_dot = make_graph(model.adjacency_matrix_, labels=labels)\n", "\n", "data_df = pd.DataFrame(data=data, columns=labels)\n", "\n", "# Define Causal Model\n", "model_est=CausalModel(\n", " data = data_df,\n", " treatment='pip2',\n", " outcome='pkc',\n", " graph=str_to_dot(graph_dot.source))\n", "\n", "# Identification\n", "identified_estimand = model_est.identify_effect(proceed_when_unidentifiable=False)\n", "print(identified_estimand)\n", "\n", "# Estimation\n", "estimate = model_est.estimate_effect(identified_estimand,\n", " method_name=\"backdoor.linear_regression\",\n", " control_value=0,\n", " treatment_value=1,\n", " confidence_intervals=True,\n", " test_significance=True)\n", "print(\"Causal Estimate is \" + str(estimate.value))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.17" }, "metadata": { "interpreter": { "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" } }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": false, "sideBar": true, "skip_h1_title": true, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }

kunwuz

67b305db5224bf718067a21acfe4baa92a7d2c8c

7eb4a0c253514a920588d1ab222e1aeb5e07cb51

Consider adding a link to the top-level documentation to help readers explore all the other methods supported by causal-learn

emrekiciman

py-why/dowhy

Update the causal discovery notebook with examples using causal-learn

Updating the old notebook as mentioned in #1021.

2023-08-30 21:25:09+00:00

2023-10-05 21:26:19+00:00

docs/source/example_notebooks/dowhy_causal_discovery_example.ipynb

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery example\n", "\n", "The goal of this notebook is to show how causal discovery methods can work with DoWhy. We use discovery methods from [Causal Discovery Tool (CDT)](https://github.com/FenTechSolutions/CausalDiscoveryToolbox) repo. As we will see, causal discovery methods are not fool-proof and there is no guarantee that they will recover the correct causal graph. Even for the simple examples below, there is a large variance in results. These methods, however, may be combined usefully with domain knowledge to construct the final causal graph." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import dowhy\n", "from dowhy import CausalModel\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import graphviz\n", "import networkx as nx \n", "\n", "np.set_printoptions(precision=3, suppress=True)\n", "np.random.seed(0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Utility function\n", "We define a utility function to draw the directed acyclic graph." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def make_graph(adjacency_matrix, labels=None):\n", " idx = np.abs(adjacency_matrix) > 0.01\n", " dirs = np.where(idx)\n", " d = graphviz.Digraph(engine='dot')\n", " names = labels if labels else [f'x{i}' for i in range(len(adjacency_matrix))]\n", " for name in names:\n", " d.node(name)\n", " for to, from_, coef in zip(dirs[0], dirs[1], adjacency_matrix[idx]):\n", " d.edge(names[from_], names[to], label=str(coef))\n", " return d\n", "\n", "def str_to_dot(string):\n", " '''\n", " Converts input string from graphviz library to valid DOT graph format.\n", " '''\n", " graph = string.strip().replace('\\n', ';').replace('\\t','')\n", " graph = graph[:9] + graph[10:-2] + graph[-1] # Removing unnecessary characters from string\n", " return graph" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Experiments on the Auto-MPG dataset\n", "\n", "In this section, we will use a dataset on the technical specification of cars. The dataset is downloaded from UCI Machine Learning Repository. The dataset contains 9 attributes and 398 instances. We do not know the true causal graph for the dataset and will use CDT to discover it. The causal graph obtained will then be used to estimate the causal effect.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Load the data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data_mpg = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data-original',\n", " delim_whitespace=True, header=None,\n", " names = ['mpg', 'cylinders', 'displacement',\n", " 'horsepower', 'weight', 'acceleration',\n", " 'model year', 'origin', 'car name'])\n", "data_mpg.dropna(inplace=True)\n", "data_mpg.drop(['model year', 'origin', 'car name'], axis=1, inplace=True)\n", "print(data_mpg.shape)\n", "data_mpg.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery with Causal Discovery Tool (CDT)\n", "\n", "We use the CDT library to perform causal discovery on the Auto-MPG dataset. We use three methods for causal discovery here -LiNGAM, PC and GES. These methods are widely used and do not take much time to run. Hence, these are ideal for an introduction to the topic. Other neural network based methods are also available in CDT and the users are encouraged to try them out by themselves. \n", "\n", "The documentation for the methods used are as follows:\n", "- LiNGAM [[link]](https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/_modules/cdt/causality/graph/LiNGAM.html)\n", "- PC [[link]](https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/_modules/cdt/causality/graph/PC.html)\n", "- GES [[link]](https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/_modules/cdt/causality/graph/GES.html)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from cdt.causality.graph import LiNGAM, PC, GES\n", "\n", "graphs = {}\n", "labels = [f'{col}' for i, col in enumerate(data_mpg.columns)]\n", "functions = {\n", " 'LiNGAM' : LiNGAM,\n", " 'PC' : PC,\n", " 'GES' : GES,\n", "}\n", "\n", "for method, lib in functions.items():\n", " obj = lib()\n", " output = obj.predict(data_mpg)\n", " adj_matrix = nx.to_numpy_array(output)\n", " adj_matrix = np.asarray(adj_matrix)\n", " graph_dot = make_graph(adj_matrix, labels)\n", " graphs[method] = graph_dot\n", "\n", "# Visualize graphs\n", "for method, graph in graphs.items():\n", " print(\"Method : %s\"%(method))\n", " display(graph)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, no two methods agree on the graphs. PC and GES effectively produce an undirected graph whereas LiNGAM produces a directed graph. We use only the LiNGAM method in the next section." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimate causal effects using Linear Regression\n", "\n", "Now let us see whether these differences in the graphs also lead to significant differences in the causal estimate of effect of *mpg* on *weight*." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for method, graph in graphs.items():\n", " if method != \"LiNGAM\":\n", " continue\n", " print('\\n*****************************************************************************\\n')\n", " print(\"Causal Discovery Method : %s\"%(method))\n", " \n", " # Obtain valid dot format\n", " graph_dot = str_to_dot(graph.source)\n", "\n", " # Define Causal Model\n", " model=CausalModel(\n", " data = data_mpg,\n", " treatment='mpg',\n", " outcome='weight',\n", " graph=graph_dot)\n", "\n", " # Identification\n", " identified_estimand = model.identify_effect(proceed_when_unidentifiable=True)\n", " print(identified_estimand)\n", " \n", " # Estimation\n", " estimate = model.estimate_effect(identified_estimand,\n", " method_name=\"backdoor.linear_regression\",\n", " control_value=0,\n", " treatment_value=1,\n", " confidence_intervals=True,\n", " test_significance=True)\n", " print(\"Causal Estimate is \" + str(estimate.value))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As mentioned earlier, due to the absence of directed edges, no backdoor, instrmental or frontdoor variables can be found out for PC and GES. Thus, causal effect estimation is not possible for these methods. However, LiNGAM does discover a DAG and hence, its possible to output a causal estimate for LiNGAM. The estimate is still pretty far from the original estimate of -70.466 (which can be calculated from the graph)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Experiments on the Sachs dataset\n", "\n", "The dataset consists of the simultaneous measurements of 11 phosphorylated proteins and phospholipids derived from thousands of individual primary immune system cells, subjected to both general and specific molecular interventions (Sachs et al., 2005).\n", "\n", "The specifications of the dataset are as follows - \n", "- Number of nodes: 11\n", "- Number of arcs: 17\n", "- Number of parameters: 178\n", "- Average Markov blanket size: 3.09\n", "- Average degree: 3.09\n", "- Maximum in-degree: 3\n", "- Number of instances: 7466\n", "\n", "The original causal graph is known for the Sachs dataset and we compare the original graph with the ones discovered using CDT in this section." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Load the data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from cdt.data import load_dataset\n", "data_sachs, graph_sachs = load_dataset(\"sachs\")\n", "\n", "data_sachs.dropna(inplace=True)\n", "print(data_sachs.shape)\n", "data_sachs.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Ground truth of the causal graph" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "labels = [f'{col}' for i, col in enumerate(data_sachs.columns)]\n", "adj_matrix = nx.to_numpy_array(graph_sachs)\n", "adj_matrix = np.asarray(adj_matrix)\n", "graph_dot = make_graph(adj_matrix, labels)\n", "display(graph_dot)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery with Causal Discovery Tool (CDT)\n", "\n", "We use the CDT library to perform causal discovery on the Auto-MPG dataset. We use three methods for causal discovery here -LiNGAM, PC and GES. These methods are widely used and do not take much time to run. Hence, these are ideal for an introduction to the topic. Other neural network based methods are also available in CDT and the users the encourages to try them out by themselves. \n", "\n", "The documentation for the methods used in as follows:\n", "- LiNGAM [[link]](https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/_modules/cdt/causality/graph/LiNGAM.html)\n", "- PC [[link]](https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/_modules/cdt/causality/graph/PC.html)\n", "- GES [[link]](https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/_modules/cdt/causality/graph/GES.html)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from cdt.causality.graph import LiNGAM, PC, GES\n", "\n", "graphs = {}\n", "graphs_nx = {}\n", "labels = [f'{col}' for i, col in enumerate(data_sachs.columns)]\n", "functions = {\n", " 'LiNGAM' : LiNGAM,\n", " 'PC' : PC,\n", " 'GES' : GES,\n", "}\n", "\n", "for method, lib in functions.items():\n", " obj = lib()\n", " output = obj.predict(data_sachs)\n", " graphs_nx[method] = output\n", " adj_matrix = nx.to_numpy_array(output)\n", " adj_matrix = np.asarray(adj_matrix)\n", " graph_dot = make_graph(adj_matrix, labels)\n", " graphs[method] = graph_dot\n", "\n", "# Visualize graphs\n", "for method, graph in graphs.items():\n", " print(\"Method : %s\"%(method))\n", " display(graph)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, no two methods agree on the graphs. Next we study the causal effects of these different graphs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimate effects using Linear Regression\n", "\n", "Now let us see whether these differences in the graphs also lead to significant differences in the causal estimate of effect of *PIP2* on *PKC*." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for method, graph in graphs.items():\n", " if method != \"LiNGAM\":\n", " continue\n", " print('\\n*****************************************************************************\\n')\n", " print(\"Causal Discovery Method : %s\"%(method))\n", "\n", " # Obtain valid dot format\n", " graph_dot = str_to_dot(graph.source)\n", "\n", " # Define Causal Model\n", " model=CausalModel(\n", " data = data_sachs,\n", " treatment='PIP2',\n", " outcome='PKC',\n", " graph=graph_dot)\n", "\n", " # Identification\n", " identified_estimand = model.identify_effect(proceed_when_unidentifiable=True)\n", " print(identified_estimand)\n", "\n", " # Estimation\n", " estimate = model.estimate_effect(identified_estimand,\n", " method_name=\"backdoor.linear_regression\",\n", " control_value=0,\n", " treatment_value=1,\n", " confidence_intervals=True,\n", " test_significance=True)\n", " print(\"Causal Estimate is \" + str(estimate.value))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the causal estimates obtained, it can be seen that the three estimates differ in different aspects. The graph obtained using LiNGAM contains a backdoor path and instrumental variables. On the other hand, the graph obtained using PC contains a backdoor path and a frontdoor path. However, despite these differences, both obtain the same mean causal estimate.\n", "\n", "The graph obtained using GES contains only a backdoor path with different backdoor variables and obtains a different causal estimate than the first two cases. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Graph Validation\n", "\n", "We compare the graphs obtained with the true causal graph using the causal discovery methods using 2 graph distance metrics - Structural Hamming Distance (SHD) and Structural Intervention Distance (SID). SHD between two graphs is, in simple terms, the number of edge insertions, deletions or flips in order to transform one graph to another graph. SID, on the other hand, is based on a graphical criterion only and quantifies the closeness between two DAGs in terms of their corresponding causal inference statements." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from cdt.metrics import SHD, SHD_CPDAG, SID, SID_CPDAG\n", "from numpy.random import randint\n", "\n", "for method, graph in graphs_nx.items():\n", " print(\"***********************************************************\")\n", " print(\"Method: %s\"%(method))\n", " tar, pred = graph_sachs, graph\n", " print(\"SHD_CPDAG = %f\"%(SHD_CPDAG(tar, pred)))\n", " print(\"SHD = %f\"%(SHD(tar, pred, double_for_anticausal=False)))\n", " print(\"SID_CPDAG = [%f, %f]\"%(SID_CPDAG(tar, pred)))\n", " print(\"SID = %f\"%(SID(tar, pred)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The graph similarity metrics show that the scores are the lowest for the LiNGAM method of graph extraction. Hence, of the three methods used, LiNGAM provides the graph that is most similar to the original graph." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Graph Refutation\n", "\n", "Here, we use the same SHD and SID metric to find out how different the discovered graph are from each other." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import itertools\n", "from numpy.random import randint\n", "from cdt.metrics import SHD, SHD_CPDAG, SID, SID_CPDAG\n", "\n", "# Find combinations of pair of methods to compare\n", "combinations = list(itertools.combinations(graphs_nx, 2))\n", "\n", "for pair in combinations:\n", " print(\"***********************************************************\")\n", " graph1 = graphs_nx[pair[0]]\n", " graph2 = graphs_nx[pair[1]]\n", " print(\"Methods: %s and %s\"%(pair[0], pair[1]))\n", " print(\"SHD_CPDAG = %f\"%(SHD_CPDAG(graph1, graph2)))\n", " print(\"SHD = %f\"%(SHD(graph1, graph2, double_for_anticausal=False)))\n", " print(\"SID_CPDAG = [%f, %f]\"%(SID_CPDAG(graph1, graph2)))\n", " print(\"SID = %f\"%(SID(graph1, graph2)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The values for the metrics show how different the graphs are from each other. A higher distance value implies that the difference between the graphs is more." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.12" }, "metadata": { "interpreter": { "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" } }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": false, "sideBar": true, "skip_h1_title": true, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery example\n", "\n", "The goal of this notebook is to show how causal discovery methods can work with DoWhy. We use discovery methods from [causal-learn](https://github.com/py-why/causal-learn) repo. As we will see, causal discovery methods require appropriate assumptions for the correctness guarantees, adn thus there will be variance across results returned by different methods in practice. These methods, however, may be combined usefully with domain knowledge to construct the final causal graph." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import dowhy\n", "from dowhy import CausalModel\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import graphviz\n", "import networkx as nx \n", "\n", "np.set_printoptions(precision=3, suppress=True)\n", "np.random.seed(0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Utility function\n", "We define a utility function to draw the directed acyclic graph." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def make_graph(adjacency_matrix, labels=None):\n", " idx = np.abs(adjacency_matrix) > 0.01\n", " dirs = np.where(idx)\n", " d = graphviz.Digraph(engine='dot')\n", " names = labels if labels else [f'x{i}' for i in range(len(adjacency_matrix))]\n", " for name in names:\n", " d.node(name)\n", " for to, from_, coef in zip(dirs[0], dirs[1], adjacency_matrix[idx]):\n", " d.edge(names[from_], names[to], label=str(coef))\n", " return d\n", "\n", "def str_to_dot(string):\n", " '''\n", " Converts input string from graphviz library to valid DOT graph format.\n", " '''\n", " graph = string.strip().replace('\\n', ';').replace('\\t','')\n", " graph = graph[:9] + graph[10:-2] + graph[-1] # Removing unnecessary characters from string\n", " return graph" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Experiments on the Auto-MPG dataset\n", "\n", "In this section, we will use a dataset on the technical specification of cars. The dataset is downloaded from UCI Machine Learning Repository. The dataset contains 9 attributes and 398 instances. We do not know the true causal graph for the dataset and will use causal-learn to discover it. The causal graph obtained will then be used to estimate the causal effect.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Load the data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(392, 6)\n" ] }, { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>mpg</th>\n", " <th>cylinders</th>\n", " <th>displacement</th>\n", " <th>horsepower</th>\n", " <th>weight</th>\n", " <th>acceleration</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>18.0</td>\n", " <td>8.0</td>\n", " <td>307.0</td>\n", " <td>130.0</td>\n", " <td>3504.0</td>\n", " <td>12.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>15.0</td>\n", " <td>8.0</td>\n", " <td>350.0</td>\n", " <td>165.0</td>\n", " <td>3693.0</td>\n", " <td>11.5</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>18.0</td>\n", " <td>8.0</td>\n", " <td>318.0</td>\n", " <td>150.0</td>\n", " <td>3436.0</td>\n", " <td>11.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>16.0</td>\n", " <td>8.0</td>\n", " <td>304.0</td>\n", " <td>150.0</td>\n", " <td>3433.0</td>\n", " <td>12.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>17.0</td>\n", " <td>8.0</td>\n", " <td>302.0</td>\n", " <td>140.0</td>\n", " <td>3449.0</td>\n", " <td>10.5</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " mpg cylinders displacement horsepower weight acceleration\n", "0 18.0 8.0 307.0 130.0 3504.0 12.0\n", "1 15.0 8.0 350.0 165.0 3693.0 11.5\n", "2 18.0 8.0 318.0 150.0 3436.0 11.0\n", "3 16.0 8.0 304.0 150.0 3433.0 12.0\n", "4 17.0 8.0 302.0 140.0 3449.0 10.5" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_mpg = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data-original',\n", " delim_whitespace=True, header=None,\n", " names = ['mpg', 'cylinders', 'displacement',\n", " 'horsepower', 'weight', 'acceleration',\n", " 'model year', 'origin', 'car name'])\n", "data_mpg.dropna(inplace=True)\n", "data_mpg.drop(['model year', 'origin', 'car name'], axis=1, inplace=True)\n", "print(data_mpg.shape)\n", "data_mpg.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery with causal-learn\n", "\n", "We use the causal-learn library to perform causal discovery on the Auto-MPG dataset. We use three methods for causal discovery here: PC, FCI and GES. These methods are widely used and do not take much time to run. Hence, these are ideal for an introduction to the topic. Causal-learn provides a comprehensive list of well-tested causal-discovery methods, and readers are welcome to explore.\n", "\n", "The documentation for the methods used are as follows:\n", "- PC [[link]](https://causal-learn.readthedocs.io/en/latest/search_methods_index/Constraint-based%20causal%20discovery%20methods/PC.html)\n", "- GES [[link]](https://causal-learn.readthedocs.io/en/latest/search_methods_index/Score-based%20causal%20discovery%20methods/GES.html)\n", "- LiNGAM [[link]](https://causal-learn.readthedocs.io/en/latest/search_methods_index/Causal%20discovery%20methods%20based%20on%20constrained%20functional%20causal%20models/lingam.html#ica-based-lingam)\n", "\n", "More methods could be found in the causal-learn documentation [[link]](https://causal-learn.readthedocs.io/en/latest/)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We first try the PC algorithm with default parameters." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1ed197e9f5ec42c8bf7fc51c5ece4485", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/6 [00:00<?, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from causallearn.search.ConstraintBased.PC import pc\n", "\n", "labels = [f'{col}' for i, col in enumerate(data_mpg.columns)]\n", "data = data_mpg.to_numpy()\n", "\n", "cg = pc(data)\n", "\n", "# Visualization using pydot\n", "from causallearn.utils.GraphUtils import GraphUtils\n", "import matplotlib.image as mpimg\n", "import matplotlib.pyplot as plt\n", "import io\n", "\n", "pyd = GraphUtils.to_pydot(cg.G, labels=labels)\n", "tmp_png = pyd.create_png(f=\"png\")\n", "fp = io.BytesIO(tmp_png)\n", "img = mpimg.imread(fp, format='png')\n", "plt.axis('off')\n", "plt.imshow(img)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we have a causal graph discovered by PC. Let us also try GES to see its result." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from causallearn.search.ScoreBased.GES import ges\n", "\n", "# default parameters\n", "Record = ges(data)\n", "\n", "# Visualization using pydot\n", "from causallearn.utils.GraphUtils import GraphUtils\n", "import matplotlib.image as mpimg\n", "import matplotlib.pyplot as plt\n", "import io\n", "\n", "pyd = GraphUtils.to_pydot(Record['G'], labels=labels)\n", "tmp_png = pyd.create_png(f=\"png\")\n", "fp = io.BytesIO(tmp_png)\n", "img = mpimg.imread(fp, format='png')\n", "plt.axis('off')\n", "plt.imshow(img)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Well, these two results are different, which is not rare when applying causal discovery on real-world dataset, since the required assumptions on the data-generating process are hard to verify.\n", "\n", "In addition, the graphs returned by PC and GES are CPDAGs instead of DAGs, so it is possible to have undirected edges (e.g., the result returned by GES). Thus, causal effect estimataion is difficult for those methods, since there may be absence of backdoor, instrumental or frontdoor variables. In order to get a DAG, we decide to try LiNGAM on our dataset." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n", "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n", " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n", "<!-- Generated by graphviz version 2.43.0 (0)\n", " -->\n", "<!-- Title: %3 Pages: 1 -->\n", "<svg width=\"369pt\" height=\"392pt\"\n", " viewBox=\"0.00 0.00 369.40 392.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n", "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 388)\">\n", "<title>%3</title>\n", "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-388 365.4,-388 365.4,4 -4,4\"/>\n", "<!-- mpg -->\n", "<g id=\"node1\" class=\"node\">\n", "<title>mpg</title>\n", "<ellipse fill=\"none\" stroke=\"black\" cx=\"34.8\" cy=\"-279\" rx=\"31.4\" 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"text/plain": [ "<graphviz.graphs.Digraph at 0x7f957464c040>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from causallearn.search.FCMBased import lingam\n", "model = lingam.ICALiNGAM()\n", "model.fit(data)\n", "\n", "from causallearn.search.FCMBased.lingam.utils import make_dot\n", "make_dot(model.adjacency_matrix_, labels=labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we have a DAG and are ready to estimate the causal effects based on that." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimate causal effects using Linear Regression\n", "\n", "Now let us see the estimate of causal effect of *mpg* on *weight*." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Estimand type: EstimandType.NONPARAMETRIC_ATE\n", "\n", "### Estimand : 1\n", "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", "──────(E[weight|cylinders])\n", "d[mpg] \n", "Estimand assumption 1, Unconfoundedness: If U→{mpg} and U→weight then P(weight|mpg,cylinders,U) = P(weight|mpg,cylinders)\n", "\n", "### Estimand : 2\n", "Estimand name: iv\n", "No such variable(s) found!\n", "\n", "### Estimand : 3\n", "Estimand name: frontdoor\n", "No such variable(s) found!\n", "\n", "Causal Estimate is -38.940973656209735\n" ] } ], "source": [ "# Obtain valid dot format\n", "graph_dot = make_graph(model.adjacency_matrix_, labels=labels)\n", "\n", "# Define Causal Model\n", "model=CausalModel(\n", " data = data_mpg,\n", " treatment='mpg',\n", " outcome='weight',\n", " graph=str_to_dot(graph_dot.source))\n", "\n", "# Identification\n", "identified_estimand = model.identify_effect(proceed_when_unidentifiable=True)\n", "print(identified_estimand)\n", "\n", "# Estimation\n", "estimate = model.estimate_effect(identified_estimand,\n", " method_name=\"backdoor.linear_regression\",\n", " control_value=0,\n", " treatment_value=1,\n", " confidence_intervals=True,\n", " test_significance=True)\n", "print(\"Causal Estimate is \" + str(estimate.value))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Experiments on the Sachs dataset\n", "\n", "The dataset consists of the simultaneous measurements of 11 phosphorylated proteins and phospholipids derived from thousands of individual primary immune system cells, subjected to both general and specific molecular interventions (Sachs et al., 2005).\n", "\n", "The specifications of the dataset are as follows - \n", "- Number of nodes: 11\n", "- Number of arcs: 17\n", "- Number of parameters: 178\n", "- Average Markov blanket size: 3.09\n", "- Average degree: 3.09\n", "- Maximum in-degree: 3\n", "- Number of instances: 7466" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Load the data" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(7466, 11)\n", "['raf', 'mek', 'plc', 'pip2', 'pip3', 'erk', 'akt', 'pka', 'pkc', 'p38', 'jnk']\n" ] } ], "source": [ "from causallearn.utils.Dataset import load_dataset\n", "\n", "data_sachs, labels = load_dataset(\"sachs\")\n", "\n", "print(data.shape)\n", "print(labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery with causal-learn\n", "\n", "We use the three causal discovery methods mentioned above (PC, GES, and LiNGAM) to find the causal graphs." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, let us take a look at how PC works." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "bc0f31d1492e4934994a6d4ba68f1ad3", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/11 [00:00<?, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "graphs = {}\n", "graphs_nx = {}\n", "labels = [f'{col}' for i, col in enumerate(labels)]\n", "data = data_sachs\n", "\n", "from causallearn.search.ConstraintBased.PC import pc\n", "\n", "cg = pc(data)\n", "\n", "# Visualization using pydot\n", "from causallearn.utils.GraphUtils import GraphUtils\n", "import matplotlib.image as mpimg\n", "import matplotlib.pyplot as plt\n", "import io\n", "\n", "pyd = GraphUtils.to_pydot(cg.G, labels=labels)\n", "tmp_png = pyd.create_png(f=\"png\")\n", "fp = io.BytesIO(tmp_png)\n", "img = mpimg.imread(fp, format='png')\n", "plt.axis('off')\n", "plt.imshow(img)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then, let us try GES." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from causallearn.search.ScoreBased.GES import ges\n", "\n", "# default parameters\n", "Record = ges(data)\n", "\n", "# Visualization using pydot\n", "from causallearn.utils.GraphUtils import GraphUtils\n", "import matplotlib.image as mpimg\n", "import matplotlib.pyplot as plt\n", "import io\n", "\n", "pyd = GraphUtils.to_pydot(Record['G'], labels=labels)\n", "tmp_png = pyd.create_png(f=\"png\")\n", "fp = io.BytesIO(tmp_png)\n", "img = mpimg.imread(fp, format='png')\n", "plt.axis('off')\n", "plt.imshow(img)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And also LiNGAM." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n", "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n", " 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points=\"674.5,-46.18 671,-36.18 667.5,-46.18 674.5,-46.18\"/>\n", "<text text-anchor=\"middle\" x=\"687\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">0.04</text>\n", "</g>\n", "</g>\n", "</svg>\n" ], "text/plain": [ "<graphviz.graphs.Digraph at 0x7f96cd974ca0>" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from causallearn.search.FCMBased import lingam\n", "model = lingam.ICALiNGAM()\n", "model.fit(data)\n", "\n", "from causallearn.search.FCMBased.lingam.utils import make_dot\n", "make_dot(model.adjacency_matrix_, labels=labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimate effects using Linear Regression\n", "\n", "Similarly, let us use the DAG returned by LiNGAM to estimate the causal effect of *PIP2* on *PKC*." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Estimand type: EstimandType.NONPARAMETRIC_ATE\n", "\n", "### Estimand : 1\n", "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", "───────(E[pkc|plc,pip3])\n", "d[pip₂] \n", "Estimand assumption 1, Unconfoundedness: If U→{pip2} and U→pkc then P(pkc|pip2,plc,pip3,U) = P(pkc|pip2,plc,pip3)\n", "\n", "### Estimand : 2\n", "Estimand name: iv\n", "No such variable(s) found!\n", "\n", "### Estimand : 3\n", "Estimand name: frontdoor\n", "No such variable(s) found!\n", "\n", "Causal Estimate is 0.03397189228452291\n" ] } ], "source": [ "# Obtain valid dot format\n", "graph_dot = make_graph(model.adjacency_matrix_, labels=labels)\n", "\n", "data_df = pd.DataFrame(data=data, columns=labels)\n", "\n", "# Define Causal Model\n", "model_est=CausalModel(\n", " data = data_df,\n", " treatment='pip2',\n", " outcome='pkc',\n", " graph=str_to_dot(graph_dot.source))\n", "\n", "# Identification\n", "identified_estimand = model_est.identify_effect(proceed_when_unidentifiable=False)\n", "print(identified_estimand)\n", "\n", "# Estimation\n", "estimate = model_est.estimate_effect(identified_estimand,\n", " method_name=\"backdoor.linear_regression\",\n", " control_value=0,\n", " treatment_value=1,\n", " confidence_intervals=True,\n", " test_significance=True)\n", "print(\"Causal Estimate is \" + str(estimate.value))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.17" }, "metadata": { "interpreter": { "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" } }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": false, "sideBar": true, "skip_h1_title": true, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }

kunwuz

67b305db5224bf718067a21acfe4baa92a7d2c8c

7eb4a0c253514a920588d1ab222e1aeb5e07cb51

the sachs dataset is still being loaded via CDT. Rather than require installing all of CDT, can we just copy the load_sachs function, giving credit and following license requirements. I believe it's a short function, though we'd also need an abbreviated version of the read_list_edges function. https://github.com/FenTechSolutions/CausalDiscoveryToolbox/blob/master/cdt/data/loader.py#L87

emrekiciman

py-why/dowhy

Update the causal discovery notebook with examples using causal-learn

Updating the old notebook as mentioned in #1021.

2023-08-30 21:25:09+00:00

2023-10-05 21:26:19+00:00

docs/source/example_notebooks/dowhy_causal_discovery_example.ipynb

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery example\n", "\n", "The goal of this notebook is to show how causal discovery methods can work with DoWhy. We use discovery methods from [Causal Discovery Tool (CDT)](https://github.com/FenTechSolutions/CausalDiscoveryToolbox) repo. As we will see, causal discovery methods are not fool-proof and there is no guarantee that they will recover the correct causal graph. Even for the simple examples below, there is a large variance in results. These methods, however, may be combined usefully with domain knowledge to construct the final causal graph." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import dowhy\n", "from dowhy import CausalModel\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import graphviz\n", "import networkx as nx \n", "\n", "np.set_printoptions(precision=3, suppress=True)\n", "np.random.seed(0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Utility function\n", "We define a utility function to draw the directed acyclic graph." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def make_graph(adjacency_matrix, labels=None):\n", " idx = np.abs(adjacency_matrix) > 0.01\n", " dirs = np.where(idx)\n", " d = graphviz.Digraph(engine='dot')\n", " names = labels if labels else [f'x{i}' for i in range(len(adjacency_matrix))]\n", " for name in names:\n", " d.node(name)\n", " for to, from_, coef in zip(dirs[0], dirs[1], adjacency_matrix[idx]):\n", " d.edge(names[from_], names[to], label=str(coef))\n", " return d\n", "\n", "def str_to_dot(string):\n", " '''\n", " Converts input string from graphviz library to valid DOT graph format.\n", " '''\n", " graph = string.strip().replace('\\n', ';').replace('\\t','')\n", " graph = graph[:9] + graph[10:-2] + graph[-1] # Removing unnecessary characters from string\n", " return graph" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Experiments on the Auto-MPG dataset\n", "\n", "In this section, we will use a dataset on the technical specification of cars. The dataset is downloaded from UCI Machine Learning Repository. The dataset contains 9 attributes and 398 instances. We do not know the true causal graph for the dataset and will use CDT to discover it. The causal graph obtained will then be used to estimate the causal effect.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Load the data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data_mpg = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data-original',\n", " delim_whitespace=True, header=None,\n", " names = ['mpg', 'cylinders', 'displacement',\n", " 'horsepower', 'weight', 'acceleration',\n", " 'model year', 'origin', 'car name'])\n", "data_mpg.dropna(inplace=True)\n", "data_mpg.drop(['model year', 'origin', 'car name'], axis=1, inplace=True)\n", "print(data_mpg.shape)\n", "data_mpg.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery with Causal Discovery Tool (CDT)\n", "\n", "We use the CDT library to perform causal discovery on the Auto-MPG dataset. We use three methods for causal discovery here -LiNGAM, PC and GES. These methods are widely used and do not take much time to run. Hence, these are ideal for an introduction to the topic. Other neural network based methods are also available in CDT and the users are encouraged to try them out by themselves. \n", "\n", "The documentation for the methods used are as follows:\n", "- LiNGAM [[link]](https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/_modules/cdt/causality/graph/LiNGAM.html)\n", "- PC [[link]](https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/_modules/cdt/causality/graph/PC.html)\n", "- GES [[link]](https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/_modules/cdt/causality/graph/GES.html)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from cdt.causality.graph import LiNGAM, PC, GES\n", "\n", "graphs = {}\n", "labels = [f'{col}' for i, col in enumerate(data_mpg.columns)]\n", "functions = {\n", " 'LiNGAM' : LiNGAM,\n", " 'PC' : PC,\n", " 'GES' : GES,\n", "}\n", "\n", "for method, lib in functions.items():\n", " obj = lib()\n", " output = obj.predict(data_mpg)\n", " adj_matrix = nx.to_numpy_array(output)\n", " adj_matrix = np.asarray(adj_matrix)\n", " graph_dot = make_graph(adj_matrix, labels)\n", " graphs[method] = graph_dot\n", "\n", "# Visualize graphs\n", "for method, graph in graphs.items():\n", " print(\"Method : %s\"%(method))\n", " display(graph)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, no two methods agree on the graphs. PC and GES effectively produce an undirected graph whereas LiNGAM produces a directed graph. We use only the LiNGAM method in the next section." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimate causal effects using Linear Regression\n", "\n", "Now let us see whether these differences in the graphs also lead to significant differences in the causal estimate of effect of *mpg* on *weight*." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for method, graph in graphs.items():\n", " if method != \"LiNGAM\":\n", " continue\n", " print('\\n*****************************************************************************\\n')\n", " print(\"Causal Discovery Method : %s\"%(method))\n", " \n", " # Obtain valid dot format\n", " graph_dot = str_to_dot(graph.source)\n", "\n", " # Define Causal Model\n", " model=CausalModel(\n", " data = data_mpg,\n", " treatment='mpg',\n", " outcome='weight',\n", " graph=graph_dot)\n", "\n", " # Identification\n", " identified_estimand = model.identify_effect(proceed_when_unidentifiable=True)\n", " print(identified_estimand)\n", " \n", " # Estimation\n", " estimate = model.estimate_effect(identified_estimand,\n", " method_name=\"backdoor.linear_regression\",\n", " control_value=0,\n", " treatment_value=1,\n", " confidence_intervals=True,\n", " test_significance=True)\n", " print(\"Causal Estimate is \" + str(estimate.value))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As mentioned earlier, due to the absence of directed edges, no backdoor, instrmental or frontdoor variables can be found out for PC and GES. Thus, causal effect estimation is not possible for these methods. However, LiNGAM does discover a DAG and hence, its possible to output a causal estimate for LiNGAM. The estimate is still pretty far from the original estimate of -70.466 (which can be calculated from the graph)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Experiments on the Sachs dataset\n", "\n", "The dataset consists of the simultaneous measurements of 11 phosphorylated proteins and phospholipids derived from thousands of individual primary immune system cells, subjected to both general and specific molecular interventions (Sachs et al., 2005).\n", "\n", "The specifications of the dataset are as follows - \n", "- Number of nodes: 11\n", "- Number of arcs: 17\n", "- Number of parameters: 178\n", "- Average Markov blanket size: 3.09\n", "- Average degree: 3.09\n", "- Maximum in-degree: 3\n", "- Number of instances: 7466\n", "\n", "The original causal graph is known for the Sachs dataset and we compare the original graph with the ones discovered using CDT in this section." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Load the data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from cdt.data import load_dataset\n", "data_sachs, graph_sachs = load_dataset(\"sachs\")\n", "\n", "data_sachs.dropna(inplace=True)\n", "print(data_sachs.shape)\n", "data_sachs.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Ground truth of the causal graph" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "labels = [f'{col}' for i, col in enumerate(data_sachs.columns)]\n", "adj_matrix = nx.to_numpy_array(graph_sachs)\n", "adj_matrix = np.asarray(adj_matrix)\n", "graph_dot = make_graph(adj_matrix, labels)\n", "display(graph_dot)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery with Causal Discovery Tool (CDT)\n", "\n", "We use the CDT library to perform causal discovery on the Auto-MPG dataset. We use three methods for causal discovery here -LiNGAM, PC and GES. These methods are widely used and do not take much time to run. Hence, these are ideal for an introduction to the topic. Other neural network based methods are also available in CDT and the users the encourages to try them out by themselves. \n", "\n", "The documentation for the methods used in as follows:\n", "- LiNGAM [[link]](https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/_modules/cdt/causality/graph/LiNGAM.html)\n", "- PC [[link]](https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/_modules/cdt/causality/graph/PC.html)\n", "- GES [[link]](https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/_modules/cdt/causality/graph/GES.html)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from cdt.causality.graph import LiNGAM, PC, GES\n", "\n", "graphs = {}\n", "graphs_nx = {}\n", "labels = [f'{col}' for i, col in enumerate(data_sachs.columns)]\n", "functions = {\n", " 'LiNGAM' : LiNGAM,\n", " 'PC' : PC,\n", " 'GES' : GES,\n", "}\n", "\n", "for method, lib in functions.items():\n", " obj = lib()\n", " output = obj.predict(data_sachs)\n", " graphs_nx[method] = output\n", " adj_matrix = nx.to_numpy_array(output)\n", " adj_matrix = np.asarray(adj_matrix)\n", " graph_dot = make_graph(adj_matrix, labels)\n", " graphs[method] = graph_dot\n", "\n", "# Visualize graphs\n", "for method, graph in graphs.items():\n", " print(\"Method : %s\"%(method))\n", " display(graph)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, no two methods agree on the graphs. Next we study the causal effects of these different graphs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimate effects using Linear Regression\n", "\n", "Now let us see whether these differences in the graphs also lead to significant differences in the causal estimate of effect of *PIP2* on *PKC*." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for method, graph in graphs.items():\n", " if method != \"LiNGAM\":\n", " continue\n", " print('\\n*****************************************************************************\\n')\n", " print(\"Causal Discovery Method : %s\"%(method))\n", "\n", " # Obtain valid dot format\n", " graph_dot = str_to_dot(graph.source)\n", "\n", " # Define Causal Model\n", " model=CausalModel(\n", " data = data_sachs,\n", " treatment='PIP2',\n", " outcome='PKC',\n", " graph=graph_dot)\n", "\n", " # Identification\n", " identified_estimand = model.identify_effect(proceed_when_unidentifiable=True)\n", " print(identified_estimand)\n", "\n", " # Estimation\n", " estimate = model.estimate_effect(identified_estimand,\n", " method_name=\"backdoor.linear_regression\",\n", " control_value=0,\n", " treatment_value=1,\n", " confidence_intervals=True,\n", " test_significance=True)\n", " print(\"Causal Estimate is \" + str(estimate.value))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the causal estimates obtained, it can be seen that the three estimates differ in different aspects. The graph obtained using LiNGAM contains a backdoor path and instrumental variables. On the other hand, the graph obtained using PC contains a backdoor path and a frontdoor path. However, despite these differences, both obtain the same mean causal estimate.\n", "\n", "The graph obtained using GES contains only a backdoor path with different backdoor variables and obtains a different causal estimate than the first two cases. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Graph Validation\n", "\n", "We compare the graphs obtained with the true causal graph using the causal discovery methods using 2 graph distance metrics - Structural Hamming Distance (SHD) and Structural Intervention Distance (SID). SHD between two graphs is, in simple terms, the number of edge insertions, deletions or flips in order to transform one graph to another graph. SID, on the other hand, is based on a graphical criterion only and quantifies the closeness between two DAGs in terms of their corresponding causal inference statements." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from cdt.metrics import SHD, SHD_CPDAG, SID, SID_CPDAG\n", "from numpy.random import randint\n", "\n", "for method, graph in graphs_nx.items():\n", " print(\"***********************************************************\")\n", " print(\"Method: %s\"%(method))\n", " tar, pred = graph_sachs, graph\n", " print(\"SHD_CPDAG = %f\"%(SHD_CPDAG(tar, pred)))\n", " print(\"SHD = %f\"%(SHD(tar, pred, double_for_anticausal=False)))\n", " print(\"SID_CPDAG = [%f, %f]\"%(SID_CPDAG(tar, pred)))\n", " print(\"SID = %f\"%(SID(tar, pred)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The graph similarity metrics show that the scores are the lowest for the LiNGAM method of graph extraction. Hence, of the three methods used, LiNGAM provides the graph that is most similar to the original graph." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Graph Refutation\n", "\n", "Here, we use the same SHD and SID metric to find out how different the discovered graph are from each other." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import itertools\n", "from numpy.random import randint\n", "from cdt.metrics import SHD, SHD_CPDAG, SID, SID_CPDAG\n", "\n", "# Find combinations of pair of methods to compare\n", "combinations = list(itertools.combinations(graphs_nx, 2))\n", "\n", "for pair in combinations:\n", " print(\"***********************************************************\")\n", " graph1 = graphs_nx[pair[0]]\n", " graph2 = graphs_nx[pair[1]]\n", " print(\"Methods: %s and %s\"%(pair[0], pair[1]))\n", " print(\"SHD_CPDAG = %f\"%(SHD_CPDAG(graph1, graph2)))\n", " print(\"SHD = %f\"%(SHD(graph1, graph2, double_for_anticausal=False)))\n", " print(\"SID_CPDAG = [%f, %f]\"%(SID_CPDAG(graph1, graph2)))\n", " print(\"SID = %f\"%(SID(graph1, graph2)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The values for the metrics show how different the graphs are from each other. A higher distance value implies that the difference between the graphs is more." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.12" }, "metadata": { "interpreter": { "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" } }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": false, "sideBar": true, "skip_h1_title": true, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery example\n", "\n", "The goal of this notebook is to show how causal discovery methods can work with DoWhy. We use discovery methods from [causal-learn](https://github.com/py-why/causal-learn) repo. As we will see, causal discovery methods require appropriate assumptions for the correctness guarantees, adn thus there will be variance across results returned by different methods in practice. These methods, however, may be combined usefully with domain knowledge to construct the final causal graph." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import dowhy\n", "from dowhy import CausalModel\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import graphviz\n", "import networkx as nx \n", "\n", "np.set_printoptions(precision=3, suppress=True)\n", "np.random.seed(0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Utility function\n", "We define a utility function to draw the directed acyclic graph." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def make_graph(adjacency_matrix, labels=None):\n", " idx = np.abs(adjacency_matrix) > 0.01\n", " dirs = np.where(idx)\n", " d = graphviz.Digraph(engine='dot')\n", " names = labels if labels else [f'x{i}' for i in range(len(adjacency_matrix))]\n", " for name in names:\n", " d.node(name)\n", " for to, from_, coef in zip(dirs[0], dirs[1], adjacency_matrix[idx]):\n", " d.edge(names[from_], names[to], label=str(coef))\n", " return d\n", "\n", "def str_to_dot(string):\n", " '''\n", " Converts input string from graphviz library to valid DOT graph format.\n", " '''\n", " graph = string.strip().replace('\\n', ';').replace('\\t','')\n", " graph = graph[:9] + graph[10:-2] + graph[-1] # Removing unnecessary characters from string\n", " return graph" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Experiments on the Auto-MPG dataset\n", "\n", "In this section, we will use a dataset on the technical specification of cars. The dataset is downloaded from UCI Machine Learning Repository. The dataset contains 9 attributes and 398 instances. We do not know the true causal graph for the dataset and will use causal-learn to discover it. The causal graph obtained will then be used to estimate the causal effect.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Load the data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(392, 6)\n" ] }, { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>mpg</th>\n", " <th>cylinders</th>\n", " <th>displacement</th>\n", " <th>horsepower</th>\n", " <th>weight</th>\n", " <th>acceleration</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>18.0</td>\n", " <td>8.0</td>\n", " <td>307.0</td>\n", " <td>130.0</td>\n", " <td>3504.0</td>\n", " <td>12.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>15.0</td>\n", " <td>8.0</td>\n", " <td>350.0</td>\n", " <td>165.0</td>\n", " <td>3693.0</td>\n", " <td>11.5</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>18.0</td>\n", " <td>8.0</td>\n", " <td>318.0</td>\n", " <td>150.0</td>\n", " <td>3436.0</td>\n", " <td>11.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>16.0</td>\n", " <td>8.0</td>\n", " <td>304.0</td>\n", " <td>150.0</td>\n", " <td>3433.0</td>\n", " <td>12.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>17.0</td>\n", " <td>8.0</td>\n", " <td>302.0</td>\n", " <td>140.0</td>\n", " <td>3449.0</td>\n", " <td>10.5</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " mpg cylinders displacement horsepower weight acceleration\n", "0 18.0 8.0 307.0 130.0 3504.0 12.0\n", "1 15.0 8.0 350.0 165.0 3693.0 11.5\n", "2 18.0 8.0 318.0 150.0 3436.0 11.0\n", "3 16.0 8.0 304.0 150.0 3433.0 12.0\n", "4 17.0 8.0 302.0 140.0 3449.0 10.5" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_mpg = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data-original',\n", " delim_whitespace=True, header=None,\n", " names = ['mpg', 'cylinders', 'displacement',\n", " 'horsepower', 'weight', 'acceleration',\n", " 'model year', 'origin', 'car name'])\n", "data_mpg.dropna(inplace=True)\n", "data_mpg.drop(['model year', 'origin', 'car name'], axis=1, inplace=True)\n", "print(data_mpg.shape)\n", "data_mpg.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery with causal-learn\n", "\n", "We use the causal-learn library to perform causal discovery on the Auto-MPG dataset. We use three methods for causal discovery here: PC, FCI and GES. These methods are widely used and do not take much time to run. Hence, these are ideal for an introduction to the topic. Causal-learn provides a comprehensive list of well-tested causal-discovery methods, and readers are welcome to explore.\n", "\n", "The documentation for the methods used are as follows:\n", "- PC [[link]](https://causal-learn.readthedocs.io/en/latest/search_methods_index/Constraint-based%20causal%20discovery%20methods/PC.html)\n", "- GES [[link]](https://causal-learn.readthedocs.io/en/latest/search_methods_index/Score-based%20causal%20discovery%20methods/GES.html)\n", "- LiNGAM [[link]](https://causal-learn.readthedocs.io/en/latest/search_methods_index/Causal%20discovery%20methods%20based%20on%20constrained%20functional%20causal%20models/lingam.html#ica-based-lingam)\n", "\n", "More methods could be found in the causal-learn documentation [[link]](https://causal-learn.readthedocs.io/en/latest/)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We first try the PC algorithm with default parameters." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1ed197e9f5ec42c8bf7fc51c5ece4485", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/6 [00:00<?, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from causallearn.search.ConstraintBased.PC import pc\n", "\n", "labels = [f'{col}' for i, col in enumerate(data_mpg.columns)]\n", "data = data_mpg.to_numpy()\n", "\n", "cg = pc(data)\n", "\n", "# Visualization using pydot\n", "from causallearn.utils.GraphUtils import GraphUtils\n", "import matplotlib.image as mpimg\n", "import matplotlib.pyplot as plt\n", "import io\n", "\n", "pyd = GraphUtils.to_pydot(cg.G, labels=labels)\n", "tmp_png = pyd.create_png(f=\"png\")\n", "fp = io.BytesIO(tmp_png)\n", "img = mpimg.imread(fp, format='png')\n", "plt.axis('off')\n", "plt.imshow(img)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we have a causal graph discovered by PC. Let us also try GES to see its result." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from causallearn.search.ScoreBased.GES import ges\n", "\n", "# default parameters\n", "Record = ges(data)\n", "\n", "# Visualization using pydot\n", "from causallearn.utils.GraphUtils import GraphUtils\n", "import matplotlib.image as mpimg\n", "import matplotlib.pyplot as plt\n", "import io\n", "\n", "pyd = GraphUtils.to_pydot(Record['G'], labels=labels)\n", "tmp_png = pyd.create_png(f=\"png\")\n", "fp = io.BytesIO(tmp_png)\n", "img = mpimg.imread(fp, format='png')\n", "plt.axis('off')\n", "plt.imshow(img)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Well, these two results are different, which is not rare when applying causal discovery on real-world dataset, since the required assumptions on the data-generating process are hard to verify.\n", "\n", "In addition, the graphs returned by PC and GES are CPDAGs instead of DAGs, so it is possible to have undirected edges (e.g., the result returned by GES). Thus, causal effect estimataion is difficult for those methods, since there may be absence of backdoor, instrumental or frontdoor variables. In order to get a DAG, we decide to try LiNGAM on our dataset." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n", "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n", " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n", "<!-- Generated by graphviz version 2.43.0 (0)\n", " -->\n", "<!-- Title: %3 Pages: 1 -->\n", "<svg width=\"369pt\" height=\"392pt\"\n", " viewBox=\"0.00 0.00 369.40 392.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n", "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 388)\">\n", "<title>%3</title>\n", "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-388 365.4,-388 365.4,4 -4,4\"/>\n", "<!-- mpg -->\n", "<g id=\"node1\" class=\"node\">\n", "<title>mpg</title>\n", "<ellipse fill=\"none\" stroke=\"black\" cx=\"34.8\" cy=\"-279\" rx=\"31.4\" 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"text/plain": [ "<graphviz.graphs.Digraph at 0x7f957464c040>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from causallearn.search.FCMBased import lingam\n", "model = lingam.ICALiNGAM()\n", "model.fit(data)\n", "\n", "from causallearn.search.FCMBased.lingam.utils import make_dot\n", "make_dot(model.adjacency_matrix_, labels=labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we have a DAG and are ready to estimate the causal effects based on that." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimate causal effects using Linear Regression\n", "\n", "Now let us see the estimate of causal effect of *mpg* on *weight*." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Estimand type: EstimandType.NONPARAMETRIC_ATE\n", "\n", "### Estimand : 1\n", "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", "──────(E[weight|cylinders])\n", "d[mpg] \n", "Estimand assumption 1, Unconfoundedness: If U→{mpg} and U→weight then P(weight|mpg,cylinders,U) = P(weight|mpg,cylinders)\n", "\n", "### Estimand : 2\n", "Estimand name: iv\n", "No such variable(s) found!\n", "\n", "### Estimand : 3\n", "Estimand name: frontdoor\n", "No such variable(s) found!\n", "\n", "Causal Estimate is -38.940973656209735\n" ] } ], "source": [ "# Obtain valid dot format\n", "graph_dot = make_graph(model.adjacency_matrix_, labels=labels)\n", "\n", "# Define Causal Model\n", "model=CausalModel(\n", " data = data_mpg,\n", " treatment='mpg',\n", " outcome='weight',\n", " graph=str_to_dot(graph_dot.source))\n", "\n", "# Identification\n", "identified_estimand = model.identify_effect(proceed_when_unidentifiable=True)\n", "print(identified_estimand)\n", "\n", "# Estimation\n", "estimate = model.estimate_effect(identified_estimand,\n", " method_name=\"backdoor.linear_regression\",\n", " control_value=0,\n", " treatment_value=1,\n", " confidence_intervals=True,\n", " test_significance=True)\n", "print(\"Causal Estimate is \" + str(estimate.value))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Experiments on the Sachs dataset\n", "\n", "The dataset consists of the simultaneous measurements of 11 phosphorylated proteins and phospholipids derived from thousands of individual primary immune system cells, subjected to both general and specific molecular interventions (Sachs et al., 2005).\n", "\n", "The specifications of the dataset are as follows - \n", "- Number of nodes: 11\n", "- Number of arcs: 17\n", "- Number of parameters: 178\n", "- Average Markov blanket size: 3.09\n", "- Average degree: 3.09\n", "- Maximum in-degree: 3\n", "- Number of instances: 7466" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Load the data" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(7466, 11)\n", "['raf', 'mek', 'plc', 'pip2', 'pip3', 'erk', 'akt', 'pka', 'pkc', 'p38', 'jnk']\n" ] } ], "source": [ "from causallearn.utils.Dataset import load_dataset\n", "\n", "data_sachs, labels = load_dataset(\"sachs\")\n", "\n", "print(data.shape)\n", "print(labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery with causal-learn\n", "\n", "We use the three causal discovery methods mentioned above (PC, GES, and LiNGAM) to find the causal graphs." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, let us take a look at how PC works." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "bc0f31d1492e4934994a6d4ba68f1ad3", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/11 [00:00<?, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "graphs = {}\n", "graphs_nx = {}\n", "labels = [f'{col}' for i, col in enumerate(labels)]\n", "data = data_sachs\n", "\n", "from causallearn.search.ConstraintBased.PC import pc\n", "\n", "cg = pc(data)\n", "\n", "# Visualization using pydot\n", "from causallearn.utils.GraphUtils import GraphUtils\n", "import matplotlib.image as mpimg\n", "import matplotlib.pyplot as plt\n", "import io\n", "\n", "pyd = GraphUtils.to_pydot(cg.G, labels=labels)\n", "tmp_png = pyd.create_png(f=\"png\")\n", "fp = io.BytesIO(tmp_png)\n", "img = mpimg.imread(fp, format='png')\n", "plt.axis('off')\n", "plt.imshow(img)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then, let us try GES." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from causallearn.search.ScoreBased.GES import ges\n", "\n", "# default parameters\n", "Record = ges(data)\n", "\n", "# Visualization using pydot\n", "from causallearn.utils.GraphUtils import GraphUtils\n", "import matplotlib.image as mpimg\n", "import matplotlib.pyplot as plt\n", "import io\n", "\n", "pyd = GraphUtils.to_pydot(Record['G'], labels=labels)\n", "tmp_png = pyd.create_png(f=\"png\")\n", "fp = io.BytesIO(tmp_png)\n", "img = mpimg.imread(fp, format='png')\n", "plt.axis('off')\n", "plt.imshow(img)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And also LiNGAM." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n", "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n", " 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points=\"674.5,-46.18 671,-36.18 667.5,-46.18 674.5,-46.18\"/>\n", "<text text-anchor=\"middle\" x=\"687\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">0.04</text>\n", "</g>\n", "</g>\n", "</svg>\n" ], "text/plain": [ "<graphviz.graphs.Digraph at 0x7f96cd974ca0>" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from causallearn.search.FCMBased import lingam\n", "model = lingam.ICALiNGAM()\n", "model.fit(data)\n", "\n", "from causallearn.search.FCMBased.lingam.utils import make_dot\n", "make_dot(model.adjacency_matrix_, labels=labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimate effects using Linear Regression\n", "\n", "Similarly, let us use the DAG returned by LiNGAM to estimate the causal effect of *PIP2* on *PKC*." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Estimand type: EstimandType.NONPARAMETRIC_ATE\n", "\n", "### Estimand : 1\n", "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", "───────(E[pkc|plc,pip3])\n", "d[pip₂] \n", "Estimand assumption 1, Unconfoundedness: If U→{pip2} and U→pkc then P(pkc|pip2,plc,pip3,U) = P(pkc|pip2,plc,pip3)\n", "\n", "### Estimand : 2\n", "Estimand name: iv\n", "No such variable(s) found!\n", "\n", "### Estimand : 3\n", "Estimand name: frontdoor\n", "No such variable(s) found!\n", "\n", "Causal Estimate is 0.03397189228452291\n" ] } ], "source": [ "# Obtain valid dot format\n", "graph_dot = make_graph(model.adjacency_matrix_, labels=labels)\n", "\n", "data_df = pd.DataFrame(data=data, columns=labels)\n", "\n", "# Define Causal Model\n", "model_est=CausalModel(\n", " data = data_df,\n", " treatment='pip2',\n", " outcome='pkc',\n", " graph=str_to_dot(graph_dot.source))\n", "\n", "# Identification\n", "identified_estimand = model_est.identify_effect(proceed_when_unidentifiable=False)\n", "print(identified_estimand)\n", "\n", "# Estimation\n", "estimate = model_est.estimate_effect(identified_estimand,\n", " method_name=\"backdoor.linear_regression\",\n", " control_value=0,\n", " treatment_value=1,\n", " confidence_intervals=True,\n", " test_significance=True)\n", "print(\"Causal Estimate is \" + str(estimate.value))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.17" }, "metadata": { "interpreter": { "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" } }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": false, "sideBar": true, "skip_h1_title": true, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }

kunwuz

67b305db5224bf718067a21acfe4baa92a7d2c8c

7eb4a0c253514a920588d1ab222e1aeb5e07cb51

Oh thanks for reminding this!

kunwuz

py-why/dowhy

Update the causal discovery notebook with examples using causal-learn

Updating the old notebook as mentioned in #1021.

2023-08-30 21:25:09+00:00

2023-10-05 21:26:19+00:00

docs/source/example_notebooks/dowhy_causal_discovery_example.ipynb

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery example\n", "\n", "The goal of this notebook is to show how causal discovery methods can work with DoWhy. We use discovery methods from [Causal Discovery Tool (CDT)](https://github.com/FenTechSolutions/CausalDiscoveryToolbox) repo. As we will see, causal discovery methods are not fool-proof and there is no guarantee that they will recover the correct causal graph. Even for the simple examples below, there is a large variance in results. These methods, however, may be combined usefully with domain knowledge to construct the final causal graph." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import dowhy\n", "from dowhy import CausalModel\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import graphviz\n", "import networkx as nx \n", "\n", "np.set_printoptions(precision=3, suppress=True)\n", "np.random.seed(0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Utility function\n", "We define a utility function to draw the directed acyclic graph." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def make_graph(adjacency_matrix, labels=None):\n", " idx = np.abs(adjacency_matrix) > 0.01\n", " dirs = np.where(idx)\n", " d = graphviz.Digraph(engine='dot')\n", " names = labels if labels else [f'x{i}' for i in range(len(adjacency_matrix))]\n", " for name in names:\n", " d.node(name)\n", " for to, from_, coef in zip(dirs[0], dirs[1], adjacency_matrix[idx]):\n", " d.edge(names[from_], names[to], label=str(coef))\n", " return d\n", "\n", "def str_to_dot(string):\n", " '''\n", " Converts input string from graphviz library to valid DOT graph format.\n", " '''\n", " graph = string.strip().replace('\\n', ';').replace('\\t','')\n", " graph = graph[:9] + graph[10:-2] + graph[-1] # Removing unnecessary characters from string\n", " return graph" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Experiments on the Auto-MPG dataset\n", "\n", "In this section, we will use a dataset on the technical specification of cars. The dataset is downloaded from UCI Machine Learning Repository. The dataset contains 9 attributes and 398 instances. We do not know the true causal graph for the dataset and will use CDT to discover it. The causal graph obtained will then be used to estimate the causal effect.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Load the data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data_mpg = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data-original',\n", " delim_whitespace=True, header=None,\n", " names = ['mpg', 'cylinders', 'displacement',\n", " 'horsepower', 'weight', 'acceleration',\n", " 'model year', 'origin', 'car name'])\n", "data_mpg.dropna(inplace=True)\n", "data_mpg.drop(['model year', 'origin', 'car name'], axis=1, inplace=True)\n", "print(data_mpg.shape)\n", "data_mpg.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery with Causal Discovery Tool (CDT)\n", "\n", "We use the CDT library to perform causal discovery on the Auto-MPG dataset. We use three methods for causal discovery here -LiNGAM, PC and GES. These methods are widely used and do not take much time to run. Hence, these are ideal for an introduction to the topic. Other neural network based methods are also available in CDT and the users are encouraged to try them out by themselves. \n", "\n", "The documentation for the methods used are as follows:\n", "- LiNGAM [[link]](https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/_modules/cdt/causality/graph/LiNGAM.html)\n", "- PC [[link]](https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/_modules/cdt/causality/graph/PC.html)\n", "- GES [[link]](https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/_modules/cdt/causality/graph/GES.html)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from cdt.causality.graph import LiNGAM, PC, GES\n", "\n", "graphs = {}\n", "labels = [f'{col}' for i, col in enumerate(data_mpg.columns)]\n", "functions = {\n", " 'LiNGAM' : LiNGAM,\n", " 'PC' : PC,\n", " 'GES' : GES,\n", "}\n", "\n", "for method, lib in functions.items():\n", " obj = lib()\n", " output = obj.predict(data_mpg)\n", " adj_matrix = nx.to_numpy_array(output)\n", " adj_matrix = np.asarray(adj_matrix)\n", " graph_dot = make_graph(adj_matrix, labels)\n", " graphs[method] = graph_dot\n", "\n", "# Visualize graphs\n", "for method, graph in graphs.items():\n", " print(\"Method : %s\"%(method))\n", " display(graph)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, no two methods agree on the graphs. PC and GES effectively produce an undirected graph whereas LiNGAM produces a directed graph. We use only the LiNGAM method in the next section." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimate causal effects using Linear Regression\n", "\n", "Now let us see whether these differences in the graphs also lead to significant differences in the causal estimate of effect of *mpg* on *weight*." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for method, graph in graphs.items():\n", " if method != \"LiNGAM\":\n", " continue\n", " print('\\n*****************************************************************************\\n')\n", " print(\"Causal Discovery Method : %s\"%(method))\n", " \n", " # Obtain valid dot format\n", " graph_dot = str_to_dot(graph.source)\n", "\n", " # Define Causal Model\n", " model=CausalModel(\n", " data = data_mpg,\n", " treatment='mpg',\n", " outcome='weight',\n", " graph=graph_dot)\n", "\n", " # Identification\n", " identified_estimand = model.identify_effect(proceed_when_unidentifiable=True)\n", " print(identified_estimand)\n", " \n", " # Estimation\n", " estimate = model.estimate_effect(identified_estimand,\n", " method_name=\"backdoor.linear_regression\",\n", " control_value=0,\n", " treatment_value=1,\n", " confidence_intervals=True,\n", " test_significance=True)\n", " print(\"Causal Estimate is \" + str(estimate.value))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As mentioned earlier, due to the absence of directed edges, no backdoor, instrmental or frontdoor variables can be found out for PC and GES. Thus, causal effect estimation is not possible for these methods. However, LiNGAM does discover a DAG and hence, its possible to output a causal estimate for LiNGAM. The estimate is still pretty far from the original estimate of -70.466 (which can be calculated from the graph)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Experiments on the Sachs dataset\n", "\n", "The dataset consists of the simultaneous measurements of 11 phosphorylated proteins and phospholipids derived from thousands of individual primary immune system cells, subjected to both general and specific molecular interventions (Sachs et al., 2005).\n", "\n", "The specifications of the dataset are as follows - \n", "- Number of nodes: 11\n", "- Number of arcs: 17\n", "- Number of parameters: 178\n", "- Average Markov blanket size: 3.09\n", "- Average degree: 3.09\n", "- Maximum in-degree: 3\n", "- Number of instances: 7466\n", "\n", "The original causal graph is known for the Sachs dataset and we compare the original graph with the ones discovered using CDT in this section." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Load the data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from cdt.data import load_dataset\n", "data_sachs, graph_sachs = load_dataset(\"sachs\")\n", "\n", "data_sachs.dropna(inplace=True)\n", "print(data_sachs.shape)\n", "data_sachs.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Ground truth of the causal graph" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "labels = [f'{col}' for i, col in enumerate(data_sachs.columns)]\n", "adj_matrix = nx.to_numpy_array(graph_sachs)\n", "adj_matrix = np.asarray(adj_matrix)\n", "graph_dot = make_graph(adj_matrix, labels)\n", "display(graph_dot)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery with Causal Discovery Tool (CDT)\n", "\n", "We use the CDT library to perform causal discovery on the Auto-MPG dataset. We use three methods for causal discovery here -LiNGAM, PC and GES. These methods are widely used and do not take much time to run. Hence, these are ideal for an introduction to the topic. Other neural network based methods are also available in CDT and the users the encourages to try them out by themselves. \n", "\n", "The documentation for the methods used in as follows:\n", "- LiNGAM [[link]](https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/_modules/cdt/causality/graph/LiNGAM.html)\n", "- PC [[link]](https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/_modules/cdt/causality/graph/PC.html)\n", "- GES [[link]](https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/_modules/cdt/causality/graph/GES.html)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from cdt.causality.graph import LiNGAM, PC, GES\n", "\n", "graphs = {}\n", "graphs_nx = {}\n", "labels = [f'{col}' for i, col in enumerate(data_sachs.columns)]\n", "functions = {\n", " 'LiNGAM' : LiNGAM,\n", " 'PC' : PC,\n", " 'GES' : GES,\n", "}\n", "\n", "for method, lib in functions.items():\n", " obj = lib()\n", " output = obj.predict(data_sachs)\n", " graphs_nx[method] = output\n", " adj_matrix = nx.to_numpy_array(output)\n", " adj_matrix = np.asarray(adj_matrix)\n", " graph_dot = make_graph(adj_matrix, labels)\n", " graphs[method] = graph_dot\n", "\n", "# Visualize graphs\n", "for method, graph in graphs.items():\n", " print(\"Method : %s\"%(method))\n", " display(graph)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, no two methods agree on the graphs. Next we study the causal effects of these different graphs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimate effects using Linear Regression\n", "\n", "Now let us see whether these differences in the graphs also lead to significant differences in the causal estimate of effect of *PIP2* on *PKC*." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for method, graph in graphs.items():\n", " if method != \"LiNGAM\":\n", " continue\n", " print('\\n*****************************************************************************\\n')\n", " print(\"Causal Discovery Method : %s\"%(method))\n", "\n", " # Obtain valid dot format\n", " graph_dot = str_to_dot(graph.source)\n", "\n", " # Define Causal Model\n", " model=CausalModel(\n", " data = data_sachs,\n", " treatment='PIP2',\n", " outcome='PKC',\n", " graph=graph_dot)\n", "\n", " # Identification\n", " identified_estimand = model.identify_effect(proceed_when_unidentifiable=True)\n", " print(identified_estimand)\n", "\n", " # Estimation\n", " estimate = model.estimate_effect(identified_estimand,\n", " method_name=\"backdoor.linear_regression\",\n", " control_value=0,\n", " treatment_value=1,\n", " confidence_intervals=True,\n", " test_significance=True)\n", " print(\"Causal Estimate is \" + str(estimate.value))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the causal estimates obtained, it can be seen that the three estimates differ in different aspects. The graph obtained using LiNGAM contains a backdoor path and instrumental variables. On the other hand, the graph obtained using PC contains a backdoor path and a frontdoor path. However, despite these differences, both obtain the same mean causal estimate.\n", "\n", "The graph obtained using GES contains only a backdoor path with different backdoor variables and obtains a different causal estimate than the first two cases. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Graph Validation\n", "\n", "We compare the graphs obtained with the true causal graph using the causal discovery methods using 2 graph distance metrics - Structural Hamming Distance (SHD) and Structural Intervention Distance (SID). SHD between two graphs is, in simple terms, the number of edge insertions, deletions or flips in order to transform one graph to another graph. SID, on the other hand, is based on a graphical criterion only and quantifies the closeness between two DAGs in terms of their corresponding causal inference statements." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from cdt.metrics import SHD, SHD_CPDAG, SID, SID_CPDAG\n", "from numpy.random import randint\n", "\n", "for method, graph in graphs_nx.items():\n", " print(\"***********************************************************\")\n", " print(\"Method: %s\"%(method))\n", " tar, pred = graph_sachs, graph\n", " print(\"SHD_CPDAG = %f\"%(SHD_CPDAG(tar, pred)))\n", " print(\"SHD = %f\"%(SHD(tar, pred, double_for_anticausal=False)))\n", " print(\"SID_CPDAG = [%f, %f]\"%(SID_CPDAG(tar, pred)))\n", " print(\"SID = %f\"%(SID(tar, pred)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The graph similarity metrics show that the scores are the lowest for the LiNGAM method of graph extraction. Hence, of the three methods used, LiNGAM provides the graph that is most similar to the original graph." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Graph Refutation\n", "\n", "Here, we use the same SHD and SID metric to find out how different the discovered graph are from each other." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import itertools\n", "from numpy.random import randint\n", "from cdt.metrics import SHD, SHD_CPDAG, SID, SID_CPDAG\n", "\n", "# Find combinations of pair of methods to compare\n", "combinations = list(itertools.combinations(graphs_nx, 2))\n", "\n", "for pair in combinations:\n", " print(\"***********************************************************\")\n", " graph1 = graphs_nx[pair[0]]\n", " graph2 = graphs_nx[pair[1]]\n", " print(\"Methods: %s and %s\"%(pair[0], pair[1]))\n", " print(\"SHD_CPDAG = %f\"%(SHD_CPDAG(graph1, graph2)))\n", " print(\"SHD = %f\"%(SHD(graph1, graph2, double_for_anticausal=False)))\n", " print(\"SID_CPDAG = [%f, %f]\"%(SID_CPDAG(graph1, graph2)))\n", " print(\"SID = %f\"%(SID(graph1, graph2)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The values for the metrics show how different the graphs are from each other. A higher distance value implies that the difference between the graphs is more." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.12" }, "metadata": { "interpreter": { "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" } }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": false, "sideBar": true, "skip_h1_title": true, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery example\n", "\n", "The goal of this notebook is to show how causal discovery methods can work with DoWhy. We use discovery methods from [causal-learn](https://github.com/py-why/causal-learn) repo. As we will see, causal discovery methods require appropriate assumptions for the correctness guarantees, adn thus there will be variance across results returned by different methods in practice. These methods, however, may be combined usefully with domain knowledge to construct the final causal graph." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import dowhy\n", "from dowhy import CausalModel\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import graphviz\n", "import networkx as nx \n", "\n", "np.set_printoptions(precision=3, suppress=True)\n", "np.random.seed(0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Utility function\n", "We define a utility function to draw the directed acyclic graph." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def make_graph(adjacency_matrix, labels=None):\n", " idx = np.abs(adjacency_matrix) > 0.01\n", " dirs = np.where(idx)\n", " d = graphviz.Digraph(engine='dot')\n", " names = labels if labels else [f'x{i}' for i in range(len(adjacency_matrix))]\n", " for name in names:\n", " d.node(name)\n", " for to, from_, coef in zip(dirs[0], dirs[1], adjacency_matrix[idx]):\n", " d.edge(names[from_], names[to], label=str(coef))\n", " return d\n", "\n", "def str_to_dot(string):\n", " '''\n", " Converts input string from graphviz library to valid DOT graph format.\n", " '''\n", " graph = string.strip().replace('\\n', ';').replace('\\t','')\n", " graph = graph[:9] + graph[10:-2] + graph[-1] # Removing unnecessary characters from string\n", " return graph" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Experiments on the Auto-MPG dataset\n", "\n", "In this section, we will use a dataset on the technical specification of cars. The dataset is downloaded from UCI Machine Learning Repository. The dataset contains 9 attributes and 398 instances. We do not know the true causal graph for the dataset and will use causal-learn to discover it. The causal graph obtained will then be used to estimate the causal effect.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Load the data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(392, 6)\n" ] }, { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>mpg</th>\n", " <th>cylinders</th>\n", " <th>displacement</th>\n", " <th>horsepower</th>\n", " <th>weight</th>\n", " <th>acceleration</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>18.0</td>\n", " <td>8.0</td>\n", " <td>307.0</td>\n", " <td>130.0</td>\n", " <td>3504.0</td>\n", " <td>12.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>15.0</td>\n", " <td>8.0</td>\n", " <td>350.0</td>\n", " <td>165.0</td>\n", " <td>3693.0</td>\n", " <td>11.5</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>18.0</td>\n", " <td>8.0</td>\n", " <td>318.0</td>\n", " <td>150.0</td>\n", " <td>3436.0</td>\n", " <td>11.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>16.0</td>\n", " <td>8.0</td>\n", " <td>304.0</td>\n", " <td>150.0</td>\n", " <td>3433.0</td>\n", " <td>12.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>17.0</td>\n", " <td>8.0</td>\n", " <td>302.0</td>\n", " <td>140.0</td>\n", " <td>3449.0</td>\n", " <td>10.5</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " mpg cylinders displacement horsepower weight acceleration\n", "0 18.0 8.0 307.0 130.0 3504.0 12.0\n", "1 15.0 8.0 350.0 165.0 3693.0 11.5\n", "2 18.0 8.0 318.0 150.0 3436.0 11.0\n", "3 16.0 8.0 304.0 150.0 3433.0 12.0\n", "4 17.0 8.0 302.0 140.0 3449.0 10.5" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_mpg = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data-original',\n", " delim_whitespace=True, header=None,\n", " names = ['mpg', 'cylinders', 'displacement',\n", " 'horsepower', 'weight', 'acceleration',\n", " 'model year', 'origin', 'car name'])\n", "data_mpg.dropna(inplace=True)\n", "data_mpg.drop(['model year', 'origin', 'car name'], axis=1, inplace=True)\n", "print(data_mpg.shape)\n", "data_mpg.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery with causal-learn\n", "\n", "We use the causal-learn library to perform causal discovery on the Auto-MPG dataset. We use three methods for causal discovery here: PC, FCI and GES. These methods are widely used and do not take much time to run. Hence, these are ideal for an introduction to the topic. Causal-learn provides a comprehensive list of well-tested causal-discovery methods, and readers are welcome to explore.\n", "\n", "The documentation for the methods used are as follows:\n", "- PC [[link]](https://causal-learn.readthedocs.io/en/latest/search_methods_index/Constraint-based%20causal%20discovery%20methods/PC.html)\n", "- GES [[link]](https://causal-learn.readthedocs.io/en/latest/search_methods_index/Score-based%20causal%20discovery%20methods/GES.html)\n", "- LiNGAM [[link]](https://causal-learn.readthedocs.io/en/latest/search_methods_index/Causal%20discovery%20methods%20based%20on%20constrained%20functional%20causal%20models/lingam.html#ica-based-lingam)\n", "\n", "More methods could be found in the causal-learn documentation [[link]](https://causal-learn.readthedocs.io/en/latest/)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We first try the PC algorithm with default parameters." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1ed197e9f5ec42c8bf7fc51c5ece4485", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/6 [00:00<?, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from causallearn.search.ConstraintBased.PC import pc\n", "\n", "labels = [f'{col}' for i, col in enumerate(data_mpg.columns)]\n", "data = data_mpg.to_numpy()\n", "\n", "cg = pc(data)\n", "\n", "# Visualization using pydot\n", "from causallearn.utils.GraphUtils import GraphUtils\n", "import matplotlib.image as mpimg\n", "import matplotlib.pyplot as plt\n", "import io\n", "\n", "pyd = GraphUtils.to_pydot(cg.G, labels=labels)\n", "tmp_png = pyd.create_png(f=\"png\")\n", "fp = io.BytesIO(tmp_png)\n", "img = mpimg.imread(fp, format='png')\n", "plt.axis('off')\n", "plt.imshow(img)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we have a causal graph discovered by PC. Let us also try GES to see its result." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from causallearn.search.ScoreBased.GES import ges\n", "\n", "# default parameters\n", "Record = ges(data)\n", "\n", "# Visualization using pydot\n", "from causallearn.utils.GraphUtils import GraphUtils\n", "import matplotlib.image as mpimg\n", "import matplotlib.pyplot as plt\n", "import io\n", "\n", "pyd = GraphUtils.to_pydot(Record['G'], labels=labels)\n", "tmp_png = pyd.create_png(f=\"png\")\n", "fp = io.BytesIO(tmp_png)\n", "img = mpimg.imread(fp, format='png')\n", "plt.axis('off')\n", "plt.imshow(img)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Well, these two results are different, which is not rare when applying causal discovery on real-world dataset, since the required assumptions on the data-generating process are hard to verify.\n", "\n", "In addition, the graphs returned by PC and GES are CPDAGs instead of DAGs, so it is possible to have undirected edges (e.g., the result returned by GES). Thus, causal effect estimataion is difficult for those methods, since there may be absence of backdoor, instrumental or frontdoor variables. In order to get a DAG, we decide to try LiNGAM on our dataset." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n", "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n", " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n", "<!-- Generated by graphviz version 2.43.0 (0)\n", " -->\n", "<!-- Title: %3 Pages: 1 -->\n", "<svg width=\"369pt\" height=\"392pt\"\n", " viewBox=\"0.00 0.00 369.40 392.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n", "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 388)\">\n", "<title>%3</title>\n", "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-388 365.4,-388 365.4,4 -4,4\"/>\n", "<!-- mpg -->\n", "<g id=\"node1\" class=\"node\">\n", "<title>mpg</title>\n", "<ellipse fill=\"none\" stroke=\"black\" cx=\"34.8\" cy=\"-279\" rx=\"31.4\" 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"text/plain": [ "<graphviz.graphs.Digraph at 0x7f957464c040>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from causallearn.search.FCMBased import lingam\n", "model = lingam.ICALiNGAM()\n", "model.fit(data)\n", "\n", "from causallearn.search.FCMBased.lingam.utils import make_dot\n", "make_dot(model.adjacency_matrix_, labels=labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we have a DAG and are ready to estimate the causal effects based on that." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimate causal effects using Linear Regression\n", "\n", "Now let us see the estimate of causal effect of *mpg* on *weight*." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Estimand type: EstimandType.NONPARAMETRIC_ATE\n", "\n", "### Estimand : 1\n", "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", "──────(E[weight|cylinders])\n", "d[mpg] \n", "Estimand assumption 1, Unconfoundedness: If U→{mpg} and U→weight then P(weight|mpg,cylinders,U) = P(weight|mpg,cylinders)\n", "\n", "### Estimand : 2\n", "Estimand name: iv\n", "No such variable(s) found!\n", "\n", "### Estimand : 3\n", "Estimand name: frontdoor\n", "No such variable(s) found!\n", "\n", "Causal Estimate is -38.940973656209735\n" ] } ], "source": [ "# Obtain valid dot format\n", "graph_dot = make_graph(model.adjacency_matrix_, labels=labels)\n", "\n", "# Define Causal Model\n", "model=CausalModel(\n", " data = data_mpg,\n", " treatment='mpg',\n", " outcome='weight',\n", " graph=str_to_dot(graph_dot.source))\n", "\n", "# Identification\n", "identified_estimand = model.identify_effect(proceed_when_unidentifiable=True)\n", "print(identified_estimand)\n", "\n", "# Estimation\n", "estimate = model.estimate_effect(identified_estimand,\n", " method_name=\"backdoor.linear_regression\",\n", " control_value=0,\n", " treatment_value=1,\n", " confidence_intervals=True,\n", " test_significance=True)\n", "print(\"Causal Estimate is \" + str(estimate.value))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Experiments on the Sachs dataset\n", "\n", "The dataset consists of the simultaneous measurements of 11 phosphorylated proteins and phospholipids derived from thousands of individual primary immune system cells, subjected to both general and specific molecular interventions (Sachs et al., 2005).\n", "\n", "The specifications of the dataset are as follows - \n", "- Number of nodes: 11\n", "- Number of arcs: 17\n", "- Number of parameters: 178\n", "- Average Markov blanket size: 3.09\n", "- Average degree: 3.09\n", "- Maximum in-degree: 3\n", "- Number of instances: 7466" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Load the data" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(7466, 11)\n", "['raf', 'mek', 'plc', 'pip2', 'pip3', 'erk', 'akt', 'pka', 'pkc', 'p38', 'jnk']\n" ] } ], "source": [ "from causallearn.utils.Dataset import load_dataset\n", "\n", "data_sachs, labels = load_dataset(\"sachs\")\n", "\n", "print(data.shape)\n", "print(labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery with causal-learn\n", "\n", "We use the three causal discovery methods mentioned above (PC, GES, and LiNGAM) to find the causal graphs." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, let us take a look at how PC works." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "bc0f31d1492e4934994a6d4ba68f1ad3", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/11 [00:00<?, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "graphs = {}\n", "graphs_nx = {}\n", "labels = [f'{col}' for i, col in enumerate(labels)]\n", "data = data_sachs\n", "\n", "from causallearn.search.ConstraintBased.PC import pc\n", "\n", "cg = pc(data)\n", "\n", "# Visualization using pydot\n", "from causallearn.utils.GraphUtils import GraphUtils\n", "import matplotlib.image as mpimg\n", "import matplotlib.pyplot as plt\n", "import io\n", "\n", "pyd = GraphUtils.to_pydot(cg.G, labels=labels)\n", "tmp_png = pyd.create_png(f=\"png\")\n", "fp = io.BytesIO(tmp_png)\n", "img = mpimg.imread(fp, format='png')\n", "plt.axis('off')\n", "plt.imshow(img)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then, let us try GES." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from causallearn.search.ScoreBased.GES import ges\n", "\n", "# default parameters\n", "Record = ges(data)\n", "\n", "# Visualization using pydot\n", "from causallearn.utils.GraphUtils import GraphUtils\n", "import matplotlib.image as mpimg\n", "import matplotlib.pyplot as plt\n", "import io\n", "\n", "pyd = GraphUtils.to_pydot(Record['G'], labels=labels)\n", "tmp_png = pyd.create_png(f=\"png\")\n", "fp = io.BytesIO(tmp_png)\n", "img = mpimg.imread(fp, format='png')\n", "plt.axis('off')\n", "plt.imshow(img)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And also LiNGAM." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n", "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n", " 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points=\"674.5,-46.18 671,-36.18 667.5,-46.18 674.5,-46.18\"/>\n", "<text text-anchor=\"middle\" x=\"687\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">0.04</text>\n", "</g>\n", "</g>\n", "</svg>\n" ], "text/plain": [ "<graphviz.graphs.Digraph at 0x7f96cd974ca0>" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from causallearn.search.FCMBased import lingam\n", "model = lingam.ICALiNGAM()\n", "model.fit(data)\n", "\n", "from causallearn.search.FCMBased.lingam.utils import make_dot\n", "make_dot(model.adjacency_matrix_, labels=labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimate effects using Linear Regression\n", "\n", "Similarly, let us use the DAG returned by LiNGAM to estimate the causal effect of *PIP2* on *PKC*." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Estimand type: EstimandType.NONPARAMETRIC_ATE\n", "\n", "### Estimand : 1\n", "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", "───────(E[pkc|plc,pip3])\n", "d[pip₂] \n", "Estimand assumption 1, Unconfoundedness: If U→{pip2} and U→pkc then P(pkc|pip2,plc,pip3,U) = P(pkc|pip2,plc,pip3)\n", "\n", "### Estimand : 2\n", "Estimand name: iv\n", "No such variable(s) found!\n", "\n", "### Estimand : 3\n", "Estimand name: frontdoor\n", "No such variable(s) found!\n", "\n", "Causal Estimate is 0.03397189228452291\n" ] } ], "source": [ "# Obtain valid dot format\n", "graph_dot = make_graph(model.adjacency_matrix_, labels=labels)\n", "\n", "data_df = pd.DataFrame(data=data, columns=labels)\n", "\n", "# Define Causal Model\n", "model_est=CausalModel(\n", " data = data_df,\n", " treatment='pip2',\n", " outcome='pkc',\n", " graph=str_to_dot(graph_dot.source))\n", "\n", "# Identification\n", "identified_estimand = model_est.identify_effect(proceed_when_unidentifiable=False)\n", "print(identified_estimand)\n", "\n", "# Estimation\n", "estimate = model_est.estimate_effect(identified_estimand,\n", " method_name=\"backdoor.linear_regression\",\n", " control_value=0,\n", " treatment_value=1,\n", " confidence_intervals=True,\n", " test_significance=True)\n", "print(\"Causal Estimate is \" + str(estimate.value))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.17" }, "metadata": { "interpreter": { "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" } }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": false, "sideBar": true, "skip_h1_title": true, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }

kunwuz

67b305db5224bf718067a21acfe4baa92a7d2c8c

7eb4a0c253514a920588d1ab222e1aeb5e07cb51

Sure, that's a great idea.

kunwuz

py-why/dowhy

Update the causal discovery notebook with examples using causal-learn

Updating the old notebook as mentioned in #1021.

2023-08-30 21:25:09+00:00

2023-10-05 21:26:19+00:00

docs/source/example_notebooks/dowhy_causal_discovery_example.ipynb

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery example\n", "\n", "The goal of this notebook is to show how causal discovery methods can work with DoWhy. We use discovery methods from [Causal Discovery Tool (CDT)](https://github.com/FenTechSolutions/CausalDiscoveryToolbox) repo. As we will see, causal discovery methods are not fool-proof and there is no guarantee that they will recover the correct causal graph. Even for the simple examples below, there is a large variance in results. These methods, however, may be combined usefully with domain knowledge to construct the final causal graph." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import dowhy\n", "from dowhy import CausalModel\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import graphviz\n", "import networkx as nx \n", "\n", "np.set_printoptions(precision=3, suppress=True)\n", "np.random.seed(0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Utility function\n", "We define a utility function to draw the directed acyclic graph." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def make_graph(adjacency_matrix, labels=None):\n", " idx = np.abs(adjacency_matrix) > 0.01\n", " dirs = np.where(idx)\n", " d = graphviz.Digraph(engine='dot')\n", " names = labels if labels else [f'x{i}' for i in range(len(adjacency_matrix))]\n", " for name in names:\n", " d.node(name)\n", " for to, from_, coef in zip(dirs[0], dirs[1], adjacency_matrix[idx]):\n", " d.edge(names[from_], names[to], label=str(coef))\n", " return d\n", "\n", "def str_to_dot(string):\n", " '''\n", " Converts input string from graphviz library to valid DOT graph format.\n", " '''\n", " graph = string.strip().replace('\\n', ';').replace('\\t','')\n", " graph = graph[:9] + graph[10:-2] + graph[-1] # Removing unnecessary characters from string\n", " return graph" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Experiments on the Auto-MPG dataset\n", "\n", "In this section, we will use a dataset on the technical specification of cars. The dataset is downloaded from UCI Machine Learning Repository. The dataset contains 9 attributes and 398 instances. We do not know the true causal graph for the dataset and will use CDT to discover it. The causal graph obtained will then be used to estimate the causal effect.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Load the data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data_mpg = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data-original',\n", " delim_whitespace=True, header=None,\n", " names = ['mpg', 'cylinders', 'displacement',\n", " 'horsepower', 'weight', 'acceleration',\n", " 'model year', 'origin', 'car name'])\n", "data_mpg.dropna(inplace=True)\n", "data_mpg.drop(['model year', 'origin', 'car name'], axis=1, inplace=True)\n", "print(data_mpg.shape)\n", "data_mpg.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery with Causal Discovery Tool (CDT)\n", "\n", "We use the CDT library to perform causal discovery on the Auto-MPG dataset. We use three methods for causal discovery here -LiNGAM, PC and GES. These methods are widely used and do not take much time to run. Hence, these are ideal for an introduction to the topic. Other neural network based methods are also available in CDT and the users are encouraged to try them out by themselves. \n", "\n", "The documentation for the methods used are as follows:\n", "- LiNGAM [[link]](https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/_modules/cdt/causality/graph/LiNGAM.html)\n", "- PC [[link]](https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/_modules/cdt/causality/graph/PC.html)\n", "- GES [[link]](https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/_modules/cdt/causality/graph/GES.html)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from cdt.causality.graph import LiNGAM, PC, GES\n", "\n", "graphs = {}\n", "labels = [f'{col}' for i, col in enumerate(data_mpg.columns)]\n", "functions = {\n", " 'LiNGAM' : LiNGAM,\n", " 'PC' : PC,\n", " 'GES' : GES,\n", "}\n", "\n", "for method, lib in functions.items():\n", " obj = lib()\n", " output = obj.predict(data_mpg)\n", " adj_matrix = nx.to_numpy_array(output)\n", " adj_matrix = np.asarray(adj_matrix)\n", " graph_dot = make_graph(adj_matrix, labels)\n", " graphs[method] = graph_dot\n", "\n", "# Visualize graphs\n", "for method, graph in graphs.items():\n", " print(\"Method : %s\"%(method))\n", " display(graph)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, no two methods agree on the graphs. PC and GES effectively produce an undirected graph whereas LiNGAM produces a directed graph. We use only the LiNGAM method in the next section." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimate causal effects using Linear Regression\n", "\n", "Now let us see whether these differences in the graphs also lead to significant differences in the causal estimate of effect of *mpg* on *weight*." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for method, graph in graphs.items():\n", " if method != \"LiNGAM\":\n", " continue\n", " print('\\n*****************************************************************************\\n')\n", " print(\"Causal Discovery Method : %s\"%(method))\n", " \n", " # Obtain valid dot format\n", " graph_dot = str_to_dot(graph.source)\n", "\n", " # Define Causal Model\n", " model=CausalModel(\n", " data = data_mpg,\n", " treatment='mpg',\n", " outcome='weight',\n", " graph=graph_dot)\n", "\n", " # Identification\n", " identified_estimand = model.identify_effect(proceed_when_unidentifiable=True)\n", " print(identified_estimand)\n", " \n", " # Estimation\n", " estimate = model.estimate_effect(identified_estimand,\n", " method_name=\"backdoor.linear_regression\",\n", " control_value=0,\n", " treatment_value=1,\n", " confidence_intervals=True,\n", " test_significance=True)\n", " print(\"Causal Estimate is \" + str(estimate.value))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As mentioned earlier, due to the absence of directed edges, no backdoor, instrmental or frontdoor variables can be found out for PC and GES. Thus, causal effect estimation is not possible for these methods. However, LiNGAM does discover a DAG and hence, its possible to output a causal estimate for LiNGAM. The estimate is still pretty far from the original estimate of -70.466 (which can be calculated from the graph)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Experiments on the Sachs dataset\n", "\n", "The dataset consists of the simultaneous measurements of 11 phosphorylated proteins and phospholipids derived from thousands of individual primary immune system cells, subjected to both general and specific molecular interventions (Sachs et al., 2005).\n", "\n", "The specifications of the dataset are as follows - \n", "- Number of nodes: 11\n", "- Number of arcs: 17\n", "- Number of parameters: 178\n", "- Average Markov blanket size: 3.09\n", "- Average degree: 3.09\n", "- Maximum in-degree: 3\n", "- Number of instances: 7466\n", "\n", "The original causal graph is known for the Sachs dataset and we compare the original graph with the ones discovered using CDT in this section." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Load the data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from cdt.data import load_dataset\n", "data_sachs, graph_sachs = load_dataset(\"sachs\")\n", "\n", "data_sachs.dropna(inplace=True)\n", "print(data_sachs.shape)\n", "data_sachs.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Ground truth of the causal graph" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "labels = [f'{col}' for i, col in enumerate(data_sachs.columns)]\n", "adj_matrix = nx.to_numpy_array(graph_sachs)\n", "adj_matrix = np.asarray(adj_matrix)\n", "graph_dot = make_graph(adj_matrix, labels)\n", "display(graph_dot)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery with Causal Discovery Tool (CDT)\n", "\n", "We use the CDT library to perform causal discovery on the Auto-MPG dataset. We use three methods for causal discovery here -LiNGAM, PC and GES. These methods are widely used and do not take much time to run. Hence, these are ideal for an introduction to the topic. Other neural network based methods are also available in CDT and the users the encourages to try them out by themselves. \n", "\n", "The documentation for the methods used in as follows:\n", "- LiNGAM [[link]](https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/_modules/cdt/causality/graph/LiNGAM.html)\n", "- PC [[link]](https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/_modules/cdt/causality/graph/PC.html)\n", "- GES [[link]](https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/_modules/cdt/causality/graph/GES.html)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from cdt.causality.graph import LiNGAM, PC, GES\n", "\n", "graphs = {}\n", "graphs_nx = {}\n", "labels = [f'{col}' for i, col in enumerate(data_sachs.columns)]\n", "functions = {\n", " 'LiNGAM' : LiNGAM,\n", " 'PC' : PC,\n", " 'GES' : GES,\n", "}\n", "\n", "for method, lib in functions.items():\n", " obj = lib()\n", " output = obj.predict(data_sachs)\n", " graphs_nx[method] = output\n", " adj_matrix = nx.to_numpy_array(output)\n", " adj_matrix = np.asarray(adj_matrix)\n", " graph_dot = make_graph(adj_matrix, labels)\n", " graphs[method] = graph_dot\n", "\n", "# Visualize graphs\n", "for method, graph in graphs.items():\n", " print(\"Method : %s\"%(method))\n", " display(graph)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, no two methods agree on the graphs. Next we study the causal effects of these different graphs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimate effects using Linear Regression\n", "\n", "Now let us see whether these differences in the graphs also lead to significant differences in the causal estimate of effect of *PIP2* on *PKC*." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for method, graph in graphs.items():\n", " if method != \"LiNGAM\":\n", " continue\n", " print('\\n*****************************************************************************\\n')\n", " print(\"Causal Discovery Method : %s\"%(method))\n", "\n", " # Obtain valid dot format\n", " graph_dot = str_to_dot(graph.source)\n", "\n", " # Define Causal Model\n", " model=CausalModel(\n", " data = data_sachs,\n", " treatment='PIP2',\n", " outcome='PKC',\n", " graph=graph_dot)\n", "\n", " # Identification\n", " identified_estimand = model.identify_effect(proceed_when_unidentifiable=True)\n", " print(identified_estimand)\n", "\n", " # Estimation\n", " estimate = model.estimate_effect(identified_estimand,\n", " method_name=\"backdoor.linear_regression\",\n", " control_value=0,\n", " treatment_value=1,\n", " confidence_intervals=True,\n", " test_significance=True)\n", " print(\"Causal Estimate is \" + str(estimate.value))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the causal estimates obtained, it can be seen that the three estimates differ in different aspects. The graph obtained using LiNGAM contains a backdoor path and instrumental variables. On the other hand, the graph obtained using PC contains a backdoor path and a frontdoor path. However, despite these differences, both obtain the same mean causal estimate.\n", "\n", "The graph obtained using GES contains only a backdoor path with different backdoor variables and obtains a different causal estimate than the first two cases. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Graph Validation\n", "\n", "We compare the graphs obtained with the true causal graph using the causal discovery methods using 2 graph distance metrics - Structural Hamming Distance (SHD) and Structural Intervention Distance (SID). SHD between two graphs is, in simple terms, the number of edge insertions, deletions or flips in order to transform one graph to another graph. SID, on the other hand, is based on a graphical criterion only and quantifies the closeness between two DAGs in terms of their corresponding causal inference statements." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from cdt.metrics import SHD, SHD_CPDAG, SID, SID_CPDAG\n", "from numpy.random import randint\n", "\n", "for method, graph in graphs_nx.items():\n", " print(\"***********************************************************\")\n", " print(\"Method: %s\"%(method))\n", " tar, pred = graph_sachs, graph\n", " print(\"SHD_CPDAG = %f\"%(SHD_CPDAG(tar, pred)))\n", " print(\"SHD = %f\"%(SHD(tar, pred, double_for_anticausal=False)))\n", " print(\"SID_CPDAG = [%f, %f]\"%(SID_CPDAG(tar, pred)))\n", " print(\"SID = %f\"%(SID(tar, pred)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The graph similarity metrics show that the scores are the lowest for the LiNGAM method of graph extraction. Hence, of the three methods used, LiNGAM provides the graph that is most similar to the original graph." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Graph Refutation\n", "\n", "Here, we use the same SHD and SID metric to find out how different the discovered graph are from each other." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import itertools\n", "from numpy.random import randint\n", "from cdt.metrics import SHD, SHD_CPDAG, SID, SID_CPDAG\n", "\n", "# Find combinations of pair of methods to compare\n", "combinations = list(itertools.combinations(graphs_nx, 2))\n", "\n", "for pair in combinations:\n", " print(\"***********************************************************\")\n", " graph1 = graphs_nx[pair[0]]\n", " graph2 = graphs_nx[pair[1]]\n", " print(\"Methods: %s and %s\"%(pair[0], pair[1]))\n", " print(\"SHD_CPDAG = %f\"%(SHD_CPDAG(graph1, graph2)))\n", " print(\"SHD = %f\"%(SHD(graph1, graph2, double_for_anticausal=False)))\n", " print(\"SID_CPDAG = [%f, %f]\"%(SID_CPDAG(graph1, graph2)))\n", " print(\"SID = %f\"%(SID(graph1, graph2)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The values for the metrics show how different the graphs are from each other. A higher distance value implies that the difference between the graphs is more." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.12" }, "metadata": { "interpreter": { "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" } }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": false, "sideBar": true, "skip_h1_title": true, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery example\n", "\n", "The goal of this notebook is to show how causal discovery methods can work with DoWhy. We use discovery methods from [causal-learn](https://github.com/py-why/causal-learn) repo. As we will see, causal discovery methods require appropriate assumptions for the correctness guarantees, adn thus there will be variance across results returned by different methods in practice. These methods, however, may be combined usefully with domain knowledge to construct the final causal graph." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import dowhy\n", "from dowhy import CausalModel\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import graphviz\n", "import networkx as nx \n", "\n", "np.set_printoptions(precision=3, suppress=True)\n", "np.random.seed(0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Utility function\n", "We define a utility function to draw the directed acyclic graph." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def make_graph(adjacency_matrix, labels=None):\n", " idx = np.abs(adjacency_matrix) > 0.01\n", " dirs = np.where(idx)\n", " d = graphviz.Digraph(engine='dot')\n", " names = labels if labels else [f'x{i}' for i in range(len(adjacency_matrix))]\n", " for name in names:\n", " d.node(name)\n", " for to, from_, coef in zip(dirs[0], dirs[1], adjacency_matrix[idx]):\n", " d.edge(names[from_], names[to], label=str(coef))\n", " return d\n", "\n", "def str_to_dot(string):\n", " '''\n", " Converts input string from graphviz library to valid DOT graph format.\n", " '''\n", " graph = string.strip().replace('\\n', ';').replace('\\t','')\n", " graph = graph[:9] + graph[10:-2] + graph[-1] # Removing unnecessary characters from string\n", " return graph" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Experiments on the Auto-MPG dataset\n", "\n", "In this section, we will use a dataset on the technical specification of cars. The dataset is downloaded from UCI Machine Learning Repository. The dataset contains 9 attributes and 398 instances. We do not know the true causal graph for the dataset and will use causal-learn to discover it. The causal graph obtained will then be used to estimate the causal effect.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Load the data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(392, 6)\n" ] }, { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>mpg</th>\n", " <th>cylinders</th>\n", " <th>displacement</th>\n", " <th>horsepower</th>\n", " <th>weight</th>\n", " <th>acceleration</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>18.0</td>\n", " <td>8.0</td>\n", " <td>307.0</td>\n", " <td>130.0</td>\n", " <td>3504.0</td>\n", " <td>12.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>15.0</td>\n", " <td>8.0</td>\n", " <td>350.0</td>\n", " <td>165.0</td>\n", " <td>3693.0</td>\n", " <td>11.5</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>18.0</td>\n", " <td>8.0</td>\n", " <td>318.0</td>\n", " <td>150.0</td>\n", " <td>3436.0</td>\n", " <td>11.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>16.0</td>\n", " <td>8.0</td>\n", " <td>304.0</td>\n", " <td>150.0</td>\n", " <td>3433.0</td>\n", " <td>12.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>17.0</td>\n", " <td>8.0</td>\n", " <td>302.0</td>\n", " <td>140.0</td>\n", " <td>3449.0</td>\n", " <td>10.5</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " mpg cylinders displacement horsepower weight acceleration\n", "0 18.0 8.0 307.0 130.0 3504.0 12.0\n", "1 15.0 8.0 350.0 165.0 3693.0 11.5\n", "2 18.0 8.0 318.0 150.0 3436.0 11.0\n", "3 16.0 8.0 304.0 150.0 3433.0 12.0\n", "4 17.0 8.0 302.0 140.0 3449.0 10.5" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_mpg = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data-original',\n", " delim_whitespace=True, header=None,\n", " names = ['mpg', 'cylinders', 'displacement',\n", " 'horsepower', 'weight', 'acceleration',\n", " 'model year', 'origin', 'car name'])\n", "data_mpg.dropna(inplace=True)\n", "data_mpg.drop(['model year', 'origin', 'car name'], axis=1, inplace=True)\n", "print(data_mpg.shape)\n", "data_mpg.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery with causal-learn\n", "\n", "We use the causal-learn library to perform causal discovery on the Auto-MPG dataset. We use three methods for causal discovery here: PC, FCI and GES. These methods are widely used and do not take much time to run. Hence, these are ideal for an introduction to the topic. Causal-learn provides a comprehensive list of well-tested causal-discovery methods, and readers are welcome to explore.\n", "\n", "The documentation for the methods used are as follows:\n", "- PC [[link]](https://causal-learn.readthedocs.io/en/latest/search_methods_index/Constraint-based%20causal%20discovery%20methods/PC.html)\n", "- GES [[link]](https://causal-learn.readthedocs.io/en/latest/search_methods_index/Score-based%20causal%20discovery%20methods/GES.html)\n", "- LiNGAM [[link]](https://causal-learn.readthedocs.io/en/latest/search_methods_index/Causal%20discovery%20methods%20based%20on%20constrained%20functional%20causal%20models/lingam.html#ica-based-lingam)\n", "\n", "More methods could be found in the causal-learn documentation [[link]](https://causal-learn.readthedocs.io/en/latest/)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We first try the PC algorithm with default parameters." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1ed197e9f5ec42c8bf7fc51c5ece4485", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/6 [00:00<?, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from causallearn.search.ConstraintBased.PC import pc\n", "\n", "labels = [f'{col}' for i, col in enumerate(data_mpg.columns)]\n", "data = data_mpg.to_numpy()\n", "\n", "cg = pc(data)\n", "\n", "# Visualization using pydot\n", "from causallearn.utils.GraphUtils import GraphUtils\n", "import matplotlib.image as mpimg\n", "import matplotlib.pyplot as plt\n", "import io\n", "\n", "pyd = GraphUtils.to_pydot(cg.G, labels=labels)\n", "tmp_png = pyd.create_png(f=\"png\")\n", "fp = io.BytesIO(tmp_png)\n", "img = mpimg.imread(fp, format='png')\n", "plt.axis('off')\n", "plt.imshow(img)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we have a causal graph discovered by PC. Let us also try GES to see its result." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from causallearn.search.ScoreBased.GES import ges\n", "\n", "# default parameters\n", "Record = ges(data)\n", "\n", "# Visualization using pydot\n", "from causallearn.utils.GraphUtils import GraphUtils\n", "import matplotlib.image as mpimg\n", "import matplotlib.pyplot as plt\n", "import io\n", "\n", "pyd = GraphUtils.to_pydot(Record['G'], labels=labels)\n", "tmp_png = pyd.create_png(f=\"png\")\n", "fp = io.BytesIO(tmp_png)\n", "img = mpimg.imread(fp, format='png')\n", "plt.axis('off')\n", "plt.imshow(img)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Well, these two results are different, which is not rare when applying causal discovery on real-world dataset, since the required assumptions on the data-generating process are hard to verify.\n", "\n", "In addition, the graphs returned by PC and GES are CPDAGs instead of DAGs, so it is possible to have undirected edges (e.g., the result returned by GES). Thus, causal effect estimataion is difficult for those methods, since there may be absence of backdoor, instrumental or frontdoor variables. In order to get a DAG, we decide to try LiNGAM on our dataset." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n", "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n", " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n", "<!-- Generated by graphviz version 2.43.0 (0)\n", " -->\n", "<!-- Title: %3 Pages: 1 -->\n", "<svg width=\"369pt\" height=\"392pt\"\n", " viewBox=\"0.00 0.00 369.40 392.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n", "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 388)\">\n", "<title>%3</title>\n", "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-388 365.4,-388 365.4,4 -4,4\"/>\n", "<!-- mpg -->\n", "<g id=\"node1\" class=\"node\">\n", "<title>mpg</title>\n", "<ellipse fill=\"none\" stroke=\"black\" cx=\"34.8\" cy=\"-279\" rx=\"31.4\" 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"text/plain": [ "<graphviz.graphs.Digraph at 0x7f957464c040>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from causallearn.search.FCMBased import lingam\n", "model = lingam.ICALiNGAM()\n", "model.fit(data)\n", "\n", "from causallearn.search.FCMBased.lingam.utils import make_dot\n", "make_dot(model.adjacency_matrix_, labels=labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we have a DAG and are ready to estimate the causal effects based on that." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimate causal effects using Linear Regression\n", "\n", "Now let us see the estimate of causal effect of *mpg* on *weight*." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Estimand type: EstimandType.NONPARAMETRIC_ATE\n", "\n", "### Estimand : 1\n", "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", "──────(E[weight|cylinders])\n", "d[mpg] \n", "Estimand assumption 1, Unconfoundedness: If U→{mpg} and U→weight then P(weight|mpg,cylinders,U) = P(weight|mpg,cylinders)\n", "\n", "### Estimand : 2\n", "Estimand name: iv\n", "No such variable(s) found!\n", "\n", "### Estimand : 3\n", "Estimand name: frontdoor\n", "No such variable(s) found!\n", "\n", "Causal Estimate is -38.940973656209735\n" ] } ], "source": [ "# Obtain valid dot format\n", "graph_dot = make_graph(model.adjacency_matrix_, labels=labels)\n", "\n", "# Define Causal Model\n", "model=CausalModel(\n", " data = data_mpg,\n", " treatment='mpg',\n", " outcome='weight',\n", " graph=str_to_dot(graph_dot.source))\n", "\n", "# Identification\n", "identified_estimand = model.identify_effect(proceed_when_unidentifiable=True)\n", "print(identified_estimand)\n", "\n", "# Estimation\n", "estimate = model.estimate_effect(identified_estimand,\n", " method_name=\"backdoor.linear_regression\",\n", " control_value=0,\n", " treatment_value=1,\n", " confidence_intervals=True,\n", " test_significance=True)\n", "print(\"Causal Estimate is \" + str(estimate.value))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Experiments on the Sachs dataset\n", "\n", "The dataset consists of the simultaneous measurements of 11 phosphorylated proteins and phospholipids derived from thousands of individual primary immune system cells, subjected to both general and specific molecular interventions (Sachs et al., 2005).\n", "\n", "The specifications of the dataset are as follows - \n", "- Number of nodes: 11\n", "- Number of arcs: 17\n", "- Number of parameters: 178\n", "- Average Markov blanket size: 3.09\n", "- Average degree: 3.09\n", "- Maximum in-degree: 3\n", "- Number of instances: 7466" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Load the data" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(7466, 11)\n", "['raf', 'mek', 'plc', 'pip2', 'pip3', 'erk', 'akt', 'pka', 'pkc', 'p38', 'jnk']\n" ] } ], "source": [ "from causallearn.utils.Dataset import load_dataset\n", "\n", "data_sachs, labels = load_dataset(\"sachs\")\n", "\n", "print(data.shape)\n", "print(labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery with causal-learn\n", "\n", "We use the three causal discovery methods mentioned above (PC, GES, and LiNGAM) to find the causal graphs." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, let us take a look at how PC works." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "bc0f31d1492e4934994a6d4ba68f1ad3", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/11 [00:00<?, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "graphs = {}\n", "graphs_nx = {}\n", "labels = [f'{col}' for i, col in enumerate(labels)]\n", "data = data_sachs\n", "\n", "from causallearn.search.ConstraintBased.PC import pc\n", "\n", "cg = pc(data)\n", "\n", "# Visualization using pydot\n", "from causallearn.utils.GraphUtils import GraphUtils\n", "import matplotlib.image as mpimg\n", "import matplotlib.pyplot as plt\n", "import io\n", "\n", "pyd = GraphUtils.to_pydot(cg.G, labels=labels)\n", "tmp_png = pyd.create_png(f=\"png\")\n", "fp = io.BytesIO(tmp_png)\n", "img = mpimg.imread(fp, format='png')\n", "plt.axis('off')\n", "plt.imshow(img)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then, let us try GES." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from causallearn.search.ScoreBased.GES import ges\n", "\n", "# default parameters\n", "Record = ges(data)\n", "\n", "# Visualization using pydot\n", "from causallearn.utils.GraphUtils import GraphUtils\n", "import matplotlib.image as mpimg\n", "import matplotlib.pyplot as plt\n", "import io\n", "\n", "pyd = GraphUtils.to_pydot(Record['G'], labels=labels)\n", "tmp_png = pyd.create_png(f=\"png\")\n", "fp = io.BytesIO(tmp_png)\n", "img = mpimg.imread(fp, format='png')\n", "plt.axis('off')\n", "plt.imshow(img)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And also LiNGAM." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n", "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n", " 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points=\"674.5,-46.18 671,-36.18 667.5,-46.18 674.5,-46.18\"/>\n", "<text text-anchor=\"middle\" x=\"687\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">0.04</text>\n", "</g>\n", "</g>\n", "</svg>\n" ], "text/plain": [ "<graphviz.graphs.Digraph at 0x7f96cd974ca0>" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from causallearn.search.FCMBased import lingam\n", "model = lingam.ICALiNGAM()\n", "model.fit(data)\n", "\n", "from causallearn.search.FCMBased.lingam.utils import make_dot\n", "make_dot(model.adjacency_matrix_, labels=labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimate effects using Linear Regression\n", "\n", "Similarly, let us use the DAG returned by LiNGAM to estimate the causal effect of *PIP2* on *PKC*." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Estimand type: EstimandType.NONPARAMETRIC_ATE\n", "\n", "### Estimand : 1\n", "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", "───────(E[pkc|plc,pip3])\n", "d[pip₂] \n", "Estimand assumption 1, Unconfoundedness: If U→{pip2} and U→pkc then P(pkc|pip2,plc,pip3,U) = P(pkc|pip2,plc,pip3)\n", "\n", "### Estimand : 2\n", "Estimand name: iv\n", "No such variable(s) found!\n", "\n", "### Estimand : 3\n", "Estimand name: frontdoor\n", "No such variable(s) found!\n", "\n", "Causal Estimate is 0.03397189228452291\n" ] } ], "source": [ "# Obtain valid dot format\n", "graph_dot = make_graph(model.adjacency_matrix_, labels=labels)\n", "\n", "data_df = pd.DataFrame(data=data, columns=labels)\n", "\n", "# Define Causal Model\n", "model_est=CausalModel(\n", " data = data_df,\n", " treatment='pip2',\n", " outcome='pkc',\n", " graph=str_to_dot(graph_dot.source))\n", "\n", "# Identification\n", "identified_estimand = model_est.identify_effect(proceed_when_unidentifiable=False)\n", "print(identified_estimand)\n", "\n", "# Estimation\n", "estimate = model_est.estimate_effect(identified_estimand,\n", " method_name=\"backdoor.linear_regression\",\n", " control_value=0,\n", " treatment_value=1,\n", " confidence_intervals=True,\n", " test_significance=True)\n", "print(\"Causal Estimate is \" + str(estimate.value))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.17" }, "metadata": { "interpreter": { "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" } }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": false, "sideBar": true, "skip_h1_title": true, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }

kunwuz

67b305db5224bf718067a21acfe4baa92a7d2c8c

7eb4a0c253514a920588d1ab222e1aeb5e07cb51

Yeah, you are totally right. It could be even better to have a causal-learn function that could automatically load various real-world datasets. I will code it up and update it in causal-learn.

kunwuz

py-why/dowhy

Update the causal discovery notebook with examples using causal-learn

Updating the old notebook as mentioned in #1021.

2023-08-30 21:25:09+00:00

2023-10-05 21:26:19+00:00

docs/source/example_notebooks/dowhy_causal_discovery_example.ipynb

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery example\n", "\n", "The goal of this notebook is to show how causal discovery methods can work with DoWhy. We use discovery methods from [Causal Discovery Tool (CDT)](https://github.com/FenTechSolutions/CausalDiscoveryToolbox) repo. As we will see, causal discovery methods are not fool-proof and there is no guarantee that they will recover the correct causal graph. Even for the simple examples below, there is a large variance in results. These methods, however, may be combined usefully with domain knowledge to construct the final causal graph." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import dowhy\n", "from dowhy import CausalModel\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import graphviz\n", "import networkx as nx \n", "\n", "np.set_printoptions(precision=3, suppress=True)\n", "np.random.seed(0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Utility function\n", "We define a utility function to draw the directed acyclic graph." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def make_graph(adjacency_matrix, labels=None):\n", " idx = np.abs(adjacency_matrix) > 0.01\n", " dirs = np.where(idx)\n", " d = graphviz.Digraph(engine='dot')\n", " names = labels if labels else [f'x{i}' for i in range(len(adjacency_matrix))]\n", " for name in names:\n", " d.node(name)\n", " for to, from_, coef in zip(dirs[0], dirs[1], adjacency_matrix[idx]):\n", " d.edge(names[from_], names[to], label=str(coef))\n", " return d\n", "\n", "def str_to_dot(string):\n", " '''\n", " Converts input string from graphviz library to valid DOT graph format.\n", " '''\n", " graph = string.strip().replace('\\n', ';').replace('\\t','')\n", " graph = graph[:9] + graph[10:-2] + graph[-1] # Removing unnecessary characters from string\n", " return graph" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Experiments on the Auto-MPG dataset\n", "\n", "In this section, we will use a dataset on the technical specification of cars. The dataset is downloaded from UCI Machine Learning Repository. The dataset contains 9 attributes and 398 instances. We do not know the true causal graph for the dataset and will use CDT to discover it. The causal graph obtained will then be used to estimate the causal effect.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Load the data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data_mpg = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data-original',\n", " delim_whitespace=True, header=None,\n", " names = ['mpg', 'cylinders', 'displacement',\n", " 'horsepower', 'weight', 'acceleration',\n", " 'model year', 'origin', 'car name'])\n", "data_mpg.dropna(inplace=True)\n", "data_mpg.drop(['model year', 'origin', 'car name'], axis=1, inplace=True)\n", "print(data_mpg.shape)\n", "data_mpg.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery with Causal Discovery Tool (CDT)\n", "\n", "We use the CDT library to perform causal discovery on the Auto-MPG dataset. We use three methods for causal discovery here -LiNGAM, PC and GES. These methods are widely used and do not take much time to run. Hence, these are ideal for an introduction to the topic. Other neural network based methods are also available in CDT and the users are encouraged to try them out by themselves. \n", "\n", "The documentation for the methods used are as follows:\n", "- LiNGAM [[link]](https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/_modules/cdt/causality/graph/LiNGAM.html)\n", "- PC [[link]](https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/_modules/cdt/causality/graph/PC.html)\n", "- GES [[link]](https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/_modules/cdt/causality/graph/GES.html)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from cdt.causality.graph import LiNGAM, PC, GES\n", "\n", "graphs = {}\n", "labels = [f'{col}' for i, col in enumerate(data_mpg.columns)]\n", "functions = {\n", " 'LiNGAM' : LiNGAM,\n", " 'PC' : PC,\n", " 'GES' : GES,\n", "}\n", "\n", "for method, lib in functions.items():\n", " obj = lib()\n", " output = obj.predict(data_mpg)\n", " adj_matrix = nx.to_numpy_array(output)\n", " adj_matrix = np.asarray(adj_matrix)\n", " graph_dot = make_graph(adj_matrix, labels)\n", " graphs[method] = graph_dot\n", "\n", "# Visualize graphs\n", "for method, graph in graphs.items():\n", " print(\"Method : %s\"%(method))\n", " display(graph)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, no two methods agree on the graphs. PC and GES effectively produce an undirected graph whereas LiNGAM produces a directed graph. We use only the LiNGAM method in the next section." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimate causal effects using Linear Regression\n", "\n", "Now let us see whether these differences in the graphs also lead to significant differences in the causal estimate of effect of *mpg* on *weight*." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for method, graph in graphs.items():\n", " if method != \"LiNGAM\":\n", " continue\n", " print('\\n*****************************************************************************\\n')\n", " print(\"Causal Discovery Method : %s\"%(method))\n", " \n", " # Obtain valid dot format\n", " graph_dot = str_to_dot(graph.source)\n", "\n", " # Define Causal Model\n", " model=CausalModel(\n", " data = data_mpg,\n", " treatment='mpg',\n", " outcome='weight',\n", " graph=graph_dot)\n", "\n", " # Identification\n", " identified_estimand = model.identify_effect(proceed_when_unidentifiable=True)\n", " print(identified_estimand)\n", " \n", " # Estimation\n", " estimate = model.estimate_effect(identified_estimand,\n", " method_name=\"backdoor.linear_regression\",\n", " control_value=0,\n", " treatment_value=1,\n", " confidence_intervals=True,\n", " test_significance=True)\n", " print(\"Causal Estimate is \" + str(estimate.value))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As mentioned earlier, due to the absence of directed edges, no backdoor, instrmental or frontdoor variables can be found out for PC and GES. Thus, causal effect estimation is not possible for these methods. However, LiNGAM does discover a DAG and hence, its possible to output a causal estimate for LiNGAM. The estimate is still pretty far from the original estimate of -70.466 (which can be calculated from the graph)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Experiments on the Sachs dataset\n", "\n", "The dataset consists of the simultaneous measurements of 11 phosphorylated proteins and phospholipids derived from thousands of individual primary immune system cells, subjected to both general and specific molecular interventions (Sachs et al., 2005).\n", "\n", "The specifications of the dataset are as follows - \n", "- Number of nodes: 11\n", "- Number of arcs: 17\n", "- Number of parameters: 178\n", "- Average Markov blanket size: 3.09\n", "- Average degree: 3.09\n", "- Maximum in-degree: 3\n", "- Number of instances: 7466\n", "\n", "The original causal graph is known for the Sachs dataset and we compare the original graph with the ones discovered using CDT in this section." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Load the data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from cdt.data import load_dataset\n", "data_sachs, graph_sachs = load_dataset(\"sachs\")\n", "\n", "data_sachs.dropna(inplace=True)\n", "print(data_sachs.shape)\n", "data_sachs.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Ground truth of the causal graph" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "labels = [f'{col}' for i, col in enumerate(data_sachs.columns)]\n", "adj_matrix = nx.to_numpy_array(graph_sachs)\n", "adj_matrix = np.asarray(adj_matrix)\n", "graph_dot = make_graph(adj_matrix, labels)\n", "display(graph_dot)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery with Causal Discovery Tool (CDT)\n", "\n", "We use the CDT library to perform causal discovery on the Auto-MPG dataset. We use three methods for causal discovery here -LiNGAM, PC and GES. These methods are widely used and do not take much time to run. Hence, these are ideal for an introduction to the topic. Other neural network based methods are also available in CDT and the users the encourages to try them out by themselves. \n", "\n", "The documentation for the methods used in as follows:\n", "- LiNGAM [[link]](https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/_modules/cdt/causality/graph/LiNGAM.html)\n", "- PC [[link]](https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/_modules/cdt/causality/graph/PC.html)\n", "- GES [[link]](https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/_modules/cdt/causality/graph/GES.html)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from cdt.causality.graph import LiNGAM, PC, GES\n", "\n", "graphs = {}\n", "graphs_nx = {}\n", "labels = [f'{col}' for i, col in enumerate(data_sachs.columns)]\n", "functions = {\n", " 'LiNGAM' : LiNGAM,\n", " 'PC' : PC,\n", " 'GES' : GES,\n", "}\n", "\n", "for method, lib in functions.items():\n", " obj = lib()\n", " output = obj.predict(data_sachs)\n", " graphs_nx[method] = output\n", " adj_matrix = nx.to_numpy_array(output)\n", " adj_matrix = np.asarray(adj_matrix)\n", " graph_dot = make_graph(adj_matrix, labels)\n", " graphs[method] = graph_dot\n", "\n", "# Visualize graphs\n", "for method, graph in graphs.items():\n", " print(\"Method : %s\"%(method))\n", " display(graph)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, no two methods agree on the graphs. Next we study the causal effects of these different graphs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimate effects using Linear Regression\n", "\n", "Now let us see whether these differences in the graphs also lead to significant differences in the causal estimate of effect of *PIP2* on *PKC*." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for method, graph in graphs.items():\n", " if method != \"LiNGAM\":\n", " continue\n", " print('\\n*****************************************************************************\\n')\n", " print(\"Causal Discovery Method : %s\"%(method))\n", "\n", " # Obtain valid dot format\n", " graph_dot = str_to_dot(graph.source)\n", "\n", " # Define Causal Model\n", " model=CausalModel(\n", " data = data_sachs,\n", " treatment='PIP2',\n", " outcome='PKC',\n", " graph=graph_dot)\n", "\n", " # Identification\n", " identified_estimand = model.identify_effect(proceed_when_unidentifiable=True)\n", " print(identified_estimand)\n", "\n", " # Estimation\n", " estimate = model.estimate_effect(identified_estimand,\n", " method_name=\"backdoor.linear_regression\",\n", " control_value=0,\n", " treatment_value=1,\n", " confidence_intervals=True,\n", " test_significance=True)\n", " print(\"Causal Estimate is \" + str(estimate.value))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the causal estimates obtained, it can be seen that the three estimates differ in different aspects. The graph obtained using LiNGAM contains a backdoor path and instrumental variables. On the other hand, the graph obtained using PC contains a backdoor path and a frontdoor path. However, despite these differences, both obtain the same mean causal estimate.\n", "\n", "The graph obtained using GES contains only a backdoor path with different backdoor variables and obtains a different causal estimate than the first two cases. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Graph Validation\n", "\n", "We compare the graphs obtained with the true causal graph using the causal discovery methods using 2 graph distance metrics - Structural Hamming Distance (SHD) and Structural Intervention Distance (SID). SHD between two graphs is, in simple terms, the number of edge insertions, deletions or flips in order to transform one graph to another graph. SID, on the other hand, is based on a graphical criterion only and quantifies the closeness between two DAGs in terms of their corresponding causal inference statements." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from cdt.metrics import SHD, SHD_CPDAG, SID, SID_CPDAG\n", "from numpy.random import randint\n", "\n", "for method, graph in graphs_nx.items():\n", " print(\"***********************************************************\")\n", " print(\"Method: %s\"%(method))\n", " tar, pred = graph_sachs, graph\n", " print(\"SHD_CPDAG = %f\"%(SHD_CPDAG(tar, pred)))\n", " print(\"SHD = %f\"%(SHD(tar, pred, double_for_anticausal=False)))\n", " print(\"SID_CPDAG = [%f, %f]\"%(SID_CPDAG(tar, pred)))\n", " print(\"SID = %f\"%(SID(tar, pred)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The graph similarity metrics show that the scores are the lowest for the LiNGAM method of graph extraction. Hence, of the three methods used, LiNGAM provides the graph that is most similar to the original graph." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Graph Refutation\n", "\n", "Here, we use the same SHD and SID metric to find out how different the discovered graph are from each other." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import itertools\n", "from numpy.random import randint\n", "from cdt.metrics import SHD, SHD_CPDAG, SID, SID_CPDAG\n", "\n", "# Find combinations of pair of methods to compare\n", "combinations = list(itertools.combinations(graphs_nx, 2))\n", "\n", "for pair in combinations:\n", " print(\"***********************************************************\")\n", " graph1 = graphs_nx[pair[0]]\n", " graph2 = graphs_nx[pair[1]]\n", " print(\"Methods: %s and %s\"%(pair[0], pair[1]))\n", " print(\"SHD_CPDAG = %f\"%(SHD_CPDAG(graph1, graph2)))\n", " print(\"SHD = %f\"%(SHD(graph1, graph2, double_for_anticausal=False)))\n", " print(\"SID_CPDAG = [%f, %f]\"%(SID_CPDAG(graph1, graph2)))\n", " print(\"SID = %f\"%(SID(graph1, graph2)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The values for the metrics show how different the graphs are from each other. A higher distance value implies that the difference between the graphs is more." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.12" }, "metadata": { "interpreter": { "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" } }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": false, "sideBar": true, "skip_h1_title": true, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery example\n", "\n", "The goal of this notebook is to show how causal discovery methods can work with DoWhy. We use discovery methods from [causal-learn](https://github.com/py-why/causal-learn) repo. As we will see, causal discovery methods require appropriate assumptions for the correctness guarantees, adn thus there will be variance across results returned by different methods in practice. These methods, however, may be combined usefully with domain knowledge to construct the final causal graph." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import dowhy\n", "from dowhy import CausalModel\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import graphviz\n", "import networkx as nx \n", "\n", "np.set_printoptions(precision=3, suppress=True)\n", "np.random.seed(0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Utility function\n", "We define a utility function to draw the directed acyclic graph." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def make_graph(adjacency_matrix, labels=None):\n", " idx = np.abs(adjacency_matrix) > 0.01\n", " dirs = np.where(idx)\n", " d = graphviz.Digraph(engine='dot')\n", " names = labels if labels else [f'x{i}' for i in range(len(adjacency_matrix))]\n", " for name in names:\n", " d.node(name)\n", " for to, from_, coef in zip(dirs[0], dirs[1], adjacency_matrix[idx]):\n", " d.edge(names[from_], names[to], label=str(coef))\n", " return d\n", "\n", "def str_to_dot(string):\n", " '''\n", " Converts input string from graphviz library to valid DOT graph format.\n", " '''\n", " graph = string.strip().replace('\\n', ';').replace('\\t','')\n", " graph = graph[:9] + graph[10:-2] + graph[-1] # Removing unnecessary characters from string\n", " return graph" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Experiments on the Auto-MPG dataset\n", "\n", "In this section, we will use a dataset on the technical specification of cars. The dataset is downloaded from UCI Machine Learning Repository. The dataset contains 9 attributes and 398 instances. We do not know the true causal graph for the dataset and will use causal-learn to discover it. The causal graph obtained will then be used to estimate the causal effect.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Load the data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(392, 6)\n" ] }, { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>mpg</th>\n", " <th>cylinders</th>\n", " <th>displacement</th>\n", " <th>horsepower</th>\n", " <th>weight</th>\n", " <th>acceleration</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>18.0</td>\n", " <td>8.0</td>\n", " <td>307.0</td>\n", " <td>130.0</td>\n", " <td>3504.0</td>\n", " <td>12.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>15.0</td>\n", " <td>8.0</td>\n", " <td>350.0</td>\n", " <td>165.0</td>\n", " <td>3693.0</td>\n", " <td>11.5</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>18.0</td>\n", " <td>8.0</td>\n", " <td>318.0</td>\n", " <td>150.0</td>\n", " <td>3436.0</td>\n", " <td>11.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>16.0</td>\n", " <td>8.0</td>\n", " <td>304.0</td>\n", " <td>150.0</td>\n", " <td>3433.0</td>\n", " <td>12.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>17.0</td>\n", " <td>8.0</td>\n", " <td>302.0</td>\n", " <td>140.0</td>\n", " <td>3449.0</td>\n", " <td>10.5</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " mpg cylinders displacement horsepower weight acceleration\n", "0 18.0 8.0 307.0 130.0 3504.0 12.0\n", "1 15.0 8.0 350.0 165.0 3693.0 11.5\n", "2 18.0 8.0 318.0 150.0 3436.0 11.0\n", "3 16.0 8.0 304.0 150.0 3433.0 12.0\n", "4 17.0 8.0 302.0 140.0 3449.0 10.5" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_mpg = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data-original',\n", " delim_whitespace=True, header=None,\n", " names = ['mpg', 'cylinders', 'displacement',\n", " 'horsepower', 'weight', 'acceleration',\n", " 'model year', 'origin', 'car name'])\n", "data_mpg.dropna(inplace=True)\n", "data_mpg.drop(['model year', 'origin', 'car name'], axis=1, inplace=True)\n", "print(data_mpg.shape)\n", "data_mpg.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery with causal-learn\n", "\n", "We use the causal-learn library to perform causal discovery on the Auto-MPG dataset. We use three methods for causal discovery here: PC, FCI and GES. These methods are widely used and do not take much time to run. Hence, these are ideal for an introduction to the topic. Causal-learn provides a comprehensive list of well-tested causal-discovery methods, and readers are welcome to explore.\n", "\n", "The documentation for the methods used are as follows:\n", "- PC [[link]](https://causal-learn.readthedocs.io/en/latest/search_methods_index/Constraint-based%20causal%20discovery%20methods/PC.html)\n", "- GES [[link]](https://causal-learn.readthedocs.io/en/latest/search_methods_index/Score-based%20causal%20discovery%20methods/GES.html)\n", "- LiNGAM [[link]](https://causal-learn.readthedocs.io/en/latest/search_methods_index/Causal%20discovery%20methods%20based%20on%20constrained%20functional%20causal%20models/lingam.html#ica-based-lingam)\n", "\n", "More methods could be found in the causal-learn documentation [[link]](https://causal-learn.readthedocs.io/en/latest/)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We first try the PC algorithm with default parameters." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1ed197e9f5ec42c8bf7fc51c5ece4485", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/6 [00:00<?, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from causallearn.search.ConstraintBased.PC import pc\n", "\n", "labels = [f'{col}' for i, col in enumerate(data_mpg.columns)]\n", "data = data_mpg.to_numpy()\n", "\n", "cg = pc(data)\n", "\n", "# Visualization using pydot\n", "from causallearn.utils.GraphUtils import GraphUtils\n", "import matplotlib.image as mpimg\n", "import matplotlib.pyplot as plt\n", "import io\n", "\n", "pyd = GraphUtils.to_pydot(cg.G, labels=labels)\n", "tmp_png = pyd.create_png(f=\"png\")\n", "fp = io.BytesIO(tmp_png)\n", "img = mpimg.imread(fp, format='png')\n", "plt.axis('off')\n", "plt.imshow(img)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we have a causal graph discovered by PC. Let us also try GES to see its result." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from causallearn.search.ScoreBased.GES import ges\n", "\n", "# default parameters\n", "Record = ges(data)\n", "\n", "# Visualization using pydot\n", "from causallearn.utils.GraphUtils import GraphUtils\n", "import matplotlib.image as mpimg\n", "import matplotlib.pyplot as plt\n", "import io\n", "\n", "pyd = GraphUtils.to_pydot(Record['G'], labels=labels)\n", "tmp_png = pyd.create_png(f=\"png\")\n", "fp = io.BytesIO(tmp_png)\n", "img = mpimg.imread(fp, format='png')\n", "plt.axis('off')\n", "plt.imshow(img)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Well, these two results are different, which is not rare when applying causal discovery on real-world dataset, since the required assumptions on the data-generating process are hard to verify.\n", "\n", "In addition, the graphs returned by PC and GES are CPDAGs instead of DAGs, so it is possible to have undirected edges (e.g., the result returned by GES). Thus, causal effect estimataion is difficult for those methods, since there may be absence of backdoor, instrumental or frontdoor variables. In order to get a DAG, we decide to try LiNGAM on our dataset." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n", "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n", " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n", "<!-- Generated by graphviz version 2.43.0 (0)\n", " -->\n", "<!-- Title: %3 Pages: 1 -->\n", "<svg width=\"369pt\" height=\"392pt\"\n", " viewBox=\"0.00 0.00 369.40 392.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n", "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 388)\">\n", "<title>%3</title>\n", "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-388 365.4,-388 365.4,4 -4,4\"/>\n", "<!-- mpg -->\n", "<g id=\"node1\" class=\"node\">\n", "<title>mpg</title>\n", "<ellipse fill=\"none\" stroke=\"black\" cx=\"34.8\" cy=\"-279\" rx=\"31.4\" 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"text/plain": [ "<graphviz.graphs.Digraph at 0x7f957464c040>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from causallearn.search.FCMBased import lingam\n", "model = lingam.ICALiNGAM()\n", "model.fit(data)\n", "\n", "from causallearn.search.FCMBased.lingam.utils import make_dot\n", "make_dot(model.adjacency_matrix_, labels=labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we have a DAG and are ready to estimate the causal effects based on that." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimate causal effects using Linear Regression\n", "\n", "Now let us see the estimate of causal effect of *mpg* on *weight*." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Estimand type: EstimandType.NONPARAMETRIC_ATE\n", "\n", "### Estimand : 1\n", "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", "──────(E[weight|cylinders])\n", "d[mpg] \n", "Estimand assumption 1, Unconfoundedness: If U→{mpg} and U→weight then P(weight|mpg,cylinders,U) = P(weight|mpg,cylinders)\n", "\n", "### Estimand : 2\n", "Estimand name: iv\n", "No such variable(s) found!\n", "\n", "### Estimand : 3\n", "Estimand name: frontdoor\n", "No such variable(s) found!\n", "\n", "Causal Estimate is -38.940973656209735\n" ] } ], "source": [ "# Obtain valid dot format\n", "graph_dot = make_graph(model.adjacency_matrix_, labels=labels)\n", "\n", "# Define Causal Model\n", "model=CausalModel(\n", " data = data_mpg,\n", " treatment='mpg',\n", " outcome='weight',\n", " graph=str_to_dot(graph_dot.source))\n", "\n", "# Identification\n", "identified_estimand = model.identify_effect(proceed_when_unidentifiable=True)\n", "print(identified_estimand)\n", "\n", "# Estimation\n", "estimate = model.estimate_effect(identified_estimand,\n", " method_name=\"backdoor.linear_regression\",\n", " control_value=0,\n", " treatment_value=1,\n", " confidence_intervals=True,\n", " test_significance=True)\n", "print(\"Causal Estimate is \" + str(estimate.value))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Experiments on the Sachs dataset\n", "\n", "The dataset consists of the simultaneous measurements of 11 phosphorylated proteins and phospholipids derived from thousands of individual primary immune system cells, subjected to both general and specific molecular interventions (Sachs et al., 2005).\n", "\n", "The specifications of the dataset are as follows - \n", "- Number of nodes: 11\n", "- Number of arcs: 17\n", "- Number of parameters: 178\n", "- Average Markov blanket size: 3.09\n", "- Average degree: 3.09\n", "- Maximum in-degree: 3\n", "- Number of instances: 7466" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Load the data" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(7466, 11)\n", "['raf', 'mek', 'plc', 'pip2', 'pip3', 'erk', 'akt', 'pka', 'pkc', 'p38', 'jnk']\n" ] } ], "source": [ "from causallearn.utils.Dataset import load_dataset\n", "\n", "data_sachs, labels = load_dataset(\"sachs\")\n", "\n", "print(data.shape)\n", "print(labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery with causal-learn\n", "\n", "We use the three causal discovery methods mentioned above (PC, GES, and LiNGAM) to find the causal graphs." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, let us take a look at how PC works." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "bc0f31d1492e4934994a6d4ba68f1ad3", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/11 [00:00<?, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "graphs = {}\n", "graphs_nx = {}\n", "labels = [f'{col}' for i, col in enumerate(labels)]\n", "data = data_sachs\n", "\n", "from causallearn.search.ConstraintBased.PC import pc\n", "\n", "cg = pc(data)\n", "\n", "# Visualization using pydot\n", "from causallearn.utils.GraphUtils import GraphUtils\n", "import matplotlib.image as mpimg\n", "import matplotlib.pyplot as plt\n", "import io\n", "\n", "pyd = GraphUtils.to_pydot(cg.G, labels=labels)\n", "tmp_png = pyd.create_png(f=\"png\")\n", "fp = io.BytesIO(tmp_png)\n", "img = mpimg.imread(fp, format='png')\n", "plt.axis('off')\n", "plt.imshow(img)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then, let us try GES." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from causallearn.search.ScoreBased.GES import ges\n", "\n", "# default parameters\n", "Record = ges(data)\n", "\n", "# Visualization using pydot\n", "from causallearn.utils.GraphUtils import GraphUtils\n", "import matplotlib.image as mpimg\n", "import matplotlib.pyplot as plt\n", "import io\n", "\n", "pyd = GraphUtils.to_pydot(Record['G'], labels=labels)\n", "tmp_png = pyd.create_png(f=\"png\")\n", "fp = io.BytesIO(tmp_png)\n", "img = mpimg.imread(fp, format='png')\n", "plt.axis('off')\n", "plt.imshow(img)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And also LiNGAM." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n", "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n", " 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points=\"674.5,-46.18 671,-36.18 667.5,-46.18 674.5,-46.18\"/>\n", "<text text-anchor=\"middle\" x=\"687\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">0.04</text>\n", "</g>\n", "</g>\n", "</svg>\n" ], "text/plain": [ "<graphviz.graphs.Digraph at 0x7f96cd974ca0>" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from causallearn.search.FCMBased import lingam\n", "model = lingam.ICALiNGAM()\n", "model.fit(data)\n", "\n", "from causallearn.search.FCMBased.lingam.utils import make_dot\n", "make_dot(model.adjacency_matrix_, labels=labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimate effects using Linear Regression\n", "\n", "Similarly, let us use the DAG returned by LiNGAM to estimate the causal effect of *PIP2* on *PKC*." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Estimand type: EstimandType.NONPARAMETRIC_ATE\n", "\n", "### Estimand : 1\n", "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", "───────(E[pkc|plc,pip3])\n", "d[pip₂] \n", "Estimand assumption 1, Unconfoundedness: If U→{pip2} and U→pkc then P(pkc|pip2,plc,pip3,U) = P(pkc|pip2,plc,pip3)\n", "\n", "### Estimand : 2\n", "Estimand name: iv\n", "No such variable(s) found!\n", "\n", "### Estimand : 3\n", "Estimand name: frontdoor\n", "No such variable(s) found!\n", "\n", "Causal Estimate is 0.03397189228452291\n" ] } ], "source": [ "# Obtain valid dot format\n", "graph_dot = make_graph(model.adjacency_matrix_, labels=labels)\n", "\n", "data_df = pd.DataFrame(data=data, columns=labels)\n", "\n", "# Define Causal Model\n", "model_est=CausalModel(\n", " data = data_df,\n", " treatment='pip2',\n", " outcome='pkc',\n", " graph=str_to_dot(graph_dot.source))\n", "\n", "# Identification\n", "identified_estimand = model_est.identify_effect(proceed_when_unidentifiable=False)\n", "print(identified_estimand)\n", "\n", "# Estimation\n", "estimate = model_est.estimate_effect(identified_estimand,\n", " method_name=\"backdoor.linear_regression\",\n", " control_value=0,\n", " treatment_value=1,\n", " confidence_intervals=True,\n", " test_significance=True)\n", "print(\"Causal Estimate is \" + str(estimate.value))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.17" }, "metadata": { "interpreter": { "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" } }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": false, "sideBar": true, "skip_h1_title": true, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }

kunwuz

67b305db5224bf718067a21acfe4baa92a7d2c8c

7eb4a0c253514a920588d1ab222e1aeb5e07cb51

There's still a reference to CDT in the beginning of the sentence.

emrekiciman

py-why/dowhy

Update the causal discovery notebook with examples using causal-learn

Updating the old notebook as mentioned in #1021.

2023-08-30 21:25:09+00:00

2023-10-05 21:26:19+00:00

docs/source/example_notebooks/dowhy_causal_discovery_example.ipynb

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery example\n", "\n", "The goal of this notebook is to show how causal discovery methods can work with DoWhy. We use discovery methods from [Causal Discovery Tool (CDT)](https://github.com/FenTechSolutions/CausalDiscoveryToolbox) repo. As we will see, causal discovery methods are not fool-proof and there is no guarantee that they will recover the correct causal graph. Even for the simple examples below, there is a large variance in results. These methods, however, may be combined usefully with domain knowledge to construct the final causal graph." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import dowhy\n", "from dowhy import CausalModel\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import graphviz\n", "import networkx as nx \n", "\n", "np.set_printoptions(precision=3, suppress=True)\n", "np.random.seed(0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Utility function\n", "We define a utility function to draw the directed acyclic graph." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def make_graph(adjacency_matrix, labels=None):\n", " idx = np.abs(adjacency_matrix) > 0.01\n", " dirs = np.where(idx)\n", " d = graphviz.Digraph(engine='dot')\n", " names = labels if labels else [f'x{i}' for i in range(len(adjacency_matrix))]\n", " for name in names:\n", " d.node(name)\n", " for to, from_, coef in zip(dirs[0], dirs[1], adjacency_matrix[idx]):\n", " d.edge(names[from_], names[to], label=str(coef))\n", " return d\n", "\n", "def str_to_dot(string):\n", " '''\n", " Converts input string from graphviz library to valid DOT graph format.\n", " '''\n", " graph = string.strip().replace('\\n', ';').replace('\\t','')\n", " graph = graph[:9] + graph[10:-2] + graph[-1] # Removing unnecessary characters from string\n", " return graph" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Experiments on the Auto-MPG dataset\n", "\n", "In this section, we will use a dataset on the technical specification of cars. The dataset is downloaded from UCI Machine Learning Repository. The dataset contains 9 attributes and 398 instances. We do not know the true causal graph for the dataset and will use CDT to discover it. The causal graph obtained will then be used to estimate the causal effect.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Load the data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data_mpg = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data-original',\n", " delim_whitespace=True, header=None,\n", " names = ['mpg', 'cylinders', 'displacement',\n", " 'horsepower', 'weight', 'acceleration',\n", " 'model year', 'origin', 'car name'])\n", "data_mpg.dropna(inplace=True)\n", "data_mpg.drop(['model year', 'origin', 'car name'], axis=1, inplace=True)\n", "print(data_mpg.shape)\n", "data_mpg.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery with Causal Discovery Tool (CDT)\n", "\n", "We use the CDT library to perform causal discovery on the Auto-MPG dataset. We use three methods for causal discovery here -LiNGAM, PC and GES. These methods are widely used and do not take much time to run. Hence, these are ideal for an introduction to the topic. Other neural network based methods are also available in CDT and the users are encouraged to try them out by themselves. \n", "\n", "The documentation for the methods used are as follows:\n", "- LiNGAM [[link]](https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/_modules/cdt/causality/graph/LiNGAM.html)\n", "- PC [[link]](https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/_modules/cdt/causality/graph/PC.html)\n", "- GES [[link]](https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/_modules/cdt/causality/graph/GES.html)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from cdt.causality.graph import LiNGAM, PC, GES\n", "\n", "graphs = {}\n", "labels = [f'{col}' for i, col in enumerate(data_mpg.columns)]\n", "functions = {\n", " 'LiNGAM' : LiNGAM,\n", " 'PC' : PC,\n", " 'GES' : GES,\n", "}\n", "\n", "for method, lib in functions.items():\n", " obj = lib()\n", " output = obj.predict(data_mpg)\n", " adj_matrix = nx.to_numpy_array(output)\n", " adj_matrix = np.asarray(adj_matrix)\n", " graph_dot = make_graph(adj_matrix, labels)\n", " graphs[method] = graph_dot\n", "\n", "# Visualize graphs\n", "for method, graph in graphs.items():\n", " print(\"Method : %s\"%(method))\n", " display(graph)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, no two methods agree on the graphs. PC and GES effectively produce an undirected graph whereas LiNGAM produces a directed graph. We use only the LiNGAM method in the next section." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimate causal effects using Linear Regression\n", "\n", "Now let us see whether these differences in the graphs also lead to significant differences in the causal estimate of effect of *mpg* on *weight*." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for method, graph in graphs.items():\n", " if method != \"LiNGAM\":\n", " continue\n", " print('\\n*****************************************************************************\\n')\n", " print(\"Causal Discovery Method : %s\"%(method))\n", " \n", " # Obtain valid dot format\n", " graph_dot = str_to_dot(graph.source)\n", "\n", " # Define Causal Model\n", " model=CausalModel(\n", " data = data_mpg,\n", " treatment='mpg',\n", " outcome='weight',\n", " graph=graph_dot)\n", "\n", " # Identification\n", " identified_estimand = model.identify_effect(proceed_when_unidentifiable=True)\n", " print(identified_estimand)\n", " \n", " # Estimation\n", " estimate = model.estimate_effect(identified_estimand,\n", " method_name=\"backdoor.linear_regression\",\n", " control_value=0,\n", " treatment_value=1,\n", " confidence_intervals=True,\n", " test_significance=True)\n", " print(\"Causal Estimate is \" + str(estimate.value))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As mentioned earlier, due to the absence of directed edges, no backdoor, instrmental or frontdoor variables can be found out for PC and GES. Thus, causal effect estimation is not possible for these methods. However, LiNGAM does discover a DAG and hence, its possible to output a causal estimate for LiNGAM. The estimate is still pretty far from the original estimate of -70.466 (which can be calculated from the graph)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Experiments on the Sachs dataset\n", "\n", "The dataset consists of the simultaneous measurements of 11 phosphorylated proteins and phospholipids derived from thousands of individual primary immune system cells, subjected to both general and specific molecular interventions (Sachs et al., 2005).\n", "\n", "The specifications of the dataset are as follows - \n", "- Number of nodes: 11\n", "- Number of arcs: 17\n", "- Number of parameters: 178\n", "- Average Markov blanket size: 3.09\n", "- Average degree: 3.09\n", "- Maximum in-degree: 3\n", "- Number of instances: 7466\n", "\n", "The original causal graph is known for the Sachs dataset and we compare the original graph with the ones discovered using CDT in this section." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Load the data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from cdt.data import load_dataset\n", "data_sachs, graph_sachs = load_dataset(\"sachs\")\n", "\n", "data_sachs.dropna(inplace=True)\n", "print(data_sachs.shape)\n", "data_sachs.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Ground truth of the causal graph" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "labels = [f'{col}' for i, col in enumerate(data_sachs.columns)]\n", "adj_matrix = nx.to_numpy_array(graph_sachs)\n", "adj_matrix = np.asarray(adj_matrix)\n", "graph_dot = make_graph(adj_matrix, labels)\n", "display(graph_dot)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery with Causal Discovery Tool (CDT)\n", "\n", "We use the CDT library to perform causal discovery on the Auto-MPG dataset. We use three methods for causal discovery here -LiNGAM, PC and GES. These methods are widely used and do not take much time to run. Hence, these are ideal for an introduction to the topic. Other neural network based methods are also available in CDT and the users the encourages to try them out by themselves. \n", "\n", "The documentation for the methods used in as follows:\n", "- LiNGAM [[link]](https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/_modules/cdt/causality/graph/LiNGAM.html)\n", "- PC [[link]](https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/_modules/cdt/causality/graph/PC.html)\n", "- GES [[link]](https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/_modules/cdt/causality/graph/GES.html)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from cdt.causality.graph import LiNGAM, PC, GES\n", "\n", "graphs = {}\n", "graphs_nx = {}\n", "labels = [f'{col}' for i, col in enumerate(data_sachs.columns)]\n", "functions = {\n", " 'LiNGAM' : LiNGAM,\n", " 'PC' : PC,\n", " 'GES' : GES,\n", "}\n", "\n", "for method, lib in functions.items():\n", " obj = lib()\n", " output = obj.predict(data_sachs)\n", " graphs_nx[method] = output\n", " adj_matrix = nx.to_numpy_array(output)\n", " adj_matrix = np.asarray(adj_matrix)\n", " graph_dot = make_graph(adj_matrix, labels)\n", " graphs[method] = graph_dot\n", "\n", "# Visualize graphs\n", "for method, graph in graphs.items():\n", " print(\"Method : %s\"%(method))\n", " display(graph)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, no two methods agree on the graphs. Next we study the causal effects of these different graphs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimate effects using Linear Regression\n", "\n", "Now let us see whether these differences in the graphs also lead to significant differences in the causal estimate of effect of *PIP2* on *PKC*." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for method, graph in graphs.items():\n", " if method != \"LiNGAM\":\n", " continue\n", " print('\\n*****************************************************************************\\n')\n", " print(\"Causal Discovery Method : %s\"%(method))\n", "\n", " # Obtain valid dot format\n", " graph_dot = str_to_dot(graph.source)\n", "\n", " # Define Causal Model\n", " model=CausalModel(\n", " data = data_sachs,\n", " treatment='PIP2',\n", " outcome='PKC',\n", " graph=graph_dot)\n", "\n", " # Identification\n", " identified_estimand = model.identify_effect(proceed_when_unidentifiable=True)\n", " print(identified_estimand)\n", "\n", " # Estimation\n", " estimate = model.estimate_effect(identified_estimand,\n", " method_name=\"backdoor.linear_regression\",\n", " control_value=0,\n", " treatment_value=1,\n", " confidence_intervals=True,\n", " test_significance=True)\n", " print(\"Causal Estimate is \" + str(estimate.value))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the causal estimates obtained, it can be seen that the three estimates differ in different aspects. The graph obtained using LiNGAM contains a backdoor path and instrumental variables. On the other hand, the graph obtained using PC contains a backdoor path and a frontdoor path. However, despite these differences, both obtain the same mean causal estimate.\n", "\n", "The graph obtained using GES contains only a backdoor path with different backdoor variables and obtains a different causal estimate than the first two cases. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Graph Validation\n", "\n", "We compare the graphs obtained with the true causal graph using the causal discovery methods using 2 graph distance metrics - Structural Hamming Distance (SHD) and Structural Intervention Distance (SID). SHD between two graphs is, in simple terms, the number of edge insertions, deletions or flips in order to transform one graph to another graph. SID, on the other hand, is based on a graphical criterion only and quantifies the closeness between two DAGs in terms of their corresponding causal inference statements." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from cdt.metrics import SHD, SHD_CPDAG, SID, SID_CPDAG\n", "from numpy.random import randint\n", "\n", "for method, graph in graphs_nx.items():\n", " print(\"***********************************************************\")\n", " print(\"Method: %s\"%(method))\n", " tar, pred = graph_sachs, graph\n", " print(\"SHD_CPDAG = %f\"%(SHD_CPDAG(tar, pred)))\n", " print(\"SHD = %f\"%(SHD(tar, pred, double_for_anticausal=False)))\n", " print(\"SID_CPDAG = [%f, %f]\"%(SID_CPDAG(tar, pred)))\n", " print(\"SID = %f\"%(SID(tar, pred)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The graph similarity metrics show that the scores are the lowest for the LiNGAM method of graph extraction. Hence, of the three methods used, LiNGAM provides the graph that is most similar to the original graph." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Graph Refutation\n", "\n", "Here, we use the same SHD and SID metric to find out how different the discovered graph are from each other." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import itertools\n", "from numpy.random import randint\n", "from cdt.metrics import SHD, SHD_CPDAG, SID, SID_CPDAG\n", "\n", "# Find combinations of pair of methods to compare\n", "combinations = list(itertools.combinations(graphs_nx, 2))\n", "\n", "for pair in combinations:\n", " print(\"***********************************************************\")\n", " graph1 = graphs_nx[pair[0]]\n", " graph2 = graphs_nx[pair[1]]\n", " print(\"Methods: %s and %s\"%(pair[0], pair[1]))\n", " print(\"SHD_CPDAG = %f\"%(SHD_CPDAG(graph1, graph2)))\n", " print(\"SHD = %f\"%(SHD(graph1, graph2, double_for_anticausal=False)))\n", " print(\"SID_CPDAG = [%f, %f]\"%(SID_CPDAG(graph1, graph2)))\n", " print(\"SID = %f\"%(SID(graph1, graph2)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The values for the metrics show how different the graphs are from each other. A higher distance value implies that the difference between the graphs is more." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.12" }, "metadata": { "interpreter": { "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" } }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": false, "sideBar": true, "skip_h1_title": true, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery example\n", "\n", "The goal of this notebook is to show how causal discovery methods can work with DoWhy. We use discovery methods from [causal-learn](https://github.com/py-why/causal-learn) repo. As we will see, causal discovery methods require appropriate assumptions for the correctness guarantees, adn thus there will be variance across results returned by different methods in practice. These methods, however, may be combined usefully with domain knowledge to construct the final causal graph." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import dowhy\n", "from dowhy import CausalModel\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import graphviz\n", "import networkx as nx \n", "\n", "np.set_printoptions(precision=3, suppress=True)\n", "np.random.seed(0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Utility function\n", "We define a utility function to draw the directed acyclic graph." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def make_graph(adjacency_matrix, labels=None):\n", " idx = np.abs(adjacency_matrix) > 0.01\n", " dirs = np.where(idx)\n", " d = graphviz.Digraph(engine='dot')\n", " names = labels if labels else [f'x{i}' for i in range(len(adjacency_matrix))]\n", " for name in names:\n", " d.node(name)\n", " for to, from_, coef in zip(dirs[0], dirs[1], adjacency_matrix[idx]):\n", " d.edge(names[from_], names[to], label=str(coef))\n", " return d\n", "\n", "def str_to_dot(string):\n", " '''\n", " Converts input string from graphviz library to valid DOT graph format.\n", " '''\n", " graph = string.strip().replace('\\n', ';').replace('\\t','')\n", " graph = graph[:9] + graph[10:-2] + graph[-1] # Removing unnecessary characters from string\n", " return graph" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Experiments on the Auto-MPG dataset\n", "\n", "In this section, we will use a dataset on the technical specification of cars. The dataset is downloaded from UCI Machine Learning Repository. The dataset contains 9 attributes and 398 instances. We do not know the true causal graph for the dataset and will use causal-learn to discover it. The causal graph obtained will then be used to estimate the causal effect.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Load the data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(392, 6)\n" ] }, { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>mpg</th>\n", " <th>cylinders</th>\n", " <th>displacement</th>\n", " <th>horsepower</th>\n", " <th>weight</th>\n", " <th>acceleration</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>18.0</td>\n", " <td>8.0</td>\n", " <td>307.0</td>\n", " <td>130.0</td>\n", " <td>3504.0</td>\n", " <td>12.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>15.0</td>\n", " <td>8.0</td>\n", " <td>350.0</td>\n", " <td>165.0</td>\n", " <td>3693.0</td>\n", " <td>11.5</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>18.0</td>\n", " <td>8.0</td>\n", " <td>318.0</td>\n", " <td>150.0</td>\n", " <td>3436.0</td>\n", " <td>11.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>16.0</td>\n", " <td>8.0</td>\n", " <td>304.0</td>\n", " <td>150.0</td>\n", " <td>3433.0</td>\n", " <td>12.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>17.0</td>\n", " <td>8.0</td>\n", " <td>302.0</td>\n", " <td>140.0</td>\n", " <td>3449.0</td>\n", " <td>10.5</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " mpg cylinders displacement horsepower weight acceleration\n", "0 18.0 8.0 307.0 130.0 3504.0 12.0\n", "1 15.0 8.0 350.0 165.0 3693.0 11.5\n", "2 18.0 8.0 318.0 150.0 3436.0 11.0\n", "3 16.0 8.0 304.0 150.0 3433.0 12.0\n", "4 17.0 8.0 302.0 140.0 3449.0 10.5" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_mpg = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data-original',\n", " delim_whitespace=True, header=None,\n", " names = ['mpg', 'cylinders', 'displacement',\n", " 'horsepower', 'weight', 'acceleration',\n", " 'model year', 'origin', 'car name'])\n", "data_mpg.dropna(inplace=True)\n", "data_mpg.drop(['model year', 'origin', 'car name'], axis=1, inplace=True)\n", "print(data_mpg.shape)\n", "data_mpg.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery with causal-learn\n", "\n", "We use the causal-learn library to perform causal discovery on the Auto-MPG dataset. We use three methods for causal discovery here: PC, FCI and GES. These methods are widely used and do not take much time to run. Hence, these are ideal for an introduction to the topic. Causal-learn provides a comprehensive list of well-tested causal-discovery methods, and readers are welcome to explore.\n", "\n", "The documentation for the methods used are as follows:\n", "- PC [[link]](https://causal-learn.readthedocs.io/en/latest/search_methods_index/Constraint-based%20causal%20discovery%20methods/PC.html)\n", "- GES [[link]](https://causal-learn.readthedocs.io/en/latest/search_methods_index/Score-based%20causal%20discovery%20methods/GES.html)\n", "- LiNGAM [[link]](https://causal-learn.readthedocs.io/en/latest/search_methods_index/Causal%20discovery%20methods%20based%20on%20constrained%20functional%20causal%20models/lingam.html#ica-based-lingam)\n", "\n", "More methods could be found in the causal-learn documentation [[link]](https://causal-learn.readthedocs.io/en/latest/)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We first try the PC algorithm with default parameters." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1ed197e9f5ec42c8bf7fc51c5ece4485", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/6 [00:00<?, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from causallearn.search.ConstraintBased.PC import pc\n", "\n", "labels = [f'{col}' for i, col in enumerate(data_mpg.columns)]\n", "data = data_mpg.to_numpy()\n", "\n", "cg = pc(data)\n", "\n", "# Visualization using pydot\n", "from causallearn.utils.GraphUtils import GraphUtils\n", "import matplotlib.image as mpimg\n", "import matplotlib.pyplot as plt\n", "import io\n", "\n", "pyd = GraphUtils.to_pydot(cg.G, labels=labels)\n", "tmp_png = pyd.create_png(f=\"png\")\n", "fp = io.BytesIO(tmp_png)\n", "img = mpimg.imread(fp, format='png')\n", "plt.axis('off')\n", "plt.imshow(img)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we have a causal graph discovered by PC. Let us also try GES to see its result." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from causallearn.search.ScoreBased.GES import ges\n", "\n", "# default parameters\n", "Record = ges(data)\n", "\n", "# Visualization using pydot\n", "from causallearn.utils.GraphUtils import GraphUtils\n", "import matplotlib.image as mpimg\n", "import matplotlib.pyplot as plt\n", "import io\n", "\n", "pyd = GraphUtils.to_pydot(Record['G'], labels=labels)\n", "tmp_png = pyd.create_png(f=\"png\")\n", "fp = io.BytesIO(tmp_png)\n", "img = mpimg.imread(fp, format='png')\n", "plt.axis('off')\n", "plt.imshow(img)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Well, these two results are different, which is not rare when applying causal discovery on real-world dataset, since the required assumptions on the data-generating process are hard to verify.\n", "\n", "In addition, the graphs returned by PC and GES are CPDAGs instead of DAGs, so it is possible to have undirected edges (e.g., the result returned by GES). Thus, causal effect estimataion is difficult for those methods, since there may be absence of backdoor, instrumental or frontdoor variables. In order to get a DAG, we decide to try LiNGAM on our dataset." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n", "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n", " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n", "<!-- Generated by graphviz version 2.43.0 (0)\n", " -->\n", "<!-- Title: %3 Pages: 1 -->\n", "<svg width=\"369pt\" height=\"392pt\"\n", " viewBox=\"0.00 0.00 369.40 392.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n", "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 388)\">\n", "<title>%3</title>\n", "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-388 365.4,-388 365.4,4 -4,4\"/>\n", "<!-- mpg -->\n", "<g id=\"node1\" class=\"node\">\n", "<title>mpg</title>\n", "<ellipse fill=\"none\" stroke=\"black\" cx=\"34.8\" cy=\"-279\" rx=\"31.4\" 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"text/plain": [ "<graphviz.graphs.Digraph at 0x7f957464c040>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from causallearn.search.FCMBased import lingam\n", "model = lingam.ICALiNGAM()\n", "model.fit(data)\n", "\n", "from causallearn.search.FCMBased.lingam.utils import make_dot\n", "make_dot(model.adjacency_matrix_, labels=labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we have a DAG and are ready to estimate the causal effects based on that." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimate causal effects using Linear Regression\n", "\n", "Now let us see the estimate of causal effect of *mpg* on *weight*." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Estimand type: EstimandType.NONPARAMETRIC_ATE\n", "\n", "### Estimand : 1\n", "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", "──────(E[weight|cylinders])\n", "d[mpg] \n", "Estimand assumption 1, Unconfoundedness: If U→{mpg} and U→weight then P(weight|mpg,cylinders,U) = P(weight|mpg,cylinders)\n", "\n", "### Estimand : 2\n", "Estimand name: iv\n", "No such variable(s) found!\n", "\n", "### Estimand : 3\n", "Estimand name: frontdoor\n", "No such variable(s) found!\n", "\n", "Causal Estimate is -38.940973656209735\n" ] } ], "source": [ "# Obtain valid dot format\n", "graph_dot = make_graph(model.adjacency_matrix_, labels=labels)\n", "\n", "# Define Causal Model\n", "model=CausalModel(\n", " data = data_mpg,\n", " treatment='mpg',\n", " outcome='weight',\n", " graph=str_to_dot(graph_dot.source))\n", "\n", "# Identification\n", "identified_estimand = model.identify_effect(proceed_when_unidentifiable=True)\n", "print(identified_estimand)\n", "\n", "# Estimation\n", "estimate = model.estimate_effect(identified_estimand,\n", " method_name=\"backdoor.linear_regression\",\n", " control_value=0,\n", " treatment_value=1,\n", " confidence_intervals=True,\n", " test_significance=True)\n", "print(\"Causal Estimate is \" + str(estimate.value))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Experiments on the Sachs dataset\n", "\n", "The dataset consists of the simultaneous measurements of 11 phosphorylated proteins and phospholipids derived from thousands of individual primary immune system cells, subjected to both general and specific molecular interventions (Sachs et al., 2005).\n", "\n", "The specifications of the dataset are as follows - \n", "- Number of nodes: 11\n", "- Number of arcs: 17\n", "- Number of parameters: 178\n", "- Average Markov blanket size: 3.09\n", "- Average degree: 3.09\n", "- Maximum in-degree: 3\n", "- Number of instances: 7466" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Load the data" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(7466, 11)\n", "['raf', 'mek', 'plc', 'pip2', 'pip3', 'erk', 'akt', 'pka', 'pkc', 'p38', 'jnk']\n" ] } ], "source": [ "from causallearn.utils.Dataset import load_dataset\n", "\n", "data_sachs, labels = load_dataset(\"sachs\")\n", "\n", "print(data.shape)\n", "print(labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Causal Discovery with causal-learn\n", "\n", "We use the three causal discovery methods mentioned above (PC, GES, and LiNGAM) to find the causal graphs." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, let us take a look at how PC works." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "bc0f31d1492e4934994a6d4ba68f1ad3", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/11 [00:00<?, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "graphs = {}\n", "graphs_nx = {}\n", "labels = [f'{col}' for i, col in enumerate(labels)]\n", "data = data_sachs\n", "\n", "from causallearn.search.ConstraintBased.PC import pc\n", "\n", "cg = pc(data)\n", "\n", "# Visualization using pydot\n", "from causallearn.utils.GraphUtils import GraphUtils\n", "import matplotlib.image as mpimg\n", "import matplotlib.pyplot as plt\n", "import io\n", "\n", "pyd = GraphUtils.to_pydot(cg.G, labels=labels)\n", "tmp_png = pyd.create_png(f=\"png\")\n", "fp = io.BytesIO(tmp_png)\n", "img = mpimg.imread(fp, format='png')\n", "plt.axis('off')\n", "plt.imshow(img)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then, let us try GES." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from causallearn.search.ScoreBased.GES import ges\n", "\n", "# default parameters\n", "Record = ges(data)\n", "\n", "# Visualization using pydot\n", "from causallearn.utils.GraphUtils import GraphUtils\n", "import matplotlib.image as mpimg\n", "import matplotlib.pyplot as plt\n", "import io\n", "\n", "pyd = GraphUtils.to_pydot(Record['G'], labels=labels)\n", "tmp_png = pyd.create_png(f=\"png\")\n", "fp = io.BytesIO(tmp_png)\n", "img = mpimg.imread(fp, format='png')\n", "plt.axis('off')\n", "plt.imshow(img)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And also LiNGAM." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n", "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n", " 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points=\"674.5,-46.18 671,-36.18 667.5,-46.18 674.5,-46.18\"/>\n", "<text text-anchor=\"middle\" x=\"687\" y=\"-57.8\" font-family=\"Times,serif\" font-size=\"14.00\">0.04</text>\n", "</g>\n", "</g>\n", "</svg>\n" ], "text/plain": [ "<graphviz.graphs.Digraph at 0x7f96cd974ca0>" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from causallearn.search.FCMBased import lingam\n", "model = lingam.ICALiNGAM()\n", "model.fit(data)\n", "\n", "from causallearn.search.FCMBased.lingam.utils import make_dot\n", "make_dot(model.adjacency_matrix_, labels=labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimate effects using Linear Regression\n", "\n", "Similarly, let us use the DAG returned by LiNGAM to estimate the causal effect of *PIP2* on *PKC*." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Estimand type: EstimandType.NONPARAMETRIC_ATE\n", "\n", "### Estimand : 1\n", "Estimand name: backdoor\n", "Estimand expression:\n", " d \n", "───────(E[pkc|plc,pip3])\n", "d[pip₂] \n", "Estimand assumption 1, Unconfoundedness: If U→{pip2} and U→pkc then P(pkc|pip2,plc,pip3,U) = P(pkc|pip2,plc,pip3)\n", "\n", "### Estimand : 2\n", "Estimand name: iv\n", "No such variable(s) found!\n", "\n", "### Estimand : 3\n", "Estimand name: frontdoor\n", "No such variable(s) found!\n", "\n", "Causal Estimate is 0.03397189228452291\n" ] } ], "source": [ "# Obtain valid dot format\n", "graph_dot = make_graph(model.adjacency_matrix_, labels=labels)\n", "\n", "data_df = pd.DataFrame(data=data, columns=labels)\n", "\n", "# Define Causal Model\n", "model_est=CausalModel(\n", " data = data_df,\n", " treatment='pip2',\n", " outcome='pkc',\n", " graph=str_to_dot(graph_dot.source))\n", "\n", "# Identification\n", "identified_estimand = model_est.identify_effect(proceed_when_unidentifiable=False)\n", "print(identified_estimand)\n", "\n", "# Estimation\n", "estimate = model_est.estimate_effect(identified_estimand,\n", " method_name=\"backdoor.linear_regression\",\n", " control_value=0,\n", " treatment_value=1,\n", " confidence_intervals=True,\n", " test_significance=True)\n", "print(\"Causal Estimate is \" + str(estimate.value))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.17" }, "metadata": { "interpreter": { "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" } }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": false, "sideBar": true, "skip_h1_title": true, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }

kunwuz

67b305db5224bf718067a21acfe4baa92a7d2c8c

7eb4a0c253514a920588d1ab222e1aeb5e07cb51

Also, 1 final reference to CDT on an earlier cell "Experiments on the Auto-MPG dataset")

emrekiciman

py-why/dowhy

Invariant nodes removal

Feature - Now users can enter the list of invariant nodes to the distribution_change function to remove them from analysis

2023-08-15 12:59:48+00:00

2023-08-24 16:12:46+00:00

dowhy/gcm/auto.py

import warnings from enum import Enum, auto from functools import partial from typing import Callable, List, Optional, Union import numpy as np import pandas as pd from joblib import Parallel, delayed from sklearn import metrics from sklearn.exceptions import ConvergenceWarning from sklearn.linear_model import LinearRegression, LogisticRegression from sklearn.model_selection import KFold, train_test_split from sklearn.preprocessing import MultiLabelBinarizer from dowhy.gcm import config from dowhy.gcm.causal_mechanisms import AdditiveNoiseModel, ClassifierFCM from dowhy.gcm.causal_models import CAUSAL_MECHANISM, ProbabilisticCausalModel, validate_causal_model_assignment from dowhy.gcm.ml import ( ClassificationModel, PredictionModel, create_hist_gradient_boost_classifier, create_hist_gradient_boost_regressor, create_lasso_regressor, create_linear_regressor, create_logistic_regression_classifier, create_random_forest_regressor, create_ridge_regressor, create_support_vector_regressor, ) from dowhy.gcm.ml.classification import ( create_ada_boost_classifier, create_extra_trees_classifier, create_gaussian_nb_classifier, create_knn_classifier, create_polynom_logistic_regression_classifier, create_random_forest_classifier, create_support_vector_classifier, ) from dowhy.gcm.ml.regression import ( create_ada_boost_regressor, create_extra_trees_regressor, create_knn_regressor, create_polynom_regressor, ) from dowhy.gcm.stochastic_models import EmpiricalDistribution from dowhy.gcm.util.general import ( auto_apply_encoders, auto_fit_encoders, is_categorical, set_random_seed, shape_into_2d, ) from dowhy.graph import get_ordered_predecessors, is_root_node _LIST_OF_POTENTIAL_CLASSIFIERS_GOOD = [ partial(create_logistic_regression_classifier, max_iter=1000), create_hist_gradient_boost_classifier, ] _LIST_OF_POTENTIAL_REGRESSORS_GOOD = [ create_linear_regressor, create_hist_gradient_boost_regressor, ] _LIST_OF_POTENTIAL_CLASSIFIERS_BETTER = _LIST_OF_POTENTIAL_CLASSIFIERS_GOOD + [ create_random_forest_classifier, create_extra_trees_classifier, create_support_vector_classifier, create_knn_classifier, create_gaussian_nb_classifier, create_ada_boost_classifier, ] _LIST_OF_POTENTIAL_REGRESSORS_BETTER = _LIST_OF_POTENTIAL_REGRESSORS_GOOD + [ create_ridge_regressor, create_polynom_regressor, partial(create_lasso_regressor, max_iter=5000), create_random_forest_regressor, create_support_vector_regressor, create_extra_trees_regressor, create_knn_regressor, create_ada_boost_regressor, ] class AssignmentQuality(Enum): GOOD = auto() BETTER = auto() BEST = auto() def assign_causal_mechanisms( causal_model: ProbabilisticCausalModel, based_on: pd.DataFrame, quality: AssignmentQuality = AssignmentQuality.GOOD, override_models: bool = False, ) -> None: """Automatically assigns appropriate causal models. If causal models are already assigned to nodes and override_models is set to False, this function only validates the assignments with respect to the graph structure. Here, the validation checks whether root nodes have StochasticModels and non-root ConditionalStochasticModels assigned. :param causal_model: The causal model to whose nodes to assign causal models. :param based_on: Jointly sampled data corresponding to the nodes of the given graph. :param quality: AssignmentQuality for the automatic model selection and model accuracy. This changes the type of prediction model and time spent on the selection. Options are: - AssignmentQuality.GOOD: Compares a linear, polynomial and gradient boost model on small test-training split of the data. The best performing model is then selected. Model selection speed: Fast Model training speed: Fast Model inference speed: Fast Model accuracy: Medium - AssignmentQuality.BETTER: Compares multiple model types and uses the one with the best performance averaged over multiple splits of the training data. By default, the model with the smallest root mean squared error is selected for regression problems and the model with the highest F1 score is selected for classification problems. For a list of possible models, see _LIST_OF_POTENTIAL_REGRESSORS_BETTER and _LIST_OF_POTENTIAL_CLASSIFIERS_BETTER, respectively. Model selection speed: Medium Model training speed: Fast Model inference speed: Fast Model accuracy: Good - AssignmentQuality.BEST: Uses an AutoGluon (auto ML) model with default settings defined by the AutoGluon wrapper. While the model selection itself is fast, the training and inference speed can be significantly slower than in the other options. NOTE: This requires the optional autogluon.tabular dependency. Model selection speed: Instant Model training speed: Slow Model inference speed: Slow-Medium Model accuracy: Best :param override_models: If set to True, existing model assignments are replaced with automatically selected ones. If set to False, the assigned models are only validated with respect to the graph structure. :return: None """ for node in causal_model.graph.nodes: if not override_models and CAUSAL_MECHANISM in causal_model.graph.nodes[node]: validate_causal_model_assignment(causal_model.graph, node) continue if is_root_node(causal_model.graph, node): causal_model.set_causal_mechanism(node, EmpiricalDistribution()) else: prediction_model = select_model( based_on[get_ordered_predecessors(causal_model.graph, node)].to_numpy(), based_on[node].to_numpy(), quality, ) if isinstance(prediction_model, ClassificationModel): causal_model.set_causal_mechanism(node, ClassifierFCM(prediction_model)) else: causal_model.set_causal_mechanism(node, AdditiveNoiseModel(prediction_model)) def select_model( X: np.ndarray, Y: np.ndarray, model_selection_quality: AssignmentQuality ) -> Union[PredictionModel, ClassificationModel]: if model_selection_quality == AssignmentQuality.BEST: try: from dowhy.gcm.ml.autogluon import AutoGluonClassifier, AutoGluonRegressor if is_categorical(Y): return AutoGluonClassifier() else: return AutoGluonRegressor() except ImportError: raise RuntimeError( "AutoGluon module not found! For the BEST auto assign quality, consider installing the " "optional AutoGluon dependency." ) elif model_selection_quality == AssignmentQuality.GOOD: list_of_regressor = list(_LIST_OF_POTENTIAL_REGRESSORS_GOOD) list_of_classifier = list(_LIST_OF_POTENTIAL_CLASSIFIERS_GOOD) model_selection_splits = 2 elif model_selection_quality == AssignmentQuality.BETTER: list_of_regressor = list(_LIST_OF_POTENTIAL_REGRESSORS_BETTER) list_of_classifier = list(_LIST_OF_POTENTIAL_CLASSIFIERS_BETTER) model_selection_splits = 5 else: raise ValueError("Invalid model selection quality.") if auto_apply_encoders(X, auto_fit_encoders(X)).shape[1] <= 5: # Avoid too many features list_of_regressor += [create_polynom_regressor] list_of_classifier += [partial(create_polynom_logistic_regression_classifier, max_iter=1000)] if is_categorical(Y): return find_best_model(list_of_classifier, X, Y, model_selection_splits=model_selection_splits)() else: return find_best_model(list_of_regressor, X, Y, model_selection_splits=model_selection_splits)() def has_linear_relationship(X: np.ndarray, Y: np.ndarray, max_num_samples: int = 3000) -> bool: X, Y = shape_into_2d(X, Y) target_is_categorical = is_categorical(Y) # Making sure there are at least 30% test samples. num_trainings_samples = min(max_num_samples, round(X.shape[0] * 0.7)) num_test_samples = min(X.shape[0] - num_trainings_samples, max_num_samples) if target_is_categorical: all_classes, indices, counts = np.unique(Y, return_counts=True, return_index=True) for i in range(all_classes.size): # Making sure that there are at least 2 samples from one class (here, simply duplicate the point). if counts[i] == 1: X = np.row_stack([X, X[indices[i], :]]) Y = np.row_stack([Y, Y[indices[i], :]]) x_train, x_test, y_train, y_test = train_test_split( X, Y, train_size=num_trainings_samples, test_size=num_test_samples, stratify=Y ) else: x_train, x_test, y_train, y_test = train_test_split( X, Y, train_size=num_trainings_samples, test_size=num_test_samples ) encoders = auto_fit_encoders(x_train, y_train) x_train = auto_apply_encoders(x_train, encoders) x_test = auto_apply_encoders(x_test, encoders) if target_is_categorical: linear_mdl = LogisticRegression(max_iter=1000) nonlinear_mdl = create_hist_gradient_boost_classifier() linear_mdl.fit(x_train, y_train.squeeze()) nonlinear_mdl.fit(x_train, y_train.squeeze()) # Compare number of correct classifications. return np.sum(shape_into_2d(linear_mdl.predict(x_test)) == y_test) >= np.sum( shape_into_2d(nonlinear_mdl.predict(x_test)) == y_test ) else: linear_mdl = LinearRegression() nonlinear_mdl = create_hist_gradient_boost_regressor() linear_mdl.fit(x_train, y_train.squeeze()) nonlinear_mdl.fit(x_train, y_train.squeeze()) return np.mean((y_test - shape_into_2d(linear_mdl.predict(x_test))) ** 2) <= np.mean( (y_test - shape_into_2d(nonlinear_mdl.predict(x_test))) ** 2 ) def find_best_model( prediction_model_factories: List[Callable[[], PredictionModel]], X: np.ndarray, Y: np.ndarray, metric: Optional[Callable[[np.ndarray, np.ndarray], float]] = None, max_samples_per_split: int = 10000, model_selection_splits: int = 5, n_jobs: Optional[int] = None, ) -> Callable[[], PredictionModel]: n_jobs = config.default_n_jobs if n_jobs is None else n_jobs X, Y = shape_into_2d(X, Y) is_classification_problem = isinstance(prediction_model_factories[0](), ClassificationModel) if metric is None: if is_classification_problem: metric = lambda y_true, y_preds: -metrics.f1_score( y_true, y_preds, average="macro", zero_division=0 ) # Higher score is better else: metric = metrics.mean_squared_error labelBinarizer = None if is_classification_problem: labelBinarizer = MultiLabelBinarizer() labelBinarizer.fit(Y) kfolds = list(KFold(n_splits=model_selection_splits).split(range(X.shape[0]))) def estimate_average_score(prediction_model_factory: Callable[[], PredictionModel], random_seed: int) -> float: set_random_seed(random_seed) average_result = 0 with warnings.catch_warnings(): warnings.filterwarnings("ignore", category=ConvergenceWarning) for train_indices, test_indices in kfolds: model_instance = prediction_model_factory() model_instance.fit(X[train_indices[:max_samples_per_split]], Y[train_indices[:max_samples_per_split]]) y_true = Y[test_indices[:max_samples_per_split]] y_pred = model_instance.predict(X[test_indices[:max_samples_per_split]]) if labelBinarizer is not None: y_true = labelBinarizer.transform(y_true) y_pred = labelBinarizer.transform(y_pred) average_result += metric(y_true, y_pred) return average_result / model_selection_splits random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(prediction_model_factories)) average_metric_scores = Parallel(n_jobs=n_jobs)( delayed(estimate_average_score)(prediction_model_factory, int(random_seed)) for prediction_model_factory, random_seed in zip(prediction_model_factories, random_seeds) ) return sorted(zip(prediction_model_factories, average_metric_scores), key=lambda x: x[1])[0][0]

import warnings from enum import Enum, auto from functools import partial from typing import Callable, List, Optional, Union import numpy as np import pandas as pd from joblib import Parallel, delayed from sklearn import metrics from sklearn.exceptions import ConvergenceWarning from sklearn.linear_model import LinearRegression, LogisticRegression from sklearn.model_selection import KFold, train_test_split from sklearn.preprocessing import MultiLabelBinarizer from dowhy.gcm import config from dowhy.gcm.causal_mechanisms import AdditiveNoiseModel, ClassifierFCM from dowhy.gcm.causal_models import CAUSAL_MECHANISM, ProbabilisticCausalModel, validate_causal_model_assignment from dowhy.gcm.ml import ( ClassificationModel, PredictionModel, create_hist_gradient_boost_classifier, create_hist_gradient_boost_regressor, create_lasso_regressor, create_linear_regressor, create_logistic_regression_classifier, create_random_forest_regressor, create_ridge_regressor, create_support_vector_regressor, ) from dowhy.gcm.ml.classification import ( create_ada_boost_classifier, create_extra_trees_classifier, create_gaussian_nb_classifier, create_knn_classifier, create_polynom_logistic_regression_classifier, create_random_forest_classifier, create_support_vector_classifier, ) from dowhy.gcm.ml.regression import ( create_ada_boost_regressor, create_extra_trees_regressor, create_knn_regressor, create_polynom_regressor, ) from dowhy.gcm.stochastic_models import EmpiricalDistribution from dowhy.gcm.util.general import ( auto_apply_encoders, auto_fit_encoders, is_categorical, set_random_seed, shape_into_2d, ) from dowhy.graph import get_ordered_predecessors, is_root_node _LIST_OF_POTENTIAL_CLASSIFIERS_GOOD = [ partial(create_logistic_regression_classifier, max_iter=1000), create_hist_gradient_boost_classifier, ] _LIST_OF_POTENTIAL_REGRESSORS_GOOD = [ create_linear_regressor, create_hist_gradient_boost_regressor, ] _LIST_OF_POTENTIAL_CLASSIFIERS_BETTER = _LIST_OF_POTENTIAL_CLASSIFIERS_GOOD + [ create_random_forest_classifier, create_extra_trees_classifier, create_support_vector_classifier, create_knn_classifier, create_gaussian_nb_classifier, create_ada_boost_classifier, ] _LIST_OF_POTENTIAL_REGRESSORS_BETTER = _LIST_OF_POTENTIAL_REGRESSORS_GOOD + [ create_ridge_regressor, create_polynom_regressor, partial(create_lasso_regressor, max_iter=5000), create_random_forest_regressor, create_support_vector_regressor, create_extra_trees_regressor, create_knn_regressor, create_ada_boost_regressor, ] class AssignmentQuality(Enum): GOOD = auto() BETTER = auto() BEST = auto() def assign_causal_mechanisms( causal_model: ProbabilisticCausalModel, based_on: pd.DataFrame, quality: AssignmentQuality = AssignmentQuality.GOOD, override_models: bool = False, ) -> None: """Automatically assigns appropriate causal models. If causal models are already assigned to nodes and override_models is set to False, this function only validates the assignments with respect to the graph structure. Here, the validation checks whether root nodes have StochasticModels and non-root ConditionalStochasticModels assigned. :param causal_model: The causal model to whose nodes to assign causal models. :param based_on: Jointly sampled data corresponding to the nodes of the given graph. :param quality: AssignmentQuality for the automatic model selection and model accuracy. This changes the type of prediction model and time spent on the selection. Options are: - AssignmentQuality.GOOD: Compares a linear, polynomial and gradient boost model on small test-training split of the data. The best performing model is then selected. Model selection speed: Fast Model training speed: Fast Model inference speed: Fast Model accuracy: Medium - AssignmentQuality.BETTER: Compares multiple model types and uses the one with the best performance averaged over multiple splits of the training data. By default, the model with the smallest root mean squared error is selected for regression problems and the model with the highest F1 score is selected for classification problems. For a list of possible models, see _LIST_OF_POTENTIAL_REGRESSORS_BETTER and _LIST_OF_POTENTIAL_CLASSIFIERS_BETTER, respectively. Model selection speed: Medium Model training speed: Fast Model inference speed: Fast Model accuracy: Good - AssignmentQuality.BEST: Uses an AutoGluon (auto ML) model with default settings defined by the AutoGluon wrapper. While the model selection itself is fast, the training and inference speed can be significantly slower than in the other options. NOTE: This requires the optional autogluon.tabular dependency. Model selection speed: Instant Model training speed: Slow Model inference speed: Slow-Medium Model accuracy: Best :param override_models: If set to True, existing model assignments are replaced with automatically selected ones. If set to False, the assigned models are only validated with respect to the graph structure. :return: None """ for node in causal_model.graph.nodes: if not override_models and CAUSAL_MECHANISM in causal_model.graph.nodes[node]: validate_causal_model_assignment(causal_model.graph, node) continue assign_causal_mechanism_node(causal_model, node, based_on, quality) def assign_causal_mechanism_node( causal_model: ProbabilisticCausalModel, node: str, based_on: pd.DataFrame, quality: AssignmentQuality = AssignmentQuality.GOOD, ) -> None: if is_root_node(causal_model.graph, node): causal_model.set_causal_mechanism(node, EmpiricalDistribution()) else: prediction_model = select_model( based_on[get_ordered_predecessors(causal_model.graph, node)].to_numpy(), based_on[node].to_numpy(), quality, ) if isinstance(prediction_model, ClassificationModel): causal_model.set_causal_mechanism(node, ClassifierFCM(prediction_model)) else: causal_model.set_causal_mechanism(node, AdditiveNoiseModel(prediction_model)) def select_model( X: np.ndarray, Y: np.ndarray, model_selection_quality: AssignmentQuality ) -> Union[PredictionModel, ClassificationModel]: if model_selection_quality == AssignmentQuality.BEST: try: from dowhy.gcm.ml.autogluon import AutoGluonClassifier, AutoGluonRegressor if is_categorical(Y): return AutoGluonClassifier() else: return AutoGluonRegressor() except ImportError: raise RuntimeError( "AutoGluon module not found! For the BEST auto assign quality, consider installing the " "optional AutoGluon dependency." ) elif model_selection_quality == AssignmentQuality.GOOD: list_of_regressor = list(_LIST_OF_POTENTIAL_REGRESSORS_GOOD) list_of_classifier = list(_LIST_OF_POTENTIAL_CLASSIFIERS_GOOD) model_selection_splits = 2 elif model_selection_quality == AssignmentQuality.BETTER: list_of_regressor = list(_LIST_OF_POTENTIAL_REGRESSORS_BETTER) list_of_classifier = list(_LIST_OF_POTENTIAL_CLASSIFIERS_BETTER) model_selection_splits = 5 else: raise ValueError("Invalid model selection quality.") if auto_apply_encoders(X, auto_fit_encoders(X)).shape[1] <= 5: # Avoid too many features list_of_regressor += [create_polynom_regressor] list_of_classifier += [partial(create_polynom_logistic_regression_classifier, max_iter=1000)] if is_categorical(Y): return find_best_model(list_of_classifier, X, Y, model_selection_splits=model_selection_splits)() else: return find_best_model(list_of_regressor, X, Y, model_selection_splits=model_selection_splits)() def has_linear_relationship(X: np.ndarray, Y: np.ndarray, max_num_samples: int = 3000) -> bool: X, Y = shape_into_2d(X, Y) target_is_categorical = is_categorical(Y) # Making sure there are at least 30% test samples. num_trainings_samples = min(max_num_samples, round(X.shape[0] * 0.7)) num_test_samples = min(X.shape[0] - num_trainings_samples, max_num_samples) if target_is_categorical: all_classes, indices, counts = np.unique(Y, return_counts=True, return_index=True) for i in range(all_classes.size): # Making sure that there are at least 2 samples from one class (here, simply duplicate the point). if counts[i] == 1: X = np.row_stack([X, X[indices[i], :]]) Y = np.row_stack([Y, Y[indices[i], :]]) x_train, x_test, y_train, y_test = train_test_split( X, Y, train_size=num_trainings_samples, test_size=num_test_samples, stratify=Y ) else: x_train, x_test, y_train, y_test = train_test_split( X, Y, train_size=num_trainings_samples, test_size=num_test_samples ) encoders = auto_fit_encoders(x_train, y_train) x_train = auto_apply_encoders(x_train, encoders) x_test = auto_apply_encoders(x_test, encoders) if target_is_categorical: linear_mdl = LogisticRegression(max_iter=1000) nonlinear_mdl = create_hist_gradient_boost_classifier() linear_mdl.fit(x_train, y_train.squeeze()) nonlinear_mdl.fit(x_train, y_train.squeeze()) # Compare number of correct classifications. return np.sum(shape_into_2d(linear_mdl.predict(x_test)) == y_test) >= np.sum( shape_into_2d(nonlinear_mdl.predict(x_test)) == y_test ) else: linear_mdl = LinearRegression() nonlinear_mdl = create_hist_gradient_boost_regressor() linear_mdl.fit(x_train, y_train.squeeze()) nonlinear_mdl.fit(x_train, y_train.squeeze()) return np.mean((y_test - shape_into_2d(linear_mdl.predict(x_test))) ** 2) <= np.mean( (y_test - shape_into_2d(nonlinear_mdl.predict(x_test))) ** 2 ) def find_best_model( prediction_model_factories: List[Callable[[], PredictionModel]], X: np.ndarray, Y: np.ndarray, metric: Optional[Callable[[np.ndarray, np.ndarray], float]] = None, max_samples_per_split: int = 10000, model_selection_splits: int = 5, n_jobs: Optional[int] = None, ) -> Callable[[], PredictionModel]: n_jobs = config.default_n_jobs if n_jobs is None else n_jobs X, Y = shape_into_2d(X, Y) is_classification_problem = isinstance(prediction_model_factories[0](), ClassificationModel) if metric is None: if is_classification_problem: metric = lambda y_true, y_preds: -metrics.f1_score( y_true, y_preds, average="macro", zero_division=0 ) # Higher score is better else: metric = metrics.mean_squared_error labelBinarizer = None if is_classification_problem: labelBinarizer = MultiLabelBinarizer() labelBinarizer.fit(Y) kfolds = list(KFold(n_splits=model_selection_splits).split(range(X.shape[0]))) def estimate_average_score(prediction_model_factory: Callable[[], PredictionModel], random_seed: int) -> float: set_random_seed(random_seed) average_result = 0 with warnings.catch_warnings(): warnings.filterwarnings("ignore", category=ConvergenceWarning) for train_indices, test_indices in kfolds: model_instance = prediction_model_factory() model_instance.fit(X[train_indices[:max_samples_per_split]], Y[train_indices[:max_samples_per_split]]) y_true = Y[test_indices[:max_samples_per_split]] y_pred = model_instance.predict(X[test_indices[:max_samples_per_split]]) if labelBinarizer is not None: y_true = labelBinarizer.transform(y_true) y_pred = labelBinarizer.transform(y_pred) average_result += metric(y_true, y_pred) return average_result / model_selection_splits random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(prediction_model_factories)) average_metric_scores = Parallel(n_jobs=n_jobs)( delayed(estimate_average_score)(prediction_model_factory, int(random_seed)) for prediction_model_factory, random_seed in zip(prediction_model_factories, random_seeds) ) return sorted(zip(prediction_model_factories, average_metric_scores), key=lambda x: x[1])[0][0]

bhatt-priyadutt

734b61671d130cf21a4e437084b9083dbd5c4a39

b4f80dcb2251a03aff4c816d7c6f6eb2aaf98e73

Good idea taking this is out as a separate function! It is probably useful for other use cases as well. Just a minor suggestion; consider renaming it to `assign_causal_mechanism_node` to be more consistent with the names of other methods.

bloebp

py-why/dowhy

Invariant nodes removal

Feature - Now users can enter the list of invariant nodes to the distribution_change function to remove them from analysis

2023-08-15 12:59:48+00:00

2023-08-24 16:12:46+00:00

dowhy/gcm/auto.py

import warnings from enum import Enum, auto from functools import partial from typing import Callable, List, Optional, Union import numpy as np import pandas as pd from joblib import Parallel, delayed from sklearn import metrics from sklearn.exceptions import ConvergenceWarning from sklearn.linear_model import LinearRegression, LogisticRegression from sklearn.model_selection import KFold, train_test_split from sklearn.preprocessing import MultiLabelBinarizer from dowhy.gcm import config from dowhy.gcm.causal_mechanisms import AdditiveNoiseModel, ClassifierFCM from dowhy.gcm.causal_models import CAUSAL_MECHANISM, ProbabilisticCausalModel, validate_causal_model_assignment from dowhy.gcm.ml import ( ClassificationModel, PredictionModel, create_hist_gradient_boost_classifier, create_hist_gradient_boost_regressor, create_lasso_regressor, create_linear_regressor, create_logistic_regression_classifier, create_random_forest_regressor, create_ridge_regressor, create_support_vector_regressor, ) from dowhy.gcm.ml.classification import ( create_ada_boost_classifier, create_extra_trees_classifier, create_gaussian_nb_classifier, create_knn_classifier, create_polynom_logistic_regression_classifier, create_random_forest_classifier, create_support_vector_classifier, ) from dowhy.gcm.ml.regression import ( create_ada_boost_regressor, create_extra_trees_regressor, create_knn_regressor, create_polynom_regressor, ) from dowhy.gcm.stochastic_models import EmpiricalDistribution from dowhy.gcm.util.general import ( auto_apply_encoders, auto_fit_encoders, is_categorical, set_random_seed, shape_into_2d, ) from dowhy.graph import get_ordered_predecessors, is_root_node _LIST_OF_POTENTIAL_CLASSIFIERS_GOOD = [ partial(create_logistic_regression_classifier, max_iter=1000), create_hist_gradient_boost_classifier, ] _LIST_OF_POTENTIAL_REGRESSORS_GOOD = [ create_linear_regressor, create_hist_gradient_boost_regressor, ] _LIST_OF_POTENTIAL_CLASSIFIERS_BETTER = _LIST_OF_POTENTIAL_CLASSIFIERS_GOOD + [ create_random_forest_classifier, create_extra_trees_classifier, create_support_vector_classifier, create_knn_classifier, create_gaussian_nb_classifier, create_ada_boost_classifier, ] _LIST_OF_POTENTIAL_REGRESSORS_BETTER = _LIST_OF_POTENTIAL_REGRESSORS_GOOD + [ create_ridge_regressor, create_polynom_regressor, partial(create_lasso_regressor, max_iter=5000), create_random_forest_regressor, create_support_vector_regressor, create_extra_trees_regressor, create_knn_regressor, create_ada_boost_regressor, ] class AssignmentQuality(Enum): GOOD = auto() BETTER = auto() BEST = auto() def assign_causal_mechanisms( causal_model: ProbabilisticCausalModel, based_on: pd.DataFrame, quality: AssignmentQuality = AssignmentQuality.GOOD, override_models: bool = False, ) -> None: """Automatically assigns appropriate causal models. If causal models are already assigned to nodes and override_models is set to False, this function only validates the assignments with respect to the graph structure. Here, the validation checks whether root nodes have StochasticModels and non-root ConditionalStochasticModels assigned. :param causal_model: The causal model to whose nodes to assign causal models. :param based_on: Jointly sampled data corresponding to the nodes of the given graph. :param quality: AssignmentQuality for the automatic model selection and model accuracy. This changes the type of prediction model and time spent on the selection. Options are: - AssignmentQuality.GOOD: Compares a linear, polynomial and gradient boost model on small test-training split of the data. The best performing model is then selected. Model selection speed: Fast Model training speed: Fast Model inference speed: Fast Model accuracy: Medium - AssignmentQuality.BETTER: Compares multiple model types and uses the one with the best performance averaged over multiple splits of the training data. By default, the model with the smallest root mean squared error is selected for regression problems and the model with the highest F1 score is selected for classification problems. For a list of possible models, see _LIST_OF_POTENTIAL_REGRESSORS_BETTER and _LIST_OF_POTENTIAL_CLASSIFIERS_BETTER, respectively. Model selection speed: Medium Model training speed: Fast Model inference speed: Fast Model accuracy: Good - AssignmentQuality.BEST: Uses an AutoGluon (auto ML) model with default settings defined by the AutoGluon wrapper. While the model selection itself is fast, the training and inference speed can be significantly slower than in the other options. NOTE: This requires the optional autogluon.tabular dependency. Model selection speed: Instant Model training speed: Slow Model inference speed: Slow-Medium Model accuracy: Best :param override_models: If set to True, existing model assignments are replaced with automatically selected ones. If set to False, the assigned models are only validated with respect to the graph structure. :return: None """ for node in causal_model.graph.nodes: if not override_models and CAUSAL_MECHANISM in causal_model.graph.nodes[node]: validate_causal_model_assignment(causal_model.graph, node) continue if is_root_node(causal_model.graph, node): causal_model.set_causal_mechanism(node, EmpiricalDistribution()) else: prediction_model = select_model( based_on[get_ordered_predecessors(causal_model.graph, node)].to_numpy(), based_on[node].to_numpy(), quality, ) if isinstance(prediction_model, ClassificationModel): causal_model.set_causal_mechanism(node, ClassifierFCM(prediction_model)) else: causal_model.set_causal_mechanism(node, AdditiveNoiseModel(prediction_model)) def select_model( X: np.ndarray, Y: np.ndarray, model_selection_quality: AssignmentQuality ) -> Union[PredictionModel, ClassificationModel]: if model_selection_quality == AssignmentQuality.BEST: try: from dowhy.gcm.ml.autogluon import AutoGluonClassifier, AutoGluonRegressor if is_categorical(Y): return AutoGluonClassifier() else: return AutoGluonRegressor() except ImportError: raise RuntimeError( "AutoGluon module not found! For the BEST auto assign quality, consider installing the " "optional AutoGluon dependency." ) elif model_selection_quality == AssignmentQuality.GOOD: list_of_regressor = list(_LIST_OF_POTENTIAL_REGRESSORS_GOOD) list_of_classifier = list(_LIST_OF_POTENTIAL_CLASSIFIERS_GOOD) model_selection_splits = 2 elif model_selection_quality == AssignmentQuality.BETTER: list_of_regressor = list(_LIST_OF_POTENTIAL_REGRESSORS_BETTER) list_of_classifier = list(_LIST_OF_POTENTIAL_CLASSIFIERS_BETTER) model_selection_splits = 5 else: raise ValueError("Invalid model selection quality.") if auto_apply_encoders(X, auto_fit_encoders(X)).shape[1] <= 5: # Avoid too many features list_of_regressor += [create_polynom_regressor] list_of_classifier += [partial(create_polynom_logistic_regression_classifier, max_iter=1000)] if is_categorical(Y): return find_best_model(list_of_classifier, X, Y, model_selection_splits=model_selection_splits)() else: return find_best_model(list_of_regressor, X, Y, model_selection_splits=model_selection_splits)() def has_linear_relationship(X: np.ndarray, Y: np.ndarray, max_num_samples: int = 3000) -> bool: X, Y = shape_into_2d(X, Y) target_is_categorical = is_categorical(Y) # Making sure there are at least 30% test samples. num_trainings_samples = min(max_num_samples, round(X.shape[0] * 0.7)) num_test_samples = min(X.shape[0] - num_trainings_samples, max_num_samples) if target_is_categorical: all_classes, indices, counts = np.unique(Y, return_counts=True, return_index=True) for i in range(all_classes.size): # Making sure that there are at least 2 samples from one class (here, simply duplicate the point). if counts[i] == 1: X = np.row_stack([X, X[indices[i], :]]) Y = np.row_stack([Y, Y[indices[i], :]]) x_train, x_test, y_train, y_test = train_test_split( X, Y, train_size=num_trainings_samples, test_size=num_test_samples, stratify=Y ) else: x_train, x_test, y_train, y_test = train_test_split( X, Y, train_size=num_trainings_samples, test_size=num_test_samples ) encoders = auto_fit_encoders(x_train, y_train) x_train = auto_apply_encoders(x_train, encoders) x_test = auto_apply_encoders(x_test, encoders) if target_is_categorical: linear_mdl = LogisticRegression(max_iter=1000) nonlinear_mdl = create_hist_gradient_boost_classifier() linear_mdl.fit(x_train, y_train.squeeze()) nonlinear_mdl.fit(x_train, y_train.squeeze()) # Compare number of correct classifications. return np.sum(shape_into_2d(linear_mdl.predict(x_test)) == y_test) >= np.sum( shape_into_2d(nonlinear_mdl.predict(x_test)) == y_test ) else: linear_mdl = LinearRegression() nonlinear_mdl = create_hist_gradient_boost_regressor() linear_mdl.fit(x_train, y_train.squeeze()) nonlinear_mdl.fit(x_train, y_train.squeeze()) return np.mean((y_test - shape_into_2d(linear_mdl.predict(x_test))) ** 2) <= np.mean( (y_test - shape_into_2d(nonlinear_mdl.predict(x_test))) ** 2 ) def find_best_model( prediction_model_factories: List[Callable[[], PredictionModel]], X: np.ndarray, Y: np.ndarray, metric: Optional[Callable[[np.ndarray, np.ndarray], float]] = None, max_samples_per_split: int = 10000, model_selection_splits: int = 5, n_jobs: Optional[int] = None, ) -> Callable[[], PredictionModel]: n_jobs = config.default_n_jobs if n_jobs is None else n_jobs X, Y = shape_into_2d(X, Y) is_classification_problem = isinstance(prediction_model_factories[0](), ClassificationModel) if metric is None: if is_classification_problem: metric = lambda y_true, y_preds: -metrics.f1_score( y_true, y_preds, average="macro", zero_division=0 ) # Higher score is better else: metric = metrics.mean_squared_error labelBinarizer = None if is_classification_problem: labelBinarizer = MultiLabelBinarizer() labelBinarizer.fit(Y) kfolds = list(KFold(n_splits=model_selection_splits).split(range(X.shape[0]))) def estimate_average_score(prediction_model_factory: Callable[[], PredictionModel], random_seed: int) -> float: set_random_seed(random_seed) average_result = 0 with warnings.catch_warnings(): warnings.filterwarnings("ignore", category=ConvergenceWarning) for train_indices, test_indices in kfolds: model_instance = prediction_model_factory() model_instance.fit(X[train_indices[:max_samples_per_split]], Y[train_indices[:max_samples_per_split]]) y_true = Y[test_indices[:max_samples_per_split]] y_pred = model_instance.predict(X[test_indices[:max_samples_per_split]]) if labelBinarizer is not None: y_true = labelBinarizer.transform(y_true) y_pred = labelBinarizer.transform(y_pred) average_result += metric(y_true, y_pred) return average_result / model_selection_splits random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(prediction_model_factories)) average_metric_scores = Parallel(n_jobs=n_jobs)( delayed(estimate_average_score)(prediction_model_factory, int(random_seed)) for prediction_model_factory, random_seed in zip(prediction_model_factories, random_seeds) ) return sorted(zip(prediction_model_factories, average_metric_scores), key=lambda x: x[1])[0][0]

import warnings from enum import Enum, auto from functools import partial from typing import Callable, List, Optional, Union import numpy as np import pandas as pd from joblib import Parallel, delayed from sklearn import metrics from sklearn.exceptions import ConvergenceWarning from sklearn.linear_model import LinearRegression, LogisticRegression from sklearn.model_selection import KFold, train_test_split from sklearn.preprocessing import MultiLabelBinarizer from dowhy.gcm import config from dowhy.gcm.causal_mechanisms import AdditiveNoiseModel, ClassifierFCM from dowhy.gcm.causal_models import CAUSAL_MECHANISM, ProbabilisticCausalModel, validate_causal_model_assignment from dowhy.gcm.ml import ( ClassificationModel, PredictionModel, create_hist_gradient_boost_classifier, create_hist_gradient_boost_regressor, create_lasso_regressor, create_linear_regressor, create_logistic_regression_classifier, create_random_forest_regressor, create_ridge_regressor, create_support_vector_regressor, ) from dowhy.gcm.ml.classification import ( create_ada_boost_classifier, create_extra_trees_classifier, create_gaussian_nb_classifier, create_knn_classifier, create_polynom_logistic_regression_classifier, create_random_forest_classifier, create_support_vector_classifier, ) from dowhy.gcm.ml.regression import ( create_ada_boost_regressor, create_extra_trees_regressor, create_knn_regressor, create_polynom_regressor, ) from dowhy.gcm.stochastic_models import EmpiricalDistribution from dowhy.gcm.util.general import ( auto_apply_encoders, auto_fit_encoders, is_categorical, set_random_seed, shape_into_2d, ) from dowhy.graph import get_ordered_predecessors, is_root_node _LIST_OF_POTENTIAL_CLASSIFIERS_GOOD = [ partial(create_logistic_regression_classifier, max_iter=1000), create_hist_gradient_boost_classifier, ] _LIST_OF_POTENTIAL_REGRESSORS_GOOD = [ create_linear_regressor, create_hist_gradient_boost_regressor, ] _LIST_OF_POTENTIAL_CLASSIFIERS_BETTER = _LIST_OF_POTENTIAL_CLASSIFIERS_GOOD + [ create_random_forest_classifier, create_extra_trees_classifier, create_support_vector_classifier, create_knn_classifier, create_gaussian_nb_classifier, create_ada_boost_classifier, ] _LIST_OF_POTENTIAL_REGRESSORS_BETTER = _LIST_OF_POTENTIAL_REGRESSORS_GOOD + [ create_ridge_regressor, create_polynom_regressor, partial(create_lasso_regressor, max_iter=5000), create_random_forest_regressor, create_support_vector_regressor, create_extra_trees_regressor, create_knn_regressor, create_ada_boost_regressor, ] class AssignmentQuality(Enum): GOOD = auto() BETTER = auto() BEST = auto() def assign_causal_mechanisms( causal_model: ProbabilisticCausalModel, based_on: pd.DataFrame, quality: AssignmentQuality = AssignmentQuality.GOOD, override_models: bool = False, ) -> None: """Automatically assigns appropriate causal models. If causal models are already assigned to nodes and override_models is set to False, this function only validates the assignments with respect to the graph structure. Here, the validation checks whether root nodes have StochasticModels and non-root ConditionalStochasticModels assigned. :param causal_model: The causal model to whose nodes to assign causal models. :param based_on: Jointly sampled data corresponding to the nodes of the given graph. :param quality: AssignmentQuality for the automatic model selection and model accuracy. This changes the type of prediction model and time spent on the selection. Options are: - AssignmentQuality.GOOD: Compares a linear, polynomial and gradient boost model on small test-training split of the data. The best performing model is then selected. Model selection speed: Fast Model training speed: Fast Model inference speed: Fast Model accuracy: Medium - AssignmentQuality.BETTER: Compares multiple model types and uses the one with the best performance averaged over multiple splits of the training data. By default, the model with the smallest root mean squared error is selected for regression problems and the model with the highest F1 score is selected for classification problems. For a list of possible models, see _LIST_OF_POTENTIAL_REGRESSORS_BETTER and _LIST_OF_POTENTIAL_CLASSIFIERS_BETTER, respectively. Model selection speed: Medium Model training speed: Fast Model inference speed: Fast Model accuracy: Good - AssignmentQuality.BEST: Uses an AutoGluon (auto ML) model with default settings defined by the AutoGluon wrapper. While the model selection itself is fast, the training and inference speed can be significantly slower than in the other options. NOTE: This requires the optional autogluon.tabular dependency. Model selection speed: Instant Model training speed: Slow Model inference speed: Slow-Medium Model accuracy: Best :param override_models: If set to True, existing model assignments are replaced with automatically selected ones. If set to False, the assigned models are only validated with respect to the graph structure. :return: None """ for node in causal_model.graph.nodes: if not override_models and CAUSAL_MECHANISM in causal_model.graph.nodes[node]: validate_causal_model_assignment(causal_model.graph, node) continue assign_causal_mechanism_node(causal_model, node, based_on, quality) def assign_causal_mechanism_node( causal_model: ProbabilisticCausalModel, node: str, based_on: pd.DataFrame, quality: AssignmentQuality = AssignmentQuality.GOOD, ) -> None: if is_root_node(causal_model.graph, node): causal_model.set_causal_mechanism(node, EmpiricalDistribution()) else: prediction_model = select_model( based_on[get_ordered_predecessors(causal_model.graph, node)].to_numpy(), based_on[node].to_numpy(), quality, ) if isinstance(prediction_model, ClassificationModel): causal_model.set_causal_mechanism(node, ClassifierFCM(prediction_model)) else: causal_model.set_causal_mechanism(node, AdditiveNoiseModel(prediction_model)) def select_model( X: np.ndarray, Y: np.ndarray, model_selection_quality: AssignmentQuality ) -> Union[PredictionModel, ClassificationModel]: if model_selection_quality == AssignmentQuality.BEST: try: from dowhy.gcm.ml.autogluon import AutoGluonClassifier, AutoGluonRegressor if is_categorical(Y): return AutoGluonClassifier() else: return AutoGluonRegressor() except ImportError: raise RuntimeError( "AutoGluon module not found! For the BEST auto assign quality, consider installing the " "optional AutoGluon dependency." ) elif model_selection_quality == AssignmentQuality.GOOD: list_of_regressor = list(_LIST_OF_POTENTIAL_REGRESSORS_GOOD) list_of_classifier = list(_LIST_OF_POTENTIAL_CLASSIFIERS_GOOD) model_selection_splits = 2 elif model_selection_quality == AssignmentQuality.BETTER: list_of_regressor = list(_LIST_OF_POTENTIAL_REGRESSORS_BETTER) list_of_classifier = list(_LIST_OF_POTENTIAL_CLASSIFIERS_BETTER) model_selection_splits = 5 else: raise ValueError("Invalid model selection quality.") if auto_apply_encoders(X, auto_fit_encoders(X)).shape[1] <= 5: # Avoid too many features list_of_regressor += [create_polynom_regressor] list_of_classifier += [partial(create_polynom_logistic_regression_classifier, max_iter=1000)] if is_categorical(Y): return find_best_model(list_of_classifier, X, Y, model_selection_splits=model_selection_splits)() else: return find_best_model(list_of_regressor, X, Y, model_selection_splits=model_selection_splits)() def has_linear_relationship(X: np.ndarray, Y: np.ndarray, max_num_samples: int = 3000) -> bool: X, Y = shape_into_2d(X, Y) target_is_categorical = is_categorical(Y) # Making sure there are at least 30% test samples. num_trainings_samples = min(max_num_samples, round(X.shape[0] * 0.7)) num_test_samples = min(X.shape[0] - num_trainings_samples, max_num_samples) if target_is_categorical: all_classes, indices, counts = np.unique(Y, return_counts=True, return_index=True) for i in range(all_classes.size): # Making sure that there are at least 2 samples from one class (here, simply duplicate the point). if counts[i] == 1: X = np.row_stack([X, X[indices[i], :]]) Y = np.row_stack([Y, Y[indices[i], :]]) x_train, x_test, y_train, y_test = train_test_split( X, Y, train_size=num_trainings_samples, test_size=num_test_samples, stratify=Y ) else: x_train, x_test, y_train, y_test = train_test_split( X, Y, train_size=num_trainings_samples, test_size=num_test_samples ) encoders = auto_fit_encoders(x_train, y_train) x_train = auto_apply_encoders(x_train, encoders) x_test = auto_apply_encoders(x_test, encoders) if target_is_categorical: linear_mdl = LogisticRegression(max_iter=1000) nonlinear_mdl = create_hist_gradient_boost_classifier() linear_mdl.fit(x_train, y_train.squeeze()) nonlinear_mdl.fit(x_train, y_train.squeeze()) # Compare number of correct classifications. return np.sum(shape_into_2d(linear_mdl.predict(x_test)) == y_test) >= np.sum( shape_into_2d(nonlinear_mdl.predict(x_test)) == y_test ) else: linear_mdl = LinearRegression() nonlinear_mdl = create_hist_gradient_boost_regressor() linear_mdl.fit(x_train, y_train.squeeze()) nonlinear_mdl.fit(x_train, y_train.squeeze()) return np.mean((y_test - shape_into_2d(linear_mdl.predict(x_test))) ** 2) <= np.mean( (y_test - shape_into_2d(nonlinear_mdl.predict(x_test))) ** 2 ) def find_best_model( prediction_model_factories: List[Callable[[], PredictionModel]], X: np.ndarray, Y: np.ndarray, metric: Optional[Callable[[np.ndarray, np.ndarray], float]] = None, max_samples_per_split: int = 10000, model_selection_splits: int = 5, n_jobs: Optional[int] = None, ) -> Callable[[], PredictionModel]: n_jobs = config.default_n_jobs if n_jobs is None else n_jobs X, Y = shape_into_2d(X, Y) is_classification_problem = isinstance(prediction_model_factories[0](), ClassificationModel) if metric is None: if is_classification_problem: metric = lambda y_true, y_preds: -metrics.f1_score( y_true, y_preds, average="macro", zero_division=0 ) # Higher score is better else: metric = metrics.mean_squared_error labelBinarizer = None if is_classification_problem: labelBinarizer = MultiLabelBinarizer() labelBinarizer.fit(Y) kfolds = list(KFold(n_splits=model_selection_splits).split(range(X.shape[0]))) def estimate_average_score(prediction_model_factory: Callable[[], PredictionModel], random_seed: int) -> float: set_random_seed(random_seed) average_result = 0 with warnings.catch_warnings(): warnings.filterwarnings("ignore", category=ConvergenceWarning) for train_indices, test_indices in kfolds: model_instance = prediction_model_factory() model_instance.fit(X[train_indices[:max_samples_per_split]], Y[train_indices[:max_samples_per_split]]) y_true = Y[test_indices[:max_samples_per_split]] y_pred = model_instance.predict(X[test_indices[:max_samples_per_split]]) if labelBinarizer is not None: y_true = labelBinarizer.transform(y_true) y_pred = labelBinarizer.transform(y_pred) average_result += metric(y_true, y_pred) return average_result / model_selection_splits random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(prediction_model_factories)) average_metric_scores = Parallel(n_jobs=n_jobs)( delayed(estimate_average_score)(prediction_model_factory, int(random_seed)) for prediction_model_factory, random_seed in zip(prediction_model_factories, random_seeds) ) return sorted(zip(prediction_model_factories, average_metric_scores), key=lambda x: x[1])[0][0]

bhatt-priyadutt

734b61671d130cf21a4e437084b9083dbd5c4a39

b4f80dcb2251a03aff4c816d7c6f6eb2aaf98e73

Instead of allowing `None` and setting a default value, let's make this explicit in the function definition by setting `quality: AssignmentQuality = AssignmentQuality.GOOD` and remove the `Optional` accordingly.

bloebp

py-why/dowhy

Invariant nodes removal

Feature - Now users can enter the list of invariant nodes to the distribution_change function to remove them from analysis

2023-08-15 12:59:48+00:00

2023-08-24 16:12:46+00:00

dowhy/gcm/auto.py

import warnings from enum import Enum, auto from functools import partial from typing import Callable, List, Optional, Union import numpy as np import pandas as pd from joblib import Parallel, delayed from sklearn import metrics from sklearn.exceptions import ConvergenceWarning from sklearn.linear_model import LinearRegression, LogisticRegression from sklearn.model_selection import KFold, train_test_split from sklearn.preprocessing import MultiLabelBinarizer from dowhy.gcm import config from dowhy.gcm.causal_mechanisms import AdditiveNoiseModel, ClassifierFCM from dowhy.gcm.causal_models import CAUSAL_MECHANISM, ProbabilisticCausalModel, validate_causal_model_assignment from dowhy.gcm.ml import ( ClassificationModel, PredictionModel, create_hist_gradient_boost_classifier, create_hist_gradient_boost_regressor, create_lasso_regressor, create_linear_regressor, create_logistic_regression_classifier, create_random_forest_regressor, create_ridge_regressor, create_support_vector_regressor, ) from dowhy.gcm.ml.classification import ( create_ada_boost_classifier, create_extra_trees_classifier, create_gaussian_nb_classifier, create_knn_classifier, create_polynom_logistic_regression_classifier, create_random_forest_classifier, create_support_vector_classifier, ) from dowhy.gcm.ml.regression import ( create_ada_boost_regressor, create_extra_trees_regressor, create_knn_regressor, create_polynom_regressor, ) from dowhy.gcm.stochastic_models import EmpiricalDistribution from dowhy.gcm.util.general import ( auto_apply_encoders, auto_fit_encoders, is_categorical, set_random_seed, shape_into_2d, ) from dowhy.graph import get_ordered_predecessors, is_root_node _LIST_OF_POTENTIAL_CLASSIFIERS_GOOD = [ partial(create_logistic_regression_classifier, max_iter=1000), create_hist_gradient_boost_classifier, ] _LIST_OF_POTENTIAL_REGRESSORS_GOOD = [ create_linear_regressor, create_hist_gradient_boost_regressor, ] _LIST_OF_POTENTIAL_CLASSIFIERS_BETTER = _LIST_OF_POTENTIAL_CLASSIFIERS_GOOD + [ create_random_forest_classifier, create_extra_trees_classifier, create_support_vector_classifier, create_knn_classifier, create_gaussian_nb_classifier, create_ada_boost_classifier, ] _LIST_OF_POTENTIAL_REGRESSORS_BETTER = _LIST_OF_POTENTIAL_REGRESSORS_GOOD + [ create_ridge_regressor, create_polynom_regressor, partial(create_lasso_regressor, max_iter=5000), create_random_forest_regressor, create_support_vector_regressor, create_extra_trees_regressor, create_knn_regressor, create_ada_boost_regressor, ] class AssignmentQuality(Enum): GOOD = auto() BETTER = auto() BEST = auto() def assign_causal_mechanisms( causal_model: ProbabilisticCausalModel, based_on: pd.DataFrame, quality: AssignmentQuality = AssignmentQuality.GOOD, override_models: bool = False, ) -> None: """Automatically assigns appropriate causal models. If causal models are already assigned to nodes and override_models is set to False, this function only validates the assignments with respect to the graph structure. Here, the validation checks whether root nodes have StochasticModels and non-root ConditionalStochasticModels assigned. :param causal_model: The causal model to whose nodes to assign causal models. :param based_on: Jointly sampled data corresponding to the nodes of the given graph. :param quality: AssignmentQuality for the automatic model selection and model accuracy. This changes the type of prediction model and time spent on the selection. Options are: - AssignmentQuality.GOOD: Compares a linear, polynomial and gradient boost model on small test-training split of the data. The best performing model is then selected. Model selection speed: Fast Model training speed: Fast Model inference speed: Fast Model accuracy: Medium - AssignmentQuality.BETTER: Compares multiple model types and uses the one with the best performance averaged over multiple splits of the training data. By default, the model with the smallest root mean squared error is selected for regression problems and the model with the highest F1 score is selected for classification problems. For a list of possible models, see _LIST_OF_POTENTIAL_REGRESSORS_BETTER and _LIST_OF_POTENTIAL_CLASSIFIERS_BETTER, respectively. Model selection speed: Medium Model training speed: Fast Model inference speed: Fast Model accuracy: Good - AssignmentQuality.BEST: Uses an AutoGluon (auto ML) model with default settings defined by the AutoGluon wrapper. While the model selection itself is fast, the training and inference speed can be significantly slower than in the other options. NOTE: This requires the optional autogluon.tabular dependency. Model selection speed: Instant Model training speed: Slow Model inference speed: Slow-Medium Model accuracy: Best :param override_models: If set to True, existing model assignments are replaced with automatically selected ones. If set to False, the assigned models are only validated with respect to the graph structure. :return: None """ for node in causal_model.graph.nodes: if not override_models and CAUSAL_MECHANISM in causal_model.graph.nodes[node]: validate_causal_model_assignment(causal_model.graph, node) continue if is_root_node(causal_model.graph, node): causal_model.set_causal_mechanism(node, EmpiricalDistribution()) else: prediction_model = select_model( based_on[get_ordered_predecessors(causal_model.graph, node)].to_numpy(), based_on[node].to_numpy(), quality, ) if isinstance(prediction_model, ClassificationModel): causal_model.set_causal_mechanism(node, ClassifierFCM(prediction_model)) else: causal_model.set_causal_mechanism(node, AdditiveNoiseModel(prediction_model)) def select_model( X: np.ndarray, Y: np.ndarray, model_selection_quality: AssignmentQuality ) -> Union[PredictionModel, ClassificationModel]: if model_selection_quality == AssignmentQuality.BEST: try: from dowhy.gcm.ml.autogluon import AutoGluonClassifier, AutoGluonRegressor if is_categorical(Y): return AutoGluonClassifier() else: return AutoGluonRegressor() except ImportError: raise RuntimeError( "AutoGluon module not found! For the BEST auto assign quality, consider installing the " "optional AutoGluon dependency." ) elif model_selection_quality == AssignmentQuality.GOOD: list_of_regressor = list(_LIST_OF_POTENTIAL_REGRESSORS_GOOD) list_of_classifier = list(_LIST_OF_POTENTIAL_CLASSIFIERS_GOOD) model_selection_splits = 2 elif model_selection_quality == AssignmentQuality.BETTER: list_of_regressor = list(_LIST_OF_POTENTIAL_REGRESSORS_BETTER) list_of_classifier = list(_LIST_OF_POTENTIAL_CLASSIFIERS_BETTER) model_selection_splits = 5 else: raise ValueError("Invalid model selection quality.") if auto_apply_encoders(X, auto_fit_encoders(X)).shape[1] <= 5: # Avoid too many features list_of_regressor += [create_polynom_regressor] list_of_classifier += [partial(create_polynom_logistic_regression_classifier, max_iter=1000)] if is_categorical(Y): return find_best_model(list_of_classifier, X, Y, model_selection_splits=model_selection_splits)() else: return find_best_model(list_of_regressor, X, Y, model_selection_splits=model_selection_splits)() def has_linear_relationship(X: np.ndarray, Y: np.ndarray, max_num_samples: int = 3000) -> bool: X, Y = shape_into_2d(X, Y) target_is_categorical = is_categorical(Y) # Making sure there are at least 30% test samples. num_trainings_samples = min(max_num_samples, round(X.shape[0] * 0.7)) num_test_samples = min(X.shape[0] - num_trainings_samples, max_num_samples) if target_is_categorical: all_classes, indices, counts = np.unique(Y, return_counts=True, return_index=True) for i in range(all_classes.size): # Making sure that there are at least 2 samples from one class (here, simply duplicate the point). if counts[i] == 1: X = np.row_stack([X, X[indices[i], :]]) Y = np.row_stack([Y, Y[indices[i], :]]) x_train, x_test, y_train, y_test = train_test_split( X, Y, train_size=num_trainings_samples, test_size=num_test_samples, stratify=Y ) else: x_train, x_test, y_train, y_test = train_test_split( X, Y, train_size=num_trainings_samples, test_size=num_test_samples ) encoders = auto_fit_encoders(x_train, y_train) x_train = auto_apply_encoders(x_train, encoders) x_test = auto_apply_encoders(x_test, encoders) if target_is_categorical: linear_mdl = LogisticRegression(max_iter=1000) nonlinear_mdl = create_hist_gradient_boost_classifier() linear_mdl.fit(x_train, y_train.squeeze()) nonlinear_mdl.fit(x_train, y_train.squeeze()) # Compare number of correct classifications. return np.sum(shape_into_2d(linear_mdl.predict(x_test)) == y_test) >= np.sum( shape_into_2d(nonlinear_mdl.predict(x_test)) == y_test ) else: linear_mdl = LinearRegression() nonlinear_mdl = create_hist_gradient_boost_regressor() linear_mdl.fit(x_train, y_train.squeeze()) nonlinear_mdl.fit(x_train, y_train.squeeze()) return np.mean((y_test - shape_into_2d(linear_mdl.predict(x_test))) ** 2) <= np.mean( (y_test - shape_into_2d(nonlinear_mdl.predict(x_test))) ** 2 ) def find_best_model( prediction_model_factories: List[Callable[[], PredictionModel]], X: np.ndarray, Y: np.ndarray, metric: Optional[Callable[[np.ndarray, np.ndarray], float]] = None, max_samples_per_split: int = 10000, model_selection_splits: int = 5, n_jobs: Optional[int] = None, ) -> Callable[[], PredictionModel]: n_jobs = config.default_n_jobs if n_jobs is None else n_jobs X, Y = shape_into_2d(X, Y) is_classification_problem = isinstance(prediction_model_factories[0](), ClassificationModel) if metric is None: if is_classification_problem: metric = lambda y_true, y_preds: -metrics.f1_score( y_true, y_preds, average="macro", zero_division=0 ) # Higher score is better else: metric = metrics.mean_squared_error labelBinarizer = None if is_classification_problem: labelBinarizer = MultiLabelBinarizer() labelBinarizer.fit(Y) kfolds = list(KFold(n_splits=model_selection_splits).split(range(X.shape[0]))) def estimate_average_score(prediction_model_factory: Callable[[], PredictionModel], random_seed: int) -> float: set_random_seed(random_seed) average_result = 0 with warnings.catch_warnings(): warnings.filterwarnings("ignore", category=ConvergenceWarning) for train_indices, test_indices in kfolds: model_instance = prediction_model_factory() model_instance.fit(X[train_indices[:max_samples_per_split]], Y[train_indices[:max_samples_per_split]]) y_true = Y[test_indices[:max_samples_per_split]] y_pred = model_instance.predict(X[test_indices[:max_samples_per_split]]) if labelBinarizer is not None: y_true = labelBinarizer.transform(y_true) y_pred = labelBinarizer.transform(y_pred) average_result += metric(y_true, y_pred) return average_result / model_selection_splits random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(prediction_model_factories)) average_metric_scores = Parallel(n_jobs=n_jobs)( delayed(estimate_average_score)(prediction_model_factory, int(random_seed)) for prediction_model_factory, random_seed in zip(prediction_model_factories, random_seeds) ) return sorted(zip(prediction_model_factories, average_metric_scores), key=lambda x: x[1])[0][0]

import warnings from enum import Enum, auto from functools import partial from typing import Callable, List, Optional, Union import numpy as np import pandas as pd from joblib import Parallel, delayed from sklearn import metrics from sklearn.exceptions import ConvergenceWarning from sklearn.linear_model import LinearRegression, LogisticRegression from sklearn.model_selection import KFold, train_test_split from sklearn.preprocessing import MultiLabelBinarizer from dowhy.gcm import config from dowhy.gcm.causal_mechanisms import AdditiveNoiseModel, ClassifierFCM from dowhy.gcm.causal_models import CAUSAL_MECHANISM, ProbabilisticCausalModel, validate_causal_model_assignment from dowhy.gcm.ml import ( ClassificationModel, PredictionModel, create_hist_gradient_boost_classifier, create_hist_gradient_boost_regressor, create_lasso_regressor, create_linear_regressor, create_logistic_regression_classifier, create_random_forest_regressor, create_ridge_regressor, create_support_vector_regressor, ) from dowhy.gcm.ml.classification import ( create_ada_boost_classifier, create_extra_trees_classifier, create_gaussian_nb_classifier, create_knn_classifier, create_polynom_logistic_regression_classifier, create_random_forest_classifier, create_support_vector_classifier, ) from dowhy.gcm.ml.regression import ( create_ada_boost_regressor, create_extra_trees_regressor, create_knn_regressor, create_polynom_regressor, ) from dowhy.gcm.stochastic_models import EmpiricalDistribution from dowhy.gcm.util.general import ( auto_apply_encoders, auto_fit_encoders, is_categorical, set_random_seed, shape_into_2d, ) from dowhy.graph import get_ordered_predecessors, is_root_node _LIST_OF_POTENTIAL_CLASSIFIERS_GOOD = [ partial(create_logistic_regression_classifier, max_iter=1000), create_hist_gradient_boost_classifier, ] _LIST_OF_POTENTIAL_REGRESSORS_GOOD = [ create_linear_regressor, create_hist_gradient_boost_regressor, ] _LIST_OF_POTENTIAL_CLASSIFIERS_BETTER = _LIST_OF_POTENTIAL_CLASSIFIERS_GOOD + [ create_random_forest_classifier, create_extra_trees_classifier, create_support_vector_classifier, create_knn_classifier, create_gaussian_nb_classifier, create_ada_boost_classifier, ] _LIST_OF_POTENTIAL_REGRESSORS_BETTER = _LIST_OF_POTENTIAL_REGRESSORS_GOOD + [ create_ridge_regressor, create_polynom_regressor, partial(create_lasso_regressor, max_iter=5000), create_random_forest_regressor, create_support_vector_regressor, create_extra_trees_regressor, create_knn_regressor, create_ada_boost_regressor, ] class AssignmentQuality(Enum): GOOD = auto() BETTER = auto() BEST = auto() def assign_causal_mechanisms( causal_model: ProbabilisticCausalModel, based_on: pd.DataFrame, quality: AssignmentQuality = AssignmentQuality.GOOD, override_models: bool = False, ) -> None: """Automatically assigns appropriate causal models. If causal models are already assigned to nodes and override_models is set to False, this function only validates the assignments with respect to the graph structure. Here, the validation checks whether root nodes have StochasticModels and non-root ConditionalStochasticModels assigned. :param causal_model: The causal model to whose nodes to assign causal models. :param based_on: Jointly sampled data corresponding to the nodes of the given graph. :param quality: AssignmentQuality for the automatic model selection and model accuracy. This changes the type of prediction model and time spent on the selection. Options are: - AssignmentQuality.GOOD: Compares a linear, polynomial and gradient boost model on small test-training split of the data. The best performing model is then selected. Model selection speed: Fast Model training speed: Fast Model inference speed: Fast Model accuracy: Medium - AssignmentQuality.BETTER: Compares multiple model types and uses the one with the best performance averaged over multiple splits of the training data. By default, the model with the smallest root mean squared error is selected for regression problems and the model with the highest F1 score is selected for classification problems. For a list of possible models, see _LIST_OF_POTENTIAL_REGRESSORS_BETTER and _LIST_OF_POTENTIAL_CLASSIFIERS_BETTER, respectively. Model selection speed: Medium Model training speed: Fast Model inference speed: Fast Model accuracy: Good - AssignmentQuality.BEST: Uses an AutoGluon (auto ML) model with default settings defined by the AutoGluon wrapper. While the model selection itself is fast, the training and inference speed can be significantly slower than in the other options. NOTE: This requires the optional autogluon.tabular dependency. Model selection speed: Instant Model training speed: Slow Model inference speed: Slow-Medium Model accuracy: Best :param override_models: If set to True, existing model assignments are replaced with automatically selected ones. If set to False, the assigned models are only validated with respect to the graph structure. :return: None """ for node in causal_model.graph.nodes: if not override_models and CAUSAL_MECHANISM in causal_model.graph.nodes[node]: validate_causal_model_assignment(causal_model.graph, node) continue assign_causal_mechanism_node(causal_model, node, based_on, quality) def assign_causal_mechanism_node( causal_model: ProbabilisticCausalModel, node: str, based_on: pd.DataFrame, quality: AssignmentQuality = AssignmentQuality.GOOD, ) -> None: if is_root_node(causal_model.graph, node): causal_model.set_causal_mechanism(node, EmpiricalDistribution()) else: prediction_model = select_model( based_on[get_ordered_predecessors(causal_model.graph, node)].to_numpy(), based_on[node].to_numpy(), quality, ) if isinstance(prediction_model, ClassificationModel): causal_model.set_causal_mechanism(node, ClassifierFCM(prediction_model)) else: causal_model.set_causal_mechanism(node, AdditiveNoiseModel(prediction_model)) def select_model( X: np.ndarray, Y: np.ndarray, model_selection_quality: AssignmentQuality ) -> Union[PredictionModel, ClassificationModel]: if model_selection_quality == AssignmentQuality.BEST: try: from dowhy.gcm.ml.autogluon import AutoGluonClassifier, AutoGluonRegressor if is_categorical(Y): return AutoGluonClassifier() else: return AutoGluonRegressor() except ImportError: raise RuntimeError( "AutoGluon module not found! For the BEST auto assign quality, consider installing the " "optional AutoGluon dependency." ) elif model_selection_quality == AssignmentQuality.GOOD: list_of_regressor = list(_LIST_OF_POTENTIAL_REGRESSORS_GOOD) list_of_classifier = list(_LIST_OF_POTENTIAL_CLASSIFIERS_GOOD) model_selection_splits = 2 elif model_selection_quality == AssignmentQuality.BETTER: list_of_regressor = list(_LIST_OF_POTENTIAL_REGRESSORS_BETTER) list_of_classifier = list(_LIST_OF_POTENTIAL_CLASSIFIERS_BETTER) model_selection_splits = 5 else: raise ValueError("Invalid model selection quality.") if auto_apply_encoders(X, auto_fit_encoders(X)).shape[1] <= 5: # Avoid too many features list_of_regressor += [create_polynom_regressor] list_of_classifier += [partial(create_polynom_logistic_regression_classifier, max_iter=1000)] if is_categorical(Y): return find_best_model(list_of_classifier, X, Y, model_selection_splits=model_selection_splits)() else: return find_best_model(list_of_regressor, X, Y, model_selection_splits=model_selection_splits)() def has_linear_relationship(X: np.ndarray, Y: np.ndarray, max_num_samples: int = 3000) -> bool: X, Y = shape_into_2d(X, Y) target_is_categorical = is_categorical(Y) # Making sure there are at least 30% test samples. num_trainings_samples = min(max_num_samples, round(X.shape[0] * 0.7)) num_test_samples = min(X.shape[0] - num_trainings_samples, max_num_samples) if target_is_categorical: all_classes, indices, counts = np.unique(Y, return_counts=True, return_index=True) for i in range(all_classes.size): # Making sure that there are at least 2 samples from one class (here, simply duplicate the point). if counts[i] == 1: X = np.row_stack([X, X[indices[i], :]]) Y = np.row_stack([Y, Y[indices[i], :]]) x_train, x_test, y_train, y_test = train_test_split( X, Y, train_size=num_trainings_samples, test_size=num_test_samples, stratify=Y ) else: x_train, x_test, y_train, y_test = train_test_split( X, Y, train_size=num_trainings_samples, test_size=num_test_samples ) encoders = auto_fit_encoders(x_train, y_train) x_train = auto_apply_encoders(x_train, encoders) x_test = auto_apply_encoders(x_test, encoders) if target_is_categorical: linear_mdl = LogisticRegression(max_iter=1000) nonlinear_mdl = create_hist_gradient_boost_classifier() linear_mdl.fit(x_train, y_train.squeeze()) nonlinear_mdl.fit(x_train, y_train.squeeze()) # Compare number of correct classifications. return np.sum(shape_into_2d(linear_mdl.predict(x_test)) == y_test) >= np.sum( shape_into_2d(nonlinear_mdl.predict(x_test)) == y_test ) else: linear_mdl = LinearRegression() nonlinear_mdl = create_hist_gradient_boost_regressor() linear_mdl.fit(x_train, y_train.squeeze()) nonlinear_mdl.fit(x_train, y_train.squeeze()) return np.mean((y_test - shape_into_2d(linear_mdl.predict(x_test))) ** 2) <= np.mean( (y_test - shape_into_2d(nonlinear_mdl.predict(x_test))) ** 2 ) def find_best_model( prediction_model_factories: List[Callable[[], PredictionModel]], X: np.ndarray, Y: np.ndarray, metric: Optional[Callable[[np.ndarray, np.ndarray], float]] = None, max_samples_per_split: int = 10000, model_selection_splits: int = 5, n_jobs: Optional[int] = None, ) -> Callable[[], PredictionModel]: n_jobs = config.default_n_jobs if n_jobs is None else n_jobs X, Y = shape_into_2d(X, Y) is_classification_problem = isinstance(prediction_model_factories[0](), ClassificationModel) if metric is None: if is_classification_problem: metric = lambda y_true, y_preds: -metrics.f1_score( y_true, y_preds, average="macro", zero_division=0 ) # Higher score is better else: metric = metrics.mean_squared_error labelBinarizer = None if is_classification_problem: labelBinarizer = MultiLabelBinarizer() labelBinarizer.fit(Y) kfolds = list(KFold(n_splits=model_selection_splits).split(range(X.shape[0]))) def estimate_average_score(prediction_model_factory: Callable[[], PredictionModel], random_seed: int) -> float: set_random_seed(random_seed) average_result = 0 with warnings.catch_warnings(): warnings.filterwarnings("ignore", category=ConvergenceWarning) for train_indices, test_indices in kfolds: model_instance = prediction_model_factory() model_instance.fit(X[train_indices[:max_samples_per_split]], Y[train_indices[:max_samples_per_split]]) y_true = Y[test_indices[:max_samples_per_split]] y_pred = model_instance.predict(X[test_indices[:max_samples_per_split]]) if labelBinarizer is not None: y_true = labelBinarizer.transform(y_true) y_pred = labelBinarizer.transform(y_pred) average_result += metric(y_true, y_pred) return average_result / model_selection_splits random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(prediction_model_factories)) average_metric_scores = Parallel(n_jobs=n_jobs)( delayed(estimate_average_score)(prediction_model_factory, int(random_seed)) for prediction_model_factory, random_seed in zip(prediction_model_factories, random_seeds) ) return sorted(zip(prediction_model_factories, average_metric_scores), key=lambda x: x[1])[0][0]

bhatt-priyadutt

734b61671d130cf21a4e437084b9083dbd5c4a39

b4f80dcb2251a03aff4c816d7c6f6eb2aaf98e73

Here, If you see where the **_remove_invariant_nodes** is called, the **quality** can be **None** as well and so to handle that case, I chose to write it like that. If I make the default to be the value you have said above, I will have to handle in the upstream **_remove_invariant_nodes** to make sure that None does not gets passed....

bhatt-priyadutt

py-why/dowhy

Invariant nodes removal

Feature - Now users can enter the list of invariant nodes to the distribution_change function to remove them from analysis

2023-08-15 12:59:48+00:00

2023-08-24 16:12:46+00:00

dowhy/gcm/auto.py

import warnings from enum import Enum, auto from functools import partial from typing import Callable, List, Optional, Union import numpy as np import pandas as pd from joblib import Parallel, delayed from sklearn import metrics from sklearn.exceptions import ConvergenceWarning from sklearn.linear_model import LinearRegression, LogisticRegression from sklearn.model_selection import KFold, train_test_split from sklearn.preprocessing import MultiLabelBinarizer from dowhy.gcm import config from dowhy.gcm.causal_mechanisms import AdditiveNoiseModel, ClassifierFCM from dowhy.gcm.causal_models import CAUSAL_MECHANISM, ProbabilisticCausalModel, validate_causal_model_assignment from dowhy.gcm.ml import ( ClassificationModel, PredictionModel, create_hist_gradient_boost_classifier, create_hist_gradient_boost_regressor, create_lasso_regressor, create_linear_regressor, create_logistic_regression_classifier, create_random_forest_regressor, create_ridge_regressor, create_support_vector_regressor, ) from dowhy.gcm.ml.classification import ( create_ada_boost_classifier, create_extra_trees_classifier, create_gaussian_nb_classifier, create_knn_classifier, create_polynom_logistic_regression_classifier, create_random_forest_classifier, create_support_vector_classifier, ) from dowhy.gcm.ml.regression import ( create_ada_boost_regressor, create_extra_trees_regressor, create_knn_regressor, create_polynom_regressor, ) from dowhy.gcm.stochastic_models import EmpiricalDistribution from dowhy.gcm.util.general import ( auto_apply_encoders, auto_fit_encoders, is_categorical, set_random_seed, shape_into_2d, ) from dowhy.graph import get_ordered_predecessors, is_root_node _LIST_OF_POTENTIAL_CLASSIFIERS_GOOD = [ partial(create_logistic_regression_classifier, max_iter=1000), create_hist_gradient_boost_classifier, ] _LIST_OF_POTENTIAL_REGRESSORS_GOOD = [ create_linear_regressor, create_hist_gradient_boost_regressor, ] _LIST_OF_POTENTIAL_CLASSIFIERS_BETTER = _LIST_OF_POTENTIAL_CLASSIFIERS_GOOD + [ create_random_forest_classifier, create_extra_trees_classifier, create_support_vector_classifier, create_knn_classifier, create_gaussian_nb_classifier, create_ada_boost_classifier, ] _LIST_OF_POTENTIAL_REGRESSORS_BETTER = _LIST_OF_POTENTIAL_REGRESSORS_GOOD + [ create_ridge_regressor, create_polynom_regressor, partial(create_lasso_regressor, max_iter=5000), create_random_forest_regressor, create_support_vector_regressor, create_extra_trees_regressor, create_knn_regressor, create_ada_boost_regressor, ] class AssignmentQuality(Enum): GOOD = auto() BETTER = auto() BEST = auto() def assign_causal_mechanisms( causal_model: ProbabilisticCausalModel, based_on: pd.DataFrame, quality: AssignmentQuality = AssignmentQuality.GOOD, override_models: bool = False, ) -> None: """Automatically assigns appropriate causal models. If causal models are already assigned to nodes and override_models is set to False, this function only validates the assignments with respect to the graph structure. Here, the validation checks whether root nodes have StochasticModels and non-root ConditionalStochasticModels assigned. :param causal_model: The causal model to whose nodes to assign causal models. :param based_on: Jointly sampled data corresponding to the nodes of the given graph. :param quality: AssignmentQuality for the automatic model selection and model accuracy. This changes the type of prediction model and time spent on the selection. Options are: - AssignmentQuality.GOOD: Compares a linear, polynomial and gradient boost model on small test-training split of the data. The best performing model is then selected. Model selection speed: Fast Model training speed: Fast Model inference speed: Fast Model accuracy: Medium - AssignmentQuality.BETTER: Compares multiple model types and uses the one with the best performance averaged over multiple splits of the training data. By default, the model with the smallest root mean squared error is selected for regression problems and the model with the highest F1 score is selected for classification problems. For a list of possible models, see _LIST_OF_POTENTIAL_REGRESSORS_BETTER and _LIST_OF_POTENTIAL_CLASSIFIERS_BETTER, respectively. Model selection speed: Medium Model training speed: Fast Model inference speed: Fast Model accuracy: Good - AssignmentQuality.BEST: Uses an AutoGluon (auto ML) model with default settings defined by the AutoGluon wrapper. While the model selection itself is fast, the training and inference speed can be significantly slower than in the other options. NOTE: This requires the optional autogluon.tabular dependency. Model selection speed: Instant Model training speed: Slow Model inference speed: Slow-Medium Model accuracy: Best :param override_models: If set to True, existing model assignments are replaced with automatically selected ones. If set to False, the assigned models are only validated with respect to the graph structure. :return: None """ for node in causal_model.graph.nodes: if not override_models and CAUSAL_MECHANISM in causal_model.graph.nodes[node]: validate_causal_model_assignment(causal_model.graph, node) continue if is_root_node(causal_model.graph, node): causal_model.set_causal_mechanism(node, EmpiricalDistribution()) else: prediction_model = select_model( based_on[get_ordered_predecessors(causal_model.graph, node)].to_numpy(), based_on[node].to_numpy(), quality, ) if isinstance(prediction_model, ClassificationModel): causal_model.set_causal_mechanism(node, ClassifierFCM(prediction_model)) else: causal_model.set_causal_mechanism(node, AdditiveNoiseModel(prediction_model)) def select_model( X: np.ndarray, Y: np.ndarray, model_selection_quality: AssignmentQuality ) -> Union[PredictionModel, ClassificationModel]: if model_selection_quality == AssignmentQuality.BEST: try: from dowhy.gcm.ml.autogluon import AutoGluonClassifier, AutoGluonRegressor if is_categorical(Y): return AutoGluonClassifier() else: return AutoGluonRegressor() except ImportError: raise RuntimeError( "AutoGluon module not found! For the BEST auto assign quality, consider installing the " "optional AutoGluon dependency." ) elif model_selection_quality == AssignmentQuality.GOOD: list_of_regressor = list(_LIST_OF_POTENTIAL_REGRESSORS_GOOD) list_of_classifier = list(_LIST_OF_POTENTIAL_CLASSIFIERS_GOOD) model_selection_splits = 2 elif model_selection_quality == AssignmentQuality.BETTER: list_of_regressor = list(_LIST_OF_POTENTIAL_REGRESSORS_BETTER) list_of_classifier = list(_LIST_OF_POTENTIAL_CLASSIFIERS_BETTER) model_selection_splits = 5 else: raise ValueError("Invalid model selection quality.") if auto_apply_encoders(X, auto_fit_encoders(X)).shape[1] <= 5: # Avoid too many features list_of_regressor += [create_polynom_regressor] list_of_classifier += [partial(create_polynom_logistic_regression_classifier, max_iter=1000)] if is_categorical(Y): return find_best_model(list_of_classifier, X, Y, model_selection_splits=model_selection_splits)() else: return find_best_model(list_of_regressor, X, Y, model_selection_splits=model_selection_splits)() def has_linear_relationship(X: np.ndarray, Y: np.ndarray, max_num_samples: int = 3000) -> bool: X, Y = shape_into_2d(X, Y) target_is_categorical = is_categorical(Y) # Making sure there are at least 30% test samples. num_trainings_samples = min(max_num_samples, round(X.shape[0] * 0.7)) num_test_samples = min(X.shape[0] - num_trainings_samples, max_num_samples) if target_is_categorical: all_classes, indices, counts = np.unique(Y, return_counts=True, return_index=True) for i in range(all_classes.size): # Making sure that there are at least 2 samples from one class (here, simply duplicate the point). if counts[i] == 1: X = np.row_stack([X, X[indices[i], :]]) Y = np.row_stack([Y, Y[indices[i], :]]) x_train, x_test, y_train, y_test = train_test_split( X, Y, train_size=num_trainings_samples, test_size=num_test_samples, stratify=Y ) else: x_train, x_test, y_train, y_test = train_test_split( X, Y, train_size=num_trainings_samples, test_size=num_test_samples ) encoders = auto_fit_encoders(x_train, y_train) x_train = auto_apply_encoders(x_train, encoders) x_test = auto_apply_encoders(x_test, encoders) if target_is_categorical: linear_mdl = LogisticRegression(max_iter=1000) nonlinear_mdl = create_hist_gradient_boost_classifier() linear_mdl.fit(x_train, y_train.squeeze()) nonlinear_mdl.fit(x_train, y_train.squeeze()) # Compare number of correct classifications. return np.sum(shape_into_2d(linear_mdl.predict(x_test)) == y_test) >= np.sum( shape_into_2d(nonlinear_mdl.predict(x_test)) == y_test ) else: linear_mdl = LinearRegression() nonlinear_mdl = create_hist_gradient_boost_regressor() linear_mdl.fit(x_train, y_train.squeeze()) nonlinear_mdl.fit(x_train, y_train.squeeze()) return np.mean((y_test - shape_into_2d(linear_mdl.predict(x_test))) ** 2) <= np.mean( (y_test - shape_into_2d(nonlinear_mdl.predict(x_test))) ** 2 ) def find_best_model( prediction_model_factories: List[Callable[[], PredictionModel]], X: np.ndarray, Y: np.ndarray, metric: Optional[Callable[[np.ndarray, np.ndarray], float]] = None, max_samples_per_split: int = 10000, model_selection_splits: int = 5, n_jobs: Optional[int] = None, ) -> Callable[[], PredictionModel]: n_jobs = config.default_n_jobs if n_jobs is None else n_jobs X, Y = shape_into_2d(X, Y) is_classification_problem = isinstance(prediction_model_factories[0](), ClassificationModel) if metric is None: if is_classification_problem: metric = lambda y_true, y_preds: -metrics.f1_score( y_true, y_preds, average="macro", zero_division=0 ) # Higher score is better else: metric = metrics.mean_squared_error labelBinarizer = None if is_classification_problem: labelBinarizer = MultiLabelBinarizer() labelBinarizer.fit(Y) kfolds = list(KFold(n_splits=model_selection_splits).split(range(X.shape[0]))) def estimate_average_score(prediction_model_factory: Callable[[], PredictionModel], random_seed: int) -> float: set_random_seed(random_seed) average_result = 0 with warnings.catch_warnings(): warnings.filterwarnings("ignore", category=ConvergenceWarning) for train_indices, test_indices in kfolds: model_instance = prediction_model_factory() model_instance.fit(X[train_indices[:max_samples_per_split]], Y[train_indices[:max_samples_per_split]]) y_true = Y[test_indices[:max_samples_per_split]] y_pred = model_instance.predict(X[test_indices[:max_samples_per_split]]) if labelBinarizer is not None: y_true = labelBinarizer.transform(y_true) y_pred = labelBinarizer.transform(y_pred) average_result += metric(y_true, y_pred) return average_result / model_selection_splits random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(prediction_model_factories)) average_metric_scores = Parallel(n_jobs=n_jobs)( delayed(estimate_average_score)(prediction_model_factory, int(random_seed)) for prediction_model_factory, random_seed in zip(prediction_model_factories, random_seeds) ) return sorted(zip(prediction_model_factories, average_metric_scores), key=lambda x: x[1])[0][0]

import warnings from enum import Enum, auto from functools import partial from typing import Callable, List, Optional, Union import numpy as np import pandas as pd from joblib import Parallel, delayed from sklearn import metrics from sklearn.exceptions import ConvergenceWarning from sklearn.linear_model import LinearRegression, LogisticRegression from sklearn.model_selection import KFold, train_test_split from sklearn.preprocessing import MultiLabelBinarizer from dowhy.gcm import config from dowhy.gcm.causal_mechanisms import AdditiveNoiseModel, ClassifierFCM from dowhy.gcm.causal_models import CAUSAL_MECHANISM, ProbabilisticCausalModel, validate_causal_model_assignment from dowhy.gcm.ml import ( ClassificationModel, PredictionModel, create_hist_gradient_boost_classifier, create_hist_gradient_boost_regressor, create_lasso_regressor, create_linear_regressor, create_logistic_regression_classifier, create_random_forest_regressor, create_ridge_regressor, create_support_vector_regressor, ) from dowhy.gcm.ml.classification import ( create_ada_boost_classifier, create_extra_trees_classifier, create_gaussian_nb_classifier, create_knn_classifier, create_polynom_logistic_regression_classifier, create_random_forest_classifier, create_support_vector_classifier, ) from dowhy.gcm.ml.regression import ( create_ada_boost_regressor, create_extra_trees_regressor, create_knn_regressor, create_polynom_regressor, ) from dowhy.gcm.stochastic_models import EmpiricalDistribution from dowhy.gcm.util.general import ( auto_apply_encoders, auto_fit_encoders, is_categorical, set_random_seed, shape_into_2d, ) from dowhy.graph import get_ordered_predecessors, is_root_node _LIST_OF_POTENTIAL_CLASSIFIERS_GOOD = [ partial(create_logistic_regression_classifier, max_iter=1000), create_hist_gradient_boost_classifier, ] _LIST_OF_POTENTIAL_REGRESSORS_GOOD = [ create_linear_regressor, create_hist_gradient_boost_regressor, ] _LIST_OF_POTENTIAL_CLASSIFIERS_BETTER = _LIST_OF_POTENTIAL_CLASSIFIERS_GOOD + [ create_random_forest_classifier, create_extra_trees_classifier, create_support_vector_classifier, create_knn_classifier, create_gaussian_nb_classifier, create_ada_boost_classifier, ] _LIST_OF_POTENTIAL_REGRESSORS_BETTER = _LIST_OF_POTENTIAL_REGRESSORS_GOOD + [ create_ridge_regressor, create_polynom_regressor, partial(create_lasso_regressor, max_iter=5000), create_random_forest_regressor, create_support_vector_regressor, create_extra_trees_regressor, create_knn_regressor, create_ada_boost_regressor, ] class AssignmentQuality(Enum): GOOD = auto() BETTER = auto() BEST = auto() def assign_causal_mechanisms( causal_model: ProbabilisticCausalModel, based_on: pd.DataFrame, quality: AssignmentQuality = AssignmentQuality.GOOD, override_models: bool = False, ) -> None: """Automatically assigns appropriate causal models. If causal models are already assigned to nodes and override_models is set to False, this function only validates the assignments with respect to the graph structure. Here, the validation checks whether root nodes have StochasticModels and non-root ConditionalStochasticModels assigned. :param causal_model: The causal model to whose nodes to assign causal models. :param based_on: Jointly sampled data corresponding to the nodes of the given graph. :param quality: AssignmentQuality for the automatic model selection and model accuracy. This changes the type of prediction model and time spent on the selection. Options are: - AssignmentQuality.GOOD: Compares a linear, polynomial and gradient boost model on small test-training split of the data. The best performing model is then selected. Model selection speed: Fast Model training speed: Fast Model inference speed: Fast Model accuracy: Medium - AssignmentQuality.BETTER: Compares multiple model types and uses the one with the best performance averaged over multiple splits of the training data. By default, the model with the smallest root mean squared error is selected for regression problems and the model with the highest F1 score is selected for classification problems. For a list of possible models, see _LIST_OF_POTENTIAL_REGRESSORS_BETTER and _LIST_OF_POTENTIAL_CLASSIFIERS_BETTER, respectively. Model selection speed: Medium Model training speed: Fast Model inference speed: Fast Model accuracy: Good - AssignmentQuality.BEST: Uses an AutoGluon (auto ML) model with default settings defined by the AutoGluon wrapper. While the model selection itself is fast, the training and inference speed can be significantly slower than in the other options. NOTE: This requires the optional autogluon.tabular dependency. Model selection speed: Instant Model training speed: Slow Model inference speed: Slow-Medium Model accuracy: Best :param override_models: If set to True, existing model assignments are replaced with automatically selected ones. If set to False, the assigned models are only validated with respect to the graph structure. :return: None """ for node in causal_model.graph.nodes: if not override_models and CAUSAL_MECHANISM in causal_model.graph.nodes[node]: validate_causal_model_assignment(causal_model.graph, node) continue assign_causal_mechanism_node(causal_model, node, based_on, quality) def assign_causal_mechanism_node( causal_model: ProbabilisticCausalModel, node: str, based_on: pd.DataFrame, quality: AssignmentQuality = AssignmentQuality.GOOD, ) -> None: if is_root_node(causal_model.graph, node): causal_model.set_causal_mechanism(node, EmpiricalDistribution()) else: prediction_model = select_model( based_on[get_ordered_predecessors(causal_model.graph, node)].to_numpy(), based_on[node].to_numpy(), quality, ) if isinstance(prediction_model, ClassificationModel): causal_model.set_causal_mechanism(node, ClassifierFCM(prediction_model)) else: causal_model.set_causal_mechanism(node, AdditiveNoiseModel(prediction_model)) def select_model( X: np.ndarray, Y: np.ndarray, model_selection_quality: AssignmentQuality ) -> Union[PredictionModel, ClassificationModel]: if model_selection_quality == AssignmentQuality.BEST: try: from dowhy.gcm.ml.autogluon import AutoGluonClassifier, AutoGluonRegressor if is_categorical(Y): return AutoGluonClassifier() else: return AutoGluonRegressor() except ImportError: raise RuntimeError( "AutoGluon module not found! For the BEST auto assign quality, consider installing the " "optional AutoGluon dependency." ) elif model_selection_quality == AssignmentQuality.GOOD: list_of_regressor = list(_LIST_OF_POTENTIAL_REGRESSORS_GOOD) list_of_classifier = list(_LIST_OF_POTENTIAL_CLASSIFIERS_GOOD) model_selection_splits = 2 elif model_selection_quality == AssignmentQuality.BETTER: list_of_regressor = list(_LIST_OF_POTENTIAL_REGRESSORS_BETTER) list_of_classifier = list(_LIST_OF_POTENTIAL_CLASSIFIERS_BETTER) model_selection_splits = 5 else: raise ValueError("Invalid model selection quality.") if auto_apply_encoders(X, auto_fit_encoders(X)).shape[1] <= 5: # Avoid too many features list_of_regressor += [create_polynom_regressor] list_of_classifier += [partial(create_polynom_logistic_regression_classifier, max_iter=1000)] if is_categorical(Y): return find_best_model(list_of_classifier, X, Y, model_selection_splits=model_selection_splits)() else: return find_best_model(list_of_regressor, X, Y, model_selection_splits=model_selection_splits)() def has_linear_relationship(X: np.ndarray, Y: np.ndarray, max_num_samples: int = 3000) -> bool: X, Y = shape_into_2d(X, Y) target_is_categorical = is_categorical(Y) # Making sure there are at least 30% test samples. num_trainings_samples = min(max_num_samples, round(X.shape[0] * 0.7)) num_test_samples = min(X.shape[0] - num_trainings_samples, max_num_samples) if target_is_categorical: all_classes, indices, counts = np.unique(Y, return_counts=True, return_index=True) for i in range(all_classes.size): # Making sure that there are at least 2 samples from one class (here, simply duplicate the point). if counts[i] == 1: X = np.row_stack([X, X[indices[i], :]]) Y = np.row_stack([Y, Y[indices[i], :]]) x_train, x_test, y_train, y_test = train_test_split( X, Y, train_size=num_trainings_samples, test_size=num_test_samples, stratify=Y ) else: x_train, x_test, y_train, y_test = train_test_split( X, Y, train_size=num_trainings_samples, test_size=num_test_samples ) encoders = auto_fit_encoders(x_train, y_train) x_train = auto_apply_encoders(x_train, encoders) x_test = auto_apply_encoders(x_test, encoders) if target_is_categorical: linear_mdl = LogisticRegression(max_iter=1000) nonlinear_mdl = create_hist_gradient_boost_classifier() linear_mdl.fit(x_train, y_train.squeeze()) nonlinear_mdl.fit(x_train, y_train.squeeze()) # Compare number of correct classifications. return np.sum(shape_into_2d(linear_mdl.predict(x_test)) == y_test) >= np.sum( shape_into_2d(nonlinear_mdl.predict(x_test)) == y_test ) else: linear_mdl = LinearRegression() nonlinear_mdl = create_hist_gradient_boost_regressor() linear_mdl.fit(x_train, y_train.squeeze()) nonlinear_mdl.fit(x_train, y_train.squeeze()) return np.mean((y_test - shape_into_2d(linear_mdl.predict(x_test))) ** 2) <= np.mean( (y_test - shape_into_2d(nonlinear_mdl.predict(x_test))) ** 2 ) def find_best_model( prediction_model_factories: List[Callable[[], PredictionModel]], X: np.ndarray, Y: np.ndarray, metric: Optional[Callable[[np.ndarray, np.ndarray], float]] = None, max_samples_per_split: int = 10000, model_selection_splits: int = 5, n_jobs: Optional[int] = None, ) -> Callable[[], PredictionModel]: n_jobs = config.default_n_jobs if n_jobs is None else n_jobs X, Y = shape_into_2d(X, Y) is_classification_problem = isinstance(prediction_model_factories[0](), ClassificationModel) if metric is None: if is_classification_problem: metric = lambda y_true, y_preds: -metrics.f1_score( y_true, y_preds, average="macro", zero_division=0 ) # Higher score is better else: metric = metrics.mean_squared_error labelBinarizer = None if is_classification_problem: labelBinarizer = MultiLabelBinarizer() labelBinarizer.fit(Y) kfolds = list(KFold(n_splits=model_selection_splits).split(range(X.shape[0]))) def estimate_average_score(prediction_model_factory: Callable[[], PredictionModel], random_seed: int) -> float: set_random_seed(random_seed) average_result = 0 with warnings.catch_warnings(): warnings.filterwarnings("ignore", category=ConvergenceWarning) for train_indices, test_indices in kfolds: model_instance = prediction_model_factory() model_instance.fit(X[train_indices[:max_samples_per_split]], Y[train_indices[:max_samples_per_split]]) y_true = Y[test_indices[:max_samples_per_split]] y_pred = model_instance.predict(X[test_indices[:max_samples_per_split]]) if labelBinarizer is not None: y_true = labelBinarizer.transform(y_true) y_pred = labelBinarizer.transform(y_pred) average_result += metric(y_true, y_pred) return average_result / model_selection_splits random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(prediction_model_factories)) average_metric_scores = Parallel(n_jobs=n_jobs)( delayed(estimate_average_score)(prediction_model_factory, int(random_seed)) for prediction_model_factory, random_seed in zip(prediction_model_factories, random_seeds) ) return sorted(zip(prediction_model_factories, average_metric_scores), key=lambda x: x[1])[0][0]

bhatt-priyadutt

734b61671d130cf21a4e437084b9083dbd5c4a39

b4f80dcb2251a03aff4c816d7c6f6eb2aaf98e73

I see. The reason why `None` is an option in `distribution_change` is to indicate to use the existing models and not do an auto assignment. However, at the point of calling `assign_causal_mechanism_node`, a `None` parameter doesn't make sense anymore. So, I would then rather check before calling this whether it is `None` and set it to a default value. This is more consistent seeing `assign_causal_mechanism_node` as a standalone function.

bloebp

py-why/dowhy

Invariant nodes removal

Feature - Now users can enter the list of invariant nodes to the distribution_change function to remove them from analysis

2023-08-15 12:59:48+00:00

2023-08-24 16:12:46+00:00

dowhy/gcm/auto.py

import warnings from enum import Enum, auto from functools import partial from typing import Callable, List, Optional, Union import numpy as np import pandas as pd from joblib import Parallel, delayed from sklearn import metrics from sklearn.exceptions import ConvergenceWarning from sklearn.linear_model import LinearRegression, LogisticRegression from sklearn.model_selection import KFold, train_test_split from sklearn.preprocessing import MultiLabelBinarizer from dowhy.gcm import config from dowhy.gcm.causal_mechanisms import AdditiveNoiseModel, ClassifierFCM from dowhy.gcm.causal_models import CAUSAL_MECHANISM, ProbabilisticCausalModel, validate_causal_model_assignment from dowhy.gcm.ml import ( ClassificationModel, PredictionModel, create_hist_gradient_boost_classifier, create_hist_gradient_boost_regressor, create_lasso_regressor, create_linear_regressor, create_logistic_regression_classifier, create_random_forest_regressor, create_ridge_regressor, create_support_vector_regressor, ) from dowhy.gcm.ml.classification import ( create_ada_boost_classifier, create_extra_trees_classifier, create_gaussian_nb_classifier, create_knn_classifier, create_polynom_logistic_regression_classifier, create_random_forest_classifier, create_support_vector_classifier, ) from dowhy.gcm.ml.regression import ( create_ada_boost_regressor, create_extra_trees_regressor, create_knn_regressor, create_polynom_regressor, ) from dowhy.gcm.stochastic_models import EmpiricalDistribution from dowhy.gcm.util.general import ( auto_apply_encoders, auto_fit_encoders, is_categorical, set_random_seed, shape_into_2d, ) from dowhy.graph import get_ordered_predecessors, is_root_node _LIST_OF_POTENTIAL_CLASSIFIERS_GOOD = [ partial(create_logistic_regression_classifier, max_iter=1000), create_hist_gradient_boost_classifier, ] _LIST_OF_POTENTIAL_REGRESSORS_GOOD = [ create_linear_regressor, create_hist_gradient_boost_regressor, ] _LIST_OF_POTENTIAL_CLASSIFIERS_BETTER = _LIST_OF_POTENTIAL_CLASSIFIERS_GOOD + [ create_random_forest_classifier, create_extra_trees_classifier, create_support_vector_classifier, create_knn_classifier, create_gaussian_nb_classifier, create_ada_boost_classifier, ] _LIST_OF_POTENTIAL_REGRESSORS_BETTER = _LIST_OF_POTENTIAL_REGRESSORS_GOOD + [ create_ridge_regressor, create_polynom_regressor, partial(create_lasso_regressor, max_iter=5000), create_random_forest_regressor, create_support_vector_regressor, create_extra_trees_regressor, create_knn_regressor, create_ada_boost_regressor, ] class AssignmentQuality(Enum): GOOD = auto() BETTER = auto() BEST = auto() def assign_causal_mechanisms( causal_model: ProbabilisticCausalModel, based_on: pd.DataFrame, quality: AssignmentQuality = AssignmentQuality.GOOD, override_models: bool = False, ) -> None: """Automatically assigns appropriate causal models. If causal models are already assigned to nodes and override_models is set to False, this function only validates the assignments with respect to the graph structure. Here, the validation checks whether root nodes have StochasticModels and non-root ConditionalStochasticModels assigned. :param causal_model: The causal model to whose nodes to assign causal models. :param based_on: Jointly sampled data corresponding to the nodes of the given graph. :param quality: AssignmentQuality for the automatic model selection and model accuracy. This changes the type of prediction model and time spent on the selection. Options are: - AssignmentQuality.GOOD: Compares a linear, polynomial and gradient boost model on small test-training split of the data. The best performing model is then selected. Model selection speed: Fast Model training speed: Fast Model inference speed: Fast Model accuracy: Medium - AssignmentQuality.BETTER: Compares multiple model types and uses the one with the best performance averaged over multiple splits of the training data. By default, the model with the smallest root mean squared error is selected for regression problems and the model with the highest F1 score is selected for classification problems. For a list of possible models, see _LIST_OF_POTENTIAL_REGRESSORS_BETTER and _LIST_OF_POTENTIAL_CLASSIFIERS_BETTER, respectively. Model selection speed: Medium Model training speed: Fast Model inference speed: Fast Model accuracy: Good - AssignmentQuality.BEST: Uses an AutoGluon (auto ML) model with default settings defined by the AutoGluon wrapper. While the model selection itself is fast, the training and inference speed can be significantly slower than in the other options. NOTE: This requires the optional autogluon.tabular dependency. Model selection speed: Instant Model training speed: Slow Model inference speed: Slow-Medium Model accuracy: Best :param override_models: If set to True, existing model assignments are replaced with automatically selected ones. If set to False, the assigned models are only validated with respect to the graph structure. :return: None """ for node in causal_model.graph.nodes: if not override_models and CAUSAL_MECHANISM in causal_model.graph.nodes[node]: validate_causal_model_assignment(causal_model.graph, node) continue if is_root_node(causal_model.graph, node): causal_model.set_causal_mechanism(node, EmpiricalDistribution()) else: prediction_model = select_model( based_on[get_ordered_predecessors(causal_model.graph, node)].to_numpy(), based_on[node].to_numpy(), quality, ) if isinstance(prediction_model, ClassificationModel): causal_model.set_causal_mechanism(node, ClassifierFCM(prediction_model)) else: causal_model.set_causal_mechanism(node, AdditiveNoiseModel(prediction_model)) def select_model( X: np.ndarray, Y: np.ndarray, model_selection_quality: AssignmentQuality ) -> Union[PredictionModel, ClassificationModel]: if model_selection_quality == AssignmentQuality.BEST: try: from dowhy.gcm.ml.autogluon import AutoGluonClassifier, AutoGluonRegressor if is_categorical(Y): return AutoGluonClassifier() else: return AutoGluonRegressor() except ImportError: raise RuntimeError( "AutoGluon module not found! For the BEST auto assign quality, consider installing the " "optional AutoGluon dependency." ) elif model_selection_quality == AssignmentQuality.GOOD: list_of_regressor = list(_LIST_OF_POTENTIAL_REGRESSORS_GOOD) list_of_classifier = list(_LIST_OF_POTENTIAL_CLASSIFIERS_GOOD) model_selection_splits = 2 elif model_selection_quality == AssignmentQuality.BETTER: list_of_regressor = list(_LIST_OF_POTENTIAL_REGRESSORS_BETTER) list_of_classifier = list(_LIST_OF_POTENTIAL_CLASSIFIERS_BETTER) model_selection_splits = 5 else: raise ValueError("Invalid model selection quality.") if auto_apply_encoders(X, auto_fit_encoders(X)).shape[1] <= 5: # Avoid too many features list_of_regressor += [create_polynom_regressor] list_of_classifier += [partial(create_polynom_logistic_regression_classifier, max_iter=1000)] if is_categorical(Y): return find_best_model(list_of_classifier, X, Y, model_selection_splits=model_selection_splits)() else: return find_best_model(list_of_regressor, X, Y, model_selection_splits=model_selection_splits)() def has_linear_relationship(X: np.ndarray, Y: np.ndarray, max_num_samples: int = 3000) -> bool: X, Y = shape_into_2d(X, Y) target_is_categorical = is_categorical(Y) # Making sure there are at least 30% test samples. num_trainings_samples = min(max_num_samples, round(X.shape[0] * 0.7)) num_test_samples = min(X.shape[0] - num_trainings_samples, max_num_samples) if target_is_categorical: all_classes, indices, counts = np.unique(Y, return_counts=True, return_index=True) for i in range(all_classes.size): # Making sure that there are at least 2 samples from one class (here, simply duplicate the point). if counts[i] == 1: X = np.row_stack([X, X[indices[i], :]]) Y = np.row_stack([Y, Y[indices[i], :]]) x_train, x_test, y_train, y_test = train_test_split( X, Y, train_size=num_trainings_samples, test_size=num_test_samples, stratify=Y ) else: x_train, x_test, y_train, y_test = train_test_split( X, Y, train_size=num_trainings_samples, test_size=num_test_samples ) encoders = auto_fit_encoders(x_train, y_train) x_train = auto_apply_encoders(x_train, encoders) x_test = auto_apply_encoders(x_test, encoders) if target_is_categorical: linear_mdl = LogisticRegression(max_iter=1000) nonlinear_mdl = create_hist_gradient_boost_classifier() linear_mdl.fit(x_train, y_train.squeeze()) nonlinear_mdl.fit(x_train, y_train.squeeze()) # Compare number of correct classifications. return np.sum(shape_into_2d(linear_mdl.predict(x_test)) == y_test) >= np.sum( shape_into_2d(nonlinear_mdl.predict(x_test)) == y_test ) else: linear_mdl = LinearRegression() nonlinear_mdl = create_hist_gradient_boost_regressor() linear_mdl.fit(x_train, y_train.squeeze()) nonlinear_mdl.fit(x_train, y_train.squeeze()) return np.mean((y_test - shape_into_2d(linear_mdl.predict(x_test))) ** 2) <= np.mean( (y_test - shape_into_2d(nonlinear_mdl.predict(x_test))) ** 2 ) def find_best_model( prediction_model_factories: List[Callable[[], PredictionModel]], X: np.ndarray, Y: np.ndarray, metric: Optional[Callable[[np.ndarray, np.ndarray], float]] = None, max_samples_per_split: int = 10000, model_selection_splits: int = 5, n_jobs: Optional[int] = None, ) -> Callable[[], PredictionModel]: n_jobs = config.default_n_jobs if n_jobs is None else n_jobs X, Y = shape_into_2d(X, Y) is_classification_problem = isinstance(prediction_model_factories[0](), ClassificationModel) if metric is None: if is_classification_problem: metric = lambda y_true, y_preds: -metrics.f1_score( y_true, y_preds, average="macro", zero_division=0 ) # Higher score is better else: metric = metrics.mean_squared_error labelBinarizer = None if is_classification_problem: labelBinarizer = MultiLabelBinarizer() labelBinarizer.fit(Y) kfolds = list(KFold(n_splits=model_selection_splits).split(range(X.shape[0]))) def estimate_average_score(prediction_model_factory: Callable[[], PredictionModel], random_seed: int) -> float: set_random_seed(random_seed) average_result = 0 with warnings.catch_warnings(): warnings.filterwarnings("ignore", category=ConvergenceWarning) for train_indices, test_indices in kfolds: model_instance = prediction_model_factory() model_instance.fit(X[train_indices[:max_samples_per_split]], Y[train_indices[:max_samples_per_split]]) y_true = Y[test_indices[:max_samples_per_split]] y_pred = model_instance.predict(X[test_indices[:max_samples_per_split]]) if labelBinarizer is not None: y_true = labelBinarizer.transform(y_true) y_pred = labelBinarizer.transform(y_pred) average_result += metric(y_true, y_pred) return average_result / model_selection_splits random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(prediction_model_factories)) average_metric_scores = Parallel(n_jobs=n_jobs)( delayed(estimate_average_score)(prediction_model_factory, int(random_seed)) for prediction_model_factory, random_seed in zip(prediction_model_factories, random_seeds) ) return sorted(zip(prediction_model_factories, average_metric_scores), key=lambda x: x[1])[0][0]

import warnings from enum import Enum, auto from functools import partial from typing import Callable, List, Optional, Union import numpy as np import pandas as pd from joblib import Parallel, delayed from sklearn import metrics from sklearn.exceptions import ConvergenceWarning from sklearn.linear_model import LinearRegression, LogisticRegression from sklearn.model_selection import KFold, train_test_split from sklearn.preprocessing import MultiLabelBinarizer from dowhy.gcm import config from dowhy.gcm.causal_mechanisms import AdditiveNoiseModel, ClassifierFCM from dowhy.gcm.causal_models import CAUSAL_MECHANISM, ProbabilisticCausalModel, validate_causal_model_assignment from dowhy.gcm.ml import ( ClassificationModel, PredictionModel, create_hist_gradient_boost_classifier, create_hist_gradient_boost_regressor, create_lasso_regressor, create_linear_regressor, create_logistic_regression_classifier, create_random_forest_regressor, create_ridge_regressor, create_support_vector_regressor, ) from dowhy.gcm.ml.classification import ( create_ada_boost_classifier, create_extra_trees_classifier, create_gaussian_nb_classifier, create_knn_classifier, create_polynom_logistic_regression_classifier, create_random_forest_classifier, create_support_vector_classifier, ) from dowhy.gcm.ml.regression import ( create_ada_boost_regressor, create_extra_trees_regressor, create_knn_regressor, create_polynom_regressor, ) from dowhy.gcm.stochastic_models import EmpiricalDistribution from dowhy.gcm.util.general import ( auto_apply_encoders, auto_fit_encoders, is_categorical, set_random_seed, shape_into_2d, ) from dowhy.graph import get_ordered_predecessors, is_root_node _LIST_OF_POTENTIAL_CLASSIFIERS_GOOD = [ partial(create_logistic_regression_classifier, max_iter=1000), create_hist_gradient_boost_classifier, ] _LIST_OF_POTENTIAL_REGRESSORS_GOOD = [ create_linear_regressor, create_hist_gradient_boost_regressor, ] _LIST_OF_POTENTIAL_CLASSIFIERS_BETTER = _LIST_OF_POTENTIAL_CLASSIFIERS_GOOD + [ create_random_forest_classifier, create_extra_trees_classifier, create_support_vector_classifier, create_knn_classifier, create_gaussian_nb_classifier, create_ada_boost_classifier, ] _LIST_OF_POTENTIAL_REGRESSORS_BETTER = _LIST_OF_POTENTIAL_REGRESSORS_GOOD + [ create_ridge_regressor, create_polynom_regressor, partial(create_lasso_regressor, max_iter=5000), create_random_forest_regressor, create_support_vector_regressor, create_extra_trees_regressor, create_knn_regressor, create_ada_boost_regressor, ] class AssignmentQuality(Enum): GOOD = auto() BETTER = auto() BEST = auto() def assign_causal_mechanisms( causal_model: ProbabilisticCausalModel, based_on: pd.DataFrame, quality: AssignmentQuality = AssignmentQuality.GOOD, override_models: bool = False, ) -> None: """Automatically assigns appropriate causal models. If causal models are already assigned to nodes and override_models is set to False, this function only validates the assignments with respect to the graph structure. Here, the validation checks whether root nodes have StochasticModels and non-root ConditionalStochasticModels assigned. :param causal_model: The causal model to whose nodes to assign causal models. :param based_on: Jointly sampled data corresponding to the nodes of the given graph. :param quality: AssignmentQuality for the automatic model selection and model accuracy. This changes the type of prediction model and time spent on the selection. Options are: - AssignmentQuality.GOOD: Compares a linear, polynomial and gradient boost model on small test-training split of the data. The best performing model is then selected. Model selection speed: Fast Model training speed: Fast Model inference speed: Fast Model accuracy: Medium - AssignmentQuality.BETTER: Compares multiple model types and uses the one with the best performance averaged over multiple splits of the training data. By default, the model with the smallest root mean squared error is selected for regression problems and the model with the highest F1 score is selected for classification problems. For a list of possible models, see _LIST_OF_POTENTIAL_REGRESSORS_BETTER and _LIST_OF_POTENTIAL_CLASSIFIERS_BETTER, respectively. Model selection speed: Medium Model training speed: Fast Model inference speed: Fast Model accuracy: Good - AssignmentQuality.BEST: Uses an AutoGluon (auto ML) model with default settings defined by the AutoGluon wrapper. While the model selection itself is fast, the training and inference speed can be significantly slower than in the other options. NOTE: This requires the optional autogluon.tabular dependency. Model selection speed: Instant Model training speed: Slow Model inference speed: Slow-Medium Model accuracy: Best :param override_models: If set to True, existing model assignments are replaced with automatically selected ones. If set to False, the assigned models are only validated with respect to the graph structure. :return: None """ for node in causal_model.graph.nodes: if not override_models and CAUSAL_MECHANISM in causal_model.graph.nodes[node]: validate_causal_model_assignment(causal_model.graph, node) continue assign_causal_mechanism_node(causal_model, node, based_on, quality) def assign_causal_mechanism_node( causal_model: ProbabilisticCausalModel, node: str, based_on: pd.DataFrame, quality: AssignmentQuality = AssignmentQuality.GOOD, ) -> None: if is_root_node(causal_model.graph, node): causal_model.set_causal_mechanism(node, EmpiricalDistribution()) else: prediction_model = select_model( based_on[get_ordered_predecessors(causal_model.graph, node)].to_numpy(), based_on[node].to_numpy(), quality, ) if isinstance(prediction_model, ClassificationModel): causal_model.set_causal_mechanism(node, ClassifierFCM(prediction_model)) else: causal_model.set_causal_mechanism(node, AdditiveNoiseModel(prediction_model)) def select_model( X: np.ndarray, Y: np.ndarray, model_selection_quality: AssignmentQuality ) -> Union[PredictionModel, ClassificationModel]: if model_selection_quality == AssignmentQuality.BEST: try: from dowhy.gcm.ml.autogluon import AutoGluonClassifier, AutoGluonRegressor if is_categorical(Y): return AutoGluonClassifier() else: return AutoGluonRegressor() except ImportError: raise RuntimeError( "AutoGluon module not found! For the BEST auto assign quality, consider installing the " "optional AutoGluon dependency." ) elif model_selection_quality == AssignmentQuality.GOOD: list_of_regressor = list(_LIST_OF_POTENTIAL_REGRESSORS_GOOD) list_of_classifier = list(_LIST_OF_POTENTIAL_CLASSIFIERS_GOOD) model_selection_splits = 2 elif model_selection_quality == AssignmentQuality.BETTER: list_of_regressor = list(_LIST_OF_POTENTIAL_REGRESSORS_BETTER) list_of_classifier = list(_LIST_OF_POTENTIAL_CLASSIFIERS_BETTER) model_selection_splits = 5 else: raise ValueError("Invalid model selection quality.") if auto_apply_encoders(X, auto_fit_encoders(X)).shape[1] <= 5: # Avoid too many features list_of_regressor += [create_polynom_regressor] list_of_classifier += [partial(create_polynom_logistic_regression_classifier, max_iter=1000)] if is_categorical(Y): return find_best_model(list_of_classifier, X, Y, model_selection_splits=model_selection_splits)() else: return find_best_model(list_of_regressor, X, Y, model_selection_splits=model_selection_splits)() def has_linear_relationship(X: np.ndarray, Y: np.ndarray, max_num_samples: int = 3000) -> bool: X, Y = shape_into_2d(X, Y) target_is_categorical = is_categorical(Y) # Making sure there are at least 30% test samples. num_trainings_samples = min(max_num_samples, round(X.shape[0] * 0.7)) num_test_samples = min(X.shape[0] - num_trainings_samples, max_num_samples) if target_is_categorical: all_classes, indices, counts = np.unique(Y, return_counts=True, return_index=True) for i in range(all_classes.size): # Making sure that there are at least 2 samples from one class (here, simply duplicate the point). if counts[i] == 1: X = np.row_stack([X, X[indices[i], :]]) Y = np.row_stack([Y, Y[indices[i], :]]) x_train, x_test, y_train, y_test = train_test_split( X, Y, train_size=num_trainings_samples, test_size=num_test_samples, stratify=Y ) else: x_train, x_test, y_train, y_test = train_test_split( X, Y, train_size=num_trainings_samples, test_size=num_test_samples ) encoders = auto_fit_encoders(x_train, y_train) x_train = auto_apply_encoders(x_train, encoders) x_test = auto_apply_encoders(x_test, encoders) if target_is_categorical: linear_mdl = LogisticRegression(max_iter=1000) nonlinear_mdl = create_hist_gradient_boost_classifier() linear_mdl.fit(x_train, y_train.squeeze()) nonlinear_mdl.fit(x_train, y_train.squeeze()) # Compare number of correct classifications. return np.sum(shape_into_2d(linear_mdl.predict(x_test)) == y_test) >= np.sum( shape_into_2d(nonlinear_mdl.predict(x_test)) == y_test ) else: linear_mdl = LinearRegression() nonlinear_mdl = create_hist_gradient_boost_regressor() linear_mdl.fit(x_train, y_train.squeeze()) nonlinear_mdl.fit(x_train, y_train.squeeze()) return np.mean((y_test - shape_into_2d(linear_mdl.predict(x_test))) ** 2) <= np.mean( (y_test - shape_into_2d(nonlinear_mdl.predict(x_test))) ** 2 ) def find_best_model( prediction_model_factories: List[Callable[[], PredictionModel]], X: np.ndarray, Y: np.ndarray, metric: Optional[Callable[[np.ndarray, np.ndarray], float]] = None, max_samples_per_split: int = 10000, model_selection_splits: int = 5, n_jobs: Optional[int] = None, ) -> Callable[[], PredictionModel]: n_jobs = config.default_n_jobs if n_jobs is None else n_jobs X, Y = shape_into_2d(X, Y) is_classification_problem = isinstance(prediction_model_factories[0](), ClassificationModel) if metric is None: if is_classification_problem: metric = lambda y_true, y_preds: -metrics.f1_score( y_true, y_preds, average="macro", zero_division=0 ) # Higher score is better else: metric = metrics.mean_squared_error labelBinarizer = None if is_classification_problem: labelBinarizer = MultiLabelBinarizer() labelBinarizer.fit(Y) kfolds = list(KFold(n_splits=model_selection_splits).split(range(X.shape[0]))) def estimate_average_score(prediction_model_factory: Callable[[], PredictionModel], random_seed: int) -> float: set_random_seed(random_seed) average_result = 0 with warnings.catch_warnings(): warnings.filterwarnings("ignore", category=ConvergenceWarning) for train_indices, test_indices in kfolds: model_instance = prediction_model_factory() model_instance.fit(X[train_indices[:max_samples_per_split]], Y[train_indices[:max_samples_per_split]]) y_true = Y[test_indices[:max_samples_per_split]] y_pred = model_instance.predict(X[test_indices[:max_samples_per_split]]) if labelBinarizer is not None: y_true = labelBinarizer.transform(y_true) y_pred = labelBinarizer.transform(y_pred) average_result += metric(y_true, y_pred) return average_result / model_selection_splits random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(prediction_model_factories)) average_metric_scores = Parallel(n_jobs=n_jobs)( delayed(estimate_average_score)(prediction_model_factory, int(random_seed)) for prediction_model_factory, random_seed in zip(prediction_model_factories, random_seeds) ) return sorted(zip(prediction_model_factories, average_metric_scores), key=lambda x: x[1])[0][0]

bhatt-priyadutt

734b61671d130cf21a4e437084b9083dbd5c4a39

b4f80dcb2251a03aff4c816d7c6f6eb2aaf98e73

done

bhatt-priyadutt

py-why/dowhy

Invariant nodes removal

Feature - Now users can enter the list of invariant nodes to the distribution_change function to remove them from analysis

2023-08-15 12:59:48+00:00

2023-08-24 16:12:46+00:00

dowhy/gcm/distribution_change.py

"""This module defines functions to attribute distribution changes. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging from typing import Any, Callable, Dict, Optional, Tuple, Union import networkx as nx import numpy as np import pandas as pd from numpy.matlib import repmat from statsmodels.stats.multitest import multipletests from tqdm import tqdm from dowhy.gcm.auto import AssignmentQuality, assign_causal_mechanisms from dowhy.gcm.causal_mechanisms import ConditionalStochasticModel from dowhy.gcm.causal_models import ( PARENTS_DURING_FIT, ProbabilisticCausalModel, clone_causal_models, validate_causal_dag, ) from dowhy.gcm.divergence import auto_estimate_kl_divergence from dowhy.gcm.fitting_sampling import draw_samples, fit_causal_model_of_target from dowhy.gcm.independence_test.kernel import kernel_based from dowhy.gcm.shapley import ShapleyConfig, estimate_shapley_values from dowhy.gcm.util.general import shape_into_2d from dowhy.graph import DirectedGraph, get_ordered_predecessors, is_root_node, node_connected_subgraph_view _logger = logging.getLogger(__name__) def mechanism_change_test( target_original_data: np.ndarray, target_new_data: np.ndarray, parents_original_data: Optional[np.ndarray] = None, parents_new_data: Optional[np.ndarray] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, ) -> float: """Estimates a p-value for the null hypothesis that the original and new data were generated by the same mechanism. Here, we check the dependency between binary labels indicating whether a sample is from the original or a new data set. If the labels do not provide information to determine if a sample is coming from the original/new distribution, then it is likely that the mechanism has not changed. For non-root nodes, samples from parent variables are needed as conditioning variables. This is, testing the null hypothesis that the data were generated by the same mechanism given the parent samples. By this, we incorporate upstream changes that might have impacted the parents, but not the target node itself. :param target_original_data: Samples of the node from the original data set. :param target_new_data: Samples of the node from the new data set. :param parents_original_data: Samples from parents of the node from the original data set. :param parents_new_data: Samples from parents of the node from the new data set. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in nodes without parents. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in nodes with parents. :return: A p-value for the null hypothesis that the mechanism has not changed. """ causal_graph: DirectedGraph if parents_original_data is not None and parents_new_data is None: raise ValueError("Original parents data were given, but no new parents data!") if parents_original_data is None and parents_new_data is not None: raise ValueError("New parents data were given, but no original parents data!") num_samples_for_testing = min(target_original_data.shape[0], target_new_data.shape[0]) data_set_indices = np.ones(num_samples_for_testing * 2) data_set_indices[num_samples_for_testing:] = -1 data_set_indices = data_set_indices.astype(str) original_indices = np.random.choice(target_original_data.shape[0], num_samples_for_testing, replace=False) new_indices = np.random.choice(target_new_data.shape[0], num_samples_for_testing, replace=False) joint_target_samples = np.vstack( [shape_into_2d(target_original_data[original_indices]), shape_into_2d(target_new_data[new_indices])] ) if parents_original_data is None: return independence_test(joint_target_samples, data_set_indices) else: parents_new_data: np.ndarray joint_parent_data = np.vstack( [shape_into_2d(parents_original_data[original_indices]), shape_into_2d(parents_new_data[new_indices])] ) return conditional_independence_test(joint_target_samples, data_set_indices, joint_parent_data) def distribution_change( causal_model: ProbabilisticCausalModel, old_data: pd.DataFrame, new_data: pd.DataFrame, target_node: Any, num_samples: int = 2000, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float] = auto_estimate_kl_divergence, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, mechanism_change_test_significance_level: float = 0.05, mechanism_change_test_fdr_control_method: Optional[str] = "fdr_bh", auto_assignment_quality: Optional[AssignmentQuality] = None, return_additional_info: bool = False, shapley_config: Optional[ShapleyConfig] = None, graph_factory: Callable[[Any], DirectedGraph] = nx.DiGraph, ) -> Union[ Dict[Any, float], Tuple[Dict[Any, float], Dict[Any, bool], ProbabilisticCausalModel, ProbabilisticCausalModel] ]: """Attributes the change in the marginal distribution of the target_node to nodes upstream in the causal DAG. Note that this method creates two copies of the causal DAG. The causal models of one causal DAG are learned from old data and those of another DAG are learned from new data. **Research Paper**: Kailash Budhathoki, Dominik Janzing, Patrick Bloebaum, Hoiyi Ng. *Why did the distribution change?*. Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, PMLR 130:1666-1674, 2021. :param causal_model: Reference causal model. :param old_data: Joint samples from the 'old' distribution. :param new_data: Joint samples from the 'new' distribution. :param target_node: Target node of interest for attributing the marginal distribution change. :param num_samples: Number of samples used for estimating Shapley values. This can have a significant influence on runtime and accuracy. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions, e.g. difference in average values. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in root nodes. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in non-root nodes. :param mechanism_change_test_significance_level: A significance level for rejecting the null hypothesis that the causal mechanism of a node has not changed. :param mechanism_change_test_fdr_control_method: The false discovery rate control method for mechanism change tests. For more options, checkout `statsmodels manual <https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.multipletests.html>`_. :param auto_assignment_quality: If set to None, the assigned models from the given causal models are used for the old and new graph. However, they are re-fitted on the given data. If set to a valid assignment quality, new models are automatically assigned to the old and new graph based on the respective data. :param return_additional_info: If set to True, three additional items are returned: a dictionary indicating whether each node's mechanism changed, the causal DAG whose causal models are learned from old data, and the causal DAG whose causal models are learned from new data. :param shapley_config: Configuration for the Shapley estimator. :param graph_factory: Allows customization in case a graph class different than networkx.DiGraph should be used. This function *must* copy nodes and edges. Attributes of nodes will be overridden in the copy, so the algorithm is independent of the attribute copy behavior of this factory. :return: By default, if `return_additional_info` is set to False, only the dictionary containing contribution of each upstream node is returned. If `return_additional_info` is set to True, three additional items are returned: a dictionary indicating whether each node's mechanism changed, the causal DAG whose causal models learned from old data, and the causal DAG whose causal models are learned from new data. """ causal_graph_old = graph_factory(node_connected_subgraph_view(causal_model.graph, target_node)) causal_model_old = ProbabilisticCausalModel(causal_graph_old) if auto_assignment_quality is None: clone_causal_models(causal_model.graph, causal_model_old.graph) else: assign_causal_mechanisms(causal_model_old, old_data, override_models=True, quality=auto_assignment_quality) causal_graph_new = graph_factory(causal_graph_old) causal_model_new = ProbabilisticCausalModel(causal_graph_new) if auto_assignment_quality is None: clone_causal_models(causal_graph_old, causal_model_new.graph) else: assign_causal_mechanisms(causal_model_new, new_data, override_models=True, quality=auto_assignment_quality) mechanism_changes = _fit_accounting_for_mechanism_change( causal_model_old, causal_model_new, old_data[list(causal_graph_old.nodes)], new_data[list(causal_graph_new.nodes)], independence_test, conditional_independence_test, mechanism_change_test_significance_level, mechanism_change_test_fdr_control_method, ) attributions = distribution_change_of_graphs( causal_model_old, causal_model_new, target_node, num_samples, difference_estimation_func, shapley_config, graph_factory, ) if return_additional_info: return attributions, mechanism_changes, causal_model_old, causal_model_new else: return attributions def distribution_change_of_graphs( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, target_node: Any, num_samples: int = 2000, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float] = auto_estimate_kl_divergence, shapley_config: Optional[ShapleyConfig] = None, graph_factory: Callable[[Any], DirectedGraph] = nx.DiGraph, ) -> Dict[Any, float]: """Attributes the change of the marginal distribution of target_node to upstream nodes based on the distributions generated by the 'old' and 'new' causal graphs. These graphs are assumed to represent the same causal structure and to be fitted on the respective data. Note: This method creates a copy of the given causal models, i.e. the original objects will not be modified. Related paper: Budhathoki, K., Janzing, D., Bloebaum, P., & Ng, H. (2021). Why did the distribution change? arXiv preprint arXiv:2102.13384. :param causal_model_old: The ProbabilisticCausalModel fitted on the 'old' data. :param causal_model_new: The ProbabilisticCausalModel fitted on the 'new' data. :param target_node: Node of interest for attributing the marginal distribution change. :param num_samples: Number of samples used for the estimation. This can have a significant influence on the runtime and accuracy. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions. An example could be the KL divergence. :param shapley_config: Config for the Shapley estimator. :param graph_factory: Allows customization in case a graph class different than networkx.DiGraph should be used. This function *must* copy nodes and edges. Attributes of nodes will be overridden in the copy, so the algorithm is independent of the attribute copy behavior of this factory. :return: A dictionary containing the contributions of upstream nodes to the marginal distribution change in the target node. """ validate_causal_dag(causal_model_old.graph) validate_causal_dag(causal_model_new.graph) return _estimate_marginal_distribution_change( ProbabilisticCausalModel(node_connected_subgraph_view(causal_model_old.graph, target_node)), ProbabilisticCausalModel(node_connected_subgraph_view(causal_model_new.graph, target_node)), target_node, num_samples, difference_estimation_func, shapley_config, graph_factory, ) def _fit_accounting_for_mechanism_change( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, old_data: pd.DataFrame, new_data: pd.DataFrame, independence_test: Callable[[np.ndarray, np.ndarray], float], conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float], significance_level: float, fdr_control_method: Optional[str], ) -> Dict[Any, bool]: mechanism_changed_for_node = _check_significant_mechanism_change( causal_model_old.graph, old_data, new_data, independence_test, conditional_independence_test, significance_level, fdr_control_method, ) joint_data = pd.concat([old_data, new_data], ignore_index=True, sort=True) for node in causal_model_new.graph.nodes: if mechanism_changed_for_node[node]: fit_causal_model_of_target(causal_model_old, node, old_data) fit_causal_model_of_target(causal_model_new, node, new_data) else: fit_causal_model_of_target(causal_model_old, node, joint_data) fit_causal_model_of_target(causal_model_new, node, joint_data) return mechanism_changed_for_node def _estimate_marginal_distribution_change( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, target_node: Any, num_samples: int, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float], shapley_config: Optional[ShapleyConfig], graph_factory: Callable[[Any], DirectedGraph], ) -> Dict[Any, float]: old_causal_models = [causal_model_old.causal_mechanism(x) for x in sorted(causal_model_old.graph.nodes)] new_causal_models = [causal_model_new.causal_mechanism(x) for x in sorted(causal_model_new.graph.nodes)] target_samples_old = draw_samples(causal_model_old, num_samples)[target_node].to_numpy() def attribution_set_function(subset): if np.all(subset == 0): return 0 causal_model = ProbabilisticCausalModel(graph_factory(causal_model_old.graph)) nodes = sorted(list(causal_model.graph.nodes)) for i in range(len(old_causal_models)): if subset[i] == 1: causal_model.set_causal_mechanism(nodes[i], new_causal_models[i]) else: causal_model.set_causal_mechanism(nodes[i], old_causal_models[i]) for node in causal_model.graph.nodes: causal_model.graph.nodes[node][PARENTS_DURING_FIT] = get_ordered_predecessors(causal_model.graph, node) target_samples_new = draw_samples(causal_model, num_samples)[target_node].to_numpy() return difference_estimation_func(target_samples_old, target_samples_new) attributions = estimate_shapley_values(attribution_set_function, len(old_causal_models), shapley_config) return {x: attributions[i] for i, x in enumerate(sorted(causal_model_old.graph.nodes))} def estimate_distribution_change_scores( causal_model: ProbabilisticCausalModel, original_data: pd.DataFrame, new_data: pd.DataFrame, difference_estimation_func: Callable[ [np.ndarray, np.ndarray], Union[np.ndarray, float] ] = auto_estimate_kl_divergence, max_num_evaluation_samples: int = 1000, num_joint_samples: int = 500, early_stopping_percentage: float = 0.01, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, mechanism_change_test_significance_level: float = 0.05, mechanism_change_test_fdr_control_method: Optional[str] = "fdr_bh", ) -> Dict[Any, float]: """Given newly observed and original samples from the joint distribution of the given causal graphical model, this method estimates a score for each node that quantifies how much the distribution of the node has changed. For this, it first checks whether the underlying causal mechanism has changed at all and, if this is the case, it estimates the difference between the new and original distributions. The score is based on the quantity measured by the provided difference_estimation_func or 0 if no mechanism change has been detected. Note that for each parent sample, num_joint_samples conditional samples are generated based on the original and new causal mechanism and evaluated by the given difference_estimation_func function. These results are then averaged over multiple different parent samples. :param causal_model: The underlying causal model based on the original data. :param original_data: Samples from the original data. :param new_data: Samples from the new data. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions. An example could be the KL divergence. :param max_num_evaluation_samples: Maximum number of (parent) samples for evaluating the difference in distributions. :param num_joint_samples: Number of samples generated in a node per parent sample. :param early_stopping_percentage: If the change in percentage between multiple consecutive runs is below this threshold, the evaluation stops before evaluating all max_num_evaluation_samples. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in root nodes. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in non-root nodes. :param mechanism_change_test_significance_level: A significance level for rejecting the null hypothesis that the causal mechanism of a node has not changed. :param mechanism_change_test_fdr_control_method: The false discovery rate control method for mechanism change tests. For more options, checkout `statsmodels manual <https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.multipletests.html>`_. :return: A dictionary assining a score to each node in the causal graph. """ validate_causal_dag(causal_model.graph) mechanism_changed_for_node = _check_significant_mechanism_change( causal_model.graph, original_data, new_data, independence_test, conditional_independence_test, mechanism_change_test_significance_level, mechanism_change_test_fdr_control_method, ) results = {} for node in tqdm( causal_model.graph.nodes, desc="Estimating mechanism change anomaly scores", position=0, leave=True ): if mechanism_changed_for_node[node]: if is_root_node(causal_model.graph, node): results[node] = difference_estimation_func(original_data[node].to_numpy(), new_data[node].to_numpy()) else: parent_nodes = get_ordered_predecessors(causal_model.graph, node) results[node] = _estimate_distribution_change_score( original_data[parent_nodes].to_numpy(), new_data[parent_nodes].to_numpy(), new_data[node].to_numpy(), causal_model.causal_mechanism(node), difference_estimation_func, max_num_evaluation_samples, num_joint_samples, early_stopping_percentage, ) else: results[node] = 0 return results def _check_significant_mechanism_change( graph: DirectedGraph, old_data: pd.DataFrame, new_data: pd.DataFrame, independence_test: Callable[[np.ndarray, np.ndarray], float], conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float], significance_level: float, fdr_control_method: Optional[str], ) -> Dict[Any, bool]: all_p_values = [] for node in graph.nodes: if is_root_node(graph, node): parents_org_data = None parents_new_data = None else: parents_org_data = old_data[get_ordered_predecessors(graph, node)].to_numpy() parents_new_data = new_data[get_ordered_predecessors(graph, node)].to_numpy() all_p_values.append( mechanism_change_test( old_data[node].to_numpy(), new_data[node].to_numpy(), parents_org_data, parents_new_data, independence_test=independence_test, conditional_independence_test=conditional_independence_test, ) ) if fdr_control_method is None: successes = np.array(all_p_values) <= significance_level else: successes = multipletests(all_p_values, significance_level, method=fdr_control_method)[0] return dict(zip(graph.nodes, successes)) def _estimate_distribution_change_score( parent_original_data: np.ndarray, parent_new_data: np.ndarray, target_new_data: np.ndarray, causal_model_original: ConditionalStochasticModel, difference_estimation_func: Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]], max_num_evaluation_samples: int, num_joint_samples: int, early_stopping_percentage: float, ) -> float: parent_original_data, parent_new_data, target_new_data = shape_into_2d( parent_original_data, parent_new_data, target_new_data ) causal_model_new = causal_model_original.clone() causal_model_new.fit(X=parent_new_data, Y=target_new_data) joint_parent_samples = np.vstack([parent_original_data, parent_new_data]) joint_parent_samples = joint_parent_samples[ np.random.choice( joint_parent_samples.shape[0], min(joint_parent_samples.shape[0], max_num_evaluation_samples), replace=False ) ] result = 0 run = 0 for joint_parent_sample in joint_parent_samples: old_result = result samples = repmat(joint_parent_sample, num_joint_samples, 1) result += difference_estimation_func( causal_model_original.draw_samples(samples), causal_model_new.draw_samples(samples) ) run += 1 if old_result != 0 and (1 - result / old_result) <= early_stopping_percentage: # If the relative change of the score is less than the given threshold, we stop the estimation early. _logger.info( "Early stopping: Result only changed by %f percent and a threshold of %f is set." % (1 - result / old_result, early_stopping_percentage) ) break return result / run

"""This module defines functions to attribute distribution changes. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging from typing import Any, Callable, Dict, List, Optional, Tuple, Union import networkx as nx import numpy as np import pandas as pd from numpy.matlib import repmat from statsmodels.stats.multitest import multipletests from tqdm import tqdm from dowhy.gcm.auto import AssignmentQuality, assign_causal_mechanism_node, assign_causal_mechanisms from dowhy.gcm.causal_mechanisms import ConditionalStochasticModel from dowhy.gcm.causal_models import ( PARENTS_DURING_FIT, ProbabilisticCausalModel, clone_causal_models, validate_causal_dag, ) from dowhy.gcm.divergence import auto_estimate_kl_divergence from dowhy.gcm.fitting_sampling import draw_samples, fit_causal_model_of_target from dowhy.gcm.independence_test.kernel import kernel_based from dowhy.gcm.shapley import ShapleyConfig, estimate_shapley_values from dowhy.gcm.util.general import shape_into_2d from dowhy.graph import DirectedGraph, get_ordered_predecessors, is_root_node, node_connected_subgraph_view _logger = logging.getLogger(__name__) def mechanism_change_test( target_original_data: np.ndarray, target_new_data: np.ndarray, parents_original_data: Optional[np.ndarray] = None, parents_new_data: Optional[np.ndarray] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, ) -> float: """Estimates a p-value for the null hypothesis that the original and new data were generated by the same mechanism. Here, we check the dependency between binary labels indicating whether a sample is from the original or a new data set. If the labels do not provide information to determine if a sample is coming from the original/new distribution, then it is likely that the mechanism has not changed. For non-root nodes, samples from parent variables are needed as conditioning variables. This is, testing the null hypothesis that the data were generated by the same mechanism given the parent samples. By this, we incorporate upstream changes that might have impacted the parents, but not the target node itself. :param target_original_data: Samples of the node from the original data set. :param target_new_data: Samples of the node from the new data set. :param parents_original_data: Samples from parents of the node from the original data set. :param parents_new_data: Samples from parents of the node from the new data set. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in nodes without parents. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in nodes with parents. :return: A p-value for the null hypothesis that the mechanism has not changed. """ causal_graph: DirectedGraph if parents_original_data is not None and parents_new_data is None: raise ValueError("Original parents data were given, but no new parents data!") if parents_original_data is None and parents_new_data is not None: raise ValueError("New parents data were given, but no original parents data!") num_samples_for_testing = min(target_original_data.shape[0], target_new_data.shape[0]) data_set_indices = np.ones(num_samples_for_testing * 2) data_set_indices[num_samples_for_testing:] = -1 data_set_indices = data_set_indices.astype(str) original_indices = np.random.choice(target_original_data.shape[0], num_samples_for_testing, replace=False) new_indices = np.random.choice(target_new_data.shape[0], num_samples_for_testing, replace=False) joint_target_samples = np.vstack( [shape_into_2d(target_original_data[original_indices]), shape_into_2d(target_new_data[new_indices])] ) if parents_original_data is None: return independence_test(joint_target_samples, data_set_indices) else: parents_new_data: np.ndarray joint_parent_data = np.vstack( [shape_into_2d(parents_original_data[original_indices]), shape_into_2d(parents_new_data[new_indices])] ) return conditional_independence_test(joint_target_samples, data_set_indices, joint_parent_data) def distribution_change( causal_model: ProbabilisticCausalModel, old_data: pd.DataFrame, new_data: pd.DataFrame, target_node: Any, invariant_nodes: List[Any] = None, num_samples: int = 2000, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float] = auto_estimate_kl_divergence, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, mechanism_change_test_significance_level: float = 0.05, mechanism_change_test_fdr_control_method: Optional[str] = "fdr_bh", auto_assignment_quality: Optional[AssignmentQuality] = None, return_additional_info: bool = False, shapley_config: Optional[ShapleyConfig] = None, graph_factory: Callable[[Any], DirectedGraph] = nx.DiGraph, ) -> Union[ Dict[Any, float], Tuple[Dict[Any, float], Dict[Any, bool], ProbabilisticCausalModel, ProbabilisticCausalModel] ]: """Attributes the change in the marginal distribution of the target_node to nodes upstream in the causal DAG. Note that this method creates two copies of the causal DAG. The causal models of one causal DAG are learned from old data and those of another DAG are learned from new data. **Research Paper**: Kailash Budhathoki, Dominik Janzing, Patrick Bloebaum, Hoiyi Ng. *Why did the distribution change?*. Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, PMLR 130:1666-1674, 2021. :param causal_model: Reference causal model. :param old_data: Joint samples from the 'old' distribution. :param new_data: Joint samples from the 'new' distribution. :param target_node: Target node of interest for attributing the marginal distribution change. :param invariant_nodes: List of nodes where the mechanism is kept constant regardless of changes in the datasets being analyzed. :param num_samples: Number of samples used for estimating Shapley values. This can have a significant influence on runtime and accuracy. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions, e.g. difference in average values. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in root nodes. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in non-root nodes. :param mechanism_change_test_significance_level: A significance level for rejecting the null hypothesis that the causal mechanism of a node has not changed. :param mechanism_change_test_fdr_control_method: The false discovery rate control method for mechanism change tests. For more options, checkout `statsmodels manual <https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.multipletests.html>`_. :param auto_assignment_quality: If set to None, the assigned models from the given causal models are used for the old and new graph. However, they are re-fitted on the given data. If set to a valid assignment quality, new models are automatically assigned to the old and new graph based on the respective data. :param return_additional_info: If set to True, three additional items are returned: a dictionary indicating whether each node's mechanism changed, the causal DAG whose causal models are learned from old data, and the causal DAG whose causal models are learned from new data. :param shapley_config: Configuration for the Shapley estimator. :param graph_factory: Allows customization in case a graph class different than networkx.DiGraph should be used. This function *must* copy nodes and edges. Attributes of nodes will be overridden in the copy, so the algorithm is independent of the attribute copy behavior of this factory. :return: By default, if `return_additional_info` is set to False, only the dictionary containing contribution of each upstream node is returned. If `return_additional_info` is set to True, three additional items are returned: a dictionary indicating whether each node's mechanism changed, the causal DAG whose causal models learned from old data, and the causal DAG whose causal models are learned from new data. """ if invariant_nodes is None: invariant_nodes = [] causal_graph_old = graph_factory(node_connected_subgraph_view(causal_model.graph, target_node)) causal_model_old = ProbabilisticCausalModel(causal_graph_old) if auto_assignment_quality is None: clone_causal_models(causal_model.graph, causal_model_old.graph) else: assign_causal_mechanisms(causal_model_old, old_data, override_models=True, quality=auto_assignment_quality) invariant_nodes = list(set(invariant_nodes).intersection(set(causal_graph_old.nodes))) _remove_invariant_nodes(invariant_nodes, causal_model_old, old_data, auto_assignment_quality) causal_graph_new = graph_factory(causal_graph_old) causal_model_new = ProbabilisticCausalModel(causal_graph_new) if auto_assignment_quality is None: clone_causal_models(causal_graph_old, causal_model_new.graph) else: assign_causal_mechanisms(causal_model_new, new_data, override_models=True, quality=auto_assignment_quality) mechanism_changes = _fit_accounting_for_mechanism_change( causal_model_old, causal_model_new, old_data[list(causal_graph_old.nodes)], new_data[list(causal_graph_new.nodes)], independence_test, conditional_independence_test, mechanism_change_test_significance_level, mechanism_change_test_fdr_control_method, invariant_nodes, ) attributions = distribution_change_of_graphs( causal_model_old, causal_model_new, target_node, num_samples, difference_estimation_func, shapley_config, graph_factory, ) # set attributions to zero for left out invariant nodes for node in invariant_nodes: attributions[node] = 0 if return_additional_info: return attributions, mechanism_changes, causal_model_old, causal_model_new else: return attributions def distribution_change_of_graphs( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, target_node: Any, num_samples: int = 2000, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float] = auto_estimate_kl_divergence, shapley_config: Optional[ShapleyConfig] = None, graph_factory: Callable[[Any], DirectedGraph] = nx.DiGraph, ) -> Dict[Any, float]: """Attributes the change of the marginal distribution of target_node to upstream nodes based on the distributions generated by the 'old' and 'new' causal graphs. These graphs are assumed to represent the same causal structure and to be fitted on the respective data. Note: This method creates a copy of the given causal models, i.e. the original objects will not be modified. Related paper: Budhathoki, K., Janzing, D., Bloebaum, P., & Ng, H. (2021). Why did the distribution change? arXiv preprint arXiv:2102.13384. :param causal_model_old: The ProbabilisticCausalModel fitted on the 'old' data. :param causal_model_new: The ProbabilisticCausalModel fitted on the 'new' data. :param target_node: Node of interest for attributing the marginal distribution change. :param num_samples: Number of samples used for the estimation. This can have a significant influence on the runtime and accuracy. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions. An example could be the KL divergence. :param shapley_config: Config for the Shapley estimator. :param graph_factory: Allows customization in case a graph class different than networkx.DiGraph should be used. This function *must* copy nodes and edges. Attributes of nodes will be overridden in the copy, so the algorithm is independent of the attribute copy behavior of this factory. :return: A dictionary containing the contributions of upstream nodes to the marginal distribution change in the target node. """ validate_causal_dag(causal_model_old.graph) validate_causal_dag(causal_model_new.graph) return _estimate_marginal_distribution_change( ProbabilisticCausalModel(node_connected_subgraph_view(causal_model_old.graph, target_node)), ProbabilisticCausalModel(node_connected_subgraph_view(causal_model_new.graph, target_node)), target_node, num_samples, difference_estimation_func, shapley_config, graph_factory, ) def _remove_invariant_nodes( invariant_nodes: List[Any], causal_model: ProbabilisticCausalModel, old_data: pd.DataFrame, auto_assignment_quality: Optional[AssignmentQuality], ) -> None: if auto_assignment_quality is None: auto_assignment_quality = AssignmentQuality.GOOD for invar_node in invariant_nodes: # Get parent and child nodes parents = get_ordered_predecessors(causal_model.graph, invar_node) children = list(causal_model.graph.successors(invar_node)) # Don't remove node if node has more than 1 children nodes as it can introduce # hidden confounders. if len(children) > 1: continue # Remove the middle node causal_model.graph.remove_node(invar_node) # Connect parent and child nodes for parent in parents: for child in children: causal_model.graph.add_edge(parent, child) # Update the causal mechanism for the child nodes for child in children: assign_causal_mechanism_node(causal_model, child, old_data, quality=auto_assignment_quality) def _fit_accounting_for_mechanism_change( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, old_data: pd.DataFrame, new_data: pd.DataFrame, independence_test: Callable[[np.ndarray, np.ndarray], float], conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float], significance_level: float, fdr_control_method: Optional[str], invariant_nodes: List[Any], ) -> Dict[Any, bool]: mechanism_changed_for_node = _check_significant_mechanism_change( causal_model_old.graph, old_data, new_data, independence_test, conditional_independence_test, significance_level, fdr_control_method, ) joint_data = pd.concat([old_data, new_data], ignore_index=True, sort=True) for node in causal_model_new.graph.nodes: if node in invariant_nodes: mechanism_changed_for_node[node] = False if mechanism_changed_for_node[node]: fit_causal_model_of_target(causal_model_old, node, old_data) fit_causal_model_of_target(causal_model_new, node, new_data) else: fit_causal_model_of_target(causal_model_old, node, joint_data) fit_causal_model_of_target(causal_model_new, node, joint_data) return mechanism_changed_for_node def _estimate_marginal_distribution_change( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, target_node: Any, num_samples: int, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float], shapley_config: Optional[ShapleyConfig], graph_factory: Callable[[Any], DirectedGraph], ) -> Dict[Any, float]: old_causal_models = [causal_model_old.causal_mechanism(x) for x in sorted(causal_model_old.graph.nodes)] new_causal_models = [causal_model_new.causal_mechanism(x) for x in sorted(causal_model_new.graph.nodes)] target_samples_old = draw_samples(causal_model_old, num_samples)[target_node].to_numpy() def attribution_set_function(subset): if np.all(subset == 0): return 0 causal_model = ProbabilisticCausalModel(graph_factory(causal_model_old.graph)) nodes = sorted(list(causal_model.graph.nodes)) for i in range(len(old_causal_models)): if subset[i] == 1: causal_model.set_causal_mechanism(nodes[i], new_causal_models[i]) else: causal_model.set_causal_mechanism(nodes[i], old_causal_models[i]) for node in causal_model.graph.nodes: causal_model.graph.nodes[node][PARENTS_DURING_FIT] = get_ordered_predecessors(causal_model.graph, node) target_samples_new = draw_samples(causal_model, num_samples)[target_node].to_numpy() return difference_estimation_func(target_samples_old, target_samples_new) attributions = estimate_shapley_values(attribution_set_function, len(old_causal_models), shapley_config) return {x: attributions[i] for i, x in enumerate(sorted(causal_model_old.graph.nodes))} def estimate_distribution_change_scores( causal_model: ProbabilisticCausalModel, original_data: pd.DataFrame, new_data: pd.DataFrame, difference_estimation_func: Callable[ [np.ndarray, np.ndarray], Union[np.ndarray, float] ] = auto_estimate_kl_divergence, max_num_evaluation_samples: int = 1000, num_joint_samples: int = 500, early_stopping_percentage: float = 0.01, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, mechanism_change_test_significance_level: float = 0.05, mechanism_change_test_fdr_control_method: Optional[str] = "fdr_bh", ) -> Dict[Any, float]: """Given newly observed and original samples from the joint distribution of the given causal graphical model, this method estimates a score for each node that quantifies how much the distribution of the node has changed. For this, it first checks whether the underlying causal mechanism has changed at all and, if this is the case, it estimates the difference between the new and original distributions. The score is based on the quantity measured by the provided difference_estimation_func or 0 if no mechanism change has been detected. Note that for each parent sample, num_joint_samples conditional samples are generated based on the original and new causal mechanism and evaluated by the given difference_estimation_func function. These results are then averaged over multiple different parent samples. :param causal_model: The underlying causal model based on the original data. :param original_data: Samples from the original data. :param new_data: Samples from the new data. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions. An example could be the KL divergence. :param max_num_evaluation_samples: Maximum number of (parent) samples for evaluating the difference in distributions. :param num_joint_samples: Number of samples generated in a node per parent sample. :param early_stopping_percentage: If the change in percentage between multiple consecutive runs is below this threshold, the evaluation stops before evaluating all max_num_evaluation_samples. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in root nodes. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in non-root nodes. :param mechanism_change_test_significance_level: A significance level for rejecting the null hypothesis that the causal mechanism of a node has not changed. :param mechanism_change_test_fdr_control_method: The false discovery rate control method for mechanism change tests. For more options, checkout `statsmodels manual <https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.multipletests.html>`_. :return: A dictionary assining a score to each node in the causal graph. """ validate_causal_dag(causal_model.graph) mechanism_changed_for_node = _check_significant_mechanism_change( causal_model.graph, original_data, new_data, independence_test, conditional_independence_test, mechanism_change_test_significance_level, mechanism_change_test_fdr_control_method, ) results = {} for node in tqdm( causal_model.graph.nodes, desc="Estimating mechanism change anomaly scores", position=0, leave=True ): if mechanism_changed_for_node[node]: if is_root_node(causal_model.graph, node): results[node] = difference_estimation_func(original_data[node].to_numpy(), new_data[node].to_numpy()) else: parent_nodes = get_ordered_predecessors(causal_model.graph, node) results[node] = _estimate_distribution_change_score( original_data[parent_nodes].to_numpy(), new_data[parent_nodes].to_numpy(), new_data[node].to_numpy(), causal_model.causal_mechanism(node), difference_estimation_func, max_num_evaluation_samples, num_joint_samples, early_stopping_percentage, ) else: results[node] = 0 return results def _check_significant_mechanism_change( graph: DirectedGraph, old_data: pd.DataFrame, new_data: pd.DataFrame, independence_test: Callable[[np.ndarray, np.ndarray], float], conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float], significance_level: float, fdr_control_method: Optional[str], ) -> Dict[Any, bool]: all_p_values = [] for node in graph.nodes: if is_root_node(graph, node): parents_org_data = None parents_new_data = None else: parents_org_data = old_data[get_ordered_predecessors(graph, node)].to_numpy() parents_new_data = new_data[get_ordered_predecessors(graph, node)].to_numpy() all_p_values.append( mechanism_change_test( old_data[node].to_numpy(), new_data[node].to_numpy(), parents_org_data, parents_new_data, independence_test=independence_test, conditional_independence_test=conditional_independence_test, ) ) if fdr_control_method is None: successes = np.array(all_p_values) <= significance_level else: successes = multipletests(all_p_values, significance_level, method=fdr_control_method)[0] return dict(zip(graph.nodes, successes)) def _estimate_distribution_change_score( parent_original_data: np.ndarray, parent_new_data: np.ndarray, target_new_data: np.ndarray, causal_model_original: ConditionalStochasticModel, difference_estimation_func: Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]], max_num_evaluation_samples: int, num_joint_samples: int, early_stopping_percentage: float, ) -> float: parent_original_data, parent_new_data, target_new_data = shape_into_2d( parent_original_data, parent_new_data, target_new_data ) causal_model_new = causal_model_original.clone() causal_model_new.fit(X=parent_new_data, Y=target_new_data) joint_parent_samples = np.vstack([parent_original_data, parent_new_data]) joint_parent_samples = joint_parent_samples[ np.random.choice( joint_parent_samples.shape[0], min(joint_parent_samples.shape[0], max_num_evaluation_samples), replace=False ) ] result = 0 run = 0 for joint_parent_sample in joint_parent_samples: old_result = result samples = repmat(joint_parent_sample, num_joint_samples, 1) result += difference_estimation_func( causal_model_original.draw_samples(samples), causal_model_new.draw_samples(samples) ) run += 1 if old_result != 0 and (1 - result / old_result) <= early_stopping_percentage: # If the relative change of the score is less than the given threshold, we stop the estimation early. _logger.info( "Early stopping: Result only changed by %f percent and a threshold of %f is set." % (1 - result / old_result, early_stopping_percentage) ) break return result / run

bhatt-priyadutt

734b61671d130cf21a4e437084b9083dbd5c4a39

b4f80dcb2251a03aff4c816d7c6f6eb2aaf98e73

Instead of 'constant' what about something like: "List of nodes where the mechanism is kept constant regardless of changes in the datasets being analyzed"

bloebp

py-why/dowhy

Invariant nodes removal

Feature - Now users can enter the list of invariant nodes to the distribution_change function to remove them from analysis

2023-08-15 12:59:48+00:00

2023-08-24 16:12:46+00:00

dowhy/gcm/distribution_change.py

"""This module defines functions to attribute distribution changes. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging from typing import Any, Callable, Dict, Optional, Tuple, Union import networkx as nx import numpy as np import pandas as pd from numpy.matlib import repmat from statsmodels.stats.multitest import multipletests from tqdm import tqdm from dowhy.gcm.auto import AssignmentQuality, assign_causal_mechanisms from dowhy.gcm.causal_mechanisms import ConditionalStochasticModel from dowhy.gcm.causal_models import ( PARENTS_DURING_FIT, ProbabilisticCausalModel, clone_causal_models, validate_causal_dag, ) from dowhy.gcm.divergence import auto_estimate_kl_divergence from dowhy.gcm.fitting_sampling import draw_samples, fit_causal_model_of_target from dowhy.gcm.independence_test.kernel import kernel_based from dowhy.gcm.shapley import ShapleyConfig, estimate_shapley_values from dowhy.gcm.util.general import shape_into_2d from dowhy.graph import DirectedGraph, get_ordered_predecessors, is_root_node, node_connected_subgraph_view _logger = logging.getLogger(__name__) def mechanism_change_test( target_original_data: np.ndarray, target_new_data: np.ndarray, parents_original_data: Optional[np.ndarray] = None, parents_new_data: Optional[np.ndarray] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, ) -> float: """Estimates a p-value for the null hypothesis that the original and new data were generated by the same mechanism. Here, we check the dependency between binary labels indicating whether a sample is from the original or a new data set. If the labels do not provide information to determine if a sample is coming from the original/new distribution, then it is likely that the mechanism has not changed. For non-root nodes, samples from parent variables are needed as conditioning variables. This is, testing the null hypothesis that the data were generated by the same mechanism given the parent samples. By this, we incorporate upstream changes that might have impacted the parents, but not the target node itself. :param target_original_data: Samples of the node from the original data set. :param target_new_data: Samples of the node from the new data set. :param parents_original_data: Samples from parents of the node from the original data set. :param parents_new_data: Samples from parents of the node from the new data set. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in nodes without parents. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in nodes with parents. :return: A p-value for the null hypothesis that the mechanism has not changed. """ causal_graph: DirectedGraph if parents_original_data is not None and parents_new_data is None: raise ValueError("Original parents data were given, but no new parents data!") if parents_original_data is None and parents_new_data is not None: raise ValueError("New parents data were given, but no original parents data!") num_samples_for_testing = min(target_original_data.shape[0], target_new_data.shape[0]) data_set_indices = np.ones(num_samples_for_testing * 2) data_set_indices[num_samples_for_testing:] = -1 data_set_indices = data_set_indices.astype(str) original_indices = np.random.choice(target_original_data.shape[0], num_samples_for_testing, replace=False) new_indices = np.random.choice(target_new_data.shape[0], num_samples_for_testing, replace=False) joint_target_samples = np.vstack( [shape_into_2d(target_original_data[original_indices]), shape_into_2d(target_new_data[new_indices])] ) if parents_original_data is None: return independence_test(joint_target_samples, data_set_indices) else: parents_new_data: np.ndarray joint_parent_data = np.vstack( [shape_into_2d(parents_original_data[original_indices]), shape_into_2d(parents_new_data[new_indices])] ) return conditional_independence_test(joint_target_samples, data_set_indices, joint_parent_data) def distribution_change( causal_model: ProbabilisticCausalModel, old_data: pd.DataFrame, new_data: pd.DataFrame, target_node: Any, num_samples: int = 2000, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float] = auto_estimate_kl_divergence, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, mechanism_change_test_significance_level: float = 0.05, mechanism_change_test_fdr_control_method: Optional[str] = "fdr_bh", auto_assignment_quality: Optional[AssignmentQuality] = None, return_additional_info: bool = False, shapley_config: Optional[ShapleyConfig] = None, graph_factory: Callable[[Any], DirectedGraph] = nx.DiGraph, ) -> Union[ Dict[Any, float], Tuple[Dict[Any, float], Dict[Any, bool], ProbabilisticCausalModel, ProbabilisticCausalModel] ]: """Attributes the change in the marginal distribution of the target_node to nodes upstream in the causal DAG. Note that this method creates two copies of the causal DAG. The causal models of one causal DAG are learned from old data and those of another DAG are learned from new data. **Research Paper**: Kailash Budhathoki, Dominik Janzing, Patrick Bloebaum, Hoiyi Ng. *Why did the distribution change?*. Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, PMLR 130:1666-1674, 2021. :param causal_model: Reference causal model. :param old_data: Joint samples from the 'old' distribution. :param new_data: Joint samples from the 'new' distribution. :param target_node: Target node of interest for attributing the marginal distribution change. :param num_samples: Number of samples used for estimating Shapley values. This can have a significant influence on runtime and accuracy. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions, e.g. difference in average values. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in root nodes. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in non-root nodes. :param mechanism_change_test_significance_level: A significance level for rejecting the null hypothesis that the causal mechanism of a node has not changed. :param mechanism_change_test_fdr_control_method: The false discovery rate control method for mechanism change tests. For more options, checkout `statsmodels manual <https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.multipletests.html>`_. :param auto_assignment_quality: If set to None, the assigned models from the given causal models are used for the old and new graph. However, they are re-fitted on the given data. If set to a valid assignment quality, new models are automatically assigned to the old and new graph based on the respective data. :param return_additional_info: If set to True, three additional items are returned: a dictionary indicating whether each node's mechanism changed, the causal DAG whose causal models are learned from old data, and the causal DAG whose causal models are learned from new data. :param shapley_config: Configuration for the Shapley estimator. :param graph_factory: Allows customization in case a graph class different than networkx.DiGraph should be used. This function *must* copy nodes and edges. Attributes of nodes will be overridden in the copy, so the algorithm is independent of the attribute copy behavior of this factory. :return: By default, if `return_additional_info` is set to False, only the dictionary containing contribution of each upstream node is returned. If `return_additional_info` is set to True, three additional items are returned: a dictionary indicating whether each node's mechanism changed, the causal DAG whose causal models learned from old data, and the causal DAG whose causal models are learned from new data. """ causal_graph_old = graph_factory(node_connected_subgraph_view(causal_model.graph, target_node)) causal_model_old = ProbabilisticCausalModel(causal_graph_old) if auto_assignment_quality is None: clone_causal_models(causal_model.graph, causal_model_old.graph) else: assign_causal_mechanisms(causal_model_old, old_data, override_models=True, quality=auto_assignment_quality) causal_graph_new = graph_factory(causal_graph_old) causal_model_new = ProbabilisticCausalModel(causal_graph_new) if auto_assignment_quality is None: clone_causal_models(causal_graph_old, causal_model_new.graph) else: assign_causal_mechanisms(causal_model_new, new_data, override_models=True, quality=auto_assignment_quality) mechanism_changes = _fit_accounting_for_mechanism_change( causal_model_old, causal_model_new, old_data[list(causal_graph_old.nodes)], new_data[list(causal_graph_new.nodes)], independence_test, conditional_independence_test, mechanism_change_test_significance_level, mechanism_change_test_fdr_control_method, ) attributions = distribution_change_of_graphs( causal_model_old, causal_model_new, target_node, num_samples, difference_estimation_func, shapley_config, graph_factory, ) if return_additional_info: return attributions, mechanism_changes, causal_model_old, causal_model_new else: return attributions def distribution_change_of_graphs( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, target_node: Any, num_samples: int = 2000, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float] = auto_estimate_kl_divergence, shapley_config: Optional[ShapleyConfig] = None, graph_factory: Callable[[Any], DirectedGraph] = nx.DiGraph, ) -> Dict[Any, float]: """Attributes the change of the marginal distribution of target_node to upstream nodes based on the distributions generated by the 'old' and 'new' causal graphs. These graphs are assumed to represent the same causal structure and to be fitted on the respective data. Note: This method creates a copy of the given causal models, i.e. the original objects will not be modified. Related paper: Budhathoki, K., Janzing, D., Bloebaum, P., & Ng, H. (2021). Why did the distribution change? arXiv preprint arXiv:2102.13384. :param causal_model_old: The ProbabilisticCausalModel fitted on the 'old' data. :param causal_model_new: The ProbabilisticCausalModel fitted on the 'new' data. :param target_node: Node of interest for attributing the marginal distribution change. :param num_samples: Number of samples used for the estimation. This can have a significant influence on the runtime and accuracy. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions. An example could be the KL divergence. :param shapley_config: Config for the Shapley estimator. :param graph_factory: Allows customization in case a graph class different than networkx.DiGraph should be used. This function *must* copy nodes and edges. Attributes of nodes will be overridden in the copy, so the algorithm is independent of the attribute copy behavior of this factory. :return: A dictionary containing the contributions of upstream nodes to the marginal distribution change in the target node. """ validate_causal_dag(causal_model_old.graph) validate_causal_dag(causal_model_new.graph) return _estimate_marginal_distribution_change( ProbabilisticCausalModel(node_connected_subgraph_view(causal_model_old.graph, target_node)), ProbabilisticCausalModel(node_connected_subgraph_view(causal_model_new.graph, target_node)), target_node, num_samples, difference_estimation_func, shapley_config, graph_factory, ) def _fit_accounting_for_mechanism_change( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, old_data: pd.DataFrame, new_data: pd.DataFrame, independence_test: Callable[[np.ndarray, np.ndarray], float], conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float], significance_level: float, fdr_control_method: Optional[str], ) -> Dict[Any, bool]: mechanism_changed_for_node = _check_significant_mechanism_change( causal_model_old.graph, old_data, new_data, independence_test, conditional_independence_test, significance_level, fdr_control_method, ) joint_data = pd.concat([old_data, new_data], ignore_index=True, sort=True) for node in causal_model_new.graph.nodes: if mechanism_changed_for_node[node]: fit_causal_model_of_target(causal_model_old, node, old_data) fit_causal_model_of_target(causal_model_new, node, new_data) else: fit_causal_model_of_target(causal_model_old, node, joint_data) fit_causal_model_of_target(causal_model_new, node, joint_data) return mechanism_changed_for_node def _estimate_marginal_distribution_change( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, target_node: Any, num_samples: int, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float], shapley_config: Optional[ShapleyConfig], graph_factory: Callable[[Any], DirectedGraph], ) -> Dict[Any, float]: old_causal_models = [causal_model_old.causal_mechanism(x) for x in sorted(causal_model_old.graph.nodes)] new_causal_models = [causal_model_new.causal_mechanism(x) for x in sorted(causal_model_new.graph.nodes)] target_samples_old = draw_samples(causal_model_old, num_samples)[target_node].to_numpy() def attribution_set_function(subset): if np.all(subset == 0): return 0 causal_model = ProbabilisticCausalModel(graph_factory(causal_model_old.graph)) nodes = sorted(list(causal_model.graph.nodes)) for i in range(len(old_causal_models)): if subset[i] == 1: causal_model.set_causal_mechanism(nodes[i], new_causal_models[i]) else: causal_model.set_causal_mechanism(nodes[i], old_causal_models[i]) for node in causal_model.graph.nodes: causal_model.graph.nodes[node][PARENTS_DURING_FIT] = get_ordered_predecessors(causal_model.graph, node) target_samples_new = draw_samples(causal_model, num_samples)[target_node].to_numpy() return difference_estimation_func(target_samples_old, target_samples_new) attributions = estimate_shapley_values(attribution_set_function, len(old_causal_models), shapley_config) return {x: attributions[i] for i, x in enumerate(sorted(causal_model_old.graph.nodes))} def estimate_distribution_change_scores( causal_model: ProbabilisticCausalModel, original_data: pd.DataFrame, new_data: pd.DataFrame, difference_estimation_func: Callable[ [np.ndarray, np.ndarray], Union[np.ndarray, float] ] = auto_estimate_kl_divergence, max_num_evaluation_samples: int = 1000, num_joint_samples: int = 500, early_stopping_percentage: float = 0.01, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, mechanism_change_test_significance_level: float = 0.05, mechanism_change_test_fdr_control_method: Optional[str] = "fdr_bh", ) -> Dict[Any, float]: """Given newly observed and original samples from the joint distribution of the given causal graphical model, this method estimates a score for each node that quantifies how much the distribution of the node has changed. For this, it first checks whether the underlying causal mechanism has changed at all and, if this is the case, it estimates the difference between the new and original distributions. The score is based on the quantity measured by the provided difference_estimation_func or 0 if no mechanism change has been detected. Note that for each parent sample, num_joint_samples conditional samples are generated based on the original and new causal mechanism and evaluated by the given difference_estimation_func function. These results are then averaged over multiple different parent samples. :param causal_model: The underlying causal model based on the original data. :param original_data: Samples from the original data. :param new_data: Samples from the new data. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions. An example could be the KL divergence. :param max_num_evaluation_samples: Maximum number of (parent) samples for evaluating the difference in distributions. :param num_joint_samples: Number of samples generated in a node per parent sample. :param early_stopping_percentage: If the change in percentage between multiple consecutive runs is below this threshold, the evaluation stops before evaluating all max_num_evaluation_samples. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in root nodes. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in non-root nodes. :param mechanism_change_test_significance_level: A significance level for rejecting the null hypothesis that the causal mechanism of a node has not changed. :param mechanism_change_test_fdr_control_method: The false discovery rate control method for mechanism change tests. For more options, checkout `statsmodels manual <https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.multipletests.html>`_. :return: A dictionary assining a score to each node in the causal graph. """ validate_causal_dag(causal_model.graph) mechanism_changed_for_node = _check_significant_mechanism_change( causal_model.graph, original_data, new_data, independence_test, conditional_independence_test, mechanism_change_test_significance_level, mechanism_change_test_fdr_control_method, ) results = {} for node in tqdm( causal_model.graph.nodes, desc="Estimating mechanism change anomaly scores", position=0, leave=True ): if mechanism_changed_for_node[node]: if is_root_node(causal_model.graph, node): results[node] = difference_estimation_func(original_data[node].to_numpy(), new_data[node].to_numpy()) else: parent_nodes = get_ordered_predecessors(causal_model.graph, node) results[node] = _estimate_distribution_change_score( original_data[parent_nodes].to_numpy(), new_data[parent_nodes].to_numpy(), new_data[node].to_numpy(), causal_model.causal_mechanism(node), difference_estimation_func, max_num_evaluation_samples, num_joint_samples, early_stopping_percentage, ) else: results[node] = 0 return results def _check_significant_mechanism_change( graph: DirectedGraph, old_data: pd.DataFrame, new_data: pd.DataFrame, independence_test: Callable[[np.ndarray, np.ndarray], float], conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float], significance_level: float, fdr_control_method: Optional[str], ) -> Dict[Any, bool]: all_p_values = [] for node in graph.nodes: if is_root_node(graph, node): parents_org_data = None parents_new_data = None else: parents_org_data = old_data[get_ordered_predecessors(graph, node)].to_numpy() parents_new_data = new_data[get_ordered_predecessors(graph, node)].to_numpy() all_p_values.append( mechanism_change_test( old_data[node].to_numpy(), new_data[node].to_numpy(), parents_org_data, parents_new_data, independence_test=independence_test, conditional_independence_test=conditional_independence_test, ) ) if fdr_control_method is None: successes = np.array(all_p_values) <= significance_level else: successes = multipletests(all_p_values, significance_level, method=fdr_control_method)[0] return dict(zip(graph.nodes, successes)) def _estimate_distribution_change_score( parent_original_data: np.ndarray, parent_new_data: np.ndarray, target_new_data: np.ndarray, causal_model_original: ConditionalStochasticModel, difference_estimation_func: Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]], max_num_evaluation_samples: int, num_joint_samples: int, early_stopping_percentage: float, ) -> float: parent_original_data, parent_new_data, target_new_data = shape_into_2d( parent_original_data, parent_new_data, target_new_data ) causal_model_new = causal_model_original.clone() causal_model_new.fit(X=parent_new_data, Y=target_new_data) joint_parent_samples = np.vstack([parent_original_data, parent_new_data]) joint_parent_samples = joint_parent_samples[ np.random.choice( joint_parent_samples.shape[0], min(joint_parent_samples.shape[0], max_num_evaluation_samples), replace=False ) ] result = 0 run = 0 for joint_parent_sample in joint_parent_samples: old_result = result samples = repmat(joint_parent_sample, num_joint_samples, 1) result += difference_estimation_func( causal_model_original.draw_samples(samples), causal_model_new.draw_samples(samples) ) run += 1 if old_result != 0 and (1 - result / old_result) <= early_stopping_percentage: # If the relative change of the score is less than the given threshold, we stop the estimation early. _logger.info( "Early stopping: Result only changed by %f percent and a threshold of %f is set." % (1 - result / old_result, early_stopping_percentage) ) break return result / run

"""This module defines functions to attribute distribution changes. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging from typing import Any, Callable, Dict, List, Optional, Tuple, Union import networkx as nx import numpy as np import pandas as pd from numpy.matlib import repmat from statsmodels.stats.multitest import multipletests from tqdm import tqdm from dowhy.gcm.auto import AssignmentQuality, assign_causal_mechanism_node, assign_causal_mechanisms from dowhy.gcm.causal_mechanisms import ConditionalStochasticModel from dowhy.gcm.causal_models import ( PARENTS_DURING_FIT, ProbabilisticCausalModel, clone_causal_models, validate_causal_dag, ) from dowhy.gcm.divergence import auto_estimate_kl_divergence from dowhy.gcm.fitting_sampling import draw_samples, fit_causal_model_of_target from dowhy.gcm.independence_test.kernel import kernel_based from dowhy.gcm.shapley import ShapleyConfig, estimate_shapley_values from dowhy.gcm.util.general import shape_into_2d from dowhy.graph import DirectedGraph, get_ordered_predecessors, is_root_node, node_connected_subgraph_view _logger = logging.getLogger(__name__) def mechanism_change_test( target_original_data: np.ndarray, target_new_data: np.ndarray, parents_original_data: Optional[np.ndarray] = None, parents_new_data: Optional[np.ndarray] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, ) -> float: """Estimates a p-value for the null hypothesis that the original and new data were generated by the same mechanism. Here, we check the dependency between binary labels indicating whether a sample is from the original or a new data set. If the labels do not provide information to determine if a sample is coming from the original/new distribution, then it is likely that the mechanism has not changed. For non-root nodes, samples from parent variables are needed as conditioning variables. This is, testing the null hypothesis that the data were generated by the same mechanism given the parent samples. By this, we incorporate upstream changes that might have impacted the parents, but not the target node itself. :param target_original_data: Samples of the node from the original data set. :param target_new_data: Samples of the node from the new data set. :param parents_original_data: Samples from parents of the node from the original data set. :param parents_new_data: Samples from parents of the node from the new data set. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in nodes without parents. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in nodes with parents. :return: A p-value for the null hypothesis that the mechanism has not changed. """ causal_graph: DirectedGraph if parents_original_data is not None and parents_new_data is None: raise ValueError("Original parents data were given, but no new parents data!") if parents_original_data is None and parents_new_data is not None: raise ValueError("New parents data were given, but no original parents data!") num_samples_for_testing = min(target_original_data.shape[0], target_new_data.shape[0]) data_set_indices = np.ones(num_samples_for_testing * 2) data_set_indices[num_samples_for_testing:] = -1 data_set_indices = data_set_indices.astype(str) original_indices = np.random.choice(target_original_data.shape[0], num_samples_for_testing, replace=False) new_indices = np.random.choice(target_new_data.shape[0], num_samples_for_testing, replace=False) joint_target_samples = np.vstack( [shape_into_2d(target_original_data[original_indices]), shape_into_2d(target_new_data[new_indices])] ) if parents_original_data is None: return independence_test(joint_target_samples, data_set_indices) else: parents_new_data: np.ndarray joint_parent_data = np.vstack( [shape_into_2d(parents_original_data[original_indices]), shape_into_2d(parents_new_data[new_indices])] ) return conditional_independence_test(joint_target_samples, data_set_indices, joint_parent_data) def distribution_change( causal_model: ProbabilisticCausalModel, old_data: pd.DataFrame, new_data: pd.DataFrame, target_node: Any, invariant_nodes: List[Any] = None, num_samples: int = 2000, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float] = auto_estimate_kl_divergence, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, mechanism_change_test_significance_level: float = 0.05, mechanism_change_test_fdr_control_method: Optional[str] = "fdr_bh", auto_assignment_quality: Optional[AssignmentQuality] = None, return_additional_info: bool = False, shapley_config: Optional[ShapleyConfig] = None, graph_factory: Callable[[Any], DirectedGraph] = nx.DiGraph, ) -> Union[ Dict[Any, float], Tuple[Dict[Any, float], Dict[Any, bool], ProbabilisticCausalModel, ProbabilisticCausalModel] ]: """Attributes the change in the marginal distribution of the target_node to nodes upstream in the causal DAG. Note that this method creates two copies of the causal DAG. The causal models of one causal DAG are learned from old data and those of another DAG are learned from new data. **Research Paper**: Kailash Budhathoki, Dominik Janzing, Patrick Bloebaum, Hoiyi Ng. *Why did the distribution change?*. Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, PMLR 130:1666-1674, 2021. :param causal_model: Reference causal model. :param old_data: Joint samples from the 'old' distribution. :param new_data: Joint samples from the 'new' distribution. :param target_node: Target node of interest for attributing the marginal distribution change. :param invariant_nodes: List of nodes where the mechanism is kept constant regardless of changes in the datasets being analyzed. :param num_samples: Number of samples used for estimating Shapley values. This can have a significant influence on runtime and accuracy. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions, e.g. difference in average values. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in root nodes. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in non-root nodes. :param mechanism_change_test_significance_level: A significance level for rejecting the null hypothesis that the causal mechanism of a node has not changed. :param mechanism_change_test_fdr_control_method: The false discovery rate control method for mechanism change tests. For more options, checkout `statsmodels manual <https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.multipletests.html>`_. :param auto_assignment_quality: If set to None, the assigned models from the given causal models are used for the old and new graph. However, they are re-fitted on the given data. If set to a valid assignment quality, new models are automatically assigned to the old and new graph based on the respective data. :param return_additional_info: If set to True, three additional items are returned: a dictionary indicating whether each node's mechanism changed, the causal DAG whose causal models are learned from old data, and the causal DAG whose causal models are learned from new data. :param shapley_config: Configuration for the Shapley estimator. :param graph_factory: Allows customization in case a graph class different than networkx.DiGraph should be used. This function *must* copy nodes and edges. Attributes of nodes will be overridden in the copy, so the algorithm is independent of the attribute copy behavior of this factory. :return: By default, if `return_additional_info` is set to False, only the dictionary containing contribution of each upstream node is returned. If `return_additional_info` is set to True, three additional items are returned: a dictionary indicating whether each node's mechanism changed, the causal DAG whose causal models learned from old data, and the causal DAG whose causal models are learned from new data. """ if invariant_nodes is None: invariant_nodes = [] causal_graph_old = graph_factory(node_connected_subgraph_view(causal_model.graph, target_node)) causal_model_old = ProbabilisticCausalModel(causal_graph_old) if auto_assignment_quality is None: clone_causal_models(causal_model.graph, causal_model_old.graph) else: assign_causal_mechanisms(causal_model_old, old_data, override_models=True, quality=auto_assignment_quality) invariant_nodes = list(set(invariant_nodes).intersection(set(causal_graph_old.nodes))) _remove_invariant_nodes(invariant_nodes, causal_model_old, old_data, auto_assignment_quality) causal_graph_new = graph_factory(causal_graph_old) causal_model_new = ProbabilisticCausalModel(causal_graph_new) if auto_assignment_quality is None: clone_causal_models(causal_graph_old, causal_model_new.graph) else: assign_causal_mechanisms(causal_model_new, new_data, override_models=True, quality=auto_assignment_quality) mechanism_changes = _fit_accounting_for_mechanism_change( causal_model_old, causal_model_new, old_data[list(causal_graph_old.nodes)], new_data[list(causal_graph_new.nodes)], independence_test, conditional_independence_test, mechanism_change_test_significance_level, mechanism_change_test_fdr_control_method, invariant_nodes, ) attributions = distribution_change_of_graphs( causal_model_old, causal_model_new, target_node, num_samples, difference_estimation_func, shapley_config, graph_factory, ) # set attributions to zero for left out invariant nodes for node in invariant_nodes: attributions[node] = 0 if return_additional_info: return attributions, mechanism_changes, causal_model_old, causal_model_new else: return attributions def distribution_change_of_graphs( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, target_node: Any, num_samples: int = 2000, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float] = auto_estimate_kl_divergence, shapley_config: Optional[ShapleyConfig] = None, graph_factory: Callable[[Any], DirectedGraph] = nx.DiGraph, ) -> Dict[Any, float]: """Attributes the change of the marginal distribution of target_node to upstream nodes based on the distributions generated by the 'old' and 'new' causal graphs. These graphs are assumed to represent the same causal structure and to be fitted on the respective data. Note: This method creates a copy of the given causal models, i.e. the original objects will not be modified. Related paper: Budhathoki, K., Janzing, D., Bloebaum, P., & Ng, H. (2021). Why did the distribution change? arXiv preprint arXiv:2102.13384. :param causal_model_old: The ProbabilisticCausalModel fitted on the 'old' data. :param causal_model_new: The ProbabilisticCausalModel fitted on the 'new' data. :param target_node: Node of interest for attributing the marginal distribution change. :param num_samples: Number of samples used for the estimation. This can have a significant influence on the runtime and accuracy. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions. An example could be the KL divergence. :param shapley_config: Config for the Shapley estimator. :param graph_factory: Allows customization in case a graph class different than networkx.DiGraph should be used. This function *must* copy nodes and edges. Attributes of nodes will be overridden in the copy, so the algorithm is independent of the attribute copy behavior of this factory. :return: A dictionary containing the contributions of upstream nodes to the marginal distribution change in the target node. """ validate_causal_dag(causal_model_old.graph) validate_causal_dag(causal_model_new.graph) return _estimate_marginal_distribution_change( ProbabilisticCausalModel(node_connected_subgraph_view(causal_model_old.graph, target_node)), ProbabilisticCausalModel(node_connected_subgraph_view(causal_model_new.graph, target_node)), target_node, num_samples, difference_estimation_func, shapley_config, graph_factory, ) def _remove_invariant_nodes( invariant_nodes: List[Any], causal_model: ProbabilisticCausalModel, old_data: pd.DataFrame, auto_assignment_quality: Optional[AssignmentQuality], ) -> None: if auto_assignment_quality is None: auto_assignment_quality = AssignmentQuality.GOOD for invar_node in invariant_nodes: # Get parent and child nodes parents = get_ordered_predecessors(causal_model.graph, invar_node) children = list(causal_model.graph.successors(invar_node)) # Don't remove node if node has more than 1 children nodes as it can introduce # hidden confounders. if len(children) > 1: continue # Remove the middle node causal_model.graph.remove_node(invar_node) # Connect parent and child nodes for parent in parents: for child in children: causal_model.graph.add_edge(parent, child) # Update the causal mechanism for the child nodes for child in children: assign_causal_mechanism_node(causal_model, child, old_data, quality=auto_assignment_quality) def _fit_accounting_for_mechanism_change( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, old_data: pd.DataFrame, new_data: pd.DataFrame, independence_test: Callable[[np.ndarray, np.ndarray], float], conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float], significance_level: float, fdr_control_method: Optional[str], invariant_nodes: List[Any], ) -> Dict[Any, bool]: mechanism_changed_for_node = _check_significant_mechanism_change( causal_model_old.graph, old_data, new_data, independence_test, conditional_independence_test, significance_level, fdr_control_method, ) joint_data = pd.concat([old_data, new_data], ignore_index=True, sort=True) for node in causal_model_new.graph.nodes: if node in invariant_nodes: mechanism_changed_for_node[node] = False if mechanism_changed_for_node[node]: fit_causal_model_of_target(causal_model_old, node, old_data) fit_causal_model_of_target(causal_model_new, node, new_data) else: fit_causal_model_of_target(causal_model_old, node, joint_data) fit_causal_model_of_target(causal_model_new, node, joint_data) return mechanism_changed_for_node def _estimate_marginal_distribution_change( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, target_node: Any, num_samples: int, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float], shapley_config: Optional[ShapleyConfig], graph_factory: Callable[[Any], DirectedGraph], ) -> Dict[Any, float]: old_causal_models = [causal_model_old.causal_mechanism(x) for x in sorted(causal_model_old.graph.nodes)] new_causal_models = [causal_model_new.causal_mechanism(x) for x in sorted(causal_model_new.graph.nodes)] target_samples_old = draw_samples(causal_model_old, num_samples)[target_node].to_numpy() def attribution_set_function(subset): if np.all(subset == 0): return 0 causal_model = ProbabilisticCausalModel(graph_factory(causal_model_old.graph)) nodes = sorted(list(causal_model.graph.nodes)) for i in range(len(old_causal_models)): if subset[i] == 1: causal_model.set_causal_mechanism(nodes[i], new_causal_models[i]) else: causal_model.set_causal_mechanism(nodes[i], old_causal_models[i]) for node in causal_model.graph.nodes: causal_model.graph.nodes[node][PARENTS_DURING_FIT] = get_ordered_predecessors(causal_model.graph, node) target_samples_new = draw_samples(causal_model, num_samples)[target_node].to_numpy() return difference_estimation_func(target_samples_old, target_samples_new) attributions = estimate_shapley_values(attribution_set_function, len(old_causal_models), shapley_config) return {x: attributions[i] for i, x in enumerate(sorted(causal_model_old.graph.nodes))} def estimate_distribution_change_scores( causal_model: ProbabilisticCausalModel, original_data: pd.DataFrame, new_data: pd.DataFrame, difference_estimation_func: Callable[ [np.ndarray, np.ndarray], Union[np.ndarray, float] ] = auto_estimate_kl_divergence, max_num_evaluation_samples: int = 1000, num_joint_samples: int = 500, early_stopping_percentage: float = 0.01, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, mechanism_change_test_significance_level: float = 0.05, mechanism_change_test_fdr_control_method: Optional[str] = "fdr_bh", ) -> Dict[Any, float]: """Given newly observed and original samples from the joint distribution of the given causal graphical model, this method estimates a score for each node that quantifies how much the distribution of the node has changed. For this, it first checks whether the underlying causal mechanism has changed at all and, if this is the case, it estimates the difference between the new and original distributions. The score is based on the quantity measured by the provided difference_estimation_func or 0 if no mechanism change has been detected. Note that for each parent sample, num_joint_samples conditional samples are generated based on the original and new causal mechanism and evaluated by the given difference_estimation_func function. These results are then averaged over multiple different parent samples. :param causal_model: The underlying causal model based on the original data. :param original_data: Samples from the original data. :param new_data: Samples from the new data. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions. An example could be the KL divergence. :param max_num_evaluation_samples: Maximum number of (parent) samples for evaluating the difference in distributions. :param num_joint_samples: Number of samples generated in a node per parent sample. :param early_stopping_percentage: If the change in percentage between multiple consecutive runs is below this threshold, the evaluation stops before evaluating all max_num_evaluation_samples. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in root nodes. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in non-root nodes. :param mechanism_change_test_significance_level: A significance level for rejecting the null hypothesis that the causal mechanism of a node has not changed. :param mechanism_change_test_fdr_control_method: The false discovery rate control method for mechanism change tests. For more options, checkout `statsmodels manual <https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.multipletests.html>`_. :return: A dictionary assining a score to each node in the causal graph. """ validate_causal_dag(causal_model.graph) mechanism_changed_for_node = _check_significant_mechanism_change( causal_model.graph, original_data, new_data, independence_test, conditional_independence_test, mechanism_change_test_significance_level, mechanism_change_test_fdr_control_method, ) results = {} for node in tqdm( causal_model.graph.nodes, desc="Estimating mechanism change anomaly scores", position=0, leave=True ): if mechanism_changed_for_node[node]: if is_root_node(causal_model.graph, node): results[node] = difference_estimation_func(original_data[node].to_numpy(), new_data[node].to_numpy()) else: parent_nodes = get_ordered_predecessors(causal_model.graph, node) results[node] = _estimate_distribution_change_score( original_data[parent_nodes].to_numpy(), new_data[parent_nodes].to_numpy(), new_data[node].to_numpy(), causal_model.causal_mechanism(node), difference_estimation_func, max_num_evaluation_samples, num_joint_samples, early_stopping_percentage, ) else: results[node] = 0 return results def _check_significant_mechanism_change( graph: DirectedGraph, old_data: pd.DataFrame, new_data: pd.DataFrame, independence_test: Callable[[np.ndarray, np.ndarray], float], conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float], significance_level: float, fdr_control_method: Optional[str], ) -> Dict[Any, bool]: all_p_values = [] for node in graph.nodes: if is_root_node(graph, node): parents_org_data = None parents_new_data = None else: parents_org_data = old_data[get_ordered_predecessors(graph, node)].to_numpy() parents_new_data = new_data[get_ordered_predecessors(graph, node)].to_numpy() all_p_values.append( mechanism_change_test( old_data[node].to_numpy(), new_data[node].to_numpy(), parents_org_data, parents_new_data, independence_test=independence_test, conditional_independence_test=conditional_independence_test, ) ) if fdr_control_method is None: successes = np.array(all_p_values) <= significance_level else: successes = multipletests(all_p_values, significance_level, method=fdr_control_method)[0] return dict(zip(graph.nodes, successes)) def _estimate_distribution_change_score( parent_original_data: np.ndarray, parent_new_data: np.ndarray, target_new_data: np.ndarray, causal_model_original: ConditionalStochasticModel, difference_estimation_func: Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]], max_num_evaluation_samples: int, num_joint_samples: int, early_stopping_percentage: float, ) -> float: parent_original_data, parent_new_data, target_new_data = shape_into_2d( parent_original_data, parent_new_data, target_new_data ) causal_model_new = causal_model_original.clone() causal_model_new.fit(X=parent_new_data, Y=target_new_data) joint_parent_samples = np.vstack([parent_original_data, parent_new_data]) joint_parent_samples = joint_parent_samples[ np.random.choice( joint_parent_samples.shape[0], min(joint_parent_samples.shape[0], max_num_evaluation_samples), replace=False ) ] result = 0 run = 0 for joint_parent_sample in joint_parent_samples: old_result = result samples = repmat(joint_parent_sample, num_joint_samples, 1) result += difference_estimation_func( causal_model_original.draw_samples(samples), causal_model_new.draw_samples(samples) ) run += 1 if old_result != 0 and (1 - result / old_result) <= early_stopping_percentage: # If the relative change of the score is less than the given threshold, we stop the estimation early. _logger.info( "Early stopping: Result only changed by %f percent and a threshold of %f is set." % (1 - result / old_result, early_stopping_percentage) ) break return result / run

bhatt-priyadutt

734b61671d130cf21a4e437084b9083dbd5c4a39

b4f80dcb2251a03aff4c816d7c6f6eb2aaf98e73

While there might be use cases outside of the distribution change, let's for now keep it as an internal function here, i.e., add an `_` in front of the name. Also consider moving it at the bottom of the function so that the public facing methods are at the top.

bloebp

py-why/dowhy

Invariant nodes removal

Feature - Now users can enter the list of invariant nodes to the distribution_change function to remove them from analysis

2023-08-15 12:59:48+00:00

2023-08-24 16:12:46+00:00

dowhy/gcm/distribution_change.py

"""This module defines functions to attribute distribution changes. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging from typing import Any, Callable, Dict, Optional, Tuple, Union import networkx as nx import numpy as np import pandas as pd from numpy.matlib import repmat from statsmodels.stats.multitest import multipletests from tqdm import tqdm from dowhy.gcm.auto import AssignmentQuality, assign_causal_mechanisms from dowhy.gcm.causal_mechanisms import ConditionalStochasticModel from dowhy.gcm.causal_models import ( PARENTS_DURING_FIT, ProbabilisticCausalModel, clone_causal_models, validate_causal_dag, ) from dowhy.gcm.divergence import auto_estimate_kl_divergence from dowhy.gcm.fitting_sampling import draw_samples, fit_causal_model_of_target from dowhy.gcm.independence_test.kernel import kernel_based from dowhy.gcm.shapley import ShapleyConfig, estimate_shapley_values from dowhy.gcm.util.general import shape_into_2d from dowhy.graph import DirectedGraph, get_ordered_predecessors, is_root_node, node_connected_subgraph_view _logger = logging.getLogger(__name__) def mechanism_change_test( target_original_data: np.ndarray, target_new_data: np.ndarray, parents_original_data: Optional[np.ndarray] = None, parents_new_data: Optional[np.ndarray] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, ) -> float: """Estimates a p-value for the null hypothesis that the original and new data were generated by the same mechanism. Here, we check the dependency between binary labels indicating whether a sample is from the original or a new data set. If the labels do not provide information to determine if a sample is coming from the original/new distribution, then it is likely that the mechanism has not changed. For non-root nodes, samples from parent variables are needed as conditioning variables. This is, testing the null hypothesis that the data were generated by the same mechanism given the parent samples. By this, we incorporate upstream changes that might have impacted the parents, but not the target node itself. :param target_original_data: Samples of the node from the original data set. :param target_new_data: Samples of the node from the new data set. :param parents_original_data: Samples from parents of the node from the original data set. :param parents_new_data: Samples from parents of the node from the new data set. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in nodes without parents. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in nodes with parents. :return: A p-value for the null hypothesis that the mechanism has not changed. """ causal_graph: DirectedGraph if parents_original_data is not None and parents_new_data is None: raise ValueError("Original parents data were given, but no new parents data!") if parents_original_data is None and parents_new_data is not None: raise ValueError("New parents data were given, but no original parents data!") num_samples_for_testing = min(target_original_data.shape[0], target_new_data.shape[0]) data_set_indices = np.ones(num_samples_for_testing * 2) data_set_indices[num_samples_for_testing:] = -1 data_set_indices = data_set_indices.astype(str) original_indices = np.random.choice(target_original_data.shape[0], num_samples_for_testing, replace=False) new_indices = np.random.choice(target_new_data.shape[0], num_samples_for_testing, replace=False) joint_target_samples = np.vstack( [shape_into_2d(target_original_data[original_indices]), shape_into_2d(target_new_data[new_indices])] ) if parents_original_data is None: return independence_test(joint_target_samples, data_set_indices) else: parents_new_data: np.ndarray joint_parent_data = np.vstack( [shape_into_2d(parents_original_data[original_indices]), shape_into_2d(parents_new_data[new_indices])] ) return conditional_independence_test(joint_target_samples, data_set_indices, joint_parent_data) def distribution_change( causal_model: ProbabilisticCausalModel, old_data: pd.DataFrame, new_data: pd.DataFrame, target_node: Any, num_samples: int = 2000, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float] = auto_estimate_kl_divergence, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, mechanism_change_test_significance_level: float = 0.05, mechanism_change_test_fdr_control_method: Optional[str] = "fdr_bh", auto_assignment_quality: Optional[AssignmentQuality] = None, return_additional_info: bool = False, shapley_config: Optional[ShapleyConfig] = None, graph_factory: Callable[[Any], DirectedGraph] = nx.DiGraph, ) -> Union[ Dict[Any, float], Tuple[Dict[Any, float], Dict[Any, bool], ProbabilisticCausalModel, ProbabilisticCausalModel] ]: """Attributes the change in the marginal distribution of the target_node to nodes upstream in the causal DAG. Note that this method creates two copies of the causal DAG. The causal models of one causal DAG are learned from old data and those of another DAG are learned from new data. **Research Paper**: Kailash Budhathoki, Dominik Janzing, Patrick Bloebaum, Hoiyi Ng. *Why did the distribution change?*. Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, PMLR 130:1666-1674, 2021. :param causal_model: Reference causal model. :param old_data: Joint samples from the 'old' distribution. :param new_data: Joint samples from the 'new' distribution. :param target_node: Target node of interest for attributing the marginal distribution change. :param num_samples: Number of samples used for estimating Shapley values. This can have a significant influence on runtime and accuracy. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions, e.g. difference in average values. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in root nodes. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in non-root nodes. :param mechanism_change_test_significance_level: A significance level for rejecting the null hypothesis that the causal mechanism of a node has not changed. :param mechanism_change_test_fdr_control_method: The false discovery rate control method for mechanism change tests. For more options, checkout `statsmodels manual <https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.multipletests.html>`_. :param auto_assignment_quality: If set to None, the assigned models from the given causal models are used for the old and new graph. However, they are re-fitted on the given data. If set to a valid assignment quality, new models are automatically assigned to the old and new graph based on the respective data. :param return_additional_info: If set to True, three additional items are returned: a dictionary indicating whether each node's mechanism changed, the causal DAG whose causal models are learned from old data, and the causal DAG whose causal models are learned from new data. :param shapley_config: Configuration for the Shapley estimator. :param graph_factory: Allows customization in case a graph class different than networkx.DiGraph should be used. This function *must* copy nodes and edges. Attributes of nodes will be overridden in the copy, so the algorithm is independent of the attribute copy behavior of this factory. :return: By default, if `return_additional_info` is set to False, only the dictionary containing contribution of each upstream node is returned. If `return_additional_info` is set to True, three additional items are returned: a dictionary indicating whether each node's mechanism changed, the causal DAG whose causal models learned from old data, and the causal DAG whose causal models are learned from new data. """ causal_graph_old = graph_factory(node_connected_subgraph_view(causal_model.graph, target_node)) causal_model_old = ProbabilisticCausalModel(causal_graph_old) if auto_assignment_quality is None: clone_causal_models(causal_model.graph, causal_model_old.graph) else: assign_causal_mechanisms(causal_model_old, old_data, override_models=True, quality=auto_assignment_quality) causal_graph_new = graph_factory(causal_graph_old) causal_model_new = ProbabilisticCausalModel(causal_graph_new) if auto_assignment_quality is None: clone_causal_models(causal_graph_old, causal_model_new.graph) else: assign_causal_mechanisms(causal_model_new, new_data, override_models=True, quality=auto_assignment_quality) mechanism_changes = _fit_accounting_for_mechanism_change( causal_model_old, causal_model_new, old_data[list(causal_graph_old.nodes)], new_data[list(causal_graph_new.nodes)], independence_test, conditional_independence_test, mechanism_change_test_significance_level, mechanism_change_test_fdr_control_method, ) attributions = distribution_change_of_graphs( causal_model_old, causal_model_new, target_node, num_samples, difference_estimation_func, shapley_config, graph_factory, ) if return_additional_info: return attributions, mechanism_changes, causal_model_old, causal_model_new else: return attributions def distribution_change_of_graphs( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, target_node: Any, num_samples: int = 2000, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float] = auto_estimate_kl_divergence, shapley_config: Optional[ShapleyConfig] = None, graph_factory: Callable[[Any], DirectedGraph] = nx.DiGraph, ) -> Dict[Any, float]: """Attributes the change of the marginal distribution of target_node to upstream nodes based on the distributions generated by the 'old' and 'new' causal graphs. These graphs are assumed to represent the same causal structure and to be fitted on the respective data. Note: This method creates a copy of the given causal models, i.e. the original objects will not be modified. Related paper: Budhathoki, K., Janzing, D., Bloebaum, P., & Ng, H. (2021). Why did the distribution change? arXiv preprint arXiv:2102.13384. :param causal_model_old: The ProbabilisticCausalModel fitted on the 'old' data. :param causal_model_new: The ProbabilisticCausalModel fitted on the 'new' data. :param target_node: Node of interest for attributing the marginal distribution change. :param num_samples: Number of samples used for the estimation. This can have a significant influence on the runtime and accuracy. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions. An example could be the KL divergence. :param shapley_config: Config for the Shapley estimator. :param graph_factory: Allows customization in case a graph class different than networkx.DiGraph should be used. This function *must* copy nodes and edges. Attributes of nodes will be overridden in the copy, so the algorithm is independent of the attribute copy behavior of this factory. :return: A dictionary containing the contributions of upstream nodes to the marginal distribution change in the target node. """ validate_causal_dag(causal_model_old.graph) validate_causal_dag(causal_model_new.graph) return _estimate_marginal_distribution_change( ProbabilisticCausalModel(node_connected_subgraph_view(causal_model_old.graph, target_node)), ProbabilisticCausalModel(node_connected_subgraph_view(causal_model_new.graph, target_node)), target_node, num_samples, difference_estimation_func, shapley_config, graph_factory, ) def _fit_accounting_for_mechanism_change( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, old_data: pd.DataFrame, new_data: pd.DataFrame, independence_test: Callable[[np.ndarray, np.ndarray], float], conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float], significance_level: float, fdr_control_method: Optional[str], ) -> Dict[Any, bool]: mechanism_changed_for_node = _check_significant_mechanism_change( causal_model_old.graph, old_data, new_data, independence_test, conditional_independence_test, significance_level, fdr_control_method, ) joint_data = pd.concat([old_data, new_data], ignore_index=True, sort=True) for node in causal_model_new.graph.nodes: if mechanism_changed_for_node[node]: fit_causal_model_of_target(causal_model_old, node, old_data) fit_causal_model_of_target(causal_model_new, node, new_data) else: fit_causal_model_of_target(causal_model_old, node, joint_data) fit_causal_model_of_target(causal_model_new, node, joint_data) return mechanism_changed_for_node def _estimate_marginal_distribution_change( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, target_node: Any, num_samples: int, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float], shapley_config: Optional[ShapleyConfig], graph_factory: Callable[[Any], DirectedGraph], ) -> Dict[Any, float]: old_causal_models = [causal_model_old.causal_mechanism(x) for x in sorted(causal_model_old.graph.nodes)] new_causal_models = [causal_model_new.causal_mechanism(x) for x in sorted(causal_model_new.graph.nodes)] target_samples_old = draw_samples(causal_model_old, num_samples)[target_node].to_numpy() def attribution_set_function(subset): if np.all(subset == 0): return 0 causal_model = ProbabilisticCausalModel(graph_factory(causal_model_old.graph)) nodes = sorted(list(causal_model.graph.nodes)) for i in range(len(old_causal_models)): if subset[i] == 1: causal_model.set_causal_mechanism(nodes[i], new_causal_models[i]) else: causal_model.set_causal_mechanism(nodes[i], old_causal_models[i]) for node in causal_model.graph.nodes: causal_model.graph.nodes[node][PARENTS_DURING_FIT] = get_ordered_predecessors(causal_model.graph, node) target_samples_new = draw_samples(causal_model, num_samples)[target_node].to_numpy() return difference_estimation_func(target_samples_old, target_samples_new) attributions = estimate_shapley_values(attribution_set_function, len(old_causal_models), shapley_config) return {x: attributions[i] for i, x in enumerate(sorted(causal_model_old.graph.nodes))} def estimate_distribution_change_scores( causal_model: ProbabilisticCausalModel, original_data: pd.DataFrame, new_data: pd.DataFrame, difference_estimation_func: Callable[ [np.ndarray, np.ndarray], Union[np.ndarray, float] ] = auto_estimate_kl_divergence, max_num_evaluation_samples: int = 1000, num_joint_samples: int = 500, early_stopping_percentage: float = 0.01, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, mechanism_change_test_significance_level: float = 0.05, mechanism_change_test_fdr_control_method: Optional[str] = "fdr_bh", ) -> Dict[Any, float]: """Given newly observed and original samples from the joint distribution of the given causal graphical model, this method estimates a score for each node that quantifies how much the distribution of the node has changed. For this, it first checks whether the underlying causal mechanism has changed at all and, if this is the case, it estimates the difference between the new and original distributions. The score is based on the quantity measured by the provided difference_estimation_func or 0 if no mechanism change has been detected. Note that for each parent sample, num_joint_samples conditional samples are generated based on the original and new causal mechanism and evaluated by the given difference_estimation_func function. These results are then averaged over multiple different parent samples. :param causal_model: The underlying causal model based on the original data. :param original_data: Samples from the original data. :param new_data: Samples from the new data. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions. An example could be the KL divergence. :param max_num_evaluation_samples: Maximum number of (parent) samples for evaluating the difference in distributions. :param num_joint_samples: Number of samples generated in a node per parent sample. :param early_stopping_percentage: If the change in percentage between multiple consecutive runs is below this threshold, the evaluation stops before evaluating all max_num_evaluation_samples. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in root nodes. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in non-root nodes. :param mechanism_change_test_significance_level: A significance level for rejecting the null hypothesis that the causal mechanism of a node has not changed. :param mechanism_change_test_fdr_control_method: The false discovery rate control method for mechanism change tests. For more options, checkout `statsmodels manual <https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.multipletests.html>`_. :return: A dictionary assining a score to each node in the causal graph. """ validate_causal_dag(causal_model.graph) mechanism_changed_for_node = _check_significant_mechanism_change( causal_model.graph, original_data, new_data, independence_test, conditional_independence_test, mechanism_change_test_significance_level, mechanism_change_test_fdr_control_method, ) results = {} for node in tqdm( causal_model.graph.nodes, desc="Estimating mechanism change anomaly scores", position=0, leave=True ): if mechanism_changed_for_node[node]: if is_root_node(causal_model.graph, node): results[node] = difference_estimation_func(original_data[node].to_numpy(), new_data[node].to_numpy()) else: parent_nodes = get_ordered_predecessors(causal_model.graph, node) results[node] = _estimate_distribution_change_score( original_data[parent_nodes].to_numpy(), new_data[parent_nodes].to_numpy(), new_data[node].to_numpy(), causal_model.causal_mechanism(node), difference_estimation_func, max_num_evaluation_samples, num_joint_samples, early_stopping_percentage, ) else: results[node] = 0 return results def _check_significant_mechanism_change( graph: DirectedGraph, old_data: pd.DataFrame, new_data: pd.DataFrame, independence_test: Callable[[np.ndarray, np.ndarray], float], conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float], significance_level: float, fdr_control_method: Optional[str], ) -> Dict[Any, bool]: all_p_values = [] for node in graph.nodes: if is_root_node(graph, node): parents_org_data = None parents_new_data = None else: parents_org_data = old_data[get_ordered_predecessors(graph, node)].to_numpy() parents_new_data = new_data[get_ordered_predecessors(graph, node)].to_numpy() all_p_values.append( mechanism_change_test( old_data[node].to_numpy(), new_data[node].to_numpy(), parents_org_data, parents_new_data, independence_test=independence_test, conditional_independence_test=conditional_independence_test, ) ) if fdr_control_method is None: successes = np.array(all_p_values) <= significance_level else: successes = multipletests(all_p_values, significance_level, method=fdr_control_method)[0] return dict(zip(graph.nodes, successes)) def _estimate_distribution_change_score( parent_original_data: np.ndarray, parent_new_data: np.ndarray, target_new_data: np.ndarray, causal_model_original: ConditionalStochasticModel, difference_estimation_func: Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]], max_num_evaluation_samples: int, num_joint_samples: int, early_stopping_percentage: float, ) -> float: parent_original_data, parent_new_data, target_new_data = shape_into_2d( parent_original_data, parent_new_data, target_new_data ) causal_model_new = causal_model_original.clone() causal_model_new.fit(X=parent_new_data, Y=target_new_data) joint_parent_samples = np.vstack([parent_original_data, parent_new_data]) joint_parent_samples = joint_parent_samples[ np.random.choice( joint_parent_samples.shape[0], min(joint_parent_samples.shape[0], max_num_evaluation_samples), replace=False ) ] result = 0 run = 0 for joint_parent_sample in joint_parent_samples: old_result = result samples = repmat(joint_parent_sample, num_joint_samples, 1) result += difference_estimation_func( causal_model_original.draw_samples(samples), causal_model_new.draw_samples(samples) ) run += 1 if old_result != 0 and (1 - result / old_result) <= early_stopping_percentage: # If the relative change of the score is less than the given threshold, we stop the estimation early. _logger.info( "Early stopping: Result only changed by %f percent and a threshold of %f is set." % (1 - result / old_result, early_stopping_percentage) ) break return result / run

"""This module defines functions to attribute distribution changes. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging from typing import Any, Callable, Dict, List, Optional, Tuple, Union import networkx as nx import numpy as np import pandas as pd from numpy.matlib import repmat from statsmodels.stats.multitest import multipletests from tqdm import tqdm from dowhy.gcm.auto import AssignmentQuality, assign_causal_mechanism_node, assign_causal_mechanisms from dowhy.gcm.causal_mechanisms import ConditionalStochasticModel from dowhy.gcm.causal_models import ( PARENTS_DURING_FIT, ProbabilisticCausalModel, clone_causal_models, validate_causal_dag, ) from dowhy.gcm.divergence import auto_estimate_kl_divergence from dowhy.gcm.fitting_sampling import draw_samples, fit_causal_model_of_target from dowhy.gcm.independence_test.kernel import kernel_based from dowhy.gcm.shapley import ShapleyConfig, estimate_shapley_values from dowhy.gcm.util.general import shape_into_2d from dowhy.graph import DirectedGraph, get_ordered_predecessors, is_root_node, node_connected_subgraph_view _logger = logging.getLogger(__name__) def mechanism_change_test( target_original_data: np.ndarray, target_new_data: np.ndarray, parents_original_data: Optional[np.ndarray] = None, parents_new_data: Optional[np.ndarray] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, ) -> float: """Estimates a p-value for the null hypothesis that the original and new data were generated by the same mechanism. Here, we check the dependency between binary labels indicating whether a sample is from the original or a new data set. If the labels do not provide information to determine if a sample is coming from the original/new distribution, then it is likely that the mechanism has not changed. For non-root nodes, samples from parent variables are needed as conditioning variables. This is, testing the null hypothesis that the data were generated by the same mechanism given the parent samples. By this, we incorporate upstream changes that might have impacted the parents, but not the target node itself. :param target_original_data: Samples of the node from the original data set. :param target_new_data: Samples of the node from the new data set. :param parents_original_data: Samples from parents of the node from the original data set. :param parents_new_data: Samples from parents of the node from the new data set. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in nodes without parents. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in nodes with parents. :return: A p-value for the null hypothesis that the mechanism has not changed. """ causal_graph: DirectedGraph if parents_original_data is not None and parents_new_data is None: raise ValueError("Original parents data were given, but no new parents data!") if parents_original_data is None and parents_new_data is not None: raise ValueError("New parents data were given, but no original parents data!") num_samples_for_testing = min(target_original_data.shape[0], target_new_data.shape[0]) data_set_indices = np.ones(num_samples_for_testing * 2) data_set_indices[num_samples_for_testing:] = -1 data_set_indices = data_set_indices.astype(str) original_indices = np.random.choice(target_original_data.shape[0], num_samples_for_testing, replace=False) new_indices = np.random.choice(target_new_data.shape[0], num_samples_for_testing, replace=False) joint_target_samples = np.vstack( [shape_into_2d(target_original_data[original_indices]), shape_into_2d(target_new_data[new_indices])] ) if parents_original_data is None: return independence_test(joint_target_samples, data_set_indices) else: parents_new_data: np.ndarray joint_parent_data = np.vstack( [shape_into_2d(parents_original_data[original_indices]), shape_into_2d(parents_new_data[new_indices])] ) return conditional_independence_test(joint_target_samples, data_set_indices, joint_parent_data) def distribution_change( causal_model: ProbabilisticCausalModel, old_data: pd.DataFrame, new_data: pd.DataFrame, target_node: Any, invariant_nodes: List[Any] = None, num_samples: int = 2000, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float] = auto_estimate_kl_divergence, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, mechanism_change_test_significance_level: float = 0.05, mechanism_change_test_fdr_control_method: Optional[str] = "fdr_bh", auto_assignment_quality: Optional[AssignmentQuality] = None, return_additional_info: bool = False, shapley_config: Optional[ShapleyConfig] = None, graph_factory: Callable[[Any], DirectedGraph] = nx.DiGraph, ) -> Union[ Dict[Any, float], Tuple[Dict[Any, float], Dict[Any, bool], ProbabilisticCausalModel, ProbabilisticCausalModel] ]: """Attributes the change in the marginal distribution of the target_node to nodes upstream in the causal DAG. Note that this method creates two copies of the causal DAG. The causal models of one causal DAG are learned from old data and those of another DAG are learned from new data. **Research Paper**: Kailash Budhathoki, Dominik Janzing, Patrick Bloebaum, Hoiyi Ng. *Why did the distribution change?*. Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, PMLR 130:1666-1674, 2021. :param causal_model: Reference causal model. :param old_data: Joint samples from the 'old' distribution. :param new_data: Joint samples from the 'new' distribution. :param target_node: Target node of interest for attributing the marginal distribution change. :param invariant_nodes: List of nodes where the mechanism is kept constant regardless of changes in the datasets being analyzed. :param num_samples: Number of samples used for estimating Shapley values. This can have a significant influence on runtime and accuracy. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions, e.g. difference in average values. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in root nodes. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in non-root nodes. :param mechanism_change_test_significance_level: A significance level for rejecting the null hypothesis that the causal mechanism of a node has not changed. :param mechanism_change_test_fdr_control_method: The false discovery rate control method for mechanism change tests. For more options, checkout `statsmodels manual <https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.multipletests.html>`_. :param auto_assignment_quality: If set to None, the assigned models from the given causal models are used for the old and new graph. However, they are re-fitted on the given data. If set to a valid assignment quality, new models are automatically assigned to the old and new graph based on the respective data. :param return_additional_info: If set to True, three additional items are returned: a dictionary indicating whether each node's mechanism changed, the causal DAG whose causal models are learned from old data, and the causal DAG whose causal models are learned from new data. :param shapley_config: Configuration for the Shapley estimator. :param graph_factory: Allows customization in case a graph class different than networkx.DiGraph should be used. This function *must* copy nodes and edges. Attributes of nodes will be overridden in the copy, so the algorithm is independent of the attribute copy behavior of this factory. :return: By default, if `return_additional_info` is set to False, only the dictionary containing contribution of each upstream node is returned. If `return_additional_info` is set to True, three additional items are returned: a dictionary indicating whether each node's mechanism changed, the causal DAG whose causal models learned from old data, and the causal DAG whose causal models are learned from new data. """ if invariant_nodes is None: invariant_nodes = [] causal_graph_old = graph_factory(node_connected_subgraph_view(causal_model.graph, target_node)) causal_model_old = ProbabilisticCausalModel(causal_graph_old) if auto_assignment_quality is None: clone_causal_models(causal_model.graph, causal_model_old.graph) else: assign_causal_mechanisms(causal_model_old, old_data, override_models=True, quality=auto_assignment_quality) invariant_nodes = list(set(invariant_nodes).intersection(set(causal_graph_old.nodes))) _remove_invariant_nodes(invariant_nodes, causal_model_old, old_data, auto_assignment_quality) causal_graph_new = graph_factory(causal_graph_old) causal_model_new = ProbabilisticCausalModel(causal_graph_new) if auto_assignment_quality is None: clone_causal_models(causal_graph_old, causal_model_new.graph) else: assign_causal_mechanisms(causal_model_new, new_data, override_models=True, quality=auto_assignment_quality) mechanism_changes = _fit_accounting_for_mechanism_change( causal_model_old, causal_model_new, old_data[list(causal_graph_old.nodes)], new_data[list(causal_graph_new.nodes)], independence_test, conditional_independence_test, mechanism_change_test_significance_level, mechanism_change_test_fdr_control_method, invariant_nodes, ) attributions = distribution_change_of_graphs( causal_model_old, causal_model_new, target_node, num_samples, difference_estimation_func, shapley_config, graph_factory, ) # set attributions to zero for left out invariant nodes for node in invariant_nodes: attributions[node] = 0 if return_additional_info: return attributions, mechanism_changes, causal_model_old, causal_model_new else: return attributions def distribution_change_of_graphs( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, target_node: Any, num_samples: int = 2000, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float] = auto_estimate_kl_divergence, shapley_config: Optional[ShapleyConfig] = None, graph_factory: Callable[[Any], DirectedGraph] = nx.DiGraph, ) -> Dict[Any, float]: """Attributes the change of the marginal distribution of target_node to upstream nodes based on the distributions generated by the 'old' and 'new' causal graphs. These graphs are assumed to represent the same causal structure and to be fitted on the respective data. Note: This method creates a copy of the given causal models, i.e. the original objects will not be modified. Related paper: Budhathoki, K., Janzing, D., Bloebaum, P., & Ng, H. (2021). Why did the distribution change? arXiv preprint arXiv:2102.13384. :param causal_model_old: The ProbabilisticCausalModel fitted on the 'old' data. :param causal_model_new: The ProbabilisticCausalModel fitted on the 'new' data. :param target_node: Node of interest for attributing the marginal distribution change. :param num_samples: Number of samples used for the estimation. This can have a significant influence on the runtime and accuracy. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions. An example could be the KL divergence. :param shapley_config: Config for the Shapley estimator. :param graph_factory: Allows customization in case a graph class different than networkx.DiGraph should be used. This function *must* copy nodes and edges. Attributes of nodes will be overridden in the copy, so the algorithm is independent of the attribute copy behavior of this factory. :return: A dictionary containing the contributions of upstream nodes to the marginal distribution change in the target node. """ validate_causal_dag(causal_model_old.graph) validate_causal_dag(causal_model_new.graph) return _estimate_marginal_distribution_change( ProbabilisticCausalModel(node_connected_subgraph_view(causal_model_old.graph, target_node)), ProbabilisticCausalModel(node_connected_subgraph_view(causal_model_new.graph, target_node)), target_node, num_samples, difference_estimation_func, shapley_config, graph_factory, ) def _remove_invariant_nodes( invariant_nodes: List[Any], causal_model: ProbabilisticCausalModel, old_data: pd.DataFrame, auto_assignment_quality: Optional[AssignmentQuality], ) -> None: if auto_assignment_quality is None: auto_assignment_quality = AssignmentQuality.GOOD for invar_node in invariant_nodes: # Get parent and child nodes parents = get_ordered_predecessors(causal_model.graph, invar_node) children = list(causal_model.graph.successors(invar_node)) # Don't remove node if node has more than 1 children nodes as it can introduce # hidden confounders. if len(children) > 1: continue # Remove the middle node causal_model.graph.remove_node(invar_node) # Connect parent and child nodes for parent in parents: for child in children: causal_model.graph.add_edge(parent, child) # Update the causal mechanism for the child nodes for child in children: assign_causal_mechanism_node(causal_model, child, old_data, quality=auto_assignment_quality) def _fit_accounting_for_mechanism_change( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, old_data: pd.DataFrame, new_data: pd.DataFrame, independence_test: Callable[[np.ndarray, np.ndarray], float], conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float], significance_level: float, fdr_control_method: Optional[str], invariant_nodes: List[Any], ) -> Dict[Any, bool]: mechanism_changed_for_node = _check_significant_mechanism_change( causal_model_old.graph, old_data, new_data, independence_test, conditional_independence_test, significance_level, fdr_control_method, ) joint_data = pd.concat([old_data, new_data], ignore_index=True, sort=True) for node in causal_model_new.graph.nodes: if node in invariant_nodes: mechanism_changed_for_node[node] = False if mechanism_changed_for_node[node]: fit_causal_model_of_target(causal_model_old, node, old_data) fit_causal_model_of_target(causal_model_new, node, new_data) else: fit_causal_model_of_target(causal_model_old, node, joint_data) fit_causal_model_of_target(causal_model_new, node, joint_data) return mechanism_changed_for_node def _estimate_marginal_distribution_change( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, target_node: Any, num_samples: int, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float], shapley_config: Optional[ShapleyConfig], graph_factory: Callable[[Any], DirectedGraph], ) -> Dict[Any, float]: old_causal_models = [causal_model_old.causal_mechanism(x) for x in sorted(causal_model_old.graph.nodes)] new_causal_models = [causal_model_new.causal_mechanism(x) for x in sorted(causal_model_new.graph.nodes)] target_samples_old = draw_samples(causal_model_old, num_samples)[target_node].to_numpy() def attribution_set_function(subset): if np.all(subset == 0): return 0 causal_model = ProbabilisticCausalModel(graph_factory(causal_model_old.graph)) nodes = sorted(list(causal_model.graph.nodes)) for i in range(len(old_causal_models)): if subset[i] == 1: causal_model.set_causal_mechanism(nodes[i], new_causal_models[i]) else: causal_model.set_causal_mechanism(nodes[i], old_causal_models[i]) for node in causal_model.graph.nodes: causal_model.graph.nodes[node][PARENTS_DURING_FIT] = get_ordered_predecessors(causal_model.graph, node) target_samples_new = draw_samples(causal_model, num_samples)[target_node].to_numpy() return difference_estimation_func(target_samples_old, target_samples_new) attributions = estimate_shapley_values(attribution_set_function, len(old_causal_models), shapley_config) return {x: attributions[i] for i, x in enumerate(sorted(causal_model_old.graph.nodes))} def estimate_distribution_change_scores( causal_model: ProbabilisticCausalModel, original_data: pd.DataFrame, new_data: pd.DataFrame, difference_estimation_func: Callable[ [np.ndarray, np.ndarray], Union[np.ndarray, float] ] = auto_estimate_kl_divergence, max_num_evaluation_samples: int = 1000, num_joint_samples: int = 500, early_stopping_percentage: float = 0.01, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, mechanism_change_test_significance_level: float = 0.05, mechanism_change_test_fdr_control_method: Optional[str] = "fdr_bh", ) -> Dict[Any, float]: """Given newly observed and original samples from the joint distribution of the given causal graphical model, this method estimates a score for each node that quantifies how much the distribution of the node has changed. For this, it first checks whether the underlying causal mechanism has changed at all and, if this is the case, it estimates the difference between the new and original distributions. The score is based on the quantity measured by the provided difference_estimation_func or 0 if no mechanism change has been detected. Note that for each parent sample, num_joint_samples conditional samples are generated based on the original and new causal mechanism and evaluated by the given difference_estimation_func function. These results are then averaged over multiple different parent samples. :param causal_model: The underlying causal model based on the original data. :param original_data: Samples from the original data. :param new_data: Samples from the new data. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions. An example could be the KL divergence. :param max_num_evaluation_samples: Maximum number of (parent) samples for evaluating the difference in distributions. :param num_joint_samples: Number of samples generated in a node per parent sample. :param early_stopping_percentage: If the change in percentage between multiple consecutive runs is below this threshold, the evaluation stops before evaluating all max_num_evaluation_samples. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in root nodes. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in non-root nodes. :param mechanism_change_test_significance_level: A significance level for rejecting the null hypothesis that the causal mechanism of a node has not changed. :param mechanism_change_test_fdr_control_method: The false discovery rate control method for mechanism change tests. For more options, checkout `statsmodels manual <https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.multipletests.html>`_. :return: A dictionary assining a score to each node in the causal graph. """ validate_causal_dag(causal_model.graph) mechanism_changed_for_node = _check_significant_mechanism_change( causal_model.graph, original_data, new_data, independence_test, conditional_independence_test, mechanism_change_test_significance_level, mechanism_change_test_fdr_control_method, ) results = {} for node in tqdm( causal_model.graph.nodes, desc="Estimating mechanism change anomaly scores", position=0, leave=True ): if mechanism_changed_for_node[node]: if is_root_node(causal_model.graph, node): results[node] = difference_estimation_func(original_data[node].to_numpy(), new_data[node].to_numpy()) else: parent_nodes = get_ordered_predecessors(causal_model.graph, node) results[node] = _estimate_distribution_change_score( original_data[parent_nodes].to_numpy(), new_data[parent_nodes].to_numpy(), new_data[node].to_numpy(), causal_model.causal_mechanism(node), difference_estimation_func, max_num_evaluation_samples, num_joint_samples, early_stopping_percentage, ) else: results[node] = 0 return results def _check_significant_mechanism_change( graph: DirectedGraph, old_data: pd.DataFrame, new_data: pd.DataFrame, independence_test: Callable[[np.ndarray, np.ndarray], float], conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float], significance_level: float, fdr_control_method: Optional[str], ) -> Dict[Any, bool]: all_p_values = [] for node in graph.nodes: if is_root_node(graph, node): parents_org_data = None parents_new_data = None else: parents_org_data = old_data[get_ordered_predecessors(graph, node)].to_numpy() parents_new_data = new_data[get_ordered_predecessors(graph, node)].to_numpy() all_p_values.append( mechanism_change_test( old_data[node].to_numpy(), new_data[node].to_numpy(), parents_org_data, parents_new_data, independence_test=independence_test, conditional_independence_test=conditional_independence_test, ) ) if fdr_control_method is None: successes = np.array(all_p_values) <= significance_level else: successes = multipletests(all_p_values, significance_level, method=fdr_control_method)[0] return dict(zip(graph.nodes, successes)) def _estimate_distribution_change_score( parent_original_data: np.ndarray, parent_new_data: np.ndarray, target_new_data: np.ndarray, causal_model_original: ConditionalStochasticModel, difference_estimation_func: Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]], max_num_evaluation_samples: int, num_joint_samples: int, early_stopping_percentage: float, ) -> float: parent_original_data, parent_new_data, target_new_data = shape_into_2d( parent_original_data, parent_new_data, target_new_data ) causal_model_new = causal_model_original.clone() causal_model_new.fit(X=parent_new_data, Y=target_new_data) joint_parent_samples = np.vstack([parent_original_data, parent_new_data]) joint_parent_samples = joint_parent_samples[ np.random.choice( joint_parent_samples.shape[0], min(joint_parent_samples.shape[0], max_num_evaluation_samples), replace=False ) ] result = 0 run = 0 for joint_parent_sample in joint_parent_samples: old_result = result samples = repmat(joint_parent_sample, num_joint_samples, 1) result += difference_estimation_func( causal_model_original.draw_samples(samples), causal_model_new.draw_samples(samples) ) run += 1 if old_result != 0 and (1 - result / old_result) <= early_stopping_percentage: # If the relative change of the score is less than the given threshold, we stop the estimation early. _logger.info( "Early stopping: Result only changed by %f percent and a threshold of %f is set." % (1 - result / old_result, early_stopping_percentage) ) break return result / run

bhatt-priyadutt

734b61671d130cf21a4e437084b9083dbd5c4a39

b4f80dcb2251a03aff4c816d7c6f6eb2aaf98e73

The type for `invariant_nodes` should be `List[Any]` for consistency.

bloebp

py-why/dowhy

Invariant nodes removal

Feature - Now users can enter the list of invariant nodes to the distribution_change function to remove them from analysis

2023-08-15 12:59:48+00:00

2023-08-24 16:12:46+00:00

dowhy/gcm/distribution_change.py

"""This module defines functions to attribute distribution changes. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging from typing import Any, Callable, Dict, Optional, Tuple, Union import networkx as nx import numpy as np import pandas as pd from numpy.matlib import repmat from statsmodels.stats.multitest import multipletests from tqdm import tqdm from dowhy.gcm.auto import AssignmentQuality, assign_causal_mechanisms from dowhy.gcm.causal_mechanisms import ConditionalStochasticModel from dowhy.gcm.causal_models import ( PARENTS_DURING_FIT, ProbabilisticCausalModel, clone_causal_models, validate_causal_dag, ) from dowhy.gcm.divergence import auto_estimate_kl_divergence from dowhy.gcm.fitting_sampling import draw_samples, fit_causal_model_of_target from dowhy.gcm.independence_test.kernel import kernel_based from dowhy.gcm.shapley import ShapleyConfig, estimate_shapley_values from dowhy.gcm.util.general import shape_into_2d from dowhy.graph import DirectedGraph, get_ordered_predecessors, is_root_node, node_connected_subgraph_view _logger = logging.getLogger(__name__) def mechanism_change_test( target_original_data: np.ndarray, target_new_data: np.ndarray, parents_original_data: Optional[np.ndarray] = None, parents_new_data: Optional[np.ndarray] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, ) -> float: """Estimates a p-value for the null hypothesis that the original and new data were generated by the same mechanism. Here, we check the dependency between binary labels indicating whether a sample is from the original or a new data set. If the labels do not provide information to determine if a sample is coming from the original/new distribution, then it is likely that the mechanism has not changed. For non-root nodes, samples from parent variables are needed as conditioning variables. This is, testing the null hypothesis that the data were generated by the same mechanism given the parent samples. By this, we incorporate upstream changes that might have impacted the parents, but not the target node itself. :param target_original_data: Samples of the node from the original data set. :param target_new_data: Samples of the node from the new data set. :param parents_original_data: Samples from parents of the node from the original data set. :param parents_new_data: Samples from parents of the node from the new data set. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in nodes without parents. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in nodes with parents. :return: A p-value for the null hypothesis that the mechanism has not changed. """ causal_graph: DirectedGraph if parents_original_data is not None and parents_new_data is None: raise ValueError("Original parents data were given, but no new parents data!") if parents_original_data is None and parents_new_data is not None: raise ValueError("New parents data were given, but no original parents data!") num_samples_for_testing = min(target_original_data.shape[0], target_new_data.shape[0]) data_set_indices = np.ones(num_samples_for_testing * 2) data_set_indices[num_samples_for_testing:] = -1 data_set_indices = data_set_indices.astype(str) original_indices = np.random.choice(target_original_data.shape[0], num_samples_for_testing, replace=False) new_indices = np.random.choice(target_new_data.shape[0], num_samples_for_testing, replace=False) joint_target_samples = np.vstack( [shape_into_2d(target_original_data[original_indices]), shape_into_2d(target_new_data[new_indices])] ) if parents_original_data is None: return independence_test(joint_target_samples, data_set_indices) else: parents_new_data: np.ndarray joint_parent_data = np.vstack( [shape_into_2d(parents_original_data[original_indices]), shape_into_2d(parents_new_data[new_indices])] ) return conditional_independence_test(joint_target_samples, data_set_indices, joint_parent_data) def distribution_change( causal_model: ProbabilisticCausalModel, old_data: pd.DataFrame, new_data: pd.DataFrame, target_node: Any, num_samples: int = 2000, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float] = auto_estimate_kl_divergence, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, mechanism_change_test_significance_level: float = 0.05, mechanism_change_test_fdr_control_method: Optional[str] = "fdr_bh", auto_assignment_quality: Optional[AssignmentQuality] = None, return_additional_info: bool = False, shapley_config: Optional[ShapleyConfig] = None, graph_factory: Callable[[Any], DirectedGraph] = nx.DiGraph, ) -> Union[ Dict[Any, float], Tuple[Dict[Any, float], Dict[Any, bool], ProbabilisticCausalModel, ProbabilisticCausalModel] ]: """Attributes the change in the marginal distribution of the target_node to nodes upstream in the causal DAG. Note that this method creates two copies of the causal DAG. The causal models of one causal DAG are learned from old data and those of another DAG are learned from new data. **Research Paper**: Kailash Budhathoki, Dominik Janzing, Patrick Bloebaum, Hoiyi Ng. *Why did the distribution change?*. Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, PMLR 130:1666-1674, 2021. :param causal_model: Reference causal model. :param old_data: Joint samples from the 'old' distribution. :param new_data: Joint samples from the 'new' distribution. :param target_node: Target node of interest for attributing the marginal distribution change. :param num_samples: Number of samples used for estimating Shapley values. This can have a significant influence on runtime and accuracy. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions, e.g. difference in average values. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in root nodes. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in non-root nodes. :param mechanism_change_test_significance_level: A significance level for rejecting the null hypothesis that the causal mechanism of a node has not changed. :param mechanism_change_test_fdr_control_method: The false discovery rate control method for mechanism change tests. For more options, checkout `statsmodels manual <https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.multipletests.html>`_. :param auto_assignment_quality: If set to None, the assigned models from the given causal models are used for the old and new graph. However, they are re-fitted on the given data. If set to a valid assignment quality, new models are automatically assigned to the old and new graph based on the respective data. :param return_additional_info: If set to True, three additional items are returned: a dictionary indicating whether each node's mechanism changed, the causal DAG whose causal models are learned from old data, and the causal DAG whose causal models are learned from new data. :param shapley_config: Configuration for the Shapley estimator. :param graph_factory: Allows customization in case a graph class different than networkx.DiGraph should be used. This function *must* copy nodes and edges. Attributes of nodes will be overridden in the copy, so the algorithm is independent of the attribute copy behavior of this factory. :return: By default, if `return_additional_info` is set to False, only the dictionary containing contribution of each upstream node is returned. If `return_additional_info` is set to True, three additional items are returned: a dictionary indicating whether each node's mechanism changed, the causal DAG whose causal models learned from old data, and the causal DAG whose causal models are learned from new data. """ causal_graph_old = graph_factory(node_connected_subgraph_view(causal_model.graph, target_node)) causal_model_old = ProbabilisticCausalModel(causal_graph_old) if auto_assignment_quality is None: clone_causal_models(causal_model.graph, causal_model_old.graph) else: assign_causal_mechanisms(causal_model_old, old_data, override_models=True, quality=auto_assignment_quality) causal_graph_new = graph_factory(causal_graph_old) causal_model_new = ProbabilisticCausalModel(causal_graph_new) if auto_assignment_quality is None: clone_causal_models(causal_graph_old, causal_model_new.graph) else: assign_causal_mechanisms(causal_model_new, new_data, override_models=True, quality=auto_assignment_quality) mechanism_changes = _fit_accounting_for_mechanism_change( causal_model_old, causal_model_new, old_data[list(causal_graph_old.nodes)], new_data[list(causal_graph_new.nodes)], independence_test, conditional_independence_test, mechanism_change_test_significance_level, mechanism_change_test_fdr_control_method, ) attributions = distribution_change_of_graphs( causal_model_old, causal_model_new, target_node, num_samples, difference_estimation_func, shapley_config, graph_factory, ) if return_additional_info: return attributions, mechanism_changes, causal_model_old, causal_model_new else: return attributions def distribution_change_of_graphs( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, target_node: Any, num_samples: int = 2000, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float] = auto_estimate_kl_divergence, shapley_config: Optional[ShapleyConfig] = None, graph_factory: Callable[[Any], DirectedGraph] = nx.DiGraph, ) -> Dict[Any, float]: """Attributes the change of the marginal distribution of target_node to upstream nodes based on the distributions generated by the 'old' and 'new' causal graphs. These graphs are assumed to represent the same causal structure and to be fitted on the respective data. Note: This method creates a copy of the given causal models, i.e. the original objects will not be modified. Related paper: Budhathoki, K., Janzing, D., Bloebaum, P., & Ng, H. (2021). Why did the distribution change? arXiv preprint arXiv:2102.13384. :param causal_model_old: The ProbabilisticCausalModel fitted on the 'old' data. :param causal_model_new: The ProbabilisticCausalModel fitted on the 'new' data. :param target_node: Node of interest for attributing the marginal distribution change. :param num_samples: Number of samples used for the estimation. This can have a significant influence on the runtime and accuracy. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions. An example could be the KL divergence. :param shapley_config: Config for the Shapley estimator. :param graph_factory: Allows customization in case a graph class different than networkx.DiGraph should be used. This function *must* copy nodes and edges. Attributes of nodes will be overridden in the copy, so the algorithm is independent of the attribute copy behavior of this factory. :return: A dictionary containing the contributions of upstream nodes to the marginal distribution change in the target node. """ validate_causal_dag(causal_model_old.graph) validate_causal_dag(causal_model_new.graph) return _estimate_marginal_distribution_change( ProbabilisticCausalModel(node_connected_subgraph_view(causal_model_old.graph, target_node)), ProbabilisticCausalModel(node_connected_subgraph_view(causal_model_new.graph, target_node)), target_node, num_samples, difference_estimation_func, shapley_config, graph_factory, ) def _fit_accounting_for_mechanism_change( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, old_data: pd.DataFrame, new_data: pd.DataFrame, independence_test: Callable[[np.ndarray, np.ndarray], float], conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float], significance_level: float, fdr_control_method: Optional[str], ) -> Dict[Any, bool]: mechanism_changed_for_node = _check_significant_mechanism_change( causal_model_old.graph, old_data, new_data, independence_test, conditional_independence_test, significance_level, fdr_control_method, ) joint_data = pd.concat([old_data, new_data], ignore_index=True, sort=True) for node in causal_model_new.graph.nodes: if mechanism_changed_for_node[node]: fit_causal_model_of_target(causal_model_old, node, old_data) fit_causal_model_of_target(causal_model_new, node, new_data) else: fit_causal_model_of_target(causal_model_old, node, joint_data) fit_causal_model_of_target(causal_model_new, node, joint_data) return mechanism_changed_for_node def _estimate_marginal_distribution_change( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, target_node: Any, num_samples: int, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float], shapley_config: Optional[ShapleyConfig], graph_factory: Callable[[Any], DirectedGraph], ) -> Dict[Any, float]: old_causal_models = [causal_model_old.causal_mechanism(x) for x in sorted(causal_model_old.graph.nodes)] new_causal_models = [causal_model_new.causal_mechanism(x) for x in sorted(causal_model_new.graph.nodes)] target_samples_old = draw_samples(causal_model_old, num_samples)[target_node].to_numpy() def attribution_set_function(subset): if np.all(subset == 0): return 0 causal_model = ProbabilisticCausalModel(graph_factory(causal_model_old.graph)) nodes = sorted(list(causal_model.graph.nodes)) for i in range(len(old_causal_models)): if subset[i] == 1: causal_model.set_causal_mechanism(nodes[i], new_causal_models[i]) else: causal_model.set_causal_mechanism(nodes[i], old_causal_models[i]) for node in causal_model.graph.nodes: causal_model.graph.nodes[node][PARENTS_DURING_FIT] = get_ordered_predecessors(causal_model.graph, node) target_samples_new = draw_samples(causal_model, num_samples)[target_node].to_numpy() return difference_estimation_func(target_samples_old, target_samples_new) attributions = estimate_shapley_values(attribution_set_function, len(old_causal_models), shapley_config) return {x: attributions[i] for i, x in enumerate(sorted(causal_model_old.graph.nodes))} def estimate_distribution_change_scores( causal_model: ProbabilisticCausalModel, original_data: pd.DataFrame, new_data: pd.DataFrame, difference_estimation_func: Callable[ [np.ndarray, np.ndarray], Union[np.ndarray, float] ] = auto_estimate_kl_divergence, max_num_evaluation_samples: int = 1000, num_joint_samples: int = 500, early_stopping_percentage: float = 0.01, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, mechanism_change_test_significance_level: float = 0.05, mechanism_change_test_fdr_control_method: Optional[str] = "fdr_bh", ) -> Dict[Any, float]: """Given newly observed and original samples from the joint distribution of the given causal graphical model, this method estimates a score for each node that quantifies how much the distribution of the node has changed. For this, it first checks whether the underlying causal mechanism has changed at all and, if this is the case, it estimates the difference between the new and original distributions. The score is based on the quantity measured by the provided difference_estimation_func or 0 if no mechanism change has been detected. Note that for each parent sample, num_joint_samples conditional samples are generated based on the original and new causal mechanism and evaluated by the given difference_estimation_func function. These results are then averaged over multiple different parent samples. :param causal_model: The underlying causal model based on the original data. :param original_data: Samples from the original data. :param new_data: Samples from the new data. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions. An example could be the KL divergence. :param max_num_evaluation_samples: Maximum number of (parent) samples for evaluating the difference in distributions. :param num_joint_samples: Number of samples generated in a node per parent sample. :param early_stopping_percentage: If the change in percentage between multiple consecutive runs is below this threshold, the evaluation stops before evaluating all max_num_evaluation_samples. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in root nodes. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in non-root nodes. :param mechanism_change_test_significance_level: A significance level for rejecting the null hypothesis that the causal mechanism of a node has not changed. :param mechanism_change_test_fdr_control_method: The false discovery rate control method for mechanism change tests. For more options, checkout `statsmodels manual <https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.multipletests.html>`_. :return: A dictionary assining a score to each node in the causal graph. """ validate_causal_dag(causal_model.graph) mechanism_changed_for_node = _check_significant_mechanism_change( causal_model.graph, original_data, new_data, independence_test, conditional_independence_test, mechanism_change_test_significance_level, mechanism_change_test_fdr_control_method, ) results = {} for node in tqdm( causal_model.graph.nodes, desc="Estimating mechanism change anomaly scores", position=0, leave=True ): if mechanism_changed_for_node[node]: if is_root_node(causal_model.graph, node): results[node] = difference_estimation_func(original_data[node].to_numpy(), new_data[node].to_numpy()) else: parent_nodes = get_ordered_predecessors(causal_model.graph, node) results[node] = _estimate_distribution_change_score( original_data[parent_nodes].to_numpy(), new_data[parent_nodes].to_numpy(), new_data[node].to_numpy(), causal_model.causal_mechanism(node), difference_estimation_func, max_num_evaluation_samples, num_joint_samples, early_stopping_percentage, ) else: results[node] = 0 return results def _check_significant_mechanism_change( graph: DirectedGraph, old_data: pd.DataFrame, new_data: pd.DataFrame, independence_test: Callable[[np.ndarray, np.ndarray], float], conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float], significance_level: float, fdr_control_method: Optional[str], ) -> Dict[Any, bool]: all_p_values = [] for node in graph.nodes: if is_root_node(graph, node): parents_org_data = None parents_new_data = None else: parents_org_data = old_data[get_ordered_predecessors(graph, node)].to_numpy() parents_new_data = new_data[get_ordered_predecessors(graph, node)].to_numpy() all_p_values.append( mechanism_change_test( old_data[node].to_numpy(), new_data[node].to_numpy(), parents_org_data, parents_new_data, independence_test=independence_test, conditional_independence_test=conditional_independence_test, ) ) if fdr_control_method is None: successes = np.array(all_p_values) <= significance_level else: successes = multipletests(all_p_values, significance_level, method=fdr_control_method)[0] return dict(zip(graph.nodes, successes)) def _estimate_distribution_change_score( parent_original_data: np.ndarray, parent_new_data: np.ndarray, target_new_data: np.ndarray, causal_model_original: ConditionalStochasticModel, difference_estimation_func: Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]], max_num_evaluation_samples: int, num_joint_samples: int, early_stopping_percentage: float, ) -> float: parent_original_data, parent_new_data, target_new_data = shape_into_2d( parent_original_data, parent_new_data, target_new_data ) causal_model_new = causal_model_original.clone() causal_model_new.fit(X=parent_new_data, Y=target_new_data) joint_parent_samples = np.vstack([parent_original_data, parent_new_data]) joint_parent_samples = joint_parent_samples[ np.random.choice( joint_parent_samples.shape[0], min(joint_parent_samples.shape[0], max_num_evaluation_samples), replace=False ) ] result = 0 run = 0 for joint_parent_sample in joint_parent_samples: old_result = result samples = repmat(joint_parent_sample, num_joint_samples, 1) result += difference_estimation_func( causal_model_original.draw_samples(samples), causal_model_new.draw_samples(samples) ) run += 1 if old_result != 0 and (1 - result / old_result) <= early_stopping_percentage: # If the relative change of the score is less than the given threshold, we stop the estimation early. _logger.info( "Early stopping: Result only changed by %f percent and a threshold of %f is set." % (1 - result / old_result, early_stopping_percentage) ) break return result / run

"""This module defines functions to attribute distribution changes. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging from typing import Any, Callable, Dict, List, Optional, Tuple, Union import networkx as nx import numpy as np import pandas as pd from numpy.matlib import repmat from statsmodels.stats.multitest import multipletests from tqdm import tqdm from dowhy.gcm.auto import AssignmentQuality, assign_causal_mechanism_node, assign_causal_mechanisms from dowhy.gcm.causal_mechanisms import ConditionalStochasticModel from dowhy.gcm.causal_models import ( PARENTS_DURING_FIT, ProbabilisticCausalModel, clone_causal_models, validate_causal_dag, ) from dowhy.gcm.divergence import auto_estimate_kl_divergence from dowhy.gcm.fitting_sampling import draw_samples, fit_causal_model_of_target from dowhy.gcm.independence_test.kernel import kernel_based from dowhy.gcm.shapley import ShapleyConfig, estimate_shapley_values from dowhy.gcm.util.general import shape_into_2d from dowhy.graph import DirectedGraph, get_ordered_predecessors, is_root_node, node_connected_subgraph_view _logger = logging.getLogger(__name__) def mechanism_change_test( target_original_data: np.ndarray, target_new_data: np.ndarray, parents_original_data: Optional[np.ndarray] = None, parents_new_data: Optional[np.ndarray] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, ) -> float: """Estimates a p-value for the null hypothesis that the original and new data were generated by the same mechanism. Here, we check the dependency between binary labels indicating whether a sample is from the original or a new data set. If the labels do not provide information to determine if a sample is coming from the original/new distribution, then it is likely that the mechanism has not changed. For non-root nodes, samples from parent variables are needed as conditioning variables. This is, testing the null hypothesis that the data were generated by the same mechanism given the parent samples. By this, we incorporate upstream changes that might have impacted the parents, but not the target node itself. :param target_original_data: Samples of the node from the original data set. :param target_new_data: Samples of the node from the new data set. :param parents_original_data: Samples from parents of the node from the original data set. :param parents_new_data: Samples from parents of the node from the new data set. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in nodes without parents. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in nodes with parents. :return: A p-value for the null hypothesis that the mechanism has not changed. """ causal_graph: DirectedGraph if parents_original_data is not None and parents_new_data is None: raise ValueError("Original parents data were given, but no new parents data!") if parents_original_data is None and parents_new_data is not None: raise ValueError("New parents data were given, but no original parents data!") num_samples_for_testing = min(target_original_data.shape[0], target_new_data.shape[0]) data_set_indices = np.ones(num_samples_for_testing * 2) data_set_indices[num_samples_for_testing:] = -1 data_set_indices = data_set_indices.astype(str) original_indices = np.random.choice(target_original_data.shape[0], num_samples_for_testing, replace=False) new_indices = np.random.choice(target_new_data.shape[0], num_samples_for_testing, replace=False) joint_target_samples = np.vstack( [shape_into_2d(target_original_data[original_indices]), shape_into_2d(target_new_data[new_indices])] ) if parents_original_data is None: return independence_test(joint_target_samples, data_set_indices) else: parents_new_data: np.ndarray joint_parent_data = np.vstack( [shape_into_2d(parents_original_data[original_indices]), shape_into_2d(parents_new_data[new_indices])] ) return conditional_independence_test(joint_target_samples, data_set_indices, joint_parent_data) def distribution_change( causal_model: ProbabilisticCausalModel, old_data: pd.DataFrame, new_data: pd.DataFrame, target_node: Any, invariant_nodes: List[Any] = None, num_samples: int = 2000, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float] = auto_estimate_kl_divergence, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, mechanism_change_test_significance_level: float = 0.05, mechanism_change_test_fdr_control_method: Optional[str] = "fdr_bh", auto_assignment_quality: Optional[AssignmentQuality] = None, return_additional_info: bool = False, shapley_config: Optional[ShapleyConfig] = None, graph_factory: Callable[[Any], DirectedGraph] = nx.DiGraph, ) -> Union[ Dict[Any, float], Tuple[Dict[Any, float], Dict[Any, bool], ProbabilisticCausalModel, ProbabilisticCausalModel] ]: """Attributes the change in the marginal distribution of the target_node to nodes upstream in the causal DAG. Note that this method creates two copies of the causal DAG. The causal models of one causal DAG are learned from old data and those of another DAG are learned from new data. **Research Paper**: Kailash Budhathoki, Dominik Janzing, Patrick Bloebaum, Hoiyi Ng. *Why did the distribution change?*. Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, PMLR 130:1666-1674, 2021. :param causal_model: Reference causal model. :param old_data: Joint samples from the 'old' distribution. :param new_data: Joint samples from the 'new' distribution. :param target_node: Target node of interest for attributing the marginal distribution change. :param invariant_nodes: List of nodes where the mechanism is kept constant regardless of changes in the datasets being analyzed. :param num_samples: Number of samples used for estimating Shapley values. This can have a significant influence on runtime and accuracy. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions, e.g. difference in average values. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in root nodes. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in non-root nodes. :param mechanism_change_test_significance_level: A significance level for rejecting the null hypothesis that the causal mechanism of a node has not changed. :param mechanism_change_test_fdr_control_method: The false discovery rate control method for mechanism change tests. For more options, checkout `statsmodels manual <https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.multipletests.html>`_. :param auto_assignment_quality: If set to None, the assigned models from the given causal models are used for the old and new graph. However, they are re-fitted on the given data. If set to a valid assignment quality, new models are automatically assigned to the old and new graph based on the respective data. :param return_additional_info: If set to True, three additional items are returned: a dictionary indicating whether each node's mechanism changed, the causal DAG whose causal models are learned from old data, and the causal DAG whose causal models are learned from new data. :param shapley_config: Configuration for the Shapley estimator. :param graph_factory: Allows customization in case a graph class different than networkx.DiGraph should be used. This function *must* copy nodes and edges. Attributes of nodes will be overridden in the copy, so the algorithm is independent of the attribute copy behavior of this factory. :return: By default, if `return_additional_info` is set to False, only the dictionary containing contribution of each upstream node is returned. If `return_additional_info` is set to True, three additional items are returned: a dictionary indicating whether each node's mechanism changed, the causal DAG whose causal models learned from old data, and the causal DAG whose causal models are learned from new data. """ if invariant_nodes is None: invariant_nodes = [] causal_graph_old = graph_factory(node_connected_subgraph_view(causal_model.graph, target_node)) causal_model_old = ProbabilisticCausalModel(causal_graph_old) if auto_assignment_quality is None: clone_causal_models(causal_model.graph, causal_model_old.graph) else: assign_causal_mechanisms(causal_model_old, old_data, override_models=True, quality=auto_assignment_quality) invariant_nodes = list(set(invariant_nodes).intersection(set(causal_graph_old.nodes))) _remove_invariant_nodes(invariant_nodes, causal_model_old, old_data, auto_assignment_quality) causal_graph_new = graph_factory(causal_graph_old) causal_model_new = ProbabilisticCausalModel(causal_graph_new) if auto_assignment_quality is None: clone_causal_models(causal_graph_old, causal_model_new.graph) else: assign_causal_mechanisms(causal_model_new, new_data, override_models=True, quality=auto_assignment_quality) mechanism_changes = _fit_accounting_for_mechanism_change( causal_model_old, causal_model_new, old_data[list(causal_graph_old.nodes)], new_data[list(causal_graph_new.nodes)], independence_test, conditional_independence_test, mechanism_change_test_significance_level, mechanism_change_test_fdr_control_method, invariant_nodes, ) attributions = distribution_change_of_graphs( causal_model_old, causal_model_new, target_node, num_samples, difference_estimation_func, shapley_config, graph_factory, ) # set attributions to zero for left out invariant nodes for node in invariant_nodes: attributions[node] = 0 if return_additional_info: return attributions, mechanism_changes, causal_model_old, causal_model_new else: return attributions def distribution_change_of_graphs( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, target_node: Any, num_samples: int = 2000, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float] = auto_estimate_kl_divergence, shapley_config: Optional[ShapleyConfig] = None, graph_factory: Callable[[Any], DirectedGraph] = nx.DiGraph, ) -> Dict[Any, float]: """Attributes the change of the marginal distribution of target_node to upstream nodes based on the distributions generated by the 'old' and 'new' causal graphs. These graphs are assumed to represent the same causal structure and to be fitted on the respective data. Note: This method creates a copy of the given causal models, i.e. the original objects will not be modified. Related paper: Budhathoki, K., Janzing, D., Bloebaum, P., & Ng, H. (2021). Why did the distribution change? arXiv preprint arXiv:2102.13384. :param causal_model_old: The ProbabilisticCausalModel fitted on the 'old' data. :param causal_model_new: The ProbabilisticCausalModel fitted on the 'new' data. :param target_node: Node of interest for attributing the marginal distribution change. :param num_samples: Number of samples used for the estimation. This can have a significant influence on the runtime and accuracy. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions. An example could be the KL divergence. :param shapley_config: Config for the Shapley estimator. :param graph_factory: Allows customization in case a graph class different than networkx.DiGraph should be used. This function *must* copy nodes and edges. Attributes of nodes will be overridden in the copy, so the algorithm is independent of the attribute copy behavior of this factory. :return: A dictionary containing the contributions of upstream nodes to the marginal distribution change in the target node. """ validate_causal_dag(causal_model_old.graph) validate_causal_dag(causal_model_new.graph) return _estimate_marginal_distribution_change( ProbabilisticCausalModel(node_connected_subgraph_view(causal_model_old.graph, target_node)), ProbabilisticCausalModel(node_connected_subgraph_view(causal_model_new.graph, target_node)), target_node, num_samples, difference_estimation_func, shapley_config, graph_factory, ) def _remove_invariant_nodes( invariant_nodes: List[Any], causal_model: ProbabilisticCausalModel, old_data: pd.DataFrame, auto_assignment_quality: Optional[AssignmentQuality], ) -> None: if auto_assignment_quality is None: auto_assignment_quality = AssignmentQuality.GOOD for invar_node in invariant_nodes: # Get parent and child nodes parents = get_ordered_predecessors(causal_model.graph, invar_node) children = list(causal_model.graph.successors(invar_node)) # Don't remove node if node has more than 1 children nodes as it can introduce # hidden confounders. if len(children) > 1: continue # Remove the middle node causal_model.graph.remove_node(invar_node) # Connect parent and child nodes for parent in parents: for child in children: causal_model.graph.add_edge(parent, child) # Update the causal mechanism for the child nodes for child in children: assign_causal_mechanism_node(causal_model, child, old_data, quality=auto_assignment_quality) def _fit_accounting_for_mechanism_change( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, old_data: pd.DataFrame, new_data: pd.DataFrame, independence_test: Callable[[np.ndarray, np.ndarray], float], conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float], significance_level: float, fdr_control_method: Optional[str], invariant_nodes: List[Any], ) -> Dict[Any, bool]: mechanism_changed_for_node = _check_significant_mechanism_change( causal_model_old.graph, old_data, new_data, independence_test, conditional_independence_test, significance_level, fdr_control_method, ) joint_data = pd.concat([old_data, new_data], ignore_index=True, sort=True) for node in causal_model_new.graph.nodes: if node in invariant_nodes: mechanism_changed_for_node[node] = False if mechanism_changed_for_node[node]: fit_causal_model_of_target(causal_model_old, node, old_data) fit_causal_model_of_target(causal_model_new, node, new_data) else: fit_causal_model_of_target(causal_model_old, node, joint_data) fit_causal_model_of_target(causal_model_new, node, joint_data) return mechanism_changed_for_node def _estimate_marginal_distribution_change( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, target_node: Any, num_samples: int, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float], shapley_config: Optional[ShapleyConfig], graph_factory: Callable[[Any], DirectedGraph], ) -> Dict[Any, float]: old_causal_models = [causal_model_old.causal_mechanism(x) for x in sorted(causal_model_old.graph.nodes)] new_causal_models = [causal_model_new.causal_mechanism(x) for x in sorted(causal_model_new.graph.nodes)] target_samples_old = draw_samples(causal_model_old, num_samples)[target_node].to_numpy() def attribution_set_function(subset): if np.all(subset == 0): return 0 causal_model = ProbabilisticCausalModel(graph_factory(causal_model_old.graph)) nodes = sorted(list(causal_model.graph.nodes)) for i in range(len(old_causal_models)): if subset[i] == 1: causal_model.set_causal_mechanism(nodes[i], new_causal_models[i]) else: causal_model.set_causal_mechanism(nodes[i], old_causal_models[i]) for node in causal_model.graph.nodes: causal_model.graph.nodes[node][PARENTS_DURING_FIT] = get_ordered_predecessors(causal_model.graph, node) target_samples_new = draw_samples(causal_model, num_samples)[target_node].to_numpy() return difference_estimation_func(target_samples_old, target_samples_new) attributions = estimate_shapley_values(attribution_set_function, len(old_causal_models), shapley_config) return {x: attributions[i] for i, x in enumerate(sorted(causal_model_old.graph.nodes))} def estimate_distribution_change_scores( causal_model: ProbabilisticCausalModel, original_data: pd.DataFrame, new_data: pd.DataFrame, difference_estimation_func: Callable[ [np.ndarray, np.ndarray], Union[np.ndarray, float] ] = auto_estimate_kl_divergence, max_num_evaluation_samples: int = 1000, num_joint_samples: int = 500, early_stopping_percentage: float = 0.01, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, mechanism_change_test_significance_level: float = 0.05, mechanism_change_test_fdr_control_method: Optional[str] = "fdr_bh", ) -> Dict[Any, float]: """Given newly observed and original samples from the joint distribution of the given causal graphical model, this method estimates a score for each node that quantifies how much the distribution of the node has changed. For this, it first checks whether the underlying causal mechanism has changed at all and, if this is the case, it estimates the difference between the new and original distributions. The score is based on the quantity measured by the provided difference_estimation_func or 0 if no mechanism change has been detected. Note that for each parent sample, num_joint_samples conditional samples are generated based on the original and new causal mechanism and evaluated by the given difference_estimation_func function. These results are then averaged over multiple different parent samples. :param causal_model: The underlying causal model based on the original data. :param original_data: Samples from the original data. :param new_data: Samples from the new data. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions. An example could be the KL divergence. :param max_num_evaluation_samples: Maximum number of (parent) samples for evaluating the difference in distributions. :param num_joint_samples: Number of samples generated in a node per parent sample. :param early_stopping_percentage: If the change in percentage between multiple consecutive runs is below this threshold, the evaluation stops before evaluating all max_num_evaluation_samples. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in root nodes. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in non-root nodes. :param mechanism_change_test_significance_level: A significance level for rejecting the null hypothesis that the causal mechanism of a node has not changed. :param mechanism_change_test_fdr_control_method: The false discovery rate control method for mechanism change tests. For more options, checkout `statsmodels manual <https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.multipletests.html>`_. :return: A dictionary assining a score to each node in the causal graph. """ validate_causal_dag(causal_model.graph) mechanism_changed_for_node = _check_significant_mechanism_change( causal_model.graph, original_data, new_data, independence_test, conditional_independence_test, mechanism_change_test_significance_level, mechanism_change_test_fdr_control_method, ) results = {} for node in tqdm( causal_model.graph.nodes, desc="Estimating mechanism change anomaly scores", position=0, leave=True ): if mechanism_changed_for_node[node]: if is_root_node(causal_model.graph, node): results[node] = difference_estimation_func(original_data[node].to_numpy(), new_data[node].to_numpy()) else: parent_nodes = get_ordered_predecessors(causal_model.graph, node) results[node] = _estimate_distribution_change_score( original_data[parent_nodes].to_numpy(), new_data[parent_nodes].to_numpy(), new_data[node].to_numpy(), causal_model.causal_mechanism(node), difference_estimation_func, max_num_evaluation_samples, num_joint_samples, early_stopping_percentage, ) else: results[node] = 0 return results def _check_significant_mechanism_change( graph: DirectedGraph, old_data: pd.DataFrame, new_data: pd.DataFrame, independence_test: Callable[[np.ndarray, np.ndarray], float], conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float], significance_level: float, fdr_control_method: Optional[str], ) -> Dict[Any, bool]: all_p_values = [] for node in graph.nodes: if is_root_node(graph, node): parents_org_data = None parents_new_data = None else: parents_org_data = old_data[get_ordered_predecessors(graph, node)].to_numpy() parents_new_data = new_data[get_ordered_predecessors(graph, node)].to_numpy() all_p_values.append( mechanism_change_test( old_data[node].to_numpy(), new_data[node].to_numpy(), parents_org_data, parents_new_data, independence_test=independence_test, conditional_independence_test=conditional_independence_test, ) ) if fdr_control_method is None: successes = np.array(all_p_values) <= significance_level else: successes = multipletests(all_p_values, significance_level, method=fdr_control_method)[0] return dict(zip(graph.nodes, successes)) def _estimate_distribution_change_score( parent_original_data: np.ndarray, parent_new_data: np.ndarray, target_new_data: np.ndarray, causal_model_original: ConditionalStochasticModel, difference_estimation_func: Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]], max_num_evaluation_samples: int, num_joint_samples: int, early_stopping_percentage: float, ) -> float: parent_original_data, parent_new_data, target_new_data = shape_into_2d( parent_original_data, parent_new_data, target_new_data ) causal_model_new = causal_model_original.clone() causal_model_new.fit(X=parent_new_data, Y=target_new_data) joint_parent_samples = np.vstack([parent_original_data, parent_new_data]) joint_parent_samples = joint_parent_samples[ np.random.choice( joint_parent_samples.shape[0], min(joint_parent_samples.shape[0], max_num_evaluation_samples), replace=False ) ] result = 0 run = 0 for joint_parent_sample in joint_parent_samples: old_result = result samples = repmat(joint_parent_sample, num_joint_samples, 1) result += difference_estimation_func( causal_model_original.draw_samples(samples), causal_model_new.draw_samples(samples) ) run += 1 if old_result != 0 and (1 - result / old_result) <= early_stopping_percentage: # If the relative change of the score is less than the given threshold, we stop the estimation early. _logger.info( "Early stopping: Result only changed by %f percent and a threshold of %f is set." % (1 - result / old_result, early_stopping_percentage) ) break return result / run

bhatt-priyadutt

734b61671d130cf21a4e437084b9083dbd5c4a39

b4f80dcb2251a03aff4c816d7c6f6eb2aaf98e73

Similar as above: The type for `invariant_nodes` should be `List[Any]` for consistency.

bloebp

py-why/dowhy

Invariant nodes removal

Feature - Now users can enter the list of invariant nodes to the distribution_change function to remove them from analysis

2023-08-15 12:59:48+00:00

2023-08-24 16:12:46+00:00

dowhy/gcm/distribution_change.py

"""This module defines functions to attribute distribution changes. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging from typing import Any, Callable, Dict, Optional, Tuple, Union import networkx as nx import numpy as np import pandas as pd from numpy.matlib import repmat from statsmodels.stats.multitest import multipletests from tqdm import tqdm from dowhy.gcm.auto import AssignmentQuality, assign_causal_mechanisms from dowhy.gcm.causal_mechanisms import ConditionalStochasticModel from dowhy.gcm.causal_models import ( PARENTS_DURING_FIT, ProbabilisticCausalModel, clone_causal_models, validate_causal_dag, ) from dowhy.gcm.divergence import auto_estimate_kl_divergence from dowhy.gcm.fitting_sampling import draw_samples, fit_causal_model_of_target from dowhy.gcm.independence_test.kernel import kernel_based from dowhy.gcm.shapley import ShapleyConfig, estimate_shapley_values from dowhy.gcm.util.general import shape_into_2d from dowhy.graph import DirectedGraph, get_ordered_predecessors, is_root_node, node_connected_subgraph_view _logger = logging.getLogger(__name__) def mechanism_change_test( target_original_data: np.ndarray, target_new_data: np.ndarray, parents_original_data: Optional[np.ndarray] = None, parents_new_data: Optional[np.ndarray] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, ) -> float: """Estimates a p-value for the null hypothesis that the original and new data were generated by the same mechanism. Here, we check the dependency between binary labels indicating whether a sample is from the original or a new data set. If the labels do not provide information to determine if a sample is coming from the original/new distribution, then it is likely that the mechanism has not changed. For non-root nodes, samples from parent variables are needed as conditioning variables. This is, testing the null hypothesis that the data were generated by the same mechanism given the parent samples. By this, we incorporate upstream changes that might have impacted the parents, but not the target node itself. :param target_original_data: Samples of the node from the original data set. :param target_new_data: Samples of the node from the new data set. :param parents_original_data: Samples from parents of the node from the original data set. :param parents_new_data: Samples from parents of the node from the new data set. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in nodes without parents. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in nodes with parents. :return: A p-value for the null hypothesis that the mechanism has not changed. """ causal_graph: DirectedGraph if parents_original_data is not None and parents_new_data is None: raise ValueError("Original parents data were given, but no new parents data!") if parents_original_data is None and parents_new_data is not None: raise ValueError("New parents data were given, but no original parents data!") num_samples_for_testing = min(target_original_data.shape[0], target_new_data.shape[0]) data_set_indices = np.ones(num_samples_for_testing * 2) data_set_indices[num_samples_for_testing:] = -1 data_set_indices = data_set_indices.astype(str) original_indices = np.random.choice(target_original_data.shape[0], num_samples_for_testing, replace=False) new_indices = np.random.choice(target_new_data.shape[0], num_samples_for_testing, replace=False) joint_target_samples = np.vstack( [shape_into_2d(target_original_data[original_indices]), shape_into_2d(target_new_data[new_indices])] ) if parents_original_data is None: return independence_test(joint_target_samples, data_set_indices) else: parents_new_data: np.ndarray joint_parent_data = np.vstack( [shape_into_2d(parents_original_data[original_indices]), shape_into_2d(parents_new_data[new_indices])] ) return conditional_independence_test(joint_target_samples, data_set_indices, joint_parent_data) def distribution_change( causal_model: ProbabilisticCausalModel, old_data: pd.DataFrame, new_data: pd.DataFrame, target_node: Any, num_samples: int = 2000, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float] = auto_estimate_kl_divergence, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, mechanism_change_test_significance_level: float = 0.05, mechanism_change_test_fdr_control_method: Optional[str] = "fdr_bh", auto_assignment_quality: Optional[AssignmentQuality] = None, return_additional_info: bool = False, shapley_config: Optional[ShapleyConfig] = None, graph_factory: Callable[[Any], DirectedGraph] = nx.DiGraph, ) -> Union[ Dict[Any, float], Tuple[Dict[Any, float], Dict[Any, bool], ProbabilisticCausalModel, ProbabilisticCausalModel] ]: """Attributes the change in the marginal distribution of the target_node to nodes upstream in the causal DAG. Note that this method creates two copies of the causal DAG. The causal models of one causal DAG are learned from old data and those of another DAG are learned from new data. **Research Paper**: Kailash Budhathoki, Dominik Janzing, Patrick Bloebaum, Hoiyi Ng. *Why did the distribution change?*. Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, PMLR 130:1666-1674, 2021. :param causal_model: Reference causal model. :param old_data: Joint samples from the 'old' distribution. :param new_data: Joint samples from the 'new' distribution. :param target_node: Target node of interest for attributing the marginal distribution change. :param num_samples: Number of samples used for estimating Shapley values. This can have a significant influence on runtime and accuracy. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions, e.g. difference in average values. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in root nodes. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in non-root nodes. :param mechanism_change_test_significance_level: A significance level for rejecting the null hypothesis that the causal mechanism of a node has not changed. :param mechanism_change_test_fdr_control_method: The false discovery rate control method for mechanism change tests. For more options, checkout `statsmodels manual <https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.multipletests.html>`_. :param auto_assignment_quality: If set to None, the assigned models from the given causal models are used for the old and new graph. However, they are re-fitted on the given data. If set to a valid assignment quality, new models are automatically assigned to the old and new graph based on the respective data. :param return_additional_info: If set to True, three additional items are returned: a dictionary indicating whether each node's mechanism changed, the causal DAG whose causal models are learned from old data, and the causal DAG whose causal models are learned from new data. :param shapley_config: Configuration for the Shapley estimator. :param graph_factory: Allows customization in case a graph class different than networkx.DiGraph should be used. This function *must* copy nodes and edges. Attributes of nodes will be overridden in the copy, so the algorithm is independent of the attribute copy behavior of this factory. :return: By default, if `return_additional_info` is set to False, only the dictionary containing contribution of each upstream node is returned. If `return_additional_info` is set to True, three additional items are returned: a dictionary indicating whether each node's mechanism changed, the causal DAG whose causal models learned from old data, and the causal DAG whose causal models are learned from new data. """ causal_graph_old = graph_factory(node_connected_subgraph_view(causal_model.graph, target_node)) causal_model_old = ProbabilisticCausalModel(causal_graph_old) if auto_assignment_quality is None: clone_causal_models(causal_model.graph, causal_model_old.graph) else: assign_causal_mechanisms(causal_model_old, old_data, override_models=True, quality=auto_assignment_quality) causal_graph_new = graph_factory(causal_graph_old) causal_model_new = ProbabilisticCausalModel(causal_graph_new) if auto_assignment_quality is None: clone_causal_models(causal_graph_old, causal_model_new.graph) else: assign_causal_mechanisms(causal_model_new, new_data, override_models=True, quality=auto_assignment_quality) mechanism_changes = _fit_accounting_for_mechanism_change( causal_model_old, causal_model_new, old_data[list(causal_graph_old.nodes)], new_data[list(causal_graph_new.nodes)], independence_test, conditional_independence_test, mechanism_change_test_significance_level, mechanism_change_test_fdr_control_method, ) attributions = distribution_change_of_graphs( causal_model_old, causal_model_new, target_node, num_samples, difference_estimation_func, shapley_config, graph_factory, ) if return_additional_info: return attributions, mechanism_changes, causal_model_old, causal_model_new else: return attributions def distribution_change_of_graphs( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, target_node: Any, num_samples: int = 2000, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float] = auto_estimate_kl_divergence, shapley_config: Optional[ShapleyConfig] = None, graph_factory: Callable[[Any], DirectedGraph] = nx.DiGraph, ) -> Dict[Any, float]: """Attributes the change of the marginal distribution of target_node to upstream nodes based on the distributions generated by the 'old' and 'new' causal graphs. These graphs are assumed to represent the same causal structure and to be fitted on the respective data. Note: This method creates a copy of the given causal models, i.e. the original objects will not be modified. Related paper: Budhathoki, K., Janzing, D., Bloebaum, P., & Ng, H. (2021). Why did the distribution change? arXiv preprint arXiv:2102.13384. :param causal_model_old: The ProbabilisticCausalModel fitted on the 'old' data. :param causal_model_new: The ProbabilisticCausalModel fitted on the 'new' data. :param target_node: Node of interest for attributing the marginal distribution change. :param num_samples: Number of samples used for the estimation. This can have a significant influence on the runtime and accuracy. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions. An example could be the KL divergence. :param shapley_config: Config for the Shapley estimator. :param graph_factory: Allows customization in case a graph class different than networkx.DiGraph should be used. This function *must* copy nodes and edges. Attributes of nodes will be overridden in the copy, so the algorithm is independent of the attribute copy behavior of this factory. :return: A dictionary containing the contributions of upstream nodes to the marginal distribution change in the target node. """ validate_causal_dag(causal_model_old.graph) validate_causal_dag(causal_model_new.graph) return _estimate_marginal_distribution_change( ProbabilisticCausalModel(node_connected_subgraph_view(causal_model_old.graph, target_node)), ProbabilisticCausalModel(node_connected_subgraph_view(causal_model_new.graph, target_node)), target_node, num_samples, difference_estimation_func, shapley_config, graph_factory, ) def _fit_accounting_for_mechanism_change( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, old_data: pd.DataFrame, new_data: pd.DataFrame, independence_test: Callable[[np.ndarray, np.ndarray], float], conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float], significance_level: float, fdr_control_method: Optional[str], ) -> Dict[Any, bool]: mechanism_changed_for_node = _check_significant_mechanism_change( causal_model_old.graph, old_data, new_data, independence_test, conditional_independence_test, significance_level, fdr_control_method, ) joint_data = pd.concat([old_data, new_data], ignore_index=True, sort=True) for node in causal_model_new.graph.nodes: if mechanism_changed_for_node[node]: fit_causal_model_of_target(causal_model_old, node, old_data) fit_causal_model_of_target(causal_model_new, node, new_data) else: fit_causal_model_of_target(causal_model_old, node, joint_data) fit_causal_model_of_target(causal_model_new, node, joint_data) return mechanism_changed_for_node def _estimate_marginal_distribution_change( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, target_node: Any, num_samples: int, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float], shapley_config: Optional[ShapleyConfig], graph_factory: Callable[[Any], DirectedGraph], ) -> Dict[Any, float]: old_causal_models = [causal_model_old.causal_mechanism(x) for x in sorted(causal_model_old.graph.nodes)] new_causal_models = [causal_model_new.causal_mechanism(x) for x in sorted(causal_model_new.graph.nodes)] target_samples_old = draw_samples(causal_model_old, num_samples)[target_node].to_numpy() def attribution_set_function(subset): if np.all(subset == 0): return 0 causal_model = ProbabilisticCausalModel(graph_factory(causal_model_old.graph)) nodes = sorted(list(causal_model.graph.nodes)) for i in range(len(old_causal_models)): if subset[i] == 1: causal_model.set_causal_mechanism(nodes[i], new_causal_models[i]) else: causal_model.set_causal_mechanism(nodes[i], old_causal_models[i]) for node in causal_model.graph.nodes: causal_model.graph.nodes[node][PARENTS_DURING_FIT] = get_ordered_predecessors(causal_model.graph, node) target_samples_new = draw_samples(causal_model, num_samples)[target_node].to_numpy() return difference_estimation_func(target_samples_old, target_samples_new) attributions = estimate_shapley_values(attribution_set_function, len(old_causal_models), shapley_config) return {x: attributions[i] for i, x in enumerate(sorted(causal_model_old.graph.nodes))} def estimate_distribution_change_scores( causal_model: ProbabilisticCausalModel, original_data: pd.DataFrame, new_data: pd.DataFrame, difference_estimation_func: Callable[ [np.ndarray, np.ndarray], Union[np.ndarray, float] ] = auto_estimate_kl_divergence, max_num_evaluation_samples: int = 1000, num_joint_samples: int = 500, early_stopping_percentage: float = 0.01, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, mechanism_change_test_significance_level: float = 0.05, mechanism_change_test_fdr_control_method: Optional[str] = "fdr_bh", ) -> Dict[Any, float]: """Given newly observed and original samples from the joint distribution of the given causal graphical model, this method estimates a score for each node that quantifies how much the distribution of the node has changed. For this, it first checks whether the underlying causal mechanism has changed at all and, if this is the case, it estimates the difference between the new and original distributions. The score is based on the quantity measured by the provided difference_estimation_func or 0 if no mechanism change has been detected. Note that for each parent sample, num_joint_samples conditional samples are generated based on the original and new causal mechanism and evaluated by the given difference_estimation_func function. These results are then averaged over multiple different parent samples. :param causal_model: The underlying causal model based on the original data. :param original_data: Samples from the original data. :param new_data: Samples from the new data. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions. An example could be the KL divergence. :param max_num_evaluation_samples: Maximum number of (parent) samples for evaluating the difference in distributions. :param num_joint_samples: Number of samples generated in a node per parent sample. :param early_stopping_percentage: If the change in percentage between multiple consecutive runs is below this threshold, the evaluation stops before evaluating all max_num_evaluation_samples. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in root nodes. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in non-root nodes. :param mechanism_change_test_significance_level: A significance level for rejecting the null hypothesis that the causal mechanism of a node has not changed. :param mechanism_change_test_fdr_control_method: The false discovery rate control method for mechanism change tests. For more options, checkout `statsmodels manual <https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.multipletests.html>`_. :return: A dictionary assining a score to each node in the causal graph. """ validate_causal_dag(causal_model.graph) mechanism_changed_for_node = _check_significant_mechanism_change( causal_model.graph, original_data, new_data, independence_test, conditional_independence_test, mechanism_change_test_significance_level, mechanism_change_test_fdr_control_method, ) results = {} for node in tqdm( causal_model.graph.nodes, desc="Estimating mechanism change anomaly scores", position=0, leave=True ): if mechanism_changed_for_node[node]: if is_root_node(causal_model.graph, node): results[node] = difference_estimation_func(original_data[node].to_numpy(), new_data[node].to_numpy()) else: parent_nodes = get_ordered_predecessors(causal_model.graph, node) results[node] = _estimate_distribution_change_score( original_data[parent_nodes].to_numpy(), new_data[parent_nodes].to_numpy(), new_data[node].to_numpy(), causal_model.causal_mechanism(node), difference_estimation_func, max_num_evaluation_samples, num_joint_samples, early_stopping_percentage, ) else: results[node] = 0 return results def _check_significant_mechanism_change( graph: DirectedGraph, old_data: pd.DataFrame, new_data: pd.DataFrame, independence_test: Callable[[np.ndarray, np.ndarray], float], conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float], significance_level: float, fdr_control_method: Optional[str], ) -> Dict[Any, bool]: all_p_values = [] for node in graph.nodes: if is_root_node(graph, node): parents_org_data = None parents_new_data = None else: parents_org_data = old_data[get_ordered_predecessors(graph, node)].to_numpy() parents_new_data = new_data[get_ordered_predecessors(graph, node)].to_numpy() all_p_values.append( mechanism_change_test( old_data[node].to_numpy(), new_data[node].to_numpy(), parents_org_data, parents_new_data, independence_test=independence_test, conditional_independence_test=conditional_independence_test, ) ) if fdr_control_method is None: successes = np.array(all_p_values) <= significance_level else: successes = multipletests(all_p_values, significance_level, method=fdr_control_method)[0] return dict(zip(graph.nodes, successes)) def _estimate_distribution_change_score( parent_original_data: np.ndarray, parent_new_data: np.ndarray, target_new_data: np.ndarray, causal_model_original: ConditionalStochasticModel, difference_estimation_func: Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]], max_num_evaluation_samples: int, num_joint_samples: int, early_stopping_percentage: float, ) -> float: parent_original_data, parent_new_data, target_new_data = shape_into_2d( parent_original_data, parent_new_data, target_new_data ) causal_model_new = causal_model_original.clone() causal_model_new.fit(X=parent_new_data, Y=target_new_data) joint_parent_samples = np.vstack([parent_original_data, parent_new_data]) joint_parent_samples = joint_parent_samples[ np.random.choice( joint_parent_samples.shape[0], min(joint_parent_samples.shape[0], max_num_evaluation_samples), replace=False ) ] result = 0 run = 0 for joint_parent_sample in joint_parent_samples: old_result = result samples = repmat(joint_parent_sample, num_joint_samples, 1) result += difference_estimation_func( causal_model_original.draw_samples(samples), causal_model_new.draw_samples(samples) ) run += 1 if old_result != 0 and (1 - result / old_result) <= early_stopping_percentage: # If the relative change of the score is less than the given threshold, we stop the estimation early. _logger.info( "Early stopping: Result only changed by %f percent and a threshold of %f is set." % (1 - result / old_result, early_stopping_percentage) ) break return result / run

"""This module defines functions to attribute distribution changes. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging from typing import Any, Callable, Dict, List, Optional, Tuple, Union import networkx as nx import numpy as np import pandas as pd from numpy.matlib import repmat from statsmodels.stats.multitest import multipletests from tqdm import tqdm from dowhy.gcm.auto import AssignmentQuality, assign_causal_mechanism_node, assign_causal_mechanisms from dowhy.gcm.causal_mechanisms import ConditionalStochasticModel from dowhy.gcm.causal_models import ( PARENTS_DURING_FIT, ProbabilisticCausalModel, clone_causal_models, validate_causal_dag, ) from dowhy.gcm.divergence import auto_estimate_kl_divergence from dowhy.gcm.fitting_sampling import draw_samples, fit_causal_model_of_target from dowhy.gcm.independence_test.kernel import kernel_based from dowhy.gcm.shapley import ShapleyConfig, estimate_shapley_values from dowhy.gcm.util.general import shape_into_2d from dowhy.graph import DirectedGraph, get_ordered_predecessors, is_root_node, node_connected_subgraph_view _logger = logging.getLogger(__name__) def mechanism_change_test( target_original_data: np.ndarray, target_new_data: np.ndarray, parents_original_data: Optional[np.ndarray] = None, parents_new_data: Optional[np.ndarray] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, ) -> float: """Estimates a p-value for the null hypothesis that the original and new data were generated by the same mechanism. Here, we check the dependency between binary labels indicating whether a sample is from the original or a new data set. If the labels do not provide information to determine if a sample is coming from the original/new distribution, then it is likely that the mechanism has not changed. For non-root nodes, samples from parent variables are needed as conditioning variables. This is, testing the null hypothesis that the data were generated by the same mechanism given the parent samples. By this, we incorporate upstream changes that might have impacted the parents, but not the target node itself. :param target_original_data: Samples of the node from the original data set. :param target_new_data: Samples of the node from the new data set. :param parents_original_data: Samples from parents of the node from the original data set. :param parents_new_data: Samples from parents of the node from the new data set. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in nodes without parents. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in nodes with parents. :return: A p-value for the null hypothesis that the mechanism has not changed. """ causal_graph: DirectedGraph if parents_original_data is not None and parents_new_data is None: raise ValueError("Original parents data were given, but no new parents data!") if parents_original_data is None and parents_new_data is not None: raise ValueError("New parents data were given, but no original parents data!") num_samples_for_testing = min(target_original_data.shape[0], target_new_data.shape[0]) data_set_indices = np.ones(num_samples_for_testing * 2) data_set_indices[num_samples_for_testing:] = -1 data_set_indices = data_set_indices.astype(str) original_indices = np.random.choice(target_original_data.shape[0], num_samples_for_testing, replace=False) new_indices = np.random.choice(target_new_data.shape[0], num_samples_for_testing, replace=False) joint_target_samples = np.vstack( [shape_into_2d(target_original_data[original_indices]), shape_into_2d(target_new_data[new_indices])] ) if parents_original_data is None: return independence_test(joint_target_samples, data_set_indices) else: parents_new_data: np.ndarray joint_parent_data = np.vstack( [shape_into_2d(parents_original_data[original_indices]), shape_into_2d(parents_new_data[new_indices])] ) return conditional_independence_test(joint_target_samples, data_set_indices, joint_parent_data) def distribution_change( causal_model: ProbabilisticCausalModel, old_data: pd.DataFrame, new_data: pd.DataFrame, target_node: Any, invariant_nodes: List[Any] = None, num_samples: int = 2000, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float] = auto_estimate_kl_divergence, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, mechanism_change_test_significance_level: float = 0.05, mechanism_change_test_fdr_control_method: Optional[str] = "fdr_bh", auto_assignment_quality: Optional[AssignmentQuality] = None, return_additional_info: bool = False, shapley_config: Optional[ShapleyConfig] = None, graph_factory: Callable[[Any], DirectedGraph] = nx.DiGraph, ) -> Union[ Dict[Any, float], Tuple[Dict[Any, float], Dict[Any, bool], ProbabilisticCausalModel, ProbabilisticCausalModel] ]: """Attributes the change in the marginal distribution of the target_node to nodes upstream in the causal DAG. Note that this method creates two copies of the causal DAG. The causal models of one causal DAG are learned from old data and those of another DAG are learned from new data. **Research Paper**: Kailash Budhathoki, Dominik Janzing, Patrick Bloebaum, Hoiyi Ng. *Why did the distribution change?*. Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, PMLR 130:1666-1674, 2021. :param causal_model: Reference causal model. :param old_data: Joint samples from the 'old' distribution. :param new_data: Joint samples from the 'new' distribution. :param target_node: Target node of interest for attributing the marginal distribution change. :param invariant_nodes: List of nodes where the mechanism is kept constant regardless of changes in the datasets being analyzed. :param num_samples: Number of samples used for estimating Shapley values. This can have a significant influence on runtime and accuracy. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions, e.g. difference in average values. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in root nodes. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in non-root nodes. :param mechanism_change_test_significance_level: A significance level for rejecting the null hypothesis that the causal mechanism of a node has not changed. :param mechanism_change_test_fdr_control_method: The false discovery rate control method for mechanism change tests. For more options, checkout `statsmodels manual <https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.multipletests.html>`_. :param auto_assignment_quality: If set to None, the assigned models from the given causal models are used for the old and new graph. However, they are re-fitted on the given data. If set to a valid assignment quality, new models are automatically assigned to the old and new graph based on the respective data. :param return_additional_info: If set to True, three additional items are returned: a dictionary indicating whether each node's mechanism changed, the causal DAG whose causal models are learned from old data, and the causal DAG whose causal models are learned from new data. :param shapley_config: Configuration for the Shapley estimator. :param graph_factory: Allows customization in case a graph class different than networkx.DiGraph should be used. This function *must* copy nodes and edges. Attributes of nodes will be overridden in the copy, so the algorithm is independent of the attribute copy behavior of this factory. :return: By default, if `return_additional_info` is set to False, only the dictionary containing contribution of each upstream node is returned. If `return_additional_info` is set to True, three additional items are returned: a dictionary indicating whether each node's mechanism changed, the causal DAG whose causal models learned from old data, and the causal DAG whose causal models are learned from new data. """ if invariant_nodes is None: invariant_nodes = [] causal_graph_old = graph_factory(node_connected_subgraph_view(causal_model.graph, target_node)) causal_model_old = ProbabilisticCausalModel(causal_graph_old) if auto_assignment_quality is None: clone_causal_models(causal_model.graph, causal_model_old.graph) else: assign_causal_mechanisms(causal_model_old, old_data, override_models=True, quality=auto_assignment_quality) invariant_nodes = list(set(invariant_nodes).intersection(set(causal_graph_old.nodes))) _remove_invariant_nodes(invariant_nodes, causal_model_old, old_data, auto_assignment_quality) causal_graph_new = graph_factory(causal_graph_old) causal_model_new = ProbabilisticCausalModel(causal_graph_new) if auto_assignment_quality is None: clone_causal_models(causal_graph_old, causal_model_new.graph) else: assign_causal_mechanisms(causal_model_new, new_data, override_models=True, quality=auto_assignment_quality) mechanism_changes = _fit_accounting_for_mechanism_change( causal_model_old, causal_model_new, old_data[list(causal_graph_old.nodes)], new_data[list(causal_graph_new.nodes)], independence_test, conditional_independence_test, mechanism_change_test_significance_level, mechanism_change_test_fdr_control_method, invariant_nodes, ) attributions = distribution_change_of_graphs( causal_model_old, causal_model_new, target_node, num_samples, difference_estimation_func, shapley_config, graph_factory, ) # set attributions to zero for left out invariant nodes for node in invariant_nodes: attributions[node] = 0 if return_additional_info: return attributions, mechanism_changes, causal_model_old, causal_model_new else: return attributions def distribution_change_of_graphs( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, target_node: Any, num_samples: int = 2000, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float] = auto_estimate_kl_divergence, shapley_config: Optional[ShapleyConfig] = None, graph_factory: Callable[[Any], DirectedGraph] = nx.DiGraph, ) -> Dict[Any, float]: """Attributes the change of the marginal distribution of target_node to upstream nodes based on the distributions generated by the 'old' and 'new' causal graphs. These graphs are assumed to represent the same causal structure and to be fitted on the respective data. Note: This method creates a copy of the given causal models, i.e. the original objects will not be modified. Related paper: Budhathoki, K., Janzing, D., Bloebaum, P., & Ng, H. (2021). Why did the distribution change? arXiv preprint arXiv:2102.13384. :param causal_model_old: The ProbabilisticCausalModel fitted on the 'old' data. :param causal_model_new: The ProbabilisticCausalModel fitted on the 'new' data. :param target_node: Node of interest for attributing the marginal distribution change. :param num_samples: Number of samples used for the estimation. This can have a significant influence on the runtime and accuracy. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions. An example could be the KL divergence. :param shapley_config: Config for the Shapley estimator. :param graph_factory: Allows customization in case a graph class different than networkx.DiGraph should be used. This function *must* copy nodes and edges. Attributes of nodes will be overridden in the copy, so the algorithm is independent of the attribute copy behavior of this factory. :return: A dictionary containing the contributions of upstream nodes to the marginal distribution change in the target node. """ validate_causal_dag(causal_model_old.graph) validate_causal_dag(causal_model_new.graph) return _estimate_marginal_distribution_change( ProbabilisticCausalModel(node_connected_subgraph_view(causal_model_old.graph, target_node)), ProbabilisticCausalModel(node_connected_subgraph_view(causal_model_new.graph, target_node)), target_node, num_samples, difference_estimation_func, shapley_config, graph_factory, ) def _remove_invariant_nodes( invariant_nodes: List[Any], causal_model: ProbabilisticCausalModel, old_data: pd.DataFrame, auto_assignment_quality: Optional[AssignmentQuality], ) -> None: if auto_assignment_quality is None: auto_assignment_quality = AssignmentQuality.GOOD for invar_node in invariant_nodes: # Get parent and child nodes parents = get_ordered_predecessors(causal_model.graph, invar_node) children = list(causal_model.graph.successors(invar_node)) # Don't remove node if node has more than 1 children nodes as it can introduce # hidden confounders. if len(children) > 1: continue # Remove the middle node causal_model.graph.remove_node(invar_node) # Connect parent and child nodes for parent in parents: for child in children: causal_model.graph.add_edge(parent, child) # Update the causal mechanism for the child nodes for child in children: assign_causal_mechanism_node(causal_model, child, old_data, quality=auto_assignment_quality) def _fit_accounting_for_mechanism_change( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, old_data: pd.DataFrame, new_data: pd.DataFrame, independence_test: Callable[[np.ndarray, np.ndarray], float], conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float], significance_level: float, fdr_control_method: Optional[str], invariant_nodes: List[Any], ) -> Dict[Any, bool]: mechanism_changed_for_node = _check_significant_mechanism_change( causal_model_old.graph, old_data, new_data, independence_test, conditional_independence_test, significance_level, fdr_control_method, ) joint_data = pd.concat([old_data, new_data], ignore_index=True, sort=True) for node in causal_model_new.graph.nodes: if node in invariant_nodes: mechanism_changed_for_node[node] = False if mechanism_changed_for_node[node]: fit_causal_model_of_target(causal_model_old, node, old_data) fit_causal_model_of_target(causal_model_new, node, new_data) else: fit_causal_model_of_target(causal_model_old, node, joint_data) fit_causal_model_of_target(causal_model_new, node, joint_data) return mechanism_changed_for_node def _estimate_marginal_distribution_change( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, target_node: Any, num_samples: int, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float], shapley_config: Optional[ShapleyConfig], graph_factory: Callable[[Any], DirectedGraph], ) -> Dict[Any, float]: old_causal_models = [causal_model_old.causal_mechanism(x) for x in sorted(causal_model_old.graph.nodes)] new_causal_models = [causal_model_new.causal_mechanism(x) for x in sorted(causal_model_new.graph.nodes)] target_samples_old = draw_samples(causal_model_old, num_samples)[target_node].to_numpy() def attribution_set_function(subset): if np.all(subset == 0): return 0 causal_model = ProbabilisticCausalModel(graph_factory(causal_model_old.graph)) nodes = sorted(list(causal_model.graph.nodes)) for i in range(len(old_causal_models)): if subset[i] == 1: causal_model.set_causal_mechanism(nodes[i], new_causal_models[i]) else: causal_model.set_causal_mechanism(nodes[i], old_causal_models[i]) for node in causal_model.graph.nodes: causal_model.graph.nodes[node][PARENTS_DURING_FIT] = get_ordered_predecessors(causal_model.graph, node) target_samples_new = draw_samples(causal_model, num_samples)[target_node].to_numpy() return difference_estimation_func(target_samples_old, target_samples_new) attributions = estimate_shapley_values(attribution_set_function, len(old_causal_models), shapley_config) return {x: attributions[i] for i, x in enumerate(sorted(causal_model_old.graph.nodes))} def estimate_distribution_change_scores( causal_model: ProbabilisticCausalModel, original_data: pd.DataFrame, new_data: pd.DataFrame, difference_estimation_func: Callable[ [np.ndarray, np.ndarray], Union[np.ndarray, float] ] = auto_estimate_kl_divergence, max_num_evaluation_samples: int = 1000, num_joint_samples: int = 500, early_stopping_percentage: float = 0.01, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, mechanism_change_test_significance_level: float = 0.05, mechanism_change_test_fdr_control_method: Optional[str] = "fdr_bh", ) -> Dict[Any, float]: """Given newly observed and original samples from the joint distribution of the given causal graphical model, this method estimates a score for each node that quantifies how much the distribution of the node has changed. For this, it first checks whether the underlying causal mechanism has changed at all and, if this is the case, it estimates the difference between the new and original distributions. The score is based on the quantity measured by the provided difference_estimation_func or 0 if no mechanism change has been detected. Note that for each parent sample, num_joint_samples conditional samples are generated based on the original and new causal mechanism and evaluated by the given difference_estimation_func function. These results are then averaged over multiple different parent samples. :param causal_model: The underlying causal model based on the original data. :param original_data: Samples from the original data. :param new_data: Samples from the new data. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions. An example could be the KL divergence. :param max_num_evaluation_samples: Maximum number of (parent) samples for evaluating the difference in distributions. :param num_joint_samples: Number of samples generated in a node per parent sample. :param early_stopping_percentage: If the change in percentage between multiple consecutive runs is below this threshold, the evaluation stops before evaluating all max_num_evaluation_samples. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in root nodes. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in non-root nodes. :param mechanism_change_test_significance_level: A significance level for rejecting the null hypothesis that the causal mechanism of a node has not changed. :param mechanism_change_test_fdr_control_method: The false discovery rate control method for mechanism change tests. For more options, checkout `statsmodels manual <https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.multipletests.html>`_. :return: A dictionary assining a score to each node in the causal graph. """ validate_causal_dag(causal_model.graph) mechanism_changed_for_node = _check_significant_mechanism_change( causal_model.graph, original_data, new_data, independence_test, conditional_independence_test, mechanism_change_test_significance_level, mechanism_change_test_fdr_control_method, ) results = {} for node in tqdm( causal_model.graph.nodes, desc="Estimating mechanism change anomaly scores", position=0, leave=True ): if mechanism_changed_for_node[node]: if is_root_node(causal_model.graph, node): results[node] = difference_estimation_func(original_data[node].to_numpy(), new_data[node].to_numpy()) else: parent_nodes = get_ordered_predecessors(causal_model.graph, node) results[node] = _estimate_distribution_change_score( original_data[parent_nodes].to_numpy(), new_data[parent_nodes].to_numpy(), new_data[node].to_numpy(), causal_model.causal_mechanism(node), difference_estimation_func, max_num_evaluation_samples, num_joint_samples, early_stopping_percentage, ) else: results[node] = 0 return results def _check_significant_mechanism_change( graph: DirectedGraph, old_data: pd.DataFrame, new_data: pd.DataFrame, independence_test: Callable[[np.ndarray, np.ndarray], float], conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float], significance_level: float, fdr_control_method: Optional[str], ) -> Dict[Any, bool]: all_p_values = [] for node in graph.nodes: if is_root_node(graph, node): parents_org_data = None parents_new_data = None else: parents_org_data = old_data[get_ordered_predecessors(graph, node)].to_numpy() parents_new_data = new_data[get_ordered_predecessors(graph, node)].to_numpy() all_p_values.append( mechanism_change_test( old_data[node].to_numpy(), new_data[node].to_numpy(), parents_org_data, parents_new_data, independence_test=independence_test, conditional_independence_test=conditional_independence_test, ) ) if fdr_control_method is None: successes = np.array(all_p_values) <= significance_level else: successes = multipletests(all_p_values, significance_level, method=fdr_control_method)[0] return dict(zip(graph.nodes, successes)) def _estimate_distribution_change_score( parent_original_data: np.ndarray, parent_new_data: np.ndarray, target_new_data: np.ndarray, causal_model_original: ConditionalStochasticModel, difference_estimation_func: Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]], max_num_evaluation_samples: int, num_joint_samples: int, early_stopping_percentage: float, ) -> float: parent_original_data, parent_new_data, target_new_data = shape_into_2d( parent_original_data, parent_new_data, target_new_data ) causal_model_new = causal_model_original.clone() causal_model_new.fit(X=parent_new_data, Y=target_new_data) joint_parent_samples = np.vstack([parent_original_data, parent_new_data]) joint_parent_samples = joint_parent_samples[ np.random.choice( joint_parent_samples.shape[0], min(joint_parent_samples.shape[0], max_num_evaluation_samples), replace=False ) ] result = 0 run = 0 for joint_parent_sample in joint_parent_samples: old_result = result samples = repmat(joint_parent_sample, num_joint_samples, 1) result += difference_estimation_func( causal_model_original.draw_samples(samples), causal_model_new.draw_samples(samples) ) run += 1 if old_result != 0 and (1 - result / old_result) <= early_stopping_percentage: # If the relative change of the score is less than the given threshold, we stop the estimation early. _logger.info( "Early stopping: Result only changed by %f percent and a threshold of %f is set." % (1 - result / old_result, early_stopping_percentage) ) break return result / run

bhatt-priyadutt

734b61671d130cf21a4e437084b9083dbd5c4a39

b4f80dcb2251a03aff4c816d7c6f6eb2aaf98e73

What about skipping this if `auto_assignment_quality` is `None` since you allow it to be optional. In that way, we can also use this method to just get the new graph.

bloebp

py-why/dowhy

Invariant nodes removal

Feature - Now users can enter the list of invariant nodes to the distribution_change function to remove them from analysis

2023-08-15 12:59:48+00:00

2023-08-24 16:12:46+00:00

dowhy/gcm/distribution_change.py

"""This module defines functions to attribute distribution changes. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging from typing import Any, Callable, Dict, Optional, Tuple, Union import networkx as nx import numpy as np import pandas as pd from numpy.matlib import repmat from statsmodels.stats.multitest import multipletests from tqdm import tqdm from dowhy.gcm.auto import AssignmentQuality, assign_causal_mechanisms from dowhy.gcm.causal_mechanisms import ConditionalStochasticModel from dowhy.gcm.causal_models import ( PARENTS_DURING_FIT, ProbabilisticCausalModel, clone_causal_models, validate_causal_dag, ) from dowhy.gcm.divergence import auto_estimate_kl_divergence from dowhy.gcm.fitting_sampling import draw_samples, fit_causal_model_of_target from dowhy.gcm.independence_test.kernel import kernel_based from dowhy.gcm.shapley import ShapleyConfig, estimate_shapley_values from dowhy.gcm.util.general import shape_into_2d from dowhy.graph import DirectedGraph, get_ordered_predecessors, is_root_node, node_connected_subgraph_view _logger = logging.getLogger(__name__) def mechanism_change_test( target_original_data: np.ndarray, target_new_data: np.ndarray, parents_original_data: Optional[np.ndarray] = None, parents_new_data: Optional[np.ndarray] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, ) -> float: """Estimates a p-value for the null hypothesis that the original and new data were generated by the same mechanism. Here, we check the dependency between binary labels indicating whether a sample is from the original or a new data set. If the labels do not provide information to determine if a sample is coming from the original/new distribution, then it is likely that the mechanism has not changed. For non-root nodes, samples from parent variables are needed as conditioning variables. This is, testing the null hypothesis that the data were generated by the same mechanism given the parent samples. By this, we incorporate upstream changes that might have impacted the parents, but not the target node itself. :param target_original_data: Samples of the node from the original data set. :param target_new_data: Samples of the node from the new data set. :param parents_original_data: Samples from parents of the node from the original data set. :param parents_new_data: Samples from parents of the node from the new data set. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in nodes without parents. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in nodes with parents. :return: A p-value for the null hypothesis that the mechanism has not changed. """ causal_graph: DirectedGraph if parents_original_data is not None and parents_new_data is None: raise ValueError("Original parents data were given, but no new parents data!") if parents_original_data is None and parents_new_data is not None: raise ValueError("New parents data were given, but no original parents data!") num_samples_for_testing = min(target_original_data.shape[0], target_new_data.shape[0]) data_set_indices = np.ones(num_samples_for_testing * 2) data_set_indices[num_samples_for_testing:] = -1 data_set_indices = data_set_indices.astype(str) original_indices = np.random.choice(target_original_data.shape[0], num_samples_for_testing, replace=False) new_indices = np.random.choice(target_new_data.shape[0], num_samples_for_testing, replace=False) joint_target_samples = np.vstack( [shape_into_2d(target_original_data[original_indices]), shape_into_2d(target_new_data[new_indices])] ) if parents_original_data is None: return independence_test(joint_target_samples, data_set_indices) else: parents_new_data: np.ndarray joint_parent_data = np.vstack( [shape_into_2d(parents_original_data[original_indices]), shape_into_2d(parents_new_data[new_indices])] ) return conditional_independence_test(joint_target_samples, data_set_indices, joint_parent_data) def distribution_change( causal_model: ProbabilisticCausalModel, old_data: pd.DataFrame, new_data: pd.DataFrame, target_node: Any, num_samples: int = 2000, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float] = auto_estimate_kl_divergence, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, mechanism_change_test_significance_level: float = 0.05, mechanism_change_test_fdr_control_method: Optional[str] = "fdr_bh", auto_assignment_quality: Optional[AssignmentQuality] = None, return_additional_info: bool = False, shapley_config: Optional[ShapleyConfig] = None, graph_factory: Callable[[Any], DirectedGraph] = nx.DiGraph, ) -> Union[ Dict[Any, float], Tuple[Dict[Any, float], Dict[Any, bool], ProbabilisticCausalModel, ProbabilisticCausalModel] ]: """Attributes the change in the marginal distribution of the target_node to nodes upstream in the causal DAG. Note that this method creates two copies of the causal DAG. The causal models of one causal DAG are learned from old data and those of another DAG are learned from new data. **Research Paper**: Kailash Budhathoki, Dominik Janzing, Patrick Bloebaum, Hoiyi Ng. *Why did the distribution change?*. Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, PMLR 130:1666-1674, 2021. :param causal_model: Reference causal model. :param old_data: Joint samples from the 'old' distribution. :param new_data: Joint samples from the 'new' distribution. :param target_node: Target node of interest for attributing the marginal distribution change. :param num_samples: Number of samples used for estimating Shapley values. This can have a significant influence on runtime and accuracy. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions, e.g. difference in average values. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in root nodes. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in non-root nodes. :param mechanism_change_test_significance_level: A significance level for rejecting the null hypothesis that the causal mechanism of a node has not changed. :param mechanism_change_test_fdr_control_method: The false discovery rate control method for mechanism change tests. For more options, checkout `statsmodels manual <https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.multipletests.html>`_. :param auto_assignment_quality: If set to None, the assigned models from the given causal models are used for the old and new graph. However, they are re-fitted on the given data. If set to a valid assignment quality, new models are automatically assigned to the old and new graph based on the respective data. :param return_additional_info: If set to True, three additional items are returned: a dictionary indicating whether each node's mechanism changed, the causal DAG whose causal models are learned from old data, and the causal DAG whose causal models are learned from new data. :param shapley_config: Configuration for the Shapley estimator. :param graph_factory: Allows customization in case a graph class different than networkx.DiGraph should be used. This function *must* copy nodes and edges. Attributes of nodes will be overridden in the copy, so the algorithm is independent of the attribute copy behavior of this factory. :return: By default, if `return_additional_info` is set to False, only the dictionary containing contribution of each upstream node is returned. If `return_additional_info` is set to True, three additional items are returned: a dictionary indicating whether each node's mechanism changed, the causal DAG whose causal models learned from old data, and the causal DAG whose causal models are learned from new data. """ causal_graph_old = graph_factory(node_connected_subgraph_view(causal_model.graph, target_node)) causal_model_old = ProbabilisticCausalModel(causal_graph_old) if auto_assignment_quality is None: clone_causal_models(causal_model.graph, causal_model_old.graph) else: assign_causal_mechanisms(causal_model_old, old_data, override_models=True, quality=auto_assignment_quality) causal_graph_new = graph_factory(causal_graph_old) causal_model_new = ProbabilisticCausalModel(causal_graph_new) if auto_assignment_quality is None: clone_causal_models(causal_graph_old, causal_model_new.graph) else: assign_causal_mechanisms(causal_model_new, new_data, override_models=True, quality=auto_assignment_quality) mechanism_changes = _fit_accounting_for_mechanism_change( causal_model_old, causal_model_new, old_data[list(causal_graph_old.nodes)], new_data[list(causal_graph_new.nodes)], independence_test, conditional_independence_test, mechanism_change_test_significance_level, mechanism_change_test_fdr_control_method, ) attributions = distribution_change_of_graphs( causal_model_old, causal_model_new, target_node, num_samples, difference_estimation_func, shapley_config, graph_factory, ) if return_additional_info: return attributions, mechanism_changes, causal_model_old, causal_model_new else: return attributions def distribution_change_of_graphs( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, target_node: Any, num_samples: int = 2000, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float] = auto_estimate_kl_divergence, shapley_config: Optional[ShapleyConfig] = None, graph_factory: Callable[[Any], DirectedGraph] = nx.DiGraph, ) -> Dict[Any, float]: """Attributes the change of the marginal distribution of target_node to upstream nodes based on the distributions generated by the 'old' and 'new' causal graphs. These graphs are assumed to represent the same causal structure and to be fitted on the respective data. Note: This method creates a copy of the given causal models, i.e. the original objects will not be modified. Related paper: Budhathoki, K., Janzing, D., Bloebaum, P., & Ng, H. (2021). Why did the distribution change? arXiv preprint arXiv:2102.13384. :param causal_model_old: The ProbabilisticCausalModel fitted on the 'old' data. :param causal_model_new: The ProbabilisticCausalModel fitted on the 'new' data. :param target_node: Node of interest for attributing the marginal distribution change. :param num_samples: Number of samples used for the estimation. This can have a significant influence on the runtime and accuracy. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions. An example could be the KL divergence. :param shapley_config: Config for the Shapley estimator. :param graph_factory: Allows customization in case a graph class different than networkx.DiGraph should be used. This function *must* copy nodes and edges. Attributes of nodes will be overridden in the copy, so the algorithm is independent of the attribute copy behavior of this factory. :return: A dictionary containing the contributions of upstream nodes to the marginal distribution change in the target node. """ validate_causal_dag(causal_model_old.graph) validate_causal_dag(causal_model_new.graph) return _estimate_marginal_distribution_change( ProbabilisticCausalModel(node_connected_subgraph_view(causal_model_old.graph, target_node)), ProbabilisticCausalModel(node_connected_subgraph_view(causal_model_new.graph, target_node)), target_node, num_samples, difference_estimation_func, shapley_config, graph_factory, ) def _fit_accounting_for_mechanism_change( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, old_data: pd.DataFrame, new_data: pd.DataFrame, independence_test: Callable[[np.ndarray, np.ndarray], float], conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float], significance_level: float, fdr_control_method: Optional[str], ) -> Dict[Any, bool]: mechanism_changed_for_node = _check_significant_mechanism_change( causal_model_old.graph, old_data, new_data, independence_test, conditional_independence_test, significance_level, fdr_control_method, ) joint_data = pd.concat([old_data, new_data], ignore_index=True, sort=True) for node in causal_model_new.graph.nodes: if mechanism_changed_for_node[node]: fit_causal_model_of_target(causal_model_old, node, old_data) fit_causal_model_of_target(causal_model_new, node, new_data) else: fit_causal_model_of_target(causal_model_old, node, joint_data) fit_causal_model_of_target(causal_model_new, node, joint_data) return mechanism_changed_for_node def _estimate_marginal_distribution_change( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, target_node: Any, num_samples: int, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float], shapley_config: Optional[ShapleyConfig], graph_factory: Callable[[Any], DirectedGraph], ) -> Dict[Any, float]: old_causal_models = [causal_model_old.causal_mechanism(x) for x in sorted(causal_model_old.graph.nodes)] new_causal_models = [causal_model_new.causal_mechanism(x) for x in sorted(causal_model_new.graph.nodes)] target_samples_old = draw_samples(causal_model_old, num_samples)[target_node].to_numpy() def attribution_set_function(subset): if np.all(subset == 0): return 0 causal_model = ProbabilisticCausalModel(graph_factory(causal_model_old.graph)) nodes = sorted(list(causal_model.graph.nodes)) for i in range(len(old_causal_models)): if subset[i] == 1: causal_model.set_causal_mechanism(nodes[i], new_causal_models[i]) else: causal_model.set_causal_mechanism(nodes[i], old_causal_models[i]) for node in causal_model.graph.nodes: causal_model.graph.nodes[node][PARENTS_DURING_FIT] = get_ordered_predecessors(causal_model.graph, node) target_samples_new = draw_samples(causal_model, num_samples)[target_node].to_numpy() return difference_estimation_func(target_samples_old, target_samples_new) attributions = estimate_shapley_values(attribution_set_function, len(old_causal_models), shapley_config) return {x: attributions[i] for i, x in enumerate(sorted(causal_model_old.graph.nodes))} def estimate_distribution_change_scores( causal_model: ProbabilisticCausalModel, original_data: pd.DataFrame, new_data: pd.DataFrame, difference_estimation_func: Callable[ [np.ndarray, np.ndarray], Union[np.ndarray, float] ] = auto_estimate_kl_divergence, max_num_evaluation_samples: int = 1000, num_joint_samples: int = 500, early_stopping_percentage: float = 0.01, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, mechanism_change_test_significance_level: float = 0.05, mechanism_change_test_fdr_control_method: Optional[str] = "fdr_bh", ) -> Dict[Any, float]: """Given newly observed and original samples from the joint distribution of the given causal graphical model, this method estimates a score for each node that quantifies how much the distribution of the node has changed. For this, it first checks whether the underlying causal mechanism has changed at all and, if this is the case, it estimates the difference between the new and original distributions. The score is based on the quantity measured by the provided difference_estimation_func or 0 if no mechanism change has been detected. Note that for each parent sample, num_joint_samples conditional samples are generated based on the original and new causal mechanism and evaluated by the given difference_estimation_func function. These results are then averaged over multiple different parent samples. :param causal_model: The underlying causal model based on the original data. :param original_data: Samples from the original data. :param new_data: Samples from the new data. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions. An example could be the KL divergence. :param max_num_evaluation_samples: Maximum number of (parent) samples for evaluating the difference in distributions. :param num_joint_samples: Number of samples generated in a node per parent sample. :param early_stopping_percentage: If the change in percentage between multiple consecutive runs is below this threshold, the evaluation stops before evaluating all max_num_evaluation_samples. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in root nodes. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in non-root nodes. :param mechanism_change_test_significance_level: A significance level for rejecting the null hypothesis that the causal mechanism of a node has not changed. :param mechanism_change_test_fdr_control_method: The false discovery rate control method for mechanism change tests. For more options, checkout `statsmodels manual <https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.multipletests.html>`_. :return: A dictionary assining a score to each node in the causal graph. """ validate_causal_dag(causal_model.graph) mechanism_changed_for_node = _check_significant_mechanism_change( causal_model.graph, original_data, new_data, independence_test, conditional_independence_test, mechanism_change_test_significance_level, mechanism_change_test_fdr_control_method, ) results = {} for node in tqdm( causal_model.graph.nodes, desc="Estimating mechanism change anomaly scores", position=0, leave=True ): if mechanism_changed_for_node[node]: if is_root_node(causal_model.graph, node): results[node] = difference_estimation_func(original_data[node].to_numpy(), new_data[node].to_numpy()) else: parent_nodes = get_ordered_predecessors(causal_model.graph, node) results[node] = _estimate_distribution_change_score( original_data[parent_nodes].to_numpy(), new_data[parent_nodes].to_numpy(), new_data[node].to_numpy(), causal_model.causal_mechanism(node), difference_estimation_func, max_num_evaluation_samples, num_joint_samples, early_stopping_percentage, ) else: results[node] = 0 return results def _check_significant_mechanism_change( graph: DirectedGraph, old_data: pd.DataFrame, new_data: pd.DataFrame, independence_test: Callable[[np.ndarray, np.ndarray], float], conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float], significance_level: float, fdr_control_method: Optional[str], ) -> Dict[Any, bool]: all_p_values = [] for node in graph.nodes: if is_root_node(graph, node): parents_org_data = None parents_new_data = None else: parents_org_data = old_data[get_ordered_predecessors(graph, node)].to_numpy() parents_new_data = new_data[get_ordered_predecessors(graph, node)].to_numpy() all_p_values.append( mechanism_change_test( old_data[node].to_numpy(), new_data[node].to_numpy(), parents_org_data, parents_new_data, independence_test=independence_test, conditional_independence_test=conditional_independence_test, ) ) if fdr_control_method is None: successes = np.array(all_p_values) <= significance_level else: successes = multipletests(all_p_values, significance_level, method=fdr_control_method)[0] return dict(zip(graph.nodes, successes)) def _estimate_distribution_change_score( parent_original_data: np.ndarray, parent_new_data: np.ndarray, target_new_data: np.ndarray, causal_model_original: ConditionalStochasticModel, difference_estimation_func: Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]], max_num_evaluation_samples: int, num_joint_samples: int, early_stopping_percentage: float, ) -> float: parent_original_data, parent_new_data, target_new_data = shape_into_2d( parent_original_data, parent_new_data, target_new_data ) causal_model_new = causal_model_original.clone() causal_model_new.fit(X=parent_new_data, Y=target_new_data) joint_parent_samples = np.vstack([parent_original_data, parent_new_data]) joint_parent_samples = joint_parent_samples[ np.random.choice( joint_parent_samples.shape[0], min(joint_parent_samples.shape[0], max_num_evaluation_samples), replace=False ) ] result = 0 run = 0 for joint_parent_sample in joint_parent_samples: old_result = result samples = repmat(joint_parent_sample, num_joint_samples, 1) result += difference_estimation_func( causal_model_original.draw_samples(samples), causal_model_new.draw_samples(samples) ) run += 1 if old_result != 0 and (1 - result / old_result) <= early_stopping_percentage: # If the relative change of the score is less than the given threshold, we stop the estimation early. _logger.info( "Early stopping: Result only changed by %f percent and a threshold of %f is set." % (1 - result / old_result, early_stopping_percentage) ) break return result / run

"""This module defines functions to attribute distribution changes. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging from typing import Any, Callable, Dict, List, Optional, Tuple, Union import networkx as nx import numpy as np import pandas as pd from numpy.matlib import repmat from statsmodels.stats.multitest import multipletests from tqdm import tqdm from dowhy.gcm.auto import AssignmentQuality, assign_causal_mechanism_node, assign_causal_mechanisms from dowhy.gcm.causal_mechanisms import ConditionalStochasticModel from dowhy.gcm.causal_models import ( PARENTS_DURING_FIT, ProbabilisticCausalModel, clone_causal_models, validate_causal_dag, ) from dowhy.gcm.divergence import auto_estimate_kl_divergence from dowhy.gcm.fitting_sampling import draw_samples, fit_causal_model_of_target from dowhy.gcm.independence_test.kernel import kernel_based from dowhy.gcm.shapley import ShapleyConfig, estimate_shapley_values from dowhy.gcm.util.general import shape_into_2d from dowhy.graph import DirectedGraph, get_ordered_predecessors, is_root_node, node_connected_subgraph_view _logger = logging.getLogger(__name__) def mechanism_change_test( target_original_data: np.ndarray, target_new_data: np.ndarray, parents_original_data: Optional[np.ndarray] = None, parents_new_data: Optional[np.ndarray] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, ) -> float: """Estimates a p-value for the null hypothesis that the original and new data were generated by the same mechanism. Here, we check the dependency between binary labels indicating whether a sample is from the original or a new data set. If the labels do not provide information to determine if a sample is coming from the original/new distribution, then it is likely that the mechanism has not changed. For non-root nodes, samples from parent variables are needed as conditioning variables. This is, testing the null hypothesis that the data were generated by the same mechanism given the parent samples. By this, we incorporate upstream changes that might have impacted the parents, but not the target node itself. :param target_original_data: Samples of the node from the original data set. :param target_new_data: Samples of the node from the new data set. :param parents_original_data: Samples from parents of the node from the original data set. :param parents_new_data: Samples from parents of the node from the new data set. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in nodes without parents. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in nodes with parents. :return: A p-value for the null hypothesis that the mechanism has not changed. """ causal_graph: DirectedGraph if parents_original_data is not None and parents_new_data is None: raise ValueError("Original parents data were given, but no new parents data!") if parents_original_data is None and parents_new_data is not None: raise ValueError("New parents data were given, but no original parents data!") num_samples_for_testing = min(target_original_data.shape[0], target_new_data.shape[0]) data_set_indices = np.ones(num_samples_for_testing * 2) data_set_indices[num_samples_for_testing:] = -1 data_set_indices = data_set_indices.astype(str) original_indices = np.random.choice(target_original_data.shape[0], num_samples_for_testing, replace=False) new_indices = np.random.choice(target_new_data.shape[0], num_samples_for_testing, replace=False) joint_target_samples = np.vstack( [shape_into_2d(target_original_data[original_indices]), shape_into_2d(target_new_data[new_indices])] ) if parents_original_data is None: return independence_test(joint_target_samples, data_set_indices) else: parents_new_data: np.ndarray joint_parent_data = np.vstack( [shape_into_2d(parents_original_data[original_indices]), shape_into_2d(parents_new_data[new_indices])] ) return conditional_independence_test(joint_target_samples, data_set_indices, joint_parent_data) def distribution_change( causal_model: ProbabilisticCausalModel, old_data: pd.DataFrame, new_data: pd.DataFrame, target_node: Any, invariant_nodes: List[Any] = None, num_samples: int = 2000, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float] = auto_estimate_kl_divergence, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, mechanism_change_test_significance_level: float = 0.05, mechanism_change_test_fdr_control_method: Optional[str] = "fdr_bh", auto_assignment_quality: Optional[AssignmentQuality] = None, return_additional_info: bool = False, shapley_config: Optional[ShapleyConfig] = None, graph_factory: Callable[[Any], DirectedGraph] = nx.DiGraph, ) -> Union[ Dict[Any, float], Tuple[Dict[Any, float], Dict[Any, bool], ProbabilisticCausalModel, ProbabilisticCausalModel] ]: """Attributes the change in the marginal distribution of the target_node to nodes upstream in the causal DAG. Note that this method creates two copies of the causal DAG. The causal models of one causal DAG are learned from old data and those of another DAG are learned from new data. **Research Paper**: Kailash Budhathoki, Dominik Janzing, Patrick Bloebaum, Hoiyi Ng. *Why did the distribution change?*. Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, PMLR 130:1666-1674, 2021. :param causal_model: Reference causal model. :param old_data: Joint samples from the 'old' distribution. :param new_data: Joint samples from the 'new' distribution. :param target_node: Target node of interest for attributing the marginal distribution change. :param invariant_nodes: List of nodes where the mechanism is kept constant regardless of changes in the datasets being analyzed. :param num_samples: Number of samples used for estimating Shapley values. This can have a significant influence on runtime and accuracy. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions, e.g. difference in average values. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in root nodes. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in non-root nodes. :param mechanism_change_test_significance_level: A significance level for rejecting the null hypothesis that the causal mechanism of a node has not changed. :param mechanism_change_test_fdr_control_method: The false discovery rate control method for mechanism change tests. For more options, checkout `statsmodels manual <https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.multipletests.html>`_. :param auto_assignment_quality: If set to None, the assigned models from the given causal models are used for the old and new graph. However, they are re-fitted on the given data. If set to a valid assignment quality, new models are automatically assigned to the old and new graph based on the respective data. :param return_additional_info: If set to True, three additional items are returned: a dictionary indicating whether each node's mechanism changed, the causal DAG whose causal models are learned from old data, and the causal DAG whose causal models are learned from new data. :param shapley_config: Configuration for the Shapley estimator. :param graph_factory: Allows customization in case a graph class different than networkx.DiGraph should be used. This function *must* copy nodes and edges. Attributes of nodes will be overridden in the copy, so the algorithm is independent of the attribute copy behavior of this factory. :return: By default, if `return_additional_info` is set to False, only the dictionary containing contribution of each upstream node is returned. If `return_additional_info` is set to True, three additional items are returned: a dictionary indicating whether each node's mechanism changed, the causal DAG whose causal models learned from old data, and the causal DAG whose causal models are learned from new data. """ if invariant_nodes is None: invariant_nodes = [] causal_graph_old = graph_factory(node_connected_subgraph_view(causal_model.graph, target_node)) causal_model_old = ProbabilisticCausalModel(causal_graph_old) if auto_assignment_quality is None: clone_causal_models(causal_model.graph, causal_model_old.graph) else: assign_causal_mechanisms(causal_model_old, old_data, override_models=True, quality=auto_assignment_quality) invariant_nodes = list(set(invariant_nodes).intersection(set(causal_graph_old.nodes))) _remove_invariant_nodes(invariant_nodes, causal_model_old, old_data, auto_assignment_quality) causal_graph_new = graph_factory(causal_graph_old) causal_model_new = ProbabilisticCausalModel(causal_graph_new) if auto_assignment_quality is None: clone_causal_models(causal_graph_old, causal_model_new.graph) else: assign_causal_mechanisms(causal_model_new, new_data, override_models=True, quality=auto_assignment_quality) mechanism_changes = _fit_accounting_for_mechanism_change( causal_model_old, causal_model_new, old_data[list(causal_graph_old.nodes)], new_data[list(causal_graph_new.nodes)], independence_test, conditional_independence_test, mechanism_change_test_significance_level, mechanism_change_test_fdr_control_method, invariant_nodes, ) attributions = distribution_change_of_graphs( causal_model_old, causal_model_new, target_node, num_samples, difference_estimation_func, shapley_config, graph_factory, ) # set attributions to zero for left out invariant nodes for node in invariant_nodes: attributions[node] = 0 if return_additional_info: return attributions, mechanism_changes, causal_model_old, causal_model_new else: return attributions def distribution_change_of_graphs( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, target_node: Any, num_samples: int = 2000, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float] = auto_estimate_kl_divergence, shapley_config: Optional[ShapleyConfig] = None, graph_factory: Callable[[Any], DirectedGraph] = nx.DiGraph, ) -> Dict[Any, float]: """Attributes the change of the marginal distribution of target_node to upstream nodes based on the distributions generated by the 'old' and 'new' causal graphs. These graphs are assumed to represent the same causal structure and to be fitted on the respective data. Note: This method creates a copy of the given causal models, i.e. the original objects will not be modified. Related paper: Budhathoki, K., Janzing, D., Bloebaum, P., & Ng, H. (2021). Why did the distribution change? arXiv preprint arXiv:2102.13384. :param causal_model_old: The ProbabilisticCausalModel fitted on the 'old' data. :param causal_model_new: The ProbabilisticCausalModel fitted on the 'new' data. :param target_node: Node of interest for attributing the marginal distribution change. :param num_samples: Number of samples used for the estimation. This can have a significant influence on the runtime and accuracy. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions. An example could be the KL divergence. :param shapley_config: Config for the Shapley estimator. :param graph_factory: Allows customization in case a graph class different than networkx.DiGraph should be used. This function *must* copy nodes and edges. Attributes of nodes will be overridden in the copy, so the algorithm is independent of the attribute copy behavior of this factory. :return: A dictionary containing the contributions of upstream nodes to the marginal distribution change in the target node. """ validate_causal_dag(causal_model_old.graph) validate_causal_dag(causal_model_new.graph) return _estimate_marginal_distribution_change( ProbabilisticCausalModel(node_connected_subgraph_view(causal_model_old.graph, target_node)), ProbabilisticCausalModel(node_connected_subgraph_view(causal_model_new.graph, target_node)), target_node, num_samples, difference_estimation_func, shapley_config, graph_factory, ) def _remove_invariant_nodes( invariant_nodes: List[Any], causal_model: ProbabilisticCausalModel, old_data: pd.DataFrame, auto_assignment_quality: Optional[AssignmentQuality], ) -> None: if auto_assignment_quality is None: auto_assignment_quality = AssignmentQuality.GOOD for invar_node in invariant_nodes: # Get parent and child nodes parents = get_ordered_predecessors(causal_model.graph, invar_node) children = list(causal_model.graph.successors(invar_node)) # Don't remove node if node has more than 1 children nodes as it can introduce # hidden confounders. if len(children) > 1: continue # Remove the middle node causal_model.graph.remove_node(invar_node) # Connect parent and child nodes for parent in parents: for child in children: causal_model.graph.add_edge(parent, child) # Update the causal mechanism for the child nodes for child in children: assign_causal_mechanism_node(causal_model, child, old_data, quality=auto_assignment_quality) def _fit_accounting_for_mechanism_change( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, old_data: pd.DataFrame, new_data: pd.DataFrame, independence_test: Callable[[np.ndarray, np.ndarray], float], conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float], significance_level: float, fdr_control_method: Optional[str], invariant_nodes: List[Any], ) -> Dict[Any, bool]: mechanism_changed_for_node = _check_significant_mechanism_change( causal_model_old.graph, old_data, new_data, independence_test, conditional_independence_test, significance_level, fdr_control_method, ) joint_data = pd.concat([old_data, new_data], ignore_index=True, sort=True) for node in causal_model_new.graph.nodes: if node in invariant_nodes: mechanism_changed_for_node[node] = False if mechanism_changed_for_node[node]: fit_causal_model_of_target(causal_model_old, node, old_data) fit_causal_model_of_target(causal_model_new, node, new_data) else: fit_causal_model_of_target(causal_model_old, node, joint_data) fit_causal_model_of_target(causal_model_new, node, joint_data) return mechanism_changed_for_node def _estimate_marginal_distribution_change( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, target_node: Any, num_samples: int, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float], shapley_config: Optional[ShapleyConfig], graph_factory: Callable[[Any], DirectedGraph], ) -> Dict[Any, float]: old_causal_models = [causal_model_old.causal_mechanism(x) for x in sorted(causal_model_old.graph.nodes)] new_causal_models = [causal_model_new.causal_mechanism(x) for x in sorted(causal_model_new.graph.nodes)] target_samples_old = draw_samples(causal_model_old, num_samples)[target_node].to_numpy() def attribution_set_function(subset): if np.all(subset == 0): return 0 causal_model = ProbabilisticCausalModel(graph_factory(causal_model_old.graph)) nodes = sorted(list(causal_model.graph.nodes)) for i in range(len(old_causal_models)): if subset[i] == 1: causal_model.set_causal_mechanism(nodes[i], new_causal_models[i]) else: causal_model.set_causal_mechanism(nodes[i], old_causal_models[i]) for node in causal_model.graph.nodes: causal_model.graph.nodes[node][PARENTS_DURING_FIT] = get_ordered_predecessors(causal_model.graph, node) target_samples_new = draw_samples(causal_model, num_samples)[target_node].to_numpy() return difference_estimation_func(target_samples_old, target_samples_new) attributions = estimate_shapley_values(attribution_set_function, len(old_causal_models), shapley_config) return {x: attributions[i] for i, x in enumerate(sorted(causal_model_old.graph.nodes))} def estimate_distribution_change_scores( causal_model: ProbabilisticCausalModel, original_data: pd.DataFrame, new_data: pd.DataFrame, difference_estimation_func: Callable[ [np.ndarray, np.ndarray], Union[np.ndarray, float] ] = auto_estimate_kl_divergence, max_num_evaluation_samples: int = 1000, num_joint_samples: int = 500, early_stopping_percentage: float = 0.01, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, mechanism_change_test_significance_level: float = 0.05, mechanism_change_test_fdr_control_method: Optional[str] = "fdr_bh", ) -> Dict[Any, float]: """Given newly observed and original samples from the joint distribution of the given causal graphical model, this method estimates a score for each node that quantifies how much the distribution of the node has changed. For this, it first checks whether the underlying causal mechanism has changed at all and, if this is the case, it estimates the difference between the new and original distributions. The score is based on the quantity measured by the provided difference_estimation_func or 0 if no mechanism change has been detected. Note that for each parent sample, num_joint_samples conditional samples are generated based on the original and new causal mechanism and evaluated by the given difference_estimation_func function. These results are then averaged over multiple different parent samples. :param causal_model: The underlying causal model based on the original data. :param original_data: Samples from the original data. :param new_data: Samples from the new data. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions. An example could be the KL divergence. :param max_num_evaluation_samples: Maximum number of (parent) samples for evaluating the difference in distributions. :param num_joint_samples: Number of samples generated in a node per parent sample. :param early_stopping_percentage: If the change in percentage between multiple consecutive runs is below this threshold, the evaluation stops before evaluating all max_num_evaluation_samples. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in root nodes. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in non-root nodes. :param mechanism_change_test_significance_level: A significance level for rejecting the null hypothesis that the causal mechanism of a node has not changed. :param mechanism_change_test_fdr_control_method: The false discovery rate control method for mechanism change tests. For more options, checkout `statsmodels manual <https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.multipletests.html>`_. :return: A dictionary assining a score to each node in the causal graph. """ validate_causal_dag(causal_model.graph) mechanism_changed_for_node = _check_significant_mechanism_change( causal_model.graph, original_data, new_data, independence_test, conditional_independence_test, mechanism_change_test_significance_level, mechanism_change_test_fdr_control_method, ) results = {} for node in tqdm( causal_model.graph.nodes, desc="Estimating mechanism change anomaly scores", position=0, leave=True ): if mechanism_changed_for_node[node]: if is_root_node(causal_model.graph, node): results[node] = difference_estimation_func(original_data[node].to_numpy(), new_data[node].to_numpy()) else: parent_nodes = get_ordered_predecessors(causal_model.graph, node) results[node] = _estimate_distribution_change_score( original_data[parent_nodes].to_numpy(), new_data[parent_nodes].to_numpy(), new_data[node].to_numpy(), causal_model.causal_mechanism(node), difference_estimation_func, max_num_evaluation_samples, num_joint_samples, early_stopping_percentage, ) else: results[node] = 0 return results def _check_significant_mechanism_change( graph: DirectedGraph, old_data: pd.DataFrame, new_data: pd.DataFrame, independence_test: Callable[[np.ndarray, np.ndarray], float], conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float], significance_level: float, fdr_control_method: Optional[str], ) -> Dict[Any, bool]: all_p_values = [] for node in graph.nodes: if is_root_node(graph, node): parents_org_data = None parents_new_data = None else: parents_org_data = old_data[get_ordered_predecessors(graph, node)].to_numpy() parents_new_data = new_data[get_ordered_predecessors(graph, node)].to_numpy() all_p_values.append( mechanism_change_test( old_data[node].to_numpy(), new_data[node].to_numpy(), parents_org_data, parents_new_data, independence_test=independence_test, conditional_independence_test=conditional_independence_test, ) ) if fdr_control_method is None: successes = np.array(all_p_values) <= significance_level else: successes = multipletests(all_p_values, significance_level, method=fdr_control_method)[0] return dict(zip(graph.nodes, successes)) def _estimate_distribution_change_score( parent_original_data: np.ndarray, parent_new_data: np.ndarray, target_new_data: np.ndarray, causal_model_original: ConditionalStochasticModel, difference_estimation_func: Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]], max_num_evaluation_samples: int, num_joint_samples: int, early_stopping_percentage: float, ) -> float: parent_original_data, parent_new_data, target_new_data = shape_into_2d( parent_original_data, parent_new_data, target_new_data ) causal_model_new = causal_model_original.clone() causal_model_new.fit(X=parent_new_data, Y=target_new_data) joint_parent_samples = np.vstack([parent_original_data, parent_new_data]) joint_parent_samples = joint_parent_samples[ np.random.choice( joint_parent_samples.shape[0], min(joint_parent_samples.shape[0], max_num_evaluation_samples), replace=False ) ] result = 0 run = 0 for joint_parent_sample in joint_parent_samples: old_result = result samples = repmat(joint_parent_sample, num_joint_samples, 1) result += difference_estimation_func( causal_model_original.draw_samples(samples), causal_model_new.draw_samples(samples) ) run += 1 if old_result != 0 and (1 - result / old_result) <= early_stopping_percentage: # If the relative change of the score is less than the given threshold, we stop the estimation early. _logger.info( "Early stopping: Result only changed by %f percent and a threshold of %f is set." % (1 - result / old_result, early_stopping_percentage) ) break return result / run

bhatt-priyadutt

734b61671d130cf21a4e437084b9083dbd5c4a39

b4f80dcb2251a03aff4c816d7c6f6eb2aaf98e73

Check explicitly: `if invariant_nodes is not None:`

bloebp

py-why/dowhy

Invariant nodes removal

Feature - Now users can enter the list of invariant nodes to the distribution_change function to remove them from analysis

2023-08-15 12:59:48+00:00

2023-08-24 16:12:46+00:00

dowhy/gcm/distribution_change.py

"""This module defines functions to attribute distribution changes. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging from typing import Any, Callable, Dict, Optional, Tuple, Union import networkx as nx import numpy as np import pandas as pd from numpy.matlib import repmat from statsmodels.stats.multitest import multipletests from tqdm import tqdm from dowhy.gcm.auto import AssignmentQuality, assign_causal_mechanisms from dowhy.gcm.causal_mechanisms import ConditionalStochasticModel from dowhy.gcm.causal_models import ( PARENTS_DURING_FIT, ProbabilisticCausalModel, clone_causal_models, validate_causal_dag, ) from dowhy.gcm.divergence import auto_estimate_kl_divergence from dowhy.gcm.fitting_sampling import draw_samples, fit_causal_model_of_target from dowhy.gcm.independence_test.kernel import kernel_based from dowhy.gcm.shapley import ShapleyConfig, estimate_shapley_values from dowhy.gcm.util.general import shape_into_2d from dowhy.graph import DirectedGraph, get_ordered_predecessors, is_root_node, node_connected_subgraph_view _logger = logging.getLogger(__name__) def mechanism_change_test( target_original_data: np.ndarray, target_new_data: np.ndarray, parents_original_data: Optional[np.ndarray] = None, parents_new_data: Optional[np.ndarray] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, ) -> float: """Estimates a p-value for the null hypothesis that the original and new data were generated by the same mechanism. Here, we check the dependency between binary labels indicating whether a sample is from the original or a new data set. If the labels do not provide information to determine if a sample is coming from the original/new distribution, then it is likely that the mechanism has not changed. For non-root nodes, samples from parent variables are needed as conditioning variables. This is, testing the null hypothesis that the data were generated by the same mechanism given the parent samples. By this, we incorporate upstream changes that might have impacted the parents, but not the target node itself. :param target_original_data: Samples of the node from the original data set. :param target_new_data: Samples of the node from the new data set. :param parents_original_data: Samples from parents of the node from the original data set. :param parents_new_data: Samples from parents of the node from the new data set. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in nodes without parents. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in nodes with parents. :return: A p-value for the null hypothesis that the mechanism has not changed. """ causal_graph: DirectedGraph if parents_original_data is not None and parents_new_data is None: raise ValueError("Original parents data were given, but no new parents data!") if parents_original_data is None and parents_new_data is not None: raise ValueError("New parents data were given, but no original parents data!") num_samples_for_testing = min(target_original_data.shape[0], target_new_data.shape[0]) data_set_indices = np.ones(num_samples_for_testing * 2) data_set_indices[num_samples_for_testing:] = -1 data_set_indices = data_set_indices.astype(str) original_indices = np.random.choice(target_original_data.shape[0], num_samples_for_testing, replace=False) new_indices = np.random.choice(target_new_data.shape[0], num_samples_for_testing, replace=False) joint_target_samples = np.vstack( [shape_into_2d(target_original_data[original_indices]), shape_into_2d(target_new_data[new_indices])] ) if parents_original_data is None: return independence_test(joint_target_samples, data_set_indices) else: parents_new_data: np.ndarray joint_parent_data = np.vstack( [shape_into_2d(parents_original_data[original_indices]), shape_into_2d(parents_new_data[new_indices])] ) return conditional_independence_test(joint_target_samples, data_set_indices, joint_parent_data) def distribution_change( causal_model: ProbabilisticCausalModel, old_data: pd.DataFrame, new_data: pd.DataFrame, target_node: Any, num_samples: int = 2000, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float] = auto_estimate_kl_divergence, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, mechanism_change_test_significance_level: float = 0.05, mechanism_change_test_fdr_control_method: Optional[str] = "fdr_bh", auto_assignment_quality: Optional[AssignmentQuality] = None, return_additional_info: bool = False, shapley_config: Optional[ShapleyConfig] = None, graph_factory: Callable[[Any], DirectedGraph] = nx.DiGraph, ) -> Union[ Dict[Any, float], Tuple[Dict[Any, float], Dict[Any, bool], ProbabilisticCausalModel, ProbabilisticCausalModel] ]: """Attributes the change in the marginal distribution of the target_node to nodes upstream in the causal DAG. Note that this method creates two copies of the causal DAG. The causal models of one causal DAG are learned from old data and those of another DAG are learned from new data. **Research Paper**: Kailash Budhathoki, Dominik Janzing, Patrick Bloebaum, Hoiyi Ng. *Why did the distribution change?*. Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, PMLR 130:1666-1674, 2021. :param causal_model: Reference causal model. :param old_data: Joint samples from the 'old' distribution. :param new_data: Joint samples from the 'new' distribution. :param target_node: Target node of interest for attributing the marginal distribution change. :param num_samples: Number of samples used for estimating Shapley values. This can have a significant influence on runtime and accuracy. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions, e.g. difference in average values. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in root nodes. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in non-root nodes. :param mechanism_change_test_significance_level: A significance level for rejecting the null hypothesis that the causal mechanism of a node has not changed. :param mechanism_change_test_fdr_control_method: The false discovery rate control method for mechanism change tests. For more options, checkout `statsmodels manual <https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.multipletests.html>`_. :param auto_assignment_quality: If set to None, the assigned models from the given causal models are used for the old and new graph. However, they are re-fitted on the given data. If set to a valid assignment quality, new models are automatically assigned to the old and new graph based on the respective data. :param return_additional_info: If set to True, three additional items are returned: a dictionary indicating whether each node's mechanism changed, the causal DAG whose causal models are learned from old data, and the causal DAG whose causal models are learned from new data. :param shapley_config: Configuration for the Shapley estimator. :param graph_factory: Allows customization in case a graph class different than networkx.DiGraph should be used. This function *must* copy nodes and edges. Attributes of nodes will be overridden in the copy, so the algorithm is independent of the attribute copy behavior of this factory. :return: By default, if `return_additional_info` is set to False, only the dictionary containing contribution of each upstream node is returned. If `return_additional_info` is set to True, three additional items are returned: a dictionary indicating whether each node's mechanism changed, the causal DAG whose causal models learned from old data, and the causal DAG whose causal models are learned from new data. """ causal_graph_old = graph_factory(node_connected_subgraph_view(causal_model.graph, target_node)) causal_model_old = ProbabilisticCausalModel(causal_graph_old) if auto_assignment_quality is None: clone_causal_models(causal_model.graph, causal_model_old.graph) else: assign_causal_mechanisms(causal_model_old, old_data, override_models=True, quality=auto_assignment_quality) causal_graph_new = graph_factory(causal_graph_old) causal_model_new = ProbabilisticCausalModel(causal_graph_new) if auto_assignment_quality is None: clone_causal_models(causal_graph_old, causal_model_new.graph) else: assign_causal_mechanisms(causal_model_new, new_data, override_models=True, quality=auto_assignment_quality) mechanism_changes = _fit_accounting_for_mechanism_change( causal_model_old, causal_model_new, old_data[list(causal_graph_old.nodes)], new_data[list(causal_graph_new.nodes)], independence_test, conditional_independence_test, mechanism_change_test_significance_level, mechanism_change_test_fdr_control_method, ) attributions = distribution_change_of_graphs( causal_model_old, causal_model_new, target_node, num_samples, difference_estimation_func, shapley_config, graph_factory, ) if return_additional_info: return attributions, mechanism_changes, causal_model_old, causal_model_new else: return attributions def distribution_change_of_graphs( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, target_node: Any, num_samples: int = 2000, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float] = auto_estimate_kl_divergence, shapley_config: Optional[ShapleyConfig] = None, graph_factory: Callable[[Any], DirectedGraph] = nx.DiGraph, ) -> Dict[Any, float]: """Attributes the change of the marginal distribution of target_node to upstream nodes based on the distributions generated by the 'old' and 'new' causal graphs. These graphs are assumed to represent the same causal structure and to be fitted on the respective data. Note: This method creates a copy of the given causal models, i.e. the original objects will not be modified. Related paper: Budhathoki, K., Janzing, D., Bloebaum, P., & Ng, H. (2021). Why did the distribution change? arXiv preprint arXiv:2102.13384. :param causal_model_old: The ProbabilisticCausalModel fitted on the 'old' data. :param causal_model_new: The ProbabilisticCausalModel fitted on the 'new' data. :param target_node: Node of interest for attributing the marginal distribution change. :param num_samples: Number of samples used for the estimation. This can have a significant influence on the runtime and accuracy. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions. An example could be the KL divergence. :param shapley_config: Config for the Shapley estimator. :param graph_factory: Allows customization in case a graph class different than networkx.DiGraph should be used. This function *must* copy nodes and edges. Attributes of nodes will be overridden in the copy, so the algorithm is independent of the attribute copy behavior of this factory. :return: A dictionary containing the contributions of upstream nodes to the marginal distribution change in the target node. """ validate_causal_dag(causal_model_old.graph) validate_causal_dag(causal_model_new.graph) return _estimate_marginal_distribution_change( ProbabilisticCausalModel(node_connected_subgraph_view(causal_model_old.graph, target_node)), ProbabilisticCausalModel(node_connected_subgraph_view(causal_model_new.graph, target_node)), target_node, num_samples, difference_estimation_func, shapley_config, graph_factory, ) def _fit_accounting_for_mechanism_change( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, old_data: pd.DataFrame, new_data: pd.DataFrame, independence_test: Callable[[np.ndarray, np.ndarray], float], conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float], significance_level: float, fdr_control_method: Optional[str], ) -> Dict[Any, bool]: mechanism_changed_for_node = _check_significant_mechanism_change( causal_model_old.graph, old_data, new_data, independence_test, conditional_independence_test, significance_level, fdr_control_method, ) joint_data = pd.concat([old_data, new_data], ignore_index=True, sort=True) for node in causal_model_new.graph.nodes: if mechanism_changed_for_node[node]: fit_causal_model_of_target(causal_model_old, node, old_data) fit_causal_model_of_target(causal_model_new, node, new_data) else: fit_causal_model_of_target(causal_model_old, node, joint_data) fit_causal_model_of_target(causal_model_new, node, joint_data) return mechanism_changed_for_node def _estimate_marginal_distribution_change( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, target_node: Any, num_samples: int, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float], shapley_config: Optional[ShapleyConfig], graph_factory: Callable[[Any], DirectedGraph], ) -> Dict[Any, float]: old_causal_models = [causal_model_old.causal_mechanism(x) for x in sorted(causal_model_old.graph.nodes)] new_causal_models = [causal_model_new.causal_mechanism(x) for x in sorted(causal_model_new.graph.nodes)] target_samples_old = draw_samples(causal_model_old, num_samples)[target_node].to_numpy() def attribution_set_function(subset): if np.all(subset == 0): return 0 causal_model = ProbabilisticCausalModel(graph_factory(causal_model_old.graph)) nodes = sorted(list(causal_model.graph.nodes)) for i in range(len(old_causal_models)): if subset[i] == 1: causal_model.set_causal_mechanism(nodes[i], new_causal_models[i]) else: causal_model.set_causal_mechanism(nodes[i], old_causal_models[i]) for node in causal_model.graph.nodes: causal_model.graph.nodes[node][PARENTS_DURING_FIT] = get_ordered_predecessors(causal_model.graph, node) target_samples_new = draw_samples(causal_model, num_samples)[target_node].to_numpy() return difference_estimation_func(target_samples_old, target_samples_new) attributions = estimate_shapley_values(attribution_set_function, len(old_causal_models), shapley_config) return {x: attributions[i] for i, x in enumerate(sorted(causal_model_old.graph.nodes))} def estimate_distribution_change_scores( causal_model: ProbabilisticCausalModel, original_data: pd.DataFrame, new_data: pd.DataFrame, difference_estimation_func: Callable[ [np.ndarray, np.ndarray], Union[np.ndarray, float] ] = auto_estimate_kl_divergence, max_num_evaluation_samples: int = 1000, num_joint_samples: int = 500, early_stopping_percentage: float = 0.01, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, mechanism_change_test_significance_level: float = 0.05, mechanism_change_test_fdr_control_method: Optional[str] = "fdr_bh", ) -> Dict[Any, float]: """Given newly observed and original samples from the joint distribution of the given causal graphical model, this method estimates a score for each node that quantifies how much the distribution of the node has changed. For this, it first checks whether the underlying causal mechanism has changed at all and, if this is the case, it estimates the difference between the new and original distributions. The score is based on the quantity measured by the provided difference_estimation_func or 0 if no mechanism change has been detected. Note that for each parent sample, num_joint_samples conditional samples are generated based on the original and new causal mechanism and evaluated by the given difference_estimation_func function. These results are then averaged over multiple different parent samples. :param causal_model: The underlying causal model based on the original data. :param original_data: Samples from the original data. :param new_data: Samples from the new data. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions. An example could be the KL divergence. :param max_num_evaluation_samples: Maximum number of (parent) samples for evaluating the difference in distributions. :param num_joint_samples: Number of samples generated in a node per parent sample. :param early_stopping_percentage: If the change in percentage between multiple consecutive runs is below this threshold, the evaluation stops before evaluating all max_num_evaluation_samples. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in root nodes. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in non-root nodes. :param mechanism_change_test_significance_level: A significance level for rejecting the null hypothesis that the causal mechanism of a node has not changed. :param mechanism_change_test_fdr_control_method: The false discovery rate control method for mechanism change tests. For more options, checkout `statsmodels manual <https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.multipletests.html>`_. :return: A dictionary assining a score to each node in the causal graph. """ validate_causal_dag(causal_model.graph) mechanism_changed_for_node = _check_significant_mechanism_change( causal_model.graph, original_data, new_data, independence_test, conditional_independence_test, mechanism_change_test_significance_level, mechanism_change_test_fdr_control_method, ) results = {} for node in tqdm( causal_model.graph.nodes, desc="Estimating mechanism change anomaly scores", position=0, leave=True ): if mechanism_changed_for_node[node]: if is_root_node(causal_model.graph, node): results[node] = difference_estimation_func(original_data[node].to_numpy(), new_data[node].to_numpy()) else: parent_nodes = get_ordered_predecessors(causal_model.graph, node) results[node] = _estimate_distribution_change_score( original_data[parent_nodes].to_numpy(), new_data[parent_nodes].to_numpy(), new_data[node].to_numpy(), causal_model.causal_mechanism(node), difference_estimation_func, max_num_evaluation_samples, num_joint_samples, early_stopping_percentage, ) else: results[node] = 0 return results def _check_significant_mechanism_change( graph: DirectedGraph, old_data: pd.DataFrame, new_data: pd.DataFrame, independence_test: Callable[[np.ndarray, np.ndarray], float], conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float], significance_level: float, fdr_control_method: Optional[str], ) -> Dict[Any, bool]: all_p_values = [] for node in graph.nodes: if is_root_node(graph, node): parents_org_data = None parents_new_data = None else: parents_org_data = old_data[get_ordered_predecessors(graph, node)].to_numpy() parents_new_data = new_data[get_ordered_predecessors(graph, node)].to_numpy() all_p_values.append( mechanism_change_test( old_data[node].to_numpy(), new_data[node].to_numpy(), parents_org_data, parents_new_data, independence_test=independence_test, conditional_independence_test=conditional_independence_test, ) ) if fdr_control_method is None: successes = np.array(all_p_values) <= significance_level else: successes = multipletests(all_p_values, significance_level, method=fdr_control_method)[0] return dict(zip(graph.nodes, successes)) def _estimate_distribution_change_score( parent_original_data: np.ndarray, parent_new_data: np.ndarray, target_new_data: np.ndarray, causal_model_original: ConditionalStochasticModel, difference_estimation_func: Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]], max_num_evaluation_samples: int, num_joint_samples: int, early_stopping_percentage: float, ) -> float: parent_original_data, parent_new_data, target_new_data = shape_into_2d( parent_original_data, parent_new_data, target_new_data ) causal_model_new = causal_model_original.clone() causal_model_new.fit(X=parent_new_data, Y=target_new_data) joint_parent_samples = np.vstack([parent_original_data, parent_new_data]) joint_parent_samples = joint_parent_samples[ np.random.choice( joint_parent_samples.shape[0], min(joint_parent_samples.shape[0], max_num_evaluation_samples), replace=False ) ] result = 0 run = 0 for joint_parent_sample in joint_parent_samples: old_result = result samples = repmat(joint_parent_sample, num_joint_samples, 1) result += difference_estimation_func( causal_model_original.draw_samples(samples), causal_model_new.draw_samples(samples) ) run += 1 if old_result != 0 and (1 - result / old_result) <= early_stopping_percentage: # If the relative change of the score is less than the given threshold, we stop the estimation early. _logger.info( "Early stopping: Result only changed by %f percent and a threshold of %f is set." % (1 - result / old_result, early_stopping_percentage) ) break return result / run

"""This module defines functions to attribute distribution changes. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging from typing import Any, Callable, Dict, List, Optional, Tuple, Union import networkx as nx import numpy as np import pandas as pd from numpy.matlib import repmat from statsmodels.stats.multitest import multipletests from tqdm import tqdm from dowhy.gcm.auto import AssignmentQuality, assign_causal_mechanism_node, assign_causal_mechanisms from dowhy.gcm.causal_mechanisms import ConditionalStochasticModel from dowhy.gcm.causal_models import ( PARENTS_DURING_FIT, ProbabilisticCausalModel, clone_causal_models, validate_causal_dag, ) from dowhy.gcm.divergence import auto_estimate_kl_divergence from dowhy.gcm.fitting_sampling import draw_samples, fit_causal_model_of_target from dowhy.gcm.independence_test.kernel import kernel_based from dowhy.gcm.shapley import ShapleyConfig, estimate_shapley_values from dowhy.gcm.util.general import shape_into_2d from dowhy.graph import DirectedGraph, get_ordered_predecessors, is_root_node, node_connected_subgraph_view _logger = logging.getLogger(__name__) def mechanism_change_test( target_original_data: np.ndarray, target_new_data: np.ndarray, parents_original_data: Optional[np.ndarray] = None, parents_new_data: Optional[np.ndarray] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, ) -> float: """Estimates a p-value for the null hypothesis that the original and new data were generated by the same mechanism. Here, we check the dependency between binary labels indicating whether a sample is from the original or a new data set. If the labels do not provide information to determine if a sample is coming from the original/new distribution, then it is likely that the mechanism has not changed. For non-root nodes, samples from parent variables are needed as conditioning variables. This is, testing the null hypothesis that the data were generated by the same mechanism given the parent samples. By this, we incorporate upstream changes that might have impacted the parents, but not the target node itself. :param target_original_data: Samples of the node from the original data set. :param target_new_data: Samples of the node from the new data set. :param parents_original_data: Samples from parents of the node from the original data set. :param parents_new_data: Samples from parents of the node from the new data set. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in nodes without parents. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in nodes with parents. :return: A p-value for the null hypothesis that the mechanism has not changed. """ causal_graph: DirectedGraph if parents_original_data is not None and parents_new_data is None: raise ValueError("Original parents data were given, but no new parents data!") if parents_original_data is None and parents_new_data is not None: raise ValueError("New parents data were given, but no original parents data!") num_samples_for_testing = min(target_original_data.shape[0], target_new_data.shape[0]) data_set_indices = np.ones(num_samples_for_testing * 2) data_set_indices[num_samples_for_testing:] = -1 data_set_indices = data_set_indices.astype(str) original_indices = np.random.choice(target_original_data.shape[0], num_samples_for_testing, replace=False) new_indices = np.random.choice(target_new_data.shape[0], num_samples_for_testing, replace=False) joint_target_samples = np.vstack( [shape_into_2d(target_original_data[original_indices]), shape_into_2d(target_new_data[new_indices])] ) if parents_original_data is None: return independence_test(joint_target_samples, data_set_indices) else: parents_new_data: np.ndarray joint_parent_data = np.vstack( [shape_into_2d(parents_original_data[original_indices]), shape_into_2d(parents_new_data[new_indices])] ) return conditional_independence_test(joint_target_samples, data_set_indices, joint_parent_data) def distribution_change( causal_model: ProbabilisticCausalModel, old_data: pd.DataFrame, new_data: pd.DataFrame, target_node: Any, invariant_nodes: List[Any] = None, num_samples: int = 2000, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float] = auto_estimate_kl_divergence, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, mechanism_change_test_significance_level: float = 0.05, mechanism_change_test_fdr_control_method: Optional[str] = "fdr_bh", auto_assignment_quality: Optional[AssignmentQuality] = None, return_additional_info: bool = False, shapley_config: Optional[ShapleyConfig] = None, graph_factory: Callable[[Any], DirectedGraph] = nx.DiGraph, ) -> Union[ Dict[Any, float], Tuple[Dict[Any, float], Dict[Any, bool], ProbabilisticCausalModel, ProbabilisticCausalModel] ]: """Attributes the change in the marginal distribution of the target_node to nodes upstream in the causal DAG. Note that this method creates two copies of the causal DAG. The causal models of one causal DAG are learned from old data and those of another DAG are learned from new data. **Research Paper**: Kailash Budhathoki, Dominik Janzing, Patrick Bloebaum, Hoiyi Ng. *Why did the distribution change?*. Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, PMLR 130:1666-1674, 2021. :param causal_model: Reference causal model. :param old_data: Joint samples from the 'old' distribution. :param new_data: Joint samples from the 'new' distribution. :param target_node: Target node of interest for attributing the marginal distribution change. :param invariant_nodes: List of nodes where the mechanism is kept constant regardless of changes in the datasets being analyzed. :param num_samples: Number of samples used for estimating Shapley values. This can have a significant influence on runtime and accuracy. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions, e.g. difference in average values. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in root nodes. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in non-root nodes. :param mechanism_change_test_significance_level: A significance level for rejecting the null hypothesis that the causal mechanism of a node has not changed. :param mechanism_change_test_fdr_control_method: The false discovery rate control method for mechanism change tests. For more options, checkout `statsmodels manual <https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.multipletests.html>`_. :param auto_assignment_quality: If set to None, the assigned models from the given causal models are used for the old and new graph. However, they are re-fitted on the given data. If set to a valid assignment quality, new models are automatically assigned to the old and new graph based on the respective data. :param return_additional_info: If set to True, three additional items are returned: a dictionary indicating whether each node's mechanism changed, the causal DAG whose causal models are learned from old data, and the causal DAG whose causal models are learned from new data. :param shapley_config: Configuration for the Shapley estimator. :param graph_factory: Allows customization in case a graph class different than networkx.DiGraph should be used. This function *must* copy nodes and edges. Attributes of nodes will be overridden in the copy, so the algorithm is independent of the attribute copy behavior of this factory. :return: By default, if `return_additional_info` is set to False, only the dictionary containing contribution of each upstream node is returned. If `return_additional_info` is set to True, three additional items are returned: a dictionary indicating whether each node's mechanism changed, the causal DAG whose causal models learned from old data, and the causal DAG whose causal models are learned from new data. """ if invariant_nodes is None: invariant_nodes = [] causal_graph_old = graph_factory(node_connected_subgraph_view(causal_model.graph, target_node)) causal_model_old = ProbabilisticCausalModel(causal_graph_old) if auto_assignment_quality is None: clone_causal_models(causal_model.graph, causal_model_old.graph) else: assign_causal_mechanisms(causal_model_old, old_data, override_models=True, quality=auto_assignment_quality) invariant_nodes = list(set(invariant_nodes).intersection(set(causal_graph_old.nodes))) _remove_invariant_nodes(invariant_nodes, causal_model_old, old_data, auto_assignment_quality) causal_graph_new = graph_factory(causal_graph_old) causal_model_new = ProbabilisticCausalModel(causal_graph_new) if auto_assignment_quality is None: clone_causal_models(causal_graph_old, causal_model_new.graph) else: assign_causal_mechanisms(causal_model_new, new_data, override_models=True, quality=auto_assignment_quality) mechanism_changes = _fit_accounting_for_mechanism_change( causal_model_old, causal_model_new, old_data[list(causal_graph_old.nodes)], new_data[list(causal_graph_new.nodes)], independence_test, conditional_independence_test, mechanism_change_test_significance_level, mechanism_change_test_fdr_control_method, invariant_nodes, ) attributions = distribution_change_of_graphs( causal_model_old, causal_model_new, target_node, num_samples, difference_estimation_func, shapley_config, graph_factory, ) # set attributions to zero for left out invariant nodes for node in invariant_nodes: attributions[node] = 0 if return_additional_info: return attributions, mechanism_changes, causal_model_old, causal_model_new else: return attributions def distribution_change_of_graphs( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, target_node: Any, num_samples: int = 2000, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float] = auto_estimate_kl_divergence, shapley_config: Optional[ShapleyConfig] = None, graph_factory: Callable[[Any], DirectedGraph] = nx.DiGraph, ) -> Dict[Any, float]: """Attributes the change of the marginal distribution of target_node to upstream nodes based on the distributions generated by the 'old' and 'new' causal graphs. These graphs are assumed to represent the same causal structure and to be fitted on the respective data. Note: This method creates a copy of the given causal models, i.e. the original objects will not be modified. Related paper: Budhathoki, K., Janzing, D., Bloebaum, P., & Ng, H. (2021). Why did the distribution change? arXiv preprint arXiv:2102.13384. :param causal_model_old: The ProbabilisticCausalModel fitted on the 'old' data. :param causal_model_new: The ProbabilisticCausalModel fitted on the 'new' data. :param target_node: Node of interest for attributing the marginal distribution change. :param num_samples: Number of samples used for the estimation. This can have a significant influence on the runtime and accuracy. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions. An example could be the KL divergence. :param shapley_config: Config for the Shapley estimator. :param graph_factory: Allows customization in case a graph class different than networkx.DiGraph should be used. This function *must* copy nodes and edges. Attributes of nodes will be overridden in the copy, so the algorithm is independent of the attribute copy behavior of this factory. :return: A dictionary containing the contributions of upstream nodes to the marginal distribution change in the target node. """ validate_causal_dag(causal_model_old.graph) validate_causal_dag(causal_model_new.graph) return _estimate_marginal_distribution_change( ProbabilisticCausalModel(node_connected_subgraph_view(causal_model_old.graph, target_node)), ProbabilisticCausalModel(node_connected_subgraph_view(causal_model_new.graph, target_node)), target_node, num_samples, difference_estimation_func, shapley_config, graph_factory, ) def _remove_invariant_nodes( invariant_nodes: List[Any], causal_model: ProbabilisticCausalModel, old_data: pd.DataFrame, auto_assignment_quality: Optional[AssignmentQuality], ) -> None: if auto_assignment_quality is None: auto_assignment_quality = AssignmentQuality.GOOD for invar_node in invariant_nodes: # Get parent and child nodes parents = get_ordered_predecessors(causal_model.graph, invar_node) children = list(causal_model.graph.successors(invar_node)) # Don't remove node if node has more than 1 children nodes as it can introduce # hidden confounders. if len(children) > 1: continue # Remove the middle node causal_model.graph.remove_node(invar_node) # Connect parent and child nodes for parent in parents: for child in children: causal_model.graph.add_edge(parent, child) # Update the causal mechanism for the child nodes for child in children: assign_causal_mechanism_node(causal_model, child, old_data, quality=auto_assignment_quality) def _fit_accounting_for_mechanism_change( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, old_data: pd.DataFrame, new_data: pd.DataFrame, independence_test: Callable[[np.ndarray, np.ndarray], float], conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float], significance_level: float, fdr_control_method: Optional[str], invariant_nodes: List[Any], ) -> Dict[Any, bool]: mechanism_changed_for_node = _check_significant_mechanism_change( causal_model_old.graph, old_data, new_data, independence_test, conditional_independence_test, significance_level, fdr_control_method, ) joint_data = pd.concat([old_data, new_data], ignore_index=True, sort=True) for node in causal_model_new.graph.nodes: if node in invariant_nodes: mechanism_changed_for_node[node] = False if mechanism_changed_for_node[node]: fit_causal_model_of_target(causal_model_old, node, old_data) fit_causal_model_of_target(causal_model_new, node, new_data) else: fit_causal_model_of_target(causal_model_old, node, joint_data) fit_causal_model_of_target(causal_model_new, node, joint_data) return mechanism_changed_for_node def _estimate_marginal_distribution_change( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, target_node: Any, num_samples: int, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float], shapley_config: Optional[ShapleyConfig], graph_factory: Callable[[Any], DirectedGraph], ) -> Dict[Any, float]: old_causal_models = [causal_model_old.causal_mechanism(x) for x in sorted(causal_model_old.graph.nodes)] new_causal_models = [causal_model_new.causal_mechanism(x) for x in sorted(causal_model_new.graph.nodes)] target_samples_old = draw_samples(causal_model_old, num_samples)[target_node].to_numpy() def attribution_set_function(subset): if np.all(subset == 0): return 0 causal_model = ProbabilisticCausalModel(graph_factory(causal_model_old.graph)) nodes = sorted(list(causal_model.graph.nodes)) for i in range(len(old_causal_models)): if subset[i] == 1: causal_model.set_causal_mechanism(nodes[i], new_causal_models[i]) else: causal_model.set_causal_mechanism(nodes[i], old_causal_models[i]) for node in causal_model.graph.nodes: causal_model.graph.nodes[node][PARENTS_DURING_FIT] = get_ordered_predecessors(causal_model.graph, node) target_samples_new = draw_samples(causal_model, num_samples)[target_node].to_numpy() return difference_estimation_func(target_samples_old, target_samples_new) attributions = estimate_shapley_values(attribution_set_function, len(old_causal_models), shapley_config) return {x: attributions[i] for i, x in enumerate(sorted(causal_model_old.graph.nodes))} def estimate_distribution_change_scores( causal_model: ProbabilisticCausalModel, original_data: pd.DataFrame, new_data: pd.DataFrame, difference_estimation_func: Callable[ [np.ndarray, np.ndarray], Union[np.ndarray, float] ] = auto_estimate_kl_divergence, max_num_evaluation_samples: int = 1000, num_joint_samples: int = 500, early_stopping_percentage: float = 0.01, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, mechanism_change_test_significance_level: float = 0.05, mechanism_change_test_fdr_control_method: Optional[str] = "fdr_bh", ) -> Dict[Any, float]: """Given newly observed and original samples from the joint distribution of the given causal graphical model, this method estimates a score for each node that quantifies how much the distribution of the node has changed. For this, it first checks whether the underlying causal mechanism has changed at all and, if this is the case, it estimates the difference between the new and original distributions. The score is based on the quantity measured by the provided difference_estimation_func or 0 if no mechanism change has been detected. Note that for each parent sample, num_joint_samples conditional samples are generated based on the original and new causal mechanism and evaluated by the given difference_estimation_func function. These results are then averaged over multiple different parent samples. :param causal_model: The underlying causal model based on the original data. :param original_data: Samples from the original data. :param new_data: Samples from the new data. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions. An example could be the KL divergence. :param max_num_evaluation_samples: Maximum number of (parent) samples for evaluating the difference in distributions. :param num_joint_samples: Number of samples generated in a node per parent sample. :param early_stopping_percentage: If the change in percentage between multiple consecutive runs is below this threshold, the evaluation stops before evaluating all max_num_evaluation_samples. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in root nodes. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in non-root nodes. :param mechanism_change_test_significance_level: A significance level for rejecting the null hypothesis that the causal mechanism of a node has not changed. :param mechanism_change_test_fdr_control_method: The false discovery rate control method for mechanism change tests. For more options, checkout `statsmodels manual <https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.multipletests.html>`_. :return: A dictionary assining a score to each node in the causal graph. """ validate_causal_dag(causal_model.graph) mechanism_changed_for_node = _check_significant_mechanism_change( causal_model.graph, original_data, new_data, independence_test, conditional_independence_test, mechanism_change_test_significance_level, mechanism_change_test_fdr_control_method, ) results = {} for node in tqdm( causal_model.graph.nodes, desc="Estimating mechanism change anomaly scores", position=0, leave=True ): if mechanism_changed_for_node[node]: if is_root_node(causal_model.graph, node): results[node] = difference_estimation_func(original_data[node].to_numpy(), new_data[node].to_numpy()) else: parent_nodes = get_ordered_predecessors(causal_model.graph, node) results[node] = _estimate_distribution_change_score( original_data[parent_nodes].to_numpy(), new_data[parent_nodes].to_numpy(), new_data[node].to_numpy(), causal_model.causal_mechanism(node), difference_estimation_func, max_num_evaluation_samples, num_joint_samples, early_stopping_percentage, ) else: results[node] = 0 return results def _check_significant_mechanism_change( graph: DirectedGraph, old_data: pd.DataFrame, new_data: pd.DataFrame, independence_test: Callable[[np.ndarray, np.ndarray], float], conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float], significance_level: float, fdr_control_method: Optional[str], ) -> Dict[Any, bool]: all_p_values = [] for node in graph.nodes: if is_root_node(graph, node): parents_org_data = None parents_new_data = None else: parents_org_data = old_data[get_ordered_predecessors(graph, node)].to_numpy() parents_new_data = new_data[get_ordered_predecessors(graph, node)].to_numpy() all_p_values.append( mechanism_change_test( old_data[node].to_numpy(), new_data[node].to_numpy(), parents_org_data, parents_new_data, independence_test=independence_test, conditional_independence_test=conditional_independence_test, ) ) if fdr_control_method is None: successes = np.array(all_p_values) <= significance_level else: successes = multipletests(all_p_values, significance_level, method=fdr_control_method)[0] return dict(zip(graph.nodes, successes)) def _estimate_distribution_change_score( parent_original_data: np.ndarray, parent_new_data: np.ndarray, target_new_data: np.ndarray, causal_model_original: ConditionalStochasticModel, difference_estimation_func: Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]], max_num_evaluation_samples: int, num_joint_samples: int, early_stopping_percentage: float, ) -> float: parent_original_data, parent_new_data, target_new_data = shape_into_2d( parent_original_data, parent_new_data, target_new_data ) causal_model_new = causal_model_original.clone() causal_model_new.fit(X=parent_new_data, Y=target_new_data) joint_parent_samples = np.vstack([parent_original_data, parent_new_data]) joint_parent_samples = joint_parent_samples[ np.random.choice( joint_parent_samples.shape[0], min(joint_parent_samples.shape[0], max_num_evaluation_samples), replace=False ) ] result = 0 run = 0 for joint_parent_sample in joint_parent_samples: old_result = result samples = repmat(joint_parent_sample, num_joint_samples, 1) result += difference_estimation_func( causal_model_original.draw_samples(samples), causal_model_new.draw_samples(samples) ) run += 1 if old_result != 0 and (1 - result / old_result) <= early_stopping_percentage: # If the relative change of the score is less than the given threshold, we stop the estimation early. _logger.info( "Early stopping: Result only changed by %f percent and a threshold of %f is set." % (1 - result / old_result, early_stopping_percentage) ) break return result / run

bhatt-priyadutt

734b61671d130cf21a4e437084b9083dbd5c4a39

b4f80dcb2251a03aff4c816d7c6f6eb2aaf98e73

@bloebp can you please explain more on this....i didnt understood here!

bhatt-priyadutt

py-why/dowhy

Invariant nodes removal

Feature - Now users can enter the list of invariant nodes to the distribution_change function to remove them from analysis

2023-08-15 12:59:48+00:00

2023-08-24 16:12:46+00:00

dowhy/gcm/distribution_change.py

"""This module defines functions to attribute distribution changes. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging from typing import Any, Callable, Dict, Optional, Tuple, Union import networkx as nx import numpy as np import pandas as pd from numpy.matlib import repmat from statsmodels.stats.multitest import multipletests from tqdm import tqdm from dowhy.gcm.auto import AssignmentQuality, assign_causal_mechanisms from dowhy.gcm.causal_mechanisms import ConditionalStochasticModel from dowhy.gcm.causal_models import ( PARENTS_DURING_FIT, ProbabilisticCausalModel, clone_causal_models, validate_causal_dag, ) from dowhy.gcm.divergence import auto_estimate_kl_divergence from dowhy.gcm.fitting_sampling import draw_samples, fit_causal_model_of_target from dowhy.gcm.independence_test.kernel import kernel_based from dowhy.gcm.shapley import ShapleyConfig, estimate_shapley_values from dowhy.gcm.util.general import shape_into_2d from dowhy.graph import DirectedGraph, get_ordered_predecessors, is_root_node, node_connected_subgraph_view _logger = logging.getLogger(__name__) def mechanism_change_test( target_original_data: np.ndarray, target_new_data: np.ndarray, parents_original_data: Optional[np.ndarray] = None, parents_new_data: Optional[np.ndarray] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, ) -> float: """Estimates a p-value for the null hypothesis that the original and new data were generated by the same mechanism. Here, we check the dependency between binary labels indicating whether a sample is from the original or a new data set. If the labels do not provide information to determine if a sample is coming from the original/new distribution, then it is likely that the mechanism has not changed. For non-root nodes, samples from parent variables are needed as conditioning variables. This is, testing the null hypothesis that the data were generated by the same mechanism given the parent samples. By this, we incorporate upstream changes that might have impacted the parents, but not the target node itself. :param target_original_data: Samples of the node from the original data set. :param target_new_data: Samples of the node from the new data set. :param parents_original_data: Samples from parents of the node from the original data set. :param parents_new_data: Samples from parents of the node from the new data set. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in nodes without parents. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in nodes with parents. :return: A p-value for the null hypothesis that the mechanism has not changed. """ causal_graph: DirectedGraph if parents_original_data is not None and parents_new_data is None: raise ValueError("Original parents data were given, but no new parents data!") if parents_original_data is None and parents_new_data is not None: raise ValueError("New parents data were given, but no original parents data!") num_samples_for_testing = min(target_original_data.shape[0], target_new_data.shape[0]) data_set_indices = np.ones(num_samples_for_testing * 2) data_set_indices[num_samples_for_testing:] = -1 data_set_indices = data_set_indices.astype(str) original_indices = np.random.choice(target_original_data.shape[0], num_samples_for_testing, replace=False) new_indices = np.random.choice(target_new_data.shape[0], num_samples_for_testing, replace=False) joint_target_samples = np.vstack( [shape_into_2d(target_original_data[original_indices]), shape_into_2d(target_new_data[new_indices])] ) if parents_original_data is None: return independence_test(joint_target_samples, data_set_indices) else: parents_new_data: np.ndarray joint_parent_data = np.vstack( [shape_into_2d(parents_original_data[original_indices]), shape_into_2d(parents_new_data[new_indices])] ) return conditional_independence_test(joint_target_samples, data_set_indices, joint_parent_data) def distribution_change( causal_model: ProbabilisticCausalModel, old_data: pd.DataFrame, new_data: pd.DataFrame, target_node: Any, num_samples: int = 2000, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float] = auto_estimate_kl_divergence, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, mechanism_change_test_significance_level: float = 0.05, mechanism_change_test_fdr_control_method: Optional[str] = "fdr_bh", auto_assignment_quality: Optional[AssignmentQuality] = None, return_additional_info: bool = False, shapley_config: Optional[ShapleyConfig] = None, graph_factory: Callable[[Any], DirectedGraph] = nx.DiGraph, ) -> Union[ Dict[Any, float], Tuple[Dict[Any, float], Dict[Any, bool], ProbabilisticCausalModel, ProbabilisticCausalModel] ]: """Attributes the change in the marginal distribution of the target_node to nodes upstream in the causal DAG. Note that this method creates two copies of the causal DAG. The causal models of one causal DAG are learned from old data and those of another DAG are learned from new data. **Research Paper**: Kailash Budhathoki, Dominik Janzing, Patrick Bloebaum, Hoiyi Ng. *Why did the distribution change?*. Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, PMLR 130:1666-1674, 2021. :param causal_model: Reference causal model. :param old_data: Joint samples from the 'old' distribution. :param new_data: Joint samples from the 'new' distribution. :param target_node: Target node of interest for attributing the marginal distribution change. :param num_samples: Number of samples used for estimating Shapley values. This can have a significant influence on runtime and accuracy. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions, e.g. difference in average values. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in root nodes. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in non-root nodes. :param mechanism_change_test_significance_level: A significance level for rejecting the null hypothesis that the causal mechanism of a node has not changed. :param mechanism_change_test_fdr_control_method: The false discovery rate control method for mechanism change tests. For more options, checkout `statsmodels manual <https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.multipletests.html>`_. :param auto_assignment_quality: If set to None, the assigned models from the given causal models are used for the old and new graph. However, they are re-fitted on the given data. If set to a valid assignment quality, new models are automatically assigned to the old and new graph based on the respective data. :param return_additional_info: If set to True, three additional items are returned: a dictionary indicating whether each node's mechanism changed, the causal DAG whose causal models are learned from old data, and the causal DAG whose causal models are learned from new data. :param shapley_config: Configuration for the Shapley estimator. :param graph_factory: Allows customization in case a graph class different than networkx.DiGraph should be used. This function *must* copy nodes and edges. Attributes of nodes will be overridden in the copy, so the algorithm is independent of the attribute copy behavior of this factory. :return: By default, if `return_additional_info` is set to False, only the dictionary containing contribution of each upstream node is returned. If `return_additional_info` is set to True, three additional items are returned: a dictionary indicating whether each node's mechanism changed, the causal DAG whose causal models learned from old data, and the causal DAG whose causal models are learned from new data. """ causal_graph_old = graph_factory(node_connected_subgraph_view(causal_model.graph, target_node)) causal_model_old = ProbabilisticCausalModel(causal_graph_old) if auto_assignment_quality is None: clone_causal_models(causal_model.graph, causal_model_old.graph) else: assign_causal_mechanisms(causal_model_old, old_data, override_models=True, quality=auto_assignment_quality) causal_graph_new = graph_factory(causal_graph_old) causal_model_new = ProbabilisticCausalModel(causal_graph_new) if auto_assignment_quality is None: clone_causal_models(causal_graph_old, causal_model_new.graph) else: assign_causal_mechanisms(causal_model_new, new_data, override_models=True, quality=auto_assignment_quality) mechanism_changes = _fit_accounting_for_mechanism_change( causal_model_old, causal_model_new, old_data[list(causal_graph_old.nodes)], new_data[list(causal_graph_new.nodes)], independence_test, conditional_independence_test, mechanism_change_test_significance_level, mechanism_change_test_fdr_control_method, ) attributions = distribution_change_of_graphs( causal_model_old, causal_model_new, target_node, num_samples, difference_estimation_func, shapley_config, graph_factory, ) if return_additional_info: return attributions, mechanism_changes, causal_model_old, causal_model_new else: return attributions def distribution_change_of_graphs( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, target_node: Any, num_samples: int = 2000, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float] = auto_estimate_kl_divergence, shapley_config: Optional[ShapleyConfig] = None, graph_factory: Callable[[Any], DirectedGraph] = nx.DiGraph, ) -> Dict[Any, float]: """Attributes the change of the marginal distribution of target_node to upstream nodes based on the distributions generated by the 'old' and 'new' causal graphs. These graphs are assumed to represent the same causal structure and to be fitted on the respective data. Note: This method creates a copy of the given causal models, i.e. the original objects will not be modified. Related paper: Budhathoki, K., Janzing, D., Bloebaum, P., & Ng, H. (2021). Why did the distribution change? arXiv preprint arXiv:2102.13384. :param causal_model_old: The ProbabilisticCausalModel fitted on the 'old' data. :param causal_model_new: The ProbabilisticCausalModel fitted on the 'new' data. :param target_node: Node of interest for attributing the marginal distribution change. :param num_samples: Number of samples used for the estimation. This can have a significant influence on the runtime and accuracy. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions. An example could be the KL divergence. :param shapley_config: Config for the Shapley estimator. :param graph_factory: Allows customization in case a graph class different than networkx.DiGraph should be used. This function *must* copy nodes and edges. Attributes of nodes will be overridden in the copy, so the algorithm is independent of the attribute copy behavior of this factory. :return: A dictionary containing the contributions of upstream nodes to the marginal distribution change in the target node. """ validate_causal_dag(causal_model_old.graph) validate_causal_dag(causal_model_new.graph) return _estimate_marginal_distribution_change( ProbabilisticCausalModel(node_connected_subgraph_view(causal_model_old.graph, target_node)), ProbabilisticCausalModel(node_connected_subgraph_view(causal_model_new.graph, target_node)), target_node, num_samples, difference_estimation_func, shapley_config, graph_factory, ) def _fit_accounting_for_mechanism_change( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, old_data: pd.DataFrame, new_data: pd.DataFrame, independence_test: Callable[[np.ndarray, np.ndarray], float], conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float], significance_level: float, fdr_control_method: Optional[str], ) -> Dict[Any, bool]: mechanism_changed_for_node = _check_significant_mechanism_change( causal_model_old.graph, old_data, new_data, independence_test, conditional_independence_test, significance_level, fdr_control_method, ) joint_data = pd.concat([old_data, new_data], ignore_index=True, sort=True) for node in causal_model_new.graph.nodes: if mechanism_changed_for_node[node]: fit_causal_model_of_target(causal_model_old, node, old_data) fit_causal_model_of_target(causal_model_new, node, new_data) else: fit_causal_model_of_target(causal_model_old, node, joint_data) fit_causal_model_of_target(causal_model_new, node, joint_data) return mechanism_changed_for_node def _estimate_marginal_distribution_change( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, target_node: Any, num_samples: int, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float], shapley_config: Optional[ShapleyConfig], graph_factory: Callable[[Any], DirectedGraph], ) -> Dict[Any, float]: old_causal_models = [causal_model_old.causal_mechanism(x) for x in sorted(causal_model_old.graph.nodes)] new_causal_models = [causal_model_new.causal_mechanism(x) for x in sorted(causal_model_new.graph.nodes)] target_samples_old = draw_samples(causal_model_old, num_samples)[target_node].to_numpy() def attribution_set_function(subset): if np.all(subset == 0): return 0 causal_model = ProbabilisticCausalModel(graph_factory(causal_model_old.graph)) nodes = sorted(list(causal_model.graph.nodes)) for i in range(len(old_causal_models)): if subset[i] == 1: causal_model.set_causal_mechanism(nodes[i], new_causal_models[i]) else: causal_model.set_causal_mechanism(nodes[i], old_causal_models[i]) for node in causal_model.graph.nodes: causal_model.graph.nodes[node][PARENTS_DURING_FIT] = get_ordered_predecessors(causal_model.graph, node) target_samples_new = draw_samples(causal_model, num_samples)[target_node].to_numpy() return difference_estimation_func(target_samples_old, target_samples_new) attributions = estimate_shapley_values(attribution_set_function, len(old_causal_models), shapley_config) return {x: attributions[i] for i, x in enumerate(sorted(causal_model_old.graph.nodes))} def estimate_distribution_change_scores( causal_model: ProbabilisticCausalModel, original_data: pd.DataFrame, new_data: pd.DataFrame, difference_estimation_func: Callable[ [np.ndarray, np.ndarray], Union[np.ndarray, float] ] = auto_estimate_kl_divergence, max_num_evaluation_samples: int = 1000, num_joint_samples: int = 500, early_stopping_percentage: float = 0.01, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, mechanism_change_test_significance_level: float = 0.05, mechanism_change_test_fdr_control_method: Optional[str] = "fdr_bh", ) -> Dict[Any, float]: """Given newly observed and original samples from the joint distribution of the given causal graphical model, this method estimates a score for each node that quantifies how much the distribution of the node has changed. For this, it first checks whether the underlying causal mechanism has changed at all and, if this is the case, it estimates the difference between the new and original distributions. The score is based on the quantity measured by the provided difference_estimation_func or 0 if no mechanism change has been detected. Note that for each parent sample, num_joint_samples conditional samples are generated based on the original and new causal mechanism and evaluated by the given difference_estimation_func function. These results are then averaged over multiple different parent samples. :param causal_model: The underlying causal model based on the original data. :param original_data: Samples from the original data. :param new_data: Samples from the new data. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions. An example could be the KL divergence. :param max_num_evaluation_samples: Maximum number of (parent) samples for evaluating the difference in distributions. :param num_joint_samples: Number of samples generated in a node per parent sample. :param early_stopping_percentage: If the change in percentage between multiple consecutive runs is below this threshold, the evaluation stops before evaluating all max_num_evaluation_samples. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in root nodes. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in non-root nodes. :param mechanism_change_test_significance_level: A significance level for rejecting the null hypothesis that the causal mechanism of a node has not changed. :param mechanism_change_test_fdr_control_method: The false discovery rate control method for mechanism change tests. For more options, checkout `statsmodels manual <https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.multipletests.html>`_. :return: A dictionary assining a score to each node in the causal graph. """ validate_causal_dag(causal_model.graph) mechanism_changed_for_node = _check_significant_mechanism_change( causal_model.graph, original_data, new_data, independence_test, conditional_independence_test, mechanism_change_test_significance_level, mechanism_change_test_fdr_control_method, ) results = {} for node in tqdm( causal_model.graph.nodes, desc="Estimating mechanism change anomaly scores", position=0, leave=True ): if mechanism_changed_for_node[node]: if is_root_node(causal_model.graph, node): results[node] = difference_estimation_func(original_data[node].to_numpy(), new_data[node].to_numpy()) else: parent_nodes = get_ordered_predecessors(causal_model.graph, node) results[node] = _estimate_distribution_change_score( original_data[parent_nodes].to_numpy(), new_data[parent_nodes].to_numpy(), new_data[node].to_numpy(), causal_model.causal_mechanism(node), difference_estimation_func, max_num_evaluation_samples, num_joint_samples, early_stopping_percentage, ) else: results[node] = 0 return results def _check_significant_mechanism_change( graph: DirectedGraph, old_data: pd.DataFrame, new_data: pd.DataFrame, independence_test: Callable[[np.ndarray, np.ndarray], float], conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float], significance_level: float, fdr_control_method: Optional[str], ) -> Dict[Any, bool]: all_p_values = [] for node in graph.nodes: if is_root_node(graph, node): parents_org_data = None parents_new_data = None else: parents_org_data = old_data[get_ordered_predecessors(graph, node)].to_numpy() parents_new_data = new_data[get_ordered_predecessors(graph, node)].to_numpy() all_p_values.append( mechanism_change_test( old_data[node].to_numpy(), new_data[node].to_numpy(), parents_org_data, parents_new_data, independence_test=independence_test, conditional_independence_test=conditional_independence_test, ) ) if fdr_control_method is None: successes = np.array(all_p_values) <= significance_level else: successes = multipletests(all_p_values, significance_level, method=fdr_control_method)[0] return dict(zip(graph.nodes, successes)) def _estimate_distribution_change_score( parent_original_data: np.ndarray, parent_new_data: np.ndarray, target_new_data: np.ndarray, causal_model_original: ConditionalStochasticModel, difference_estimation_func: Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]], max_num_evaluation_samples: int, num_joint_samples: int, early_stopping_percentage: float, ) -> float: parent_original_data, parent_new_data, target_new_data = shape_into_2d( parent_original_data, parent_new_data, target_new_data ) causal_model_new = causal_model_original.clone() causal_model_new.fit(X=parent_new_data, Y=target_new_data) joint_parent_samples = np.vstack([parent_original_data, parent_new_data]) joint_parent_samples = joint_parent_samples[ np.random.choice( joint_parent_samples.shape[0], min(joint_parent_samples.shape[0], max_num_evaluation_samples), replace=False ) ] result = 0 run = 0 for joint_parent_sample in joint_parent_samples: old_result = result samples = repmat(joint_parent_sample, num_joint_samples, 1) result += difference_estimation_func( causal_model_original.draw_samples(samples), causal_model_new.draw_samples(samples) ) run += 1 if old_result != 0 and (1 - result / old_result) <= early_stopping_percentage: # If the relative change of the score is less than the given threshold, we stop the estimation early. _logger.info( "Early stopping: Result only changed by %f percent and a threshold of %f is set." % (1 - result / old_result, early_stopping_percentage) ) break return result / run

"""This module defines functions to attribute distribution changes. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging from typing import Any, Callable, Dict, List, Optional, Tuple, Union import networkx as nx import numpy as np import pandas as pd from numpy.matlib import repmat from statsmodels.stats.multitest import multipletests from tqdm import tqdm from dowhy.gcm.auto import AssignmentQuality, assign_causal_mechanism_node, assign_causal_mechanisms from dowhy.gcm.causal_mechanisms import ConditionalStochasticModel from dowhy.gcm.causal_models import ( PARENTS_DURING_FIT, ProbabilisticCausalModel, clone_causal_models, validate_causal_dag, ) from dowhy.gcm.divergence import auto_estimate_kl_divergence from dowhy.gcm.fitting_sampling import draw_samples, fit_causal_model_of_target from dowhy.gcm.independence_test.kernel import kernel_based from dowhy.gcm.shapley import ShapleyConfig, estimate_shapley_values from dowhy.gcm.util.general import shape_into_2d from dowhy.graph import DirectedGraph, get_ordered_predecessors, is_root_node, node_connected_subgraph_view _logger = logging.getLogger(__name__) def mechanism_change_test( target_original_data: np.ndarray, target_new_data: np.ndarray, parents_original_data: Optional[np.ndarray] = None, parents_new_data: Optional[np.ndarray] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, ) -> float: """Estimates a p-value for the null hypothesis that the original and new data were generated by the same mechanism. Here, we check the dependency between binary labels indicating whether a sample is from the original or a new data set. If the labels do not provide information to determine if a sample is coming from the original/new distribution, then it is likely that the mechanism has not changed. For non-root nodes, samples from parent variables are needed as conditioning variables. This is, testing the null hypothesis that the data were generated by the same mechanism given the parent samples. By this, we incorporate upstream changes that might have impacted the parents, but not the target node itself. :param target_original_data: Samples of the node from the original data set. :param target_new_data: Samples of the node from the new data set. :param parents_original_data: Samples from parents of the node from the original data set. :param parents_new_data: Samples from parents of the node from the new data set. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in nodes without parents. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in nodes with parents. :return: A p-value for the null hypothesis that the mechanism has not changed. """ causal_graph: DirectedGraph if parents_original_data is not None and parents_new_data is None: raise ValueError("Original parents data were given, but no new parents data!") if parents_original_data is None and parents_new_data is not None: raise ValueError("New parents data were given, but no original parents data!") num_samples_for_testing = min(target_original_data.shape[0], target_new_data.shape[0]) data_set_indices = np.ones(num_samples_for_testing * 2) data_set_indices[num_samples_for_testing:] = -1 data_set_indices = data_set_indices.astype(str) original_indices = np.random.choice(target_original_data.shape[0], num_samples_for_testing, replace=False) new_indices = np.random.choice(target_new_data.shape[0], num_samples_for_testing, replace=False) joint_target_samples = np.vstack( [shape_into_2d(target_original_data[original_indices]), shape_into_2d(target_new_data[new_indices])] ) if parents_original_data is None: return independence_test(joint_target_samples, data_set_indices) else: parents_new_data: np.ndarray joint_parent_data = np.vstack( [shape_into_2d(parents_original_data[original_indices]), shape_into_2d(parents_new_data[new_indices])] ) return conditional_independence_test(joint_target_samples, data_set_indices, joint_parent_data) def distribution_change( causal_model: ProbabilisticCausalModel, old_data: pd.DataFrame, new_data: pd.DataFrame, target_node: Any, invariant_nodes: List[Any] = None, num_samples: int = 2000, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float] = auto_estimate_kl_divergence, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, mechanism_change_test_significance_level: float = 0.05, mechanism_change_test_fdr_control_method: Optional[str] = "fdr_bh", auto_assignment_quality: Optional[AssignmentQuality] = None, return_additional_info: bool = False, shapley_config: Optional[ShapleyConfig] = None, graph_factory: Callable[[Any], DirectedGraph] = nx.DiGraph, ) -> Union[ Dict[Any, float], Tuple[Dict[Any, float], Dict[Any, bool], ProbabilisticCausalModel, ProbabilisticCausalModel] ]: """Attributes the change in the marginal distribution of the target_node to nodes upstream in the causal DAG. Note that this method creates two copies of the causal DAG. The causal models of one causal DAG are learned from old data and those of another DAG are learned from new data. **Research Paper**: Kailash Budhathoki, Dominik Janzing, Patrick Bloebaum, Hoiyi Ng. *Why did the distribution change?*. Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, PMLR 130:1666-1674, 2021. :param causal_model: Reference causal model. :param old_data: Joint samples from the 'old' distribution. :param new_data: Joint samples from the 'new' distribution. :param target_node: Target node of interest for attributing the marginal distribution change. :param invariant_nodes: List of nodes where the mechanism is kept constant regardless of changes in the datasets being analyzed. :param num_samples: Number of samples used for estimating Shapley values. This can have a significant influence on runtime and accuracy. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions, e.g. difference in average values. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in root nodes. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in non-root nodes. :param mechanism_change_test_significance_level: A significance level for rejecting the null hypothesis that the causal mechanism of a node has not changed. :param mechanism_change_test_fdr_control_method: The false discovery rate control method for mechanism change tests. For more options, checkout `statsmodels manual <https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.multipletests.html>`_. :param auto_assignment_quality: If set to None, the assigned models from the given causal models are used for the old and new graph. However, they are re-fitted on the given data. If set to a valid assignment quality, new models are automatically assigned to the old and new graph based on the respective data. :param return_additional_info: If set to True, three additional items are returned: a dictionary indicating whether each node's mechanism changed, the causal DAG whose causal models are learned from old data, and the causal DAG whose causal models are learned from new data. :param shapley_config: Configuration for the Shapley estimator. :param graph_factory: Allows customization in case a graph class different than networkx.DiGraph should be used. This function *must* copy nodes and edges. Attributes of nodes will be overridden in the copy, so the algorithm is independent of the attribute copy behavior of this factory. :return: By default, if `return_additional_info` is set to False, only the dictionary containing contribution of each upstream node is returned. If `return_additional_info` is set to True, three additional items are returned: a dictionary indicating whether each node's mechanism changed, the causal DAG whose causal models learned from old data, and the causal DAG whose causal models are learned from new data. """ if invariant_nodes is None: invariant_nodes = [] causal_graph_old = graph_factory(node_connected_subgraph_view(causal_model.graph, target_node)) causal_model_old = ProbabilisticCausalModel(causal_graph_old) if auto_assignment_quality is None: clone_causal_models(causal_model.graph, causal_model_old.graph) else: assign_causal_mechanisms(causal_model_old, old_data, override_models=True, quality=auto_assignment_quality) invariant_nodes = list(set(invariant_nodes).intersection(set(causal_graph_old.nodes))) _remove_invariant_nodes(invariant_nodes, causal_model_old, old_data, auto_assignment_quality) causal_graph_new = graph_factory(causal_graph_old) causal_model_new = ProbabilisticCausalModel(causal_graph_new) if auto_assignment_quality is None: clone_causal_models(causal_graph_old, causal_model_new.graph) else: assign_causal_mechanisms(causal_model_new, new_data, override_models=True, quality=auto_assignment_quality) mechanism_changes = _fit_accounting_for_mechanism_change( causal_model_old, causal_model_new, old_data[list(causal_graph_old.nodes)], new_data[list(causal_graph_new.nodes)], independence_test, conditional_independence_test, mechanism_change_test_significance_level, mechanism_change_test_fdr_control_method, invariant_nodes, ) attributions = distribution_change_of_graphs( causal_model_old, causal_model_new, target_node, num_samples, difference_estimation_func, shapley_config, graph_factory, ) # set attributions to zero for left out invariant nodes for node in invariant_nodes: attributions[node] = 0 if return_additional_info: return attributions, mechanism_changes, causal_model_old, causal_model_new else: return attributions def distribution_change_of_graphs( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, target_node: Any, num_samples: int = 2000, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float] = auto_estimate_kl_divergence, shapley_config: Optional[ShapleyConfig] = None, graph_factory: Callable[[Any], DirectedGraph] = nx.DiGraph, ) -> Dict[Any, float]: """Attributes the change of the marginal distribution of target_node to upstream nodes based on the distributions generated by the 'old' and 'new' causal graphs. These graphs are assumed to represent the same causal structure and to be fitted on the respective data. Note: This method creates a copy of the given causal models, i.e. the original objects will not be modified. Related paper: Budhathoki, K., Janzing, D., Bloebaum, P., & Ng, H. (2021). Why did the distribution change? arXiv preprint arXiv:2102.13384. :param causal_model_old: The ProbabilisticCausalModel fitted on the 'old' data. :param causal_model_new: The ProbabilisticCausalModel fitted on the 'new' data. :param target_node: Node of interest for attributing the marginal distribution change. :param num_samples: Number of samples used for the estimation. This can have a significant influence on the runtime and accuracy. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions. An example could be the KL divergence. :param shapley_config: Config for the Shapley estimator. :param graph_factory: Allows customization in case a graph class different than networkx.DiGraph should be used. This function *must* copy nodes and edges. Attributes of nodes will be overridden in the copy, so the algorithm is independent of the attribute copy behavior of this factory. :return: A dictionary containing the contributions of upstream nodes to the marginal distribution change in the target node. """ validate_causal_dag(causal_model_old.graph) validate_causal_dag(causal_model_new.graph) return _estimate_marginal_distribution_change( ProbabilisticCausalModel(node_connected_subgraph_view(causal_model_old.graph, target_node)), ProbabilisticCausalModel(node_connected_subgraph_view(causal_model_new.graph, target_node)), target_node, num_samples, difference_estimation_func, shapley_config, graph_factory, ) def _remove_invariant_nodes( invariant_nodes: List[Any], causal_model: ProbabilisticCausalModel, old_data: pd.DataFrame, auto_assignment_quality: Optional[AssignmentQuality], ) -> None: if auto_assignment_quality is None: auto_assignment_quality = AssignmentQuality.GOOD for invar_node in invariant_nodes: # Get parent and child nodes parents = get_ordered_predecessors(causal_model.graph, invar_node) children = list(causal_model.graph.successors(invar_node)) # Don't remove node if node has more than 1 children nodes as it can introduce # hidden confounders. if len(children) > 1: continue # Remove the middle node causal_model.graph.remove_node(invar_node) # Connect parent and child nodes for parent in parents: for child in children: causal_model.graph.add_edge(parent, child) # Update the causal mechanism for the child nodes for child in children: assign_causal_mechanism_node(causal_model, child, old_data, quality=auto_assignment_quality) def _fit_accounting_for_mechanism_change( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, old_data: pd.DataFrame, new_data: pd.DataFrame, independence_test: Callable[[np.ndarray, np.ndarray], float], conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float], significance_level: float, fdr_control_method: Optional[str], invariant_nodes: List[Any], ) -> Dict[Any, bool]: mechanism_changed_for_node = _check_significant_mechanism_change( causal_model_old.graph, old_data, new_data, independence_test, conditional_independence_test, significance_level, fdr_control_method, ) joint_data = pd.concat([old_data, new_data], ignore_index=True, sort=True) for node in causal_model_new.graph.nodes: if node in invariant_nodes: mechanism_changed_for_node[node] = False if mechanism_changed_for_node[node]: fit_causal_model_of_target(causal_model_old, node, old_data) fit_causal_model_of_target(causal_model_new, node, new_data) else: fit_causal_model_of_target(causal_model_old, node, joint_data) fit_causal_model_of_target(causal_model_new, node, joint_data) return mechanism_changed_for_node def _estimate_marginal_distribution_change( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, target_node: Any, num_samples: int, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float], shapley_config: Optional[ShapleyConfig], graph_factory: Callable[[Any], DirectedGraph], ) -> Dict[Any, float]: old_causal_models = [causal_model_old.causal_mechanism(x) for x in sorted(causal_model_old.graph.nodes)] new_causal_models = [causal_model_new.causal_mechanism(x) for x in sorted(causal_model_new.graph.nodes)] target_samples_old = draw_samples(causal_model_old, num_samples)[target_node].to_numpy() def attribution_set_function(subset): if np.all(subset == 0): return 0 causal_model = ProbabilisticCausalModel(graph_factory(causal_model_old.graph)) nodes = sorted(list(causal_model.graph.nodes)) for i in range(len(old_causal_models)): if subset[i] == 1: causal_model.set_causal_mechanism(nodes[i], new_causal_models[i]) else: causal_model.set_causal_mechanism(nodes[i], old_causal_models[i]) for node in causal_model.graph.nodes: causal_model.graph.nodes[node][PARENTS_DURING_FIT] = get_ordered_predecessors(causal_model.graph, node) target_samples_new = draw_samples(causal_model, num_samples)[target_node].to_numpy() return difference_estimation_func(target_samples_old, target_samples_new) attributions = estimate_shapley_values(attribution_set_function, len(old_causal_models), shapley_config) return {x: attributions[i] for i, x in enumerate(sorted(causal_model_old.graph.nodes))} def estimate_distribution_change_scores( causal_model: ProbabilisticCausalModel, original_data: pd.DataFrame, new_data: pd.DataFrame, difference_estimation_func: Callable[ [np.ndarray, np.ndarray], Union[np.ndarray, float] ] = auto_estimate_kl_divergence, max_num_evaluation_samples: int = 1000, num_joint_samples: int = 500, early_stopping_percentage: float = 0.01, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, mechanism_change_test_significance_level: float = 0.05, mechanism_change_test_fdr_control_method: Optional[str] = "fdr_bh", ) -> Dict[Any, float]: """Given newly observed and original samples from the joint distribution of the given causal graphical model, this method estimates a score for each node that quantifies how much the distribution of the node has changed. For this, it first checks whether the underlying causal mechanism has changed at all and, if this is the case, it estimates the difference between the new and original distributions. The score is based on the quantity measured by the provided difference_estimation_func or 0 if no mechanism change has been detected. Note that for each parent sample, num_joint_samples conditional samples are generated based on the original and new causal mechanism and evaluated by the given difference_estimation_func function. These results are then averaged over multiple different parent samples. :param causal_model: The underlying causal model based on the original data. :param original_data: Samples from the original data. :param new_data: Samples from the new data. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions. An example could be the KL divergence. :param max_num_evaluation_samples: Maximum number of (parent) samples for evaluating the difference in distributions. :param num_joint_samples: Number of samples generated in a node per parent sample. :param early_stopping_percentage: If the change in percentage between multiple consecutive runs is below this threshold, the evaluation stops before evaluating all max_num_evaluation_samples. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in root nodes. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in non-root nodes. :param mechanism_change_test_significance_level: A significance level for rejecting the null hypothesis that the causal mechanism of a node has not changed. :param mechanism_change_test_fdr_control_method: The false discovery rate control method for mechanism change tests. For more options, checkout `statsmodels manual <https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.multipletests.html>`_. :return: A dictionary assining a score to each node in the causal graph. """ validate_causal_dag(causal_model.graph) mechanism_changed_for_node = _check_significant_mechanism_change( causal_model.graph, original_data, new_data, independence_test, conditional_independence_test, mechanism_change_test_significance_level, mechanism_change_test_fdr_control_method, ) results = {} for node in tqdm( causal_model.graph.nodes, desc="Estimating mechanism change anomaly scores", position=0, leave=True ): if mechanism_changed_for_node[node]: if is_root_node(causal_model.graph, node): results[node] = difference_estimation_func(original_data[node].to_numpy(), new_data[node].to_numpy()) else: parent_nodes = get_ordered_predecessors(causal_model.graph, node) results[node] = _estimate_distribution_change_score( original_data[parent_nodes].to_numpy(), new_data[parent_nodes].to_numpy(), new_data[node].to_numpy(), causal_model.causal_mechanism(node), difference_estimation_func, max_num_evaluation_samples, num_joint_samples, early_stopping_percentage, ) else: results[node] = 0 return results def _check_significant_mechanism_change( graph: DirectedGraph, old_data: pd.DataFrame, new_data: pd.DataFrame, independence_test: Callable[[np.ndarray, np.ndarray], float], conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float], significance_level: float, fdr_control_method: Optional[str], ) -> Dict[Any, bool]: all_p_values = [] for node in graph.nodes: if is_root_node(graph, node): parents_org_data = None parents_new_data = None else: parents_org_data = old_data[get_ordered_predecessors(graph, node)].to_numpy() parents_new_data = new_data[get_ordered_predecessors(graph, node)].to_numpy() all_p_values.append( mechanism_change_test( old_data[node].to_numpy(), new_data[node].to_numpy(), parents_org_data, parents_new_data, independence_test=independence_test, conditional_independence_test=conditional_independence_test, ) ) if fdr_control_method is None: successes = np.array(all_p_values) <= significance_level else: successes = multipletests(all_p_values, significance_level, method=fdr_control_method)[0] return dict(zip(graph.nodes, successes)) def _estimate_distribution_change_score( parent_original_data: np.ndarray, parent_new_data: np.ndarray, target_new_data: np.ndarray, causal_model_original: ConditionalStochasticModel, difference_estimation_func: Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]], max_num_evaluation_samples: int, num_joint_samples: int, early_stopping_percentage: float, ) -> float: parent_original_data, parent_new_data, target_new_data = shape_into_2d( parent_original_data, parent_new_data, target_new_data ) causal_model_new = causal_model_original.clone() causal_model_new.fit(X=parent_new_data, Y=target_new_data) joint_parent_samples = np.vstack([parent_original_data, parent_new_data]) joint_parent_samples = joint_parent_samples[ np.random.choice( joint_parent_samples.shape[0], min(joint_parent_samples.shape[0], max_num_evaluation_samples), replace=False ) ] result = 0 run = 0 for joint_parent_sample in joint_parent_samples: old_result = result samples = repmat(joint_parent_sample, num_joint_samples, 1) result += difference_estimation_func( causal_model_original.draw_samples(samples), causal_model_new.draw_samples(samples) ) run += 1 if old_result != 0 and (1 - result / old_result) <= early_stopping_percentage: # If the relative change of the score is less than the given threshold, we stop the estimation early. _logger.info( "Early stopping: Result only changed by %f percent and a threshold of %f is set." % (1 - result / old_result, early_stopping_percentage) ) break return result / run

bhatt-priyadutt

734b61671d130cf21a4e437084b9083dbd5c4a39

b4f80dcb2251a03aff4c816d7c6f6eb2aaf98e73

What I meant is something like: ``` if auto_assignment_quality is not None: for child in children: set_causal_mechanism_for_node(causal_model, child, old_data, quality=auto_assignment_quality) ``` In that way, one can still get the modified model without running the auto assignment on the children nodes by providing `auto_assignment_quality=None`.

bloebp

py-why/dowhy

Invariant nodes removal

Feature - Now users can enter the list of invariant nodes to the distribution_change function to remove them from analysis

2023-08-15 12:59:48+00:00

2023-08-24 16:12:46+00:00

dowhy/gcm/distribution_change.py

"""This module defines functions to attribute distribution changes. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging from typing import Any, Callable, Dict, Optional, Tuple, Union import networkx as nx import numpy as np import pandas as pd from numpy.matlib import repmat from statsmodels.stats.multitest import multipletests from tqdm import tqdm from dowhy.gcm.auto import AssignmentQuality, assign_causal_mechanisms from dowhy.gcm.causal_mechanisms import ConditionalStochasticModel from dowhy.gcm.causal_models import ( PARENTS_DURING_FIT, ProbabilisticCausalModel, clone_causal_models, validate_causal_dag, ) from dowhy.gcm.divergence import auto_estimate_kl_divergence from dowhy.gcm.fitting_sampling import draw_samples, fit_causal_model_of_target from dowhy.gcm.independence_test.kernel import kernel_based from dowhy.gcm.shapley import ShapleyConfig, estimate_shapley_values from dowhy.gcm.util.general import shape_into_2d from dowhy.graph import DirectedGraph, get_ordered_predecessors, is_root_node, node_connected_subgraph_view _logger = logging.getLogger(__name__) def mechanism_change_test( target_original_data: np.ndarray, target_new_data: np.ndarray, parents_original_data: Optional[np.ndarray] = None, parents_new_data: Optional[np.ndarray] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, ) -> float: """Estimates a p-value for the null hypothesis that the original and new data were generated by the same mechanism. Here, we check the dependency between binary labels indicating whether a sample is from the original or a new data set. If the labels do not provide information to determine if a sample is coming from the original/new distribution, then it is likely that the mechanism has not changed. For non-root nodes, samples from parent variables are needed as conditioning variables. This is, testing the null hypothesis that the data were generated by the same mechanism given the parent samples. By this, we incorporate upstream changes that might have impacted the parents, but not the target node itself. :param target_original_data: Samples of the node from the original data set. :param target_new_data: Samples of the node from the new data set. :param parents_original_data: Samples from parents of the node from the original data set. :param parents_new_data: Samples from parents of the node from the new data set. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in nodes without parents. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in nodes with parents. :return: A p-value for the null hypothesis that the mechanism has not changed. """ causal_graph: DirectedGraph if parents_original_data is not None and parents_new_data is None: raise ValueError("Original parents data were given, but no new parents data!") if parents_original_data is None and parents_new_data is not None: raise ValueError("New parents data were given, but no original parents data!") num_samples_for_testing = min(target_original_data.shape[0], target_new_data.shape[0]) data_set_indices = np.ones(num_samples_for_testing * 2) data_set_indices[num_samples_for_testing:] = -1 data_set_indices = data_set_indices.astype(str) original_indices = np.random.choice(target_original_data.shape[0], num_samples_for_testing, replace=False) new_indices = np.random.choice(target_new_data.shape[0], num_samples_for_testing, replace=False) joint_target_samples = np.vstack( [shape_into_2d(target_original_data[original_indices]), shape_into_2d(target_new_data[new_indices])] ) if parents_original_data is None: return independence_test(joint_target_samples, data_set_indices) else: parents_new_data: np.ndarray joint_parent_data = np.vstack( [shape_into_2d(parents_original_data[original_indices]), shape_into_2d(parents_new_data[new_indices])] ) return conditional_independence_test(joint_target_samples, data_set_indices, joint_parent_data) def distribution_change( causal_model: ProbabilisticCausalModel, old_data: pd.DataFrame, new_data: pd.DataFrame, target_node: Any, num_samples: int = 2000, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float] = auto_estimate_kl_divergence, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, mechanism_change_test_significance_level: float = 0.05, mechanism_change_test_fdr_control_method: Optional[str] = "fdr_bh", auto_assignment_quality: Optional[AssignmentQuality] = None, return_additional_info: bool = False, shapley_config: Optional[ShapleyConfig] = None, graph_factory: Callable[[Any], DirectedGraph] = nx.DiGraph, ) -> Union[ Dict[Any, float], Tuple[Dict[Any, float], Dict[Any, bool], ProbabilisticCausalModel, ProbabilisticCausalModel] ]: """Attributes the change in the marginal distribution of the target_node to nodes upstream in the causal DAG. Note that this method creates two copies of the causal DAG. The causal models of one causal DAG are learned from old data and those of another DAG are learned from new data. **Research Paper**: Kailash Budhathoki, Dominik Janzing, Patrick Bloebaum, Hoiyi Ng. *Why did the distribution change?*. Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, PMLR 130:1666-1674, 2021. :param causal_model: Reference causal model. :param old_data: Joint samples from the 'old' distribution. :param new_data: Joint samples from the 'new' distribution. :param target_node: Target node of interest for attributing the marginal distribution change. :param num_samples: Number of samples used for estimating Shapley values. This can have a significant influence on runtime and accuracy. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions, e.g. difference in average values. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in root nodes. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in non-root nodes. :param mechanism_change_test_significance_level: A significance level for rejecting the null hypothesis that the causal mechanism of a node has not changed. :param mechanism_change_test_fdr_control_method: The false discovery rate control method for mechanism change tests. For more options, checkout `statsmodels manual <https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.multipletests.html>`_. :param auto_assignment_quality: If set to None, the assigned models from the given causal models are used for the old and new graph. However, they are re-fitted on the given data. If set to a valid assignment quality, new models are automatically assigned to the old and new graph based on the respective data. :param return_additional_info: If set to True, three additional items are returned: a dictionary indicating whether each node's mechanism changed, the causal DAG whose causal models are learned from old data, and the causal DAG whose causal models are learned from new data. :param shapley_config: Configuration for the Shapley estimator. :param graph_factory: Allows customization in case a graph class different than networkx.DiGraph should be used. This function *must* copy nodes and edges. Attributes of nodes will be overridden in the copy, so the algorithm is independent of the attribute copy behavior of this factory. :return: By default, if `return_additional_info` is set to False, only the dictionary containing contribution of each upstream node is returned. If `return_additional_info` is set to True, three additional items are returned: a dictionary indicating whether each node's mechanism changed, the causal DAG whose causal models learned from old data, and the causal DAG whose causal models are learned from new data. """ causal_graph_old = graph_factory(node_connected_subgraph_view(causal_model.graph, target_node)) causal_model_old = ProbabilisticCausalModel(causal_graph_old) if auto_assignment_quality is None: clone_causal_models(causal_model.graph, causal_model_old.graph) else: assign_causal_mechanisms(causal_model_old, old_data, override_models=True, quality=auto_assignment_quality) causal_graph_new = graph_factory(causal_graph_old) causal_model_new = ProbabilisticCausalModel(causal_graph_new) if auto_assignment_quality is None: clone_causal_models(causal_graph_old, causal_model_new.graph) else: assign_causal_mechanisms(causal_model_new, new_data, override_models=True, quality=auto_assignment_quality) mechanism_changes = _fit_accounting_for_mechanism_change( causal_model_old, causal_model_new, old_data[list(causal_graph_old.nodes)], new_data[list(causal_graph_new.nodes)], independence_test, conditional_independence_test, mechanism_change_test_significance_level, mechanism_change_test_fdr_control_method, ) attributions = distribution_change_of_graphs( causal_model_old, causal_model_new, target_node, num_samples, difference_estimation_func, shapley_config, graph_factory, ) if return_additional_info: return attributions, mechanism_changes, causal_model_old, causal_model_new else: return attributions def distribution_change_of_graphs( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, target_node: Any, num_samples: int = 2000, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float] = auto_estimate_kl_divergence, shapley_config: Optional[ShapleyConfig] = None, graph_factory: Callable[[Any], DirectedGraph] = nx.DiGraph, ) -> Dict[Any, float]: """Attributes the change of the marginal distribution of target_node to upstream nodes based on the distributions generated by the 'old' and 'new' causal graphs. These graphs are assumed to represent the same causal structure and to be fitted on the respective data. Note: This method creates a copy of the given causal models, i.e. the original objects will not be modified. Related paper: Budhathoki, K., Janzing, D., Bloebaum, P., & Ng, H. (2021). Why did the distribution change? arXiv preprint arXiv:2102.13384. :param causal_model_old: The ProbabilisticCausalModel fitted on the 'old' data. :param causal_model_new: The ProbabilisticCausalModel fitted on the 'new' data. :param target_node: Node of interest for attributing the marginal distribution change. :param num_samples: Number of samples used for the estimation. This can have a significant influence on the runtime and accuracy. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions. An example could be the KL divergence. :param shapley_config: Config for the Shapley estimator. :param graph_factory: Allows customization in case a graph class different than networkx.DiGraph should be used. This function *must* copy nodes and edges. Attributes of nodes will be overridden in the copy, so the algorithm is independent of the attribute copy behavior of this factory. :return: A dictionary containing the contributions of upstream nodes to the marginal distribution change in the target node. """ validate_causal_dag(causal_model_old.graph) validate_causal_dag(causal_model_new.graph) return _estimate_marginal_distribution_change( ProbabilisticCausalModel(node_connected_subgraph_view(causal_model_old.graph, target_node)), ProbabilisticCausalModel(node_connected_subgraph_view(causal_model_new.graph, target_node)), target_node, num_samples, difference_estimation_func, shapley_config, graph_factory, ) def _fit_accounting_for_mechanism_change( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, old_data: pd.DataFrame, new_data: pd.DataFrame, independence_test: Callable[[np.ndarray, np.ndarray], float], conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float], significance_level: float, fdr_control_method: Optional[str], ) -> Dict[Any, bool]: mechanism_changed_for_node = _check_significant_mechanism_change( causal_model_old.graph, old_data, new_data, independence_test, conditional_independence_test, significance_level, fdr_control_method, ) joint_data = pd.concat([old_data, new_data], ignore_index=True, sort=True) for node in causal_model_new.graph.nodes: if mechanism_changed_for_node[node]: fit_causal_model_of_target(causal_model_old, node, old_data) fit_causal_model_of_target(causal_model_new, node, new_data) else: fit_causal_model_of_target(causal_model_old, node, joint_data) fit_causal_model_of_target(causal_model_new, node, joint_data) return mechanism_changed_for_node def _estimate_marginal_distribution_change( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, target_node: Any, num_samples: int, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float], shapley_config: Optional[ShapleyConfig], graph_factory: Callable[[Any], DirectedGraph], ) -> Dict[Any, float]: old_causal_models = [causal_model_old.causal_mechanism(x) for x in sorted(causal_model_old.graph.nodes)] new_causal_models = [causal_model_new.causal_mechanism(x) for x in sorted(causal_model_new.graph.nodes)] target_samples_old = draw_samples(causal_model_old, num_samples)[target_node].to_numpy() def attribution_set_function(subset): if np.all(subset == 0): return 0 causal_model = ProbabilisticCausalModel(graph_factory(causal_model_old.graph)) nodes = sorted(list(causal_model.graph.nodes)) for i in range(len(old_causal_models)): if subset[i] == 1: causal_model.set_causal_mechanism(nodes[i], new_causal_models[i]) else: causal_model.set_causal_mechanism(nodes[i], old_causal_models[i]) for node in causal_model.graph.nodes: causal_model.graph.nodes[node][PARENTS_DURING_FIT] = get_ordered_predecessors(causal_model.graph, node) target_samples_new = draw_samples(causal_model, num_samples)[target_node].to_numpy() return difference_estimation_func(target_samples_old, target_samples_new) attributions = estimate_shapley_values(attribution_set_function, len(old_causal_models), shapley_config) return {x: attributions[i] for i, x in enumerate(sorted(causal_model_old.graph.nodes))} def estimate_distribution_change_scores( causal_model: ProbabilisticCausalModel, original_data: pd.DataFrame, new_data: pd.DataFrame, difference_estimation_func: Callable[ [np.ndarray, np.ndarray], Union[np.ndarray, float] ] = auto_estimate_kl_divergence, max_num_evaluation_samples: int = 1000, num_joint_samples: int = 500, early_stopping_percentage: float = 0.01, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, mechanism_change_test_significance_level: float = 0.05, mechanism_change_test_fdr_control_method: Optional[str] = "fdr_bh", ) -> Dict[Any, float]: """Given newly observed and original samples from the joint distribution of the given causal graphical model, this method estimates a score for each node that quantifies how much the distribution of the node has changed. For this, it first checks whether the underlying causal mechanism has changed at all and, if this is the case, it estimates the difference between the new and original distributions. The score is based on the quantity measured by the provided difference_estimation_func or 0 if no mechanism change has been detected. Note that for each parent sample, num_joint_samples conditional samples are generated based on the original and new causal mechanism and evaluated by the given difference_estimation_func function. These results are then averaged over multiple different parent samples. :param causal_model: The underlying causal model based on the original data. :param original_data: Samples from the original data. :param new_data: Samples from the new data. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions. An example could be the KL divergence. :param max_num_evaluation_samples: Maximum number of (parent) samples for evaluating the difference in distributions. :param num_joint_samples: Number of samples generated in a node per parent sample. :param early_stopping_percentage: If the change in percentage between multiple consecutive runs is below this threshold, the evaluation stops before evaluating all max_num_evaluation_samples. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in root nodes. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in non-root nodes. :param mechanism_change_test_significance_level: A significance level for rejecting the null hypothesis that the causal mechanism of a node has not changed. :param mechanism_change_test_fdr_control_method: The false discovery rate control method for mechanism change tests. For more options, checkout `statsmodels manual <https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.multipletests.html>`_. :return: A dictionary assining a score to each node in the causal graph. """ validate_causal_dag(causal_model.graph) mechanism_changed_for_node = _check_significant_mechanism_change( causal_model.graph, original_data, new_data, independence_test, conditional_independence_test, mechanism_change_test_significance_level, mechanism_change_test_fdr_control_method, ) results = {} for node in tqdm( causal_model.graph.nodes, desc="Estimating mechanism change anomaly scores", position=0, leave=True ): if mechanism_changed_for_node[node]: if is_root_node(causal_model.graph, node): results[node] = difference_estimation_func(original_data[node].to_numpy(), new_data[node].to_numpy()) else: parent_nodes = get_ordered_predecessors(causal_model.graph, node) results[node] = _estimate_distribution_change_score( original_data[parent_nodes].to_numpy(), new_data[parent_nodes].to_numpy(), new_data[node].to_numpy(), causal_model.causal_mechanism(node), difference_estimation_func, max_num_evaluation_samples, num_joint_samples, early_stopping_percentage, ) else: results[node] = 0 return results def _check_significant_mechanism_change( graph: DirectedGraph, old_data: pd.DataFrame, new_data: pd.DataFrame, independence_test: Callable[[np.ndarray, np.ndarray], float], conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float], significance_level: float, fdr_control_method: Optional[str], ) -> Dict[Any, bool]: all_p_values = [] for node in graph.nodes: if is_root_node(graph, node): parents_org_data = None parents_new_data = None else: parents_org_data = old_data[get_ordered_predecessors(graph, node)].to_numpy() parents_new_data = new_data[get_ordered_predecessors(graph, node)].to_numpy() all_p_values.append( mechanism_change_test( old_data[node].to_numpy(), new_data[node].to_numpy(), parents_org_data, parents_new_data, independence_test=independence_test, conditional_independence_test=conditional_independence_test, ) ) if fdr_control_method is None: successes = np.array(all_p_values) <= significance_level else: successes = multipletests(all_p_values, significance_level, method=fdr_control_method)[0] return dict(zip(graph.nodes, successes)) def _estimate_distribution_change_score( parent_original_data: np.ndarray, parent_new_data: np.ndarray, target_new_data: np.ndarray, causal_model_original: ConditionalStochasticModel, difference_estimation_func: Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]], max_num_evaluation_samples: int, num_joint_samples: int, early_stopping_percentage: float, ) -> float: parent_original_data, parent_new_data, target_new_data = shape_into_2d( parent_original_data, parent_new_data, target_new_data ) causal_model_new = causal_model_original.clone() causal_model_new.fit(X=parent_new_data, Y=target_new_data) joint_parent_samples = np.vstack([parent_original_data, parent_new_data]) joint_parent_samples = joint_parent_samples[ np.random.choice( joint_parent_samples.shape[0], min(joint_parent_samples.shape[0], max_num_evaluation_samples), replace=False ) ] result = 0 run = 0 for joint_parent_sample in joint_parent_samples: old_result = result samples = repmat(joint_parent_sample, num_joint_samples, 1) result += difference_estimation_func( causal_model_original.draw_samples(samples), causal_model_new.draw_samples(samples) ) run += 1 if old_result != 0 and (1 - result / old_result) <= early_stopping_percentage: # If the relative change of the score is less than the given threshold, we stop the estimation early. _logger.info( "Early stopping: Result only changed by %f percent and a threshold of %f is set." % (1 - result / old_result, early_stopping_percentage) ) break return result / run

"""This module defines functions to attribute distribution changes. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging from typing import Any, Callable, Dict, List, Optional, Tuple, Union import networkx as nx import numpy as np import pandas as pd from numpy.matlib import repmat from statsmodels.stats.multitest import multipletests from tqdm import tqdm from dowhy.gcm.auto import AssignmentQuality, assign_causal_mechanism_node, assign_causal_mechanisms from dowhy.gcm.causal_mechanisms import ConditionalStochasticModel from dowhy.gcm.causal_models import ( PARENTS_DURING_FIT, ProbabilisticCausalModel, clone_causal_models, validate_causal_dag, ) from dowhy.gcm.divergence import auto_estimate_kl_divergence from dowhy.gcm.fitting_sampling import draw_samples, fit_causal_model_of_target from dowhy.gcm.independence_test.kernel import kernel_based from dowhy.gcm.shapley import ShapleyConfig, estimate_shapley_values from dowhy.gcm.util.general import shape_into_2d from dowhy.graph import DirectedGraph, get_ordered_predecessors, is_root_node, node_connected_subgraph_view _logger = logging.getLogger(__name__) def mechanism_change_test( target_original_data: np.ndarray, target_new_data: np.ndarray, parents_original_data: Optional[np.ndarray] = None, parents_new_data: Optional[np.ndarray] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, ) -> float: """Estimates a p-value for the null hypothesis that the original and new data were generated by the same mechanism. Here, we check the dependency between binary labels indicating whether a sample is from the original or a new data set. If the labels do not provide information to determine if a sample is coming from the original/new distribution, then it is likely that the mechanism has not changed. For non-root nodes, samples from parent variables are needed as conditioning variables. This is, testing the null hypothesis that the data were generated by the same mechanism given the parent samples. By this, we incorporate upstream changes that might have impacted the parents, but not the target node itself. :param target_original_data: Samples of the node from the original data set. :param target_new_data: Samples of the node from the new data set. :param parents_original_data: Samples from parents of the node from the original data set. :param parents_new_data: Samples from parents of the node from the new data set. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in nodes without parents. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in nodes with parents. :return: A p-value for the null hypothesis that the mechanism has not changed. """ causal_graph: DirectedGraph if parents_original_data is not None and parents_new_data is None: raise ValueError("Original parents data were given, but no new parents data!") if parents_original_data is None and parents_new_data is not None: raise ValueError("New parents data were given, but no original parents data!") num_samples_for_testing = min(target_original_data.shape[0], target_new_data.shape[0]) data_set_indices = np.ones(num_samples_for_testing * 2) data_set_indices[num_samples_for_testing:] = -1 data_set_indices = data_set_indices.astype(str) original_indices = np.random.choice(target_original_data.shape[0], num_samples_for_testing, replace=False) new_indices = np.random.choice(target_new_data.shape[0], num_samples_for_testing, replace=False) joint_target_samples = np.vstack( [shape_into_2d(target_original_data[original_indices]), shape_into_2d(target_new_data[new_indices])] ) if parents_original_data is None: return independence_test(joint_target_samples, data_set_indices) else: parents_new_data: np.ndarray joint_parent_data = np.vstack( [shape_into_2d(parents_original_data[original_indices]), shape_into_2d(parents_new_data[new_indices])] ) return conditional_independence_test(joint_target_samples, data_set_indices, joint_parent_data) def distribution_change( causal_model: ProbabilisticCausalModel, old_data: pd.DataFrame, new_data: pd.DataFrame, target_node: Any, invariant_nodes: List[Any] = None, num_samples: int = 2000, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float] = auto_estimate_kl_divergence, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, mechanism_change_test_significance_level: float = 0.05, mechanism_change_test_fdr_control_method: Optional[str] = "fdr_bh", auto_assignment_quality: Optional[AssignmentQuality] = None, return_additional_info: bool = False, shapley_config: Optional[ShapleyConfig] = None, graph_factory: Callable[[Any], DirectedGraph] = nx.DiGraph, ) -> Union[ Dict[Any, float], Tuple[Dict[Any, float], Dict[Any, bool], ProbabilisticCausalModel, ProbabilisticCausalModel] ]: """Attributes the change in the marginal distribution of the target_node to nodes upstream in the causal DAG. Note that this method creates two copies of the causal DAG. The causal models of one causal DAG are learned from old data and those of another DAG are learned from new data. **Research Paper**: Kailash Budhathoki, Dominik Janzing, Patrick Bloebaum, Hoiyi Ng. *Why did the distribution change?*. Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, PMLR 130:1666-1674, 2021. :param causal_model: Reference causal model. :param old_data: Joint samples from the 'old' distribution. :param new_data: Joint samples from the 'new' distribution. :param target_node: Target node of interest for attributing the marginal distribution change. :param invariant_nodes: List of nodes where the mechanism is kept constant regardless of changes in the datasets being analyzed. :param num_samples: Number of samples used for estimating Shapley values. This can have a significant influence on runtime and accuracy. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions, e.g. difference in average values. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in root nodes. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in non-root nodes. :param mechanism_change_test_significance_level: A significance level for rejecting the null hypothesis that the causal mechanism of a node has not changed. :param mechanism_change_test_fdr_control_method: The false discovery rate control method for mechanism change tests. For more options, checkout `statsmodels manual <https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.multipletests.html>`_. :param auto_assignment_quality: If set to None, the assigned models from the given causal models are used for the old and new graph. However, they are re-fitted on the given data. If set to a valid assignment quality, new models are automatically assigned to the old and new graph based on the respective data. :param return_additional_info: If set to True, three additional items are returned: a dictionary indicating whether each node's mechanism changed, the causal DAG whose causal models are learned from old data, and the causal DAG whose causal models are learned from new data. :param shapley_config: Configuration for the Shapley estimator. :param graph_factory: Allows customization in case a graph class different than networkx.DiGraph should be used. This function *must* copy nodes and edges. Attributes of nodes will be overridden in the copy, so the algorithm is independent of the attribute copy behavior of this factory. :return: By default, if `return_additional_info` is set to False, only the dictionary containing contribution of each upstream node is returned. If `return_additional_info` is set to True, three additional items are returned: a dictionary indicating whether each node's mechanism changed, the causal DAG whose causal models learned from old data, and the causal DAG whose causal models are learned from new data. """ if invariant_nodes is None: invariant_nodes = [] causal_graph_old = graph_factory(node_connected_subgraph_view(causal_model.graph, target_node)) causal_model_old = ProbabilisticCausalModel(causal_graph_old) if auto_assignment_quality is None: clone_causal_models(causal_model.graph, causal_model_old.graph) else: assign_causal_mechanisms(causal_model_old, old_data, override_models=True, quality=auto_assignment_quality) invariant_nodes = list(set(invariant_nodes).intersection(set(causal_graph_old.nodes))) _remove_invariant_nodes(invariant_nodes, causal_model_old, old_data, auto_assignment_quality) causal_graph_new = graph_factory(causal_graph_old) causal_model_new = ProbabilisticCausalModel(causal_graph_new) if auto_assignment_quality is None: clone_causal_models(causal_graph_old, causal_model_new.graph) else: assign_causal_mechanisms(causal_model_new, new_data, override_models=True, quality=auto_assignment_quality) mechanism_changes = _fit_accounting_for_mechanism_change( causal_model_old, causal_model_new, old_data[list(causal_graph_old.nodes)], new_data[list(causal_graph_new.nodes)], independence_test, conditional_independence_test, mechanism_change_test_significance_level, mechanism_change_test_fdr_control_method, invariant_nodes, ) attributions = distribution_change_of_graphs( causal_model_old, causal_model_new, target_node, num_samples, difference_estimation_func, shapley_config, graph_factory, ) # set attributions to zero for left out invariant nodes for node in invariant_nodes: attributions[node] = 0 if return_additional_info: return attributions, mechanism_changes, causal_model_old, causal_model_new else: return attributions def distribution_change_of_graphs( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, target_node: Any, num_samples: int = 2000, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float] = auto_estimate_kl_divergence, shapley_config: Optional[ShapleyConfig] = None, graph_factory: Callable[[Any], DirectedGraph] = nx.DiGraph, ) -> Dict[Any, float]: """Attributes the change of the marginal distribution of target_node to upstream nodes based on the distributions generated by the 'old' and 'new' causal graphs. These graphs are assumed to represent the same causal structure and to be fitted on the respective data. Note: This method creates a copy of the given causal models, i.e. the original objects will not be modified. Related paper: Budhathoki, K., Janzing, D., Bloebaum, P., & Ng, H. (2021). Why did the distribution change? arXiv preprint arXiv:2102.13384. :param causal_model_old: The ProbabilisticCausalModel fitted on the 'old' data. :param causal_model_new: The ProbabilisticCausalModel fitted on the 'new' data. :param target_node: Node of interest for attributing the marginal distribution change. :param num_samples: Number of samples used for the estimation. This can have a significant influence on the runtime and accuracy. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions. An example could be the KL divergence. :param shapley_config: Config for the Shapley estimator. :param graph_factory: Allows customization in case a graph class different than networkx.DiGraph should be used. This function *must* copy nodes and edges. Attributes of nodes will be overridden in the copy, so the algorithm is independent of the attribute copy behavior of this factory. :return: A dictionary containing the contributions of upstream nodes to the marginal distribution change in the target node. """ validate_causal_dag(causal_model_old.graph) validate_causal_dag(causal_model_new.graph) return _estimate_marginal_distribution_change( ProbabilisticCausalModel(node_connected_subgraph_view(causal_model_old.graph, target_node)), ProbabilisticCausalModel(node_connected_subgraph_view(causal_model_new.graph, target_node)), target_node, num_samples, difference_estimation_func, shapley_config, graph_factory, ) def _remove_invariant_nodes( invariant_nodes: List[Any], causal_model: ProbabilisticCausalModel, old_data: pd.DataFrame, auto_assignment_quality: Optional[AssignmentQuality], ) -> None: if auto_assignment_quality is None: auto_assignment_quality = AssignmentQuality.GOOD for invar_node in invariant_nodes: # Get parent and child nodes parents = get_ordered_predecessors(causal_model.graph, invar_node) children = list(causal_model.graph.successors(invar_node)) # Don't remove node if node has more than 1 children nodes as it can introduce # hidden confounders. if len(children) > 1: continue # Remove the middle node causal_model.graph.remove_node(invar_node) # Connect parent and child nodes for parent in parents: for child in children: causal_model.graph.add_edge(parent, child) # Update the causal mechanism for the child nodes for child in children: assign_causal_mechanism_node(causal_model, child, old_data, quality=auto_assignment_quality) def _fit_accounting_for_mechanism_change( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, old_data: pd.DataFrame, new_data: pd.DataFrame, independence_test: Callable[[np.ndarray, np.ndarray], float], conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float], significance_level: float, fdr_control_method: Optional[str], invariant_nodes: List[Any], ) -> Dict[Any, bool]: mechanism_changed_for_node = _check_significant_mechanism_change( causal_model_old.graph, old_data, new_data, independence_test, conditional_independence_test, significance_level, fdr_control_method, ) joint_data = pd.concat([old_data, new_data], ignore_index=True, sort=True) for node in causal_model_new.graph.nodes: if node in invariant_nodes: mechanism_changed_for_node[node] = False if mechanism_changed_for_node[node]: fit_causal_model_of_target(causal_model_old, node, old_data) fit_causal_model_of_target(causal_model_new, node, new_data) else: fit_causal_model_of_target(causal_model_old, node, joint_data) fit_causal_model_of_target(causal_model_new, node, joint_data) return mechanism_changed_for_node def _estimate_marginal_distribution_change( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, target_node: Any, num_samples: int, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float], shapley_config: Optional[ShapleyConfig], graph_factory: Callable[[Any], DirectedGraph], ) -> Dict[Any, float]: old_causal_models = [causal_model_old.causal_mechanism(x) for x in sorted(causal_model_old.graph.nodes)] new_causal_models = [causal_model_new.causal_mechanism(x) for x in sorted(causal_model_new.graph.nodes)] target_samples_old = draw_samples(causal_model_old, num_samples)[target_node].to_numpy() def attribution_set_function(subset): if np.all(subset == 0): return 0 causal_model = ProbabilisticCausalModel(graph_factory(causal_model_old.graph)) nodes = sorted(list(causal_model.graph.nodes)) for i in range(len(old_causal_models)): if subset[i] == 1: causal_model.set_causal_mechanism(nodes[i], new_causal_models[i]) else: causal_model.set_causal_mechanism(nodes[i], old_causal_models[i]) for node in causal_model.graph.nodes: causal_model.graph.nodes[node][PARENTS_DURING_FIT] = get_ordered_predecessors(causal_model.graph, node) target_samples_new = draw_samples(causal_model, num_samples)[target_node].to_numpy() return difference_estimation_func(target_samples_old, target_samples_new) attributions = estimate_shapley_values(attribution_set_function, len(old_causal_models), shapley_config) return {x: attributions[i] for i, x in enumerate(sorted(causal_model_old.graph.nodes))} def estimate_distribution_change_scores( causal_model: ProbabilisticCausalModel, original_data: pd.DataFrame, new_data: pd.DataFrame, difference_estimation_func: Callable[ [np.ndarray, np.ndarray], Union[np.ndarray, float] ] = auto_estimate_kl_divergence, max_num_evaluation_samples: int = 1000, num_joint_samples: int = 500, early_stopping_percentage: float = 0.01, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, mechanism_change_test_significance_level: float = 0.05, mechanism_change_test_fdr_control_method: Optional[str] = "fdr_bh", ) -> Dict[Any, float]: """Given newly observed and original samples from the joint distribution of the given causal graphical model, this method estimates a score for each node that quantifies how much the distribution of the node has changed. For this, it first checks whether the underlying causal mechanism has changed at all and, if this is the case, it estimates the difference between the new and original distributions. The score is based on the quantity measured by the provided difference_estimation_func or 0 if no mechanism change has been detected. Note that for each parent sample, num_joint_samples conditional samples are generated based on the original and new causal mechanism and evaluated by the given difference_estimation_func function. These results are then averaged over multiple different parent samples. :param causal_model: The underlying causal model based on the original data. :param original_data: Samples from the original data. :param new_data: Samples from the new data. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions. An example could be the KL divergence. :param max_num_evaluation_samples: Maximum number of (parent) samples for evaluating the difference in distributions. :param num_joint_samples: Number of samples generated in a node per parent sample. :param early_stopping_percentage: If the change in percentage between multiple consecutive runs is below this threshold, the evaluation stops before evaluating all max_num_evaluation_samples. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in root nodes. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in non-root nodes. :param mechanism_change_test_significance_level: A significance level for rejecting the null hypothesis that the causal mechanism of a node has not changed. :param mechanism_change_test_fdr_control_method: The false discovery rate control method for mechanism change tests. For more options, checkout `statsmodels manual <https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.multipletests.html>`_. :return: A dictionary assining a score to each node in the causal graph. """ validate_causal_dag(causal_model.graph) mechanism_changed_for_node = _check_significant_mechanism_change( causal_model.graph, original_data, new_data, independence_test, conditional_independence_test, mechanism_change_test_significance_level, mechanism_change_test_fdr_control_method, ) results = {} for node in tqdm( causal_model.graph.nodes, desc="Estimating mechanism change anomaly scores", position=0, leave=True ): if mechanism_changed_for_node[node]: if is_root_node(causal_model.graph, node): results[node] = difference_estimation_func(original_data[node].to_numpy(), new_data[node].to_numpy()) else: parent_nodes = get_ordered_predecessors(causal_model.graph, node) results[node] = _estimate_distribution_change_score( original_data[parent_nodes].to_numpy(), new_data[parent_nodes].to_numpy(), new_data[node].to_numpy(), causal_model.causal_mechanism(node), difference_estimation_func, max_num_evaluation_samples, num_joint_samples, early_stopping_percentage, ) else: results[node] = 0 return results def _check_significant_mechanism_change( graph: DirectedGraph, old_data: pd.DataFrame, new_data: pd.DataFrame, independence_test: Callable[[np.ndarray, np.ndarray], float], conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float], significance_level: float, fdr_control_method: Optional[str], ) -> Dict[Any, bool]: all_p_values = [] for node in graph.nodes: if is_root_node(graph, node): parents_org_data = None parents_new_data = None else: parents_org_data = old_data[get_ordered_predecessors(graph, node)].to_numpy() parents_new_data = new_data[get_ordered_predecessors(graph, node)].to_numpy() all_p_values.append( mechanism_change_test( old_data[node].to_numpy(), new_data[node].to_numpy(), parents_org_data, parents_new_data, independence_test=independence_test, conditional_independence_test=conditional_independence_test, ) ) if fdr_control_method is None: successes = np.array(all_p_values) <= significance_level else: successes = multipletests(all_p_values, significance_level, method=fdr_control_method)[0] return dict(zip(graph.nodes, successes)) def _estimate_distribution_change_score( parent_original_data: np.ndarray, parent_new_data: np.ndarray, target_new_data: np.ndarray, causal_model_original: ConditionalStochasticModel, difference_estimation_func: Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]], max_num_evaluation_samples: int, num_joint_samples: int, early_stopping_percentage: float, ) -> float: parent_original_data, parent_new_data, target_new_data = shape_into_2d( parent_original_data, parent_new_data, target_new_data ) causal_model_new = causal_model_original.clone() causal_model_new.fit(X=parent_new_data, Y=target_new_data) joint_parent_samples = np.vstack([parent_original_data, parent_new_data]) joint_parent_samples = joint_parent_samples[ np.random.choice( joint_parent_samples.shape[0], min(joint_parent_samples.shape[0], max_num_evaluation_samples), replace=False ) ] result = 0 run = 0 for joint_parent_sample in joint_parent_samples: old_result = result samples = repmat(joint_parent_sample, num_joint_samples, 1) result += difference_estimation_func( causal_model_original.draw_samples(samples), causal_model_new.draw_samples(samples) ) run += 1 if old_result != 0 and (1 - result / old_result) <= early_stopping_percentage: # If the relative change of the score is less than the given threshold, we stop the estimation early. _logger.info( "Early stopping: Result only changed by %f percent and a threshold of %f is set." % (1 - result / old_result, early_stopping_percentage) ) break return result / run

bhatt-priyadutt

734b61671d130cf21a4e437084b9083dbd5c4a39

b4f80dcb2251a03aff4c816d7c6f6eb2aaf98e73

If we do this then we will not be able to get updated model for child nodes when assignment quality is None right? and this is which we dont want. Or i am still unable to understand the reason. Also, please see the below image where this function is getting called, its called after the assigning of the old causal graph models... 

bhatt-priyadutt

py-why/dowhy

Invariant nodes removal

Feature - Now users can enter the list of invariant nodes to the distribution_change function to remove them from analysis

2023-08-15 12:59:48+00:00

2023-08-24 16:12:46+00:00

dowhy/gcm/distribution_change.py

"""This module defines functions to attribute distribution changes. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging from typing import Any, Callable, Dict, Optional, Tuple, Union import networkx as nx import numpy as np import pandas as pd from numpy.matlib import repmat from statsmodels.stats.multitest import multipletests from tqdm import tqdm from dowhy.gcm.auto import AssignmentQuality, assign_causal_mechanisms from dowhy.gcm.causal_mechanisms import ConditionalStochasticModel from dowhy.gcm.causal_models import ( PARENTS_DURING_FIT, ProbabilisticCausalModel, clone_causal_models, validate_causal_dag, ) from dowhy.gcm.divergence import auto_estimate_kl_divergence from dowhy.gcm.fitting_sampling import draw_samples, fit_causal_model_of_target from dowhy.gcm.independence_test.kernel import kernel_based from dowhy.gcm.shapley import ShapleyConfig, estimate_shapley_values from dowhy.gcm.util.general import shape_into_2d from dowhy.graph import DirectedGraph, get_ordered_predecessors, is_root_node, node_connected_subgraph_view _logger = logging.getLogger(__name__) def mechanism_change_test( target_original_data: np.ndarray, target_new_data: np.ndarray, parents_original_data: Optional[np.ndarray] = None, parents_new_data: Optional[np.ndarray] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, ) -> float: """Estimates a p-value for the null hypothesis that the original and new data were generated by the same mechanism. Here, we check the dependency between binary labels indicating whether a sample is from the original or a new data set. If the labels do not provide information to determine if a sample is coming from the original/new distribution, then it is likely that the mechanism has not changed. For non-root nodes, samples from parent variables are needed as conditioning variables. This is, testing the null hypothesis that the data were generated by the same mechanism given the parent samples. By this, we incorporate upstream changes that might have impacted the parents, but not the target node itself. :param target_original_data: Samples of the node from the original data set. :param target_new_data: Samples of the node from the new data set. :param parents_original_data: Samples from parents of the node from the original data set. :param parents_new_data: Samples from parents of the node from the new data set. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in nodes without parents. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in nodes with parents. :return: A p-value for the null hypothesis that the mechanism has not changed. """ causal_graph: DirectedGraph if parents_original_data is not None and parents_new_data is None: raise ValueError("Original parents data were given, but no new parents data!") if parents_original_data is None and parents_new_data is not None: raise ValueError("New parents data were given, but no original parents data!") num_samples_for_testing = min(target_original_data.shape[0], target_new_data.shape[0]) data_set_indices = np.ones(num_samples_for_testing * 2) data_set_indices[num_samples_for_testing:] = -1 data_set_indices = data_set_indices.astype(str) original_indices = np.random.choice(target_original_data.shape[0], num_samples_for_testing, replace=False) new_indices = np.random.choice(target_new_data.shape[0], num_samples_for_testing, replace=False) joint_target_samples = np.vstack( [shape_into_2d(target_original_data[original_indices]), shape_into_2d(target_new_data[new_indices])] ) if parents_original_data is None: return independence_test(joint_target_samples, data_set_indices) else: parents_new_data: np.ndarray joint_parent_data = np.vstack( [shape_into_2d(parents_original_data[original_indices]), shape_into_2d(parents_new_data[new_indices])] ) return conditional_independence_test(joint_target_samples, data_set_indices, joint_parent_data) def distribution_change( causal_model: ProbabilisticCausalModel, old_data: pd.DataFrame, new_data: pd.DataFrame, target_node: Any, num_samples: int = 2000, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float] = auto_estimate_kl_divergence, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, mechanism_change_test_significance_level: float = 0.05, mechanism_change_test_fdr_control_method: Optional[str] = "fdr_bh", auto_assignment_quality: Optional[AssignmentQuality] = None, return_additional_info: bool = False, shapley_config: Optional[ShapleyConfig] = None, graph_factory: Callable[[Any], DirectedGraph] = nx.DiGraph, ) -> Union[ Dict[Any, float], Tuple[Dict[Any, float], Dict[Any, bool], ProbabilisticCausalModel, ProbabilisticCausalModel] ]: """Attributes the change in the marginal distribution of the target_node to nodes upstream in the causal DAG. Note that this method creates two copies of the causal DAG. The causal models of one causal DAG are learned from old data and those of another DAG are learned from new data. **Research Paper**: Kailash Budhathoki, Dominik Janzing, Patrick Bloebaum, Hoiyi Ng. *Why did the distribution change?*. Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, PMLR 130:1666-1674, 2021. :param causal_model: Reference causal model. :param old_data: Joint samples from the 'old' distribution. :param new_data: Joint samples from the 'new' distribution. :param target_node: Target node of interest for attributing the marginal distribution change. :param num_samples: Number of samples used for estimating Shapley values. This can have a significant influence on runtime and accuracy. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions, e.g. difference in average values. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in root nodes. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in non-root nodes. :param mechanism_change_test_significance_level: A significance level for rejecting the null hypothesis that the causal mechanism of a node has not changed. :param mechanism_change_test_fdr_control_method: The false discovery rate control method for mechanism change tests. For more options, checkout `statsmodels manual <https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.multipletests.html>`_. :param auto_assignment_quality: If set to None, the assigned models from the given causal models are used for the old and new graph. However, they are re-fitted on the given data. If set to a valid assignment quality, new models are automatically assigned to the old and new graph based on the respective data. :param return_additional_info: If set to True, three additional items are returned: a dictionary indicating whether each node's mechanism changed, the causal DAG whose causal models are learned from old data, and the causal DAG whose causal models are learned from new data. :param shapley_config: Configuration for the Shapley estimator. :param graph_factory: Allows customization in case a graph class different than networkx.DiGraph should be used. This function *must* copy nodes and edges. Attributes of nodes will be overridden in the copy, so the algorithm is independent of the attribute copy behavior of this factory. :return: By default, if `return_additional_info` is set to False, only the dictionary containing contribution of each upstream node is returned. If `return_additional_info` is set to True, three additional items are returned: a dictionary indicating whether each node's mechanism changed, the causal DAG whose causal models learned from old data, and the causal DAG whose causal models are learned from new data. """ causal_graph_old = graph_factory(node_connected_subgraph_view(causal_model.graph, target_node)) causal_model_old = ProbabilisticCausalModel(causal_graph_old) if auto_assignment_quality is None: clone_causal_models(causal_model.graph, causal_model_old.graph) else: assign_causal_mechanisms(causal_model_old, old_data, override_models=True, quality=auto_assignment_quality) causal_graph_new = graph_factory(causal_graph_old) causal_model_new = ProbabilisticCausalModel(causal_graph_new) if auto_assignment_quality is None: clone_causal_models(causal_graph_old, causal_model_new.graph) else: assign_causal_mechanisms(causal_model_new, new_data, override_models=True, quality=auto_assignment_quality) mechanism_changes = _fit_accounting_for_mechanism_change( causal_model_old, causal_model_new, old_data[list(causal_graph_old.nodes)], new_data[list(causal_graph_new.nodes)], independence_test, conditional_independence_test, mechanism_change_test_significance_level, mechanism_change_test_fdr_control_method, ) attributions = distribution_change_of_graphs( causal_model_old, causal_model_new, target_node, num_samples, difference_estimation_func, shapley_config, graph_factory, ) if return_additional_info: return attributions, mechanism_changes, causal_model_old, causal_model_new else: return attributions def distribution_change_of_graphs( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, target_node: Any, num_samples: int = 2000, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float] = auto_estimate_kl_divergence, shapley_config: Optional[ShapleyConfig] = None, graph_factory: Callable[[Any], DirectedGraph] = nx.DiGraph, ) -> Dict[Any, float]: """Attributes the change of the marginal distribution of target_node to upstream nodes based on the distributions generated by the 'old' and 'new' causal graphs. These graphs are assumed to represent the same causal structure and to be fitted on the respective data. Note: This method creates a copy of the given causal models, i.e. the original objects will not be modified. Related paper: Budhathoki, K., Janzing, D., Bloebaum, P., & Ng, H. (2021). Why did the distribution change? arXiv preprint arXiv:2102.13384. :param causal_model_old: The ProbabilisticCausalModel fitted on the 'old' data. :param causal_model_new: The ProbabilisticCausalModel fitted on the 'new' data. :param target_node: Node of interest for attributing the marginal distribution change. :param num_samples: Number of samples used for the estimation. This can have a significant influence on the runtime and accuracy. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions. An example could be the KL divergence. :param shapley_config: Config for the Shapley estimator. :param graph_factory: Allows customization in case a graph class different than networkx.DiGraph should be used. This function *must* copy nodes and edges. Attributes of nodes will be overridden in the copy, so the algorithm is independent of the attribute copy behavior of this factory. :return: A dictionary containing the contributions of upstream nodes to the marginal distribution change in the target node. """ validate_causal_dag(causal_model_old.graph) validate_causal_dag(causal_model_new.graph) return _estimate_marginal_distribution_change( ProbabilisticCausalModel(node_connected_subgraph_view(causal_model_old.graph, target_node)), ProbabilisticCausalModel(node_connected_subgraph_view(causal_model_new.graph, target_node)), target_node, num_samples, difference_estimation_func, shapley_config, graph_factory, ) def _fit_accounting_for_mechanism_change( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, old_data: pd.DataFrame, new_data: pd.DataFrame, independence_test: Callable[[np.ndarray, np.ndarray], float], conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float], significance_level: float, fdr_control_method: Optional[str], ) -> Dict[Any, bool]: mechanism_changed_for_node = _check_significant_mechanism_change( causal_model_old.graph, old_data, new_data, independence_test, conditional_independence_test, significance_level, fdr_control_method, ) joint_data = pd.concat([old_data, new_data], ignore_index=True, sort=True) for node in causal_model_new.graph.nodes: if mechanism_changed_for_node[node]: fit_causal_model_of_target(causal_model_old, node, old_data) fit_causal_model_of_target(causal_model_new, node, new_data) else: fit_causal_model_of_target(causal_model_old, node, joint_data) fit_causal_model_of_target(causal_model_new, node, joint_data) return mechanism_changed_for_node def _estimate_marginal_distribution_change( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, target_node: Any, num_samples: int, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float], shapley_config: Optional[ShapleyConfig], graph_factory: Callable[[Any], DirectedGraph], ) -> Dict[Any, float]: old_causal_models = [causal_model_old.causal_mechanism(x) for x in sorted(causal_model_old.graph.nodes)] new_causal_models = [causal_model_new.causal_mechanism(x) for x in sorted(causal_model_new.graph.nodes)] target_samples_old = draw_samples(causal_model_old, num_samples)[target_node].to_numpy() def attribution_set_function(subset): if np.all(subset == 0): return 0 causal_model = ProbabilisticCausalModel(graph_factory(causal_model_old.graph)) nodes = sorted(list(causal_model.graph.nodes)) for i in range(len(old_causal_models)): if subset[i] == 1: causal_model.set_causal_mechanism(nodes[i], new_causal_models[i]) else: causal_model.set_causal_mechanism(nodes[i], old_causal_models[i]) for node in causal_model.graph.nodes: causal_model.graph.nodes[node][PARENTS_DURING_FIT] = get_ordered_predecessors(causal_model.graph, node) target_samples_new = draw_samples(causal_model, num_samples)[target_node].to_numpy() return difference_estimation_func(target_samples_old, target_samples_new) attributions = estimate_shapley_values(attribution_set_function, len(old_causal_models), shapley_config) return {x: attributions[i] for i, x in enumerate(sorted(causal_model_old.graph.nodes))} def estimate_distribution_change_scores( causal_model: ProbabilisticCausalModel, original_data: pd.DataFrame, new_data: pd.DataFrame, difference_estimation_func: Callable[ [np.ndarray, np.ndarray], Union[np.ndarray, float] ] = auto_estimate_kl_divergence, max_num_evaluation_samples: int = 1000, num_joint_samples: int = 500, early_stopping_percentage: float = 0.01, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, mechanism_change_test_significance_level: float = 0.05, mechanism_change_test_fdr_control_method: Optional[str] = "fdr_bh", ) -> Dict[Any, float]: """Given newly observed and original samples from the joint distribution of the given causal graphical model, this method estimates a score for each node that quantifies how much the distribution of the node has changed. For this, it first checks whether the underlying causal mechanism has changed at all and, if this is the case, it estimates the difference between the new and original distributions. The score is based on the quantity measured by the provided difference_estimation_func or 0 if no mechanism change has been detected. Note that for each parent sample, num_joint_samples conditional samples are generated based on the original and new causal mechanism and evaluated by the given difference_estimation_func function. These results are then averaged over multiple different parent samples. :param causal_model: The underlying causal model based on the original data. :param original_data: Samples from the original data. :param new_data: Samples from the new data. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions. An example could be the KL divergence. :param max_num_evaluation_samples: Maximum number of (parent) samples for evaluating the difference in distributions. :param num_joint_samples: Number of samples generated in a node per parent sample. :param early_stopping_percentage: If the change in percentage between multiple consecutive runs is below this threshold, the evaluation stops before evaluating all max_num_evaluation_samples. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in root nodes. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in non-root nodes. :param mechanism_change_test_significance_level: A significance level for rejecting the null hypothesis that the causal mechanism of a node has not changed. :param mechanism_change_test_fdr_control_method: The false discovery rate control method for mechanism change tests. For more options, checkout `statsmodels manual <https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.multipletests.html>`_. :return: A dictionary assining a score to each node in the causal graph. """ validate_causal_dag(causal_model.graph) mechanism_changed_for_node = _check_significant_mechanism_change( causal_model.graph, original_data, new_data, independence_test, conditional_independence_test, mechanism_change_test_significance_level, mechanism_change_test_fdr_control_method, ) results = {} for node in tqdm( causal_model.graph.nodes, desc="Estimating mechanism change anomaly scores", position=0, leave=True ): if mechanism_changed_for_node[node]: if is_root_node(causal_model.graph, node): results[node] = difference_estimation_func(original_data[node].to_numpy(), new_data[node].to_numpy()) else: parent_nodes = get_ordered_predecessors(causal_model.graph, node) results[node] = _estimate_distribution_change_score( original_data[parent_nodes].to_numpy(), new_data[parent_nodes].to_numpy(), new_data[node].to_numpy(), causal_model.causal_mechanism(node), difference_estimation_func, max_num_evaluation_samples, num_joint_samples, early_stopping_percentage, ) else: results[node] = 0 return results def _check_significant_mechanism_change( graph: DirectedGraph, old_data: pd.DataFrame, new_data: pd.DataFrame, independence_test: Callable[[np.ndarray, np.ndarray], float], conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float], significance_level: float, fdr_control_method: Optional[str], ) -> Dict[Any, bool]: all_p_values = [] for node in graph.nodes: if is_root_node(graph, node): parents_org_data = None parents_new_data = None else: parents_org_data = old_data[get_ordered_predecessors(graph, node)].to_numpy() parents_new_data = new_data[get_ordered_predecessors(graph, node)].to_numpy() all_p_values.append( mechanism_change_test( old_data[node].to_numpy(), new_data[node].to_numpy(), parents_org_data, parents_new_data, independence_test=independence_test, conditional_independence_test=conditional_independence_test, ) ) if fdr_control_method is None: successes = np.array(all_p_values) <= significance_level else: successes = multipletests(all_p_values, significance_level, method=fdr_control_method)[0] return dict(zip(graph.nodes, successes)) def _estimate_distribution_change_score( parent_original_data: np.ndarray, parent_new_data: np.ndarray, target_new_data: np.ndarray, causal_model_original: ConditionalStochasticModel, difference_estimation_func: Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]], max_num_evaluation_samples: int, num_joint_samples: int, early_stopping_percentage: float, ) -> float: parent_original_data, parent_new_data, target_new_data = shape_into_2d( parent_original_data, parent_new_data, target_new_data ) causal_model_new = causal_model_original.clone() causal_model_new.fit(X=parent_new_data, Y=target_new_data) joint_parent_samples = np.vstack([parent_original_data, parent_new_data]) joint_parent_samples = joint_parent_samples[ np.random.choice( joint_parent_samples.shape[0], min(joint_parent_samples.shape[0], max_num_evaluation_samples), replace=False ) ] result = 0 run = 0 for joint_parent_sample in joint_parent_samples: old_result = result samples = repmat(joint_parent_sample, num_joint_samples, 1) result += difference_estimation_func( causal_model_original.draw_samples(samples), causal_model_new.draw_samples(samples) ) run += 1 if old_result != 0 and (1 - result / old_result) <= early_stopping_percentage: # If the relative change of the score is less than the given threshold, we stop the estimation early. _logger.info( "Early stopping: Result only changed by %f percent and a threshold of %f is set." % (1 - result / old_result, early_stopping_percentage) ) break return result / run

"""This module defines functions to attribute distribution changes. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging from typing import Any, Callable, Dict, List, Optional, Tuple, Union import networkx as nx import numpy as np import pandas as pd from numpy.matlib import repmat from statsmodels.stats.multitest import multipletests from tqdm import tqdm from dowhy.gcm.auto import AssignmentQuality, assign_causal_mechanism_node, assign_causal_mechanisms from dowhy.gcm.causal_mechanisms import ConditionalStochasticModel from dowhy.gcm.causal_models import ( PARENTS_DURING_FIT, ProbabilisticCausalModel, clone_causal_models, validate_causal_dag, ) from dowhy.gcm.divergence import auto_estimate_kl_divergence from dowhy.gcm.fitting_sampling import draw_samples, fit_causal_model_of_target from dowhy.gcm.independence_test.kernel import kernel_based from dowhy.gcm.shapley import ShapleyConfig, estimate_shapley_values from dowhy.gcm.util.general import shape_into_2d from dowhy.graph import DirectedGraph, get_ordered_predecessors, is_root_node, node_connected_subgraph_view _logger = logging.getLogger(__name__) def mechanism_change_test( target_original_data: np.ndarray, target_new_data: np.ndarray, parents_original_data: Optional[np.ndarray] = None, parents_new_data: Optional[np.ndarray] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, ) -> float: """Estimates a p-value for the null hypothesis that the original and new data were generated by the same mechanism. Here, we check the dependency between binary labels indicating whether a sample is from the original or a new data set. If the labels do not provide information to determine if a sample is coming from the original/new distribution, then it is likely that the mechanism has not changed. For non-root nodes, samples from parent variables are needed as conditioning variables. This is, testing the null hypothesis that the data were generated by the same mechanism given the parent samples. By this, we incorporate upstream changes that might have impacted the parents, but not the target node itself. :param target_original_data: Samples of the node from the original data set. :param target_new_data: Samples of the node from the new data set. :param parents_original_data: Samples from parents of the node from the original data set. :param parents_new_data: Samples from parents of the node from the new data set. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in nodes without parents. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in nodes with parents. :return: A p-value for the null hypothesis that the mechanism has not changed. """ causal_graph: DirectedGraph if parents_original_data is not None and parents_new_data is None: raise ValueError("Original parents data were given, but no new parents data!") if parents_original_data is None and parents_new_data is not None: raise ValueError("New parents data were given, but no original parents data!") num_samples_for_testing = min(target_original_data.shape[0], target_new_data.shape[0]) data_set_indices = np.ones(num_samples_for_testing * 2) data_set_indices[num_samples_for_testing:] = -1 data_set_indices = data_set_indices.astype(str) original_indices = np.random.choice(target_original_data.shape[0], num_samples_for_testing, replace=False) new_indices = np.random.choice(target_new_data.shape[0], num_samples_for_testing, replace=False) joint_target_samples = np.vstack( [shape_into_2d(target_original_data[original_indices]), shape_into_2d(target_new_data[new_indices])] ) if parents_original_data is None: return independence_test(joint_target_samples, data_set_indices) else: parents_new_data: np.ndarray joint_parent_data = np.vstack( [shape_into_2d(parents_original_data[original_indices]), shape_into_2d(parents_new_data[new_indices])] ) return conditional_independence_test(joint_target_samples, data_set_indices, joint_parent_data) def distribution_change( causal_model: ProbabilisticCausalModel, old_data: pd.DataFrame, new_data: pd.DataFrame, target_node: Any, invariant_nodes: List[Any] = None, num_samples: int = 2000, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float] = auto_estimate_kl_divergence, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, mechanism_change_test_significance_level: float = 0.05, mechanism_change_test_fdr_control_method: Optional[str] = "fdr_bh", auto_assignment_quality: Optional[AssignmentQuality] = None, return_additional_info: bool = False, shapley_config: Optional[ShapleyConfig] = None, graph_factory: Callable[[Any], DirectedGraph] = nx.DiGraph, ) -> Union[ Dict[Any, float], Tuple[Dict[Any, float], Dict[Any, bool], ProbabilisticCausalModel, ProbabilisticCausalModel] ]: """Attributes the change in the marginal distribution of the target_node to nodes upstream in the causal DAG. Note that this method creates two copies of the causal DAG. The causal models of one causal DAG are learned from old data and those of another DAG are learned from new data. **Research Paper**: Kailash Budhathoki, Dominik Janzing, Patrick Bloebaum, Hoiyi Ng. *Why did the distribution change?*. Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, PMLR 130:1666-1674, 2021. :param causal_model: Reference causal model. :param old_data: Joint samples from the 'old' distribution. :param new_data: Joint samples from the 'new' distribution. :param target_node: Target node of interest for attributing the marginal distribution change. :param invariant_nodes: List of nodes where the mechanism is kept constant regardless of changes in the datasets being analyzed. :param num_samples: Number of samples used for estimating Shapley values. This can have a significant influence on runtime and accuracy. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions, e.g. difference in average values. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in root nodes. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in non-root nodes. :param mechanism_change_test_significance_level: A significance level for rejecting the null hypothesis that the causal mechanism of a node has not changed. :param mechanism_change_test_fdr_control_method: The false discovery rate control method for mechanism change tests. For more options, checkout `statsmodels manual <https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.multipletests.html>`_. :param auto_assignment_quality: If set to None, the assigned models from the given causal models are used for the old and new graph. However, they are re-fitted on the given data. If set to a valid assignment quality, new models are automatically assigned to the old and new graph based on the respective data. :param return_additional_info: If set to True, three additional items are returned: a dictionary indicating whether each node's mechanism changed, the causal DAG whose causal models are learned from old data, and the causal DAG whose causal models are learned from new data. :param shapley_config: Configuration for the Shapley estimator. :param graph_factory: Allows customization in case a graph class different than networkx.DiGraph should be used. This function *must* copy nodes and edges. Attributes of nodes will be overridden in the copy, so the algorithm is independent of the attribute copy behavior of this factory. :return: By default, if `return_additional_info` is set to False, only the dictionary containing contribution of each upstream node is returned. If `return_additional_info` is set to True, three additional items are returned: a dictionary indicating whether each node's mechanism changed, the causal DAG whose causal models learned from old data, and the causal DAG whose causal models are learned from new data. """ if invariant_nodes is None: invariant_nodes = [] causal_graph_old = graph_factory(node_connected_subgraph_view(causal_model.graph, target_node)) causal_model_old = ProbabilisticCausalModel(causal_graph_old) if auto_assignment_quality is None: clone_causal_models(causal_model.graph, causal_model_old.graph) else: assign_causal_mechanisms(causal_model_old, old_data, override_models=True, quality=auto_assignment_quality) invariant_nodes = list(set(invariant_nodes).intersection(set(causal_graph_old.nodes))) _remove_invariant_nodes(invariant_nodes, causal_model_old, old_data, auto_assignment_quality) causal_graph_new = graph_factory(causal_graph_old) causal_model_new = ProbabilisticCausalModel(causal_graph_new) if auto_assignment_quality is None: clone_causal_models(causal_graph_old, causal_model_new.graph) else: assign_causal_mechanisms(causal_model_new, new_data, override_models=True, quality=auto_assignment_quality) mechanism_changes = _fit_accounting_for_mechanism_change( causal_model_old, causal_model_new, old_data[list(causal_graph_old.nodes)], new_data[list(causal_graph_new.nodes)], independence_test, conditional_independence_test, mechanism_change_test_significance_level, mechanism_change_test_fdr_control_method, invariant_nodes, ) attributions = distribution_change_of_graphs( causal_model_old, causal_model_new, target_node, num_samples, difference_estimation_func, shapley_config, graph_factory, ) # set attributions to zero for left out invariant nodes for node in invariant_nodes: attributions[node] = 0 if return_additional_info: return attributions, mechanism_changes, causal_model_old, causal_model_new else: return attributions def distribution_change_of_graphs( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, target_node: Any, num_samples: int = 2000, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float] = auto_estimate_kl_divergence, shapley_config: Optional[ShapleyConfig] = None, graph_factory: Callable[[Any], DirectedGraph] = nx.DiGraph, ) -> Dict[Any, float]: """Attributes the change of the marginal distribution of target_node to upstream nodes based on the distributions generated by the 'old' and 'new' causal graphs. These graphs are assumed to represent the same causal structure and to be fitted on the respective data. Note: This method creates a copy of the given causal models, i.e. the original objects will not be modified. Related paper: Budhathoki, K., Janzing, D., Bloebaum, P., & Ng, H. (2021). Why did the distribution change? arXiv preprint arXiv:2102.13384. :param causal_model_old: The ProbabilisticCausalModel fitted on the 'old' data. :param causal_model_new: The ProbabilisticCausalModel fitted on the 'new' data. :param target_node: Node of interest for attributing the marginal distribution change. :param num_samples: Number of samples used for the estimation. This can have a significant influence on the runtime and accuracy. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions. An example could be the KL divergence. :param shapley_config: Config for the Shapley estimator. :param graph_factory: Allows customization in case a graph class different than networkx.DiGraph should be used. This function *must* copy nodes and edges. Attributes of nodes will be overridden in the copy, so the algorithm is independent of the attribute copy behavior of this factory. :return: A dictionary containing the contributions of upstream nodes to the marginal distribution change in the target node. """ validate_causal_dag(causal_model_old.graph) validate_causal_dag(causal_model_new.graph) return _estimate_marginal_distribution_change( ProbabilisticCausalModel(node_connected_subgraph_view(causal_model_old.graph, target_node)), ProbabilisticCausalModel(node_connected_subgraph_view(causal_model_new.graph, target_node)), target_node, num_samples, difference_estimation_func, shapley_config, graph_factory, ) def _remove_invariant_nodes( invariant_nodes: List[Any], causal_model: ProbabilisticCausalModel, old_data: pd.DataFrame, auto_assignment_quality: Optional[AssignmentQuality], ) -> None: if auto_assignment_quality is None: auto_assignment_quality = AssignmentQuality.GOOD for invar_node in invariant_nodes: # Get parent and child nodes parents = get_ordered_predecessors(causal_model.graph, invar_node) children = list(causal_model.graph.successors(invar_node)) # Don't remove node if node has more than 1 children nodes as it can introduce # hidden confounders. if len(children) > 1: continue # Remove the middle node causal_model.graph.remove_node(invar_node) # Connect parent and child nodes for parent in parents: for child in children: causal_model.graph.add_edge(parent, child) # Update the causal mechanism for the child nodes for child in children: assign_causal_mechanism_node(causal_model, child, old_data, quality=auto_assignment_quality) def _fit_accounting_for_mechanism_change( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, old_data: pd.DataFrame, new_data: pd.DataFrame, independence_test: Callable[[np.ndarray, np.ndarray], float], conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float], significance_level: float, fdr_control_method: Optional[str], invariant_nodes: List[Any], ) -> Dict[Any, bool]: mechanism_changed_for_node = _check_significant_mechanism_change( causal_model_old.graph, old_data, new_data, independence_test, conditional_independence_test, significance_level, fdr_control_method, ) joint_data = pd.concat([old_data, new_data], ignore_index=True, sort=True) for node in causal_model_new.graph.nodes: if node in invariant_nodes: mechanism_changed_for_node[node] = False if mechanism_changed_for_node[node]: fit_causal_model_of_target(causal_model_old, node, old_data) fit_causal_model_of_target(causal_model_new, node, new_data) else: fit_causal_model_of_target(causal_model_old, node, joint_data) fit_causal_model_of_target(causal_model_new, node, joint_data) return mechanism_changed_for_node def _estimate_marginal_distribution_change( causal_model_old: ProbabilisticCausalModel, causal_model_new: ProbabilisticCausalModel, target_node: Any, num_samples: int, difference_estimation_func: Callable[[np.ndarray, np.ndarray], float], shapley_config: Optional[ShapleyConfig], graph_factory: Callable[[Any], DirectedGraph], ) -> Dict[Any, float]: old_causal_models = [causal_model_old.causal_mechanism(x) for x in sorted(causal_model_old.graph.nodes)] new_causal_models = [causal_model_new.causal_mechanism(x) for x in sorted(causal_model_new.graph.nodes)] target_samples_old = draw_samples(causal_model_old, num_samples)[target_node].to_numpy() def attribution_set_function(subset): if np.all(subset == 0): return 0 causal_model = ProbabilisticCausalModel(graph_factory(causal_model_old.graph)) nodes = sorted(list(causal_model.graph.nodes)) for i in range(len(old_causal_models)): if subset[i] == 1: causal_model.set_causal_mechanism(nodes[i], new_causal_models[i]) else: causal_model.set_causal_mechanism(nodes[i], old_causal_models[i]) for node in causal_model.graph.nodes: causal_model.graph.nodes[node][PARENTS_DURING_FIT] = get_ordered_predecessors(causal_model.graph, node) target_samples_new = draw_samples(causal_model, num_samples)[target_node].to_numpy() return difference_estimation_func(target_samples_old, target_samples_new) attributions = estimate_shapley_values(attribution_set_function, len(old_causal_models), shapley_config) return {x: attributions[i] for i, x in enumerate(sorted(causal_model_old.graph.nodes))} def estimate_distribution_change_scores( causal_model: ProbabilisticCausalModel, original_data: pd.DataFrame, new_data: pd.DataFrame, difference_estimation_func: Callable[ [np.ndarray, np.ndarray], Union[np.ndarray, float] ] = auto_estimate_kl_divergence, max_num_evaluation_samples: int = 1000, num_joint_samples: int = 500, early_stopping_percentage: float = 0.01, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, mechanism_change_test_significance_level: float = 0.05, mechanism_change_test_fdr_control_method: Optional[str] = "fdr_bh", ) -> Dict[Any, float]: """Given newly observed and original samples from the joint distribution of the given causal graphical model, this method estimates a score for each node that quantifies how much the distribution of the node has changed. For this, it first checks whether the underlying causal mechanism has changed at all and, if this is the case, it estimates the difference between the new and original distributions. The score is based on the quantity measured by the provided difference_estimation_func or 0 if no mechanism change has been detected. Note that for each parent sample, num_joint_samples conditional samples are generated based on the original and new causal mechanism and evaluated by the given difference_estimation_func function. These results are then averaged over multiple different parent samples. :param causal_model: The underlying causal model based on the original data. :param original_data: Samples from the original data. :param new_data: Samples from the new data. :param difference_estimation_func: Function for quantifying the distribution change. This function should expect two inputs which represent samples from two different distributions. An example could be the KL divergence. :param max_num_evaluation_samples: Maximum number of (parent) samples for evaluating the difference in distributions. :param num_joint_samples: Number of samples generated in a node per parent sample. :param early_stopping_percentage: If the change in percentage between multiple consecutive runs is below this threshold, the evaluation stops before evaluating all max_num_evaluation_samples. :param independence_test: Unconditional independence test. This is used to identify mechanism changes in root nodes. :param conditional_independence_test: Conditional independence test. This is used to identify mechanism changes in non-root nodes. :param mechanism_change_test_significance_level: A significance level for rejecting the null hypothesis that the causal mechanism of a node has not changed. :param mechanism_change_test_fdr_control_method: The false discovery rate control method for mechanism change tests. For more options, checkout `statsmodels manual <https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.multipletests.html>`_. :return: A dictionary assining a score to each node in the causal graph. """ validate_causal_dag(causal_model.graph) mechanism_changed_for_node = _check_significant_mechanism_change( causal_model.graph, original_data, new_data, independence_test, conditional_independence_test, mechanism_change_test_significance_level, mechanism_change_test_fdr_control_method, ) results = {} for node in tqdm( causal_model.graph.nodes, desc="Estimating mechanism change anomaly scores", position=0, leave=True ): if mechanism_changed_for_node[node]: if is_root_node(causal_model.graph, node): results[node] = difference_estimation_func(original_data[node].to_numpy(), new_data[node].to_numpy()) else: parent_nodes = get_ordered_predecessors(causal_model.graph, node) results[node] = _estimate_distribution_change_score( original_data[parent_nodes].to_numpy(), new_data[parent_nodes].to_numpy(), new_data[node].to_numpy(), causal_model.causal_mechanism(node), difference_estimation_func, max_num_evaluation_samples, num_joint_samples, early_stopping_percentage, ) else: results[node] = 0 return results def _check_significant_mechanism_change( graph: DirectedGraph, old_data: pd.DataFrame, new_data: pd.DataFrame, independence_test: Callable[[np.ndarray, np.ndarray], float], conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float], significance_level: float, fdr_control_method: Optional[str], ) -> Dict[Any, bool]: all_p_values = [] for node in graph.nodes: if is_root_node(graph, node): parents_org_data = None parents_new_data = None else: parents_org_data = old_data[get_ordered_predecessors(graph, node)].to_numpy() parents_new_data = new_data[get_ordered_predecessors(graph, node)].to_numpy() all_p_values.append( mechanism_change_test( old_data[node].to_numpy(), new_data[node].to_numpy(), parents_org_data, parents_new_data, independence_test=independence_test, conditional_independence_test=conditional_independence_test, ) ) if fdr_control_method is None: successes = np.array(all_p_values) <= significance_level else: successes = multipletests(all_p_values, significance_level, method=fdr_control_method)[0] return dict(zip(graph.nodes, successes)) def _estimate_distribution_change_score( parent_original_data: np.ndarray, parent_new_data: np.ndarray, target_new_data: np.ndarray, causal_model_original: ConditionalStochasticModel, difference_estimation_func: Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]], max_num_evaluation_samples: int, num_joint_samples: int, early_stopping_percentage: float, ) -> float: parent_original_data, parent_new_data, target_new_data = shape_into_2d( parent_original_data, parent_new_data, target_new_data ) causal_model_new = causal_model_original.clone() causal_model_new.fit(X=parent_new_data, Y=target_new_data) joint_parent_samples = np.vstack([parent_original_data, parent_new_data]) joint_parent_samples = joint_parent_samples[ np.random.choice( joint_parent_samples.shape[0], min(joint_parent_samples.shape[0], max_num_evaluation_samples), replace=False ) ] result = 0 run = 0 for joint_parent_sample in joint_parent_samples: old_result = result samples = repmat(joint_parent_sample, num_joint_samples, 1) result += difference_estimation_func( causal_model_original.draw_samples(samples), causal_model_new.draw_samples(samples) ) run += 1 if old_result != 0 and (1 - result / old_result) <= early_stopping_percentage: # If the relative change of the score is less than the given threshold, we stop the estimation early. _logger.info( "Early stopping: Result only changed by %f percent and a threshold of %f is set." % (1 - result / old_result, early_stopping_percentage) ) break return result / run

bhatt-priyadutt

734b61671d130cf21a4e437084b9083dbd5c4a39

b4f80dcb2251a03aff4c816d7c6f6eb2aaf98e73

Ok yes, nevermind my comment, I was thinking of a use-case outside of this method where a user just wants the corrected graph structure without necessarily having a new mechanism assigned. But then we would also need to make the data optional. Let's keep the logic as is, but to avoid confusion, the assignment quality should not be `Optional`. This means, in the `assign_causal_mechanism_node` function, set it to the default `quality: AssignmentQuality = AssignmentQuality.GOOD` in the method definition and remove the `Optional[...]` typing in `assign_causal_mechanism_node` as well as in `_remove_invariant_nodes`. It should be clearer then.

bloebp

py-why/dowhy

Remove kwargs from falsify, improve docstrings

This commit updates `gcm/falsify` in the following ways: - Remove the `**kwargs` for `validate_graph` and `falsify_graph`. Instead, validation methods given to those functions should be wrapped in `wrap_partial`, e.g. if we want to validate a graph `G` and test local Markov conditions and pairwise dependencies on observations `data`, we would do ``` validate_graph(G, methods=( wrap_partial(validate_lmc, data=data, independence_test=..., conditional_independence_test=...), wrap_partial(validate_pd, data=data, independence_test=...), ) ) ``` By default (`methods=None`), `validate_graph` tests local Markov conditions and `falsify_graph` tests local Markov conditions and d-separation of parental triples. In this case `data` has to be set and optionally one can provide `independence_test`, `conditional_independence_test`, `significance_level`, and `n_jobs` via the keyword arguments of `validate_graph` / `falsify_graph`. - Use `Enum` for some of the constants - In `_PValuesMemory` the method `add_p_value ` always sets a p-value even if it was set already. This removes the need for the method `clear_placeholders` and allows `p_values` to be set to `-1`, indicating that the CI is not testable (because of missing data for a node, c.f. `_compute_p_value`) - If for some CI: X, Y | Z any of the nodes has no data (column missing in the provided dataframe), we skip this test and raise a warning. - Instead of using the raw number of violations in the plotted histogram and to compute the p-value, we use the relative number of violations. This will result in different p-values only if we have missing data for some nodes (and thus the number of tests is potentially different between different permutations) - Improve docstrings

2023-07-04 13:58:30+00:00

2023-07-18 14:19:10+00:00

dowhy/gcm/falsify.py

"""This module provides functionality to falsify a user-given DAG given observed data. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from dataclasses import dataclass, field from enum import Enum, auto from functools import partial from itertools import permutations from typing import Any, Callable, Dict, FrozenSet, List, Optional, Set, Tuple, Union import matplotlib.colors as mcolors import matplotlib.pyplot as plt import networkx as nx import numpy as np import pandas as pd from joblib import Parallel, delayed from tqdm import tqdm import dowhy.gcm.config as config from dowhy.gcm.independence_test import kernel_based from dowhy.gcm.util import plot from dowhy.gcm.util.general import set_random_seed from dowhy.gcm.validation import _get_non_descendants from dowhy.graph import DirectedGraph, get_ordered_predecessors VIOLATION_COLOR = "red" COLORS = list(mcolors.TABLEAU_COLORS.values()) class FalsifyConst(Enum): N_VIOLATIONS = auto() N_TESTS = auto() P_VALUE = auto() P_VALUES = auto() GIVEN_VIOLATIONS = auto() PERM_VIOLATIONS = auto() F_GIVEN_VIOLATIONS = auto() F_PERM_VIOLATIONS = auto() LOCAL_VIOLATION_INSIGHT = auto() METHOD = auto() VALIDATE_LMC = auto() VALIDATE_TPA = auto() VALIDATE_PD = auto() VALIDATE_CM = auto() PERM_GRAPHS = auto() MEC = auto() FALSIFY_METHODS = { FalsifyConst.VALIDATE_LMC: "LMC", FalsifyConst.VALIDATE_PD: "Faithfulness", FalsifyConst.VALIDATE_TPA: "tPa", FalsifyConst.VALIDATE_CM: "Causal Minimality", } @dataclass class _PValuesMemory: """A class to store and access results of independence tests. This class is useful if, e.g. we validate LMC on graph X -> Y -> Z and also on the permuted nodes Z -> Y -> X. Since X ind Z | Y == Z ind X | Y, we only need to perform the conditional independence test once. We use frozensets of nodes since standard (immutable) sets are not hashable. """ p_values: Dict[Tuple[FrozenSet, FrozenSet, FrozenSet], float] = field(default_factory=dict) def add_p_value( self, p_value: Union[None, float], X: Union[Set, List, str], Y: Union[Set, List, str], Z: Optional[Union[Set, List, str]] = None, ): if not Z: Z = set() self.p_values[(_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z))] = p_value def get_p_value( self, X: Union[Set, List, str], Y: Union[Set, List, str], Z: Optional[Union[Set, List, str]] = None ): if not Z: Z = set() # Independence tests are symmetric if (_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z)) in self.p_values: return self.p_values[(_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z))] elif (_to_frozenset(Y), _to_frozenset(X), _to_frozenset(Z)) in self.p_values: return self.p_values[(_to_frozenset(Y), _to_frozenset(X), _to_frozenset(Z))] return def __contains__(self, item: Tuple[Union[Set, List, str], ...]) -> bool: X, Y = (_to_frozenset(i) for i in item[:2]) if len(item) == 2 or item[2] is None: Z = frozenset() else: Z = _to_frozenset(item[2]) return (X, Y, Z) in self.p_values or (Y, X, Z) in self.p_values def validate_lmc( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, include_unconditional: bool = True, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[str, float]]]: """ Validate the local markov condition for a given directed graph. Return number of violations and p values for each node. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory instance, where results of previously performed tests are stored. :param independence_test: Test to use for unconditional independencies (only used if include_unconditional=True) :param conditional_independence_test: Conditional independence test to use for checking local Markov condition. :param significance_level: Significance level for (conditional) independence tests. :param include_unconditional: Test also unconditional independencies of root nodes. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Outcome of validation containing number of violations in the graph and p values/violation for each tuple (node, non_desc) """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_LMC, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, FalsifyConst.P_VALUES: dict(), } # Find out which tests to do triples = _get_parental_triples(causal_graph, include_unconditional) to_test = [] for node, non_desc, parents in triples: if not (node, non_desc, parents) in p_values_memory: to_test.append((node, non_desc, parents)) p_values_memory.add_p_value(None, node, non_desc, parents) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=node, Y=non_desc, Z=parents, independence_test=independence_test, conditional_independence_test=conditional_independence_test, seed=int(seed), ) for (node, non_desc, parents), seed in zip(to_test, random_seeds) ) # Gather results for i, (node, non_desc, parents) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], node, non_desc, parents) # Summarize for node, non_desc, parents in triples: lmc_p_value = p_values_memory.get_p_value(node, non_desc, parents) if lmc_p_value is not None: validation_summary[FalsifyConst.N_TESTS] += 1 validation_summary[FalsifyConst.P_VALUES][(node, non_desc)] = ( lmc_p_value, lmc_p_value <= significance_level, ) if lmc_p_value <= significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_tpa( causal_graph: DirectedGraph, causal_graph_reference: DirectedGraph, include_unconditional: bool = True, data: Optional[pd.DataFrame] = None, ) -> Dict[str, int]: """ Graphical criterion to evaluate which pairwise parental d-separations (parental triples) in `causal_graph` are violated, assuming `causal_graph_reference` is the ground truth graph. If none are violated, then both graphs lie in the same Markov equivalence class. Specifically we test: X _|_G' Y | Z and X _/|_G Y | Z for Y \in ND{X}^G', Z = PA{X}^G :param causal_graph: Causal graph for which to evaluate parental d-separations (G') :param causal_graph_reference: Causal graph where we test if d-separation holds (G) :param include_unconditional: Test also unconditional independencies of root nodes. :param data: IGNORED! No data is needed for this validation method and thus the data argument is ignored! :return: Validation summary with number of d-separations implied by `causal_graph` and number of times these are violated in the graph `causal_graph_reference`. """ validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_TPA, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, } triples = _get_parental_triples(causal_graph, include_unconditional) for node, non_desc, parents in triples: validation_summary[FalsifyConst.N_TESTS] += 1 if not nx.d_separated(causal_graph_reference, {node}, {non_desc}, set(parents)): validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_pd( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, n_pairs: int = -1, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, adjacent_only: bool = False, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[tuple, float]]]: """ Validate pairwise dependencies (pd) for a given causal graph and data. Test for each node if it is statistically dependent of all its ancestors. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory object, where results of previously performed tests are stored. :param n_pairs: Evaluate dependencies for n_pairs <= all pairs in the DAG. If n_pairs=-1, evaluate dependencies for all (ancestor, node) pairs (default). :param independence_test: Independence test to use for checking pairwise dependencies. :param significance_level: Significance level for independence tests. :param adjacent_only: Only test adjacent node pairs. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Summary dict: {n_violations: int, n_tests: int, p_values: {(ancestor, node): float, ...}} """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() pairs = [(ancestor, node) for node in causal_graph.nodes for ancestor in nx.ancestors(causal_graph, node)] if adjacent_only: pairs = [ (ancestor, node) for (ancestor, node) in pairs if ancestor in get_ordered_predecessors(causal_graph, node) ] if n_pairs < 0: n_pairs = len(pairs) if n_pairs > len(pairs): raise ValueError(f"n_pairs ({n_pairs}) > number of pairs in the DAG ({len(pairs)})") validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_PD, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: n_pairs, FalsifyConst.P_VALUES: dict(), } pair_idxs = np.random.choice(len(pairs), size=n_pairs, replace=False) # Find out which tests to do to_test = [] for pair_idx in pair_idxs: ancestor, node = pairs[pair_idx] if not (ancestor, node) in p_values_memory: to_test.append((ancestor, node)) p_values_memory.add_p_value(None, ancestor, node) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=ancestor, Y=node, Z=None, independence_test=independence_test, conditional_independence_test=None, seed=int(seed), ) for (ancestor, node), seed in zip(to_test, random_seeds) ) # Gather results for i, (ancestor, node) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], ancestor, node) # Summarize for pair_idx in pair_idxs: ancestor, node = pairs[pair_idx] p_value = p_values_memory.get_p_value(ancestor, node) if p_value is not None: validation_summary[FalsifyConst.P_VALUES][(node, ancestor)] = (p_value, p_value > significance_level) if p_value > significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_cm( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[tuple, float]]]: """ Function to test causal minimality of a DAG (see [1], Proposition 6.36). [1] J. Peters, D. Janzing, and B. Schölkopf, Elements of Causal Inference: Foundations and Learning Algorithms. Cambridge, MA, USA: MIT Press, 2017. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory object, where results of previously performed tests are stored. :param independence_test: Independence test to use. :param conditional_independence_test: Conditional independence test to use. :param significance_level: Significance level for independence tests. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Validation summary as dict. """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_CM, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, FalsifyConst.P_VALUES: dict(), } # Find out which tests to do triples = [] to_test = [] for node in causal_graph.nodes: parents = set(causal_graph.predecessors(node)) if parents: for p in parents: other_parents = parents.difference({p}) triples.append((node, p, other_parents)) if not (node, p, other_parents) in p_values_memory: to_test.append((node, p, other_parents)) p_values_memory.add_p_value(None, node, p, other_parents) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=node, Y=p, Z=list(other_parents), independence_test=independence_test, conditional_independence_test=conditional_independence_test, seed=int(seed), ) for (node, p, other_parents), seed in zip(to_test, random_seeds) ) # Gather results for i, (node, p, other_parents) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], node, p, other_parents) # Summarize for node, p, other_parents in triples: p_value = p_values_memory.get_p_value(node, p, other_parents) if p_value is not None: validation_summary[FalsifyConst.N_TESTS] += 1 validation_summary[FalsifyConst.P_VALUES][(node, p, tuple(other_parents))] = ( p_value, p_value > significance_level, ) if p_value > significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_graph( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Optional[Union[Callable, Tuple[Callable, ...], List[Callable]]] = partial( validate_lmc, independence_test=kernel_based, conditional_independence_test=kernel_based ), ) -> Dict[str, Dict]: """ Validate a given causal graph using observational data and some given methods. If methods are provided, they must be wrapped in a partial object, with their respective parameters. E.g., if one wants to test the local Markov conditions and the pairwise dependencies (unconditional faithfulness), then call validate_graph(G, data, methods=( partial(validate_lmc, independence_test=..., conditional_independence_test=...), partial(validate_pd, independence_test=...), ) ) :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param methods: Method functions wrapped in wrap_partial. E.g. wrap_partial(validate_lmc, data=data, independence_test=..., conditional_independence_test=...). If no methods are provided we run validate_lmc with optional keyword arguments provided to validate_graph. :return: Validation summary as dict. """ if not isinstance(methods, (tuple, list)): methods = (methods,) validation_summary = dict() for m in methods: m_summary = m(causal_graph=causal_graph, data=data) m_name = m_summary.pop(FalsifyConst.METHOD) validation_summary[m_name] = m_summary return validation_summary @dataclass class EvaluationResult: """ Dataset class containing the evaluation result of falsifying a graph using a node-permutation test. ... Attributes ---------- summary : dict Dictionary containing the summary of the evaluation. significance_level : float Significance level based on which we falsify the given DAG falsifiable : bool Whether the given DAG is falsifiable. falsified : bool Whether the given DAG is falsified. """ summary: dict significance_level: float suggestions: Optional[dict] = None def update_significance_level(self, significance_level: float): """ Update the significance level to decide if we falsify a given DAG. """ self.significance_level = significance_level self.__post_init__() def __post_init__(self): self.can_evaluate = self._can_evaluate() if not self.can_evaluate: self.falsified = None self.falsifiable = None elif ( self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.P_VALUE] > self.significance_level > self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE] ): self.falsified = True self.falsifiable = True elif self.significance_level < self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE]: self.falsified = False self.falsifiable = False else: self.falsified = False self.falsifiable = True def __repr__(self): # DAG Evaluation if self.can_evaluate: decision = " " if self.falsified else " do not " informative = " " if self.falsifiable else " not " frac_MEC = f"{len(self.summary[FalsifyConst.MEC])} / {len(self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.PERM_VIOLATIONS])}" frac_VLMC = f"{self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.GIVEN_VIOLATIONS]}/{self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.N_TESTS]}" p_LMC = self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.P_VALUE] p_dSep = self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE] validation_repr = [ f"The given DAG is{informative}informative because {frac_MEC} of the permutations lie in the Markov", f"equivalence class of the given DAG (p-value: {p_dSep:.2f}).", f"The given DAG violates {frac_VLMC} LMCs and is better than {(1 - p_LMC) * 100:.1f}% of the permuted DAGs (p-value: {p_LMC:.2f}).", f"Based on the provided significance level ({self.significance_level}) and because the DAG is{informative}informative,", f"we{decision}reject the DAG.", ] else: validation_repr = ["Cannot be evaluated!"] # Suggestions suggestion_repr = {} for m in self.suggestions: suggestion_repr[FALSIFY_METHODS[m]] = [ f"Remove edge {node[1]} --> {node[0]}" for (node, r) in self.suggestions[m][FalsifyConst.P_VALUES].items() if r[1] ] return _generate_table(validation_repr, suggestion_repr) def _can_evaluate(self): can_evaluate = True for m in (FalsifyConst.VALIDATE_LMC, FalsifyConst.VALIDATE_TPA): if m not in self.summary: can_evaluate = False return can_evaluate def falsify_graph( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Optional[Union[Callable, Tuple[Callable, ...]]] = None, suggestion_methods: Optional[Union[Callable, Tuple[Callable, ...]]] = None, suggestions: bool = False, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, significance_ci: float = 0.05, n_permutations: Optional[int] = None, show_progress_bar: Optional[bool] = None, n_jobs: Optional[int] = None, plot_histogram: bool = False, plot_kwargs: Optional[Dict] = None, ) -> EvaluationResult: """ Falsify a given DAG using observational data. This method returns the result of a permutation-test to falsify a user-given DAG using observational data. To this end we construct the test statistics by testing the violations of local Markov conditions (LMC) implied by the graph using conditional independence (CI) tests. The null is the number of LMC violations of a random node-permutation of the given graph. Our test can be interpreted as whether the given graph is significantly better than random in terms of the CIs it entails. To determine whether a given graph is falsifiable by our metric, we implement a second test, which reports whether given graph is "characteristic" enough in terms of the CIs it entails. For this, we compute how many of the random node permutations lie in the same Markov equivalence class (MEC) as the given graph and conclude that the given graph is falsifiable only if the fraction of permuted DAGs in the same MEC as the given graph is "reasonably" small. The returned EvaluationResult object has two attributes: `falsified` and `falsifiable`: `falsifiable`: The given graph lies in a different MEC than >= 1-`significance_level` of the permuted DAGs `falsified`: The given graph is falsifiable and violates fewer LMCs than >= 1-`significance_level` of the permuted DAGs By default, we only run 1 / `significance_level` permutations as those are enough to falsify a graph with type I error probability `significance_level` at some given `significance_level`. If you are interested in a more exact estimate of the p-value of whish to plot a histogram to see how the given DAG compares to random node permutations, you should set `n_permutations` to some larger value (e.g. 100 or 1000). If `n_permutations=-1` we test on all n_nodes! permutations. `methods` and `suggestion_methods` must be wrapped in partial(method, **kwargs) (c.f. `validate_graph`). Related paper: Eulig, E., Mastakouri, A. A., Blöbaum, P., Hardt, M., & Janzing, D. (2023). Toward Falsifying Causal Graphs Using a Permutation-Based Test. https://arxiv.org/abs/2305.09565 :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param methods: Validation methods to perform. :param suggestion_methods: Methods to run on the given graph to provide additional suggestions. :param suggestions: Provide suggestions generated using the `suggestion_methods`. :param independence_test: Independence test to use for checking pairwise independencies. :param conditional_independence_test: Conditional independence test to use. :param significance_level: Significance level for the permutation test. :param significance_ci: Significance level for (conditional) independence tests. :param n_permutations: Number of permutations to perform. If -1 use all n_nodes! permutations. :param show_progress_bar: Whether to show progress bar over permutations. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :param plot_histogram: Plot histogram of results from permutation baseline. :param plot_kwargs: Additional plot arguments to be passed to plot_evaluation_results. :return: EvaluationResult """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs show_progress_bar = config.show_progress_bars if show_progress_bar is None else show_progress_bar p_values_memory = _PValuesMemory() if n_permutations is None: n_permutations = int(1 / significance_level) if not plot_histogram else -1 if not plot_kwargs: plot_kwargs = {} # If no methods are provided, use default ones: validate_lmc, validate_tpa if not methods: methods = ( partial( validate_lmc, independence_test=independence_test, conditional_independence_test=conditional_independence_test, significance_level=significance_ci, p_values_memory=p_values_memory, n_jobs=n_jobs, ), partial(validate_tpa, causal_graph_reference=causal_graph), ) elif isinstance(methods, Callable): methods = (methods,) # If no suggestion methods are provided, but suggestions=True, use default ones: validate_cm if not suggestions: suggestion_methods = tuple() elif suggestions and isinstance(suggestion_methods, Callable): suggestion_methods = (suggestion_methods,) elif suggestions: suggestion_methods = ( partial( validate_cm, independence_test=independence_test, conditional_independence_test=conditional_independence_test, significance_level=significance_ci, p_values_memory=p_values_memory, n_jobs=n_jobs, ), ) summary_given = validate_graph( causal_graph, data, methods=methods + suggestion_methods, ) summary_perm = _permutation_based( causal_graph, data, methods=methods, exclude_original_order=False, n_permutations=n_permutations, show_progress_bar=show_progress_bar, ) summary = dict() validation_methods = set(summary_perm.keys()) - {FalsifyConst.PERM_GRAPHS} for m in validation_methods: summary[m] = dict() summary[m][FalsifyConst.PERM_VIOLATIONS] = [perm[FalsifyConst.N_VIOLATIONS] for perm in summary_perm[m]] summary[m][FalsifyConst.GIVEN_VIOLATIONS] = summary_given[m][FalsifyConst.N_VIOLATIONS] summary[m][FalsifyConst.N_TESTS] = summary_given[m][FalsifyConst.N_TESTS] summary[m][FalsifyConst.F_PERM_VIOLATIONS] = [ perm[FalsifyConst.N_VIOLATIONS] / perm[FalsifyConst.N_TESTS] for perm in summary_perm[m] ] summary[m][FalsifyConst.F_GIVEN_VIOLATIONS] = ( summary[m][FalsifyConst.GIVEN_VIOLATIONS] / summary[m][FalsifyConst.N_TESTS] ) summary[m][FalsifyConst.P_VALUE] = sum( [ 1 for perm in summary[m][FalsifyConst.F_PERM_VIOLATIONS] if perm <= summary[m][FalsifyConst.F_GIVEN_VIOLATIONS] ] ) / len(summary[m][FalsifyConst.PERM_VIOLATIONS]) if m != FalsifyConst.VALIDATE_TPA: # Append list of violations (node, non_desc) to get local information summary[m][FalsifyConst.LOCAL_VIOLATION_INSIGHT] = summary_given[m][FalsifyConst.P_VALUES] if FalsifyConst.VALIDATE_TPA in summary: summary[FalsifyConst.MEC] = [ summary_perm[FalsifyConst.PERM_GRAPHS][i] for i, v in enumerate(summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.PERM_VIOLATIONS]) if v == 0 ] result = EvaluationResult( summary=summary, significance_level=significance_level, suggestions={m: summary_given[m] for m in summary_given if m not in validation_methods}, ) if plot_histogram: plot_evaluation_results(result, **plot_kwargs) return result def apply_suggestions( causal_graph: DirectedGraph, evaluation_result: EvaluationResult, edges_to_keep: Optional[List[Tuple[Any, Any]]] = None, ): if not hasattr(evaluation_result, "suggestions"): raise ValueError("EvaluationResult object has no attribute suggestions. Please run with suggestion=True!") causal_graph = causal_graph.copy() for m in evaluation_result.suggestions: for node, res in evaluation_result.suggestions[m][FalsifyConst.P_VALUES].items(): edge = (node[1], node[0]) if (res[1] and edges_to_keep is not None and edge not in edges_to_keep) or ( res[1] and edges_to_keep is None ): causal_graph.remove_edge(edge[0], edge[1]) return causal_graph def plot_evaluation_results(evaluation_result, figsize=(8, 3), bins=None, title="", savepath="", display=True): fig, ax = plt.subplots(figsize=figsize) # Plot histograms p_values = "" data = [] labels = [] evaluation_summary = {k: v for k, v in evaluation_result.summary.items() if k != FalsifyConst.MEC} for i, (m, m_summary) in enumerate(evaluation_summary.items()): data.append(m_summary[FalsifyConst.F_PERM_VIOLATIONS]) labels.append(f"Violations of {FALSIFY_METHODS[m]} of permuted DAGs") p_values += f"p-value {FALSIFY_METHODS[m]} = {m_summary[FalsifyConst.P_VALUE]:.2f}\n" ax.hist(data, color=COLORS[: len(evaluation_summary)], bins=bins, alpha=0.5, label=labels, edgecolor="k") # Plot given violations for i, (m, m_summary) in enumerate(evaluation_summary.items()): ylim = ax.get_ylim()[1] ax.plot( [m_summary[FalsifyConst.F_GIVEN_VIOLATIONS]] * 2, [0, ylim], "--", c=COLORS[i], label=f"Violations of {FALSIFY_METHODS[m]} of given DAG", ) ax.set_ylim([0, ylim]) ax.set_xlabel("Fraction of violations") ax.set_ylabel("# Permutations") plt.legend(loc="upper left", bbox_to_anchor=(1.05, 1), borderaxespad=0.0, title=p_values) if title: plt.title(title) plt.tight_layout() if savepath: plt.savefig(savepath, dpi=300, bbox_inches="tight") if display: plt.show() else: plt.close() def plot_local_insights( causal_graph: DirectedGraph, evaluation_result: Union[EvaluationResult, Dict], method: Optional[str] = FalsifyConst.VALIDATE_LMC, ): """ For some given graph and evaluation result plot local violations. :param causal_graph: DiGraph :param evaluation_result: EvaluationResult :param method: Method for which to plot violations """ colors = {} if isinstance(evaluation_result, EvaluationResult) and method in evaluation_result.summary: local_insight_dict = evaluation_result.summary[method][FalsifyConst.LOCAL_VIOLATION_INSIGHT] elif ( isinstance(evaluation_result, EvaluationResult) and hasattr(evaluation_result, "suggestions") and method in evaluation_result.suggestions ): local_insight_dict = evaluation_result.suggestions[method][FalsifyConst.P_VALUES] elif isinstance(evaluation_result, Dict): if method not in evaluation_result: raise ValueError(f"Validation method {method} does not exist in given evaluation_result!") if FalsifyConst.P_VALUES not in evaluation_result[method]: raise ValueError( f"Validation method {method} has no key {FalsifyConst.P_VALUES} where information on local violations " f"are stored!" ) local_insight_dict = evaluation_result[method][FalsifyConst.P_VALUES] else: raise ValueError(f"Cannot plot local violation insights from method {method} in evaluation_result!") for nodes, result in local_insight_dict.items(): if result[1]: if method == FalsifyConst.VALIDATE_LMC: # For LMC we highlight X for which X _|/|_ Y \in ND_X | Pa_X colors[nodes[0]] = VIOLATION_COLOR elif method == FalsifyConst.VALIDATE_PD: # For PD we highlight the edge (if Y\in Anc_X -> X are adjacent) colors[(nodes[1], nodes[0])] = VIOLATION_COLOR elif method == FalsifyConst.VALIDATE_CM: # For causal minimality we highlight the edge Y \in Pa_X -> X colors[(nodes[1], nodes[0])] = VIOLATION_COLOR plot(causal_graph, colors=colors) def _generate_table( validation_repr, suggestion_repr, width=105, validation_name="Falsificaton Summary", suggestion_name="Suggestions" ): # Create Validation header _repr = [ "+" + "-" * (width - 2) + "+\n", "|" + " " * int(np.floor((width - 2 - len(validation_name)) / 2)), validation_name, " " * int(np.ceil((width - 2 - len(validation_name)) / 2)) + "|\n", "+" + "-" * (width - 2) + "+\n", ] # Create Validation summary _repr += [f"| {v_line}" + " " * (width - 3 - len(v_line)) + "|\n" for v_line in validation_repr] # Close Validation _repr += ["+" + "-" * (width - 2) + "+\n"] # Create Suggestions header if suggestion_repr: _repr += [ "|" + " " * int(np.floor((width - 2 - len(suggestion_name)) / 2)), suggestion_name, " " * int(np.ceil((width - 2 - len(suggestion_name)) / 2)) + "|\n", "+" + "-" * (width - 2) + "+\n", ] # Iterate over suggestions for m, suggestions in suggestion_repr.items(): left_col = "| " + m + " |" if not suggestions: right_col = " " + " " * (width - 2 - len(left_col)) + "|\n" _repr += [left_col, right_col] for i, s in enumerate(suggestions): if i > 0: left_col = "| " + " " * len(m) + " |" right_col = " - " + s + " " * (width - 4 - len(s) - len(left_col)) + "|\n" _repr += [left_col, right_col] _repr += ["+" + "-" * (width - 2) + "+\n"] return "".join(_repr)[:-1] def _compute_p_value( data: pd.DataFrame, X: Union[List, str], Y: Union[List, str], Z: Optional[Union[Set, List, str]], independence_test: Optional[Callable[[np.ndarray, np.ndarray], float]], conditional_independence_test: Optional[Callable[[np.ndarray, np.ndarray, np.ndarray], float]], seed: int, ) -> float: """Perform (conditional) independence test and report p-value. If any nodes needed for the test do not exist in the data, this test is skipped and a value of -1 is returned and a warning is raised. :param data: Observations of variables in the DAG. :param X: Variable to test (conditional) independence with Y :param Y: Variable to test (conditional) independence with X :param Z: Set to condition independence test on. Can be empty (None, empty set, or empty list). :param independence_test: Independence test to use. :param conditional_independence_test: Conditional independence test to use. :param seed: Random seed :return: p-value """ set_random_seed(seed) # Test if we have data for X and Y for node in [X, Y]: if not node in data.columns: warnings.warn(f"WARN: Couldn't find data for node {node}. Skip this test.") return if Z: # Test if we have data for Z for node in Z: if not node in data.columns: warnings.warn(f"WARN: Couldn't find data for node {node}. Skip this test.") return p_value = conditional_independence_test(data[X].values, data[Y].values, data[Z].values) else: p_value = independence_test(data[X].values, data[Y].values) return p_value def _get_parental_triples(causal_graph: DirectedGraph, include_unconditional: bool): """ For a given graph collect all parental triples, that is, the triple (X, Y, Z) is a parental triple iff Y is non-descendant of X, and Z are the parents of X (can be empty if include_unconditional=True) """ triples = [] for node in causal_graph.nodes: parents = get_ordered_predecessors(causal_graph, node) non_descendants = _get_non_descendants(causal_graph, node, exclude_parents=True) if (parents or include_unconditional) and non_descendants: for non_desc in non_descendants: triples.append((node, non_desc, parents)) return triples def _permutation_based( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Union[Callable, Tuple[Callable, ...], List[Callable]], exclude_original_order: bool, n_permutations: int, show_progress_bar: bool, ) -> Dict[str, List[Union[DirectedGraph, Dict]]]: """ Generate baseline for node permutations. :param causal_graph: A directed acyclic graph (DAG). :param methods: Validation methods to perform. :param exclude_original_order: Exclude the original ordering of the nodes (default=False) :param n_permutations: Number of permutations to perform. If -1 use all n_nodes! - int(exclude_orig) permutations :param show_progress_bar: Whether to show progress bar over tested permutations. :return: Dictionary containing summary of validation for each individual graph as well as the permuted graphs """ if not isinstance(methods, (tuple, list)): methods = (methods,) perm_gen = _PermuteNodes(causal_graph, n_permutations=n_permutations, exclude_original_order=exclude_original_order) validation_summary = {FalsifyConst.PERM_GRAPHS: []} for permuted_graph in tqdm(perm_gen, desc="Test permutations of given graph", disable=not show_progress_bar): res = validate_graph( causal_graph=permuted_graph, data=data, methods=methods, ) validation_summary[FalsifyConst.PERM_GRAPHS].append(permuted_graph) for m_name, summary in res.items(): if m_name not in validation_summary: validation_summary[m_name] = [] validation_summary[m_name].append(summary) return validation_summary class _PermuteNodes: def __init__(self, causal_graph: DirectedGraph, exclude_original_order: bool, n_permutations: int): """ Randomly permute the nodes of a given causal graph while keeping the underlying graph structure the same. :param causal_graph: A directed acyclic graph (DAG). :param exclude_original_order: Do not return the original order. :param n_permutations: Return a generator with n_permutations permutations. If n_permutations = -1 (default), we return all n_nodes! - int(exclude_orig) permutations. :return: Copy of causal_graph with nodes randomly permuted. """ self.causal_graph = causal_graph self.exclude_original_order = exclude_original_order self.n_permutations = n_permutations self.max_perms = np.math.factorial(self.causal_graph.number_of_nodes()) - int(self.exclude_original_order) if n_permutations == -1 or n_permutations > self.max_perms: self.it = self.iter_all_permutations() self.length = self.max_perms if self.length > 2**63 - 1: raise ValueError( f"Too many permutations specified. Did you accidently set 'n_permutations'=-1 for a " f"large (>20 nodes) graph? " f"Given graph has {causal_graph.number_of_nodes()} nodes." ) else: self.it = self.iter_random_permutations() self.length = self.n_permutations def iter_all_permutations(self): for i, perm in enumerate(permutations(self.causal_graph.nodes)): if self.exclude_original_order and i == 0: continue mapping = {node: perm[i] for i, node in enumerate(self.causal_graph.nodes)} yield nx.relabel_nodes(self.causal_graph, mapping, copy=True) def iter_random_permutations(self): for _ in range(self.n_permutations): if self.exclude_original_order: is_orig = True while is_orig: perm = list(np.random.permutation(self.causal_graph.nodes)) if perm != list(self.causal_graph.nodes): is_orig = False else: perm = list(np.random.permutation(self.causal_graph.nodes)) mapping = {node: perm[i] for i, node in enumerate(self.causal_graph.nodes)} yield nx.relabel_nodes(self.causal_graph, mapping, copy=True) def __iter__(self): yield from self.it def __len__(self): return self.length def _to_frozenset(x: Union[Set, List, str]): """Converts a set, list or string into a hashable frozenset""" assert ( isinstance(x, Set) or isinstance(x, List) or isinstance(x, str) ), f"{x} must be list, set or str. Got {type(x)} instead!" if isinstance(x, str): return frozenset({x}) return frozenset(x)

"""This module provides functionality to falsify a user-given DAG given observed data. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from dataclasses import dataclass, field from enum import Enum, auto from functools import partial from inspect import getfullargspec from itertools import permutations from typing import Any, Callable, Dict, FrozenSet, List, Optional, Set, Tuple, Union import matplotlib.colors as mcolors import matplotlib.pyplot as plt import networkx as nx import numpy as np import pandas as pd from joblib import Parallel, delayed from tqdm import tqdm import dowhy.gcm.config as config from dowhy.gcm.independence_test import kernel_based from dowhy.gcm.util import plot from dowhy.gcm.util.general import set_random_seed from dowhy.gcm.validation import _get_non_descendants from dowhy.graph import DirectedGraph, get_ordered_predecessors VIOLATION_COLOR = "red" COLORS = list(mcolors.TABLEAU_COLORS.values()) class FalsifyConst(Enum): N_VIOLATIONS = auto() N_TESTS = auto() P_VALUE = auto() P_VALUES = auto() GIVEN_VIOLATIONS = auto() PERM_VIOLATIONS = auto() F_GIVEN_VIOLATIONS = auto() F_PERM_VIOLATIONS = auto() LOCAL_VIOLATION_INSIGHT = auto() METHOD = auto() VALIDATE_LMC = auto() VALIDATE_TPA = auto() VALIDATE_PD = auto() VALIDATE_CM = auto() PERM_GRAPHS = auto() MEC = auto() FALSIFY_METHODS = { FalsifyConst.VALIDATE_LMC: "LMC", FalsifyConst.VALIDATE_PD: "Faithfulness", FalsifyConst.VALIDATE_TPA: "TPa", FalsifyConst.VALIDATE_CM: "Causal Minimality", } @dataclass class _PValuesMemory: """A class to store and access results of independence tests. This class is useful if, e.g. we validate LMC on graph X -> Y -> Z and also on the permuted nodes Z -> Y -> X. Since X ind Z | Y == Z ind X | Y, we only need to perform the conditional independence test once. We use frozensets of nodes since standard (immutable) sets are not hashable. """ p_values: Dict[Tuple[FrozenSet, FrozenSet, FrozenSet], float] = field(default_factory=dict) def add_p_value( self, p_value: Union[None, float], X: Union[Set, List, str], Y: Union[Set, List, str], Z: Optional[Union[Set, List, str]] = None, ): if not Z: Z = set() self.p_values[(_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z))] = p_value def get_p_value( self, X: Union[Set, List, str], Y: Union[Set, List, str], Z: Optional[Union[Set, List, str]] = None ): if not Z: Z = set() # Independence tests are symmetric if (_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z)) in self.p_values: return self.p_values[(_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z))] elif (_to_frozenset(Y), _to_frozenset(X), _to_frozenset(Z)) in self.p_values: return self.p_values[(_to_frozenset(Y), _to_frozenset(X), _to_frozenset(Z))] return def __contains__(self, item: Tuple[Union[Set, List, str], ...]) -> bool: X, Y = (_to_frozenset(i) for i in item[:2]) if len(item) == 2 or item[2] is None: Z = frozenset() else: Z = _to_frozenset(item[2]) return (X, Y, Z) in self.p_values or (Y, X, Z) in self.p_values def validate_lmc( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, include_unconditional: bool = True, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[str, float]]]: """ Validate the local markov condition for a given directed graph. Return number of violations and p values for each node. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory instance, where results of previously performed tests are stored. :param independence_test: Test to use for unconditional independencies (only used if include_unconditional=True) :param conditional_independence_test: Conditional independence test to use for checking local Markov condition. :param significance_level: Significance level for (conditional) independence tests. :param include_unconditional: Test also unconditional independencies of root nodes. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Outcome of validation containing number of violations in the graph and p values/violation for each tuple (node, non_desc) """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_LMC, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, FalsifyConst.P_VALUES: dict(), } # Find out which tests to do triples = _get_parental_triples(causal_graph, include_unconditional) to_test = [] for node, non_desc, parents in triples: if not (node, non_desc, parents) in p_values_memory: to_test.append((node, non_desc, parents)) p_values_memory.add_p_value(None, node, non_desc, parents) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=node, Y=non_desc, Z=parents, independence_test=independence_test, conditional_independence_test=conditional_independence_test, seed=int(seed), ) for (node, non_desc, parents), seed in zip(to_test, random_seeds) ) # Gather results for i, (node, non_desc, parents) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], node, non_desc, parents) # Summarize for node, non_desc, parents in triples: lmc_p_value = p_values_memory.get_p_value(node, non_desc, parents) if lmc_p_value is not None: validation_summary[FalsifyConst.N_TESTS] += 1 validation_summary[FalsifyConst.P_VALUES][(node, non_desc)] = ( lmc_p_value, lmc_p_value <= significance_level, ) if lmc_p_value <= significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_tpa( causal_graph: DirectedGraph, causal_graph_reference: DirectedGraph, include_unconditional: bool = True, ) -> Dict[str, int]: """ Graphical criterion to evaluate which pairwise parental d-separations (parental triples) in `causal_graph` are violated, assuming `causal_graph_reference` is the ground truth graph. If none are violated, then both graphs lie in the same Markov equivalence class. Specifically we test: X _|_G' Y | Z and X _/|_G Y | Z for Y \in ND{X}^G', Z = PA{X}^G :param causal_graph: Causal graph for which to evaluate parental d-separations (G') :param causal_graph_reference: Causal graph where we test if d-separation holds (G) :param include_unconditional: Test also unconditional independencies of root nodes. :return: Validation summary with number of d-separations implied by `causal_graph` and number of times these are violated in the graph `causal_graph_reference`. """ validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_TPA, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, } triples = _get_parental_triples(causal_graph, include_unconditional) for node, non_desc, parents in triples: validation_summary[FalsifyConst.N_TESTS] += 1 if not nx.d_separated(causal_graph_reference, {node}, {non_desc}, set(parents)): validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_pd( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, n_pairs: int = -1, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, adjacent_only: bool = False, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[tuple, float]]]: """ Validate pairwise dependencies (pd) for a given causal graph and data. Test for each node if it is statistically dependent of all its ancestors. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory object, where results of previously performed tests are stored. :param n_pairs: Evaluate dependencies for n_pairs <= all pairs in the DAG. If n_pairs=-1, evaluate dependencies for all (ancestor, node) pairs (default). :param independence_test: Independence test to use for checking pairwise dependencies. :param significance_level: Significance level for independence tests. :param adjacent_only: Only test adjacent node pairs. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Summary dict: {n_violations: int, n_tests: int, p_values: {(ancestor, node): float, ...}} """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() pairs = [(ancestor, node) for node in causal_graph.nodes for ancestor in nx.ancestors(causal_graph, node)] if adjacent_only: pairs = [ (ancestor, node) for (ancestor, node) in pairs if ancestor in get_ordered_predecessors(causal_graph, node) ] if n_pairs < 0: n_pairs = len(pairs) if n_pairs > len(pairs): raise ValueError(f"n_pairs ({n_pairs}) > number of pairs in the DAG ({len(pairs)})") validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_PD, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: n_pairs, FalsifyConst.P_VALUES: dict(), } pair_idxs = np.random.choice(len(pairs), size=n_pairs, replace=False) # Find out which tests to do to_test = [] for pair_idx in pair_idxs: ancestor, node = pairs[pair_idx] if not (ancestor, node) in p_values_memory: to_test.append((ancestor, node)) p_values_memory.add_p_value(None, ancestor, node) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=ancestor, Y=node, Z=None, independence_test=independence_test, conditional_independence_test=None, seed=int(seed), ) for (ancestor, node), seed in zip(to_test, random_seeds) ) # Gather results for i, (ancestor, node) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], ancestor, node) # Summarize for pair_idx in pair_idxs: ancestor, node = pairs[pair_idx] p_value = p_values_memory.get_p_value(ancestor, node) if p_value is not None: validation_summary[FalsifyConst.P_VALUES][(node, ancestor)] = (p_value, p_value > significance_level) if p_value > significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_cm( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[tuple, float]]]: """ Function to test causal minimality of a DAG (see [1], Proposition 6.36). [1] J. Peters, D. Janzing, and B. Schölkopf, Elements of Causal Inference: Foundations and Learning Algorithms. Cambridge, MA, USA: MIT Press, 2017. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory object, where results of previously performed tests are stored. :param independence_test: Independence test to use. :param conditional_independence_test: Conditional independence test to use. :param significance_level: Significance level for independence tests. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Validation summary as dict. """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_CM, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, FalsifyConst.P_VALUES: dict(), } # Find out which tests to do triples = [] to_test = [] for node in causal_graph.nodes: parents = set(causal_graph.predecessors(node)) if parents: for p in parents: other_parents = parents.difference({p}) triples.append((node, p, other_parents)) if not (node, p, other_parents) in p_values_memory: to_test.append((node, p, other_parents)) p_values_memory.add_p_value(None, node, p, other_parents) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=node, Y=p, Z=list(other_parents), independence_test=independence_test, conditional_independence_test=conditional_independence_test, seed=int(seed), ) for (node, p, other_parents), seed in zip(to_test, random_seeds) ) # Gather results for i, (node, p, other_parents) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], node, p, other_parents) # Summarize for node, p, other_parents in triples: p_value = p_values_memory.get_p_value(node, p, other_parents) if p_value is not None: validation_summary[FalsifyConst.N_TESTS] += 1 validation_summary[FalsifyConst.P_VALUES][(node, p, tuple(other_parents))] = ( p_value, p_value > significance_level, ) if p_value > significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def run_validations( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Optional[Union[Callable, Tuple[Callable, ...], List[Callable]]] = partial( validate_lmc, independence_test=kernel_based, conditional_independence_test=kernel_based ), ) -> Dict[str, Dict]: """ Validate a given causal graph using observational data and some given methods. If methods are provided, they must be wrapped in a partial object, with their respective parameters. E.g., if one wants to test the local Markov conditions and the pairwise dependencies (unconditional faithfulness), then call run_validations(G, data, methods=( partial(validate_lmc, independence_test=..., conditional_independence_test=...), partial(validate_pd, independence_test=...), ) ) :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param methods: Method functions wrapped in wrap_partial. E.g. wrap_partial(validate_lmc, data=data, independence_test=..., conditional_independence_test=...). If no methods are provided we run validate_lmc with optional keyword arguments provided to run_validations. :return: Validation summary as dict. """ if not isinstance(methods, (tuple, list)): methods = (methods,) validation_summary = dict() for m in methods: if "data" in getfullargspec(m).args: m = partial(m, data=data) m_summary = m(causal_graph=causal_graph) m_name = m_summary.pop(FalsifyConst.METHOD) validation_summary[m_name] = m_summary return validation_summary @dataclass class EvaluationResult: """ Dataset class containing the evaluation result of falsifying a graph using a node-permutation test. ... Attributes ---------- summary : dict Dictionary containing the summary of the evaluation. significance_level : float Significance level based on which we falsify the given DAG falsifiable : bool Whether the given DAG is falsifiable. falsified : bool Whether the given DAG is falsified. """ summary: dict significance_level: float suggestions: Optional[dict] = None def update_significance_level(self, significance_level: float): """ Update the significance level to decide if we falsify a given DAG. """ self.significance_level = significance_level self.__post_init__() def __post_init__(self): self.can_evaluate = self._can_evaluate() if not self.can_evaluate: self.falsified = None self.falsifiable = None elif ( self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.P_VALUE] > self.significance_level > self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE] ): self.falsified = True self.falsifiable = True elif self.significance_level < self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE]: self.falsified = False self.falsifiable = False else: self.falsified = False self.falsifiable = True def __repr__(self): # DAG Evaluation if self.can_evaluate: decision = " " if self.falsified else " do not " informative = " " if self.falsifiable else " not " frac_MEC = f"{len(self.summary[FalsifyConst.MEC])} / {len(self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.PERM_VIOLATIONS])}" frac_VLMC = f"{self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.GIVEN_VIOLATIONS]}/{self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.N_TESTS]}" p_LMC = self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.P_VALUE] p_dSep = self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE] validation_repr = [ f"The given DAG is{informative}informative because {frac_MEC} of the permutations lie in the Markov", f"equivalence class of the given DAG (p-value: {p_dSep:.2f}).", f"The given DAG violates {frac_VLMC} LMCs and is better than {(1 - p_LMC) * 100:.1f}% of the permuted DAGs (p-value: {p_LMC:.2f}).", f"Based on the provided significance level ({self.significance_level}) and because the DAG is{informative}informative,", f"we{decision}reject the DAG.", ] else: validation_repr = ["Cannot be evaluated!"] # Suggestions suggestion_repr = {} for m in self.suggestions: suggestion_repr[FALSIFY_METHODS[m]] = [ f"Remove edge {node[1]} --> {node[0]}" for (node, r) in self.suggestions[m][FalsifyConst.P_VALUES].items() if r[1] ] return _generate_table(validation_repr, suggestion_repr) def _can_evaluate(self): can_evaluate = True for m in (FalsifyConst.VALIDATE_LMC, FalsifyConst.VALIDATE_TPA): if m not in self.summary: can_evaluate = False return can_evaluate def falsify_graph( causal_graph: DirectedGraph, data: pd.DataFrame, suggestions: bool = False, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, significance_ci: float = 0.05, n_permutations: Optional[int] = None, show_progress_bar: Optional[bool] = None, n_jobs: Optional[int] = None, plot_histogram: bool = False, plot_kwargs: Optional[Dict] = None, ) -> EvaluationResult: """ Falsify a given DAG using observational data. This method returns the result of a permutation-test to falsify a user-given DAG using observational data. To this end we construct the test statistics by testing the violations of local Markov conditions (LMC) implied by the graph using conditional independence (CI) tests. The null is the number of LMC violations of a random node-permutation of the given graph. Our test can be interpreted as whether the given graph is significantly better than random in terms of the CIs it entails. To determine whether a given graph is falsifiable by our metric, we implement a second test, which reports whether given graph is "characteristic" enough in terms of the CIs it entails. For this, we compute how many of the random node permutations lie in the same Markov equivalence class (MEC) as the given graph and conclude that the given graph is falsifiable only if the fraction of permuted DAGs in the same MEC as the given graph is "reasonably" small. The returned EvaluationResult object has two attributes: `falsified` and `falsifiable`: `falsifiable`: The given graph lies in a different MEC than >= 1-`significance_level` of the permuted DAGs `falsified`: The given graph is falsifiable and violates fewer LMCs than >= 1-`significance_level` of the permuted DAGs By default, we only run 1 / `significance_level` permutations as those are enough to falsify a graph with type I error probability `significance_level` at some given `significance_level`. If you are interested in a more exact estimate of the p-value or wish to plot a histogram to see how the given DAG compares to random node permutations, you should set `n_permutations` to some larger value (e.g. 100 or 1000). If `n_permutations=-1` we test on all n_nodes! permutations (the default if plot_histogram=True). Additionally, this method allows to return suggestions to the user (suggestions=True). This is done by testing for violations of causal minimality via `validate_cm`. Related paper: Eulig, E., Mastakouri, A. A., Blöbaum, P., Hardt, M., & Janzing, D. (2023). Toward Falsifying Causal Graphs Using a Permutation-Based Test. https://arxiv.org/abs/2305.09565 :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param suggestions: Provide suggestions to the user. At the moment the only source of suggestions comes from validating causal minimality (using validate_cm). :param independence_test: Independence test to use for checking pairwise independencies. :param conditional_independence_test: Conditional independence test to use. :param significance_level: Significance level for the permutation test. :param significance_ci: Significance level for (conditional) independence tests. :param n_permutations: Number of permutations to perform. If -1 use all n_nodes! permutations. :param show_progress_bar: Whether to show progress bar over permutations. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :param plot_histogram: Plot histogram of results from permutation baseline. :param plot_kwargs: Additional plot arguments to be passed to plot_evaluation_results. :return: EvaluationResult """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs show_progress_bar = config.show_progress_bars if show_progress_bar is None else show_progress_bar p_values_memory = _PValuesMemory() if n_permutations is None: n_permutations = int(1 / significance_level) if not plot_histogram else -1 if not plot_kwargs: plot_kwargs = {} methods = ( partial( validate_lmc, independence_test=independence_test, conditional_independence_test=conditional_independence_test, significance_level=significance_ci, p_values_memory=p_values_memory, n_jobs=n_jobs, ), partial(validate_tpa, causal_graph_reference=causal_graph), ) if suggestions: suggestion_methods = ( partial( validate_cm, independence_test=independence_test, conditional_independence_test=conditional_independence_test, significance_level=significance_ci, p_values_memory=p_values_memory, n_jobs=n_jobs, ), ) else: suggestion_methods = tuple() summary_given = run_validations( causal_graph, data, methods=methods + suggestion_methods, ) summary_perm = _permutation_based( causal_graph, data, methods=methods, exclude_original_order=False, n_permutations=n_permutations, show_progress_bar=show_progress_bar, ) summary = dict() validation_methods = set(summary_perm.keys()) - {FalsifyConst.PERM_GRAPHS} for m in validation_methods: summary[m] = dict() summary[m][FalsifyConst.PERM_VIOLATIONS] = [perm[FalsifyConst.N_VIOLATIONS] for perm in summary_perm[m]] summary[m][FalsifyConst.GIVEN_VIOLATIONS] = summary_given[m][FalsifyConst.N_VIOLATIONS] summary[m][FalsifyConst.N_TESTS] = summary_given[m][FalsifyConst.N_TESTS] summary[m][FalsifyConst.F_PERM_VIOLATIONS] = [ perm[FalsifyConst.N_VIOLATIONS] / perm[FalsifyConst.N_TESTS] for perm in summary_perm[m] ] summary[m][FalsifyConst.F_GIVEN_VIOLATIONS] = ( summary[m][FalsifyConst.GIVEN_VIOLATIONS] / summary[m][FalsifyConst.N_TESTS] ) summary[m][FalsifyConst.P_VALUE] = sum( [ 1 for perm in summary[m][FalsifyConst.F_PERM_VIOLATIONS] if perm <= summary[m][FalsifyConst.F_GIVEN_VIOLATIONS] ] ) / len(summary[m][FalsifyConst.PERM_VIOLATIONS]) if m != FalsifyConst.VALIDATE_TPA: # Append list of violations (node, non_desc) to get local information summary[m][FalsifyConst.LOCAL_VIOLATION_INSIGHT] = summary_given[m][FalsifyConst.P_VALUES] if FalsifyConst.VALIDATE_TPA in summary: summary[FalsifyConst.MEC] = [ summary_perm[FalsifyConst.PERM_GRAPHS][i] for i, v in enumerate(summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.PERM_VIOLATIONS]) if v == 0 ] result = EvaluationResult( summary=summary, significance_level=significance_level, suggestions={m: summary_given[m] for m in summary_given if m not in validation_methods}, ) if plot_histogram: plot_evaluation_results(result, **plot_kwargs) return result def apply_suggestions( causal_graph: DirectedGraph, evaluation_result: EvaluationResult, edges_to_keep: Optional[List[Tuple[Any, Any]]] = None, ): if not hasattr(evaluation_result, "suggestions"): raise ValueError("EvaluationResult object has no attribute suggestions. Please run with suggestion=True!") causal_graph = causal_graph.copy() for m in evaluation_result.suggestions: for node, res in evaluation_result.suggestions[m][FalsifyConst.P_VALUES].items(): edge = (node[1], node[0]) if (res[1] and edges_to_keep is not None and edge not in edges_to_keep) or ( res[1] and edges_to_keep is None ): causal_graph.remove_edge(edge[0], edge[1]) return causal_graph def plot_evaluation_results(evaluation_result, figsize=(8, 3), bins=None, title="", savepath="", display=True): fig, ax = plt.subplots(figsize=figsize) # Plot histograms p_values = "" data = [] labels = [] evaluation_summary = {k: v for k, v in evaluation_result.summary.items() if k != FalsifyConst.MEC} for i, (m, m_summary) in enumerate(evaluation_summary.items()): data.append(m_summary[FalsifyConst.F_PERM_VIOLATIONS]) labels.append(f"Violations of {FALSIFY_METHODS[m]} of permuted DAGs") p_values += f"p-value {FALSIFY_METHODS[m]} = {m_summary[FalsifyConst.P_VALUE]:.2f}\n" ax.hist(data, color=COLORS[: len(evaluation_summary)], bins=bins, alpha=0.5, label=labels, edgecolor="k") # Plot given violations for i, (m, m_summary) in enumerate(evaluation_summary.items()): ylim = ax.get_ylim()[1] ax.plot( [m_summary[FalsifyConst.F_GIVEN_VIOLATIONS]] * 2, [0, ylim], "--", c=COLORS[i], label=f"Violations of {FALSIFY_METHODS[m]} of given DAG", ) ax.set_ylim([0, ylim]) ax.set_xlabel("Fraction of violations") ax.set_ylabel("# Permutations") plt.legend(loc="upper left", bbox_to_anchor=(1.05, 1), borderaxespad=0.0, title=p_values) if title: plt.title(title) plt.tight_layout() if savepath: plt.savefig(savepath, dpi=300, bbox_inches="tight") if display: plt.show() else: plt.close() def plot_local_insights( causal_graph: DirectedGraph, evaluation_result: Union[EvaluationResult, Dict], method: Optional[str] = FalsifyConst.VALIDATE_LMC, ): """ For some given graph and evaluation result plot local violations. :param causal_graph: DiGraph :param evaluation_result: EvaluationResult :param method: Method for which to plot violations """ colors = {} if isinstance(evaluation_result, EvaluationResult) and method in evaluation_result.summary: local_insight_dict = evaluation_result.summary[method][FalsifyConst.LOCAL_VIOLATION_INSIGHT] elif ( isinstance(evaluation_result, EvaluationResult) and hasattr(evaluation_result, "suggestions") and method in evaluation_result.suggestions ): local_insight_dict = evaluation_result.suggestions[method][FalsifyConst.P_VALUES] elif isinstance(evaluation_result, Dict): if method not in evaluation_result: raise ValueError(f"Validation method {method} does not exist in given evaluation_result!") if FalsifyConst.P_VALUES not in evaluation_result[method]: raise ValueError( f"Validation method {method} has no key {FalsifyConst.P_VALUES} where information on local violations " f"are stored!" ) local_insight_dict = evaluation_result[method][FalsifyConst.P_VALUES] else: raise ValueError(f"Cannot plot local violation insights from method {method} in evaluation_result!") for nodes, result in local_insight_dict.items(): if result[1]: if method == FalsifyConst.VALIDATE_LMC: # For LMC we highlight X for which X _|/|_ Y \in ND_X | Pa_X colors[nodes[0]] = VIOLATION_COLOR elif method == FalsifyConst.VALIDATE_PD: # For PD we highlight the edge (if Y\in Anc_X -> X are adjacent) colors[(nodes[1], nodes[0])] = VIOLATION_COLOR elif method == FalsifyConst.VALIDATE_CM: # For causal minimality we highlight the edge Y \in Pa_X -> X colors[(nodes[1], nodes[0])] = VIOLATION_COLOR plot(causal_graph, colors=colors) def _generate_table( validation_repr, suggestion_repr, width=105, validation_name="Falsificaton Summary", suggestion_name="Suggestions" ): # Create Validation header _repr = [ "+" + "-" * (width - 2) + "+\n", "|" + " " * int(np.floor((width - 2 - len(validation_name)) / 2)), validation_name, " " * int(np.ceil((width - 2 - len(validation_name)) / 2)) + "|\n", "+" + "-" * (width - 2) + "+\n", ] # Create Validation summary _repr += [f"| {v_line}" + " " * (width - 3 - len(v_line)) + "|\n" for v_line in validation_repr] # Close Validation _repr += ["+" + "-" * (width - 2) + "+\n"] # Create Suggestions header if suggestion_repr: _repr += [ "|" + " " * int(np.floor((width - 2 - len(suggestion_name)) / 2)), suggestion_name, " " * int(np.ceil((width - 2 - len(suggestion_name)) / 2)) + "|\n", "+" + "-" * (width - 2) + "+\n", ] # Iterate over suggestions for m, suggestions in suggestion_repr.items(): left_col = "| " + m + " |" if not suggestions: right_col = " " + " " * (width - 2 - len(left_col)) + "|\n" _repr += [left_col, right_col] for i, s in enumerate(suggestions): if i > 0: left_col = "| " + " " * len(m) + " |" right_col = " - " + s + " " * (width - 4 - len(s) - len(left_col)) + "|\n" _repr += [left_col, right_col] _repr += ["+" + "-" * (width - 2) + "+\n"] return "".join(_repr)[:-1] def _compute_p_value( data: pd.DataFrame, X: Union[List, str], Y: Union[List, str], Z: Optional[Union[Set, List, str]], independence_test: Optional[Callable[[np.ndarray, np.ndarray], float]], conditional_independence_test: Optional[Callable[[np.ndarray, np.ndarray, np.ndarray], float]], seed: int, ) -> float: """Perform (conditional) independence test and report p-value. If any nodes needed for the test do not exist in the data, this test is skipped and a value of -1 is returned and a warning is raised. :param data: Observations of variables in the DAG. :param X: Variable to test (conditional) independence with Y :param Y: Variable to test (conditional) independence with X :param Z: Set to condition independence test on. Can be empty (None, empty set, or empty list). :param independence_test: Independence test to use. :param conditional_independence_test: Conditional independence test to use. :param seed: Random seed :return: p-value """ set_random_seed(seed) # Test if we have data for X and Y for node in [X, Y]: if not node in data.columns: warnings.warn(f"WARN: Couldn't find data for node {node}. Skip this test.") return if Z: # Test if we have data for Z for node in Z: if not node in data.columns: warnings.warn(f"WARN: Couldn't find data for node {node}. Skip this test.") return p_value = conditional_independence_test(data[X].values, data[Y].values, data[Z].values) else: p_value = independence_test(data[X].values, data[Y].values) return p_value def _get_parental_triples(causal_graph: DirectedGraph, include_unconditional: bool): """ For a given graph collect all parental triples, that is, the triple (X, Y, Z) is a parental triple iff Y is non-descendant of X, and Z are the parents of X (can be empty if include_unconditional=True) """ triples = [] for node in causal_graph.nodes: parents = get_ordered_predecessors(causal_graph, node) non_descendants = _get_non_descendants(causal_graph, node, exclude_parents=True) if (parents or include_unconditional) and non_descendants: for non_desc in non_descendants: triples.append((node, non_desc, parents)) return triples def _permutation_based( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Union[Callable, Tuple[Callable, ...], List[Callable]], exclude_original_order: bool, n_permutations: int, show_progress_bar: bool, ) -> Dict[str, List[Union[DirectedGraph, Dict]]]: """ Generate baseline for node permutations. :param causal_graph: A directed acyclic graph (DAG). :param methods: Validation methods to perform. :param exclude_original_order: Exclude the original ordering of the nodes (default=False) :param n_permutations: Number of permutations to perform. If -1 use all n_nodes! - int(exclude_orig) permutations :param show_progress_bar: Whether to show progress bar over tested permutations. :return: Dictionary containing summary of validation for each individual graph as well as the permuted graphs """ if not isinstance(methods, (tuple, list)): methods = (methods,) perm_gen = _PermuteNodes(causal_graph, n_permutations=n_permutations, exclude_original_order=exclude_original_order) validation_summary = {FalsifyConst.PERM_GRAPHS: []} for permuted_graph in tqdm(perm_gen, desc="Test permutations of given graph", disable=not show_progress_bar): res = run_validations( causal_graph=permuted_graph, data=data, methods=methods, ) validation_summary[FalsifyConst.PERM_GRAPHS].append(permuted_graph) for m_name, summary in res.items(): if m_name not in validation_summary: validation_summary[m_name] = [] validation_summary[m_name].append(summary) return validation_summary class _PermuteNodes: def __init__(self, causal_graph: DirectedGraph, exclude_original_order: bool, n_permutations: int): """ Randomly permute the nodes of a given causal graph while keeping the underlying graph structure the same. :param causal_graph: A directed acyclic graph (DAG). :param exclude_original_order: Do not return the original order. :param n_permutations: Return a generator with n_permutations permutations. If n_permutations = -1 (default), we return all n_nodes! - int(exclude_orig) permutations. :return: Copy of causal_graph with nodes randomly permuted. """ self.causal_graph = causal_graph self.exclude_original_order = exclude_original_order self.n_permutations = n_permutations self.max_perms = np.math.factorial(self.causal_graph.number_of_nodes()) - int(self.exclude_original_order) if n_permutations == -1 or n_permutations > self.max_perms: self.it = self.iter_all_permutations() self.length = self.max_perms if self.length > 2**63 - 1: raise ValueError( f"Too many permutations specified. Did you accidently set 'n_permutations'=-1 for a " f"large (>20 nodes) graph? " f"Given graph has {causal_graph.number_of_nodes()} nodes." ) else: self.it = self.iter_random_permutations() self.length = self.n_permutations def iter_all_permutations(self): for i, perm in enumerate(permutations(self.causal_graph.nodes)): if self.exclude_original_order and i == 0: continue mapping = {node: perm[i] for i, node in enumerate(self.causal_graph.nodes)} yield nx.relabel_nodes(self.causal_graph, mapping, copy=True) def iter_random_permutations(self): for _ in range(self.n_permutations): if self.exclude_original_order: is_orig = True while is_orig: perm = list(np.random.permutation(self.causal_graph.nodes)) if perm != list(self.causal_graph.nodes): is_orig = False else: perm = list(np.random.permutation(self.causal_graph.nodes)) mapping = {node: perm[i] for i, node in enumerate(self.causal_graph.nodes)} yield nx.relabel_nodes(self.causal_graph, mapping, copy=True) def __iter__(self): yield from self.it def __len__(self): return self.length def _to_frozenset(x: Union[Set, List, str]): """Converts a set, list or string into a hashable frozenset""" assert ( isinstance(x, Set) or isinstance(x, List) or isinstance(x, str) ), f"{x} must be list, set or str. Got {type(x)} instead!" if isinstance(x, str): return frozenset({x}) return frozenset(x)

eeulig

379f188d674d5be6d644559f9f54852b87403f7c

4e30c63fe1113e35259e7fb3400a6f09bca4d699

What about using `nan` instead of `-1`? That makes it clearer.

bloebp

py-why/dowhy

Remove kwargs from falsify, improve docstrings

This commit updates `gcm/falsify` in the following ways: - Remove the `**kwargs` for `validate_graph` and `falsify_graph`. Instead, validation methods given to those functions should be wrapped in `wrap_partial`, e.g. if we want to validate a graph `G` and test local Markov conditions and pairwise dependencies on observations `data`, we would do ``` validate_graph(G, methods=( wrap_partial(validate_lmc, data=data, independence_test=..., conditional_independence_test=...), wrap_partial(validate_pd, data=data, independence_test=...), ) ) ``` By default (`methods=None`), `validate_graph` tests local Markov conditions and `falsify_graph` tests local Markov conditions and d-separation of parental triples. In this case `data` has to be set and optionally one can provide `independence_test`, `conditional_independence_test`, `significance_level`, and `n_jobs` via the keyword arguments of `validate_graph` / `falsify_graph`. - Use `Enum` for some of the constants - In `_PValuesMemory` the method `add_p_value ` always sets a p-value even if it was set already. This removes the need for the method `clear_placeholders` and allows `p_values` to be set to `-1`, indicating that the CI is not testable (because of missing data for a node, c.f. `_compute_p_value`) - If for some CI: X, Y | Z any of the nodes has no data (column missing in the provided dataframe), we skip this test and raise a warning. - Instead of using the raw number of violations in the plotted histogram and to compute the p-value, we use the relative number of violations. This will result in different p-values only if we have missing data for some nodes (and thus the number of tests is potentially different between different permutations) - Improve docstrings

2023-07-04 13:58:30+00:00

2023-07-18 14:19:10+00:00

dowhy/gcm/falsify.py

"""This module provides functionality to falsify a user-given DAG given observed data. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from dataclasses import dataclass, field from enum import Enum, auto from functools import partial from itertools import permutations from typing import Any, Callable, Dict, FrozenSet, List, Optional, Set, Tuple, Union import matplotlib.colors as mcolors import matplotlib.pyplot as plt import networkx as nx import numpy as np import pandas as pd from joblib import Parallel, delayed from tqdm import tqdm import dowhy.gcm.config as config from dowhy.gcm.independence_test import kernel_based from dowhy.gcm.util import plot from dowhy.gcm.util.general import set_random_seed from dowhy.gcm.validation import _get_non_descendants from dowhy.graph import DirectedGraph, get_ordered_predecessors VIOLATION_COLOR = "red" COLORS = list(mcolors.TABLEAU_COLORS.values()) class FalsifyConst(Enum): N_VIOLATIONS = auto() N_TESTS = auto() P_VALUE = auto() P_VALUES = auto() GIVEN_VIOLATIONS = auto() PERM_VIOLATIONS = auto() F_GIVEN_VIOLATIONS = auto() F_PERM_VIOLATIONS = auto() LOCAL_VIOLATION_INSIGHT = auto() METHOD = auto() VALIDATE_LMC = auto() VALIDATE_TPA = auto() VALIDATE_PD = auto() VALIDATE_CM = auto() PERM_GRAPHS = auto() MEC = auto() FALSIFY_METHODS = { FalsifyConst.VALIDATE_LMC: "LMC", FalsifyConst.VALIDATE_PD: "Faithfulness", FalsifyConst.VALIDATE_TPA: "tPa", FalsifyConst.VALIDATE_CM: "Causal Minimality", } @dataclass class _PValuesMemory: """A class to store and access results of independence tests. This class is useful if, e.g. we validate LMC on graph X -> Y -> Z and also on the permuted nodes Z -> Y -> X. Since X ind Z | Y == Z ind X | Y, we only need to perform the conditional independence test once. We use frozensets of nodes since standard (immutable) sets are not hashable. """ p_values: Dict[Tuple[FrozenSet, FrozenSet, FrozenSet], float] = field(default_factory=dict) def add_p_value( self, p_value: Union[None, float], X: Union[Set, List, str], Y: Union[Set, List, str], Z: Optional[Union[Set, List, str]] = None, ): if not Z: Z = set() self.p_values[(_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z))] = p_value def get_p_value( self, X: Union[Set, List, str], Y: Union[Set, List, str], Z: Optional[Union[Set, List, str]] = None ): if not Z: Z = set() # Independence tests are symmetric if (_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z)) in self.p_values: return self.p_values[(_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z))] elif (_to_frozenset(Y), _to_frozenset(X), _to_frozenset(Z)) in self.p_values: return self.p_values[(_to_frozenset(Y), _to_frozenset(X), _to_frozenset(Z))] return def __contains__(self, item: Tuple[Union[Set, List, str], ...]) -> bool: X, Y = (_to_frozenset(i) for i in item[:2]) if len(item) == 2 or item[2] is None: Z = frozenset() else: Z = _to_frozenset(item[2]) return (X, Y, Z) in self.p_values or (Y, X, Z) in self.p_values def validate_lmc( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, include_unconditional: bool = True, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[str, float]]]: """ Validate the local markov condition for a given directed graph. Return number of violations and p values for each node. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory instance, where results of previously performed tests are stored. :param independence_test: Test to use for unconditional independencies (only used if include_unconditional=True) :param conditional_independence_test: Conditional independence test to use for checking local Markov condition. :param significance_level: Significance level for (conditional) independence tests. :param include_unconditional: Test also unconditional independencies of root nodes. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Outcome of validation containing number of violations in the graph and p values/violation for each tuple (node, non_desc) """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_LMC, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, FalsifyConst.P_VALUES: dict(), } # Find out which tests to do triples = _get_parental_triples(causal_graph, include_unconditional) to_test = [] for node, non_desc, parents in triples: if not (node, non_desc, parents) in p_values_memory: to_test.append((node, non_desc, parents)) p_values_memory.add_p_value(None, node, non_desc, parents) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=node, Y=non_desc, Z=parents, independence_test=independence_test, conditional_independence_test=conditional_independence_test, seed=int(seed), ) for (node, non_desc, parents), seed in zip(to_test, random_seeds) ) # Gather results for i, (node, non_desc, parents) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], node, non_desc, parents) # Summarize for node, non_desc, parents in triples: lmc_p_value = p_values_memory.get_p_value(node, non_desc, parents) if lmc_p_value is not None: validation_summary[FalsifyConst.N_TESTS] += 1 validation_summary[FalsifyConst.P_VALUES][(node, non_desc)] = ( lmc_p_value, lmc_p_value <= significance_level, ) if lmc_p_value <= significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_tpa( causal_graph: DirectedGraph, causal_graph_reference: DirectedGraph, include_unconditional: bool = True, data: Optional[pd.DataFrame] = None, ) -> Dict[str, int]: """ Graphical criterion to evaluate which pairwise parental d-separations (parental triples) in `causal_graph` are violated, assuming `causal_graph_reference` is the ground truth graph. If none are violated, then both graphs lie in the same Markov equivalence class. Specifically we test: X _|_G' Y | Z and X _/|_G Y | Z for Y \in ND{X}^G', Z = PA{X}^G :param causal_graph: Causal graph for which to evaluate parental d-separations (G') :param causal_graph_reference: Causal graph where we test if d-separation holds (G) :param include_unconditional: Test also unconditional independencies of root nodes. :param data: IGNORED! No data is needed for this validation method and thus the data argument is ignored! :return: Validation summary with number of d-separations implied by `causal_graph` and number of times these are violated in the graph `causal_graph_reference`. """ validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_TPA, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, } triples = _get_parental_triples(causal_graph, include_unconditional) for node, non_desc, parents in triples: validation_summary[FalsifyConst.N_TESTS] += 1 if not nx.d_separated(causal_graph_reference, {node}, {non_desc}, set(parents)): validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_pd( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, n_pairs: int = -1, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, adjacent_only: bool = False, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[tuple, float]]]: """ Validate pairwise dependencies (pd) for a given causal graph and data. Test for each node if it is statistically dependent of all its ancestors. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory object, where results of previously performed tests are stored. :param n_pairs: Evaluate dependencies for n_pairs <= all pairs in the DAG. If n_pairs=-1, evaluate dependencies for all (ancestor, node) pairs (default). :param independence_test: Independence test to use for checking pairwise dependencies. :param significance_level: Significance level for independence tests. :param adjacent_only: Only test adjacent node pairs. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Summary dict: {n_violations: int, n_tests: int, p_values: {(ancestor, node): float, ...}} """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() pairs = [(ancestor, node) for node in causal_graph.nodes for ancestor in nx.ancestors(causal_graph, node)] if adjacent_only: pairs = [ (ancestor, node) for (ancestor, node) in pairs if ancestor in get_ordered_predecessors(causal_graph, node) ] if n_pairs < 0: n_pairs = len(pairs) if n_pairs > len(pairs): raise ValueError(f"n_pairs ({n_pairs}) > number of pairs in the DAG ({len(pairs)})") validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_PD, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: n_pairs, FalsifyConst.P_VALUES: dict(), } pair_idxs = np.random.choice(len(pairs), size=n_pairs, replace=False) # Find out which tests to do to_test = [] for pair_idx in pair_idxs: ancestor, node = pairs[pair_idx] if not (ancestor, node) in p_values_memory: to_test.append((ancestor, node)) p_values_memory.add_p_value(None, ancestor, node) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=ancestor, Y=node, Z=None, independence_test=independence_test, conditional_independence_test=None, seed=int(seed), ) for (ancestor, node), seed in zip(to_test, random_seeds) ) # Gather results for i, (ancestor, node) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], ancestor, node) # Summarize for pair_idx in pair_idxs: ancestor, node = pairs[pair_idx] p_value = p_values_memory.get_p_value(ancestor, node) if p_value is not None: validation_summary[FalsifyConst.P_VALUES][(node, ancestor)] = (p_value, p_value > significance_level) if p_value > significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_cm( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[tuple, float]]]: """ Function to test causal minimality of a DAG (see [1], Proposition 6.36). [1] J. Peters, D. Janzing, and B. Schölkopf, Elements of Causal Inference: Foundations and Learning Algorithms. Cambridge, MA, USA: MIT Press, 2017. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory object, where results of previously performed tests are stored. :param independence_test: Independence test to use. :param conditional_independence_test: Conditional independence test to use. :param significance_level: Significance level for independence tests. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Validation summary as dict. """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_CM, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, FalsifyConst.P_VALUES: dict(), } # Find out which tests to do triples = [] to_test = [] for node in causal_graph.nodes: parents = set(causal_graph.predecessors(node)) if parents: for p in parents: other_parents = parents.difference({p}) triples.append((node, p, other_parents)) if not (node, p, other_parents) in p_values_memory: to_test.append((node, p, other_parents)) p_values_memory.add_p_value(None, node, p, other_parents) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=node, Y=p, Z=list(other_parents), independence_test=independence_test, conditional_independence_test=conditional_independence_test, seed=int(seed), ) for (node, p, other_parents), seed in zip(to_test, random_seeds) ) # Gather results for i, (node, p, other_parents) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], node, p, other_parents) # Summarize for node, p, other_parents in triples: p_value = p_values_memory.get_p_value(node, p, other_parents) if p_value is not None: validation_summary[FalsifyConst.N_TESTS] += 1 validation_summary[FalsifyConst.P_VALUES][(node, p, tuple(other_parents))] = ( p_value, p_value > significance_level, ) if p_value > significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_graph( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Optional[Union[Callable, Tuple[Callable, ...], List[Callable]]] = partial( validate_lmc, independence_test=kernel_based, conditional_independence_test=kernel_based ), ) -> Dict[str, Dict]: """ Validate a given causal graph using observational data and some given methods. If methods are provided, they must be wrapped in a partial object, with their respective parameters. E.g., if one wants to test the local Markov conditions and the pairwise dependencies (unconditional faithfulness), then call validate_graph(G, data, methods=( partial(validate_lmc, independence_test=..., conditional_independence_test=...), partial(validate_pd, independence_test=...), ) ) :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param methods: Method functions wrapped in wrap_partial. E.g. wrap_partial(validate_lmc, data=data, independence_test=..., conditional_independence_test=...). If no methods are provided we run validate_lmc with optional keyword arguments provided to validate_graph. :return: Validation summary as dict. """ if not isinstance(methods, (tuple, list)): methods = (methods,) validation_summary = dict() for m in methods: m_summary = m(causal_graph=causal_graph, data=data) m_name = m_summary.pop(FalsifyConst.METHOD) validation_summary[m_name] = m_summary return validation_summary @dataclass class EvaluationResult: """ Dataset class containing the evaluation result of falsifying a graph using a node-permutation test. ... Attributes ---------- summary : dict Dictionary containing the summary of the evaluation. significance_level : float Significance level based on which we falsify the given DAG falsifiable : bool Whether the given DAG is falsifiable. falsified : bool Whether the given DAG is falsified. """ summary: dict significance_level: float suggestions: Optional[dict] = None def update_significance_level(self, significance_level: float): """ Update the significance level to decide if we falsify a given DAG. """ self.significance_level = significance_level self.__post_init__() def __post_init__(self): self.can_evaluate = self._can_evaluate() if not self.can_evaluate: self.falsified = None self.falsifiable = None elif ( self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.P_VALUE] > self.significance_level > self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE] ): self.falsified = True self.falsifiable = True elif self.significance_level < self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE]: self.falsified = False self.falsifiable = False else: self.falsified = False self.falsifiable = True def __repr__(self): # DAG Evaluation if self.can_evaluate: decision = " " if self.falsified else " do not " informative = " " if self.falsifiable else " not " frac_MEC = f"{len(self.summary[FalsifyConst.MEC])} / {len(self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.PERM_VIOLATIONS])}" frac_VLMC = f"{self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.GIVEN_VIOLATIONS]}/{self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.N_TESTS]}" p_LMC = self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.P_VALUE] p_dSep = self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE] validation_repr = [ f"The given DAG is{informative}informative because {frac_MEC} of the permutations lie in the Markov", f"equivalence class of the given DAG (p-value: {p_dSep:.2f}).", f"The given DAG violates {frac_VLMC} LMCs and is better than {(1 - p_LMC) * 100:.1f}% of the permuted DAGs (p-value: {p_LMC:.2f}).", f"Based on the provided significance level ({self.significance_level}) and because the DAG is{informative}informative,", f"we{decision}reject the DAG.", ] else: validation_repr = ["Cannot be evaluated!"] # Suggestions suggestion_repr = {} for m in self.suggestions: suggestion_repr[FALSIFY_METHODS[m]] = [ f"Remove edge {node[1]} --> {node[0]}" for (node, r) in self.suggestions[m][FalsifyConst.P_VALUES].items() if r[1] ] return _generate_table(validation_repr, suggestion_repr) def _can_evaluate(self): can_evaluate = True for m in (FalsifyConst.VALIDATE_LMC, FalsifyConst.VALIDATE_TPA): if m not in self.summary: can_evaluate = False return can_evaluate def falsify_graph( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Optional[Union[Callable, Tuple[Callable, ...]]] = None, suggestion_methods: Optional[Union[Callable, Tuple[Callable, ...]]] = None, suggestions: bool = False, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, significance_ci: float = 0.05, n_permutations: Optional[int] = None, show_progress_bar: Optional[bool] = None, n_jobs: Optional[int] = None, plot_histogram: bool = False, plot_kwargs: Optional[Dict] = None, ) -> EvaluationResult: """ Falsify a given DAG using observational data. This method returns the result of a permutation-test to falsify a user-given DAG using observational data. To this end we construct the test statistics by testing the violations of local Markov conditions (LMC) implied by the graph using conditional independence (CI) tests. The null is the number of LMC violations of a random node-permutation of the given graph. Our test can be interpreted as whether the given graph is significantly better than random in terms of the CIs it entails. To determine whether a given graph is falsifiable by our metric, we implement a second test, which reports whether given graph is "characteristic" enough in terms of the CIs it entails. For this, we compute how many of the random node permutations lie in the same Markov equivalence class (MEC) as the given graph and conclude that the given graph is falsifiable only if the fraction of permuted DAGs in the same MEC as the given graph is "reasonably" small. The returned EvaluationResult object has two attributes: `falsified` and `falsifiable`: `falsifiable`: The given graph lies in a different MEC than >= 1-`significance_level` of the permuted DAGs `falsified`: The given graph is falsifiable and violates fewer LMCs than >= 1-`significance_level` of the permuted DAGs By default, we only run 1 / `significance_level` permutations as those are enough to falsify a graph with type I error probability `significance_level` at some given `significance_level`. If you are interested in a more exact estimate of the p-value of whish to plot a histogram to see how the given DAG compares to random node permutations, you should set `n_permutations` to some larger value (e.g. 100 or 1000). If `n_permutations=-1` we test on all n_nodes! permutations. `methods` and `suggestion_methods` must be wrapped in partial(method, **kwargs) (c.f. `validate_graph`). Related paper: Eulig, E., Mastakouri, A. A., Blöbaum, P., Hardt, M., & Janzing, D. (2023). Toward Falsifying Causal Graphs Using a Permutation-Based Test. https://arxiv.org/abs/2305.09565 :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param methods: Validation methods to perform. :param suggestion_methods: Methods to run on the given graph to provide additional suggestions. :param suggestions: Provide suggestions generated using the `suggestion_methods`. :param independence_test: Independence test to use for checking pairwise independencies. :param conditional_independence_test: Conditional independence test to use. :param significance_level: Significance level for the permutation test. :param significance_ci: Significance level for (conditional) independence tests. :param n_permutations: Number of permutations to perform. If -1 use all n_nodes! permutations. :param show_progress_bar: Whether to show progress bar over permutations. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :param plot_histogram: Plot histogram of results from permutation baseline. :param plot_kwargs: Additional plot arguments to be passed to plot_evaluation_results. :return: EvaluationResult """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs show_progress_bar = config.show_progress_bars if show_progress_bar is None else show_progress_bar p_values_memory = _PValuesMemory() if n_permutations is None: n_permutations = int(1 / significance_level) if not plot_histogram else -1 if not plot_kwargs: plot_kwargs = {} # If no methods are provided, use default ones: validate_lmc, validate_tpa if not methods: methods = ( partial( validate_lmc, independence_test=independence_test, conditional_independence_test=conditional_independence_test, significance_level=significance_ci, p_values_memory=p_values_memory, n_jobs=n_jobs, ), partial(validate_tpa, causal_graph_reference=causal_graph), ) elif isinstance(methods, Callable): methods = (methods,) # If no suggestion methods are provided, but suggestions=True, use default ones: validate_cm if not suggestions: suggestion_methods = tuple() elif suggestions and isinstance(suggestion_methods, Callable): suggestion_methods = (suggestion_methods,) elif suggestions: suggestion_methods = ( partial( validate_cm, independence_test=independence_test, conditional_independence_test=conditional_independence_test, significance_level=significance_ci, p_values_memory=p_values_memory, n_jobs=n_jobs, ), ) summary_given = validate_graph( causal_graph, data, methods=methods + suggestion_methods, ) summary_perm = _permutation_based( causal_graph, data, methods=methods, exclude_original_order=False, n_permutations=n_permutations, show_progress_bar=show_progress_bar, ) summary = dict() validation_methods = set(summary_perm.keys()) - {FalsifyConst.PERM_GRAPHS} for m in validation_methods: summary[m] = dict() summary[m][FalsifyConst.PERM_VIOLATIONS] = [perm[FalsifyConst.N_VIOLATIONS] for perm in summary_perm[m]] summary[m][FalsifyConst.GIVEN_VIOLATIONS] = summary_given[m][FalsifyConst.N_VIOLATIONS] summary[m][FalsifyConst.N_TESTS] = summary_given[m][FalsifyConst.N_TESTS] summary[m][FalsifyConst.F_PERM_VIOLATIONS] = [ perm[FalsifyConst.N_VIOLATIONS] / perm[FalsifyConst.N_TESTS] for perm in summary_perm[m] ] summary[m][FalsifyConst.F_GIVEN_VIOLATIONS] = ( summary[m][FalsifyConst.GIVEN_VIOLATIONS] / summary[m][FalsifyConst.N_TESTS] ) summary[m][FalsifyConst.P_VALUE] = sum( [ 1 for perm in summary[m][FalsifyConst.F_PERM_VIOLATIONS] if perm <= summary[m][FalsifyConst.F_GIVEN_VIOLATIONS] ] ) / len(summary[m][FalsifyConst.PERM_VIOLATIONS]) if m != FalsifyConst.VALIDATE_TPA: # Append list of violations (node, non_desc) to get local information summary[m][FalsifyConst.LOCAL_VIOLATION_INSIGHT] = summary_given[m][FalsifyConst.P_VALUES] if FalsifyConst.VALIDATE_TPA in summary: summary[FalsifyConst.MEC] = [ summary_perm[FalsifyConst.PERM_GRAPHS][i] for i, v in enumerate(summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.PERM_VIOLATIONS]) if v == 0 ] result = EvaluationResult( summary=summary, significance_level=significance_level, suggestions={m: summary_given[m] for m in summary_given if m not in validation_methods}, ) if plot_histogram: plot_evaluation_results(result, **plot_kwargs) return result def apply_suggestions( causal_graph: DirectedGraph, evaluation_result: EvaluationResult, edges_to_keep: Optional[List[Tuple[Any, Any]]] = None, ): if not hasattr(evaluation_result, "suggestions"): raise ValueError("EvaluationResult object has no attribute suggestions. Please run with suggestion=True!") causal_graph = causal_graph.copy() for m in evaluation_result.suggestions: for node, res in evaluation_result.suggestions[m][FalsifyConst.P_VALUES].items(): edge = (node[1], node[0]) if (res[1] and edges_to_keep is not None and edge not in edges_to_keep) or ( res[1] and edges_to_keep is None ): causal_graph.remove_edge(edge[0], edge[1]) return causal_graph def plot_evaluation_results(evaluation_result, figsize=(8, 3), bins=None, title="", savepath="", display=True): fig, ax = plt.subplots(figsize=figsize) # Plot histograms p_values = "" data = [] labels = [] evaluation_summary = {k: v for k, v in evaluation_result.summary.items() if k != FalsifyConst.MEC} for i, (m, m_summary) in enumerate(evaluation_summary.items()): data.append(m_summary[FalsifyConst.F_PERM_VIOLATIONS]) labels.append(f"Violations of {FALSIFY_METHODS[m]} of permuted DAGs") p_values += f"p-value {FALSIFY_METHODS[m]} = {m_summary[FalsifyConst.P_VALUE]:.2f}\n" ax.hist(data, color=COLORS[: len(evaluation_summary)], bins=bins, alpha=0.5, label=labels, edgecolor="k") # Plot given violations for i, (m, m_summary) in enumerate(evaluation_summary.items()): ylim = ax.get_ylim()[1] ax.plot( [m_summary[FalsifyConst.F_GIVEN_VIOLATIONS]] * 2, [0, ylim], "--", c=COLORS[i], label=f"Violations of {FALSIFY_METHODS[m]} of given DAG", ) ax.set_ylim([0, ylim]) ax.set_xlabel("Fraction of violations") ax.set_ylabel("# Permutations") plt.legend(loc="upper left", bbox_to_anchor=(1.05, 1), borderaxespad=0.0, title=p_values) if title: plt.title(title) plt.tight_layout() if savepath: plt.savefig(savepath, dpi=300, bbox_inches="tight") if display: plt.show() else: plt.close() def plot_local_insights( causal_graph: DirectedGraph, evaluation_result: Union[EvaluationResult, Dict], method: Optional[str] = FalsifyConst.VALIDATE_LMC, ): """ For some given graph and evaluation result plot local violations. :param causal_graph: DiGraph :param evaluation_result: EvaluationResult :param method: Method for which to plot violations """ colors = {} if isinstance(evaluation_result, EvaluationResult) and method in evaluation_result.summary: local_insight_dict = evaluation_result.summary[method][FalsifyConst.LOCAL_VIOLATION_INSIGHT] elif ( isinstance(evaluation_result, EvaluationResult) and hasattr(evaluation_result, "suggestions") and method in evaluation_result.suggestions ): local_insight_dict = evaluation_result.suggestions[method][FalsifyConst.P_VALUES] elif isinstance(evaluation_result, Dict): if method not in evaluation_result: raise ValueError(f"Validation method {method} does not exist in given evaluation_result!") if FalsifyConst.P_VALUES not in evaluation_result[method]: raise ValueError( f"Validation method {method} has no key {FalsifyConst.P_VALUES} where information on local violations " f"are stored!" ) local_insight_dict = evaluation_result[method][FalsifyConst.P_VALUES] else: raise ValueError(f"Cannot plot local violation insights from method {method} in evaluation_result!") for nodes, result in local_insight_dict.items(): if result[1]: if method == FalsifyConst.VALIDATE_LMC: # For LMC we highlight X for which X _|/|_ Y \in ND_X | Pa_X colors[nodes[0]] = VIOLATION_COLOR elif method == FalsifyConst.VALIDATE_PD: # For PD we highlight the edge (if Y\in Anc_X -> X are adjacent) colors[(nodes[1], nodes[0])] = VIOLATION_COLOR elif method == FalsifyConst.VALIDATE_CM: # For causal minimality we highlight the edge Y \in Pa_X -> X colors[(nodes[1], nodes[0])] = VIOLATION_COLOR plot(causal_graph, colors=colors) def _generate_table( validation_repr, suggestion_repr, width=105, validation_name="Falsificaton Summary", suggestion_name="Suggestions" ): # Create Validation header _repr = [ "+" + "-" * (width - 2) + "+\n", "|" + " " * int(np.floor((width - 2 - len(validation_name)) / 2)), validation_name, " " * int(np.ceil((width - 2 - len(validation_name)) / 2)) + "|\n", "+" + "-" * (width - 2) + "+\n", ] # Create Validation summary _repr += [f"| {v_line}" + " " * (width - 3 - len(v_line)) + "|\n" for v_line in validation_repr] # Close Validation _repr += ["+" + "-" * (width - 2) + "+\n"] # Create Suggestions header if suggestion_repr: _repr += [ "|" + " " * int(np.floor((width - 2 - len(suggestion_name)) / 2)), suggestion_name, " " * int(np.ceil((width - 2 - len(suggestion_name)) / 2)) + "|\n", "+" + "-" * (width - 2) + "+\n", ] # Iterate over suggestions for m, suggestions in suggestion_repr.items(): left_col = "| " + m + " |" if not suggestions: right_col = " " + " " * (width - 2 - len(left_col)) + "|\n" _repr += [left_col, right_col] for i, s in enumerate(suggestions): if i > 0: left_col = "| " + " " * len(m) + " |" right_col = " - " + s + " " * (width - 4 - len(s) - len(left_col)) + "|\n" _repr += [left_col, right_col] _repr += ["+" + "-" * (width - 2) + "+\n"] return "".join(_repr)[:-1] def _compute_p_value( data: pd.DataFrame, X: Union[List, str], Y: Union[List, str], Z: Optional[Union[Set, List, str]], independence_test: Optional[Callable[[np.ndarray, np.ndarray], float]], conditional_independence_test: Optional[Callable[[np.ndarray, np.ndarray, np.ndarray], float]], seed: int, ) -> float: """Perform (conditional) independence test and report p-value. If any nodes needed for the test do not exist in the data, this test is skipped and a value of -1 is returned and a warning is raised. :param data: Observations of variables in the DAG. :param X: Variable to test (conditional) independence with Y :param Y: Variable to test (conditional) independence with X :param Z: Set to condition independence test on. Can be empty (None, empty set, or empty list). :param independence_test: Independence test to use. :param conditional_independence_test: Conditional independence test to use. :param seed: Random seed :return: p-value """ set_random_seed(seed) # Test if we have data for X and Y for node in [X, Y]: if not node in data.columns: warnings.warn(f"WARN: Couldn't find data for node {node}. Skip this test.") return if Z: # Test if we have data for Z for node in Z: if not node in data.columns: warnings.warn(f"WARN: Couldn't find data for node {node}. Skip this test.") return p_value = conditional_independence_test(data[X].values, data[Y].values, data[Z].values) else: p_value = independence_test(data[X].values, data[Y].values) return p_value def _get_parental_triples(causal_graph: DirectedGraph, include_unconditional: bool): """ For a given graph collect all parental triples, that is, the triple (X, Y, Z) is a parental triple iff Y is non-descendant of X, and Z are the parents of X (can be empty if include_unconditional=True) """ triples = [] for node in causal_graph.nodes: parents = get_ordered_predecessors(causal_graph, node) non_descendants = _get_non_descendants(causal_graph, node, exclude_parents=True) if (parents or include_unconditional) and non_descendants: for non_desc in non_descendants: triples.append((node, non_desc, parents)) return triples def _permutation_based( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Union[Callable, Tuple[Callable, ...], List[Callable]], exclude_original_order: bool, n_permutations: int, show_progress_bar: bool, ) -> Dict[str, List[Union[DirectedGraph, Dict]]]: """ Generate baseline for node permutations. :param causal_graph: A directed acyclic graph (DAG). :param methods: Validation methods to perform. :param exclude_original_order: Exclude the original ordering of the nodes (default=False) :param n_permutations: Number of permutations to perform. If -1 use all n_nodes! - int(exclude_orig) permutations :param show_progress_bar: Whether to show progress bar over tested permutations. :return: Dictionary containing summary of validation for each individual graph as well as the permuted graphs """ if not isinstance(methods, (tuple, list)): methods = (methods,) perm_gen = _PermuteNodes(causal_graph, n_permutations=n_permutations, exclude_original_order=exclude_original_order) validation_summary = {FalsifyConst.PERM_GRAPHS: []} for permuted_graph in tqdm(perm_gen, desc="Test permutations of given graph", disable=not show_progress_bar): res = validate_graph( causal_graph=permuted_graph, data=data, methods=methods, ) validation_summary[FalsifyConst.PERM_GRAPHS].append(permuted_graph) for m_name, summary in res.items(): if m_name not in validation_summary: validation_summary[m_name] = [] validation_summary[m_name].append(summary) return validation_summary class _PermuteNodes: def __init__(self, causal_graph: DirectedGraph, exclude_original_order: bool, n_permutations: int): """ Randomly permute the nodes of a given causal graph while keeping the underlying graph structure the same. :param causal_graph: A directed acyclic graph (DAG). :param exclude_original_order: Do not return the original order. :param n_permutations: Return a generator with n_permutations permutations. If n_permutations = -1 (default), we return all n_nodes! - int(exclude_orig) permutations. :return: Copy of causal_graph with nodes randomly permuted. """ self.causal_graph = causal_graph self.exclude_original_order = exclude_original_order self.n_permutations = n_permutations self.max_perms = np.math.factorial(self.causal_graph.number_of_nodes()) - int(self.exclude_original_order) if n_permutations == -1 or n_permutations > self.max_perms: self.it = self.iter_all_permutations() self.length = self.max_perms if self.length > 2**63 - 1: raise ValueError( f"Too many permutations specified. Did you accidently set 'n_permutations'=-1 for a " f"large (>20 nodes) graph? " f"Given graph has {causal_graph.number_of_nodes()} nodes." ) else: self.it = self.iter_random_permutations() self.length = self.n_permutations def iter_all_permutations(self): for i, perm in enumerate(permutations(self.causal_graph.nodes)): if self.exclude_original_order and i == 0: continue mapping = {node: perm[i] for i, node in enumerate(self.causal_graph.nodes)} yield nx.relabel_nodes(self.causal_graph, mapping, copy=True) def iter_random_permutations(self): for _ in range(self.n_permutations): if self.exclude_original_order: is_orig = True while is_orig: perm = list(np.random.permutation(self.causal_graph.nodes)) if perm != list(self.causal_graph.nodes): is_orig = False else: perm = list(np.random.permutation(self.causal_graph.nodes)) mapping = {node: perm[i] for i, node in enumerate(self.causal_graph.nodes)} yield nx.relabel_nodes(self.causal_graph, mapping, copy=True) def __iter__(self): yield from self.it def __len__(self): return self.length def _to_frozenset(x: Union[Set, List, str]): """Converts a set, list or string into a hashable frozenset""" assert ( isinstance(x, Set) or isinstance(x, List) or isinstance(x, str) ), f"{x} must be list, set or str. Got {type(x)} instead!" if isinstance(x, str): return frozenset({x}) return frozenset(x)

"""This module provides functionality to falsify a user-given DAG given observed data. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from dataclasses import dataclass, field from enum import Enum, auto from functools import partial from inspect import getfullargspec from itertools import permutations from typing import Any, Callable, Dict, FrozenSet, List, Optional, Set, Tuple, Union import matplotlib.colors as mcolors import matplotlib.pyplot as plt import networkx as nx import numpy as np import pandas as pd from joblib import Parallel, delayed from tqdm import tqdm import dowhy.gcm.config as config from dowhy.gcm.independence_test import kernel_based from dowhy.gcm.util import plot from dowhy.gcm.util.general import set_random_seed from dowhy.gcm.validation import _get_non_descendants from dowhy.graph import DirectedGraph, get_ordered_predecessors VIOLATION_COLOR = "red" COLORS = list(mcolors.TABLEAU_COLORS.values()) class FalsifyConst(Enum): N_VIOLATIONS = auto() N_TESTS = auto() P_VALUE = auto() P_VALUES = auto() GIVEN_VIOLATIONS = auto() PERM_VIOLATIONS = auto() F_GIVEN_VIOLATIONS = auto() F_PERM_VIOLATIONS = auto() LOCAL_VIOLATION_INSIGHT = auto() METHOD = auto() VALIDATE_LMC = auto() VALIDATE_TPA = auto() VALIDATE_PD = auto() VALIDATE_CM = auto() PERM_GRAPHS = auto() MEC = auto() FALSIFY_METHODS = { FalsifyConst.VALIDATE_LMC: "LMC", FalsifyConst.VALIDATE_PD: "Faithfulness", FalsifyConst.VALIDATE_TPA: "TPa", FalsifyConst.VALIDATE_CM: "Causal Minimality", } @dataclass class _PValuesMemory: """A class to store and access results of independence tests. This class is useful if, e.g. we validate LMC on graph X -> Y -> Z and also on the permuted nodes Z -> Y -> X. Since X ind Z | Y == Z ind X | Y, we only need to perform the conditional independence test once. We use frozensets of nodes since standard (immutable) sets are not hashable. """ p_values: Dict[Tuple[FrozenSet, FrozenSet, FrozenSet], float] = field(default_factory=dict) def add_p_value( self, p_value: Union[None, float], X: Union[Set, List, str], Y: Union[Set, List, str], Z: Optional[Union[Set, List, str]] = None, ): if not Z: Z = set() self.p_values[(_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z))] = p_value def get_p_value( self, X: Union[Set, List, str], Y: Union[Set, List, str], Z: Optional[Union[Set, List, str]] = None ): if not Z: Z = set() # Independence tests are symmetric if (_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z)) in self.p_values: return self.p_values[(_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z))] elif (_to_frozenset(Y), _to_frozenset(X), _to_frozenset(Z)) in self.p_values: return self.p_values[(_to_frozenset(Y), _to_frozenset(X), _to_frozenset(Z))] return def __contains__(self, item: Tuple[Union[Set, List, str], ...]) -> bool: X, Y = (_to_frozenset(i) for i in item[:2]) if len(item) == 2 or item[2] is None: Z = frozenset() else: Z = _to_frozenset(item[2]) return (X, Y, Z) in self.p_values or (Y, X, Z) in self.p_values def validate_lmc( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, include_unconditional: bool = True, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[str, float]]]: """ Validate the local markov condition for a given directed graph. Return number of violations and p values for each node. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory instance, where results of previously performed tests are stored. :param independence_test: Test to use for unconditional independencies (only used if include_unconditional=True) :param conditional_independence_test: Conditional independence test to use for checking local Markov condition. :param significance_level: Significance level for (conditional) independence tests. :param include_unconditional: Test also unconditional independencies of root nodes. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Outcome of validation containing number of violations in the graph and p values/violation for each tuple (node, non_desc) """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_LMC, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, FalsifyConst.P_VALUES: dict(), } # Find out which tests to do triples = _get_parental_triples(causal_graph, include_unconditional) to_test = [] for node, non_desc, parents in triples: if not (node, non_desc, parents) in p_values_memory: to_test.append((node, non_desc, parents)) p_values_memory.add_p_value(None, node, non_desc, parents) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=node, Y=non_desc, Z=parents, independence_test=independence_test, conditional_independence_test=conditional_independence_test, seed=int(seed), ) for (node, non_desc, parents), seed in zip(to_test, random_seeds) ) # Gather results for i, (node, non_desc, parents) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], node, non_desc, parents) # Summarize for node, non_desc, parents in triples: lmc_p_value = p_values_memory.get_p_value(node, non_desc, parents) if lmc_p_value is not None: validation_summary[FalsifyConst.N_TESTS] += 1 validation_summary[FalsifyConst.P_VALUES][(node, non_desc)] = ( lmc_p_value, lmc_p_value <= significance_level, ) if lmc_p_value <= significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_tpa( causal_graph: DirectedGraph, causal_graph_reference: DirectedGraph, include_unconditional: bool = True, ) -> Dict[str, int]: """ Graphical criterion to evaluate which pairwise parental d-separations (parental triples) in `causal_graph` are violated, assuming `causal_graph_reference` is the ground truth graph. If none are violated, then both graphs lie in the same Markov equivalence class. Specifically we test: X _|_G' Y | Z and X _/|_G Y | Z for Y \in ND{X}^G', Z = PA{X}^G :param causal_graph: Causal graph for which to evaluate parental d-separations (G') :param causal_graph_reference: Causal graph where we test if d-separation holds (G) :param include_unconditional: Test also unconditional independencies of root nodes. :return: Validation summary with number of d-separations implied by `causal_graph` and number of times these are violated in the graph `causal_graph_reference`. """ validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_TPA, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, } triples = _get_parental_triples(causal_graph, include_unconditional) for node, non_desc, parents in triples: validation_summary[FalsifyConst.N_TESTS] += 1 if not nx.d_separated(causal_graph_reference, {node}, {non_desc}, set(parents)): validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_pd( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, n_pairs: int = -1, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, adjacent_only: bool = False, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[tuple, float]]]: """ Validate pairwise dependencies (pd) for a given causal graph and data. Test for each node if it is statistically dependent of all its ancestors. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory object, where results of previously performed tests are stored. :param n_pairs: Evaluate dependencies for n_pairs <= all pairs in the DAG. If n_pairs=-1, evaluate dependencies for all (ancestor, node) pairs (default). :param independence_test: Independence test to use for checking pairwise dependencies. :param significance_level: Significance level for independence tests. :param adjacent_only: Only test adjacent node pairs. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Summary dict: {n_violations: int, n_tests: int, p_values: {(ancestor, node): float, ...}} """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() pairs = [(ancestor, node) for node in causal_graph.nodes for ancestor in nx.ancestors(causal_graph, node)] if adjacent_only: pairs = [ (ancestor, node) for (ancestor, node) in pairs if ancestor in get_ordered_predecessors(causal_graph, node) ] if n_pairs < 0: n_pairs = len(pairs) if n_pairs > len(pairs): raise ValueError(f"n_pairs ({n_pairs}) > number of pairs in the DAG ({len(pairs)})") validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_PD, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: n_pairs, FalsifyConst.P_VALUES: dict(), } pair_idxs = np.random.choice(len(pairs), size=n_pairs, replace=False) # Find out which tests to do to_test = [] for pair_idx in pair_idxs: ancestor, node = pairs[pair_idx] if not (ancestor, node) in p_values_memory: to_test.append((ancestor, node)) p_values_memory.add_p_value(None, ancestor, node) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=ancestor, Y=node, Z=None, independence_test=independence_test, conditional_independence_test=None, seed=int(seed), ) for (ancestor, node), seed in zip(to_test, random_seeds) ) # Gather results for i, (ancestor, node) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], ancestor, node) # Summarize for pair_idx in pair_idxs: ancestor, node = pairs[pair_idx] p_value = p_values_memory.get_p_value(ancestor, node) if p_value is not None: validation_summary[FalsifyConst.P_VALUES][(node, ancestor)] = (p_value, p_value > significance_level) if p_value > significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_cm( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[tuple, float]]]: """ Function to test causal minimality of a DAG (see [1], Proposition 6.36). [1] J. Peters, D. Janzing, and B. Schölkopf, Elements of Causal Inference: Foundations and Learning Algorithms. Cambridge, MA, USA: MIT Press, 2017. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory object, where results of previously performed tests are stored. :param independence_test: Independence test to use. :param conditional_independence_test: Conditional independence test to use. :param significance_level: Significance level for independence tests. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Validation summary as dict. """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_CM, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, FalsifyConst.P_VALUES: dict(), } # Find out which tests to do triples = [] to_test = [] for node in causal_graph.nodes: parents = set(causal_graph.predecessors(node)) if parents: for p in parents: other_parents = parents.difference({p}) triples.append((node, p, other_parents)) if not (node, p, other_parents) in p_values_memory: to_test.append((node, p, other_parents)) p_values_memory.add_p_value(None, node, p, other_parents) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=node, Y=p, Z=list(other_parents), independence_test=independence_test, conditional_independence_test=conditional_independence_test, seed=int(seed), ) for (node, p, other_parents), seed in zip(to_test, random_seeds) ) # Gather results for i, (node, p, other_parents) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], node, p, other_parents) # Summarize for node, p, other_parents in triples: p_value = p_values_memory.get_p_value(node, p, other_parents) if p_value is not None: validation_summary[FalsifyConst.N_TESTS] += 1 validation_summary[FalsifyConst.P_VALUES][(node, p, tuple(other_parents))] = ( p_value, p_value > significance_level, ) if p_value > significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def run_validations( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Optional[Union[Callable, Tuple[Callable, ...], List[Callable]]] = partial( validate_lmc, independence_test=kernel_based, conditional_independence_test=kernel_based ), ) -> Dict[str, Dict]: """ Validate a given causal graph using observational data and some given methods. If methods are provided, they must be wrapped in a partial object, with their respective parameters. E.g., if one wants to test the local Markov conditions and the pairwise dependencies (unconditional faithfulness), then call run_validations(G, data, methods=( partial(validate_lmc, independence_test=..., conditional_independence_test=...), partial(validate_pd, independence_test=...), ) ) :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param methods: Method functions wrapped in wrap_partial. E.g. wrap_partial(validate_lmc, data=data, independence_test=..., conditional_independence_test=...). If no methods are provided we run validate_lmc with optional keyword arguments provided to run_validations. :return: Validation summary as dict. """ if not isinstance(methods, (tuple, list)): methods = (methods,) validation_summary = dict() for m in methods: if "data" in getfullargspec(m).args: m = partial(m, data=data) m_summary = m(causal_graph=causal_graph) m_name = m_summary.pop(FalsifyConst.METHOD) validation_summary[m_name] = m_summary return validation_summary @dataclass class EvaluationResult: """ Dataset class containing the evaluation result of falsifying a graph using a node-permutation test. ... Attributes ---------- summary : dict Dictionary containing the summary of the evaluation. significance_level : float Significance level based on which we falsify the given DAG falsifiable : bool Whether the given DAG is falsifiable. falsified : bool Whether the given DAG is falsified. """ summary: dict significance_level: float suggestions: Optional[dict] = None def update_significance_level(self, significance_level: float): """ Update the significance level to decide if we falsify a given DAG. """ self.significance_level = significance_level self.__post_init__() def __post_init__(self): self.can_evaluate = self._can_evaluate() if not self.can_evaluate: self.falsified = None self.falsifiable = None elif ( self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.P_VALUE] > self.significance_level > self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE] ): self.falsified = True self.falsifiable = True elif self.significance_level < self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE]: self.falsified = False self.falsifiable = False else: self.falsified = False self.falsifiable = True def __repr__(self): # DAG Evaluation if self.can_evaluate: decision = " " if self.falsified else " do not " informative = " " if self.falsifiable else " not " frac_MEC = f"{len(self.summary[FalsifyConst.MEC])} / {len(self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.PERM_VIOLATIONS])}" frac_VLMC = f"{self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.GIVEN_VIOLATIONS]}/{self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.N_TESTS]}" p_LMC = self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.P_VALUE] p_dSep = self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE] validation_repr = [ f"The given DAG is{informative}informative because {frac_MEC} of the permutations lie in the Markov", f"equivalence class of the given DAG (p-value: {p_dSep:.2f}).", f"The given DAG violates {frac_VLMC} LMCs and is better than {(1 - p_LMC) * 100:.1f}% of the permuted DAGs (p-value: {p_LMC:.2f}).", f"Based on the provided significance level ({self.significance_level}) and because the DAG is{informative}informative,", f"we{decision}reject the DAG.", ] else: validation_repr = ["Cannot be evaluated!"] # Suggestions suggestion_repr = {} for m in self.suggestions: suggestion_repr[FALSIFY_METHODS[m]] = [ f"Remove edge {node[1]} --> {node[0]}" for (node, r) in self.suggestions[m][FalsifyConst.P_VALUES].items() if r[1] ] return _generate_table(validation_repr, suggestion_repr) def _can_evaluate(self): can_evaluate = True for m in (FalsifyConst.VALIDATE_LMC, FalsifyConst.VALIDATE_TPA): if m not in self.summary: can_evaluate = False return can_evaluate def falsify_graph( causal_graph: DirectedGraph, data: pd.DataFrame, suggestions: bool = False, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, significance_ci: float = 0.05, n_permutations: Optional[int] = None, show_progress_bar: Optional[bool] = None, n_jobs: Optional[int] = None, plot_histogram: bool = False, plot_kwargs: Optional[Dict] = None, ) -> EvaluationResult: """ Falsify a given DAG using observational data. This method returns the result of a permutation-test to falsify a user-given DAG using observational data. To this end we construct the test statistics by testing the violations of local Markov conditions (LMC) implied by the graph using conditional independence (CI) tests. The null is the number of LMC violations of a random node-permutation of the given graph. Our test can be interpreted as whether the given graph is significantly better than random in terms of the CIs it entails. To determine whether a given graph is falsifiable by our metric, we implement a second test, which reports whether given graph is "characteristic" enough in terms of the CIs it entails. For this, we compute how many of the random node permutations lie in the same Markov equivalence class (MEC) as the given graph and conclude that the given graph is falsifiable only if the fraction of permuted DAGs in the same MEC as the given graph is "reasonably" small. The returned EvaluationResult object has two attributes: `falsified` and `falsifiable`: `falsifiable`: The given graph lies in a different MEC than >= 1-`significance_level` of the permuted DAGs `falsified`: The given graph is falsifiable and violates fewer LMCs than >= 1-`significance_level` of the permuted DAGs By default, we only run 1 / `significance_level` permutations as those are enough to falsify a graph with type I error probability `significance_level` at some given `significance_level`. If you are interested in a more exact estimate of the p-value or wish to plot a histogram to see how the given DAG compares to random node permutations, you should set `n_permutations` to some larger value (e.g. 100 or 1000). If `n_permutations=-1` we test on all n_nodes! permutations (the default if plot_histogram=True). Additionally, this method allows to return suggestions to the user (suggestions=True). This is done by testing for violations of causal minimality via `validate_cm`. Related paper: Eulig, E., Mastakouri, A. A., Blöbaum, P., Hardt, M., & Janzing, D. (2023). Toward Falsifying Causal Graphs Using a Permutation-Based Test. https://arxiv.org/abs/2305.09565 :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param suggestions: Provide suggestions to the user. At the moment the only source of suggestions comes from validating causal minimality (using validate_cm). :param independence_test: Independence test to use for checking pairwise independencies. :param conditional_independence_test: Conditional independence test to use. :param significance_level: Significance level for the permutation test. :param significance_ci: Significance level for (conditional) independence tests. :param n_permutations: Number of permutations to perform. If -1 use all n_nodes! permutations. :param show_progress_bar: Whether to show progress bar over permutations. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :param plot_histogram: Plot histogram of results from permutation baseline. :param plot_kwargs: Additional plot arguments to be passed to plot_evaluation_results. :return: EvaluationResult """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs show_progress_bar = config.show_progress_bars if show_progress_bar is None else show_progress_bar p_values_memory = _PValuesMemory() if n_permutations is None: n_permutations = int(1 / significance_level) if not plot_histogram else -1 if not plot_kwargs: plot_kwargs = {} methods = ( partial( validate_lmc, independence_test=independence_test, conditional_independence_test=conditional_independence_test, significance_level=significance_ci, p_values_memory=p_values_memory, n_jobs=n_jobs, ), partial(validate_tpa, causal_graph_reference=causal_graph), ) if suggestions: suggestion_methods = ( partial( validate_cm, independence_test=independence_test, conditional_independence_test=conditional_independence_test, significance_level=significance_ci, p_values_memory=p_values_memory, n_jobs=n_jobs, ), ) else: suggestion_methods = tuple() summary_given = run_validations( causal_graph, data, methods=methods + suggestion_methods, ) summary_perm = _permutation_based( causal_graph, data, methods=methods, exclude_original_order=False, n_permutations=n_permutations, show_progress_bar=show_progress_bar, ) summary = dict() validation_methods = set(summary_perm.keys()) - {FalsifyConst.PERM_GRAPHS} for m in validation_methods: summary[m] = dict() summary[m][FalsifyConst.PERM_VIOLATIONS] = [perm[FalsifyConst.N_VIOLATIONS] for perm in summary_perm[m]] summary[m][FalsifyConst.GIVEN_VIOLATIONS] = summary_given[m][FalsifyConst.N_VIOLATIONS] summary[m][FalsifyConst.N_TESTS] = summary_given[m][FalsifyConst.N_TESTS] summary[m][FalsifyConst.F_PERM_VIOLATIONS] = [ perm[FalsifyConst.N_VIOLATIONS] / perm[FalsifyConst.N_TESTS] for perm in summary_perm[m] ] summary[m][FalsifyConst.F_GIVEN_VIOLATIONS] = ( summary[m][FalsifyConst.GIVEN_VIOLATIONS] / summary[m][FalsifyConst.N_TESTS] ) summary[m][FalsifyConst.P_VALUE] = sum( [ 1 for perm in summary[m][FalsifyConst.F_PERM_VIOLATIONS] if perm <= summary[m][FalsifyConst.F_GIVEN_VIOLATIONS] ] ) / len(summary[m][FalsifyConst.PERM_VIOLATIONS]) if m != FalsifyConst.VALIDATE_TPA: # Append list of violations (node, non_desc) to get local information summary[m][FalsifyConst.LOCAL_VIOLATION_INSIGHT] = summary_given[m][FalsifyConst.P_VALUES] if FalsifyConst.VALIDATE_TPA in summary: summary[FalsifyConst.MEC] = [ summary_perm[FalsifyConst.PERM_GRAPHS][i] for i, v in enumerate(summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.PERM_VIOLATIONS]) if v == 0 ] result = EvaluationResult( summary=summary, significance_level=significance_level, suggestions={m: summary_given[m] for m in summary_given if m not in validation_methods}, ) if plot_histogram: plot_evaluation_results(result, **plot_kwargs) return result def apply_suggestions( causal_graph: DirectedGraph, evaluation_result: EvaluationResult, edges_to_keep: Optional[List[Tuple[Any, Any]]] = None, ): if not hasattr(evaluation_result, "suggestions"): raise ValueError("EvaluationResult object has no attribute suggestions. Please run with suggestion=True!") causal_graph = causal_graph.copy() for m in evaluation_result.suggestions: for node, res in evaluation_result.suggestions[m][FalsifyConst.P_VALUES].items(): edge = (node[1], node[0]) if (res[1] and edges_to_keep is not None and edge not in edges_to_keep) or ( res[1] and edges_to_keep is None ): causal_graph.remove_edge(edge[0], edge[1]) return causal_graph def plot_evaluation_results(evaluation_result, figsize=(8, 3), bins=None, title="", savepath="", display=True): fig, ax = plt.subplots(figsize=figsize) # Plot histograms p_values = "" data = [] labels = [] evaluation_summary = {k: v for k, v in evaluation_result.summary.items() if k != FalsifyConst.MEC} for i, (m, m_summary) in enumerate(evaluation_summary.items()): data.append(m_summary[FalsifyConst.F_PERM_VIOLATIONS]) labels.append(f"Violations of {FALSIFY_METHODS[m]} of permuted DAGs") p_values += f"p-value {FALSIFY_METHODS[m]} = {m_summary[FalsifyConst.P_VALUE]:.2f}\n" ax.hist(data, color=COLORS[: len(evaluation_summary)], bins=bins, alpha=0.5, label=labels, edgecolor="k") # Plot given violations for i, (m, m_summary) in enumerate(evaluation_summary.items()): ylim = ax.get_ylim()[1] ax.plot( [m_summary[FalsifyConst.F_GIVEN_VIOLATIONS]] * 2, [0, ylim], "--", c=COLORS[i], label=f"Violations of {FALSIFY_METHODS[m]} of given DAG", ) ax.set_ylim([0, ylim]) ax.set_xlabel("Fraction of violations") ax.set_ylabel("# Permutations") plt.legend(loc="upper left", bbox_to_anchor=(1.05, 1), borderaxespad=0.0, title=p_values) if title: plt.title(title) plt.tight_layout() if savepath: plt.savefig(savepath, dpi=300, bbox_inches="tight") if display: plt.show() else: plt.close() def plot_local_insights( causal_graph: DirectedGraph, evaluation_result: Union[EvaluationResult, Dict], method: Optional[str] = FalsifyConst.VALIDATE_LMC, ): """ For some given graph and evaluation result plot local violations. :param causal_graph: DiGraph :param evaluation_result: EvaluationResult :param method: Method for which to plot violations """ colors = {} if isinstance(evaluation_result, EvaluationResult) and method in evaluation_result.summary: local_insight_dict = evaluation_result.summary[method][FalsifyConst.LOCAL_VIOLATION_INSIGHT] elif ( isinstance(evaluation_result, EvaluationResult) and hasattr(evaluation_result, "suggestions") and method in evaluation_result.suggestions ): local_insight_dict = evaluation_result.suggestions[method][FalsifyConst.P_VALUES] elif isinstance(evaluation_result, Dict): if method not in evaluation_result: raise ValueError(f"Validation method {method} does not exist in given evaluation_result!") if FalsifyConst.P_VALUES not in evaluation_result[method]: raise ValueError( f"Validation method {method} has no key {FalsifyConst.P_VALUES} where information on local violations " f"are stored!" ) local_insight_dict = evaluation_result[method][FalsifyConst.P_VALUES] else: raise ValueError(f"Cannot plot local violation insights from method {method} in evaluation_result!") for nodes, result in local_insight_dict.items(): if result[1]: if method == FalsifyConst.VALIDATE_LMC: # For LMC we highlight X for which X _|/|_ Y \in ND_X | Pa_X colors[nodes[0]] = VIOLATION_COLOR elif method == FalsifyConst.VALIDATE_PD: # For PD we highlight the edge (if Y\in Anc_X -> X are adjacent) colors[(nodes[1], nodes[0])] = VIOLATION_COLOR elif method == FalsifyConst.VALIDATE_CM: # For causal minimality we highlight the edge Y \in Pa_X -> X colors[(nodes[1], nodes[0])] = VIOLATION_COLOR plot(causal_graph, colors=colors) def _generate_table( validation_repr, suggestion_repr, width=105, validation_name="Falsificaton Summary", suggestion_name="Suggestions" ): # Create Validation header _repr = [ "+" + "-" * (width - 2) + "+\n", "|" + " " * int(np.floor((width - 2 - len(validation_name)) / 2)), validation_name, " " * int(np.ceil((width - 2 - len(validation_name)) / 2)) + "|\n", "+" + "-" * (width - 2) + "+\n", ] # Create Validation summary _repr += [f"| {v_line}" + " " * (width - 3 - len(v_line)) + "|\n" for v_line in validation_repr] # Close Validation _repr += ["+" + "-" * (width - 2) + "+\n"] # Create Suggestions header if suggestion_repr: _repr += [ "|" + " " * int(np.floor((width - 2 - len(suggestion_name)) / 2)), suggestion_name, " " * int(np.ceil((width - 2 - len(suggestion_name)) / 2)) + "|\n", "+" + "-" * (width - 2) + "+\n", ] # Iterate over suggestions for m, suggestions in suggestion_repr.items(): left_col = "| " + m + " |" if not suggestions: right_col = " " + " " * (width - 2 - len(left_col)) + "|\n" _repr += [left_col, right_col] for i, s in enumerate(suggestions): if i > 0: left_col = "| " + " " * len(m) + " |" right_col = " - " + s + " " * (width - 4 - len(s) - len(left_col)) + "|\n" _repr += [left_col, right_col] _repr += ["+" + "-" * (width - 2) + "+\n"] return "".join(_repr)[:-1] def _compute_p_value( data: pd.DataFrame, X: Union[List, str], Y: Union[List, str], Z: Optional[Union[Set, List, str]], independence_test: Optional[Callable[[np.ndarray, np.ndarray], float]], conditional_independence_test: Optional[Callable[[np.ndarray, np.ndarray, np.ndarray], float]], seed: int, ) -> float: """Perform (conditional) independence test and report p-value. If any nodes needed for the test do not exist in the data, this test is skipped and a value of -1 is returned and a warning is raised. :param data: Observations of variables in the DAG. :param X: Variable to test (conditional) independence with Y :param Y: Variable to test (conditional) independence with X :param Z: Set to condition independence test on. Can be empty (None, empty set, or empty list). :param independence_test: Independence test to use. :param conditional_independence_test: Conditional independence test to use. :param seed: Random seed :return: p-value """ set_random_seed(seed) # Test if we have data for X and Y for node in [X, Y]: if not node in data.columns: warnings.warn(f"WARN: Couldn't find data for node {node}. Skip this test.") return if Z: # Test if we have data for Z for node in Z: if not node in data.columns: warnings.warn(f"WARN: Couldn't find data for node {node}. Skip this test.") return p_value = conditional_independence_test(data[X].values, data[Y].values, data[Z].values) else: p_value = independence_test(data[X].values, data[Y].values) return p_value def _get_parental_triples(causal_graph: DirectedGraph, include_unconditional: bool): """ For a given graph collect all parental triples, that is, the triple (X, Y, Z) is a parental triple iff Y is non-descendant of X, and Z are the parents of X (can be empty if include_unconditional=True) """ triples = [] for node in causal_graph.nodes: parents = get_ordered_predecessors(causal_graph, node) non_descendants = _get_non_descendants(causal_graph, node, exclude_parents=True) if (parents or include_unconditional) and non_descendants: for non_desc in non_descendants: triples.append((node, non_desc, parents)) return triples def _permutation_based( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Union[Callable, Tuple[Callable, ...], List[Callable]], exclude_original_order: bool, n_permutations: int, show_progress_bar: bool, ) -> Dict[str, List[Union[DirectedGraph, Dict]]]: """ Generate baseline for node permutations. :param causal_graph: A directed acyclic graph (DAG). :param methods: Validation methods to perform. :param exclude_original_order: Exclude the original ordering of the nodes (default=False) :param n_permutations: Number of permutations to perform. If -1 use all n_nodes! - int(exclude_orig) permutations :param show_progress_bar: Whether to show progress bar over tested permutations. :return: Dictionary containing summary of validation for each individual graph as well as the permuted graphs """ if not isinstance(methods, (tuple, list)): methods = (methods,) perm_gen = _PermuteNodes(causal_graph, n_permutations=n_permutations, exclude_original_order=exclude_original_order) validation_summary = {FalsifyConst.PERM_GRAPHS: []} for permuted_graph in tqdm(perm_gen, desc="Test permutations of given graph", disable=not show_progress_bar): res = run_validations( causal_graph=permuted_graph, data=data, methods=methods, ) validation_summary[FalsifyConst.PERM_GRAPHS].append(permuted_graph) for m_name, summary in res.items(): if m_name not in validation_summary: validation_summary[m_name] = [] validation_summary[m_name].append(summary) return validation_summary class _PermuteNodes: def __init__(self, causal_graph: DirectedGraph, exclude_original_order: bool, n_permutations: int): """ Randomly permute the nodes of a given causal graph while keeping the underlying graph structure the same. :param causal_graph: A directed acyclic graph (DAG). :param exclude_original_order: Do not return the original order. :param n_permutations: Return a generator with n_permutations permutations. If n_permutations = -1 (default), we return all n_nodes! - int(exclude_orig) permutations. :return: Copy of causal_graph with nodes randomly permuted. """ self.causal_graph = causal_graph self.exclude_original_order = exclude_original_order self.n_permutations = n_permutations self.max_perms = np.math.factorial(self.causal_graph.number_of_nodes()) - int(self.exclude_original_order) if n_permutations == -1 or n_permutations > self.max_perms: self.it = self.iter_all_permutations() self.length = self.max_perms if self.length > 2**63 - 1: raise ValueError( f"Too many permutations specified. Did you accidently set 'n_permutations'=-1 for a " f"large (>20 nodes) graph? " f"Given graph has {causal_graph.number_of_nodes()} nodes." ) else: self.it = self.iter_random_permutations() self.length = self.n_permutations def iter_all_permutations(self): for i, perm in enumerate(permutations(self.causal_graph.nodes)): if self.exclude_original_order and i == 0: continue mapping = {node: perm[i] for i, node in enumerate(self.causal_graph.nodes)} yield nx.relabel_nodes(self.causal_graph, mapping, copy=True) def iter_random_permutations(self): for _ in range(self.n_permutations): if self.exclude_original_order: is_orig = True while is_orig: perm = list(np.random.permutation(self.causal_graph.nodes)) if perm != list(self.causal_graph.nodes): is_orig = False else: perm = list(np.random.permutation(self.causal_graph.nodes)) mapping = {node: perm[i] for i, node in enumerate(self.causal_graph.nodes)} yield nx.relabel_nodes(self.causal_graph, mapping, copy=True) def __iter__(self): yield from self.it def __len__(self): return self.length def _to_frozenset(x: Union[Set, List, str]): """Converts a set, list or string into a hashable frozenset""" assert ( isinstance(x, Set) or isinstance(x, List) or isinstance(x, str) ), f"{x} must be list, set or str. Got {type(x)} instead!" if isinstance(x, str): return frozenset({x}) return frozenset(x)

eeulig

379f188d674d5be6d644559f9f54852b87403f7c

4e30c63fe1113e35259e7fb3400a6f09bca4d699

What does `tpa` stand for? Isn't `dsep` already a good hint what this method validates?

bloebp

py-why/dowhy

Remove kwargs from falsify, improve docstrings

This commit updates `gcm/falsify` in the following ways: - Remove the `**kwargs` for `validate_graph` and `falsify_graph`. Instead, validation methods given to those functions should be wrapped in `wrap_partial`, e.g. if we want to validate a graph `G` and test local Markov conditions and pairwise dependencies on observations `data`, we would do ``` validate_graph(G, methods=( wrap_partial(validate_lmc, data=data, independence_test=..., conditional_independence_test=...), wrap_partial(validate_pd, data=data, independence_test=...), ) ) ``` By default (`methods=None`), `validate_graph` tests local Markov conditions and `falsify_graph` tests local Markov conditions and d-separation of parental triples. In this case `data` has to be set and optionally one can provide `independence_test`, `conditional_independence_test`, `significance_level`, and `n_jobs` via the keyword arguments of `validate_graph` / `falsify_graph`. - Use `Enum` for some of the constants - In `_PValuesMemory` the method `add_p_value ` always sets a p-value even if it was set already. This removes the need for the method `clear_placeholders` and allows `p_values` to be set to `-1`, indicating that the CI is not testable (because of missing data for a node, c.f. `_compute_p_value`) - If for some CI: X, Y | Z any of the nodes has no data (column missing in the provided dataframe), we skip this test and raise a warning. - Instead of using the raw number of violations in the plotted histogram and to compute the p-value, we use the relative number of violations. This will result in different p-values only if we have missing data for some nodes (and thus the number of tests is potentially different between different permutations) - Improve docstrings

2023-07-04 13:58:30+00:00

2023-07-18 14:19:10+00:00

dowhy/gcm/falsify.py

"""This module provides functionality to falsify a user-given DAG given observed data. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from dataclasses import dataclass, field from enum import Enum, auto from functools import partial from itertools import permutations from typing import Any, Callable, Dict, FrozenSet, List, Optional, Set, Tuple, Union import matplotlib.colors as mcolors import matplotlib.pyplot as plt import networkx as nx import numpy as np import pandas as pd from joblib import Parallel, delayed from tqdm import tqdm import dowhy.gcm.config as config from dowhy.gcm.independence_test import kernel_based from dowhy.gcm.util import plot from dowhy.gcm.util.general import set_random_seed from dowhy.gcm.validation import _get_non_descendants from dowhy.graph import DirectedGraph, get_ordered_predecessors VIOLATION_COLOR = "red" COLORS = list(mcolors.TABLEAU_COLORS.values()) class FalsifyConst(Enum): N_VIOLATIONS = auto() N_TESTS = auto() P_VALUE = auto() P_VALUES = auto() GIVEN_VIOLATIONS = auto() PERM_VIOLATIONS = auto() F_GIVEN_VIOLATIONS = auto() F_PERM_VIOLATIONS = auto() LOCAL_VIOLATION_INSIGHT = auto() METHOD = auto() VALIDATE_LMC = auto() VALIDATE_TPA = auto() VALIDATE_PD = auto() VALIDATE_CM = auto() PERM_GRAPHS = auto() MEC = auto() FALSIFY_METHODS = { FalsifyConst.VALIDATE_LMC: "LMC", FalsifyConst.VALIDATE_PD: "Faithfulness", FalsifyConst.VALIDATE_TPA: "tPa", FalsifyConst.VALIDATE_CM: "Causal Minimality", } @dataclass class _PValuesMemory: """A class to store and access results of independence tests. This class is useful if, e.g. we validate LMC on graph X -> Y -> Z and also on the permuted nodes Z -> Y -> X. Since X ind Z | Y == Z ind X | Y, we only need to perform the conditional independence test once. We use frozensets of nodes since standard (immutable) sets are not hashable. """ p_values: Dict[Tuple[FrozenSet, FrozenSet, FrozenSet], float] = field(default_factory=dict) def add_p_value( self, p_value: Union[None, float], X: Union[Set, List, str], Y: Union[Set, List, str], Z: Optional[Union[Set, List, str]] = None, ): if not Z: Z = set() self.p_values[(_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z))] = p_value def get_p_value( self, X: Union[Set, List, str], Y: Union[Set, List, str], Z: Optional[Union[Set, List, str]] = None ): if not Z: Z = set() # Independence tests are symmetric if (_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z)) in self.p_values: return self.p_values[(_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z))] elif (_to_frozenset(Y), _to_frozenset(X), _to_frozenset(Z)) in self.p_values: return self.p_values[(_to_frozenset(Y), _to_frozenset(X), _to_frozenset(Z))] return def __contains__(self, item: Tuple[Union[Set, List, str], ...]) -> bool: X, Y = (_to_frozenset(i) for i in item[:2]) if len(item) == 2 or item[2] is None: Z = frozenset() else: Z = _to_frozenset(item[2]) return (X, Y, Z) in self.p_values or (Y, X, Z) in self.p_values def validate_lmc( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, include_unconditional: bool = True, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[str, float]]]: """ Validate the local markov condition for a given directed graph. Return number of violations and p values for each node. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory instance, where results of previously performed tests are stored. :param independence_test: Test to use for unconditional independencies (only used if include_unconditional=True) :param conditional_independence_test: Conditional independence test to use for checking local Markov condition. :param significance_level: Significance level for (conditional) independence tests. :param include_unconditional: Test also unconditional independencies of root nodes. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Outcome of validation containing number of violations in the graph and p values/violation for each tuple (node, non_desc) """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_LMC, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, FalsifyConst.P_VALUES: dict(), } # Find out which tests to do triples = _get_parental_triples(causal_graph, include_unconditional) to_test = [] for node, non_desc, parents in triples: if not (node, non_desc, parents) in p_values_memory: to_test.append((node, non_desc, parents)) p_values_memory.add_p_value(None, node, non_desc, parents) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=node, Y=non_desc, Z=parents, independence_test=independence_test, conditional_independence_test=conditional_independence_test, seed=int(seed), ) for (node, non_desc, parents), seed in zip(to_test, random_seeds) ) # Gather results for i, (node, non_desc, parents) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], node, non_desc, parents) # Summarize for node, non_desc, parents in triples: lmc_p_value = p_values_memory.get_p_value(node, non_desc, parents) if lmc_p_value is not None: validation_summary[FalsifyConst.N_TESTS] += 1 validation_summary[FalsifyConst.P_VALUES][(node, non_desc)] = ( lmc_p_value, lmc_p_value <= significance_level, ) if lmc_p_value <= significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_tpa( causal_graph: DirectedGraph, causal_graph_reference: DirectedGraph, include_unconditional: bool = True, data: Optional[pd.DataFrame] = None, ) -> Dict[str, int]: """ Graphical criterion to evaluate which pairwise parental d-separations (parental triples) in `causal_graph` are violated, assuming `causal_graph_reference` is the ground truth graph. If none are violated, then both graphs lie in the same Markov equivalence class. Specifically we test: X _|_G' Y | Z and X _/|_G Y | Z for Y \in ND{X}^G', Z = PA{X}^G :param causal_graph: Causal graph for which to evaluate parental d-separations (G') :param causal_graph_reference: Causal graph where we test if d-separation holds (G) :param include_unconditional: Test also unconditional independencies of root nodes. :param data: IGNORED! No data is needed for this validation method and thus the data argument is ignored! :return: Validation summary with number of d-separations implied by `causal_graph` and number of times these are violated in the graph `causal_graph_reference`. """ validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_TPA, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, } triples = _get_parental_triples(causal_graph, include_unconditional) for node, non_desc, parents in triples: validation_summary[FalsifyConst.N_TESTS] += 1 if not nx.d_separated(causal_graph_reference, {node}, {non_desc}, set(parents)): validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_pd( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, n_pairs: int = -1, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, adjacent_only: bool = False, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[tuple, float]]]: """ Validate pairwise dependencies (pd) for a given causal graph and data. Test for each node if it is statistically dependent of all its ancestors. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory object, where results of previously performed tests are stored. :param n_pairs: Evaluate dependencies for n_pairs <= all pairs in the DAG. If n_pairs=-1, evaluate dependencies for all (ancestor, node) pairs (default). :param independence_test: Independence test to use for checking pairwise dependencies. :param significance_level: Significance level for independence tests. :param adjacent_only: Only test adjacent node pairs. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Summary dict: {n_violations: int, n_tests: int, p_values: {(ancestor, node): float, ...}} """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() pairs = [(ancestor, node) for node in causal_graph.nodes for ancestor in nx.ancestors(causal_graph, node)] if adjacent_only: pairs = [ (ancestor, node) for (ancestor, node) in pairs if ancestor in get_ordered_predecessors(causal_graph, node) ] if n_pairs < 0: n_pairs = len(pairs) if n_pairs > len(pairs): raise ValueError(f"n_pairs ({n_pairs}) > number of pairs in the DAG ({len(pairs)})") validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_PD, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: n_pairs, FalsifyConst.P_VALUES: dict(), } pair_idxs = np.random.choice(len(pairs), size=n_pairs, replace=False) # Find out which tests to do to_test = [] for pair_idx in pair_idxs: ancestor, node = pairs[pair_idx] if not (ancestor, node) in p_values_memory: to_test.append((ancestor, node)) p_values_memory.add_p_value(None, ancestor, node) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=ancestor, Y=node, Z=None, independence_test=independence_test, conditional_independence_test=None, seed=int(seed), ) for (ancestor, node), seed in zip(to_test, random_seeds) ) # Gather results for i, (ancestor, node) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], ancestor, node) # Summarize for pair_idx in pair_idxs: ancestor, node = pairs[pair_idx] p_value = p_values_memory.get_p_value(ancestor, node) if p_value is not None: validation_summary[FalsifyConst.P_VALUES][(node, ancestor)] = (p_value, p_value > significance_level) if p_value > significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_cm( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[tuple, float]]]: """ Function to test causal minimality of a DAG (see [1], Proposition 6.36). [1] J. Peters, D. Janzing, and B. Schölkopf, Elements of Causal Inference: Foundations and Learning Algorithms. Cambridge, MA, USA: MIT Press, 2017. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory object, where results of previously performed tests are stored. :param independence_test: Independence test to use. :param conditional_independence_test: Conditional independence test to use. :param significance_level: Significance level for independence tests. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Validation summary as dict. """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_CM, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, FalsifyConst.P_VALUES: dict(), } # Find out which tests to do triples = [] to_test = [] for node in causal_graph.nodes: parents = set(causal_graph.predecessors(node)) if parents: for p in parents: other_parents = parents.difference({p}) triples.append((node, p, other_parents)) if not (node, p, other_parents) in p_values_memory: to_test.append((node, p, other_parents)) p_values_memory.add_p_value(None, node, p, other_parents) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=node, Y=p, Z=list(other_parents), independence_test=independence_test, conditional_independence_test=conditional_independence_test, seed=int(seed), ) for (node, p, other_parents), seed in zip(to_test, random_seeds) ) # Gather results for i, (node, p, other_parents) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], node, p, other_parents) # Summarize for node, p, other_parents in triples: p_value = p_values_memory.get_p_value(node, p, other_parents) if p_value is not None: validation_summary[FalsifyConst.N_TESTS] += 1 validation_summary[FalsifyConst.P_VALUES][(node, p, tuple(other_parents))] = ( p_value, p_value > significance_level, ) if p_value > significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_graph( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Optional[Union[Callable, Tuple[Callable, ...], List[Callable]]] = partial( validate_lmc, independence_test=kernel_based, conditional_independence_test=kernel_based ), ) -> Dict[str, Dict]: """ Validate a given causal graph using observational data and some given methods. If methods are provided, they must be wrapped in a partial object, with their respective parameters. E.g., if one wants to test the local Markov conditions and the pairwise dependencies (unconditional faithfulness), then call validate_graph(G, data, methods=( partial(validate_lmc, independence_test=..., conditional_independence_test=...), partial(validate_pd, independence_test=...), ) ) :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param methods: Method functions wrapped in wrap_partial. E.g. wrap_partial(validate_lmc, data=data, independence_test=..., conditional_independence_test=...). If no methods are provided we run validate_lmc with optional keyword arguments provided to validate_graph. :return: Validation summary as dict. """ if not isinstance(methods, (tuple, list)): methods = (methods,) validation_summary = dict() for m in methods: m_summary = m(causal_graph=causal_graph, data=data) m_name = m_summary.pop(FalsifyConst.METHOD) validation_summary[m_name] = m_summary return validation_summary @dataclass class EvaluationResult: """ Dataset class containing the evaluation result of falsifying a graph using a node-permutation test. ... Attributes ---------- summary : dict Dictionary containing the summary of the evaluation. significance_level : float Significance level based on which we falsify the given DAG falsifiable : bool Whether the given DAG is falsifiable. falsified : bool Whether the given DAG is falsified. """ summary: dict significance_level: float suggestions: Optional[dict] = None def update_significance_level(self, significance_level: float): """ Update the significance level to decide if we falsify a given DAG. """ self.significance_level = significance_level self.__post_init__() def __post_init__(self): self.can_evaluate = self._can_evaluate() if not self.can_evaluate: self.falsified = None self.falsifiable = None elif ( self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.P_VALUE] > self.significance_level > self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE] ): self.falsified = True self.falsifiable = True elif self.significance_level < self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE]: self.falsified = False self.falsifiable = False else: self.falsified = False self.falsifiable = True def __repr__(self): # DAG Evaluation if self.can_evaluate: decision = " " if self.falsified else " do not " informative = " " if self.falsifiable else " not " frac_MEC = f"{len(self.summary[FalsifyConst.MEC])} / {len(self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.PERM_VIOLATIONS])}" frac_VLMC = f"{self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.GIVEN_VIOLATIONS]}/{self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.N_TESTS]}" p_LMC = self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.P_VALUE] p_dSep = self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE] validation_repr = [ f"The given DAG is{informative}informative because {frac_MEC} of the permutations lie in the Markov", f"equivalence class of the given DAG (p-value: {p_dSep:.2f}).", f"The given DAG violates {frac_VLMC} LMCs and is better than {(1 - p_LMC) * 100:.1f}% of the permuted DAGs (p-value: {p_LMC:.2f}).", f"Based on the provided significance level ({self.significance_level}) and because the DAG is{informative}informative,", f"we{decision}reject the DAG.", ] else: validation_repr = ["Cannot be evaluated!"] # Suggestions suggestion_repr = {} for m in self.suggestions: suggestion_repr[FALSIFY_METHODS[m]] = [ f"Remove edge {node[1]} --> {node[0]}" for (node, r) in self.suggestions[m][FalsifyConst.P_VALUES].items() if r[1] ] return _generate_table(validation_repr, suggestion_repr) def _can_evaluate(self): can_evaluate = True for m in (FalsifyConst.VALIDATE_LMC, FalsifyConst.VALIDATE_TPA): if m not in self.summary: can_evaluate = False return can_evaluate def falsify_graph( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Optional[Union[Callable, Tuple[Callable, ...]]] = None, suggestion_methods: Optional[Union[Callable, Tuple[Callable, ...]]] = None, suggestions: bool = False, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, significance_ci: float = 0.05, n_permutations: Optional[int] = None, show_progress_bar: Optional[bool] = None, n_jobs: Optional[int] = None, plot_histogram: bool = False, plot_kwargs: Optional[Dict] = None, ) -> EvaluationResult: """ Falsify a given DAG using observational data. This method returns the result of a permutation-test to falsify a user-given DAG using observational data. To this end we construct the test statistics by testing the violations of local Markov conditions (LMC) implied by the graph using conditional independence (CI) tests. The null is the number of LMC violations of a random node-permutation of the given graph. Our test can be interpreted as whether the given graph is significantly better than random in terms of the CIs it entails. To determine whether a given graph is falsifiable by our metric, we implement a second test, which reports whether given graph is "characteristic" enough in terms of the CIs it entails. For this, we compute how many of the random node permutations lie in the same Markov equivalence class (MEC) as the given graph and conclude that the given graph is falsifiable only if the fraction of permuted DAGs in the same MEC as the given graph is "reasonably" small. The returned EvaluationResult object has two attributes: `falsified` and `falsifiable`: `falsifiable`: The given graph lies in a different MEC than >= 1-`significance_level` of the permuted DAGs `falsified`: The given graph is falsifiable and violates fewer LMCs than >= 1-`significance_level` of the permuted DAGs By default, we only run 1 / `significance_level` permutations as those are enough to falsify a graph with type I error probability `significance_level` at some given `significance_level`. If you are interested in a more exact estimate of the p-value of whish to plot a histogram to see how the given DAG compares to random node permutations, you should set `n_permutations` to some larger value (e.g. 100 or 1000). If `n_permutations=-1` we test on all n_nodes! permutations. `methods` and `suggestion_methods` must be wrapped in partial(method, **kwargs) (c.f. `validate_graph`). Related paper: Eulig, E., Mastakouri, A. A., Blöbaum, P., Hardt, M., & Janzing, D. (2023). Toward Falsifying Causal Graphs Using a Permutation-Based Test. https://arxiv.org/abs/2305.09565 :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param methods: Validation methods to perform. :param suggestion_methods: Methods to run on the given graph to provide additional suggestions. :param suggestions: Provide suggestions generated using the `suggestion_methods`. :param independence_test: Independence test to use for checking pairwise independencies. :param conditional_independence_test: Conditional independence test to use. :param significance_level: Significance level for the permutation test. :param significance_ci: Significance level for (conditional) independence tests. :param n_permutations: Number of permutations to perform. If -1 use all n_nodes! permutations. :param show_progress_bar: Whether to show progress bar over permutations. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :param plot_histogram: Plot histogram of results from permutation baseline. :param plot_kwargs: Additional plot arguments to be passed to plot_evaluation_results. :return: EvaluationResult """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs show_progress_bar = config.show_progress_bars if show_progress_bar is None else show_progress_bar p_values_memory = _PValuesMemory() if n_permutations is None: n_permutations = int(1 / significance_level) if not plot_histogram else -1 if not plot_kwargs: plot_kwargs = {} # If no methods are provided, use default ones: validate_lmc, validate_tpa if not methods: methods = ( partial( validate_lmc, independence_test=independence_test, conditional_independence_test=conditional_independence_test, significance_level=significance_ci, p_values_memory=p_values_memory, n_jobs=n_jobs, ), partial(validate_tpa, causal_graph_reference=causal_graph), ) elif isinstance(methods, Callable): methods = (methods,) # If no suggestion methods are provided, but suggestions=True, use default ones: validate_cm if not suggestions: suggestion_methods = tuple() elif suggestions and isinstance(suggestion_methods, Callable): suggestion_methods = (suggestion_methods,) elif suggestions: suggestion_methods = ( partial( validate_cm, independence_test=independence_test, conditional_independence_test=conditional_independence_test, significance_level=significance_ci, p_values_memory=p_values_memory, n_jobs=n_jobs, ), ) summary_given = validate_graph( causal_graph, data, methods=methods + suggestion_methods, ) summary_perm = _permutation_based( causal_graph, data, methods=methods, exclude_original_order=False, n_permutations=n_permutations, show_progress_bar=show_progress_bar, ) summary = dict() validation_methods = set(summary_perm.keys()) - {FalsifyConst.PERM_GRAPHS} for m in validation_methods: summary[m] = dict() summary[m][FalsifyConst.PERM_VIOLATIONS] = [perm[FalsifyConst.N_VIOLATIONS] for perm in summary_perm[m]] summary[m][FalsifyConst.GIVEN_VIOLATIONS] = summary_given[m][FalsifyConst.N_VIOLATIONS] summary[m][FalsifyConst.N_TESTS] = summary_given[m][FalsifyConst.N_TESTS] summary[m][FalsifyConst.F_PERM_VIOLATIONS] = [ perm[FalsifyConst.N_VIOLATIONS] / perm[FalsifyConst.N_TESTS] for perm in summary_perm[m] ] summary[m][FalsifyConst.F_GIVEN_VIOLATIONS] = ( summary[m][FalsifyConst.GIVEN_VIOLATIONS] / summary[m][FalsifyConst.N_TESTS] ) summary[m][FalsifyConst.P_VALUE] = sum( [ 1 for perm in summary[m][FalsifyConst.F_PERM_VIOLATIONS] if perm <= summary[m][FalsifyConst.F_GIVEN_VIOLATIONS] ] ) / len(summary[m][FalsifyConst.PERM_VIOLATIONS]) if m != FalsifyConst.VALIDATE_TPA: # Append list of violations (node, non_desc) to get local information summary[m][FalsifyConst.LOCAL_VIOLATION_INSIGHT] = summary_given[m][FalsifyConst.P_VALUES] if FalsifyConst.VALIDATE_TPA in summary: summary[FalsifyConst.MEC] = [ summary_perm[FalsifyConst.PERM_GRAPHS][i] for i, v in enumerate(summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.PERM_VIOLATIONS]) if v == 0 ] result = EvaluationResult( summary=summary, significance_level=significance_level, suggestions={m: summary_given[m] for m in summary_given if m not in validation_methods}, ) if plot_histogram: plot_evaluation_results(result, **plot_kwargs) return result def apply_suggestions( causal_graph: DirectedGraph, evaluation_result: EvaluationResult, edges_to_keep: Optional[List[Tuple[Any, Any]]] = None, ): if not hasattr(evaluation_result, "suggestions"): raise ValueError("EvaluationResult object has no attribute suggestions. Please run with suggestion=True!") causal_graph = causal_graph.copy() for m in evaluation_result.suggestions: for node, res in evaluation_result.suggestions[m][FalsifyConst.P_VALUES].items(): edge = (node[1], node[0]) if (res[1] and edges_to_keep is not None and edge not in edges_to_keep) or ( res[1] and edges_to_keep is None ): causal_graph.remove_edge(edge[0], edge[1]) return causal_graph def plot_evaluation_results(evaluation_result, figsize=(8, 3), bins=None, title="", savepath="", display=True): fig, ax = plt.subplots(figsize=figsize) # Plot histograms p_values = "" data = [] labels = [] evaluation_summary = {k: v for k, v in evaluation_result.summary.items() if k != FalsifyConst.MEC} for i, (m, m_summary) in enumerate(evaluation_summary.items()): data.append(m_summary[FalsifyConst.F_PERM_VIOLATIONS]) labels.append(f"Violations of {FALSIFY_METHODS[m]} of permuted DAGs") p_values += f"p-value {FALSIFY_METHODS[m]} = {m_summary[FalsifyConst.P_VALUE]:.2f}\n" ax.hist(data, color=COLORS[: len(evaluation_summary)], bins=bins, alpha=0.5, label=labels, edgecolor="k") # Plot given violations for i, (m, m_summary) in enumerate(evaluation_summary.items()): ylim = ax.get_ylim()[1] ax.plot( [m_summary[FalsifyConst.F_GIVEN_VIOLATIONS]] * 2, [0, ylim], "--", c=COLORS[i], label=f"Violations of {FALSIFY_METHODS[m]} of given DAG", ) ax.set_ylim([0, ylim]) ax.set_xlabel("Fraction of violations") ax.set_ylabel("# Permutations") plt.legend(loc="upper left", bbox_to_anchor=(1.05, 1), borderaxespad=0.0, title=p_values) if title: plt.title(title) plt.tight_layout() if savepath: plt.savefig(savepath, dpi=300, bbox_inches="tight") if display: plt.show() else: plt.close() def plot_local_insights( causal_graph: DirectedGraph, evaluation_result: Union[EvaluationResult, Dict], method: Optional[str] = FalsifyConst.VALIDATE_LMC, ): """ For some given graph and evaluation result plot local violations. :param causal_graph: DiGraph :param evaluation_result: EvaluationResult :param method: Method for which to plot violations """ colors = {} if isinstance(evaluation_result, EvaluationResult) and method in evaluation_result.summary: local_insight_dict = evaluation_result.summary[method][FalsifyConst.LOCAL_VIOLATION_INSIGHT] elif ( isinstance(evaluation_result, EvaluationResult) and hasattr(evaluation_result, "suggestions") and method in evaluation_result.suggestions ): local_insight_dict = evaluation_result.suggestions[method][FalsifyConst.P_VALUES] elif isinstance(evaluation_result, Dict): if method not in evaluation_result: raise ValueError(f"Validation method {method} does not exist in given evaluation_result!") if FalsifyConst.P_VALUES not in evaluation_result[method]: raise ValueError( f"Validation method {method} has no key {FalsifyConst.P_VALUES} where information on local violations " f"are stored!" ) local_insight_dict = evaluation_result[method][FalsifyConst.P_VALUES] else: raise ValueError(f"Cannot plot local violation insights from method {method} in evaluation_result!") for nodes, result in local_insight_dict.items(): if result[1]: if method == FalsifyConst.VALIDATE_LMC: # For LMC we highlight X for which X _|/|_ Y \in ND_X | Pa_X colors[nodes[0]] = VIOLATION_COLOR elif method == FalsifyConst.VALIDATE_PD: # For PD we highlight the edge (if Y\in Anc_X -> X are adjacent) colors[(nodes[1], nodes[0])] = VIOLATION_COLOR elif method == FalsifyConst.VALIDATE_CM: # For causal minimality we highlight the edge Y \in Pa_X -> X colors[(nodes[1], nodes[0])] = VIOLATION_COLOR plot(causal_graph, colors=colors) def _generate_table( validation_repr, suggestion_repr, width=105, validation_name="Falsificaton Summary", suggestion_name="Suggestions" ): # Create Validation header _repr = [ "+" + "-" * (width - 2) + "+\n", "|" + " " * int(np.floor((width - 2 - len(validation_name)) / 2)), validation_name, " " * int(np.ceil((width - 2 - len(validation_name)) / 2)) + "|\n", "+" + "-" * (width - 2) + "+\n", ] # Create Validation summary _repr += [f"| {v_line}" + " " * (width - 3 - len(v_line)) + "|\n" for v_line in validation_repr] # Close Validation _repr += ["+" + "-" * (width - 2) + "+\n"] # Create Suggestions header if suggestion_repr: _repr += [ "|" + " " * int(np.floor((width - 2 - len(suggestion_name)) / 2)), suggestion_name, " " * int(np.ceil((width - 2 - len(suggestion_name)) / 2)) + "|\n", "+" + "-" * (width - 2) + "+\n", ] # Iterate over suggestions for m, suggestions in suggestion_repr.items(): left_col = "| " + m + " |" if not suggestions: right_col = " " + " " * (width - 2 - len(left_col)) + "|\n" _repr += [left_col, right_col] for i, s in enumerate(suggestions): if i > 0: left_col = "| " + " " * len(m) + " |" right_col = " - " + s + " " * (width - 4 - len(s) - len(left_col)) + "|\n" _repr += [left_col, right_col] _repr += ["+" + "-" * (width - 2) + "+\n"] return "".join(_repr)[:-1] def _compute_p_value( data: pd.DataFrame, X: Union[List, str], Y: Union[List, str], Z: Optional[Union[Set, List, str]], independence_test: Optional[Callable[[np.ndarray, np.ndarray], float]], conditional_independence_test: Optional[Callable[[np.ndarray, np.ndarray, np.ndarray], float]], seed: int, ) -> float: """Perform (conditional) independence test and report p-value. If any nodes needed for the test do not exist in the data, this test is skipped and a value of -1 is returned and a warning is raised. :param data: Observations of variables in the DAG. :param X: Variable to test (conditional) independence with Y :param Y: Variable to test (conditional) independence with X :param Z: Set to condition independence test on. Can be empty (None, empty set, or empty list). :param independence_test: Independence test to use. :param conditional_independence_test: Conditional independence test to use. :param seed: Random seed :return: p-value """ set_random_seed(seed) # Test if we have data for X and Y for node in [X, Y]: if not node in data.columns: warnings.warn(f"WARN: Couldn't find data for node {node}. Skip this test.") return if Z: # Test if we have data for Z for node in Z: if not node in data.columns: warnings.warn(f"WARN: Couldn't find data for node {node}. Skip this test.") return p_value = conditional_independence_test(data[X].values, data[Y].values, data[Z].values) else: p_value = independence_test(data[X].values, data[Y].values) return p_value def _get_parental_triples(causal_graph: DirectedGraph, include_unconditional: bool): """ For a given graph collect all parental triples, that is, the triple (X, Y, Z) is a parental triple iff Y is non-descendant of X, and Z are the parents of X (can be empty if include_unconditional=True) """ triples = [] for node in causal_graph.nodes: parents = get_ordered_predecessors(causal_graph, node) non_descendants = _get_non_descendants(causal_graph, node, exclude_parents=True) if (parents or include_unconditional) and non_descendants: for non_desc in non_descendants: triples.append((node, non_desc, parents)) return triples def _permutation_based( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Union[Callable, Tuple[Callable, ...], List[Callable]], exclude_original_order: bool, n_permutations: int, show_progress_bar: bool, ) -> Dict[str, List[Union[DirectedGraph, Dict]]]: """ Generate baseline for node permutations. :param causal_graph: A directed acyclic graph (DAG). :param methods: Validation methods to perform. :param exclude_original_order: Exclude the original ordering of the nodes (default=False) :param n_permutations: Number of permutations to perform. If -1 use all n_nodes! - int(exclude_orig) permutations :param show_progress_bar: Whether to show progress bar over tested permutations. :return: Dictionary containing summary of validation for each individual graph as well as the permuted graphs """ if not isinstance(methods, (tuple, list)): methods = (methods,) perm_gen = _PermuteNodes(causal_graph, n_permutations=n_permutations, exclude_original_order=exclude_original_order) validation_summary = {FalsifyConst.PERM_GRAPHS: []} for permuted_graph in tqdm(perm_gen, desc="Test permutations of given graph", disable=not show_progress_bar): res = validate_graph( causal_graph=permuted_graph, data=data, methods=methods, ) validation_summary[FalsifyConst.PERM_GRAPHS].append(permuted_graph) for m_name, summary in res.items(): if m_name not in validation_summary: validation_summary[m_name] = [] validation_summary[m_name].append(summary) return validation_summary class _PermuteNodes: def __init__(self, causal_graph: DirectedGraph, exclude_original_order: bool, n_permutations: int): """ Randomly permute the nodes of a given causal graph while keeping the underlying graph structure the same. :param causal_graph: A directed acyclic graph (DAG). :param exclude_original_order: Do not return the original order. :param n_permutations: Return a generator with n_permutations permutations. If n_permutations = -1 (default), we return all n_nodes! - int(exclude_orig) permutations. :return: Copy of causal_graph with nodes randomly permuted. """ self.causal_graph = causal_graph self.exclude_original_order = exclude_original_order self.n_permutations = n_permutations self.max_perms = np.math.factorial(self.causal_graph.number_of_nodes()) - int(self.exclude_original_order) if n_permutations == -1 or n_permutations > self.max_perms: self.it = self.iter_all_permutations() self.length = self.max_perms if self.length > 2**63 - 1: raise ValueError( f"Too many permutations specified. Did you accidently set 'n_permutations'=-1 for a " f"large (>20 nodes) graph? " f"Given graph has {causal_graph.number_of_nodes()} nodes." ) else: self.it = self.iter_random_permutations() self.length = self.n_permutations def iter_all_permutations(self): for i, perm in enumerate(permutations(self.causal_graph.nodes)): if self.exclude_original_order and i == 0: continue mapping = {node: perm[i] for i, node in enumerate(self.causal_graph.nodes)} yield nx.relabel_nodes(self.causal_graph, mapping, copy=True) def iter_random_permutations(self): for _ in range(self.n_permutations): if self.exclude_original_order: is_orig = True while is_orig: perm = list(np.random.permutation(self.causal_graph.nodes)) if perm != list(self.causal_graph.nodes): is_orig = False else: perm = list(np.random.permutation(self.causal_graph.nodes)) mapping = {node: perm[i] for i, node in enumerate(self.causal_graph.nodes)} yield nx.relabel_nodes(self.causal_graph, mapping, copy=True) def __iter__(self): yield from self.it def __len__(self): return self.length def _to_frozenset(x: Union[Set, List, str]): """Converts a set, list or string into a hashable frozenset""" assert ( isinstance(x, Set) or isinstance(x, List) or isinstance(x, str) ), f"{x} must be list, set or str. Got {type(x)} instead!" if isinstance(x, str): return frozenset({x}) return frozenset(x)

"""This module provides functionality to falsify a user-given DAG given observed data. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from dataclasses import dataclass, field from enum import Enum, auto from functools import partial from inspect import getfullargspec from itertools import permutations from typing import Any, Callable, Dict, FrozenSet, List, Optional, Set, Tuple, Union import matplotlib.colors as mcolors import matplotlib.pyplot as plt import networkx as nx import numpy as np import pandas as pd from joblib import Parallel, delayed from tqdm import tqdm import dowhy.gcm.config as config from dowhy.gcm.independence_test import kernel_based from dowhy.gcm.util import plot from dowhy.gcm.util.general import set_random_seed from dowhy.gcm.validation import _get_non_descendants from dowhy.graph import DirectedGraph, get_ordered_predecessors VIOLATION_COLOR = "red" COLORS = list(mcolors.TABLEAU_COLORS.values()) class FalsifyConst(Enum): N_VIOLATIONS = auto() N_TESTS = auto() P_VALUE = auto() P_VALUES = auto() GIVEN_VIOLATIONS = auto() PERM_VIOLATIONS = auto() F_GIVEN_VIOLATIONS = auto() F_PERM_VIOLATIONS = auto() LOCAL_VIOLATION_INSIGHT = auto() METHOD = auto() VALIDATE_LMC = auto() VALIDATE_TPA = auto() VALIDATE_PD = auto() VALIDATE_CM = auto() PERM_GRAPHS = auto() MEC = auto() FALSIFY_METHODS = { FalsifyConst.VALIDATE_LMC: "LMC", FalsifyConst.VALIDATE_PD: "Faithfulness", FalsifyConst.VALIDATE_TPA: "TPa", FalsifyConst.VALIDATE_CM: "Causal Minimality", } @dataclass class _PValuesMemory: """A class to store and access results of independence tests. This class is useful if, e.g. we validate LMC on graph X -> Y -> Z and also on the permuted nodes Z -> Y -> X. Since X ind Z | Y == Z ind X | Y, we only need to perform the conditional independence test once. We use frozensets of nodes since standard (immutable) sets are not hashable. """ p_values: Dict[Tuple[FrozenSet, FrozenSet, FrozenSet], float] = field(default_factory=dict) def add_p_value( self, p_value: Union[None, float], X: Union[Set, List, str], Y: Union[Set, List, str], Z: Optional[Union[Set, List, str]] = None, ): if not Z: Z = set() self.p_values[(_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z))] = p_value def get_p_value( self, X: Union[Set, List, str], Y: Union[Set, List, str], Z: Optional[Union[Set, List, str]] = None ): if not Z: Z = set() # Independence tests are symmetric if (_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z)) in self.p_values: return self.p_values[(_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z))] elif (_to_frozenset(Y), _to_frozenset(X), _to_frozenset(Z)) in self.p_values: return self.p_values[(_to_frozenset(Y), _to_frozenset(X), _to_frozenset(Z))] return def __contains__(self, item: Tuple[Union[Set, List, str], ...]) -> bool: X, Y = (_to_frozenset(i) for i in item[:2]) if len(item) == 2 or item[2] is None: Z = frozenset() else: Z = _to_frozenset(item[2]) return (X, Y, Z) in self.p_values or (Y, X, Z) in self.p_values def validate_lmc( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, include_unconditional: bool = True, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[str, float]]]: """ Validate the local markov condition for a given directed graph. Return number of violations and p values for each node. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory instance, where results of previously performed tests are stored. :param independence_test: Test to use for unconditional independencies (only used if include_unconditional=True) :param conditional_independence_test: Conditional independence test to use for checking local Markov condition. :param significance_level: Significance level for (conditional) independence tests. :param include_unconditional: Test also unconditional independencies of root nodes. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Outcome of validation containing number of violations in the graph and p values/violation for each tuple (node, non_desc) """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_LMC, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, FalsifyConst.P_VALUES: dict(), } # Find out which tests to do triples = _get_parental_triples(causal_graph, include_unconditional) to_test = [] for node, non_desc, parents in triples: if not (node, non_desc, parents) in p_values_memory: to_test.append((node, non_desc, parents)) p_values_memory.add_p_value(None, node, non_desc, parents) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=node, Y=non_desc, Z=parents, independence_test=independence_test, conditional_independence_test=conditional_independence_test, seed=int(seed), ) for (node, non_desc, parents), seed in zip(to_test, random_seeds) ) # Gather results for i, (node, non_desc, parents) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], node, non_desc, parents) # Summarize for node, non_desc, parents in triples: lmc_p_value = p_values_memory.get_p_value(node, non_desc, parents) if lmc_p_value is not None: validation_summary[FalsifyConst.N_TESTS] += 1 validation_summary[FalsifyConst.P_VALUES][(node, non_desc)] = ( lmc_p_value, lmc_p_value <= significance_level, ) if lmc_p_value <= significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_tpa( causal_graph: DirectedGraph, causal_graph_reference: DirectedGraph, include_unconditional: bool = True, ) -> Dict[str, int]: """ Graphical criterion to evaluate which pairwise parental d-separations (parental triples) in `causal_graph` are violated, assuming `causal_graph_reference` is the ground truth graph. If none are violated, then both graphs lie in the same Markov equivalence class. Specifically we test: X _|_G' Y | Z and X _/|_G Y | Z for Y \in ND{X}^G', Z = PA{X}^G :param causal_graph: Causal graph for which to evaluate parental d-separations (G') :param causal_graph_reference: Causal graph where we test if d-separation holds (G) :param include_unconditional: Test also unconditional independencies of root nodes. :return: Validation summary with number of d-separations implied by `causal_graph` and number of times these are violated in the graph `causal_graph_reference`. """ validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_TPA, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, } triples = _get_parental_triples(causal_graph, include_unconditional) for node, non_desc, parents in triples: validation_summary[FalsifyConst.N_TESTS] += 1 if not nx.d_separated(causal_graph_reference, {node}, {non_desc}, set(parents)): validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_pd( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, n_pairs: int = -1, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, adjacent_only: bool = False, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[tuple, float]]]: """ Validate pairwise dependencies (pd) for a given causal graph and data. Test for each node if it is statistically dependent of all its ancestors. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory object, where results of previously performed tests are stored. :param n_pairs: Evaluate dependencies for n_pairs <= all pairs in the DAG. If n_pairs=-1, evaluate dependencies for all (ancestor, node) pairs (default). :param independence_test: Independence test to use for checking pairwise dependencies. :param significance_level: Significance level for independence tests. :param adjacent_only: Only test adjacent node pairs. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Summary dict: {n_violations: int, n_tests: int, p_values: {(ancestor, node): float, ...}} """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() pairs = [(ancestor, node) for node in causal_graph.nodes for ancestor in nx.ancestors(causal_graph, node)] if adjacent_only: pairs = [ (ancestor, node) for (ancestor, node) in pairs if ancestor in get_ordered_predecessors(causal_graph, node) ] if n_pairs < 0: n_pairs = len(pairs) if n_pairs > len(pairs): raise ValueError(f"n_pairs ({n_pairs}) > number of pairs in the DAG ({len(pairs)})") validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_PD, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: n_pairs, FalsifyConst.P_VALUES: dict(), } pair_idxs = np.random.choice(len(pairs), size=n_pairs, replace=False) # Find out which tests to do to_test = [] for pair_idx in pair_idxs: ancestor, node = pairs[pair_idx] if not (ancestor, node) in p_values_memory: to_test.append((ancestor, node)) p_values_memory.add_p_value(None, ancestor, node) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=ancestor, Y=node, Z=None, independence_test=independence_test, conditional_independence_test=None, seed=int(seed), ) for (ancestor, node), seed in zip(to_test, random_seeds) ) # Gather results for i, (ancestor, node) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], ancestor, node) # Summarize for pair_idx in pair_idxs: ancestor, node = pairs[pair_idx] p_value = p_values_memory.get_p_value(ancestor, node) if p_value is not None: validation_summary[FalsifyConst.P_VALUES][(node, ancestor)] = (p_value, p_value > significance_level) if p_value > significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_cm( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[tuple, float]]]: """ Function to test causal minimality of a DAG (see [1], Proposition 6.36). [1] J. Peters, D. Janzing, and B. Schölkopf, Elements of Causal Inference: Foundations and Learning Algorithms. Cambridge, MA, USA: MIT Press, 2017. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory object, where results of previously performed tests are stored. :param independence_test: Independence test to use. :param conditional_independence_test: Conditional independence test to use. :param significance_level: Significance level for independence tests. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Validation summary as dict. """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_CM, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, FalsifyConst.P_VALUES: dict(), } # Find out which tests to do triples = [] to_test = [] for node in causal_graph.nodes: parents = set(causal_graph.predecessors(node)) if parents: for p in parents: other_parents = parents.difference({p}) triples.append((node, p, other_parents)) if not (node, p, other_parents) in p_values_memory: to_test.append((node, p, other_parents)) p_values_memory.add_p_value(None, node, p, other_parents) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=node, Y=p, Z=list(other_parents), independence_test=independence_test, conditional_independence_test=conditional_independence_test, seed=int(seed), ) for (node, p, other_parents), seed in zip(to_test, random_seeds) ) # Gather results for i, (node, p, other_parents) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], node, p, other_parents) # Summarize for node, p, other_parents in triples: p_value = p_values_memory.get_p_value(node, p, other_parents) if p_value is not None: validation_summary[FalsifyConst.N_TESTS] += 1 validation_summary[FalsifyConst.P_VALUES][(node, p, tuple(other_parents))] = ( p_value, p_value > significance_level, ) if p_value > significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def run_validations( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Optional[Union[Callable, Tuple[Callable, ...], List[Callable]]] = partial( validate_lmc, independence_test=kernel_based, conditional_independence_test=kernel_based ), ) -> Dict[str, Dict]: """ Validate a given causal graph using observational data and some given methods. If methods are provided, they must be wrapped in a partial object, with their respective parameters. E.g., if one wants to test the local Markov conditions and the pairwise dependencies (unconditional faithfulness), then call run_validations(G, data, methods=( partial(validate_lmc, independence_test=..., conditional_independence_test=...), partial(validate_pd, independence_test=...), ) ) :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param methods: Method functions wrapped in wrap_partial. E.g. wrap_partial(validate_lmc, data=data, independence_test=..., conditional_independence_test=...). If no methods are provided we run validate_lmc with optional keyword arguments provided to run_validations. :return: Validation summary as dict. """ if not isinstance(methods, (tuple, list)): methods = (methods,) validation_summary = dict() for m in methods: if "data" in getfullargspec(m).args: m = partial(m, data=data) m_summary = m(causal_graph=causal_graph) m_name = m_summary.pop(FalsifyConst.METHOD) validation_summary[m_name] = m_summary return validation_summary @dataclass class EvaluationResult: """ Dataset class containing the evaluation result of falsifying a graph using a node-permutation test. ... Attributes ---------- summary : dict Dictionary containing the summary of the evaluation. significance_level : float Significance level based on which we falsify the given DAG falsifiable : bool Whether the given DAG is falsifiable. falsified : bool Whether the given DAG is falsified. """ summary: dict significance_level: float suggestions: Optional[dict] = None def update_significance_level(self, significance_level: float): """ Update the significance level to decide if we falsify a given DAG. """ self.significance_level = significance_level self.__post_init__() def __post_init__(self): self.can_evaluate = self._can_evaluate() if not self.can_evaluate: self.falsified = None self.falsifiable = None elif ( self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.P_VALUE] > self.significance_level > self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE] ): self.falsified = True self.falsifiable = True elif self.significance_level < self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE]: self.falsified = False self.falsifiable = False else: self.falsified = False self.falsifiable = True def __repr__(self): # DAG Evaluation if self.can_evaluate: decision = " " if self.falsified else " do not " informative = " " if self.falsifiable else " not " frac_MEC = f"{len(self.summary[FalsifyConst.MEC])} / {len(self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.PERM_VIOLATIONS])}" frac_VLMC = f"{self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.GIVEN_VIOLATIONS]}/{self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.N_TESTS]}" p_LMC = self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.P_VALUE] p_dSep = self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE] validation_repr = [ f"The given DAG is{informative}informative because {frac_MEC} of the permutations lie in the Markov", f"equivalence class of the given DAG (p-value: {p_dSep:.2f}).", f"The given DAG violates {frac_VLMC} LMCs and is better than {(1 - p_LMC) * 100:.1f}% of the permuted DAGs (p-value: {p_LMC:.2f}).", f"Based on the provided significance level ({self.significance_level}) and because the DAG is{informative}informative,", f"we{decision}reject the DAG.", ] else: validation_repr = ["Cannot be evaluated!"] # Suggestions suggestion_repr = {} for m in self.suggestions: suggestion_repr[FALSIFY_METHODS[m]] = [ f"Remove edge {node[1]} --> {node[0]}" for (node, r) in self.suggestions[m][FalsifyConst.P_VALUES].items() if r[1] ] return _generate_table(validation_repr, suggestion_repr) def _can_evaluate(self): can_evaluate = True for m in (FalsifyConst.VALIDATE_LMC, FalsifyConst.VALIDATE_TPA): if m not in self.summary: can_evaluate = False return can_evaluate def falsify_graph( causal_graph: DirectedGraph, data: pd.DataFrame, suggestions: bool = False, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, significance_ci: float = 0.05, n_permutations: Optional[int] = None, show_progress_bar: Optional[bool] = None, n_jobs: Optional[int] = None, plot_histogram: bool = False, plot_kwargs: Optional[Dict] = None, ) -> EvaluationResult: """ Falsify a given DAG using observational data. This method returns the result of a permutation-test to falsify a user-given DAG using observational data. To this end we construct the test statistics by testing the violations of local Markov conditions (LMC) implied by the graph using conditional independence (CI) tests. The null is the number of LMC violations of a random node-permutation of the given graph. Our test can be interpreted as whether the given graph is significantly better than random in terms of the CIs it entails. To determine whether a given graph is falsifiable by our metric, we implement a second test, which reports whether given graph is "characteristic" enough in terms of the CIs it entails. For this, we compute how many of the random node permutations lie in the same Markov equivalence class (MEC) as the given graph and conclude that the given graph is falsifiable only if the fraction of permuted DAGs in the same MEC as the given graph is "reasonably" small. The returned EvaluationResult object has two attributes: `falsified` and `falsifiable`: `falsifiable`: The given graph lies in a different MEC than >= 1-`significance_level` of the permuted DAGs `falsified`: The given graph is falsifiable and violates fewer LMCs than >= 1-`significance_level` of the permuted DAGs By default, we only run 1 / `significance_level` permutations as those are enough to falsify a graph with type I error probability `significance_level` at some given `significance_level`. If you are interested in a more exact estimate of the p-value or wish to plot a histogram to see how the given DAG compares to random node permutations, you should set `n_permutations` to some larger value (e.g. 100 or 1000). If `n_permutations=-1` we test on all n_nodes! permutations (the default if plot_histogram=True). Additionally, this method allows to return suggestions to the user (suggestions=True). This is done by testing for violations of causal minimality via `validate_cm`. Related paper: Eulig, E., Mastakouri, A. A., Blöbaum, P., Hardt, M., & Janzing, D. (2023). Toward Falsifying Causal Graphs Using a Permutation-Based Test. https://arxiv.org/abs/2305.09565 :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param suggestions: Provide suggestions to the user. At the moment the only source of suggestions comes from validating causal minimality (using validate_cm). :param independence_test: Independence test to use for checking pairwise independencies. :param conditional_independence_test: Conditional independence test to use. :param significance_level: Significance level for the permutation test. :param significance_ci: Significance level for (conditional) independence tests. :param n_permutations: Number of permutations to perform. If -1 use all n_nodes! permutations. :param show_progress_bar: Whether to show progress bar over permutations. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :param plot_histogram: Plot histogram of results from permutation baseline. :param plot_kwargs: Additional plot arguments to be passed to plot_evaluation_results. :return: EvaluationResult """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs show_progress_bar = config.show_progress_bars if show_progress_bar is None else show_progress_bar p_values_memory = _PValuesMemory() if n_permutations is None: n_permutations = int(1 / significance_level) if not plot_histogram else -1 if not plot_kwargs: plot_kwargs = {} methods = ( partial( validate_lmc, independence_test=independence_test, conditional_independence_test=conditional_independence_test, significance_level=significance_ci, p_values_memory=p_values_memory, n_jobs=n_jobs, ), partial(validate_tpa, causal_graph_reference=causal_graph), ) if suggestions: suggestion_methods = ( partial( validate_cm, independence_test=independence_test, conditional_independence_test=conditional_independence_test, significance_level=significance_ci, p_values_memory=p_values_memory, n_jobs=n_jobs, ), ) else: suggestion_methods = tuple() summary_given = run_validations( causal_graph, data, methods=methods + suggestion_methods, ) summary_perm = _permutation_based( causal_graph, data, methods=methods, exclude_original_order=False, n_permutations=n_permutations, show_progress_bar=show_progress_bar, ) summary = dict() validation_methods = set(summary_perm.keys()) - {FalsifyConst.PERM_GRAPHS} for m in validation_methods: summary[m] = dict() summary[m][FalsifyConst.PERM_VIOLATIONS] = [perm[FalsifyConst.N_VIOLATIONS] for perm in summary_perm[m]] summary[m][FalsifyConst.GIVEN_VIOLATIONS] = summary_given[m][FalsifyConst.N_VIOLATIONS] summary[m][FalsifyConst.N_TESTS] = summary_given[m][FalsifyConst.N_TESTS] summary[m][FalsifyConst.F_PERM_VIOLATIONS] = [ perm[FalsifyConst.N_VIOLATIONS] / perm[FalsifyConst.N_TESTS] for perm in summary_perm[m] ] summary[m][FalsifyConst.F_GIVEN_VIOLATIONS] = ( summary[m][FalsifyConst.GIVEN_VIOLATIONS] / summary[m][FalsifyConst.N_TESTS] ) summary[m][FalsifyConst.P_VALUE] = sum( [ 1 for perm in summary[m][FalsifyConst.F_PERM_VIOLATIONS] if perm <= summary[m][FalsifyConst.F_GIVEN_VIOLATIONS] ] ) / len(summary[m][FalsifyConst.PERM_VIOLATIONS]) if m != FalsifyConst.VALIDATE_TPA: # Append list of violations (node, non_desc) to get local information summary[m][FalsifyConst.LOCAL_VIOLATION_INSIGHT] = summary_given[m][FalsifyConst.P_VALUES] if FalsifyConst.VALIDATE_TPA in summary: summary[FalsifyConst.MEC] = [ summary_perm[FalsifyConst.PERM_GRAPHS][i] for i, v in enumerate(summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.PERM_VIOLATIONS]) if v == 0 ] result = EvaluationResult( summary=summary, significance_level=significance_level, suggestions={m: summary_given[m] for m in summary_given if m not in validation_methods}, ) if plot_histogram: plot_evaluation_results(result, **plot_kwargs) return result def apply_suggestions( causal_graph: DirectedGraph, evaluation_result: EvaluationResult, edges_to_keep: Optional[List[Tuple[Any, Any]]] = None, ): if not hasattr(evaluation_result, "suggestions"): raise ValueError("EvaluationResult object has no attribute suggestions. Please run with suggestion=True!") causal_graph = causal_graph.copy() for m in evaluation_result.suggestions: for node, res in evaluation_result.suggestions[m][FalsifyConst.P_VALUES].items(): edge = (node[1], node[0]) if (res[1] and edges_to_keep is not None and edge not in edges_to_keep) or ( res[1] and edges_to_keep is None ): causal_graph.remove_edge(edge[0], edge[1]) return causal_graph def plot_evaluation_results(evaluation_result, figsize=(8, 3), bins=None, title="", savepath="", display=True): fig, ax = plt.subplots(figsize=figsize) # Plot histograms p_values = "" data = [] labels = [] evaluation_summary = {k: v for k, v in evaluation_result.summary.items() if k != FalsifyConst.MEC} for i, (m, m_summary) in enumerate(evaluation_summary.items()): data.append(m_summary[FalsifyConst.F_PERM_VIOLATIONS]) labels.append(f"Violations of {FALSIFY_METHODS[m]} of permuted DAGs") p_values += f"p-value {FALSIFY_METHODS[m]} = {m_summary[FalsifyConst.P_VALUE]:.2f}\n" ax.hist(data, color=COLORS[: len(evaluation_summary)], bins=bins, alpha=0.5, label=labels, edgecolor="k") # Plot given violations for i, (m, m_summary) in enumerate(evaluation_summary.items()): ylim = ax.get_ylim()[1] ax.plot( [m_summary[FalsifyConst.F_GIVEN_VIOLATIONS]] * 2, [0, ylim], "--", c=COLORS[i], label=f"Violations of {FALSIFY_METHODS[m]} of given DAG", ) ax.set_ylim([0, ylim]) ax.set_xlabel("Fraction of violations") ax.set_ylabel("# Permutations") plt.legend(loc="upper left", bbox_to_anchor=(1.05, 1), borderaxespad=0.0, title=p_values) if title: plt.title(title) plt.tight_layout() if savepath: plt.savefig(savepath, dpi=300, bbox_inches="tight") if display: plt.show() else: plt.close() def plot_local_insights( causal_graph: DirectedGraph, evaluation_result: Union[EvaluationResult, Dict], method: Optional[str] = FalsifyConst.VALIDATE_LMC, ): """ For some given graph and evaluation result plot local violations. :param causal_graph: DiGraph :param evaluation_result: EvaluationResult :param method: Method for which to plot violations """ colors = {} if isinstance(evaluation_result, EvaluationResult) and method in evaluation_result.summary: local_insight_dict = evaluation_result.summary[method][FalsifyConst.LOCAL_VIOLATION_INSIGHT] elif ( isinstance(evaluation_result, EvaluationResult) and hasattr(evaluation_result, "suggestions") and method in evaluation_result.suggestions ): local_insight_dict = evaluation_result.suggestions[method][FalsifyConst.P_VALUES] elif isinstance(evaluation_result, Dict): if method not in evaluation_result: raise ValueError(f"Validation method {method} does not exist in given evaluation_result!") if FalsifyConst.P_VALUES not in evaluation_result[method]: raise ValueError( f"Validation method {method} has no key {FalsifyConst.P_VALUES} where information on local violations " f"are stored!" ) local_insight_dict = evaluation_result[method][FalsifyConst.P_VALUES] else: raise ValueError(f"Cannot plot local violation insights from method {method} in evaluation_result!") for nodes, result in local_insight_dict.items(): if result[1]: if method == FalsifyConst.VALIDATE_LMC: # For LMC we highlight X for which X _|/|_ Y \in ND_X | Pa_X colors[nodes[0]] = VIOLATION_COLOR elif method == FalsifyConst.VALIDATE_PD: # For PD we highlight the edge (if Y\in Anc_X -> X are adjacent) colors[(nodes[1], nodes[0])] = VIOLATION_COLOR elif method == FalsifyConst.VALIDATE_CM: # For causal minimality we highlight the edge Y \in Pa_X -> X colors[(nodes[1], nodes[0])] = VIOLATION_COLOR plot(causal_graph, colors=colors) def _generate_table( validation_repr, suggestion_repr, width=105, validation_name="Falsificaton Summary", suggestion_name="Suggestions" ): # Create Validation header _repr = [ "+" + "-" * (width - 2) + "+\n", "|" + " " * int(np.floor((width - 2 - len(validation_name)) / 2)), validation_name, " " * int(np.ceil((width - 2 - len(validation_name)) / 2)) + "|\n", "+" + "-" * (width - 2) + "+\n", ] # Create Validation summary _repr += [f"| {v_line}" + " " * (width - 3 - len(v_line)) + "|\n" for v_line in validation_repr] # Close Validation _repr += ["+" + "-" * (width - 2) + "+\n"] # Create Suggestions header if suggestion_repr: _repr += [ "|" + " " * int(np.floor((width - 2 - len(suggestion_name)) / 2)), suggestion_name, " " * int(np.ceil((width - 2 - len(suggestion_name)) / 2)) + "|\n", "+" + "-" * (width - 2) + "+\n", ] # Iterate over suggestions for m, suggestions in suggestion_repr.items(): left_col = "| " + m + " |" if not suggestions: right_col = " " + " " * (width - 2 - len(left_col)) + "|\n" _repr += [left_col, right_col] for i, s in enumerate(suggestions): if i > 0: left_col = "| " + " " * len(m) + " |" right_col = " - " + s + " " * (width - 4 - len(s) - len(left_col)) + "|\n" _repr += [left_col, right_col] _repr += ["+" + "-" * (width - 2) + "+\n"] return "".join(_repr)[:-1] def _compute_p_value( data: pd.DataFrame, X: Union[List, str], Y: Union[List, str], Z: Optional[Union[Set, List, str]], independence_test: Optional[Callable[[np.ndarray, np.ndarray], float]], conditional_independence_test: Optional[Callable[[np.ndarray, np.ndarray, np.ndarray], float]], seed: int, ) -> float: """Perform (conditional) independence test and report p-value. If any nodes needed for the test do not exist in the data, this test is skipped and a value of -1 is returned and a warning is raised. :param data: Observations of variables in the DAG. :param X: Variable to test (conditional) independence with Y :param Y: Variable to test (conditional) independence with X :param Z: Set to condition independence test on. Can be empty (None, empty set, or empty list). :param independence_test: Independence test to use. :param conditional_independence_test: Conditional independence test to use. :param seed: Random seed :return: p-value """ set_random_seed(seed) # Test if we have data for X and Y for node in [X, Y]: if not node in data.columns: warnings.warn(f"WARN: Couldn't find data for node {node}. Skip this test.") return if Z: # Test if we have data for Z for node in Z: if not node in data.columns: warnings.warn(f"WARN: Couldn't find data for node {node}. Skip this test.") return p_value = conditional_independence_test(data[X].values, data[Y].values, data[Z].values) else: p_value = independence_test(data[X].values, data[Y].values) return p_value def _get_parental_triples(causal_graph: DirectedGraph, include_unconditional: bool): """ For a given graph collect all parental triples, that is, the triple (X, Y, Z) is a parental triple iff Y is non-descendant of X, and Z are the parents of X (can be empty if include_unconditional=True) """ triples = [] for node in causal_graph.nodes: parents = get_ordered_predecessors(causal_graph, node) non_descendants = _get_non_descendants(causal_graph, node, exclude_parents=True) if (parents or include_unconditional) and non_descendants: for non_desc in non_descendants: triples.append((node, non_desc, parents)) return triples def _permutation_based( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Union[Callable, Tuple[Callable, ...], List[Callable]], exclude_original_order: bool, n_permutations: int, show_progress_bar: bool, ) -> Dict[str, List[Union[DirectedGraph, Dict]]]: """ Generate baseline for node permutations. :param causal_graph: A directed acyclic graph (DAG). :param methods: Validation methods to perform. :param exclude_original_order: Exclude the original ordering of the nodes (default=False) :param n_permutations: Number of permutations to perform. If -1 use all n_nodes! - int(exclude_orig) permutations :param show_progress_bar: Whether to show progress bar over tested permutations. :return: Dictionary containing summary of validation for each individual graph as well as the permuted graphs """ if not isinstance(methods, (tuple, list)): methods = (methods,) perm_gen = _PermuteNodes(causal_graph, n_permutations=n_permutations, exclude_original_order=exclude_original_order) validation_summary = {FalsifyConst.PERM_GRAPHS: []} for permuted_graph in tqdm(perm_gen, desc="Test permutations of given graph", disable=not show_progress_bar): res = run_validations( causal_graph=permuted_graph, data=data, methods=methods, ) validation_summary[FalsifyConst.PERM_GRAPHS].append(permuted_graph) for m_name, summary in res.items(): if m_name not in validation_summary: validation_summary[m_name] = [] validation_summary[m_name].append(summary) return validation_summary class _PermuteNodes: def __init__(self, causal_graph: DirectedGraph, exclude_original_order: bool, n_permutations: int): """ Randomly permute the nodes of a given causal graph while keeping the underlying graph structure the same. :param causal_graph: A directed acyclic graph (DAG). :param exclude_original_order: Do not return the original order. :param n_permutations: Return a generator with n_permutations permutations. If n_permutations = -1 (default), we return all n_nodes! - int(exclude_orig) permutations. :return: Copy of causal_graph with nodes randomly permuted. """ self.causal_graph = causal_graph self.exclude_original_order = exclude_original_order self.n_permutations = n_permutations self.max_perms = np.math.factorial(self.causal_graph.number_of_nodes()) - int(self.exclude_original_order) if n_permutations == -1 or n_permutations > self.max_perms: self.it = self.iter_all_permutations() self.length = self.max_perms if self.length > 2**63 - 1: raise ValueError( f"Too many permutations specified. Did you accidently set 'n_permutations'=-1 for a " f"large (>20 nodes) graph? " f"Given graph has {causal_graph.number_of_nodes()} nodes." ) else: self.it = self.iter_random_permutations() self.length = self.n_permutations def iter_all_permutations(self): for i, perm in enumerate(permutations(self.causal_graph.nodes)): if self.exclude_original_order and i == 0: continue mapping = {node: perm[i] for i, node in enumerate(self.causal_graph.nodes)} yield nx.relabel_nodes(self.causal_graph, mapping, copy=True) def iter_random_permutations(self): for _ in range(self.n_permutations): if self.exclude_original_order: is_orig = True while is_orig: perm = list(np.random.permutation(self.causal_graph.nodes)) if perm != list(self.causal_graph.nodes): is_orig = False else: perm = list(np.random.permutation(self.causal_graph.nodes)) mapping = {node: perm[i] for i, node in enumerate(self.causal_graph.nodes)} yield nx.relabel_nodes(self.causal_graph, mapping, copy=True) def __iter__(self): yield from self.it def __len__(self): return self.length def _to_frozenset(x: Union[Set, List, str]): """Converts a set, list or string into a hashable frozenset""" assert ( isinstance(x, Set) or isinstance(x, List) or isinstance(x, str) ), f"{x} must be list, set or str. Got {type(x)} instead!" if isinstance(x, str): return frozenset({x}) return frozenset(x)

eeulig

379f188d674d5be6d644559f9f54852b87403f7c

4e30c63fe1113e35259e7fb3400a6f09bca4d699

Wondering if there is really a use case where someone wants to use different data for the tests? To make things simpler, I would probably leave this as required and pass it to the methods. Or are there tests that do not require data (i.e., we cannot blindly pass it)?

bloebp

py-why/dowhy

Remove kwargs from falsify, improve docstrings

This commit updates `gcm/falsify` in the following ways: - Remove the `**kwargs` for `validate_graph` and `falsify_graph`. Instead, validation methods given to those functions should be wrapped in `wrap_partial`, e.g. if we want to validate a graph `G` and test local Markov conditions and pairwise dependencies on observations `data`, we would do ``` validate_graph(G, methods=( wrap_partial(validate_lmc, data=data, independence_test=..., conditional_independence_test=...), wrap_partial(validate_pd, data=data, independence_test=...), ) ) ``` By default (`methods=None`), `validate_graph` tests local Markov conditions and `falsify_graph` tests local Markov conditions and d-separation of parental triples. In this case `data` has to be set and optionally one can provide `independence_test`, `conditional_independence_test`, `significance_level`, and `n_jobs` via the keyword arguments of `validate_graph` / `falsify_graph`. - Use `Enum` for some of the constants - In `_PValuesMemory` the method `add_p_value ` always sets a p-value even if it was set already. This removes the need for the method `clear_placeholders` and allows `p_values` to be set to `-1`, indicating that the CI is not testable (because of missing data for a node, c.f. `_compute_p_value`) - If for some CI: X, Y | Z any of the nodes has no data (column missing in the provided dataframe), we skip this test and raise a warning. - Instead of using the raw number of violations in the plotted histogram and to compute the p-value, we use the relative number of violations. This will result in different p-values only if we have missing data for some nodes (and thus the number of tests is potentially different between different permutations) - Improve docstrings

2023-07-04 13:58:30+00:00

2023-07-18 14:19:10+00:00

dowhy/gcm/falsify.py

"""This module provides functionality to falsify a user-given DAG given observed data. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from dataclasses import dataclass, field from enum import Enum, auto from functools import partial from itertools import permutations from typing import Any, Callable, Dict, FrozenSet, List, Optional, Set, Tuple, Union import matplotlib.colors as mcolors import matplotlib.pyplot as plt import networkx as nx import numpy as np import pandas as pd from joblib import Parallel, delayed from tqdm import tqdm import dowhy.gcm.config as config from dowhy.gcm.independence_test import kernel_based from dowhy.gcm.util import plot from dowhy.gcm.util.general import set_random_seed from dowhy.gcm.validation import _get_non_descendants from dowhy.graph import DirectedGraph, get_ordered_predecessors VIOLATION_COLOR = "red" COLORS = list(mcolors.TABLEAU_COLORS.values()) class FalsifyConst(Enum): N_VIOLATIONS = auto() N_TESTS = auto() P_VALUE = auto() P_VALUES = auto() GIVEN_VIOLATIONS = auto() PERM_VIOLATIONS = auto() F_GIVEN_VIOLATIONS = auto() F_PERM_VIOLATIONS = auto() LOCAL_VIOLATION_INSIGHT = auto() METHOD = auto() VALIDATE_LMC = auto() VALIDATE_TPA = auto() VALIDATE_PD = auto() VALIDATE_CM = auto() PERM_GRAPHS = auto() MEC = auto() FALSIFY_METHODS = { FalsifyConst.VALIDATE_LMC: "LMC", FalsifyConst.VALIDATE_PD: "Faithfulness", FalsifyConst.VALIDATE_TPA: "tPa", FalsifyConst.VALIDATE_CM: "Causal Minimality", } @dataclass class _PValuesMemory: """A class to store and access results of independence tests. This class is useful if, e.g. we validate LMC on graph X -> Y -> Z and also on the permuted nodes Z -> Y -> X. Since X ind Z | Y == Z ind X | Y, we only need to perform the conditional independence test once. We use frozensets of nodes since standard (immutable) sets are not hashable. """ p_values: Dict[Tuple[FrozenSet, FrozenSet, FrozenSet], float] = field(default_factory=dict) def add_p_value( self, p_value: Union[None, float], X: Union[Set, List, str], Y: Union[Set, List, str], Z: Optional[Union[Set, List, str]] = None, ): if not Z: Z = set() self.p_values[(_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z))] = p_value def get_p_value( self, X: Union[Set, List, str], Y: Union[Set, List, str], Z: Optional[Union[Set, List, str]] = None ): if not Z: Z = set() # Independence tests are symmetric if (_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z)) in self.p_values: return self.p_values[(_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z))] elif (_to_frozenset(Y), _to_frozenset(X), _to_frozenset(Z)) in self.p_values: return self.p_values[(_to_frozenset(Y), _to_frozenset(X), _to_frozenset(Z))] return def __contains__(self, item: Tuple[Union[Set, List, str], ...]) -> bool: X, Y = (_to_frozenset(i) for i in item[:2]) if len(item) == 2 or item[2] is None: Z = frozenset() else: Z = _to_frozenset(item[2]) return (X, Y, Z) in self.p_values or (Y, X, Z) in self.p_values def validate_lmc( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, include_unconditional: bool = True, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[str, float]]]: """ Validate the local markov condition for a given directed graph. Return number of violations and p values for each node. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory instance, where results of previously performed tests are stored. :param independence_test: Test to use for unconditional independencies (only used if include_unconditional=True) :param conditional_independence_test: Conditional independence test to use for checking local Markov condition. :param significance_level: Significance level for (conditional) independence tests. :param include_unconditional: Test also unconditional independencies of root nodes. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Outcome of validation containing number of violations in the graph and p values/violation for each tuple (node, non_desc) """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_LMC, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, FalsifyConst.P_VALUES: dict(), } # Find out which tests to do triples = _get_parental_triples(causal_graph, include_unconditional) to_test = [] for node, non_desc, parents in triples: if not (node, non_desc, parents) in p_values_memory: to_test.append((node, non_desc, parents)) p_values_memory.add_p_value(None, node, non_desc, parents) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=node, Y=non_desc, Z=parents, independence_test=independence_test, conditional_independence_test=conditional_independence_test, seed=int(seed), ) for (node, non_desc, parents), seed in zip(to_test, random_seeds) ) # Gather results for i, (node, non_desc, parents) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], node, non_desc, parents) # Summarize for node, non_desc, parents in triples: lmc_p_value = p_values_memory.get_p_value(node, non_desc, parents) if lmc_p_value is not None: validation_summary[FalsifyConst.N_TESTS] += 1 validation_summary[FalsifyConst.P_VALUES][(node, non_desc)] = ( lmc_p_value, lmc_p_value <= significance_level, ) if lmc_p_value <= significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_tpa( causal_graph: DirectedGraph, causal_graph_reference: DirectedGraph, include_unconditional: bool = True, data: Optional[pd.DataFrame] = None, ) -> Dict[str, int]: """ Graphical criterion to evaluate which pairwise parental d-separations (parental triples) in `causal_graph` are violated, assuming `causal_graph_reference` is the ground truth graph. If none are violated, then both graphs lie in the same Markov equivalence class. Specifically we test: X _|_G' Y | Z and X _/|_G Y | Z for Y \in ND{X}^G', Z = PA{X}^G :param causal_graph: Causal graph for which to evaluate parental d-separations (G') :param causal_graph_reference: Causal graph where we test if d-separation holds (G) :param include_unconditional: Test also unconditional independencies of root nodes. :param data: IGNORED! No data is needed for this validation method and thus the data argument is ignored! :return: Validation summary with number of d-separations implied by `causal_graph` and number of times these are violated in the graph `causal_graph_reference`. """ validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_TPA, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, } triples = _get_parental_triples(causal_graph, include_unconditional) for node, non_desc, parents in triples: validation_summary[FalsifyConst.N_TESTS] += 1 if not nx.d_separated(causal_graph_reference, {node}, {non_desc}, set(parents)): validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_pd( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, n_pairs: int = -1, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, adjacent_only: bool = False, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[tuple, float]]]: """ Validate pairwise dependencies (pd) for a given causal graph and data. Test for each node if it is statistically dependent of all its ancestors. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory object, where results of previously performed tests are stored. :param n_pairs: Evaluate dependencies for n_pairs <= all pairs in the DAG. If n_pairs=-1, evaluate dependencies for all (ancestor, node) pairs (default). :param independence_test: Independence test to use for checking pairwise dependencies. :param significance_level: Significance level for independence tests. :param adjacent_only: Only test adjacent node pairs. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Summary dict: {n_violations: int, n_tests: int, p_values: {(ancestor, node): float, ...}} """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() pairs = [(ancestor, node) for node in causal_graph.nodes for ancestor in nx.ancestors(causal_graph, node)] if adjacent_only: pairs = [ (ancestor, node) for (ancestor, node) in pairs if ancestor in get_ordered_predecessors(causal_graph, node) ] if n_pairs < 0: n_pairs = len(pairs) if n_pairs > len(pairs): raise ValueError(f"n_pairs ({n_pairs}) > number of pairs in the DAG ({len(pairs)})") validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_PD, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: n_pairs, FalsifyConst.P_VALUES: dict(), } pair_idxs = np.random.choice(len(pairs), size=n_pairs, replace=False) # Find out which tests to do to_test = [] for pair_idx in pair_idxs: ancestor, node = pairs[pair_idx] if not (ancestor, node) in p_values_memory: to_test.append((ancestor, node)) p_values_memory.add_p_value(None, ancestor, node) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=ancestor, Y=node, Z=None, independence_test=independence_test, conditional_independence_test=None, seed=int(seed), ) for (ancestor, node), seed in zip(to_test, random_seeds) ) # Gather results for i, (ancestor, node) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], ancestor, node) # Summarize for pair_idx in pair_idxs: ancestor, node = pairs[pair_idx] p_value = p_values_memory.get_p_value(ancestor, node) if p_value is not None: validation_summary[FalsifyConst.P_VALUES][(node, ancestor)] = (p_value, p_value > significance_level) if p_value > significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_cm( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[tuple, float]]]: """ Function to test causal minimality of a DAG (see [1], Proposition 6.36). [1] J. Peters, D. Janzing, and B. Schölkopf, Elements of Causal Inference: Foundations and Learning Algorithms. Cambridge, MA, USA: MIT Press, 2017. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory object, where results of previously performed tests are stored. :param independence_test: Independence test to use. :param conditional_independence_test: Conditional independence test to use. :param significance_level: Significance level for independence tests. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Validation summary as dict. """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_CM, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, FalsifyConst.P_VALUES: dict(), } # Find out which tests to do triples = [] to_test = [] for node in causal_graph.nodes: parents = set(causal_graph.predecessors(node)) if parents: for p in parents: other_parents = parents.difference({p}) triples.append((node, p, other_parents)) if not (node, p, other_parents) in p_values_memory: to_test.append((node, p, other_parents)) p_values_memory.add_p_value(None, node, p, other_parents) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=node, Y=p, Z=list(other_parents), independence_test=independence_test, conditional_independence_test=conditional_independence_test, seed=int(seed), ) for (node, p, other_parents), seed in zip(to_test, random_seeds) ) # Gather results for i, (node, p, other_parents) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], node, p, other_parents) # Summarize for node, p, other_parents in triples: p_value = p_values_memory.get_p_value(node, p, other_parents) if p_value is not None: validation_summary[FalsifyConst.N_TESTS] += 1 validation_summary[FalsifyConst.P_VALUES][(node, p, tuple(other_parents))] = ( p_value, p_value > significance_level, ) if p_value > significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_graph( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Optional[Union[Callable, Tuple[Callable, ...], List[Callable]]] = partial( validate_lmc, independence_test=kernel_based, conditional_independence_test=kernel_based ), ) -> Dict[str, Dict]: """ Validate a given causal graph using observational data and some given methods. If methods are provided, they must be wrapped in a partial object, with their respective parameters. E.g., if one wants to test the local Markov conditions and the pairwise dependencies (unconditional faithfulness), then call validate_graph(G, data, methods=( partial(validate_lmc, independence_test=..., conditional_independence_test=...), partial(validate_pd, independence_test=...), ) ) :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param methods: Method functions wrapped in wrap_partial. E.g. wrap_partial(validate_lmc, data=data, independence_test=..., conditional_independence_test=...). If no methods are provided we run validate_lmc with optional keyword arguments provided to validate_graph. :return: Validation summary as dict. """ if not isinstance(methods, (tuple, list)): methods = (methods,) validation_summary = dict() for m in methods: m_summary = m(causal_graph=causal_graph, data=data) m_name = m_summary.pop(FalsifyConst.METHOD) validation_summary[m_name] = m_summary return validation_summary @dataclass class EvaluationResult: """ Dataset class containing the evaluation result of falsifying a graph using a node-permutation test. ... Attributes ---------- summary : dict Dictionary containing the summary of the evaluation. significance_level : float Significance level based on which we falsify the given DAG falsifiable : bool Whether the given DAG is falsifiable. falsified : bool Whether the given DAG is falsified. """ summary: dict significance_level: float suggestions: Optional[dict] = None def update_significance_level(self, significance_level: float): """ Update the significance level to decide if we falsify a given DAG. """ self.significance_level = significance_level self.__post_init__() def __post_init__(self): self.can_evaluate = self._can_evaluate() if not self.can_evaluate: self.falsified = None self.falsifiable = None elif ( self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.P_VALUE] > self.significance_level > self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE] ): self.falsified = True self.falsifiable = True elif self.significance_level < self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE]: self.falsified = False self.falsifiable = False else: self.falsified = False self.falsifiable = True def __repr__(self): # DAG Evaluation if self.can_evaluate: decision = " " if self.falsified else " do not " informative = " " if self.falsifiable else " not " frac_MEC = f"{len(self.summary[FalsifyConst.MEC])} / {len(self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.PERM_VIOLATIONS])}" frac_VLMC = f"{self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.GIVEN_VIOLATIONS]}/{self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.N_TESTS]}" p_LMC = self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.P_VALUE] p_dSep = self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE] validation_repr = [ f"The given DAG is{informative}informative because {frac_MEC} of the permutations lie in the Markov", f"equivalence class of the given DAG (p-value: {p_dSep:.2f}).", f"The given DAG violates {frac_VLMC} LMCs and is better than {(1 - p_LMC) * 100:.1f}% of the permuted DAGs (p-value: {p_LMC:.2f}).", f"Based on the provided significance level ({self.significance_level}) and because the DAG is{informative}informative,", f"we{decision}reject the DAG.", ] else: validation_repr = ["Cannot be evaluated!"] # Suggestions suggestion_repr = {} for m in self.suggestions: suggestion_repr[FALSIFY_METHODS[m]] = [ f"Remove edge {node[1]} --> {node[0]}" for (node, r) in self.suggestions[m][FalsifyConst.P_VALUES].items() if r[1] ] return _generate_table(validation_repr, suggestion_repr) def _can_evaluate(self): can_evaluate = True for m in (FalsifyConst.VALIDATE_LMC, FalsifyConst.VALIDATE_TPA): if m not in self.summary: can_evaluate = False return can_evaluate def falsify_graph( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Optional[Union[Callable, Tuple[Callable, ...]]] = None, suggestion_methods: Optional[Union[Callable, Tuple[Callable, ...]]] = None, suggestions: bool = False, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, significance_ci: float = 0.05, n_permutations: Optional[int] = None, show_progress_bar: Optional[bool] = None, n_jobs: Optional[int] = None, plot_histogram: bool = False, plot_kwargs: Optional[Dict] = None, ) -> EvaluationResult: """ Falsify a given DAG using observational data. This method returns the result of a permutation-test to falsify a user-given DAG using observational data. To this end we construct the test statistics by testing the violations of local Markov conditions (LMC) implied by the graph using conditional independence (CI) tests. The null is the number of LMC violations of a random node-permutation of the given graph. Our test can be interpreted as whether the given graph is significantly better than random in terms of the CIs it entails. To determine whether a given graph is falsifiable by our metric, we implement a second test, which reports whether given graph is "characteristic" enough in terms of the CIs it entails. For this, we compute how many of the random node permutations lie in the same Markov equivalence class (MEC) as the given graph and conclude that the given graph is falsifiable only if the fraction of permuted DAGs in the same MEC as the given graph is "reasonably" small. The returned EvaluationResult object has two attributes: `falsified` and `falsifiable`: `falsifiable`: The given graph lies in a different MEC than >= 1-`significance_level` of the permuted DAGs `falsified`: The given graph is falsifiable and violates fewer LMCs than >= 1-`significance_level` of the permuted DAGs By default, we only run 1 / `significance_level` permutations as those are enough to falsify a graph with type I error probability `significance_level` at some given `significance_level`. If you are interested in a more exact estimate of the p-value of whish to plot a histogram to see how the given DAG compares to random node permutations, you should set `n_permutations` to some larger value (e.g. 100 or 1000). If `n_permutations=-1` we test on all n_nodes! permutations. `methods` and `suggestion_methods` must be wrapped in partial(method, **kwargs) (c.f. `validate_graph`). Related paper: Eulig, E., Mastakouri, A. A., Blöbaum, P., Hardt, M., & Janzing, D. (2023). Toward Falsifying Causal Graphs Using a Permutation-Based Test. https://arxiv.org/abs/2305.09565 :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param methods: Validation methods to perform. :param suggestion_methods: Methods to run on the given graph to provide additional suggestions. :param suggestions: Provide suggestions generated using the `suggestion_methods`. :param independence_test: Independence test to use for checking pairwise independencies. :param conditional_independence_test: Conditional independence test to use. :param significance_level: Significance level for the permutation test. :param significance_ci: Significance level for (conditional) independence tests. :param n_permutations: Number of permutations to perform. If -1 use all n_nodes! permutations. :param show_progress_bar: Whether to show progress bar over permutations. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :param plot_histogram: Plot histogram of results from permutation baseline. :param plot_kwargs: Additional plot arguments to be passed to plot_evaluation_results. :return: EvaluationResult """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs show_progress_bar = config.show_progress_bars if show_progress_bar is None else show_progress_bar p_values_memory = _PValuesMemory() if n_permutations is None: n_permutations = int(1 / significance_level) if not plot_histogram else -1 if not plot_kwargs: plot_kwargs = {} # If no methods are provided, use default ones: validate_lmc, validate_tpa if not methods: methods = ( partial( validate_lmc, independence_test=independence_test, conditional_independence_test=conditional_independence_test, significance_level=significance_ci, p_values_memory=p_values_memory, n_jobs=n_jobs, ), partial(validate_tpa, causal_graph_reference=causal_graph), ) elif isinstance(methods, Callable): methods = (methods,) # If no suggestion methods are provided, but suggestions=True, use default ones: validate_cm if not suggestions: suggestion_methods = tuple() elif suggestions and isinstance(suggestion_methods, Callable): suggestion_methods = (suggestion_methods,) elif suggestions: suggestion_methods = ( partial( validate_cm, independence_test=independence_test, conditional_independence_test=conditional_independence_test, significance_level=significance_ci, p_values_memory=p_values_memory, n_jobs=n_jobs, ), ) summary_given = validate_graph( causal_graph, data, methods=methods + suggestion_methods, ) summary_perm = _permutation_based( causal_graph, data, methods=methods, exclude_original_order=False, n_permutations=n_permutations, show_progress_bar=show_progress_bar, ) summary = dict() validation_methods = set(summary_perm.keys()) - {FalsifyConst.PERM_GRAPHS} for m in validation_methods: summary[m] = dict() summary[m][FalsifyConst.PERM_VIOLATIONS] = [perm[FalsifyConst.N_VIOLATIONS] for perm in summary_perm[m]] summary[m][FalsifyConst.GIVEN_VIOLATIONS] = summary_given[m][FalsifyConst.N_VIOLATIONS] summary[m][FalsifyConst.N_TESTS] = summary_given[m][FalsifyConst.N_TESTS] summary[m][FalsifyConst.F_PERM_VIOLATIONS] = [ perm[FalsifyConst.N_VIOLATIONS] / perm[FalsifyConst.N_TESTS] for perm in summary_perm[m] ] summary[m][FalsifyConst.F_GIVEN_VIOLATIONS] = ( summary[m][FalsifyConst.GIVEN_VIOLATIONS] / summary[m][FalsifyConst.N_TESTS] ) summary[m][FalsifyConst.P_VALUE] = sum( [ 1 for perm in summary[m][FalsifyConst.F_PERM_VIOLATIONS] if perm <= summary[m][FalsifyConst.F_GIVEN_VIOLATIONS] ] ) / len(summary[m][FalsifyConst.PERM_VIOLATIONS]) if m != FalsifyConst.VALIDATE_TPA: # Append list of violations (node, non_desc) to get local information summary[m][FalsifyConst.LOCAL_VIOLATION_INSIGHT] = summary_given[m][FalsifyConst.P_VALUES] if FalsifyConst.VALIDATE_TPA in summary: summary[FalsifyConst.MEC] = [ summary_perm[FalsifyConst.PERM_GRAPHS][i] for i, v in enumerate(summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.PERM_VIOLATIONS]) if v == 0 ] result = EvaluationResult( summary=summary, significance_level=significance_level, suggestions={m: summary_given[m] for m in summary_given if m not in validation_methods}, ) if plot_histogram: plot_evaluation_results(result, **plot_kwargs) return result def apply_suggestions( causal_graph: DirectedGraph, evaluation_result: EvaluationResult, edges_to_keep: Optional[List[Tuple[Any, Any]]] = None, ): if not hasattr(evaluation_result, "suggestions"): raise ValueError("EvaluationResult object has no attribute suggestions. Please run with suggestion=True!") causal_graph = causal_graph.copy() for m in evaluation_result.suggestions: for node, res in evaluation_result.suggestions[m][FalsifyConst.P_VALUES].items(): edge = (node[1], node[0]) if (res[1] and edges_to_keep is not None and edge not in edges_to_keep) or ( res[1] and edges_to_keep is None ): causal_graph.remove_edge(edge[0], edge[1]) return causal_graph def plot_evaluation_results(evaluation_result, figsize=(8, 3), bins=None, title="", savepath="", display=True): fig, ax = plt.subplots(figsize=figsize) # Plot histograms p_values = "" data = [] labels = [] evaluation_summary = {k: v for k, v in evaluation_result.summary.items() if k != FalsifyConst.MEC} for i, (m, m_summary) in enumerate(evaluation_summary.items()): data.append(m_summary[FalsifyConst.F_PERM_VIOLATIONS]) labels.append(f"Violations of {FALSIFY_METHODS[m]} of permuted DAGs") p_values += f"p-value {FALSIFY_METHODS[m]} = {m_summary[FalsifyConst.P_VALUE]:.2f}\n" ax.hist(data, color=COLORS[: len(evaluation_summary)], bins=bins, alpha=0.5, label=labels, edgecolor="k") # Plot given violations for i, (m, m_summary) in enumerate(evaluation_summary.items()): ylim = ax.get_ylim()[1] ax.plot( [m_summary[FalsifyConst.F_GIVEN_VIOLATIONS]] * 2, [0, ylim], "--", c=COLORS[i], label=f"Violations of {FALSIFY_METHODS[m]} of given DAG", ) ax.set_ylim([0, ylim]) ax.set_xlabel("Fraction of violations") ax.set_ylabel("# Permutations") plt.legend(loc="upper left", bbox_to_anchor=(1.05, 1), borderaxespad=0.0, title=p_values) if title: plt.title(title) plt.tight_layout() if savepath: plt.savefig(savepath, dpi=300, bbox_inches="tight") if display: plt.show() else: plt.close() def plot_local_insights( causal_graph: DirectedGraph, evaluation_result: Union[EvaluationResult, Dict], method: Optional[str] = FalsifyConst.VALIDATE_LMC, ): """ For some given graph and evaluation result plot local violations. :param causal_graph: DiGraph :param evaluation_result: EvaluationResult :param method: Method for which to plot violations """ colors = {} if isinstance(evaluation_result, EvaluationResult) and method in evaluation_result.summary: local_insight_dict = evaluation_result.summary[method][FalsifyConst.LOCAL_VIOLATION_INSIGHT] elif ( isinstance(evaluation_result, EvaluationResult) and hasattr(evaluation_result, "suggestions") and method in evaluation_result.suggestions ): local_insight_dict = evaluation_result.suggestions[method][FalsifyConst.P_VALUES] elif isinstance(evaluation_result, Dict): if method not in evaluation_result: raise ValueError(f"Validation method {method} does not exist in given evaluation_result!") if FalsifyConst.P_VALUES not in evaluation_result[method]: raise ValueError( f"Validation method {method} has no key {FalsifyConst.P_VALUES} where information on local violations " f"are stored!" ) local_insight_dict = evaluation_result[method][FalsifyConst.P_VALUES] else: raise ValueError(f"Cannot plot local violation insights from method {method} in evaluation_result!") for nodes, result in local_insight_dict.items(): if result[1]: if method == FalsifyConst.VALIDATE_LMC: # For LMC we highlight X for which X _|/|_ Y \in ND_X | Pa_X colors[nodes[0]] = VIOLATION_COLOR elif method == FalsifyConst.VALIDATE_PD: # For PD we highlight the edge (if Y\in Anc_X -> X are adjacent) colors[(nodes[1], nodes[0])] = VIOLATION_COLOR elif method == FalsifyConst.VALIDATE_CM: # For causal minimality we highlight the edge Y \in Pa_X -> X colors[(nodes[1], nodes[0])] = VIOLATION_COLOR plot(causal_graph, colors=colors) def _generate_table( validation_repr, suggestion_repr, width=105, validation_name="Falsificaton Summary", suggestion_name="Suggestions" ): # Create Validation header _repr = [ "+" + "-" * (width - 2) + "+\n", "|" + " " * int(np.floor((width - 2 - len(validation_name)) / 2)), validation_name, " " * int(np.ceil((width - 2 - len(validation_name)) / 2)) + "|\n", "+" + "-" * (width - 2) + "+\n", ] # Create Validation summary _repr += [f"| {v_line}" + " " * (width - 3 - len(v_line)) + "|\n" for v_line in validation_repr] # Close Validation _repr += ["+" + "-" * (width - 2) + "+\n"] # Create Suggestions header if suggestion_repr: _repr += [ "|" + " " * int(np.floor((width - 2 - len(suggestion_name)) / 2)), suggestion_name, " " * int(np.ceil((width - 2 - len(suggestion_name)) / 2)) + "|\n", "+" + "-" * (width - 2) + "+\n", ] # Iterate over suggestions for m, suggestions in suggestion_repr.items(): left_col = "| " + m + " |" if not suggestions: right_col = " " + " " * (width - 2 - len(left_col)) + "|\n" _repr += [left_col, right_col] for i, s in enumerate(suggestions): if i > 0: left_col = "| " + " " * len(m) + " |" right_col = " - " + s + " " * (width - 4 - len(s) - len(left_col)) + "|\n" _repr += [left_col, right_col] _repr += ["+" + "-" * (width - 2) + "+\n"] return "".join(_repr)[:-1] def _compute_p_value( data: pd.DataFrame, X: Union[List, str], Y: Union[List, str], Z: Optional[Union[Set, List, str]], independence_test: Optional[Callable[[np.ndarray, np.ndarray], float]], conditional_independence_test: Optional[Callable[[np.ndarray, np.ndarray, np.ndarray], float]], seed: int, ) -> float: """Perform (conditional) independence test and report p-value. If any nodes needed for the test do not exist in the data, this test is skipped and a value of -1 is returned and a warning is raised. :param data: Observations of variables in the DAG. :param X: Variable to test (conditional) independence with Y :param Y: Variable to test (conditional) independence with X :param Z: Set to condition independence test on. Can be empty (None, empty set, or empty list). :param independence_test: Independence test to use. :param conditional_independence_test: Conditional independence test to use. :param seed: Random seed :return: p-value """ set_random_seed(seed) # Test if we have data for X and Y for node in [X, Y]: if not node in data.columns: warnings.warn(f"WARN: Couldn't find data for node {node}. Skip this test.") return if Z: # Test if we have data for Z for node in Z: if not node in data.columns: warnings.warn(f"WARN: Couldn't find data for node {node}. Skip this test.") return p_value = conditional_independence_test(data[X].values, data[Y].values, data[Z].values) else: p_value = independence_test(data[X].values, data[Y].values) return p_value def _get_parental_triples(causal_graph: DirectedGraph, include_unconditional: bool): """ For a given graph collect all parental triples, that is, the triple (X, Y, Z) is a parental triple iff Y is non-descendant of X, and Z are the parents of X (can be empty if include_unconditional=True) """ triples = [] for node in causal_graph.nodes: parents = get_ordered_predecessors(causal_graph, node) non_descendants = _get_non_descendants(causal_graph, node, exclude_parents=True) if (parents or include_unconditional) and non_descendants: for non_desc in non_descendants: triples.append((node, non_desc, parents)) return triples def _permutation_based( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Union[Callable, Tuple[Callable, ...], List[Callable]], exclude_original_order: bool, n_permutations: int, show_progress_bar: bool, ) -> Dict[str, List[Union[DirectedGraph, Dict]]]: """ Generate baseline for node permutations. :param causal_graph: A directed acyclic graph (DAG). :param methods: Validation methods to perform. :param exclude_original_order: Exclude the original ordering of the nodes (default=False) :param n_permutations: Number of permutations to perform. If -1 use all n_nodes! - int(exclude_orig) permutations :param show_progress_bar: Whether to show progress bar over tested permutations. :return: Dictionary containing summary of validation for each individual graph as well as the permuted graphs """ if not isinstance(methods, (tuple, list)): methods = (methods,) perm_gen = _PermuteNodes(causal_graph, n_permutations=n_permutations, exclude_original_order=exclude_original_order) validation_summary = {FalsifyConst.PERM_GRAPHS: []} for permuted_graph in tqdm(perm_gen, desc="Test permutations of given graph", disable=not show_progress_bar): res = validate_graph( causal_graph=permuted_graph, data=data, methods=methods, ) validation_summary[FalsifyConst.PERM_GRAPHS].append(permuted_graph) for m_name, summary in res.items(): if m_name not in validation_summary: validation_summary[m_name] = [] validation_summary[m_name].append(summary) return validation_summary class _PermuteNodes: def __init__(self, causal_graph: DirectedGraph, exclude_original_order: bool, n_permutations: int): """ Randomly permute the nodes of a given causal graph while keeping the underlying graph structure the same. :param causal_graph: A directed acyclic graph (DAG). :param exclude_original_order: Do not return the original order. :param n_permutations: Return a generator with n_permutations permutations. If n_permutations = -1 (default), we return all n_nodes! - int(exclude_orig) permutations. :return: Copy of causal_graph with nodes randomly permuted. """ self.causal_graph = causal_graph self.exclude_original_order = exclude_original_order self.n_permutations = n_permutations self.max_perms = np.math.factorial(self.causal_graph.number_of_nodes()) - int(self.exclude_original_order) if n_permutations == -1 or n_permutations > self.max_perms: self.it = self.iter_all_permutations() self.length = self.max_perms if self.length > 2**63 - 1: raise ValueError( f"Too many permutations specified. Did you accidently set 'n_permutations'=-1 for a " f"large (>20 nodes) graph? " f"Given graph has {causal_graph.number_of_nodes()} nodes." ) else: self.it = self.iter_random_permutations() self.length = self.n_permutations def iter_all_permutations(self): for i, perm in enumerate(permutations(self.causal_graph.nodes)): if self.exclude_original_order and i == 0: continue mapping = {node: perm[i] for i, node in enumerate(self.causal_graph.nodes)} yield nx.relabel_nodes(self.causal_graph, mapping, copy=True) def iter_random_permutations(self): for _ in range(self.n_permutations): if self.exclude_original_order: is_orig = True while is_orig: perm = list(np.random.permutation(self.causal_graph.nodes)) if perm != list(self.causal_graph.nodes): is_orig = False else: perm = list(np.random.permutation(self.causal_graph.nodes)) mapping = {node: perm[i] for i, node in enumerate(self.causal_graph.nodes)} yield nx.relabel_nodes(self.causal_graph, mapping, copy=True) def __iter__(self): yield from self.it def __len__(self): return self.length def _to_frozenset(x: Union[Set, List, str]): """Converts a set, list or string into a hashable frozenset""" assert ( isinstance(x, Set) or isinstance(x, List) or isinstance(x, str) ), f"{x} must be list, set or str. Got {type(x)} instead!" if isinstance(x, str): return frozenset({x}) return frozenset(x)

"""This module provides functionality to falsify a user-given DAG given observed data. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from dataclasses import dataclass, field from enum import Enum, auto from functools import partial from inspect import getfullargspec from itertools import permutations from typing import Any, Callable, Dict, FrozenSet, List, Optional, Set, Tuple, Union import matplotlib.colors as mcolors import matplotlib.pyplot as plt import networkx as nx import numpy as np import pandas as pd from joblib import Parallel, delayed from tqdm import tqdm import dowhy.gcm.config as config from dowhy.gcm.independence_test import kernel_based from dowhy.gcm.util import plot from dowhy.gcm.util.general import set_random_seed from dowhy.gcm.validation import _get_non_descendants from dowhy.graph import DirectedGraph, get_ordered_predecessors VIOLATION_COLOR = "red" COLORS = list(mcolors.TABLEAU_COLORS.values()) class FalsifyConst(Enum): N_VIOLATIONS = auto() N_TESTS = auto() P_VALUE = auto() P_VALUES = auto() GIVEN_VIOLATIONS = auto() PERM_VIOLATIONS = auto() F_GIVEN_VIOLATIONS = auto() F_PERM_VIOLATIONS = auto() LOCAL_VIOLATION_INSIGHT = auto() METHOD = auto() VALIDATE_LMC = auto() VALIDATE_TPA = auto() VALIDATE_PD = auto() VALIDATE_CM = auto() PERM_GRAPHS = auto() MEC = auto() FALSIFY_METHODS = { FalsifyConst.VALIDATE_LMC: "LMC", FalsifyConst.VALIDATE_PD: "Faithfulness", FalsifyConst.VALIDATE_TPA: "TPa", FalsifyConst.VALIDATE_CM: "Causal Minimality", } @dataclass class _PValuesMemory: """A class to store and access results of independence tests. This class is useful if, e.g. we validate LMC on graph X -> Y -> Z and also on the permuted nodes Z -> Y -> X. Since X ind Z | Y == Z ind X | Y, we only need to perform the conditional independence test once. We use frozensets of nodes since standard (immutable) sets are not hashable. """ p_values: Dict[Tuple[FrozenSet, FrozenSet, FrozenSet], float] = field(default_factory=dict) def add_p_value( self, p_value: Union[None, float], X: Union[Set, List, str], Y: Union[Set, List, str], Z: Optional[Union[Set, List, str]] = None, ): if not Z: Z = set() self.p_values[(_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z))] = p_value def get_p_value( self, X: Union[Set, List, str], Y: Union[Set, List, str], Z: Optional[Union[Set, List, str]] = None ): if not Z: Z = set() # Independence tests are symmetric if (_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z)) in self.p_values: return self.p_values[(_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z))] elif (_to_frozenset(Y), _to_frozenset(X), _to_frozenset(Z)) in self.p_values: return self.p_values[(_to_frozenset(Y), _to_frozenset(X), _to_frozenset(Z))] return def __contains__(self, item: Tuple[Union[Set, List, str], ...]) -> bool: X, Y = (_to_frozenset(i) for i in item[:2]) if len(item) == 2 or item[2] is None: Z = frozenset() else: Z = _to_frozenset(item[2]) return (X, Y, Z) in self.p_values or (Y, X, Z) in self.p_values def validate_lmc( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, include_unconditional: bool = True, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[str, float]]]: """ Validate the local markov condition for a given directed graph. Return number of violations and p values for each node. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory instance, where results of previously performed tests are stored. :param independence_test: Test to use for unconditional independencies (only used if include_unconditional=True) :param conditional_independence_test: Conditional independence test to use for checking local Markov condition. :param significance_level: Significance level for (conditional) independence tests. :param include_unconditional: Test also unconditional independencies of root nodes. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Outcome of validation containing number of violations in the graph and p values/violation for each tuple (node, non_desc) """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_LMC, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, FalsifyConst.P_VALUES: dict(), } # Find out which tests to do triples = _get_parental_triples(causal_graph, include_unconditional) to_test = [] for node, non_desc, parents in triples: if not (node, non_desc, parents) in p_values_memory: to_test.append((node, non_desc, parents)) p_values_memory.add_p_value(None, node, non_desc, parents) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=node, Y=non_desc, Z=parents, independence_test=independence_test, conditional_independence_test=conditional_independence_test, seed=int(seed), ) for (node, non_desc, parents), seed in zip(to_test, random_seeds) ) # Gather results for i, (node, non_desc, parents) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], node, non_desc, parents) # Summarize for node, non_desc, parents in triples: lmc_p_value = p_values_memory.get_p_value(node, non_desc, parents) if lmc_p_value is not None: validation_summary[FalsifyConst.N_TESTS] += 1 validation_summary[FalsifyConst.P_VALUES][(node, non_desc)] = ( lmc_p_value, lmc_p_value <= significance_level, ) if lmc_p_value <= significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_tpa( causal_graph: DirectedGraph, causal_graph_reference: DirectedGraph, include_unconditional: bool = True, ) -> Dict[str, int]: """ Graphical criterion to evaluate which pairwise parental d-separations (parental triples) in `causal_graph` are violated, assuming `causal_graph_reference` is the ground truth graph. If none are violated, then both graphs lie in the same Markov equivalence class. Specifically we test: X _|_G' Y | Z and X _/|_G Y | Z for Y \in ND{X}^G', Z = PA{X}^G :param causal_graph: Causal graph for which to evaluate parental d-separations (G') :param causal_graph_reference: Causal graph where we test if d-separation holds (G) :param include_unconditional: Test also unconditional independencies of root nodes. :return: Validation summary with number of d-separations implied by `causal_graph` and number of times these are violated in the graph `causal_graph_reference`. """ validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_TPA, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, } triples = _get_parental_triples(causal_graph, include_unconditional) for node, non_desc, parents in triples: validation_summary[FalsifyConst.N_TESTS] += 1 if not nx.d_separated(causal_graph_reference, {node}, {non_desc}, set(parents)): validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_pd( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, n_pairs: int = -1, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, adjacent_only: bool = False, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[tuple, float]]]: """ Validate pairwise dependencies (pd) for a given causal graph and data. Test for each node if it is statistically dependent of all its ancestors. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory object, where results of previously performed tests are stored. :param n_pairs: Evaluate dependencies for n_pairs <= all pairs in the DAG. If n_pairs=-1, evaluate dependencies for all (ancestor, node) pairs (default). :param independence_test: Independence test to use for checking pairwise dependencies. :param significance_level: Significance level for independence tests. :param adjacent_only: Only test adjacent node pairs. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Summary dict: {n_violations: int, n_tests: int, p_values: {(ancestor, node): float, ...}} """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() pairs = [(ancestor, node) for node in causal_graph.nodes for ancestor in nx.ancestors(causal_graph, node)] if adjacent_only: pairs = [ (ancestor, node) for (ancestor, node) in pairs if ancestor in get_ordered_predecessors(causal_graph, node) ] if n_pairs < 0: n_pairs = len(pairs) if n_pairs > len(pairs): raise ValueError(f"n_pairs ({n_pairs}) > number of pairs in the DAG ({len(pairs)})") validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_PD, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: n_pairs, FalsifyConst.P_VALUES: dict(), } pair_idxs = np.random.choice(len(pairs), size=n_pairs, replace=False) # Find out which tests to do to_test = [] for pair_idx in pair_idxs: ancestor, node = pairs[pair_idx] if not (ancestor, node) in p_values_memory: to_test.append((ancestor, node)) p_values_memory.add_p_value(None, ancestor, node) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=ancestor, Y=node, Z=None, independence_test=independence_test, conditional_independence_test=None, seed=int(seed), ) for (ancestor, node), seed in zip(to_test, random_seeds) ) # Gather results for i, (ancestor, node) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], ancestor, node) # Summarize for pair_idx in pair_idxs: ancestor, node = pairs[pair_idx] p_value = p_values_memory.get_p_value(ancestor, node) if p_value is not None: validation_summary[FalsifyConst.P_VALUES][(node, ancestor)] = (p_value, p_value > significance_level) if p_value > significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_cm( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[tuple, float]]]: """ Function to test causal minimality of a DAG (see [1], Proposition 6.36). [1] J. Peters, D. Janzing, and B. Schölkopf, Elements of Causal Inference: Foundations and Learning Algorithms. Cambridge, MA, USA: MIT Press, 2017. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory object, where results of previously performed tests are stored. :param independence_test: Independence test to use. :param conditional_independence_test: Conditional independence test to use. :param significance_level: Significance level for independence tests. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Validation summary as dict. """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_CM, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, FalsifyConst.P_VALUES: dict(), } # Find out which tests to do triples = [] to_test = [] for node in causal_graph.nodes: parents = set(causal_graph.predecessors(node)) if parents: for p in parents: other_parents = parents.difference({p}) triples.append((node, p, other_parents)) if not (node, p, other_parents) in p_values_memory: to_test.append((node, p, other_parents)) p_values_memory.add_p_value(None, node, p, other_parents) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=node, Y=p, Z=list(other_parents), independence_test=independence_test, conditional_independence_test=conditional_independence_test, seed=int(seed), ) for (node, p, other_parents), seed in zip(to_test, random_seeds) ) # Gather results for i, (node, p, other_parents) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], node, p, other_parents) # Summarize for node, p, other_parents in triples: p_value = p_values_memory.get_p_value(node, p, other_parents) if p_value is not None: validation_summary[FalsifyConst.N_TESTS] += 1 validation_summary[FalsifyConst.P_VALUES][(node, p, tuple(other_parents))] = ( p_value, p_value > significance_level, ) if p_value > significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def run_validations( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Optional[Union[Callable, Tuple[Callable, ...], List[Callable]]] = partial( validate_lmc, independence_test=kernel_based, conditional_independence_test=kernel_based ), ) -> Dict[str, Dict]: """ Validate a given causal graph using observational data and some given methods. If methods are provided, they must be wrapped in a partial object, with their respective parameters. E.g., if one wants to test the local Markov conditions and the pairwise dependencies (unconditional faithfulness), then call run_validations(G, data, methods=( partial(validate_lmc, independence_test=..., conditional_independence_test=...), partial(validate_pd, independence_test=...), ) ) :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param methods: Method functions wrapped in wrap_partial. E.g. wrap_partial(validate_lmc, data=data, independence_test=..., conditional_independence_test=...). If no methods are provided we run validate_lmc with optional keyword arguments provided to run_validations. :return: Validation summary as dict. """ if not isinstance(methods, (tuple, list)): methods = (methods,) validation_summary = dict() for m in methods: if "data" in getfullargspec(m).args: m = partial(m, data=data) m_summary = m(causal_graph=causal_graph) m_name = m_summary.pop(FalsifyConst.METHOD) validation_summary[m_name] = m_summary return validation_summary @dataclass class EvaluationResult: """ Dataset class containing the evaluation result of falsifying a graph using a node-permutation test. ... Attributes ---------- summary : dict Dictionary containing the summary of the evaluation. significance_level : float Significance level based on which we falsify the given DAG falsifiable : bool Whether the given DAG is falsifiable. falsified : bool Whether the given DAG is falsified. """ summary: dict significance_level: float suggestions: Optional[dict] = None def update_significance_level(self, significance_level: float): """ Update the significance level to decide if we falsify a given DAG. """ self.significance_level = significance_level self.__post_init__() def __post_init__(self): self.can_evaluate = self._can_evaluate() if not self.can_evaluate: self.falsified = None self.falsifiable = None elif ( self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.P_VALUE] > self.significance_level > self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE] ): self.falsified = True self.falsifiable = True elif self.significance_level < self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE]: self.falsified = False self.falsifiable = False else: self.falsified = False self.falsifiable = True def __repr__(self): # DAG Evaluation if self.can_evaluate: decision = " " if self.falsified else " do not " informative = " " if self.falsifiable else " not " frac_MEC = f"{len(self.summary[FalsifyConst.MEC])} / {len(self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.PERM_VIOLATIONS])}" frac_VLMC = f"{self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.GIVEN_VIOLATIONS]}/{self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.N_TESTS]}" p_LMC = self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.P_VALUE] p_dSep = self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE] validation_repr = [ f"The given DAG is{informative}informative because {frac_MEC} of the permutations lie in the Markov", f"equivalence class of the given DAG (p-value: {p_dSep:.2f}).", f"The given DAG violates {frac_VLMC} LMCs and is better than {(1 - p_LMC) * 100:.1f}% of the permuted DAGs (p-value: {p_LMC:.2f}).", f"Based on the provided significance level ({self.significance_level}) and because the DAG is{informative}informative,", f"we{decision}reject the DAG.", ] else: validation_repr = ["Cannot be evaluated!"] # Suggestions suggestion_repr = {} for m in self.suggestions: suggestion_repr[FALSIFY_METHODS[m]] = [ f"Remove edge {node[1]} --> {node[0]}" for (node, r) in self.suggestions[m][FalsifyConst.P_VALUES].items() if r[1] ] return _generate_table(validation_repr, suggestion_repr) def _can_evaluate(self): can_evaluate = True for m in (FalsifyConst.VALIDATE_LMC, FalsifyConst.VALIDATE_TPA): if m not in self.summary: can_evaluate = False return can_evaluate def falsify_graph( causal_graph: DirectedGraph, data: pd.DataFrame, suggestions: bool = False, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, significance_ci: float = 0.05, n_permutations: Optional[int] = None, show_progress_bar: Optional[bool] = None, n_jobs: Optional[int] = None, plot_histogram: bool = False, plot_kwargs: Optional[Dict] = None, ) -> EvaluationResult: """ Falsify a given DAG using observational data. This method returns the result of a permutation-test to falsify a user-given DAG using observational data. To this end we construct the test statistics by testing the violations of local Markov conditions (LMC) implied by the graph using conditional independence (CI) tests. The null is the number of LMC violations of a random node-permutation of the given graph. Our test can be interpreted as whether the given graph is significantly better than random in terms of the CIs it entails. To determine whether a given graph is falsifiable by our metric, we implement a second test, which reports whether given graph is "characteristic" enough in terms of the CIs it entails. For this, we compute how many of the random node permutations lie in the same Markov equivalence class (MEC) as the given graph and conclude that the given graph is falsifiable only if the fraction of permuted DAGs in the same MEC as the given graph is "reasonably" small. The returned EvaluationResult object has two attributes: `falsified` and `falsifiable`: `falsifiable`: The given graph lies in a different MEC than >= 1-`significance_level` of the permuted DAGs `falsified`: The given graph is falsifiable and violates fewer LMCs than >= 1-`significance_level` of the permuted DAGs By default, we only run 1 / `significance_level` permutations as those are enough to falsify a graph with type I error probability `significance_level` at some given `significance_level`. If you are interested in a more exact estimate of the p-value or wish to plot a histogram to see how the given DAG compares to random node permutations, you should set `n_permutations` to some larger value (e.g. 100 or 1000). If `n_permutations=-1` we test on all n_nodes! permutations (the default if plot_histogram=True). Additionally, this method allows to return suggestions to the user (suggestions=True). This is done by testing for violations of causal minimality via `validate_cm`. Related paper: Eulig, E., Mastakouri, A. A., Blöbaum, P., Hardt, M., & Janzing, D. (2023). Toward Falsifying Causal Graphs Using a Permutation-Based Test. https://arxiv.org/abs/2305.09565 :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param suggestions: Provide suggestions to the user. At the moment the only source of suggestions comes from validating causal minimality (using validate_cm). :param independence_test: Independence test to use for checking pairwise independencies. :param conditional_independence_test: Conditional independence test to use. :param significance_level: Significance level for the permutation test. :param significance_ci: Significance level for (conditional) independence tests. :param n_permutations: Number of permutations to perform. If -1 use all n_nodes! permutations. :param show_progress_bar: Whether to show progress bar over permutations. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :param plot_histogram: Plot histogram of results from permutation baseline. :param plot_kwargs: Additional plot arguments to be passed to plot_evaluation_results. :return: EvaluationResult """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs show_progress_bar = config.show_progress_bars if show_progress_bar is None else show_progress_bar p_values_memory = _PValuesMemory() if n_permutations is None: n_permutations = int(1 / significance_level) if not plot_histogram else -1 if not plot_kwargs: plot_kwargs = {} methods = ( partial( validate_lmc, independence_test=independence_test, conditional_independence_test=conditional_independence_test, significance_level=significance_ci, p_values_memory=p_values_memory, n_jobs=n_jobs, ), partial(validate_tpa, causal_graph_reference=causal_graph), ) if suggestions: suggestion_methods = ( partial( validate_cm, independence_test=independence_test, conditional_independence_test=conditional_independence_test, significance_level=significance_ci, p_values_memory=p_values_memory, n_jobs=n_jobs, ), ) else: suggestion_methods = tuple() summary_given = run_validations( causal_graph, data, methods=methods + suggestion_methods, ) summary_perm = _permutation_based( causal_graph, data, methods=methods, exclude_original_order=False, n_permutations=n_permutations, show_progress_bar=show_progress_bar, ) summary = dict() validation_methods = set(summary_perm.keys()) - {FalsifyConst.PERM_GRAPHS} for m in validation_methods: summary[m] = dict() summary[m][FalsifyConst.PERM_VIOLATIONS] = [perm[FalsifyConst.N_VIOLATIONS] for perm in summary_perm[m]] summary[m][FalsifyConst.GIVEN_VIOLATIONS] = summary_given[m][FalsifyConst.N_VIOLATIONS] summary[m][FalsifyConst.N_TESTS] = summary_given[m][FalsifyConst.N_TESTS] summary[m][FalsifyConst.F_PERM_VIOLATIONS] = [ perm[FalsifyConst.N_VIOLATIONS] / perm[FalsifyConst.N_TESTS] for perm in summary_perm[m] ] summary[m][FalsifyConst.F_GIVEN_VIOLATIONS] = ( summary[m][FalsifyConst.GIVEN_VIOLATIONS] / summary[m][FalsifyConst.N_TESTS] ) summary[m][FalsifyConst.P_VALUE] = sum( [ 1 for perm in summary[m][FalsifyConst.F_PERM_VIOLATIONS] if perm <= summary[m][FalsifyConst.F_GIVEN_VIOLATIONS] ] ) / len(summary[m][FalsifyConst.PERM_VIOLATIONS]) if m != FalsifyConst.VALIDATE_TPA: # Append list of violations (node, non_desc) to get local information summary[m][FalsifyConst.LOCAL_VIOLATION_INSIGHT] = summary_given[m][FalsifyConst.P_VALUES] if FalsifyConst.VALIDATE_TPA in summary: summary[FalsifyConst.MEC] = [ summary_perm[FalsifyConst.PERM_GRAPHS][i] for i, v in enumerate(summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.PERM_VIOLATIONS]) if v == 0 ] result = EvaluationResult( summary=summary, significance_level=significance_level, suggestions={m: summary_given[m] for m in summary_given if m not in validation_methods}, ) if plot_histogram: plot_evaluation_results(result, **plot_kwargs) return result def apply_suggestions( causal_graph: DirectedGraph, evaluation_result: EvaluationResult, edges_to_keep: Optional[List[Tuple[Any, Any]]] = None, ): if not hasattr(evaluation_result, "suggestions"): raise ValueError("EvaluationResult object has no attribute suggestions. Please run with suggestion=True!") causal_graph = causal_graph.copy() for m in evaluation_result.suggestions: for node, res in evaluation_result.suggestions[m][FalsifyConst.P_VALUES].items(): edge = (node[1], node[0]) if (res[1] and edges_to_keep is not None and edge not in edges_to_keep) or ( res[1] and edges_to_keep is None ): causal_graph.remove_edge(edge[0], edge[1]) return causal_graph def plot_evaluation_results(evaluation_result, figsize=(8, 3), bins=None, title="", savepath="", display=True): fig, ax = plt.subplots(figsize=figsize) # Plot histograms p_values = "" data = [] labels = [] evaluation_summary = {k: v for k, v in evaluation_result.summary.items() if k != FalsifyConst.MEC} for i, (m, m_summary) in enumerate(evaluation_summary.items()): data.append(m_summary[FalsifyConst.F_PERM_VIOLATIONS]) labels.append(f"Violations of {FALSIFY_METHODS[m]} of permuted DAGs") p_values += f"p-value {FALSIFY_METHODS[m]} = {m_summary[FalsifyConst.P_VALUE]:.2f}\n" ax.hist(data, color=COLORS[: len(evaluation_summary)], bins=bins, alpha=0.5, label=labels, edgecolor="k") # Plot given violations for i, (m, m_summary) in enumerate(evaluation_summary.items()): ylim = ax.get_ylim()[1] ax.plot( [m_summary[FalsifyConst.F_GIVEN_VIOLATIONS]] * 2, [0, ylim], "--", c=COLORS[i], label=f"Violations of {FALSIFY_METHODS[m]} of given DAG", ) ax.set_ylim([0, ylim]) ax.set_xlabel("Fraction of violations") ax.set_ylabel("# Permutations") plt.legend(loc="upper left", bbox_to_anchor=(1.05, 1), borderaxespad=0.0, title=p_values) if title: plt.title(title) plt.tight_layout() if savepath: plt.savefig(savepath, dpi=300, bbox_inches="tight") if display: plt.show() else: plt.close() def plot_local_insights( causal_graph: DirectedGraph, evaluation_result: Union[EvaluationResult, Dict], method: Optional[str] = FalsifyConst.VALIDATE_LMC, ): """ For some given graph and evaluation result plot local violations. :param causal_graph: DiGraph :param evaluation_result: EvaluationResult :param method: Method for which to plot violations """ colors = {} if isinstance(evaluation_result, EvaluationResult) and method in evaluation_result.summary: local_insight_dict = evaluation_result.summary[method][FalsifyConst.LOCAL_VIOLATION_INSIGHT] elif ( isinstance(evaluation_result, EvaluationResult) and hasattr(evaluation_result, "suggestions") and method in evaluation_result.suggestions ): local_insight_dict = evaluation_result.suggestions[method][FalsifyConst.P_VALUES] elif isinstance(evaluation_result, Dict): if method not in evaluation_result: raise ValueError(f"Validation method {method} does not exist in given evaluation_result!") if FalsifyConst.P_VALUES not in evaluation_result[method]: raise ValueError( f"Validation method {method} has no key {FalsifyConst.P_VALUES} where information on local violations " f"are stored!" ) local_insight_dict = evaluation_result[method][FalsifyConst.P_VALUES] else: raise ValueError(f"Cannot plot local violation insights from method {method} in evaluation_result!") for nodes, result in local_insight_dict.items(): if result[1]: if method == FalsifyConst.VALIDATE_LMC: # For LMC we highlight X for which X _|/|_ Y \in ND_X | Pa_X colors[nodes[0]] = VIOLATION_COLOR elif method == FalsifyConst.VALIDATE_PD: # For PD we highlight the edge (if Y\in Anc_X -> X are adjacent) colors[(nodes[1], nodes[0])] = VIOLATION_COLOR elif method == FalsifyConst.VALIDATE_CM: # For causal minimality we highlight the edge Y \in Pa_X -> X colors[(nodes[1], nodes[0])] = VIOLATION_COLOR plot(causal_graph, colors=colors) def _generate_table( validation_repr, suggestion_repr, width=105, validation_name="Falsificaton Summary", suggestion_name="Suggestions" ): # Create Validation header _repr = [ "+" + "-" * (width - 2) + "+\n", "|" + " " * int(np.floor((width - 2 - len(validation_name)) / 2)), validation_name, " " * int(np.ceil((width - 2 - len(validation_name)) / 2)) + "|\n", "+" + "-" * (width - 2) + "+\n", ] # Create Validation summary _repr += [f"| {v_line}" + " " * (width - 3 - len(v_line)) + "|\n" for v_line in validation_repr] # Close Validation _repr += ["+" + "-" * (width - 2) + "+\n"] # Create Suggestions header if suggestion_repr: _repr += [ "|" + " " * int(np.floor((width - 2 - len(suggestion_name)) / 2)), suggestion_name, " " * int(np.ceil((width - 2 - len(suggestion_name)) / 2)) + "|\n", "+" + "-" * (width - 2) + "+\n", ] # Iterate over suggestions for m, suggestions in suggestion_repr.items(): left_col = "| " + m + " |" if not suggestions: right_col = " " + " " * (width - 2 - len(left_col)) + "|\n" _repr += [left_col, right_col] for i, s in enumerate(suggestions): if i > 0: left_col = "| " + " " * len(m) + " |" right_col = " - " + s + " " * (width - 4 - len(s) - len(left_col)) + "|\n" _repr += [left_col, right_col] _repr += ["+" + "-" * (width - 2) + "+\n"] return "".join(_repr)[:-1] def _compute_p_value( data: pd.DataFrame, X: Union[List, str], Y: Union[List, str], Z: Optional[Union[Set, List, str]], independence_test: Optional[Callable[[np.ndarray, np.ndarray], float]], conditional_independence_test: Optional[Callable[[np.ndarray, np.ndarray, np.ndarray], float]], seed: int, ) -> float: """Perform (conditional) independence test and report p-value. If any nodes needed for the test do not exist in the data, this test is skipped and a value of -1 is returned and a warning is raised. :param data: Observations of variables in the DAG. :param X: Variable to test (conditional) independence with Y :param Y: Variable to test (conditional) independence with X :param Z: Set to condition independence test on. Can be empty (None, empty set, or empty list). :param independence_test: Independence test to use. :param conditional_independence_test: Conditional independence test to use. :param seed: Random seed :return: p-value """ set_random_seed(seed) # Test if we have data for X and Y for node in [X, Y]: if not node in data.columns: warnings.warn(f"WARN: Couldn't find data for node {node}. Skip this test.") return if Z: # Test if we have data for Z for node in Z: if not node in data.columns: warnings.warn(f"WARN: Couldn't find data for node {node}. Skip this test.") return p_value = conditional_independence_test(data[X].values, data[Y].values, data[Z].values) else: p_value = independence_test(data[X].values, data[Y].values) return p_value def _get_parental_triples(causal_graph: DirectedGraph, include_unconditional: bool): """ For a given graph collect all parental triples, that is, the triple (X, Y, Z) is a parental triple iff Y is non-descendant of X, and Z are the parents of X (can be empty if include_unconditional=True) """ triples = [] for node in causal_graph.nodes: parents = get_ordered_predecessors(causal_graph, node) non_descendants = _get_non_descendants(causal_graph, node, exclude_parents=True) if (parents or include_unconditional) and non_descendants: for non_desc in non_descendants: triples.append((node, non_desc, parents)) return triples def _permutation_based( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Union[Callable, Tuple[Callable, ...], List[Callable]], exclude_original_order: bool, n_permutations: int, show_progress_bar: bool, ) -> Dict[str, List[Union[DirectedGraph, Dict]]]: """ Generate baseline for node permutations. :param causal_graph: A directed acyclic graph (DAG). :param methods: Validation methods to perform. :param exclude_original_order: Exclude the original ordering of the nodes (default=False) :param n_permutations: Number of permutations to perform. If -1 use all n_nodes! - int(exclude_orig) permutations :param show_progress_bar: Whether to show progress bar over tested permutations. :return: Dictionary containing summary of validation for each individual graph as well as the permuted graphs """ if not isinstance(methods, (tuple, list)): methods = (methods,) perm_gen = _PermuteNodes(causal_graph, n_permutations=n_permutations, exclude_original_order=exclude_original_order) validation_summary = {FalsifyConst.PERM_GRAPHS: []} for permuted_graph in tqdm(perm_gen, desc="Test permutations of given graph", disable=not show_progress_bar): res = run_validations( causal_graph=permuted_graph, data=data, methods=methods, ) validation_summary[FalsifyConst.PERM_GRAPHS].append(permuted_graph) for m_name, summary in res.items(): if m_name not in validation_summary: validation_summary[m_name] = [] validation_summary[m_name].append(summary) return validation_summary class _PermuteNodes: def __init__(self, causal_graph: DirectedGraph, exclude_original_order: bool, n_permutations: int): """ Randomly permute the nodes of a given causal graph while keeping the underlying graph structure the same. :param causal_graph: A directed acyclic graph (DAG). :param exclude_original_order: Do not return the original order. :param n_permutations: Return a generator with n_permutations permutations. If n_permutations = -1 (default), we return all n_nodes! - int(exclude_orig) permutations. :return: Copy of causal_graph with nodes randomly permuted. """ self.causal_graph = causal_graph self.exclude_original_order = exclude_original_order self.n_permutations = n_permutations self.max_perms = np.math.factorial(self.causal_graph.number_of_nodes()) - int(self.exclude_original_order) if n_permutations == -1 or n_permutations > self.max_perms: self.it = self.iter_all_permutations() self.length = self.max_perms if self.length > 2**63 - 1: raise ValueError( f"Too many permutations specified. Did you accidently set 'n_permutations'=-1 for a " f"large (>20 nodes) graph? " f"Given graph has {causal_graph.number_of_nodes()} nodes." ) else: self.it = self.iter_random_permutations() self.length = self.n_permutations def iter_all_permutations(self): for i, perm in enumerate(permutations(self.causal_graph.nodes)): if self.exclude_original_order and i == 0: continue mapping = {node: perm[i] for i, node in enumerate(self.causal_graph.nodes)} yield nx.relabel_nodes(self.causal_graph, mapping, copy=True) def iter_random_permutations(self): for _ in range(self.n_permutations): if self.exclude_original_order: is_orig = True while is_orig: perm = list(np.random.permutation(self.causal_graph.nodes)) if perm != list(self.causal_graph.nodes): is_orig = False else: perm = list(np.random.permutation(self.causal_graph.nodes)) mapping = {node: perm[i] for i, node in enumerate(self.causal_graph.nodes)} yield nx.relabel_nodes(self.causal_graph, mapping, copy=True) def __iter__(self): yield from self.it def __len__(self): return self.length def _to_frozenset(x: Union[Set, List, str]): """Converts a set, list or string into a hashable frozenset""" assert ( isinstance(x, Set) or isinstance(x, List) or isinstance(x, str) ), f"{x} must be list, set or str. Got {type(x)} instead!" if isinstance(x, str): return frozenset({x}) return frozenset(x)

eeulig

379f188d674d5be6d644559f9f54852b87403f7c

4e30c63fe1113e35259e7fb3400a6f09bca4d699

Isn't the `n_jobs` also method dependent or does every method expect this?

bloebp

py-why/dowhy

Remove kwargs from falsify, improve docstrings

This commit updates `gcm/falsify` in the following ways: - Remove the `**kwargs` for `validate_graph` and `falsify_graph`. Instead, validation methods given to those functions should be wrapped in `wrap_partial`, e.g. if we want to validate a graph `G` and test local Markov conditions and pairwise dependencies on observations `data`, we would do ``` validate_graph(G, methods=( wrap_partial(validate_lmc, data=data, independence_test=..., conditional_independence_test=...), wrap_partial(validate_pd, data=data, independence_test=...), ) ) ``` By default (`methods=None`), `validate_graph` tests local Markov conditions and `falsify_graph` tests local Markov conditions and d-separation of parental triples. In this case `data` has to be set and optionally one can provide `independence_test`, `conditional_independence_test`, `significance_level`, and `n_jobs` via the keyword arguments of `validate_graph` / `falsify_graph`. - Use `Enum` for some of the constants - In `_PValuesMemory` the method `add_p_value ` always sets a p-value even if it was set already. This removes the need for the method `clear_placeholders` and allows `p_values` to be set to `-1`, indicating that the CI is not testable (because of missing data for a node, c.f. `_compute_p_value`) - If for some CI: X, Y | Z any of the nodes has no data (column missing in the provided dataframe), we skip this test and raise a warning. - Instead of using the raw number of violations in the plotted histogram and to compute the p-value, we use the relative number of violations. This will result in different p-values only if we have missing data for some nodes (and thus the number of tests is potentially different between different permutations) - Improve docstrings

2023-07-04 13:58:30+00:00

2023-07-18 14:19:10+00:00

dowhy/gcm/falsify.py

"""This module provides functionality to falsify a user-given DAG given observed data. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from dataclasses import dataclass, field from enum import Enum, auto from functools import partial from itertools import permutations from typing import Any, Callable, Dict, FrozenSet, List, Optional, Set, Tuple, Union import matplotlib.colors as mcolors import matplotlib.pyplot as plt import networkx as nx import numpy as np import pandas as pd from joblib import Parallel, delayed from tqdm import tqdm import dowhy.gcm.config as config from dowhy.gcm.independence_test import kernel_based from dowhy.gcm.util import plot from dowhy.gcm.util.general import set_random_seed from dowhy.gcm.validation import _get_non_descendants from dowhy.graph import DirectedGraph, get_ordered_predecessors VIOLATION_COLOR = "red" COLORS = list(mcolors.TABLEAU_COLORS.values()) class FalsifyConst(Enum): N_VIOLATIONS = auto() N_TESTS = auto() P_VALUE = auto() P_VALUES = auto() GIVEN_VIOLATIONS = auto() PERM_VIOLATIONS = auto() F_GIVEN_VIOLATIONS = auto() F_PERM_VIOLATIONS = auto() LOCAL_VIOLATION_INSIGHT = auto() METHOD = auto() VALIDATE_LMC = auto() VALIDATE_TPA = auto() VALIDATE_PD = auto() VALIDATE_CM = auto() PERM_GRAPHS = auto() MEC = auto() FALSIFY_METHODS = { FalsifyConst.VALIDATE_LMC: "LMC", FalsifyConst.VALIDATE_PD: "Faithfulness", FalsifyConst.VALIDATE_TPA: "tPa", FalsifyConst.VALIDATE_CM: "Causal Minimality", } @dataclass class _PValuesMemory: """A class to store and access results of independence tests. This class is useful if, e.g. we validate LMC on graph X -> Y -> Z and also on the permuted nodes Z -> Y -> X. Since X ind Z | Y == Z ind X | Y, we only need to perform the conditional independence test once. We use frozensets of nodes since standard (immutable) sets are not hashable. """ p_values: Dict[Tuple[FrozenSet, FrozenSet, FrozenSet], float] = field(default_factory=dict) def add_p_value( self, p_value: Union[None, float], X: Union[Set, List, str], Y: Union[Set, List, str], Z: Optional[Union[Set, List, str]] = None, ): if not Z: Z = set() self.p_values[(_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z))] = p_value def get_p_value( self, X: Union[Set, List, str], Y: Union[Set, List, str], Z: Optional[Union[Set, List, str]] = None ): if not Z: Z = set() # Independence tests are symmetric if (_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z)) in self.p_values: return self.p_values[(_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z))] elif (_to_frozenset(Y), _to_frozenset(X), _to_frozenset(Z)) in self.p_values: return self.p_values[(_to_frozenset(Y), _to_frozenset(X), _to_frozenset(Z))] return def __contains__(self, item: Tuple[Union[Set, List, str], ...]) -> bool: X, Y = (_to_frozenset(i) for i in item[:2]) if len(item) == 2 or item[2] is None: Z = frozenset() else: Z = _to_frozenset(item[2]) return (X, Y, Z) in self.p_values or (Y, X, Z) in self.p_values def validate_lmc( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, include_unconditional: bool = True, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[str, float]]]: """ Validate the local markov condition for a given directed graph. Return number of violations and p values for each node. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory instance, where results of previously performed tests are stored. :param independence_test: Test to use for unconditional independencies (only used if include_unconditional=True) :param conditional_independence_test: Conditional independence test to use for checking local Markov condition. :param significance_level: Significance level for (conditional) independence tests. :param include_unconditional: Test also unconditional independencies of root nodes. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Outcome of validation containing number of violations in the graph and p values/violation for each tuple (node, non_desc) """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_LMC, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, FalsifyConst.P_VALUES: dict(), } # Find out which tests to do triples = _get_parental_triples(causal_graph, include_unconditional) to_test = [] for node, non_desc, parents in triples: if not (node, non_desc, parents) in p_values_memory: to_test.append((node, non_desc, parents)) p_values_memory.add_p_value(None, node, non_desc, parents) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=node, Y=non_desc, Z=parents, independence_test=independence_test, conditional_independence_test=conditional_independence_test, seed=int(seed), ) for (node, non_desc, parents), seed in zip(to_test, random_seeds) ) # Gather results for i, (node, non_desc, parents) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], node, non_desc, parents) # Summarize for node, non_desc, parents in triples: lmc_p_value = p_values_memory.get_p_value(node, non_desc, parents) if lmc_p_value is not None: validation_summary[FalsifyConst.N_TESTS] += 1 validation_summary[FalsifyConst.P_VALUES][(node, non_desc)] = ( lmc_p_value, lmc_p_value <= significance_level, ) if lmc_p_value <= significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_tpa( causal_graph: DirectedGraph, causal_graph_reference: DirectedGraph, include_unconditional: bool = True, data: Optional[pd.DataFrame] = None, ) -> Dict[str, int]: """ Graphical criterion to evaluate which pairwise parental d-separations (parental triples) in `causal_graph` are violated, assuming `causal_graph_reference` is the ground truth graph. If none are violated, then both graphs lie in the same Markov equivalence class. Specifically we test: X _|_G' Y | Z and X _/|_G Y | Z for Y \in ND{X}^G', Z = PA{X}^G :param causal_graph: Causal graph for which to evaluate parental d-separations (G') :param causal_graph_reference: Causal graph where we test if d-separation holds (G) :param include_unconditional: Test also unconditional independencies of root nodes. :param data: IGNORED! No data is needed for this validation method and thus the data argument is ignored! :return: Validation summary with number of d-separations implied by `causal_graph` and number of times these are violated in the graph `causal_graph_reference`. """ validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_TPA, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, } triples = _get_parental_triples(causal_graph, include_unconditional) for node, non_desc, parents in triples: validation_summary[FalsifyConst.N_TESTS] += 1 if not nx.d_separated(causal_graph_reference, {node}, {non_desc}, set(parents)): validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_pd( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, n_pairs: int = -1, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, adjacent_only: bool = False, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[tuple, float]]]: """ Validate pairwise dependencies (pd) for a given causal graph and data. Test for each node if it is statistically dependent of all its ancestors. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory object, where results of previously performed tests are stored. :param n_pairs: Evaluate dependencies for n_pairs <= all pairs in the DAG. If n_pairs=-1, evaluate dependencies for all (ancestor, node) pairs (default). :param independence_test: Independence test to use for checking pairwise dependencies. :param significance_level: Significance level for independence tests. :param adjacent_only: Only test adjacent node pairs. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Summary dict: {n_violations: int, n_tests: int, p_values: {(ancestor, node): float, ...}} """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() pairs = [(ancestor, node) for node in causal_graph.nodes for ancestor in nx.ancestors(causal_graph, node)] if adjacent_only: pairs = [ (ancestor, node) for (ancestor, node) in pairs if ancestor in get_ordered_predecessors(causal_graph, node) ] if n_pairs < 0: n_pairs = len(pairs) if n_pairs > len(pairs): raise ValueError(f"n_pairs ({n_pairs}) > number of pairs in the DAG ({len(pairs)})") validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_PD, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: n_pairs, FalsifyConst.P_VALUES: dict(), } pair_idxs = np.random.choice(len(pairs), size=n_pairs, replace=False) # Find out which tests to do to_test = [] for pair_idx in pair_idxs: ancestor, node = pairs[pair_idx] if not (ancestor, node) in p_values_memory: to_test.append((ancestor, node)) p_values_memory.add_p_value(None, ancestor, node) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=ancestor, Y=node, Z=None, independence_test=independence_test, conditional_independence_test=None, seed=int(seed), ) for (ancestor, node), seed in zip(to_test, random_seeds) ) # Gather results for i, (ancestor, node) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], ancestor, node) # Summarize for pair_idx in pair_idxs: ancestor, node = pairs[pair_idx] p_value = p_values_memory.get_p_value(ancestor, node) if p_value is not None: validation_summary[FalsifyConst.P_VALUES][(node, ancestor)] = (p_value, p_value > significance_level) if p_value > significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_cm( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[tuple, float]]]: """ Function to test causal minimality of a DAG (see [1], Proposition 6.36). [1] J. Peters, D. Janzing, and B. Schölkopf, Elements of Causal Inference: Foundations and Learning Algorithms. Cambridge, MA, USA: MIT Press, 2017. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory object, where results of previously performed tests are stored. :param independence_test: Independence test to use. :param conditional_independence_test: Conditional independence test to use. :param significance_level: Significance level for independence tests. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Validation summary as dict. """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_CM, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, FalsifyConst.P_VALUES: dict(), } # Find out which tests to do triples = [] to_test = [] for node in causal_graph.nodes: parents = set(causal_graph.predecessors(node)) if parents: for p in parents: other_parents = parents.difference({p}) triples.append((node, p, other_parents)) if not (node, p, other_parents) in p_values_memory: to_test.append((node, p, other_parents)) p_values_memory.add_p_value(None, node, p, other_parents) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=node, Y=p, Z=list(other_parents), independence_test=independence_test, conditional_independence_test=conditional_independence_test, seed=int(seed), ) for (node, p, other_parents), seed in zip(to_test, random_seeds) ) # Gather results for i, (node, p, other_parents) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], node, p, other_parents) # Summarize for node, p, other_parents in triples: p_value = p_values_memory.get_p_value(node, p, other_parents) if p_value is not None: validation_summary[FalsifyConst.N_TESTS] += 1 validation_summary[FalsifyConst.P_VALUES][(node, p, tuple(other_parents))] = ( p_value, p_value > significance_level, ) if p_value > significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_graph( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Optional[Union[Callable, Tuple[Callable, ...], List[Callable]]] = partial( validate_lmc, independence_test=kernel_based, conditional_independence_test=kernel_based ), ) -> Dict[str, Dict]: """ Validate a given causal graph using observational data and some given methods. If methods are provided, they must be wrapped in a partial object, with their respective parameters. E.g., if one wants to test the local Markov conditions and the pairwise dependencies (unconditional faithfulness), then call validate_graph(G, data, methods=( partial(validate_lmc, independence_test=..., conditional_independence_test=...), partial(validate_pd, independence_test=...), ) ) :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param methods: Method functions wrapped in wrap_partial. E.g. wrap_partial(validate_lmc, data=data, independence_test=..., conditional_independence_test=...). If no methods are provided we run validate_lmc with optional keyword arguments provided to validate_graph. :return: Validation summary as dict. """ if not isinstance(methods, (tuple, list)): methods = (methods,) validation_summary = dict() for m in methods: m_summary = m(causal_graph=causal_graph, data=data) m_name = m_summary.pop(FalsifyConst.METHOD) validation_summary[m_name] = m_summary return validation_summary @dataclass class EvaluationResult: """ Dataset class containing the evaluation result of falsifying a graph using a node-permutation test. ... Attributes ---------- summary : dict Dictionary containing the summary of the evaluation. significance_level : float Significance level based on which we falsify the given DAG falsifiable : bool Whether the given DAG is falsifiable. falsified : bool Whether the given DAG is falsified. """ summary: dict significance_level: float suggestions: Optional[dict] = None def update_significance_level(self, significance_level: float): """ Update the significance level to decide if we falsify a given DAG. """ self.significance_level = significance_level self.__post_init__() def __post_init__(self): self.can_evaluate = self._can_evaluate() if not self.can_evaluate: self.falsified = None self.falsifiable = None elif ( self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.P_VALUE] > self.significance_level > self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE] ): self.falsified = True self.falsifiable = True elif self.significance_level < self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE]: self.falsified = False self.falsifiable = False else: self.falsified = False self.falsifiable = True def __repr__(self): # DAG Evaluation if self.can_evaluate: decision = " " if self.falsified else " do not " informative = " " if self.falsifiable else " not " frac_MEC = f"{len(self.summary[FalsifyConst.MEC])} / {len(self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.PERM_VIOLATIONS])}" frac_VLMC = f"{self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.GIVEN_VIOLATIONS]}/{self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.N_TESTS]}" p_LMC = self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.P_VALUE] p_dSep = self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE] validation_repr = [ f"The given DAG is{informative}informative because {frac_MEC} of the permutations lie in the Markov", f"equivalence class of the given DAG (p-value: {p_dSep:.2f}).", f"The given DAG violates {frac_VLMC} LMCs and is better than {(1 - p_LMC) * 100:.1f}% of the permuted DAGs (p-value: {p_LMC:.2f}).", f"Based on the provided significance level ({self.significance_level}) and because the DAG is{informative}informative,", f"we{decision}reject the DAG.", ] else: validation_repr = ["Cannot be evaluated!"] # Suggestions suggestion_repr = {} for m in self.suggestions: suggestion_repr[FALSIFY_METHODS[m]] = [ f"Remove edge {node[1]} --> {node[0]}" for (node, r) in self.suggestions[m][FalsifyConst.P_VALUES].items() if r[1] ] return _generate_table(validation_repr, suggestion_repr) def _can_evaluate(self): can_evaluate = True for m in (FalsifyConst.VALIDATE_LMC, FalsifyConst.VALIDATE_TPA): if m not in self.summary: can_evaluate = False return can_evaluate def falsify_graph( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Optional[Union[Callable, Tuple[Callable, ...]]] = None, suggestion_methods: Optional[Union[Callable, Tuple[Callable, ...]]] = None, suggestions: bool = False, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, significance_ci: float = 0.05, n_permutations: Optional[int] = None, show_progress_bar: Optional[bool] = None, n_jobs: Optional[int] = None, plot_histogram: bool = False, plot_kwargs: Optional[Dict] = None, ) -> EvaluationResult: """ Falsify a given DAG using observational data. This method returns the result of a permutation-test to falsify a user-given DAG using observational data. To this end we construct the test statistics by testing the violations of local Markov conditions (LMC) implied by the graph using conditional independence (CI) tests. The null is the number of LMC violations of a random node-permutation of the given graph. Our test can be interpreted as whether the given graph is significantly better than random in terms of the CIs it entails. To determine whether a given graph is falsifiable by our metric, we implement a second test, which reports whether given graph is "characteristic" enough in terms of the CIs it entails. For this, we compute how many of the random node permutations lie in the same Markov equivalence class (MEC) as the given graph and conclude that the given graph is falsifiable only if the fraction of permuted DAGs in the same MEC as the given graph is "reasonably" small. The returned EvaluationResult object has two attributes: `falsified` and `falsifiable`: `falsifiable`: The given graph lies in a different MEC than >= 1-`significance_level` of the permuted DAGs `falsified`: The given graph is falsifiable and violates fewer LMCs than >= 1-`significance_level` of the permuted DAGs By default, we only run 1 / `significance_level` permutations as those are enough to falsify a graph with type I error probability `significance_level` at some given `significance_level`. If you are interested in a more exact estimate of the p-value of whish to plot a histogram to see how the given DAG compares to random node permutations, you should set `n_permutations` to some larger value (e.g. 100 or 1000). If `n_permutations=-1` we test on all n_nodes! permutations. `methods` and `suggestion_methods` must be wrapped in partial(method, **kwargs) (c.f. `validate_graph`). Related paper: Eulig, E., Mastakouri, A. A., Blöbaum, P., Hardt, M., & Janzing, D. (2023). Toward Falsifying Causal Graphs Using a Permutation-Based Test. https://arxiv.org/abs/2305.09565 :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param methods: Validation methods to perform. :param suggestion_methods: Methods to run on the given graph to provide additional suggestions. :param suggestions: Provide suggestions generated using the `suggestion_methods`. :param independence_test: Independence test to use for checking pairwise independencies. :param conditional_independence_test: Conditional independence test to use. :param significance_level: Significance level for the permutation test. :param significance_ci: Significance level for (conditional) independence tests. :param n_permutations: Number of permutations to perform. If -1 use all n_nodes! permutations. :param show_progress_bar: Whether to show progress bar over permutations. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :param plot_histogram: Plot histogram of results from permutation baseline. :param plot_kwargs: Additional plot arguments to be passed to plot_evaluation_results. :return: EvaluationResult """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs show_progress_bar = config.show_progress_bars if show_progress_bar is None else show_progress_bar p_values_memory = _PValuesMemory() if n_permutations is None: n_permutations = int(1 / significance_level) if not plot_histogram else -1 if not plot_kwargs: plot_kwargs = {} # If no methods are provided, use default ones: validate_lmc, validate_tpa if not methods: methods = ( partial( validate_lmc, independence_test=independence_test, conditional_independence_test=conditional_independence_test, significance_level=significance_ci, p_values_memory=p_values_memory, n_jobs=n_jobs, ), partial(validate_tpa, causal_graph_reference=causal_graph), ) elif isinstance(methods, Callable): methods = (methods,) # If no suggestion methods are provided, but suggestions=True, use default ones: validate_cm if not suggestions: suggestion_methods = tuple() elif suggestions and isinstance(suggestion_methods, Callable): suggestion_methods = (suggestion_methods,) elif suggestions: suggestion_methods = ( partial( validate_cm, independence_test=independence_test, conditional_independence_test=conditional_independence_test, significance_level=significance_ci, p_values_memory=p_values_memory, n_jobs=n_jobs, ), ) summary_given = validate_graph( causal_graph, data, methods=methods + suggestion_methods, ) summary_perm = _permutation_based( causal_graph, data, methods=methods, exclude_original_order=False, n_permutations=n_permutations, show_progress_bar=show_progress_bar, ) summary = dict() validation_methods = set(summary_perm.keys()) - {FalsifyConst.PERM_GRAPHS} for m in validation_methods: summary[m] = dict() summary[m][FalsifyConst.PERM_VIOLATIONS] = [perm[FalsifyConst.N_VIOLATIONS] for perm in summary_perm[m]] summary[m][FalsifyConst.GIVEN_VIOLATIONS] = summary_given[m][FalsifyConst.N_VIOLATIONS] summary[m][FalsifyConst.N_TESTS] = summary_given[m][FalsifyConst.N_TESTS] summary[m][FalsifyConst.F_PERM_VIOLATIONS] = [ perm[FalsifyConst.N_VIOLATIONS] / perm[FalsifyConst.N_TESTS] for perm in summary_perm[m] ] summary[m][FalsifyConst.F_GIVEN_VIOLATIONS] = ( summary[m][FalsifyConst.GIVEN_VIOLATIONS] / summary[m][FalsifyConst.N_TESTS] ) summary[m][FalsifyConst.P_VALUE] = sum( [ 1 for perm in summary[m][FalsifyConst.F_PERM_VIOLATIONS] if perm <= summary[m][FalsifyConst.F_GIVEN_VIOLATIONS] ] ) / len(summary[m][FalsifyConst.PERM_VIOLATIONS]) if m != FalsifyConst.VALIDATE_TPA: # Append list of violations (node, non_desc) to get local information summary[m][FalsifyConst.LOCAL_VIOLATION_INSIGHT] = summary_given[m][FalsifyConst.P_VALUES] if FalsifyConst.VALIDATE_TPA in summary: summary[FalsifyConst.MEC] = [ summary_perm[FalsifyConst.PERM_GRAPHS][i] for i, v in enumerate(summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.PERM_VIOLATIONS]) if v == 0 ] result = EvaluationResult( summary=summary, significance_level=significance_level, suggestions={m: summary_given[m] for m in summary_given if m not in validation_methods}, ) if plot_histogram: plot_evaluation_results(result, **plot_kwargs) return result def apply_suggestions( causal_graph: DirectedGraph, evaluation_result: EvaluationResult, edges_to_keep: Optional[List[Tuple[Any, Any]]] = None, ): if not hasattr(evaluation_result, "suggestions"): raise ValueError("EvaluationResult object has no attribute suggestions. Please run with suggestion=True!") causal_graph = causal_graph.copy() for m in evaluation_result.suggestions: for node, res in evaluation_result.suggestions[m][FalsifyConst.P_VALUES].items(): edge = (node[1], node[0]) if (res[1] and edges_to_keep is not None and edge not in edges_to_keep) or ( res[1] and edges_to_keep is None ): causal_graph.remove_edge(edge[0], edge[1]) return causal_graph def plot_evaluation_results(evaluation_result, figsize=(8, 3), bins=None, title="", savepath="", display=True): fig, ax = plt.subplots(figsize=figsize) # Plot histograms p_values = "" data = [] labels = [] evaluation_summary = {k: v for k, v in evaluation_result.summary.items() if k != FalsifyConst.MEC} for i, (m, m_summary) in enumerate(evaluation_summary.items()): data.append(m_summary[FalsifyConst.F_PERM_VIOLATIONS]) labels.append(f"Violations of {FALSIFY_METHODS[m]} of permuted DAGs") p_values += f"p-value {FALSIFY_METHODS[m]} = {m_summary[FalsifyConst.P_VALUE]:.2f}\n" ax.hist(data, color=COLORS[: len(evaluation_summary)], bins=bins, alpha=0.5, label=labels, edgecolor="k") # Plot given violations for i, (m, m_summary) in enumerate(evaluation_summary.items()): ylim = ax.get_ylim()[1] ax.plot( [m_summary[FalsifyConst.F_GIVEN_VIOLATIONS]] * 2, [0, ylim], "--", c=COLORS[i], label=f"Violations of {FALSIFY_METHODS[m]} of given DAG", ) ax.set_ylim([0, ylim]) ax.set_xlabel("Fraction of violations") ax.set_ylabel("# Permutations") plt.legend(loc="upper left", bbox_to_anchor=(1.05, 1), borderaxespad=0.0, title=p_values) if title: plt.title(title) plt.tight_layout() if savepath: plt.savefig(savepath, dpi=300, bbox_inches="tight") if display: plt.show() else: plt.close() def plot_local_insights( causal_graph: DirectedGraph, evaluation_result: Union[EvaluationResult, Dict], method: Optional[str] = FalsifyConst.VALIDATE_LMC, ): """ For some given graph and evaluation result plot local violations. :param causal_graph: DiGraph :param evaluation_result: EvaluationResult :param method: Method for which to plot violations """ colors = {} if isinstance(evaluation_result, EvaluationResult) and method in evaluation_result.summary: local_insight_dict = evaluation_result.summary[method][FalsifyConst.LOCAL_VIOLATION_INSIGHT] elif ( isinstance(evaluation_result, EvaluationResult) and hasattr(evaluation_result, "suggestions") and method in evaluation_result.suggestions ): local_insight_dict = evaluation_result.suggestions[method][FalsifyConst.P_VALUES] elif isinstance(evaluation_result, Dict): if method not in evaluation_result: raise ValueError(f"Validation method {method} does not exist in given evaluation_result!") if FalsifyConst.P_VALUES not in evaluation_result[method]: raise ValueError( f"Validation method {method} has no key {FalsifyConst.P_VALUES} where information on local violations " f"are stored!" ) local_insight_dict = evaluation_result[method][FalsifyConst.P_VALUES] else: raise ValueError(f"Cannot plot local violation insights from method {method} in evaluation_result!") for nodes, result in local_insight_dict.items(): if result[1]: if method == FalsifyConst.VALIDATE_LMC: # For LMC we highlight X for which X _|/|_ Y \in ND_X | Pa_X colors[nodes[0]] = VIOLATION_COLOR elif method == FalsifyConst.VALIDATE_PD: # For PD we highlight the edge (if Y\in Anc_X -> X are adjacent) colors[(nodes[1], nodes[0])] = VIOLATION_COLOR elif method == FalsifyConst.VALIDATE_CM: # For causal minimality we highlight the edge Y \in Pa_X -> X colors[(nodes[1], nodes[0])] = VIOLATION_COLOR plot(causal_graph, colors=colors) def _generate_table( validation_repr, suggestion_repr, width=105, validation_name="Falsificaton Summary", suggestion_name="Suggestions" ): # Create Validation header _repr = [ "+" + "-" * (width - 2) + "+\n", "|" + " " * int(np.floor((width - 2 - len(validation_name)) / 2)), validation_name, " " * int(np.ceil((width - 2 - len(validation_name)) / 2)) + "|\n", "+" + "-" * (width - 2) + "+\n", ] # Create Validation summary _repr += [f"| {v_line}" + " " * (width - 3 - len(v_line)) + "|\n" for v_line in validation_repr] # Close Validation _repr += ["+" + "-" * (width - 2) + "+\n"] # Create Suggestions header if suggestion_repr: _repr += [ "|" + " " * int(np.floor((width - 2 - len(suggestion_name)) / 2)), suggestion_name, " " * int(np.ceil((width - 2 - len(suggestion_name)) / 2)) + "|\n", "+" + "-" * (width - 2) + "+\n", ] # Iterate over suggestions for m, suggestions in suggestion_repr.items(): left_col = "| " + m + " |" if not suggestions: right_col = " " + " " * (width - 2 - len(left_col)) + "|\n" _repr += [left_col, right_col] for i, s in enumerate(suggestions): if i > 0: left_col = "| " + " " * len(m) + " |" right_col = " - " + s + " " * (width - 4 - len(s) - len(left_col)) + "|\n" _repr += [left_col, right_col] _repr += ["+" + "-" * (width - 2) + "+\n"] return "".join(_repr)[:-1] def _compute_p_value( data: pd.DataFrame, X: Union[List, str], Y: Union[List, str], Z: Optional[Union[Set, List, str]], independence_test: Optional[Callable[[np.ndarray, np.ndarray], float]], conditional_independence_test: Optional[Callable[[np.ndarray, np.ndarray, np.ndarray], float]], seed: int, ) -> float: """Perform (conditional) independence test and report p-value. If any nodes needed for the test do not exist in the data, this test is skipped and a value of -1 is returned and a warning is raised. :param data: Observations of variables in the DAG. :param X: Variable to test (conditional) independence with Y :param Y: Variable to test (conditional) independence with X :param Z: Set to condition independence test on. Can be empty (None, empty set, or empty list). :param independence_test: Independence test to use. :param conditional_independence_test: Conditional independence test to use. :param seed: Random seed :return: p-value """ set_random_seed(seed) # Test if we have data for X and Y for node in [X, Y]: if not node in data.columns: warnings.warn(f"WARN: Couldn't find data for node {node}. Skip this test.") return if Z: # Test if we have data for Z for node in Z: if not node in data.columns: warnings.warn(f"WARN: Couldn't find data for node {node}. Skip this test.") return p_value = conditional_independence_test(data[X].values, data[Y].values, data[Z].values) else: p_value = independence_test(data[X].values, data[Y].values) return p_value def _get_parental_triples(causal_graph: DirectedGraph, include_unconditional: bool): """ For a given graph collect all parental triples, that is, the triple (X, Y, Z) is a parental triple iff Y is non-descendant of X, and Z are the parents of X (can be empty if include_unconditional=True) """ triples = [] for node in causal_graph.nodes: parents = get_ordered_predecessors(causal_graph, node) non_descendants = _get_non_descendants(causal_graph, node, exclude_parents=True) if (parents or include_unconditional) and non_descendants: for non_desc in non_descendants: triples.append((node, non_desc, parents)) return triples def _permutation_based( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Union[Callable, Tuple[Callable, ...], List[Callable]], exclude_original_order: bool, n_permutations: int, show_progress_bar: bool, ) -> Dict[str, List[Union[DirectedGraph, Dict]]]: """ Generate baseline for node permutations. :param causal_graph: A directed acyclic graph (DAG). :param methods: Validation methods to perform. :param exclude_original_order: Exclude the original ordering of the nodes (default=False) :param n_permutations: Number of permutations to perform. If -1 use all n_nodes! - int(exclude_orig) permutations :param show_progress_bar: Whether to show progress bar over tested permutations. :return: Dictionary containing summary of validation for each individual graph as well as the permuted graphs """ if not isinstance(methods, (tuple, list)): methods = (methods,) perm_gen = _PermuteNodes(causal_graph, n_permutations=n_permutations, exclude_original_order=exclude_original_order) validation_summary = {FalsifyConst.PERM_GRAPHS: []} for permuted_graph in tqdm(perm_gen, desc="Test permutations of given graph", disable=not show_progress_bar): res = validate_graph( causal_graph=permuted_graph, data=data, methods=methods, ) validation_summary[FalsifyConst.PERM_GRAPHS].append(permuted_graph) for m_name, summary in res.items(): if m_name not in validation_summary: validation_summary[m_name] = [] validation_summary[m_name].append(summary) return validation_summary class _PermuteNodes: def __init__(self, causal_graph: DirectedGraph, exclude_original_order: bool, n_permutations: int): """ Randomly permute the nodes of a given causal graph while keeping the underlying graph structure the same. :param causal_graph: A directed acyclic graph (DAG). :param exclude_original_order: Do not return the original order. :param n_permutations: Return a generator with n_permutations permutations. If n_permutations = -1 (default), we return all n_nodes! - int(exclude_orig) permutations. :return: Copy of causal_graph with nodes randomly permuted. """ self.causal_graph = causal_graph self.exclude_original_order = exclude_original_order self.n_permutations = n_permutations self.max_perms = np.math.factorial(self.causal_graph.number_of_nodes()) - int(self.exclude_original_order) if n_permutations == -1 or n_permutations > self.max_perms: self.it = self.iter_all_permutations() self.length = self.max_perms if self.length > 2**63 - 1: raise ValueError( f"Too many permutations specified. Did you accidently set 'n_permutations'=-1 for a " f"large (>20 nodes) graph? " f"Given graph has {causal_graph.number_of_nodes()} nodes." ) else: self.it = self.iter_random_permutations() self.length = self.n_permutations def iter_all_permutations(self): for i, perm in enumerate(permutations(self.causal_graph.nodes)): if self.exclude_original_order and i == 0: continue mapping = {node: perm[i] for i, node in enumerate(self.causal_graph.nodes)} yield nx.relabel_nodes(self.causal_graph, mapping, copy=True) def iter_random_permutations(self): for _ in range(self.n_permutations): if self.exclude_original_order: is_orig = True while is_orig: perm = list(np.random.permutation(self.causal_graph.nodes)) if perm != list(self.causal_graph.nodes): is_orig = False else: perm = list(np.random.permutation(self.causal_graph.nodes)) mapping = {node: perm[i] for i, node in enumerate(self.causal_graph.nodes)} yield nx.relabel_nodes(self.causal_graph, mapping, copy=True) def __iter__(self): yield from self.it def __len__(self): return self.length def _to_frozenset(x: Union[Set, List, str]): """Converts a set, list or string into a hashable frozenset""" assert ( isinstance(x, Set) or isinstance(x, List) or isinstance(x, str) ), f"{x} must be list, set or str. Got {type(x)} instead!" if isinstance(x, str): return frozenset({x}) return frozenset(x)

"""This module provides functionality to falsify a user-given DAG given observed data. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from dataclasses import dataclass, field from enum import Enum, auto from functools import partial from inspect import getfullargspec from itertools import permutations from typing import Any, Callable, Dict, FrozenSet, List, Optional, Set, Tuple, Union import matplotlib.colors as mcolors import matplotlib.pyplot as plt import networkx as nx import numpy as np import pandas as pd from joblib import Parallel, delayed from tqdm import tqdm import dowhy.gcm.config as config from dowhy.gcm.independence_test import kernel_based from dowhy.gcm.util import plot from dowhy.gcm.util.general import set_random_seed from dowhy.gcm.validation import _get_non_descendants from dowhy.graph import DirectedGraph, get_ordered_predecessors VIOLATION_COLOR = "red" COLORS = list(mcolors.TABLEAU_COLORS.values()) class FalsifyConst(Enum): N_VIOLATIONS = auto() N_TESTS = auto() P_VALUE = auto() P_VALUES = auto() GIVEN_VIOLATIONS = auto() PERM_VIOLATIONS = auto() F_GIVEN_VIOLATIONS = auto() F_PERM_VIOLATIONS = auto() LOCAL_VIOLATION_INSIGHT = auto() METHOD = auto() VALIDATE_LMC = auto() VALIDATE_TPA = auto() VALIDATE_PD = auto() VALIDATE_CM = auto() PERM_GRAPHS = auto() MEC = auto() FALSIFY_METHODS = { FalsifyConst.VALIDATE_LMC: "LMC", FalsifyConst.VALIDATE_PD: "Faithfulness", FalsifyConst.VALIDATE_TPA: "TPa", FalsifyConst.VALIDATE_CM: "Causal Minimality", } @dataclass class _PValuesMemory: """A class to store and access results of independence tests. This class is useful if, e.g. we validate LMC on graph X -> Y -> Z and also on the permuted nodes Z -> Y -> X. Since X ind Z | Y == Z ind X | Y, we only need to perform the conditional independence test once. We use frozensets of nodes since standard (immutable) sets are not hashable. """ p_values: Dict[Tuple[FrozenSet, FrozenSet, FrozenSet], float] = field(default_factory=dict) def add_p_value( self, p_value: Union[None, float], X: Union[Set, List, str], Y: Union[Set, List, str], Z: Optional[Union[Set, List, str]] = None, ): if not Z: Z = set() self.p_values[(_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z))] = p_value def get_p_value( self, X: Union[Set, List, str], Y: Union[Set, List, str], Z: Optional[Union[Set, List, str]] = None ): if not Z: Z = set() # Independence tests are symmetric if (_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z)) in self.p_values: return self.p_values[(_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z))] elif (_to_frozenset(Y), _to_frozenset(X), _to_frozenset(Z)) in self.p_values: return self.p_values[(_to_frozenset(Y), _to_frozenset(X), _to_frozenset(Z))] return def __contains__(self, item: Tuple[Union[Set, List, str], ...]) -> bool: X, Y = (_to_frozenset(i) for i in item[:2]) if len(item) == 2 or item[2] is None: Z = frozenset() else: Z = _to_frozenset(item[2]) return (X, Y, Z) in self.p_values or (Y, X, Z) in self.p_values def validate_lmc( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, include_unconditional: bool = True, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[str, float]]]: """ Validate the local markov condition for a given directed graph. Return number of violations and p values for each node. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory instance, where results of previously performed tests are stored. :param independence_test: Test to use for unconditional independencies (only used if include_unconditional=True) :param conditional_independence_test: Conditional independence test to use for checking local Markov condition. :param significance_level: Significance level for (conditional) independence tests. :param include_unconditional: Test also unconditional independencies of root nodes. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Outcome of validation containing number of violations in the graph and p values/violation for each tuple (node, non_desc) """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_LMC, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, FalsifyConst.P_VALUES: dict(), } # Find out which tests to do triples = _get_parental_triples(causal_graph, include_unconditional) to_test = [] for node, non_desc, parents in triples: if not (node, non_desc, parents) in p_values_memory: to_test.append((node, non_desc, parents)) p_values_memory.add_p_value(None, node, non_desc, parents) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=node, Y=non_desc, Z=parents, independence_test=independence_test, conditional_independence_test=conditional_independence_test, seed=int(seed), ) for (node, non_desc, parents), seed in zip(to_test, random_seeds) ) # Gather results for i, (node, non_desc, parents) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], node, non_desc, parents) # Summarize for node, non_desc, parents in triples: lmc_p_value = p_values_memory.get_p_value(node, non_desc, parents) if lmc_p_value is not None: validation_summary[FalsifyConst.N_TESTS] += 1 validation_summary[FalsifyConst.P_VALUES][(node, non_desc)] = ( lmc_p_value, lmc_p_value <= significance_level, ) if lmc_p_value <= significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_tpa( causal_graph: DirectedGraph, causal_graph_reference: DirectedGraph, include_unconditional: bool = True, ) -> Dict[str, int]: """ Graphical criterion to evaluate which pairwise parental d-separations (parental triples) in `causal_graph` are violated, assuming `causal_graph_reference` is the ground truth graph. If none are violated, then both graphs lie in the same Markov equivalence class. Specifically we test: X _|_G' Y | Z and X _/|_G Y | Z for Y \in ND{X}^G', Z = PA{X}^G :param causal_graph: Causal graph for which to evaluate parental d-separations (G') :param causal_graph_reference: Causal graph where we test if d-separation holds (G) :param include_unconditional: Test also unconditional independencies of root nodes. :return: Validation summary with number of d-separations implied by `causal_graph` and number of times these are violated in the graph `causal_graph_reference`. """ validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_TPA, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, } triples = _get_parental_triples(causal_graph, include_unconditional) for node, non_desc, parents in triples: validation_summary[FalsifyConst.N_TESTS] += 1 if not nx.d_separated(causal_graph_reference, {node}, {non_desc}, set(parents)): validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_pd( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, n_pairs: int = -1, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, adjacent_only: bool = False, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[tuple, float]]]: """ Validate pairwise dependencies (pd) for a given causal graph and data. Test for each node if it is statistically dependent of all its ancestors. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory object, where results of previously performed tests are stored. :param n_pairs: Evaluate dependencies for n_pairs <= all pairs in the DAG. If n_pairs=-1, evaluate dependencies for all (ancestor, node) pairs (default). :param independence_test: Independence test to use for checking pairwise dependencies. :param significance_level: Significance level for independence tests. :param adjacent_only: Only test adjacent node pairs. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Summary dict: {n_violations: int, n_tests: int, p_values: {(ancestor, node): float, ...}} """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() pairs = [(ancestor, node) for node in causal_graph.nodes for ancestor in nx.ancestors(causal_graph, node)] if adjacent_only: pairs = [ (ancestor, node) for (ancestor, node) in pairs if ancestor in get_ordered_predecessors(causal_graph, node) ] if n_pairs < 0: n_pairs = len(pairs) if n_pairs > len(pairs): raise ValueError(f"n_pairs ({n_pairs}) > number of pairs in the DAG ({len(pairs)})") validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_PD, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: n_pairs, FalsifyConst.P_VALUES: dict(), } pair_idxs = np.random.choice(len(pairs), size=n_pairs, replace=False) # Find out which tests to do to_test = [] for pair_idx in pair_idxs: ancestor, node = pairs[pair_idx] if not (ancestor, node) in p_values_memory: to_test.append((ancestor, node)) p_values_memory.add_p_value(None, ancestor, node) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=ancestor, Y=node, Z=None, independence_test=independence_test, conditional_independence_test=None, seed=int(seed), ) for (ancestor, node), seed in zip(to_test, random_seeds) ) # Gather results for i, (ancestor, node) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], ancestor, node) # Summarize for pair_idx in pair_idxs: ancestor, node = pairs[pair_idx] p_value = p_values_memory.get_p_value(ancestor, node) if p_value is not None: validation_summary[FalsifyConst.P_VALUES][(node, ancestor)] = (p_value, p_value > significance_level) if p_value > significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_cm( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[tuple, float]]]: """ Function to test causal minimality of a DAG (see [1], Proposition 6.36). [1] J. Peters, D. Janzing, and B. Schölkopf, Elements of Causal Inference: Foundations and Learning Algorithms. Cambridge, MA, USA: MIT Press, 2017. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory object, where results of previously performed tests are stored. :param independence_test: Independence test to use. :param conditional_independence_test: Conditional independence test to use. :param significance_level: Significance level for independence tests. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Validation summary as dict. """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_CM, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, FalsifyConst.P_VALUES: dict(), } # Find out which tests to do triples = [] to_test = [] for node in causal_graph.nodes: parents = set(causal_graph.predecessors(node)) if parents: for p in parents: other_parents = parents.difference({p}) triples.append((node, p, other_parents)) if not (node, p, other_parents) in p_values_memory: to_test.append((node, p, other_parents)) p_values_memory.add_p_value(None, node, p, other_parents) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=node, Y=p, Z=list(other_parents), independence_test=independence_test, conditional_independence_test=conditional_independence_test, seed=int(seed), ) for (node, p, other_parents), seed in zip(to_test, random_seeds) ) # Gather results for i, (node, p, other_parents) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], node, p, other_parents) # Summarize for node, p, other_parents in triples: p_value = p_values_memory.get_p_value(node, p, other_parents) if p_value is not None: validation_summary[FalsifyConst.N_TESTS] += 1 validation_summary[FalsifyConst.P_VALUES][(node, p, tuple(other_parents))] = ( p_value, p_value > significance_level, ) if p_value > significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def run_validations( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Optional[Union[Callable, Tuple[Callable, ...], List[Callable]]] = partial( validate_lmc, independence_test=kernel_based, conditional_independence_test=kernel_based ), ) -> Dict[str, Dict]: """ Validate a given causal graph using observational data and some given methods. If methods are provided, they must be wrapped in a partial object, with their respective parameters. E.g., if one wants to test the local Markov conditions and the pairwise dependencies (unconditional faithfulness), then call run_validations(G, data, methods=( partial(validate_lmc, independence_test=..., conditional_independence_test=...), partial(validate_pd, independence_test=...), ) ) :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param methods: Method functions wrapped in wrap_partial. E.g. wrap_partial(validate_lmc, data=data, independence_test=..., conditional_independence_test=...). If no methods are provided we run validate_lmc with optional keyword arguments provided to run_validations. :return: Validation summary as dict. """ if not isinstance(methods, (tuple, list)): methods = (methods,) validation_summary = dict() for m in methods: if "data" in getfullargspec(m).args: m = partial(m, data=data) m_summary = m(causal_graph=causal_graph) m_name = m_summary.pop(FalsifyConst.METHOD) validation_summary[m_name] = m_summary return validation_summary @dataclass class EvaluationResult: """ Dataset class containing the evaluation result of falsifying a graph using a node-permutation test. ... Attributes ---------- summary : dict Dictionary containing the summary of the evaluation. significance_level : float Significance level based on which we falsify the given DAG falsifiable : bool Whether the given DAG is falsifiable. falsified : bool Whether the given DAG is falsified. """ summary: dict significance_level: float suggestions: Optional[dict] = None def update_significance_level(self, significance_level: float): """ Update the significance level to decide if we falsify a given DAG. """ self.significance_level = significance_level self.__post_init__() def __post_init__(self): self.can_evaluate = self._can_evaluate() if not self.can_evaluate: self.falsified = None self.falsifiable = None elif ( self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.P_VALUE] > self.significance_level > self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE] ): self.falsified = True self.falsifiable = True elif self.significance_level < self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE]: self.falsified = False self.falsifiable = False else: self.falsified = False self.falsifiable = True def __repr__(self): # DAG Evaluation if self.can_evaluate: decision = " " if self.falsified else " do not " informative = " " if self.falsifiable else " not " frac_MEC = f"{len(self.summary[FalsifyConst.MEC])} / {len(self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.PERM_VIOLATIONS])}" frac_VLMC = f"{self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.GIVEN_VIOLATIONS]}/{self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.N_TESTS]}" p_LMC = self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.P_VALUE] p_dSep = self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE] validation_repr = [ f"The given DAG is{informative}informative because {frac_MEC} of the permutations lie in the Markov", f"equivalence class of the given DAG (p-value: {p_dSep:.2f}).", f"The given DAG violates {frac_VLMC} LMCs and is better than {(1 - p_LMC) * 100:.1f}% of the permuted DAGs (p-value: {p_LMC:.2f}).", f"Based on the provided significance level ({self.significance_level}) and because the DAG is{informative}informative,", f"we{decision}reject the DAG.", ] else: validation_repr = ["Cannot be evaluated!"] # Suggestions suggestion_repr = {} for m in self.suggestions: suggestion_repr[FALSIFY_METHODS[m]] = [ f"Remove edge {node[1]} --> {node[0]}" for (node, r) in self.suggestions[m][FalsifyConst.P_VALUES].items() if r[1] ] return _generate_table(validation_repr, suggestion_repr) def _can_evaluate(self): can_evaluate = True for m in (FalsifyConst.VALIDATE_LMC, FalsifyConst.VALIDATE_TPA): if m not in self.summary: can_evaluate = False return can_evaluate def falsify_graph( causal_graph: DirectedGraph, data: pd.DataFrame, suggestions: bool = False, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, significance_ci: float = 0.05, n_permutations: Optional[int] = None, show_progress_bar: Optional[bool] = None, n_jobs: Optional[int] = None, plot_histogram: bool = False, plot_kwargs: Optional[Dict] = None, ) -> EvaluationResult: """ Falsify a given DAG using observational data. This method returns the result of a permutation-test to falsify a user-given DAG using observational data. To this end we construct the test statistics by testing the violations of local Markov conditions (LMC) implied by the graph using conditional independence (CI) tests. The null is the number of LMC violations of a random node-permutation of the given graph. Our test can be interpreted as whether the given graph is significantly better than random in terms of the CIs it entails. To determine whether a given graph is falsifiable by our metric, we implement a second test, which reports whether given graph is "characteristic" enough in terms of the CIs it entails. For this, we compute how many of the random node permutations lie in the same Markov equivalence class (MEC) as the given graph and conclude that the given graph is falsifiable only if the fraction of permuted DAGs in the same MEC as the given graph is "reasonably" small. The returned EvaluationResult object has two attributes: `falsified` and `falsifiable`: `falsifiable`: The given graph lies in a different MEC than >= 1-`significance_level` of the permuted DAGs `falsified`: The given graph is falsifiable and violates fewer LMCs than >= 1-`significance_level` of the permuted DAGs By default, we only run 1 / `significance_level` permutations as those are enough to falsify a graph with type I error probability `significance_level` at some given `significance_level`. If you are interested in a more exact estimate of the p-value or wish to plot a histogram to see how the given DAG compares to random node permutations, you should set `n_permutations` to some larger value (e.g. 100 or 1000). If `n_permutations=-1` we test on all n_nodes! permutations (the default if plot_histogram=True). Additionally, this method allows to return suggestions to the user (suggestions=True). This is done by testing for violations of causal minimality via `validate_cm`. Related paper: Eulig, E., Mastakouri, A. A., Blöbaum, P., Hardt, M., & Janzing, D. (2023). Toward Falsifying Causal Graphs Using a Permutation-Based Test. https://arxiv.org/abs/2305.09565 :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param suggestions: Provide suggestions to the user. At the moment the only source of suggestions comes from validating causal minimality (using validate_cm). :param independence_test: Independence test to use for checking pairwise independencies. :param conditional_independence_test: Conditional independence test to use. :param significance_level: Significance level for the permutation test. :param significance_ci: Significance level for (conditional) independence tests. :param n_permutations: Number of permutations to perform. If -1 use all n_nodes! permutations. :param show_progress_bar: Whether to show progress bar over permutations. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :param plot_histogram: Plot histogram of results from permutation baseline. :param plot_kwargs: Additional plot arguments to be passed to plot_evaluation_results. :return: EvaluationResult """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs show_progress_bar = config.show_progress_bars if show_progress_bar is None else show_progress_bar p_values_memory = _PValuesMemory() if n_permutations is None: n_permutations = int(1 / significance_level) if not plot_histogram else -1 if not plot_kwargs: plot_kwargs = {} methods = ( partial( validate_lmc, independence_test=independence_test, conditional_independence_test=conditional_independence_test, significance_level=significance_ci, p_values_memory=p_values_memory, n_jobs=n_jobs, ), partial(validate_tpa, causal_graph_reference=causal_graph), ) if suggestions: suggestion_methods = ( partial( validate_cm, independence_test=independence_test, conditional_independence_test=conditional_independence_test, significance_level=significance_ci, p_values_memory=p_values_memory, n_jobs=n_jobs, ), ) else: suggestion_methods = tuple() summary_given = run_validations( causal_graph, data, methods=methods + suggestion_methods, ) summary_perm = _permutation_based( causal_graph, data, methods=methods, exclude_original_order=False, n_permutations=n_permutations, show_progress_bar=show_progress_bar, ) summary = dict() validation_methods = set(summary_perm.keys()) - {FalsifyConst.PERM_GRAPHS} for m in validation_methods: summary[m] = dict() summary[m][FalsifyConst.PERM_VIOLATIONS] = [perm[FalsifyConst.N_VIOLATIONS] for perm in summary_perm[m]] summary[m][FalsifyConst.GIVEN_VIOLATIONS] = summary_given[m][FalsifyConst.N_VIOLATIONS] summary[m][FalsifyConst.N_TESTS] = summary_given[m][FalsifyConst.N_TESTS] summary[m][FalsifyConst.F_PERM_VIOLATIONS] = [ perm[FalsifyConst.N_VIOLATIONS] / perm[FalsifyConst.N_TESTS] for perm in summary_perm[m] ] summary[m][FalsifyConst.F_GIVEN_VIOLATIONS] = ( summary[m][FalsifyConst.GIVEN_VIOLATIONS] / summary[m][FalsifyConst.N_TESTS] ) summary[m][FalsifyConst.P_VALUE] = sum( [ 1 for perm in summary[m][FalsifyConst.F_PERM_VIOLATIONS] if perm <= summary[m][FalsifyConst.F_GIVEN_VIOLATIONS] ] ) / len(summary[m][FalsifyConst.PERM_VIOLATIONS]) if m != FalsifyConst.VALIDATE_TPA: # Append list of violations (node, non_desc) to get local information summary[m][FalsifyConst.LOCAL_VIOLATION_INSIGHT] = summary_given[m][FalsifyConst.P_VALUES] if FalsifyConst.VALIDATE_TPA in summary: summary[FalsifyConst.MEC] = [ summary_perm[FalsifyConst.PERM_GRAPHS][i] for i, v in enumerate(summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.PERM_VIOLATIONS]) if v == 0 ] result = EvaluationResult( summary=summary, significance_level=significance_level, suggestions={m: summary_given[m] for m in summary_given if m not in validation_methods}, ) if plot_histogram: plot_evaluation_results(result, **plot_kwargs) return result def apply_suggestions( causal_graph: DirectedGraph, evaluation_result: EvaluationResult, edges_to_keep: Optional[List[Tuple[Any, Any]]] = None, ): if not hasattr(evaluation_result, "suggestions"): raise ValueError("EvaluationResult object has no attribute suggestions. Please run with suggestion=True!") causal_graph = causal_graph.copy() for m in evaluation_result.suggestions: for node, res in evaluation_result.suggestions[m][FalsifyConst.P_VALUES].items(): edge = (node[1], node[0]) if (res[1] and edges_to_keep is not None and edge not in edges_to_keep) or ( res[1] and edges_to_keep is None ): causal_graph.remove_edge(edge[0], edge[1]) return causal_graph def plot_evaluation_results(evaluation_result, figsize=(8, 3), bins=None, title="", savepath="", display=True): fig, ax = plt.subplots(figsize=figsize) # Plot histograms p_values = "" data = [] labels = [] evaluation_summary = {k: v for k, v in evaluation_result.summary.items() if k != FalsifyConst.MEC} for i, (m, m_summary) in enumerate(evaluation_summary.items()): data.append(m_summary[FalsifyConst.F_PERM_VIOLATIONS]) labels.append(f"Violations of {FALSIFY_METHODS[m]} of permuted DAGs") p_values += f"p-value {FALSIFY_METHODS[m]} = {m_summary[FalsifyConst.P_VALUE]:.2f}\n" ax.hist(data, color=COLORS[: len(evaluation_summary)], bins=bins, alpha=0.5, label=labels, edgecolor="k") # Plot given violations for i, (m, m_summary) in enumerate(evaluation_summary.items()): ylim = ax.get_ylim()[1] ax.plot( [m_summary[FalsifyConst.F_GIVEN_VIOLATIONS]] * 2, [0, ylim], "--", c=COLORS[i], label=f"Violations of {FALSIFY_METHODS[m]} of given DAG", ) ax.set_ylim([0, ylim]) ax.set_xlabel("Fraction of violations") ax.set_ylabel("# Permutations") plt.legend(loc="upper left", bbox_to_anchor=(1.05, 1), borderaxespad=0.0, title=p_values) if title: plt.title(title) plt.tight_layout() if savepath: plt.savefig(savepath, dpi=300, bbox_inches="tight") if display: plt.show() else: plt.close() def plot_local_insights( causal_graph: DirectedGraph, evaluation_result: Union[EvaluationResult, Dict], method: Optional[str] = FalsifyConst.VALIDATE_LMC, ): """ For some given graph and evaluation result plot local violations. :param causal_graph: DiGraph :param evaluation_result: EvaluationResult :param method: Method for which to plot violations """ colors = {} if isinstance(evaluation_result, EvaluationResult) and method in evaluation_result.summary: local_insight_dict = evaluation_result.summary[method][FalsifyConst.LOCAL_VIOLATION_INSIGHT] elif ( isinstance(evaluation_result, EvaluationResult) and hasattr(evaluation_result, "suggestions") and method in evaluation_result.suggestions ): local_insight_dict = evaluation_result.suggestions[method][FalsifyConst.P_VALUES] elif isinstance(evaluation_result, Dict): if method not in evaluation_result: raise ValueError(f"Validation method {method} does not exist in given evaluation_result!") if FalsifyConst.P_VALUES not in evaluation_result[method]: raise ValueError( f"Validation method {method} has no key {FalsifyConst.P_VALUES} where information on local violations " f"are stored!" ) local_insight_dict = evaluation_result[method][FalsifyConst.P_VALUES] else: raise ValueError(f"Cannot plot local violation insights from method {method} in evaluation_result!") for nodes, result in local_insight_dict.items(): if result[1]: if method == FalsifyConst.VALIDATE_LMC: # For LMC we highlight X for which X _|/|_ Y \in ND_X | Pa_X colors[nodes[0]] = VIOLATION_COLOR elif method == FalsifyConst.VALIDATE_PD: # For PD we highlight the edge (if Y\in Anc_X -> X are adjacent) colors[(nodes[1], nodes[0])] = VIOLATION_COLOR elif method == FalsifyConst.VALIDATE_CM: # For causal minimality we highlight the edge Y \in Pa_X -> X colors[(nodes[1], nodes[0])] = VIOLATION_COLOR plot(causal_graph, colors=colors) def _generate_table( validation_repr, suggestion_repr, width=105, validation_name="Falsificaton Summary", suggestion_name="Suggestions" ): # Create Validation header _repr = [ "+" + "-" * (width - 2) + "+\n", "|" + " " * int(np.floor((width - 2 - len(validation_name)) / 2)), validation_name, " " * int(np.ceil((width - 2 - len(validation_name)) / 2)) + "|\n", "+" + "-" * (width - 2) + "+\n", ] # Create Validation summary _repr += [f"| {v_line}" + " " * (width - 3 - len(v_line)) + "|\n" for v_line in validation_repr] # Close Validation _repr += ["+" + "-" * (width - 2) + "+\n"] # Create Suggestions header if suggestion_repr: _repr += [ "|" + " " * int(np.floor((width - 2 - len(suggestion_name)) / 2)), suggestion_name, " " * int(np.ceil((width - 2 - len(suggestion_name)) / 2)) + "|\n", "+" + "-" * (width - 2) + "+\n", ] # Iterate over suggestions for m, suggestions in suggestion_repr.items(): left_col = "| " + m + " |" if not suggestions: right_col = " " + " " * (width - 2 - len(left_col)) + "|\n" _repr += [left_col, right_col] for i, s in enumerate(suggestions): if i > 0: left_col = "| " + " " * len(m) + " |" right_col = " - " + s + " " * (width - 4 - len(s) - len(left_col)) + "|\n" _repr += [left_col, right_col] _repr += ["+" + "-" * (width - 2) + "+\n"] return "".join(_repr)[:-1] def _compute_p_value( data: pd.DataFrame, X: Union[List, str], Y: Union[List, str], Z: Optional[Union[Set, List, str]], independence_test: Optional[Callable[[np.ndarray, np.ndarray], float]], conditional_independence_test: Optional[Callable[[np.ndarray, np.ndarray, np.ndarray], float]], seed: int, ) -> float: """Perform (conditional) independence test and report p-value. If any nodes needed for the test do not exist in the data, this test is skipped and a value of -1 is returned and a warning is raised. :param data: Observations of variables in the DAG. :param X: Variable to test (conditional) independence with Y :param Y: Variable to test (conditional) independence with X :param Z: Set to condition independence test on. Can be empty (None, empty set, or empty list). :param independence_test: Independence test to use. :param conditional_independence_test: Conditional independence test to use. :param seed: Random seed :return: p-value """ set_random_seed(seed) # Test if we have data for X and Y for node in [X, Y]: if not node in data.columns: warnings.warn(f"WARN: Couldn't find data for node {node}. Skip this test.") return if Z: # Test if we have data for Z for node in Z: if not node in data.columns: warnings.warn(f"WARN: Couldn't find data for node {node}. Skip this test.") return p_value = conditional_independence_test(data[X].values, data[Y].values, data[Z].values) else: p_value = independence_test(data[X].values, data[Y].values) return p_value def _get_parental_triples(causal_graph: DirectedGraph, include_unconditional: bool): """ For a given graph collect all parental triples, that is, the triple (X, Y, Z) is a parental triple iff Y is non-descendant of X, and Z are the parents of X (can be empty if include_unconditional=True) """ triples = [] for node in causal_graph.nodes: parents = get_ordered_predecessors(causal_graph, node) non_descendants = _get_non_descendants(causal_graph, node, exclude_parents=True) if (parents or include_unconditional) and non_descendants: for non_desc in non_descendants: triples.append((node, non_desc, parents)) return triples def _permutation_based( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Union[Callable, Tuple[Callable, ...], List[Callable]], exclude_original_order: bool, n_permutations: int, show_progress_bar: bool, ) -> Dict[str, List[Union[DirectedGraph, Dict]]]: """ Generate baseline for node permutations. :param causal_graph: A directed acyclic graph (DAG). :param methods: Validation methods to perform. :param exclude_original_order: Exclude the original ordering of the nodes (default=False) :param n_permutations: Number of permutations to perform. If -1 use all n_nodes! - int(exclude_orig) permutations :param show_progress_bar: Whether to show progress bar over tested permutations. :return: Dictionary containing summary of validation for each individual graph as well as the permuted graphs """ if not isinstance(methods, (tuple, list)): methods = (methods,) perm_gen = _PermuteNodes(causal_graph, n_permutations=n_permutations, exclude_original_order=exclude_original_order) validation_summary = {FalsifyConst.PERM_GRAPHS: []} for permuted_graph in tqdm(perm_gen, desc="Test permutations of given graph", disable=not show_progress_bar): res = run_validations( causal_graph=permuted_graph, data=data, methods=methods, ) validation_summary[FalsifyConst.PERM_GRAPHS].append(permuted_graph) for m_name, summary in res.items(): if m_name not in validation_summary: validation_summary[m_name] = [] validation_summary[m_name].append(summary) return validation_summary class _PermuteNodes: def __init__(self, causal_graph: DirectedGraph, exclude_original_order: bool, n_permutations: int): """ Randomly permute the nodes of a given causal graph while keeping the underlying graph structure the same. :param causal_graph: A directed acyclic graph (DAG). :param exclude_original_order: Do not return the original order. :param n_permutations: Return a generator with n_permutations permutations. If n_permutations = -1 (default), we return all n_nodes! - int(exclude_orig) permutations. :return: Copy of causal_graph with nodes randomly permuted. """ self.causal_graph = causal_graph self.exclude_original_order = exclude_original_order self.n_permutations = n_permutations self.max_perms = np.math.factorial(self.causal_graph.number_of_nodes()) - int(self.exclude_original_order) if n_permutations == -1 or n_permutations > self.max_perms: self.it = self.iter_all_permutations() self.length = self.max_perms if self.length > 2**63 - 1: raise ValueError( f"Too many permutations specified. Did you accidently set 'n_permutations'=-1 for a " f"large (>20 nodes) graph? " f"Given graph has {causal_graph.number_of_nodes()} nodes." ) else: self.it = self.iter_random_permutations() self.length = self.n_permutations def iter_all_permutations(self): for i, perm in enumerate(permutations(self.causal_graph.nodes)): if self.exclude_original_order and i == 0: continue mapping = {node: perm[i] for i, node in enumerate(self.causal_graph.nodes)} yield nx.relabel_nodes(self.causal_graph, mapping, copy=True) def iter_random_permutations(self): for _ in range(self.n_permutations): if self.exclude_original_order: is_orig = True while is_orig: perm = list(np.random.permutation(self.causal_graph.nodes)) if perm != list(self.causal_graph.nodes): is_orig = False else: perm = list(np.random.permutation(self.causal_graph.nodes)) mapping = {node: perm[i] for i, node in enumerate(self.causal_graph.nodes)} yield nx.relabel_nodes(self.causal_graph, mapping, copy=True) def __iter__(self): yield from self.it def __len__(self): return self.length def _to_frozenset(x: Union[Set, List, str]): """Converts a set, list or string into a hashable frozenset""" assert ( isinstance(x, Set) or isinstance(x, List) or isinstance(x, str) ), f"{x} must be list, set or str. Got {type(x)} instead!" if isinstance(x, str): return frozenset({x}) return frozenset(x)

eeulig

379f188d674d5be6d644559f9f54852b87403f7c

4e30c63fe1113e35259e7fb3400a6f09bca4d699

Similar comments as for `validate_graph`.

bloebp

py-why/dowhy

Remove kwargs from falsify, improve docstrings

This commit updates `gcm/falsify` in the following ways: - Remove the `**kwargs` for `validate_graph` and `falsify_graph`. Instead, validation methods given to those functions should be wrapped in `wrap_partial`, e.g. if we want to validate a graph `G` and test local Markov conditions and pairwise dependencies on observations `data`, we would do ``` validate_graph(G, methods=( wrap_partial(validate_lmc, data=data, independence_test=..., conditional_independence_test=...), wrap_partial(validate_pd, data=data, independence_test=...), ) ) ``` By default (`methods=None`), `validate_graph` tests local Markov conditions and `falsify_graph` tests local Markov conditions and d-separation of parental triples. In this case `data` has to be set and optionally one can provide `independence_test`, `conditional_independence_test`, `significance_level`, and `n_jobs` via the keyword arguments of `validate_graph` / `falsify_graph`. - Use `Enum` for some of the constants - In `_PValuesMemory` the method `add_p_value ` always sets a p-value even if it was set already. This removes the need for the method `clear_placeholders` and allows `p_values` to be set to `-1`, indicating that the CI is not testable (because of missing data for a node, c.f. `_compute_p_value`) - If for some CI: X, Y | Z any of the nodes has no data (column missing in the provided dataframe), we skip this test and raise a warning. - Instead of using the raw number of violations in the plotted histogram and to compute the p-value, we use the relative number of violations. This will result in different p-values only if we have missing data for some nodes (and thus the number of tests is potentially different between different permutations) - Improve docstrings

2023-07-04 13:58:30+00:00

2023-07-18 14:19:10+00:00

dowhy/gcm/falsify.py

"""This module provides functionality to falsify a user-given DAG given observed data. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from dataclasses import dataclass, field from enum import Enum, auto from functools import partial from itertools import permutations from typing import Any, Callable, Dict, FrozenSet, List, Optional, Set, Tuple, Union import matplotlib.colors as mcolors import matplotlib.pyplot as plt import networkx as nx import numpy as np import pandas as pd from joblib import Parallel, delayed from tqdm import tqdm import dowhy.gcm.config as config from dowhy.gcm.independence_test import kernel_based from dowhy.gcm.util import plot from dowhy.gcm.util.general import set_random_seed from dowhy.gcm.validation import _get_non_descendants from dowhy.graph import DirectedGraph, get_ordered_predecessors VIOLATION_COLOR = "red" COLORS = list(mcolors.TABLEAU_COLORS.values()) class FalsifyConst(Enum): N_VIOLATIONS = auto() N_TESTS = auto() P_VALUE = auto() P_VALUES = auto() GIVEN_VIOLATIONS = auto() PERM_VIOLATIONS = auto() F_GIVEN_VIOLATIONS = auto() F_PERM_VIOLATIONS = auto() LOCAL_VIOLATION_INSIGHT = auto() METHOD = auto() VALIDATE_LMC = auto() VALIDATE_TPA = auto() VALIDATE_PD = auto() VALIDATE_CM = auto() PERM_GRAPHS = auto() MEC = auto() FALSIFY_METHODS = { FalsifyConst.VALIDATE_LMC: "LMC", FalsifyConst.VALIDATE_PD: "Faithfulness", FalsifyConst.VALIDATE_TPA: "tPa", FalsifyConst.VALIDATE_CM: "Causal Minimality", } @dataclass class _PValuesMemory: """A class to store and access results of independence tests. This class is useful if, e.g. we validate LMC on graph X -> Y -> Z and also on the permuted nodes Z -> Y -> X. Since X ind Z | Y == Z ind X | Y, we only need to perform the conditional independence test once. We use frozensets of nodes since standard (immutable) sets are not hashable. """ p_values: Dict[Tuple[FrozenSet, FrozenSet, FrozenSet], float] = field(default_factory=dict) def add_p_value( self, p_value: Union[None, float], X: Union[Set, List, str], Y: Union[Set, List, str], Z: Optional[Union[Set, List, str]] = None, ): if not Z: Z = set() self.p_values[(_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z))] = p_value def get_p_value( self, X: Union[Set, List, str], Y: Union[Set, List, str], Z: Optional[Union[Set, List, str]] = None ): if not Z: Z = set() # Independence tests are symmetric if (_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z)) in self.p_values: return self.p_values[(_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z))] elif (_to_frozenset(Y), _to_frozenset(X), _to_frozenset(Z)) in self.p_values: return self.p_values[(_to_frozenset(Y), _to_frozenset(X), _to_frozenset(Z))] return def __contains__(self, item: Tuple[Union[Set, List, str], ...]) -> bool: X, Y = (_to_frozenset(i) for i in item[:2]) if len(item) == 2 or item[2] is None: Z = frozenset() else: Z = _to_frozenset(item[2]) return (X, Y, Z) in self.p_values or (Y, X, Z) in self.p_values def validate_lmc( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, include_unconditional: bool = True, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[str, float]]]: """ Validate the local markov condition for a given directed graph. Return number of violations and p values for each node. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory instance, where results of previously performed tests are stored. :param independence_test: Test to use for unconditional independencies (only used if include_unconditional=True) :param conditional_independence_test: Conditional independence test to use for checking local Markov condition. :param significance_level: Significance level for (conditional) independence tests. :param include_unconditional: Test also unconditional independencies of root nodes. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Outcome of validation containing number of violations in the graph and p values/violation for each tuple (node, non_desc) """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_LMC, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, FalsifyConst.P_VALUES: dict(), } # Find out which tests to do triples = _get_parental_triples(causal_graph, include_unconditional) to_test = [] for node, non_desc, parents in triples: if not (node, non_desc, parents) in p_values_memory: to_test.append((node, non_desc, parents)) p_values_memory.add_p_value(None, node, non_desc, parents) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=node, Y=non_desc, Z=parents, independence_test=independence_test, conditional_independence_test=conditional_independence_test, seed=int(seed), ) for (node, non_desc, parents), seed in zip(to_test, random_seeds) ) # Gather results for i, (node, non_desc, parents) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], node, non_desc, parents) # Summarize for node, non_desc, parents in triples: lmc_p_value = p_values_memory.get_p_value(node, non_desc, parents) if lmc_p_value is not None: validation_summary[FalsifyConst.N_TESTS] += 1 validation_summary[FalsifyConst.P_VALUES][(node, non_desc)] = ( lmc_p_value, lmc_p_value <= significance_level, ) if lmc_p_value <= significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_tpa( causal_graph: DirectedGraph, causal_graph_reference: DirectedGraph, include_unconditional: bool = True, data: Optional[pd.DataFrame] = None, ) -> Dict[str, int]: """ Graphical criterion to evaluate which pairwise parental d-separations (parental triples) in `causal_graph` are violated, assuming `causal_graph_reference` is the ground truth graph. If none are violated, then both graphs lie in the same Markov equivalence class. Specifically we test: X _|_G' Y | Z and X _/|_G Y | Z for Y \in ND{X}^G', Z = PA{X}^G :param causal_graph: Causal graph for which to evaluate parental d-separations (G') :param causal_graph_reference: Causal graph where we test if d-separation holds (G) :param include_unconditional: Test also unconditional independencies of root nodes. :param data: IGNORED! No data is needed for this validation method and thus the data argument is ignored! :return: Validation summary with number of d-separations implied by `causal_graph` and number of times these are violated in the graph `causal_graph_reference`. """ validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_TPA, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, } triples = _get_parental_triples(causal_graph, include_unconditional) for node, non_desc, parents in triples: validation_summary[FalsifyConst.N_TESTS] += 1 if not nx.d_separated(causal_graph_reference, {node}, {non_desc}, set(parents)): validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_pd( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, n_pairs: int = -1, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, adjacent_only: bool = False, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[tuple, float]]]: """ Validate pairwise dependencies (pd) for a given causal graph and data. Test for each node if it is statistically dependent of all its ancestors. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory object, where results of previously performed tests are stored. :param n_pairs: Evaluate dependencies for n_pairs <= all pairs in the DAG. If n_pairs=-1, evaluate dependencies for all (ancestor, node) pairs (default). :param independence_test: Independence test to use for checking pairwise dependencies. :param significance_level: Significance level for independence tests. :param adjacent_only: Only test adjacent node pairs. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Summary dict: {n_violations: int, n_tests: int, p_values: {(ancestor, node): float, ...}} """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() pairs = [(ancestor, node) for node in causal_graph.nodes for ancestor in nx.ancestors(causal_graph, node)] if adjacent_only: pairs = [ (ancestor, node) for (ancestor, node) in pairs if ancestor in get_ordered_predecessors(causal_graph, node) ] if n_pairs < 0: n_pairs = len(pairs) if n_pairs > len(pairs): raise ValueError(f"n_pairs ({n_pairs}) > number of pairs in the DAG ({len(pairs)})") validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_PD, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: n_pairs, FalsifyConst.P_VALUES: dict(), } pair_idxs = np.random.choice(len(pairs), size=n_pairs, replace=False) # Find out which tests to do to_test = [] for pair_idx in pair_idxs: ancestor, node = pairs[pair_idx] if not (ancestor, node) in p_values_memory: to_test.append((ancestor, node)) p_values_memory.add_p_value(None, ancestor, node) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=ancestor, Y=node, Z=None, independence_test=independence_test, conditional_independence_test=None, seed=int(seed), ) for (ancestor, node), seed in zip(to_test, random_seeds) ) # Gather results for i, (ancestor, node) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], ancestor, node) # Summarize for pair_idx in pair_idxs: ancestor, node = pairs[pair_idx] p_value = p_values_memory.get_p_value(ancestor, node) if p_value is not None: validation_summary[FalsifyConst.P_VALUES][(node, ancestor)] = (p_value, p_value > significance_level) if p_value > significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_cm( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[tuple, float]]]: """ Function to test causal minimality of a DAG (see [1], Proposition 6.36). [1] J. Peters, D. Janzing, and B. Schölkopf, Elements of Causal Inference: Foundations and Learning Algorithms. Cambridge, MA, USA: MIT Press, 2017. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory object, where results of previously performed tests are stored. :param independence_test: Independence test to use. :param conditional_independence_test: Conditional independence test to use. :param significance_level: Significance level for independence tests. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Validation summary as dict. """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_CM, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, FalsifyConst.P_VALUES: dict(), } # Find out which tests to do triples = [] to_test = [] for node in causal_graph.nodes: parents = set(causal_graph.predecessors(node)) if parents: for p in parents: other_parents = parents.difference({p}) triples.append((node, p, other_parents)) if not (node, p, other_parents) in p_values_memory: to_test.append((node, p, other_parents)) p_values_memory.add_p_value(None, node, p, other_parents) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=node, Y=p, Z=list(other_parents), independence_test=independence_test, conditional_independence_test=conditional_independence_test, seed=int(seed), ) for (node, p, other_parents), seed in zip(to_test, random_seeds) ) # Gather results for i, (node, p, other_parents) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], node, p, other_parents) # Summarize for node, p, other_parents in triples: p_value = p_values_memory.get_p_value(node, p, other_parents) if p_value is not None: validation_summary[FalsifyConst.N_TESTS] += 1 validation_summary[FalsifyConst.P_VALUES][(node, p, tuple(other_parents))] = ( p_value, p_value > significance_level, ) if p_value > significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_graph( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Optional[Union[Callable, Tuple[Callable, ...], List[Callable]]] = partial( validate_lmc, independence_test=kernel_based, conditional_independence_test=kernel_based ), ) -> Dict[str, Dict]: """ Validate a given causal graph using observational data and some given methods. If methods are provided, they must be wrapped in a partial object, with their respective parameters. E.g., if one wants to test the local Markov conditions and the pairwise dependencies (unconditional faithfulness), then call validate_graph(G, data, methods=( partial(validate_lmc, independence_test=..., conditional_independence_test=...), partial(validate_pd, independence_test=...), ) ) :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param methods: Method functions wrapped in wrap_partial. E.g. wrap_partial(validate_lmc, data=data, independence_test=..., conditional_independence_test=...). If no methods are provided we run validate_lmc with optional keyword arguments provided to validate_graph. :return: Validation summary as dict. """ if not isinstance(methods, (tuple, list)): methods = (methods,) validation_summary = dict() for m in methods: m_summary = m(causal_graph=causal_graph, data=data) m_name = m_summary.pop(FalsifyConst.METHOD) validation_summary[m_name] = m_summary return validation_summary @dataclass class EvaluationResult: """ Dataset class containing the evaluation result of falsifying a graph using a node-permutation test. ... Attributes ---------- summary : dict Dictionary containing the summary of the evaluation. significance_level : float Significance level based on which we falsify the given DAG falsifiable : bool Whether the given DAG is falsifiable. falsified : bool Whether the given DAG is falsified. """ summary: dict significance_level: float suggestions: Optional[dict] = None def update_significance_level(self, significance_level: float): """ Update the significance level to decide if we falsify a given DAG. """ self.significance_level = significance_level self.__post_init__() def __post_init__(self): self.can_evaluate = self._can_evaluate() if not self.can_evaluate: self.falsified = None self.falsifiable = None elif ( self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.P_VALUE] > self.significance_level > self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE] ): self.falsified = True self.falsifiable = True elif self.significance_level < self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE]: self.falsified = False self.falsifiable = False else: self.falsified = False self.falsifiable = True def __repr__(self): # DAG Evaluation if self.can_evaluate: decision = " " if self.falsified else " do not " informative = " " if self.falsifiable else " not " frac_MEC = f"{len(self.summary[FalsifyConst.MEC])} / {len(self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.PERM_VIOLATIONS])}" frac_VLMC = f"{self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.GIVEN_VIOLATIONS]}/{self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.N_TESTS]}" p_LMC = self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.P_VALUE] p_dSep = self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE] validation_repr = [ f"The given DAG is{informative}informative because {frac_MEC} of the permutations lie in the Markov", f"equivalence class of the given DAG (p-value: {p_dSep:.2f}).", f"The given DAG violates {frac_VLMC} LMCs and is better than {(1 - p_LMC) * 100:.1f}% of the permuted DAGs (p-value: {p_LMC:.2f}).", f"Based on the provided significance level ({self.significance_level}) and because the DAG is{informative}informative,", f"we{decision}reject the DAG.", ] else: validation_repr = ["Cannot be evaluated!"] # Suggestions suggestion_repr = {} for m in self.suggestions: suggestion_repr[FALSIFY_METHODS[m]] = [ f"Remove edge {node[1]} --> {node[0]}" for (node, r) in self.suggestions[m][FalsifyConst.P_VALUES].items() if r[1] ] return _generate_table(validation_repr, suggestion_repr) def _can_evaluate(self): can_evaluate = True for m in (FalsifyConst.VALIDATE_LMC, FalsifyConst.VALIDATE_TPA): if m not in self.summary: can_evaluate = False return can_evaluate def falsify_graph( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Optional[Union[Callable, Tuple[Callable, ...]]] = None, suggestion_methods: Optional[Union[Callable, Tuple[Callable, ...]]] = None, suggestions: bool = False, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, significance_ci: float = 0.05, n_permutations: Optional[int] = None, show_progress_bar: Optional[bool] = None, n_jobs: Optional[int] = None, plot_histogram: bool = False, plot_kwargs: Optional[Dict] = None, ) -> EvaluationResult: """ Falsify a given DAG using observational data. This method returns the result of a permutation-test to falsify a user-given DAG using observational data. To this end we construct the test statistics by testing the violations of local Markov conditions (LMC) implied by the graph using conditional independence (CI) tests. The null is the number of LMC violations of a random node-permutation of the given graph. Our test can be interpreted as whether the given graph is significantly better than random in terms of the CIs it entails. To determine whether a given graph is falsifiable by our metric, we implement a second test, which reports whether given graph is "characteristic" enough in terms of the CIs it entails. For this, we compute how many of the random node permutations lie in the same Markov equivalence class (MEC) as the given graph and conclude that the given graph is falsifiable only if the fraction of permuted DAGs in the same MEC as the given graph is "reasonably" small. The returned EvaluationResult object has two attributes: `falsified` and `falsifiable`: `falsifiable`: The given graph lies in a different MEC than >= 1-`significance_level` of the permuted DAGs `falsified`: The given graph is falsifiable and violates fewer LMCs than >= 1-`significance_level` of the permuted DAGs By default, we only run 1 / `significance_level` permutations as those are enough to falsify a graph with type I error probability `significance_level` at some given `significance_level`. If you are interested in a more exact estimate of the p-value of whish to plot a histogram to see how the given DAG compares to random node permutations, you should set `n_permutations` to some larger value (e.g. 100 or 1000). If `n_permutations=-1` we test on all n_nodes! permutations. `methods` and `suggestion_methods` must be wrapped in partial(method, **kwargs) (c.f. `validate_graph`). Related paper: Eulig, E., Mastakouri, A. A., Blöbaum, P., Hardt, M., & Janzing, D. (2023). Toward Falsifying Causal Graphs Using a Permutation-Based Test. https://arxiv.org/abs/2305.09565 :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param methods: Validation methods to perform. :param suggestion_methods: Methods to run on the given graph to provide additional suggestions. :param suggestions: Provide suggestions generated using the `suggestion_methods`. :param independence_test: Independence test to use for checking pairwise independencies. :param conditional_independence_test: Conditional independence test to use. :param significance_level: Significance level for the permutation test. :param significance_ci: Significance level for (conditional) independence tests. :param n_permutations: Number of permutations to perform. If -1 use all n_nodes! permutations. :param show_progress_bar: Whether to show progress bar over permutations. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :param plot_histogram: Plot histogram of results from permutation baseline. :param plot_kwargs: Additional plot arguments to be passed to plot_evaluation_results. :return: EvaluationResult """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs show_progress_bar = config.show_progress_bars if show_progress_bar is None else show_progress_bar p_values_memory = _PValuesMemory() if n_permutations is None: n_permutations = int(1 / significance_level) if not plot_histogram else -1 if not plot_kwargs: plot_kwargs = {} # If no methods are provided, use default ones: validate_lmc, validate_tpa if not methods: methods = ( partial( validate_lmc, independence_test=independence_test, conditional_independence_test=conditional_independence_test, significance_level=significance_ci, p_values_memory=p_values_memory, n_jobs=n_jobs, ), partial(validate_tpa, causal_graph_reference=causal_graph), ) elif isinstance(methods, Callable): methods = (methods,) # If no suggestion methods are provided, but suggestions=True, use default ones: validate_cm if not suggestions: suggestion_methods = tuple() elif suggestions and isinstance(suggestion_methods, Callable): suggestion_methods = (suggestion_methods,) elif suggestions: suggestion_methods = ( partial( validate_cm, independence_test=independence_test, conditional_independence_test=conditional_independence_test, significance_level=significance_ci, p_values_memory=p_values_memory, n_jobs=n_jobs, ), ) summary_given = validate_graph( causal_graph, data, methods=methods + suggestion_methods, ) summary_perm = _permutation_based( causal_graph, data, methods=methods, exclude_original_order=False, n_permutations=n_permutations, show_progress_bar=show_progress_bar, ) summary = dict() validation_methods = set(summary_perm.keys()) - {FalsifyConst.PERM_GRAPHS} for m in validation_methods: summary[m] = dict() summary[m][FalsifyConst.PERM_VIOLATIONS] = [perm[FalsifyConst.N_VIOLATIONS] for perm in summary_perm[m]] summary[m][FalsifyConst.GIVEN_VIOLATIONS] = summary_given[m][FalsifyConst.N_VIOLATIONS] summary[m][FalsifyConst.N_TESTS] = summary_given[m][FalsifyConst.N_TESTS] summary[m][FalsifyConst.F_PERM_VIOLATIONS] = [ perm[FalsifyConst.N_VIOLATIONS] / perm[FalsifyConst.N_TESTS] for perm in summary_perm[m] ] summary[m][FalsifyConst.F_GIVEN_VIOLATIONS] = ( summary[m][FalsifyConst.GIVEN_VIOLATIONS] / summary[m][FalsifyConst.N_TESTS] ) summary[m][FalsifyConst.P_VALUE] = sum( [ 1 for perm in summary[m][FalsifyConst.F_PERM_VIOLATIONS] if perm <= summary[m][FalsifyConst.F_GIVEN_VIOLATIONS] ] ) / len(summary[m][FalsifyConst.PERM_VIOLATIONS]) if m != FalsifyConst.VALIDATE_TPA: # Append list of violations (node, non_desc) to get local information summary[m][FalsifyConst.LOCAL_VIOLATION_INSIGHT] = summary_given[m][FalsifyConst.P_VALUES] if FalsifyConst.VALIDATE_TPA in summary: summary[FalsifyConst.MEC] = [ summary_perm[FalsifyConst.PERM_GRAPHS][i] for i, v in enumerate(summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.PERM_VIOLATIONS]) if v == 0 ] result = EvaluationResult( summary=summary, significance_level=significance_level, suggestions={m: summary_given[m] for m in summary_given if m not in validation_methods}, ) if plot_histogram: plot_evaluation_results(result, **plot_kwargs) return result def apply_suggestions( causal_graph: DirectedGraph, evaluation_result: EvaluationResult, edges_to_keep: Optional[List[Tuple[Any, Any]]] = None, ): if not hasattr(evaluation_result, "suggestions"): raise ValueError("EvaluationResult object has no attribute suggestions. Please run with suggestion=True!") causal_graph = causal_graph.copy() for m in evaluation_result.suggestions: for node, res in evaluation_result.suggestions[m][FalsifyConst.P_VALUES].items(): edge = (node[1], node[0]) if (res[1] and edges_to_keep is not None and edge not in edges_to_keep) or ( res[1] and edges_to_keep is None ): causal_graph.remove_edge(edge[0], edge[1]) return causal_graph def plot_evaluation_results(evaluation_result, figsize=(8, 3), bins=None, title="", savepath="", display=True): fig, ax = plt.subplots(figsize=figsize) # Plot histograms p_values = "" data = [] labels = [] evaluation_summary = {k: v for k, v in evaluation_result.summary.items() if k != FalsifyConst.MEC} for i, (m, m_summary) in enumerate(evaluation_summary.items()): data.append(m_summary[FalsifyConst.F_PERM_VIOLATIONS]) labels.append(f"Violations of {FALSIFY_METHODS[m]} of permuted DAGs") p_values += f"p-value {FALSIFY_METHODS[m]} = {m_summary[FalsifyConst.P_VALUE]:.2f}\n" ax.hist(data, color=COLORS[: len(evaluation_summary)], bins=bins, alpha=0.5, label=labels, edgecolor="k") # Plot given violations for i, (m, m_summary) in enumerate(evaluation_summary.items()): ylim = ax.get_ylim()[1] ax.plot( [m_summary[FalsifyConst.F_GIVEN_VIOLATIONS]] * 2, [0, ylim], "--", c=COLORS[i], label=f"Violations of {FALSIFY_METHODS[m]} of given DAG", ) ax.set_ylim([0, ylim]) ax.set_xlabel("Fraction of violations") ax.set_ylabel("# Permutations") plt.legend(loc="upper left", bbox_to_anchor=(1.05, 1), borderaxespad=0.0, title=p_values) if title: plt.title(title) plt.tight_layout() if savepath: plt.savefig(savepath, dpi=300, bbox_inches="tight") if display: plt.show() else: plt.close() def plot_local_insights( causal_graph: DirectedGraph, evaluation_result: Union[EvaluationResult, Dict], method: Optional[str] = FalsifyConst.VALIDATE_LMC, ): """ For some given graph and evaluation result plot local violations. :param causal_graph: DiGraph :param evaluation_result: EvaluationResult :param method: Method for which to plot violations """ colors = {} if isinstance(evaluation_result, EvaluationResult) and method in evaluation_result.summary: local_insight_dict = evaluation_result.summary[method][FalsifyConst.LOCAL_VIOLATION_INSIGHT] elif ( isinstance(evaluation_result, EvaluationResult) and hasattr(evaluation_result, "suggestions") and method in evaluation_result.suggestions ): local_insight_dict = evaluation_result.suggestions[method][FalsifyConst.P_VALUES] elif isinstance(evaluation_result, Dict): if method not in evaluation_result: raise ValueError(f"Validation method {method} does not exist in given evaluation_result!") if FalsifyConst.P_VALUES not in evaluation_result[method]: raise ValueError( f"Validation method {method} has no key {FalsifyConst.P_VALUES} where information on local violations " f"are stored!" ) local_insight_dict = evaluation_result[method][FalsifyConst.P_VALUES] else: raise ValueError(f"Cannot plot local violation insights from method {method} in evaluation_result!") for nodes, result in local_insight_dict.items(): if result[1]: if method == FalsifyConst.VALIDATE_LMC: # For LMC we highlight X for which X _|/|_ Y \in ND_X | Pa_X colors[nodes[0]] = VIOLATION_COLOR elif method == FalsifyConst.VALIDATE_PD: # For PD we highlight the edge (if Y\in Anc_X -> X are adjacent) colors[(nodes[1], nodes[0])] = VIOLATION_COLOR elif method == FalsifyConst.VALIDATE_CM: # For causal minimality we highlight the edge Y \in Pa_X -> X colors[(nodes[1], nodes[0])] = VIOLATION_COLOR plot(causal_graph, colors=colors) def _generate_table( validation_repr, suggestion_repr, width=105, validation_name="Falsificaton Summary", suggestion_name="Suggestions" ): # Create Validation header _repr = [ "+" + "-" * (width - 2) + "+\n", "|" + " " * int(np.floor((width - 2 - len(validation_name)) / 2)), validation_name, " " * int(np.ceil((width - 2 - len(validation_name)) / 2)) + "|\n", "+" + "-" * (width - 2) + "+\n", ] # Create Validation summary _repr += [f"| {v_line}" + " " * (width - 3 - len(v_line)) + "|\n" for v_line in validation_repr] # Close Validation _repr += ["+" + "-" * (width - 2) + "+\n"] # Create Suggestions header if suggestion_repr: _repr += [ "|" + " " * int(np.floor((width - 2 - len(suggestion_name)) / 2)), suggestion_name, " " * int(np.ceil((width - 2 - len(suggestion_name)) / 2)) + "|\n", "+" + "-" * (width - 2) + "+\n", ] # Iterate over suggestions for m, suggestions in suggestion_repr.items(): left_col = "| " + m + " |" if not suggestions: right_col = " " + " " * (width - 2 - len(left_col)) + "|\n" _repr += [left_col, right_col] for i, s in enumerate(suggestions): if i > 0: left_col = "| " + " " * len(m) + " |" right_col = " - " + s + " " * (width - 4 - len(s) - len(left_col)) + "|\n" _repr += [left_col, right_col] _repr += ["+" + "-" * (width - 2) + "+\n"] return "".join(_repr)[:-1] def _compute_p_value( data: pd.DataFrame, X: Union[List, str], Y: Union[List, str], Z: Optional[Union[Set, List, str]], independence_test: Optional[Callable[[np.ndarray, np.ndarray], float]], conditional_independence_test: Optional[Callable[[np.ndarray, np.ndarray, np.ndarray], float]], seed: int, ) -> float: """Perform (conditional) independence test and report p-value. If any nodes needed for the test do not exist in the data, this test is skipped and a value of -1 is returned and a warning is raised. :param data: Observations of variables in the DAG. :param X: Variable to test (conditional) independence with Y :param Y: Variable to test (conditional) independence with X :param Z: Set to condition independence test on. Can be empty (None, empty set, or empty list). :param independence_test: Independence test to use. :param conditional_independence_test: Conditional independence test to use. :param seed: Random seed :return: p-value """ set_random_seed(seed) # Test if we have data for X and Y for node in [X, Y]: if not node in data.columns: warnings.warn(f"WARN: Couldn't find data for node {node}. Skip this test.") return if Z: # Test if we have data for Z for node in Z: if not node in data.columns: warnings.warn(f"WARN: Couldn't find data for node {node}. Skip this test.") return p_value = conditional_independence_test(data[X].values, data[Y].values, data[Z].values) else: p_value = independence_test(data[X].values, data[Y].values) return p_value def _get_parental_triples(causal_graph: DirectedGraph, include_unconditional: bool): """ For a given graph collect all parental triples, that is, the triple (X, Y, Z) is a parental triple iff Y is non-descendant of X, and Z are the parents of X (can be empty if include_unconditional=True) """ triples = [] for node in causal_graph.nodes: parents = get_ordered_predecessors(causal_graph, node) non_descendants = _get_non_descendants(causal_graph, node, exclude_parents=True) if (parents or include_unconditional) and non_descendants: for non_desc in non_descendants: triples.append((node, non_desc, parents)) return triples def _permutation_based( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Union[Callable, Tuple[Callable, ...], List[Callable]], exclude_original_order: bool, n_permutations: int, show_progress_bar: bool, ) -> Dict[str, List[Union[DirectedGraph, Dict]]]: """ Generate baseline for node permutations. :param causal_graph: A directed acyclic graph (DAG). :param methods: Validation methods to perform. :param exclude_original_order: Exclude the original ordering of the nodes (default=False) :param n_permutations: Number of permutations to perform. If -1 use all n_nodes! - int(exclude_orig) permutations :param show_progress_bar: Whether to show progress bar over tested permutations. :return: Dictionary containing summary of validation for each individual graph as well as the permuted graphs """ if not isinstance(methods, (tuple, list)): methods = (methods,) perm_gen = _PermuteNodes(causal_graph, n_permutations=n_permutations, exclude_original_order=exclude_original_order) validation_summary = {FalsifyConst.PERM_GRAPHS: []} for permuted_graph in tqdm(perm_gen, desc="Test permutations of given graph", disable=not show_progress_bar): res = validate_graph( causal_graph=permuted_graph, data=data, methods=methods, ) validation_summary[FalsifyConst.PERM_GRAPHS].append(permuted_graph) for m_name, summary in res.items(): if m_name not in validation_summary: validation_summary[m_name] = [] validation_summary[m_name].append(summary) return validation_summary class _PermuteNodes: def __init__(self, causal_graph: DirectedGraph, exclude_original_order: bool, n_permutations: int): """ Randomly permute the nodes of a given causal graph while keeping the underlying graph structure the same. :param causal_graph: A directed acyclic graph (DAG). :param exclude_original_order: Do not return the original order. :param n_permutations: Return a generator with n_permutations permutations. If n_permutations = -1 (default), we return all n_nodes! - int(exclude_orig) permutations. :return: Copy of causal_graph with nodes randomly permuted. """ self.causal_graph = causal_graph self.exclude_original_order = exclude_original_order self.n_permutations = n_permutations self.max_perms = np.math.factorial(self.causal_graph.number_of_nodes()) - int(self.exclude_original_order) if n_permutations == -1 or n_permutations > self.max_perms: self.it = self.iter_all_permutations() self.length = self.max_perms if self.length > 2**63 - 1: raise ValueError( f"Too many permutations specified. Did you accidently set 'n_permutations'=-1 for a " f"large (>20 nodes) graph? " f"Given graph has {causal_graph.number_of_nodes()} nodes." ) else: self.it = self.iter_random_permutations() self.length = self.n_permutations def iter_all_permutations(self): for i, perm in enumerate(permutations(self.causal_graph.nodes)): if self.exclude_original_order and i == 0: continue mapping = {node: perm[i] for i, node in enumerate(self.causal_graph.nodes)} yield nx.relabel_nodes(self.causal_graph, mapping, copy=True) def iter_random_permutations(self): for _ in range(self.n_permutations): if self.exclude_original_order: is_orig = True while is_orig: perm = list(np.random.permutation(self.causal_graph.nodes)) if perm != list(self.causal_graph.nodes): is_orig = False else: perm = list(np.random.permutation(self.causal_graph.nodes)) mapping = {node: perm[i] for i, node in enumerate(self.causal_graph.nodes)} yield nx.relabel_nodes(self.causal_graph, mapping, copy=True) def __iter__(self): yield from self.it def __len__(self): return self.length def _to_frozenset(x: Union[Set, List, str]): """Converts a set, list or string into a hashable frozenset""" assert ( isinstance(x, Set) or isinstance(x, List) or isinstance(x, str) ), f"{x} must be list, set or str. Got {type(x)} instead!" if isinstance(x, str): return frozenset({x}) return frozenset(x)

"""This module provides functionality to falsify a user-given DAG given observed data. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from dataclasses import dataclass, field from enum import Enum, auto from functools import partial from inspect import getfullargspec from itertools import permutations from typing import Any, Callable, Dict, FrozenSet, List, Optional, Set, Tuple, Union import matplotlib.colors as mcolors import matplotlib.pyplot as plt import networkx as nx import numpy as np import pandas as pd from joblib import Parallel, delayed from tqdm import tqdm import dowhy.gcm.config as config from dowhy.gcm.independence_test import kernel_based from dowhy.gcm.util import plot from dowhy.gcm.util.general import set_random_seed from dowhy.gcm.validation import _get_non_descendants from dowhy.graph import DirectedGraph, get_ordered_predecessors VIOLATION_COLOR = "red" COLORS = list(mcolors.TABLEAU_COLORS.values()) class FalsifyConst(Enum): N_VIOLATIONS = auto() N_TESTS = auto() P_VALUE = auto() P_VALUES = auto() GIVEN_VIOLATIONS = auto() PERM_VIOLATIONS = auto() F_GIVEN_VIOLATIONS = auto() F_PERM_VIOLATIONS = auto() LOCAL_VIOLATION_INSIGHT = auto() METHOD = auto() VALIDATE_LMC = auto() VALIDATE_TPA = auto() VALIDATE_PD = auto() VALIDATE_CM = auto() PERM_GRAPHS = auto() MEC = auto() FALSIFY_METHODS = { FalsifyConst.VALIDATE_LMC: "LMC", FalsifyConst.VALIDATE_PD: "Faithfulness", FalsifyConst.VALIDATE_TPA: "TPa", FalsifyConst.VALIDATE_CM: "Causal Minimality", } @dataclass class _PValuesMemory: """A class to store and access results of independence tests. This class is useful if, e.g. we validate LMC on graph X -> Y -> Z and also on the permuted nodes Z -> Y -> X. Since X ind Z | Y == Z ind X | Y, we only need to perform the conditional independence test once. We use frozensets of nodes since standard (immutable) sets are not hashable. """ p_values: Dict[Tuple[FrozenSet, FrozenSet, FrozenSet], float] = field(default_factory=dict) def add_p_value( self, p_value: Union[None, float], X: Union[Set, List, str], Y: Union[Set, List, str], Z: Optional[Union[Set, List, str]] = None, ): if not Z: Z = set() self.p_values[(_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z))] = p_value def get_p_value( self, X: Union[Set, List, str], Y: Union[Set, List, str], Z: Optional[Union[Set, List, str]] = None ): if not Z: Z = set() # Independence tests are symmetric if (_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z)) in self.p_values: return self.p_values[(_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z))] elif (_to_frozenset(Y), _to_frozenset(X), _to_frozenset(Z)) in self.p_values: return self.p_values[(_to_frozenset(Y), _to_frozenset(X), _to_frozenset(Z))] return def __contains__(self, item: Tuple[Union[Set, List, str], ...]) -> bool: X, Y = (_to_frozenset(i) for i in item[:2]) if len(item) == 2 or item[2] is None: Z = frozenset() else: Z = _to_frozenset(item[2]) return (X, Y, Z) in self.p_values or (Y, X, Z) in self.p_values def validate_lmc( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, include_unconditional: bool = True, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[str, float]]]: """ Validate the local markov condition for a given directed graph. Return number of violations and p values for each node. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory instance, where results of previously performed tests are stored. :param independence_test: Test to use for unconditional independencies (only used if include_unconditional=True) :param conditional_independence_test: Conditional independence test to use for checking local Markov condition. :param significance_level: Significance level for (conditional) independence tests. :param include_unconditional: Test also unconditional independencies of root nodes. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Outcome of validation containing number of violations in the graph and p values/violation for each tuple (node, non_desc) """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_LMC, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, FalsifyConst.P_VALUES: dict(), } # Find out which tests to do triples = _get_parental_triples(causal_graph, include_unconditional) to_test = [] for node, non_desc, parents in triples: if not (node, non_desc, parents) in p_values_memory: to_test.append((node, non_desc, parents)) p_values_memory.add_p_value(None, node, non_desc, parents) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=node, Y=non_desc, Z=parents, independence_test=independence_test, conditional_independence_test=conditional_independence_test, seed=int(seed), ) for (node, non_desc, parents), seed in zip(to_test, random_seeds) ) # Gather results for i, (node, non_desc, parents) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], node, non_desc, parents) # Summarize for node, non_desc, parents in triples: lmc_p_value = p_values_memory.get_p_value(node, non_desc, parents) if lmc_p_value is not None: validation_summary[FalsifyConst.N_TESTS] += 1 validation_summary[FalsifyConst.P_VALUES][(node, non_desc)] = ( lmc_p_value, lmc_p_value <= significance_level, ) if lmc_p_value <= significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_tpa( causal_graph: DirectedGraph, causal_graph_reference: DirectedGraph, include_unconditional: bool = True, ) -> Dict[str, int]: """ Graphical criterion to evaluate which pairwise parental d-separations (parental triples) in `causal_graph` are violated, assuming `causal_graph_reference` is the ground truth graph. If none are violated, then both graphs lie in the same Markov equivalence class. Specifically we test: X _|_G' Y | Z and X _/|_G Y | Z for Y \in ND{X}^G', Z = PA{X}^G :param causal_graph: Causal graph for which to evaluate parental d-separations (G') :param causal_graph_reference: Causal graph where we test if d-separation holds (G) :param include_unconditional: Test also unconditional independencies of root nodes. :return: Validation summary with number of d-separations implied by `causal_graph` and number of times these are violated in the graph `causal_graph_reference`. """ validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_TPA, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, } triples = _get_parental_triples(causal_graph, include_unconditional) for node, non_desc, parents in triples: validation_summary[FalsifyConst.N_TESTS] += 1 if not nx.d_separated(causal_graph_reference, {node}, {non_desc}, set(parents)): validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_pd( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, n_pairs: int = -1, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, adjacent_only: bool = False, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[tuple, float]]]: """ Validate pairwise dependencies (pd) for a given causal graph and data. Test for each node if it is statistically dependent of all its ancestors. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory object, where results of previously performed tests are stored. :param n_pairs: Evaluate dependencies for n_pairs <= all pairs in the DAG. If n_pairs=-1, evaluate dependencies for all (ancestor, node) pairs (default). :param independence_test: Independence test to use for checking pairwise dependencies. :param significance_level: Significance level for independence tests. :param adjacent_only: Only test adjacent node pairs. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Summary dict: {n_violations: int, n_tests: int, p_values: {(ancestor, node): float, ...}} """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() pairs = [(ancestor, node) for node in causal_graph.nodes for ancestor in nx.ancestors(causal_graph, node)] if adjacent_only: pairs = [ (ancestor, node) for (ancestor, node) in pairs if ancestor in get_ordered_predecessors(causal_graph, node) ] if n_pairs < 0: n_pairs = len(pairs) if n_pairs > len(pairs): raise ValueError(f"n_pairs ({n_pairs}) > number of pairs in the DAG ({len(pairs)})") validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_PD, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: n_pairs, FalsifyConst.P_VALUES: dict(), } pair_idxs = np.random.choice(len(pairs), size=n_pairs, replace=False) # Find out which tests to do to_test = [] for pair_idx in pair_idxs: ancestor, node = pairs[pair_idx] if not (ancestor, node) in p_values_memory: to_test.append((ancestor, node)) p_values_memory.add_p_value(None, ancestor, node) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=ancestor, Y=node, Z=None, independence_test=independence_test, conditional_independence_test=None, seed=int(seed), ) for (ancestor, node), seed in zip(to_test, random_seeds) ) # Gather results for i, (ancestor, node) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], ancestor, node) # Summarize for pair_idx in pair_idxs: ancestor, node = pairs[pair_idx] p_value = p_values_memory.get_p_value(ancestor, node) if p_value is not None: validation_summary[FalsifyConst.P_VALUES][(node, ancestor)] = (p_value, p_value > significance_level) if p_value > significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_cm( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[tuple, float]]]: """ Function to test causal minimality of a DAG (see [1], Proposition 6.36). [1] J. Peters, D. Janzing, and B. Schölkopf, Elements of Causal Inference: Foundations and Learning Algorithms. Cambridge, MA, USA: MIT Press, 2017. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory object, where results of previously performed tests are stored. :param independence_test: Independence test to use. :param conditional_independence_test: Conditional independence test to use. :param significance_level: Significance level for independence tests. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Validation summary as dict. """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_CM, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, FalsifyConst.P_VALUES: dict(), } # Find out which tests to do triples = [] to_test = [] for node in causal_graph.nodes: parents = set(causal_graph.predecessors(node)) if parents: for p in parents: other_parents = parents.difference({p}) triples.append((node, p, other_parents)) if not (node, p, other_parents) in p_values_memory: to_test.append((node, p, other_parents)) p_values_memory.add_p_value(None, node, p, other_parents) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=node, Y=p, Z=list(other_parents), independence_test=independence_test, conditional_independence_test=conditional_independence_test, seed=int(seed), ) for (node, p, other_parents), seed in zip(to_test, random_seeds) ) # Gather results for i, (node, p, other_parents) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], node, p, other_parents) # Summarize for node, p, other_parents in triples: p_value = p_values_memory.get_p_value(node, p, other_parents) if p_value is not None: validation_summary[FalsifyConst.N_TESTS] += 1 validation_summary[FalsifyConst.P_VALUES][(node, p, tuple(other_parents))] = ( p_value, p_value > significance_level, ) if p_value > significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def run_validations( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Optional[Union[Callable, Tuple[Callable, ...], List[Callable]]] = partial( validate_lmc, independence_test=kernel_based, conditional_independence_test=kernel_based ), ) -> Dict[str, Dict]: """ Validate a given causal graph using observational data and some given methods. If methods are provided, they must be wrapped in a partial object, with their respective parameters. E.g., if one wants to test the local Markov conditions and the pairwise dependencies (unconditional faithfulness), then call run_validations(G, data, methods=( partial(validate_lmc, independence_test=..., conditional_independence_test=...), partial(validate_pd, independence_test=...), ) ) :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param methods: Method functions wrapped in wrap_partial. E.g. wrap_partial(validate_lmc, data=data, independence_test=..., conditional_independence_test=...). If no methods are provided we run validate_lmc with optional keyword arguments provided to run_validations. :return: Validation summary as dict. """ if not isinstance(methods, (tuple, list)): methods = (methods,) validation_summary = dict() for m in methods: if "data" in getfullargspec(m).args: m = partial(m, data=data) m_summary = m(causal_graph=causal_graph) m_name = m_summary.pop(FalsifyConst.METHOD) validation_summary[m_name] = m_summary return validation_summary @dataclass class EvaluationResult: """ Dataset class containing the evaluation result of falsifying a graph using a node-permutation test. ... Attributes ---------- summary : dict Dictionary containing the summary of the evaluation. significance_level : float Significance level based on which we falsify the given DAG falsifiable : bool Whether the given DAG is falsifiable. falsified : bool Whether the given DAG is falsified. """ summary: dict significance_level: float suggestions: Optional[dict] = None def update_significance_level(self, significance_level: float): """ Update the significance level to decide if we falsify a given DAG. """ self.significance_level = significance_level self.__post_init__() def __post_init__(self): self.can_evaluate = self._can_evaluate() if not self.can_evaluate: self.falsified = None self.falsifiable = None elif ( self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.P_VALUE] > self.significance_level > self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE] ): self.falsified = True self.falsifiable = True elif self.significance_level < self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE]: self.falsified = False self.falsifiable = False else: self.falsified = False self.falsifiable = True def __repr__(self): # DAG Evaluation if self.can_evaluate: decision = " " if self.falsified else " do not " informative = " " if self.falsifiable else " not " frac_MEC = f"{len(self.summary[FalsifyConst.MEC])} / {len(self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.PERM_VIOLATIONS])}" frac_VLMC = f"{self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.GIVEN_VIOLATIONS]}/{self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.N_TESTS]}" p_LMC = self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.P_VALUE] p_dSep = self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE] validation_repr = [ f"The given DAG is{informative}informative because {frac_MEC} of the permutations lie in the Markov", f"equivalence class of the given DAG (p-value: {p_dSep:.2f}).", f"The given DAG violates {frac_VLMC} LMCs and is better than {(1 - p_LMC) * 100:.1f}% of the permuted DAGs (p-value: {p_LMC:.2f}).", f"Based on the provided significance level ({self.significance_level}) and because the DAG is{informative}informative,", f"we{decision}reject the DAG.", ] else: validation_repr = ["Cannot be evaluated!"] # Suggestions suggestion_repr = {} for m in self.suggestions: suggestion_repr[FALSIFY_METHODS[m]] = [ f"Remove edge {node[1]} --> {node[0]}" for (node, r) in self.suggestions[m][FalsifyConst.P_VALUES].items() if r[1] ] return _generate_table(validation_repr, suggestion_repr) def _can_evaluate(self): can_evaluate = True for m in (FalsifyConst.VALIDATE_LMC, FalsifyConst.VALIDATE_TPA): if m not in self.summary: can_evaluate = False return can_evaluate def falsify_graph( causal_graph: DirectedGraph, data: pd.DataFrame, suggestions: bool = False, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, significance_ci: float = 0.05, n_permutations: Optional[int] = None, show_progress_bar: Optional[bool] = None, n_jobs: Optional[int] = None, plot_histogram: bool = False, plot_kwargs: Optional[Dict] = None, ) -> EvaluationResult: """ Falsify a given DAG using observational data. This method returns the result of a permutation-test to falsify a user-given DAG using observational data. To this end we construct the test statistics by testing the violations of local Markov conditions (LMC) implied by the graph using conditional independence (CI) tests. The null is the number of LMC violations of a random node-permutation of the given graph. Our test can be interpreted as whether the given graph is significantly better than random in terms of the CIs it entails. To determine whether a given graph is falsifiable by our metric, we implement a second test, which reports whether given graph is "characteristic" enough in terms of the CIs it entails. For this, we compute how many of the random node permutations lie in the same Markov equivalence class (MEC) as the given graph and conclude that the given graph is falsifiable only if the fraction of permuted DAGs in the same MEC as the given graph is "reasonably" small. The returned EvaluationResult object has two attributes: `falsified` and `falsifiable`: `falsifiable`: The given graph lies in a different MEC than >= 1-`significance_level` of the permuted DAGs `falsified`: The given graph is falsifiable and violates fewer LMCs than >= 1-`significance_level` of the permuted DAGs By default, we only run 1 / `significance_level` permutations as those are enough to falsify a graph with type I error probability `significance_level` at some given `significance_level`. If you are interested in a more exact estimate of the p-value or wish to plot a histogram to see how the given DAG compares to random node permutations, you should set `n_permutations` to some larger value (e.g. 100 or 1000). If `n_permutations=-1` we test on all n_nodes! permutations (the default if plot_histogram=True). Additionally, this method allows to return suggestions to the user (suggestions=True). This is done by testing for violations of causal minimality via `validate_cm`. Related paper: Eulig, E., Mastakouri, A. A., Blöbaum, P., Hardt, M., & Janzing, D. (2023). Toward Falsifying Causal Graphs Using a Permutation-Based Test. https://arxiv.org/abs/2305.09565 :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param suggestions: Provide suggestions to the user. At the moment the only source of suggestions comes from validating causal minimality (using validate_cm). :param independence_test: Independence test to use for checking pairwise independencies. :param conditional_independence_test: Conditional independence test to use. :param significance_level: Significance level for the permutation test. :param significance_ci: Significance level for (conditional) independence tests. :param n_permutations: Number of permutations to perform. If -1 use all n_nodes! permutations. :param show_progress_bar: Whether to show progress bar over permutations. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :param plot_histogram: Plot histogram of results from permutation baseline. :param plot_kwargs: Additional plot arguments to be passed to plot_evaluation_results. :return: EvaluationResult """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs show_progress_bar = config.show_progress_bars if show_progress_bar is None else show_progress_bar p_values_memory = _PValuesMemory() if n_permutations is None: n_permutations = int(1 / significance_level) if not plot_histogram else -1 if not plot_kwargs: plot_kwargs = {} methods = ( partial( validate_lmc, independence_test=independence_test, conditional_independence_test=conditional_independence_test, significance_level=significance_ci, p_values_memory=p_values_memory, n_jobs=n_jobs, ), partial(validate_tpa, causal_graph_reference=causal_graph), ) if suggestions: suggestion_methods = ( partial( validate_cm, independence_test=independence_test, conditional_independence_test=conditional_independence_test, significance_level=significance_ci, p_values_memory=p_values_memory, n_jobs=n_jobs, ), ) else: suggestion_methods = tuple() summary_given = run_validations( causal_graph, data, methods=methods + suggestion_methods, ) summary_perm = _permutation_based( causal_graph, data, methods=methods, exclude_original_order=False, n_permutations=n_permutations, show_progress_bar=show_progress_bar, ) summary = dict() validation_methods = set(summary_perm.keys()) - {FalsifyConst.PERM_GRAPHS} for m in validation_methods: summary[m] = dict() summary[m][FalsifyConst.PERM_VIOLATIONS] = [perm[FalsifyConst.N_VIOLATIONS] for perm in summary_perm[m]] summary[m][FalsifyConst.GIVEN_VIOLATIONS] = summary_given[m][FalsifyConst.N_VIOLATIONS] summary[m][FalsifyConst.N_TESTS] = summary_given[m][FalsifyConst.N_TESTS] summary[m][FalsifyConst.F_PERM_VIOLATIONS] = [ perm[FalsifyConst.N_VIOLATIONS] / perm[FalsifyConst.N_TESTS] for perm in summary_perm[m] ] summary[m][FalsifyConst.F_GIVEN_VIOLATIONS] = ( summary[m][FalsifyConst.GIVEN_VIOLATIONS] / summary[m][FalsifyConst.N_TESTS] ) summary[m][FalsifyConst.P_VALUE] = sum( [ 1 for perm in summary[m][FalsifyConst.F_PERM_VIOLATIONS] if perm <= summary[m][FalsifyConst.F_GIVEN_VIOLATIONS] ] ) / len(summary[m][FalsifyConst.PERM_VIOLATIONS]) if m != FalsifyConst.VALIDATE_TPA: # Append list of violations (node, non_desc) to get local information summary[m][FalsifyConst.LOCAL_VIOLATION_INSIGHT] = summary_given[m][FalsifyConst.P_VALUES] if FalsifyConst.VALIDATE_TPA in summary: summary[FalsifyConst.MEC] = [ summary_perm[FalsifyConst.PERM_GRAPHS][i] for i, v in enumerate(summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.PERM_VIOLATIONS]) if v == 0 ] result = EvaluationResult( summary=summary, significance_level=significance_level, suggestions={m: summary_given[m] for m in summary_given if m not in validation_methods}, ) if plot_histogram: plot_evaluation_results(result, **plot_kwargs) return result def apply_suggestions( causal_graph: DirectedGraph, evaluation_result: EvaluationResult, edges_to_keep: Optional[List[Tuple[Any, Any]]] = None, ): if not hasattr(evaluation_result, "suggestions"): raise ValueError("EvaluationResult object has no attribute suggestions. Please run with suggestion=True!") causal_graph = causal_graph.copy() for m in evaluation_result.suggestions: for node, res in evaluation_result.suggestions[m][FalsifyConst.P_VALUES].items(): edge = (node[1], node[0]) if (res[1] and edges_to_keep is not None and edge not in edges_to_keep) or ( res[1] and edges_to_keep is None ): causal_graph.remove_edge(edge[0], edge[1]) return causal_graph def plot_evaluation_results(evaluation_result, figsize=(8, 3), bins=None, title="", savepath="", display=True): fig, ax = plt.subplots(figsize=figsize) # Plot histograms p_values = "" data = [] labels = [] evaluation_summary = {k: v for k, v in evaluation_result.summary.items() if k != FalsifyConst.MEC} for i, (m, m_summary) in enumerate(evaluation_summary.items()): data.append(m_summary[FalsifyConst.F_PERM_VIOLATIONS]) labels.append(f"Violations of {FALSIFY_METHODS[m]} of permuted DAGs") p_values += f"p-value {FALSIFY_METHODS[m]} = {m_summary[FalsifyConst.P_VALUE]:.2f}\n" ax.hist(data, color=COLORS[: len(evaluation_summary)], bins=bins, alpha=0.5, label=labels, edgecolor="k") # Plot given violations for i, (m, m_summary) in enumerate(evaluation_summary.items()): ylim = ax.get_ylim()[1] ax.plot( [m_summary[FalsifyConst.F_GIVEN_VIOLATIONS]] * 2, [0, ylim], "--", c=COLORS[i], label=f"Violations of {FALSIFY_METHODS[m]} of given DAG", ) ax.set_ylim([0, ylim]) ax.set_xlabel("Fraction of violations") ax.set_ylabel("# Permutations") plt.legend(loc="upper left", bbox_to_anchor=(1.05, 1), borderaxespad=0.0, title=p_values) if title: plt.title(title) plt.tight_layout() if savepath: plt.savefig(savepath, dpi=300, bbox_inches="tight") if display: plt.show() else: plt.close() def plot_local_insights( causal_graph: DirectedGraph, evaluation_result: Union[EvaluationResult, Dict], method: Optional[str] = FalsifyConst.VALIDATE_LMC, ): """ For some given graph and evaluation result plot local violations. :param causal_graph: DiGraph :param evaluation_result: EvaluationResult :param method: Method for which to plot violations """ colors = {} if isinstance(evaluation_result, EvaluationResult) and method in evaluation_result.summary: local_insight_dict = evaluation_result.summary[method][FalsifyConst.LOCAL_VIOLATION_INSIGHT] elif ( isinstance(evaluation_result, EvaluationResult) and hasattr(evaluation_result, "suggestions") and method in evaluation_result.suggestions ): local_insight_dict = evaluation_result.suggestions[method][FalsifyConst.P_VALUES] elif isinstance(evaluation_result, Dict): if method not in evaluation_result: raise ValueError(f"Validation method {method} does not exist in given evaluation_result!") if FalsifyConst.P_VALUES not in evaluation_result[method]: raise ValueError( f"Validation method {method} has no key {FalsifyConst.P_VALUES} where information on local violations " f"are stored!" ) local_insight_dict = evaluation_result[method][FalsifyConst.P_VALUES] else: raise ValueError(f"Cannot plot local violation insights from method {method} in evaluation_result!") for nodes, result in local_insight_dict.items(): if result[1]: if method == FalsifyConst.VALIDATE_LMC: # For LMC we highlight X for which X _|/|_ Y \in ND_X | Pa_X colors[nodes[0]] = VIOLATION_COLOR elif method == FalsifyConst.VALIDATE_PD: # For PD we highlight the edge (if Y\in Anc_X -> X are adjacent) colors[(nodes[1], nodes[0])] = VIOLATION_COLOR elif method == FalsifyConst.VALIDATE_CM: # For causal minimality we highlight the edge Y \in Pa_X -> X colors[(nodes[1], nodes[0])] = VIOLATION_COLOR plot(causal_graph, colors=colors) def _generate_table( validation_repr, suggestion_repr, width=105, validation_name="Falsificaton Summary", suggestion_name="Suggestions" ): # Create Validation header _repr = [ "+" + "-" * (width - 2) + "+\n", "|" + " " * int(np.floor((width - 2 - len(validation_name)) / 2)), validation_name, " " * int(np.ceil((width - 2 - len(validation_name)) / 2)) + "|\n", "+" + "-" * (width - 2) + "+\n", ] # Create Validation summary _repr += [f"| {v_line}" + " " * (width - 3 - len(v_line)) + "|\n" for v_line in validation_repr] # Close Validation _repr += ["+" + "-" * (width - 2) + "+\n"] # Create Suggestions header if suggestion_repr: _repr += [ "|" + " " * int(np.floor((width - 2 - len(suggestion_name)) / 2)), suggestion_name, " " * int(np.ceil((width - 2 - len(suggestion_name)) / 2)) + "|\n", "+" + "-" * (width - 2) + "+\n", ] # Iterate over suggestions for m, suggestions in suggestion_repr.items(): left_col = "| " + m + " |" if not suggestions: right_col = " " + " " * (width - 2 - len(left_col)) + "|\n" _repr += [left_col, right_col] for i, s in enumerate(suggestions): if i > 0: left_col = "| " + " " * len(m) + " |" right_col = " - " + s + " " * (width - 4 - len(s) - len(left_col)) + "|\n" _repr += [left_col, right_col] _repr += ["+" + "-" * (width - 2) + "+\n"] return "".join(_repr)[:-1] def _compute_p_value( data: pd.DataFrame, X: Union[List, str], Y: Union[List, str], Z: Optional[Union[Set, List, str]], independence_test: Optional[Callable[[np.ndarray, np.ndarray], float]], conditional_independence_test: Optional[Callable[[np.ndarray, np.ndarray, np.ndarray], float]], seed: int, ) -> float: """Perform (conditional) independence test and report p-value. If any nodes needed for the test do not exist in the data, this test is skipped and a value of -1 is returned and a warning is raised. :param data: Observations of variables in the DAG. :param X: Variable to test (conditional) independence with Y :param Y: Variable to test (conditional) independence with X :param Z: Set to condition independence test on. Can be empty (None, empty set, or empty list). :param independence_test: Independence test to use. :param conditional_independence_test: Conditional independence test to use. :param seed: Random seed :return: p-value """ set_random_seed(seed) # Test if we have data for X and Y for node in [X, Y]: if not node in data.columns: warnings.warn(f"WARN: Couldn't find data for node {node}. Skip this test.") return if Z: # Test if we have data for Z for node in Z: if not node in data.columns: warnings.warn(f"WARN: Couldn't find data for node {node}. Skip this test.") return p_value = conditional_independence_test(data[X].values, data[Y].values, data[Z].values) else: p_value = independence_test(data[X].values, data[Y].values) return p_value def _get_parental_triples(causal_graph: DirectedGraph, include_unconditional: bool): """ For a given graph collect all parental triples, that is, the triple (X, Y, Z) is a parental triple iff Y is non-descendant of X, and Z are the parents of X (can be empty if include_unconditional=True) """ triples = [] for node in causal_graph.nodes: parents = get_ordered_predecessors(causal_graph, node) non_descendants = _get_non_descendants(causal_graph, node, exclude_parents=True) if (parents or include_unconditional) and non_descendants: for non_desc in non_descendants: triples.append((node, non_desc, parents)) return triples def _permutation_based( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Union[Callable, Tuple[Callable, ...], List[Callable]], exclude_original_order: bool, n_permutations: int, show_progress_bar: bool, ) -> Dict[str, List[Union[DirectedGraph, Dict]]]: """ Generate baseline for node permutations. :param causal_graph: A directed acyclic graph (DAG). :param methods: Validation methods to perform. :param exclude_original_order: Exclude the original ordering of the nodes (default=False) :param n_permutations: Number of permutations to perform. If -1 use all n_nodes! - int(exclude_orig) permutations :param show_progress_bar: Whether to show progress bar over tested permutations. :return: Dictionary containing summary of validation for each individual graph as well as the permuted graphs """ if not isinstance(methods, (tuple, list)): methods = (methods,) perm_gen = _PermuteNodes(causal_graph, n_permutations=n_permutations, exclude_original_order=exclude_original_order) validation_summary = {FalsifyConst.PERM_GRAPHS: []} for permuted_graph in tqdm(perm_gen, desc="Test permutations of given graph", disable=not show_progress_bar): res = run_validations( causal_graph=permuted_graph, data=data, methods=methods, ) validation_summary[FalsifyConst.PERM_GRAPHS].append(permuted_graph) for m_name, summary in res.items(): if m_name not in validation_summary: validation_summary[m_name] = [] validation_summary[m_name].append(summary) return validation_summary class _PermuteNodes: def __init__(self, causal_graph: DirectedGraph, exclude_original_order: bool, n_permutations: int): """ Randomly permute the nodes of a given causal graph while keeping the underlying graph structure the same. :param causal_graph: A directed acyclic graph (DAG). :param exclude_original_order: Do not return the original order. :param n_permutations: Return a generator with n_permutations permutations. If n_permutations = -1 (default), we return all n_nodes! - int(exclude_orig) permutations. :return: Copy of causal_graph with nodes randomly permuted. """ self.causal_graph = causal_graph self.exclude_original_order = exclude_original_order self.n_permutations = n_permutations self.max_perms = np.math.factorial(self.causal_graph.number_of_nodes()) - int(self.exclude_original_order) if n_permutations == -1 or n_permutations > self.max_perms: self.it = self.iter_all_permutations() self.length = self.max_perms if self.length > 2**63 - 1: raise ValueError( f"Too many permutations specified. Did you accidently set 'n_permutations'=-1 for a " f"large (>20 nodes) graph? " f"Given graph has {causal_graph.number_of_nodes()} nodes." ) else: self.it = self.iter_random_permutations() self.length = self.n_permutations def iter_all_permutations(self): for i, perm in enumerate(permutations(self.causal_graph.nodes)): if self.exclude_original_order and i == 0: continue mapping = {node: perm[i] for i, node in enumerate(self.causal_graph.nodes)} yield nx.relabel_nodes(self.causal_graph, mapping, copy=True) def iter_random_permutations(self): for _ in range(self.n_permutations): if self.exclude_original_order: is_orig = True while is_orig: perm = list(np.random.permutation(self.causal_graph.nodes)) if perm != list(self.causal_graph.nodes): is_orig = False else: perm = list(np.random.permutation(self.causal_graph.nodes)) mapping = {node: perm[i] for i, node in enumerate(self.causal_graph.nodes)} yield nx.relabel_nodes(self.causal_graph, mapping, copy=True) def __iter__(self): yield from self.it def __len__(self): return self.length def _to_frozenset(x: Union[Set, List, str]): """Converts a set, list or string into a hashable frozenset""" assert ( isinstance(x, Set) or isinstance(x, List) or isinstance(x, str) ), f"{x} must be list, set or str. Got {type(x)} instead!" if isinstance(x, str): return frozenset({x}) return frozenset(x)

eeulig

379f188d674d5be6d644559f9f54852b87403f7c

4e30c63fe1113e35259e7fb3400a6f09bca4d699

What about the following alternative: Remove all the method specific parameters here and set a default method partial function. This is, something like: ``` def validate_graph( causal_graph: DirectedGraph, methods=( wrap_partial(validate_lmc, data=data, independence_test=kernel_based, conditional_independence_test=kernel_based) ), significance_level: float = 0.05, n_jobs: Optional[int] = None ) ``` Then there is no need to have these mix of parameters for the default behavior and customization, since now the default behavior is also defined as a partial.

bloebp

py-why/dowhy

Remove kwargs from falsify, improve docstrings

This commit updates `gcm/falsify` in the following ways: - Remove the `**kwargs` for `validate_graph` and `falsify_graph`. Instead, validation methods given to those functions should be wrapped in `wrap_partial`, e.g. if we want to validate a graph `G` and test local Markov conditions and pairwise dependencies on observations `data`, we would do ``` validate_graph(G, methods=( wrap_partial(validate_lmc, data=data, independence_test=..., conditional_independence_test=...), wrap_partial(validate_pd, data=data, independence_test=...), ) ) ``` By default (`methods=None`), `validate_graph` tests local Markov conditions and `falsify_graph` tests local Markov conditions and d-separation of parental triples. In this case `data` has to be set and optionally one can provide `independence_test`, `conditional_independence_test`, `significance_level`, and `n_jobs` via the keyword arguments of `validate_graph` / `falsify_graph`. - Use `Enum` for some of the constants - In `_PValuesMemory` the method `add_p_value ` always sets a p-value even if it was set already. This removes the need for the method `clear_placeholders` and allows `p_values` to be set to `-1`, indicating that the CI is not testable (because of missing data for a node, c.f. `_compute_p_value`) - If for some CI: X, Y | Z any of the nodes has no data (column missing in the provided dataframe), we skip this test and raise a warning. - Instead of using the raw number of violations in the plotted histogram and to compute the p-value, we use the relative number of violations. This will result in different p-values only if we have missing data for some nodes (and thus the number of tests is potentially different between different permutations) - Improve docstrings

2023-07-04 13:58:30+00:00

2023-07-18 14:19:10+00:00

dowhy/gcm/falsify.py

"""This module provides functionality to falsify a user-given DAG given observed data. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from dataclasses import dataclass, field from enum import Enum, auto from functools import partial from itertools import permutations from typing import Any, Callable, Dict, FrozenSet, List, Optional, Set, Tuple, Union import matplotlib.colors as mcolors import matplotlib.pyplot as plt import networkx as nx import numpy as np import pandas as pd from joblib import Parallel, delayed from tqdm import tqdm import dowhy.gcm.config as config from dowhy.gcm.independence_test import kernel_based from dowhy.gcm.util import plot from dowhy.gcm.util.general import set_random_seed from dowhy.gcm.validation import _get_non_descendants from dowhy.graph import DirectedGraph, get_ordered_predecessors VIOLATION_COLOR = "red" COLORS = list(mcolors.TABLEAU_COLORS.values()) class FalsifyConst(Enum): N_VIOLATIONS = auto() N_TESTS = auto() P_VALUE = auto() P_VALUES = auto() GIVEN_VIOLATIONS = auto() PERM_VIOLATIONS = auto() F_GIVEN_VIOLATIONS = auto() F_PERM_VIOLATIONS = auto() LOCAL_VIOLATION_INSIGHT = auto() METHOD = auto() VALIDATE_LMC = auto() VALIDATE_TPA = auto() VALIDATE_PD = auto() VALIDATE_CM = auto() PERM_GRAPHS = auto() MEC = auto() FALSIFY_METHODS = { FalsifyConst.VALIDATE_LMC: "LMC", FalsifyConst.VALIDATE_PD: "Faithfulness", FalsifyConst.VALIDATE_TPA: "tPa", FalsifyConst.VALIDATE_CM: "Causal Minimality", } @dataclass class _PValuesMemory: """A class to store and access results of independence tests. This class is useful if, e.g. we validate LMC on graph X -> Y -> Z and also on the permuted nodes Z -> Y -> X. Since X ind Z | Y == Z ind X | Y, we only need to perform the conditional independence test once. We use frozensets of nodes since standard (immutable) sets are not hashable. """ p_values: Dict[Tuple[FrozenSet, FrozenSet, FrozenSet], float] = field(default_factory=dict) def add_p_value( self, p_value: Union[None, float], X: Union[Set, List, str], Y: Union[Set, List, str], Z: Optional[Union[Set, List, str]] = None, ): if not Z: Z = set() self.p_values[(_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z))] = p_value def get_p_value( self, X: Union[Set, List, str], Y: Union[Set, List, str], Z: Optional[Union[Set, List, str]] = None ): if not Z: Z = set() # Independence tests are symmetric if (_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z)) in self.p_values: return self.p_values[(_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z))] elif (_to_frozenset(Y), _to_frozenset(X), _to_frozenset(Z)) in self.p_values: return self.p_values[(_to_frozenset(Y), _to_frozenset(X), _to_frozenset(Z))] return def __contains__(self, item: Tuple[Union[Set, List, str], ...]) -> bool: X, Y = (_to_frozenset(i) for i in item[:2]) if len(item) == 2 or item[2] is None: Z = frozenset() else: Z = _to_frozenset(item[2]) return (X, Y, Z) in self.p_values or (Y, X, Z) in self.p_values def validate_lmc( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, include_unconditional: bool = True, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[str, float]]]: """ Validate the local markov condition for a given directed graph. Return number of violations and p values for each node. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory instance, where results of previously performed tests are stored. :param independence_test: Test to use for unconditional independencies (only used if include_unconditional=True) :param conditional_independence_test: Conditional independence test to use for checking local Markov condition. :param significance_level: Significance level for (conditional) independence tests. :param include_unconditional: Test also unconditional independencies of root nodes. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Outcome of validation containing number of violations in the graph and p values/violation for each tuple (node, non_desc) """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_LMC, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, FalsifyConst.P_VALUES: dict(), } # Find out which tests to do triples = _get_parental_triples(causal_graph, include_unconditional) to_test = [] for node, non_desc, parents in triples: if not (node, non_desc, parents) in p_values_memory: to_test.append((node, non_desc, parents)) p_values_memory.add_p_value(None, node, non_desc, parents) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=node, Y=non_desc, Z=parents, independence_test=independence_test, conditional_independence_test=conditional_independence_test, seed=int(seed), ) for (node, non_desc, parents), seed in zip(to_test, random_seeds) ) # Gather results for i, (node, non_desc, parents) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], node, non_desc, parents) # Summarize for node, non_desc, parents in triples: lmc_p_value = p_values_memory.get_p_value(node, non_desc, parents) if lmc_p_value is not None: validation_summary[FalsifyConst.N_TESTS] += 1 validation_summary[FalsifyConst.P_VALUES][(node, non_desc)] = ( lmc_p_value, lmc_p_value <= significance_level, ) if lmc_p_value <= significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_tpa( causal_graph: DirectedGraph, causal_graph_reference: DirectedGraph, include_unconditional: bool = True, data: Optional[pd.DataFrame] = None, ) -> Dict[str, int]: """ Graphical criterion to evaluate which pairwise parental d-separations (parental triples) in `causal_graph` are violated, assuming `causal_graph_reference` is the ground truth graph. If none are violated, then both graphs lie in the same Markov equivalence class. Specifically we test: X _|_G' Y | Z and X _/|_G Y | Z for Y \in ND{X}^G', Z = PA{X}^G :param causal_graph: Causal graph for which to evaluate parental d-separations (G') :param causal_graph_reference: Causal graph where we test if d-separation holds (G) :param include_unconditional: Test also unconditional independencies of root nodes. :param data: IGNORED! No data is needed for this validation method and thus the data argument is ignored! :return: Validation summary with number of d-separations implied by `causal_graph` and number of times these are violated in the graph `causal_graph_reference`. """ validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_TPA, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, } triples = _get_parental_triples(causal_graph, include_unconditional) for node, non_desc, parents in triples: validation_summary[FalsifyConst.N_TESTS] += 1 if not nx.d_separated(causal_graph_reference, {node}, {non_desc}, set(parents)): validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_pd( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, n_pairs: int = -1, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, adjacent_only: bool = False, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[tuple, float]]]: """ Validate pairwise dependencies (pd) for a given causal graph and data. Test for each node if it is statistically dependent of all its ancestors. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory object, where results of previously performed tests are stored. :param n_pairs: Evaluate dependencies for n_pairs <= all pairs in the DAG. If n_pairs=-1, evaluate dependencies for all (ancestor, node) pairs (default). :param independence_test: Independence test to use for checking pairwise dependencies. :param significance_level: Significance level for independence tests. :param adjacent_only: Only test adjacent node pairs. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Summary dict: {n_violations: int, n_tests: int, p_values: {(ancestor, node): float, ...}} """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() pairs = [(ancestor, node) for node in causal_graph.nodes for ancestor in nx.ancestors(causal_graph, node)] if adjacent_only: pairs = [ (ancestor, node) for (ancestor, node) in pairs if ancestor in get_ordered_predecessors(causal_graph, node) ] if n_pairs < 0: n_pairs = len(pairs) if n_pairs > len(pairs): raise ValueError(f"n_pairs ({n_pairs}) > number of pairs in the DAG ({len(pairs)})") validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_PD, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: n_pairs, FalsifyConst.P_VALUES: dict(), } pair_idxs = np.random.choice(len(pairs), size=n_pairs, replace=False) # Find out which tests to do to_test = [] for pair_idx in pair_idxs: ancestor, node = pairs[pair_idx] if not (ancestor, node) in p_values_memory: to_test.append((ancestor, node)) p_values_memory.add_p_value(None, ancestor, node) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=ancestor, Y=node, Z=None, independence_test=independence_test, conditional_independence_test=None, seed=int(seed), ) for (ancestor, node), seed in zip(to_test, random_seeds) ) # Gather results for i, (ancestor, node) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], ancestor, node) # Summarize for pair_idx in pair_idxs: ancestor, node = pairs[pair_idx] p_value = p_values_memory.get_p_value(ancestor, node) if p_value is not None: validation_summary[FalsifyConst.P_VALUES][(node, ancestor)] = (p_value, p_value > significance_level) if p_value > significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_cm( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[tuple, float]]]: """ Function to test causal minimality of a DAG (see [1], Proposition 6.36). [1] J. Peters, D. Janzing, and B. Schölkopf, Elements of Causal Inference: Foundations and Learning Algorithms. Cambridge, MA, USA: MIT Press, 2017. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory object, where results of previously performed tests are stored. :param independence_test: Independence test to use. :param conditional_independence_test: Conditional independence test to use. :param significance_level: Significance level for independence tests. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Validation summary as dict. """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_CM, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, FalsifyConst.P_VALUES: dict(), } # Find out which tests to do triples = [] to_test = [] for node in causal_graph.nodes: parents = set(causal_graph.predecessors(node)) if parents: for p in parents: other_parents = parents.difference({p}) triples.append((node, p, other_parents)) if not (node, p, other_parents) in p_values_memory: to_test.append((node, p, other_parents)) p_values_memory.add_p_value(None, node, p, other_parents) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=node, Y=p, Z=list(other_parents), independence_test=independence_test, conditional_independence_test=conditional_independence_test, seed=int(seed), ) for (node, p, other_parents), seed in zip(to_test, random_seeds) ) # Gather results for i, (node, p, other_parents) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], node, p, other_parents) # Summarize for node, p, other_parents in triples: p_value = p_values_memory.get_p_value(node, p, other_parents) if p_value is not None: validation_summary[FalsifyConst.N_TESTS] += 1 validation_summary[FalsifyConst.P_VALUES][(node, p, tuple(other_parents))] = ( p_value, p_value > significance_level, ) if p_value > significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_graph( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Optional[Union[Callable, Tuple[Callable, ...], List[Callable]]] = partial( validate_lmc, independence_test=kernel_based, conditional_independence_test=kernel_based ), ) -> Dict[str, Dict]: """ Validate a given causal graph using observational data and some given methods. If methods are provided, they must be wrapped in a partial object, with their respective parameters. E.g., if one wants to test the local Markov conditions and the pairwise dependencies (unconditional faithfulness), then call validate_graph(G, data, methods=( partial(validate_lmc, independence_test=..., conditional_independence_test=...), partial(validate_pd, independence_test=...), ) ) :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param methods: Method functions wrapped in wrap_partial. E.g. wrap_partial(validate_lmc, data=data, independence_test=..., conditional_independence_test=...). If no methods are provided we run validate_lmc with optional keyword arguments provided to validate_graph. :return: Validation summary as dict. """ if not isinstance(methods, (tuple, list)): methods = (methods,) validation_summary = dict() for m in methods: m_summary = m(causal_graph=causal_graph, data=data) m_name = m_summary.pop(FalsifyConst.METHOD) validation_summary[m_name] = m_summary return validation_summary @dataclass class EvaluationResult: """ Dataset class containing the evaluation result of falsifying a graph using a node-permutation test. ... Attributes ---------- summary : dict Dictionary containing the summary of the evaluation. significance_level : float Significance level based on which we falsify the given DAG falsifiable : bool Whether the given DAG is falsifiable. falsified : bool Whether the given DAG is falsified. """ summary: dict significance_level: float suggestions: Optional[dict] = None def update_significance_level(self, significance_level: float): """ Update the significance level to decide if we falsify a given DAG. """ self.significance_level = significance_level self.__post_init__() def __post_init__(self): self.can_evaluate = self._can_evaluate() if not self.can_evaluate: self.falsified = None self.falsifiable = None elif ( self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.P_VALUE] > self.significance_level > self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE] ): self.falsified = True self.falsifiable = True elif self.significance_level < self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE]: self.falsified = False self.falsifiable = False else: self.falsified = False self.falsifiable = True def __repr__(self): # DAG Evaluation if self.can_evaluate: decision = " " if self.falsified else " do not " informative = " " if self.falsifiable else " not " frac_MEC = f"{len(self.summary[FalsifyConst.MEC])} / {len(self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.PERM_VIOLATIONS])}" frac_VLMC = f"{self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.GIVEN_VIOLATIONS]}/{self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.N_TESTS]}" p_LMC = self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.P_VALUE] p_dSep = self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE] validation_repr = [ f"The given DAG is{informative}informative because {frac_MEC} of the permutations lie in the Markov", f"equivalence class of the given DAG (p-value: {p_dSep:.2f}).", f"The given DAG violates {frac_VLMC} LMCs and is better than {(1 - p_LMC) * 100:.1f}% of the permuted DAGs (p-value: {p_LMC:.2f}).", f"Based on the provided significance level ({self.significance_level}) and because the DAG is{informative}informative,", f"we{decision}reject the DAG.", ] else: validation_repr = ["Cannot be evaluated!"] # Suggestions suggestion_repr = {} for m in self.suggestions: suggestion_repr[FALSIFY_METHODS[m]] = [ f"Remove edge {node[1]} --> {node[0]}" for (node, r) in self.suggestions[m][FalsifyConst.P_VALUES].items() if r[1] ] return _generate_table(validation_repr, suggestion_repr) def _can_evaluate(self): can_evaluate = True for m in (FalsifyConst.VALIDATE_LMC, FalsifyConst.VALIDATE_TPA): if m not in self.summary: can_evaluate = False return can_evaluate def falsify_graph( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Optional[Union[Callable, Tuple[Callable, ...]]] = None, suggestion_methods: Optional[Union[Callable, Tuple[Callable, ...]]] = None, suggestions: bool = False, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, significance_ci: float = 0.05, n_permutations: Optional[int] = None, show_progress_bar: Optional[bool] = None, n_jobs: Optional[int] = None, plot_histogram: bool = False, plot_kwargs: Optional[Dict] = None, ) -> EvaluationResult: """ Falsify a given DAG using observational data. This method returns the result of a permutation-test to falsify a user-given DAG using observational data. To this end we construct the test statistics by testing the violations of local Markov conditions (LMC) implied by the graph using conditional independence (CI) tests. The null is the number of LMC violations of a random node-permutation of the given graph. Our test can be interpreted as whether the given graph is significantly better than random in terms of the CIs it entails. To determine whether a given graph is falsifiable by our metric, we implement a second test, which reports whether given graph is "characteristic" enough in terms of the CIs it entails. For this, we compute how many of the random node permutations lie in the same Markov equivalence class (MEC) as the given graph and conclude that the given graph is falsifiable only if the fraction of permuted DAGs in the same MEC as the given graph is "reasonably" small. The returned EvaluationResult object has two attributes: `falsified` and `falsifiable`: `falsifiable`: The given graph lies in a different MEC than >= 1-`significance_level` of the permuted DAGs `falsified`: The given graph is falsifiable and violates fewer LMCs than >= 1-`significance_level` of the permuted DAGs By default, we only run 1 / `significance_level` permutations as those are enough to falsify a graph with type I error probability `significance_level` at some given `significance_level`. If you are interested in a more exact estimate of the p-value of whish to plot a histogram to see how the given DAG compares to random node permutations, you should set `n_permutations` to some larger value (e.g. 100 or 1000). If `n_permutations=-1` we test on all n_nodes! permutations. `methods` and `suggestion_methods` must be wrapped in partial(method, **kwargs) (c.f. `validate_graph`). Related paper: Eulig, E., Mastakouri, A. A., Blöbaum, P., Hardt, M., & Janzing, D. (2023). Toward Falsifying Causal Graphs Using a Permutation-Based Test. https://arxiv.org/abs/2305.09565 :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param methods: Validation methods to perform. :param suggestion_methods: Methods to run on the given graph to provide additional suggestions. :param suggestions: Provide suggestions generated using the `suggestion_methods`. :param independence_test: Independence test to use for checking pairwise independencies. :param conditional_independence_test: Conditional independence test to use. :param significance_level: Significance level for the permutation test. :param significance_ci: Significance level for (conditional) independence tests. :param n_permutations: Number of permutations to perform. If -1 use all n_nodes! permutations. :param show_progress_bar: Whether to show progress bar over permutations. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :param plot_histogram: Plot histogram of results from permutation baseline. :param plot_kwargs: Additional plot arguments to be passed to plot_evaluation_results. :return: EvaluationResult """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs show_progress_bar = config.show_progress_bars if show_progress_bar is None else show_progress_bar p_values_memory = _PValuesMemory() if n_permutations is None: n_permutations = int(1 / significance_level) if not plot_histogram else -1 if not plot_kwargs: plot_kwargs = {} # If no methods are provided, use default ones: validate_lmc, validate_tpa if not methods: methods = ( partial( validate_lmc, independence_test=independence_test, conditional_independence_test=conditional_independence_test, significance_level=significance_ci, p_values_memory=p_values_memory, n_jobs=n_jobs, ), partial(validate_tpa, causal_graph_reference=causal_graph), ) elif isinstance(methods, Callable): methods = (methods,) # If no suggestion methods are provided, but suggestions=True, use default ones: validate_cm if not suggestions: suggestion_methods = tuple() elif suggestions and isinstance(suggestion_methods, Callable): suggestion_methods = (suggestion_methods,) elif suggestions: suggestion_methods = ( partial( validate_cm, independence_test=independence_test, conditional_independence_test=conditional_independence_test, significance_level=significance_ci, p_values_memory=p_values_memory, n_jobs=n_jobs, ), ) summary_given = validate_graph( causal_graph, data, methods=methods + suggestion_methods, ) summary_perm = _permutation_based( causal_graph, data, methods=methods, exclude_original_order=False, n_permutations=n_permutations, show_progress_bar=show_progress_bar, ) summary = dict() validation_methods = set(summary_perm.keys()) - {FalsifyConst.PERM_GRAPHS} for m in validation_methods: summary[m] = dict() summary[m][FalsifyConst.PERM_VIOLATIONS] = [perm[FalsifyConst.N_VIOLATIONS] for perm in summary_perm[m]] summary[m][FalsifyConst.GIVEN_VIOLATIONS] = summary_given[m][FalsifyConst.N_VIOLATIONS] summary[m][FalsifyConst.N_TESTS] = summary_given[m][FalsifyConst.N_TESTS] summary[m][FalsifyConst.F_PERM_VIOLATIONS] = [ perm[FalsifyConst.N_VIOLATIONS] / perm[FalsifyConst.N_TESTS] for perm in summary_perm[m] ] summary[m][FalsifyConst.F_GIVEN_VIOLATIONS] = ( summary[m][FalsifyConst.GIVEN_VIOLATIONS] / summary[m][FalsifyConst.N_TESTS] ) summary[m][FalsifyConst.P_VALUE] = sum( [ 1 for perm in summary[m][FalsifyConst.F_PERM_VIOLATIONS] if perm <= summary[m][FalsifyConst.F_GIVEN_VIOLATIONS] ] ) / len(summary[m][FalsifyConst.PERM_VIOLATIONS]) if m != FalsifyConst.VALIDATE_TPA: # Append list of violations (node, non_desc) to get local information summary[m][FalsifyConst.LOCAL_VIOLATION_INSIGHT] = summary_given[m][FalsifyConst.P_VALUES] if FalsifyConst.VALIDATE_TPA in summary: summary[FalsifyConst.MEC] = [ summary_perm[FalsifyConst.PERM_GRAPHS][i] for i, v in enumerate(summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.PERM_VIOLATIONS]) if v == 0 ] result = EvaluationResult( summary=summary, significance_level=significance_level, suggestions={m: summary_given[m] for m in summary_given if m not in validation_methods}, ) if plot_histogram: plot_evaluation_results(result, **plot_kwargs) return result def apply_suggestions( causal_graph: DirectedGraph, evaluation_result: EvaluationResult, edges_to_keep: Optional[List[Tuple[Any, Any]]] = None, ): if not hasattr(evaluation_result, "suggestions"): raise ValueError("EvaluationResult object has no attribute suggestions. Please run with suggestion=True!") causal_graph = causal_graph.copy() for m in evaluation_result.suggestions: for node, res in evaluation_result.suggestions[m][FalsifyConst.P_VALUES].items(): edge = (node[1], node[0]) if (res[1] and edges_to_keep is not None and edge not in edges_to_keep) or ( res[1] and edges_to_keep is None ): causal_graph.remove_edge(edge[0], edge[1]) return causal_graph def plot_evaluation_results(evaluation_result, figsize=(8, 3), bins=None, title="", savepath="", display=True): fig, ax = plt.subplots(figsize=figsize) # Plot histograms p_values = "" data = [] labels = [] evaluation_summary = {k: v for k, v in evaluation_result.summary.items() if k != FalsifyConst.MEC} for i, (m, m_summary) in enumerate(evaluation_summary.items()): data.append(m_summary[FalsifyConst.F_PERM_VIOLATIONS]) labels.append(f"Violations of {FALSIFY_METHODS[m]} of permuted DAGs") p_values += f"p-value {FALSIFY_METHODS[m]} = {m_summary[FalsifyConst.P_VALUE]:.2f}\n" ax.hist(data, color=COLORS[: len(evaluation_summary)], bins=bins, alpha=0.5, label=labels, edgecolor="k") # Plot given violations for i, (m, m_summary) in enumerate(evaluation_summary.items()): ylim = ax.get_ylim()[1] ax.plot( [m_summary[FalsifyConst.F_GIVEN_VIOLATIONS]] * 2, [0, ylim], "--", c=COLORS[i], label=f"Violations of {FALSIFY_METHODS[m]} of given DAG", ) ax.set_ylim([0, ylim]) ax.set_xlabel("Fraction of violations") ax.set_ylabel("# Permutations") plt.legend(loc="upper left", bbox_to_anchor=(1.05, 1), borderaxespad=0.0, title=p_values) if title: plt.title(title) plt.tight_layout() if savepath: plt.savefig(savepath, dpi=300, bbox_inches="tight") if display: plt.show() else: plt.close() def plot_local_insights( causal_graph: DirectedGraph, evaluation_result: Union[EvaluationResult, Dict], method: Optional[str] = FalsifyConst.VALIDATE_LMC, ): """ For some given graph and evaluation result plot local violations. :param causal_graph: DiGraph :param evaluation_result: EvaluationResult :param method: Method for which to plot violations """ colors = {} if isinstance(evaluation_result, EvaluationResult) and method in evaluation_result.summary: local_insight_dict = evaluation_result.summary[method][FalsifyConst.LOCAL_VIOLATION_INSIGHT] elif ( isinstance(evaluation_result, EvaluationResult) and hasattr(evaluation_result, "suggestions") and method in evaluation_result.suggestions ): local_insight_dict = evaluation_result.suggestions[method][FalsifyConst.P_VALUES] elif isinstance(evaluation_result, Dict): if method not in evaluation_result: raise ValueError(f"Validation method {method} does not exist in given evaluation_result!") if FalsifyConst.P_VALUES not in evaluation_result[method]: raise ValueError( f"Validation method {method} has no key {FalsifyConst.P_VALUES} where information on local violations " f"are stored!" ) local_insight_dict = evaluation_result[method][FalsifyConst.P_VALUES] else: raise ValueError(f"Cannot plot local violation insights from method {method} in evaluation_result!") for nodes, result in local_insight_dict.items(): if result[1]: if method == FalsifyConst.VALIDATE_LMC: # For LMC we highlight X for which X _|/|_ Y \in ND_X | Pa_X colors[nodes[0]] = VIOLATION_COLOR elif method == FalsifyConst.VALIDATE_PD: # For PD we highlight the edge (if Y\in Anc_X -> X are adjacent) colors[(nodes[1], nodes[0])] = VIOLATION_COLOR elif method == FalsifyConst.VALIDATE_CM: # For causal minimality we highlight the edge Y \in Pa_X -> X colors[(nodes[1], nodes[0])] = VIOLATION_COLOR plot(causal_graph, colors=colors) def _generate_table( validation_repr, suggestion_repr, width=105, validation_name="Falsificaton Summary", suggestion_name="Suggestions" ): # Create Validation header _repr = [ "+" + "-" * (width - 2) + "+\n", "|" + " " * int(np.floor((width - 2 - len(validation_name)) / 2)), validation_name, " " * int(np.ceil((width - 2 - len(validation_name)) / 2)) + "|\n", "+" + "-" * (width - 2) + "+\n", ] # Create Validation summary _repr += [f"| {v_line}" + " " * (width - 3 - len(v_line)) + "|\n" for v_line in validation_repr] # Close Validation _repr += ["+" + "-" * (width - 2) + "+\n"] # Create Suggestions header if suggestion_repr: _repr += [ "|" + " " * int(np.floor((width - 2 - len(suggestion_name)) / 2)), suggestion_name, " " * int(np.ceil((width - 2 - len(suggestion_name)) / 2)) + "|\n", "+" + "-" * (width - 2) + "+\n", ] # Iterate over suggestions for m, suggestions in suggestion_repr.items(): left_col = "| " + m + " |" if not suggestions: right_col = " " + " " * (width - 2 - len(left_col)) + "|\n" _repr += [left_col, right_col] for i, s in enumerate(suggestions): if i > 0: left_col = "| " + " " * len(m) + " |" right_col = " - " + s + " " * (width - 4 - len(s) - len(left_col)) + "|\n" _repr += [left_col, right_col] _repr += ["+" + "-" * (width - 2) + "+\n"] return "".join(_repr)[:-1] def _compute_p_value( data: pd.DataFrame, X: Union[List, str], Y: Union[List, str], Z: Optional[Union[Set, List, str]], independence_test: Optional[Callable[[np.ndarray, np.ndarray], float]], conditional_independence_test: Optional[Callable[[np.ndarray, np.ndarray, np.ndarray], float]], seed: int, ) -> float: """Perform (conditional) independence test and report p-value. If any nodes needed for the test do not exist in the data, this test is skipped and a value of -1 is returned and a warning is raised. :param data: Observations of variables in the DAG. :param X: Variable to test (conditional) independence with Y :param Y: Variable to test (conditional) independence with X :param Z: Set to condition independence test on. Can be empty (None, empty set, or empty list). :param independence_test: Independence test to use. :param conditional_independence_test: Conditional independence test to use. :param seed: Random seed :return: p-value """ set_random_seed(seed) # Test if we have data for X and Y for node in [X, Y]: if not node in data.columns: warnings.warn(f"WARN: Couldn't find data for node {node}. Skip this test.") return if Z: # Test if we have data for Z for node in Z: if not node in data.columns: warnings.warn(f"WARN: Couldn't find data for node {node}. Skip this test.") return p_value = conditional_independence_test(data[X].values, data[Y].values, data[Z].values) else: p_value = independence_test(data[X].values, data[Y].values) return p_value def _get_parental_triples(causal_graph: DirectedGraph, include_unconditional: bool): """ For a given graph collect all parental triples, that is, the triple (X, Y, Z) is a parental triple iff Y is non-descendant of X, and Z are the parents of X (can be empty if include_unconditional=True) """ triples = [] for node in causal_graph.nodes: parents = get_ordered_predecessors(causal_graph, node) non_descendants = _get_non_descendants(causal_graph, node, exclude_parents=True) if (parents or include_unconditional) and non_descendants: for non_desc in non_descendants: triples.append((node, non_desc, parents)) return triples def _permutation_based( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Union[Callable, Tuple[Callable, ...], List[Callable]], exclude_original_order: bool, n_permutations: int, show_progress_bar: bool, ) -> Dict[str, List[Union[DirectedGraph, Dict]]]: """ Generate baseline for node permutations. :param causal_graph: A directed acyclic graph (DAG). :param methods: Validation methods to perform. :param exclude_original_order: Exclude the original ordering of the nodes (default=False) :param n_permutations: Number of permutations to perform. If -1 use all n_nodes! - int(exclude_orig) permutations :param show_progress_bar: Whether to show progress bar over tested permutations. :return: Dictionary containing summary of validation for each individual graph as well as the permuted graphs """ if not isinstance(methods, (tuple, list)): methods = (methods,) perm_gen = _PermuteNodes(causal_graph, n_permutations=n_permutations, exclude_original_order=exclude_original_order) validation_summary = {FalsifyConst.PERM_GRAPHS: []} for permuted_graph in tqdm(perm_gen, desc="Test permutations of given graph", disable=not show_progress_bar): res = validate_graph( causal_graph=permuted_graph, data=data, methods=methods, ) validation_summary[FalsifyConst.PERM_GRAPHS].append(permuted_graph) for m_name, summary in res.items(): if m_name not in validation_summary: validation_summary[m_name] = [] validation_summary[m_name].append(summary) return validation_summary class _PermuteNodes: def __init__(self, causal_graph: DirectedGraph, exclude_original_order: bool, n_permutations: int): """ Randomly permute the nodes of a given causal graph while keeping the underlying graph structure the same. :param causal_graph: A directed acyclic graph (DAG). :param exclude_original_order: Do not return the original order. :param n_permutations: Return a generator with n_permutations permutations. If n_permutations = -1 (default), we return all n_nodes! - int(exclude_orig) permutations. :return: Copy of causal_graph with nodes randomly permuted. """ self.causal_graph = causal_graph self.exclude_original_order = exclude_original_order self.n_permutations = n_permutations self.max_perms = np.math.factorial(self.causal_graph.number_of_nodes()) - int(self.exclude_original_order) if n_permutations == -1 or n_permutations > self.max_perms: self.it = self.iter_all_permutations() self.length = self.max_perms if self.length > 2**63 - 1: raise ValueError( f"Too many permutations specified. Did you accidently set 'n_permutations'=-1 for a " f"large (>20 nodes) graph? " f"Given graph has {causal_graph.number_of_nodes()} nodes." ) else: self.it = self.iter_random_permutations() self.length = self.n_permutations def iter_all_permutations(self): for i, perm in enumerate(permutations(self.causal_graph.nodes)): if self.exclude_original_order and i == 0: continue mapping = {node: perm[i] for i, node in enumerate(self.causal_graph.nodes)} yield nx.relabel_nodes(self.causal_graph, mapping, copy=True) def iter_random_permutations(self): for _ in range(self.n_permutations): if self.exclude_original_order: is_orig = True while is_orig: perm = list(np.random.permutation(self.causal_graph.nodes)) if perm != list(self.causal_graph.nodes): is_orig = False else: perm = list(np.random.permutation(self.causal_graph.nodes)) mapping = {node: perm[i] for i, node in enumerate(self.causal_graph.nodes)} yield nx.relabel_nodes(self.causal_graph, mapping, copy=True) def __iter__(self): yield from self.it def __len__(self): return self.length def _to_frozenset(x: Union[Set, List, str]): """Converts a set, list or string into a hashable frozenset""" assert ( isinstance(x, Set) or isinstance(x, List) or isinstance(x, str) ), f"{x} must be list, set or str. Got {type(x)} instead!" if isinstance(x, str): return frozenset({x}) return frozenset(x)

"""This module provides functionality to falsify a user-given DAG given observed data. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from dataclasses import dataclass, field from enum import Enum, auto from functools import partial from inspect import getfullargspec from itertools import permutations from typing import Any, Callable, Dict, FrozenSet, List, Optional, Set, Tuple, Union import matplotlib.colors as mcolors import matplotlib.pyplot as plt import networkx as nx import numpy as np import pandas as pd from joblib import Parallel, delayed from tqdm import tqdm import dowhy.gcm.config as config from dowhy.gcm.independence_test import kernel_based from dowhy.gcm.util import plot from dowhy.gcm.util.general import set_random_seed from dowhy.gcm.validation import _get_non_descendants from dowhy.graph import DirectedGraph, get_ordered_predecessors VIOLATION_COLOR = "red" COLORS = list(mcolors.TABLEAU_COLORS.values()) class FalsifyConst(Enum): N_VIOLATIONS = auto() N_TESTS = auto() P_VALUE = auto() P_VALUES = auto() GIVEN_VIOLATIONS = auto() PERM_VIOLATIONS = auto() F_GIVEN_VIOLATIONS = auto() F_PERM_VIOLATIONS = auto() LOCAL_VIOLATION_INSIGHT = auto() METHOD = auto() VALIDATE_LMC = auto() VALIDATE_TPA = auto() VALIDATE_PD = auto() VALIDATE_CM = auto() PERM_GRAPHS = auto() MEC = auto() FALSIFY_METHODS = { FalsifyConst.VALIDATE_LMC: "LMC", FalsifyConst.VALIDATE_PD: "Faithfulness", FalsifyConst.VALIDATE_TPA: "TPa", FalsifyConst.VALIDATE_CM: "Causal Minimality", } @dataclass class _PValuesMemory: """A class to store and access results of independence tests. This class is useful if, e.g. we validate LMC on graph X -> Y -> Z and also on the permuted nodes Z -> Y -> X. Since X ind Z | Y == Z ind X | Y, we only need to perform the conditional independence test once. We use frozensets of nodes since standard (immutable) sets are not hashable. """ p_values: Dict[Tuple[FrozenSet, FrozenSet, FrozenSet], float] = field(default_factory=dict) def add_p_value( self, p_value: Union[None, float], X: Union[Set, List, str], Y: Union[Set, List, str], Z: Optional[Union[Set, List, str]] = None, ): if not Z: Z = set() self.p_values[(_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z))] = p_value def get_p_value( self, X: Union[Set, List, str], Y: Union[Set, List, str], Z: Optional[Union[Set, List, str]] = None ): if not Z: Z = set() # Independence tests are symmetric if (_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z)) in self.p_values: return self.p_values[(_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z))] elif (_to_frozenset(Y), _to_frozenset(X), _to_frozenset(Z)) in self.p_values: return self.p_values[(_to_frozenset(Y), _to_frozenset(X), _to_frozenset(Z))] return def __contains__(self, item: Tuple[Union[Set, List, str], ...]) -> bool: X, Y = (_to_frozenset(i) for i in item[:2]) if len(item) == 2 or item[2] is None: Z = frozenset() else: Z = _to_frozenset(item[2]) return (X, Y, Z) in self.p_values or (Y, X, Z) in self.p_values def validate_lmc( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, include_unconditional: bool = True, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[str, float]]]: """ Validate the local markov condition for a given directed graph. Return number of violations and p values for each node. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory instance, where results of previously performed tests are stored. :param independence_test: Test to use for unconditional independencies (only used if include_unconditional=True) :param conditional_independence_test: Conditional independence test to use for checking local Markov condition. :param significance_level: Significance level for (conditional) independence tests. :param include_unconditional: Test also unconditional independencies of root nodes. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Outcome of validation containing number of violations in the graph and p values/violation for each tuple (node, non_desc) """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_LMC, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, FalsifyConst.P_VALUES: dict(), } # Find out which tests to do triples = _get_parental_triples(causal_graph, include_unconditional) to_test = [] for node, non_desc, parents in triples: if not (node, non_desc, parents) in p_values_memory: to_test.append((node, non_desc, parents)) p_values_memory.add_p_value(None, node, non_desc, parents) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=node, Y=non_desc, Z=parents, independence_test=independence_test, conditional_independence_test=conditional_independence_test, seed=int(seed), ) for (node, non_desc, parents), seed in zip(to_test, random_seeds) ) # Gather results for i, (node, non_desc, parents) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], node, non_desc, parents) # Summarize for node, non_desc, parents in triples: lmc_p_value = p_values_memory.get_p_value(node, non_desc, parents) if lmc_p_value is not None: validation_summary[FalsifyConst.N_TESTS] += 1 validation_summary[FalsifyConst.P_VALUES][(node, non_desc)] = ( lmc_p_value, lmc_p_value <= significance_level, ) if lmc_p_value <= significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_tpa( causal_graph: DirectedGraph, causal_graph_reference: DirectedGraph, include_unconditional: bool = True, ) -> Dict[str, int]: """ Graphical criterion to evaluate which pairwise parental d-separations (parental triples) in `causal_graph` are violated, assuming `causal_graph_reference` is the ground truth graph. If none are violated, then both graphs lie in the same Markov equivalence class. Specifically we test: X _|_G' Y | Z and X _/|_G Y | Z for Y \in ND{X}^G', Z = PA{X}^G :param causal_graph: Causal graph for which to evaluate parental d-separations (G') :param causal_graph_reference: Causal graph where we test if d-separation holds (G) :param include_unconditional: Test also unconditional independencies of root nodes. :return: Validation summary with number of d-separations implied by `causal_graph` and number of times these are violated in the graph `causal_graph_reference`. """ validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_TPA, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, } triples = _get_parental_triples(causal_graph, include_unconditional) for node, non_desc, parents in triples: validation_summary[FalsifyConst.N_TESTS] += 1 if not nx.d_separated(causal_graph_reference, {node}, {non_desc}, set(parents)): validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_pd( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, n_pairs: int = -1, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, adjacent_only: bool = False, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[tuple, float]]]: """ Validate pairwise dependencies (pd) for a given causal graph and data. Test for each node if it is statistically dependent of all its ancestors. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory object, where results of previously performed tests are stored. :param n_pairs: Evaluate dependencies for n_pairs <= all pairs in the DAG. If n_pairs=-1, evaluate dependencies for all (ancestor, node) pairs (default). :param independence_test: Independence test to use for checking pairwise dependencies. :param significance_level: Significance level for independence tests. :param adjacent_only: Only test adjacent node pairs. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Summary dict: {n_violations: int, n_tests: int, p_values: {(ancestor, node): float, ...}} """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() pairs = [(ancestor, node) for node in causal_graph.nodes for ancestor in nx.ancestors(causal_graph, node)] if adjacent_only: pairs = [ (ancestor, node) for (ancestor, node) in pairs if ancestor in get_ordered_predecessors(causal_graph, node) ] if n_pairs < 0: n_pairs = len(pairs) if n_pairs > len(pairs): raise ValueError(f"n_pairs ({n_pairs}) > number of pairs in the DAG ({len(pairs)})") validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_PD, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: n_pairs, FalsifyConst.P_VALUES: dict(), } pair_idxs = np.random.choice(len(pairs), size=n_pairs, replace=False) # Find out which tests to do to_test = [] for pair_idx in pair_idxs: ancestor, node = pairs[pair_idx] if not (ancestor, node) in p_values_memory: to_test.append((ancestor, node)) p_values_memory.add_p_value(None, ancestor, node) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=ancestor, Y=node, Z=None, independence_test=independence_test, conditional_independence_test=None, seed=int(seed), ) for (ancestor, node), seed in zip(to_test, random_seeds) ) # Gather results for i, (ancestor, node) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], ancestor, node) # Summarize for pair_idx in pair_idxs: ancestor, node = pairs[pair_idx] p_value = p_values_memory.get_p_value(ancestor, node) if p_value is not None: validation_summary[FalsifyConst.P_VALUES][(node, ancestor)] = (p_value, p_value > significance_level) if p_value > significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_cm( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[tuple, float]]]: """ Function to test causal minimality of a DAG (see [1], Proposition 6.36). [1] J. Peters, D. Janzing, and B. Schölkopf, Elements of Causal Inference: Foundations and Learning Algorithms. Cambridge, MA, USA: MIT Press, 2017. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory object, where results of previously performed tests are stored. :param independence_test: Independence test to use. :param conditional_independence_test: Conditional independence test to use. :param significance_level: Significance level for independence tests. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Validation summary as dict. """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_CM, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, FalsifyConst.P_VALUES: dict(), } # Find out which tests to do triples = [] to_test = [] for node in causal_graph.nodes: parents = set(causal_graph.predecessors(node)) if parents: for p in parents: other_parents = parents.difference({p}) triples.append((node, p, other_parents)) if not (node, p, other_parents) in p_values_memory: to_test.append((node, p, other_parents)) p_values_memory.add_p_value(None, node, p, other_parents) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=node, Y=p, Z=list(other_parents), independence_test=independence_test, conditional_independence_test=conditional_independence_test, seed=int(seed), ) for (node, p, other_parents), seed in zip(to_test, random_seeds) ) # Gather results for i, (node, p, other_parents) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], node, p, other_parents) # Summarize for node, p, other_parents in triples: p_value = p_values_memory.get_p_value(node, p, other_parents) if p_value is not None: validation_summary[FalsifyConst.N_TESTS] += 1 validation_summary[FalsifyConst.P_VALUES][(node, p, tuple(other_parents))] = ( p_value, p_value > significance_level, ) if p_value > significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def run_validations( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Optional[Union[Callable, Tuple[Callable, ...], List[Callable]]] = partial( validate_lmc, independence_test=kernel_based, conditional_independence_test=kernel_based ), ) -> Dict[str, Dict]: """ Validate a given causal graph using observational data and some given methods. If methods are provided, they must be wrapped in a partial object, with their respective parameters. E.g., if one wants to test the local Markov conditions and the pairwise dependencies (unconditional faithfulness), then call run_validations(G, data, methods=( partial(validate_lmc, independence_test=..., conditional_independence_test=...), partial(validate_pd, independence_test=...), ) ) :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param methods: Method functions wrapped in wrap_partial. E.g. wrap_partial(validate_lmc, data=data, independence_test=..., conditional_independence_test=...). If no methods are provided we run validate_lmc with optional keyword arguments provided to run_validations. :return: Validation summary as dict. """ if not isinstance(methods, (tuple, list)): methods = (methods,) validation_summary = dict() for m in methods: if "data" in getfullargspec(m).args: m = partial(m, data=data) m_summary = m(causal_graph=causal_graph) m_name = m_summary.pop(FalsifyConst.METHOD) validation_summary[m_name] = m_summary return validation_summary @dataclass class EvaluationResult: """ Dataset class containing the evaluation result of falsifying a graph using a node-permutation test. ... Attributes ---------- summary : dict Dictionary containing the summary of the evaluation. significance_level : float Significance level based on which we falsify the given DAG falsifiable : bool Whether the given DAG is falsifiable. falsified : bool Whether the given DAG is falsified. """ summary: dict significance_level: float suggestions: Optional[dict] = None def update_significance_level(self, significance_level: float): """ Update the significance level to decide if we falsify a given DAG. """ self.significance_level = significance_level self.__post_init__() def __post_init__(self): self.can_evaluate = self._can_evaluate() if not self.can_evaluate: self.falsified = None self.falsifiable = None elif ( self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.P_VALUE] > self.significance_level > self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE] ): self.falsified = True self.falsifiable = True elif self.significance_level < self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE]: self.falsified = False self.falsifiable = False else: self.falsified = False self.falsifiable = True def __repr__(self): # DAG Evaluation if self.can_evaluate: decision = " " if self.falsified else " do not " informative = " " if self.falsifiable else " not " frac_MEC = f"{len(self.summary[FalsifyConst.MEC])} / {len(self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.PERM_VIOLATIONS])}" frac_VLMC = f"{self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.GIVEN_VIOLATIONS]}/{self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.N_TESTS]}" p_LMC = self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.P_VALUE] p_dSep = self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE] validation_repr = [ f"The given DAG is{informative}informative because {frac_MEC} of the permutations lie in the Markov", f"equivalence class of the given DAG (p-value: {p_dSep:.2f}).", f"The given DAG violates {frac_VLMC} LMCs and is better than {(1 - p_LMC) * 100:.1f}% of the permuted DAGs (p-value: {p_LMC:.2f}).", f"Based on the provided significance level ({self.significance_level}) and because the DAG is{informative}informative,", f"we{decision}reject the DAG.", ] else: validation_repr = ["Cannot be evaluated!"] # Suggestions suggestion_repr = {} for m in self.suggestions: suggestion_repr[FALSIFY_METHODS[m]] = [ f"Remove edge {node[1]} --> {node[0]}" for (node, r) in self.suggestions[m][FalsifyConst.P_VALUES].items() if r[1] ] return _generate_table(validation_repr, suggestion_repr) def _can_evaluate(self): can_evaluate = True for m in (FalsifyConst.VALIDATE_LMC, FalsifyConst.VALIDATE_TPA): if m not in self.summary: can_evaluate = False return can_evaluate def falsify_graph( causal_graph: DirectedGraph, data: pd.DataFrame, suggestions: bool = False, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, significance_ci: float = 0.05, n_permutations: Optional[int] = None, show_progress_bar: Optional[bool] = None, n_jobs: Optional[int] = None, plot_histogram: bool = False, plot_kwargs: Optional[Dict] = None, ) -> EvaluationResult: """ Falsify a given DAG using observational data. This method returns the result of a permutation-test to falsify a user-given DAG using observational data. To this end we construct the test statistics by testing the violations of local Markov conditions (LMC) implied by the graph using conditional independence (CI) tests. The null is the number of LMC violations of a random node-permutation of the given graph. Our test can be interpreted as whether the given graph is significantly better than random in terms of the CIs it entails. To determine whether a given graph is falsifiable by our metric, we implement a second test, which reports whether given graph is "characteristic" enough in terms of the CIs it entails. For this, we compute how many of the random node permutations lie in the same Markov equivalence class (MEC) as the given graph and conclude that the given graph is falsifiable only if the fraction of permuted DAGs in the same MEC as the given graph is "reasonably" small. The returned EvaluationResult object has two attributes: `falsified` and `falsifiable`: `falsifiable`: The given graph lies in a different MEC than >= 1-`significance_level` of the permuted DAGs `falsified`: The given graph is falsifiable and violates fewer LMCs than >= 1-`significance_level` of the permuted DAGs By default, we only run 1 / `significance_level` permutations as those are enough to falsify a graph with type I error probability `significance_level` at some given `significance_level`. If you are interested in a more exact estimate of the p-value or wish to plot a histogram to see how the given DAG compares to random node permutations, you should set `n_permutations` to some larger value (e.g. 100 or 1000). If `n_permutations=-1` we test on all n_nodes! permutations (the default if plot_histogram=True). Additionally, this method allows to return suggestions to the user (suggestions=True). This is done by testing for violations of causal minimality via `validate_cm`. Related paper: Eulig, E., Mastakouri, A. A., Blöbaum, P., Hardt, M., & Janzing, D. (2023). Toward Falsifying Causal Graphs Using a Permutation-Based Test. https://arxiv.org/abs/2305.09565 :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param suggestions: Provide suggestions to the user. At the moment the only source of suggestions comes from validating causal minimality (using validate_cm). :param independence_test: Independence test to use for checking pairwise independencies. :param conditional_independence_test: Conditional independence test to use. :param significance_level: Significance level for the permutation test. :param significance_ci: Significance level for (conditional) independence tests. :param n_permutations: Number of permutations to perform. If -1 use all n_nodes! permutations. :param show_progress_bar: Whether to show progress bar over permutations. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :param plot_histogram: Plot histogram of results from permutation baseline. :param plot_kwargs: Additional plot arguments to be passed to plot_evaluation_results. :return: EvaluationResult """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs show_progress_bar = config.show_progress_bars if show_progress_bar is None else show_progress_bar p_values_memory = _PValuesMemory() if n_permutations is None: n_permutations = int(1 / significance_level) if not plot_histogram else -1 if not plot_kwargs: plot_kwargs = {} methods = ( partial( validate_lmc, independence_test=independence_test, conditional_independence_test=conditional_independence_test, significance_level=significance_ci, p_values_memory=p_values_memory, n_jobs=n_jobs, ), partial(validate_tpa, causal_graph_reference=causal_graph), ) if suggestions: suggestion_methods = ( partial( validate_cm, independence_test=independence_test, conditional_independence_test=conditional_independence_test, significance_level=significance_ci, p_values_memory=p_values_memory, n_jobs=n_jobs, ), ) else: suggestion_methods = tuple() summary_given = run_validations( causal_graph, data, methods=methods + suggestion_methods, ) summary_perm = _permutation_based( causal_graph, data, methods=methods, exclude_original_order=False, n_permutations=n_permutations, show_progress_bar=show_progress_bar, ) summary = dict() validation_methods = set(summary_perm.keys()) - {FalsifyConst.PERM_GRAPHS} for m in validation_methods: summary[m] = dict() summary[m][FalsifyConst.PERM_VIOLATIONS] = [perm[FalsifyConst.N_VIOLATIONS] for perm in summary_perm[m]] summary[m][FalsifyConst.GIVEN_VIOLATIONS] = summary_given[m][FalsifyConst.N_VIOLATIONS] summary[m][FalsifyConst.N_TESTS] = summary_given[m][FalsifyConst.N_TESTS] summary[m][FalsifyConst.F_PERM_VIOLATIONS] = [ perm[FalsifyConst.N_VIOLATIONS] / perm[FalsifyConst.N_TESTS] for perm in summary_perm[m] ] summary[m][FalsifyConst.F_GIVEN_VIOLATIONS] = ( summary[m][FalsifyConst.GIVEN_VIOLATIONS] / summary[m][FalsifyConst.N_TESTS] ) summary[m][FalsifyConst.P_VALUE] = sum( [ 1 for perm in summary[m][FalsifyConst.F_PERM_VIOLATIONS] if perm <= summary[m][FalsifyConst.F_GIVEN_VIOLATIONS] ] ) / len(summary[m][FalsifyConst.PERM_VIOLATIONS]) if m != FalsifyConst.VALIDATE_TPA: # Append list of violations (node, non_desc) to get local information summary[m][FalsifyConst.LOCAL_VIOLATION_INSIGHT] = summary_given[m][FalsifyConst.P_VALUES] if FalsifyConst.VALIDATE_TPA in summary: summary[FalsifyConst.MEC] = [ summary_perm[FalsifyConst.PERM_GRAPHS][i] for i, v in enumerate(summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.PERM_VIOLATIONS]) if v == 0 ] result = EvaluationResult( summary=summary, significance_level=significance_level, suggestions={m: summary_given[m] for m in summary_given if m not in validation_methods}, ) if plot_histogram: plot_evaluation_results(result, **plot_kwargs) return result def apply_suggestions( causal_graph: DirectedGraph, evaluation_result: EvaluationResult, edges_to_keep: Optional[List[Tuple[Any, Any]]] = None, ): if not hasattr(evaluation_result, "suggestions"): raise ValueError("EvaluationResult object has no attribute suggestions. Please run with suggestion=True!") causal_graph = causal_graph.copy() for m in evaluation_result.suggestions: for node, res in evaluation_result.suggestions[m][FalsifyConst.P_VALUES].items(): edge = (node[1], node[0]) if (res[1] and edges_to_keep is not None and edge not in edges_to_keep) or ( res[1] and edges_to_keep is None ): causal_graph.remove_edge(edge[0], edge[1]) return causal_graph def plot_evaluation_results(evaluation_result, figsize=(8, 3), bins=None, title="", savepath="", display=True): fig, ax = plt.subplots(figsize=figsize) # Plot histograms p_values = "" data = [] labels = [] evaluation_summary = {k: v for k, v in evaluation_result.summary.items() if k != FalsifyConst.MEC} for i, (m, m_summary) in enumerate(evaluation_summary.items()): data.append(m_summary[FalsifyConst.F_PERM_VIOLATIONS]) labels.append(f"Violations of {FALSIFY_METHODS[m]} of permuted DAGs") p_values += f"p-value {FALSIFY_METHODS[m]} = {m_summary[FalsifyConst.P_VALUE]:.2f}\n" ax.hist(data, color=COLORS[: len(evaluation_summary)], bins=bins, alpha=0.5, label=labels, edgecolor="k") # Plot given violations for i, (m, m_summary) in enumerate(evaluation_summary.items()): ylim = ax.get_ylim()[1] ax.plot( [m_summary[FalsifyConst.F_GIVEN_VIOLATIONS]] * 2, [0, ylim], "--", c=COLORS[i], label=f"Violations of {FALSIFY_METHODS[m]} of given DAG", ) ax.set_ylim([0, ylim]) ax.set_xlabel("Fraction of violations") ax.set_ylabel("# Permutations") plt.legend(loc="upper left", bbox_to_anchor=(1.05, 1), borderaxespad=0.0, title=p_values) if title: plt.title(title) plt.tight_layout() if savepath: plt.savefig(savepath, dpi=300, bbox_inches="tight") if display: plt.show() else: plt.close() def plot_local_insights( causal_graph: DirectedGraph, evaluation_result: Union[EvaluationResult, Dict], method: Optional[str] = FalsifyConst.VALIDATE_LMC, ): """ For some given graph and evaluation result plot local violations. :param causal_graph: DiGraph :param evaluation_result: EvaluationResult :param method: Method for which to plot violations """ colors = {} if isinstance(evaluation_result, EvaluationResult) and method in evaluation_result.summary: local_insight_dict = evaluation_result.summary[method][FalsifyConst.LOCAL_VIOLATION_INSIGHT] elif ( isinstance(evaluation_result, EvaluationResult) and hasattr(evaluation_result, "suggestions") and method in evaluation_result.suggestions ): local_insight_dict = evaluation_result.suggestions[method][FalsifyConst.P_VALUES] elif isinstance(evaluation_result, Dict): if method not in evaluation_result: raise ValueError(f"Validation method {method} does not exist in given evaluation_result!") if FalsifyConst.P_VALUES not in evaluation_result[method]: raise ValueError( f"Validation method {method} has no key {FalsifyConst.P_VALUES} where information on local violations " f"are stored!" ) local_insight_dict = evaluation_result[method][FalsifyConst.P_VALUES] else: raise ValueError(f"Cannot plot local violation insights from method {method} in evaluation_result!") for nodes, result in local_insight_dict.items(): if result[1]: if method == FalsifyConst.VALIDATE_LMC: # For LMC we highlight X for which X _|/|_ Y \in ND_X | Pa_X colors[nodes[0]] = VIOLATION_COLOR elif method == FalsifyConst.VALIDATE_PD: # For PD we highlight the edge (if Y\in Anc_X -> X are adjacent) colors[(nodes[1], nodes[0])] = VIOLATION_COLOR elif method == FalsifyConst.VALIDATE_CM: # For causal minimality we highlight the edge Y \in Pa_X -> X colors[(nodes[1], nodes[0])] = VIOLATION_COLOR plot(causal_graph, colors=colors) def _generate_table( validation_repr, suggestion_repr, width=105, validation_name="Falsificaton Summary", suggestion_name="Suggestions" ): # Create Validation header _repr = [ "+" + "-" * (width - 2) + "+\n", "|" + " " * int(np.floor((width - 2 - len(validation_name)) / 2)), validation_name, " " * int(np.ceil((width - 2 - len(validation_name)) / 2)) + "|\n", "+" + "-" * (width - 2) + "+\n", ] # Create Validation summary _repr += [f"| {v_line}" + " " * (width - 3 - len(v_line)) + "|\n" for v_line in validation_repr] # Close Validation _repr += ["+" + "-" * (width - 2) + "+\n"] # Create Suggestions header if suggestion_repr: _repr += [ "|" + " " * int(np.floor((width - 2 - len(suggestion_name)) / 2)), suggestion_name, " " * int(np.ceil((width - 2 - len(suggestion_name)) / 2)) + "|\n", "+" + "-" * (width - 2) + "+\n", ] # Iterate over suggestions for m, suggestions in suggestion_repr.items(): left_col = "| " + m + " |" if not suggestions: right_col = " " + " " * (width - 2 - len(left_col)) + "|\n" _repr += [left_col, right_col] for i, s in enumerate(suggestions): if i > 0: left_col = "| " + " " * len(m) + " |" right_col = " - " + s + " " * (width - 4 - len(s) - len(left_col)) + "|\n" _repr += [left_col, right_col] _repr += ["+" + "-" * (width - 2) + "+\n"] return "".join(_repr)[:-1] def _compute_p_value( data: pd.DataFrame, X: Union[List, str], Y: Union[List, str], Z: Optional[Union[Set, List, str]], independence_test: Optional[Callable[[np.ndarray, np.ndarray], float]], conditional_independence_test: Optional[Callable[[np.ndarray, np.ndarray, np.ndarray], float]], seed: int, ) -> float: """Perform (conditional) independence test and report p-value. If any nodes needed for the test do not exist in the data, this test is skipped and a value of -1 is returned and a warning is raised. :param data: Observations of variables in the DAG. :param X: Variable to test (conditional) independence with Y :param Y: Variable to test (conditional) independence with X :param Z: Set to condition independence test on. Can be empty (None, empty set, or empty list). :param independence_test: Independence test to use. :param conditional_independence_test: Conditional independence test to use. :param seed: Random seed :return: p-value """ set_random_seed(seed) # Test if we have data for X and Y for node in [X, Y]: if not node in data.columns: warnings.warn(f"WARN: Couldn't find data for node {node}. Skip this test.") return if Z: # Test if we have data for Z for node in Z: if not node in data.columns: warnings.warn(f"WARN: Couldn't find data for node {node}. Skip this test.") return p_value = conditional_independence_test(data[X].values, data[Y].values, data[Z].values) else: p_value = independence_test(data[X].values, data[Y].values) return p_value def _get_parental_triples(causal_graph: DirectedGraph, include_unconditional: bool): """ For a given graph collect all parental triples, that is, the triple (X, Y, Z) is a parental triple iff Y is non-descendant of X, and Z are the parents of X (can be empty if include_unconditional=True) """ triples = [] for node in causal_graph.nodes: parents = get_ordered_predecessors(causal_graph, node) non_descendants = _get_non_descendants(causal_graph, node, exclude_parents=True) if (parents or include_unconditional) and non_descendants: for non_desc in non_descendants: triples.append((node, non_desc, parents)) return triples def _permutation_based( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Union[Callable, Tuple[Callable, ...], List[Callable]], exclude_original_order: bool, n_permutations: int, show_progress_bar: bool, ) -> Dict[str, List[Union[DirectedGraph, Dict]]]: """ Generate baseline for node permutations. :param causal_graph: A directed acyclic graph (DAG). :param methods: Validation methods to perform. :param exclude_original_order: Exclude the original ordering of the nodes (default=False) :param n_permutations: Number of permutations to perform. If -1 use all n_nodes! - int(exclude_orig) permutations :param show_progress_bar: Whether to show progress bar over tested permutations. :return: Dictionary containing summary of validation for each individual graph as well as the permuted graphs """ if not isinstance(methods, (tuple, list)): methods = (methods,) perm_gen = _PermuteNodes(causal_graph, n_permutations=n_permutations, exclude_original_order=exclude_original_order) validation_summary = {FalsifyConst.PERM_GRAPHS: []} for permuted_graph in tqdm(perm_gen, desc="Test permutations of given graph", disable=not show_progress_bar): res = run_validations( causal_graph=permuted_graph, data=data, methods=methods, ) validation_summary[FalsifyConst.PERM_GRAPHS].append(permuted_graph) for m_name, summary in res.items(): if m_name not in validation_summary: validation_summary[m_name] = [] validation_summary[m_name].append(summary) return validation_summary class _PermuteNodes: def __init__(self, causal_graph: DirectedGraph, exclude_original_order: bool, n_permutations: int): """ Randomly permute the nodes of a given causal graph while keeping the underlying graph structure the same. :param causal_graph: A directed acyclic graph (DAG). :param exclude_original_order: Do not return the original order. :param n_permutations: Return a generator with n_permutations permutations. If n_permutations = -1 (default), we return all n_nodes! - int(exclude_orig) permutations. :return: Copy of causal_graph with nodes randomly permuted. """ self.causal_graph = causal_graph self.exclude_original_order = exclude_original_order self.n_permutations = n_permutations self.max_perms = np.math.factorial(self.causal_graph.number_of_nodes()) - int(self.exclude_original_order) if n_permutations == -1 or n_permutations > self.max_perms: self.it = self.iter_all_permutations() self.length = self.max_perms if self.length > 2**63 - 1: raise ValueError( f"Too many permutations specified. Did you accidently set 'n_permutations'=-1 for a " f"large (>20 nodes) graph? " f"Given graph has {causal_graph.number_of_nodes()} nodes." ) else: self.it = self.iter_random_permutations() self.length = self.n_permutations def iter_all_permutations(self): for i, perm in enumerate(permutations(self.causal_graph.nodes)): if self.exclude_original_order and i == 0: continue mapping = {node: perm[i] for i, node in enumerate(self.causal_graph.nodes)} yield nx.relabel_nodes(self.causal_graph, mapping, copy=True) def iter_random_permutations(self): for _ in range(self.n_permutations): if self.exclude_original_order: is_orig = True while is_orig: perm = list(np.random.permutation(self.causal_graph.nodes)) if perm != list(self.causal_graph.nodes): is_orig = False else: perm = list(np.random.permutation(self.causal_graph.nodes)) mapping = {node: perm[i] for i, node in enumerate(self.causal_graph.nodes)} yield nx.relabel_nodes(self.causal_graph, mapping, copy=True) def __iter__(self): yield from self.it def __len__(self): return self.length def _to_frozenset(x: Union[Set, List, str]): """Converts a set, list or string into a hashable frozenset""" assert ( isinstance(x, Set) or isinstance(x, List) or isinstance(x, str) ), f"{x} must be list, set or str. Got {type(x)} instead!" if isinstance(x, str): return frozenset({x}) return frozenset(x)

eeulig

379f188d674d5be6d644559f9f54852b87403f7c

4e30c63fe1113e35259e7fb3400a6f09bca4d699

Can you add this reference to the notebook as well in case it is not in there?

bloebp

py-why/dowhy

Remove kwargs from falsify, improve docstrings

This commit updates `gcm/falsify` in the following ways: - Remove the `**kwargs` for `validate_graph` and `falsify_graph`. Instead, validation methods given to those functions should be wrapped in `wrap_partial`, e.g. if we want to validate a graph `G` and test local Markov conditions and pairwise dependencies on observations `data`, we would do ``` validate_graph(G, methods=( wrap_partial(validate_lmc, data=data, independence_test=..., conditional_independence_test=...), wrap_partial(validate_pd, data=data, independence_test=...), ) ) ``` By default (`methods=None`), `validate_graph` tests local Markov conditions and `falsify_graph` tests local Markov conditions and d-separation of parental triples. In this case `data` has to be set and optionally one can provide `independence_test`, `conditional_independence_test`, `significance_level`, and `n_jobs` via the keyword arguments of `validate_graph` / `falsify_graph`. - Use `Enum` for some of the constants - In `_PValuesMemory` the method `add_p_value ` always sets a p-value even if it was set already. This removes the need for the method `clear_placeholders` and allows `p_values` to be set to `-1`, indicating that the CI is not testable (because of missing data for a node, c.f. `_compute_p_value`) - If for some CI: X, Y | Z any of the nodes has no data (column missing in the provided dataframe), we skip this test and raise a warning. - Instead of using the raw number of violations in the plotted histogram and to compute the p-value, we use the relative number of violations. This will result in different p-values only if we have missing data for some nodes (and thus the number of tests is potentially different between different permutations) - Improve docstrings

2023-07-04 13:58:30+00:00

2023-07-18 14:19:10+00:00

dowhy/gcm/falsify.py

"""This module provides functionality to falsify a user-given DAG given observed data. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from dataclasses import dataclass, field from enum import Enum, auto from functools import partial from itertools import permutations from typing import Any, Callable, Dict, FrozenSet, List, Optional, Set, Tuple, Union import matplotlib.colors as mcolors import matplotlib.pyplot as plt import networkx as nx import numpy as np import pandas as pd from joblib import Parallel, delayed from tqdm import tqdm import dowhy.gcm.config as config from dowhy.gcm.independence_test import kernel_based from dowhy.gcm.util import plot from dowhy.gcm.util.general import set_random_seed from dowhy.gcm.validation import _get_non_descendants from dowhy.graph import DirectedGraph, get_ordered_predecessors VIOLATION_COLOR = "red" COLORS = list(mcolors.TABLEAU_COLORS.values()) class FalsifyConst(Enum): N_VIOLATIONS = auto() N_TESTS = auto() P_VALUE = auto() P_VALUES = auto() GIVEN_VIOLATIONS = auto() PERM_VIOLATIONS = auto() F_GIVEN_VIOLATIONS = auto() F_PERM_VIOLATIONS = auto() LOCAL_VIOLATION_INSIGHT = auto() METHOD = auto() VALIDATE_LMC = auto() VALIDATE_TPA = auto() VALIDATE_PD = auto() VALIDATE_CM = auto() PERM_GRAPHS = auto() MEC = auto() FALSIFY_METHODS = { FalsifyConst.VALIDATE_LMC: "LMC", FalsifyConst.VALIDATE_PD: "Faithfulness", FalsifyConst.VALIDATE_TPA: "tPa", FalsifyConst.VALIDATE_CM: "Causal Minimality", } @dataclass class _PValuesMemory: """A class to store and access results of independence tests. This class is useful if, e.g. we validate LMC on graph X -> Y -> Z and also on the permuted nodes Z -> Y -> X. Since X ind Z | Y == Z ind X | Y, we only need to perform the conditional independence test once. We use frozensets of nodes since standard (immutable) sets are not hashable. """ p_values: Dict[Tuple[FrozenSet, FrozenSet, FrozenSet], float] = field(default_factory=dict) def add_p_value( self, p_value: Union[None, float], X: Union[Set, List, str], Y: Union[Set, List, str], Z: Optional[Union[Set, List, str]] = None, ): if not Z: Z = set() self.p_values[(_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z))] = p_value def get_p_value( self, X: Union[Set, List, str], Y: Union[Set, List, str], Z: Optional[Union[Set, List, str]] = None ): if not Z: Z = set() # Independence tests are symmetric if (_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z)) in self.p_values: return self.p_values[(_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z))] elif (_to_frozenset(Y), _to_frozenset(X), _to_frozenset(Z)) in self.p_values: return self.p_values[(_to_frozenset(Y), _to_frozenset(X), _to_frozenset(Z))] return def __contains__(self, item: Tuple[Union[Set, List, str], ...]) -> bool: X, Y = (_to_frozenset(i) for i in item[:2]) if len(item) == 2 or item[2] is None: Z = frozenset() else: Z = _to_frozenset(item[2]) return (X, Y, Z) in self.p_values or (Y, X, Z) in self.p_values def validate_lmc( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, include_unconditional: bool = True, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[str, float]]]: """ Validate the local markov condition for a given directed graph. Return number of violations and p values for each node. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory instance, where results of previously performed tests are stored. :param independence_test: Test to use for unconditional independencies (only used if include_unconditional=True) :param conditional_independence_test: Conditional independence test to use for checking local Markov condition. :param significance_level: Significance level for (conditional) independence tests. :param include_unconditional: Test also unconditional independencies of root nodes. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Outcome of validation containing number of violations in the graph and p values/violation for each tuple (node, non_desc) """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_LMC, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, FalsifyConst.P_VALUES: dict(), } # Find out which tests to do triples = _get_parental_triples(causal_graph, include_unconditional) to_test = [] for node, non_desc, parents in triples: if not (node, non_desc, parents) in p_values_memory: to_test.append((node, non_desc, parents)) p_values_memory.add_p_value(None, node, non_desc, parents) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=node, Y=non_desc, Z=parents, independence_test=independence_test, conditional_independence_test=conditional_independence_test, seed=int(seed), ) for (node, non_desc, parents), seed in zip(to_test, random_seeds) ) # Gather results for i, (node, non_desc, parents) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], node, non_desc, parents) # Summarize for node, non_desc, parents in triples: lmc_p_value = p_values_memory.get_p_value(node, non_desc, parents) if lmc_p_value is not None: validation_summary[FalsifyConst.N_TESTS] += 1 validation_summary[FalsifyConst.P_VALUES][(node, non_desc)] = ( lmc_p_value, lmc_p_value <= significance_level, ) if lmc_p_value <= significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_tpa( causal_graph: DirectedGraph, causal_graph_reference: DirectedGraph, include_unconditional: bool = True, data: Optional[pd.DataFrame] = None, ) -> Dict[str, int]: """ Graphical criterion to evaluate which pairwise parental d-separations (parental triples) in `causal_graph` are violated, assuming `causal_graph_reference` is the ground truth graph. If none are violated, then both graphs lie in the same Markov equivalence class. Specifically we test: X _|_G' Y | Z and X _/|_G Y | Z for Y \in ND{X}^G', Z = PA{X}^G :param causal_graph: Causal graph for which to evaluate parental d-separations (G') :param causal_graph_reference: Causal graph where we test if d-separation holds (G) :param include_unconditional: Test also unconditional independencies of root nodes. :param data: IGNORED! No data is needed for this validation method and thus the data argument is ignored! :return: Validation summary with number of d-separations implied by `causal_graph` and number of times these are violated in the graph `causal_graph_reference`. """ validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_TPA, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, } triples = _get_parental_triples(causal_graph, include_unconditional) for node, non_desc, parents in triples: validation_summary[FalsifyConst.N_TESTS] += 1 if not nx.d_separated(causal_graph_reference, {node}, {non_desc}, set(parents)): validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_pd( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, n_pairs: int = -1, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, adjacent_only: bool = False, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[tuple, float]]]: """ Validate pairwise dependencies (pd) for a given causal graph and data. Test for each node if it is statistically dependent of all its ancestors. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory object, where results of previously performed tests are stored. :param n_pairs: Evaluate dependencies for n_pairs <= all pairs in the DAG. If n_pairs=-1, evaluate dependencies for all (ancestor, node) pairs (default). :param independence_test: Independence test to use for checking pairwise dependencies. :param significance_level: Significance level for independence tests. :param adjacent_only: Only test adjacent node pairs. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Summary dict: {n_violations: int, n_tests: int, p_values: {(ancestor, node): float, ...}} """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() pairs = [(ancestor, node) for node in causal_graph.nodes for ancestor in nx.ancestors(causal_graph, node)] if adjacent_only: pairs = [ (ancestor, node) for (ancestor, node) in pairs if ancestor in get_ordered_predecessors(causal_graph, node) ] if n_pairs < 0: n_pairs = len(pairs) if n_pairs > len(pairs): raise ValueError(f"n_pairs ({n_pairs}) > number of pairs in the DAG ({len(pairs)})") validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_PD, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: n_pairs, FalsifyConst.P_VALUES: dict(), } pair_idxs = np.random.choice(len(pairs), size=n_pairs, replace=False) # Find out which tests to do to_test = [] for pair_idx in pair_idxs: ancestor, node = pairs[pair_idx] if not (ancestor, node) in p_values_memory: to_test.append((ancestor, node)) p_values_memory.add_p_value(None, ancestor, node) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=ancestor, Y=node, Z=None, independence_test=independence_test, conditional_independence_test=None, seed=int(seed), ) for (ancestor, node), seed in zip(to_test, random_seeds) ) # Gather results for i, (ancestor, node) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], ancestor, node) # Summarize for pair_idx in pair_idxs: ancestor, node = pairs[pair_idx] p_value = p_values_memory.get_p_value(ancestor, node) if p_value is not None: validation_summary[FalsifyConst.P_VALUES][(node, ancestor)] = (p_value, p_value > significance_level) if p_value > significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_cm( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[tuple, float]]]: """ Function to test causal minimality of a DAG (see [1], Proposition 6.36). [1] J. Peters, D. Janzing, and B. Schölkopf, Elements of Causal Inference: Foundations and Learning Algorithms. Cambridge, MA, USA: MIT Press, 2017. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory object, where results of previously performed tests are stored. :param independence_test: Independence test to use. :param conditional_independence_test: Conditional independence test to use. :param significance_level: Significance level for independence tests. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Validation summary as dict. """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_CM, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, FalsifyConst.P_VALUES: dict(), } # Find out which tests to do triples = [] to_test = [] for node in causal_graph.nodes: parents = set(causal_graph.predecessors(node)) if parents: for p in parents: other_parents = parents.difference({p}) triples.append((node, p, other_parents)) if not (node, p, other_parents) in p_values_memory: to_test.append((node, p, other_parents)) p_values_memory.add_p_value(None, node, p, other_parents) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=node, Y=p, Z=list(other_parents), independence_test=independence_test, conditional_independence_test=conditional_independence_test, seed=int(seed), ) for (node, p, other_parents), seed in zip(to_test, random_seeds) ) # Gather results for i, (node, p, other_parents) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], node, p, other_parents) # Summarize for node, p, other_parents in triples: p_value = p_values_memory.get_p_value(node, p, other_parents) if p_value is not None: validation_summary[FalsifyConst.N_TESTS] += 1 validation_summary[FalsifyConst.P_VALUES][(node, p, tuple(other_parents))] = ( p_value, p_value > significance_level, ) if p_value > significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_graph( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Optional[Union[Callable, Tuple[Callable, ...], List[Callable]]] = partial( validate_lmc, independence_test=kernel_based, conditional_independence_test=kernel_based ), ) -> Dict[str, Dict]: """ Validate a given causal graph using observational data and some given methods. If methods are provided, they must be wrapped in a partial object, with their respective parameters. E.g., if one wants to test the local Markov conditions and the pairwise dependencies (unconditional faithfulness), then call validate_graph(G, data, methods=( partial(validate_lmc, independence_test=..., conditional_independence_test=...), partial(validate_pd, independence_test=...), ) ) :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param methods: Method functions wrapped in wrap_partial. E.g. wrap_partial(validate_lmc, data=data, independence_test=..., conditional_independence_test=...). If no methods are provided we run validate_lmc with optional keyword arguments provided to validate_graph. :return: Validation summary as dict. """ if not isinstance(methods, (tuple, list)): methods = (methods,) validation_summary = dict() for m in methods: m_summary = m(causal_graph=causal_graph, data=data) m_name = m_summary.pop(FalsifyConst.METHOD) validation_summary[m_name] = m_summary return validation_summary @dataclass class EvaluationResult: """ Dataset class containing the evaluation result of falsifying a graph using a node-permutation test. ... Attributes ---------- summary : dict Dictionary containing the summary of the evaluation. significance_level : float Significance level based on which we falsify the given DAG falsifiable : bool Whether the given DAG is falsifiable. falsified : bool Whether the given DAG is falsified. """ summary: dict significance_level: float suggestions: Optional[dict] = None def update_significance_level(self, significance_level: float): """ Update the significance level to decide if we falsify a given DAG. """ self.significance_level = significance_level self.__post_init__() def __post_init__(self): self.can_evaluate = self._can_evaluate() if not self.can_evaluate: self.falsified = None self.falsifiable = None elif ( self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.P_VALUE] > self.significance_level > self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE] ): self.falsified = True self.falsifiable = True elif self.significance_level < self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE]: self.falsified = False self.falsifiable = False else: self.falsified = False self.falsifiable = True def __repr__(self): # DAG Evaluation if self.can_evaluate: decision = " " if self.falsified else " do not " informative = " " if self.falsifiable else " not " frac_MEC = f"{len(self.summary[FalsifyConst.MEC])} / {len(self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.PERM_VIOLATIONS])}" frac_VLMC = f"{self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.GIVEN_VIOLATIONS]}/{self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.N_TESTS]}" p_LMC = self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.P_VALUE] p_dSep = self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE] validation_repr = [ f"The given DAG is{informative}informative because {frac_MEC} of the permutations lie in the Markov", f"equivalence class of the given DAG (p-value: {p_dSep:.2f}).", f"The given DAG violates {frac_VLMC} LMCs and is better than {(1 - p_LMC) * 100:.1f}% of the permuted DAGs (p-value: {p_LMC:.2f}).", f"Based on the provided significance level ({self.significance_level}) and because the DAG is{informative}informative,", f"we{decision}reject the DAG.", ] else: validation_repr = ["Cannot be evaluated!"] # Suggestions suggestion_repr = {} for m in self.suggestions: suggestion_repr[FALSIFY_METHODS[m]] = [ f"Remove edge {node[1]} --> {node[0]}" for (node, r) in self.suggestions[m][FalsifyConst.P_VALUES].items() if r[1] ] return _generate_table(validation_repr, suggestion_repr) def _can_evaluate(self): can_evaluate = True for m in (FalsifyConst.VALIDATE_LMC, FalsifyConst.VALIDATE_TPA): if m not in self.summary: can_evaluate = False return can_evaluate def falsify_graph( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Optional[Union[Callable, Tuple[Callable, ...]]] = None, suggestion_methods: Optional[Union[Callable, Tuple[Callable, ...]]] = None, suggestions: bool = False, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, significance_ci: float = 0.05, n_permutations: Optional[int] = None, show_progress_bar: Optional[bool] = None, n_jobs: Optional[int] = None, plot_histogram: bool = False, plot_kwargs: Optional[Dict] = None, ) -> EvaluationResult: """ Falsify a given DAG using observational data. This method returns the result of a permutation-test to falsify a user-given DAG using observational data. To this end we construct the test statistics by testing the violations of local Markov conditions (LMC) implied by the graph using conditional independence (CI) tests. The null is the number of LMC violations of a random node-permutation of the given graph. Our test can be interpreted as whether the given graph is significantly better than random in terms of the CIs it entails. To determine whether a given graph is falsifiable by our metric, we implement a second test, which reports whether given graph is "characteristic" enough in terms of the CIs it entails. For this, we compute how many of the random node permutations lie in the same Markov equivalence class (MEC) as the given graph and conclude that the given graph is falsifiable only if the fraction of permuted DAGs in the same MEC as the given graph is "reasonably" small. The returned EvaluationResult object has two attributes: `falsified` and `falsifiable`: `falsifiable`: The given graph lies in a different MEC than >= 1-`significance_level` of the permuted DAGs `falsified`: The given graph is falsifiable and violates fewer LMCs than >= 1-`significance_level` of the permuted DAGs By default, we only run 1 / `significance_level` permutations as those are enough to falsify a graph with type I error probability `significance_level` at some given `significance_level`. If you are interested in a more exact estimate of the p-value of whish to plot a histogram to see how the given DAG compares to random node permutations, you should set `n_permutations` to some larger value (e.g. 100 or 1000). If `n_permutations=-1` we test on all n_nodes! permutations. `methods` and `suggestion_methods` must be wrapped in partial(method, **kwargs) (c.f. `validate_graph`). Related paper: Eulig, E., Mastakouri, A. A., Blöbaum, P., Hardt, M., & Janzing, D. (2023). Toward Falsifying Causal Graphs Using a Permutation-Based Test. https://arxiv.org/abs/2305.09565 :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param methods: Validation methods to perform. :param suggestion_methods: Methods to run on the given graph to provide additional suggestions. :param suggestions: Provide suggestions generated using the `suggestion_methods`. :param independence_test: Independence test to use for checking pairwise independencies. :param conditional_independence_test: Conditional independence test to use. :param significance_level: Significance level for the permutation test. :param significance_ci: Significance level for (conditional) independence tests. :param n_permutations: Number of permutations to perform. If -1 use all n_nodes! permutations. :param show_progress_bar: Whether to show progress bar over permutations. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :param plot_histogram: Plot histogram of results from permutation baseline. :param plot_kwargs: Additional plot arguments to be passed to plot_evaluation_results. :return: EvaluationResult """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs show_progress_bar = config.show_progress_bars if show_progress_bar is None else show_progress_bar p_values_memory = _PValuesMemory() if n_permutations is None: n_permutations = int(1 / significance_level) if not plot_histogram else -1 if not plot_kwargs: plot_kwargs = {} # If no methods are provided, use default ones: validate_lmc, validate_tpa if not methods: methods = ( partial( validate_lmc, independence_test=independence_test, conditional_independence_test=conditional_independence_test, significance_level=significance_ci, p_values_memory=p_values_memory, n_jobs=n_jobs, ), partial(validate_tpa, causal_graph_reference=causal_graph), ) elif isinstance(methods, Callable): methods = (methods,) # If no suggestion methods are provided, but suggestions=True, use default ones: validate_cm if not suggestions: suggestion_methods = tuple() elif suggestions and isinstance(suggestion_methods, Callable): suggestion_methods = (suggestion_methods,) elif suggestions: suggestion_methods = ( partial( validate_cm, independence_test=independence_test, conditional_independence_test=conditional_independence_test, significance_level=significance_ci, p_values_memory=p_values_memory, n_jobs=n_jobs, ), ) summary_given = validate_graph( causal_graph, data, methods=methods + suggestion_methods, ) summary_perm = _permutation_based( causal_graph, data, methods=methods, exclude_original_order=False, n_permutations=n_permutations, show_progress_bar=show_progress_bar, ) summary = dict() validation_methods = set(summary_perm.keys()) - {FalsifyConst.PERM_GRAPHS} for m in validation_methods: summary[m] = dict() summary[m][FalsifyConst.PERM_VIOLATIONS] = [perm[FalsifyConst.N_VIOLATIONS] for perm in summary_perm[m]] summary[m][FalsifyConst.GIVEN_VIOLATIONS] = summary_given[m][FalsifyConst.N_VIOLATIONS] summary[m][FalsifyConst.N_TESTS] = summary_given[m][FalsifyConst.N_TESTS] summary[m][FalsifyConst.F_PERM_VIOLATIONS] = [ perm[FalsifyConst.N_VIOLATIONS] / perm[FalsifyConst.N_TESTS] for perm in summary_perm[m] ] summary[m][FalsifyConst.F_GIVEN_VIOLATIONS] = ( summary[m][FalsifyConst.GIVEN_VIOLATIONS] / summary[m][FalsifyConst.N_TESTS] ) summary[m][FalsifyConst.P_VALUE] = sum( [ 1 for perm in summary[m][FalsifyConst.F_PERM_VIOLATIONS] if perm <= summary[m][FalsifyConst.F_GIVEN_VIOLATIONS] ] ) / len(summary[m][FalsifyConst.PERM_VIOLATIONS]) if m != FalsifyConst.VALIDATE_TPA: # Append list of violations (node, non_desc) to get local information summary[m][FalsifyConst.LOCAL_VIOLATION_INSIGHT] = summary_given[m][FalsifyConst.P_VALUES] if FalsifyConst.VALIDATE_TPA in summary: summary[FalsifyConst.MEC] = [ summary_perm[FalsifyConst.PERM_GRAPHS][i] for i, v in enumerate(summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.PERM_VIOLATIONS]) if v == 0 ] result = EvaluationResult( summary=summary, significance_level=significance_level, suggestions={m: summary_given[m] for m in summary_given if m not in validation_methods}, ) if plot_histogram: plot_evaluation_results(result, **plot_kwargs) return result def apply_suggestions( causal_graph: DirectedGraph, evaluation_result: EvaluationResult, edges_to_keep: Optional[List[Tuple[Any, Any]]] = None, ): if not hasattr(evaluation_result, "suggestions"): raise ValueError("EvaluationResult object has no attribute suggestions. Please run with suggestion=True!") causal_graph = causal_graph.copy() for m in evaluation_result.suggestions: for node, res in evaluation_result.suggestions[m][FalsifyConst.P_VALUES].items(): edge = (node[1], node[0]) if (res[1] and edges_to_keep is not None and edge not in edges_to_keep) or ( res[1] and edges_to_keep is None ): causal_graph.remove_edge(edge[0], edge[1]) return causal_graph def plot_evaluation_results(evaluation_result, figsize=(8, 3), bins=None, title="", savepath="", display=True): fig, ax = plt.subplots(figsize=figsize) # Plot histograms p_values = "" data = [] labels = [] evaluation_summary = {k: v for k, v in evaluation_result.summary.items() if k != FalsifyConst.MEC} for i, (m, m_summary) in enumerate(evaluation_summary.items()): data.append(m_summary[FalsifyConst.F_PERM_VIOLATIONS]) labels.append(f"Violations of {FALSIFY_METHODS[m]} of permuted DAGs") p_values += f"p-value {FALSIFY_METHODS[m]} = {m_summary[FalsifyConst.P_VALUE]:.2f}\n" ax.hist(data, color=COLORS[: len(evaluation_summary)], bins=bins, alpha=0.5, label=labels, edgecolor="k") # Plot given violations for i, (m, m_summary) in enumerate(evaluation_summary.items()): ylim = ax.get_ylim()[1] ax.plot( [m_summary[FalsifyConst.F_GIVEN_VIOLATIONS]] * 2, [0, ylim], "--", c=COLORS[i], label=f"Violations of {FALSIFY_METHODS[m]} of given DAG", ) ax.set_ylim([0, ylim]) ax.set_xlabel("Fraction of violations") ax.set_ylabel("# Permutations") plt.legend(loc="upper left", bbox_to_anchor=(1.05, 1), borderaxespad=0.0, title=p_values) if title: plt.title(title) plt.tight_layout() if savepath: plt.savefig(savepath, dpi=300, bbox_inches="tight") if display: plt.show() else: plt.close() def plot_local_insights( causal_graph: DirectedGraph, evaluation_result: Union[EvaluationResult, Dict], method: Optional[str] = FalsifyConst.VALIDATE_LMC, ): """ For some given graph and evaluation result plot local violations. :param causal_graph: DiGraph :param evaluation_result: EvaluationResult :param method: Method for which to plot violations """ colors = {} if isinstance(evaluation_result, EvaluationResult) and method in evaluation_result.summary: local_insight_dict = evaluation_result.summary[method][FalsifyConst.LOCAL_VIOLATION_INSIGHT] elif ( isinstance(evaluation_result, EvaluationResult) and hasattr(evaluation_result, "suggestions") and method in evaluation_result.suggestions ): local_insight_dict = evaluation_result.suggestions[method][FalsifyConst.P_VALUES] elif isinstance(evaluation_result, Dict): if method not in evaluation_result: raise ValueError(f"Validation method {method} does not exist in given evaluation_result!") if FalsifyConst.P_VALUES not in evaluation_result[method]: raise ValueError( f"Validation method {method} has no key {FalsifyConst.P_VALUES} where information on local violations " f"are stored!" ) local_insight_dict = evaluation_result[method][FalsifyConst.P_VALUES] else: raise ValueError(f"Cannot plot local violation insights from method {method} in evaluation_result!") for nodes, result in local_insight_dict.items(): if result[1]: if method == FalsifyConst.VALIDATE_LMC: # For LMC we highlight X for which X _|/|_ Y \in ND_X | Pa_X colors[nodes[0]] = VIOLATION_COLOR elif method == FalsifyConst.VALIDATE_PD: # For PD we highlight the edge (if Y\in Anc_X -> X are adjacent) colors[(nodes[1], nodes[0])] = VIOLATION_COLOR elif method == FalsifyConst.VALIDATE_CM: # For causal minimality we highlight the edge Y \in Pa_X -> X colors[(nodes[1], nodes[0])] = VIOLATION_COLOR plot(causal_graph, colors=colors) def _generate_table( validation_repr, suggestion_repr, width=105, validation_name="Falsificaton Summary", suggestion_name="Suggestions" ): # Create Validation header _repr = [ "+" + "-" * (width - 2) + "+\n", "|" + " " * int(np.floor((width - 2 - len(validation_name)) / 2)), validation_name, " " * int(np.ceil((width - 2 - len(validation_name)) / 2)) + "|\n", "+" + "-" * (width - 2) + "+\n", ] # Create Validation summary _repr += [f"| {v_line}" + " " * (width - 3 - len(v_line)) + "|\n" for v_line in validation_repr] # Close Validation _repr += ["+" + "-" * (width - 2) + "+\n"] # Create Suggestions header if suggestion_repr: _repr += [ "|" + " " * int(np.floor((width - 2 - len(suggestion_name)) / 2)), suggestion_name, " " * int(np.ceil((width - 2 - len(suggestion_name)) / 2)) + "|\n", "+" + "-" * (width - 2) + "+\n", ] # Iterate over suggestions for m, suggestions in suggestion_repr.items(): left_col = "| " + m + " |" if not suggestions: right_col = " " + " " * (width - 2 - len(left_col)) + "|\n" _repr += [left_col, right_col] for i, s in enumerate(suggestions): if i > 0: left_col = "| " + " " * len(m) + " |" right_col = " - " + s + " " * (width - 4 - len(s) - len(left_col)) + "|\n" _repr += [left_col, right_col] _repr += ["+" + "-" * (width - 2) + "+\n"] return "".join(_repr)[:-1] def _compute_p_value( data: pd.DataFrame, X: Union[List, str], Y: Union[List, str], Z: Optional[Union[Set, List, str]], independence_test: Optional[Callable[[np.ndarray, np.ndarray], float]], conditional_independence_test: Optional[Callable[[np.ndarray, np.ndarray, np.ndarray], float]], seed: int, ) -> float: """Perform (conditional) independence test and report p-value. If any nodes needed for the test do not exist in the data, this test is skipped and a value of -1 is returned and a warning is raised. :param data: Observations of variables in the DAG. :param X: Variable to test (conditional) independence with Y :param Y: Variable to test (conditional) independence with X :param Z: Set to condition independence test on. Can be empty (None, empty set, or empty list). :param independence_test: Independence test to use. :param conditional_independence_test: Conditional independence test to use. :param seed: Random seed :return: p-value """ set_random_seed(seed) # Test if we have data for X and Y for node in [X, Y]: if not node in data.columns: warnings.warn(f"WARN: Couldn't find data for node {node}. Skip this test.") return if Z: # Test if we have data for Z for node in Z: if not node in data.columns: warnings.warn(f"WARN: Couldn't find data for node {node}. Skip this test.") return p_value = conditional_independence_test(data[X].values, data[Y].values, data[Z].values) else: p_value = independence_test(data[X].values, data[Y].values) return p_value def _get_parental_triples(causal_graph: DirectedGraph, include_unconditional: bool): """ For a given graph collect all parental triples, that is, the triple (X, Y, Z) is a parental triple iff Y is non-descendant of X, and Z are the parents of X (can be empty if include_unconditional=True) """ triples = [] for node in causal_graph.nodes: parents = get_ordered_predecessors(causal_graph, node) non_descendants = _get_non_descendants(causal_graph, node, exclude_parents=True) if (parents or include_unconditional) and non_descendants: for non_desc in non_descendants: triples.append((node, non_desc, parents)) return triples def _permutation_based( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Union[Callable, Tuple[Callable, ...], List[Callable]], exclude_original_order: bool, n_permutations: int, show_progress_bar: bool, ) -> Dict[str, List[Union[DirectedGraph, Dict]]]: """ Generate baseline for node permutations. :param causal_graph: A directed acyclic graph (DAG). :param methods: Validation methods to perform. :param exclude_original_order: Exclude the original ordering of the nodes (default=False) :param n_permutations: Number of permutations to perform. If -1 use all n_nodes! - int(exclude_orig) permutations :param show_progress_bar: Whether to show progress bar over tested permutations. :return: Dictionary containing summary of validation for each individual graph as well as the permuted graphs """ if not isinstance(methods, (tuple, list)): methods = (methods,) perm_gen = _PermuteNodes(causal_graph, n_permutations=n_permutations, exclude_original_order=exclude_original_order) validation_summary = {FalsifyConst.PERM_GRAPHS: []} for permuted_graph in tqdm(perm_gen, desc="Test permutations of given graph", disable=not show_progress_bar): res = validate_graph( causal_graph=permuted_graph, data=data, methods=methods, ) validation_summary[FalsifyConst.PERM_GRAPHS].append(permuted_graph) for m_name, summary in res.items(): if m_name not in validation_summary: validation_summary[m_name] = [] validation_summary[m_name].append(summary) return validation_summary class _PermuteNodes: def __init__(self, causal_graph: DirectedGraph, exclude_original_order: bool, n_permutations: int): """ Randomly permute the nodes of a given causal graph while keeping the underlying graph structure the same. :param causal_graph: A directed acyclic graph (DAG). :param exclude_original_order: Do not return the original order. :param n_permutations: Return a generator with n_permutations permutations. If n_permutations = -1 (default), we return all n_nodes! - int(exclude_orig) permutations. :return: Copy of causal_graph with nodes randomly permuted. """ self.causal_graph = causal_graph self.exclude_original_order = exclude_original_order self.n_permutations = n_permutations self.max_perms = np.math.factorial(self.causal_graph.number_of_nodes()) - int(self.exclude_original_order) if n_permutations == -1 or n_permutations > self.max_perms: self.it = self.iter_all_permutations() self.length = self.max_perms if self.length > 2**63 - 1: raise ValueError( f"Too many permutations specified. Did you accidently set 'n_permutations'=-1 for a " f"large (>20 nodes) graph? " f"Given graph has {causal_graph.number_of_nodes()} nodes." ) else: self.it = self.iter_random_permutations() self.length = self.n_permutations def iter_all_permutations(self): for i, perm in enumerate(permutations(self.causal_graph.nodes)): if self.exclude_original_order and i == 0: continue mapping = {node: perm[i] for i, node in enumerate(self.causal_graph.nodes)} yield nx.relabel_nodes(self.causal_graph, mapping, copy=True) def iter_random_permutations(self): for _ in range(self.n_permutations): if self.exclude_original_order: is_orig = True while is_orig: perm = list(np.random.permutation(self.causal_graph.nodes)) if perm != list(self.causal_graph.nodes): is_orig = False else: perm = list(np.random.permutation(self.causal_graph.nodes)) mapping = {node: perm[i] for i, node in enumerate(self.causal_graph.nodes)} yield nx.relabel_nodes(self.causal_graph, mapping, copy=True) def __iter__(self): yield from self.it def __len__(self): return self.length def _to_frozenset(x: Union[Set, List, str]): """Converts a set, list or string into a hashable frozenset""" assert ( isinstance(x, Set) or isinstance(x, List) or isinstance(x, str) ), f"{x} must be list, set or str. Got {type(x)} instead!" if isinstance(x, str): return frozenset({x}) return frozenset(x)

"""This module provides functionality to falsify a user-given DAG given observed data. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from dataclasses import dataclass, field from enum import Enum, auto from functools import partial from inspect import getfullargspec from itertools import permutations from typing import Any, Callable, Dict, FrozenSet, List, Optional, Set, Tuple, Union import matplotlib.colors as mcolors import matplotlib.pyplot as plt import networkx as nx import numpy as np import pandas as pd from joblib import Parallel, delayed from tqdm import tqdm import dowhy.gcm.config as config from dowhy.gcm.independence_test import kernel_based from dowhy.gcm.util import plot from dowhy.gcm.util.general import set_random_seed from dowhy.gcm.validation import _get_non_descendants from dowhy.graph import DirectedGraph, get_ordered_predecessors VIOLATION_COLOR = "red" COLORS = list(mcolors.TABLEAU_COLORS.values()) class FalsifyConst(Enum): N_VIOLATIONS = auto() N_TESTS = auto() P_VALUE = auto() P_VALUES = auto() GIVEN_VIOLATIONS = auto() PERM_VIOLATIONS = auto() F_GIVEN_VIOLATIONS = auto() F_PERM_VIOLATIONS = auto() LOCAL_VIOLATION_INSIGHT = auto() METHOD = auto() VALIDATE_LMC = auto() VALIDATE_TPA = auto() VALIDATE_PD = auto() VALIDATE_CM = auto() PERM_GRAPHS = auto() MEC = auto() FALSIFY_METHODS = { FalsifyConst.VALIDATE_LMC: "LMC", FalsifyConst.VALIDATE_PD: "Faithfulness", FalsifyConst.VALIDATE_TPA: "TPa", FalsifyConst.VALIDATE_CM: "Causal Minimality", } @dataclass class _PValuesMemory: """A class to store and access results of independence tests. This class is useful if, e.g. we validate LMC on graph X -> Y -> Z and also on the permuted nodes Z -> Y -> X. Since X ind Z | Y == Z ind X | Y, we only need to perform the conditional independence test once. We use frozensets of nodes since standard (immutable) sets are not hashable. """ p_values: Dict[Tuple[FrozenSet, FrozenSet, FrozenSet], float] = field(default_factory=dict) def add_p_value( self, p_value: Union[None, float], X: Union[Set, List, str], Y: Union[Set, List, str], Z: Optional[Union[Set, List, str]] = None, ): if not Z: Z = set() self.p_values[(_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z))] = p_value def get_p_value( self, X: Union[Set, List, str], Y: Union[Set, List, str], Z: Optional[Union[Set, List, str]] = None ): if not Z: Z = set() # Independence tests are symmetric if (_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z)) in self.p_values: return self.p_values[(_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z))] elif (_to_frozenset(Y), _to_frozenset(X), _to_frozenset(Z)) in self.p_values: return self.p_values[(_to_frozenset(Y), _to_frozenset(X), _to_frozenset(Z))] return def __contains__(self, item: Tuple[Union[Set, List, str], ...]) -> bool: X, Y = (_to_frozenset(i) for i in item[:2]) if len(item) == 2 or item[2] is None: Z = frozenset() else: Z = _to_frozenset(item[2]) return (X, Y, Z) in self.p_values or (Y, X, Z) in self.p_values def validate_lmc( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, include_unconditional: bool = True, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[str, float]]]: """ Validate the local markov condition for a given directed graph. Return number of violations and p values for each node. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory instance, where results of previously performed tests are stored. :param independence_test: Test to use for unconditional independencies (only used if include_unconditional=True) :param conditional_independence_test: Conditional independence test to use for checking local Markov condition. :param significance_level: Significance level for (conditional) independence tests. :param include_unconditional: Test also unconditional independencies of root nodes. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Outcome of validation containing number of violations in the graph and p values/violation for each tuple (node, non_desc) """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_LMC, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, FalsifyConst.P_VALUES: dict(), } # Find out which tests to do triples = _get_parental_triples(causal_graph, include_unconditional) to_test = [] for node, non_desc, parents in triples: if not (node, non_desc, parents) in p_values_memory: to_test.append((node, non_desc, parents)) p_values_memory.add_p_value(None, node, non_desc, parents) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=node, Y=non_desc, Z=parents, independence_test=independence_test, conditional_independence_test=conditional_independence_test, seed=int(seed), ) for (node, non_desc, parents), seed in zip(to_test, random_seeds) ) # Gather results for i, (node, non_desc, parents) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], node, non_desc, parents) # Summarize for node, non_desc, parents in triples: lmc_p_value = p_values_memory.get_p_value(node, non_desc, parents) if lmc_p_value is not None: validation_summary[FalsifyConst.N_TESTS] += 1 validation_summary[FalsifyConst.P_VALUES][(node, non_desc)] = ( lmc_p_value, lmc_p_value <= significance_level, ) if lmc_p_value <= significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_tpa( causal_graph: DirectedGraph, causal_graph_reference: DirectedGraph, include_unconditional: bool = True, ) -> Dict[str, int]: """ Graphical criterion to evaluate which pairwise parental d-separations (parental triples) in `causal_graph` are violated, assuming `causal_graph_reference` is the ground truth graph. If none are violated, then both graphs lie in the same Markov equivalence class. Specifically we test: X _|_G' Y | Z and X _/|_G Y | Z for Y \in ND{X}^G', Z = PA{X}^G :param causal_graph: Causal graph for which to evaluate parental d-separations (G') :param causal_graph_reference: Causal graph where we test if d-separation holds (G) :param include_unconditional: Test also unconditional independencies of root nodes. :return: Validation summary with number of d-separations implied by `causal_graph` and number of times these are violated in the graph `causal_graph_reference`. """ validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_TPA, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, } triples = _get_parental_triples(causal_graph, include_unconditional) for node, non_desc, parents in triples: validation_summary[FalsifyConst.N_TESTS] += 1 if not nx.d_separated(causal_graph_reference, {node}, {non_desc}, set(parents)): validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_pd( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, n_pairs: int = -1, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, adjacent_only: bool = False, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[tuple, float]]]: """ Validate pairwise dependencies (pd) for a given causal graph and data. Test for each node if it is statistically dependent of all its ancestors. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory object, where results of previously performed tests are stored. :param n_pairs: Evaluate dependencies for n_pairs <= all pairs in the DAG. If n_pairs=-1, evaluate dependencies for all (ancestor, node) pairs (default). :param independence_test: Independence test to use for checking pairwise dependencies. :param significance_level: Significance level for independence tests. :param adjacent_only: Only test adjacent node pairs. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Summary dict: {n_violations: int, n_tests: int, p_values: {(ancestor, node): float, ...}} """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() pairs = [(ancestor, node) for node in causal_graph.nodes for ancestor in nx.ancestors(causal_graph, node)] if adjacent_only: pairs = [ (ancestor, node) for (ancestor, node) in pairs if ancestor in get_ordered_predecessors(causal_graph, node) ] if n_pairs < 0: n_pairs = len(pairs) if n_pairs > len(pairs): raise ValueError(f"n_pairs ({n_pairs}) > number of pairs in the DAG ({len(pairs)})") validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_PD, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: n_pairs, FalsifyConst.P_VALUES: dict(), } pair_idxs = np.random.choice(len(pairs), size=n_pairs, replace=False) # Find out which tests to do to_test = [] for pair_idx in pair_idxs: ancestor, node = pairs[pair_idx] if not (ancestor, node) in p_values_memory: to_test.append((ancestor, node)) p_values_memory.add_p_value(None, ancestor, node) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=ancestor, Y=node, Z=None, independence_test=independence_test, conditional_independence_test=None, seed=int(seed), ) for (ancestor, node), seed in zip(to_test, random_seeds) ) # Gather results for i, (ancestor, node) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], ancestor, node) # Summarize for pair_idx in pair_idxs: ancestor, node = pairs[pair_idx] p_value = p_values_memory.get_p_value(ancestor, node) if p_value is not None: validation_summary[FalsifyConst.P_VALUES][(node, ancestor)] = (p_value, p_value > significance_level) if p_value > significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_cm( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[tuple, float]]]: """ Function to test causal minimality of a DAG (see [1], Proposition 6.36). [1] J. Peters, D. Janzing, and B. Schölkopf, Elements of Causal Inference: Foundations and Learning Algorithms. Cambridge, MA, USA: MIT Press, 2017. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory object, where results of previously performed tests are stored. :param independence_test: Independence test to use. :param conditional_independence_test: Conditional independence test to use. :param significance_level: Significance level for independence tests. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Validation summary as dict. """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_CM, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, FalsifyConst.P_VALUES: dict(), } # Find out which tests to do triples = [] to_test = [] for node in causal_graph.nodes: parents = set(causal_graph.predecessors(node)) if parents: for p in parents: other_parents = parents.difference({p}) triples.append((node, p, other_parents)) if not (node, p, other_parents) in p_values_memory: to_test.append((node, p, other_parents)) p_values_memory.add_p_value(None, node, p, other_parents) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=node, Y=p, Z=list(other_parents), independence_test=independence_test, conditional_independence_test=conditional_independence_test, seed=int(seed), ) for (node, p, other_parents), seed in zip(to_test, random_seeds) ) # Gather results for i, (node, p, other_parents) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], node, p, other_parents) # Summarize for node, p, other_parents in triples: p_value = p_values_memory.get_p_value(node, p, other_parents) if p_value is not None: validation_summary[FalsifyConst.N_TESTS] += 1 validation_summary[FalsifyConst.P_VALUES][(node, p, tuple(other_parents))] = ( p_value, p_value > significance_level, ) if p_value > significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def run_validations( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Optional[Union[Callable, Tuple[Callable, ...], List[Callable]]] = partial( validate_lmc, independence_test=kernel_based, conditional_independence_test=kernel_based ), ) -> Dict[str, Dict]: """ Validate a given causal graph using observational data and some given methods. If methods are provided, they must be wrapped in a partial object, with their respective parameters. E.g., if one wants to test the local Markov conditions and the pairwise dependencies (unconditional faithfulness), then call run_validations(G, data, methods=( partial(validate_lmc, independence_test=..., conditional_independence_test=...), partial(validate_pd, independence_test=...), ) ) :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param methods: Method functions wrapped in wrap_partial. E.g. wrap_partial(validate_lmc, data=data, independence_test=..., conditional_independence_test=...). If no methods are provided we run validate_lmc with optional keyword arguments provided to run_validations. :return: Validation summary as dict. """ if not isinstance(methods, (tuple, list)): methods = (methods,) validation_summary = dict() for m in methods: if "data" in getfullargspec(m).args: m = partial(m, data=data) m_summary = m(causal_graph=causal_graph) m_name = m_summary.pop(FalsifyConst.METHOD) validation_summary[m_name] = m_summary return validation_summary @dataclass class EvaluationResult: """ Dataset class containing the evaluation result of falsifying a graph using a node-permutation test. ... Attributes ---------- summary : dict Dictionary containing the summary of the evaluation. significance_level : float Significance level based on which we falsify the given DAG falsifiable : bool Whether the given DAG is falsifiable. falsified : bool Whether the given DAG is falsified. """ summary: dict significance_level: float suggestions: Optional[dict] = None def update_significance_level(self, significance_level: float): """ Update the significance level to decide if we falsify a given DAG. """ self.significance_level = significance_level self.__post_init__() def __post_init__(self): self.can_evaluate = self._can_evaluate() if not self.can_evaluate: self.falsified = None self.falsifiable = None elif ( self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.P_VALUE] > self.significance_level > self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE] ): self.falsified = True self.falsifiable = True elif self.significance_level < self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE]: self.falsified = False self.falsifiable = False else: self.falsified = False self.falsifiable = True def __repr__(self): # DAG Evaluation if self.can_evaluate: decision = " " if self.falsified else " do not " informative = " " if self.falsifiable else " not " frac_MEC = f"{len(self.summary[FalsifyConst.MEC])} / {len(self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.PERM_VIOLATIONS])}" frac_VLMC = f"{self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.GIVEN_VIOLATIONS]}/{self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.N_TESTS]}" p_LMC = self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.P_VALUE] p_dSep = self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE] validation_repr = [ f"The given DAG is{informative}informative because {frac_MEC} of the permutations lie in the Markov", f"equivalence class of the given DAG (p-value: {p_dSep:.2f}).", f"The given DAG violates {frac_VLMC} LMCs and is better than {(1 - p_LMC) * 100:.1f}% of the permuted DAGs (p-value: {p_LMC:.2f}).", f"Based on the provided significance level ({self.significance_level}) and because the DAG is{informative}informative,", f"we{decision}reject the DAG.", ] else: validation_repr = ["Cannot be evaluated!"] # Suggestions suggestion_repr = {} for m in self.suggestions: suggestion_repr[FALSIFY_METHODS[m]] = [ f"Remove edge {node[1]} --> {node[0]}" for (node, r) in self.suggestions[m][FalsifyConst.P_VALUES].items() if r[1] ] return _generate_table(validation_repr, suggestion_repr) def _can_evaluate(self): can_evaluate = True for m in (FalsifyConst.VALIDATE_LMC, FalsifyConst.VALIDATE_TPA): if m not in self.summary: can_evaluate = False return can_evaluate def falsify_graph( causal_graph: DirectedGraph, data: pd.DataFrame, suggestions: bool = False, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, significance_ci: float = 0.05, n_permutations: Optional[int] = None, show_progress_bar: Optional[bool] = None, n_jobs: Optional[int] = None, plot_histogram: bool = False, plot_kwargs: Optional[Dict] = None, ) -> EvaluationResult: """ Falsify a given DAG using observational data. This method returns the result of a permutation-test to falsify a user-given DAG using observational data. To this end we construct the test statistics by testing the violations of local Markov conditions (LMC) implied by the graph using conditional independence (CI) tests. The null is the number of LMC violations of a random node-permutation of the given graph. Our test can be interpreted as whether the given graph is significantly better than random in terms of the CIs it entails. To determine whether a given graph is falsifiable by our metric, we implement a second test, which reports whether given graph is "characteristic" enough in terms of the CIs it entails. For this, we compute how many of the random node permutations lie in the same Markov equivalence class (MEC) as the given graph and conclude that the given graph is falsifiable only if the fraction of permuted DAGs in the same MEC as the given graph is "reasonably" small. The returned EvaluationResult object has two attributes: `falsified` and `falsifiable`: `falsifiable`: The given graph lies in a different MEC than >= 1-`significance_level` of the permuted DAGs `falsified`: The given graph is falsifiable and violates fewer LMCs than >= 1-`significance_level` of the permuted DAGs By default, we only run 1 / `significance_level` permutations as those are enough to falsify a graph with type I error probability `significance_level` at some given `significance_level`. If you are interested in a more exact estimate of the p-value or wish to plot a histogram to see how the given DAG compares to random node permutations, you should set `n_permutations` to some larger value (e.g. 100 or 1000). If `n_permutations=-1` we test on all n_nodes! permutations (the default if plot_histogram=True). Additionally, this method allows to return suggestions to the user (suggestions=True). This is done by testing for violations of causal minimality via `validate_cm`. Related paper: Eulig, E., Mastakouri, A. A., Blöbaum, P., Hardt, M., & Janzing, D. (2023). Toward Falsifying Causal Graphs Using a Permutation-Based Test. https://arxiv.org/abs/2305.09565 :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param suggestions: Provide suggestions to the user. At the moment the only source of suggestions comes from validating causal minimality (using validate_cm). :param independence_test: Independence test to use for checking pairwise independencies. :param conditional_independence_test: Conditional independence test to use. :param significance_level: Significance level for the permutation test. :param significance_ci: Significance level for (conditional) independence tests. :param n_permutations: Number of permutations to perform. If -1 use all n_nodes! permutations. :param show_progress_bar: Whether to show progress bar over permutations. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :param plot_histogram: Plot histogram of results from permutation baseline. :param plot_kwargs: Additional plot arguments to be passed to plot_evaluation_results. :return: EvaluationResult """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs show_progress_bar = config.show_progress_bars if show_progress_bar is None else show_progress_bar p_values_memory = _PValuesMemory() if n_permutations is None: n_permutations = int(1 / significance_level) if not plot_histogram else -1 if not plot_kwargs: plot_kwargs = {} methods = ( partial( validate_lmc, independence_test=independence_test, conditional_independence_test=conditional_independence_test, significance_level=significance_ci, p_values_memory=p_values_memory, n_jobs=n_jobs, ), partial(validate_tpa, causal_graph_reference=causal_graph), ) if suggestions: suggestion_methods = ( partial( validate_cm, independence_test=independence_test, conditional_independence_test=conditional_independence_test, significance_level=significance_ci, p_values_memory=p_values_memory, n_jobs=n_jobs, ), ) else: suggestion_methods = tuple() summary_given = run_validations( causal_graph, data, methods=methods + suggestion_methods, ) summary_perm = _permutation_based( causal_graph, data, methods=methods, exclude_original_order=False, n_permutations=n_permutations, show_progress_bar=show_progress_bar, ) summary = dict() validation_methods = set(summary_perm.keys()) - {FalsifyConst.PERM_GRAPHS} for m in validation_methods: summary[m] = dict() summary[m][FalsifyConst.PERM_VIOLATIONS] = [perm[FalsifyConst.N_VIOLATIONS] for perm in summary_perm[m]] summary[m][FalsifyConst.GIVEN_VIOLATIONS] = summary_given[m][FalsifyConst.N_VIOLATIONS] summary[m][FalsifyConst.N_TESTS] = summary_given[m][FalsifyConst.N_TESTS] summary[m][FalsifyConst.F_PERM_VIOLATIONS] = [ perm[FalsifyConst.N_VIOLATIONS] / perm[FalsifyConst.N_TESTS] for perm in summary_perm[m] ] summary[m][FalsifyConst.F_GIVEN_VIOLATIONS] = ( summary[m][FalsifyConst.GIVEN_VIOLATIONS] / summary[m][FalsifyConst.N_TESTS] ) summary[m][FalsifyConst.P_VALUE] = sum( [ 1 for perm in summary[m][FalsifyConst.F_PERM_VIOLATIONS] if perm <= summary[m][FalsifyConst.F_GIVEN_VIOLATIONS] ] ) / len(summary[m][FalsifyConst.PERM_VIOLATIONS]) if m != FalsifyConst.VALIDATE_TPA: # Append list of violations (node, non_desc) to get local information summary[m][FalsifyConst.LOCAL_VIOLATION_INSIGHT] = summary_given[m][FalsifyConst.P_VALUES] if FalsifyConst.VALIDATE_TPA in summary: summary[FalsifyConst.MEC] = [ summary_perm[FalsifyConst.PERM_GRAPHS][i] for i, v in enumerate(summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.PERM_VIOLATIONS]) if v == 0 ] result = EvaluationResult( summary=summary, significance_level=significance_level, suggestions={m: summary_given[m] for m in summary_given if m not in validation_methods}, ) if plot_histogram: plot_evaluation_results(result, **plot_kwargs) return result def apply_suggestions( causal_graph: DirectedGraph, evaluation_result: EvaluationResult, edges_to_keep: Optional[List[Tuple[Any, Any]]] = None, ): if not hasattr(evaluation_result, "suggestions"): raise ValueError("EvaluationResult object has no attribute suggestions. Please run with suggestion=True!") causal_graph = causal_graph.copy() for m in evaluation_result.suggestions: for node, res in evaluation_result.suggestions[m][FalsifyConst.P_VALUES].items(): edge = (node[1], node[0]) if (res[1] and edges_to_keep is not None and edge not in edges_to_keep) or ( res[1] and edges_to_keep is None ): causal_graph.remove_edge(edge[0], edge[1]) return causal_graph def plot_evaluation_results(evaluation_result, figsize=(8, 3), bins=None, title="", savepath="", display=True): fig, ax = plt.subplots(figsize=figsize) # Plot histograms p_values = "" data = [] labels = [] evaluation_summary = {k: v for k, v in evaluation_result.summary.items() if k != FalsifyConst.MEC} for i, (m, m_summary) in enumerate(evaluation_summary.items()): data.append(m_summary[FalsifyConst.F_PERM_VIOLATIONS]) labels.append(f"Violations of {FALSIFY_METHODS[m]} of permuted DAGs") p_values += f"p-value {FALSIFY_METHODS[m]} = {m_summary[FalsifyConst.P_VALUE]:.2f}\n" ax.hist(data, color=COLORS[: len(evaluation_summary)], bins=bins, alpha=0.5, label=labels, edgecolor="k") # Plot given violations for i, (m, m_summary) in enumerate(evaluation_summary.items()): ylim = ax.get_ylim()[1] ax.plot( [m_summary[FalsifyConst.F_GIVEN_VIOLATIONS]] * 2, [0, ylim], "--", c=COLORS[i], label=f"Violations of {FALSIFY_METHODS[m]} of given DAG", ) ax.set_ylim([0, ylim]) ax.set_xlabel("Fraction of violations") ax.set_ylabel("# Permutations") plt.legend(loc="upper left", bbox_to_anchor=(1.05, 1), borderaxespad=0.0, title=p_values) if title: plt.title(title) plt.tight_layout() if savepath: plt.savefig(savepath, dpi=300, bbox_inches="tight") if display: plt.show() else: plt.close() def plot_local_insights( causal_graph: DirectedGraph, evaluation_result: Union[EvaluationResult, Dict], method: Optional[str] = FalsifyConst.VALIDATE_LMC, ): """ For some given graph and evaluation result plot local violations. :param causal_graph: DiGraph :param evaluation_result: EvaluationResult :param method: Method for which to plot violations """ colors = {} if isinstance(evaluation_result, EvaluationResult) and method in evaluation_result.summary: local_insight_dict = evaluation_result.summary[method][FalsifyConst.LOCAL_VIOLATION_INSIGHT] elif ( isinstance(evaluation_result, EvaluationResult) and hasattr(evaluation_result, "suggestions") and method in evaluation_result.suggestions ): local_insight_dict = evaluation_result.suggestions[method][FalsifyConst.P_VALUES] elif isinstance(evaluation_result, Dict): if method not in evaluation_result: raise ValueError(f"Validation method {method} does not exist in given evaluation_result!") if FalsifyConst.P_VALUES not in evaluation_result[method]: raise ValueError( f"Validation method {method} has no key {FalsifyConst.P_VALUES} where information on local violations " f"are stored!" ) local_insight_dict = evaluation_result[method][FalsifyConst.P_VALUES] else: raise ValueError(f"Cannot plot local violation insights from method {method} in evaluation_result!") for nodes, result in local_insight_dict.items(): if result[1]: if method == FalsifyConst.VALIDATE_LMC: # For LMC we highlight X for which X _|/|_ Y \in ND_X | Pa_X colors[nodes[0]] = VIOLATION_COLOR elif method == FalsifyConst.VALIDATE_PD: # For PD we highlight the edge (if Y\in Anc_X -> X are adjacent) colors[(nodes[1], nodes[0])] = VIOLATION_COLOR elif method == FalsifyConst.VALIDATE_CM: # For causal minimality we highlight the edge Y \in Pa_X -> X colors[(nodes[1], nodes[0])] = VIOLATION_COLOR plot(causal_graph, colors=colors) def _generate_table( validation_repr, suggestion_repr, width=105, validation_name="Falsificaton Summary", suggestion_name="Suggestions" ): # Create Validation header _repr = [ "+" + "-" * (width - 2) + "+\n", "|" + " " * int(np.floor((width - 2 - len(validation_name)) / 2)), validation_name, " " * int(np.ceil((width - 2 - len(validation_name)) / 2)) + "|\n", "+" + "-" * (width - 2) + "+\n", ] # Create Validation summary _repr += [f"| {v_line}" + " " * (width - 3 - len(v_line)) + "|\n" for v_line in validation_repr] # Close Validation _repr += ["+" + "-" * (width - 2) + "+\n"] # Create Suggestions header if suggestion_repr: _repr += [ "|" + " " * int(np.floor((width - 2 - len(suggestion_name)) / 2)), suggestion_name, " " * int(np.ceil((width - 2 - len(suggestion_name)) / 2)) + "|\n", "+" + "-" * (width - 2) + "+\n", ] # Iterate over suggestions for m, suggestions in suggestion_repr.items(): left_col = "| " + m + " |" if not suggestions: right_col = " " + " " * (width - 2 - len(left_col)) + "|\n" _repr += [left_col, right_col] for i, s in enumerate(suggestions): if i > 0: left_col = "| " + " " * len(m) + " |" right_col = " - " + s + " " * (width - 4 - len(s) - len(left_col)) + "|\n" _repr += [left_col, right_col] _repr += ["+" + "-" * (width - 2) + "+\n"] return "".join(_repr)[:-1] def _compute_p_value( data: pd.DataFrame, X: Union[List, str], Y: Union[List, str], Z: Optional[Union[Set, List, str]], independence_test: Optional[Callable[[np.ndarray, np.ndarray], float]], conditional_independence_test: Optional[Callable[[np.ndarray, np.ndarray, np.ndarray], float]], seed: int, ) -> float: """Perform (conditional) independence test and report p-value. If any nodes needed for the test do not exist in the data, this test is skipped and a value of -1 is returned and a warning is raised. :param data: Observations of variables in the DAG. :param X: Variable to test (conditional) independence with Y :param Y: Variable to test (conditional) independence with X :param Z: Set to condition independence test on. Can be empty (None, empty set, or empty list). :param independence_test: Independence test to use. :param conditional_independence_test: Conditional independence test to use. :param seed: Random seed :return: p-value """ set_random_seed(seed) # Test if we have data for X and Y for node in [X, Y]: if not node in data.columns: warnings.warn(f"WARN: Couldn't find data for node {node}. Skip this test.") return if Z: # Test if we have data for Z for node in Z: if not node in data.columns: warnings.warn(f"WARN: Couldn't find data for node {node}. Skip this test.") return p_value = conditional_independence_test(data[X].values, data[Y].values, data[Z].values) else: p_value = independence_test(data[X].values, data[Y].values) return p_value def _get_parental_triples(causal_graph: DirectedGraph, include_unconditional: bool): """ For a given graph collect all parental triples, that is, the triple (X, Y, Z) is a parental triple iff Y is non-descendant of X, and Z are the parents of X (can be empty if include_unconditional=True) """ triples = [] for node in causal_graph.nodes: parents = get_ordered_predecessors(causal_graph, node) non_descendants = _get_non_descendants(causal_graph, node, exclude_parents=True) if (parents or include_unconditional) and non_descendants: for non_desc in non_descendants: triples.append((node, non_desc, parents)) return triples def _permutation_based( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Union[Callable, Tuple[Callable, ...], List[Callable]], exclude_original_order: bool, n_permutations: int, show_progress_bar: bool, ) -> Dict[str, List[Union[DirectedGraph, Dict]]]: """ Generate baseline for node permutations. :param causal_graph: A directed acyclic graph (DAG). :param methods: Validation methods to perform. :param exclude_original_order: Exclude the original ordering of the nodes (default=False) :param n_permutations: Number of permutations to perform. If -1 use all n_nodes! - int(exclude_orig) permutations :param show_progress_bar: Whether to show progress bar over tested permutations. :return: Dictionary containing summary of validation for each individual graph as well as the permuted graphs """ if not isinstance(methods, (tuple, list)): methods = (methods,) perm_gen = _PermuteNodes(causal_graph, n_permutations=n_permutations, exclude_original_order=exclude_original_order) validation_summary = {FalsifyConst.PERM_GRAPHS: []} for permuted_graph in tqdm(perm_gen, desc="Test permutations of given graph", disable=not show_progress_bar): res = run_validations( causal_graph=permuted_graph, data=data, methods=methods, ) validation_summary[FalsifyConst.PERM_GRAPHS].append(permuted_graph) for m_name, summary in res.items(): if m_name not in validation_summary: validation_summary[m_name] = [] validation_summary[m_name].append(summary) return validation_summary class _PermuteNodes: def __init__(self, causal_graph: DirectedGraph, exclude_original_order: bool, n_permutations: int): """ Randomly permute the nodes of a given causal graph while keeping the underlying graph structure the same. :param causal_graph: A directed acyclic graph (DAG). :param exclude_original_order: Do not return the original order. :param n_permutations: Return a generator with n_permutations permutations. If n_permutations = -1 (default), we return all n_nodes! - int(exclude_orig) permutations. :return: Copy of causal_graph with nodes randomly permuted. """ self.causal_graph = causal_graph self.exclude_original_order = exclude_original_order self.n_permutations = n_permutations self.max_perms = np.math.factorial(self.causal_graph.number_of_nodes()) - int(self.exclude_original_order) if n_permutations == -1 or n_permutations > self.max_perms: self.it = self.iter_all_permutations() self.length = self.max_perms if self.length > 2**63 - 1: raise ValueError( f"Too many permutations specified. Did you accidently set 'n_permutations'=-1 for a " f"large (>20 nodes) graph? " f"Given graph has {causal_graph.number_of_nodes()} nodes." ) else: self.it = self.iter_random_permutations() self.length = self.n_permutations def iter_all_permutations(self): for i, perm in enumerate(permutations(self.causal_graph.nodes)): if self.exclude_original_order and i == 0: continue mapping = {node: perm[i] for i, node in enumerate(self.causal_graph.nodes)} yield nx.relabel_nodes(self.causal_graph, mapping, copy=True) def iter_random_permutations(self): for _ in range(self.n_permutations): if self.exclude_original_order: is_orig = True while is_orig: perm = list(np.random.permutation(self.causal_graph.nodes)) if perm != list(self.causal_graph.nodes): is_orig = False else: perm = list(np.random.permutation(self.causal_graph.nodes)) mapping = {node: perm[i] for i, node in enumerate(self.causal_graph.nodes)} yield nx.relabel_nodes(self.causal_graph, mapping, copy=True) def __iter__(self): yield from self.it def __len__(self): return self.length def _to_frozenset(x: Union[Set, List, str]): """Converts a set, list or string into a hashable frozenset""" assert ( isinstance(x, Set) or isinstance(x, List) or isinstance(x, str) ), f"{x} must be list, set or str. Got {type(x)} instead!" if isinstance(x, str): return frozenset({x}) return frozenset(x)

eeulig

379f188d674d5be6d644559f9f54852b87403f7c

4e30c63fe1113e35259e7fb3400a6f09bca4d699

Make this an internal function with `_`. Can you also add a docstring why we need this `wrap_partial` instead of `partial` directly (due to the `__name__` attribute). Maybe briefly explain how the `__name__` attribute is used there.

bloebp

py-why/dowhy

Remove kwargs from falsify, improve docstrings

This commit updates `gcm/falsify` in the following ways: - Remove the `**kwargs` for `validate_graph` and `falsify_graph`. Instead, validation methods given to those functions should be wrapped in `wrap_partial`, e.g. if we want to validate a graph `G` and test local Markov conditions and pairwise dependencies on observations `data`, we would do ``` validate_graph(G, methods=( wrap_partial(validate_lmc, data=data, independence_test=..., conditional_independence_test=...), wrap_partial(validate_pd, data=data, independence_test=...), ) ) ``` By default (`methods=None`), `validate_graph` tests local Markov conditions and `falsify_graph` tests local Markov conditions and d-separation of parental triples. In this case `data` has to be set and optionally one can provide `independence_test`, `conditional_independence_test`, `significance_level`, and `n_jobs` via the keyword arguments of `validate_graph` / `falsify_graph`. - Use `Enum` for some of the constants - In `_PValuesMemory` the method `add_p_value ` always sets a p-value even if it was set already. This removes the need for the method `clear_placeholders` and allows `p_values` to be set to `-1`, indicating that the CI is not testable (because of missing data for a node, c.f. `_compute_p_value`) - If for some CI: X, Y | Z any of the nodes has no data (column missing in the provided dataframe), we skip this test and raise a warning. - Instead of using the raw number of violations in the plotted histogram and to compute the p-value, we use the relative number of violations. This will result in different p-values only if we have missing data for some nodes (and thus the number of tests is potentially different between different permutations) - Improve docstrings

2023-07-04 13:58:30+00:00

2023-07-18 14:19:10+00:00

dowhy/gcm/falsify.py

"""This module provides functionality to falsify a user-given DAG given observed data. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from dataclasses import dataclass, field from enum import Enum, auto from functools import partial from itertools import permutations from typing import Any, Callable, Dict, FrozenSet, List, Optional, Set, Tuple, Union import matplotlib.colors as mcolors import matplotlib.pyplot as plt import networkx as nx import numpy as np import pandas as pd from joblib import Parallel, delayed from tqdm import tqdm import dowhy.gcm.config as config from dowhy.gcm.independence_test import kernel_based from dowhy.gcm.util import plot from dowhy.gcm.util.general import set_random_seed from dowhy.gcm.validation import _get_non_descendants from dowhy.graph import DirectedGraph, get_ordered_predecessors VIOLATION_COLOR = "red" COLORS = list(mcolors.TABLEAU_COLORS.values()) class FalsifyConst(Enum): N_VIOLATIONS = auto() N_TESTS = auto() P_VALUE = auto() P_VALUES = auto() GIVEN_VIOLATIONS = auto() PERM_VIOLATIONS = auto() F_GIVEN_VIOLATIONS = auto() F_PERM_VIOLATIONS = auto() LOCAL_VIOLATION_INSIGHT = auto() METHOD = auto() VALIDATE_LMC = auto() VALIDATE_TPA = auto() VALIDATE_PD = auto() VALIDATE_CM = auto() PERM_GRAPHS = auto() MEC = auto() FALSIFY_METHODS = { FalsifyConst.VALIDATE_LMC: "LMC", FalsifyConst.VALIDATE_PD: "Faithfulness", FalsifyConst.VALIDATE_TPA: "tPa", FalsifyConst.VALIDATE_CM: "Causal Minimality", } @dataclass class _PValuesMemory: """A class to store and access results of independence tests. This class is useful if, e.g. we validate LMC on graph X -> Y -> Z and also on the permuted nodes Z -> Y -> X. Since X ind Z | Y == Z ind X | Y, we only need to perform the conditional independence test once. We use frozensets of nodes since standard (immutable) sets are not hashable. """ p_values: Dict[Tuple[FrozenSet, FrozenSet, FrozenSet], float] = field(default_factory=dict) def add_p_value( self, p_value: Union[None, float], X: Union[Set, List, str], Y: Union[Set, List, str], Z: Optional[Union[Set, List, str]] = None, ): if not Z: Z = set() self.p_values[(_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z))] = p_value def get_p_value( self, X: Union[Set, List, str], Y: Union[Set, List, str], Z: Optional[Union[Set, List, str]] = None ): if not Z: Z = set() # Independence tests are symmetric if (_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z)) in self.p_values: return self.p_values[(_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z))] elif (_to_frozenset(Y), _to_frozenset(X), _to_frozenset(Z)) in self.p_values: return self.p_values[(_to_frozenset(Y), _to_frozenset(X), _to_frozenset(Z))] return def __contains__(self, item: Tuple[Union[Set, List, str], ...]) -> bool: X, Y = (_to_frozenset(i) for i in item[:2]) if len(item) == 2 or item[2] is None: Z = frozenset() else: Z = _to_frozenset(item[2]) return (X, Y, Z) in self.p_values or (Y, X, Z) in self.p_values def validate_lmc( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, include_unconditional: bool = True, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[str, float]]]: """ Validate the local markov condition for a given directed graph. Return number of violations and p values for each node. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory instance, where results of previously performed tests are stored. :param independence_test: Test to use for unconditional independencies (only used if include_unconditional=True) :param conditional_independence_test: Conditional independence test to use for checking local Markov condition. :param significance_level: Significance level for (conditional) independence tests. :param include_unconditional: Test also unconditional independencies of root nodes. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Outcome of validation containing number of violations in the graph and p values/violation for each tuple (node, non_desc) """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_LMC, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, FalsifyConst.P_VALUES: dict(), } # Find out which tests to do triples = _get_parental_triples(causal_graph, include_unconditional) to_test = [] for node, non_desc, parents in triples: if not (node, non_desc, parents) in p_values_memory: to_test.append((node, non_desc, parents)) p_values_memory.add_p_value(None, node, non_desc, parents) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=node, Y=non_desc, Z=parents, independence_test=independence_test, conditional_independence_test=conditional_independence_test, seed=int(seed), ) for (node, non_desc, parents), seed in zip(to_test, random_seeds) ) # Gather results for i, (node, non_desc, parents) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], node, non_desc, parents) # Summarize for node, non_desc, parents in triples: lmc_p_value = p_values_memory.get_p_value(node, non_desc, parents) if lmc_p_value is not None: validation_summary[FalsifyConst.N_TESTS] += 1 validation_summary[FalsifyConst.P_VALUES][(node, non_desc)] = ( lmc_p_value, lmc_p_value <= significance_level, ) if lmc_p_value <= significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_tpa( causal_graph: DirectedGraph, causal_graph_reference: DirectedGraph, include_unconditional: bool = True, data: Optional[pd.DataFrame] = None, ) -> Dict[str, int]: """ Graphical criterion to evaluate which pairwise parental d-separations (parental triples) in `causal_graph` are violated, assuming `causal_graph_reference` is the ground truth graph. If none are violated, then both graphs lie in the same Markov equivalence class. Specifically we test: X _|_G' Y | Z and X _/|_G Y | Z for Y \in ND{X}^G', Z = PA{X}^G :param causal_graph: Causal graph for which to evaluate parental d-separations (G') :param causal_graph_reference: Causal graph where we test if d-separation holds (G) :param include_unconditional: Test also unconditional independencies of root nodes. :param data: IGNORED! No data is needed for this validation method and thus the data argument is ignored! :return: Validation summary with number of d-separations implied by `causal_graph` and number of times these are violated in the graph `causal_graph_reference`. """ validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_TPA, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, } triples = _get_parental_triples(causal_graph, include_unconditional) for node, non_desc, parents in triples: validation_summary[FalsifyConst.N_TESTS] += 1 if not nx.d_separated(causal_graph_reference, {node}, {non_desc}, set(parents)): validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_pd( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, n_pairs: int = -1, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, adjacent_only: bool = False, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[tuple, float]]]: """ Validate pairwise dependencies (pd) for a given causal graph and data. Test for each node if it is statistically dependent of all its ancestors. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory object, where results of previously performed tests are stored. :param n_pairs: Evaluate dependencies for n_pairs <= all pairs in the DAG. If n_pairs=-1, evaluate dependencies for all (ancestor, node) pairs (default). :param independence_test: Independence test to use for checking pairwise dependencies. :param significance_level: Significance level for independence tests. :param adjacent_only: Only test adjacent node pairs. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Summary dict: {n_violations: int, n_tests: int, p_values: {(ancestor, node): float, ...}} """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() pairs = [(ancestor, node) for node in causal_graph.nodes for ancestor in nx.ancestors(causal_graph, node)] if adjacent_only: pairs = [ (ancestor, node) for (ancestor, node) in pairs if ancestor in get_ordered_predecessors(causal_graph, node) ] if n_pairs < 0: n_pairs = len(pairs) if n_pairs > len(pairs): raise ValueError(f"n_pairs ({n_pairs}) > number of pairs in the DAG ({len(pairs)})") validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_PD, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: n_pairs, FalsifyConst.P_VALUES: dict(), } pair_idxs = np.random.choice(len(pairs), size=n_pairs, replace=False) # Find out which tests to do to_test = [] for pair_idx in pair_idxs: ancestor, node = pairs[pair_idx] if not (ancestor, node) in p_values_memory: to_test.append((ancestor, node)) p_values_memory.add_p_value(None, ancestor, node) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=ancestor, Y=node, Z=None, independence_test=independence_test, conditional_independence_test=None, seed=int(seed), ) for (ancestor, node), seed in zip(to_test, random_seeds) ) # Gather results for i, (ancestor, node) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], ancestor, node) # Summarize for pair_idx in pair_idxs: ancestor, node = pairs[pair_idx] p_value = p_values_memory.get_p_value(ancestor, node) if p_value is not None: validation_summary[FalsifyConst.P_VALUES][(node, ancestor)] = (p_value, p_value > significance_level) if p_value > significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_cm( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[tuple, float]]]: """ Function to test causal minimality of a DAG (see [1], Proposition 6.36). [1] J. Peters, D. Janzing, and B. Schölkopf, Elements of Causal Inference: Foundations and Learning Algorithms. Cambridge, MA, USA: MIT Press, 2017. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory object, where results of previously performed tests are stored. :param independence_test: Independence test to use. :param conditional_independence_test: Conditional independence test to use. :param significance_level: Significance level for independence tests. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Validation summary as dict. """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_CM, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, FalsifyConst.P_VALUES: dict(), } # Find out which tests to do triples = [] to_test = [] for node in causal_graph.nodes: parents = set(causal_graph.predecessors(node)) if parents: for p in parents: other_parents = parents.difference({p}) triples.append((node, p, other_parents)) if not (node, p, other_parents) in p_values_memory: to_test.append((node, p, other_parents)) p_values_memory.add_p_value(None, node, p, other_parents) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=node, Y=p, Z=list(other_parents), independence_test=independence_test, conditional_independence_test=conditional_independence_test, seed=int(seed), ) for (node, p, other_parents), seed in zip(to_test, random_seeds) ) # Gather results for i, (node, p, other_parents) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], node, p, other_parents) # Summarize for node, p, other_parents in triples: p_value = p_values_memory.get_p_value(node, p, other_parents) if p_value is not None: validation_summary[FalsifyConst.N_TESTS] += 1 validation_summary[FalsifyConst.P_VALUES][(node, p, tuple(other_parents))] = ( p_value, p_value > significance_level, ) if p_value > significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_graph( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Optional[Union[Callable, Tuple[Callable, ...], List[Callable]]] = partial( validate_lmc, independence_test=kernel_based, conditional_independence_test=kernel_based ), ) -> Dict[str, Dict]: """ Validate a given causal graph using observational data and some given methods. If methods are provided, they must be wrapped in a partial object, with their respective parameters. E.g., if one wants to test the local Markov conditions and the pairwise dependencies (unconditional faithfulness), then call validate_graph(G, data, methods=( partial(validate_lmc, independence_test=..., conditional_independence_test=...), partial(validate_pd, independence_test=...), ) ) :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param methods: Method functions wrapped in wrap_partial. E.g. wrap_partial(validate_lmc, data=data, independence_test=..., conditional_independence_test=...). If no methods are provided we run validate_lmc with optional keyword arguments provided to validate_graph. :return: Validation summary as dict. """ if not isinstance(methods, (tuple, list)): methods = (methods,) validation_summary = dict() for m in methods: m_summary = m(causal_graph=causal_graph, data=data) m_name = m_summary.pop(FalsifyConst.METHOD) validation_summary[m_name] = m_summary return validation_summary @dataclass class EvaluationResult: """ Dataset class containing the evaluation result of falsifying a graph using a node-permutation test. ... Attributes ---------- summary : dict Dictionary containing the summary of the evaluation. significance_level : float Significance level based on which we falsify the given DAG falsifiable : bool Whether the given DAG is falsifiable. falsified : bool Whether the given DAG is falsified. """ summary: dict significance_level: float suggestions: Optional[dict] = None def update_significance_level(self, significance_level: float): """ Update the significance level to decide if we falsify a given DAG. """ self.significance_level = significance_level self.__post_init__() def __post_init__(self): self.can_evaluate = self._can_evaluate() if not self.can_evaluate: self.falsified = None self.falsifiable = None elif ( self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.P_VALUE] > self.significance_level > self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE] ): self.falsified = True self.falsifiable = True elif self.significance_level < self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE]: self.falsified = False self.falsifiable = False else: self.falsified = False self.falsifiable = True def __repr__(self): # DAG Evaluation if self.can_evaluate: decision = " " if self.falsified else " do not " informative = " " if self.falsifiable else " not " frac_MEC = f"{len(self.summary[FalsifyConst.MEC])} / {len(self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.PERM_VIOLATIONS])}" frac_VLMC = f"{self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.GIVEN_VIOLATIONS]}/{self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.N_TESTS]}" p_LMC = self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.P_VALUE] p_dSep = self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE] validation_repr = [ f"The given DAG is{informative}informative because {frac_MEC} of the permutations lie in the Markov", f"equivalence class of the given DAG (p-value: {p_dSep:.2f}).", f"The given DAG violates {frac_VLMC} LMCs and is better than {(1 - p_LMC) * 100:.1f}% of the permuted DAGs (p-value: {p_LMC:.2f}).", f"Based on the provided significance level ({self.significance_level}) and because the DAG is{informative}informative,", f"we{decision}reject the DAG.", ] else: validation_repr = ["Cannot be evaluated!"] # Suggestions suggestion_repr = {} for m in self.suggestions: suggestion_repr[FALSIFY_METHODS[m]] = [ f"Remove edge {node[1]} --> {node[0]}" for (node, r) in self.suggestions[m][FalsifyConst.P_VALUES].items() if r[1] ] return _generate_table(validation_repr, suggestion_repr) def _can_evaluate(self): can_evaluate = True for m in (FalsifyConst.VALIDATE_LMC, FalsifyConst.VALIDATE_TPA): if m not in self.summary: can_evaluate = False return can_evaluate def falsify_graph( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Optional[Union[Callable, Tuple[Callable, ...]]] = None, suggestion_methods: Optional[Union[Callable, Tuple[Callable, ...]]] = None, suggestions: bool = False, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, significance_ci: float = 0.05, n_permutations: Optional[int] = None, show_progress_bar: Optional[bool] = None, n_jobs: Optional[int] = None, plot_histogram: bool = False, plot_kwargs: Optional[Dict] = None, ) -> EvaluationResult: """ Falsify a given DAG using observational data. This method returns the result of a permutation-test to falsify a user-given DAG using observational data. To this end we construct the test statistics by testing the violations of local Markov conditions (LMC) implied by the graph using conditional independence (CI) tests. The null is the number of LMC violations of a random node-permutation of the given graph. Our test can be interpreted as whether the given graph is significantly better than random in terms of the CIs it entails. To determine whether a given graph is falsifiable by our metric, we implement a second test, which reports whether given graph is "characteristic" enough in terms of the CIs it entails. For this, we compute how many of the random node permutations lie in the same Markov equivalence class (MEC) as the given graph and conclude that the given graph is falsifiable only if the fraction of permuted DAGs in the same MEC as the given graph is "reasonably" small. The returned EvaluationResult object has two attributes: `falsified` and `falsifiable`: `falsifiable`: The given graph lies in a different MEC than >= 1-`significance_level` of the permuted DAGs `falsified`: The given graph is falsifiable and violates fewer LMCs than >= 1-`significance_level` of the permuted DAGs By default, we only run 1 / `significance_level` permutations as those are enough to falsify a graph with type I error probability `significance_level` at some given `significance_level`. If you are interested in a more exact estimate of the p-value of whish to plot a histogram to see how the given DAG compares to random node permutations, you should set `n_permutations` to some larger value (e.g. 100 or 1000). If `n_permutations=-1` we test on all n_nodes! permutations. `methods` and `suggestion_methods` must be wrapped in partial(method, **kwargs) (c.f. `validate_graph`). Related paper: Eulig, E., Mastakouri, A. A., Blöbaum, P., Hardt, M., & Janzing, D. (2023). Toward Falsifying Causal Graphs Using a Permutation-Based Test. https://arxiv.org/abs/2305.09565 :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param methods: Validation methods to perform. :param suggestion_methods: Methods to run on the given graph to provide additional suggestions. :param suggestions: Provide suggestions generated using the `suggestion_methods`. :param independence_test: Independence test to use for checking pairwise independencies. :param conditional_independence_test: Conditional independence test to use. :param significance_level: Significance level for the permutation test. :param significance_ci: Significance level for (conditional) independence tests. :param n_permutations: Number of permutations to perform. If -1 use all n_nodes! permutations. :param show_progress_bar: Whether to show progress bar over permutations. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :param plot_histogram: Plot histogram of results from permutation baseline. :param plot_kwargs: Additional plot arguments to be passed to plot_evaluation_results. :return: EvaluationResult """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs show_progress_bar = config.show_progress_bars if show_progress_bar is None else show_progress_bar p_values_memory = _PValuesMemory() if n_permutations is None: n_permutations = int(1 / significance_level) if not plot_histogram else -1 if not plot_kwargs: plot_kwargs = {} # If no methods are provided, use default ones: validate_lmc, validate_tpa if not methods: methods = ( partial( validate_lmc, independence_test=independence_test, conditional_independence_test=conditional_independence_test, significance_level=significance_ci, p_values_memory=p_values_memory, n_jobs=n_jobs, ), partial(validate_tpa, causal_graph_reference=causal_graph), ) elif isinstance(methods, Callable): methods = (methods,) # If no suggestion methods are provided, but suggestions=True, use default ones: validate_cm if not suggestions: suggestion_methods = tuple() elif suggestions and isinstance(suggestion_methods, Callable): suggestion_methods = (suggestion_methods,) elif suggestions: suggestion_methods = ( partial( validate_cm, independence_test=independence_test, conditional_independence_test=conditional_independence_test, significance_level=significance_ci, p_values_memory=p_values_memory, n_jobs=n_jobs, ), ) summary_given = validate_graph( causal_graph, data, methods=methods + suggestion_methods, ) summary_perm = _permutation_based( causal_graph, data, methods=methods, exclude_original_order=False, n_permutations=n_permutations, show_progress_bar=show_progress_bar, ) summary = dict() validation_methods = set(summary_perm.keys()) - {FalsifyConst.PERM_GRAPHS} for m in validation_methods: summary[m] = dict() summary[m][FalsifyConst.PERM_VIOLATIONS] = [perm[FalsifyConst.N_VIOLATIONS] for perm in summary_perm[m]] summary[m][FalsifyConst.GIVEN_VIOLATIONS] = summary_given[m][FalsifyConst.N_VIOLATIONS] summary[m][FalsifyConst.N_TESTS] = summary_given[m][FalsifyConst.N_TESTS] summary[m][FalsifyConst.F_PERM_VIOLATIONS] = [ perm[FalsifyConst.N_VIOLATIONS] / perm[FalsifyConst.N_TESTS] for perm in summary_perm[m] ] summary[m][FalsifyConst.F_GIVEN_VIOLATIONS] = ( summary[m][FalsifyConst.GIVEN_VIOLATIONS] / summary[m][FalsifyConst.N_TESTS] ) summary[m][FalsifyConst.P_VALUE] = sum( [ 1 for perm in summary[m][FalsifyConst.F_PERM_VIOLATIONS] if perm <= summary[m][FalsifyConst.F_GIVEN_VIOLATIONS] ] ) / len(summary[m][FalsifyConst.PERM_VIOLATIONS]) if m != FalsifyConst.VALIDATE_TPA: # Append list of violations (node, non_desc) to get local information summary[m][FalsifyConst.LOCAL_VIOLATION_INSIGHT] = summary_given[m][FalsifyConst.P_VALUES] if FalsifyConst.VALIDATE_TPA in summary: summary[FalsifyConst.MEC] = [ summary_perm[FalsifyConst.PERM_GRAPHS][i] for i, v in enumerate(summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.PERM_VIOLATIONS]) if v == 0 ] result = EvaluationResult( summary=summary, significance_level=significance_level, suggestions={m: summary_given[m] for m in summary_given if m not in validation_methods}, ) if plot_histogram: plot_evaluation_results(result, **plot_kwargs) return result def apply_suggestions( causal_graph: DirectedGraph, evaluation_result: EvaluationResult, edges_to_keep: Optional[List[Tuple[Any, Any]]] = None, ): if not hasattr(evaluation_result, "suggestions"): raise ValueError("EvaluationResult object has no attribute suggestions. Please run with suggestion=True!") causal_graph = causal_graph.copy() for m in evaluation_result.suggestions: for node, res in evaluation_result.suggestions[m][FalsifyConst.P_VALUES].items(): edge = (node[1], node[0]) if (res[1] and edges_to_keep is not None and edge not in edges_to_keep) or ( res[1] and edges_to_keep is None ): causal_graph.remove_edge(edge[0], edge[1]) return causal_graph def plot_evaluation_results(evaluation_result, figsize=(8, 3), bins=None, title="", savepath="", display=True): fig, ax = plt.subplots(figsize=figsize) # Plot histograms p_values = "" data = [] labels = [] evaluation_summary = {k: v for k, v in evaluation_result.summary.items() if k != FalsifyConst.MEC} for i, (m, m_summary) in enumerate(evaluation_summary.items()): data.append(m_summary[FalsifyConst.F_PERM_VIOLATIONS]) labels.append(f"Violations of {FALSIFY_METHODS[m]} of permuted DAGs") p_values += f"p-value {FALSIFY_METHODS[m]} = {m_summary[FalsifyConst.P_VALUE]:.2f}\n" ax.hist(data, color=COLORS[: len(evaluation_summary)], bins=bins, alpha=0.5, label=labels, edgecolor="k") # Plot given violations for i, (m, m_summary) in enumerate(evaluation_summary.items()): ylim = ax.get_ylim()[1] ax.plot( [m_summary[FalsifyConst.F_GIVEN_VIOLATIONS]] * 2, [0, ylim], "--", c=COLORS[i], label=f"Violations of {FALSIFY_METHODS[m]} of given DAG", ) ax.set_ylim([0, ylim]) ax.set_xlabel("Fraction of violations") ax.set_ylabel("# Permutations") plt.legend(loc="upper left", bbox_to_anchor=(1.05, 1), borderaxespad=0.0, title=p_values) if title: plt.title(title) plt.tight_layout() if savepath: plt.savefig(savepath, dpi=300, bbox_inches="tight") if display: plt.show() else: plt.close() def plot_local_insights( causal_graph: DirectedGraph, evaluation_result: Union[EvaluationResult, Dict], method: Optional[str] = FalsifyConst.VALIDATE_LMC, ): """ For some given graph and evaluation result plot local violations. :param causal_graph: DiGraph :param evaluation_result: EvaluationResult :param method: Method for which to plot violations """ colors = {} if isinstance(evaluation_result, EvaluationResult) and method in evaluation_result.summary: local_insight_dict = evaluation_result.summary[method][FalsifyConst.LOCAL_VIOLATION_INSIGHT] elif ( isinstance(evaluation_result, EvaluationResult) and hasattr(evaluation_result, "suggestions") and method in evaluation_result.suggestions ): local_insight_dict = evaluation_result.suggestions[method][FalsifyConst.P_VALUES] elif isinstance(evaluation_result, Dict): if method not in evaluation_result: raise ValueError(f"Validation method {method} does not exist in given evaluation_result!") if FalsifyConst.P_VALUES not in evaluation_result[method]: raise ValueError( f"Validation method {method} has no key {FalsifyConst.P_VALUES} where information on local violations " f"are stored!" ) local_insight_dict = evaluation_result[method][FalsifyConst.P_VALUES] else: raise ValueError(f"Cannot plot local violation insights from method {method} in evaluation_result!") for nodes, result in local_insight_dict.items(): if result[1]: if method == FalsifyConst.VALIDATE_LMC: # For LMC we highlight X for which X _|/|_ Y \in ND_X | Pa_X colors[nodes[0]] = VIOLATION_COLOR elif method == FalsifyConst.VALIDATE_PD: # For PD we highlight the edge (if Y\in Anc_X -> X are adjacent) colors[(nodes[1], nodes[0])] = VIOLATION_COLOR elif method == FalsifyConst.VALIDATE_CM: # For causal minimality we highlight the edge Y \in Pa_X -> X colors[(nodes[1], nodes[0])] = VIOLATION_COLOR plot(causal_graph, colors=colors) def _generate_table( validation_repr, suggestion_repr, width=105, validation_name="Falsificaton Summary", suggestion_name="Suggestions" ): # Create Validation header _repr = [ "+" + "-" * (width - 2) + "+\n", "|" + " " * int(np.floor((width - 2 - len(validation_name)) / 2)), validation_name, " " * int(np.ceil((width - 2 - len(validation_name)) / 2)) + "|\n", "+" + "-" * (width - 2) + "+\n", ] # Create Validation summary _repr += [f"| {v_line}" + " " * (width - 3 - len(v_line)) + "|\n" for v_line in validation_repr] # Close Validation _repr += ["+" + "-" * (width - 2) + "+\n"] # Create Suggestions header if suggestion_repr: _repr += [ "|" + " " * int(np.floor((width - 2 - len(suggestion_name)) / 2)), suggestion_name, " " * int(np.ceil((width - 2 - len(suggestion_name)) / 2)) + "|\n", "+" + "-" * (width - 2) + "+\n", ] # Iterate over suggestions for m, suggestions in suggestion_repr.items(): left_col = "| " + m + " |" if not suggestions: right_col = " " + " " * (width - 2 - len(left_col)) + "|\n" _repr += [left_col, right_col] for i, s in enumerate(suggestions): if i > 0: left_col = "| " + " " * len(m) + " |" right_col = " - " + s + " " * (width - 4 - len(s) - len(left_col)) + "|\n" _repr += [left_col, right_col] _repr += ["+" + "-" * (width - 2) + "+\n"] return "".join(_repr)[:-1] def _compute_p_value( data: pd.DataFrame, X: Union[List, str], Y: Union[List, str], Z: Optional[Union[Set, List, str]], independence_test: Optional[Callable[[np.ndarray, np.ndarray], float]], conditional_independence_test: Optional[Callable[[np.ndarray, np.ndarray, np.ndarray], float]], seed: int, ) -> float: """Perform (conditional) independence test and report p-value. If any nodes needed for the test do not exist in the data, this test is skipped and a value of -1 is returned and a warning is raised. :param data: Observations of variables in the DAG. :param X: Variable to test (conditional) independence with Y :param Y: Variable to test (conditional) independence with X :param Z: Set to condition independence test on. Can be empty (None, empty set, or empty list). :param independence_test: Independence test to use. :param conditional_independence_test: Conditional independence test to use. :param seed: Random seed :return: p-value """ set_random_seed(seed) # Test if we have data for X and Y for node in [X, Y]: if not node in data.columns: warnings.warn(f"WARN: Couldn't find data for node {node}. Skip this test.") return if Z: # Test if we have data for Z for node in Z: if not node in data.columns: warnings.warn(f"WARN: Couldn't find data for node {node}. Skip this test.") return p_value = conditional_independence_test(data[X].values, data[Y].values, data[Z].values) else: p_value = independence_test(data[X].values, data[Y].values) return p_value def _get_parental_triples(causal_graph: DirectedGraph, include_unconditional: bool): """ For a given graph collect all parental triples, that is, the triple (X, Y, Z) is a parental triple iff Y is non-descendant of X, and Z are the parents of X (can be empty if include_unconditional=True) """ triples = [] for node in causal_graph.nodes: parents = get_ordered_predecessors(causal_graph, node) non_descendants = _get_non_descendants(causal_graph, node, exclude_parents=True) if (parents or include_unconditional) and non_descendants: for non_desc in non_descendants: triples.append((node, non_desc, parents)) return triples def _permutation_based( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Union[Callable, Tuple[Callable, ...], List[Callable]], exclude_original_order: bool, n_permutations: int, show_progress_bar: bool, ) -> Dict[str, List[Union[DirectedGraph, Dict]]]: """ Generate baseline for node permutations. :param causal_graph: A directed acyclic graph (DAG). :param methods: Validation methods to perform. :param exclude_original_order: Exclude the original ordering of the nodes (default=False) :param n_permutations: Number of permutations to perform. If -1 use all n_nodes! - int(exclude_orig) permutations :param show_progress_bar: Whether to show progress bar over tested permutations. :return: Dictionary containing summary of validation for each individual graph as well as the permuted graphs """ if not isinstance(methods, (tuple, list)): methods = (methods,) perm_gen = _PermuteNodes(causal_graph, n_permutations=n_permutations, exclude_original_order=exclude_original_order) validation_summary = {FalsifyConst.PERM_GRAPHS: []} for permuted_graph in tqdm(perm_gen, desc="Test permutations of given graph", disable=not show_progress_bar): res = validate_graph( causal_graph=permuted_graph, data=data, methods=methods, ) validation_summary[FalsifyConst.PERM_GRAPHS].append(permuted_graph) for m_name, summary in res.items(): if m_name not in validation_summary: validation_summary[m_name] = [] validation_summary[m_name].append(summary) return validation_summary class _PermuteNodes: def __init__(self, causal_graph: DirectedGraph, exclude_original_order: bool, n_permutations: int): """ Randomly permute the nodes of a given causal graph while keeping the underlying graph structure the same. :param causal_graph: A directed acyclic graph (DAG). :param exclude_original_order: Do not return the original order. :param n_permutations: Return a generator with n_permutations permutations. If n_permutations = -1 (default), we return all n_nodes! - int(exclude_orig) permutations. :return: Copy of causal_graph with nodes randomly permuted. """ self.causal_graph = causal_graph self.exclude_original_order = exclude_original_order self.n_permutations = n_permutations self.max_perms = np.math.factorial(self.causal_graph.number_of_nodes()) - int(self.exclude_original_order) if n_permutations == -1 or n_permutations > self.max_perms: self.it = self.iter_all_permutations() self.length = self.max_perms if self.length > 2**63 - 1: raise ValueError( f"Too many permutations specified. Did you accidently set 'n_permutations'=-1 for a " f"large (>20 nodes) graph? " f"Given graph has {causal_graph.number_of_nodes()} nodes." ) else: self.it = self.iter_random_permutations() self.length = self.n_permutations def iter_all_permutations(self): for i, perm in enumerate(permutations(self.causal_graph.nodes)): if self.exclude_original_order and i == 0: continue mapping = {node: perm[i] for i, node in enumerate(self.causal_graph.nodes)} yield nx.relabel_nodes(self.causal_graph, mapping, copy=True) def iter_random_permutations(self): for _ in range(self.n_permutations): if self.exclude_original_order: is_orig = True while is_orig: perm = list(np.random.permutation(self.causal_graph.nodes)) if perm != list(self.causal_graph.nodes): is_orig = False else: perm = list(np.random.permutation(self.causal_graph.nodes)) mapping = {node: perm[i] for i, node in enumerate(self.causal_graph.nodes)} yield nx.relabel_nodes(self.causal_graph, mapping, copy=True) def __iter__(self): yield from self.it def __len__(self): return self.length def _to_frozenset(x: Union[Set, List, str]): """Converts a set, list or string into a hashable frozenset""" assert ( isinstance(x, Set) or isinstance(x, List) or isinstance(x, str) ), f"{x} must be list, set or str. Got {type(x)} instead!" if isinstance(x, str): return frozenset({x}) return frozenset(x)

"""This module provides functionality to falsify a user-given DAG given observed data. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from dataclasses import dataclass, field from enum import Enum, auto from functools import partial from inspect import getfullargspec from itertools import permutations from typing import Any, Callable, Dict, FrozenSet, List, Optional, Set, Tuple, Union import matplotlib.colors as mcolors import matplotlib.pyplot as plt import networkx as nx import numpy as np import pandas as pd from joblib import Parallel, delayed from tqdm import tqdm import dowhy.gcm.config as config from dowhy.gcm.independence_test import kernel_based from dowhy.gcm.util import plot from dowhy.gcm.util.general import set_random_seed from dowhy.gcm.validation import _get_non_descendants from dowhy.graph import DirectedGraph, get_ordered_predecessors VIOLATION_COLOR = "red" COLORS = list(mcolors.TABLEAU_COLORS.values()) class FalsifyConst(Enum): N_VIOLATIONS = auto() N_TESTS = auto() P_VALUE = auto() P_VALUES = auto() GIVEN_VIOLATIONS = auto() PERM_VIOLATIONS = auto() F_GIVEN_VIOLATIONS = auto() F_PERM_VIOLATIONS = auto() LOCAL_VIOLATION_INSIGHT = auto() METHOD = auto() VALIDATE_LMC = auto() VALIDATE_TPA = auto() VALIDATE_PD = auto() VALIDATE_CM = auto() PERM_GRAPHS = auto() MEC = auto() FALSIFY_METHODS = { FalsifyConst.VALIDATE_LMC: "LMC", FalsifyConst.VALIDATE_PD: "Faithfulness", FalsifyConst.VALIDATE_TPA: "TPa", FalsifyConst.VALIDATE_CM: "Causal Minimality", } @dataclass class _PValuesMemory: """A class to store and access results of independence tests. This class is useful if, e.g. we validate LMC on graph X -> Y -> Z and also on the permuted nodes Z -> Y -> X. Since X ind Z | Y == Z ind X | Y, we only need to perform the conditional independence test once. We use frozensets of nodes since standard (immutable) sets are not hashable. """ p_values: Dict[Tuple[FrozenSet, FrozenSet, FrozenSet], float] = field(default_factory=dict) def add_p_value( self, p_value: Union[None, float], X: Union[Set, List, str], Y: Union[Set, List, str], Z: Optional[Union[Set, List, str]] = None, ): if not Z: Z = set() self.p_values[(_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z))] = p_value def get_p_value( self, X: Union[Set, List, str], Y: Union[Set, List, str], Z: Optional[Union[Set, List, str]] = None ): if not Z: Z = set() # Independence tests are symmetric if (_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z)) in self.p_values: return self.p_values[(_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z))] elif (_to_frozenset(Y), _to_frozenset(X), _to_frozenset(Z)) in self.p_values: return self.p_values[(_to_frozenset(Y), _to_frozenset(X), _to_frozenset(Z))] return def __contains__(self, item: Tuple[Union[Set, List, str], ...]) -> bool: X, Y = (_to_frozenset(i) for i in item[:2]) if len(item) == 2 or item[2] is None: Z = frozenset() else: Z = _to_frozenset(item[2]) return (X, Y, Z) in self.p_values or (Y, X, Z) in self.p_values def validate_lmc( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, include_unconditional: bool = True, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[str, float]]]: """ Validate the local markov condition for a given directed graph. Return number of violations and p values for each node. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory instance, where results of previously performed tests are stored. :param independence_test: Test to use for unconditional independencies (only used if include_unconditional=True) :param conditional_independence_test: Conditional independence test to use for checking local Markov condition. :param significance_level: Significance level for (conditional) independence tests. :param include_unconditional: Test also unconditional independencies of root nodes. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Outcome of validation containing number of violations in the graph and p values/violation for each tuple (node, non_desc) """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_LMC, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, FalsifyConst.P_VALUES: dict(), } # Find out which tests to do triples = _get_parental_triples(causal_graph, include_unconditional) to_test = [] for node, non_desc, parents in triples: if not (node, non_desc, parents) in p_values_memory: to_test.append((node, non_desc, parents)) p_values_memory.add_p_value(None, node, non_desc, parents) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=node, Y=non_desc, Z=parents, independence_test=independence_test, conditional_independence_test=conditional_independence_test, seed=int(seed), ) for (node, non_desc, parents), seed in zip(to_test, random_seeds) ) # Gather results for i, (node, non_desc, parents) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], node, non_desc, parents) # Summarize for node, non_desc, parents in triples: lmc_p_value = p_values_memory.get_p_value(node, non_desc, parents) if lmc_p_value is not None: validation_summary[FalsifyConst.N_TESTS] += 1 validation_summary[FalsifyConst.P_VALUES][(node, non_desc)] = ( lmc_p_value, lmc_p_value <= significance_level, ) if lmc_p_value <= significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_tpa( causal_graph: DirectedGraph, causal_graph_reference: DirectedGraph, include_unconditional: bool = True, ) -> Dict[str, int]: """ Graphical criterion to evaluate which pairwise parental d-separations (parental triples) in `causal_graph` are violated, assuming `causal_graph_reference` is the ground truth graph. If none are violated, then both graphs lie in the same Markov equivalence class. Specifically we test: X _|_G' Y | Z and X _/|_G Y | Z for Y \in ND{X}^G', Z = PA{X}^G :param causal_graph: Causal graph for which to evaluate parental d-separations (G') :param causal_graph_reference: Causal graph where we test if d-separation holds (G) :param include_unconditional: Test also unconditional independencies of root nodes. :return: Validation summary with number of d-separations implied by `causal_graph` and number of times these are violated in the graph `causal_graph_reference`. """ validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_TPA, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, } triples = _get_parental_triples(causal_graph, include_unconditional) for node, non_desc, parents in triples: validation_summary[FalsifyConst.N_TESTS] += 1 if not nx.d_separated(causal_graph_reference, {node}, {non_desc}, set(parents)): validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_pd( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, n_pairs: int = -1, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, adjacent_only: bool = False, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[tuple, float]]]: """ Validate pairwise dependencies (pd) for a given causal graph and data. Test for each node if it is statistically dependent of all its ancestors. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory object, where results of previously performed tests are stored. :param n_pairs: Evaluate dependencies for n_pairs <= all pairs in the DAG. If n_pairs=-1, evaluate dependencies for all (ancestor, node) pairs (default). :param independence_test: Independence test to use for checking pairwise dependencies. :param significance_level: Significance level for independence tests. :param adjacent_only: Only test adjacent node pairs. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Summary dict: {n_violations: int, n_tests: int, p_values: {(ancestor, node): float, ...}} """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() pairs = [(ancestor, node) for node in causal_graph.nodes for ancestor in nx.ancestors(causal_graph, node)] if adjacent_only: pairs = [ (ancestor, node) for (ancestor, node) in pairs if ancestor in get_ordered_predecessors(causal_graph, node) ] if n_pairs < 0: n_pairs = len(pairs) if n_pairs > len(pairs): raise ValueError(f"n_pairs ({n_pairs}) > number of pairs in the DAG ({len(pairs)})") validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_PD, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: n_pairs, FalsifyConst.P_VALUES: dict(), } pair_idxs = np.random.choice(len(pairs), size=n_pairs, replace=False) # Find out which tests to do to_test = [] for pair_idx in pair_idxs: ancestor, node = pairs[pair_idx] if not (ancestor, node) in p_values_memory: to_test.append((ancestor, node)) p_values_memory.add_p_value(None, ancestor, node) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=ancestor, Y=node, Z=None, independence_test=independence_test, conditional_independence_test=None, seed=int(seed), ) for (ancestor, node), seed in zip(to_test, random_seeds) ) # Gather results for i, (ancestor, node) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], ancestor, node) # Summarize for pair_idx in pair_idxs: ancestor, node = pairs[pair_idx] p_value = p_values_memory.get_p_value(ancestor, node) if p_value is not None: validation_summary[FalsifyConst.P_VALUES][(node, ancestor)] = (p_value, p_value > significance_level) if p_value > significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_cm( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[tuple, float]]]: """ Function to test causal minimality of a DAG (see [1], Proposition 6.36). [1] J. Peters, D. Janzing, and B. Schölkopf, Elements of Causal Inference: Foundations and Learning Algorithms. Cambridge, MA, USA: MIT Press, 2017. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory object, where results of previously performed tests are stored. :param independence_test: Independence test to use. :param conditional_independence_test: Conditional independence test to use. :param significance_level: Significance level for independence tests. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Validation summary as dict. """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_CM, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, FalsifyConst.P_VALUES: dict(), } # Find out which tests to do triples = [] to_test = [] for node in causal_graph.nodes: parents = set(causal_graph.predecessors(node)) if parents: for p in parents: other_parents = parents.difference({p}) triples.append((node, p, other_parents)) if not (node, p, other_parents) in p_values_memory: to_test.append((node, p, other_parents)) p_values_memory.add_p_value(None, node, p, other_parents) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=node, Y=p, Z=list(other_parents), independence_test=independence_test, conditional_independence_test=conditional_independence_test, seed=int(seed), ) for (node, p, other_parents), seed in zip(to_test, random_seeds) ) # Gather results for i, (node, p, other_parents) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], node, p, other_parents) # Summarize for node, p, other_parents in triples: p_value = p_values_memory.get_p_value(node, p, other_parents) if p_value is not None: validation_summary[FalsifyConst.N_TESTS] += 1 validation_summary[FalsifyConst.P_VALUES][(node, p, tuple(other_parents))] = ( p_value, p_value > significance_level, ) if p_value > significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def run_validations( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Optional[Union[Callable, Tuple[Callable, ...], List[Callable]]] = partial( validate_lmc, independence_test=kernel_based, conditional_independence_test=kernel_based ), ) -> Dict[str, Dict]: """ Validate a given causal graph using observational data and some given methods. If methods are provided, they must be wrapped in a partial object, with their respective parameters. E.g., if one wants to test the local Markov conditions and the pairwise dependencies (unconditional faithfulness), then call run_validations(G, data, methods=( partial(validate_lmc, independence_test=..., conditional_independence_test=...), partial(validate_pd, independence_test=...), ) ) :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param methods: Method functions wrapped in wrap_partial. E.g. wrap_partial(validate_lmc, data=data, independence_test=..., conditional_independence_test=...). If no methods are provided we run validate_lmc with optional keyword arguments provided to run_validations. :return: Validation summary as dict. """ if not isinstance(methods, (tuple, list)): methods = (methods,) validation_summary = dict() for m in methods: if "data" in getfullargspec(m).args: m = partial(m, data=data) m_summary = m(causal_graph=causal_graph) m_name = m_summary.pop(FalsifyConst.METHOD) validation_summary[m_name] = m_summary return validation_summary @dataclass class EvaluationResult: """ Dataset class containing the evaluation result of falsifying a graph using a node-permutation test. ... Attributes ---------- summary : dict Dictionary containing the summary of the evaluation. significance_level : float Significance level based on which we falsify the given DAG falsifiable : bool Whether the given DAG is falsifiable. falsified : bool Whether the given DAG is falsified. """ summary: dict significance_level: float suggestions: Optional[dict] = None def update_significance_level(self, significance_level: float): """ Update the significance level to decide if we falsify a given DAG. """ self.significance_level = significance_level self.__post_init__() def __post_init__(self): self.can_evaluate = self._can_evaluate() if not self.can_evaluate: self.falsified = None self.falsifiable = None elif ( self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.P_VALUE] > self.significance_level > self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE] ): self.falsified = True self.falsifiable = True elif self.significance_level < self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE]: self.falsified = False self.falsifiable = False else: self.falsified = False self.falsifiable = True def __repr__(self): # DAG Evaluation if self.can_evaluate: decision = " " if self.falsified else " do not " informative = " " if self.falsifiable else " not " frac_MEC = f"{len(self.summary[FalsifyConst.MEC])} / {len(self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.PERM_VIOLATIONS])}" frac_VLMC = f"{self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.GIVEN_VIOLATIONS]}/{self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.N_TESTS]}" p_LMC = self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.P_VALUE] p_dSep = self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE] validation_repr = [ f"The given DAG is{informative}informative because {frac_MEC} of the permutations lie in the Markov", f"equivalence class of the given DAG (p-value: {p_dSep:.2f}).", f"The given DAG violates {frac_VLMC} LMCs and is better than {(1 - p_LMC) * 100:.1f}% of the permuted DAGs (p-value: {p_LMC:.2f}).", f"Based on the provided significance level ({self.significance_level}) and because the DAG is{informative}informative,", f"we{decision}reject the DAG.", ] else: validation_repr = ["Cannot be evaluated!"] # Suggestions suggestion_repr = {} for m in self.suggestions: suggestion_repr[FALSIFY_METHODS[m]] = [ f"Remove edge {node[1]} --> {node[0]}" for (node, r) in self.suggestions[m][FalsifyConst.P_VALUES].items() if r[1] ] return _generate_table(validation_repr, suggestion_repr) def _can_evaluate(self): can_evaluate = True for m in (FalsifyConst.VALIDATE_LMC, FalsifyConst.VALIDATE_TPA): if m not in self.summary: can_evaluate = False return can_evaluate def falsify_graph( causal_graph: DirectedGraph, data: pd.DataFrame, suggestions: bool = False, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, significance_ci: float = 0.05, n_permutations: Optional[int] = None, show_progress_bar: Optional[bool] = None, n_jobs: Optional[int] = None, plot_histogram: bool = False, plot_kwargs: Optional[Dict] = None, ) -> EvaluationResult: """ Falsify a given DAG using observational data. This method returns the result of a permutation-test to falsify a user-given DAG using observational data. To this end we construct the test statistics by testing the violations of local Markov conditions (LMC) implied by the graph using conditional independence (CI) tests. The null is the number of LMC violations of a random node-permutation of the given graph. Our test can be interpreted as whether the given graph is significantly better than random in terms of the CIs it entails. To determine whether a given graph is falsifiable by our metric, we implement a second test, which reports whether given graph is "characteristic" enough in terms of the CIs it entails. For this, we compute how many of the random node permutations lie in the same Markov equivalence class (MEC) as the given graph and conclude that the given graph is falsifiable only if the fraction of permuted DAGs in the same MEC as the given graph is "reasonably" small. The returned EvaluationResult object has two attributes: `falsified` and `falsifiable`: `falsifiable`: The given graph lies in a different MEC than >= 1-`significance_level` of the permuted DAGs `falsified`: The given graph is falsifiable and violates fewer LMCs than >= 1-`significance_level` of the permuted DAGs By default, we only run 1 / `significance_level` permutations as those are enough to falsify a graph with type I error probability `significance_level` at some given `significance_level`. If you are interested in a more exact estimate of the p-value or wish to plot a histogram to see how the given DAG compares to random node permutations, you should set `n_permutations` to some larger value (e.g. 100 or 1000). If `n_permutations=-1` we test on all n_nodes! permutations (the default if plot_histogram=True). Additionally, this method allows to return suggestions to the user (suggestions=True). This is done by testing for violations of causal minimality via `validate_cm`. Related paper: Eulig, E., Mastakouri, A. A., Blöbaum, P., Hardt, M., & Janzing, D. (2023). Toward Falsifying Causal Graphs Using a Permutation-Based Test. https://arxiv.org/abs/2305.09565 :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param suggestions: Provide suggestions to the user. At the moment the only source of suggestions comes from validating causal minimality (using validate_cm). :param independence_test: Independence test to use for checking pairwise independencies. :param conditional_independence_test: Conditional independence test to use. :param significance_level: Significance level for the permutation test. :param significance_ci: Significance level for (conditional) independence tests. :param n_permutations: Number of permutations to perform. If -1 use all n_nodes! permutations. :param show_progress_bar: Whether to show progress bar over permutations. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :param plot_histogram: Plot histogram of results from permutation baseline. :param plot_kwargs: Additional plot arguments to be passed to plot_evaluation_results. :return: EvaluationResult """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs show_progress_bar = config.show_progress_bars if show_progress_bar is None else show_progress_bar p_values_memory = _PValuesMemory() if n_permutations is None: n_permutations = int(1 / significance_level) if not plot_histogram else -1 if not plot_kwargs: plot_kwargs = {} methods = ( partial( validate_lmc, independence_test=independence_test, conditional_independence_test=conditional_independence_test, significance_level=significance_ci, p_values_memory=p_values_memory, n_jobs=n_jobs, ), partial(validate_tpa, causal_graph_reference=causal_graph), ) if suggestions: suggestion_methods = ( partial( validate_cm, independence_test=independence_test, conditional_independence_test=conditional_independence_test, significance_level=significance_ci, p_values_memory=p_values_memory, n_jobs=n_jobs, ), ) else: suggestion_methods = tuple() summary_given = run_validations( causal_graph, data, methods=methods + suggestion_methods, ) summary_perm = _permutation_based( causal_graph, data, methods=methods, exclude_original_order=False, n_permutations=n_permutations, show_progress_bar=show_progress_bar, ) summary = dict() validation_methods = set(summary_perm.keys()) - {FalsifyConst.PERM_GRAPHS} for m in validation_methods: summary[m] = dict() summary[m][FalsifyConst.PERM_VIOLATIONS] = [perm[FalsifyConst.N_VIOLATIONS] for perm in summary_perm[m]] summary[m][FalsifyConst.GIVEN_VIOLATIONS] = summary_given[m][FalsifyConst.N_VIOLATIONS] summary[m][FalsifyConst.N_TESTS] = summary_given[m][FalsifyConst.N_TESTS] summary[m][FalsifyConst.F_PERM_VIOLATIONS] = [ perm[FalsifyConst.N_VIOLATIONS] / perm[FalsifyConst.N_TESTS] for perm in summary_perm[m] ] summary[m][FalsifyConst.F_GIVEN_VIOLATIONS] = ( summary[m][FalsifyConst.GIVEN_VIOLATIONS] / summary[m][FalsifyConst.N_TESTS] ) summary[m][FalsifyConst.P_VALUE] = sum( [ 1 for perm in summary[m][FalsifyConst.F_PERM_VIOLATIONS] if perm <= summary[m][FalsifyConst.F_GIVEN_VIOLATIONS] ] ) / len(summary[m][FalsifyConst.PERM_VIOLATIONS]) if m != FalsifyConst.VALIDATE_TPA: # Append list of violations (node, non_desc) to get local information summary[m][FalsifyConst.LOCAL_VIOLATION_INSIGHT] = summary_given[m][FalsifyConst.P_VALUES] if FalsifyConst.VALIDATE_TPA in summary: summary[FalsifyConst.MEC] = [ summary_perm[FalsifyConst.PERM_GRAPHS][i] for i, v in enumerate(summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.PERM_VIOLATIONS]) if v == 0 ] result = EvaluationResult( summary=summary, significance_level=significance_level, suggestions={m: summary_given[m] for m in summary_given if m not in validation_methods}, ) if plot_histogram: plot_evaluation_results(result, **plot_kwargs) return result def apply_suggestions( causal_graph: DirectedGraph, evaluation_result: EvaluationResult, edges_to_keep: Optional[List[Tuple[Any, Any]]] = None, ): if not hasattr(evaluation_result, "suggestions"): raise ValueError("EvaluationResult object has no attribute suggestions. Please run with suggestion=True!") causal_graph = causal_graph.copy() for m in evaluation_result.suggestions: for node, res in evaluation_result.suggestions[m][FalsifyConst.P_VALUES].items(): edge = (node[1], node[0]) if (res[1] and edges_to_keep is not None and edge not in edges_to_keep) or ( res[1] and edges_to_keep is None ): causal_graph.remove_edge(edge[0], edge[1]) return causal_graph def plot_evaluation_results(evaluation_result, figsize=(8, 3), bins=None, title="", savepath="", display=True): fig, ax = plt.subplots(figsize=figsize) # Plot histograms p_values = "" data = [] labels = [] evaluation_summary = {k: v for k, v in evaluation_result.summary.items() if k != FalsifyConst.MEC} for i, (m, m_summary) in enumerate(evaluation_summary.items()): data.append(m_summary[FalsifyConst.F_PERM_VIOLATIONS]) labels.append(f"Violations of {FALSIFY_METHODS[m]} of permuted DAGs") p_values += f"p-value {FALSIFY_METHODS[m]} = {m_summary[FalsifyConst.P_VALUE]:.2f}\n" ax.hist(data, color=COLORS[: len(evaluation_summary)], bins=bins, alpha=0.5, label=labels, edgecolor="k") # Plot given violations for i, (m, m_summary) in enumerate(evaluation_summary.items()): ylim = ax.get_ylim()[1] ax.plot( [m_summary[FalsifyConst.F_GIVEN_VIOLATIONS]] * 2, [0, ylim], "--", c=COLORS[i], label=f"Violations of {FALSIFY_METHODS[m]} of given DAG", ) ax.set_ylim([0, ylim]) ax.set_xlabel("Fraction of violations") ax.set_ylabel("# Permutations") plt.legend(loc="upper left", bbox_to_anchor=(1.05, 1), borderaxespad=0.0, title=p_values) if title: plt.title(title) plt.tight_layout() if savepath: plt.savefig(savepath, dpi=300, bbox_inches="tight") if display: plt.show() else: plt.close() def plot_local_insights( causal_graph: DirectedGraph, evaluation_result: Union[EvaluationResult, Dict], method: Optional[str] = FalsifyConst.VALIDATE_LMC, ): """ For some given graph and evaluation result plot local violations. :param causal_graph: DiGraph :param evaluation_result: EvaluationResult :param method: Method for which to plot violations """ colors = {} if isinstance(evaluation_result, EvaluationResult) and method in evaluation_result.summary: local_insight_dict = evaluation_result.summary[method][FalsifyConst.LOCAL_VIOLATION_INSIGHT] elif ( isinstance(evaluation_result, EvaluationResult) and hasattr(evaluation_result, "suggestions") and method in evaluation_result.suggestions ): local_insight_dict = evaluation_result.suggestions[method][FalsifyConst.P_VALUES] elif isinstance(evaluation_result, Dict): if method not in evaluation_result: raise ValueError(f"Validation method {method} does not exist in given evaluation_result!") if FalsifyConst.P_VALUES not in evaluation_result[method]: raise ValueError( f"Validation method {method} has no key {FalsifyConst.P_VALUES} where information on local violations " f"are stored!" ) local_insight_dict = evaluation_result[method][FalsifyConst.P_VALUES] else: raise ValueError(f"Cannot plot local violation insights from method {method} in evaluation_result!") for nodes, result in local_insight_dict.items(): if result[1]: if method == FalsifyConst.VALIDATE_LMC: # For LMC we highlight X for which X _|/|_ Y \in ND_X | Pa_X colors[nodes[0]] = VIOLATION_COLOR elif method == FalsifyConst.VALIDATE_PD: # For PD we highlight the edge (if Y\in Anc_X -> X are adjacent) colors[(nodes[1], nodes[0])] = VIOLATION_COLOR elif method == FalsifyConst.VALIDATE_CM: # For causal minimality we highlight the edge Y \in Pa_X -> X colors[(nodes[1], nodes[0])] = VIOLATION_COLOR plot(causal_graph, colors=colors) def _generate_table( validation_repr, suggestion_repr, width=105, validation_name="Falsificaton Summary", suggestion_name="Suggestions" ): # Create Validation header _repr = [ "+" + "-" * (width - 2) + "+\n", "|" + " " * int(np.floor((width - 2 - len(validation_name)) / 2)), validation_name, " " * int(np.ceil((width - 2 - len(validation_name)) / 2)) + "|\n", "+" + "-" * (width - 2) + "+\n", ] # Create Validation summary _repr += [f"| {v_line}" + " " * (width - 3 - len(v_line)) + "|\n" for v_line in validation_repr] # Close Validation _repr += ["+" + "-" * (width - 2) + "+\n"] # Create Suggestions header if suggestion_repr: _repr += [ "|" + " " * int(np.floor((width - 2 - len(suggestion_name)) / 2)), suggestion_name, " " * int(np.ceil((width - 2 - len(suggestion_name)) / 2)) + "|\n", "+" + "-" * (width - 2) + "+\n", ] # Iterate over suggestions for m, suggestions in suggestion_repr.items(): left_col = "| " + m + " |" if not suggestions: right_col = " " + " " * (width - 2 - len(left_col)) + "|\n" _repr += [left_col, right_col] for i, s in enumerate(suggestions): if i > 0: left_col = "| " + " " * len(m) + " |" right_col = " - " + s + " " * (width - 4 - len(s) - len(left_col)) + "|\n" _repr += [left_col, right_col] _repr += ["+" + "-" * (width - 2) + "+\n"] return "".join(_repr)[:-1] def _compute_p_value( data: pd.DataFrame, X: Union[List, str], Y: Union[List, str], Z: Optional[Union[Set, List, str]], independence_test: Optional[Callable[[np.ndarray, np.ndarray], float]], conditional_independence_test: Optional[Callable[[np.ndarray, np.ndarray, np.ndarray], float]], seed: int, ) -> float: """Perform (conditional) independence test and report p-value. If any nodes needed for the test do not exist in the data, this test is skipped and a value of -1 is returned and a warning is raised. :param data: Observations of variables in the DAG. :param X: Variable to test (conditional) independence with Y :param Y: Variable to test (conditional) independence with X :param Z: Set to condition independence test on. Can be empty (None, empty set, or empty list). :param independence_test: Independence test to use. :param conditional_independence_test: Conditional independence test to use. :param seed: Random seed :return: p-value """ set_random_seed(seed) # Test if we have data for X and Y for node in [X, Y]: if not node in data.columns: warnings.warn(f"WARN: Couldn't find data for node {node}. Skip this test.") return if Z: # Test if we have data for Z for node in Z: if not node in data.columns: warnings.warn(f"WARN: Couldn't find data for node {node}. Skip this test.") return p_value = conditional_independence_test(data[X].values, data[Y].values, data[Z].values) else: p_value = independence_test(data[X].values, data[Y].values) return p_value def _get_parental_triples(causal_graph: DirectedGraph, include_unconditional: bool): """ For a given graph collect all parental triples, that is, the triple (X, Y, Z) is a parental triple iff Y is non-descendant of X, and Z are the parents of X (can be empty if include_unconditional=True) """ triples = [] for node in causal_graph.nodes: parents = get_ordered_predecessors(causal_graph, node) non_descendants = _get_non_descendants(causal_graph, node, exclude_parents=True) if (parents or include_unconditional) and non_descendants: for non_desc in non_descendants: triples.append((node, non_desc, parents)) return triples def _permutation_based( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Union[Callable, Tuple[Callable, ...], List[Callable]], exclude_original_order: bool, n_permutations: int, show_progress_bar: bool, ) -> Dict[str, List[Union[DirectedGraph, Dict]]]: """ Generate baseline for node permutations. :param causal_graph: A directed acyclic graph (DAG). :param methods: Validation methods to perform. :param exclude_original_order: Exclude the original ordering of the nodes (default=False) :param n_permutations: Number of permutations to perform. If -1 use all n_nodes! - int(exclude_orig) permutations :param show_progress_bar: Whether to show progress bar over tested permutations. :return: Dictionary containing summary of validation for each individual graph as well as the permuted graphs """ if not isinstance(methods, (tuple, list)): methods = (methods,) perm_gen = _PermuteNodes(causal_graph, n_permutations=n_permutations, exclude_original_order=exclude_original_order) validation_summary = {FalsifyConst.PERM_GRAPHS: []} for permuted_graph in tqdm(perm_gen, desc="Test permutations of given graph", disable=not show_progress_bar): res = run_validations( causal_graph=permuted_graph, data=data, methods=methods, ) validation_summary[FalsifyConst.PERM_GRAPHS].append(permuted_graph) for m_name, summary in res.items(): if m_name not in validation_summary: validation_summary[m_name] = [] validation_summary[m_name].append(summary) return validation_summary class _PermuteNodes: def __init__(self, causal_graph: DirectedGraph, exclude_original_order: bool, n_permutations: int): """ Randomly permute the nodes of a given causal graph while keeping the underlying graph structure the same. :param causal_graph: A directed acyclic graph (DAG). :param exclude_original_order: Do not return the original order. :param n_permutations: Return a generator with n_permutations permutations. If n_permutations = -1 (default), we return all n_nodes! - int(exclude_orig) permutations. :return: Copy of causal_graph with nodes randomly permuted. """ self.causal_graph = causal_graph self.exclude_original_order = exclude_original_order self.n_permutations = n_permutations self.max_perms = np.math.factorial(self.causal_graph.number_of_nodes()) - int(self.exclude_original_order) if n_permutations == -1 or n_permutations > self.max_perms: self.it = self.iter_all_permutations() self.length = self.max_perms if self.length > 2**63 - 1: raise ValueError( f"Too many permutations specified. Did you accidently set 'n_permutations'=-1 for a " f"large (>20 nodes) graph? " f"Given graph has {causal_graph.number_of_nodes()} nodes." ) else: self.it = self.iter_random_permutations() self.length = self.n_permutations def iter_all_permutations(self): for i, perm in enumerate(permutations(self.causal_graph.nodes)): if self.exclude_original_order and i == 0: continue mapping = {node: perm[i] for i, node in enumerate(self.causal_graph.nodes)} yield nx.relabel_nodes(self.causal_graph, mapping, copy=True) def iter_random_permutations(self): for _ in range(self.n_permutations): if self.exclude_original_order: is_orig = True while is_orig: perm = list(np.random.permutation(self.causal_graph.nodes)) if perm != list(self.causal_graph.nodes): is_orig = False else: perm = list(np.random.permutation(self.causal_graph.nodes)) mapping = {node: perm[i] for i, node in enumerate(self.causal_graph.nodes)} yield nx.relabel_nodes(self.causal_graph, mapping, copy=True) def __iter__(self): yield from self.it def __len__(self): return self.length def _to_frozenset(x: Union[Set, List, str]): """Converts a set, list or string into a hashable frozenset""" assert ( isinstance(x, Set) or isinstance(x, List) or isinstance(x, str) ), f"{x} must be list, set or str. Got {type(x)} instead!" if isinstance(x, str): return frozenset({x}) return frozenset(x)

eeulig

379f188d674d5be6d644559f9f54852b87403f7c

4e30c63fe1113e35259e7fb3400a6f09bca4d699

`tpa` stands for parental triple. This is how we call it in the paper and I think this unifies it a bit. I explained it in the docstring now!

eeulig

py-why/dowhy

Remove kwargs from falsify, improve docstrings

This commit updates `gcm/falsify` in the following ways: - Remove the `**kwargs` for `validate_graph` and `falsify_graph`. Instead, validation methods given to those functions should be wrapped in `wrap_partial`, e.g. if we want to validate a graph `G` and test local Markov conditions and pairwise dependencies on observations `data`, we would do ``` validate_graph(G, methods=( wrap_partial(validate_lmc, data=data, independence_test=..., conditional_independence_test=...), wrap_partial(validate_pd, data=data, independence_test=...), ) ) ``` By default (`methods=None`), `validate_graph` tests local Markov conditions and `falsify_graph` tests local Markov conditions and d-separation of parental triples. In this case `data` has to be set and optionally one can provide `independence_test`, `conditional_independence_test`, `significance_level`, and `n_jobs` via the keyword arguments of `validate_graph` / `falsify_graph`. - Use `Enum` for some of the constants - In `_PValuesMemory` the method `add_p_value ` always sets a p-value even if it was set already. This removes the need for the method `clear_placeholders` and allows `p_values` to be set to `-1`, indicating that the CI is not testable (because of missing data for a node, c.f. `_compute_p_value`) - If for some CI: X, Y | Z any of the nodes has no data (column missing in the provided dataframe), we skip this test and raise a warning. - Instead of using the raw number of violations in the plotted histogram and to compute the p-value, we use the relative number of violations. This will result in different p-values only if we have missing data for some nodes (and thus the number of tests is potentially different between different permutations) - Improve docstrings

2023-07-04 13:58:30+00:00

2023-07-18 14:19:10+00:00

dowhy/gcm/falsify.py

"""This module provides functionality to falsify a user-given DAG given observed data. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from dataclasses import dataclass, field from enum import Enum, auto from functools import partial from itertools import permutations from typing import Any, Callable, Dict, FrozenSet, List, Optional, Set, Tuple, Union import matplotlib.colors as mcolors import matplotlib.pyplot as plt import networkx as nx import numpy as np import pandas as pd from joblib import Parallel, delayed from tqdm import tqdm import dowhy.gcm.config as config from dowhy.gcm.independence_test import kernel_based from dowhy.gcm.util import plot from dowhy.gcm.util.general import set_random_seed from dowhy.gcm.validation import _get_non_descendants from dowhy.graph import DirectedGraph, get_ordered_predecessors VIOLATION_COLOR = "red" COLORS = list(mcolors.TABLEAU_COLORS.values()) class FalsifyConst(Enum): N_VIOLATIONS = auto() N_TESTS = auto() P_VALUE = auto() P_VALUES = auto() GIVEN_VIOLATIONS = auto() PERM_VIOLATIONS = auto() F_GIVEN_VIOLATIONS = auto() F_PERM_VIOLATIONS = auto() LOCAL_VIOLATION_INSIGHT = auto() METHOD = auto() VALIDATE_LMC = auto() VALIDATE_TPA = auto() VALIDATE_PD = auto() VALIDATE_CM = auto() PERM_GRAPHS = auto() MEC = auto() FALSIFY_METHODS = { FalsifyConst.VALIDATE_LMC: "LMC", FalsifyConst.VALIDATE_PD: "Faithfulness", FalsifyConst.VALIDATE_TPA: "tPa", FalsifyConst.VALIDATE_CM: "Causal Minimality", } @dataclass class _PValuesMemory: """A class to store and access results of independence tests. This class is useful if, e.g. we validate LMC on graph X -> Y -> Z and also on the permuted nodes Z -> Y -> X. Since X ind Z | Y == Z ind X | Y, we only need to perform the conditional independence test once. We use frozensets of nodes since standard (immutable) sets are not hashable. """ p_values: Dict[Tuple[FrozenSet, FrozenSet, FrozenSet], float] = field(default_factory=dict) def add_p_value( self, p_value: Union[None, float], X: Union[Set, List, str], Y: Union[Set, List, str], Z: Optional[Union[Set, List, str]] = None, ): if not Z: Z = set() self.p_values[(_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z))] = p_value def get_p_value( self, X: Union[Set, List, str], Y: Union[Set, List, str], Z: Optional[Union[Set, List, str]] = None ): if not Z: Z = set() # Independence tests are symmetric if (_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z)) in self.p_values: return self.p_values[(_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z))] elif (_to_frozenset(Y), _to_frozenset(X), _to_frozenset(Z)) in self.p_values: return self.p_values[(_to_frozenset(Y), _to_frozenset(X), _to_frozenset(Z))] return def __contains__(self, item: Tuple[Union[Set, List, str], ...]) -> bool: X, Y = (_to_frozenset(i) for i in item[:2]) if len(item) == 2 or item[2] is None: Z = frozenset() else: Z = _to_frozenset(item[2]) return (X, Y, Z) in self.p_values or (Y, X, Z) in self.p_values def validate_lmc( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, include_unconditional: bool = True, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[str, float]]]: """ Validate the local markov condition for a given directed graph. Return number of violations and p values for each node. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory instance, where results of previously performed tests are stored. :param independence_test: Test to use for unconditional independencies (only used if include_unconditional=True) :param conditional_independence_test: Conditional independence test to use for checking local Markov condition. :param significance_level: Significance level for (conditional) independence tests. :param include_unconditional: Test also unconditional independencies of root nodes. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Outcome of validation containing number of violations in the graph and p values/violation for each tuple (node, non_desc) """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_LMC, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, FalsifyConst.P_VALUES: dict(), } # Find out which tests to do triples = _get_parental_triples(causal_graph, include_unconditional) to_test = [] for node, non_desc, parents in triples: if not (node, non_desc, parents) in p_values_memory: to_test.append((node, non_desc, parents)) p_values_memory.add_p_value(None, node, non_desc, parents) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=node, Y=non_desc, Z=parents, independence_test=independence_test, conditional_independence_test=conditional_independence_test, seed=int(seed), ) for (node, non_desc, parents), seed in zip(to_test, random_seeds) ) # Gather results for i, (node, non_desc, parents) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], node, non_desc, parents) # Summarize for node, non_desc, parents in triples: lmc_p_value = p_values_memory.get_p_value(node, non_desc, parents) if lmc_p_value is not None: validation_summary[FalsifyConst.N_TESTS] += 1 validation_summary[FalsifyConst.P_VALUES][(node, non_desc)] = ( lmc_p_value, lmc_p_value <= significance_level, ) if lmc_p_value <= significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_tpa( causal_graph: DirectedGraph, causal_graph_reference: DirectedGraph, include_unconditional: bool = True, data: Optional[pd.DataFrame] = None, ) -> Dict[str, int]: """ Graphical criterion to evaluate which pairwise parental d-separations (parental triples) in `causal_graph` are violated, assuming `causal_graph_reference` is the ground truth graph. If none are violated, then both graphs lie in the same Markov equivalence class. Specifically we test: X _|_G' Y | Z and X _/|_G Y | Z for Y \in ND{X}^G', Z = PA{X}^G :param causal_graph: Causal graph for which to evaluate parental d-separations (G') :param causal_graph_reference: Causal graph where we test if d-separation holds (G) :param include_unconditional: Test also unconditional independencies of root nodes. :param data: IGNORED! No data is needed for this validation method and thus the data argument is ignored! :return: Validation summary with number of d-separations implied by `causal_graph` and number of times these are violated in the graph `causal_graph_reference`. """ validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_TPA, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, } triples = _get_parental_triples(causal_graph, include_unconditional) for node, non_desc, parents in triples: validation_summary[FalsifyConst.N_TESTS] += 1 if not nx.d_separated(causal_graph_reference, {node}, {non_desc}, set(parents)): validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_pd( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, n_pairs: int = -1, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, adjacent_only: bool = False, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[tuple, float]]]: """ Validate pairwise dependencies (pd) for a given causal graph and data. Test for each node if it is statistically dependent of all its ancestors. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory object, where results of previously performed tests are stored. :param n_pairs: Evaluate dependencies for n_pairs <= all pairs in the DAG. If n_pairs=-1, evaluate dependencies for all (ancestor, node) pairs (default). :param independence_test: Independence test to use for checking pairwise dependencies. :param significance_level: Significance level for independence tests. :param adjacent_only: Only test adjacent node pairs. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Summary dict: {n_violations: int, n_tests: int, p_values: {(ancestor, node): float, ...}} """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() pairs = [(ancestor, node) for node in causal_graph.nodes for ancestor in nx.ancestors(causal_graph, node)] if adjacent_only: pairs = [ (ancestor, node) for (ancestor, node) in pairs if ancestor in get_ordered_predecessors(causal_graph, node) ] if n_pairs < 0: n_pairs = len(pairs) if n_pairs > len(pairs): raise ValueError(f"n_pairs ({n_pairs}) > number of pairs in the DAG ({len(pairs)})") validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_PD, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: n_pairs, FalsifyConst.P_VALUES: dict(), } pair_idxs = np.random.choice(len(pairs), size=n_pairs, replace=False) # Find out which tests to do to_test = [] for pair_idx in pair_idxs: ancestor, node = pairs[pair_idx] if not (ancestor, node) in p_values_memory: to_test.append((ancestor, node)) p_values_memory.add_p_value(None, ancestor, node) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=ancestor, Y=node, Z=None, independence_test=independence_test, conditional_independence_test=None, seed=int(seed), ) for (ancestor, node), seed in zip(to_test, random_seeds) ) # Gather results for i, (ancestor, node) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], ancestor, node) # Summarize for pair_idx in pair_idxs: ancestor, node = pairs[pair_idx] p_value = p_values_memory.get_p_value(ancestor, node) if p_value is not None: validation_summary[FalsifyConst.P_VALUES][(node, ancestor)] = (p_value, p_value > significance_level) if p_value > significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_cm( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[tuple, float]]]: """ Function to test causal minimality of a DAG (see [1], Proposition 6.36). [1] J. Peters, D. Janzing, and B. Schölkopf, Elements of Causal Inference: Foundations and Learning Algorithms. Cambridge, MA, USA: MIT Press, 2017. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory object, where results of previously performed tests are stored. :param independence_test: Independence test to use. :param conditional_independence_test: Conditional independence test to use. :param significance_level: Significance level for independence tests. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Validation summary as dict. """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_CM, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, FalsifyConst.P_VALUES: dict(), } # Find out which tests to do triples = [] to_test = [] for node in causal_graph.nodes: parents = set(causal_graph.predecessors(node)) if parents: for p in parents: other_parents = parents.difference({p}) triples.append((node, p, other_parents)) if not (node, p, other_parents) in p_values_memory: to_test.append((node, p, other_parents)) p_values_memory.add_p_value(None, node, p, other_parents) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=node, Y=p, Z=list(other_parents), independence_test=independence_test, conditional_independence_test=conditional_independence_test, seed=int(seed), ) for (node, p, other_parents), seed in zip(to_test, random_seeds) ) # Gather results for i, (node, p, other_parents) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], node, p, other_parents) # Summarize for node, p, other_parents in triples: p_value = p_values_memory.get_p_value(node, p, other_parents) if p_value is not None: validation_summary[FalsifyConst.N_TESTS] += 1 validation_summary[FalsifyConst.P_VALUES][(node, p, tuple(other_parents))] = ( p_value, p_value > significance_level, ) if p_value > significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_graph( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Optional[Union[Callable, Tuple[Callable, ...], List[Callable]]] = partial( validate_lmc, independence_test=kernel_based, conditional_independence_test=kernel_based ), ) -> Dict[str, Dict]: """ Validate a given causal graph using observational data and some given methods. If methods are provided, they must be wrapped in a partial object, with their respective parameters. E.g., if one wants to test the local Markov conditions and the pairwise dependencies (unconditional faithfulness), then call validate_graph(G, data, methods=( partial(validate_lmc, independence_test=..., conditional_independence_test=...), partial(validate_pd, independence_test=...), ) ) :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param methods: Method functions wrapped in wrap_partial. E.g. wrap_partial(validate_lmc, data=data, independence_test=..., conditional_independence_test=...). If no methods are provided we run validate_lmc with optional keyword arguments provided to validate_graph. :return: Validation summary as dict. """ if not isinstance(methods, (tuple, list)): methods = (methods,) validation_summary = dict() for m in methods: m_summary = m(causal_graph=causal_graph, data=data) m_name = m_summary.pop(FalsifyConst.METHOD) validation_summary[m_name] = m_summary return validation_summary @dataclass class EvaluationResult: """ Dataset class containing the evaluation result of falsifying a graph using a node-permutation test. ... Attributes ---------- summary : dict Dictionary containing the summary of the evaluation. significance_level : float Significance level based on which we falsify the given DAG falsifiable : bool Whether the given DAG is falsifiable. falsified : bool Whether the given DAG is falsified. """ summary: dict significance_level: float suggestions: Optional[dict] = None def update_significance_level(self, significance_level: float): """ Update the significance level to decide if we falsify a given DAG. """ self.significance_level = significance_level self.__post_init__() def __post_init__(self): self.can_evaluate = self._can_evaluate() if not self.can_evaluate: self.falsified = None self.falsifiable = None elif ( self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.P_VALUE] > self.significance_level > self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE] ): self.falsified = True self.falsifiable = True elif self.significance_level < self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE]: self.falsified = False self.falsifiable = False else: self.falsified = False self.falsifiable = True def __repr__(self): # DAG Evaluation if self.can_evaluate: decision = " " if self.falsified else " do not " informative = " " if self.falsifiable else " not " frac_MEC = f"{len(self.summary[FalsifyConst.MEC])} / {len(self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.PERM_VIOLATIONS])}" frac_VLMC = f"{self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.GIVEN_VIOLATIONS]}/{self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.N_TESTS]}" p_LMC = self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.P_VALUE] p_dSep = self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE] validation_repr = [ f"The given DAG is{informative}informative because {frac_MEC} of the permutations lie in the Markov", f"equivalence class of the given DAG (p-value: {p_dSep:.2f}).", f"The given DAG violates {frac_VLMC} LMCs and is better than {(1 - p_LMC) * 100:.1f}% of the permuted DAGs (p-value: {p_LMC:.2f}).", f"Based on the provided significance level ({self.significance_level}) and because the DAG is{informative}informative,", f"we{decision}reject the DAG.", ] else: validation_repr = ["Cannot be evaluated!"] # Suggestions suggestion_repr = {} for m in self.suggestions: suggestion_repr[FALSIFY_METHODS[m]] = [ f"Remove edge {node[1]} --> {node[0]}" for (node, r) in self.suggestions[m][FalsifyConst.P_VALUES].items() if r[1] ] return _generate_table(validation_repr, suggestion_repr) def _can_evaluate(self): can_evaluate = True for m in (FalsifyConst.VALIDATE_LMC, FalsifyConst.VALIDATE_TPA): if m not in self.summary: can_evaluate = False return can_evaluate def falsify_graph( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Optional[Union[Callable, Tuple[Callable, ...]]] = None, suggestion_methods: Optional[Union[Callable, Tuple[Callable, ...]]] = None, suggestions: bool = False, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, significance_ci: float = 0.05, n_permutations: Optional[int] = None, show_progress_bar: Optional[bool] = None, n_jobs: Optional[int] = None, plot_histogram: bool = False, plot_kwargs: Optional[Dict] = None, ) -> EvaluationResult: """ Falsify a given DAG using observational data. This method returns the result of a permutation-test to falsify a user-given DAG using observational data. To this end we construct the test statistics by testing the violations of local Markov conditions (LMC) implied by the graph using conditional independence (CI) tests. The null is the number of LMC violations of a random node-permutation of the given graph. Our test can be interpreted as whether the given graph is significantly better than random in terms of the CIs it entails. To determine whether a given graph is falsifiable by our metric, we implement a second test, which reports whether given graph is "characteristic" enough in terms of the CIs it entails. For this, we compute how many of the random node permutations lie in the same Markov equivalence class (MEC) as the given graph and conclude that the given graph is falsifiable only if the fraction of permuted DAGs in the same MEC as the given graph is "reasonably" small. The returned EvaluationResult object has two attributes: `falsified` and `falsifiable`: `falsifiable`: The given graph lies in a different MEC than >= 1-`significance_level` of the permuted DAGs `falsified`: The given graph is falsifiable and violates fewer LMCs than >= 1-`significance_level` of the permuted DAGs By default, we only run 1 / `significance_level` permutations as those are enough to falsify a graph with type I error probability `significance_level` at some given `significance_level`. If you are interested in a more exact estimate of the p-value of whish to plot a histogram to see how the given DAG compares to random node permutations, you should set `n_permutations` to some larger value (e.g. 100 or 1000). If `n_permutations=-1` we test on all n_nodes! permutations. `methods` and `suggestion_methods` must be wrapped in partial(method, **kwargs) (c.f. `validate_graph`). Related paper: Eulig, E., Mastakouri, A. A., Blöbaum, P., Hardt, M., & Janzing, D. (2023). Toward Falsifying Causal Graphs Using a Permutation-Based Test. https://arxiv.org/abs/2305.09565 :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param methods: Validation methods to perform. :param suggestion_methods: Methods to run on the given graph to provide additional suggestions. :param suggestions: Provide suggestions generated using the `suggestion_methods`. :param independence_test: Independence test to use for checking pairwise independencies. :param conditional_independence_test: Conditional independence test to use. :param significance_level: Significance level for the permutation test. :param significance_ci: Significance level for (conditional) independence tests. :param n_permutations: Number of permutations to perform. If -1 use all n_nodes! permutations. :param show_progress_bar: Whether to show progress bar over permutations. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :param plot_histogram: Plot histogram of results from permutation baseline. :param plot_kwargs: Additional plot arguments to be passed to plot_evaluation_results. :return: EvaluationResult """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs show_progress_bar = config.show_progress_bars if show_progress_bar is None else show_progress_bar p_values_memory = _PValuesMemory() if n_permutations is None: n_permutations = int(1 / significance_level) if not plot_histogram else -1 if not plot_kwargs: plot_kwargs = {} # If no methods are provided, use default ones: validate_lmc, validate_tpa if not methods: methods = ( partial( validate_lmc, independence_test=independence_test, conditional_independence_test=conditional_independence_test, significance_level=significance_ci, p_values_memory=p_values_memory, n_jobs=n_jobs, ), partial(validate_tpa, causal_graph_reference=causal_graph), ) elif isinstance(methods, Callable): methods = (methods,) # If no suggestion methods are provided, but suggestions=True, use default ones: validate_cm if not suggestions: suggestion_methods = tuple() elif suggestions and isinstance(suggestion_methods, Callable): suggestion_methods = (suggestion_methods,) elif suggestions: suggestion_methods = ( partial( validate_cm, independence_test=independence_test, conditional_independence_test=conditional_independence_test, significance_level=significance_ci, p_values_memory=p_values_memory, n_jobs=n_jobs, ), ) summary_given = validate_graph( causal_graph, data, methods=methods + suggestion_methods, ) summary_perm = _permutation_based( causal_graph, data, methods=methods, exclude_original_order=False, n_permutations=n_permutations, show_progress_bar=show_progress_bar, ) summary = dict() validation_methods = set(summary_perm.keys()) - {FalsifyConst.PERM_GRAPHS} for m in validation_methods: summary[m] = dict() summary[m][FalsifyConst.PERM_VIOLATIONS] = [perm[FalsifyConst.N_VIOLATIONS] for perm in summary_perm[m]] summary[m][FalsifyConst.GIVEN_VIOLATIONS] = summary_given[m][FalsifyConst.N_VIOLATIONS] summary[m][FalsifyConst.N_TESTS] = summary_given[m][FalsifyConst.N_TESTS] summary[m][FalsifyConst.F_PERM_VIOLATIONS] = [ perm[FalsifyConst.N_VIOLATIONS] / perm[FalsifyConst.N_TESTS] for perm in summary_perm[m] ] summary[m][FalsifyConst.F_GIVEN_VIOLATIONS] = ( summary[m][FalsifyConst.GIVEN_VIOLATIONS] / summary[m][FalsifyConst.N_TESTS] ) summary[m][FalsifyConst.P_VALUE] = sum( [ 1 for perm in summary[m][FalsifyConst.F_PERM_VIOLATIONS] if perm <= summary[m][FalsifyConst.F_GIVEN_VIOLATIONS] ] ) / len(summary[m][FalsifyConst.PERM_VIOLATIONS]) if m != FalsifyConst.VALIDATE_TPA: # Append list of violations (node, non_desc) to get local information summary[m][FalsifyConst.LOCAL_VIOLATION_INSIGHT] = summary_given[m][FalsifyConst.P_VALUES] if FalsifyConst.VALIDATE_TPA in summary: summary[FalsifyConst.MEC] = [ summary_perm[FalsifyConst.PERM_GRAPHS][i] for i, v in enumerate(summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.PERM_VIOLATIONS]) if v == 0 ] result = EvaluationResult( summary=summary, significance_level=significance_level, suggestions={m: summary_given[m] for m in summary_given if m not in validation_methods}, ) if plot_histogram: plot_evaluation_results(result, **plot_kwargs) return result def apply_suggestions( causal_graph: DirectedGraph, evaluation_result: EvaluationResult, edges_to_keep: Optional[List[Tuple[Any, Any]]] = None, ): if not hasattr(evaluation_result, "suggestions"): raise ValueError("EvaluationResult object has no attribute suggestions. Please run with suggestion=True!") causal_graph = causal_graph.copy() for m in evaluation_result.suggestions: for node, res in evaluation_result.suggestions[m][FalsifyConst.P_VALUES].items(): edge = (node[1], node[0]) if (res[1] and edges_to_keep is not None and edge not in edges_to_keep) or ( res[1] and edges_to_keep is None ): causal_graph.remove_edge(edge[0], edge[1]) return causal_graph def plot_evaluation_results(evaluation_result, figsize=(8, 3), bins=None, title="", savepath="", display=True): fig, ax = plt.subplots(figsize=figsize) # Plot histograms p_values = "" data = [] labels = [] evaluation_summary = {k: v for k, v in evaluation_result.summary.items() if k != FalsifyConst.MEC} for i, (m, m_summary) in enumerate(evaluation_summary.items()): data.append(m_summary[FalsifyConst.F_PERM_VIOLATIONS]) labels.append(f"Violations of {FALSIFY_METHODS[m]} of permuted DAGs") p_values += f"p-value {FALSIFY_METHODS[m]} = {m_summary[FalsifyConst.P_VALUE]:.2f}\n" ax.hist(data, color=COLORS[: len(evaluation_summary)], bins=bins, alpha=0.5, label=labels, edgecolor="k") # Plot given violations for i, (m, m_summary) in enumerate(evaluation_summary.items()): ylim = ax.get_ylim()[1] ax.plot( [m_summary[FalsifyConst.F_GIVEN_VIOLATIONS]] * 2, [0, ylim], "--", c=COLORS[i], label=f"Violations of {FALSIFY_METHODS[m]} of given DAG", ) ax.set_ylim([0, ylim]) ax.set_xlabel("Fraction of violations") ax.set_ylabel("# Permutations") plt.legend(loc="upper left", bbox_to_anchor=(1.05, 1), borderaxespad=0.0, title=p_values) if title: plt.title(title) plt.tight_layout() if savepath: plt.savefig(savepath, dpi=300, bbox_inches="tight") if display: plt.show() else: plt.close() def plot_local_insights( causal_graph: DirectedGraph, evaluation_result: Union[EvaluationResult, Dict], method: Optional[str] = FalsifyConst.VALIDATE_LMC, ): """ For some given graph and evaluation result plot local violations. :param causal_graph: DiGraph :param evaluation_result: EvaluationResult :param method: Method for which to plot violations """ colors = {} if isinstance(evaluation_result, EvaluationResult) and method in evaluation_result.summary: local_insight_dict = evaluation_result.summary[method][FalsifyConst.LOCAL_VIOLATION_INSIGHT] elif ( isinstance(evaluation_result, EvaluationResult) and hasattr(evaluation_result, "suggestions") and method in evaluation_result.suggestions ): local_insight_dict = evaluation_result.suggestions[method][FalsifyConst.P_VALUES] elif isinstance(evaluation_result, Dict): if method not in evaluation_result: raise ValueError(f"Validation method {method} does not exist in given evaluation_result!") if FalsifyConst.P_VALUES not in evaluation_result[method]: raise ValueError( f"Validation method {method} has no key {FalsifyConst.P_VALUES} where information on local violations " f"are stored!" ) local_insight_dict = evaluation_result[method][FalsifyConst.P_VALUES] else: raise ValueError(f"Cannot plot local violation insights from method {method} in evaluation_result!") for nodes, result in local_insight_dict.items(): if result[1]: if method == FalsifyConst.VALIDATE_LMC: # For LMC we highlight X for which X _|/|_ Y \in ND_X | Pa_X colors[nodes[0]] = VIOLATION_COLOR elif method == FalsifyConst.VALIDATE_PD: # For PD we highlight the edge (if Y\in Anc_X -> X are adjacent) colors[(nodes[1], nodes[0])] = VIOLATION_COLOR elif method == FalsifyConst.VALIDATE_CM: # For causal minimality we highlight the edge Y \in Pa_X -> X colors[(nodes[1], nodes[0])] = VIOLATION_COLOR plot(causal_graph, colors=colors) def _generate_table( validation_repr, suggestion_repr, width=105, validation_name="Falsificaton Summary", suggestion_name="Suggestions" ): # Create Validation header _repr = [ "+" + "-" * (width - 2) + "+\n", "|" + " " * int(np.floor((width - 2 - len(validation_name)) / 2)), validation_name, " " * int(np.ceil((width - 2 - len(validation_name)) / 2)) + "|\n", "+" + "-" * (width - 2) + "+\n", ] # Create Validation summary _repr += [f"| {v_line}" + " " * (width - 3 - len(v_line)) + "|\n" for v_line in validation_repr] # Close Validation _repr += ["+" + "-" * (width - 2) + "+\n"] # Create Suggestions header if suggestion_repr: _repr += [ "|" + " " * int(np.floor((width - 2 - len(suggestion_name)) / 2)), suggestion_name, " " * int(np.ceil((width - 2 - len(suggestion_name)) / 2)) + "|\n", "+" + "-" * (width - 2) + "+\n", ] # Iterate over suggestions for m, suggestions in suggestion_repr.items(): left_col = "| " + m + " |" if not suggestions: right_col = " " + " " * (width - 2 - len(left_col)) + "|\n" _repr += [left_col, right_col] for i, s in enumerate(suggestions): if i > 0: left_col = "| " + " " * len(m) + " |" right_col = " - " + s + " " * (width - 4 - len(s) - len(left_col)) + "|\n" _repr += [left_col, right_col] _repr += ["+" + "-" * (width - 2) + "+\n"] return "".join(_repr)[:-1] def _compute_p_value( data: pd.DataFrame, X: Union[List, str], Y: Union[List, str], Z: Optional[Union[Set, List, str]], independence_test: Optional[Callable[[np.ndarray, np.ndarray], float]], conditional_independence_test: Optional[Callable[[np.ndarray, np.ndarray, np.ndarray], float]], seed: int, ) -> float: """Perform (conditional) independence test and report p-value. If any nodes needed for the test do not exist in the data, this test is skipped and a value of -1 is returned and a warning is raised. :param data: Observations of variables in the DAG. :param X: Variable to test (conditional) independence with Y :param Y: Variable to test (conditional) independence with X :param Z: Set to condition independence test on. Can be empty (None, empty set, or empty list). :param independence_test: Independence test to use. :param conditional_independence_test: Conditional independence test to use. :param seed: Random seed :return: p-value """ set_random_seed(seed) # Test if we have data for X and Y for node in [X, Y]: if not node in data.columns: warnings.warn(f"WARN: Couldn't find data for node {node}. Skip this test.") return if Z: # Test if we have data for Z for node in Z: if not node in data.columns: warnings.warn(f"WARN: Couldn't find data for node {node}. Skip this test.") return p_value = conditional_independence_test(data[X].values, data[Y].values, data[Z].values) else: p_value = independence_test(data[X].values, data[Y].values) return p_value def _get_parental_triples(causal_graph: DirectedGraph, include_unconditional: bool): """ For a given graph collect all parental triples, that is, the triple (X, Y, Z) is a parental triple iff Y is non-descendant of X, and Z are the parents of X (can be empty if include_unconditional=True) """ triples = [] for node in causal_graph.nodes: parents = get_ordered_predecessors(causal_graph, node) non_descendants = _get_non_descendants(causal_graph, node, exclude_parents=True) if (parents or include_unconditional) and non_descendants: for non_desc in non_descendants: triples.append((node, non_desc, parents)) return triples def _permutation_based( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Union[Callable, Tuple[Callable, ...], List[Callable]], exclude_original_order: bool, n_permutations: int, show_progress_bar: bool, ) -> Dict[str, List[Union[DirectedGraph, Dict]]]: """ Generate baseline for node permutations. :param causal_graph: A directed acyclic graph (DAG). :param methods: Validation methods to perform. :param exclude_original_order: Exclude the original ordering of the nodes (default=False) :param n_permutations: Number of permutations to perform. If -1 use all n_nodes! - int(exclude_orig) permutations :param show_progress_bar: Whether to show progress bar over tested permutations. :return: Dictionary containing summary of validation for each individual graph as well as the permuted graphs """ if not isinstance(methods, (tuple, list)): methods = (methods,) perm_gen = _PermuteNodes(causal_graph, n_permutations=n_permutations, exclude_original_order=exclude_original_order) validation_summary = {FalsifyConst.PERM_GRAPHS: []} for permuted_graph in tqdm(perm_gen, desc="Test permutations of given graph", disable=not show_progress_bar): res = validate_graph( causal_graph=permuted_graph, data=data, methods=methods, ) validation_summary[FalsifyConst.PERM_GRAPHS].append(permuted_graph) for m_name, summary in res.items(): if m_name not in validation_summary: validation_summary[m_name] = [] validation_summary[m_name].append(summary) return validation_summary class _PermuteNodes: def __init__(self, causal_graph: DirectedGraph, exclude_original_order: bool, n_permutations: int): """ Randomly permute the nodes of a given causal graph while keeping the underlying graph structure the same. :param causal_graph: A directed acyclic graph (DAG). :param exclude_original_order: Do not return the original order. :param n_permutations: Return a generator with n_permutations permutations. If n_permutations = -1 (default), we return all n_nodes! - int(exclude_orig) permutations. :return: Copy of causal_graph with nodes randomly permuted. """ self.causal_graph = causal_graph self.exclude_original_order = exclude_original_order self.n_permutations = n_permutations self.max_perms = np.math.factorial(self.causal_graph.number_of_nodes()) - int(self.exclude_original_order) if n_permutations == -1 or n_permutations > self.max_perms: self.it = self.iter_all_permutations() self.length = self.max_perms if self.length > 2**63 - 1: raise ValueError( f"Too many permutations specified. Did you accidently set 'n_permutations'=-1 for a " f"large (>20 nodes) graph? " f"Given graph has {causal_graph.number_of_nodes()} nodes." ) else: self.it = self.iter_random_permutations() self.length = self.n_permutations def iter_all_permutations(self): for i, perm in enumerate(permutations(self.causal_graph.nodes)): if self.exclude_original_order and i == 0: continue mapping = {node: perm[i] for i, node in enumerate(self.causal_graph.nodes)} yield nx.relabel_nodes(self.causal_graph, mapping, copy=True) def iter_random_permutations(self): for _ in range(self.n_permutations): if self.exclude_original_order: is_orig = True while is_orig: perm = list(np.random.permutation(self.causal_graph.nodes)) if perm != list(self.causal_graph.nodes): is_orig = False else: perm = list(np.random.permutation(self.causal_graph.nodes)) mapping = {node: perm[i] for i, node in enumerate(self.causal_graph.nodes)} yield nx.relabel_nodes(self.causal_graph, mapping, copy=True) def __iter__(self): yield from self.it def __len__(self): return self.length def _to_frozenset(x: Union[Set, List, str]): """Converts a set, list or string into a hashable frozenset""" assert ( isinstance(x, Set) or isinstance(x, List) or isinstance(x, str) ), f"{x} must be list, set or str. Got {type(x)} instead!" if isinstance(x, str): return frozenset({x}) return frozenset(x)

"""This module provides functionality to falsify a user-given DAG given observed data. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from dataclasses import dataclass, field from enum import Enum, auto from functools import partial from inspect import getfullargspec from itertools import permutations from typing import Any, Callable, Dict, FrozenSet, List, Optional, Set, Tuple, Union import matplotlib.colors as mcolors import matplotlib.pyplot as plt import networkx as nx import numpy as np import pandas as pd from joblib import Parallel, delayed from tqdm import tqdm import dowhy.gcm.config as config from dowhy.gcm.independence_test import kernel_based from dowhy.gcm.util import plot from dowhy.gcm.util.general import set_random_seed from dowhy.gcm.validation import _get_non_descendants from dowhy.graph import DirectedGraph, get_ordered_predecessors VIOLATION_COLOR = "red" COLORS = list(mcolors.TABLEAU_COLORS.values()) class FalsifyConst(Enum): N_VIOLATIONS = auto() N_TESTS = auto() P_VALUE = auto() P_VALUES = auto() GIVEN_VIOLATIONS = auto() PERM_VIOLATIONS = auto() F_GIVEN_VIOLATIONS = auto() F_PERM_VIOLATIONS = auto() LOCAL_VIOLATION_INSIGHT = auto() METHOD = auto() VALIDATE_LMC = auto() VALIDATE_TPA = auto() VALIDATE_PD = auto() VALIDATE_CM = auto() PERM_GRAPHS = auto() MEC = auto() FALSIFY_METHODS = { FalsifyConst.VALIDATE_LMC: "LMC", FalsifyConst.VALIDATE_PD: "Faithfulness", FalsifyConst.VALIDATE_TPA: "TPa", FalsifyConst.VALIDATE_CM: "Causal Minimality", } @dataclass class _PValuesMemory: """A class to store and access results of independence tests. This class is useful if, e.g. we validate LMC on graph X -> Y -> Z and also on the permuted nodes Z -> Y -> X. Since X ind Z | Y == Z ind X | Y, we only need to perform the conditional independence test once. We use frozensets of nodes since standard (immutable) sets are not hashable. """ p_values: Dict[Tuple[FrozenSet, FrozenSet, FrozenSet], float] = field(default_factory=dict) def add_p_value( self, p_value: Union[None, float], X: Union[Set, List, str], Y: Union[Set, List, str], Z: Optional[Union[Set, List, str]] = None, ): if not Z: Z = set() self.p_values[(_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z))] = p_value def get_p_value( self, X: Union[Set, List, str], Y: Union[Set, List, str], Z: Optional[Union[Set, List, str]] = None ): if not Z: Z = set() # Independence tests are symmetric if (_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z)) in self.p_values: return self.p_values[(_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z))] elif (_to_frozenset(Y), _to_frozenset(X), _to_frozenset(Z)) in self.p_values: return self.p_values[(_to_frozenset(Y), _to_frozenset(X), _to_frozenset(Z))] return def __contains__(self, item: Tuple[Union[Set, List, str], ...]) -> bool: X, Y = (_to_frozenset(i) for i in item[:2]) if len(item) == 2 or item[2] is None: Z = frozenset() else: Z = _to_frozenset(item[2]) return (X, Y, Z) in self.p_values or (Y, X, Z) in self.p_values def validate_lmc( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, include_unconditional: bool = True, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[str, float]]]: """ Validate the local markov condition for a given directed graph. Return number of violations and p values for each node. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory instance, where results of previously performed tests are stored. :param independence_test: Test to use for unconditional independencies (only used if include_unconditional=True) :param conditional_independence_test: Conditional independence test to use for checking local Markov condition. :param significance_level: Significance level for (conditional) independence tests. :param include_unconditional: Test also unconditional independencies of root nodes. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Outcome of validation containing number of violations in the graph and p values/violation for each tuple (node, non_desc) """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_LMC, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, FalsifyConst.P_VALUES: dict(), } # Find out which tests to do triples = _get_parental_triples(causal_graph, include_unconditional) to_test = [] for node, non_desc, parents in triples: if not (node, non_desc, parents) in p_values_memory: to_test.append((node, non_desc, parents)) p_values_memory.add_p_value(None, node, non_desc, parents) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=node, Y=non_desc, Z=parents, independence_test=independence_test, conditional_independence_test=conditional_independence_test, seed=int(seed), ) for (node, non_desc, parents), seed in zip(to_test, random_seeds) ) # Gather results for i, (node, non_desc, parents) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], node, non_desc, parents) # Summarize for node, non_desc, parents in triples: lmc_p_value = p_values_memory.get_p_value(node, non_desc, parents) if lmc_p_value is not None: validation_summary[FalsifyConst.N_TESTS] += 1 validation_summary[FalsifyConst.P_VALUES][(node, non_desc)] = ( lmc_p_value, lmc_p_value <= significance_level, ) if lmc_p_value <= significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_tpa( causal_graph: DirectedGraph, causal_graph_reference: DirectedGraph, include_unconditional: bool = True, ) -> Dict[str, int]: """ Graphical criterion to evaluate which pairwise parental d-separations (parental triples) in `causal_graph` are violated, assuming `causal_graph_reference` is the ground truth graph. If none are violated, then both graphs lie in the same Markov equivalence class. Specifically we test: X _|_G' Y | Z and X _/|_G Y | Z for Y \in ND{X}^G', Z = PA{X}^G :param causal_graph: Causal graph for which to evaluate parental d-separations (G') :param causal_graph_reference: Causal graph where we test if d-separation holds (G) :param include_unconditional: Test also unconditional independencies of root nodes. :return: Validation summary with number of d-separations implied by `causal_graph` and number of times these are violated in the graph `causal_graph_reference`. """ validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_TPA, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, } triples = _get_parental_triples(causal_graph, include_unconditional) for node, non_desc, parents in triples: validation_summary[FalsifyConst.N_TESTS] += 1 if not nx.d_separated(causal_graph_reference, {node}, {non_desc}, set(parents)): validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_pd( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, n_pairs: int = -1, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, adjacent_only: bool = False, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[tuple, float]]]: """ Validate pairwise dependencies (pd) for a given causal graph and data. Test for each node if it is statistically dependent of all its ancestors. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory object, where results of previously performed tests are stored. :param n_pairs: Evaluate dependencies for n_pairs <= all pairs in the DAG. If n_pairs=-1, evaluate dependencies for all (ancestor, node) pairs (default). :param independence_test: Independence test to use for checking pairwise dependencies. :param significance_level: Significance level for independence tests. :param adjacent_only: Only test adjacent node pairs. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Summary dict: {n_violations: int, n_tests: int, p_values: {(ancestor, node): float, ...}} """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() pairs = [(ancestor, node) for node in causal_graph.nodes for ancestor in nx.ancestors(causal_graph, node)] if adjacent_only: pairs = [ (ancestor, node) for (ancestor, node) in pairs if ancestor in get_ordered_predecessors(causal_graph, node) ] if n_pairs < 0: n_pairs = len(pairs) if n_pairs > len(pairs): raise ValueError(f"n_pairs ({n_pairs}) > number of pairs in the DAG ({len(pairs)})") validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_PD, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: n_pairs, FalsifyConst.P_VALUES: dict(), } pair_idxs = np.random.choice(len(pairs), size=n_pairs, replace=False) # Find out which tests to do to_test = [] for pair_idx in pair_idxs: ancestor, node = pairs[pair_idx] if not (ancestor, node) in p_values_memory: to_test.append((ancestor, node)) p_values_memory.add_p_value(None, ancestor, node) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=ancestor, Y=node, Z=None, independence_test=independence_test, conditional_independence_test=None, seed=int(seed), ) for (ancestor, node), seed in zip(to_test, random_seeds) ) # Gather results for i, (ancestor, node) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], ancestor, node) # Summarize for pair_idx in pair_idxs: ancestor, node = pairs[pair_idx] p_value = p_values_memory.get_p_value(ancestor, node) if p_value is not None: validation_summary[FalsifyConst.P_VALUES][(node, ancestor)] = (p_value, p_value > significance_level) if p_value > significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_cm( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[tuple, float]]]: """ Function to test causal minimality of a DAG (see [1], Proposition 6.36). [1] J. Peters, D. Janzing, and B. Schölkopf, Elements of Causal Inference: Foundations and Learning Algorithms. Cambridge, MA, USA: MIT Press, 2017. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory object, where results of previously performed tests are stored. :param independence_test: Independence test to use. :param conditional_independence_test: Conditional independence test to use. :param significance_level: Significance level for independence tests. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Validation summary as dict. """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_CM, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, FalsifyConst.P_VALUES: dict(), } # Find out which tests to do triples = [] to_test = [] for node in causal_graph.nodes: parents = set(causal_graph.predecessors(node)) if parents: for p in parents: other_parents = parents.difference({p}) triples.append((node, p, other_parents)) if not (node, p, other_parents) in p_values_memory: to_test.append((node, p, other_parents)) p_values_memory.add_p_value(None, node, p, other_parents) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=node, Y=p, Z=list(other_parents), independence_test=independence_test, conditional_independence_test=conditional_independence_test, seed=int(seed), ) for (node, p, other_parents), seed in zip(to_test, random_seeds) ) # Gather results for i, (node, p, other_parents) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], node, p, other_parents) # Summarize for node, p, other_parents in triples: p_value = p_values_memory.get_p_value(node, p, other_parents) if p_value is not None: validation_summary[FalsifyConst.N_TESTS] += 1 validation_summary[FalsifyConst.P_VALUES][(node, p, tuple(other_parents))] = ( p_value, p_value > significance_level, ) if p_value > significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def run_validations( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Optional[Union[Callable, Tuple[Callable, ...], List[Callable]]] = partial( validate_lmc, independence_test=kernel_based, conditional_independence_test=kernel_based ), ) -> Dict[str, Dict]: """ Validate a given causal graph using observational data and some given methods. If methods are provided, they must be wrapped in a partial object, with their respective parameters. E.g., if one wants to test the local Markov conditions and the pairwise dependencies (unconditional faithfulness), then call run_validations(G, data, methods=( partial(validate_lmc, independence_test=..., conditional_independence_test=...), partial(validate_pd, independence_test=...), ) ) :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param methods: Method functions wrapped in wrap_partial. E.g. wrap_partial(validate_lmc, data=data, independence_test=..., conditional_independence_test=...). If no methods are provided we run validate_lmc with optional keyword arguments provided to run_validations. :return: Validation summary as dict. """ if not isinstance(methods, (tuple, list)): methods = (methods,) validation_summary = dict() for m in methods: if "data" in getfullargspec(m).args: m = partial(m, data=data) m_summary = m(causal_graph=causal_graph) m_name = m_summary.pop(FalsifyConst.METHOD) validation_summary[m_name] = m_summary return validation_summary @dataclass class EvaluationResult: """ Dataset class containing the evaluation result of falsifying a graph using a node-permutation test. ... Attributes ---------- summary : dict Dictionary containing the summary of the evaluation. significance_level : float Significance level based on which we falsify the given DAG falsifiable : bool Whether the given DAG is falsifiable. falsified : bool Whether the given DAG is falsified. """ summary: dict significance_level: float suggestions: Optional[dict] = None def update_significance_level(self, significance_level: float): """ Update the significance level to decide if we falsify a given DAG. """ self.significance_level = significance_level self.__post_init__() def __post_init__(self): self.can_evaluate = self._can_evaluate() if not self.can_evaluate: self.falsified = None self.falsifiable = None elif ( self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.P_VALUE] > self.significance_level > self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE] ): self.falsified = True self.falsifiable = True elif self.significance_level < self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE]: self.falsified = False self.falsifiable = False else: self.falsified = False self.falsifiable = True def __repr__(self): # DAG Evaluation if self.can_evaluate: decision = " " if self.falsified else " do not " informative = " " if self.falsifiable else " not " frac_MEC = f"{len(self.summary[FalsifyConst.MEC])} / {len(self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.PERM_VIOLATIONS])}" frac_VLMC = f"{self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.GIVEN_VIOLATIONS]}/{self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.N_TESTS]}" p_LMC = self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.P_VALUE] p_dSep = self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE] validation_repr = [ f"The given DAG is{informative}informative because {frac_MEC} of the permutations lie in the Markov", f"equivalence class of the given DAG (p-value: {p_dSep:.2f}).", f"The given DAG violates {frac_VLMC} LMCs and is better than {(1 - p_LMC) * 100:.1f}% of the permuted DAGs (p-value: {p_LMC:.2f}).", f"Based on the provided significance level ({self.significance_level}) and because the DAG is{informative}informative,", f"we{decision}reject the DAG.", ] else: validation_repr = ["Cannot be evaluated!"] # Suggestions suggestion_repr = {} for m in self.suggestions: suggestion_repr[FALSIFY_METHODS[m]] = [ f"Remove edge {node[1]} --> {node[0]}" for (node, r) in self.suggestions[m][FalsifyConst.P_VALUES].items() if r[1] ] return _generate_table(validation_repr, suggestion_repr) def _can_evaluate(self): can_evaluate = True for m in (FalsifyConst.VALIDATE_LMC, FalsifyConst.VALIDATE_TPA): if m not in self.summary: can_evaluate = False return can_evaluate def falsify_graph( causal_graph: DirectedGraph, data: pd.DataFrame, suggestions: bool = False, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, significance_ci: float = 0.05, n_permutations: Optional[int] = None, show_progress_bar: Optional[bool] = None, n_jobs: Optional[int] = None, plot_histogram: bool = False, plot_kwargs: Optional[Dict] = None, ) -> EvaluationResult: """ Falsify a given DAG using observational data. This method returns the result of a permutation-test to falsify a user-given DAG using observational data. To this end we construct the test statistics by testing the violations of local Markov conditions (LMC) implied by the graph using conditional independence (CI) tests. The null is the number of LMC violations of a random node-permutation of the given graph. Our test can be interpreted as whether the given graph is significantly better than random in terms of the CIs it entails. To determine whether a given graph is falsifiable by our metric, we implement a second test, which reports whether given graph is "characteristic" enough in terms of the CIs it entails. For this, we compute how many of the random node permutations lie in the same Markov equivalence class (MEC) as the given graph and conclude that the given graph is falsifiable only if the fraction of permuted DAGs in the same MEC as the given graph is "reasonably" small. The returned EvaluationResult object has two attributes: `falsified` and `falsifiable`: `falsifiable`: The given graph lies in a different MEC than >= 1-`significance_level` of the permuted DAGs `falsified`: The given graph is falsifiable and violates fewer LMCs than >= 1-`significance_level` of the permuted DAGs By default, we only run 1 / `significance_level` permutations as those are enough to falsify a graph with type I error probability `significance_level` at some given `significance_level`. If you are interested in a more exact estimate of the p-value or wish to plot a histogram to see how the given DAG compares to random node permutations, you should set `n_permutations` to some larger value (e.g. 100 or 1000). If `n_permutations=-1` we test on all n_nodes! permutations (the default if plot_histogram=True). Additionally, this method allows to return suggestions to the user (suggestions=True). This is done by testing for violations of causal minimality via `validate_cm`. Related paper: Eulig, E., Mastakouri, A. A., Blöbaum, P., Hardt, M., & Janzing, D. (2023). Toward Falsifying Causal Graphs Using a Permutation-Based Test. https://arxiv.org/abs/2305.09565 :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param suggestions: Provide suggestions to the user. At the moment the only source of suggestions comes from validating causal minimality (using validate_cm). :param independence_test: Independence test to use for checking pairwise independencies. :param conditional_independence_test: Conditional independence test to use. :param significance_level: Significance level for the permutation test. :param significance_ci: Significance level for (conditional) independence tests. :param n_permutations: Number of permutations to perform. If -1 use all n_nodes! permutations. :param show_progress_bar: Whether to show progress bar over permutations. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :param plot_histogram: Plot histogram of results from permutation baseline. :param plot_kwargs: Additional plot arguments to be passed to plot_evaluation_results. :return: EvaluationResult """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs show_progress_bar = config.show_progress_bars if show_progress_bar is None else show_progress_bar p_values_memory = _PValuesMemory() if n_permutations is None: n_permutations = int(1 / significance_level) if not plot_histogram else -1 if not plot_kwargs: plot_kwargs = {} methods = ( partial( validate_lmc, independence_test=independence_test, conditional_independence_test=conditional_independence_test, significance_level=significance_ci, p_values_memory=p_values_memory, n_jobs=n_jobs, ), partial(validate_tpa, causal_graph_reference=causal_graph), ) if suggestions: suggestion_methods = ( partial( validate_cm, independence_test=independence_test, conditional_independence_test=conditional_independence_test, significance_level=significance_ci, p_values_memory=p_values_memory, n_jobs=n_jobs, ), ) else: suggestion_methods = tuple() summary_given = run_validations( causal_graph, data, methods=methods + suggestion_methods, ) summary_perm = _permutation_based( causal_graph, data, methods=methods, exclude_original_order=False, n_permutations=n_permutations, show_progress_bar=show_progress_bar, ) summary = dict() validation_methods = set(summary_perm.keys()) - {FalsifyConst.PERM_GRAPHS} for m in validation_methods: summary[m] = dict() summary[m][FalsifyConst.PERM_VIOLATIONS] = [perm[FalsifyConst.N_VIOLATIONS] for perm in summary_perm[m]] summary[m][FalsifyConst.GIVEN_VIOLATIONS] = summary_given[m][FalsifyConst.N_VIOLATIONS] summary[m][FalsifyConst.N_TESTS] = summary_given[m][FalsifyConst.N_TESTS] summary[m][FalsifyConst.F_PERM_VIOLATIONS] = [ perm[FalsifyConst.N_VIOLATIONS] / perm[FalsifyConst.N_TESTS] for perm in summary_perm[m] ] summary[m][FalsifyConst.F_GIVEN_VIOLATIONS] = ( summary[m][FalsifyConst.GIVEN_VIOLATIONS] / summary[m][FalsifyConst.N_TESTS] ) summary[m][FalsifyConst.P_VALUE] = sum( [ 1 for perm in summary[m][FalsifyConst.F_PERM_VIOLATIONS] if perm <= summary[m][FalsifyConst.F_GIVEN_VIOLATIONS] ] ) / len(summary[m][FalsifyConst.PERM_VIOLATIONS]) if m != FalsifyConst.VALIDATE_TPA: # Append list of violations (node, non_desc) to get local information summary[m][FalsifyConst.LOCAL_VIOLATION_INSIGHT] = summary_given[m][FalsifyConst.P_VALUES] if FalsifyConst.VALIDATE_TPA in summary: summary[FalsifyConst.MEC] = [ summary_perm[FalsifyConst.PERM_GRAPHS][i] for i, v in enumerate(summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.PERM_VIOLATIONS]) if v == 0 ] result = EvaluationResult( summary=summary, significance_level=significance_level, suggestions={m: summary_given[m] for m in summary_given if m not in validation_methods}, ) if plot_histogram: plot_evaluation_results(result, **plot_kwargs) return result def apply_suggestions( causal_graph: DirectedGraph, evaluation_result: EvaluationResult, edges_to_keep: Optional[List[Tuple[Any, Any]]] = None, ): if not hasattr(evaluation_result, "suggestions"): raise ValueError("EvaluationResult object has no attribute suggestions. Please run with suggestion=True!") causal_graph = causal_graph.copy() for m in evaluation_result.suggestions: for node, res in evaluation_result.suggestions[m][FalsifyConst.P_VALUES].items(): edge = (node[1], node[0]) if (res[1] and edges_to_keep is not None and edge not in edges_to_keep) or ( res[1] and edges_to_keep is None ): causal_graph.remove_edge(edge[0], edge[1]) return causal_graph def plot_evaluation_results(evaluation_result, figsize=(8, 3), bins=None, title="", savepath="", display=True): fig, ax = plt.subplots(figsize=figsize) # Plot histograms p_values = "" data = [] labels = [] evaluation_summary = {k: v for k, v in evaluation_result.summary.items() if k != FalsifyConst.MEC} for i, (m, m_summary) in enumerate(evaluation_summary.items()): data.append(m_summary[FalsifyConst.F_PERM_VIOLATIONS]) labels.append(f"Violations of {FALSIFY_METHODS[m]} of permuted DAGs") p_values += f"p-value {FALSIFY_METHODS[m]} = {m_summary[FalsifyConst.P_VALUE]:.2f}\n" ax.hist(data, color=COLORS[: len(evaluation_summary)], bins=bins, alpha=0.5, label=labels, edgecolor="k") # Plot given violations for i, (m, m_summary) in enumerate(evaluation_summary.items()): ylim = ax.get_ylim()[1] ax.plot( [m_summary[FalsifyConst.F_GIVEN_VIOLATIONS]] * 2, [0, ylim], "--", c=COLORS[i], label=f"Violations of {FALSIFY_METHODS[m]} of given DAG", ) ax.set_ylim([0, ylim]) ax.set_xlabel("Fraction of violations") ax.set_ylabel("# Permutations") plt.legend(loc="upper left", bbox_to_anchor=(1.05, 1), borderaxespad=0.0, title=p_values) if title: plt.title(title) plt.tight_layout() if savepath: plt.savefig(savepath, dpi=300, bbox_inches="tight") if display: plt.show() else: plt.close() def plot_local_insights( causal_graph: DirectedGraph, evaluation_result: Union[EvaluationResult, Dict], method: Optional[str] = FalsifyConst.VALIDATE_LMC, ): """ For some given graph and evaluation result plot local violations. :param causal_graph: DiGraph :param evaluation_result: EvaluationResult :param method: Method for which to plot violations """ colors = {} if isinstance(evaluation_result, EvaluationResult) and method in evaluation_result.summary: local_insight_dict = evaluation_result.summary[method][FalsifyConst.LOCAL_VIOLATION_INSIGHT] elif ( isinstance(evaluation_result, EvaluationResult) and hasattr(evaluation_result, "suggestions") and method in evaluation_result.suggestions ): local_insight_dict = evaluation_result.suggestions[method][FalsifyConst.P_VALUES] elif isinstance(evaluation_result, Dict): if method not in evaluation_result: raise ValueError(f"Validation method {method} does not exist in given evaluation_result!") if FalsifyConst.P_VALUES not in evaluation_result[method]: raise ValueError( f"Validation method {method} has no key {FalsifyConst.P_VALUES} where information on local violations " f"are stored!" ) local_insight_dict = evaluation_result[method][FalsifyConst.P_VALUES] else: raise ValueError(f"Cannot plot local violation insights from method {method} in evaluation_result!") for nodes, result in local_insight_dict.items(): if result[1]: if method == FalsifyConst.VALIDATE_LMC: # For LMC we highlight X for which X _|/|_ Y \in ND_X | Pa_X colors[nodes[0]] = VIOLATION_COLOR elif method == FalsifyConst.VALIDATE_PD: # For PD we highlight the edge (if Y\in Anc_X -> X are adjacent) colors[(nodes[1], nodes[0])] = VIOLATION_COLOR elif method == FalsifyConst.VALIDATE_CM: # For causal minimality we highlight the edge Y \in Pa_X -> X colors[(nodes[1], nodes[0])] = VIOLATION_COLOR plot(causal_graph, colors=colors) def _generate_table( validation_repr, suggestion_repr, width=105, validation_name="Falsificaton Summary", suggestion_name="Suggestions" ): # Create Validation header _repr = [ "+" + "-" * (width - 2) + "+\n", "|" + " " * int(np.floor((width - 2 - len(validation_name)) / 2)), validation_name, " " * int(np.ceil((width - 2 - len(validation_name)) / 2)) + "|\n", "+" + "-" * (width - 2) + "+\n", ] # Create Validation summary _repr += [f"| {v_line}" + " " * (width - 3 - len(v_line)) + "|\n" for v_line in validation_repr] # Close Validation _repr += ["+" + "-" * (width - 2) + "+\n"] # Create Suggestions header if suggestion_repr: _repr += [ "|" + " " * int(np.floor((width - 2 - len(suggestion_name)) / 2)), suggestion_name, " " * int(np.ceil((width - 2 - len(suggestion_name)) / 2)) + "|\n", "+" + "-" * (width - 2) + "+\n", ] # Iterate over suggestions for m, suggestions in suggestion_repr.items(): left_col = "| " + m + " |" if not suggestions: right_col = " " + " " * (width - 2 - len(left_col)) + "|\n" _repr += [left_col, right_col] for i, s in enumerate(suggestions): if i > 0: left_col = "| " + " " * len(m) + " |" right_col = " - " + s + " " * (width - 4 - len(s) - len(left_col)) + "|\n" _repr += [left_col, right_col] _repr += ["+" + "-" * (width - 2) + "+\n"] return "".join(_repr)[:-1] def _compute_p_value( data: pd.DataFrame, X: Union[List, str], Y: Union[List, str], Z: Optional[Union[Set, List, str]], independence_test: Optional[Callable[[np.ndarray, np.ndarray], float]], conditional_independence_test: Optional[Callable[[np.ndarray, np.ndarray, np.ndarray], float]], seed: int, ) -> float: """Perform (conditional) independence test and report p-value. If any nodes needed for the test do not exist in the data, this test is skipped and a value of -1 is returned and a warning is raised. :param data: Observations of variables in the DAG. :param X: Variable to test (conditional) independence with Y :param Y: Variable to test (conditional) independence with X :param Z: Set to condition independence test on. Can be empty (None, empty set, or empty list). :param independence_test: Independence test to use. :param conditional_independence_test: Conditional independence test to use. :param seed: Random seed :return: p-value """ set_random_seed(seed) # Test if we have data for X and Y for node in [X, Y]: if not node in data.columns: warnings.warn(f"WARN: Couldn't find data for node {node}. Skip this test.") return if Z: # Test if we have data for Z for node in Z: if not node in data.columns: warnings.warn(f"WARN: Couldn't find data for node {node}. Skip this test.") return p_value = conditional_independence_test(data[X].values, data[Y].values, data[Z].values) else: p_value = independence_test(data[X].values, data[Y].values) return p_value def _get_parental_triples(causal_graph: DirectedGraph, include_unconditional: bool): """ For a given graph collect all parental triples, that is, the triple (X, Y, Z) is a parental triple iff Y is non-descendant of X, and Z are the parents of X (can be empty if include_unconditional=True) """ triples = [] for node in causal_graph.nodes: parents = get_ordered_predecessors(causal_graph, node) non_descendants = _get_non_descendants(causal_graph, node, exclude_parents=True) if (parents or include_unconditional) and non_descendants: for non_desc in non_descendants: triples.append((node, non_desc, parents)) return triples def _permutation_based( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Union[Callable, Tuple[Callable, ...], List[Callable]], exclude_original_order: bool, n_permutations: int, show_progress_bar: bool, ) -> Dict[str, List[Union[DirectedGraph, Dict]]]: """ Generate baseline for node permutations. :param causal_graph: A directed acyclic graph (DAG). :param methods: Validation methods to perform. :param exclude_original_order: Exclude the original ordering of the nodes (default=False) :param n_permutations: Number of permutations to perform. If -1 use all n_nodes! - int(exclude_orig) permutations :param show_progress_bar: Whether to show progress bar over tested permutations. :return: Dictionary containing summary of validation for each individual graph as well as the permuted graphs """ if not isinstance(methods, (tuple, list)): methods = (methods,) perm_gen = _PermuteNodes(causal_graph, n_permutations=n_permutations, exclude_original_order=exclude_original_order) validation_summary = {FalsifyConst.PERM_GRAPHS: []} for permuted_graph in tqdm(perm_gen, desc="Test permutations of given graph", disable=not show_progress_bar): res = run_validations( causal_graph=permuted_graph, data=data, methods=methods, ) validation_summary[FalsifyConst.PERM_GRAPHS].append(permuted_graph) for m_name, summary in res.items(): if m_name not in validation_summary: validation_summary[m_name] = [] validation_summary[m_name].append(summary) return validation_summary class _PermuteNodes: def __init__(self, causal_graph: DirectedGraph, exclude_original_order: bool, n_permutations: int): """ Randomly permute the nodes of a given causal graph while keeping the underlying graph structure the same. :param causal_graph: A directed acyclic graph (DAG). :param exclude_original_order: Do not return the original order. :param n_permutations: Return a generator with n_permutations permutations. If n_permutations = -1 (default), we return all n_nodes! - int(exclude_orig) permutations. :return: Copy of causal_graph with nodes randomly permuted. """ self.causal_graph = causal_graph self.exclude_original_order = exclude_original_order self.n_permutations = n_permutations self.max_perms = np.math.factorial(self.causal_graph.number_of_nodes()) - int(self.exclude_original_order) if n_permutations == -1 or n_permutations > self.max_perms: self.it = self.iter_all_permutations() self.length = self.max_perms if self.length > 2**63 - 1: raise ValueError( f"Too many permutations specified. Did you accidently set 'n_permutations'=-1 for a " f"large (>20 nodes) graph? " f"Given graph has {causal_graph.number_of_nodes()} nodes." ) else: self.it = self.iter_random_permutations() self.length = self.n_permutations def iter_all_permutations(self): for i, perm in enumerate(permutations(self.causal_graph.nodes)): if self.exclude_original_order and i == 0: continue mapping = {node: perm[i] for i, node in enumerate(self.causal_graph.nodes)} yield nx.relabel_nodes(self.causal_graph, mapping, copy=True) def iter_random_permutations(self): for _ in range(self.n_permutations): if self.exclude_original_order: is_orig = True while is_orig: perm = list(np.random.permutation(self.causal_graph.nodes)) if perm != list(self.causal_graph.nodes): is_orig = False else: perm = list(np.random.permutation(self.causal_graph.nodes)) mapping = {node: perm[i] for i, node in enumerate(self.causal_graph.nodes)} yield nx.relabel_nodes(self.causal_graph, mapping, copy=True) def __iter__(self): yield from self.it def __len__(self): return self.length def _to_frozenset(x: Union[Set, List, str]): """Converts a set, list or string into a hashable frozenset""" assert ( isinstance(x, Set) or isinstance(x, List) or isinstance(x, str) ), f"{x} must be list, set or str. Got {type(x)} instead!" if isinstance(x, str): return frozenset({x}) return frozenset(x)

eeulig

379f188d674d5be6d644559f9f54852b87403f7c

4e30c63fe1113e35259e7fb3400a6f09bca4d699

Yes, the `validate_tpa` method doesn't require data. I now included an optional `data` argument there which is ignored, so that we can blindly pass it.

eeulig

py-why/dowhy

Remove kwargs from falsify, improve docstrings

This commit updates `gcm/falsify` in the following ways: - Remove the `**kwargs` for `validate_graph` and `falsify_graph`. Instead, validation methods given to those functions should be wrapped in `wrap_partial`, e.g. if we want to validate a graph `G` and test local Markov conditions and pairwise dependencies on observations `data`, we would do ``` validate_graph(G, methods=( wrap_partial(validate_lmc, data=data, independence_test=..., conditional_independence_test=...), wrap_partial(validate_pd, data=data, independence_test=...), ) ) ``` By default (`methods=None`), `validate_graph` tests local Markov conditions and `falsify_graph` tests local Markov conditions and d-separation of parental triples. In this case `data` has to be set and optionally one can provide `independence_test`, `conditional_independence_test`, `significance_level`, and `n_jobs` via the keyword arguments of `validate_graph` / `falsify_graph`. - Use `Enum` for some of the constants - In `_PValuesMemory` the method `add_p_value ` always sets a p-value even if it was set already. This removes the need for the method `clear_placeholders` and allows `p_values` to be set to `-1`, indicating that the CI is not testable (because of missing data for a node, c.f. `_compute_p_value`) - If for some CI: X, Y | Z any of the nodes has no data (column missing in the provided dataframe), we skip this test and raise a warning. - Instead of using the raw number of violations in the plotted histogram and to compute the p-value, we use the relative number of violations. This will result in different p-values only if we have missing data for some nodes (and thus the number of tests is potentially different between different permutations) - Improve docstrings

2023-07-04 13:58:30+00:00

2023-07-18 14:19:10+00:00

dowhy/gcm/falsify.py

"""This module provides functionality to falsify a user-given DAG given observed data. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from dataclasses import dataclass, field from enum import Enum, auto from functools import partial from itertools import permutations from typing import Any, Callable, Dict, FrozenSet, List, Optional, Set, Tuple, Union import matplotlib.colors as mcolors import matplotlib.pyplot as plt import networkx as nx import numpy as np import pandas as pd from joblib import Parallel, delayed from tqdm import tqdm import dowhy.gcm.config as config from dowhy.gcm.independence_test import kernel_based from dowhy.gcm.util import plot from dowhy.gcm.util.general import set_random_seed from dowhy.gcm.validation import _get_non_descendants from dowhy.graph import DirectedGraph, get_ordered_predecessors VIOLATION_COLOR = "red" COLORS = list(mcolors.TABLEAU_COLORS.values()) class FalsifyConst(Enum): N_VIOLATIONS = auto() N_TESTS = auto() P_VALUE = auto() P_VALUES = auto() GIVEN_VIOLATIONS = auto() PERM_VIOLATIONS = auto() F_GIVEN_VIOLATIONS = auto() F_PERM_VIOLATIONS = auto() LOCAL_VIOLATION_INSIGHT = auto() METHOD = auto() VALIDATE_LMC = auto() VALIDATE_TPA = auto() VALIDATE_PD = auto() VALIDATE_CM = auto() PERM_GRAPHS = auto() MEC = auto() FALSIFY_METHODS = { FalsifyConst.VALIDATE_LMC: "LMC", FalsifyConst.VALIDATE_PD: "Faithfulness", FalsifyConst.VALIDATE_TPA: "tPa", FalsifyConst.VALIDATE_CM: "Causal Minimality", } @dataclass class _PValuesMemory: """A class to store and access results of independence tests. This class is useful if, e.g. we validate LMC on graph X -> Y -> Z and also on the permuted nodes Z -> Y -> X. Since X ind Z | Y == Z ind X | Y, we only need to perform the conditional independence test once. We use frozensets of nodes since standard (immutable) sets are not hashable. """ p_values: Dict[Tuple[FrozenSet, FrozenSet, FrozenSet], float] = field(default_factory=dict) def add_p_value( self, p_value: Union[None, float], X: Union[Set, List, str], Y: Union[Set, List, str], Z: Optional[Union[Set, List, str]] = None, ): if not Z: Z = set() self.p_values[(_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z))] = p_value def get_p_value( self, X: Union[Set, List, str], Y: Union[Set, List, str], Z: Optional[Union[Set, List, str]] = None ): if not Z: Z = set() # Independence tests are symmetric if (_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z)) in self.p_values: return self.p_values[(_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z))] elif (_to_frozenset(Y), _to_frozenset(X), _to_frozenset(Z)) in self.p_values: return self.p_values[(_to_frozenset(Y), _to_frozenset(X), _to_frozenset(Z))] return def __contains__(self, item: Tuple[Union[Set, List, str], ...]) -> bool: X, Y = (_to_frozenset(i) for i in item[:2]) if len(item) == 2 or item[2] is None: Z = frozenset() else: Z = _to_frozenset(item[2]) return (X, Y, Z) in self.p_values or (Y, X, Z) in self.p_values def validate_lmc( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, include_unconditional: bool = True, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[str, float]]]: """ Validate the local markov condition for a given directed graph. Return number of violations and p values for each node. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory instance, where results of previously performed tests are stored. :param independence_test: Test to use for unconditional independencies (only used if include_unconditional=True) :param conditional_independence_test: Conditional independence test to use for checking local Markov condition. :param significance_level: Significance level for (conditional) independence tests. :param include_unconditional: Test also unconditional independencies of root nodes. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Outcome of validation containing number of violations in the graph and p values/violation for each tuple (node, non_desc) """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_LMC, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, FalsifyConst.P_VALUES: dict(), } # Find out which tests to do triples = _get_parental_triples(causal_graph, include_unconditional) to_test = [] for node, non_desc, parents in triples: if not (node, non_desc, parents) in p_values_memory: to_test.append((node, non_desc, parents)) p_values_memory.add_p_value(None, node, non_desc, parents) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=node, Y=non_desc, Z=parents, independence_test=independence_test, conditional_independence_test=conditional_independence_test, seed=int(seed), ) for (node, non_desc, parents), seed in zip(to_test, random_seeds) ) # Gather results for i, (node, non_desc, parents) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], node, non_desc, parents) # Summarize for node, non_desc, parents in triples: lmc_p_value = p_values_memory.get_p_value(node, non_desc, parents) if lmc_p_value is not None: validation_summary[FalsifyConst.N_TESTS] += 1 validation_summary[FalsifyConst.P_VALUES][(node, non_desc)] = ( lmc_p_value, lmc_p_value <= significance_level, ) if lmc_p_value <= significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_tpa( causal_graph: DirectedGraph, causal_graph_reference: DirectedGraph, include_unconditional: bool = True, data: Optional[pd.DataFrame] = None, ) -> Dict[str, int]: """ Graphical criterion to evaluate which pairwise parental d-separations (parental triples) in `causal_graph` are violated, assuming `causal_graph_reference` is the ground truth graph. If none are violated, then both graphs lie in the same Markov equivalence class. Specifically we test: X _|_G' Y | Z and X _/|_G Y | Z for Y \in ND{X}^G', Z = PA{X}^G :param causal_graph: Causal graph for which to evaluate parental d-separations (G') :param causal_graph_reference: Causal graph where we test if d-separation holds (G) :param include_unconditional: Test also unconditional independencies of root nodes. :param data: IGNORED! No data is needed for this validation method and thus the data argument is ignored! :return: Validation summary with number of d-separations implied by `causal_graph` and number of times these are violated in the graph `causal_graph_reference`. """ validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_TPA, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, } triples = _get_parental_triples(causal_graph, include_unconditional) for node, non_desc, parents in triples: validation_summary[FalsifyConst.N_TESTS] += 1 if not nx.d_separated(causal_graph_reference, {node}, {non_desc}, set(parents)): validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_pd( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, n_pairs: int = -1, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, adjacent_only: bool = False, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[tuple, float]]]: """ Validate pairwise dependencies (pd) for a given causal graph and data. Test for each node if it is statistically dependent of all its ancestors. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory object, where results of previously performed tests are stored. :param n_pairs: Evaluate dependencies for n_pairs <= all pairs in the DAG. If n_pairs=-1, evaluate dependencies for all (ancestor, node) pairs (default). :param independence_test: Independence test to use for checking pairwise dependencies. :param significance_level: Significance level for independence tests. :param adjacent_only: Only test adjacent node pairs. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Summary dict: {n_violations: int, n_tests: int, p_values: {(ancestor, node): float, ...}} """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() pairs = [(ancestor, node) for node in causal_graph.nodes for ancestor in nx.ancestors(causal_graph, node)] if adjacent_only: pairs = [ (ancestor, node) for (ancestor, node) in pairs if ancestor in get_ordered_predecessors(causal_graph, node) ] if n_pairs < 0: n_pairs = len(pairs) if n_pairs > len(pairs): raise ValueError(f"n_pairs ({n_pairs}) > number of pairs in the DAG ({len(pairs)})") validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_PD, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: n_pairs, FalsifyConst.P_VALUES: dict(), } pair_idxs = np.random.choice(len(pairs), size=n_pairs, replace=False) # Find out which tests to do to_test = [] for pair_idx in pair_idxs: ancestor, node = pairs[pair_idx] if not (ancestor, node) in p_values_memory: to_test.append((ancestor, node)) p_values_memory.add_p_value(None, ancestor, node) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=ancestor, Y=node, Z=None, independence_test=independence_test, conditional_independence_test=None, seed=int(seed), ) for (ancestor, node), seed in zip(to_test, random_seeds) ) # Gather results for i, (ancestor, node) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], ancestor, node) # Summarize for pair_idx in pair_idxs: ancestor, node = pairs[pair_idx] p_value = p_values_memory.get_p_value(ancestor, node) if p_value is not None: validation_summary[FalsifyConst.P_VALUES][(node, ancestor)] = (p_value, p_value > significance_level) if p_value > significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_cm( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[tuple, float]]]: """ Function to test causal minimality of a DAG (see [1], Proposition 6.36). [1] J. Peters, D. Janzing, and B. Schölkopf, Elements of Causal Inference: Foundations and Learning Algorithms. Cambridge, MA, USA: MIT Press, 2017. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory object, where results of previously performed tests are stored. :param independence_test: Independence test to use. :param conditional_independence_test: Conditional independence test to use. :param significance_level: Significance level for independence tests. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Validation summary as dict. """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_CM, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, FalsifyConst.P_VALUES: dict(), } # Find out which tests to do triples = [] to_test = [] for node in causal_graph.nodes: parents = set(causal_graph.predecessors(node)) if parents: for p in parents: other_parents = parents.difference({p}) triples.append((node, p, other_parents)) if not (node, p, other_parents) in p_values_memory: to_test.append((node, p, other_parents)) p_values_memory.add_p_value(None, node, p, other_parents) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=node, Y=p, Z=list(other_parents), independence_test=independence_test, conditional_independence_test=conditional_independence_test, seed=int(seed), ) for (node, p, other_parents), seed in zip(to_test, random_seeds) ) # Gather results for i, (node, p, other_parents) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], node, p, other_parents) # Summarize for node, p, other_parents in triples: p_value = p_values_memory.get_p_value(node, p, other_parents) if p_value is not None: validation_summary[FalsifyConst.N_TESTS] += 1 validation_summary[FalsifyConst.P_VALUES][(node, p, tuple(other_parents))] = ( p_value, p_value > significance_level, ) if p_value > significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_graph( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Optional[Union[Callable, Tuple[Callable, ...], List[Callable]]] = partial( validate_lmc, independence_test=kernel_based, conditional_independence_test=kernel_based ), ) -> Dict[str, Dict]: """ Validate a given causal graph using observational data and some given methods. If methods are provided, they must be wrapped in a partial object, with their respective parameters. E.g., if one wants to test the local Markov conditions and the pairwise dependencies (unconditional faithfulness), then call validate_graph(G, data, methods=( partial(validate_lmc, independence_test=..., conditional_independence_test=...), partial(validate_pd, independence_test=...), ) ) :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param methods: Method functions wrapped in wrap_partial. E.g. wrap_partial(validate_lmc, data=data, independence_test=..., conditional_independence_test=...). If no methods are provided we run validate_lmc with optional keyword arguments provided to validate_graph. :return: Validation summary as dict. """ if not isinstance(methods, (tuple, list)): methods = (methods,) validation_summary = dict() for m in methods: m_summary = m(causal_graph=causal_graph, data=data) m_name = m_summary.pop(FalsifyConst.METHOD) validation_summary[m_name] = m_summary return validation_summary @dataclass class EvaluationResult: """ Dataset class containing the evaluation result of falsifying a graph using a node-permutation test. ... Attributes ---------- summary : dict Dictionary containing the summary of the evaluation. significance_level : float Significance level based on which we falsify the given DAG falsifiable : bool Whether the given DAG is falsifiable. falsified : bool Whether the given DAG is falsified. """ summary: dict significance_level: float suggestions: Optional[dict] = None def update_significance_level(self, significance_level: float): """ Update the significance level to decide if we falsify a given DAG. """ self.significance_level = significance_level self.__post_init__() def __post_init__(self): self.can_evaluate = self._can_evaluate() if not self.can_evaluate: self.falsified = None self.falsifiable = None elif ( self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.P_VALUE] > self.significance_level > self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE] ): self.falsified = True self.falsifiable = True elif self.significance_level < self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE]: self.falsified = False self.falsifiable = False else: self.falsified = False self.falsifiable = True def __repr__(self): # DAG Evaluation if self.can_evaluate: decision = " " if self.falsified else " do not " informative = " " if self.falsifiable else " not " frac_MEC = f"{len(self.summary[FalsifyConst.MEC])} / {len(self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.PERM_VIOLATIONS])}" frac_VLMC = f"{self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.GIVEN_VIOLATIONS]}/{self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.N_TESTS]}" p_LMC = self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.P_VALUE] p_dSep = self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE] validation_repr = [ f"The given DAG is{informative}informative because {frac_MEC} of the permutations lie in the Markov", f"equivalence class of the given DAG (p-value: {p_dSep:.2f}).", f"The given DAG violates {frac_VLMC} LMCs and is better than {(1 - p_LMC) * 100:.1f}% of the permuted DAGs (p-value: {p_LMC:.2f}).", f"Based on the provided significance level ({self.significance_level}) and because the DAG is{informative}informative,", f"we{decision}reject the DAG.", ] else: validation_repr = ["Cannot be evaluated!"] # Suggestions suggestion_repr = {} for m in self.suggestions: suggestion_repr[FALSIFY_METHODS[m]] = [ f"Remove edge {node[1]} --> {node[0]}" for (node, r) in self.suggestions[m][FalsifyConst.P_VALUES].items() if r[1] ] return _generate_table(validation_repr, suggestion_repr) def _can_evaluate(self): can_evaluate = True for m in (FalsifyConst.VALIDATE_LMC, FalsifyConst.VALIDATE_TPA): if m not in self.summary: can_evaluate = False return can_evaluate def falsify_graph( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Optional[Union[Callable, Tuple[Callable, ...]]] = None, suggestion_methods: Optional[Union[Callable, Tuple[Callable, ...]]] = None, suggestions: bool = False, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, significance_ci: float = 0.05, n_permutations: Optional[int] = None, show_progress_bar: Optional[bool] = None, n_jobs: Optional[int] = None, plot_histogram: bool = False, plot_kwargs: Optional[Dict] = None, ) -> EvaluationResult: """ Falsify a given DAG using observational data. This method returns the result of a permutation-test to falsify a user-given DAG using observational data. To this end we construct the test statistics by testing the violations of local Markov conditions (LMC) implied by the graph using conditional independence (CI) tests. The null is the number of LMC violations of a random node-permutation of the given graph. Our test can be interpreted as whether the given graph is significantly better than random in terms of the CIs it entails. To determine whether a given graph is falsifiable by our metric, we implement a second test, which reports whether given graph is "characteristic" enough in terms of the CIs it entails. For this, we compute how many of the random node permutations lie in the same Markov equivalence class (MEC) as the given graph and conclude that the given graph is falsifiable only if the fraction of permuted DAGs in the same MEC as the given graph is "reasonably" small. The returned EvaluationResult object has two attributes: `falsified` and `falsifiable`: `falsifiable`: The given graph lies in a different MEC than >= 1-`significance_level` of the permuted DAGs `falsified`: The given graph is falsifiable and violates fewer LMCs than >= 1-`significance_level` of the permuted DAGs By default, we only run 1 / `significance_level` permutations as those are enough to falsify a graph with type I error probability `significance_level` at some given `significance_level`. If you are interested in a more exact estimate of the p-value of whish to plot a histogram to see how the given DAG compares to random node permutations, you should set `n_permutations` to some larger value (e.g. 100 or 1000). If `n_permutations=-1` we test on all n_nodes! permutations. `methods` and `suggestion_methods` must be wrapped in partial(method, **kwargs) (c.f. `validate_graph`). Related paper: Eulig, E., Mastakouri, A. A., Blöbaum, P., Hardt, M., & Janzing, D. (2023). Toward Falsifying Causal Graphs Using a Permutation-Based Test. https://arxiv.org/abs/2305.09565 :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param methods: Validation methods to perform. :param suggestion_methods: Methods to run on the given graph to provide additional suggestions. :param suggestions: Provide suggestions generated using the `suggestion_methods`. :param independence_test: Independence test to use for checking pairwise independencies. :param conditional_independence_test: Conditional independence test to use. :param significance_level: Significance level for the permutation test. :param significance_ci: Significance level for (conditional) independence tests. :param n_permutations: Number of permutations to perform. If -1 use all n_nodes! permutations. :param show_progress_bar: Whether to show progress bar over permutations. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :param plot_histogram: Plot histogram of results from permutation baseline. :param plot_kwargs: Additional plot arguments to be passed to plot_evaluation_results. :return: EvaluationResult """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs show_progress_bar = config.show_progress_bars if show_progress_bar is None else show_progress_bar p_values_memory = _PValuesMemory() if n_permutations is None: n_permutations = int(1 / significance_level) if not plot_histogram else -1 if not plot_kwargs: plot_kwargs = {} # If no methods are provided, use default ones: validate_lmc, validate_tpa if not methods: methods = ( partial( validate_lmc, independence_test=independence_test, conditional_independence_test=conditional_independence_test, significance_level=significance_ci, p_values_memory=p_values_memory, n_jobs=n_jobs, ), partial(validate_tpa, causal_graph_reference=causal_graph), ) elif isinstance(methods, Callable): methods = (methods,) # If no suggestion methods are provided, but suggestions=True, use default ones: validate_cm if not suggestions: suggestion_methods = tuple() elif suggestions and isinstance(suggestion_methods, Callable): suggestion_methods = (suggestion_methods,) elif suggestions: suggestion_methods = ( partial( validate_cm, independence_test=independence_test, conditional_independence_test=conditional_independence_test, significance_level=significance_ci, p_values_memory=p_values_memory, n_jobs=n_jobs, ), ) summary_given = validate_graph( causal_graph, data, methods=methods + suggestion_methods, ) summary_perm = _permutation_based( causal_graph, data, methods=methods, exclude_original_order=False, n_permutations=n_permutations, show_progress_bar=show_progress_bar, ) summary = dict() validation_methods = set(summary_perm.keys()) - {FalsifyConst.PERM_GRAPHS} for m in validation_methods: summary[m] = dict() summary[m][FalsifyConst.PERM_VIOLATIONS] = [perm[FalsifyConst.N_VIOLATIONS] for perm in summary_perm[m]] summary[m][FalsifyConst.GIVEN_VIOLATIONS] = summary_given[m][FalsifyConst.N_VIOLATIONS] summary[m][FalsifyConst.N_TESTS] = summary_given[m][FalsifyConst.N_TESTS] summary[m][FalsifyConst.F_PERM_VIOLATIONS] = [ perm[FalsifyConst.N_VIOLATIONS] / perm[FalsifyConst.N_TESTS] for perm in summary_perm[m] ] summary[m][FalsifyConst.F_GIVEN_VIOLATIONS] = ( summary[m][FalsifyConst.GIVEN_VIOLATIONS] / summary[m][FalsifyConst.N_TESTS] ) summary[m][FalsifyConst.P_VALUE] = sum( [ 1 for perm in summary[m][FalsifyConst.F_PERM_VIOLATIONS] if perm <= summary[m][FalsifyConst.F_GIVEN_VIOLATIONS] ] ) / len(summary[m][FalsifyConst.PERM_VIOLATIONS]) if m != FalsifyConst.VALIDATE_TPA: # Append list of violations (node, non_desc) to get local information summary[m][FalsifyConst.LOCAL_VIOLATION_INSIGHT] = summary_given[m][FalsifyConst.P_VALUES] if FalsifyConst.VALIDATE_TPA in summary: summary[FalsifyConst.MEC] = [ summary_perm[FalsifyConst.PERM_GRAPHS][i] for i, v in enumerate(summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.PERM_VIOLATIONS]) if v == 0 ] result = EvaluationResult( summary=summary, significance_level=significance_level, suggestions={m: summary_given[m] for m in summary_given if m not in validation_methods}, ) if plot_histogram: plot_evaluation_results(result, **plot_kwargs) return result def apply_suggestions( causal_graph: DirectedGraph, evaluation_result: EvaluationResult, edges_to_keep: Optional[List[Tuple[Any, Any]]] = None, ): if not hasattr(evaluation_result, "suggestions"): raise ValueError("EvaluationResult object has no attribute suggestions. Please run with suggestion=True!") causal_graph = causal_graph.copy() for m in evaluation_result.suggestions: for node, res in evaluation_result.suggestions[m][FalsifyConst.P_VALUES].items(): edge = (node[1], node[0]) if (res[1] and edges_to_keep is not None and edge not in edges_to_keep) or ( res[1] and edges_to_keep is None ): causal_graph.remove_edge(edge[0], edge[1]) return causal_graph def plot_evaluation_results(evaluation_result, figsize=(8, 3), bins=None, title="", savepath="", display=True): fig, ax = plt.subplots(figsize=figsize) # Plot histograms p_values = "" data = [] labels = [] evaluation_summary = {k: v for k, v in evaluation_result.summary.items() if k != FalsifyConst.MEC} for i, (m, m_summary) in enumerate(evaluation_summary.items()): data.append(m_summary[FalsifyConst.F_PERM_VIOLATIONS]) labels.append(f"Violations of {FALSIFY_METHODS[m]} of permuted DAGs") p_values += f"p-value {FALSIFY_METHODS[m]} = {m_summary[FalsifyConst.P_VALUE]:.2f}\n" ax.hist(data, color=COLORS[: len(evaluation_summary)], bins=bins, alpha=0.5, label=labels, edgecolor="k") # Plot given violations for i, (m, m_summary) in enumerate(evaluation_summary.items()): ylim = ax.get_ylim()[1] ax.plot( [m_summary[FalsifyConst.F_GIVEN_VIOLATIONS]] * 2, [0, ylim], "--", c=COLORS[i], label=f"Violations of {FALSIFY_METHODS[m]} of given DAG", ) ax.set_ylim([0, ylim]) ax.set_xlabel("Fraction of violations") ax.set_ylabel("# Permutations") plt.legend(loc="upper left", bbox_to_anchor=(1.05, 1), borderaxespad=0.0, title=p_values) if title: plt.title(title) plt.tight_layout() if savepath: plt.savefig(savepath, dpi=300, bbox_inches="tight") if display: plt.show() else: plt.close() def plot_local_insights( causal_graph: DirectedGraph, evaluation_result: Union[EvaluationResult, Dict], method: Optional[str] = FalsifyConst.VALIDATE_LMC, ): """ For some given graph and evaluation result plot local violations. :param causal_graph: DiGraph :param evaluation_result: EvaluationResult :param method: Method for which to plot violations """ colors = {} if isinstance(evaluation_result, EvaluationResult) and method in evaluation_result.summary: local_insight_dict = evaluation_result.summary[method][FalsifyConst.LOCAL_VIOLATION_INSIGHT] elif ( isinstance(evaluation_result, EvaluationResult) and hasattr(evaluation_result, "suggestions") and method in evaluation_result.suggestions ): local_insight_dict = evaluation_result.suggestions[method][FalsifyConst.P_VALUES] elif isinstance(evaluation_result, Dict): if method not in evaluation_result: raise ValueError(f"Validation method {method} does not exist in given evaluation_result!") if FalsifyConst.P_VALUES not in evaluation_result[method]: raise ValueError( f"Validation method {method} has no key {FalsifyConst.P_VALUES} where information on local violations " f"are stored!" ) local_insight_dict = evaluation_result[method][FalsifyConst.P_VALUES] else: raise ValueError(f"Cannot plot local violation insights from method {method} in evaluation_result!") for nodes, result in local_insight_dict.items(): if result[1]: if method == FalsifyConst.VALIDATE_LMC: # For LMC we highlight X for which X _|/|_ Y \in ND_X | Pa_X colors[nodes[0]] = VIOLATION_COLOR elif method == FalsifyConst.VALIDATE_PD: # For PD we highlight the edge (if Y\in Anc_X -> X are adjacent) colors[(nodes[1], nodes[0])] = VIOLATION_COLOR elif method == FalsifyConst.VALIDATE_CM: # For causal minimality we highlight the edge Y \in Pa_X -> X colors[(nodes[1], nodes[0])] = VIOLATION_COLOR plot(causal_graph, colors=colors) def _generate_table( validation_repr, suggestion_repr, width=105, validation_name="Falsificaton Summary", suggestion_name="Suggestions" ): # Create Validation header _repr = [ "+" + "-" * (width - 2) + "+\n", "|" + " " * int(np.floor((width - 2 - len(validation_name)) / 2)), validation_name, " " * int(np.ceil((width - 2 - len(validation_name)) / 2)) + "|\n", "+" + "-" * (width - 2) + "+\n", ] # Create Validation summary _repr += [f"| {v_line}" + " " * (width - 3 - len(v_line)) + "|\n" for v_line in validation_repr] # Close Validation _repr += ["+" + "-" * (width - 2) + "+\n"] # Create Suggestions header if suggestion_repr: _repr += [ "|" + " " * int(np.floor((width - 2 - len(suggestion_name)) / 2)), suggestion_name, " " * int(np.ceil((width - 2 - len(suggestion_name)) / 2)) + "|\n", "+" + "-" * (width - 2) + "+\n", ] # Iterate over suggestions for m, suggestions in suggestion_repr.items(): left_col = "| " + m + " |" if not suggestions: right_col = " " + " " * (width - 2 - len(left_col)) + "|\n" _repr += [left_col, right_col] for i, s in enumerate(suggestions): if i > 0: left_col = "| " + " " * len(m) + " |" right_col = " - " + s + " " * (width - 4 - len(s) - len(left_col)) + "|\n" _repr += [left_col, right_col] _repr += ["+" + "-" * (width - 2) + "+\n"] return "".join(_repr)[:-1] def _compute_p_value( data: pd.DataFrame, X: Union[List, str], Y: Union[List, str], Z: Optional[Union[Set, List, str]], independence_test: Optional[Callable[[np.ndarray, np.ndarray], float]], conditional_independence_test: Optional[Callable[[np.ndarray, np.ndarray, np.ndarray], float]], seed: int, ) -> float: """Perform (conditional) independence test and report p-value. If any nodes needed for the test do not exist in the data, this test is skipped and a value of -1 is returned and a warning is raised. :param data: Observations of variables in the DAG. :param X: Variable to test (conditional) independence with Y :param Y: Variable to test (conditional) independence with X :param Z: Set to condition independence test on. Can be empty (None, empty set, or empty list). :param independence_test: Independence test to use. :param conditional_independence_test: Conditional independence test to use. :param seed: Random seed :return: p-value """ set_random_seed(seed) # Test if we have data for X and Y for node in [X, Y]: if not node in data.columns: warnings.warn(f"WARN: Couldn't find data for node {node}. Skip this test.") return if Z: # Test if we have data for Z for node in Z: if not node in data.columns: warnings.warn(f"WARN: Couldn't find data for node {node}. Skip this test.") return p_value = conditional_independence_test(data[X].values, data[Y].values, data[Z].values) else: p_value = independence_test(data[X].values, data[Y].values) return p_value def _get_parental_triples(causal_graph: DirectedGraph, include_unconditional: bool): """ For a given graph collect all parental triples, that is, the triple (X, Y, Z) is a parental triple iff Y is non-descendant of X, and Z are the parents of X (can be empty if include_unconditional=True) """ triples = [] for node in causal_graph.nodes: parents = get_ordered_predecessors(causal_graph, node) non_descendants = _get_non_descendants(causal_graph, node, exclude_parents=True) if (parents or include_unconditional) and non_descendants: for non_desc in non_descendants: triples.append((node, non_desc, parents)) return triples def _permutation_based( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Union[Callable, Tuple[Callable, ...], List[Callable]], exclude_original_order: bool, n_permutations: int, show_progress_bar: bool, ) -> Dict[str, List[Union[DirectedGraph, Dict]]]: """ Generate baseline for node permutations. :param causal_graph: A directed acyclic graph (DAG). :param methods: Validation methods to perform. :param exclude_original_order: Exclude the original ordering of the nodes (default=False) :param n_permutations: Number of permutations to perform. If -1 use all n_nodes! - int(exclude_orig) permutations :param show_progress_bar: Whether to show progress bar over tested permutations. :return: Dictionary containing summary of validation for each individual graph as well as the permuted graphs """ if not isinstance(methods, (tuple, list)): methods = (methods,) perm_gen = _PermuteNodes(causal_graph, n_permutations=n_permutations, exclude_original_order=exclude_original_order) validation_summary = {FalsifyConst.PERM_GRAPHS: []} for permuted_graph in tqdm(perm_gen, desc="Test permutations of given graph", disable=not show_progress_bar): res = validate_graph( causal_graph=permuted_graph, data=data, methods=methods, ) validation_summary[FalsifyConst.PERM_GRAPHS].append(permuted_graph) for m_name, summary in res.items(): if m_name not in validation_summary: validation_summary[m_name] = [] validation_summary[m_name].append(summary) return validation_summary class _PermuteNodes: def __init__(self, causal_graph: DirectedGraph, exclude_original_order: bool, n_permutations: int): """ Randomly permute the nodes of a given causal graph while keeping the underlying graph structure the same. :param causal_graph: A directed acyclic graph (DAG). :param exclude_original_order: Do not return the original order. :param n_permutations: Return a generator with n_permutations permutations. If n_permutations = -1 (default), we return all n_nodes! - int(exclude_orig) permutations. :return: Copy of causal_graph with nodes randomly permuted. """ self.causal_graph = causal_graph self.exclude_original_order = exclude_original_order self.n_permutations = n_permutations self.max_perms = np.math.factorial(self.causal_graph.number_of_nodes()) - int(self.exclude_original_order) if n_permutations == -1 or n_permutations > self.max_perms: self.it = self.iter_all_permutations() self.length = self.max_perms if self.length > 2**63 - 1: raise ValueError( f"Too many permutations specified. Did you accidently set 'n_permutations'=-1 for a " f"large (>20 nodes) graph? " f"Given graph has {causal_graph.number_of_nodes()} nodes." ) else: self.it = self.iter_random_permutations() self.length = self.n_permutations def iter_all_permutations(self): for i, perm in enumerate(permutations(self.causal_graph.nodes)): if self.exclude_original_order and i == 0: continue mapping = {node: perm[i] for i, node in enumerate(self.causal_graph.nodes)} yield nx.relabel_nodes(self.causal_graph, mapping, copy=True) def iter_random_permutations(self): for _ in range(self.n_permutations): if self.exclude_original_order: is_orig = True while is_orig: perm = list(np.random.permutation(self.causal_graph.nodes)) if perm != list(self.causal_graph.nodes): is_orig = False else: perm = list(np.random.permutation(self.causal_graph.nodes)) mapping = {node: perm[i] for i, node in enumerate(self.causal_graph.nodes)} yield nx.relabel_nodes(self.causal_graph, mapping, copy=True) def __iter__(self): yield from self.it def __len__(self): return self.length def _to_frozenset(x: Union[Set, List, str]): """Converts a set, list or string into a hashable frozenset""" assert ( isinstance(x, Set) or isinstance(x, List) or isinstance(x, str) ), f"{x} must be list, set or str. Got {type(x)} instead!" if isinstance(x, str): return frozenset({x}) return frozenset(x)

"""This module provides functionality to falsify a user-given DAG given observed data. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from dataclasses import dataclass, field from enum import Enum, auto from functools import partial from inspect import getfullargspec from itertools import permutations from typing import Any, Callable, Dict, FrozenSet, List, Optional, Set, Tuple, Union import matplotlib.colors as mcolors import matplotlib.pyplot as plt import networkx as nx import numpy as np import pandas as pd from joblib import Parallel, delayed from tqdm import tqdm import dowhy.gcm.config as config from dowhy.gcm.independence_test import kernel_based from dowhy.gcm.util import plot from dowhy.gcm.util.general import set_random_seed from dowhy.gcm.validation import _get_non_descendants from dowhy.graph import DirectedGraph, get_ordered_predecessors VIOLATION_COLOR = "red" COLORS = list(mcolors.TABLEAU_COLORS.values()) class FalsifyConst(Enum): N_VIOLATIONS = auto() N_TESTS = auto() P_VALUE = auto() P_VALUES = auto() GIVEN_VIOLATIONS = auto() PERM_VIOLATIONS = auto() F_GIVEN_VIOLATIONS = auto() F_PERM_VIOLATIONS = auto() LOCAL_VIOLATION_INSIGHT = auto() METHOD = auto() VALIDATE_LMC = auto() VALIDATE_TPA = auto() VALIDATE_PD = auto() VALIDATE_CM = auto() PERM_GRAPHS = auto() MEC = auto() FALSIFY_METHODS = { FalsifyConst.VALIDATE_LMC: "LMC", FalsifyConst.VALIDATE_PD: "Faithfulness", FalsifyConst.VALIDATE_TPA: "TPa", FalsifyConst.VALIDATE_CM: "Causal Minimality", } @dataclass class _PValuesMemory: """A class to store and access results of independence tests. This class is useful if, e.g. we validate LMC on graph X -> Y -> Z and also on the permuted nodes Z -> Y -> X. Since X ind Z | Y == Z ind X | Y, we only need to perform the conditional independence test once. We use frozensets of nodes since standard (immutable) sets are not hashable. """ p_values: Dict[Tuple[FrozenSet, FrozenSet, FrozenSet], float] = field(default_factory=dict) def add_p_value( self, p_value: Union[None, float], X: Union[Set, List, str], Y: Union[Set, List, str], Z: Optional[Union[Set, List, str]] = None, ): if not Z: Z = set() self.p_values[(_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z))] = p_value def get_p_value( self, X: Union[Set, List, str], Y: Union[Set, List, str], Z: Optional[Union[Set, List, str]] = None ): if not Z: Z = set() # Independence tests are symmetric if (_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z)) in self.p_values: return self.p_values[(_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z))] elif (_to_frozenset(Y), _to_frozenset(X), _to_frozenset(Z)) in self.p_values: return self.p_values[(_to_frozenset(Y), _to_frozenset(X), _to_frozenset(Z))] return def __contains__(self, item: Tuple[Union[Set, List, str], ...]) -> bool: X, Y = (_to_frozenset(i) for i in item[:2]) if len(item) == 2 or item[2] is None: Z = frozenset() else: Z = _to_frozenset(item[2]) return (X, Y, Z) in self.p_values or (Y, X, Z) in self.p_values def validate_lmc( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, include_unconditional: bool = True, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[str, float]]]: """ Validate the local markov condition for a given directed graph. Return number of violations and p values for each node. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory instance, where results of previously performed tests are stored. :param independence_test: Test to use for unconditional independencies (only used if include_unconditional=True) :param conditional_independence_test: Conditional independence test to use for checking local Markov condition. :param significance_level: Significance level for (conditional) independence tests. :param include_unconditional: Test also unconditional independencies of root nodes. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Outcome of validation containing number of violations in the graph and p values/violation for each tuple (node, non_desc) """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_LMC, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, FalsifyConst.P_VALUES: dict(), } # Find out which tests to do triples = _get_parental_triples(causal_graph, include_unconditional) to_test = [] for node, non_desc, parents in triples: if not (node, non_desc, parents) in p_values_memory: to_test.append((node, non_desc, parents)) p_values_memory.add_p_value(None, node, non_desc, parents) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=node, Y=non_desc, Z=parents, independence_test=independence_test, conditional_independence_test=conditional_independence_test, seed=int(seed), ) for (node, non_desc, parents), seed in zip(to_test, random_seeds) ) # Gather results for i, (node, non_desc, parents) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], node, non_desc, parents) # Summarize for node, non_desc, parents in triples: lmc_p_value = p_values_memory.get_p_value(node, non_desc, parents) if lmc_p_value is not None: validation_summary[FalsifyConst.N_TESTS] += 1 validation_summary[FalsifyConst.P_VALUES][(node, non_desc)] = ( lmc_p_value, lmc_p_value <= significance_level, ) if lmc_p_value <= significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_tpa( causal_graph: DirectedGraph, causal_graph_reference: DirectedGraph, include_unconditional: bool = True, ) -> Dict[str, int]: """ Graphical criterion to evaluate which pairwise parental d-separations (parental triples) in `causal_graph` are violated, assuming `causal_graph_reference` is the ground truth graph. If none are violated, then both graphs lie in the same Markov equivalence class. Specifically we test: X _|_G' Y | Z and X _/|_G Y | Z for Y \in ND{X}^G', Z = PA{X}^G :param causal_graph: Causal graph for which to evaluate parental d-separations (G') :param causal_graph_reference: Causal graph where we test if d-separation holds (G) :param include_unconditional: Test also unconditional independencies of root nodes. :return: Validation summary with number of d-separations implied by `causal_graph` and number of times these are violated in the graph `causal_graph_reference`. """ validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_TPA, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, } triples = _get_parental_triples(causal_graph, include_unconditional) for node, non_desc, parents in triples: validation_summary[FalsifyConst.N_TESTS] += 1 if not nx.d_separated(causal_graph_reference, {node}, {non_desc}, set(parents)): validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_pd( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, n_pairs: int = -1, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, adjacent_only: bool = False, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[tuple, float]]]: """ Validate pairwise dependencies (pd) for a given causal graph and data. Test for each node if it is statistically dependent of all its ancestors. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory object, where results of previously performed tests are stored. :param n_pairs: Evaluate dependencies for n_pairs <= all pairs in the DAG. If n_pairs=-1, evaluate dependencies for all (ancestor, node) pairs (default). :param independence_test: Independence test to use for checking pairwise dependencies. :param significance_level: Significance level for independence tests. :param adjacent_only: Only test adjacent node pairs. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Summary dict: {n_violations: int, n_tests: int, p_values: {(ancestor, node): float, ...}} """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() pairs = [(ancestor, node) for node in causal_graph.nodes for ancestor in nx.ancestors(causal_graph, node)] if adjacent_only: pairs = [ (ancestor, node) for (ancestor, node) in pairs if ancestor in get_ordered_predecessors(causal_graph, node) ] if n_pairs < 0: n_pairs = len(pairs) if n_pairs > len(pairs): raise ValueError(f"n_pairs ({n_pairs}) > number of pairs in the DAG ({len(pairs)})") validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_PD, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: n_pairs, FalsifyConst.P_VALUES: dict(), } pair_idxs = np.random.choice(len(pairs), size=n_pairs, replace=False) # Find out which tests to do to_test = [] for pair_idx in pair_idxs: ancestor, node = pairs[pair_idx] if not (ancestor, node) in p_values_memory: to_test.append((ancestor, node)) p_values_memory.add_p_value(None, ancestor, node) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=ancestor, Y=node, Z=None, independence_test=independence_test, conditional_independence_test=None, seed=int(seed), ) for (ancestor, node), seed in zip(to_test, random_seeds) ) # Gather results for i, (ancestor, node) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], ancestor, node) # Summarize for pair_idx in pair_idxs: ancestor, node = pairs[pair_idx] p_value = p_values_memory.get_p_value(ancestor, node) if p_value is not None: validation_summary[FalsifyConst.P_VALUES][(node, ancestor)] = (p_value, p_value > significance_level) if p_value > significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_cm( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[tuple, float]]]: """ Function to test causal minimality of a DAG (see [1], Proposition 6.36). [1] J. Peters, D. Janzing, and B. Schölkopf, Elements of Causal Inference: Foundations and Learning Algorithms. Cambridge, MA, USA: MIT Press, 2017. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory object, where results of previously performed tests are stored. :param independence_test: Independence test to use. :param conditional_independence_test: Conditional independence test to use. :param significance_level: Significance level for independence tests. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Validation summary as dict. """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_CM, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, FalsifyConst.P_VALUES: dict(), } # Find out which tests to do triples = [] to_test = [] for node in causal_graph.nodes: parents = set(causal_graph.predecessors(node)) if parents: for p in parents: other_parents = parents.difference({p}) triples.append((node, p, other_parents)) if not (node, p, other_parents) in p_values_memory: to_test.append((node, p, other_parents)) p_values_memory.add_p_value(None, node, p, other_parents) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=node, Y=p, Z=list(other_parents), independence_test=independence_test, conditional_independence_test=conditional_independence_test, seed=int(seed), ) for (node, p, other_parents), seed in zip(to_test, random_seeds) ) # Gather results for i, (node, p, other_parents) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], node, p, other_parents) # Summarize for node, p, other_parents in triples: p_value = p_values_memory.get_p_value(node, p, other_parents) if p_value is not None: validation_summary[FalsifyConst.N_TESTS] += 1 validation_summary[FalsifyConst.P_VALUES][(node, p, tuple(other_parents))] = ( p_value, p_value > significance_level, ) if p_value > significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def run_validations( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Optional[Union[Callable, Tuple[Callable, ...], List[Callable]]] = partial( validate_lmc, independence_test=kernel_based, conditional_independence_test=kernel_based ), ) -> Dict[str, Dict]: """ Validate a given causal graph using observational data and some given methods. If methods are provided, they must be wrapped in a partial object, with their respective parameters. E.g., if one wants to test the local Markov conditions and the pairwise dependencies (unconditional faithfulness), then call run_validations(G, data, methods=( partial(validate_lmc, independence_test=..., conditional_independence_test=...), partial(validate_pd, independence_test=...), ) ) :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param methods: Method functions wrapped in wrap_partial. E.g. wrap_partial(validate_lmc, data=data, independence_test=..., conditional_independence_test=...). If no methods are provided we run validate_lmc with optional keyword arguments provided to run_validations. :return: Validation summary as dict. """ if not isinstance(methods, (tuple, list)): methods = (methods,) validation_summary = dict() for m in methods: if "data" in getfullargspec(m).args: m = partial(m, data=data) m_summary = m(causal_graph=causal_graph) m_name = m_summary.pop(FalsifyConst.METHOD) validation_summary[m_name] = m_summary return validation_summary @dataclass class EvaluationResult: """ Dataset class containing the evaluation result of falsifying a graph using a node-permutation test. ... Attributes ---------- summary : dict Dictionary containing the summary of the evaluation. significance_level : float Significance level based on which we falsify the given DAG falsifiable : bool Whether the given DAG is falsifiable. falsified : bool Whether the given DAG is falsified. """ summary: dict significance_level: float suggestions: Optional[dict] = None def update_significance_level(self, significance_level: float): """ Update the significance level to decide if we falsify a given DAG. """ self.significance_level = significance_level self.__post_init__() def __post_init__(self): self.can_evaluate = self._can_evaluate() if not self.can_evaluate: self.falsified = None self.falsifiable = None elif ( self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.P_VALUE] > self.significance_level > self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE] ): self.falsified = True self.falsifiable = True elif self.significance_level < self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE]: self.falsified = False self.falsifiable = False else: self.falsified = False self.falsifiable = True def __repr__(self): # DAG Evaluation if self.can_evaluate: decision = " " if self.falsified else " do not " informative = " " if self.falsifiable else " not " frac_MEC = f"{len(self.summary[FalsifyConst.MEC])} / {len(self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.PERM_VIOLATIONS])}" frac_VLMC = f"{self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.GIVEN_VIOLATIONS]}/{self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.N_TESTS]}" p_LMC = self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.P_VALUE] p_dSep = self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE] validation_repr = [ f"The given DAG is{informative}informative because {frac_MEC} of the permutations lie in the Markov", f"equivalence class of the given DAG (p-value: {p_dSep:.2f}).", f"The given DAG violates {frac_VLMC} LMCs and is better than {(1 - p_LMC) * 100:.1f}% of the permuted DAGs (p-value: {p_LMC:.2f}).", f"Based on the provided significance level ({self.significance_level}) and because the DAG is{informative}informative,", f"we{decision}reject the DAG.", ] else: validation_repr = ["Cannot be evaluated!"] # Suggestions suggestion_repr = {} for m in self.suggestions: suggestion_repr[FALSIFY_METHODS[m]] = [ f"Remove edge {node[1]} --> {node[0]}" for (node, r) in self.suggestions[m][FalsifyConst.P_VALUES].items() if r[1] ] return _generate_table(validation_repr, suggestion_repr) def _can_evaluate(self): can_evaluate = True for m in (FalsifyConst.VALIDATE_LMC, FalsifyConst.VALIDATE_TPA): if m not in self.summary: can_evaluate = False return can_evaluate def falsify_graph( causal_graph: DirectedGraph, data: pd.DataFrame, suggestions: bool = False, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, significance_ci: float = 0.05, n_permutations: Optional[int] = None, show_progress_bar: Optional[bool] = None, n_jobs: Optional[int] = None, plot_histogram: bool = False, plot_kwargs: Optional[Dict] = None, ) -> EvaluationResult: """ Falsify a given DAG using observational data. This method returns the result of a permutation-test to falsify a user-given DAG using observational data. To this end we construct the test statistics by testing the violations of local Markov conditions (LMC) implied by the graph using conditional independence (CI) tests. The null is the number of LMC violations of a random node-permutation of the given graph. Our test can be interpreted as whether the given graph is significantly better than random in terms of the CIs it entails. To determine whether a given graph is falsifiable by our metric, we implement a second test, which reports whether given graph is "characteristic" enough in terms of the CIs it entails. For this, we compute how many of the random node permutations lie in the same Markov equivalence class (MEC) as the given graph and conclude that the given graph is falsifiable only if the fraction of permuted DAGs in the same MEC as the given graph is "reasonably" small. The returned EvaluationResult object has two attributes: `falsified` and `falsifiable`: `falsifiable`: The given graph lies in a different MEC than >= 1-`significance_level` of the permuted DAGs `falsified`: The given graph is falsifiable and violates fewer LMCs than >= 1-`significance_level` of the permuted DAGs By default, we only run 1 / `significance_level` permutations as those are enough to falsify a graph with type I error probability `significance_level` at some given `significance_level`. If you are interested in a more exact estimate of the p-value or wish to plot a histogram to see how the given DAG compares to random node permutations, you should set `n_permutations` to some larger value (e.g. 100 or 1000). If `n_permutations=-1` we test on all n_nodes! permutations (the default if plot_histogram=True). Additionally, this method allows to return suggestions to the user (suggestions=True). This is done by testing for violations of causal minimality via `validate_cm`. Related paper: Eulig, E., Mastakouri, A. A., Blöbaum, P., Hardt, M., & Janzing, D. (2023). Toward Falsifying Causal Graphs Using a Permutation-Based Test. https://arxiv.org/abs/2305.09565 :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param suggestions: Provide suggestions to the user. At the moment the only source of suggestions comes from validating causal minimality (using validate_cm). :param independence_test: Independence test to use for checking pairwise independencies. :param conditional_independence_test: Conditional independence test to use. :param significance_level: Significance level for the permutation test. :param significance_ci: Significance level for (conditional) independence tests. :param n_permutations: Number of permutations to perform. If -1 use all n_nodes! permutations. :param show_progress_bar: Whether to show progress bar over permutations. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :param plot_histogram: Plot histogram of results from permutation baseline. :param plot_kwargs: Additional plot arguments to be passed to plot_evaluation_results. :return: EvaluationResult """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs show_progress_bar = config.show_progress_bars if show_progress_bar is None else show_progress_bar p_values_memory = _PValuesMemory() if n_permutations is None: n_permutations = int(1 / significance_level) if not plot_histogram else -1 if not plot_kwargs: plot_kwargs = {} methods = ( partial( validate_lmc, independence_test=independence_test, conditional_independence_test=conditional_independence_test, significance_level=significance_ci, p_values_memory=p_values_memory, n_jobs=n_jobs, ), partial(validate_tpa, causal_graph_reference=causal_graph), ) if suggestions: suggestion_methods = ( partial( validate_cm, independence_test=independence_test, conditional_independence_test=conditional_independence_test, significance_level=significance_ci, p_values_memory=p_values_memory, n_jobs=n_jobs, ), ) else: suggestion_methods = tuple() summary_given = run_validations( causal_graph, data, methods=methods + suggestion_methods, ) summary_perm = _permutation_based( causal_graph, data, methods=methods, exclude_original_order=False, n_permutations=n_permutations, show_progress_bar=show_progress_bar, ) summary = dict() validation_methods = set(summary_perm.keys()) - {FalsifyConst.PERM_GRAPHS} for m in validation_methods: summary[m] = dict() summary[m][FalsifyConst.PERM_VIOLATIONS] = [perm[FalsifyConst.N_VIOLATIONS] for perm in summary_perm[m]] summary[m][FalsifyConst.GIVEN_VIOLATIONS] = summary_given[m][FalsifyConst.N_VIOLATIONS] summary[m][FalsifyConst.N_TESTS] = summary_given[m][FalsifyConst.N_TESTS] summary[m][FalsifyConst.F_PERM_VIOLATIONS] = [ perm[FalsifyConst.N_VIOLATIONS] / perm[FalsifyConst.N_TESTS] for perm in summary_perm[m] ] summary[m][FalsifyConst.F_GIVEN_VIOLATIONS] = ( summary[m][FalsifyConst.GIVEN_VIOLATIONS] / summary[m][FalsifyConst.N_TESTS] ) summary[m][FalsifyConst.P_VALUE] = sum( [ 1 for perm in summary[m][FalsifyConst.F_PERM_VIOLATIONS] if perm <= summary[m][FalsifyConst.F_GIVEN_VIOLATIONS] ] ) / len(summary[m][FalsifyConst.PERM_VIOLATIONS]) if m != FalsifyConst.VALIDATE_TPA: # Append list of violations (node, non_desc) to get local information summary[m][FalsifyConst.LOCAL_VIOLATION_INSIGHT] = summary_given[m][FalsifyConst.P_VALUES] if FalsifyConst.VALIDATE_TPA in summary: summary[FalsifyConst.MEC] = [ summary_perm[FalsifyConst.PERM_GRAPHS][i] for i, v in enumerate(summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.PERM_VIOLATIONS]) if v == 0 ] result = EvaluationResult( summary=summary, significance_level=significance_level, suggestions={m: summary_given[m] for m in summary_given if m not in validation_methods}, ) if plot_histogram: plot_evaluation_results(result, **plot_kwargs) return result def apply_suggestions( causal_graph: DirectedGraph, evaluation_result: EvaluationResult, edges_to_keep: Optional[List[Tuple[Any, Any]]] = None, ): if not hasattr(evaluation_result, "suggestions"): raise ValueError("EvaluationResult object has no attribute suggestions. Please run with suggestion=True!") causal_graph = causal_graph.copy() for m in evaluation_result.suggestions: for node, res in evaluation_result.suggestions[m][FalsifyConst.P_VALUES].items(): edge = (node[1], node[0]) if (res[1] and edges_to_keep is not None and edge not in edges_to_keep) or ( res[1] and edges_to_keep is None ): causal_graph.remove_edge(edge[0], edge[1]) return causal_graph def plot_evaluation_results(evaluation_result, figsize=(8, 3), bins=None, title="", savepath="", display=True): fig, ax = plt.subplots(figsize=figsize) # Plot histograms p_values = "" data = [] labels = [] evaluation_summary = {k: v for k, v in evaluation_result.summary.items() if k != FalsifyConst.MEC} for i, (m, m_summary) in enumerate(evaluation_summary.items()): data.append(m_summary[FalsifyConst.F_PERM_VIOLATIONS]) labels.append(f"Violations of {FALSIFY_METHODS[m]} of permuted DAGs") p_values += f"p-value {FALSIFY_METHODS[m]} = {m_summary[FalsifyConst.P_VALUE]:.2f}\n" ax.hist(data, color=COLORS[: len(evaluation_summary)], bins=bins, alpha=0.5, label=labels, edgecolor="k") # Plot given violations for i, (m, m_summary) in enumerate(evaluation_summary.items()): ylim = ax.get_ylim()[1] ax.plot( [m_summary[FalsifyConst.F_GIVEN_VIOLATIONS]] * 2, [0, ylim], "--", c=COLORS[i], label=f"Violations of {FALSIFY_METHODS[m]} of given DAG", ) ax.set_ylim([0, ylim]) ax.set_xlabel("Fraction of violations") ax.set_ylabel("# Permutations") plt.legend(loc="upper left", bbox_to_anchor=(1.05, 1), borderaxespad=0.0, title=p_values) if title: plt.title(title) plt.tight_layout() if savepath: plt.savefig(savepath, dpi=300, bbox_inches="tight") if display: plt.show() else: plt.close() def plot_local_insights( causal_graph: DirectedGraph, evaluation_result: Union[EvaluationResult, Dict], method: Optional[str] = FalsifyConst.VALIDATE_LMC, ): """ For some given graph and evaluation result plot local violations. :param causal_graph: DiGraph :param evaluation_result: EvaluationResult :param method: Method for which to plot violations """ colors = {} if isinstance(evaluation_result, EvaluationResult) and method in evaluation_result.summary: local_insight_dict = evaluation_result.summary[method][FalsifyConst.LOCAL_VIOLATION_INSIGHT] elif ( isinstance(evaluation_result, EvaluationResult) and hasattr(evaluation_result, "suggestions") and method in evaluation_result.suggestions ): local_insight_dict = evaluation_result.suggestions[method][FalsifyConst.P_VALUES] elif isinstance(evaluation_result, Dict): if method not in evaluation_result: raise ValueError(f"Validation method {method} does not exist in given evaluation_result!") if FalsifyConst.P_VALUES not in evaluation_result[method]: raise ValueError( f"Validation method {method} has no key {FalsifyConst.P_VALUES} where information on local violations " f"are stored!" ) local_insight_dict = evaluation_result[method][FalsifyConst.P_VALUES] else: raise ValueError(f"Cannot plot local violation insights from method {method} in evaluation_result!") for nodes, result in local_insight_dict.items(): if result[1]: if method == FalsifyConst.VALIDATE_LMC: # For LMC we highlight X for which X _|/|_ Y \in ND_X | Pa_X colors[nodes[0]] = VIOLATION_COLOR elif method == FalsifyConst.VALIDATE_PD: # For PD we highlight the edge (if Y\in Anc_X -> X are adjacent) colors[(nodes[1], nodes[0])] = VIOLATION_COLOR elif method == FalsifyConst.VALIDATE_CM: # For causal minimality we highlight the edge Y \in Pa_X -> X colors[(nodes[1], nodes[0])] = VIOLATION_COLOR plot(causal_graph, colors=colors) def _generate_table( validation_repr, suggestion_repr, width=105, validation_name="Falsificaton Summary", suggestion_name="Suggestions" ): # Create Validation header _repr = [ "+" + "-" * (width - 2) + "+\n", "|" + " " * int(np.floor((width - 2 - len(validation_name)) / 2)), validation_name, " " * int(np.ceil((width - 2 - len(validation_name)) / 2)) + "|\n", "+" + "-" * (width - 2) + "+\n", ] # Create Validation summary _repr += [f"| {v_line}" + " " * (width - 3 - len(v_line)) + "|\n" for v_line in validation_repr] # Close Validation _repr += ["+" + "-" * (width - 2) + "+\n"] # Create Suggestions header if suggestion_repr: _repr += [ "|" + " " * int(np.floor((width - 2 - len(suggestion_name)) / 2)), suggestion_name, " " * int(np.ceil((width - 2 - len(suggestion_name)) / 2)) + "|\n", "+" + "-" * (width - 2) + "+\n", ] # Iterate over suggestions for m, suggestions in suggestion_repr.items(): left_col = "| " + m + " |" if not suggestions: right_col = " " + " " * (width - 2 - len(left_col)) + "|\n" _repr += [left_col, right_col] for i, s in enumerate(suggestions): if i > 0: left_col = "| " + " " * len(m) + " |" right_col = " - " + s + " " * (width - 4 - len(s) - len(left_col)) + "|\n" _repr += [left_col, right_col] _repr += ["+" + "-" * (width - 2) + "+\n"] return "".join(_repr)[:-1] def _compute_p_value( data: pd.DataFrame, X: Union[List, str], Y: Union[List, str], Z: Optional[Union[Set, List, str]], independence_test: Optional[Callable[[np.ndarray, np.ndarray], float]], conditional_independence_test: Optional[Callable[[np.ndarray, np.ndarray, np.ndarray], float]], seed: int, ) -> float: """Perform (conditional) independence test and report p-value. If any nodes needed for the test do not exist in the data, this test is skipped and a value of -1 is returned and a warning is raised. :param data: Observations of variables in the DAG. :param X: Variable to test (conditional) independence with Y :param Y: Variable to test (conditional) independence with X :param Z: Set to condition independence test on. Can be empty (None, empty set, or empty list). :param independence_test: Independence test to use. :param conditional_independence_test: Conditional independence test to use. :param seed: Random seed :return: p-value """ set_random_seed(seed) # Test if we have data for X and Y for node in [X, Y]: if not node in data.columns: warnings.warn(f"WARN: Couldn't find data for node {node}. Skip this test.") return if Z: # Test if we have data for Z for node in Z: if not node in data.columns: warnings.warn(f"WARN: Couldn't find data for node {node}. Skip this test.") return p_value = conditional_independence_test(data[X].values, data[Y].values, data[Z].values) else: p_value = independence_test(data[X].values, data[Y].values) return p_value def _get_parental_triples(causal_graph: DirectedGraph, include_unconditional: bool): """ For a given graph collect all parental triples, that is, the triple (X, Y, Z) is a parental triple iff Y is non-descendant of X, and Z are the parents of X (can be empty if include_unconditional=True) """ triples = [] for node in causal_graph.nodes: parents = get_ordered_predecessors(causal_graph, node) non_descendants = _get_non_descendants(causal_graph, node, exclude_parents=True) if (parents or include_unconditional) and non_descendants: for non_desc in non_descendants: triples.append((node, non_desc, parents)) return triples def _permutation_based( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Union[Callable, Tuple[Callable, ...], List[Callable]], exclude_original_order: bool, n_permutations: int, show_progress_bar: bool, ) -> Dict[str, List[Union[DirectedGraph, Dict]]]: """ Generate baseline for node permutations. :param causal_graph: A directed acyclic graph (DAG). :param methods: Validation methods to perform. :param exclude_original_order: Exclude the original ordering of the nodes (default=False) :param n_permutations: Number of permutations to perform. If -1 use all n_nodes! - int(exclude_orig) permutations :param show_progress_bar: Whether to show progress bar over tested permutations. :return: Dictionary containing summary of validation for each individual graph as well as the permuted graphs """ if not isinstance(methods, (tuple, list)): methods = (methods,) perm_gen = _PermuteNodes(causal_graph, n_permutations=n_permutations, exclude_original_order=exclude_original_order) validation_summary = {FalsifyConst.PERM_GRAPHS: []} for permuted_graph in tqdm(perm_gen, desc="Test permutations of given graph", disable=not show_progress_bar): res = run_validations( causal_graph=permuted_graph, data=data, methods=methods, ) validation_summary[FalsifyConst.PERM_GRAPHS].append(permuted_graph) for m_name, summary in res.items(): if m_name not in validation_summary: validation_summary[m_name] = [] validation_summary[m_name].append(summary) return validation_summary class _PermuteNodes: def __init__(self, causal_graph: DirectedGraph, exclude_original_order: bool, n_permutations: int): """ Randomly permute the nodes of a given causal graph while keeping the underlying graph structure the same. :param causal_graph: A directed acyclic graph (DAG). :param exclude_original_order: Do not return the original order. :param n_permutations: Return a generator with n_permutations permutations. If n_permutations = -1 (default), we return all n_nodes! - int(exclude_orig) permutations. :return: Copy of causal_graph with nodes randomly permuted. """ self.causal_graph = causal_graph self.exclude_original_order = exclude_original_order self.n_permutations = n_permutations self.max_perms = np.math.factorial(self.causal_graph.number_of_nodes()) - int(self.exclude_original_order) if n_permutations == -1 or n_permutations > self.max_perms: self.it = self.iter_all_permutations() self.length = self.max_perms if self.length > 2**63 - 1: raise ValueError( f"Too many permutations specified. Did you accidently set 'n_permutations'=-1 for a " f"large (>20 nodes) graph? " f"Given graph has {causal_graph.number_of_nodes()} nodes." ) else: self.it = self.iter_random_permutations() self.length = self.n_permutations def iter_all_permutations(self): for i, perm in enumerate(permutations(self.causal_graph.nodes)): if self.exclude_original_order and i == 0: continue mapping = {node: perm[i] for i, node in enumerate(self.causal_graph.nodes)} yield nx.relabel_nodes(self.causal_graph, mapping, copy=True) def iter_random_permutations(self): for _ in range(self.n_permutations): if self.exclude_original_order: is_orig = True while is_orig: perm = list(np.random.permutation(self.causal_graph.nodes)) if perm != list(self.causal_graph.nodes): is_orig = False else: perm = list(np.random.permutation(self.causal_graph.nodes)) mapping = {node: perm[i] for i, node in enumerate(self.causal_graph.nodes)} yield nx.relabel_nodes(self.causal_graph, mapping, copy=True) def __iter__(self): yield from self.it def __len__(self): return self.length def _to_frozenset(x: Union[Set, List, str]): """Converts a set, list or string into a hashable frozenset""" assert ( isinstance(x, Set) or isinstance(x, List) or isinstance(x, str) ), f"{x} must be list, set or str. Got {type(x)} instead!" if isinstance(x, str): return frozenset({x}) return frozenset(x)

eeulig

379f188d674d5be6d644559f9f54852b87403f7c

4e30c63fe1113e35259e7fb3400a6f09bca4d699

I used `wrap_partial` because we needed the `__name__` attribute. I now included a `FalsifyConst.METHOD` key in the validation summary of each method so that this is not necessary anymore. Methods can now be simply wrapped using `partial`.

eeulig

py-why/dowhy

Remove kwargs from falsify, improve docstrings

This commit updates `gcm/falsify` in the following ways: - Remove the `**kwargs` for `validate_graph` and `falsify_graph`. Instead, validation methods given to those functions should be wrapped in `wrap_partial`, e.g. if we want to validate a graph `G` and test local Markov conditions and pairwise dependencies on observations `data`, we would do ``` validate_graph(G, methods=( wrap_partial(validate_lmc, data=data, independence_test=..., conditional_independence_test=...), wrap_partial(validate_pd, data=data, independence_test=...), ) ) ``` By default (`methods=None`), `validate_graph` tests local Markov conditions and `falsify_graph` tests local Markov conditions and d-separation of parental triples. In this case `data` has to be set and optionally one can provide `independence_test`, `conditional_independence_test`, `significance_level`, and `n_jobs` via the keyword arguments of `validate_graph` / `falsify_graph`. - Use `Enum` for some of the constants - In `_PValuesMemory` the method `add_p_value ` always sets a p-value even if it was set already. This removes the need for the method `clear_placeholders` and allows `p_values` to be set to `-1`, indicating that the CI is not testable (because of missing data for a node, c.f. `_compute_p_value`) - If for some CI: X, Y | Z any of the nodes has no data (column missing in the provided dataframe), we skip this test and raise a warning. - Instead of using the raw number of violations in the plotted histogram and to compute the p-value, we use the relative number of violations. This will result in different p-values only if we have missing data for some nodes (and thus the number of tests is potentially different between different permutations) - Improve docstrings

2023-07-04 13:58:30+00:00

2023-07-18 14:19:10+00:00

dowhy/gcm/falsify.py

"""This module provides functionality to falsify a user-given DAG given observed data. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from dataclasses import dataclass, field from enum import Enum, auto from functools import partial from itertools import permutations from typing import Any, Callable, Dict, FrozenSet, List, Optional, Set, Tuple, Union import matplotlib.colors as mcolors import matplotlib.pyplot as plt import networkx as nx import numpy as np import pandas as pd from joblib import Parallel, delayed from tqdm import tqdm import dowhy.gcm.config as config from dowhy.gcm.independence_test import kernel_based from dowhy.gcm.util import plot from dowhy.gcm.util.general import set_random_seed from dowhy.gcm.validation import _get_non_descendants from dowhy.graph import DirectedGraph, get_ordered_predecessors VIOLATION_COLOR = "red" COLORS = list(mcolors.TABLEAU_COLORS.values()) class FalsifyConst(Enum): N_VIOLATIONS = auto() N_TESTS = auto() P_VALUE = auto() P_VALUES = auto() GIVEN_VIOLATIONS = auto() PERM_VIOLATIONS = auto() F_GIVEN_VIOLATIONS = auto() F_PERM_VIOLATIONS = auto() LOCAL_VIOLATION_INSIGHT = auto() METHOD = auto() VALIDATE_LMC = auto() VALIDATE_TPA = auto() VALIDATE_PD = auto() VALIDATE_CM = auto() PERM_GRAPHS = auto() MEC = auto() FALSIFY_METHODS = { FalsifyConst.VALIDATE_LMC: "LMC", FalsifyConst.VALIDATE_PD: "Faithfulness", FalsifyConst.VALIDATE_TPA: "tPa", FalsifyConst.VALIDATE_CM: "Causal Minimality", } @dataclass class _PValuesMemory: """A class to store and access results of independence tests. This class is useful if, e.g. we validate LMC on graph X -> Y -> Z and also on the permuted nodes Z -> Y -> X. Since X ind Z | Y == Z ind X | Y, we only need to perform the conditional independence test once. We use frozensets of nodes since standard (immutable) sets are not hashable. """ p_values: Dict[Tuple[FrozenSet, FrozenSet, FrozenSet], float] = field(default_factory=dict) def add_p_value( self, p_value: Union[None, float], X: Union[Set, List, str], Y: Union[Set, List, str], Z: Optional[Union[Set, List, str]] = None, ): if not Z: Z = set() self.p_values[(_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z))] = p_value def get_p_value( self, X: Union[Set, List, str], Y: Union[Set, List, str], Z: Optional[Union[Set, List, str]] = None ): if not Z: Z = set() # Independence tests are symmetric if (_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z)) in self.p_values: return self.p_values[(_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z))] elif (_to_frozenset(Y), _to_frozenset(X), _to_frozenset(Z)) in self.p_values: return self.p_values[(_to_frozenset(Y), _to_frozenset(X), _to_frozenset(Z))] return def __contains__(self, item: Tuple[Union[Set, List, str], ...]) -> bool: X, Y = (_to_frozenset(i) for i in item[:2]) if len(item) == 2 or item[2] is None: Z = frozenset() else: Z = _to_frozenset(item[2]) return (X, Y, Z) in self.p_values or (Y, X, Z) in self.p_values def validate_lmc( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, include_unconditional: bool = True, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[str, float]]]: """ Validate the local markov condition for a given directed graph. Return number of violations and p values for each node. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory instance, where results of previously performed tests are stored. :param independence_test: Test to use for unconditional independencies (only used if include_unconditional=True) :param conditional_independence_test: Conditional independence test to use for checking local Markov condition. :param significance_level: Significance level for (conditional) independence tests. :param include_unconditional: Test also unconditional independencies of root nodes. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Outcome of validation containing number of violations in the graph and p values/violation for each tuple (node, non_desc) """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_LMC, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, FalsifyConst.P_VALUES: dict(), } # Find out which tests to do triples = _get_parental_triples(causal_graph, include_unconditional) to_test = [] for node, non_desc, parents in triples: if not (node, non_desc, parents) in p_values_memory: to_test.append((node, non_desc, parents)) p_values_memory.add_p_value(None, node, non_desc, parents) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=node, Y=non_desc, Z=parents, independence_test=independence_test, conditional_independence_test=conditional_independence_test, seed=int(seed), ) for (node, non_desc, parents), seed in zip(to_test, random_seeds) ) # Gather results for i, (node, non_desc, parents) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], node, non_desc, parents) # Summarize for node, non_desc, parents in triples: lmc_p_value = p_values_memory.get_p_value(node, non_desc, parents) if lmc_p_value is not None: validation_summary[FalsifyConst.N_TESTS] += 1 validation_summary[FalsifyConst.P_VALUES][(node, non_desc)] = ( lmc_p_value, lmc_p_value <= significance_level, ) if lmc_p_value <= significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_tpa( causal_graph: DirectedGraph, causal_graph_reference: DirectedGraph, include_unconditional: bool = True, data: Optional[pd.DataFrame] = None, ) -> Dict[str, int]: """ Graphical criterion to evaluate which pairwise parental d-separations (parental triples) in `causal_graph` are violated, assuming `causal_graph_reference` is the ground truth graph. If none are violated, then both graphs lie in the same Markov equivalence class. Specifically we test: X _|_G' Y | Z and X _/|_G Y | Z for Y \in ND{X}^G', Z = PA{X}^G :param causal_graph: Causal graph for which to evaluate parental d-separations (G') :param causal_graph_reference: Causal graph where we test if d-separation holds (G) :param include_unconditional: Test also unconditional independencies of root nodes. :param data: IGNORED! No data is needed for this validation method and thus the data argument is ignored! :return: Validation summary with number of d-separations implied by `causal_graph` and number of times these are violated in the graph `causal_graph_reference`. """ validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_TPA, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, } triples = _get_parental_triples(causal_graph, include_unconditional) for node, non_desc, parents in triples: validation_summary[FalsifyConst.N_TESTS] += 1 if not nx.d_separated(causal_graph_reference, {node}, {non_desc}, set(parents)): validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_pd( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, n_pairs: int = -1, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, adjacent_only: bool = False, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[tuple, float]]]: """ Validate pairwise dependencies (pd) for a given causal graph and data. Test for each node if it is statistically dependent of all its ancestors. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory object, where results of previously performed tests are stored. :param n_pairs: Evaluate dependencies for n_pairs <= all pairs in the DAG. If n_pairs=-1, evaluate dependencies for all (ancestor, node) pairs (default). :param independence_test: Independence test to use for checking pairwise dependencies. :param significance_level: Significance level for independence tests. :param adjacent_only: Only test adjacent node pairs. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Summary dict: {n_violations: int, n_tests: int, p_values: {(ancestor, node): float, ...}} """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() pairs = [(ancestor, node) for node in causal_graph.nodes for ancestor in nx.ancestors(causal_graph, node)] if adjacent_only: pairs = [ (ancestor, node) for (ancestor, node) in pairs if ancestor in get_ordered_predecessors(causal_graph, node) ] if n_pairs < 0: n_pairs = len(pairs) if n_pairs > len(pairs): raise ValueError(f"n_pairs ({n_pairs}) > number of pairs in the DAG ({len(pairs)})") validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_PD, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: n_pairs, FalsifyConst.P_VALUES: dict(), } pair_idxs = np.random.choice(len(pairs), size=n_pairs, replace=False) # Find out which tests to do to_test = [] for pair_idx in pair_idxs: ancestor, node = pairs[pair_idx] if not (ancestor, node) in p_values_memory: to_test.append((ancestor, node)) p_values_memory.add_p_value(None, ancestor, node) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=ancestor, Y=node, Z=None, independence_test=independence_test, conditional_independence_test=None, seed=int(seed), ) for (ancestor, node), seed in zip(to_test, random_seeds) ) # Gather results for i, (ancestor, node) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], ancestor, node) # Summarize for pair_idx in pair_idxs: ancestor, node = pairs[pair_idx] p_value = p_values_memory.get_p_value(ancestor, node) if p_value is not None: validation_summary[FalsifyConst.P_VALUES][(node, ancestor)] = (p_value, p_value > significance_level) if p_value > significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_cm( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[tuple, float]]]: """ Function to test causal minimality of a DAG (see [1], Proposition 6.36). [1] J. Peters, D. Janzing, and B. Schölkopf, Elements of Causal Inference: Foundations and Learning Algorithms. Cambridge, MA, USA: MIT Press, 2017. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory object, where results of previously performed tests are stored. :param independence_test: Independence test to use. :param conditional_independence_test: Conditional independence test to use. :param significance_level: Significance level for independence tests. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Validation summary as dict. """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_CM, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, FalsifyConst.P_VALUES: dict(), } # Find out which tests to do triples = [] to_test = [] for node in causal_graph.nodes: parents = set(causal_graph.predecessors(node)) if parents: for p in parents: other_parents = parents.difference({p}) triples.append((node, p, other_parents)) if not (node, p, other_parents) in p_values_memory: to_test.append((node, p, other_parents)) p_values_memory.add_p_value(None, node, p, other_parents) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=node, Y=p, Z=list(other_parents), independence_test=independence_test, conditional_independence_test=conditional_independence_test, seed=int(seed), ) for (node, p, other_parents), seed in zip(to_test, random_seeds) ) # Gather results for i, (node, p, other_parents) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], node, p, other_parents) # Summarize for node, p, other_parents in triples: p_value = p_values_memory.get_p_value(node, p, other_parents) if p_value is not None: validation_summary[FalsifyConst.N_TESTS] += 1 validation_summary[FalsifyConst.P_VALUES][(node, p, tuple(other_parents))] = ( p_value, p_value > significance_level, ) if p_value > significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_graph( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Optional[Union[Callable, Tuple[Callable, ...], List[Callable]]] = partial( validate_lmc, independence_test=kernel_based, conditional_independence_test=kernel_based ), ) -> Dict[str, Dict]: """ Validate a given causal graph using observational data and some given methods. If methods are provided, they must be wrapped in a partial object, with their respective parameters. E.g., if one wants to test the local Markov conditions and the pairwise dependencies (unconditional faithfulness), then call validate_graph(G, data, methods=( partial(validate_lmc, independence_test=..., conditional_independence_test=...), partial(validate_pd, independence_test=...), ) ) :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param methods: Method functions wrapped in wrap_partial. E.g. wrap_partial(validate_lmc, data=data, independence_test=..., conditional_independence_test=...). If no methods are provided we run validate_lmc with optional keyword arguments provided to validate_graph. :return: Validation summary as dict. """ if not isinstance(methods, (tuple, list)): methods = (methods,) validation_summary = dict() for m in methods: m_summary = m(causal_graph=causal_graph, data=data) m_name = m_summary.pop(FalsifyConst.METHOD) validation_summary[m_name] = m_summary return validation_summary @dataclass class EvaluationResult: """ Dataset class containing the evaluation result of falsifying a graph using a node-permutation test. ... Attributes ---------- summary : dict Dictionary containing the summary of the evaluation. significance_level : float Significance level based on which we falsify the given DAG falsifiable : bool Whether the given DAG is falsifiable. falsified : bool Whether the given DAG is falsified. """ summary: dict significance_level: float suggestions: Optional[dict] = None def update_significance_level(self, significance_level: float): """ Update the significance level to decide if we falsify a given DAG. """ self.significance_level = significance_level self.__post_init__() def __post_init__(self): self.can_evaluate = self._can_evaluate() if not self.can_evaluate: self.falsified = None self.falsifiable = None elif ( self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.P_VALUE] > self.significance_level > self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE] ): self.falsified = True self.falsifiable = True elif self.significance_level < self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE]: self.falsified = False self.falsifiable = False else: self.falsified = False self.falsifiable = True def __repr__(self): # DAG Evaluation if self.can_evaluate: decision = " " if self.falsified else " do not " informative = " " if self.falsifiable else " not " frac_MEC = f"{len(self.summary[FalsifyConst.MEC])} / {len(self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.PERM_VIOLATIONS])}" frac_VLMC = f"{self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.GIVEN_VIOLATIONS]}/{self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.N_TESTS]}" p_LMC = self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.P_VALUE] p_dSep = self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE] validation_repr = [ f"The given DAG is{informative}informative because {frac_MEC} of the permutations lie in the Markov", f"equivalence class of the given DAG (p-value: {p_dSep:.2f}).", f"The given DAG violates {frac_VLMC} LMCs and is better than {(1 - p_LMC) * 100:.1f}% of the permuted DAGs (p-value: {p_LMC:.2f}).", f"Based on the provided significance level ({self.significance_level}) and because the DAG is{informative}informative,", f"we{decision}reject the DAG.", ] else: validation_repr = ["Cannot be evaluated!"] # Suggestions suggestion_repr = {} for m in self.suggestions: suggestion_repr[FALSIFY_METHODS[m]] = [ f"Remove edge {node[1]} --> {node[0]}" for (node, r) in self.suggestions[m][FalsifyConst.P_VALUES].items() if r[1] ] return _generate_table(validation_repr, suggestion_repr) def _can_evaluate(self): can_evaluate = True for m in (FalsifyConst.VALIDATE_LMC, FalsifyConst.VALIDATE_TPA): if m not in self.summary: can_evaluate = False return can_evaluate def falsify_graph( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Optional[Union[Callable, Tuple[Callable, ...]]] = None, suggestion_methods: Optional[Union[Callable, Tuple[Callable, ...]]] = None, suggestions: bool = False, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, significance_ci: float = 0.05, n_permutations: Optional[int] = None, show_progress_bar: Optional[bool] = None, n_jobs: Optional[int] = None, plot_histogram: bool = False, plot_kwargs: Optional[Dict] = None, ) -> EvaluationResult: """ Falsify a given DAG using observational data. This method returns the result of a permutation-test to falsify a user-given DAG using observational data. To this end we construct the test statistics by testing the violations of local Markov conditions (LMC) implied by the graph using conditional independence (CI) tests. The null is the number of LMC violations of a random node-permutation of the given graph. Our test can be interpreted as whether the given graph is significantly better than random in terms of the CIs it entails. To determine whether a given graph is falsifiable by our metric, we implement a second test, which reports whether given graph is "characteristic" enough in terms of the CIs it entails. For this, we compute how many of the random node permutations lie in the same Markov equivalence class (MEC) as the given graph and conclude that the given graph is falsifiable only if the fraction of permuted DAGs in the same MEC as the given graph is "reasonably" small. The returned EvaluationResult object has two attributes: `falsified` and `falsifiable`: `falsifiable`: The given graph lies in a different MEC than >= 1-`significance_level` of the permuted DAGs `falsified`: The given graph is falsifiable and violates fewer LMCs than >= 1-`significance_level` of the permuted DAGs By default, we only run 1 / `significance_level` permutations as those are enough to falsify a graph with type I error probability `significance_level` at some given `significance_level`. If you are interested in a more exact estimate of the p-value of whish to plot a histogram to see how the given DAG compares to random node permutations, you should set `n_permutations` to some larger value (e.g. 100 or 1000). If `n_permutations=-1` we test on all n_nodes! permutations. `methods` and `suggestion_methods` must be wrapped in partial(method, **kwargs) (c.f. `validate_graph`). Related paper: Eulig, E., Mastakouri, A. A., Blöbaum, P., Hardt, M., & Janzing, D. (2023). Toward Falsifying Causal Graphs Using a Permutation-Based Test. https://arxiv.org/abs/2305.09565 :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param methods: Validation methods to perform. :param suggestion_methods: Methods to run on the given graph to provide additional suggestions. :param suggestions: Provide suggestions generated using the `suggestion_methods`. :param independence_test: Independence test to use for checking pairwise independencies. :param conditional_independence_test: Conditional independence test to use. :param significance_level: Significance level for the permutation test. :param significance_ci: Significance level for (conditional) independence tests. :param n_permutations: Number of permutations to perform. If -1 use all n_nodes! permutations. :param show_progress_bar: Whether to show progress bar over permutations. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :param plot_histogram: Plot histogram of results from permutation baseline. :param plot_kwargs: Additional plot arguments to be passed to plot_evaluation_results. :return: EvaluationResult """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs show_progress_bar = config.show_progress_bars if show_progress_bar is None else show_progress_bar p_values_memory = _PValuesMemory() if n_permutations is None: n_permutations = int(1 / significance_level) if not plot_histogram else -1 if not plot_kwargs: plot_kwargs = {} # If no methods are provided, use default ones: validate_lmc, validate_tpa if not methods: methods = ( partial( validate_lmc, independence_test=independence_test, conditional_independence_test=conditional_independence_test, significance_level=significance_ci, p_values_memory=p_values_memory, n_jobs=n_jobs, ), partial(validate_tpa, causal_graph_reference=causal_graph), ) elif isinstance(methods, Callable): methods = (methods,) # If no suggestion methods are provided, but suggestions=True, use default ones: validate_cm if not suggestions: suggestion_methods = tuple() elif suggestions and isinstance(suggestion_methods, Callable): suggestion_methods = (suggestion_methods,) elif suggestions: suggestion_methods = ( partial( validate_cm, independence_test=independence_test, conditional_independence_test=conditional_independence_test, significance_level=significance_ci, p_values_memory=p_values_memory, n_jobs=n_jobs, ), ) summary_given = validate_graph( causal_graph, data, methods=methods + suggestion_methods, ) summary_perm = _permutation_based( causal_graph, data, methods=methods, exclude_original_order=False, n_permutations=n_permutations, show_progress_bar=show_progress_bar, ) summary = dict() validation_methods = set(summary_perm.keys()) - {FalsifyConst.PERM_GRAPHS} for m in validation_methods: summary[m] = dict() summary[m][FalsifyConst.PERM_VIOLATIONS] = [perm[FalsifyConst.N_VIOLATIONS] for perm in summary_perm[m]] summary[m][FalsifyConst.GIVEN_VIOLATIONS] = summary_given[m][FalsifyConst.N_VIOLATIONS] summary[m][FalsifyConst.N_TESTS] = summary_given[m][FalsifyConst.N_TESTS] summary[m][FalsifyConst.F_PERM_VIOLATIONS] = [ perm[FalsifyConst.N_VIOLATIONS] / perm[FalsifyConst.N_TESTS] for perm in summary_perm[m] ] summary[m][FalsifyConst.F_GIVEN_VIOLATIONS] = ( summary[m][FalsifyConst.GIVEN_VIOLATIONS] / summary[m][FalsifyConst.N_TESTS] ) summary[m][FalsifyConst.P_VALUE] = sum( [ 1 for perm in summary[m][FalsifyConst.F_PERM_VIOLATIONS] if perm <= summary[m][FalsifyConst.F_GIVEN_VIOLATIONS] ] ) / len(summary[m][FalsifyConst.PERM_VIOLATIONS]) if m != FalsifyConst.VALIDATE_TPA: # Append list of violations (node, non_desc) to get local information summary[m][FalsifyConst.LOCAL_VIOLATION_INSIGHT] = summary_given[m][FalsifyConst.P_VALUES] if FalsifyConst.VALIDATE_TPA in summary: summary[FalsifyConst.MEC] = [ summary_perm[FalsifyConst.PERM_GRAPHS][i] for i, v in enumerate(summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.PERM_VIOLATIONS]) if v == 0 ] result = EvaluationResult( summary=summary, significance_level=significance_level, suggestions={m: summary_given[m] for m in summary_given if m not in validation_methods}, ) if plot_histogram: plot_evaluation_results(result, **plot_kwargs) return result def apply_suggestions( causal_graph: DirectedGraph, evaluation_result: EvaluationResult, edges_to_keep: Optional[List[Tuple[Any, Any]]] = None, ): if not hasattr(evaluation_result, "suggestions"): raise ValueError("EvaluationResult object has no attribute suggestions. Please run with suggestion=True!") causal_graph = causal_graph.copy() for m in evaluation_result.suggestions: for node, res in evaluation_result.suggestions[m][FalsifyConst.P_VALUES].items(): edge = (node[1], node[0]) if (res[1] and edges_to_keep is not None and edge not in edges_to_keep) or ( res[1] and edges_to_keep is None ): causal_graph.remove_edge(edge[0], edge[1]) return causal_graph def plot_evaluation_results(evaluation_result, figsize=(8, 3), bins=None, title="", savepath="", display=True): fig, ax = plt.subplots(figsize=figsize) # Plot histograms p_values = "" data = [] labels = [] evaluation_summary = {k: v for k, v in evaluation_result.summary.items() if k != FalsifyConst.MEC} for i, (m, m_summary) in enumerate(evaluation_summary.items()): data.append(m_summary[FalsifyConst.F_PERM_VIOLATIONS]) labels.append(f"Violations of {FALSIFY_METHODS[m]} of permuted DAGs") p_values += f"p-value {FALSIFY_METHODS[m]} = {m_summary[FalsifyConst.P_VALUE]:.2f}\n" ax.hist(data, color=COLORS[: len(evaluation_summary)], bins=bins, alpha=0.5, label=labels, edgecolor="k") # Plot given violations for i, (m, m_summary) in enumerate(evaluation_summary.items()): ylim = ax.get_ylim()[1] ax.plot( [m_summary[FalsifyConst.F_GIVEN_VIOLATIONS]] * 2, [0, ylim], "--", c=COLORS[i], label=f"Violations of {FALSIFY_METHODS[m]} of given DAG", ) ax.set_ylim([0, ylim]) ax.set_xlabel("Fraction of violations") ax.set_ylabel("# Permutations") plt.legend(loc="upper left", bbox_to_anchor=(1.05, 1), borderaxespad=0.0, title=p_values) if title: plt.title(title) plt.tight_layout() if savepath: plt.savefig(savepath, dpi=300, bbox_inches="tight") if display: plt.show() else: plt.close() def plot_local_insights( causal_graph: DirectedGraph, evaluation_result: Union[EvaluationResult, Dict], method: Optional[str] = FalsifyConst.VALIDATE_LMC, ): """ For some given graph and evaluation result plot local violations. :param causal_graph: DiGraph :param evaluation_result: EvaluationResult :param method: Method for which to plot violations """ colors = {} if isinstance(evaluation_result, EvaluationResult) and method in evaluation_result.summary: local_insight_dict = evaluation_result.summary[method][FalsifyConst.LOCAL_VIOLATION_INSIGHT] elif ( isinstance(evaluation_result, EvaluationResult) and hasattr(evaluation_result, "suggestions") and method in evaluation_result.suggestions ): local_insight_dict = evaluation_result.suggestions[method][FalsifyConst.P_VALUES] elif isinstance(evaluation_result, Dict): if method not in evaluation_result: raise ValueError(f"Validation method {method} does not exist in given evaluation_result!") if FalsifyConst.P_VALUES not in evaluation_result[method]: raise ValueError( f"Validation method {method} has no key {FalsifyConst.P_VALUES} where information on local violations " f"are stored!" ) local_insight_dict = evaluation_result[method][FalsifyConst.P_VALUES] else: raise ValueError(f"Cannot plot local violation insights from method {method} in evaluation_result!") for nodes, result in local_insight_dict.items(): if result[1]: if method == FalsifyConst.VALIDATE_LMC: # For LMC we highlight X for which X _|/|_ Y \in ND_X | Pa_X colors[nodes[0]] = VIOLATION_COLOR elif method == FalsifyConst.VALIDATE_PD: # For PD we highlight the edge (if Y\in Anc_X -> X are adjacent) colors[(nodes[1], nodes[0])] = VIOLATION_COLOR elif method == FalsifyConst.VALIDATE_CM: # For causal minimality we highlight the edge Y \in Pa_X -> X colors[(nodes[1], nodes[0])] = VIOLATION_COLOR plot(causal_graph, colors=colors) def _generate_table( validation_repr, suggestion_repr, width=105, validation_name="Falsificaton Summary", suggestion_name="Suggestions" ): # Create Validation header _repr = [ "+" + "-" * (width - 2) + "+\n", "|" + " " * int(np.floor((width - 2 - len(validation_name)) / 2)), validation_name, " " * int(np.ceil((width - 2 - len(validation_name)) / 2)) + "|\n", "+" + "-" * (width - 2) + "+\n", ] # Create Validation summary _repr += [f"| {v_line}" + " " * (width - 3 - len(v_line)) + "|\n" for v_line in validation_repr] # Close Validation _repr += ["+" + "-" * (width - 2) + "+\n"] # Create Suggestions header if suggestion_repr: _repr += [ "|" + " " * int(np.floor((width - 2 - len(suggestion_name)) / 2)), suggestion_name, " " * int(np.ceil((width - 2 - len(suggestion_name)) / 2)) + "|\n", "+" + "-" * (width - 2) + "+\n", ] # Iterate over suggestions for m, suggestions in suggestion_repr.items(): left_col = "| " + m + " |" if not suggestions: right_col = " " + " " * (width - 2 - len(left_col)) + "|\n" _repr += [left_col, right_col] for i, s in enumerate(suggestions): if i > 0: left_col = "| " + " " * len(m) + " |" right_col = " - " + s + " " * (width - 4 - len(s) - len(left_col)) + "|\n" _repr += [left_col, right_col] _repr += ["+" + "-" * (width - 2) + "+\n"] return "".join(_repr)[:-1] def _compute_p_value( data: pd.DataFrame, X: Union[List, str], Y: Union[List, str], Z: Optional[Union[Set, List, str]], independence_test: Optional[Callable[[np.ndarray, np.ndarray], float]], conditional_independence_test: Optional[Callable[[np.ndarray, np.ndarray, np.ndarray], float]], seed: int, ) -> float: """Perform (conditional) independence test and report p-value. If any nodes needed for the test do not exist in the data, this test is skipped and a value of -1 is returned and a warning is raised. :param data: Observations of variables in the DAG. :param X: Variable to test (conditional) independence with Y :param Y: Variable to test (conditional) independence with X :param Z: Set to condition independence test on. Can be empty (None, empty set, or empty list). :param independence_test: Independence test to use. :param conditional_independence_test: Conditional independence test to use. :param seed: Random seed :return: p-value """ set_random_seed(seed) # Test if we have data for X and Y for node in [X, Y]: if not node in data.columns: warnings.warn(f"WARN: Couldn't find data for node {node}. Skip this test.") return if Z: # Test if we have data for Z for node in Z: if not node in data.columns: warnings.warn(f"WARN: Couldn't find data for node {node}. Skip this test.") return p_value = conditional_independence_test(data[X].values, data[Y].values, data[Z].values) else: p_value = independence_test(data[X].values, data[Y].values) return p_value def _get_parental_triples(causal_graph: DirectedGraph, include_unconditional: bool): """ For a given graph collect all parental triples, that is, the triple (X, Y, Z) is a parental triple iff Y is non-descendant of X, and Z are the parents of X (can be empty if include_unconditional=True) """ triples = [] for node in causal_graph.nodes: parents = get_ordered_predecessors(causal_graph, node) non_descendants = _get_non_descendants(causal_graph, node, exclude_parents=True) if (parents or include_unconditional) and non_descendants: for non_desc in non_descendants: triples.append((node, non_desc, parents)) return triples def _permutation_based( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Union[Callable, Tuple[Callable, ...], List[Callable]], exclude_original_order: bool, n_permutations: int, show_progress_bar: bool, ) -> Dict[str, List[Union[DirectedGraph, Dict]]]: """ Generate baseline for node permutations. :param causal_graph: A directed acyclic graph (DAG). :param methods: Validation methods to perform. :param exclude_original_order: Exclude the original ordering of the nodes (default=False) :param n_permutations: Number of permutations to perform. If -1 use all n_nodes! - int(exclude_orig) permutations :param show_progress_bar: Whether to show progress bar over tested permutations. :return: Dictionary containing summary of validation for each individual graph as well as the permuted graphs """ if not isinstance(methods, (tuple, list)): methods = (methods,) perm_gen = _PermuteNodes(causal_graph, n_permutations=n_permutations, exclude_original_order=exclude_original_order) validation_summary = {FalsifyConst.PERM_GRAPHS: []} for permuted_graph in tqdm(perm_gen, desc="Test permutations of given graph", disable=not show_progress_bar): res = validate_graph( causal_graph=permuted_graph, data=data, methods=methods, ) validation_summary[FalsifyConst.PERM_GRAPHS].append(permuted_graph) for m_name, summary in res.items(): if m_name not in validation_summary: validation_summary[m_name] = [] validation_summary[m_name].append(summary) return validation_summary class _PermuteNodes: def __init__(self, causal_graph: DirectedGraph, exclude_original_order: bool, n_permutations: int): """ Randomly permute the nodes of a given causal graph while keeping the underlying graph structure the same. :param causal_graph: A directed acyclic graph (DAG). :param exclude_original_order: Do not return the original order. :param n_permutations: Return a generator with n_permutations permutations. If n_permutations = -1 (default), we return all n_nodes! - int(exclude_orig) permutations. :return: Copy of causal_graph with nodes randomly permuted. """ self.causal_graph = causal_graph self.exclude_original_order = exclude_original_order self.n_permutations = n_permutations self.max_perms = np.math.factorial(self.causal_graph.number_of_nodes()) - int(self.exclude_original_order) if n_permutations == -1 or n_permutations > self.max_perms: self.it = self.iter_all_permutations() self.length = self.max_perms if self.length > 2**63 - 1: raise ValueError( f"Too many permutations specified. Did you accidently set 'n_permutations'=-1 for a " f"large (>20 nodes) graph? " f"Given graph has {causal_graph.number_of_nodes()} nodes." ) else: self.it = self.iter_random_permutations() self.length = self.n_permutations def iter_all_permutations(self): for i, perm in enumerate(permutations(self.causal_graph.nodes)): if self.exclude_original_order and i == 0: continue mapping = {node: perm[i] for i, node in enumerate(self.causal_graph.nodes)} yield nx.relabel_nodes(self.causal_graph, mapping, copy=True) def iter_random_permutations(self): for _ in range(self.n_permutations): if self.exclude_original_order: is_orig = True while is_orig: perm = list(np.random.permutation(self.causal_graph.nodes)) if perm != list(self.causal_graph.nodes): is_orig = False else: perm = list(np.random.permutation(self.causal_graph.nodes)) mapping = {node: perm[i] for i, node in enumerate(self.causal_graph.nodes)} yield nx.relabel_nodes(self.causal_graph, mapping, copy=True) def __iter__(self): yield from self.it def __len__(self): return self.length def _to_frozenset(x: Union[Set, List, str]): """Converts a set, list or string into a hashable frozenset""" assert ( isinstance(x, Set) or isinstance(x, List) or isinstance(x, str) ), f"{x} must be list, set or str. Got {type(x)} instead!" if isinstance(x, str): return frozenset({x}) return frozenset(x)

"""This module provides functionality to falsify a user-given DAG given observed data. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import warnings from dataclasses import dataclass, field from enum import Enum, auto from functools import partial from inspect import getfullargspec from itertools import permutations from typing import Any, Callable, Dict, FrozenSet, List, Optional, Set, Tuple, Union import matplotlib.colors as mcolors import matplotlib.pyplot as plt import networkx as nx import numpy as np import pandas as pd from joblib import Parallel, delayed from tqdm import tqdm import dowhy.gcm.config as config from dowhy.gcm.independence_test import kernel_based from dowhy.gcm.util import plot from dowhy.gcm.util.general import set_random_seed from dowhy.gcm.validation import _get_non_descendants from dowhy.graph import DirectedGraph, get_ordered_predecessors VIOLATION_COLOR = "red" COLORS = list(mcolors.TABLEAU_COLORS.values()) class FalsifyConst(Enum): N_VIOLATIONS = auto() N_TESTS = auto() P_VALUE = auto() P_VALUES = auto() GIVEN_VIOLATIONS = auto() PERM_VIOLATIONS = auto() F_GIVEN_VIOLATIONS = auto() F_PERM_VIOLATIONS = auto() LOCAL_VIOLATION_INSIGHT = auto() METHOD = auto() VALIDATE_LMC = auto() VALIDATE_TPA = auto() VALIDATE_PD = auto() VALIDATE_CM = auto() PERM_GRAPHS = auto() MEC = auto() FALSIFY_METHODS = { FalsifyConst.VALIDATE_LMC: "LMC", FalsifyConst.VALIDATE_PD: "Faithfulness", FalsifyConst.VALIDATE_TPA: "TPa", FalsifyConst.VALIDATE_CM: "Causal Minimality", } @dataclass class _PValuesMemory: """A class to store and access results of independence tests. This class is useful if, e.g. we validate LMC on graph X -> Y -> Z and also on the permuted nodes Z -> Y -> X. Since X ind Z | Y == Z ind X | Y, we only need to perform the conditional independence test once. We use frozensets of nodes since standard (immutable) sets are not hashable. """ p_values: Dict[Tuple[FrozenSet, FrozenSet, FrozenSet], float] = field(default_factory=dict) def add_p_value( self, p_value: Union[None, float], X: Union[Set, List, str], Y: Union[Set, List, str], Z: Optional[Union[Set, List, str]] = None, ): if not Z: Z = set() self.p_values[(_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z))] = p_value def get_p_value( self, X: Union[Set, List, str], Y: Union[Set, List, str], Z: Optional[Union[Set, List, str]] = None ): if not Z: Z = set() # Independence tests are symmetric if (_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z)) in self.p_values: return self.p_values[(_to_frozenset(X), _to_frozenset(Y), _to_frozenset(Z))] elif (_to_frozenset(Y), _to_frozenset(X), _to_frozenset(Z)) in self.p_values: return self.p_values[(_to_frozenset(Y), _to_frozenset(X), _to_frozenset(Z))] return def __contains__(self, item: Tuple[Union[Set, List, str], ...]) -> bool: X, Y = (_to_frozenset(i) for i in item[:2]) if len(item) == 2 or item[2] is None: Z = frozenset() else: Z = _to_frozenset(item[2]) return (X, Y, Z) in self.p_values or (Y, X, Z) in self.p_values def validate_lmc( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, include_unconditional: bool = True, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[str, float]]]: """ Validate the local markov condition for a given directed graph. Return number of violations and p values for each node. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory instance, where results of previously performed tests are stored. :param independence_test: Test to use for unconditional independencies (only used if include_unconditional=True) :param conditional_independence_test: Conditional independence test to use for checking local Markov condition. :param significance_level: Significance level for (conditional) independence tests. :param include_unconditional: Test also unconditional independencies of root nodes. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Outcome of validation containing number of violations in the graph and p values/violation for each tuple (node, non_desc) """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_LMC, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, FalsifyConst.P_VALUES: dict(), } # Find out which tests to do triples = _get_parental_triples(causal_graph, include_unconditional) to_test = [] for node, non_desc, parents in triples: if not (node, non_desc, parents) in p_values_memory: to_test.append((node, non_desc, parents)) p_values_memory.add_p_value(None, node, non_desc, parents) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=node, Y=non_desc, Z=parents, independence_test=independence_test, conditional_independence_test=conditional_independence_test, seed=int(seed), ) for (node, non_desc, parents), seed in zip(to_test, random_seeds) ) # Gather results for i, (node, non_desc, parents) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], node, non_desc, parents) # Summarize for node, non_desc, parents in triples: lmc_p_value = p_values_memory.get_p_value(node, non_desc, parents) if lmc_p_value is not None: validation_summary[FalsifyConst.N_TESTS] += 1 validation_summary[FalsifyConst.P_VALUES][(node, non_desc)] = ( lmc_p_value, lmc_p_value <= significance_level, ) if lmc_p_value <= significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_tpa( causal_graph: DirectedGraph, causal_graph_reference: DirectedGraph, include_unconditional: bool = True, ) -> Dict[str, int]: """ Graphical criterion to evaluate which pairwise parental d-separations (parental triples) in `causal_graph` are violated, assuming `causal_graph_reference` is the ground truth graph. If none are violated, then both graphs lie in the same Markov equivalence class. Specifically we test: X _|_G' Y | Z and X _/|_G Y | Z for Y \in ND{X}^G', Z = PA{X}^G :param causal_graph: Causal graph for which to evaluate parental d-separations (G') :param causal_graph_reference: Causal graph where we test if d-separation holds (G) :param include_unconditional: Test also unconditional independencies of root nodes. :return: Validation summary with number of d-separations implied by `causal_graph` and number of times these are violated in the graph `causal_graph_reference`. """ validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_TPA, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, } triples = _get_parental_triples(causal_graph, include_unconditional) for node, non_desc, parents in triples: validation_summary[FalsifyConst.N_TESTS] += 1 if not nx.d_separated(causal_graph_reference, {node}, {non_desc}, set(parents)): validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_pd( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, n_pairs: int = -1, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, adjacent_only: bool = False, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[tuple, float]]]: """ Validate pairwise dependencies (pd) for a given causal graph and data. Test for each node if it is statistically dependent of all its ancestors. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory object, where results of previously performed tests are stored. :param n_pairs: Evaluate dependencies for n_pairs <= all pairs in the DAG. If n_pairs=-1, evaluate dependencies for all (ancestor, node) pairs (default). :param independence_test: Independence test to use for checking pairwise dependencies. :param significance_level: Significance level for independence tests. :param adjacent_only: Only test adjacent node pairs. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Summary dict: {n_violations: int, n_tests: int, p_values: {(ancestor, node): float, ...}} """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() pairs = [(ancestor, node) for node in causal_graph.nodes for ancestor in nx.ancestors(causal_graph, node)] if adjacent_only: pairs = [ (ancestor, node) for (ancestor, node) in pairs if ancestor in get_ordered_predecessors(causal_graph, node) ] if n_pairs < 0: n_pairs = len(pairs) if n_pairs > len(pairs): raise ValueError(f"n_pairs ({n_pairs}) > number of pairs in the DAG ({len(pairs)})") validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_PD, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: n_pairs, FalsifyConst.P_VALUES: dict(), } pair_idxs = np.random.choice(len(pairs), size=n_pairs, replace=False) # Find out which tests to do to_test = [] for pair_idx in pair_idxs: ancestor, node = pairs[pair_idx] if not (ancestor, node) in p_values_memory: to_test.append((ancestor, node)) p_values_memory.add_p_value(None, ancestor, node) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=ancestor, Y=node, Z=None, independence_test=independence_test, conditional_independence_test=None, seed=int(seed), ) for (ancestor, node), seed in zip(to_test, random_seeds) ) # Gather results for i, (ancestor, node) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], ancestor, node) # Summarize for pair_idx in pair_idxs: ancestor, node = pairs[pair_idx] p_value = p_values_memory.get_p_value(ancestor, node) if p_value is not None: validation_summary[FalsifyConst.P_VALUES][(node, ancestor)] = (p_value, p_value > significance_level) if p_value > significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def validate_cm( causal_graph: DirectedGraph, data: pd.DataFrame, p_values_memory: Optional[_PValuesMemory] = None, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, n_jobs: Optional[int] = None, ) -> Dict[str, Union[int, Dict[tuple, float]]]: """ Function to test causal minimality of a DAG (see [1], Proposition 6.36). [1] J. Peters, D. Janzing, and B. Schölkopf, Elements of Causal Inference: Foundations and Learning Algorithms. Cambridge, MA, USA: MIT Press, 2017. :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param p_values_memory: _PValuesMemory object, where results of previously performed tests are stored. :param independence_test: Independence test to use. :param conditional_independence_test: Conditional independence test to use. :param significance_level: Significance level for independence tests. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :return: Validation summary as dict. """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs if p_values_memory is None: p_values_memory = _PValuesMemory() validation_summary = { FalsifyConst.METHOD: FalsifyConst.VALIDATE_CM, FalsifyConst.N_VIOLATIONS: 0, FalsifyConst.N_TESTS: 0, FalsifyConst.P_VALUES: dict(), } # Find out which tests to do triples = [] to_test = [] for node in causal_graph.nodes: parents = set(causal_graph.predecessors(node)) if parents: for p in parents: other_parents = parents.difference({p}) triples.append((node, p, other_parents)) if not (node, p, other_parents) in p_values_memory: to_test.append((node, p, other_parents)) p_values_memory.add_p_value(None, node, p, other_parents) # Placeholder # Parallelize over tests random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(to_test)) p_values = Parallel(n_jobs=n_jobs)( delayed(_compute_p_value)( data=data, X=node, Y=p, Z=list(other_parents), independence_test=independence_test, conditional_independence_test=conditional_independence_test, seed=int(seed), ) for (node, p, other_parents), seed in zip(to_test, random_seeds) ) # Gather results for i, (node, p, other_parents) in enumerate(to_test): p_values_memory.add_p_value(p_values[i], node, p, other_parents) # Summarize for node, p, other_parents in triples: p_value = p_values_memory.get_p_value(node, p, other_parents) if p_value is not None: validation_summary[FalsifyConst.N_TESTS] += 1 validation_summary[FalsifyConst.P_VALUES][(node, p, tuple(other_parents))] = ( p_value, p_value > significance_level, ) if p_value > significance_level: validation_summary[FalsifyConst.N_VIOLATIONS] += 1 return validation_summary def run_validations( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Optional[Union[Callable, Tuple[Callable, ...], List[Callable]]] = partial( validate_lmc, independence_test=kernel_based, conditional_independence_test=kernel_based ), ) -> Dict[str, Dict]: """ Validate a given causal graph using observational data and some given methods. If methods are provided, they must be wrapped in a partial object, with their respective parameters. E.g., if one wants to test the local Markov conditions and the pairwise dependencies (unconditional faithfulness), then call run_validations(G, data, methods=( partial(validate_lmc, independence_test=..., conditional_independence_test=...), partial(validate_pd, independence_test=...), ) ) :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param methods: Method functions wrapped in wrap_partial. E.g. wrap_partial(validate_lmc, data=data, independence_test=..., conditional_independence_test=...). If no methods are provided we run validate_lmc with optional keyword arguments provided to run_validations. :return: Validation summary as dict. """ if not isinstance(methods, (tuple, list)): methods = (methods,) validation_summary = dict() for m in methods: if "data" in getfullargspec(m).args: m = partial(m, data=data) m_summary = m(causal_graph=causal_graph) m_name = m_summary.pop(FalsifyConst.METHOD) validation_summary[m_name] = m_summary return validation_summary @dataclass class EvaluationResult: """ Dataset class containing the evaluation result of falsifying a graph using a node-permutation test. ... Attributes ---------- summary : dict Dictionary containing the summary of the evaluation. significance_level : float Significance level based on which we falsify the given DAG falsifiable : bool Whether the given DAG is falsifiable. falsified : bool Whether the given DAG is falsified. """ summary: dict significance_level: float suggestions: Optional[dict] = None def update_significance_level(self, significance_level: float): """ Update the significance level to decide if we falsify a given DAG. """ self.significance_level = significance_level self.__post_init__() def __post_init__(self): self.can_evaluate = self._can_evaluate() if not self.can_evaluate: self.falsified = None self.falsifiable = None elif ( self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.P_VALUE] > self.significance_level > self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE] ): self.falsified = True self.falsifiable = True elif self.significance_level < self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE]: self.falsified = False self.falsifiable = False else: self.falsified = False self.falsifiable = True def __repr__(self): # DAG Evaluation if self.can_evaluate: decision = " " if self.falsified else " do not " informative = " " if self.falsifiable else " not " frac_MEC = f"{len(self.summary[FalsifyConst.MEC])} / {len(self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.PERM_VIOLATIONS])}" frac_VLMC = f"{self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.GIVEN_VIOLATIONS]}/{self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.N_TESTS]}" p_LMC = self.summary[FalsifyConst.VALIDATE_LMC][FalsifyConst.P_VALUE] p_dSep = self.summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.P_VALUE] validation_repr = [ f"The given DAG is{informative}informative because {frac_MEC} of the permutations lie in the Markov", f"equivalence class of the given DAG (p-value: {p_dSep:.2f}).", f"The given DAG violates {frac_VLMC} LMCs and is better than {(1 - p_LMC) * 100:.1f}% of the permuted DAGs (p-value: {p_LMC:.2f}).", f"Based on the provided significance level ({self.significance_level}) and because the DAG is{informative}informative,", f"we{decision}reject the DAG.", ] else: validation_repr = ["Cannot be evaluated!"] # Suggestions suggestion_repr = {} for m in self.suggestions: suggestion_repr[FALSIFY_METHODS[m]] = [ f"Remove edge {node[1]} --> {node[0]}" for (node, r) in self.suggestions[m][FalsifyConst.P_VALUES].items() if r[1] ] return _generate_table(validation_repr, suggestion_repr) def _can_evaluate(self): can_evaluate = True for m in (FalsifyConst.VALIDATE_LMC, FalsifyConst.VALIDATE_TPA): if m not in self.summary: can_evaluate = False return can_evaluate def falsify_graph( causal_graph: DirectedGraph, data: pd.DataFrame, suggestions: bool = False, independence_test: Callable[[np.ndarray, np.ndarray], float] = kernel_based, conditional_independence_test: Callable[[np.ndarray, np.ndarray, np.ndarray], float] = kernel_based, significance_level: float = 0.05, significance_ci: float = 0.05, n_permutations: Optional[int] = None, show_progress_bar: Optional[bool] = None, n_jobs: Optional[int] = None, plot_histogram: bool = False, plot_kwargs: Optional[Dict] = None, ) -> EvaluationResult: """ Falsify a given DAG using observational data. This method returns the result of a permutation-test to falsify a user-given DAG using observational data. To this end we construct the test statistics by testing the violations of local Markov conditions (LMC) implied by the graph using conditional independence (CI) tests. The null is the number of LMC violations of a random node-permutation of the given graph. Our test can be interpreted as whether the given graph is significantly better than random in terms of the CIs it entails. To determine whether a given graph is falsifiable by our metric, we implement a second test, which reports whether given graph is "characteristic" enough in terms of the CIs it entails. For this, we compute how many of the random node permutations lie in the same Markov equivalence class (MEC) as the given graph and conclude that the given graph is falsifiable only if the fraction of permuted DAGs in the same MEC as the given graph is "reasonably" small. The returned EvaluationResult object has two attributes: `falsified` and `falsifiable`: `falsifiable`: The given graph lies in a different MEC than >= 1-`significance_level` of the permuted DAGs `falsified`: The given graph is falsifiable and violates fewer LMCs than >= 1-`significance_level` of the permuted DAGs By default, we only run 1 / `significance_level` permutations as those are enough to falsify a graph with type I error probability `significance_level` at some given `significance_level`. If you are interested in a more exact estimate of the p-value or wish to plot a histogram to see how the given DAG compares to random node permutations, you should set `n_permutations` to some larger value (e.g. 100 or 1000). If `n_permutations=-1` we test on all n_nodes! permutations (the default if plot_histogram=True). Additionally, this method allows to return suggestions to the user (suggestions=True). This is done by testing for violations of causal minimality via `validate_cm`. Related paper: Eulig, E., Mastakouri, A. A., Blöbaum, P., Hardt, M., & Janzing, D. (2023). Toward Falsifying Causal Graphs Using a Permutation-Based Test. https://arxiv.org/abs/2305.09565 :param causal_graph: A directed acyclic graph (DAG). :param data: Observations of variables in the DAG. :param suggestions: Provide suggestions to the user. At the moment the only source of suggestions comes from validating causal minimality (using validate_cm). :param independence_test: Independence test to use for checking pairwise independencies. :param conditional_independence_test: Conditional independence test to use. :param significance_level: Significance level for the permutation test. :param significance_ci: Significance level for (conditional) independence tests. :param n_permutations: Number of permutations to perform. If -1 use all n_nodes! permutations. :param show_progress_bar: Whether to show progress bar over permutations. :param n_jobs: Number of jobs to use for parallel execution of (conditional) independence tests. :param plot_histogram: Plot histogram of results from permutation baseline. :param plot_kwargs: Additional plot arguments to be passed to plot_evaluation_results. :return: EvaluationResult """ n_jobs = config.default_n_jobs if n_jobs is None else n_jobs show_progress_bar = config.show_progress_bars if show_progress_bar is None else show_progress_bar p_values_memory = _PValuesMemory() if n_permutations is None: n_permutations = int(1 / significance_level) if not plot_histogram else -1 if not plot_kwargs: plot_kwargs = {} methods = ( partial( validate_lmc, independence_test=independence_test, conditional_independence_test=conditional_independence_test, significance_level=significance_ci, p_values_memory=p_values_memory, n_jobs=n_jobs, ), partial(validate_tpa, causal_graph_reference=causal_graph), ) if suggestions: suggestion_methods = ( partial( validate_cm, independence_test=independence_test, conditional_independence_test=conditional_independence_test, significance_level=significance_ci, p_values_memory=p_values_memory, n_jobs=n_jobs, ), ) else: suggestion_methods = tuple() summary_given = run_validations( causal_graph, data, methods=methods + suggestion_methods, ) summary_perm = _permutation_based( causal_graph, data, methods=methods, exclude_original_order=False, n_permutations=n_permutations, show_progress_bar=show_progress_bar, ) summary = dict() validation_methods = set(summary_perm.keys()) - {FalsifyConst.PERM_GRAPHS} for m in validation_methods: summary[m] = dict() summary[m][FalsifyConst.PERM_VIOLATIONS] = [perm[FalsifyConst.N_VIOLATIONS] for perm in summary_perm[m]] summary[m][FalsifyConst.GIVEN_VIOLATIONS] = summary_given[m][FalsifyConst.N_VIOLATIONS] summary[m][FalsifyConst.N_TESTS] = summary_given[m][FalsifyConst.N_TESTS] summary[m][FalsifyConst.F_PERM_VIOLATIONS] = [ perm[FalsifyConst.N_VIOLATIONS] / perm[FalsifyConst.N_TESTS] for perm in summary_perm[m] ] summary[m][FalsifyConst.F_GIVEN_VIOLATIONS] = ( summary[m][FalsifyConst.GIVEN_VIOLATIONS] / summary[m][FalsifyConst.N_TESTS] ) summary[m][FalsifyConst.P_VALUE] = sum( [ 1 for perm in summary[m][FalsifyConst.F_PERM_VIOLATIONS] if perm <= summary[m][FalsifyConst.F_GIVEN_VIOLATIONS] ] ) / len(summary[m][FalsifyConst.PERM_VIOLATIONS]) if m != FalsifyConst.VALIDATE_TPA: # Append list of violations (node, non_desc) to get local information summary[m][FalsifyConst.LOCAL_VIOLATION_INSIGHT] = summary_given[m][FalsifyConst.P_VALUES] if FalsifyConst.VALIDATE_TPA in summary: summary[FalsifyConst.MEC] = [ summary_perm[FalsifyConst.PERM_GRAPHS][i] for i, v in enumerate(summary[FalsifyConst.VALIDATE_TPA][FalsifyConst.PERM_VIOLATIONS]) if v == 0 ] result = EvaluationResult( summary=summary, significance_level=significance_level, suggestions={m: summary_given[m] for m in summary_given if m not in validation_methods}, ) if plot_histogram: plot_evaluation_results(result, **plot_kwargs) return result def apply_suggestions( causal_graph: DirectedGraph, evaluation_result: EvaluationResult, edges_to_keep: Optional[List[Tuple[Any, Any]]] = None, ): if not hasattr(evaluation_result, "suggestions"): raise ValueError("EvaluationResult object has no attribute suggestions. Please run with suggestion=True!") causal_graph = causal_graph.copy() for m in evaluation_result.suggestions: for node, res in evaluation_result.suggestions[m][FalsifyConst.P_VALUES].items(): edge = (node[1], node[0]) if (res[1] and edges_to_keep is not None and edge not in edges_to_keep) or ( res[1] and edges_to_keep is None ): causal_graph.remove_edge(edge[0], edge[1]) return causal_graph def plot_evaluation_results(evaluation_result, figsize=(8, 3), bins=None, title="", savepath="", display=True): fig, ax = plt.subplots(figsize=figsize) # Plot histograms p_values = "" data = [] labels = [] evaluation_summary = {k: v for k, v in evaluation_result.summary.items() if k != FalsifyConst.MEC} for i, (m, m_summary) in enumerate(evaluation_summary.items()): data.append(m_summary[FalsifyConst.F_PERM_VIOLATIONS]) labels.append(f"Violations of {FALSIFY_METHODS[m]} of permuted DAGs") p_values += f"p-value {FALSIFY_METHODS[m]} = {m_summary[FalsifyConst.P_VALUE]:.2f}\n" ax.hist(data, color=COLORS[: len(evaluation_summary)], bins=bins, alpha=0.5, label=labels, edgecolor="k") # Plot given violations for i, (m, m_summary) in enumerate(evaluation_summary.items()): ylim = ax.get_ylim()[1] ax.plot( [m_summary[FalsifyConst.F_GIVEN_VIOLATIONS]] * 2, [0, ylim], "--", c=COLORS[i], label=f"Violations of {FALSIFY_METHODS[m]} of given DAG", ) ax.set_ylim([0, ylim]) ax.set_xlabel("Fraction of violations") ax.set_ylabel("# Permutations") plt.legend(loc="upper left", bbox_to_anchor=(1.05, 1), borderaxespad=0.0, title=p_values) if title: plt.title(title) plt.tight_layout() if savepath: plt.savefig(savepath, dpi=300, bbox_inches="tight") if display: plt.show() else: plt.close() def plot_local_insights( causal_graph: DirectedGraph, evaluation_result: Union[EvaluationResult, Dict], method: Optional[str] = FalsifyConst.VALIDATE_LMC, ): """ For some given graph and evaluation result plot local violations. :param causal_graph: DiGraph :param evaluation_result: EvaluationResult :param method: Method for which to plot violations """ colors = {} if isinstance(evaluation_result, EvaluationResult) and method in evaluation_result.summary: local_insight_dict = evaluation_result.summary[method][FalsifyConst.LOCAL_VIOLATION_INSIGHT] elif ( isinstance(evaluation_result, EvaluationResult) and hasattr(evaluation_result, "suggestions") and method in evaluation_result.suggestions ): local_insight_dict = evaluation_result.suggestions[method][FalsifyConst.P_VALUES] elif isinstance(evaluation_result, Dict): if method not in evaluation_result: raise ValueError(f"Validation method {method} does not exist in given evaluation_result!") if FalsifyConst.P_VALUES not in evaluation_result[method]: raise ValueError( f"Validation method {method} has no key {FalsifyConst.P_VALUES} where information on local violations " f"are stored!" ) local_insight_dict = evaluation_result[method][FalsifyConst.P_VALUES] else: raise ValueError(f"Cannot plot local violation insights from method {method} in evaluation_result!") for nodes, result in local_insight_dict.items(): if result[1]: if method == FalsifyConst.VALIDATE_LMC: # For LMC we highlight X for which X _|/|_ Y \in ND_X | Pa_X colors[nodes[0]] = VIOLATION_COLOR elif method == FalsifyConst.VALIDATE_PD: # For PD we highlight the edge (if Y\in Anc_X -> X are adjacent) colors[(nodes[1], nodes[0])] = VIOLATION_COLOR elif method == FalsifyConst.VALIDATE_CM: # For causal minimality we highlight the edge Y \in Pa_X -> X colors[(nodes[1], nodes[0])] = VIOLATION_COLOR plot(causal_graph, colors=colors) def _generate_table( validation_repr, suggestion_repr, width=105, validation_name="Falsificaton Summary", suggestion_name="Suggestions" ): # Create Validation header _repr = [ "+" + "-" * (width - 2) + "+\n", "|" + " " * int(np.floor((width - 2 - len(validation_name)) / 2)), validation_name, " " * int(np.ceil((width - 2 - len(validation_name)) / 2)) + "|\n", "+" + "-" * (width - 2) + "+\n", ] # Create Validation summary _repr += [f"| {v_line}" + " " * (width - 3 - len(v_line)) + "|\n" for v_line in validation_repr] # Close Validation _repr += ["+" + "-" * (width - 2) + "+\n"] # Create Suggestions header if suggestion_repr: _repr += [ "|" + " " * int(np.floor((width - 2 - len(suggestion_name)) / 2)), suggestion_name, " " * int(np.ceil((width - 2 - len(suggestion_name)) / 2)) + "|\n", "+" + "-" * (width - 2) + "+\n", ] # Iterate over suggestions for m, suggestions in suggestion_repr.items(): left_col = "| " + m + " |" if not suggestions: right_col = " " + " " * (width - 2 - len(left_col)) + "|\n" _repr += [left_col, right_col] for i, s in enumerate(suggestions): if i > 0: left_col = "| " + " " * len(m) + " |" right_col = " - " + s + " " * (width - 4 - len(s) - len(left_col)) + "|\n" _repr += [left_col, right_col] _repr += ["+" + "-" * (width - 2) + "+\n"] return "".join(_repr)[:-1] def _compute_p_value( data: pd.DataFrame, X: Union[List, str], Y: Union[List, str], Z: Optional[Union[Set, List, str]], independence_test: Optional[Callable[[np.ndarray, np.ndarray], float]], conditional_independence_test: Optional[Callable[[np.ndarray, np.ndarray, np.ndarray], float]], seed: int, ) -> float: """Perform (conditional) independence test and report p-value. If any nodes needed for the test do not exist in the data, this test is skipped and a value of -1 is returned and a warning is raised. :param data: Observations of variables in the DAG. :param X: Variable to test (conditional) independence with Y :param Y: Variable to test (conditional) independence with X :param Z: Set to condition independence test on. Can be empty (None, empty set, or empty list). :param independence_test: Independence test to use. :param conditional_independence_test: Conditional independence test to use. :param seed: Random seed :return: p-value """ set_random_seed(seed) # Test if we have data for X and Y for node in [X, Y]: if not node in data.columns: warnings.warn(f"WARN: Couldn't find data for node {node}. Skip this test.") return if Z: # Test if we have data for Z for node in Z: if not node in data.columns: warnings.warn(f"WARN: Couldn't find data for node {node}. Skip this test.") return p_value = conditional_independence_test(data[X].values, data[Y].values, data[Z].values) else: p_value = independence_test(data[X].values, data[Y].values) return p_value def _get_parental_triples(causal_graph: DirectedGraph, include_unconditional: bool): """ For a given graph collect all parental triples, that is, the triple (X, Y, Z) is a parental triple iff Y is non-descendant of X, and Z are the parents of X (can be empty if include_unconditional=True) """ triples = [] for node in causal_graph.nodes: parents = get_ordered_predecessors(causal_graph, node) non_descendants = _get_non_descendants(causal_graph, node, exclude_parents=True) if (parents or include_unconditional) and non_descendants: for non_desc in non_descendants: triples.append((node, non_desc, parents)) return triples def _permutation_based( causal_graph: DirectedGraph, data: pd.DataFrame, methods: Union[Callable, Tuple[Callable, ...], List[Callable]], exclude_original_order: bool, n_permutations: int, show_progress_bar: bool, ) -> Dict[str, List[Union[DirectedGraph, Dict]]]: """ Generate baseline for node permutations. :param causal_graph: A directed acyclic graph (DAG). :param methods: Validation methods to perform. :param exclude_original_order: Exclude the original ordering of the nodes (default=False) :param n_permutations: Number of permutations to perform. If -1 use all n_nodes! - int(exclude_orig) permutations :param show_progress_bar: Whether to show progress bar over tested permutations. :return: Dictionary containing summary of validation for each individual graph as well as the permuted graphs """ if not isinstance(methods, (tuple, list)): methods = (methods,) perm_gen = _PermuteNodes(causal_graph, n_permutations=n_permutations, exclude_original_order=exclude_original_order) validation_summary = {FalsifyConst.PERM_GRAPHS: []} for permuted_graph in tqdm(perm_gen, desc="Test permutations of given graph", disable=not show_progress_bar): res = run_validations( causal_graph=permuted_graph, data=data, methods=methods, ) validation_summary[FalsifyConst.PERM_GRAPHS].append(permuted_graph) for m_name, summary in res.items(): if m_name not in validation_summary: validation_summary[m_name] = [] validation_summary[m_name].append(summary) return validation_summary class _PermuteNodes: def __init__(self, causal_graph: DirectedGraph, exclude_original_order: bool, n_permutations: int): """ Randomly permute the nodes of a given causal graph while keeping the underlying graph structure the same. :param causal_graph: A directed acyclic graph (DAG). :param exclude_original_order: Do not return the original order. :param n_permutations: Return a generator with n_permutations permutations. If n_permutations = -1 (default), we return all n_nodes! - int(exclude_orig) permutations. :return: Copy of causal_graph with nodes randomly permuted. """ self.causal_graph = causal_graph self.exclude_original_order = exclude_original_order self.n_permutations = n_permutations self.max_perms = np.math.factorial(self.causal_graph.number_of_nodes()) - int(self.exclude_original_order) if n_permutations == -1 or n_permutations > self.max_perms: self.it = self.iter_all_permutations() self.length = self.max_perms if self.length > 2**63 - 1: raise ValueError( f"Too many permutations specified. Did you accidently set 'n_permutations'=-1 for a " f"large (>20 nodes) graph? " f"Given graph has {causal_graph.number_of_nodes()} nodes." ) else: self.it = self.iter_random_permutations() self.length = self.n_permutations def iter_all_permutations(self): for i, perm in enumerate(permutations(self.causal_graph.nodes)): if self.exclude_original_order and i == 0: continue mapping = {node: perm[i] for i, node in enumerate(self.causal_graph.nodes)} yield nx.relabel_nodes(self.causal_graph, mapping, copy=True) def iter_random_permutations(self): for _ in range(self.n_permutations): if self.exclude_original_order: is_orig = True while is_orig: perm = list(np.random.permutation(self.causal_graph.nodes)) if perm != list(self.causal_graph.nodes): is_orig = False else: perm = list(np.random.permutation(self.causal_graph.nodes)) mapping = {node: perm[i] for i, node in enumerate(self.causal_graph.nodes)} yield nx.relabel_nodes(self.causal_graph, mapping, copy=True) def __iter__(self): yield from self.it def __len__(self): return self.length def _to_frozenset(x: Union[Set, List, str]): """Converts a set, list or string into a hashable frozenset""" assert ( isinstance(x, Set) or isinstance(x, List) or isinstance(x, str) ), f"{x} must be list, set or str. Got {type(x)} instead!" if isinstance(x, str): return frozenset({x}) return frozenset(x)

eeulig

379f188d674d5be6d644559f9f54852b87403f7c

4e30c63fe1113e35259e7fb3400a6f09bca4d699

I made data non-optional. Defining the default methods similar to how we do it for `validate_graph` now is a bit tricky though because of `causal_graph_reference` for `validate_tpa` and the p-value cache that is initialized after calling the function.

eeulig

py-why/dowhy

Removing autolguon as optional dependency

The autogluon models can still be used with DoWhy, but it is not listed as an optional dependency anymore to reduce the number of restrictions on other packages.

2023-06-22 14:55:47+00:00

2023-06-26 16:58:14+00:00

pyproject.toml

[tool.poetry] name = "dowhy" # # 0.0.0 is standard placeholder for poetry-dynamic-versioning # any changes to this should not be checked in # version = "0.0.0" description = "DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions" authors = ["PyWhy Community <amshar@microsoft.com>"] maintainers = [] license = "MIT" documentation = "https://py-why.github.io/dowhy" repository = "https://github.com/py-why/dowhy" classifiers = [ 'Development Status :: 4 - Beta', 'License :: OSI Approved :: MIT License', 'Programming Language :: Python :: 3.8', 'Programming Language :: Python :: 3.9', 'Programming Language :: Python :: 3.10', ] keywords = [ 'causality', 'machine-learning', 'causal-inference', 'statistics', 'graphical-model', ] include = ['docs', 'tests', 'CONTRIBUTING.md', 'LICENSE'] readme = 'README.rst' [build-system] requires = ["poetry-core>=1.0.0"] build-backend = "poetry.core.masonry.api" [tool.poetry-dynamic-versioning] enable = true vcs = "git" [tool.poetry-dynamic-versioning.substitution] files = ["dowhy/__init__.py"] # # Dependency compatibility notes: # * numba (imported by econml) requires python <3.11 # [tool.poetry.dependencies] python = ">=3.8,<3.12" cython = "^0.29.32" scipy = ">=1.4.1" statsmodels = "^0.13.2" numpy = ">=1.23.1" pandas = ">=1.4.3" networkx = ">=2.8.5" sympy = "^1.10.1" scikit-learn = ">1.0,<2.0" pydot = { version = "^1.4.2", optional = true } joblib = ">=1.1.0" pygraphviz = { version = "^1.9", optional = true } econml = { version = "*", optional = true } tqdm = ">=4.64.0" causal-learn = ">=0.1.3.0" #Plotting Extra matplotlib = { version = ">=3.5.3", optional = true } sphinx_design = "^0.3.0" cvxpy = "^1.2.2" [tool.poetry.extras] pygraphviz = ["pygraphviz"] pydot = ["pydot"] plotting = ["matplotlib"] econml = ["econml"] [tool.poetry.group.dev.dependencies] poethepoet = "^0.16.0" flake8 = "^4.0.1" black = { version = "^22.6.0", extras = ["jupyter"] } isort = "^5.10.1" pytest = "^7.1.2" pytest-cov = "^3.0.0" pytest-split = "^0.8.0" nbformat = "^5.4.0" jupyter = "^1.0.0" flaky = "^3.7.0" keras = "^2.9.0" xgboost = "^1.7.0" mypy = "^0.971" torch = "^1.12.1" torchvision = "^0.13.1" pytorch-lightning = "^1.7.7" [tool.poetry.group.docs] optional = true [tool.poetry.group.docs.dependencies] # # Dependencies for Documentation Generation # sphinx = "^5.3.0" sphinxcontrib-googleanalytics = { git = "https://github.com/sphinx-contrib/googleanalytics.git", branch = "master" } nbsphinx = "^0.8.9" sphinx-rtd-theme = "^1.0.0" pydata-sphinx-theme = "^0.9.0" ipykernel = "^6.15.1" sphinx-copybutton = "0.5.0" seaborn = "^0.12.1" tensorflow = "^2.11.0" cdt ="^0.6.0" # # Versions defined for security reasons # # https://github.com/py-why/dowhy/security/dependabot/1 - CVE-2022-34749 nbconvert = { version = "7.0.0rc3", allow-prereleases = true } [tool.pytest.ini_options] markers = [ "advanced: not be to run each time. only on package updates.", "notebook: jupyter notebook tests", "econml: a marker to mark tests requiring econml", "focused: a debug marker to focus on specific tests", ] [tool.poe.tasks] # stop the build if there are Python syntax errors or undefined names _flake8Errors = "flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics" _flake8Warnings = "flake8 . --count --exit-zero --statistics" _black = 'black .' _isort = 'isort .' _black_check = 'black --check .' _isort_check = 'isort --check .' # testing tasks test = "pytest -v -m 'not advanced and not econml' --durations=0 --durations-min=60.0" test_no_notebooks= "pytest -v -m 'not advanced and not econml and not notebook' --durations=0 --durations-min=60.0" test_durations = "poetry run poe test --store-durations" test_econml = "pytest -v -m 'econml' --durations=0 --durations-min=60.0" test_advanced = "pytest -v" test_focused = "pytest -v -m 'focused'" [tool.poe.tasks.format] sequence = ['_black', '_isort'] ignore_fail = 'return_non_zero' [tool.poe.tasks.format_check] sequence = ['_black_check', '_isort_check'] ignore_fail = 'return_non_zero' [tool.poe.tasks.lint] sequence = ['_flake8Errors', '_flake8Warnings'] ignore_fail = 'return_non_zero' [tool.poe.tasks.verify] sequence = ['lint', 'format_check', 'test'] ignore_fail = "return_non_zero" [tool.black] line-length = 120 target-version = ['py38'] include = '\.pyi?$' extend-exclude = ''' ( __pycache__ | \.github ) ''' [tool.pylint] max-line-length = 120 disable = ["W0511"] [tool.isort] profile = 'black' multi_line_output = 3 line_length = 120 py_version = 38

[tool.poetry] name = "dowhy" # # 0.0.0 is standard placeholder for poetry-dynamic-versioning # any changes to this should not be checked in # version = "0.0.0" description = "DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions" authors = ["PyWhy Community <amshar@microsoft.com>"] maintainers = [] license = "MIT" documentation = "https://py-why.github.io/dowhy" repository = "https://github.com/py-why/dowhy" classifiers = [ 'Development Status :: 4 - Beta', 'License :: OSI Approved :: MIT License', 'Programming Language :: Python :: 3.8', 'Programming Language :: Python :: 3.9', 'Programming Language :: Python :: 3.10', ] keywords = [ 'causality', 'machine-learning', 'causal-inference', 'statistics', 'graphical-model', ] include = ['docs', 'tests', 'CONTRIBUTING.md', 'LICENSE'] readme = 'README.rst' [build-system] requires = ["poetry-core>=1.0.0"] build-backend = "poetry.core.masonry.api" [tool.poetry-dynamic-versioning] enable = true vcs = "git" [tool.poetry-dynamic-versioning.substitution] files = ["dowhy/__init__.py"] # # Dependency compatibility notes: # * numba (imported by econml) requires python <3.11 # [tool.poetry.dependencies] python = ">=3.8,<3.12" cython = "^0.29.32" scipy = ">=1.4.1" statsmodels = "^0.13.5" numpy = ">=1.23.1" pandas = ">=1.4.3" networkx = ">=2.8.5" sympy = "^1.10.1" scikit-learn = ">1.0,<2.0" pydot = { version = "^1.4.2", optional = true } joblib = ">=1.1.0" pygraphviz = { version = "^1.9", optional = true } econml = { version = "*", optional = true } tqdm = ">=4.64.0" causal-learn = ">=0.1.3.0" #Plotting Extra matplotlib = { version = ">=3.5.3", optional = true } sphinx_design = "^0.3.0" cvxpy = "^1.2.2" [tool.poetry.extras] pygraphviz = ["pygraphviz"] pydot = ["pydot"] plotting = ["matplotlib"] econml = ["econml"] [tool.poetry.group.dev.dependencies] poethepoet = "^0.16.0" flake8 = "^4.0.1" black = { version = "^22.6.0", extras = ["jupyter"] } isort = "^5.10.1" pytest = "^7.1.2" pytest-cov = "^3.0.0" pytest-split = "^0.8.0" nbformat = "^5.4.0" jupyter = "^1.0.0" flaky = "^3.7.0" keras = "^2.9.0" xgboost = "^1.7.0" mypy = "^0.971" torch = "^1.12.1" torchvision = "^0.13.1" pytorch-lightning = "^1.7.7" [tool.poetry.group.docs] optional = true [tool.poetry.group.docs.dependencies] # # Dependencies for Documentation Generation # sphinx = "^5.3.0" sphinxcontrib-googleanalytics = { git = "https://github.com/sphinx-contrib/googleanalytics.git", branch = "master" } nbsphinx = "^0.8.9" sphinx-rtd-theme = "^1.0.0" pydata-sphinx-theme = "^0.9.0" ipykernel = "^6.15.1" sphinx-copybutton = "0.5.0" seaborn = "^0.12.1" tensorflow = "^2.11.0" cdt ="^0.6.0" # # Versions defined for security reasons # # https://github.com/py-why/dowhy/security/dependabot/1 - CVE-2022-34749 nbconvert = { version = "7.0.0rc3", allow-prereleases = true } [tool.pytest.ini_options] markers = [ "advanced: not be to run each time. only on package updates.", "notebook: jupyter notebook tests", "econml: a marker to mark tests requiring econml", "focused: a debug marker to focus on specific tests", ] [tool.poe.tasks] # stop the build if there are Python syntax errors or undefined names _flake8Errors = "flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics" _flake8Warnings = "flake8 . --count --exit-zero --statistics" _black = 'black .' _isort = 'isort .' _black_check = 'black --check .' _isort_check = 'isort --check .' # testing tasks test = "pytest -v -m 'not advanced and not econml' --durations=0 --durations-min=60.0" test_no_notebooks= "pytest -v -m 'not advanced and not econml and not notebook' --durations=0 --durations-min=60.0" test_durations = "poetry run poe test --store-durations" test_econml = "pytest -v -m 'econml' --durations=0 --durations-min=60.0" test_advanced = "pytest -v" test_focused = "pytest -v -m 'focused'" [tool.poe.tasks.format] sequence = ['_black', '_isort'] ignore_fail = 'return_non_zero' [tool.poe.tasks.format_check] sequence = ['_black_check', '_isort_check'] ignore_fail = 'return_non_zero' [tool.poe.tasks.lint] sequence = ['_flake8Errors', '_flake8Warnings'] ignore_fail = 'return_non_zero' [tool.poe.tasks.verify] sequence = ['lint', 'format_check', 'test'] ignore_fail = "return_non_zero" [tool.black] line-length = 120 target-version = ['py38'] include = '\.pyi?$' extend-exclude = ''' ( __pycache__ | \.github ) ''' [tool.pylint] max-line-length = 120 disable = ["W0511"] [tool.isort] profile = 'black' multi_line_output = 3 line_length = 120 py_version = 38

bloebp

21a37a9453d0a018318e16af22ffabab5d6d4fc6

a1c6c042ca5bdfba98063e78b348854cf1709307

`>=0.13.2,<1.0` isn't this the same as `^0.13.2` in poetry?

Zethson

py-why/dowhy

Removing autolguon as optional dependency

The autogluon models can still be used with DoWhy, but it is not listed as an optional dependency anymore to reduce the number of restrictions on other packages.

2023-06-22 14:55:47+00:00

2023-06-26 16:58:14+00:00

pyproject.toml

[tool.poetry] name = "dowhy" # # 0.0.0 is standard placeholder for poetry-dynamic-versioning # any changes to this should not be checked in # version = "0.0.0" description = "DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions" authors = ["PyWhy Community <amshar@microsoft.com>"] maintainers = [] license = "MIT" documentation = "https://py-why.github.io/dowhy" repository = "https://github.com/py-why/dowhy" classifiers = [ 'Development Status :: 4 - Beta', 'License :: OSI Approved :: MIT License', 'Programming Language :: Python :: 3.8', 'Programming Language :: Python :: 3.9', 'Programming Language :: Python :: 3.10', ] keywords = [ 'causality', 'machine-learning', 'causal-inference', 'statistics', 'graphical-model', ] include = ['docs', 'tests', 'CONTRIBUTING.md', 'LICENSE'] readme = 'README.rst' [build-system] requires = ["poetry-core>=1.0.0"] build-backend = "poetry.core.masonry.api" [tool.poetry-dynamic-versioning] enable = true vcs = "git" [tool.poetry-dynamic-versioning.substitution] files = ["dowhy/__init__.py"] # # Dependency compatibility notes: # * numba (imported by econml) requires python <3.11 # [tool.poetry.dependencies] python = ">=3.8,<3.12" cython = "^0.29.32" scipy = ">=1.4.1" statsmodels = "^0.13.2" numpy = ">=1.23.1" pandas = ">=1.4.3" networkx = ">=2.8.5" sympy = "^1.10.1" scikit-learn = ">1.0,<2.0" pydot = { version = "^1.4.2", optional = true } joblib = ">=1.1.0" pygraphviz = { version = "^1.9", optional = true } econml = { version = "*", optional = true } tqdm = ">=4.64.0" causal-learn = ">=0.1.3.0" #Plotting Extra matplotlib = { version = ">=3.5.3", optional = true } sphinx_design = "^0.3.0" cvxpy = "^1.2.2" [tool.poetry.extras] pygraphviz = ["pygraphviz"] pydot = ["pydot"] plotting = ["matplotlib"] econml = ["econml"] [tool.poetry.group.dev.dependencies] poethepoet = "^0.16.0" flake8 = "^4.0.1" black = { version = "^22.6.0", extras = ["jupyter"] } isort = "^5.10.1" pytest = "^7.1.2" pytest-cov = "^3.0.0" pytest-split = "^0.8.0" nbformat = "^5.4.0" jupyter = "^1.0.0" flaky = "^3.7.0" keras = "^2.9.0" xgboost = "^1.7.0" mypy = "^0.971" torch = "^1.12.1" torchvision = "^0.13.1" pytorch-lightning = "^1.7.7" [tool.poetry.group.docs] optional = true [tool.poetry.group.docs.dependencies] # # Dependencies for Documentation Generation # sphinx = "^5.3.0" sphinxcontrib-googleanalytics = { git = "https://github.com/sphinx-contrib/googleanalytics.git", branch = "master" } nbsphinx = "^0.8.9" sphinx-rtd-theme = "^1.0.0" pydata-sphinx-theme = "^0.9.0" ipykernel = "^6.15.1" sphinx-copybutton = "0.5.0" seaborn = "^0.12.1" tensorflow = "^2.11.0" cdt ="^0.6.0" # # Versions defined for security reasons # # https://github.com/py-why/dowhy/security/dependabot/1 - CVE-2022-34749 nbconvert = { version = "7.0.0rc3", allow-prereleases = true } [tool.pytest.ini_options] markers = [ "advanced: not be to run each time. only on package updates.", "notebook: jupyter notebook tests", "econml: a marker to mark tests requiring econml", "focused: a debug marker to focus on specific tests", ] [tool.poe.tasks] # stop the build if there are Python syntax errors or undefined names _flake8Errors = "flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics" _flake8Warnings = "flake8 . --count --exit-zero --statistics" _black = 'black .' _isort = 'isort .' _black_check = 'black --check .' _isort_check = 'isort --check .' # testing tasks test = "pytest -v -m 'not advanced and not econml' --durations=0 --durations-min=60.0" test_no_notebooks= "pytest -v -m 'not advanced and not econml and not notebook' --durations=0 --durations-min=60.0" test_durations = "poetry run poe test --store-durations" test_econml = "pytest -v -m 'econml' --durations=0 --durations-min=60.0" test_advanced = "pytest -v" test_focused = "pytest -v -m 'focused'" [tool.poe.tasks.format] sequence = ['_black', '_isort'] ignore_fail = 'return_non_zero' [tool.poe.tasks.format_check] sequence = ['_black_check', '_isort_check'] ignore_fail = 'return_non_zero' [tool.poe.tasks.lint] sequence = ['_flake8Errors', '_flake8Warnings'] ignore_fail = 'return_non_zero' [tool.poe.tasks.verify] sequence = ['lint', 'format_check', 'test'] ignore_fail = "return_non_zero" [tool.black] line-length = 120 target-version = ['py38'] include = '\.pyi?$' extend-exclude = ''' ( __pycache__ | \.github ) ''' [tool.pylint] max-line-length = 120 disable = ["W0511"] [tool.isort] profile = 'black' multi_line_output = 3 line_length = 120 py_version = 38

[tool.poetry] name = "dowhy" # # 0.0.0 is standard placeholder for poetry-dynamic-versioning # any changes to this should not be checked in # version = "0.0.0" description = "DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions" authors = ["PyWhy Community <amshar@microsoft.com>"] maintainers = [] license = "MIT" documentation = "https://py-why.github.io/dowhy" repository = "https://github.com/py-why/dowhy" classifiers = [ 'Development Status :: 4 - Beta', 'License :: OSI Approved :: MIT License', 'Programming Language :: Python :: 3.8', 'Programming Language :: Python :: 3.9', 'Programming Language :: Python :: 3.10', ] keywords = [ 'causality', 'machine-learning', 'causal-inference', 'statistics', 'graphical-model', ] include = ['docs', 'tests', 'CONTRIBUTING.md', 'LICENSE'] readme = 'README.rst' [build-system] requires = ["poetry-core>=1.0.0"] build-backend = "poetry.core.masonry.api" [tool.poetry-dynamic-versioning] enable = true vcs = "git" [tool.poetry-dynamic-versioning.substitution] files = ["dowhy/__init__.py"] # # Dependency compatibility notes: # * numba (imported by econml) requires python <3.11 # [tool.poetry.dependencies] python = ">=3.8,<3.12" cython = "^0.29.32" scipy = ">=1.4.1" statsmodels = "^0.13.5" numpy = ">=1.23.1" pandas = ">=1.4.3" networkx = ">=2.8.5" sympy = "^1.10.1" scikit-learn = ">1.0,<2.0" pydot = { version = "^1.4.2", optional = true } joblib = ">=1.1.0" pygraphviz = { version = "^1.9", optional = true } econml = { version = "*", optional = true } tqdm = ">=4.64.0" causal-learn = ">=0.1.3.0" #Plotting Extra matplotlib = { version = ">=3.5.3", optional = true } sphinx_design = "^0.3.0" cvxpy = "^1.2.2" [tool.poetry.extras] pygraphviz = ["pygraphviz"] pydot = ["pydot"] plotting = ["matplotlib"] econml = ["econml"] [tool.poetry.group.dev.dependencies] poethepoet = "^0.16.0" flake8 = "^4.0.1" black = { version = "^22.6.0", extras = ["jupyter"] } isort = "^5.10.1" pytest = "^7.1.2" pytest-cov = "^3.0.0" pytest-split = "^0.8.0" nbformat = "^5.4.0" jupyter = "^1.0.0" flaky = "^3.7.0" keras = "^2.9.0" xgboost = "^1.7.0" mypy = "^0.971" torch = "^1.12.1" torchvision = "^0.13.1" pytorch-lightning = "^1.7.7" [tool.poetry.group.docs] optional = true [tool.poetry.group.docs.dependencies] # # Dependencies for Documentation Generation # sphinx = "^5.3.0" sphinxcontrib-googleanalytics = { git = "https://github.com/sphinx-contrib/googleanalytics.git", branch = "master" } nbsphinx = "^0.8.9" sphinx-rtd-theme = "^1.0.0" pydata-sphinx-theme = "^0.9.0" ipykernel = "^6.15.1" sphinx-copybutton = "0.5.0" seaborn = "^0.12.1" tensorflow = "^2.11.0" cdt ="^0.6.0" # # Versions defined for security reasons # # https://github.com/py-why/dowhy/security/dependabot/1 - CVE-2022-34749 nbconvert = { version = "7.0.0rc3", allow-prereleases = true } [tool.pytest.ini_options] markers = [ "advanced: not be to run each time. only on package updates.", "notebook: jupyter notebook tests", "econml: a marker to mark tests requiring econml", "focused: a debug marker to focus on specific tests", ] [tool.poe.tasks] # stop the build if there are Python syntax errors or undefined names _flake8Errors = "flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics" _flake8Warnings = "flake8 . --count --exit-zero --statistics" _black = 'black .' _isort = 'isort .' _black_check = 'black --check .' _isort_check = 'isort --check .' # testing tasks test = "pytest -v -m 'not advanced and not econml' --durations=0 --durations-min=60.0" test_no_notebooks= "pytest -v -m 'not advanced and not econml and not notebook' --durations=0 --durations-min=60.0" test_durations = "poetry run poe test --store-durations" test_econml = "pytest -v -m 'econml' --durations=0 --durations-min=60.0" test_advanced = "pytest -v" test_focused = "pytest -v -m 'focused'" [tool.poe.tasks.format] sequence = ['_black', '_isort'] ignore_fail = 'return_non_zero' [tool.poe.tasks.format_check] sequence = ['_black_check', '_isort_check'] ignore_fail = 'return_non_zero' [tool.poe.tasks.lint] sequence = ['_flake8Errors', '_flake8Warnings'] ignore_fail = 'return_non_zero' [tool.poe.tasks.verify] sequence = ['lint', 'format_check', 'test'] ignore_fail = "return_non_zero" [tool.black] line-length = 120 target-version = ['py38'] include = '\.pyi?$' extend-exclude = ''' ( __pycache__ | \.github ) ''' [tool.pylint] max-line-length = 120 disable = ["W0511"] [tool.isort] profile = 'black' multi_line_output = 3 line_length = 120 py_version = 38

bloebp

21a37a9453d0a018318e16af22ffabab5d6d4fc6

a1c6c042ca5bdfba98063e78b348854cf1709307

I thought so too, but somehow it behaved slightly differently. Might be that it was something local, but just wanted to make sure :)

bloebp

py-why/dowhy

Removing autolguon as optional dependency

The autogluon models can still be used with DoWhy, but it is not listed as an optional dependency anymore to reduce the number of restrictions on other packages.

2023-06-22 14:55:47+00:00

2023-06-26 16:58:14+00:00

pyproject.toml

[tool.poetry] name = "dowhy" # # 0.0.0 is standard placeholder for poetry-dynamic-versioning # any changes to this should not be checked in # version = "0.0.0" description = "DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions" authors = ["PyWhy Community <amshar@microsoft.com>"] maintainers = [] license = "MIT" documentation = "https://py-why.github.io/dowhy" repository = "https://github.com/py-why/dowhy" classifiers = [ 'Development Status :: 4 - Beta', 'License :: OSI Approved :: MIT License', 'Programming Language :: Python :: 3.8', 'Programming Language :: Python :: 3.9', 'Programming Language :: Python :: 3.10', ] keywords = [ 'causality', 'machine-learning', 'causal-inference', 'statistics', 'graphical-model', ] include = ['docs', 'tests', 'CONTRIBUTING.md', 'LICENSE'] readme = 'README.rst' [build-system] requires = ["poetry-core>=1.0.0"] build-backend = "poetry.core.masonry.api" [tool.poetry-dynamic-versioning] enable = true vcs = "git" [tool.poetry-dynamic-versioning.substitution] files = ["dowhy/__init__.py"] # # Dependency compatibility notes: # * numba (imported by econml) requires python <3.11 # [tool.poetry.dependencies] python = ">=3.8,<3.12" cython = "^0.29.32" scipy = ">=1.4.1" statsmodels = "^0.13.2" numpy = ">=1.23.1" pandas = ">=1.4.3" networkx = ">=2.8.5" sympy = "^1.10.1" scikit-learn = ">1.0,<2.0" pydot = { version = "^1.4.2", optional = true } joblib = ">=1.1.0" pygraphviz = { version = "^1.9", optional = true } econml = { version = "*", optional = true } tqdm = ">=4.64.0" causal-learn = ">=0.1.3.0" #Plotting Extra matplotlib = { version = ">=3.5.3", optional = true } sphinx_design = "^0.3.0" cvxpy = "^1.2.2" [tool.poetry.extras] pygraphviz = ["pygraphviz"] pydot = ["pydot"] plotting = ["matplotlib"] econml = ["econml"] [tool.poetry.group.dev.dependencies] poethepoet = "^0.16.0" flake8 = "^4.0.1" black = { version = "^22.6.0", extras = ["jupyter"] } isort = "^5.10.1" pytest = "^7.1.2" pytest-cov = "^3.0.0" pytest-split = "^0.8.0" nbformat = "^5.4.0" jupyter = "^1.0.0" flaky = "^3.7.0" keras = "^2.9.0" xgboost = "^1.7.0" mypy = "^0.971" torch = "^1.12.1" torchvision = "^0.13.1" pytorch-lightning = "^1.7.7" [tool.poetry.group.docs] optional = true [tool.poetry.group.docs.dependencies] # # Dependencies for Documentation Generation # sphinx = "^5.3.0" sphinxcontrib-googleanalytics = { git = "https://github.com/sphinx-contrib/googleanalytics.git", branch = "master" } nbsphinx = "^0.8.9" sphinx-rtd-theme = "^1.0.0" pydata-sphinx-theme = "^0.9.0" ipykernel = "^6.15.1" sphinx-copybutton = "0.5.0" seaborn = "^0.12.1" tensorflow = "^2.11.0" cdt ="^0.6.0" # # Versions defined for security reasons # # https://github.com/py-why/dowhy/security/dependabot/1 - CVE-2022-34749 nbconvert = { version = "7.0.0rc3", allow-prereleases = true } [tool.pytest.ini_options] markers = [ "advanced: not be to run each time. only on package updates.", "notebook: jupyter notebook tests", "econml: a marker to mark tests requiring econml", "focused: a debug marker to focus on specific tests", ] [tool.poe.tasks] # stop the build if there are Python syntax errors or undefined names _flake8Errors = "flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics" _flake8Warnings = "flake8 . --count --exit-zero --statistics" _black = 'black .' _isort = 'isort .' _black_check = 'black --check .' _isort_check = 'isort --check .' # testing tasks test = "pytest -v -m 'not advanced and not econml' --durations=0 --durations-min=60.0" test_no_notebooks= "pytest -v -m 'not advanced and not econml and not notebook' --durations=0 --durations-min=60.0" test_durations = "poetry run poe test --store-durations" test_econml = "pytest -v -m 'econml' --durations=0 --durations-min=60.0" test_advanced = "pytest -v" test_focused = "pytest -v -m 'focused'" [tool.poe.tasks.format] sequence = ['_black', '_isort'] ignore_fail = 'return_non_zero' [tool.poe.tasks.format_check] sequence = ['_black_check', '_isort_check'] ignore_fail = 'return_non_zero' [tool.poe.tasks.lint] sequence = ['_flake8Errors', '_flake8Warnings'] ignore_fail = 'return_non_zero' [tool.poe.tasks.verify] sequence = ['lint', 'format_check', 'test'] ignore_fail = "return_non_zero" [tool.black] line-length = 120 target-version = ['py38'] include = '\.pyi?$' extend-exclude = ''' ( __pycache__ | \.github ) ''' [tool.pylint] max-line-length = 120 disable = ["W0511"] [tool.isort] profile = 'black' multi_line_output = 3 line_length = 120 py_version = 38

[tool.poetry] name = "dowhy" # # 0.0.0 is standard placeholder for poetry-dynamic-versioning # any changes to this should not be checked in # version = "0.0.0" description = "DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions" authors = ["PyWhy Community <amshar@microsoft.com>"] maintainers = [] license = "MIT" documentation = "https://py-why.github.io/dowhy" repository = "https://github.com/py-why/dowhy" classifiers = [ 'Development Status :: 4 - Beta', 'License :: OSI Approved :: MIT License', 'Programming Language :: Python :: 3.8', 'Programming Language :: Python :: 3.9', 'Programming Language :: Python :: 3.10', ] keywords = [ 'causality', 'machine-learning', 'causal-inference', 'statistics', 'graphical-model', ] include = ['docs', 'tests', 'CONTRIBUTING.md', 'LICENSE'] readme = 'README.rst' [build-system] requires = ["poetry-core>=1.0.0"] build-backend = "poetry.core.masonry.api" [tool.poetry-dynamic-versioning] enable = true vcs = "git" [tool.poetry-dynamic-versioning.substitution] files = ["dowhy/__init__.py"] # # Dependency compatibility notes: # * numba (imported by econml) requires python <3.11 # [tool.poetry.dependencies] python = ">=3.8,<3.12" cython = "^0.29.32" scipy = ">=1.4.1" statsmodels = "^0.13.5" numpy = ">=1.23.1" pandas = ">=1.4.3" networkx = ">=2.8.5" sympy = "^1.10.1" scikit-learn = ">1.0,<2.0" pydot = { version = "^1.4.2", optional = true } joblib = ">=1.1.0" pygraphviz = { version = "^1.9", optional = true } econml = { version = "*", optional = true } tqdm = ">=4.64.0" causal-learn = ">=0.1.3.0" #Plotting Extra matplotlib = { version = ">=3.5.3", optional = true } sphinx_design = "^0.3.0" cvxpy = "^1.2.2" [tool.poetry.extras] pygraphviz = ["pygraphviz"] pydot = ["pydot"] plotting = ["matplotlib"] econml = ["econml"] [tool.poetry.group.dev.dependencies] poethepoet = "^0.16.0" flake8 = "^4.0.1" black = { version = "^22.6.0", extras = ["jupyter"] } isort = "^5.10.1" pytest = "^7.1.2" pytest-cov = "^3.0.0" pytest-split = "^0.8.0" nbformat = "^5.4.0" jupyter = "^1.0.0" flaky = "^3.7.0" keras = "^2.9.0" xgboost = "^1.7.0" mypy = "^0.971" torch = "^1.12.1" torchvision = "^0.13.1" pytorch-lightning = "^1.7.7" [tool.poetry.group.docs] optional = true [tool.poetry.group.docs.dependencies] # # Dependencies for Documentation Generation # sphinx = "^5.3.0" sphinxcontrib-googleanalytics = { git = "https://github.com/sphinx-contrib/googleanalytics.git", branch = "master" } nbsphinx = "^0.8.9" sphinx-rtd-theme = "^1.0.0" pydata-sphinx-theme = "^0.9.0" ipykernel = "^6.15.1" sphinx-copybutton = "0.5.0" seaborn = "^0.12.1" tensorflow = "^2.11.0" cdt ="^0.6.0" # # Versions defined for security reasons # # https://github.com/py-why/dowhy/security/dependabot/1 - CVE-2022-34749 nbconvert = { version = "7.0.0rc3", allow-prereleases = true } [tool.pytest.ini_options] markers = [ "advanced: not be to run each time. only on package updates.", "notebook: jupyter notebook tests", "econml: a marker to mark tests requiring econml", "focused: a debug marker to focus on specific tests", ] [tool.poe.tasks] # stop the build if there are Python syntax errors or undefined names _flake8Errors = "flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics" _flake8Warnings = "flake8 . --count --exit-zero --statistics" _black = 'black .' _isort = 'isort .' _black_check = 'black --check .' _isort_check = 'isort --check .' # testing tasks test = "pytest -v -m 'not advanced and not econml' --durations=0 --durations-min=60.0" test_no_notebooks= "pytest -v -m 'not advanced and not econml and not notebook' --durations=0 --durations-min=60.0" test_durations = "poetry run poe test --store-durations" test_econml = "pytest -v -m 'econml' --durations=0 --durations-min=60.0" test_advanced = "pytest -v" test_focused = "pytest -v -m 'focused'" [tool.poe.tasks.format] sequence = ['_black', '_isort'] ignore_fail = 'return_non_zero' [tool.poe.tasks.format_check] sequence = ['_black_check', '_isort_check'] ignore_fail = 'return_non_zero' [tool.poe.tasks.lint] sequence = ['_flake8Errors', '_flake8Warnings'] ignore_fail = 'return_non_zero' [tool.poe.tasks.verify] sequence = ['lint', 'format_check', 'test'] ignore_fail = "return_non_zero" [tool.black] line-length = 120 target-version = ['py38'] include = '\.pyi?$' extend-exclude = ''' ( __pycache__ | \.github ) ''' [tool.pylint] max-line-length = 120 disable = ["W0511"] [tool.isort] profile = 'black' multi_line_output = 3 line_length = 120 py_version = 38

bloebp

21a37a9453d0a018318e16af22ffabab5d6d4fc6

a1c6c042ca5bdfba98063e78b348854cf1709307

we can change this to >=3.8,<3.12. the latest version of numba supports 3.11. can update line 45 too.

amit-sharma

py-why/dowhy

Removing autolguon as optional dependency

The autogluon models can still be used with DoWhy, but it is not listed as an optional dependency anymore to reduce the number of restrictions on other packages.

2023-06-22 14:55:47+00:00

2023-06-26 16:58:14+00:00

pyproject.toml

[tool.poetry] name = "dowhy" # # 0.0.0 is standard placeholder for poetry-dynamic-versioning # any changes to this should not be checked in # version = "0.0.0" description = "DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions" authors = ["PyWhy Community <amshar@microsoft.com>"] maintainers = [] license = "MIT" documentation = "https://py-why.github.io/dowhy" repository = "https://github.com/py-why/dowhy" classifiers = [ 'Development Status :: 4 - Beta', 'License :: OSI Approved :: MIT License', 'Programming Language :: Python :: 3.8', 'Programming Language :: Python :: 3.9', 'Programming Language :: Python :: 3.10', ] keywords = [ 'causality', 'machine-learning', 'causal-inference', 'statistics', 'graphical-model', ] include = ['docs', 'tests', 'CONTRIBUTING.md', 'LICENSE'] readme = 'README.rst' [build-system] requires = ["poetry-core>=1.0.0"] build-backend = "poetry.core.masonry.api" [tool.poetry-dynamic-versioning] enable = true vcs = "git" [tool.poetry-dynamic-versioning.substitution] files = ["dowhy/__init__.py"] # # Dependency compatibility notes: # * numba (imported by econml) requires python <3.11 # [tool.poetry.dependencies] python = ">=3.8,<3.12" cython = "^0.29.32" scipy = ">=1.4.1" statsmodels = "^0.13.2" numpy = ">=1.23.1" pandas = ">=1.4.3" networkx = ">=2.8.5" sympy = "^1.10.1" scikit-learn = ">1.0,<2.0" pydot = { version = "^1.4.2", optional = true } joblib = ">=1.1.0" pygraphviz = { version = "^1.9", optional = true } econml = { version = "*", optional = true } tqdm = ">=4.64.0" causal-learn = ">=0.1.3.0" #Plotting Extra matplotlib = { version = ">=3.5.3", optional = true } sphinx_design = "^0.3.0" cvxpy = "^1.2.2" [tool.poetry.extras] pygraphviz = ["pygraphviz"] pydot = ["pydot"] plotting = ["matplotlib"] econml = ["econml"] [tool.poetry.group.dev.dependencies] poethepoet = "^0.16.0" flake8 = "^4.0.1" black = { version = "^22.6.0", extras = ["jupyter"] } isort = "^5.10.1" pytest = "^7.1.2" pytest-cov = "^3.0.0" pytest-split = "^0.8.0" nbformat = "^5.4.0" jupyter = "^1.0.0" flaky = "^3.7.0" keras = "^2.9.0" xgboost = "^1.7.0" mypy = "^0.971" torch = "^1.12.1" torchvision = "^0.13.1" pytorch-lightning = "^1.7.7" [tool.poetry.group.docs] optional = true [tool.poetry.group.docs.dependencies] # # Dependencies for Documentation Generation # sphinx = "^5.3.0" sphinxcontrib-googleanalytics = { git = "https://github.com/sphinx-contrib/googleanalytics.git", branch = "master" } nbsphinx = "^0.8.9" sphinx-rtd-theme = "^1.0.0" pydata-sphinx-theme = "^0.9.0" ipykernel = "^6.15.1" sphinx-copybutton = "0.5.0" seaborn = "^0.12.1" tensorflow = "^2.11.0" cdt ="^0.6.0" # # Versions defined for security reasons # # https://github.com/py-why/dowhy/security/dependabot/1 - CVE-2022-34749 nbconvert = { version = "7.0.0rc3", allow-prereleases = true } [tool.pytest.ini_options] markers = [ "advanced: not be to run each time. only on package updates.", "notebook: jupyter notebook tests", "econml: a marker to mark tests requiring econml", "focused: a debug marker to focus on specific tests", ] [tool.poe.tasks] # stop the build if there are Python syntax errors or undefined names _flake8Errors = "flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics" _flake8Warnings = "flake8 . --count --exit-zero --statistics" _black = 'black .' _isort = 'isort .' _black_check = 'black --check .' _isort_check = 'isort --check .' # testing tasks test = "pytest -v -m 'not advanced and not econml' --durations=0 --durations-min=60.0" test_no_notebooks= "pytest -v -m 'not advanced and not econml and not notebook' --durations=0 --durations-min=60.0" test_durations = "poetry run poe test --store-durations" test_econml = "pytest -v -m 'econml' --durations=0 --durations-min=60.0" test_advanced = "pytest -v" test_focused = "pytest -v -m 'focused'" [tool.poe.tasks.format] sequence = ['_black', '_isort'] ignore_fail = 'return_non_zero' [tool.poe.tasks.format_check] sequence = ['_black_check', '_isort_check'] ignore_fail = 'return_non_zero' [tool.poe.tasks.lint] sequence = ['_flake8Errors', '_flake8Warnings'] ignore_fail = 'return_non_zero' [tool.poe.tasks.verify] sequence = ['lint', 'format_check', 'test'] ignore_fail = "return_non_zero" [tool.black] line-length = 120 target-version = ['py38'] include = '\.pyi?$' extend-exclude = ''' ( __pycache__ | \.github ) ''' [tool.pylint] max-line-length = 120 disable = ["W0511"] [tool.isort] profile = 'black' multi_line_output = 3 line_length = 120 py_version = 38

[tool.poetry] name = "dowhy" # # 0.0.0 is standard placeholder for poetry-dynamic-versioning # any changes to this should not be checked in # version = "0.0.0" description = "DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions" authors = ["PyWhy Community <amshar@microsoft.com>"] maintainers = [] license = "MIT" documentation = "https://py-why.github.io/dowhy" repository = "https://github.com/py-why/dowhy" classifiers = [ 'Development Status :: 4 - Beta', 'License :: OSI Approved :: MIT License', 'Programming Language :: Python :: 3.8', 'Programming Language :: Python :: 3.9', 'Programming Language :: Python :: 3.10', ] keywords = [ 'causality', 'machine-learning', 'causal-inference', 'statistics', 'graphical-model', ] include = ['docs', 'tests', 'CONTRIBUTING.md', 'LICENSE'] readme = 'README.rst' [build-system] requires = ["poetry-core>=1.0.0"] build-backend = "poetry.core.masonry.api" [tool.poetry-dynamic-versioning] enable = true vcs = "git" [tool.poetry-dynamic-versioning.substitution] files = ["dowhy/__init__.py"] # # Dependency compatibility notes: # * numba (imported by econml) requires python <3.11 # [tool.poetry.dependencies] python = ">=3.8,<3.12" cython = "^0.29.32" scipy = ">=1.4.1" statsmodels = "^0.13.5" numpy = ">=1.23.1" pandas = ">=1.4.3" networkx = ">=2.8.5" sympy = "^1.10.1" scikit-learn = ">1.0,<2.0" pydot = { version = "^1.4.2", optional = true } joblib = ">=1.1.0" pygraphviz = { version = "^1.9", optional = true } econml = { version = "*", optional = true } tqdm = ">=4.64.0" causal-learn = ">=0.1.3.0" #Plotting Extra matplotlib = { version = ">=3.5.3", optional = true } sphinx_design = "^0.3.0" cvxpy = "^1.2.2" [tool.poetry.extras] pygraphviz = ["pygraphviz"] pydot = ["pydot"] plotting = ["matplotlib"] econml = ["econml"] [tool.poetry.group.dev.dependencies] poethepoet = "^0.16.0" flake8 = "^4.0.1" black = { version = "^22.6.0", extras = ["jupyter"] } isort = "^5.10.1" pytest = "^7.1.2" pytest-cov = "^3.0.0" pytest-split = "^0.8.0" nbformat = "^5.4.0" jupyter = "^1.0.0" flaky = "^3.7.0" keras = "^2.9.0" xgboost = "^1.7.0" mypy = "^0.971" torch = "^1.12.1" torchvision = "^0.13.1" pytorch-lightning = "^1.7.7" [tool.poetry.group.docs] optional = true [tool.poetry.group.docs.dependencies] # # Dependencies for Documentation Generation # sphinx = "^5.3.0" sphinxcontrib-googleanalytics = { git = "https://github.com/sphinx-contrib/googleanalytics.git", branch = "master" } nbsphinx = "^0.8.9" sphinx-rtd-theme = "^1.0.0" pydata-sphinx-theme = "^0.9.0" ipykernel = "^6.15.1" sphinx-copybutton = "0.5.0" seaborn = "^0.12.1" tensorflow = "^2.11.0" cdt ="^0.6.0" # # Versions defined for security reasons # # https://github.com/py-why/dowhy/security/dependabot/1 - CVE-2022-34749 nbconvert = { version = "7.0.0rc3", allow-prereleases = true } [tool.pytest.ini_options] markers = [ "advanced: not be to run each time. only on package updates.", "notebook: jupyter notebook tests", "econml: a marker to mark tests requiring econml", "focused: a debug marker to focus on specific tests", ] [tool.poe.tasks] # stop the build if there are Python syntax errors or undefined names _flake8Errors = "flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics" _flake8Warnings = "flake8 . --count --exit-zero --statistics" _black = 'black .' _isort = 'isort .' _black_check = 'black --check .' _isort_check = 'isort --check .' # testing tasks test = "pytest -v -m 'not advanced and not econml' --durations=0 --durations-min=60.0" test_no_notebooks= "pytest -v -m 'not advanced and not econml and not notebook' --durations=0 --durations-min=60.0" test_durations = "poetry run poe test --store-durations" test_econml = "pytest -v -m 'econml' --durations=0 --durations-min=60.0" test_advanced = "pytest -v" test_focused = "pytest -v -m 'focused'" [tool.poe.tasks.format] sequence = ['_black', '_isort'] ignore_fail = 'return_non_zero' [tool.poe.tasks.format_check] sequence = ['_black_check', '_isort_check'] ignore_fail = 'return_non_zero' [tool.poe.tasks.lint] sequence = ['_flake8Errors', '_flake8Warnings'] ignore_fail = 'return_non_zero' [tool.poe.tasks.verify] sequence = ['lint', 'format_check', 'test'] ignore_fail = "return_non_zero" [tool.black] line-length = 120 target-version = ['py38'] include = '\.pyi?$' extend-exclude = ''' ( __pycache__ | \.github ) ''' [tool.pylint] max-line-length = 120 disable = ["W0511"] [tool.isort] profile = 'black' multi_line_output = 3 line_length = 120 py_version = 38

bloebp

21a37a9453d0a018318e16af22ffabab5d6d4fc6

a1c6c042ca5bdfba98063e78b348854cf1709307

Both should be fine! :)

amit-sharma

py-why/dowhy

Proposal: Finalize functional API refactor - deprecate causal graph

- The graph should now be defined via a networkx graph. Most identification methods now expect an additional "observed_nodes" parameter accordingly. - CausalModel and CausalGraph still exist and should be compatible with the old API. Open task is still to replace the usage of CausalModel in the tests and notebooks. There are also some smaller details with the identification methods, which should be double checked.

2023-05-16 16:07:18+00:00

2023-11-27 17:48:56+00:00

dowhy/causal_identifier/auto_identifier.py

import itertools import logging from enum import Enum from typing import Dict, List, Optional, Union import sympy as sp import sympy.stats as spstats from dowhy.causal_graph import CausalGraph from dowhy.causal_identifier.efficient_backdoor import EfficientBackdoor from dowhy.causal_identifier.identified_estimand import IdentifiedEstimand from dowhy.utils.api import parse_state logger = logging.getLogger(__name__) class EstimandType(Enum): # Average total effect NONPARAMETRIC_ATE = "nonparametric-ate" # Natural direct effect NONPARAMETRIC_NDE = "nonparametric-nde" # Natural indirect effect NONPARAMETRIC_NIE = "nonparametric-nie" # Controlled direct effect NONPARAMETRIC_CDE = "nonparametric-cde" class BackdoorAdjustment(Enum): # Backdoor method names BACKDOOR_DEFAULT = "default" BACKDOOR_EXHAUSTIVE = "exhaustive-search" BACKDOOR_MIN = "minimal-adjustment" BACKDOOR_MAX = "maximal-adjustment" BACKDOOR_EFFICIENT = "efficient-adjustment" BACKDOOR_MIN_EFFICIENT = "efficient-minimal-adjustment" BACKDOOR_MINCOST_EFFICIENT = "efficient-mincost-adjustment" MAX_BACKDOOR_ITERATIONS = 100000 METHOD_NAMES = { BackdoorAdjustment.BACKDOOR_DEFAULT, BackdoorAdjustment.BACKDOOR_EXHAUSTIVE, BackdoorAdjustment.BACKDOOR_MIN, BackdoorAdjustment.BACKDOOR_MAX, BackdoorAdjustment.BACKDOOR_EFFICIENT, BackdoorAdjustment.BACKDOOR_MIN_EFFICIENT, BackdoorAdjustment.BACKDOOR_MINCOST_EFFICIENT, } EFFICIENT_METHODS = { BackdoorAdjustment.BACKDOOR_EFFICIENT, BackdoorAdjustment.BACKDOOR_MIN_EFFICIENT, BackdoorAdjustment.BACKDOOR_MINCOST_EFFICIENT, } DEFAULT_BACKDOOR_METHOD = BackdoorAdjustment.BACKDOOR_DEFAULT class AutoIdentifier: """Class that implements different identification methods. Currently supports backdoor and instrumental variable identification methods. The identification is based on the causal graph provided. This class is for backwards compatibility with CausalModel Will be deprecated in the future in favor of function call auto_identify_effect() """ def __init__( self, estimand_type: EstimandType, backdoor_adjustment: BackdoorAdjustment = BackdoorAdjustment.BACKDOOR_DEFAULT, proceed_when_unidentifiable: bool = False, optimize_backdoor: bool = False, costs: Optional[List] = None, ): self.estimand_type = estimand_type self.backdoor_adjustment = backdoor_adjustment self._proceed_when_unidentifiable = proceed_when_unidentifiable self.optimize_backdoor = optimize_backdoor self.costs = costs self.logger = logging.getLogger(__name__) def identify_effect( self, graph: CausalGraph, treatment_name: Union[str, List[str]], outcome_name: Union[str, List[str]], conditional_node_names: List[str] = None, **kwargs, ): estimand = identify_effect_auto( graph, treatment_name, outcome_name, self.estimand_type, conditional_node_names, self.backdoor_adjustment, self._proceed_when_unidentifiable, self.optimize_backdoor, self.costs, **kwargs, ) estimand.identifier = self return estimand def identify_backdoor( self, graph: CausalGraph, treatment_name: List[str], outcome_name: str, include_unobserved: bool = False, dseparation_algo: str = "default", direct_effect: bool = False, ): return identify_backdoor( graph, treatment_name, outcome_name, self.backdoor_adjustment, include_unobserved, dseparation_algo, direct_effect, ) def identify_effect_auto( graph: CausalGraph, treatment_name: Union[str, List[str]], outcome_name: Union[str, List[str]], estimand_type: EstimandType, conditional_node_names: List[str] = None, backdoor_adjustment: BackdoorAdjustment = BackdoorAdjustment.BACKDOOR_DEFAULT, proceed_when_unidentifiable: bool = False, optimize_backdoor: bool = False, costs: Optional[List] = None, **kwargs, ) -> IdentifiedEstimand: """Main method that returns an identified estimand (if one exists). If estimand_type is non-parametric ATE, then uses backdoor, instrumental variable and frontdoor identification methods, to check if an identified estimand exists, based on the causal graph. :param optimize_backdoor: if True, uses an optimised algorithm to compute the backdoor sets :param costs: non-negative costs associated with variables in the graph. Only used for estimand_type='non-parametric-ate' and backdoor_adjustment='efficient-mincost-adjustment'. If no costs are provided by the user, and backdoor_adjustment='efficient-mincost-adjustment', costs are assumed to be equal to one for all variables in the graph. :param conditional_node_names: variables that are used to determine treatment. If none are provided, it is assumed that the intervention is static. :returns: target estimand, an instance of the IdentifiedEstimand class """ treatment_name = parse_state(treatment_name) outcome_name = parse_state(outcome_name) # First, check if there is a directed path from action to outcome if not graph.has_directed_path(treatment_name, outcome_name): logger.warn("No directed path from treatment to outcome. Causal Effect is zero.") return IdentifiedEstimand( None, treatment_variable=treatment_name, outcome_variable=outcome_name, no_directed_path=True, ) if estimand_type == EstimandType.NONPARAMETRIC_ATE: return identify_ate_effect( graph, treatment_name, outcome_name, backdoor_adjustment, optimize_backdoor, estimand_type, costs, conditional_node_names, proceed_when_unidentifiable, ) elif estimand_type == EstimandType.NONPARAMETRIC_NDE: return identify_nde_effect( graph, treatment_name, outcome_name, backdoor_adjustment, estimand_type, proceed_when_unidentifiable ) elif estimand_type == EstimandType.NONPARAMETRIC_NIE: return identify_nie_effect( graph, treatment_name, outcome_name, backdoor_adjustment, estimand_type, proceed_when_unidentifiable ) elif estimand_type == EstimandType.NONPARAMETRIC_CDE: return identify_cde_effect( graph, treatment_name, outcome_name, backdoor_adjustment, estimand_type, proceed_when_unidentifiable ) else: raise ValueError( "Estimand type is not supported. Use either {0}, {1}, or {2}.".format( EstimandType.NONPARAMETRIC_ATE, EstimandType.NONPARAMETRIC_CDE, EstimandType.NONPARAMETRIC_NDE, EstimandType.NONPARAMETRIC_NIE, ) ) def identify_ate_effect( graph: CausalGraph, treatment_name: List[str], outcome_name: str, backdoor_adjustment: BackdoorAdjustment, optimize_backdoor: bool, estimand_type: EstimandType, costs: List, conditional_node_names: List[str] = None, proceed_when_unidentifiable: bool = False, ): estimands_dict = {} mediation_first_stage_confounders = None mediation_second_stage_confounders = None ### 1. BACKDOOR IDENTIFICATION # Pick algorithm to compute backdoor sets according to method chosen if backdoor_adjustment not in EFFICIENT_METHODS: # First, checking if there are any valid backdoor adjustment sets if optimize_backdoor == False: backdoor_sets = identify_backdoor(graph, treatment_name, outcome_name, backdoor_adjustment) else: from dowhy.causal_identifier.backdoor import Backdoor path = Backdoor(graph._graph, treatment_name, outcome_name) backdoor_sets = path.get_backdoor_vars() elif backdoor_adjustment in EFFICIENT_METHODS: backdoor_sets = identify_efficient_backdoor( graph, backdoor_adjustment, costs, conditional_node_names=conditional_node_names ) estimands_dict, backdoor_variables_dict = build_backdoor_estimands_dict( graph, treatment_name, outcome_name, backdoor_sets, estimands_dict ) # Setting default "backdoor" identification adjustment set default_backdoor_id = get_default_backdoor_set_id(graph, treatment_name, outcome_name, backdoor_variables_dict) if len(backdoor_variables_dict) > 0: estimands_dict["backdoor"] = estimands_dict.get(str(default_backdoor_id), None) backdoor_variables_dict["backdoor"] = backdoor_variables_dict.get(str(default_backdoor_id), None) else: estimands_dict["backdoor"] = None ### 2. INSTRUMENTAL VARIABLE IDENTIFICATION # Now checking if there is also a valid iv estimand instrument_names = graph.get_instruments(treatment_name, outcome_name) logger.info("Instrumental variables for treatment and outcome:" + str(instrument_names)) if len(instrument_names) > 0: iv_estimand_expr = construct_iv_estimand( treatment_name, outcome_name, instrument_names, ) logger.debug("Identified expression = " + str(iv_estimand_expr)) estimands_dict["iv"] = iv_estimand_expr else: estimands_dict["iv"] = None ### 3. FRONTDOOR IDENTIFICATION # Now checking if there is a valid frontdoor variable frontdoor_variables_names = identify_frontdoor(graph, treatment_name, outcome_name) logger.info("Frontdoor variables for treatment and outcome:" + str(frontdoor_variables_names)) if len(frontdoor_variables_names) > 0: frontdoor_estimand_expr = construct_frontdoor_estimand( treatment_name, outcome_name, frontdoor_variables_names, ) logger.debug("Identified expression = " + str(frontdoor_estimand_expr)) estimands_dict["frontdoor"] = frontdoor_estimand_expr mediation_first_stage_confounders = identify_mediation_first_stage_confounders( graph, treatment_name, outcome_name, frontdoor_variables_names, backdoor_adjustment ) mediation_second_stage_confounders = identify_mediation_second_stage_confounders( graph, treatment_name, frontdoor_variables_names, outcome_name, backdoor_adjustment ) else: estimands_dict["frontdoor"] = None # Finally returning the estimand object estimand = IdentifiedEstimand( None, treatment_variable=treatment_name, outcome_variable=outcome_name, estimand_type=estimand_type, estimands=estimands_dict, backdoor_variables=backdoor_variables_dict, instrumental_variables=instrument_names, frontdoor_variables=frontdoor_variables_names, mediation_first_stage_confounders=mediation_first_stage_confounders, mediation_second_stage_confounders=mediation_second_stage_confounders, default_backdoor_id=default_backdoor_id, ) return estimand def identify_cde_effect( graph: CausalGraph, treatment_name: List[str], outcome_name: str, backdoor_adjustment: BackdoorAdjustment, estimand_type: EstimandType, proceed_when_unidentifiable: bool = False, ): """Identify controlled direct effect. For a definition, see Vanderwheele (2011). Controlled direct and mediated effects: definition, identification and bounds. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4193506/ Using do-calculus rules, identification yields a adjustment set. It is based on the principle that under a graph where the direct edge from treatment to outcome is removed, conditioning on the adjustment set should d-separate treatment and outcome. """ estimands_dict = {} # Pick algorithm to compute backdoor sets according to method chosen backdoor_sets = identify_backdoor(graph, treatment_name, outcome_name, backdoor_adjustment, direct_effect=True) estimands_dict, backdoor_variables_dict = build_backdoor_estimands_dict( graph, treatment_name, outcome_name, backdoor_sets, estimands_dict ) # Setting default "backdoor" identification adjustment set default_backdoor_id = get_default_backdoor_set_id(graph, treatment_name, outcome_name, backdoor_variables_dict) if len(backdoor_variables_dict) > 0: estimands_dict["backdoor"] = estimands_dict.get(str(default_backdoor_id), None) backdoor_variables_dict["backdoor"] = backdoor_variables_dict.get(str(default_backdoor_id), None) else: estimands_dict["backdoor"] = None # Finally returning the estimand object estimand = IdentifiedEstimand( None, treatment_variable=treatment_name, outcome_variable=outcome_name, estimand_type=estimand_type, estimands=estimands_dict, backdoor_variables=backdoor_variables_dict, instrumental_variables=None, frontdoor_variables=None, mediation_first_stage_confounders=None, mediation_second_stage_confounders=None, default_backdoor_id=default_backdoor_id, ) return estimand def identify_nie_effect( graph: CausalGraph, treatment_name: List[str], outcome_name: str, backdoor_adjustment: BackdoorAdjustment, estimand_type: EstimandType, proceed_when_unidentifiable: bool = False, ): estimands_dict = {} ### 1. FIRST DOING BACKDOOR IDENTIFICATION # First, checking if there are any valid backdoor adjustment sets backdoor_sets = identify_backdoor(graph, treatment_name, outcome_name, backdoor_adjustment) estimands_dict, backdoor_variables_dict = build_backdoor_estimands_dict( graph, treatment_name, outcome_name, backdoor_sets, estimands_dict ) # Setting default "backdoor" identification adjustment set default_backdoor_id = get_default_backdoor_set_id(graph, treatment_name, outcome_name, backdoor_variables_dict) backdoor_variables_dict["backdoor"] = backdoor_variables_dict.get(str(default_backdoor_id), None) ### 2. SECOND, CHECKING FOR MEDIATORS # Now checking if there are valid mediator variables estimands_dict = {} # Need to reinitialize this dictionary to avoid including the backdoor sets mediation_first_stage_confounders = None mediation_second_stage_confounders = None mediators_names = identify_mediation(graph, treatment_name, outcome_name) logger.info("Mediators for treatment and outcome:" + str(mediators_names)) if len(mediators_names) > 0: mediation_estimand_expr = construct_mediation_estimand( estimand_type, treatment_name, outcome_name, mediators_names, ) logger.debug("Identified expression = " + str(mediation_estimand_expr)) estimands_dict["mediation"] = mediation_estimand_expr mediation_first_stage_confounders = identify_mediation_first_stage_confounders( graph, treatment_name, outcome_name, mediators_names, backdoor_adjustment ) mediation_second_stage_confounders = identify_mediation_second_stage_confounders( graph, treatment_name, mediators_names, outcome_name, backdoor_adjustment ) else: estimands_dict["mediation"] = None # Finally returning the estimand object estimand = IdentifiedEstimand( None, treatment_variable=treatment_name, outcome_variable=outcome_name, estimand_type=estimand_type, estimands=estimands_dict, backdoor_variables=backdoor_variables_dict, instrumental_variables=None, frontdoor_variables=None, mediator_variables=mediators_names, mediation_first_stage_confounders=mediation_first_stage_confounders, mediation_second_stage_confounders=mediation_second_stage_confounders, default_backdoor_id=None, ) return estimand def identify_nde_effect( graph: CausalGraph, treatment_name: List[str], outcome_name: str, backdoor_adjustment: BackdoorAdjustment, estimand_type: EstimandType, proceed_when_unidentifiable: bool = False, ): estimands_dict = {} ### 1. FIRST DOING BACKDOOR IDENTIFICATION # First, checking if there are any valid backdoor adjustment sets backdoor_sets = identify_backdoor(graph, treatment_name, outcome_name, backdoor_adjustment) estimands_dict, backdoor_variables_dict = build_backdoor_estimands_dict( graph, treatment_name, outcome_name, backdoor_sets, estimands_dict ) # Setting default "backdoor" identification adjustment set default_backdoor_id = get_default_backdoor_set_id(graph, treatment_name, outcome_name, backdoor_variables_dict) backdoor_variables_dict["backdoor"] = backdoor_variables_dict.get(str(default_backdoor_id), None) ### 2. SECOND, CHECKING FOR MEDIATORS # Now checking if there are valid mediator variables estimands_dict = {} mediation_first_stage_confounders = None mediation_second_stage_confounders = None mediators_names = identify_mediation(graph, treatment_name, outcome_name) logger.info("Mediators for treatment and outcome:" + str(mediators_names)) if len(mediators_names) > 0: mediation_estimand_expr = construct_mediation_estimand( estimand_type, treatment_name, outcome_name, mediators_names, ) logger.debug("Identified expression = " + str(mediation_estimand_expr)) estimands_dict["mediation"] = mediation_estimand_expr mediation_first_stage_confounders = identify_mediation_first_stage_confounders( graph, treatment_name, outcome_name, mediators_names, backdoor_adjustment ) mediation_second_stage_confounders = identify_mediation_second_stage_confounders( graph, treatment_name, mediators_names, outcome_name, backdoor_adjustment ) else: estimands_dict["mediation"] = None # Finally returning the estimand object estimand = IdentifiedEstimand( None, treatment_variable=treatment_name, outcome_variable=outcome_name, estimand_type=estimand_type, estimands=estimands_dict, backdoor_variables=backdoor_variables_dict, instrumental_variables=None, frontdoor_variables=None, mediator_variables=mediators_names, mediation_first_stage_confounders=mediation_first_stage_confounders, mediation_second_stage_confounders=mediation_second_stage_confounders, default_backdoor_id=None, ) return estimand def identify_backdoor( graph: CausalGraph, treatment_name: List[str], outcome_name: str, backdoor_adjustment: BackdoorAdjustment, include_unobserved: bool = False, dseparation_algo: str = "default", direct_effect: bool = False, ): backdoor_sets = [] backdoor_paths = None bdoor_graph = None if dseparation_algo == "naive": backdoor_paths = graph.get_backdoor_paths(treatment_name, outcome_name) elif dseparation_algo == "default": bdoor_graph = graph.do_surgery( treatment_name, target_node_names=outcome_name, remove_outgoing_edges=True, remove_only_direct_edges_to_target=direct_effect, ) else: raise ValueError(f"d-separation algorithm {dseparation_algo} is not supported") backdoor_adjustment = ( backdoor_adjustment if backdoor_adjustment != BackdoorAdjustment.BACKDOOR_DEFAULT else DEFAULT_BACKDOOR_METHOD ) # First, checking if empty set is a valid backdoor set empty_set = set() check = graph.check_valid_backdoor_set( treatment_name, outcome_name, empty_set, backdoor_paths=backdoor_paths, new_graph=bdoor_graph, dseparation_algo=dseparation_algo, ) if check["is_dseparated"]: backdoor_sets.append({"backdoor_set": empty_set}) # If the method is `minimal-adjustment`, return the empty set right away. if backdoor_adjustment == BackdoorAdjustment.BACKDOOR_MIN: return backdoor_sets # Second, checking for all other sets of variables. If include_unobserved is false, then only observed variables are eligible. eligible_variables = ( graph.get_all_nodes(include_unobserved=include_unobserved) - set(treatment_name) - set(outcome_name) ) if direct_effect: # only remove descendants of Y # also allow any causes of Y that are not caused by T (for lower variance) eligible_variables -= graph.get_descendants(outcome_name) else: # remove descendants of T (mediators) and descendants of Y eligible_variables -= graph.get_descendants(treatment_name) # If var is d-separated from both treatment or outcome, it cannot # be a part of the backdoor set filt_eligible_variables = set() for var in eligible_variables: dsep_treat_var = graph.check_dseparation(treatment_name, parse_state(var), set()) dsep_outcome_var = graph.check_dseparation(outcome_name, parse_state(var), set()) if not dsep_outcome_var or not dsep_treat_var: filt_eligible_variables.add(var) if backdoor_adjustment in METHOD_NAMES: backdoor_sets, found_valid_adjustment_set = find_valid_adjustment_sets( graph, treatment_name, outcome_name, backdoor_paths, bdoor_graph, dseparation_algo, backdoor_sets, filt_eligible_variables, backdoor_adjustment=backdoor_adjustment, max_iterations=MAX_BACKDOOR_ITERATIONS, ) if backdoor_adjustment == BackdoorAdjustment.BACKDOOR_DEFAULT and found_valid_adjustment_set: # repeat the above search with BACKDOOR_MIN backdoor_sets, _ = find_valid_adjustment_sets( graph, treatment_name, outcome_name, backdoor_paths, bdoor_graph, dseparation_algo, backdoor_sets, filt_eligible_variables, backdoor_adjustment=BackdoorAdjustment.BACKDOOR_MIN, max_iterations=MAX_BACKDOOR_ITERATIONS, ) else: raise ValueError( f"Identifier method {backdoor_adjustment} not supported. Try one of the following: {METHOD_NAMES}" ) return backdoor_sets def identify_efficient_backdoor( graph: CausalGraph, backdoor_adjustment: BackdoorAdjustment, costs: List, conditional_node_names: List[str] = None, ): """Method implementing algorithms to compute efficient backdoor sets, as described in Rotnitzky and Smucler (2020), Smucler, Sapienza and Rotnitzky (2021) and Smucler and Rotnitzky (2022). For backdoor_adjustment='efficient-adjustment', computes an optimal backdoor set, that is, a backdoor set comprised of observable variables that yields non-parametric estimators of the interventional mean with the smallest asymptotic variance among those that are based on observable backdoor sets. This optimal backdoor set always exists when no variables are latent, and the algorithm is guaranteed to compute it in this case. Under a non-parametric graphical model with latent variables, such a backdoor set can fail to exist. When certain sufficient conditions under which it is known that such a backdoor set exists are not satisfied, an error is raised. For backdoor_adjustment='efficient-minimal-adjustment', computes an optimal minimal backdoor set, that is, a minimal backdoor set comprised of observable variables that yields non-parametric estimators of the interventional mean with the smallest asymptotic variance among those that are based on observable minimal backdoor sets. For backdoor_adjustment='efficient-mincost-adjustment', computes an optimal minimum cost backdoor set, that is, a minimum cost backdoor set comprised of observable variables that yields non-parametric estimators of the interventional mean with the smallest asymptotic variance among those that are based on observable minimum cost backdoor sets. The cost of a backdoor set is defined as the sum of the costs of the variables that comprise it. The various optimal backdoor sets computed by this method are not only optimal under non-parametric graphical models and non-parametric estimators of interventional mean, but also under linear graphical models and OLS estimators, per results in Henckel, Perkovic and Maathuis (2020). :param costs: a list with non-negative costs associated with variables in the graph. Only used for estimatand_type='non-parametric-ate' and backdoor_adjustment='efficient-mincost-adjustment'. If not costs are provided by the user, and backdoor_adjustment='efficient-mincost-adjustment', costs are assumed to be equal to one for all variables in the graph. The structure of the list should be of the form [(node, {"cost": x}) for node in nodes]. :param conditional_node_names: variables that are used to determine treatment. If none are provided, it is assumed that the intervention sets the treatment to a constant. :returns: backdoor_sets, a list of dictionaries, with each dictionary having as values a backdoor set. """ if costs is None and backdoor_adjustment == "efficient-mincost-adjustment": logger.warning("No costs were passed, so they will be assumed to be constant and equal to 1.") efficient_bd = EfficientBackdoor( graph=graph, conditional_node_names=conditional_node_names, costs=costs, ) if backdoor_adjustment == BackdoorAdjustment.BACKDOOR_EFFICIENT: backdoor_set = efficient_bd.optimal_adj_set() backdoor_sets = [{"backdoor_set": tuple(backdoor_set)}] elif backdoor_adjustment == BackdoorAdjustment.BACKDOOR_MIN_EFFICIENT: backdoor_set = efficient_bd.optimal_minimal_adj_set() backdoor_sets = [{"backdoor_set": tuple(backdoor_set)}] elif backdoor_adjustment == BackdoorAdjustment.BACKDOOR_MINCOST_EFFICIENT: backdoor_set = efficient_bd.optimal_mincost_adj_set() backdoor_sets = [{"backdoor_set": tuple(backdoor_set)}] return backdoor_sets def find_valid_adjustment_sets( graph: CausalGraph, treatment_name: List, outcome_name: List, backdoor_paths: List, bdoor_graph: CausalGraph, dseparation_algo: str, backdoor_sets: List, filt_eligible_variables: List, backdoor_adjustment: BackdoorAdjustment, max_iterations: int, ): num_iterations = 0 found_valid_adjustment_set = False all_nodes_observed = graph.all_observed(graph.get_all_nodes()) # If `minimal-adjustment` method is specified, start the search from the set with minimum size. Otherwise, start from the largest. set_sizes = ( range(1, len(filt_eligible_variables) + 1, 1) if backdoor_adjustment == BackdoorAdjustment.BACKDOOR_MIN else range(len(filt_eligible_variables), 0, -1) ) for size_candidate_set in set_sizes: for candidate_set in itertools.combinations(filt_eligible_variables, size_candidate_set): check = graph.check_valid_backdoor_set( treatment_name, outcome_name, candidate_set, backdoor_paths=backdoor_paths, new_graph=bdoor_graph, dseparation_algo=dseparation_algo, ) logger.debug( "Candidate backdoor set: {0}, is_dseparated: {1}".format(candidate_set, check["is_dseparated"]) ) if check["is_dseparated"]: backdoor_sets.append({"backdoor_set": candidate_set}) found_valid_adjustment_set = True num_iterations += 1 if backdoor_adjustment == BackdoorAdjustment.BACKDOOR_EXHAUSTIVE and num_iterations > max_iterations: logger.warning(f"Max number of iterations {max_iterations} reached.") break # If the backdoor method is `maximal-adjustment` or `minimal-adjustment`, return the first found adjustment set. if ( backdoor_adjustment in { BackdoorAdjustment.BACKDOOR_DEFAULT, BackdoorAdjustment.BACKDOOR_MAX, BackdoorAdjustment.BACKDOOR_MIN, } and found_valid_adjustment_set ): break # If all variables are observed, and the biggest eligible set # does not satisfy backdoor, then none of its subsets will. if ( backdoor_adjustment in {BackdoorAdjustment.BACKDOOR_DEFAULT, BackdoorAdjustment.BACKDOOR_MAX} and all_nodes_observed ): break if num_iterations > max_iterations: logger.warning(f"Max number of iterations {max_iterations} reached. Could not find a valid backdoor set.") break return backdoor_sets, found_valid_adjustment_set def get_default_backdoor_set_id( graph: CausalGraph, treatment_name: List[str], outcome_name: List[str], backdoor_sets_dict: Dict ): # Adding a None estimand if no backdoor set found if len(backdoor_sets_dict) == 0: return None # Default set contains minimum possible number of instrumental variables, to prevent lowering variance in the treatment variable. instrument_names = set(graph.get_instruments(treatment_name, outcome_name)) iv_count_dict = { key: len(set(bdoor_set).intersection(instrument_names)) for key, bdoor_set in backdoor_sets_dict.items() } min_iv_count = min(iv_count_dict.values()) min_iv_keys = {key for key, iv_count in iv_count_dict.items() if iv_count == min_iv_count} min_iv_backdoor_sets_dict = {key: backdoor_sets_dict[key] for key in min_iv_keys} # Default set is the one with the least number of adjustment variables (optimizing for efficiency) min_set_length = 1000000 default_key = None for key, bdoor_set in min_iv_backdoor_sets_dict.items(): if len(bdoor_set) < min_set_length: min_set_length = len(bdoor_set) default_key = key return default_key def build_backdoor_estimands_dict( graph: CausalGraph, treatment_name: List[str], outcome_name: List[str], backdoor_sets: List[str], estimands_dict: Dict, ): """Build the final dict for backdoor sets by filtering unobserved variables if needed.""" backdoor_variables_dict = {} is_identified = [graph.all_observed(bset["backdoor_set"]) for bset in backdoor_sets] if any(is_identified): logger.info("Causal effect can be identified.") backdoor_sets_arr = [ list(bset["backdoor_set"]) for bset in backdoor_sets if graph.all_observed(bset["backdoor_set"]) ] else: # there is unobserved confounding logger.warning("Backdoor identification failed.") backdoor_sets_arr = [] for i in range(len(backdoor_sets_arr)): backdoor_estimand_expr = construct_backdoor_estimand(treatment_name, outcome_name, backdoor_sets_arr[i]) logger.debug("Identified expression = " + str(backdoor_estimand_expr)) estimands_dict["backdoor" + str(i + 1)] = backdoor_estimand_expr backdoor_variables_dict["backdoor" + str(i + 1)] = backdoor_sets_arr[i] return estimands_dict, backdoor_variables_dict def identify_frontdoor( graph: CausalGraph, treatment_name: List[str], outcome_name: List[str], dseparation_algo: str = "default" ): """Find a valid frontdoor variable if it exists. Currently only supports a single variable frontdoor set. """ frontdoor_var = None frontdoor_paths = None fdoor_graph = None if dseparation_algo == "default": cond1_graph = graph.do_surgery(treatment_name, remove_incoming_edges=True) bdoor_graph1 = graph.do_surgery(treatment_name, remove_outgoing_edges=True) elif dseparation_algo == "naive": frontdoor_paths = graph.get_all_directed_paths(treatment_name, outcome_name) else: raise ValueError(f"d-separation algorithm {dseparation_algo} is not supported") eligible_variables = ( graph.get_descendants(treatment_name) - set(outcome_name) - set(graph.get_descendants(outcome_name)) ) # For simplicity, assuming a one-variable frontdoor set for candidate_var in eligible_variables: # Cond 1: All directed paths intercepted by candidate_var cond1 = graph.check_valid_frontdoor_set( treatment_name, outcome_name, parse_state(candidate_var), frontdoor_paths=frontdoor_paths, new_graph=cond1_graph, dseparation_algo=dseparation_algo, ) logger.debug("Candidate frontdoor set: {0}, is_dseparated: {1}".format(candidate_var, cond1)) if not cond1: continue # Cond 2: No confounding between treatment and candidate var cond2 = graph.check_valid_backdoor_set( treatment_name, parse_state(candidate_var), set(), backdoor_paths=None, new_graph=bdoor_graph1, dseparation_algo=dseparation_algo, ) if not cond2: continue # Cond 3: treatment blocks all confounding between candidate_var and outcome bdoor_graph2 = graph.do_surgery(candidate_var, remove_outgoing_edges=True) cond3 = graph.check_valid_backdoor_set( parse_state(candidate_var), outcome_name, treatment_name, backdoor_paths=None, new_graph=bdoor_graph2, dseparation_algo=dseparation_algo, ) is_valid_frontdoor = cond1 and cond2 and cond3 if is_valid_frontdoor: frontdoor_var = candidate_var break return parse_state(frontdoor_var) def identify_mediation(graph: CausalGraph, treatment_name: List[str], outcome_name: List[str]): """Find a valid mediator if it exists. Currently only supports a single variable mediator set. """ mediation_var = None mediation_paths = graph.get_all_directed_paths(treatment_name, outcome_name) eligible_variables = graph.get_descendants(treatment_name) - set(outcome_name) # For simplicity, assuming a one-variable mediation set for candidate_var in eligible_variables: is_valid_mediation = graph.check_valid_mediation_set( treatment_name, outcome_name, parse_state(candidate_var), mediation_paths=mediation_paths, ) logger.debug("Candidate mediation set: {0}, on_mediating_path: {1}".format(candidate_var, is_valid_mediation)) if is_valid_mediation: mediation_var = candidate_var break return parse_state(mediation_var) def identify_mediation_first_stage_confounders( graph: CausalGraph, treatment_name: List[str], outcome_name: List[str], mediators_names: List[str], backdoor_adjustment: BackdoorAdjustment, ): # Create estimands dict as per the API for backdoor, but do not return it estimands_dict = {} backdoor_sets = identify_backdoor(graph, treatment_name, mediators_names, backdoor_adjustment) estimands_dict, backdoor_variables_dict = build_backdoor_estimands_dict( graph, treatment_name, mediators_names, backdoor_sets, estimands_dict, ) # Setting default "backdoor" identification adjustment set default_backdoor_id = get_default_backdoor_set_id(graph, treatment_name, outcome_name, backdoor_variables_dict) estimands_dict["backdoor"] = estimands_dict.get(str(default_backdoor_id), None) backdoor_variables_dict["backdoor"] = backdoor_variables_dict.get(str(default_backdoor_id), None) return backdoor_variables_dict def identify_mediation_second_stage_confounders( graph: CausalGraph, treatment_name: List[str], mediators_names: List[str], outcome_name: List[str], backdoor_adjustment: BackdoorAdjustment, ): # Create estimands dict as per the API for backdoor, but do not return it estimands_dict = {} backdoor_sets = identify_backdoor(graph, mediators_names, outcome_name, backdoor_adjustment) estimands_dict, backdoor_variables_dict = build_backdoor_estimands_dict( graph, mediators_names, outcome_name, backdoor_sets, estimands_dict, ) # Setting default "backdoor" identification adjustment set default_backdoor_id = get_default_backdoor_set_id(graph, treatment_name, outcome_name, backdoor_variables_dict) estimands_dict["backdoor"] = estimands_dict.get(str(default_backdoor_id), None) backdoor_variables_dict["backdoor"] = backdoor_variables_dict.get(str(default_backdoor_id), None) return backdoor_variables_dict def construct_backdoor_estimand(treatment_name: List[str], outcome_name: List[str], common_causes: List[str]): # TODO: outputs string for now, but ideally should do symbolic # expressions Mon 19 Feb 2018 04:54:17 PM DST # TODO Better support for multivariate treatments expr = None outcome_name = outcome_name[0] num_expr_str = outcome_name if len(common_causes) > 0: num_expr_str += "|" + ",".join(common_causes) expr = "d(" + num_expr_str + ")/d" + ",".join(treatment_name) sym_mu = sp.Symbol("mu") sym_sigma = sp.Symbol("sigma", positive=True) sym_outcome = spstats.Normal(num_expr_str, sym_mu, sym_sigma) sym_treatment_symbols = [sp.Symbol(t) for t in treatment_name] sym_treatment = sp.Array(sym_treatment_symbols) sym_conditional_outcome = spstats.Expectation(sym_outcome) sym_effect = sp.Derivative(sym_conditional_outcome, sym_treatment) sym_assumptions = { "Unconfoundedness": ( "If U\N{RIGHTWARDS ARROW}{{{0}}} and U\N{RIGHTWARDS ARROW}{1}" " then P({1}|{0},{2},U) = P({1}|{0},{2})" ).format(",".join(treatment_name), outcome_name, ",".join(common_causes)) } estimand = {"estimand": sym_effect, "assumptions": sym_assumptions} return estimand def construct_iv_estimand(treatment_name: List[str], outcome_name: List[str], instrument_names: List[str]): # TODO: support multivariate treatments better. expr = None outcome_name = outcome_name[0] sym_outcome = spstats.Normal(outcome_name, 0, 1) sym_treatment_symbols = [spstats.Normal(t, 0, 1) for t in treatment_name] sym_treatment = sp.Array(sym_treatment_symbols) sym_instrument_symbols = [sp.Symbol(inst) for inst in instrument_names] sym_instrument = sp.Array(sym_instrument_symbols) # ",".join(instrument_names)) sym_outcome_derivative = sp.Derivative(sym_outcome, sym_instrument) sym_treatment_derivative = sp.Derivative(sym_treatment, sym_instrument) sym_effect = spstats.Expectation(sym_outcome_derivative / sym_treatment_derivative) sym_assumptions = { "As-if-random": ( "If U\N{RIGHTWARDS ARROW}\N{RIGHTWARDS ARROW}{0} then " "\N{NOT SIGN}(U \N{RIGHTWARDS ARROW}\N{RIGHTWARDS ARROW}{{{1}}})" ).format(outcome_name, ",".join(instrument_names)), "Exclusion": ( "If we remove {{{0}}}\N{RIGHTWARDS ARROW}{{{1}}}, then " "\N{NOT SIGN}({{{0}}}\N{RIGHTWARDS ARROW}{2})" ).format(",".join(instrument_names), ",".join(treatment_name), outcome_name), } estimand = {"estimand": sym_effect, "assumptions": sym_assumptions} return estimand def construct_frontdoor_estimand( treatment_name: List[str], outcome_name: List[str], frontdoor_variables_names: List[str] ): # TODO: support multivariate treatments better. expr = None outcome_name = outcome_name[0] sym_outcome = spstats.Normal(outcome_name, 0, 1) sym_treatment_symbols = [spstats.Normal(t, 0, 1) for t in treatment_name] sym_treatment = sp.Array(sym_treatment_symbols) sym_frontdoor_symbols = [sp.Symbol(inst) for inst in frontdoor_variables_names] sym_frontdoor = sp.Array(sym_frontdoor_symbols) # ",".join(instrument_names)) sym_outcome_derivative = sp.Derivative(sym_outcome, sym_frontdoor) sym_treatment_derivative = sp.Derivative(sym_frontdoor, sym_treatment) sym_effect = spstats.Expectation(sym_treatment_derivative * sym_outcome_derivative) sym_assumptions = { "Full-mediation": ("{2} intercepts (blocks) all directed paths from {0} to {1}.").format( ",".join(treatment_name), ",".join(outcome_name), ",".join(frontdoor_variables_names), ), "First-stage-unconfoundedness": ( "If U\N{RIGHTWARDS ARROW}{{{0}}} and U\N{RIGHTWARDS ARROW}{{{1}}}" " then P({1}|{0},U) = P({1}|{0})" ).format(",".join(treatment_name), ",".join(frontdoor_variables_names)), "Second-stage-unconfoundedness": ( "If U\N{RIGHTWARDS ARROW}{{{2}}} and U\N{RIGHTWARDS ARROW}{1}" " then P({1}|{2}, {0}, U) = P({1}|{2}, {0})" ).format( ",".join(treatment_name), outcome_name, ",".join(frontdoor_variables_names), ), } estimand = {"estimand": sym_effect, "assumptions": sym_assumptions} return estimand def construct_mediation_estimand( estimand_type: EstimandType, treatment_name: List[str], outcome_name: List[str], mediators_names: List[str] ): # TODO: support multivariate treatments better. expr = None if estimand_type in ( EstimandType.NONPARAMETRIC_NDE, EstimandType.NONPARAMETRIC_NIE, ): outcome_name = outcome_name[0] sym_outcome = spstats.Normal(outcome_name, 0, 1) sym_treatment_symbols = [spstats.Normal(t, 0, 1) for t in treatment_name] sym_treatment = sp.Array(sym_treatment_symbols) sym_mediators_symbols = [sp.Symbol(inst) for inst in mediators_names] sym_mediators = sp.Array(sym_mediators_symbols) sym_outcome_derivative = sp.Derivative(sym_outcome, sym_mediators) sym_treatment_derivative = sp.Derivative(sym_mediators, sym_treatment) # For direct effect num_expr_str = outcome_name if len(mediators_names) > 0: num_expr_str += "|" + ",".join(mediators_names) sym_mu = sp.Symbol("mu") sym_sigma = sp.Symbol("sigma", positive=True) sym_conditional_outcome = spstats.Normal(num_expr_str, sym_mu, sym_sigma) sym_directeffect_derivative = sp.Derivative(sym_conditional_outcome, sym_treatment) if estimand_type == EstimandType.NONPARAMETRIC_NIE: sym_effect = spstats.Expectation(sym_treatment_derivative * sym_outcome_derivative) elif estimand_type == EstimandType.NONPARAMETRIC_NDE: sym_effect = spstats.Expectation(sym_directeffect_derivative) sym_assumptions = { "Mediation": ( "{2} intercepts (blocks) all directed paths from {0} to {1} except the path {{{0}}}\N{RIGHTWARDS ARROW}{{{1}}}." ).format( ",".join(treatment_name), ",".join(outcome_name), ",".join(mediators_names), ), "First-stage-unconfoundedness": ( "If U\N{RIGHTWARDS ARROW}{{{0}}} and U\N{RIGHTWARDS ARROW}{{{1}}}" " then P({1}|{0},U) = P({1}|{0})" ).format(",".join(treatment_name), ",".join(mediators_names)), "Second-stage-unconfoundedness": ( "If U\N{RIGHTWARDS ARROW}{{{2}}} and U\N{RIGHTWARDS ARROW}{1}" " then P({1}|{2}, {0}, U) = P({1}|{2}, {0})" ).format(",".join(treatment_name), outcome_name, ",".join(mediators_names)), } else: raise ValueError( "Estimand type not supported. Supported estimand types are {0} or {1}'.".format( EstimandType.NONPARAMETRIC_NDE, EstimandType.NONPARAMETRIC_NIE, ) ) estimand = {"estimand": sym_effect, "assumptions": sym_assumptions} return estimand

import itertools import logging from enum import Enum from typing import Dict, List, Optional, Union import networkx as nx import sympy as sp import sympy.stats as spstats from dowhy.causal_identifier.efficient_backdoor import EfficientBackdoor from dowhy.causal_identifier.identified_estimand import IdentifiedEstimand from dowhy.graph import ( check_dseparation, check_valid_backdoor_set, check_valid_frontdoor_set, check_valid_mediation_set, do_surgery, get_all_directed_paths, get_backdoor_paths, get_descendants, get_instruments, has_directed_path, ) from dowhy.utils.api import parse_state logger = logging.getLogger(__name__) class EstimandType(Enum): # Average total effect NONPARAMETRIC_ATE = "nonparametric-ate" # Natural direct effect NONPARAMETRIC_NDE = "nonparametric-nde" # Natural indirect effect NONPARAMETRIC_NIE = "nonparametric-nie" # Controlled direct effect NONPARAMETRIC_CDE = "nonparametric-cde" class BackdoorAdjustment(Enum): # Backdoor method names BACKDOOR_DEFAULT = "default" BACKDOOR_EXHAUSTIVE = "exhaustive-search" BACKDOOR_MIN = "minimal-adjustment" BACKDOOR_MAX = "maximal-adjustment" BACKDOOR_EFFICIENT = "efficient-adjustment" BACKDOOR_MIN_EFFICIENT = "efficient-minimal-adjustment" BACKDOOR_MINCOST_EFFICIENT = "efficient-mincost-adjustment" MAX_BACKDOOR_ITERATIONS = 100000 METHOD_NAMES = { BackdoorAdjustment.BACKDOOR_DEFAULT, BackdoorAdjustment.BACKDOOR_EXHAUSTIVE, BackdoorAdjustment.BACKDOOR_MIN, BackdoorAdjustment.BACKDOOR_MAX, BackdoorAdjustment.BACKDOOR_EFFICIENT, BackdoorAdjustment.BACKDOOR_MIN_EFFICIENT, BackdoorAdjustment.BACKDOOR_MINCOST_EFFICIENT, } EFFICIENT_METHODS = { BackdoorAdjustment.BACKDOOR_EFFICIENT, BackdoorAdjustment.BACKDOOR_MIN_EFFICIENT, BackdoorAdjustment.BACKDOOR_MINCOST_EFFICIENT, } DEFAULT_BACKDOOR_METHOD = BackdoorAdjustment.BACKDOOR_DEFAULT class AutoIdentifier: """Class that implements different identification methods. Currently supports backdoor and instrumental variable identification methods. The identification is based on the causal graph provided. This class is for backwards compatibility with CausalModel Will be deprecated in the future in favor of function call auto_identify_effect() """ def __init__( self, estimand_type: EstimandType, backdoor_adjustment: BackdoorAdjustment = BackdoorAdjustment.BACKDOOR_DEFAULT, optimize_backdoor: bool = False, costs: Optional[List] = None, ): self.estimand_type = estimand_type self.backdoor_adjustment = backdoor_adjustment self.optimize_backdoor = optimize_backdoor self.costs = costs self.logger = logging.getLogger(__name__) def identify_effect( self, graph: nx.DiGraph, action_nodes: Union[str, List[str]], outcome_nodes: Union[str, List[str]], observed_nodes: Union[str, List[str]], conditional_node_names: List[str] = None, ): estimand = identify_effect_auto( graph, action_nodes, outcome_nodes, observed_nodes, self.estimand_type, conditional_node_names, self.backdoor_adjustment, self.optimize_backdoor, self.costs, ) estimand.identifier = self return estimand def identify_backdoor( self, graph: nx.DiGraph, action_nodes: List[str], outcome_nodes: List[str], observed_nodes: List[str], include_unobserved: bool = False, dseparation_algo: str = "default", direct_effect: bool = False, ): return identify_backdoor( graph, action_nodes, outcome_nodes, observed_nodes, self.backdoor_adjustment, include_unobserved, dseparation_algo, direct_effect, ) def identify_effect_auto( graph: nx.DiGraph, action_nodes: Union[str, List[str]], outcome_nodes: Union[str, List[str]], observed_nodes: Union[str, List[str]], estimand_type: EstimandType, conditional_node_names: List[str] = None, backdoor_adjustment: BackdoorAdjustment = BackdoorAdjustment.BACKDOOR_DEFAULT, optimize_backdoor: bool = False, costs: Optional[List] = None, ) -> IdentifiedEstimand: """Main method that returns an identified estimand (if one exists). If estimand_type is non-parametric ATE, then uses backdoor, instrumental variable and frontdoor identification methods, to check if an identified estimand exists, based on the causal graph. :param optimize_backdoor: if True, uses an optimised algorithm to compute the backdoor sets :param costs: non-negative costs associated with variables in the graph. Only used for estimand_type='non-parametric-ate' and backdoor_adjustment='efficient-mincost-adjustment'. If no costs are provided by the user, and backdoor_adjustment='efficient-mincost-adjustment', costs are assumed to be equal to one for all variables in the graph. :param conditional_node_names: variables that are used to determine treatment. If none are provided, it is assumed that the intervention is static. :returns: target estimand, an instance of the IdentifiedEstimand class """ observed_nodes = parse_state(observed_nodes) action_nodes = parse_state(action_nodes) outcome_nodes = parse_state(outcome_nodes) # First, check if there is a directed path from action to outcome if not has_directed_path(graph, action_nodes, outcome_nodes): logger.warn("No directed path from treatment to outcome. Causal Effect is zero.") return IdentifiedEstimand( None, treatment_variable=action_nodes, outcome_variable=outcome_nodes, no_directed_path=True, ) if estimand_type == EstimandType.NONPARAMETRIC_ATE: return identify_ate_effect( graph, action_nodes, outcome_nodes, observed_nodes, backdoor_adjustment, optimize_backdoor, estimand_type, costs, conditional_node_names, ) elif estimand_type == EstimandType.NONPARAMETRIC_NDE: return identify_nde_effect( graph, action_nodes, outcome_nodes, observed_nodes, backdoor_adjustment, estimand_type ) elif estimand_type == EstimandType.NONPARAMETRIC_NIE: return identify_nie_effect( graph, action_nodes, outcome_nodes, observed_nodes, backdoor_adjustment, estimand_type ) elif estimand_type == EstimandType.NONPARAMETRIC_CDE: return identify_cde_effect( graph, action_nodes, outcome_nodes, observed_nodes, backdoor_adjustment, estimand_type ) else: raise ValueError( "Estimand type is not supported. Use either {0}, {1}, or {2}.".format( EstimandType.NONPARAMETRIC_ATE, EstimandType.NONPARAMETRIC_CDE, EstimandType.NONPARAMETRIC_NDE, EstimandType.NONPARAMETRIC_NIE, ) ) def identify_ate_effect( graph: nx.DiGraph, action_nodes: List[str], outcome_nodes: List[str], observed_nodes: List[str], backdoor_adjustment: BackdoorAdjustment, optimize_backdoor: bool, estimand_type: EstimandType, costs: List, conditional_node_names: List[str] = None, ): estimands_dict = {} mediation_first_stage_confounders = None mediation_second_stage_confounders = None ### 1. BACKDOOR IDENTIFICATION # Pick algorithm to compute backdoor sets according to method chosen if backdoor_adjustment not in EFFICIENT_METHODS: # First, checking if there are any valid backdoor adjustment sets if optimize_backdoor == False: backdoor_sets = identify_backdoor(graph, action_nodes, outcome_nodes, observed_nodes, backdoor_adjustment) else: from dowhy.causal_identifier.backdoor import Backdoor path = Backdoor(graph, action_nodes, outcome_nodes) backdoor_sets = path.get_backdoor_vars() elif backdoor_adjustment in EFFICIENT_METHODS: backdoor_sets = identify_efficient_backdoor( graph, action_nodes, outcome_nodes, observed_nodes, backdoor_adjustment, costs, conditional_node_names=conditional_node_names, ) estimands_dict, backdoor_variables_dict = build_backdoor_estimands_dict( action_nodes, outcome_nodes, observed_nodes, backdoor_sets, estimands_dict ) # Setting default "backdoor" identification adjustment set default_backdoor_id = get_default_backdoor_set_id(graph, action_nodes, outcome_nodes, backdoor_variables_dict) if len(backdoor_variables_dict) > 0: estimands_dict["backdoor"] = estimands_dict.get(str(default_backdoor_id), None) backdoor_variables_dict["backdoor"] = backdoor_variables_dict.get(str(default_backdoor_id), None) else: estimands_dict["backdoor"] = None ### 2. INSTRUMENTAL VARIABLE IDENTIFICATION # Now checking if there is also a valid iv estimand instrument_names = get_instruments(graph, action_nodes, outcome_nodes) logger.info("Instrumental variables for treatment and outcome:" + str(instrument_names)) if len(instrument_names) > 0: iv_estimand_expr = construct_iv_estimand( action_nodes, outcome_nodes, instrument_names, ) logger.debug("Identified expression = " + str(iv_estimand_expr)) estimands_dict["iv"] = iv_estimand_expr else: estimands_dict["iv"] = None ### 3. FRONTDOOR IDENTIFICATION # Now checking if there is a valid frontdoor variable frontdoor_variables_names = identify_frontdoor(graph, action_nodes, outcome_nodes) logger.info("Frontdoor variables for treatment and outcome:" + str(frontdoor_variables_names)) if len(frontdoor_variables_names) > 0: frontdoor_estimand_expr = construct_frontdoor_estimand( action_nodes, outcome_nodes, frontdoor_variables_names, ) logger.debug("Identified expression = " + str(frontdoor_estimand_expr)) estimands_dict["frontdoor"] = frontdoor_estimand_expr mediation_first_stage_confounders = identify_mediation_first_stage_confounders( graph, action_nodes, outcome_nodes, frontdoor_variables_names, observed_nodes, backdoor_adjustment ) mediation_second_stage_confounders = identify_mediation_second_stage_confounders( graph, action_nodes, frontdoor_variables_names, outcome_nodes, observed_nodes, backdoor_adjustment ) else: estimands_dict["frontdoor"] = None # Finally returning the estimand object estimand = IdentifiedEstimand( None, treatment_variable=action_nodes, outcome_variable=outcome_nodes, estimand_type=estimand_type, estimands=estimands_dict, backdoor_variables=backdoor_variables_dict, instrumental_variables=instrument_names, frontdoor_variables=frontdoor_variables_names, mediation_first_stage_confounders=mediation_first_stage_confounders, mediation_second_stage_confounders=mediation_second_stage_confounders, default_backdoor_id=default_backdoor_id, ) return estimand def identify_cde_effect( graph: nx.DiGraph, action_nodes: List[str], outcome_nodes: List[str], observed_nodes: List[str], backdoor_adjustment: BackdoorAdjustment, estimand_type: EstimandType, ): """Identify controlled direct effect. For a definition, see Vanderwheele (2011). Controlled direct and mediated effects: definition, identification and bounds. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4193506/ Using do-calculus rules, identification yields a adjustment set. It is based on the principle that under a graph where the direct edge from treatment to outcome is removed, conditioning on the adjustment set should d-separate treatment and outcome. """ estimands_dict = {} # Pick algorithm to compute backdoor sets according to method chosen backdoor_sets = identify_backdoor( graph, action_nodes, outcome_nodes, observed_nodes, backdoor_adjustment, direct_effect=True ) estimands_dict, backdoor_variables_dict = build_backdoor_estimands_dict( action_nodes, outcome_nodes, observed_nodes, backdoor_sets, estimands_dict ) # Setting default "backdoor" identification adjustment set default_backdoor_id = get_default_backdoor_set_id(graph, action_nodes, outcome_nodes, backdoor_variables_dict) if len(backdoor_variables_dict) > 0: estimands_dict["backdoor"] = estimands_dict.get(str(default_backdoor_id), None) backdoor_variables_dict["backdoor"] = backdoor_variables_dict.get(str(default_backdoor_id), None) else: estimands_dict["backdoor"] = None # Finally returning the estimand object estimand = IdentifiedEstimand( None, treatment_variable=action_nodes, outcome_variable=outcome_nodes, estimand_type=estimand_type, estimands=estimands_dict, backdoor_variables=backdoor_variables_dict, instrumental_variables=None, frontdoor_variables=None, mediation_first_stage_confounders=None, mediation_second_stage_confounders=None, default_backdoor_id=default_backdoor_id, ) return estimand def identify_nie_effect( graph: nx.DiGraph, action_nodes: List[str], outcome_nodes: List[str], observed_nodes: List[str], backdoor_adjustment: BackdoorAdjustment, estimand_type: EstimandType, ): estimands_dict = {} ### 1. FIRST DOING BACKDOOR IDENTIFICATION # First, checking if there are any valid backdoor adjustment sets backdoor_sets = identify_backdoor(graph, action_nodes, outcome_nodes, observed_nodes, backdoor_adjustment) estimands_dict, backdoor_variables_dict = build_backdoor_estimands_dict( action_nodes, outcome_nodes, observed_nodes, backdoor_sets, estimands_dict ) # Setting default "backdoor" identification adjustment set default_backdoor_id = get_default_backdoor_set_id(graph, action_nodes, outcome_nodes, backdoor_variables_dict) backdoor_variables_dict["backdoor"] = backdoor_variables_dict.get(str(default_backdoor_id), None) ### 2. SECOND, CHECKING FOR MEDIATORS # Now checking if there are valid mediator variables estimands_dict = {} # Need to reinitialize this dictionary to avoid including the backdoor sets mediation_first_stage_confounders = None mediation_second_stage_confounders = None mediators_names = identify_mediation(graph, action_nodes, outcome_nodes) logger.info("Mediators for treatment and outcome:" + str(mediators_names)) if len(mediators_names) > 0: mediation_estimand_expr = construct_mediation_estimand( estimand_type, action_nodes, outcome_nodes, mediators_names, ) logger.debug("Identified expression = " + str(mediation_estimand_expr)) estimands_dict["mediation"] = mediation_estimand_expr mediation_first_stage_confounders = identify_mediation_first_stage_confounders( graph, action_nodes, outcome_nodes, mediators_names, observed_nodes, backdoor_adjustment ) mediation_second_stage_confounders = identify_mediation_second_stage_confounders( graph, action_nodes, mediators_names, outcome_nodes, observed_nodes, backdoor_adjustment ) else: estimands_dict["mediation"] = None # Finally returning the estimand object estimand = IdentifiedEstimand( None, treatment_variable=action_nodes, outcome_variable=outcome_nodes, estimand_type=estimand_type, estimands=estimands_dict, backdoor_variables=backdoor_variables_dict, instrumental_variables=None, frontdoor_variables=None, mediator_variables=mediators_names, mediation_first_stage_confounders=mediation_first_stage_confounders, mediation_second_stage_confounders=mediation_second_stage_confounders, default_backdoor_id=None, ) return estimand def identify_nde_effect( graph: nx.DiGraph, action_nodes: List[str], outcome_nodes: List[str], observed_nodes: List[str], backdoor_adjustment: BackdoorAdjustment, estimand_type: EstimandType, ): estimands_dict = {} ### 1. FIRST DOING BACKDOOR IDENTIFICATION # First, checking if there are any valid backdoor adjustment sets backdoor_sets = identify_backdoor(graph, action_nodes, outcome_nodes, observed_nodes, backdoor_adjustment) estimands_dict, backdoor_variables_dict = build_backdoor_estimands_dict( action_nodes, outcome_nodes, observed_nodes, backdoor_sets, estimands_dict ) # Setting default "backdoor" identification adjustment set default_backdoor_id = get_default_backdoor_set_id(graph, action_nodes, outcome_nodes, backdoor_variables_dict) backdoor_variables_dict["backdoor"] = backdoor_variables_dict.get(str(default_backdoor_id), None) ### 2. SECOND, CHECKING FOR MEDIATORS # Now checking if there are valid mediator variables estimands_dict = {} mediation_first_stage_confounders = None mediation_second_stage_confounders = None mediators_names = identify_mediation(graph, action_nodes, outcome_nodes) logger.info("Mediators for treatment and outcome:" + str(mediators_names)) if len(mediators_names) > 0: mediation_estimand_expr = construct_mediation_estimand( estimand_type, action_nodes, outcome_nodes, mediators_names, ) logger.debug("Identified expression = " + str(mediation_estimand_expr)) estimands_dict["mediation"] = mediation_estimand_expr mediation_first_stage_confounders = identify_mediation_first_stage_confounders( graph, action_nodes, outcome_nodes, mediators_names, observed_nodes, backdoor_adjustment ) mediation_second_stage_confounders = identify_mediation_second_stage_confounders( graph, action_nodes, mediators_names, outcome_nodes, observed_nodes, backdoor_adjustment ) else: estimands_dict["mediation"] = None # Finally returning the estimand object estimand = IdentifiedEstimand( None, treatment_variable=action_nodes, outcome_variable=outcome_nodes, estimand_type=estimand_type, estimands=estimands_dict, backdoor_variables=backdoor_variables_dict, instrumental_variables=None, frontdoor_variables=None, mediator_variables=mediators_names, mediation_first_stage_confounders=mediation_first_stage_confounders, mediation_second_stage_confounders=mediation_second_stage_confounders, default_backdoor_id=None, ) return estimand def identify_backdoor( graph: nx.DiGraph, action_nodes: List[str], outcome_nodes: List[str], observed_nodes: List[str], backdoor_adjustment: BackdoorAdjustment, include_unobserved: bool = False, dseparation_algo: str = "default", direct_effect: bool = False, ): backdoor_sets = [] backdoor_paths = None bdoor_graph = None observed_nodes = set(observed_nodes) if dseparation_algo == "naive": backdoor_paths = get_backdoor_paths(graph, action_nodes, outcome_nodes) elif dseparation_algo == "default": bdoor_graph = do_surgery( graph, action_nodes, target_node_names=outcome_nodes, remove_outgoing_edges=True, remove_only_direct_edges_to_target=direct_effect, ) else: raise ValueError(f"d-separation algorithm {dseparation_algo} is not supported") backdoor_adjustment = ( backdoor_adjustment if backdoor_adjustment != BackdoorAdjustment.BACKDOOR_DEFAULT else DEFAULT_BACKDOOR_METHOD ) # First, checking if empty set is a valid backdoor set empty_set = set() check = check_valid_backdoor_set( graph, action_nodes, outcome_nodes, empty_set, backdoor_paths=backdoor_paths, new_graph=bdoor_graph, dseparation_algo=dseparation_algo, ) if check["is_dseparated"]: backdoor_sets.append({"backdoor_set": empty_set}) # If the method is `minimal-adjustment`, return the empty set right away. if backdoor_adjustment == BackdoorAdjustment.BACKDOOR_MIN: return backdoor_sets # Second, checking for all other sets of variables. If include_unobserved is false, then only observed variables are eligible. eligible_variables = ( set([node for node in graph.nodes if include_unobserved or node in observed_nodes]) - set(action_nodes) - set(outcome_nodes) ) if direct_effect: # only remove descendants of Y # also allow any causes of Y that are not caused by T (for lower variance) eligible_variables -= get_descendants(graph, outcome_nodes) else: # remove descendants of T (mediators) and descendants of Y eligible_variables -= get_descendants(graph, action_nodes) # If var is d-separated from both treatment or outcome, it cannot # be a part of the backdoor set filt_eligible_variables = set() for var in eligible_variables: dsep_treat_var = check_dseparation(graph, action_nodes, parse_state(var), set()) dsep_outcome_var = check_dseparation(graph, outcome_nodes, parse_state(var), set()) if not dsep_outcome_var or not dsep_treat_var: filt_eligible_variables.add(var) if backdoor_adjustment in METHOD_NAMES: backdoor_sets, found_valid_adjustment_set = find_valid_adjustment_sets( graph, action_nodes, outcome_nodes, observed_nodes, backdoor_paths, bdoor_graph, dseparation_algo, backdoor_sets, filt_eligible_variables, backdoor_adjustment=backdoor_adjustment, max_iterations=MAX_BACKDOOR_ITERATIONS, ) if backdoor_adjustment == BackdoorAdjustment.BACKDOOR_DEFAULT and found_valid_adjustment_set: # repeat the above search with BACKDOOR_MIN backdoor_sets, _ = find_valid_adjustment_sets( graph, action_nodes, outcome_nodes, observed_nodes, backdoor_paths, bdoor_graph, dseparation_algo, backdoor_sets, filt_eligible_variables, backdoor_adjustment=BackdoorAdjustment.BACKDOOR_MIN, max_iterations=MAX_BACKDOOR_ITERATIONS, ) else: raise ValueError( f"Identifier method {backdoor_adjustment} not supported. Try one of the following: {METHOD_NAMES}" ) return backdoor_sets def identify_efficient_backdoor( graph: nx.DiGraph, action_nodes: List[str], outcome_nodes: List[str], observed_nodes: List[str], backdoor_adjustment: BackdoorAdjustment, costs: List, conditional_node_names: List[str] = None, ): """Method implementing algorithms to compute efficient backdoor sets, as described in Rotnitzky and Smucler (2020), Smucler, Sapienza and Rotnitzky (2021) and Smucler and Rotnitzky (2022). For backdoor_adjustment='efficient-adjustment', computes an optimal backdoor set, that is, a backdoor set comprised of observable variables that yields non-parametric estimators of the interventional mean with the smallest asymptotic variance among those that are based on observable backdoor sets. This optimal backdoor set always exists when no variables are latent, and the algorithm is guaranteed to compute it in this case. Under a non-parametric graphical model with latent variables, such a backdoor set can fail to exist. When certain sufficient conditions under which it is known that such a backdoor set exists are not satisfied, an error is raised. For backdoor_adjustment='efficient-minimal-adjustment', computes an optimal minimal backdoor set, that is, a minimal backdoor set comprised of observable variables that yields non-parametric estimators of the interventional mean with the smallest asymptotic variance among those that are based on observable minimal backdoor sets. For backdoor_adjustment='efficient-mincost-adjustment', computes an optimal minimum cost backdoor set, that is, a minimum cost backdoor set comprised of observable variables that yields non-parametric estimators of the interventional mean with the smallest asymptotic variance among those that are based on observable minimum cost backdoor sets. The cost of a backdoor set is defined as the sum of the costs of the variables that comprise it. The various optimal backdoor sets computed by this method are not only optimal under non-parametric graphical models and non-parametric estimators of interventional mean, but also under linear graphical models and OLS estimators, per results in Henckel, Perkovic and Maathuis (2020). :param costs: a list with non-negative costs associated with variables in the graph. Only used for estimatand_type='non-parametric-ate' and backdoor_adjustment='efficient-mincost-adjustment'. If not costs are provided by the user, and backdoor_adjustment='efficient-mincost-adjustment', costs are assumed to be equal to one for all variables in the graph. The structure of the list should be of the form [(node, {"cost": x}) for node in nodes]. :param conditional_node_names: variables that are used to determine treatment. If none are provided, it is assumed that the intervention sets the treatment to a constant. :returns: backdoor_sets, a list of dictionaries, with each dictionary having as values a backdoor set. """ if costs is None and backdoor_adjustment == "efficient-mincost-adjustment": logger.warning("No costs were passed, so they will be assumed to be constant and equal to 1.") efficient_bd = EfficientBackdoor( graph=graph, action_nodes=action_nodes, outcome_nodes=outcome_nodes, observed_nodes=observed_nodes, conditional_node_names=conditional_node_names, costs=costs, ) if backdoor_adjustment == BackdoorAdjustment.BACKDOOR_EFFICIENT: backdoor_set = efficient_bd.optimal_adj_set() backdoor_sets = [{"backdoor_set": tuple(backdoor_set)}] elif backdoor_adjustment == BackdoorAdjustment.BACKDOOR_MIN_EFFICIENT: backdoor_set = efficient_bd.optimal_minimal_adj_set() backdoor_sets = [{"backdoor_set": tuple(backdoor_set)}] elif backdoor_adjustment == BackdoorAdjustment.BACKDOOR_MINCOST_EFFICIENT: backdoor_set = efficient_bd.optimal_mincost_adj_set() backdoor_sets = [{"backdoor_set": tuple(backdoor_set)}] return backdoor_sets def find_valid_adjustment_sets( graph: nx.DiGraph, action_nodes: List[str], outcome_nodes: List[str], observed_nodes: List[str], backdoor_paths: List, bdoor_graph: nx.DiGraph, dseparation_algo: str, backdoor_sets: List, filt_eligible_variables: List, backdoor_adjustment: BackdoorAdjustment, max_iterations: int, ): num_iterations = 0 found_valid_adjustment_set = False is_all_observed = set(graph.nodes) == set(observed_nodes) # If `minimal-adjustment` method is specified, start the search from the set with minimum size. Otherwise, start from the largest. set_sizes = ( range(1, len(filt_eligible_variables) + 1, 1) if backdoor_adjustment == BackdoorAdjustment.BACKDOOR_MIN else range(len(filt_eligible_variables), 0, -1) ) for size_candidate_set in set_sizes: for candidate_set in itertools.combinations(filt_eligible_variables, size_candidate_set): check = check_valid_backdoor_set( graph, action_nodes, outcome_nodes, candidate_set, backdoor_paths=backdoor_paths, new_graph=bdoor_graph, dseparation_algo=dseparation_algo, ) logger.debug( "Candidate backdoor set: {0}, is_dseparated: {1}".format(candidate_set, check["is_dseparated"]) ) if check["is_dseparated"]: backdoor_sets.append({"backdoor_set": candidate_set}) found_valid_adjustment_set = True num_iterations += 1 if backdoor_adjustment == BackdoorAdjustment.BACKDOOR_EXHAUSTIVE and num_iterations > max_iterations: logger.warning(f"Max number of iterations {max_iterations} reached.") break # If the backdoor method is `maximal-adjustment` or `minimal-adjustment`, return the first found adjustment set. if ( backdoor_adjustment in { BackdoorAdjustment.BACKDOOR_DEFAULT, BackdoorAdjustment.BACKDOOR_MAX, BackdoorAdjustment.BACKDOOR_MIN, } and found_valid_adjustment_set ): break # If all variables are observed, and the biggest eligible set # does not satisfy backdoor, then none of its subsets will. if ( backdoor_adjustment in {BackdoorAdjustment.BACKDOOR_DEFAULT, BackdoorAdjustment.BACKDOOR_MAX} and is_all_observed ): break if num_iterations > max_iterations: logger.warning(f"Max number of iterations {max_iterations} reached. Could not find a valid backdoor set.") break return backdoor_sets, found_valid_adjustment_set def get_default_backdoor_set_id( graph: nx.DiGraph, action_nodes: List[str], outcome_nodes: List[str], backdoor_sets_dict: Dict ): # Adding a None estimand if no backdoor set found if len(backdoor_sets_dict) == 0: return None # Default set contains minimum possible number of instrumental variables, to prevent lowering variance in the treatment variable. instrument_names = set(get_instruments(graph, action_nodes, outcome_nodes)) iv_count_dict = { key: len(set(bdoor_set).intersection(instrument_names)) for key, bdoor_set in backdoor_sets_dict.items() } min_iv_count = min(iv_count_dict.values()) min_iv_keys = {key for key, iv_count in iv_count_dict.items() if iv_count == min_iv_count} min_iv_backdoor_sets_dict = {key: backdoor_sets_dict[key] for key in min_iv_keys} # Default set is the one with the least number of adjustment variables (optimizing for efficiency) min_set_length = 1000000 default_key = None for key, bdoor_set in min_iv_backdoor_sets_dict.items(): if len(bdoor_set) < min_set_length: min_set_length = len(bdoor_set) default_key = key return default_key def build_backdoor_estimands_dict( treatment_names: List[str], outcome_names: List[str], observed_nodes: List[str], backdoor_sets: List[str], estimands_dict: Dict, ): """Build the final dict for backdoor sets by filtering unobserved variables if needed.""" backdoor_variables_dict = {} observed_nodes = set(observed_nodes) is_identified = [set(bset["backdoor_set"]).issubset(observed_nodes) for bset in backdoor_sets] if any(is_identified): logger.info("Causal effect can be identified.") backdoor_sets_arr = [ list(bset["backdoor_set"]) for bset in backdoor_sets if set(bset["backdoor_set"]).issubset(observed_nodes) ] else: # there is unobserved confounding logger.warning("Backdoor identification failed.") backdoor_sets_arr = [] for i in range(len(backdoor_sets_arr)): backdoor_estimand_expr = construct_backdoor_estimand(treatment_names, outcome_names, backdoor_sets_arr[i]) logger.debug("Identified expression = " + str(backdoor_estimand_expr)) estimands_dict["backdoor" + str(i + 1)] = backdoor_estimand_expr backdoor_variables_dict["backdoor" + str(i + 1)] = backdoor_sets_arr[i] return estimands_dict, backdoor_variables_dict def identify_frontdoor( graph: nx.DiGraph, action_nodes: List[str], outcome_nodes: List[str], dseparation_algo: str = "default" ): """Find a valid frontdoor variable if it exists. Currently only supports a single variable frontdoor set. """ frontdoor_var = None frontdoor_paths = None fdoor_graph = None if dseparation_algo == "default": cond1_graph = do_surgery(graph, action_nodes, remove_incoming_edges=True) bdoor_graph1 = do_surgery(graph, action_nodes, remove_outgoing_edges=True) elif dseparation_algo == "naive": frontdoor_paths = get_all_directed_paths(graph, action_nodes, outcome_nodes) else: raise ValueError(f"d-separation algorithm {dseparation_algo} is not supported") eligible_variables = ( get_descendants(graph, action_nodes) - set(outcome_nodes) - set(get_descendants(graph, outcome_nodes)) ) # For simplicity, assuming a one-variable frontdoor set for candidate_var in eligible_variables: # Cond 1: All directed paths intercepted by candidate_var cond1 = check_valid_frontdoor_set( graph, action_nodes, outcome_nodes, parse_state(candidate_var), frontdoor_paths=frontdoor_paths, new_graph=cond1_graph, dseparation_algo=dseparation_algo, ) logger.debug("Candidate frontdoor set: {0}, is_dseparated: {1}".format(candidate_var, cond1)) if not cond1: continue # Cond 2: No confounding between treatment and candidate var cond2 = check_valid_backdoor_set( graph, action_nodes, parse_state(candidate_var), set(), backdoor_paths=None, new_graph=bdoor_graph1, dseparation_algo=dseparation_algo, ) if not cond2: continue # Cond 3: treatment blocks all confounding between candidate_var and outcome bdoor_graph2 = do_surgery(graph, candidate_var, remove_outgoing_edges=True) cond3 = check_valid_backdoor_set( graph, parse_state(candidate_var), outcome_nodes, action_nodes, backdoor_paths=None, new_graph=bdoor_graph2, dseparation_algo=dseparation_algo, ) is_valid_frontdoor = cond1 and cond2 and cond3 if is_valid_frontdoor: frontdoor_var = candidate_var break return parse_state(frontdoor_var) def identify_mediation(graph: nx.DiGraph, action_nodes: List[str], outcome_nodes: List[str]): """Find a valid mediator if it exists. Currently only supports a single variable mediator set. """ mediation_var = None mediation_paths = get_all_directed_paths(graph, action_nodes, outcome_nodes) eligible_variables = get_descendants(graph, action_nodes) - set(outcome_nodes) # For simplicity, assuming a one-variable mediation set for candidate_var in eligible_variables: is_valid_mediation = check_valid_mediation_set( graph, action_nodes, outcome_nodes, parse_state(candidate_var), mediation_paths=mediation_paths, ) logger.debug("Candidate mediation set: {0}, on_mediating_path: {1}".format(candidate_var, is_valid_mediation)) if is_valid_mediation: mediation_var = candidate_var break return parse_state(mediation_var) def identify_mediation_first_stage_confounders( graph: nx.DiGraph, action_nodes: List[str], outcome_nodes: List[str], mediator_nodes: List[str], observed_nodes: List[str], backdoor_adjustment: BackdoorAdjustment, ): # Create estimands dict as per the API for backdoor, but do not return it estimands_dict = {} backdoor_sets = identify_backdoor(graph, action_nodes, mediator_nodes, observed_nodes, backdoor_adjustment) estimands_dict, backdoor_variables_dict = build_backdoor_estimands_dict( action_nodes, mediator_nodes, observed_nodes, backdoor_sets, estimands_dict, ) # Setting default "backdoor" identification adjustment set default_backdoor_id = get_default_backdoor_set_id(graph, action_nodes, outcome_nodes, backdoor_variables_dict) estimands_dict["backdoor"] = estimands_dict.get(str(default_backdoor_id), None) backdoor_variables_dict["backdoor"] = backdoor_variables_dict.get(str(default_backdoor_id), None) return backdoor_variables_dict def identify_mediation_second_stage_confounders( graph: nx.DiGraph, action_nodes: List[str], mediator_nodes: List[str], outcome_nodes: List[str], observed_nodes: List[str], backdoor_adjustment: BackdoorAdjustment, ): # Create estimands dict as per the API for backdoor, but do not return it estimands_dict = {} backdoor_sets = identify_backdoor(graph, mediator_nodes, outcome_nodes, observed_nodes, backdoor_adjustment) estimands_dict, backdoor_variables_dict = build_backdoor_estimands_dict( mediator_nodes, outcome_nodes, observed_nodes, backdoor_sets, estimands_dict, ) # Setting default "backdoor" identification adjustment set default_backdoor_id = get_default_backdoor_set_id(graph, action_nodes, outcome_nodes, backdoor_variables_dict) estimands_dict["backdoor"] = estimands_dict.get(str(default_backdoor_id), None) backdoor_variables_dict["backdoor"] = backdoor_variables_dict.get(str(default_backdoor_id), None) return backdoor_variables_dict def construct_backdoor_estimand(treatment_name: List[str], outcome_name: List[str], common_causes: List[str]): # TODO: outputs string for now, but ideally should do symbolic # expressions Mon 19 Feb 2018 04:54:17 PM DST # TODO Better support for multivariate treatments expr = None outcome_name = outcome_name[0] num_expr_str = outcome_name if len(common_causes) > 0: num_expr_str += "|" + ",".join(common_causes) expr = "d(" + num_expr_str + ")/d" + ",".join(treatment_name) sym_mu = sp.Symbol("mu") sym_sigma = sp.Symbol("sigma", positive=True) sym_outcome = spstats.Normal(num_expr_str, sym_mu, sym_sigma) sym_treatment_symbols = [sp.Symbol(t) for t in treatment_name] sym_treatment = sp.Array(sym_treatment_symbols) sym_conditional_outcome = spstats.Expectation(sym_outcome) sym_effect = sp.Derivative(sym_conditional_outcome, sym_treatment) sym_assumptions = { "Unconfoundedness": ( "If U\N{RIGHTWARDS ARROW}{{{0}}} and U\N{RIGHTWARDS ARROW}{1}" " then P({1}|{0},{2},U) = P({1}|{0},{2})" ).format(",".join(treatment_name), outcome_name, ",".join(common_causes)) } estimand = {"estimand": sym_effect, "assumptions": sym_assumptions} return estimand def construct_iv_estimand(treatment_name: List[str], outcome_name: List[str], instrument_names: List[str]): # TODO: support multivariate treatments better. expr = None outcome_name = outcome_name[0] sym_outcome = spstats.Normal(outcome_name, 0, 1) sym_treatment_symbols = [spstats.Normal(t, 0, 1) for t in treatment_name] sym_treatment = sp.Array(sym_treatment_symbols) sym_instrument_symbols = [sp.Symbol(inst) for inst in instrument_names] sym_instrument = sp.Array(sym_instrument_symbols) # ",".join(instrument_names)) sym_outcome_derivative = sp.Derivative(sym_outcome, sym_instrument) sym_treatment_derivative = sp.Derivative(sym_treatment, sym_instrument) sym_effect = spstats.Expectation(sym_outcome_derivative / sym_treatment_derivative) sym_assumptions = { "As-if-random": ( "If U\N{RIGHTWARDS ARROW}\N{RIGHTWARDS ARROW}{0} then " "\N{NOT SIGN}(U \N{RIGHTWARDS ARROW}\N{RIGHTWARDS ARROW}{{{1}}})" ).format(outcome_name, ",".join(instrument_names)), "Exclusion": ( "If we remove {{{0}}}\N{RIGHTWARDS ARROW}{{{1}}}, then " "\N{NOT SIGN}({{{0}}}\N{RIGHTWARDS ARROW}{2})" ).format(",".join(instrument_names), ",".join(treatment_name), outcome_name), } estimand = {"estimand": sym_effect, "assumptions": sym_assumptions} return estimand def construct_frontdoor_estimand( treatment_name: List[str], outcome_name: List[str], frontdoor_variables_names: List[str] ): # TODO: support multivariate treatments better. expr = None outcome_name = outcome_name[0] sym_outcome = spstats.Normal(outcome_name, 0, 1) sym_treatment_symbols = [spstats.Normal(t, 0, 1) for t in treatment_name] sym_treatment = sp.Array(sym_treatment_symbols) sym_frontdoor_symbols = [sp.Symbol(inst) for inst in frontdoor_variables_names] sym_frontdoor = sp.Array(sym_frontdoor_symbols) # ",".join(instrument_names)) sym_outcome_derivative = sp.Derivative(sym_outcome, sym_frontdoor) sym_treatment_derivative = sp.Derivative(sym_frontdoor, sym_treatment) sym_effect = spstats.Expectation(sym_treatment_derivative * sym_outcome_derivative) sym_assumptions = { "Full-mediation": ("{2} intercepts (blocks) all directed paths from {0} to {1}.").format( ",".join(treatment_name), ",".join(outcome_name), ",".join(frontdoor_variables_names), ), "First-stage-unconfoundedness": ( "If U\N{RIGHTWARDS ARROW}{{{0}}} and U\N{RIGHTWARDS ARROW}{{{1}}}" " then P({1}|{0},U) = P({1}|{0})" ).format(",".join(treatment_name), ",".join(frontdoor_variables_names)), "Second-stage-unconfoundedness": ( "If U\N{RIGHTWARDS ARROW}{{{2}}} and U\N{RIGHTWARDS ARROW}{1}" " then P({1}|{2}, {0}, U) = P({1}|{2}, {0})" ).format( ",".join(treatment_name), outcome_name, ",".join(frontdoor_variables_names), ), } estimand = {"estimand": sym_effect, "assumptions": sym_assumptions} return estimand def construct_mediation_estimand( estimand_type: EstimandType, action_nodes: List[str], outcome_nodes: List[str], mediator_nodes: List[str] ): # TODO: support multivariate treatments better. expr = None if estimand_type in ( EstimandType.NONPARAMETRIC_NDE, EstimandType.NONPARAMETRIC_NIE, ): outcome_nodes = outcome_nodes[0] sym_outcome = spstats.Normal(outcome_nodes, 0, 1) sym_treatment_symbols = [spstats.Normal(t, 0, 1) for t in action_nodes] sym_treatment = sp.Array(sym_treatment_symbols) sym_mediators_symbols = [sp.Symbol(inst) for inst in mediator_nodes] sym_mediators = sp.Array(sym_mediators_symbols) sym_outcome_derivative = sp.Derivative(sym_outcome, sym_mediators) sym_treatment_derivative = sp.Derivative(sym_mediators, sym_treatment) # For direct effect num_expr_str = outcome_nodes if len(mediator_nodes) > 0: num_expr_str += "|" + ",".join(mediator_nodes) sym_mu = sp.Symbol("mu") sym_sigma = sp.Symbol("sigma", positive=True) sym_conditional_outcome = spstats.Normal(num_expr_str, sym_mu, sym_sigma) sym_directeffect_derivative = sp.Derivative(sym_conditional_outcome, sym_treatment) if estimand_type == EstimandType.NONPARAMETRIC_NIE: sym_effect = spstats.Expectation(sym_treatment_derivative * sym_outcome_derivative) elif estimand_type == EstimandType.NONPARAMETRIC_NDE: sym_effect = spstats.Expectation(sym_directeffect_derivative) sym_assumptions = { "Mediation": ( "{2} intercepts (blocks) all directed paths from {0} to {1} except the path {{{0}}}\N{RIGHTWARDS ARROW}{{{1}}}." ).format( ",".join(action_nodes), ",".join(outcome_nodes), ",".join(mediator_nodes), ), "First-stage-unconfoundedness": ( "If U\N{RIGHTWARDS ARROW}{{{0}}} and U\N{RIGHTWARDS ARROW}{{{1}}}" " then P({1}|{0},U) = P({1}|{0})" ).format(",".join(action_nodes), ",".join(mediator_nodes)), "Second-stage-unconfoundedness": ( "If U\N{RIGHTWARDS ARROW}{{{2}}} and U\N{RIGHTWARDS ARROW}{1}" " then P({1}|{2}, {0}, U) = P({1}|{2}, {0})" ).format(",".join(action_nodes), outcome_nodes, ",".join(mediator_nodes)), } else: raise ValueError( "Estimand type not supported. Supported estimand types are {0} or {1}'.".format( EstimandType.NONPARAMETRIC_NDE, EstimandType.NONPARAMETRIC_NIE, ) ) estimand = {"estimand": sym_effect, "assumptions": sym_assumptions} return estimand

bloebp

4fd0a92bd2fabbacfe6f225ea9637d3e8f08407e

2a8e49a77eb43a74d7ee6fc0925a376cd60335f2

nice, like the nomenclature of observed_nodes, action_nodes and outcome_nodes.

amit-sharma

py-why/dowhy

Proposal: Finalize functional API refactor - deprecate causal graph

- The graph should now be defined via a networkx graph. Most identification methods now expect an additional "observed_nodes" parameter accordingly. - CausalModel and CausalGraph still exist and should be compatible with the old API. Open task is still to replace the usage of CausalModel in the tests and notebooks. There are also some smaller details with the identification methods, which should be double checked.

2023-05-16 16:07:18+00:00

2023-11-27 17:48:56+00:00

dowhy/causal_identifier/auto_identifier.py

import itertools import logging from enum import Enum from typing import Dict, List, Optional, Union import sympy as sp import sympy.stats as spstats from dowhy.causal_graph import CausalGraph from dowhy.causal_identifier.efficient_backdoor import EfficientBackdoor from dowhy.causal_identifier.identified_estimand import IdentifiedEstimand from dowhy.utils.api import parse_state logger = logging.getLogger(__name__) class EstimandType(Enum): # Average total effect NONPARAMETRIC_ATE = "nonparametric-ate" # Natural direct effect NONPARAMETRIC_NDE = "nonparametric-nde" # Natural indirect effect NONPARAMETRIC_NIE = "nonparametric-nie" # Controlled direct effect NONPARAMETRIC_CDE = "nonparametric-cde" class BackdoorAdjustment(Enum): # Backdoor method names BACKDOOR_DEFAULT = "default" BACKDOOR_EXHAUSTIVE = "exhaustive-search" BACKDOOR_MIN = "minimal-adjustment" BACKDOOR_MAX = "maximal-adjustment" BACKDOOR_EFFICIENT = "efficient-adjustment" BACKDOOR_MIN_EFFICIENT = "efficient-minimal-adjustment" BACKDOOR_MINCOST_EFFICIENT = "efficient-mincost-adjustment" MAX_BACKDOOR_ITERATIONS = 100000 METHOD_NAMES = { BackdoorAdjustment.BACKDOOR_DEFAULT, BackdoorAdjustment.BACKDOOR_EXHAUSTIVE, BackdoorAdjustment.BACKDOOR_MIN, BackdoorAdjustment.BACKDOOR_MAX, BackdoorAdjustment.BACKDOOR_EFFICIENT, BackdoorAdjustment.BACKDOOR_MIN_EFFICIENT, BackdoorAdjustment.BACKDOOR_MINCOST_EFFICIENT, } EFFICIENT_METHODS = { BackdoorAdjustment.BACKDOOR_EFFICIENT, BackdoorAdjustment.BACKDOOR_MIN_EFFICIENT, BackdoorAdjustment.BACKDOOR_MINCOST_EFFICIENT, } DEFAULT_BACKDOOR_METHOD = BackdoorAdjustment.BACKDOOR_DEFAULT class AutoIdentifier: """Class that implements different identification methods. Currently supports backdoor and instrumental variable identification methods. The identification is based on the causal graph provided. This class is for backwards compatibility with CausalModel Will be deprecated in the future in favor of function call auto_identify_effect() """ def __init__( self, estimand_type: EstimandType, backdoor_adjustment: BackdoorAdjustment = BackdoorAdjustment.BACKDOOR_DEFAULT, proceed_when_unidentifiable: bool = False, optimize_backdoor: bool = False, costs: Optional[List] = None, ): self.estimand_type = estimand_type self.backdoor_adjustment = backdoor_adjustment self._proceed_when_unidentifiable = proceed_when_unidentifiable self.optimize_backdoor = optimize_backdoor self.costs = costs self.logger = logging.getLogger(__name__) def identify_effect( self, graph: CausalGraph, treatment_name: Union[str, List[str]], outcome_name: Union[str, List[str]], conditional_node_names: List[str] = None, **kwargs, ): estimand = identify_effect_auto( graph, treatment_name, outcome_name, self.estimand_type, conditional_node_names, self.backdoor_adjustment, self._proceed_when_unidentifiable, self.optimize_backdoor, self.costs, **kwargs, ) estimand.identifier = self return estimand def identify_backdoor( self, graph: CausalGraph, treatment_name: List[str], outcome_name: str, include_unobserved: bool = False, dseparation_algo: str = "default", direct_effect: bool = False, ): return identify_backdoor( graph, treatment_name, outcome_name, self.backdoor_adjustment, include_unobserved, dseparation_algo, direct_effect, ) def identify_effect_auto( graph: CausalGraph, treatment_name: Union[str, List[str]], outcome_name: Union[str, List[str]], estimand_type: EstimandType, conditional_node_names: List[str] = None, backdoor_adjustment: BackdoorAdjustment = BackdoorAdjustment.BACKDOOR_DEFAULT, proceed_when_unidentifiable: bool = False, optimize_backdoor: bool = False, costs: Optional[List] = None, **kwargs, ) -> IdentifiedEstimand: """Main method that returns an identified estimand (if one exists). If estimand_type is non-parametric ATE, then uses backdoor, instrumental variable and frontdoor identification methods, to check if an identified estimand exists, based on the causal graph. :param optimize_backdoor: if True, uses an optimised algorithm to compute the backdoor sets :param costs: non-negative costs associated with variables in the graph. Only used for estimand_type='non-parametric-ate' and backdoor_adjustment='efficient-mincost-adjustment'. If no costs are provided by the user, and backdoor_adjustment='efficient-mincost-adjustment', costs are assumed to be equal to one for all variables in the graph. :param conditional_node_names: variables that are used to determine treatment. If none are provided, it is assumed that the intervention is static. :returns: target estimand, an instance of the IdentifiedEstimand class """ treatment_name = parse_state(treatment_name) outcome_name = parse_state(outcome_name) # First, check if there is a directed path from action to outcome if not graph.has_directed_path(treatment_name, outcome_name): logger.warn("No directed path from treatment to outcome. Causal Effect is zero.") return IdentifiedEstimand( None, treatment_variable=treatment_name, outcome_variable=outcome_name, no_directed_path=True, ) if estimand_type == EstimandType.NONPARAMETRIC_ATE: return identify_ate_effect( graph, treatment_name, outcome_name, backdoor_adjustment, optimize_backdoor, estimand_type, costs, conditional_node_names, proceed_when_unidentifiable, ) elif estimand_type == EstimandType.NONPARAMETRIC_NDE: return identify_nde_effect( graph, treatment_name, outcome_name, backdoor_adjustment, estimand_type, proceed_when_unidentifiable ) elif estimand_type == EstimandType.NONPARAMETRIC_NIE: return identify_nie_effect( graph, treatment_name, outcome_name, backdoor_adjustment, estimand_type, proceed_when_unidentifiable ) elif estimand_type == EstimandType.NONPARAMETRIC_CDE: return identify_cde_effect( graph, treatment_name, outcome_name, backdoor_adjustment, estimand_type, proceed_when_unidentifiable ) else: raise ValueError( "Estimand type is not supported. Use either {0}, {1}, or {2}.".format( EstimandType.NONPARAMETRIC_ATE, EstimandType.NONPARAMETRIC_CDE, EstimandType.NONPARAMETRIC_NDE, EstimandType.NONPARAMETRIC_NIE, ) ) def identify_ate_effect( graph: CausalGraph, treatment_name: List[str], outcome_name: str, backdoor_adjustment: BackdoorAdjustment, optimize_backdoor: bool, estimand_type: EstimandType, costs: List, conditional_node_names: List[str] = None, proceed_when_unidentifiable: bool = False, ): estimands_dict = {} mediation_first_stage_confounders = None mediation_second_stage_confounders = None ### 1. BACKDOOR IDENTIFICATION # Pick algorithm to compute backdoor sets according to method chosen if backdoor_adjustment not in EFFICIENT_METHODS: # First, checking if there are any valid backdoor adjustment sets if optimize_backdoor == False: backdoor_sets = identify_backdoor(graph, treatment_name, outcome_name, backdoor_adjustment) else: from dowhy.causal_identifier.backdoor import Backdoor path = Backdoor(graph._graph, treatment_name, outcome_name) backdoor_sets = path.get_backdoor_vars() elif backdoor_adjustment in EFFICIENT_METHODS: backdoor_sets = identify_efficient_backdoor( graph, backdoor_adjustment, costs, conditional_node_names=conditional_node_names ) estimands_dict, backdoor_variables_dict = build_backdoor_estimands_dict( graph, treatment_name, outcome_name, backdoor_sets, estimands_dict ) # Setting default "backdoor" identification adjustment set default_backdoor_id = get_default_backdoor_set_id(graph, treatment_name, outcome_name, backdoor_variables_dict) if len(backdoor_variables_dict) > 0: estimands_dict["backdoor"] = estimands_dict.get(str(default_backdoor_id), None) backdoor_variables_dict["backdoor"] = backdoor_variables_dict.get(str(default_backdoor_id), None) else: estimands_dict["backdoor"] = None ### 2. INSTRUMENTAL VARIABLE IDENTIFICATION # Now checking if there is also a valid iv estimand instrument_names = graph.get_instruments(treatment_name, outcome_name) logger.info("Instrumental variables for treatment and outcome:" + str(instrument_names)) if len(instrument_names) > 0: iv_estimand_expr = construct_iv_estimand( treatment_name, outcome_name, instrument_names, ) logger.debug("Identified expression = " + str(iv_estimand_expr)) estimands_dict["iv"] = iv_estimand_expr else: estimands_dict["iv"] = None ### 3. FRONTDOOR IDENTIFICATION # Now checking if there is a valid frontdoor variable frontdoor_variables_names = identify_frontdoor(graph, treatment_name, outcome_name) logger.info("Frontdoor variables for treatment and outcome:" + str(frontdoor_variables_names)) if len(frontdoor_variables_names) > 0: frontdoor_estimand_expr = construct_frontdoor_estimand( treatment_name, outcome_name, frontdoor_variables_names, ) logger.debug("Identified expression = " + str(frontdoor_estimand_expr)) estimands_dict["frontdoor"] = frontdoor_estimand_expr mediation_first_stage_confounders = identify_mediation_first_stage_confounders( graph, treatment_name, outcome_name, frontdoor_variables_names, backdoor_adjustment ) mediation_second_stage_confounders = identify_mediation_second_stage_confounders( graph, treatment_name, frontdoor_variables_names, outcome_name, backdoor_adjustment ) else: estimands_dict["frontdoor"] = None # Finally returning the estimand object estimand = IdentifiedEstimand( None, treatment_variable=treatment_name, outcome_variable=outcome_name, estimand_type=estimand_type, estimands=estimands_dict, backdoor_variables=backdoor_variables_dict, instrumental_variables=instrument_names, frontdoor_variables=frontdoor_variables_names, mediation_first_stage_confounders=mediation_first_stage_confounders, mediation_second_stage_confounders=mediation_second_stage_confounders, default_backdoor_id=default_backdoor_id, ) return estimand def identify_cde_effect( graph: CausalGraph, treatment_name: List[str], outcome_name: str, backdoor_adjustment: BackdoorAdjustment, estimand_type: EstimandType, proceed_when_unidentifiable: bool = False, ): """Identify controlled direct effect. For a definition, see Vanderwheele (2011). Controlled direct and mediated effects: definition, identification and bounds. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4193506/ Using do-calculus rules, identification yields a adjustment set. It is based on the principle that under a graph where the direct edge from treatment to outcome is removed, conditioning on the adjustment set should d-separate treatment and outcome. """ estimands_dict = {} # Pick algorithm to compute backdoor sets according to method chosen backdoor_sets = identify_backdoor(graph, treatment_name, outcome_name, backdoor_adjustment, direct_effect=True) estimands_dict, backdoor_variables_dict = build_backdoor_estimands_dict( graph, treatment_name, outcome_name, backdoor_sets, estimands_dict ) # Setting default "backdoor" identification adjustment set default_backdoor_id = get_default_backdoor_set_id(graph, treatment_name, outcome_name, backdoor_variables_dict) if len(backdoor_variables_dict) > 0: estimands_dict["backdoor"] = estimands_dict.get(str(default_backdoor_id), None) backdoor_variables_dict["backdoor"] = backdoor_variables_dict.get(str(default_backdoor_id), None) else: estimands_dict["backdoor"] = None # Finally returning the estimand object estimand = IdentifiedEstimand( None, treatment_variable=treatment_name, outcome_variable=outcome_name, estimand_type=estimand_type, estimands=estimands_dict, backdoor_variables=backdoor_variables_dict, instrumental_variables=None, frontdoor_variables=None, mediation_first_stage_confounders=None, mediation_second_stage_confounders=None, default_backdoor_id=default_backdoor_id, ) return estimand def identify_nie_effect( graph: CausalGraph, treatment_name: List[str], outcome_name: str, backdoor_adjustment: BackdoorAdjustment, estimand_type: EstimandType, proceed_when_unidentifiable: bool = False, ): estimands_dict = {} ### 1. FIRST DOING BACKDOOR IDENTIFICATION # First, checking if there are any valid backdoor adjustment sets backdoor_sets = identify_backdoor(graph, treatment_name, outcome_name, backdoor_adjustment) estimands_dict, backdoor_variables_dict = build_backdoor_estimands_dict( graph, treatment_name, outcome_name, backdoor_sets, estimands_dict ) # Setting default "backdoor" identification adjustment set default_backdoor_id = get_default_backdoor_set_id(graph, treatment_name, outcome_name, backdoor_variables_dict) backdoor_variables_dict["backdoor"] = backdoor_variables_dict.get(str(default_backdoor_id), None) ### 2. SECOND, CHECKING FOR MEDIATORS # Now checking if there are valid mediator variables estimands_dict = {} # Need to reinitialize this dictionary to avoid including the backdoor sets mediation_first_stage_confounders = None mediation_second_stage_confounders = None mediators_names = identify_mediation(graph, treatment_name, outcome_name) logger.info("Mediators for treatment and outcome:" + str(mediators_names)) if len(mediators_names) > 0: mediation_estimand_expr = construct_mediation_estimand( estimand_type, treatment_name, outcome_name, mediators_names, ) logger.debug("Identified expression = " + str(mediation_estimand_expr)) estimands_dict["mediation"] = mediation_estimand_expr mediation_first_stage_confounders = identify_mediation_first_stage_confounders( graph, treatment_name, outcome_name, mediators_names, backdoor_adjustment ) mediation_second_stage_confounders = identify_mediation_second_stage_confounders( graph, treatment_name, mediators_names, outcome_name, backdoor_adjustment ) else: estimands_dict["mediation"] = None # Finally returning the estimand object estimand = IdentifiedEstimand( None, treatment_variable=treatment_name, outcome_variable=outcome_name, estimand_type=estimand_type, estimands=estimands_dict, backdoor_variables=backdoor_variables_dict, instrumental_variables=None, frontdoor_variables=None, mediator_variables=mediators_names, mediation_first_stage_confounders=mediation_first_stage_confounders, mediation_second_stage_confounders=mediation_second_stage_confounders, default_backdoor_id=None, ) return estimand def identify_nde_effect( graph: CausalGraph, treatment_name: List[str], outcome_name: str, backdoor_adjustment: BackdoorAdjustment, estimand_type: EstimandType, proceed_when_unidentifiable: bool = False, ): estimands_dict = {} ### 1. FIRST DOING BACKDOOR IDENTIFICATION # First, checking if there are any valid backdoor adjustment sets backdoor_sets = identify_backdoor(graph, treatment_name, outcome_name, backdoor_adjustment) estimands_dict, backdoor_variables_dict = build_backdoor_estimands_dict( graph, treatment_name, outcome_name, backdoor_sets, estimands_dict ) # Setting default "backdoor" identification adjustment set default_backdoor_id = get_default_backdoor_set_id(graph, treatment_name, outcome_name, backdoor_variables_dict) backdoor_variables_dict["backdoor"] = backdoor_variables_dict.get(str(default_backdoor_id), None) ### 2. SECOND, CHECKING FOR MEDIATORS # Now checking if there are valid mediator variables estimands_dict = {} mediation_first_stage_confounders = None mediation_second_stage_confounders = None mediators_names = identify_mediation(graph, treatment_name, outcome_name) logger.info("Mediators for treatment and outcome:" + str(mediators_names)) if len(mediators_names) > 0: mediation_estimand_expr = construct_mediation_estimand( estimand_type, treatment_name, outcome_name, mediators_names, ) logger.debug("Identified expression = " + str(mediation_estimand_expr)) estimands_dict["mediation"] = mediation_estimand_expr mediation_first_stage_confounders = identify_mediation_first_stage_confounders( graph, treatment_name, outcome_name, mediators_names, backdoor_adjustment ) mediation_second_stage_confounders = identify_mediation_second_stage_confounders( graph, treatment_name, mediators_names, outcome_name, backdoor_adjustment ) else: estimands_dict["mediation"] = None # Finally returning the estimand object estimand = IdentifiedEstimand( None, treatment_variable=treatment_name, outcome_variable=outcome_name, estimand_type=estimand_type, estimands=estimands_dict, backdoor_variables=backdoor_variables_dict, instrumental_variables=None, frontdoor_variables=None, mediator_variables=mediators_names, mediation_first_stage_confounders=mediation_first_stage_confounders, mediation_second_stage_confounders=mediation_second_stage_confounders, default_backdoor_id=None, ) return estimand def identify_backdoor( graph: CausalGraph, treatment_name: List[str], outcome_name: str, backdoor_adjustment: BackdoorAdjustment, include_unobserved: bool = False, dseparation_algo: str = "default", direct_effect: bool = False, ): backdoor_sets = [] backdoor_paths = None bdoor_graph = None if dseparation_algo == "naive": backdoor_paths = graph.get_backdoor_paths(treatment_name, outcome_name) elif dseparation_algo == "default": bdoor_graph = graph.do_surgery( treatment_name, target_node_names=outcome_name, remove_outgoing_edges=True, remove_only_direct_edges_to_target=direct_effect, ) else: raise ValueError(f"d-separation algorithm {dseparation_algo} is not supported") backdoor_adjustment = ( backdoor_adjustment if backdoor_adjustment != BackdoorAdjustment.BACKDOOR_DEFAULT else DEFAULT_BACKDOOR_METHOD ) # First, checking if empty set is a valid backdoor set empty_set = set() check = graph.check_valid_backdoor_set( treatment_name, outcome_name, empty_set, backdoor_paths=backdoor_paths, new_graph=bdoor_graph, dseparation_algo=dseparation_algo, ) if check["is_dseparated"]: backdoor_sets.append({"backdoor_set": empty_set}) # If the method is `minimal-adjustment`, return the empty set right away. if backdoor_adjustment == BackdoorAdjustment.BACKDOOR_MIN: return backdoor_sets # Second, checking for all other sets of variables. If include_unobserved is false, then only observed variables are eligible. eligible_variables = ( graph.get_all_nodes(include_unobserved=include_unobserved) - set(treatment_name) - set(outcome_name) ) if direct_effect: # only remove descendants of Y # also allow any causes of Y that are not caused by T (for lower variance) eligible_variables -= graph.get_descendants(outcome_name) else: # remove descendants of T (mediators) and descendants of Y eligible_variables -= graph.get_descendants(treatment_name) # If var is d-separated from both treatment or outcome, it cannot # be a part of the backdoor set filt_eligible_variables = set() for var in eligible_variables: dsep_treat_var = graph.check_dseparation(treatment_name, parse_state(var), set()) dsep_outcome_var = graph.check_dseparation(outcome_name, parse_state(var), set()) if not dsep_outcome_var or not dsep_treat_var: filt_eligible_variables.add(var) if backdoor_adjustment in METHOD_NAMES: backdoor_sets, found_valid_adjustment_set = find_valid_adjustment_sets( graph, treatment_name, outcome_name, backdoor_paths, bdoor_graph, dseparation_algo, backdoor_sets, filt_eligible_variables, backdoor_adjustment=backdoor_adjustment, max_iterations=MAX_BACKDOOR_ITERATIONS, ) if backdoor_adjustment == BackdoorAdjustment.BACKDOOR_DEFAULT and found_valid_adjustment_set: # repeat the above search with BACKDOOR_MIN backdoor_sets, _ = find_valid_adjustment_sets( graph, treatment_name, outcome_name, backdoor_paths, bdoor_graph, dseparation_algo, backdoor_sets, filt_eligible_variables, backdoor_adjustment=BackdoorAdjustment.BACKDOOR_MIN, max_iterations=MAX_BACKDOOR_ITERATIONS, ) else: raise ValueError( f"Identifier method {backdoor_adjustment} not supported. Try one of the following: {METHOD_NAMES}" ) return backdoor_sets def identify_efficient_backdoor( graph: CausalGraph, backdoor_adjustment: BackdoorAdjustment, costs: List, conditional_node_names: List[str] = None, ): """Method implementing algorithms to compute efficient backdoor sets, as described in Rotnitzky and Smucler (2020), Smucler, Sapienza and Rotnitzky (2021) and Smucler and Rotnitzky (2022). For backdoor_adjustment='efficient-adjustment', computes an optimal backdoor set, that is, a backdoor set comprised of observable variables that yields non-parametric estimators of the interventional mean with the smallest asymptotic variance among those that are based on observable backdoor sets. This optimal backdoor set always exists when no variables are latent, and the algorithm is guaranteed to compute it in this case. Under a non-parametric graphical model with latent variables, such a backdoor set can fail to exist. When certain sufficient conditions under which it is known that such a backdoor set exists are not satisfied, an error is raised. For backdoor_adjustment='efficient-minimal-adjustment', computes an optimal minimal backdoor set, that is, a minimal backdoor set comprised of observable variables that yields non-parametric estimators of the interventional mean with the smallest asymptotic variance among those that are based on observable minimal backdoor sets. For backdoor_adjustment='efficient-mincost-adjustment', computes an optimal minimum cost backdoor set, that is, a minimum cost backdoor set comprised of observable variables that yields non-parametric estimators of the interventional mean with the smallest asymptotic variance among those that are based on observable minimum cost backdoor sets. The cost of a backdoor set is defined as the sum of the costs of the variables that comprise it. The various optimal backdoor sets computed by this method are not only optimal under non-parametric graphical models and non-parametric estimators of interventional mean, but also under linear graphical models and OLS estimators, per results in Henckel, Perkovic and Maathuis (2020). :param costs: a list with non-negative costs associated with variables in the graph. Only used for estimatand_type='non-parametric-ate' and backdoor_adjustment='efficient-mincost-adjustment'. If not costs are provided by the user, and backdoor_adjustment='efficient-mincost-adjustment', costs are assumed to be equal to one for all variables in the graph. The structure of the list should be of the form [(node, {"cost": x}) for node in nodes]. :param conditional_node_names: variables that are used to determine treatment. If none are provided, it is assumed that the intervention sets the treatment to a constant. :returns: backdoor_sets, a list of dictionaries, with each dictionary having as values a backdoor set. """ if costs is None and backdoor_adjustment == "efficient-mincost-adjustment": logger.warning("No costs were passed, so they will be assumed to be constant and equal to 1.") efficient_bd = EfficientBackdoor( graph=graph, conditional_node_names=conditional_node_names, costs=costs, ) if backdoor_adjustment == BackdoorAdjustment.BACKDOOR_EFFICIENT: backdoor_set = efficient_bd.optimal_adj_set() backdoor_sets = [{"backdoor_set": tuple(backdoor_set)}] elif backdoor_adjustment == BackdoorAdjustment.BACKDOOR_MIN_EFFICIENT: backdoor_set = efficient_bd.optimal_minimal_adj_set() backdoor_sets = [{"backdoor_set": tuple(backdoor_set)}] elif backdoor_adjustment == BackdoorAdjustment.BACKDOOR_MINCOST_EFFICIENT: backdoor_set = efficient_bd.optimal_mincost_adj_set() backdoor_sets = [{"backdoor_set": tuple(backdoor_set)}] return backdoor_sets def find_valid_adjustment_sets( graph: CausalGraph, treatment_name: List, outcome_name: List, backdoor_paths: List, bdoor_graph: CausalGraph, dseparation_algo: str, backdoor_sets: List, filt_eligible_variables: List, backdoor_adjustment: BackdoorAdjustment, max_iterations: int, ): num_iterations = 0 found_valid_adjustment_set = False all_nodes_observed = graph.all_observed(graph.get_all_nodes()) # If `minimal-adjustment` method is specified, start the search from the set with minimum size. Otherwise, start from the largest. set_sizes = ( range(1, len(filt_eligible_variables) + 1, 1) if backdoor_adjustment == BackdoorAdjustment.BACKDOOR_MIN else range(len(filt_eligible_variables), 0, -1) ) for size_candidate_set in set_sizes: for candidate_set in itertools.combinations(filt_eligible_variables, size_candidate_set): check = graph.check_valid_backdoor_set( treatment_name, outcome_name, candidate_set, backdoor_paths=backdoor_paths, new_graph=bdoor_graph, dseparation_algo=dseparation_algo, ) logger.debug( "Candidate backdoor set: {0}, is_dseparated: {1}".format(candidate_set, check["is_dseparated"]) ) if check["is_dseparated"]: backdoor_sets.append({"backdoor_set": candidate_set}) found_valid_adjustment_set = True num_iterations += 1 if backdoor_adjustment == BackdoorAdjustment.BACKDOOR_EXHAUSTIVE and num_iterations > max_iterations: logger.warning(f"Max number of iterations {max_iterations} reached.") break # If the backdoor method is `maximal-adjustment` or `minimal-adjustment`, return the first found adjustment set. if ( backdoor_adjustment in { BackdoorAdjustment.BACKDOOR_DEFAULT, BackdoorAdjustment.BACKDOOR_MAX, BackdoorAdjustment.BACKDOOR_MIN, } and found_valid_adjustment_set ): break # If all variables are observed, and the biggest eligible set # does not satisfy backdoor, then none of its subsets will. if ( backdoor_adjustment in {BackdoorAdjustment.BACKDOOR_DEFAULT, BackdoorAdjustment.BACKDOOR_MAX} and all_nodes_observed ): break if num_iterations > max_iterations: logger.warning(f"Max number of iterations {max_iterations} reached. Could not find a valid backdoor set.") break return backdoor_sets, found_valid_adjustment_set def get_default_backdoor_set_id( graph: CausalGraph, treatment_name: List[str], outcome_name: List[str], backdoor_sets_dict: Dict ): # Adding a None estimand if no backdoor set found if len(backdoor_sets_dict) == 0: return None # Default set contains minimum possible number of instrumental variables, to prevent lowering variance in the treatment variable. instrument_names = set(graph.get_instruments(treatment_name, outcome_name)) iv_count_dict = { key: len(set(bdoor_set).intersection(instrument_names)) for key, bdoor_set in backdoor_sets_dict.items() } min_iv_count = min(iv_count_dict.values()) min_iv_keys = {key for key, iv_count in iv_count_dict.items() if iv_count == min_iv_count} min_iv_backdoor_sets_dict = {key: backdoor_sets_dict[key] for key in min_iv_keys} # Default set is the one with the least number of adjustment variables (optimizing for efficiency) min_set_length = 1000000 default_key = None for key, bdoor_set in min_iv_backdoor_sets_dict.items(): if len(bdoor_set) < min_set_length: min_set_length = len(bdoor_set) default_key = key return default_key def build_backdoor_estimands_dict( graph: CausalGraph, treatment_name: List[str], outcome_name: List[str], backdoor_sets: List[str], estimands_dict: Dict, ): """Build the final dict for backdoor sets by filtering unobserved variables if needed.""" backdoor_variables_dict = {} is_identified = [graph.all_observed(bset["backdoor_set"]) for bset in backdoor_sets] if any(is_identified): logger.info("Causal effect can be identified.") backdoor_sets_arr = [ list(bset["backdoor_set"]) for bset in backdoor_sets if graph.all_observed(bset["backdoor_set"]) ] else: # there is unobserved confounding logger.warning("Backdoor identification failed.") backdoor_sets_arr = [] for i in range(len(backdoor_sets_arr)): backdoor_estimand_expr = construct_backdoor_estimand(treatment_name, outcome_name, backdoor_sets_arr[i]) logger.debug("Identified expression = " + str(backdoor_estimand_expr)) estimands_dict["backdoor" + str(i + 1)] = backdoor_estimand_expr backdoor_variables_dict["backdoor" + str(i + 1)] = backdoor_sets_arr[i] return estimands_dict, backdoor_variables_dict def identify_frontdoor( graph: CausalGraph, treatment_name: List[str], outcome_name: List[str], dseparation_algo: str = "default" ): """Find a valid frontdoor variable if it exists. Currently only supports a single variable frontdoor set. """ frontdoor_var = None frontdoor_paths = None fdoor_graph = None if dseparation_algo == "default": cond1_graph = graph.do_surgery(treatment_name, remove_incoming_edges=True) bdoor_graph1 = graph.do_surgery(treatment_name, remove_outgoing_edges=True) elif dseparation_algo == "naive": frontdoor_paths = graph.get_all_directed_paths(treatment_name, outcome_name) else: raise ValueError(f"d-separation algorithm {dseparation_algo} is not supported") eligible_variables = ( graph.get_descendants(treatment_name) - set(outcome_name) - set(graph.get_descendants(outcome_name)) ) # For simplicity, assuming a one-variable frontdoor set for candidate_var in eligible_variables: # Cond 1: All directed paths intercepted by candidate_var cond1 = graph.check_valid_frontdoor_set( treatment_name, outcome_name, parse_state(candidate_var), frontdoor_paths=frontdoor_paths, new_graph=cond1_graph, dseparation_algo=dseparation_algo, ) logger.debug("Candidate frontdoor set: {0}, is_dseparated: {1}".format(candidate_var, cond1)) if not cond1: continue # Cond 2: No confounding between treatment and candidate var cond2 = graph.check_valid_backdoor_set( treatment_name, parse_state(candidate_var), set(), backdoor_paths=None, new_graph=bdoor_graph1, dseparation_algo=dseparation_algo, ) if not cond2: continue # Cond 3: treatment blocks all confounding between candidate_var and outcome bdoor_graph2 = graph.do_surgery(candidate_var, remove_outgoing_edges=True) cond3 = graph.check_valid_backdoor_set( parse_state(candidate_var), outcome_name, treatment_name, backdoor_paths=None, new_graph=bdoor_graph2, dseparation_algo=dseparation_algo, ) is_valid_frontdoor = cond1 and cond2 and cond3 if is_valid_frontdoor: frontdoor_var = candidate_var break return parse_state(frontdoor_var) def identify_mediation(graph: CausalGraph, treatment_name: List[str], outcome_name: List[str]): """Find a valid mediator if it exists. Currently only supports a single variable mediator set. """ mediation_var = None mediation_paths = graph.get_all_directed_paths(treatment_name, outcome_name) eligible_variables = graph.get_descendants(treatment_name) - set(outcome_name) # For simplicity, assuming a one-variable mediation set for candidate_var in eligible_variables: is_valid_mediation = graph.check_valid_mediation_set( treatment_name, outcome_name, parse_state(candidate_var), mediation_paths=mediation_paths, ) logger.debug("Candidate mediation set: {0}, on_mediating_path: {1}".format(candidate_var, is_valid_mediation)) if is_valid_mediation: mediation_var = candidate_var break return parse_state(mediation_var) def identify_mediation_first_stage_confounders( graph: CausalGraph, treatment_name: List[str], outcome_name: List[str], mediators_names: List[str], backdoor_adjustment: BackdoorAdjustment, ): # Create estimands dict as per the API for backdoor, but do not return it estimands_dict = {} backdoor_sets = identify_backdoor(graph, treatment_name, mediators_names, backdoor_adjustment) estimands_dict, backdoor_variables_dict = build_backdoor_estimands_dict( graph, treatment_name, mediators_names, backdoor_sets, estimands_dict, ) # Setting default "backdoor" identification adjustment set default_backdoor_id = get_default_backdoor_set_id(graph, treatment_name, outcome_name, backdoor_variables_dict) estimands_dict["backdoor"] = estimands_dict.get(str(default_backdoor_id), None) backdoor_variables_dict["backdoor"] = backdoor_variables_dict.get(str(default_backdoor_id), None) return backdoor_variables_dict def identify_mediation_second_stage_confounders( graph: CausalGraph, treatment_name: List[str], mediators_names: List[str], outcome_name: List[str], backdoor_adjustment: BackdoorAdjustment, ): # Create estimands dict as per the API for backdoor, but do not return it estimands_dict = {} backdoor_sets = identify_backdoor(graph, mediators_names, outcome_name, backdoor_adjustment) estimands_dict, backdoor_variables_dict = build_backdoor_estimands_dict( graph, mediators_names, outcome_name, backdoor_sets, estimands_dict, ) # Setting default "backdoor" identification adjustment set default_backdoor_id = get_default_backdoor_set_id(graph, treatment_name, outcome_name, backdoor_variables_dict) estimands_dict["backdoor"] = estimands_dict.get(str(default_backdoor_id), None) backdoor_variables_dict["backdoor"] = backdoor_variables_dict.get(str(default_backdoor_id), None) return backdoor_variables_dict def construct_backdoor_estimand(treatment_name: List[str], outcome_name: List[str], common_causes: List[str]): # TODO: outputs string for now, but ideally should do symbolic # expressions Mon 19 Feb 2018 04:54:17 PM DST # TODO Better support for multivariate treatments expr = None outcome_name = outcome_name[0] num_expr_str = outcome_name if len(common_causes) > 0: num_expr_str += "|" + ",".join(common_causes) expr = "d(" + num_expr_str + ")/d" + ",".join(treatment_name) sym_mu = sp.Symbol("mu") sym_sigma = sp.Symbol("sigma", positive=True) sym_outcome = spstats.Normal(num_expr_str, sym_mu, sym_sigma) sym_treatment_symbols = [sp.Symbol(t) for t in treatment_name] sym_treatment = sp.Array(sym_treatment_symbols) sym_conditional_outcome = spstats.Expectation(sym_outcome) sym_effect = sp.Derivative(sym_conditional_outcome, sym_treatment) sym_assumptions = { "Unconfoundedness": ( "If U\N{RIGHTWARDS ARROW}{{{0}}} and U\N{RIGHTWARDS ARROW}{1}" " then P({1}|{0},{2},U) = P({1}|{0},{2})" ).format(",".join(treatment_name), outcome_name, ",".join(common_causes)) } estimand = {"estimand": sym_effect, "assumptions": sym_assumptions} return estimand def construct_iv_estimand(treatment_name: List[str], outcome_name: List[str], instrument_names: List[str]): # TODO: support multivariate treatments better. expr = None outcome_name = outcome_name[0] sym_outcome = spstats.Normal(outcome_name, 0, 1) sym_treatment_symbols = [spstats.Normal(t, 0, 1) for t in treatment_name] sym_treatment = sp.Array(sym_treatment_symbols) sym_instrument_symbols = [sp.Symbol(inst) for inst in instrument_names] sym_instrument = sp.Array(sym_instrument_symbols) # ",".join(instrument_names)) sym_outcome_derivative = sp.Derivative(sym_outcome, sym_instrument) sym_treatment_derivative = sp.Derivative(sym_treatment, sym_instrument) sym_effect = spstats.Expectation(sym_outcome_derivative / sym_treatment_derivative) sym_assumptions = { "As-if-random": ( "If U\N{RIGHTWARDS ARROW}\N{RIGHTWARDS ARROW}{0} then " "\N{NOT SIGN}(U \N{RIGHTWARDS ARROW}\N{RIGHTWARDS ARROW}{{{1}}})" ).format(outcome_name, ",".join(instrument_names)), "Exclusion": ( "If we remove {{{0}}}\N{RIGHTWARDS ARROW}{{{1}}}, then " "\N{NOT SIGN}({{{0}}}\N{RIGHTWARDS ARROW}{2})" ).format(",".join(instrument_names), ",".join(treatment_name), outcome_name), } estimand = {"estimand": sym_effect, "assumptions": sym_assumptions} return estimand def construct_frontdoor_estimand( treatment_name: List[str], outcome_name: List[str], frontdoor_variables_names: List[str] ): # TODO: support multivariate treatments better. expr = None outcome_name = outcome_name[0] sym_outcome = spstats.Normal(outcome_name, 0, 1) sym_treatment_symbols = [spstats.Normal(t, 0, 1) for t in treatment_name] sym_treatment = sp.Array(sym_treatment_symbols) sym_frontdoor_symbols = [sp.Symbol(inst) for inst in frontdoor_variables_names] sym_frontdoor = sp.Array(sym_frontdoor_symbols) # ",".join(instrument_names)) sym_outcome_derivative = sp.Derivative(sym_outcome, sym_frontdoor) sym_treatment_derivative = sp.Derivative(sym_frontdoor, sym_treatment) sym_effect = spstats.Expectation(sym_treatment_derivative * sym_outcome_derivative) sym_assumptions = { "Full-mediation": ("{2} intercepts (blocks) all directed paths from {0} to {1}.").format( ",".join(treatment_name), ",".join(outcome_name), ",".join(frontdoor_variables_names), ), "First-stage-unconfoundedness": ( "If U\N{RIGHTWARDS ARROW}{{{0}}} and U\N{RIGHTWARDS ARROW}{{{1}}}" " then P({1}|{0},U) = P({1}|{0})" ).format(",".join(treatment_name), ",".join(frontdoor_variables_names)), "Second-stage-unconfoundedness": ( "If U\N{RIGHTWARDS ARROW}{{{2}}} and U\N{RIGHTWARDS ARROW}{1}" " then P({1}|{2}, {0}, U) = P({1}|{2}, {0})" ).format( ",".join(treatment_name), outcome_name, ",".join(frontdoor_variables_names), ), } estimand = {"estimand": sym_effect, "assumptions": sym_assumptions} return estimand def construct_mediation_estimand( estimand_type: EstimandType, treatment_name: List[str], outcome_name: List[str], mediators_names: List[str] ): # TODO: support multivariate treatments better. expr = None if estimand_type in ( EstimandType.NONPARAMETRIC_NDE, EstimandType.NONPARAMETRIC_NIE, ): outcome_name = outcome_name[0] sym_outcome = spstats.Normal(outcome_name, 0, 1) sym_treatment_symbols = [spstats.Normal(t, 0, 1) for t in treatment_name] sym_treatment = sp.Array(sym_treatment_symbols) sym_mediators_symbols = [sp.Symbol(inst) for inst in mediators_names] sym_mediators = sp.Array(sym_mediators_symbols) sym_outcome_derivative = sp.Derivative(sym_outcome, sym_mediators) sym_treatment_derivative = sp.Derivative(sym_mediators, sym_treatment) # For direct effect num_expr_str = outcome_name if len(mediators_names) > 0: num_expr_str += "|" + ",".join(mediators_names) sym_mu = sp.Symbol("mu") sym_sigma = sp.Symbol("sigma", positive=True) sym_conditional_outcome = spstats.Normal(num_expr_str, sym_mu, sym_sigma) sym_directeffect_derivative = sp.Derivative(sym_conditional_outcome, sym_treatment) if estimand_type == EstimandType.NONPARAMETRIC_NIE: sym_effect = spstats.Expectation(sym_treatment_derivative * sym_outcome_derivative) elif estimand_type == EstimandType.NONPARAMETRIC_NDE: sym_effect = spstats.Expectation(sym_directeffect_derivative) sym_assumptions = { "Mediation": ( "{2} intercepts (blocks) all directed paths from {0} to {1} except the path {{{0}}}\N{RIGHTWARDS ARROW}{{{1}}}." ).format( ",".join(treatment_name), ",".join(outcome_name), ",".join(mediators_names), ), "First-stage-unconfoundedness": ( "If U\N{RIGHTWARDS ARROW}{{{0}}} and U\N{RIGHTWARDS ARROW}{{{1}}}" " then P({1}|{0},U) = P({1}|{0})" ).format(",".join(treatment_name), ",".join(mediators_names)), "Second-stage-unconfoundedness": ( "If U\N{RIGHTWARDS ARROW}{{{2}}} and U\N{RIGHTWARDS ARROW}{1}" " then P({1}|{2}, {0}, U) = P({1}|{2}, {0})" ).format(",".join(treatment_name), outcome_name, ",".join(mediators_names)), } else: raise ValueError( "Estimand type not supported. Supported estimand types are {0} or {1}'.".format( EstimandType.NONPARAMETRIC_NDE, EstimandType.NONPARAMETRIC_NIE, ) ) estimand = {"estimand": sym_effect, "assumptions": sym_assumptions} return estimand

import itertools import logging from enum import Enum from typing import Dict, List, Optional, Union import networkx as nx import sympy as sp import sympy.stats as spstats from dowhy.causal_identifier.efficient_backdoor import EfficientBackdoor from dowhy.causal_identifier.identified_estimand import IdentifiedEstimand from dowhy.graph import ( check_dseparation, check_valid_backdoor_set, check_valid_frontdoor_set, check_valid_mediation_set, do_surgery, get_all_directed_paths, get_backdoor_paths, get_descendants, get_instruments, has_directed_path, ) from dowhy.utils.api import parse_state logger = logging.getLogger(__name__) class EstimandType(Enum): # Average total effect NONPARAMETRIC_ATE = "nonparametric-ate" # Natural direct effect NONPARAMETRIC_NDE = "nonparametric-nde" # Natural indirect effect NONPARAMETRIC_NIE = "nonparametric-nie" # Controlled direct effect NONPARAMETRIC_CDE = "nonparametric-cde" class BackdoorAdjustment(Enum): # Backdoor method names BACKDOOR_DEFAULT = "default" BACKDOOR_EXHAUSTIVE = "exhaustive-search" BACKDOOR_MIN = "minimal-adjustment" BACKDOOR_MAX = "maximal-adjustment" BACKDOOR_EFFICIENT = "efficient-adjustment" BACKDOOR_MIN_EFFICIENT = "efficient-minimal-adjustment" BACKDOOR_MINCOST_EFFICIENT = "efficient-mincost-adjustment" MAX_BACKDOOR_ITERATIONS = 100000 METHOD_NAMES = { BackdoorAdjustment.BACKDOOR_DEFAULT, BackdoorAdjustment.BACKDOOR_EXHAUSTIVE, BackdoorAdjustment.BACKDOOR_MIN, BackdoorAdjustment.BACKDOOR_MAX, BackdoorAdjustment.BACKDOOR_EFFICIENT, BackdoorAdjustment.BACKDOOR_MIN_EFFICIENT, BackdoorAdjustment.BACKDOOR_MINCOST_EFFICIENT, } EFFICIENT_METHODS = { BackdoorAdjustment.BACKDOOR_EFFICIENT, BackdoorAdjustment.BACKDOOR_MIN_EFFICIENT, BackdoorAdjustment.BACKDOOR_MINCOST_EFFICIENT, } DEFAULT_BACKDOOR_METHOD = BackdoorAdjustment.BACKDOOR_DEFAULT class AutoIdentifier: """Class that implements different identification methods. Currently supports backdoor and instrumental variable identification methods. The identification is based on the causal graph provided. This class is for backwards compatibility with CausalModel Will be deprecated in the future in favor of function call auto_identify_effect() """ def __init__( self, estimand_type: EstimandType, backdoor_adjustment: BackdoorAdjustment = BackdoorAdjustment.BACKDOOR_DEFAULT, optimize_backdoor: bool = False, costs: Optional[List] = None, ): self.estimand_type = estimand_type self.backdoor_adjustment = backdoor_adjustment self.optimize_backdoor = optimize_backdoor self.costs = costs self.logger = logging.getLogger(__name__) def identify_effect( self, graph: nx.DiGraph, action_nodes: Union[str, List[str]], outcome_nodes: Union[str, List[str]], observed_nodes: Union[str, List[str]], conditional_node_names: List[str] = None, ): estimand = identify_effect_auto( graph, action_nodes, outcome_nodes, observed_nodes, self.estimand_type, conditional_node_names, self.backdoor_adjustment, self.optimize_backdoor, self.costs, ) estimand.identifier = self return estimand def identify_backdoor( self, graph: nx.DiGraph, action_nodes: List[str], outcome_nodes: List[str], observed_nodes: List[str], include_unobserved: bool = False, dseparation_algo: str = "default", direct_effect: bool = False, ): return identify_backdoor( graph, action_nodes, outcome_nodes, observed_nodes, self.backdoor_adjustment, include_unobserved, dseparation_algo, direct_effect, ) def identify_effect_auto( graph: nx.DiGraph, action_nodes: Union[str, List[str]], outcome_nodes: Union[str, List[str]], observed_nodes: Union[str, List[str]], estimand_type: EstimandType, conditional_node_names: List[str] = None, backdoor_adjustment: BackdoorAdjustment = BackdoorAdjustment.BACKDOOR_DEFAULT, optimize_backdoor: bool = False, costs: Optional[List] = None, ) -> IdentifiedEstimand: """Main method that returns an identified estimand (if one exists). If estimand_type is non-parametric ATE, then uses backdoor, instrumental variable and frontdoor identification methods, to check if an identified estimand exists, based on the causal graph. :param optimize_backdoor: if True, uses an optimised algorithm to compute the backdoor sets :param costs: non-negative costs associated with variables in the graph. Only used for estimand_type='non-parametric-ate' and backdoor_adjustment='efficient-mincost-adjustment'. If no costs are provided by the user, and backdoor_adjustment='efficient-mincost-adjustment', costs are assumed to be equal to one for all variables in the graph. :param conditional_node_names: variables that are used to determine treatment. If none are provided, it is assumed that the intervention is static. :returns: target estimand, an instance of the IdentifiedEstimand class """ observed_nodes = parse_state(observed_nodes) action_nodes = parse_state(action_nodes) outcome_nodes = parse_state(outcome_nodes) # First, check if there is a directed path from action to outcome if not has_directed_path(graph, action_nodes, outcome_nodes): logger.warn("No directed path from treatment to outcome. Causal Effect is zero.") return IdentifiedEstimand( None, treatment_variable=action_nodes, outcome_variable=outcome_nodes, no_directed_path=True, ) if estimand_type == EstimandType.NONPARAMETRIC_ATE: return identify_ate_effect( graph, action_nodes, outcome_nodes, observed_nodes, backdoor_adjustment, optimize_backdoor, estimand_type, costs, conditional_node_names, ) elif estimand_type == EstimandType.NONPARAMETRIC_NDE: return identify_nde_effect( graph, action_nodes, outcome_nodes, observed_nodes, backdoor_adjustment, estimand_type ) elif estimand_type == EstimandType.NONPARAMETRIC_NIE: return identify_nie_effect( graph, action_nodes, outcome_nodes, observed_nodes, backdoor_adjustment, estimand_type ) elif estimand_type == EstimandType.NONPARAMETRIC_CDE: return identify_cde_effect( graph, action_nodes, outcome_nodes, observed_nodes, backdoor_adjustment, estimand_type ) else: raise ValueError( "Estimand type is not supported. Use either {0}, {1}, or {2}.".format( EstimandType.NONPARAMETRIC_ATE, EstimandType.NONPARAMETRIC_CDE, EstimandType.NONPARAMETRIC_NDE, EstimandType.NONPARAMETRIC_NIE, ) ) def identify_ate_effect( graph: nx.DiGraph, action_nodes: List[str], outcome_nodes: List[str], observed_nodes: List[str], backdoor_adjustment: BackdoorAdjustment, optimize_backdoor: bool, estimand_type: EstimandType, costs: List, conditional_node_names: List[str] = None, ): estimands_dict = {} mediation_first_stage_confounders = None mediation_second_stage_confounders = None ### 1. BACKDOOR IDENTIFICATION # Pick algorithm to compute backdoor sets according to method chosen if backdoor_adjustment not in EFFICIENT_METHODS: # First, checking if there are any valid backdoor adjustment sets if optimize_backdoor == False: backdoor_sets = identify_backdoor(graph, action_nodes, outcome_nodes, observed_nodes, backdoor_adjustment) else: from dowhy.causal_identifier.backdoor import Backdoor path = Backdoor(graph, action_nodes, outcome_nodes) backdoor_sets = path.get_backdoor_vars() elif backdoor_adjustment in EFFICIENT_METHODS: backdoor_sets = identify_efficient_backdoor( graph, action_nodes, outcome_nodes, observed_nodes, backdoor_adjustment, costs, conditional_node_names=conditional_node_names, ) estimands_dict, backdoor_variables_dict = build_backdoor_estimands_dict( action_nodes, outcome_nodes, observed_nodes, backdoor_sets, estimands_dict ) # Setting default "backdoor" identification adjustment set default_backdoor_id = get_default_backdoor_set_id(graph, action_nodes, outcome_nodes, backdoor_variables_dict) if len(backdoor_variables_dict) > 0: estimands_dict["backdoor"] = estimands_dict.get(str(default_backdoor_id), None) backdoor_variables_dict["backdoor"] = backdoor_variables_dict.get(str(default_backdoor_id), None) else: estimands_dict["backdoor"] = None ### 2. INSTRUMENTAL VARIABLE IDENTIFICATION # Now checking if there is also a valid iv estimand instrument_names = get_instruments(graph, action_nodes, outcome_nodes) logger.info("Instrumental variables for treatment and outcome:" + str(instrument_names)) if len(instrument_names) > 0: iv_estimand_expr = construct_iv_estimand( action_nodes, outcome_nodes, instrument_names, ) logger.debug("Identified expression = " + str(iv_estimand_expr)) estimands_dict["iv"] = iv_estimand_expr else: estimands_dict["iv"] = None ### 3. FRONTDOOR IDENTIFICATION # Now checking if there is a valid frontdoor variable frontdoor_variables_names = identify_frontdoor(graph, action_nodes, outcome_nodes) logger.info("Frontdoor variables for treatment and outcome:" + str(frontdoor_variables_names)) if len(frontdoor_variables_names) > 0: frontdoor_estimand_expr = construct_frontdoor_estimand( action_nodes, outcome_nodes, frontdoor_variables_names, ) logger.debug("Identified expression = " + str(frontdoor_estimand_expr)) estimands_dict["frontdoor"] = frontdoor_estimand_expr mediation_first_stage_confounders = identify_mediation_first_stage_confounders( graph, action_nodes, outcome_nodes, frontdoor_variables_names, observed_nodes, backdoor_adjustment ) mediation_second_stage_confounders = identify_mediation_second_stage_confounders( graph, action_nodes, frontdoor_variables_names, outcome_nodes, observed_nodes, backdoor_adjustment ) else: estimands_dict["frontdoor"] = None # Finally returning the estimand object estimand = IdentifiedEstimand( None, treatment_variable=action_nodes, outcome_variable=outcome_nodes, estimand_type=estimand_type, estimands=estimands_dict, backdoor_variables=backdoor_variables_dict, instrumental_variables=instrument_names, frontdoor_variables=frontdoor_variables_names, mediation_first_stage_confounders=mediation_first_stage_confounders, mediation_second_stage_confounders=mediation_second_stage_confounders, default_backdoor_id=default_backdoor_id, ) return estimand def identify_cde_effect( graph: nx.DiGraph, action_nodes: List[str], outcome_nodes: List[str], observed_nodes: List[str], backdoor_adjustment: BackdoorAdjustment, estimand_type: EstimandType, ): """Identify controlled direct effect. For a definition, see Vanderwheele (2011). Controlled direct and mediated effects: definition, identification and bounds. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4193506/ Using do-calculus rules, identification yields a adjustment set. It is based on the principle that under a graph where the direct edge from treatment to outcome is removed, conditioning on the adjustment set should d-separate treatment and outcome. """ estimands_dict = {} # Pick algorithm to compute backdoor sets according to method chosen backdoor_sets = identify_backdoor( graph, action_nodes, outcome_nodes, observed_nodes, backdoor_adjustment, direct_effect=True ) estimands_dict, backdoor_variables_dict = build_backdoor_estimands_dict( action_nodes, outcome_nodes, observed_nodes, backdoor_sets, estimands_dict ) # Setting default "backdoor" identification adjustment set default_backdoor_id = get_default_backdoor_set_id(graph, action_nodes, outcome_nodes, backdoor_variables_dict) if len(backdoor_variables_dict) > 0: estimands_dict["backdoor"] = estimands_dict.get(str(default_backdoor_id), None) backdoor_variables_dict["backdoor"] = backdoor_variables_dict.get(str(default_backdoor_id), None) else: estimands_dict["backdoor"] = None # Finally returning the estimand object estimand = IdentifiedEstimand( None, treatment_variable=action_nodes, outcome_variable=outcome_nodes, estimand_type=estimand_type, estimands=estimands_dict, backdoor_variables=backdoor_variables_dict, instrumental_variables=None, frontdoor_variables=None, mediation_first_stage_confounders=None, mediation_second_stage_confounders=None, default_backdoor_id=default_backdoor_id, ) return estimand def identify_nie_effect( graph: nx.DiGraph, action_nodes: List[str], outcome_nodes: List[str], observed_nodes: List[str], backdoor_adjustment: BackdoorAdjustment, estimand_type: EstimandType, ): estimands_dict = {} ### 1. FIRST DOING BACKDOOR IDENTIFICATION # First, checking if there are any valid backdoor adjustment sets backdoor_sets = identify_backdoor(graph, action_nodes, outcome_nodes, observed_nodes, backdoor_adjustment) estimands_dict, backdoor_variables_dict = build_backdoor_estimands_dict( action_nodes, outcome_nodes, observed_nodes, backdoor_sets, estimands_dict ) # Setting default "backdoor" identification adjustment set default_backdoor_id = get_default_backdoor_set_id(graph, action_nodes, outcome_nodes, backdoor_variables_dict) backdoor_variables_dict["backdoor"] = backdoor_variables_dict.get(str(default_backdoor_id), None) ### 2. SECOND, CHECKING FOR MEDIATORS # Now checking if there are valid mediator variables estimands_dict = {} # Need to reinitialize this dictionary to avoid including the backdoor sets mediation_first_stage_confounders = None mediation_second_stage_confounders = None mediators_names = identify_mediation(graph, action_nodes, outcome_nodes) logger.info("Mediators for treatment and outcome:" + str(mediators_names)) if len(mediators_names) > 0: mediation_estimand_expr = construct_mediation_estimand( estimand_type, action_nodes, outcome_nodes, mediators_names, ) logger.debug("Identified expression = " + str(mediation_estimand_expr)) estimands_dict["mediation"] = mediation_estimand_expr mediation_first_stage_confounders = identify_mediation_first_stage_confounders( graph, action_nodes, outcome_nodes, mediators_names, observed_nodes, backdoor_adjustment ) mediation_second_stage_confounders = identify_mediation_second_stage_confounders( graph, action_nodes, mediators_names, outcome_nodes, observed_nodes, backdoor_adjustment ) else: estimands_dict["mediation"] = None # Finally returning the estimand object estimand = IdentifiedEstimand( None, treatment_variable=action_nodes, outcome_variable=outcome_nodes, estimand_type=estimand_type, estimands=estimands_dict, backdoor_variables=backdoor_variables_dict, instrumental_variables=None, frontdoor_variables=None, mediator_variables=mediators_names, mediation_first_stage_confounders=mediation_first_stage_confounders, mediation_second_stage_confounders=mediation_second_stage_confounders, default_backdoor_id=None, ) return estimand def identify_nde_effect( graph: nx.DiGraph, action_nodes: List[str], outcome_nodes: List[str], observed_nodes: List[str], backdoor_adjustment: BackdoorAdjustment, estimand_type: EstimandType, ): estimands_dict = {} ### 1. FIRST DOING BACKDOOR IDENTIFICATION # First, checking if there are any valid backdoor adjustment sets backdoor_sets = identify_backdoor(graph, action_nodes, outcome_nodes, observed_nodes, backdoor_adjustment) estimands_dict, backdoor_variables_dict = build_backdoor_estimands_dict( action_nodes, outcome_nodes, observed_nodes, backdoor_sets, estimands_dict ) # Setting default "backdoor" identification adjustment set default_backdoor_id = get_default_backdoor_set_id(graph, action_nodes, outcome_nodes, backdoor_variables_dict) backdoor_variables_dict["backdoor"] = backdoor_variables_dict.get(str(default_backdoor_id), None) ### 2. SECOND, CHECKING FOR MEDIATORS # Now checking if there are valid mediator variables estimands_dict = {} mediation_first_stage_confounders = None mediation_second_stage_confounders = None mediators_names = identify_mediation(graph, action_nodes, outcome_nodes) logger.info("Mediators for treatment and outcome:" + str(mediators_names)) if len(mediators_names) > 0: mediation_estimand_expr = construct_mediation_estimand( estimand_type, action_nodes, outcome_nodes, mediators_names, ) logger.debug("Identified expression = " + str(mediation_estimand_expr)) estimands_dict["mediation"] = mediation_estimand_expr mediation_first_stage_confounders = identify_mediation_first_stage_confounders( graph, action_nodes, outcome_nodes, mediators_names, observed_nodes, backdoor_adjustment ) mediation_second_stage_confounders = identify_mediation_second_stage_confounders( graph, action_nodes, mediators_names, outcome_nodes, observed_nodes, backdoor_adjustment ) else: estimands_dict["mediation"] = None # Finally returning the estimand object estimand = IdentifiedEstimand( None, treatment_variable=action_nodes, outcome_variable=outcome_nodes, estimand_type=estimand_type, estimands=estimands_dict, backdoor_variables=backdoor_variables_dict, instrumental_variables=None, frontdoor_variables=None, mediator_variables=mediators_names, mediation_first_stage_confounders=mediation_first_stage_confounders, mediation_second_stage_confounders=mediation_second_stage_confounders, default_backdoor_id=None, ) return estimand def identify_backdoor( graph: nx.DiGraph, action_nodes: List[str], outcome_nodes: List[str], observed_nodes: List[str], backdoor_adjustment: BackdoorAdjustment, include_unobserved: bool = False, dseparation_algo: str = "default", direct_effect: bool = False, ): backdoor_sets = [] backdoor_paths = None bdoor_graph = None observed_nodes = set(observed_nodes) if dseparation_algo == "naive": backdoor_paths = get_backdoor_paths(graph, action_nodes, outcome_nodes) elif dseparation_algo == "default": bdoor_graph = do_surgery( graph, action_nodes, target_node_names=outcome_nodes, remove_outgoing_edges=True, remove_only_direct_edges_to_target=direct_effect, ) else: raise ValueError(f"d-separation algorithm {dseparation_algo} is not supported") backdoor_adjustment = ( backdoor_adjustment if backdoor_adjustment != BackdoorAdjustment.BACKDOOR_DEFAULT else DEFAULT_BACKDOOR_METHOD ) # First, checking if empty set is a valid backdoor set empty_set = set() check = check_valid_backdoor_set( graph, action_nodes, outcome_nodes, empty_set, backdoor_paths=backdoor_paths, new_graph=bdoor_graph, dseparation_algo=dseparation_algo, ) if check["is_dseparated"]: backdoor_sets.append({"backdoor_set": empty_set}) # If the method is `minimal-adjustment`, return the empty set right away. if backdoor_adjustment == BackdoorAdjustment.BACKDOOR_MIN: return backdoor_sets # Second, checking for all other sets of variables. If include_unobserved is false, then only observed variables are eligible. eligible_variables = ( set([node for node in graph.nodes if include_unobserved or node in observed_nodes]) - set(action_nodes) - set(outcome_nodes) ) if direct_effect: # only remove descendants of Y # also allow any causes of Y that are not caused by T (for lower variance) eligible_variables -= get_descendants(graph, outcome_nodes) else: # remove descendants of T (mediators) and descendants of Y eligible_variables -= get_descendants(graph, action_nodes) # If var is d-separated from both treatment or outcome, it cannot # be a part of the backdoor set filt_eligible_variables = set() for var in eligible_variables: dsep_treat_var = check_dseparation(graph, action_nodes, parse_state(var), set()) dsep_outcome_var = check_dseparation(graph, outcome_nodes, parse_state(var), set()) if not dsep_outcome_var or not dsep_treat_var: filt_eligible_variables.add(var) if backdoor_adjustment in METHOD_NAMES: backdoor_sets, found_valid_adjustment_set = find_valid_adjustment_sets( graph, action_nodes, outcome_nodes, observed_nodes, backdoor_paths, bdoor_graph, dseparation_algo, backdoor_sets, filt_eligible_variables, backdoor_adjustment=backdoor_adjustment, max_iterations=MAX_BACKDOOR_ITERATIONS, ) if backdoor_adjustment == BackdoorAdjustment.BACKDOOR_DEFAULT and found_valid_adjustment_set: # repeat the above search with BACKDOOR_MIN backdoor_sets, _ = find_valid_adjustment_sets( graph, action_nodes, outcome_nodes, observed_nodes, backdoor_paths, bdoor_graph, dseparation_algo, backdoor_sets, filt_eligible_variables, backdoor_adjustment=BackdoorAdjustment.BACKDOOR_MIN, max_iterations=MAX_BACKDOOR_ITERATIONS, ) else: raise ValueError( f"Identifier method {backdoor_adjustment} not supported. Try one of the following: {METHOD_NAMES}" ) return backdoor_sets def identify_efficient_backdoor( graph: nx.DiGraph, action_nodes: List[str], outcome_nodes: List[str], observed_nodes: List[str], backdoor_adjustment: BackdoorAdjustment, costs: List, conditional_node_names: List[str] = None, ): """Method implementing algorithms to compute efficient backdoor sets, as described in Rotnitzky and Smucler (2020), Smucler, Sapienza and Rotnitzky (2021) and Smucler and Rotnitzky (2022). For backdoor_adjustment='efficient-adjustment', computes an optimal backdoor set, that is, a backdoor set comprised of observable variables that yields non-parametric estimators of the interventional mean with the smallest asymptotic variance among those that are based on observable backdoor sets. This optimal backdoor set always exists when no variables are latent, and the algorithm is guaranteed to compute it in this case. Under a non-parametric graphical model with latent variables, such a backdoor set can fail to exist. When certain sufficient conditions under which it is known that such a backdoor set exists are not satisfied, an error is raised. For backdoor_adjustment='efficient-minimal-adjustment', computes an optimal minimal backdoor set, that is, a minimal backdoor set comprised of observable variables that yields non-parametric estimators of the interventional mean with the smallest asymptotic variance among those that are based on observable minimal backdoor sets. For backdoor_adjustment='efficient-mincost-adjustment', computes an optimal minimum cost backdoor set, that is, a minimum cost backdoor set comprised of observable variables that yields non-parametric estimators of the interventional mean with the smallest asymptotic variance among those that are based on observable minimum cost backdoor sets. The cost of a backdoor set is defined as the sum of the costs of the variables that comprise it. The various optimal backdoor sets computed by this method are not only optimal under non-parametric graphical models and non-parametric estimators of interventional mean, but also under linear graphical models and OLS estimators, per results in Henckel, Perkovic and Maathuis (2020). :param costs: a list with non-negative costs associated with variables in the graph. Only used for estimatand_type='non-parametric-ate' and backdoor_adjustment='efficient-mincost-adjustment'. If not costs are provided by the user, and backdoor_adjustment='efficient-mincost-adjustment', costs are assumed to be equal to one for all variables in the graph. The structure of the list should be of the form [(node, {"cost": x}) for node in nodes]. :param conditional_node_names: variables that are used to determine treatment. If none are provided, it is assumed that the intervention sets the treatment to a constant. :returns: backdoor_sets, a list of dictionaries, with each dictionary having as values a backdoor set. """ if costs is None and backdoor_adjustment == "efficient-mincost-adjustment": logger.warning("No costs were passed, so they will be assumed to be constant and equal to 1.") efficient_bd = EfficientBackdoor( graph=graph, action_nodes=action_nodes, outcome_nodes=outcome_nodes, observed_nodes=observed_nodes, conditional_node_names=conditional_node_names, costs=costs, ) if backdoor_adjustment == BackdoorAdjustment.BACKDOOR_EFFICIENT: backdoor_set = efficient_bd.optimal_adj_set() backdoor_sets = [{"backdoor_set": tuple(backdoor_set)}] elif backdoor_adjustment == BackdoorAdjustment.BACKDOOR_MIN_EFFICIENT: backdoor_set = efficient_bd.optimal_minimal_adj_set() backdoor_sets = [{"backdoor_set": tuple(backdoor_set)}] elif backdoor_adjustment == BackdoorAdjustment.BACKDOOR_MINCOST_EFFICIENT: backdoor_set = efficient_bd.optimal_mincost_adj_set() backdoor_sets = [{"backdoor_set": tuple(backdoor_set)}] return backdoor_sets def find_valid_adjustment_sets( graph: nx.DiGraph, action_nodes: List[str], outcome_nodes: List[str], observed_nodes: List[str], backdoor_paths: List, bdoor_graph: nx.DiGraph, dseparation_algo: str, backdoor_sets: List, filt_eligible_variables: List, backdoor_adjustment: BackdoorAdjustment, max_iterations: int, ): num_iterations = 0 found_valid_adjustment_set = False is_all_observed = set(graph.nodes) == set(observed_nodes) # If `minimal-adjustment` method is specified, start the search from the set with minimum size. Otherwise, start from the largest. set_sizes = ( range(1, len(filt_eligible_variables) + 1, 1) if backdoor_adjustment == BackdoorAdjustment.BACKDOOR_MIN else range(len(filt_eligible_variables), 0, -1) ) for size_candidate_set in set_sizes: for candidate_set in itertools.combinations(filt_eligible_variables, size_candidate_set): check = check_valid_backdoor_set( graph, action_nodes, outcome_nodes, candidate_set, backdoor_paths=backdoor_paths, new_graph=bdoor_graph, dseparation_algo=dseparation_algo, ) logger.debug( "Candidate backdoor set: {0}, is_dseparated: {1}".format(candidate_set, check["is_dseparated"]) ) if check["is_dseparated"]: backdoor_sets.append({"backdoor_set": candidate_set}) found_valid_adjustment_set = True num_iterations += 1 if backdoor_adjustment == BackdoorAdjustment.BACKDOOR_EXHAUSTIVE and num_iterations > max_iterations: logger.warning(f"Max number of iterations {max_iterations} reached.") break # If the backdoor method is `maximal-adjustment` or `minimal-adjustment`, return the first found adjustment set. if ( backdoor_adjustment in { BackdoorAdjustment.BACKDOOR_DEFAULT, BackdoorAdjustment.BACKDOOR_MAX, BackdoorAdjustment.BACKDOOR_MIN, } and found_valid_adjustment_set ): break # If all variables are observed, and the biggest eligible set # does not satisfy backdoor, then none of its subsets will. if ( backdoor_adjustment in {BackdoorAdjustment.BACKDOOR_DEFAULT, BackdoorAdjustment.BACKDOOR_MAX} and is_all_observed ): break if num_iterations > max_iterations: logger.warning(f"Max number of iterations {max_iterations} reached. Could not find a valid backdoor set.") break return backdoor_sets, found_valid_adjustment_set def get_default_backdoor_set_id( graph: nx.DiGraph, action_nodes: List[str], outcome_nodes: List[str], backdoor_sets_dict: Dict ): # Adding a None estimand if no backdoor set found if len(backdoor_sets_dict) == 0: return None # Default set contains minimum possible number of instrumental variables, to prevent lowering variance in the treatment variable. instrument_names = set(get_instruments(graph, action_nodes, outcome_nodes)) iv_count_dict = { key: len(set(bdoor_set).intersection(instrument_names)) for key, bdoor_set in backdoor_sets_dict.items() } min_iv_count = min(iv_count_dict.values()) min_iv_keys = {key for key, iv_count in iv_count_dict.items() if iv_count == min_iv_count} min_iv_backdoor_sets_dict = {key: backdoor_sets_dict[key] for key in min_iv_keys} # Default set is the one with the least number of adjustment variables (optimizing for efficiency) min_set_length = 1000000 default_key = None for key, bdoor_set in min_iv_backdoor_sets_dict.items(): if len(bdoor_set) < min_set_length: min_set_length = len(bdoor_set) default_key = key return default_key def build_backdoor_estimands_dict( treatment_names: List[str], outcome_names: List[str], observed_nodes: List[str], backdoor_sets: List[str], estimands_dict: Dict, ): """Build the final dict for backdoor sets by filtering unobserved variables if needed.""" backdoor_variables_dict = {} observed_nodes = set(observed_nodes) is_identified = [set(bset["backdoor_set"]).issubset(observed_nodes) for bset in backdoor_sets] if any(is_identified): logger.info("Causal effect can be identified.") backdoor_sets_arr = [ list(bset["backdoor_set"]) for bset in backdoor_sets if set(bset["backdoor_set"]).issubset(observed_nodes) ] else: # there is unobserved confounding logger.warning("Backdoor identification failed.") backdoor_sets_arr = [] for i in range(len(backdoor_sets_arr)): backdoor_estimand_expr = construct_backdoor_estimand(treatment_names, outcome_names, backdoor_sets_arr[i]) logger.debug("Identified expression = " + str(backdoor_estimand_expr)) estimands_dict["backdoor" + str(i + 1)] = backdoor_estimand_expr backdoor_variables_dict["backdoor" + str(i + 1)] = backdoor_sets_arr[i] return estimands_dict, backdoor_variables_dict def identify_frontdoor( graph: nx.DiGraph, action_nodes: List[str], outcome_nodes: List[str], dseparation_algo: str = "default" ): """Find a valid frontdoor variable if it exists. Currently only supports a single variable frontdoor set. """ frontdoor_var = None frontdoor_paths = None fdoor_graph = None if dseparation_algo == "default": cond1_graph = do_surgery(graph, action_nodes, remove_incoming_edges=True) bdoor_graph1 = do_surgery(graph, action_nodes, remove_outgoing_edges=True) elif dseparation_algo == "naive": frontdoor_paths = get_all_directed_paths(graph, action_nodes, outcome_nodes) else: raise ValueError(f"d-separation algorithm {dseparation_algo} is not supported") eligible_variables = ( get_descendants(graph, action_nodes) - set(outcome_nodes) - set(get_descendants(graph, outcome_nodes)) ) # For simplicity, assuming a one-variable frontdoor set for candidate_var in eligible_variables: # Cond 1: All directed paths intercepted by candidate_var cond1 = check_valid_frontdoor_set( graph, action_nodes, outcome_nodes, parse_state(candidate_var), frontdoor_paths=frontdoor_paths, new_graph=cond1_graph, dseparation_algo=dseparation_algo, ) logger.debug("Candidate frontdoor set: {0}, is_dseparated: {1}".format(candidate_var, cond1)) if not cond1: continue # Cond 2: No confounding between treatment and candidate var cond2 = check_valid_backdoor_set( graph, action_nodes, parse_state(candidate_var), set(), backdoor_paths=None, new_graph=bdoor_graph1, dseparation_algo=dseparation_algo, ) if not cond2: continue # Cond 3: treatment blocks all confounding between candidate_var and outcome bdoor_graph2 = do_surgery(graph, candidate_var, remove_outgoing_edges=True) cond3 = check_valid_backdoor_set( graph, parse_state(candidate_var), outcome_nodes, action_nodes, backdoor_paths=None, new_graph=bdoor_graph2, dseparation_algo=dseparation_algo, ) is_valid_frontdoor = cond1 and cond2 and cond3 if is_valid_frontdoor: frontdoor_var = candidate_var break return parse_state(frontdoor_var) def identify_mediation(graph: nx.DiGraph, action_nodes: List[str], outcome_nodes: List[str]): """Find a valid mediator if it exists. Currently only supports a single variable mediator set. """ mediation_var = None mediation_paths = get_all_directed_paths(graph, action_nodes, outcome_nodes) eligible_variables = get_descendants(graph, action_nodes) - set(outcome_nodes) # For simplicity, assuming a one-variable mediation set for candidate_var in eligible_variables: is_valid_mediation = check_valid_mediation_set( graph, action_nodes, outcome_nodes, parse_state(candidate_var), mediation_paths=mediation_paths, ) logger.debug("Candidate mediation set: {0}, on_mediating_path: {1}".format(candidate_var, is_valid_mediation)) if is_valid_mediation: mediation_var = candidate_var break return parse_state(mediation_var) def identify_mediation_first_stage_confounders( graph: nx.DiGraph, action_nodes: List[str], outcome_nodes: List[str], mediator_nodes: List[str], observed_nodes: List[str], backdoor_adjustment: BackdoorAdjustment, ): # Create estimands dict as per the API for backdoor, but do not return it estimands_dict = {} backdoor_sets = identify_backdoor(graph, action_nodes, mediator_nodes, observed_nodes, backdoor_adjustment) estimands_dict, backdoor_variables_dict = build_backdoor_estimands_dict( action_nodes, mediator_nodes, observed_nodes, backdoor_sets, estimands_dict, ) # Setting default "backdoor" identification adjustment set default_backdoor_id = get_default_backdoor_set_id(graph, action_nodes, outcome_nodes, backdoor_variables_dict) estimands_dict["backdoor"] = estimands_dict.get(str(default_backdoor_id), None) backdoor_variables_dict["backdoor"] = backdoor_variables_dict.get(str(default_backdoor_id), None) return backdoor_variables_dict def identify_mediation_second_stage_confounders( graph: nx.DiGraph, action_nodes: List[str], mediator_nodes: List[str], outcome_nodes: List[str], observed_nodes: List[str], backdoor_adjustment: BackdoorAdjustment, ): # Create estimands dict as per the API for backdoor, but do not return it estimands_dict = {} backdoor_sets = identify_backdoor(graph, mediator_nodes, outcome_nodes, observed_nodes, backdoor_adjustment) estimands_dict, backdoor_variables_dict = build_backdoor_estimands_dict( mediator_nodes, outcome_nodes, observed_nodes, backdoor_sets, estimands_dict, ) # Setting default "backdoor" identification adjustment set default_backdoor_id = get_default_backdoor_set_id(graph, action_nodes, outcome_nodes, backdoor_variables_dict) estimands_dict["backdoor"] = estimands_dict.get(str(default_backdoor_id), None) backdoor_variables_dict["backdoor"] = backdoor_variables_dict.get(str(default_backdoor_id), None) return backdoor_variables_dict def construct_backdoor_estimand(treatment_name: List[str], outcome_name: List[str], common_causes: List[str]): # TODO: outputs string for now, but ideally should do symbolic # expressions Mon 19 Feb 2018 04:54:17 PM DST # TODO Better support for multivariate treatments expr = None outcome_name = outcome_name[0] num_expr_str = outcome_name if len(common_causes) > 0: num_expr_str += "|" + ",".join(common_causes) expr = "d(" + num_expr_str + ")/d" + ",".join(treatment_name) sym_mu = sp.Symbol("mu") sym_sigma = sp.Symbol("sigma", positive=True) sym_outcome = spstats.Normal(num_expr_str, sym_mu, sym_sigma) sym_treatment_symbols = [sp.Symbol(t) for t in treatment_name] sym_treatment = sp.Array(sym_treatment_symbols) sym_conditional_outcome = spstats.Expectation(sym_outcome) sym_effect = sp.Derivative(sym_conditional_outcome, sym_treatment) sym_assumptions = { "Unconfoundedness": ( "If U\N{RIGHTWARDS ARROW}{{{0}}} and U\N{RIGHTWARDS ARROW}{1}" " then P({1}|{0},{2},U) = P({1}|{0},{2})" ).format(",".join(treatment_name), outcome_name, ",".join(common_causes)) } estimand = {"estimand": sym_effect, "assumptions": sym_assumptions} return estimand def construct_iv_estimand(treatment_name: List[str], outcome_name: List[str], instrument_names: List[str]): # TODO: support multivariate treatments better. expr = None outcome_name = outcome_name[0] sym_outcome = spstats.Normal(outcome_name, 0, 1) sym_treatment_symbols = [spstats.Normal(t, 0, 1) for t in treatment_name] sym_treatment = sp.Array(sym_treatment_symbols) sym_instrument_symbols = [sp.Symbol(inst) for inst in instrument_names] sym_instrument = sp.Array(sym_instrument_symbols) # ",".join(instrument_names)) sym_outcome_derivative = sp.Derivative(sym_outcome, sym_instrument) sym_treatment_derivative = sp.Derivative(sym_treatment, sym_instrument) sym_effect = spstats.Expectation(sym_outcome_derivative / sym_treatment_derivative) sym_assumptions = { "As-if-random": ( "If U\N{RIGHTWARDS ARROW}\N{RIGHTWARDS ARROW}{0} then " "\N{NOT SIGN}(U \N{RIGHTWARDS ARROW}\N{RIGHTWARDS ARROW}{{{1}}})" ).format(outcome_name, ",".join(instrument_names)), "Exclusion": ( "If we remove {{{0}}}\N{RIGHTWARDS ARROW}{{{1}}}, then " "\N{NOT SIGN}({{{0}}}\N{RIGHTWARDS ARROW}{2})" ).format(",".join(instrument_names), ",".join(treatment_name), outcome_name), } estimand = {"estimand": sym_effect, "assumptions": sym_assumptions} return estimand def construct_frontdoor_estimand( treatment_name: List[str], outcome_name: List[str], frontdoor_variables_names: List[str] ): # TODO: support multivariate treatments better. expr = None outcome_name = outcome_name[0] sym_outcome = spstats.Normal(outcome_name, 0, 1) sym_treatment_symbols = [spstats.Normal(t, 0, 1) for t in treatment_name] sym_treatment = sp.Array(sym_treatment_symbols) sym_frontdoor_symbols = [sp.Symbol(inst) for inst in frontdoor_variables_names] sym_frontdoor = sp.Array(sym_frontdoor_symbols) # ",".join(instrument_names)) sym_outcome_derivative = sp.Derivative(sym_outcome, sym_frontdoor) sym_treatment_derivative = sp.Derivative(sym_frontdoor, sym_treatment) sym_effect = spstats.Expectation(sym_treatment_derivative * sym_outcome_derivative) sym_assumptions = { "Full-mediation": ("{2} intercepts (blocks) all directed paths from {0} to {1}.").format( ",".join(treatment_name), ",".join(outcome_name), ",".join(frontdoor_variables_names), ), "First-stage-unconfoundedness": ( "If U\N{RIGHTWARDS ARROW}{{{0}}} and U\N{RIGHTWARDS ARROW}{{{1}}}" " then P({1}|{0},U) = P({1}|{0})" ).format(",".join(treatment_name), ",".join(frontdoor_variables_names)), "Second-stage-unconfoundedness": ( "If U\N{RIGHTWARDS ARROW}{{{2}}} and U\N{RIGHTWARDS ARROW}{1}" " then P({1}|{2}, {0}, U) = P({1}|{2}, {0})" ).format( ",".join(treatment_name), outcome_name, ",".join(frontdoor_variables_names), ), } estimand = {"estimand": sym_effect, "assumptions": sym_assumptions} return estimand def construct_mediation_estimand( estimand_type: EstimandType, action_nodes: List[str], outcome_nodes: List[str], mediator_nodes: List[str] ): # TODO: support multivariate treatments better. expr = None if estimand_type in ( EstimandType.NONPARAMETRIC_NDE, EstimandType.NONPARAMETRIC_NIE, ): outcome_nodes = outcome_nodes[0] sym_outcome = spstats.Normal(outcome_nodes, 0, 1) sym_treatment_symbols = [spstats.Normal(t, 0, 1) for t in action_nodes] sym_treatment = sp.Array(sym_treatment_symbols) sym_mediators_symbols = [sp.Symbol(inst) for inst in mediator_nodes] sym_mediators = sp.Array(sym_mediators_symbols) sym_outcome_derivative = sp.Derivative(sym_outcome, sym_mediators) sym_treatment_derivative = sp.Derivative(sym_mediators, sym_treatment) # For direct effect num_expr_str = outcome_nodes if len(mediator_nodes) > 0: num_expr_str += "|" + ",".join(mediator_nodes) sym_mu = sp.Symbol("mu") sym_sigma = sp.Symbol("sigma", positive=True) sym_conditional_outcome = spstats.Normal(num_expr_str, sym_mu, sym_sigma) sym_directeffect_derivative = sp.Derivative(sym_conditional_outcome, sym_treatment) if estimand_type == EstimandType.NONPARAMETRIC_NIE: sym_effect = spstats.Expectation(sym_treatment_derivative * sym_outcome_derivative) elif estimand_type == EstimandType.NONPARAMETRIC_NDE: sym_effect = spstats.Expectation(sym_directeffect_derivative) sym_assumptions = { "Mediation": ( "{2} intercepts (blocks) all directed paths from {0} to {1} except the path {{{0}}}\N{RIGHTWARDS ARROW}{{{1}}}." ).format( ",".join(action_nodes), ",".join(outcome_nodes), ",".join(mediator_nodes), ), "First-stage-unconfoundedness": ( "If U\N{RIGHTWARDS ARROW}{{{0}}} and U\N{RIGHTWARDS ARROW}{{{1}}}" " then P({1}|{0},U) = P({1}|{0})" ).format(",".join(action_nodes), ",".join(mediator_nodes)), "Second-stage-unconfoundedness": ( "If U\N{RIGHTWARDS ARROW}{{{2}}} and U\N{RIGHTWARDS ARROW}{1}" " then P({1}|{2}, {0}, U) = P({1}|{2}, {0})" ).format(",".join(action_nodes), outcome_nodes, ",".join(mediator_nodes)), } else: raise ValueError( "Estimand type not supported. Supported estimand types are {0} or {1}'.".format( EstimandType.NONPARAMETRIC_NDE, EstimandType.NONPARAMETRIC_NIE, ) ) estimand = {"estimand": sym_effect, "assumptions": sym_assumptions} return estimand

bloebp

4fd0a92bd2fabbacfe6f225ea9637d3e8f08407e

2a8e49a77eb43a74d7ee6fc0925a376cd60335f2

thanks for simplifying this function

amit-sharma

py-why/dowhy

Proposal: Finalize functional API refactor - deprecate causal graph

- The graph should now be defined via a networkx graph. Most identification methods now expect an additional "observed_nodes" parameter accordingly. - CausalModel and CausalGraph still exist and should be compatible with the old API. Open task is still to replace the usage of CausalModel in the tests and notebooks. There are also some smaller details with the identification methods, which should be double checked.

2023-05-16 16:07:18+00:00

2023-11-27 17:48:56+00:00

dowhy/causal_identifier/auto_identifier.py

import itertools import logging from enum import Enum from typing import Dict, List, Optional, Union import sympy as sp import sympy.stats as spstats from dowhy.causal_graph import CausalGraph from dowhy.causal_identifier.efficient_backdoor import EfficientBackdoor from dowhy.causal_identifier.identified_estimand import IdentifiedEstimand from dowhy.utils.api import parse_state logger = logging.getLogger(__name__) class EstimandType(Enum): # Average total effect NONPARAMETRIC_ATE = "nonparametric-ate" # Natural direct effect NONPARAMETRIC_NDE = "nonparametric-nde" # Natural indirect effect NONPARAMETRIC_NIE = "nonparametric-nie" # Controlled direct effect NONPARAMETRIC_CDE = "nonparametric-cde" class BackdoorAdjustment(Enum): # Backdoor method names BACKDOOR_DEFAULT = "default" BACKDOOR_EXHAUSTIVE = "exhaustive-search" BACKDOOR_MIN = "minimal-adjustment" BACKDOOR_MAX = "maximal-adjustment" BACKDOOR_EFFICIENT = "efficient-adjustment" BACKDOOR_MIN_EFFICIENT = "efficient-minimal-adjustment" BACKDOOR_MINCOST_EFFICIENT = "efficient-mincost-adjustment" MAX_BACKDOOR_ITERATIONS = 100000 METHOD_NAMES = { BackdoorAdjustment.BACKDOOR_DEFAULT, BackdoorAdjustment.BACKDOOR_EXHAUSTIVE, BackdoorAdjustment.BACKDOOR_MIN, BackdoorAdjustment.BACKDOOR_MAX, BackdoorAdjustment.BACKDOOR_EFFICIENT, BackdoorAdjustment.BACKDOOR_MIN_EFFICIENT, BackdoorAdjustment.BACKDOOR_MINCOST_EFFICIENT, } EFFICIENT_METHODS = { BackdoorAdjustment.BACKDOOR_EFFICIENT, BackdoorAdjustment.BACKDOOR_MIN_EFFICIENT, BackdoorAdjustment.BACKDOOR_MINCOST_EFFICIENT, } DEFAULT_BACKDOOR_METHOD = BackdoorAdjustment.BACKDOOR_DEFAULT class AutoIdentifier: """Class that implements different identification methods. Currently supports backdoor and instrumental variable identification methods. The identification is based on the causal graph provided. This class is for backwards compatibility with CausalModel Will be deprecated in the future in favor of function call auto_identify_effect() """ def __init__( self, estimand_type: EstimandType, backdoor_adjustment: BackdoorAdjustment = BackdoorAdjustment.BACKDOOR_DEFAULT, proceed_when_unidentifiable: bool = False, optimize_backdoor: bool = False, costs: Optional[List] = None, ): self.estimand_type = estimand_type self.backdoor_adjustment = backdoor_adjustment self._proceed_when_unidentifiable = proceed_when_unidentifiable self.optimize_backdoor = optimize_backdoor self.costs = costs self.logger = logging.getLogger(__name__) def identify_effect( self, graph: CausalGraph, treatment_name: Union[str, List[str]], outcome_name: Union[str, List[str]], conditional_node_names: List[str] = None, **kwargs, ): estimand = identify_effect_auto( graph, treatment_name, outcome_name, self.estimand_type, conditional_node_names, self.backdoor_adjustment, self._proceed_when_unidentifiable, self.optimize_backdoor, self.costs, **kwargs, ) estimand.identifier = self return estimand def identify_backdoor( self, graph: CausalGraph, treatment_name: List[str], outcome_name: str, include_unobserved: bool = False, dseparation_algo: str = "default", direct_effect: bool = False, ): return identify_backdoor( graph, treatment_name, outcome_name, self.backdoor_adjustment, include_unobserved, dseparation_algo, direct_effect, ) def identify_effect_auto( graph: CausalGraph, treatment_name: Union[str, List[str]], outcome_name: Union[str, List[str]], estimand_type: EstimandType, conditional_node_names: List[str] = None, backdoor_adjustment: BackdoorAdjustment = BackdoorAdjustment.BACKDOOR_DEFAULT, proceed_when_unidentifiable: bool = False, optimize_backdoor: bool = False, costs: Optional[List] = None, **kwargs, ) -> IdentifiedEstimand: """Main method that returns an identified estimand (if one exists). If estimand_type is non-parametric ATE, then uses backdoor, instrumental variable and frontdoor identification methods, to check if an identified estimand exists, based on the causal graph. :param optimize_backdoor: if True, uses an optimised algorithm to compute the backdoor sets :param costs: non-negative costs associated with variables in the graph. Only used for estimand_type='non-parametric-ate' and backdoor_adjustment='efficient-mincost-adjustment'. If no costs are provided by the user, and backdoor_adjustment='efficient-mincost-adjustment', costs are assumed to be equal to one for all variables in the graph. :param conditional_node_names: variables that are used to determine treatment. If none are provided, it is assumed that the intervention is static. :returns: target estimand, an instance of the IdentifiedEstimand class """ treatment_name = parse_state(treatment_name) outcome_name = parse_state(outcome_name) # First, check if there is a directed path from action to outcome if not graph.has_directed_path(treatment_name, outcome_name): logger.warn("No directed path from treatment to outcome. Causal Effect is zero.") return IdentifiedEstimand( None, treatment_variable=treatment_name, outcome_variable=outcome_name, no_directed_path=True, ) if estimand_type == EstimandType.NONPARAMETRIC_ATE: return identify_ate_effect( graph, treatment_name, outcome_name, backdoor_adjustment, optimize_backdoor, estimand_type, costs, conditional_node_names, proceed_when_unidentifiable, ) elif estimand_type == EstimandType.NONPARAMETRIC_NDE: return identify_nde_effect( graph, treatment_name, outcome_name, backdoor_adjustment, estimand_type, proceed_when_unidentifiable ) elif estimand_type == EstimandType.NONPARAMETRIC_NIE: return identify_nie_effect( graph, treatment_name, outcome_name, backdoor_adjustment, estimand_type, proceed_when_unidentifiable ) elif estimand_type == EstimandType.NONPARAMETRIC_CDE: return identify_cde_effect( graph, treatment_name, outcome_name, backdoor_adjustment, estimand_type, proceed_when_unidentifiable ) else: raise ValueError( "Estimand type is not supported. Use either {0}, {1}, or {2}.".format( EstimandType.NONPARAMETRIC_ATE, EstimandType.NONPARAMETRIC_CDE, EstimandType.NONPARAMETRIC_NDE, EstimandType.NONPARAMETRIC_NIE, ) ) def identify_ate_effect( graph: CausalGraph, treatment_name: List[str], outcome_name: str, backdoor_adjustment: BackdoorAdjustment, optimize_backdoor: bool, estimand_type: EstimandType, costs: List, conditional_node_names: List[str] = None, proceed_when_unidentifiable: bool = False, ): estimands_dict = {} mediation_first_stage_confounders = None mediation_second_stage_confounders = None ### 1. BACKDOOR IDENTIFICATION # Pick algorithm to compute backdoor sets according to method chosen if backdoor_adjustment not in EFFICIENT_METHODS: # First, checking if there are any valid backdoor adjustment sets if optimize_backdoor == False: backdoor_sets = identify_backdoor(graph, treatment_name, outcome_name, backdoor_adjustment) else: from dowhy.causal_identifier.backdoor import Backdoor path = Backdoor(graph._graph, treatment_name, outcome_name) backdoor_sets = path.get_backdoor_vars() elif backdoor_adjustment in EFFICIENT_METHODS: backdoor_sets = identify_efficient_backdoor( graph, backdoor_adjustment, costs, conditional_node_names=conditional_node_names ) estimands_dict, backdoor_variables_dict = build_backdoor_estimands_dict( graph, treatment_name, outcome_name, backdoor_sets, estimands_dict ) # Setting default "backdoor" identification adjustment set default_backdoor_id = get_default_backdoor_set_id(graph, treatment_name, outcome_name, backdoor_variables_dict) if len(backdoor_variables_dict) > 0: estimands_dict["backdoor"] = estimands_dict.get(str(default_backdoor_id), None) backdoor_variables_dict["backdoor"] = backdoor_variables_dict.get(str(default_backdoor_id), None) else: estimands_dict["backdoor"] = None ### 2. INSTRUMENTAL VARIABLE IDENTIFICATION # Now checking if there is also a valid iv estimand instrument_names = graph.get_instruments(treatment_name, outcome_name) logger.info("Instrumental variables for treatment and outcome:" + str(instrument_names)) if len(instrument_names) > 0: iv_estimand_expr = construct_iv_estimand( treatment_name, outcome_name, instrument_names, ) logger.debug("Identified expression = " + str(iv_estimand_expr)) estimands_dict["iv"] = iv_estimand_expr else: estimands_dict["iv"] = None ### 3. FRONTDOOR IDENTIFICATION # Now checking if there is a valid frontdoor variable frontdoor_variables_names = identify_frontdoor(graph, treatment_name, outcome_name) logger.info("Frontdoor variables for treatment and outcome:" + str(frontdoor_variables_names)) if len(frontdoor_variables_names) > 0: frontdoor_estimand_expr = construct_frontdoor_estimand( treatment_name, outcome_name, frontdoor_variables_names, ) logger.debug("Identified expression = " + str(frontdoor_estimand_expr)) estimands_dict["frontdoor"] = frontdoor_estimand_expr mediation_first_stage_confounders = identify_mediation_first_stage_confounders( graph, treatment_name, outcome_name, frontdoor_variables_names, backdoor_adjustment ) mediation_second_stage_confounders = identify_mediation_second_stage_confounders( graph, treatment_name, frontdoor_variables_names, outcome_name, backdoor_adjustment ) else: estimands_dict["frontdoor"] = None # Finally returning the estimand object estimand = IdentifiedEstimand( None, treatment_variable=treatment_name, outcome_variable=outcome_name, estimand_type=estimand_type, estimands=estimands_dict, backdoor_variables=backdoor_variables_dict, instrumental_variables=instrument_names, frontdoor_variables=frontdoor_variables_names, mediation_first_stage_confounders=mediation_first_stage_confounders, mediation_second_stage_confounders=mediation_second_stage_confounders, default_backdoor_id=default_backdoor_id, ) return estimand def identify_cde_effect( graph: CausalGraph, treatment_name: List[str], outcome_name: str, backdoor_adjustment: BackdoorAdjustment, estimand_type: EstimandType, proceed_when_unidentifiable: bool = False, ): """Identify controlled direct effect. For a definition, see Vanderwheele (2011). Controlled direct and mediated effects: definition, identification and bounds. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4193506/ Using do-calculus rules, identification yields a adjustment set. It is based on the principle that under a graph where the direct edge from treatment to outcome is removed, conditioning on the adjustment set should d-separate treatment and outcome. """ estimands_dict = {} # Pick algorithm to compute backdoor sets according to method chosen backdoor_sets = identify_backdoor(graph, treatment_name, outcome_name, backdoor_adjustment, direct_effect=True) estimands_dict, backdoor_variables_dict = build_backdoor_estimands_dict( graph, treatment_name, outcome_name, backdoor_sets, estimands_dict ) # Setting default "backdoor" identification adjustment set default_backdoor_id = get_default_backdoor_set_id(graph, treatment_name, outcome_name, backdoor_variables_dict) if len(backdoor_variables_dict) > 0: estimands_dict["backdoor"] = estimands_dict.get(str(default_backdoor_id), None) backdoor_variables_dict["backdoor"] = backdoor_variables_dict.get(str(default_backdoor_id), None) else: estimands_dict["backdoor"] = None # Finally returning the estimand object estimand = IdentifiedEstimand( None, treatment_variable=treatment_name, outcome_variable=outcome_name, estimand_type=estimand_type, estimands=estimands_dict, backdoor_variables=backdoor_variables_dict, instrumental_variables=None, frontdoor_variables=None, mediation_first_stage_confounders=None, mediation_second_stage_confounders=None, default_backdoor_id=default_backdoor_id, ) return estimand def identify_nie_effect( graph: CausalGraph, treatment_name: List[str], outcome_name: str, backdoor_adjustment: BackdoorAdjustment, estimand_type: EstimandType, proceed_when_unidentifiable: bool = False, ): estimands_dict = {} ### 1. FIRST DOING BACKDOOR IDENTIFICATION # First, checking if there are any valid backdoor adjustment sets backdoor_sets = identify_backdoor(graph, treatment_name, outcome_name, backdoor_adjustment) estimands_dict, backdoor_variables_dict = build_backdoor_estimands_dict( graph, treatment_name, outcome_name, backdoor_sets, estimands_dict ) # Setting default "backdoor" identification adjustment set default_backdoor_id = get_default_backdoor_set_id(graph, treatment_name, outcome_name, backdoor_variables_dict) backdoor_variables_dict["backdoor"] = backdoor_variables_dict.get(str(default_backdoor_id), None) ### 2. SECOND, CHECKING FOR MEDIATORS # Now checking if there are valid mediator variables estimands_dict = {} # Need to reinitialize this dictionary to avoid including the backdoor sets mediation_first_stage_confounders = None mediation_second_stage_confounders = None mediators_names = identify_mediation(graph, treatment_name, outcome_name) logger.info("Mediators for treatment and outcome:" + str(mediators_names)) if len(mediators_names) > 0: mediation_estimand_expr = construct_mediation_estimand( estimand_type, treatment_name, outcome_name, mediators_names, ) logger.debug("Identified expression = " + str(mediation_estimand_expr)) estimands_dict["mediation"] = mediation_estimand_expr mediation_first_stage_confounders = identify_mediation_first_stage_confounders( graph, treatment_name, outcome_name, mediators_names, backdoor_adjustment ) mediation_second_stage_confounders = identify_mediation_second_stage_confounders( graph, treatment_name, mediators_names, outcome_name, backdoor_adjustment ) else: estimands_dict["mediation"] = None # Finally returning the estimand object estimand = IdentifiedEstimand( None, treatment_variable=treatment_name, outcome_variable=outcome_name, estimand_type=estimand_type, estimands=estimands_dict, backdoor_variables=backdoor_variables_dict, instrumental_variables=None, frontdoor_variables=None, mediator_variables=mediators_names, mediation_first_stage_confounders=mediation_first_stage_confounders, mediation_second_stage_confounders=mediation_second_stage_confounders, default_backdoor_id=None, ) return estimand def identify_nde_effect( graph: CausalGraph, treatment_name: List[str], outcome_name: str, backdoor_adjustment: BackdoorAdjustment, estimand_type: EstimandType, proceed_when_unidentifiable: bool = False, ): estimands_dict = {} ### 1. FIRST DOING BACKDOOR IDENTIFICATION # First, checking if there are any valid backdoor adjustment sets backdoor_sets = identify_backdoor(graph, treatment_name, outcome_name, backdoor_adjustment) estimands_dict, backdoor_variables_dict = build_backdoor_estimands_dict( graph, treatment_name, outcome_name, backdoor_sets, estimands_dict ) # Setting default "backdoor" identification adjustment set default_backdoor_id = get_default_backdoor_set_id(graph, treatment_name, outcome_name, backdoor_variables_dict) backdoor_variables_dict["backdoor"] = backdoor_variables_dict.get(str(default_backdoor_id), None) ### 2. SECOND, CHECKING FOR MEDIATORS # Now checking if there are valid mediator variables estimands_dict = {} mediation_first_stage_confounders = None mediation_second_stage_confounders = None mediators_names = identify_mediation(graph, treatment_name, outcome_name) logger.info("Mediators for treatment and outcome:" + str(mediators_names)) if len(mediators_names) > 0: mediation_estimand_expr = construct_mediation_estimand( estimand_type, treatment_name, outcome_name, mediators_names, ) logger.debug("Identified expression = " + str(mediation_estimand_expr)) estimands_dict["mediation"] = mediation_estimand_expr mediation_first_stage_confounders = identify_mediation_first_stage_confounders( graph, treatment_name, outcome_name, mediators_names, backdoor_adjustment ) mediation_second_stage_confounders = identify_mediation_second_stage_confounders( graph, treatment_name, mediators_names, outcome_name, backdoor_adjustment ) else: estimands_dict["mediation"] = None # Finally returning the estimand object estimand = IdentifiedEstimand( None, treatment_variable=treatment_name, outcome_variable=outcome_name, estimand_type=estimand_type, estimands=estimands_dict, backdoor_variables=backdoor_variables_dict, instrumental_variables=None, frontdoor_variables=None, mediator_variables=mediators_names, mediation_first_stage_confounders=mediation_first_stage_confounders, mediation_second_stage_confounders=mediation_second_stage_confounders, default_backdoor_id=None, ) return estimand def identify_backdoor( graph: CausalGraph, treatment_name: List[str], outcome_name: str, backdoor_adjustment: BackdoorAdjustment, include_unobserved: bool = False, dseparation_algo: str = "default", direct_effect: bool = False, ): backdoor_sets = [] backdoor_paths = None bdoor_graph = None if dseparation_algo == "naive": backdoor_paths = graph.get_backdoor_paths(treatment_name, outcome_name) elif dseparation_algo == "default": bdoor_graph = graph.do_surgery( treatment_name, target_node_names=outcome_name, remove_outgoing_edges=True, remove_only_direct_edges_to_target=direct_effect, ) else: raise ValueError(f"d-separation algorithm {dseparation_algo} is not supported") backdoor_adjustment = ( backdoor_adjustment if backdoor_adjustment != BackdoorAdjustment.BACKDOOR_DEFAULT else DEFAULT_BACKDOOR_METHOD ) # First, checking if empty set is a valid backdoor set empty_set = set() check = graph.check_valid_backdoor_set( treatment_name, outcome_name, empty_set, backdoor_paths=backdoor_paths, new_graph=bdoor_graph, dseparation_algo=dseparation_algo, ) if check["is_dseparated"]: backdoor_sets.append({"backdoor_set": empty_set}) # If the method is `minimal-adjustment`, return the empty set right away. if backdoor_adjustment == BackdoorAdjustment.BACKDOOR_MIN: return backdoor_sets # Second, checking for all other sets of variables. If include_unobserved is false, then only observed variables are eligible. eligible_variables = ( graph.get_all_nodes(include_unobserved=include_unobserved) - set(treatment_name) - set(outcome_name) ) if direct_effect: # only remove descendants of Y # also allow any causes of Y that are not caused by T (for lower variance) eligible_variables -= graph.get_descendants(outcome_name) else: # remove descendants of T (mediators) and descendants of Y eligible_variables -= graph.get_descendants(treatment_name) # If var is d-separated from both treatment or outcome, it cannot # be a part of the backdoor set filt_eligible_variables = set() for var in eligible_variables: dsep_treat_var = graph.check_dseparation(treatment_name, parse_state(var), set()) dsep_outcome_var = graph.check_dseparation(outcome_name, parse_state(var), set()) if not dsep_outcome_var or not dsep_treat_var: filt_eligible_variables.add(var) if backdoor_adjustment in METHOD_NAMES: backdoor_sets, found_valid_adjustment_set = find_valid_adjustment_sets( graph, treatment_name, outcome_name, backdoor_paths, bdoor_graph, dseparation_algo, backdoor_sets, filt_eligible_variables, backdoor_adjustment=backdoor_adjustment, max_iterations=MAX_BACKDOOR_ITERATIONS, ) if backdoor_adjustment == BackdoorAdjustment.BACKDOOR_DEFAULT and found_valid_adjustment_set: # repeat the above search with BACKDOOR_MIN backdoor_sets, _ = find_valid_adjustment_sets( graph, treatment_name, outcome_name, backdoor_paths, bdoor_graph, dseparation_algo, backdoor_sets, filt_eligible_variables, backdoor_adjustment=BackdoorAdjustment.BACKDOOR_MIN, max_iterations=MAX_BACKDOOR_ITERATIONS, ) else: raise ValueError( f"Identifier method {backdoor_adjustment} not supported. Try one of the following: {METHOD_NAMES}" ) return backdoor_sets def identify_efficient_backdoor( graph: CausalGraph, backdoor_adjustment: BackdoorAdjustment, costs: List, conditional_node_names: List[str] = None, ): """Method implementing algorithms to compute efficient backdoor sets, as described in Rotnitzky and Smucler (2020), Smucler, Sapienza and Rotnitzky (2021) and Smucler and Rotnitzky (2022). For backdoor_adjustment='efficient-adjustment', computes an optimal backdoor set, that is, a backdoor set comprised of observable variables that yields non-parametric estimators of the interventional mean with the smallest asymptotic variance among those that are based on observable backdoor sets. This optimal backdoor set always exists when no variables are latent, and the algorithm is guaranteed to compute it in this case. Under a non-parametric graphical model with latent variables, such a backdoor set can fail to exist. When certain sufficient conditions under which it is known that such a backdoor set exists are not satisfied, an error is raised. For backdoor_adjustment='efficient-minimal-adjustment', computes an optimal minimal backdoor set, that is, a minimal backdoor set comprised of observable variables that yields non-parametric estimators of the interventional mean with the smallest asymptotic variance among those that are based on observable minimal backdoor sets. For backdoor_adjustment='efficient-mincost-adjustment', computes an optimal minimum cost backdoor set, that is, a minimum cost backdoor set comprised of observable variables that yields non-parametric estimators of the interventional mean with the smallest asymptotic variance among those that are based on observable minimum cost backdoor sets. The cost of a backdoor set is defined as the sum of the costs of the variables that comprise it. The various optimal backdoor sets computed by this method are not only optimal under non-parametric graphical models and non-parametric estimators of interventional mean, but also under linear graphical models and OLS estimators, per results in Henckel, Perkovic and Maathuis (2020). :param costs: a list with non-negative costs associated with variables in the graph. Only used for estimatand_type='non-parametric-ate' and backdoor_adjustment='efficient-mincost-adjustment'. If not costs are provided by the user, and backdoor_adjustment='efficient-mincost-adjustment', costs are assumed to be equal to one for all variables in the graph. The structure of the list should be of the form [(node, {"cost": x}) for node in nodes]. :param conditional_node_names: variables that are used to determine treatment. If none are provided, it is assumed that the intervention sets the treatment to a constant. :returns: backdoor_sets, a list of dictionaries, with each dictionary having as values a backdoor set. """ if costs is None and backdoor_adjustment == "efficient-mincost-adjustment": logger.warning("No costs were passed, so they will be assumed to be constant and equal to 1.") efficient_bd = EfficientBackdoor( graph=graph, conditional_node_names=conditional_node_names, costs=costs, ) if backdoor_adjustment == BackdoorAdjustment.BACKDOOR_EFFICIENT: backdoor_set = efficient_bd.optimal_adj_set() backdoor_sets = [{"backdoor_set": tuple(backdoor_set)}] elif backdoor_adjustment == BackdoorAdjustment.BACKDOOR_MIN_EFFICIENT: backdoor_set = efficient_bd.optimal_minimal_adj_set() backdoor_sets = [{"backdoor_set": tuple(backdoor_set)}] elif backdoor_adjustment == BackdoorAdjustment.BACKDOOR_MINCOST_EFFICIENT: backdoor_set = efficient_bd.optimal_mincost_adj_set() backdoor_sets = [{"backdoor_set": tuple(backdoor_set)}] return backdoor_sets def find_valid_adjustment_sets( graph: CausalGraph, treatment_name: List, outcome_name: List, backdoor_paths: List, bdoor_graph: CausalGraph, dseparation_algo: str, backdoor_sets: List, filt_eligible_variables: List, backdoor_adjustment: BackdoorAdjustment, max_iterations: int, ): num_iterations = 0 found_valid_adjustment_set = False all_nodes_observed = graph.all_observed(graph.get_all_nodes()) # If `minimal-adjustment` method is specified, start the search from the set with minimum size. Otherwise, start from the largest. set_sizes = ( range(1, len(filt_eligible_variables) + 1, 1) if backdoor_adjustment == BackdoorAdjustment.BACKDOOR_MIN else range(len(filt_eligible_variables), 0, -1) ) for size_candidate_set in set_sizes: for candidate_set in itertools.combinations(filt_eligible_variables, size_candidate_set): check = graph.check_valid_backdoor_set( treatment_name, outcome_name, candidate_set, backdoor_paths=backdoor_paths, new_graph=bdoor_graph, dseparation_algo=dseparation_algo, ) logger.debug( "Candidate backdoor set: {0}, is_dseparated: {1}".format(candidate_set, check["is_dseparated"]) ) if check["is_dseparated"]: backdoor_sets.append({"backdoor_set": candidate_set}) found_valid_adjustment_set = True num_iterations += 1 if backdoor_adjustment == BackdoorAdjustment.BACKDOOR_EXHAUSTIVE and num_iterations > max_iterations: logger.warning(f"Max number of iterations {max_iterations} reached.") break # If the backdoor method is `maximal-adjustment` or `minimal-adjustment`, return the first found adjustment set. if ( backdoor_adjustment in { BackdoorAdjustment.BACKDOOR_DEFAULT, BackdoorAdjustment.BACKDOOR_MAX, BackdoorAdjustment.BACKDOOR_MIN, } and found_valid_adjustment_set ): break # If all variables are observed, and the biggest eligible set # does not satisfy backdoor, then none of its subsets will. if ( backdoor_adjustment in {BackdoorAdjustment.BACKDOOR_DEFAULT, BackdoorAdjustment.BACKDOOR_MAX} and all_nodes_observed ): break if num_iterations > max_iterations: logger.warning(f"Max number of iterations {max_iterations} reached. Could not find a valid backdoor set.") break return backdoor_sets, found_valid_adjustment_set def get_default_backdoor_set_id( graph: CausalGraph, treatment_name: List[str], outcome_name: List[str], backdoor_sets_dict: Dict ): # Adding a None estimand if no backdoor set found if len(backdoor_sets_dict) == 0: return None # Default set contains minimum possible number of instrumental variables, to prevent lowering variance in the treatment variable. instrument_names = set(graph.get_instruments(treatment_name, outcome_name)) iv_count_dict = { key: len(set(bdoor_set).intersection(instrument_names)) for key, bdoor_set in backdoor_sets_dict.items() } min_iv_count = min(iv_count_dict.values()) min_iv_keys = {key for key, iv_count in iv_count_dict.items() if iv_count == min_iv_count} min_iv_backdoor_sets_dict = {key: backdoor_sets_dict[key] for key in min_iv_keys} # Default set is the one with the least number of adjustment variables (optimizing for efficiency) min_set_length = 1000000 default_key = None for key, bdoor_set in min_iv_backdoor_sets_dict.items(): if len(bdoor_set) < min_set_length: min_set_length = len(bdoor_set) default_key = key return default_key def build_backdoor_estimands_dict( graph: CausalGraph, treatment_name: List[str], outcome_name: List[str], backdoor_sets: List[str], estimands_dict: Dict, ): """Build the final dict for backdoor sets by filtering unobserved variables if needed.""" backdoor_variables_dict = {} is_identified = [graph.all_observed(bset["backdoor_set"]) for bset in backdoor_sets] if any(is_identified): logger.info("Causal effect can be identified.") backdoor_sets_arr = [ list(bset["backdoor_set"]) for bset in backdoor_sets if graph.all_observed(bset["backdoor_set"]) ] else: # there is unobserved confounding logger.warning("Backdoor identification failed.") backdoor_sets_arr = [] for i in range(len(backdoor_sets_arr)): backdoor_estimand_expr = construct_backdoor_estimand(treatment_name, outcome_name, backdoor_sets_arr[i]) logger.debug("Identified expression = " + str(backdoor_estimand_expr)) estimands_dict["backdoor" + str(i + 1)] = backdoor_estimand_expr backdoor_variables_dict["backdoor" + str(i + 1)] = backdoor_sets_arr[i] return estimands_dict, backdoor_variables_dict def identify_frontdoor( graph: CausalGraph, treatment_name: List[str], outcome_name: List[str], dseparation_algo: str = "default" ): """Find a valid frontdoor variable if it exists. Currently only supports a single variable frontdoor set. """ frontdoor_var = None frontdoor_paths = None fdoor_graph = None if dseparation_algo == "default": cond1_graph = graph.do_surgery(treatment_name, remove_incoming_edges=True) bdoor_graph1 = graph.do_surgery(treatment_name, remove_outgoing_edges=True) elif dseparation_algo == "naive": frontdoor_paths = graph.get_all_directed_paths(treatment_name, outcome_name) else: raise ValueError(f"d-separation algorithm {dseparation_algo} is not supported") eligible_variables = ( graph.get_descendants(treatment_name) - set(outcome_name) - set(graph.get_descendants(outcome_name)) ) # For simplicity, assuming a one-variable frontdoor set for candidate_var in eligible_variables: # Cond 1: All directed paths intercepted by candidate_var cond1 = graph.check_valid_frontdoor_set( treatment_name, outcome_name, parse_state(candidate_var), frontdoor_paths=frontdoor_paths, new_graph=cond1_graph, dseparation_algo=dseparation_algo, ) logger.debug("Candidate frontdoor set: {0}, is_dseparated: {1}".format(candidate_var, cond1)) if not cond1: continue # Cond 2: No confounding between treatment and candidate var cond2 = graph.check_valid_backdoor_set( treatment_name, parse_state(candidate_var), set(), backdoor_paths=None, new_graph=bdoor_graph1, dseparation_algo=dseparation_algo, ) if not cond2: continue # Cond 3: treatment blocks all confounding between candidate_var and outcome bdoor_graph2 = graph.do_surgery(candidate_var, remove_outgoing_edges=True) cond3 = graph.check_valid_backdoor_set( parse_state(candidate_var), outcome_name, treatment_name, backdoor_paths=None, new_graph=bdoor_graph2, dseparation_algo=dseparation_algo, ) is_valid_frontdoor = cond1 and cond2 and cond3 if is_valid_frontdoor: frontdoor_var = candidate_var break return parse_state(frontdoor_var) def identify_mediation(graph: CausalGraph, treatment_name: List[str], outcome_name: List[str]): """Find a valid mediator if it exists. Currently only supports a single variable mediator set. """ mediation_var = None mediation_paths = graph.get_all_directed_paths(treatment_name, outcome_name) eligible_variables = graph.get_descendants(treatment_name) - set(outcome_name) # For simplicity, assuming a one-variable mediation set for candidate_var in eligible_variables: is_valid_mediation = graph.check_valid_mediation_set( treatment_name, outcome_name, parse_state(candidate_var), mediation_paths=mediation_paths, ) logger.debug("Candidate mediation set: {0}, on_mediating_path: {1}".format(candidate_var, is_valid_mediation)) if is_valid_mediation: mediation_var = candidate_var break return parse_state(mediation_var) def identify_mediation_first_stage_confounders( graph: CausalGraph, treatment_name: List[str], outcome_name: List[str], mediators_names: List[str], backdoor_adjustment: BackdoorAdjustment, ): # Create estimands dict as per the API for backdoor, but do not return it estimands_dict = {} backdoor_sets = identify_backdoor(graph, treatment_name, mediators_names, backdoor_adjustment) estimands_dict, backdoor_variables_dict = build_backdoor_estimands_dict( graph, treatment_name, mediators_names, backdoor_sets, estimands_dict, ) # Setting default "backdoor" identification adjustment set default_backdoor_id = get_default_backdoor_set_id(graph, treatment_name, outcome_name, backdoor_variables_dict) estimands_dict["backdoor"] = estimands_dict.get(str(default_backdoor_id), None) backdoor_variables_dict["backdoor"] = backdoor_variables_dict.get(str(default_backdoor_id), None) return backdoor_variables_dict def identify_mediation_second_stage_confounders( graph: CausalGraph, treatment_name: List[str], mediators_names: List[str], outcome_name: List[str], backdoor_adjustment: BackdoorAdjustment, ): # Create estimands dict as per the API for backdoor, but do not return it estimands_dict = {} backdoor_sets = identify_backdoor(graph, mediators_names, outcome_name, backdoor_adjustment) estimands_dict, backdoor_variables_dict = build_backdoor_estimands_dict( graph, mediators_names, outcome_name, backdoor_sets, estimands_dict, ) # Setting default "backdoor" identification adjustment set default_backdoor_id = get_default_backdoor_set_id(graph, treatment_name, outcome_name, backdoor_variables_dict) estimands_dict["backdoor"] = estimands_dict.get(str(default_backdoor_id), None) backdoor_variables_dict["backdoor"] = backdoor_variables_dict.get(str(default_backdoor_id), None) return backdoor_variables_dict def construct_backdoor_estimand(treatment_name: List[str], outcome_name: List[str], common_causes: List[str]): # TODO: outputs string for now, but ideally should do symbolic # expressions Mon 19 Feb 2018 04:54:17 PM DST # TODO Better support for multivariate treatments expr = None outcome_name = outcome_name[0] num_expr_str = outcome_name if len(common_causes) > 0: num_expr_str += "|" + ",".join(common_causes) expr = "d(" + num_expr_str + ")/d" + ",".join(treatment_name) sym_mu = sp.Symbol("mu") sym_sigma = sp.Symbol("sigma", positive=True) sym_outcome = spstats.Normal(num_expr_str, sym_mu, sym_sigma) sym_treatment_symbols = [sp.Symbol(t) for t in treatment_name] sym_treatment = sp.Array(sym_treatment_symbols) sym_conditional_outcome = spstats.Expectation(sym_outcome) sym_effect = sp.Derivative(sym_conditional_outcome, sym_treatment) sym_assumptions = { "Unconfoundedness": ( "If U\N{RIGHTWARDS ARROW}{{{0}}} and U\N{RIGHTWARDS ARROW}{1}" " then P({1}|{0},{2},U) = P({1}|{0},{2})" ).format(",".join(treatment_name), outcome_name, ",".join(common_causes)) } estimand = {"estimand": sym_effect, "assumptions": sym_assumptions} return estimand def construct_iv_estimand(treatment_name: List[str], outcome_name: List[str], instrument_names: List[str]): # TODO: support multivariate treatments better. expr = None outcome_name = outcome_name[0] sym_outcome = spstats.Normal(outcome_name, 0, 1) sym_treatment_symbols = [spstats.Normal(t, 0, 1) for t in treatment_name] sym_treatment = sp.Array(sym_treatment_symbols) sym_instrument_symbols = [sp.Symbol(inst) for inst in instrument_names] sym_instrument = sp.Array(sym_instrument_symbols) # ",".join(instrument_names)) sym_outcome_derivative = sp.Derivative(sym_outcome, sym_instrument) sym_treatment_derivative = sp.Derivative(sym_treatment, sym_instrument) sym_effect = spstats.Expectation(sym_outcome_derivative / sym_treatment_derivative) sym_assumptions = { "As-if-random": ( "If U\N{RIGHTWARDS ARROW}\N{RIGHTWARDS ARROW}{0} then " "\N{NOT SIGN}(U \N{RIGHTWARDS ARROW}\N{RIGHTWARDS ARROW}{{{1}}})" ).format(outcome_name, ",".join(instrument_names)), "Exclusion": ( "If we remove {{{0}}}\N{RIGHTWARDS ARROW}{{{1}}}, then " "\N{NOT SIGN}({{{0}}}\N{RIGHTWARDS ARROW}{2})" ).format(",".join(instrument_names), ",".join(treatment_name), outcome_name), } estimand = {"estimand": sym_effect, "assumptions": sym_assumptions} return estimand def construct_frontdoor_estimand( treatment_name: List[str], outcome_name: List[str], frontdoor_variables_names: List[str] ): # TODO: support multivariate treatments better. expr = None outcome_name = outcome_name[0] sym_outcome = spstats.Normal(outcome_name, 0, 1) sym_treatment_symbols = [spstats.Normal(t, 0, 1) for t in treatment_name] sym_treatment = sp.Array(sym_treatment_symbols) sym_frontdoor_symbols = [sp.Symbol(inst) for inst in frontdoor_variables_names] sym_frontdoor = sp.Array(sym_frontdoor_symbols) # ",".join(instrument_names)) sym_outcome_derivative = sp.Derivative(sym_outcome, sym_frontdoor) sym_treatment_derivative = sp.Derivative(sym_frontdoor, sym_treatment) sym_effect = spstats.Expectation(sym_treatment_derivative * sym_outcome_derivative) sym_assumptions = { "Full-mediation": ("{2} intercepts (blocks) all directed paths from {0} to {1}.").format( ",".join(treatment_name), ",".join(outcome_name), ",".join(frontdoor_variables_names), ), "First-stage-unconfoundedness": ( "If U\N{RIGHTWARDS ARROW}{{{0}}} and U\N{RIGHTWARDS ARROW}{{{1}}}" " then P({1}|{0},U) = P({1}|{0})" ).format(",".join(treatment_name), ",".join(frontdoor_variables_names)), "Second-stage-unconfoundedness": ( "If U\N{RIGHTWARDS ARROW}{{{2}}} and U\N{RIGHTWARDS ARROW}{1}" " then P({1}|{2}, {0}, U) = P({1}|{2}, {0})" ).format( ",".join(treatment_name), outcome_name, ",".join(frontdoor_variables_names), ), } estimand = {"estimand": sym_effect, "assumptions": sym_assumptions} return estimand def construct_mediation_estimand( estimand_type: EstimandType, treatment_name: List[str], outcome_name: List[str], mediators_names: List[str] ): # TODO: support multivariate treatments better. expr = None if estimand_type in ( EstimandType.NONPARAMETRIC_NDE, EstimandType.NONPARAMETRIC_NIE, ): outcome_name = outcome_name[0] sym_outcome = spstats.Normal(outcome_name, 0, 1) sym_treatment_symbols = [spstats.Normal(t, 0, 1) for t in treatment_name] sym_treatment = sp.Array(sym_treatment_symbols) sym_mediators_symbols = [sp.Symbol(inst) for inst in mediators_names] sym_mediators = sp.Array(sym_mediators_symbols) sym_outcome_derivative = sp.Derivative(sym_outcome, sym_mediators) sym_treatment_derivative = sp.Derivative(sym_mediators, sym_treatment) # For direct effect num_expr_str = outcome_name if len(mediators_names) > 0: num_expr_str += "|" + ",".join(mediators_names) sym_mu = sp.Symbol("mu") sym_sigma = sp.Symbol("sigma", positive=True) sym_conditional_outcome = spstats.Normal(num_expr_str, sym_mu, sym_sigma) sym_directeffect_derivative = sp.Derivative(sym_conditional_outcome, sym_treatment) if estimand_type == EstimandType.NONPARAMETRIC_NIE: sym_effect = spstats.Expectation(sym_treatment_derivative * sym_outcome_derivative) elif estimand_type == EstimandType.NONPARAMETRIC_NDE: sym_effect = spstats.Expectation(sym_directeffect_derivative) sym_assumptions = { "Mediation": ( "{2} intercepts (blocks) all directed paths from {0} to {1} except the path {{{0}}}\N{RIGHTWARDS ARROW}{{{1}}}." ).format( ",".join(treatment_name), ",".join(outcome_name), ",".join(mediators_names), ), "First-stage-unconfoundedness": ( "If U\N{RIGHTWARDS ARROW}{{{0}}} and U\N{RIGHTWARDS ARROW}{{{1}}}" " then P({1}|{0},U) = P({1}|{0})" ).format(",".join(treatment_name), ",".join(mediators_names)), "Second-stage-unconfoundedness": ( "If U\N{RIGHTWARDS ARROW}{{{2}}} and U\N{RIGHTWARDS ARROW}{1}" " then P({1}|{2}, {0}, U) = P({1}|{2}, {0})" ).format(",".join(treatment_name), outcome_name, ",".join(mediators_names)), } else: raise ValueError( "Estimand type not supported. Supported estimand types are {0} or {1}'.".format( EstimandType.NONPARAMETRIC_NDE, EstimandType.NONPARAMETRIC_NIE, ) ) estimand = {"estimand": sym_effect, "assumptions": sym_assumptions} return estimand

import itertools import logging from enum import Enum from typing import Dict, List, Optional, Union import networkx as nx import sympy as sp import sympy.stats as spstats from dowhy.causal_identifier.efficient_backdoor import EfficientBackdoor from dowhy.causal_identifier.identified_estimand import IdentifiedEstimand from dowhy.graph import ( check_dseparation, check_valid_backdoor_set, check_valid_frontdoor_set, check_valid_mediation_set, do_surgery, get_all_directed_paths, get_backdoor_paths, get_descendants, get_instruments, has_directed_path, ) from dowhy.utils.api import parse_state logger = logging.getLogger(__name__) class EstimandType(Enum): # Average total effect NONPARAMETRIC_ATE = "nonparametric-ate" # Natural direct effect NONPARAMETRIC_NDE = "nonparametric-nde" # Natural indirect effect NONPARAMETRIC_NIE = "nonparametric-nie" # Controlled direct effect NONPARAMETRIC_CDE = "nonparametric-cde" class BackdoorAdjustment(Enum): # Backdoor method names BACKDOOR_DEFAULT = "default" BACKDOOR_EXHAUSTIVE = "exhaustive-search" BACKDOOR_MIN = "minimal-adjustment" BACKDOOR_MAX = "maximal-adjustment" BACKDOOR_EFFICIENT = "efficient-adjustment" BACKDOOR_MIN_EFFICIENT = "efficient-minimal-adjustment" BACKDOOR_MINCOST_EFFICIENT = "efficient-mincost-adjustment" MAX_BACKDOOR_ITERATIONS = 100000 METHOD_NAMES = { BackdoorAdjustment.BACKDOOR_DEFAULT, BackdoorAdjustment.BACKDOOR_EXHAUSTIVE, BackdoorAdjustment.BACKDOOR_MIN, BackdoorAdjustment.BACKDOOR_MAX, BackdoorAdjustment.BACKDOOR_EFFICIENT, BackdoorAdjustment.BACKDOOR_MIN_EFFICIENT, BackdoorAdjustment.BACKDOOR_MINCOST_EFFICIENT, } EFFICIENT_METHODS = { BackdoorAdjustment.BACKDOOR_EFFICIENT, BackdoorAdjustment.BACKDOOR_MIN_EFFICIENT, BackdoorAdjustment.BACKDOOR_MINCOST_EFFICIENT, } DEFAULT_BACKDOOR_METHOD = BackdoorAdjustment.BACKDOOR_DEFAULT class AutoIdentifier: """Class that implements different identification methods. Currently supports backdoor and instrumental variable identification methods. The identification is based on the causal graph provided. This class is for backwards compatibility with CausalModel Will be deprecated in the future in favor of function call auto_identify_effect() """ def __init__( self, estimand_type: EstimandType, backdoor_adjustment: BackdoorAdjustment = BackdoorAdjustment.BACKDOOR_DEFAULT, optimize_backdoor: bool = False, costs: Optional[List] = None, ): self.estimand_type = estimand_type self.backdoor_adjustment = backdoor_adjustment self.optimize_backdoor = optimize_backdoor self.costs = costs self.logger = logging.getLogger(__name__) def identify_effect( self, graph: nx.DiGraph, action_nodes: Union[str, List[str]], outcome_nodes: Union[str, List[str]], observed_nodes: Union[str, List[str]], conditional_node_names: List[str] = None, ): estimand = identify_effect_auto( graph, action_nodes, outcome_nodes, observed_nodes, self.estimand_type, conditional_node_names, self.backdoor_adjustment, self.optimize_backdoor, self.costs, ) estimand.identifier = self return estimand def identify_backdoor( self, graph: nx.DiGraph, action_nodes: List[str], outcome_nodes: List[str], observed_nodes: List[str], include_unobserved: bool = False, dseparation_algo: str = "default", direct_effect: bool = False, ): return identify_backdoor( graph, action_nodes, outcome_nodes, observed_nodes, self.backdoor_adjustment, include_unobserved, dseparation_algo, direct_effect, ) def identify_effect_auto( graph: nx.DiGraph, action_nodes: Union[str, List[str]], outcome_nodes: Union[str, List[str]], observed_nodes: Union[str, List[str]], estimand_type: EstimandType, conditional_node_names: List[str] = None, backdoor_adjustment: BackdoorAdjustment = BackdoorAdjustment.BACKDOOR_DEFAULT, optimize_backdoor: bool = False, costs: Optional[List] = None, ) -> IdentifiedEstimand: """Main method that returns an identified estimand (if one exists). If estimand_type is non-parametric ATE, then uses backdoor, instrumental variable and frontdoor identification methods, to check if an identified estimand exists, based on the causal graph. :param optimize_backdoor: if True, uses an optimised algorithm to compute the backdoor sets :param costs: non-negative costs associated with variables in the graph. Only used for estimand_type='non-parametric-ate' and backdoor_adjustment='efficient-mincost-adjustment'. If no costs are provided by the user, and backdoor_adjustment='efficient-mincost-adjustment', costs are assumed to be equal to one for all variables in the graph. :param conditional_node_names: variables that are used to determine treatment. If none are provided, it is assumed that the intervention is static. :returns: target estimand, an instance of the IdentifiedEstimand class """ observed_nodes = parse_state(observed_nodes) action_nodes = parse_state(action_nodes) outcome_nodes = parse_state(outcome_nodes) # First, check if there is a directed path from action to outcome if not has_directed_path(graph, action_nodes, outcome_nodes): logger.warn("No directed path from treatment to outcome. Causal Effect is zero.") return IdentifiedEstimand( None, treatment_variable=action_nodes, outcome_variable=outcome_nodes, no_directed_path=True, ) if estimand_type == EstimandType.NONPARAMETRIC_ATE: return identify_ate_effect( graph, action_nodes, outcome_nodes, observed_nodes, backdoor_adjustment, optimize_backdoor, estimand_type, costs, conditional_node_names, ) elif estimand_type == EstimandType.NONPARAMETRIC_NDE: return identify_nde_effect( graph, action_nodes, outcome_nodes, observed_nodes, backdoor_adjustment, estimand_type ) elif estimand_type == EstimandType.NONPARAMETRIC_NIE: return identify_nie_effect( graph, action_nodes, outcome_nodes, observed_nodes, backdoor_adjustment, estimand_type ) elif estimand_type == EstimandType.NONPARAMETRIC_CDE: return identify_cde_effect( graph, action_nodes, outcome_nodes, observed_nodes, backdoor_adjustment, estimand_type ) else: raise ValueError( "Estimand type is not supported. Use either {0}, {1}, or {2}.".format( EstimandType.NONPARAMETRIC_ATE, EstimandType.NONPARAMETRIC_CDE, EstimandType.NONPARAMETRIC_NDE, EstimandType.NONPARAMETRIC_NIE, ) ) def identify_ate_effect( graph: nx.DiGraph, action_nodes: List[str], outcome_nodes: List[str], observed_nodes: List[str], backdoor_adjustment: BackdoorAdjustment, optimize_backdoor: bool, estimand_type: EstimandType, costs: List, conditional_node_names: List[str] = None, ): estimands_dict = {} mediation_first_stage_confounders = None mediation_second_stage_confounders = None ### 1. BACKDOOR IDENTIFICATION # Pick algorithm to compute backdoor sets according to method chosen if backdoor_adjustment not in EFFICIENT_METHODS: # First, checking if there are any valid backdoor adjustment sets if optimize_backdoor == False: backdoor_sets = identify_backdoor(graph, action_nodes, outcome_nodes, observed_nodes, backdoor_adjustment) else: from dowhy.causal_identifier.backdoor import Backdoor path = Backdoor(graph, action_nodes, outcome_nodes) backdoor_sets = path.get_backdoor_vars() elif backdoor_adjustment in EFFICIENT_METHODS: backdoor_sets = identify_efficient_backdoor( graph, action_nodes, outcome_nodes, observed_nodes, backdoor_adjustment, costs, conditional_node_names=conditional_node_names, ) estimands_dict, backdoor_variables_dict = build_backdoor_estimands_dict( action_nodes, outcome_nodes, observed_nodes, backdoor_sets, estimands_dict ) # Setting default "backdoor" identification adjustment set default_backdoor_id = get_default_backdoor_set_id(graph, action_nodes, outcome_nodes, backdoor_variables_dict) if len(backdoor_variables_dict) > 0: estimands_dict["backdoor"] = estimands_dict.get(str(default_backdoor_id), None) backdoor_variables_dict["backdoor"] = backdoor_variables_dict.get(str(default_backdoor_id), None) else: estimands_dict["backdoor"] = None ### 2. INSTRUMENTAL VARIABLE IDENTIFICATION # Now checking if there is also a valid iv estimand instrument_names = get_instruments(graph, action_nodes, outcome_nodes) logger.info("Instrumental variables for treatment and outcome:" + str(instrument_names)) if len(instrument_names) > 0: iv_estimand_expr = construct_iv_estimand( action_nodes, outcome_nodes, instrument_names, ) logger.debug("Identified expression = " + str(iv_estimand_expr)) estimands_dict["iv"] = iv_estimand_expr else: estimands_dict["iv"] = None ### 3. FRONTDOOR IDENTIFICATION # Now checking if there is a valid frontdoor variable frontdoor_variables_names = identify_frontdoor(graph, action_nodes, outcome_nodes) logger.info("Frontdoor variables for treatment and outcome:" + str(frontdoor_variables_names)) if len(frontdoor_variables_names) > 0: frontdoor_estimand_expr = construct_frontdoor_estimand( action_nodes, outcome_nodes, frontdoor_variables_names, ) logger.debug("Identified expression = " + str(frontdoor_estimand_expr)) estimands_dict["frontdoor"] = frontdoor_estimand_expr mediation_first_stage_confounders = identify_mediation_first_stage_confounders( graph, action_nodes, outcome_nodes, frontdoor_variables_names, observed_nodes, backdoor_adjustment ) mediation_second_stage_confounders = identify_mediation_second_stage_confounders( graph, action_nodes, frontdoor_variables_names, outcome_nodes, observed_nodes, backdoor_adjustment ) else: estimands_dict["frontdoor"] = None # Finally returning the estimand object estimand = IdentifiedEstimand( None, treatment_variable=action_nodes, outcome_variable=outcome_nodes, estimand_type=estimand_type, estimands=estimands_dict, backdoor_variables=backdoor_variables_dict, instrumental_variables=instrument_names, frontdoor_variables=frontdoor_variables_names, mediation_first_stage_confounders=mediation_first_stage_confounders, mediation_second_stage_confounders=mediation_second_stage_confounders, default_backdoor_id=default_backdoor_id, ) return estimand def identify_cde_effect( graph: nx.DiGraph, action_nodes: List[str], outcome_nodes: List[str], observed_nodes: List[str], backdoor_adjustment: BackdoorAdjustment, estimand_type: EstimandType, ): """Identify controlled direct effect. For a definition, see Vanderwheele (2011). Controlled direct and mediated effects: definition, identification and bounds. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4193506/ Using do-calculus rules, identification yields a adjustment set. It is based on the principle that under a graph where the direct edge from treatment to outcome is removed, conditioning on the adjustment set should d-separate treatment and outcome. """ estimands_dict = {} # Pick algorithm to compute backdoor sets according to method chosen backdoor_sets = identify_backdoor( graph, action_nodes, outcome_nodes, observed_nodes, backdoor_adjustment, direct_effect=True ) estimands_dict, backdoor_variables_dict = build_backdoor_estimands_dict( action_nodes, outcome_nodes, observed_nodes, backdoor_sets, estimands_dict ) # Setting default "backdoor" identification adjustment set default_backdoor_id = get_default_backdoor_set_id(graph, action_nodes, outcome_nodes, backdoor_variables_dict) if len(backdoor_variables_dict) > 0: estimands_dict["backdoor"] = estimands_dict.get(str(default_backdoor_id), None) backdoor_variables_dict["backdoor"] = backdoor_variables_dict.get(str(default_backdoor_id), None) else: estimands_dict["backdoor"] = None # Finally returning the estimand object estimand = IdentifiedEstimand( None, treatment_variable=action_nodes, outcome_variable=outcome_nodes, estimand_type=estimand_type, estimands=estimands_dict, backdoor_variables=backdoor_variables_dict, instrumental_variables=None, frontdoor_variables=None, mediation_first_stage_confounders=None, mediation_second_stage_confounders=None, default_backdoor_id=default_backdoor_id, ) return estimand def identify_nie_effect( graph: nx.DiGraph, action_nodes: List[str], outcome_nodes: List[str], observed_nodes: List[str], backdoor_adjustment: BackdoorAdjustment, estimand_type: EstimandType, ): estimands_dict = {} ### 1. FIRST DOING BACKDOOR IDENTIFICATION # First, checking if there are any valid backdoor adjustment sets backdoor_sets = identify_backdoor(graph, action_nodes, outcome_nodes, observed_nodes, backdoor_adjustment) estimands_dict, backdoor_variables_dict = build_backdoor_estimands_dict( action_nodes, outcome_nodes, observed_nodes, backdoor_sets, estimands_dict ) # Setting default "backdoor" identification adjustment set default_backdoor_id = get_default_backdoor_set_id(graph, action_nodes, outcome_nodes, backdoor_variables_dict) backdoor_variables_dict["backdoor"] = backdoor_variables_dict.get(str(default_backdoor_id), None) ### 2. SECOND, CHECKING FOR MEDIATORS # Now checking if there are valid mediator variables estimands_dict = {} # Need to reinitialize this dictionary to avoid including the backdoor sets mediation_first_stage_confounders = None mediation_second_stage_confounders = None mediators_names = identify_mediation(graph, action_nodes, outcome_nodes) logger.info("Mediators for treatment and outcome:" + str(mediators_names)) if len(mediators_names) > 0: mediation_estimand_expr = construct_mediation_estimand( estimand_type, action_nodes, outcome_nodes, mediators_names, ) logger.debug("Identified expression = " + str(mediation_estimand_expr)) estimands_dict["mediation"] = mediation_estimand_expr mediation_first_stage_confounders = identify_mediation_first_stage_confounders( graph, action_nodes, outcome_nodes, mediators_names, observed_nodes, backdoor_adjustment ) mediation_second_stage_confounders = identify_mediation_second_stage_confounders( graph, action_nodes, mediators_names, outcome_nodes, observed_nodes, backdoor_adjustment ) else: estimands_dict["mediation"] = None # Finally returning the estimand object estimand = IdentifiedEstimand( None, treatment_variable=action_nodes, outcome_variable=outcome_nodes, estimand_type=estimand_type, estimands=estimands_dict, backdoor_variables=backdoor_variables_dict, instrumental_variables=None, frontdoor_variables=None, mediator_variables=mediators_names, mediation_first_stage_confounders=mediation_first_stage_confounders, mediation_second_stage_confounders=mediation_second_stage_confounders, default_backdoor_id=None, ) return estimand def identify_nde_effect( graph: nx.DiGraph, action_nodes: List[str], outcome_nodes: List[str], observed_nodes: List[str], backdoor_adjustment: BackdoorAdjustment, estimand_type: EstimandType, ): estimands_dict = {} ### 1. FIRST DOING BACKDOOR IDENTIFICATION # First, checking if there are any valid backdoor adjustment sets backdoor_sets = identify_backdoor(graph, action_nodes, outcome_nodes, observed_nodes, backdoor_adjustment) estimands_dict, backdoor_variables_dict = build_backdoor_estimands_dict( action_nodes, outcome_nodes, observed_nodes, backdoor_sets, estimands_dict ) # Setting default "backdoor" identification adjustment set default_backdoor_id = get_default_backdoor_set_id(graph, action_nodes, outcome_nodes, backdoor_variables_dict) backdoor_variables_dict["backdoor"] = backdoor_variables_dict.get(str(default_backdoor_id), None) ### 2. SECOND, CHECKING FOR MEDIATORS # Now checking if there are valid mediator variables estimands_dict = {} mediation_first_stage_confounders = None mediation_second_stage_confounders = None mediators_names = identify_mediation(graph, action_nodes, outcome_nodes) logger.info("Mediators for treatment and outcome:" + str(mediators_names)) if len(mediators_names) > 0: mediation_estimand_expr = construct_mediation_estimand( estimand_type, action_nodes, outcome_nodes, mediators_names, ) logger.debug("Identified expression = " + str(mediation_estimand_expr)) estimands_dict["mediation"] = mediation_estimand_expr mediation_first_stage_confounders = identify_mediation_first_stage_confounders( graph, action_nodes, outcome_nodes, mediators_names, observed_nodes, backdoor_adjustment ) mediation_second_stage_confounders = identify_mediation_second_stage_confounders( graph, action_nodes, mediators_names, outcome_nodes, observed_nodes, backdoor_adjustment ) else: estimands_dict["mediation"] = None # Finally returning the estimand object estimand = IdentifiedEstimand( None, treatment_variable=action_nodes, outcome_variable=outcome_nodes, estimand_type=estimand_type, estimands=estimands_dict, backdoor_variables=backdoor_variables_dict, instrumental_variables=None, frontdoor_variables=None, mediator_variables=mediators_names, mediation_first_stage_confounders=mediation_first_stage_confounders, mediation_second_stage_confounders=mediation_second_stage_confounders, default_backdoor_id=None, ) return estimand def identify_backdoor( graph: nx.DiGraph, action_nodes: List[str], outcome_nodes: List[str], observed_nodes: List[str], backdoor_adjustment: BackdoorAdjustment, include_unobserved: bool = False, dseparation_algo: str = "default", direct_effect: bool = False, ): backdoor_sets = [] backdoor_paths = None bdoor_graph = None observed_nodes = set(observed_nodes) if dseparation_algo == "naive": backdoor_paths = get_backdoor_paths(graph, action_nodes, outcome_nodes) elif dseparation_algo == "default": bdoor_graph = do_surgery( graph, action_nodes, target_node_names=outcome_nodes, remove_outgoing_edges=True, remove_only_direct_edges_to_target=direct_effect, ) else: raise ValueError(f"d-separation algorithm {dseparation_algo} is not supported") backdoor_adjustment = ( backdoor_adjustment if backdoor_adjustment != BackdoorAdjustment.BACKDOOR_DEFAULT else DEFAULT_BACKDOOR_METHOD ) # First, checking if empty set is a valid backdoor set empty_set = set() check = check_valid_backdoor_set( graph, action_nodes, outcome_nodes, empty_set, backdoor_paths=backdoor_paths, new_graph=bdoor_graph, dseparation_algo=dseparation_algo, ) if check["is_dseparated"]: backdoor_sets.append({"backdoor_set": empty_set}) # If the method is `minimal-adjustment`, return the empty set right away. if backdoor_adjustment == BackdoorAdjustment.BACKDOOR_MIN: return backdoor_sets # Second, checking for all other sets of variables. If include_unobserved is false, then only observed variables are eligible. eligible_variables = ( set([node for node in graph.nodes if include_unobserved or node in observed_nodes]) - set(action_nodes) - set(outcome_nodes) ) if direct_effect: # only remove descendants of Y # also allow any causes of Y that are not caused by T (for lower variance) eligible_variables -= get_descendants(graph, outcome_nodes) else: # remove descendants of T (mediators) and descendants of Y eligible_variables -= get_descendants(graph, action_nodes) # If var is d-separated from both treatment or outcome, it cannot # be a part of the backdoor set filt_eligible_variables = set() for var in eligible_variables: dsep_treat_var = check_dseparation(graph, action_nodes, parse_state(var), set()) dsep_outcome_var = check_dseparation(graph, outcome_nodes, parse_state(var), set()) if not dsep_outcome_var or not dsep_treat_var: filt_eligible_variables.add(var) if backdoor_adjustment in METHOD_NAMES: backdoor_sets, found_valid_adjustment_set = find_valid_adjustment_sets( graph, action_nodes, outcome_nodes, observed_nodes, backdoor_paths, bdoor_graph, dseparation_algo, backdoor_sets, filt_eligible_variables, backdoor_adjustment=backdoor_adjustment, max_iterations=MAX_BACKDOOR_ITERATIONS, ) if backdoor_adjustment == BackdoorAdjustment.BACKDOOR_DEFAULT and found_valid_adjustment_set: # repeat the above search with BACKDOOR_MIN backdoor_sets, _ = find_valid_adjustment_sets( graph, action_nodes, outcome_nodes, observed_nodes, backdoor_paths, bdoor_graph, dseparation_algo, backdoor_sets, filt_eligible_variables, backdoor_adjustment=BackdoorAdjustment.BACKDOOR_MIN, max_iterations=MAX_BACKDOOR_ITERATIONS, ) else: raise ValueError( f"Identifier method {backdoor_adjustment} not supported. Try one of the following: {METHOD_NAMES}" ) return backdoor_sets def identify_efficient_backdoor( graph: nx.DiGraph, action_nodes: List[str], outcome_nodes: List[str], observed_nodes: List[str], backdoor_adjustment: BackdoorAdjustment, costs: List, conditional_node_names: List[str] = None, ): """Method implementing algorithms to compute efficient backdoor sets, as described in Rotnitzky and Smucler (2020), Smucler, Sapienza and Rotnitzky (2021) and Smucler and Rotnitzky (2022). For backdoor_adjustment='efficient-adjustment', computes an optimal backdoor set, that is, a backdoor set comprised of observable variables that yields non-parametric estimators of the interventional mean with the smallest asymptotic variance among those that are based on observable backdoor sets. This optimal backdoor set always exists when no variables are latent, and the algorithm is guaranteed to compute it in this case. Under a non-parametric graphical model with latent variables, such a backdoor set can fail to exist. When certain sufficient conditions under which it is known that such a backdoor set exists are not satisfied, an error is raised. For backdoor_adjustment='efficient-minimal-adjustment', computes an optimal minimal backdoor set, that is, a minimal backdoor set comprised of observable variables that yields non-parametric estimators of the interventional mean with the smallest asymptotic variance among those that are based on observable minimal backdoor sets. For backdoor_adjustment='efficient-mincost-adjustment', computes an optimal minimum cost backdoor set, that is, a minimum cost backdoor set comprised of observable variables that yields non-parametric estimators of the interventional mean with the smallest asymptotic variance among those that are based on observable minimum cost backdoor sets. The cost of a backdoor set is defined as the sum of the costs of the variables that comprise it. The various optimal backdoor sets computed by this method are not only optimal under non-parametric graphical models and non-parametric estimators of interventional mean, but also under linear graphical models and OLS estimators, per results in Henckel, Perkovic and Maathuis (2020). :param costs: a list with non-negative costs associated with variables in the graph. Only used for estimatand_type='non-parametric-ate' and backdoor_adjustment='efficient-mincost-adjustment'. If not costs are provided by the user, and backdoor_adjustment='efficient-mincost-adjustment', costs are assumed to be equal to one for all variables in the graph. The structure of the list should be of the form [(node, {"cost": x}) for node in nodes]. :param conditional_node_names: variables that are used to determine treatment. If none are provided, it is assumed that the intervention sets the treatment to a constant. :returns: backdoor_sets, a list of dictionaries, with each dictionary having as values a backdoor set. """ if costs is None and backdoor_adjustment == "efficient-mincost-adjustment": logger.warning("No costs were passed, so they will be assumed to be constant and equal to 1.") efficient_bd = EfficientBackdoor( graph=graph, action_nodes=action_nodes, outcome_nodes=outcome_nodes, observed_nodes=observed_nodes, conditional_node_names=conditional_node_names, costs=costs, ) if backdoor_adjustment == BackdoorAdjustment.BACKDOOR_EFFICIENT: backdoor_set = efficient_bd.optimal_adj_set() backdoor_sets = [{"backdoor_set": tuple(backdoor_set)}] elif backdoor_adjustment == BackdoorAdjustment.BACKDOOR_MIN_EFFICIENT: backdoor_set = efficient_bd.optimal_minimal_adj_set() backdoor_sets = [{"backdoor_set": tuple(backdoor_set)}] elif backdoor_adjustment == BackdoorAdjustment.BACKDOOR_MINCOST_EFFICIENT: backdoor_set = efficient_bd.optimal_mincost_adj_set() backdoor_sets = [{"backdoor_set": tuple(backdoor_set)}] return backdoor_sets def find_valid_adjustment_sets( graph: nx.DiGraph, action_nodes: List[str], outcome_nodes: List[str], observed_nodes: List[str], backdoor_paths: List, bdoor_graph: nx.DiGraph, dseparation_algo: str, backdoor_sets: List, filt_eligible_variables: List, backdoor_adjustment: BackdoorAdjustment, max_iterations: int, ): num_iterations = 0 found_valid_adjustment_set = False is_all_observed = set(graph.nodes) == set(observed_nodes) # If `minimal-adjustment` method is specified, start the search from the set with minimum size. Otherwise, start from the largest. set_sizes = ( range(1, len(filt_eligible_variables) + 1, 1) if backdoor_adjustment == BackdoorAdjustment.BACKDOOR_MIN else range(len(filt_eligible_variables), 0, -1) ) for size_candidate_set in set_sizes: for candidate_set in itertools.combinations(filt_eligible_variables, size_candidate_set): check = check_valid_backdoor_set( graph, action_nodes, outcome_nodes, candidate_set, backdoor_paths=backdoor_paths, new_graph=bdoor_graph, dseparation_algo=dseparation_algo, ) logger.debug( "Candidate backdoor set: {0}, is_dseparated: {1}".format(candidate_set, check["is_dseparated"]) ) if check["is_dseparated"]: backdoor_sets.append({"backdoor_set": candidate_set}) found_valid_adjustment_set = True num_iterations += 1 if backdoor_adjustment == BackdoorAdjustment.BACKDOOR_EXHAUSTIVE and num_iterations > max_iterations: logger.warning(f"Max number of iterations {max_iterations} reached.") break # If the backdoor method is `maximal-adjustment` or `minimal-adjustment`, return the first found adjustment set. if ( backdoor_adjustment in { BackdoorAdjustment.BACKDOOR_DEFAULT, BackdoorAdjustment.BACKDOOR_MAX, BackdoorAdjustment.BACKDOOR_MIN, } and found_valid_adjustment_set ): break # If all variables are observed, and the biggest eligible set # does not satisfy backdoor, then none of its subsets will. if ( backdoor_adjustment in {BackdoorAdjustment.BACKDOOR_DEFAULT, BackdoorAdjustment.BACKDOOR_MAX} and is_all_observed ): break if num_iterations > max_iterations: logger.warning(f"Max number of iterations {max_iterations} reached. Could not find a valid backdoor set.") break return backdoor_sets, found_valid_adjustment_set def get_default_backdoor_set_id( graph: nx.DiGraph, action_nodes: List[str], outcome_nodes: List[str], backdoor_sets_dict: Dict ): # Adding a None estimand if no backdoor set found if len(backdoor_sets_dict) == 0: return None # Default set contains minimum possible number of instrumental variables, to prevent lowering variance in the treatment variable. instrument_names = set(get_instruments(graph, action_nodes, outcome_nodes)) iv_count_dict = { key: len(set(bdoor_set).intersection(instrument_names)) for key, bdoor_set in backdoor_sets_dict.items() } min_iv_count = min(iv_count_dict.values()) min_iv_keys = {key for key, iv_count in iv_count_dict.items() if iv_count == min_iv_count} min_iv_backdoor_sets_dict = {key: backdoor_sets_dict[key] for key in min_iv_keys} # Default set is the one with the least number of adjustment variables (optimizing for efficiency) min_set_length = 1000000 default_key = None for key, bdoor_set in min_iv_backdoor_sets_dict.items(): if len(bdoor_set) < min_set_length: min_set_length = len(bdoor_set) default_key = key return default_key def build_backdoor_estimands_dict( treatment_names: List[str], outcome_names: List[str], observed_nodes: List[str], backdoor_sets: List[str], estimands_dict: Dict, ): """Build the final dict for backdoor sets by filtering unobserved variables if needed.""" backdoor_variables_dict = {} observed_nodes = set(observed_nodes) is_identified = [set(bset["backdoor_set"]).issubset(observed_nodes) for bset in backdoor_sets] if any(is_identified): logger.info("Causal effect can be identified.") backdoor_sets_arr = [ list(bset["backdoor_set"]) for bset in backdoor_sets if set(bset["backdoor_set"]).issubset(observed_nodes) ] else: # there is unobserved confounding logger.warning("Backdoor identification failed.") backdoor_sets_arr = [] for i in range(len(backdoor_sets_arr)): backdoor_estimand_expr = construct_backdoor_estimand(treatment_names, outcome_names, backdoor_sets_arr[i]) logger.debug("Identified expression = " + str(backdoor_estimand_expr)) estimands_dict["backdoor" + str(i + 1)] = backdoor_estimand_expr backdoor_variables_dict["backdoor" + str(i + 1)] = backdoor_sets_arr[i] return estimands_dict, backdoor_variables_dict def identify_frontdoor( graph: nx.DiGraph, action_nodes: List[str], outcome_nodes: List[str], dseparation_algo: str = "default" ): """Find a valid frontdoor variable if it exists. Currently only supports a single variable frontdoor set. """ frontdoor_var = None frontdoor_paths = None fdoor_graph = None if dseparation_algo == "default": cond1_graph = do_surgery(graph, action_nodes, remove_incoming_edges=True) bdoor_graph1 = do_surgery(graph, action_nodes, remove_outgoing_edges=True) elif dseparation_algo == "naive": frontdoor_paths = get_all_directed_paths(graph, action_nodes, outcome_nodes) else: raise ValueError(f"d-separation algorithm {dseparation_algo} is not supported") eligible_variables = ( get_descendants(graph, action_nodes) - set(outcome_nodes) - set(get_descendants(graph, outcome_nodes)) ) # For simplicity, assuming a one-variable frontdoor set for candidate_var in eligible_variables: # Cond 1: All directed paths intercepted by candidate_var cond1 = check_valid_frontdoor_set( graph, action_nodes, outcome_nodes, parse_state(candidate_var), frontdoor_paths=frontdoor_paths, new_graph=cond1_graph, dseparation_algo=dseparation_algo, ) logger.debug("Candidate frontdoor set: {0}, is_dseparated: {1}".format(candidate_var, cond1)) if not cond1: continue # Cond 2: No confounding between treatment and candidate var cond2 = check_valid_backdoor_set( graph, action_nodes, parse_state(candidate_var), set(), backdoor_paths=None, new_graph=bdoor_graph1, dseparation_algo=dseparation_algo, ) if not cond2: continue # Cond 3: treatment blocks all confounding between candidate_var and outcome bdoor_graph2 = do_surgery(graph, candidate_var, remove_outgoing_edges=True) cond3 = check_valid_backdoor_set( graph, parse_state(candidate_var), outcome_nodes, action_nodes, backdoor_paths=None, new_graph=bdoor_graph2, dseparation_algo=dseparation_algo, ) is_valid_frontdoor = cond1 and cond2 and cond3 if is_valid_frontdoor: frontdoor_var = candidate_var break return parse_state(frontdoor_var) def identify_mediation(graph: nx.DiGraph, action_nodes: List[str], outcome_nodes: List[str]): """Find a valid mediator if it exists. Currently only supports a single variable mediator set. """ mediation_var = None mediation_paths = get_all_directed_paths(graph, action_nodes, outcome_nodes) eligible_variables = get_descendants(graph, action_nodes) - set(outcome_nodes) # For simplicity, assuming a one-variable mediation set for candidate_var in eligible_variables: is_valid_mediation = check_valid_mediation_set( graph, action_nodes, outcome_nodes, parse_state(candidate_var), mediation_paths=mediation_paths, ) logger.debug("Candidate mediation set: {0}, on_mediating_path: {1}".format(candidate_var, is_valid_mediation)) if is_valid_mediation: mediation_var = candidate_var break return parse_state(mediation_var) def identify_mediation_first_stage_confounders( graph: nx.DiGraph, action_nodes: List[str], outcome_nodes: List[str], mediator_nodes: List[str], observed_nodes: List[str], backdoor_adjustment: BackdoorAdjustment, ): # Create estimands dict as per the API for backdoor, but do not return it estimands_dict = {} backdoor_sets = identify_backdoor(graph, action_nodes, mediator_nodes, observed_nodes, backdoor_adjustment) estimands_dict, backdoor_variables_dict = build_backdoor_estimands_dict( action_nodes, mediator_nodes, observed_nodes, backdoor_sets, estimands_dict, ) # Setting default "backdoor" identification adjustment set default_backdoor_id = get_default_backdoor_set_id(graph, action_nodes, outcome_nodes, backdoor_variables_dict) estimands_dict["backdoor"] = estimands_dict.get(str(default_backdoor_id), None) backdoor_variables_dict["backdoor"] = backdoor_variables_dict.get(str(default_backdoor_id), None) return backdoor_variables_dict def identify_mediation_second_stage_confounders( graph: nx.DiGraph, action_nodes: List[str], mediator_nodes: List[str], outcome_nodes: List[str], observed_nodes: List[str], backdoor_adjustment: BackdoorAdjustment, ): # Create estimands dict as per the API for backdoor, but do not return it estimands_dict = {} backdoor_sets = identify_backdoor(graph, mediator_nodes, outcome_nodes, observed_nodes, backdoor_adjustment) estimands_dict, backdoor_variables_dict = build_backdoor_estimands_dict( mediator_nodes, outcome_nodes, observed_nodes, backdoor_sets, estimands_dict, ) # Setting default "backdoor" identification adjustment set default_backdoor_id = get_default_backdoor_set_id(graph, action_nodes, outcome_nodes, backdoor_variables_dict) estimands_dict["backdoor"] = estimands_dict.get(str(default_backdoor_id), None) backdoor_variables_dict["backdoor"] = backdoor_variables_dict.get(str(default_backdoor_id), None) return backdoor_variables_dict def construct_backdoor_estimand(treatment_name: List[str], outcome_name: List[str], common_causes: List[str]): # TODO: outputs string for now, but ideally should do symbolic # expressions Mon 19 Feb 2018 04:54:17 PM DST # TODO Better support for multivariate treatments expr = None outcome_name = outcome_name[0] num_expr_str = outcome_name if len(common_causes) > 0: num_expr_str += "|" + ",".join(common_causes) expr = "d(" + num_expr_str + ")/d" + ",".join(treatment_name) sym_mu = sp.Symbol("mu") sym_sigma = sp.Symbol("sigma", positive=True) sym_outcome = spstats.Normal(num_expr_str, sym_mu, sym_sigma) sym_treatment_symbols = [sp.Symbol(t) for t in treatment_name] sym_treatment = sp.Array(sym_treatment_symbols) sym_conditional_outcome = spstats.Expectation(sym_outcome) sym_effect = sp.Derivative(sym_conditional_outcome, sym_treatment) sym_assumptions = { "Unconfoundedness": ( "If U\N{RIGHTWARDS ARROW}{{{0}}} and U\N{RIGHTWARDS ARROW}{1}" " then P({1}|{0},{2},U) = P({1}|{0},{2})" ).format(",".join(treatment_name), outcome_name, ",".join(common_causes)) } estimand = {"estimand": sym_effect, "assumptions": sym_assumptions} return estimand def construct_iv_estimand(treatment_name: List[str], outcome_name: List[str], instrument_names: List[str]): # TODO: support multivariate treatments better. expr = None outcome_name = outcome_name[0] sym_outcome = spstats.Normal(outcome_name, 0, 1) sym_treatment_symbols = [spstats.Normal(t, 0, 1) for t in treatment_name] sym_treatment = sp.Array(sym_treatment_symbols) sym_instrument_symbols = [sp.Symbol(inst) for inst in instrument_names] sym_instrument = sp.Array(sym_instrument_symbols) # ",".join(instrument_names)) sym_outcome_derivative = sp.Derivative(sym_outcome, sym_instrument) sym_treatment_derivative = sp.Derivative(sym_treatment, sym_instrument) sym_effect = spstats.Expectation(sym_outcome_derivative / sym_treatment_derivative) sym_assumptions = { "As-if-random": ( "If U\N{RIGHTWARDS ARROW}\N{RIGHTWARDS ARROW}{0} then " "\N{NOT SIGN}(U \N{RIGHTWARDS ARROW}\N{RIGHTWARDS ARROW}{{{1}}})" ).format(outcome_name, ",".join(instrument_names)), "Exclusion": ( "If we remove {{{0}}}\N{RIGHTWARDS ARROW}{{{1}}}, then " "\N{NOT SIGN}({{{0}}}\N{RIGHTWARDS ARROW}{2})" ).format(",".join(instrument_names), ",".join(treatment_name), outcome_name), } estimand = {"estimand": sym_effect, "assumptions": sym_assumptions} return estimand def construct_frontdoor_estimand( treatment_name: List[str], outcome_name: List[str], frontdoor_variables_names: List[str] ): # TODO: support multivariate treatments better. expr = None outcome_name = outcome_name[0] sym_outcome = spstats.Normal(outcome_name, 0, 1) sym_treatment_symbols = [spstats.Normal(t, 0, 1) for t in treatment_name] sym_treatment = sp.Array(sym_treatment_symbols) sym_frontdoor_symbols = [sp.Symbol(inst) for inst in frontdoor_variables_names] sym_frontdoor = sp.Array(sym_frontdoor_symbols) # ",".join(instrument_names)) sym_outcome_derivative = sp.Derivative(sym_outcome, sym_frontdoor) sym_treatment_derivative = sp.Derivative(sym_frontdoor, sym_treatment) sym_effect = spstats.Expectation(sym_treatment_derivative * sym_outcome_derivative) sym_assumptions = { "Full-mediation": ("{2} intercepts (blocks) all directed paths from {0} to {1}.").format( ",".join(treatment_name), ",".join(outcome_name), ",".join(frontdoor_variables_names), ), "First-stage-unconfoundedness": ( "If U\N{RIGHTWARDS ARROW}{{{0}}} and U\N{RIGHTWARDS ARROW}{{{1}}}" " then P({1}|{0},U) = P({1}|{0})" ).format(",".join(treatment_name), ",".join(frontdoor_variables_names)), "Second-stage-unconfoundedness": ( "If U\N{RIGHTWARDS ARROW}{{{2}}} and U\N{RIGHTWARDS ARROW}{1}" " then P({1}|{2}, {0}, U) = P({1}|{2}, {0})" ).format( ",".join(treatment_name), outcome_name, ",".join(frontdoor_variables_names), ), } estimand = {"estimand": sym_effect, "assumptions": sym_assumptions} return estimand def construct_mediation_estimand( estimand_type: EstimandType, action_nodes: List[str], outcome_nodes: List[str], mediator_nodes: List[str] ): # TODO: support multivariate treatments better. expr = None if estimand_type in ( EstimandType.NONPARAMETRIC_NDE, EstimandType.NONPARAMETRIC_NIE, ): outcome_nodes = outcome_nodes[0] sym_outcome = spstats.Normal(outcome_nodes, 0, 1) sym_treatment_symbols = [spstats.Normal(t, 0, 1) for t in action_nodes] sym_treatment = sp.Array(sym_treatment_symbols) sym_mediators_symbols = [sp.Symbol(inst) for inst in mediator_nodes] sym_mediators = sp.Array(sym_mediators_symbols) sym_outcome_derivative = sp.Derivative(sym_outcome, sym_mediators) sym_treatment_derivative = sp.Derivative(sym_mediators, sym_treatment) # For direct effect num_expr_str = outcome_nodes if len(mediator_nodes) > 0: num_expr_str += "|" + ",".join(mediator_nodes) sym_mu = sp.Symbol("mu") sym_sigma = sp.Symbol("sigma", positive=True) sym_conditional_outcome = spstats.Normal(num_expr_str, sym_mu, sym_sigma) sym_directeffect_derivative = sp.Derivative(sym_conditional_outcome, sym_treatment) if estimand_type == EstimandType.NONPARAMETRIC_NIE: sym_effect = spstats.Expectation(sym_treatment_derivative * sym_outcome_derivative) elif estimand_type == EstimandType.NONPARAMETRIC_NDE: sym_effect = spstats.Expectation(sym_directeffect_derivative) sym_assumptions = { "Mediation": ( "{2} intercepts (blocks) all directed paths from {0} to {1} except the path {{{0}}}\N{RIGHTWARDS ARROW}{{{1}}}." ).format( ",".join(action_nodes), ",".join(outcome_nodes), ",".join(mediator_nodes), ), "First-stage-unconfoundedness": ( "If U\N{RIGHTWARDS ARROW}{{{0}}} and U\N{RIGHTWARDS ARROW}{{{1}}}" " then P({1}|{0},U) = P({1}|{0})" ).format(",".join(action_nodes), ",".join(mediator_nodes)), "Second-stage-unconfoundedness": ( "If U\N{RIGHTWARDS ARROW}{{{2}}} and U\N{RIGHTWARDS ARROW}{1}" " then P({1}|{2}, {0}, U) = P({1}|{2}, {0})" ).format(",".join(action_nodes), outcome_nodes, ",".join(mediator_nodes)), } else: raise ValueError( "Estimand type not supported. Supported estimand types are {0} or {1}'.".format( EstimandType.NONPARAMETRIC_NDE, EstimandType.NONPARAMETRIC_NIE, ) ) estimand = {"estimand": sym_effect, "assumptions": sym_assumptions} return estimand

bloebp

4fd0a92bd2fabbacfe6f225ea9637d3e8f08407e

2a8e49a77eb43a74d7ee6fc0925a376cd60335f2

to be consistent with naming, we can call it "mediator_nodes".

amit-sharma

py-why/dowhy

Proposal: Finalize functional API refactor - deprecate causal graph

- The graph should now be defined via a networkx graph. Most identification methods now expect an additional "observed_nodes" parameter accordingly. - CausalModel and CausalGraph still exist and should be compatible with the old API. Open task is still to replace the usage of CausalModel in the tests and notebooks. There are also some smaller details with the identification methods, which should be double checked.

2023-05-16 16:07:18+00:00

2023-11-27 17:48:56+00:00

dowhy/causal_identifier/efficient_backdoor.py

import networkx as nx import numpy as np EXCEPTION_NO_ADJ = "An adjustment set formed by observable variables does not exist" EXCEPTION_COND_NO_OPT = "Conditions to guarantee the existence of an optimal adjustment set are not satisfied" class EfficientBackdoor: """ Implements methods for finding optimal (efficient) backdoor sets. """ def __init__(self, graph, conditional_node_names=None, costs=None): """ :param graph: CausalGraph A causal graph. :param costs: list A list with non-negative costs associated with variables in the graph. Only used for estimatand_type='non-parametric-ate' and method_name='efficient-mincost-adjustment'. If not costs are provided by the user, and method_name='efficient-mincost-adjustment', costs are assumed to be equal to one for all variables in the graph. The structure of the list should be of the form [(node, {"cost": x}) for node in nodes]. :param conditional_node_names: list A list with variables that are used to determine treatment. If none are provided, it is assumed that the intervention sets the treatment to a constant. """ assert ( len(graph.treatment_name) == 1 ), "The methods for computing efficient backdoor sets are only valid for one dimensional treatments" assert ( len(graph.outcome_name) == 1 ), "The methods for computing efficient backdoor sets are only valid for one dimensional outcomes" self.graph = graph if costs is None: # If no costs are passed, use uniform costs costs = [(node, {"cost": 1}) for node in self.graph._graph.nodes] assert all([tup["cost"] > 0 for _, tup in costs]), "All costs must be positive" self.graph._graph.add_nodes_from(costs) self.observed_nodes = set( [node for node in self.graph._graph.nodes if self.graph._graph.nodes[node]["observed"] == "yes"] ) if conditional_node_names is None: conditional_node_names = [] assert set(conditional_node_names).issubset( self.observed_nodes ), "Some conditional variables are not marked as observed" self.conditional_node_names = conditional_node_names def ancestors_all(self, nodes): """Method to compute the set of all ancestors of a set of nodes. A node is always an ancestor of itself. :param nodes: list A list of nodes in the graph. :returns ancestors: set The set of nodes that are ancestors of nodes in nodes. """ ancestors = set() for node in nodes: ancestors_node = nx.ancestors(self.graph._graph, node) ancestors = ancestors.union(ancestors_node) ancestors = ancestors.union(set(nodes)) return ancestors def backdoor_graph(self, G): """Method to compute the proper back-door graph associated with treatment and outcome. :param G: nx.DiGraph A directed acyclic graph. :returns Gbd: nx.DiGraph The proper backdoor graph of G. """ Gbd = G.copy() for path in nx.all_simple_edge_paths(G, self.graph.treatment_name[0], self.graph.outcome_name[0]): first_edge = path[0] Gbd.remove_edge(first_edge[0], first_edge[1]) return Gbd def causal_vertices(self): """Method to compute the set of all vertices that lie in a causal path between treatment and outcome. :returns causal_vertices: set A set with vertices lying on some causal path between treatment and outcome. """ causal_vertices = set() causal_paths = list( nx.all_simple_paths( self.graph._graph, source=self.graph.treatment_name[0], target=self.graph.outcome_name[0], ) ) for path in causal_paths: causal_vertices = causal_vertices.union(set(path)) causal_vertices.remove(self.graph.treatment_name[0]) return causal_vertices def forbidden(self): """Method to compute the forbidden set with respect to treatment and outcome. :returns forbidden: set The forbidden set. """ forbidden = set() for node in self.causal_vertices(): forbidden = forbidden.union(nx.descendants(self.graph._graph, node).union({node})) return forbidden.union({self.graph.treatment_name[0]}) def ignore(self): """Method to compute the set of ignorable vertices with respect to treatment, outcome, conditional and observable variables. Used in the construction of the H0 and H1 graphs. See Smucler, Sapienza and Rotnitzky (2021), Biometrika, for the full definition of this set. :returns ignore: set The set of ignorable vertices. """ set1 = set( self.ancestors_all(self.conditional_node_names + [self.graph.treatment_name[0], self.graph.outcome_name[0]]) ) set1.remove(self.graph.treatment_name[0]) set1.remove(self.graph.outcome_name[0]) set2 = set(self.graph._graph.nodes()) - self.observed_nodes set2 = set2.union(self.forbidden()) ignore = set1.intersection(set2) return ignore def unblocked(self, H, Z): """Method to compute the unblocked set of Z with respect to treatment. See Smucler, Sapienza and Rotnitzky (2021), Biometrika, for the full definition of this set. :params H: nx.Graph An undirected graph. :param Z: list A list with nodes in the graph H. :returns unblocked: set The unblocked set. """ G2 = H.subgraph(H.nodes() - set(Z)) B = nx.node_connected_component(G2, self.graph.treatment_name[0]) unblocked = set(nx.node_boundary(H, B)) return unblocked def build_H0(self): """Returns the H0 graph associated with treatment, outcome, conditional and observable variables. See Smucler, Sapienza and Rotnitzky (2021), Biometrika, for the full definition of this graph. :returns H0: nx.Graph The H0 graph. """ # restriction to ancestors anc = self.ancestors_all( self.conditional_node_names + [self.graph.treatment_name[0], self.graph.outcome_name[0]] ) G2 = self.graph._graph.subgraph(anc) # back-door graph G3 = self.backdoor_graph(G2) # moralization H0 = nx.moral_graph(G3) return H0 def build_H1(self): """Returns the H1 graph associated with treatment, outcome, conditional and observable variables. See Smucler, Sapienza and Rotnitzky (2021), Biometrika, for the full definition of this graph. :returns H1: nx.Graph The H1 graph. """ H0 = self.build_H0() ignore_nodes = self.ignore() H1 = H0.copy().subgraph(H0.nodes() - ignore_nodes) H1 = nx.Graph(H1) vertices_list = list(H1.nodes()) for i, node1 in enumerate(vertices_list): for node2 in vertices_list[(i + 1) :]: for path in nx.all_simple_paths(H0, source=node1, target=node2): if set(path).issubset(ignore_nodes.union({node1, node2})): H1.add_edge(node1, node2) break for node in self.conditional_node_names: H1.add_edge(self.graph.treatment_name[0], node) H1.add_edge(node, self.graph.outcome_name[0]) return H1 def build_D(self): """Returns the D flow network associated with treatment, outcome, conditional and observable variables. If a node does not have a 'cost' attribute, this function will assume the cost is infinity. See Smucler and Rotnitzky (2022), Journal of Causa Inference, for the full definition of this flow network. :returns D: nx.DiGraph The D flow network. """ H1 = self.build_H1() D = nx.DiGraph() for node in H1.nodes.keys(): if "cost" in H1.nodes[node]: capacity = H1.nodes[node]["cost"] else: capacity = np.inf D.add_edge(node + "'", node + "''", capacity=capacity) for edge in H1.edges.keys(): D.add_edge(edge[0] + "''", edge[1] + "'", capacity=np.inf) D.add_edge(edge[1] + "''", edge[0] + "'", capacity=np.inf) return D def compute_smallest_mincut(self): """Returns a min-cut in the flow network D associated with treatment, outcome, conditional and observable variables that is contained in any other min-cut. :returns S_c: set The min-cut with the above property. """ D = self.build_D() _, flow_dict = nx.algorithms.flow.maximum_flow( flowG=D, _s=self.graph.outcome_name[0] + "''", _t=self.graph.treatment_name[0] + "'", ) queu = [self.graph.outcome_name[0] + "''"] S_c = set() visited = set() while len(queu) > 0: node = queu.pop() if node not in visited: visited.add(node) point_in = D.in_edges(node) point_out = D.out_edges(node) for edge in point_out: capacity = D.edges[edge]["capacity"] flow = flow_dict[edge[0]][edge[1]] if flow < capacity: queu.append(edge[1]) S_c.add(edge[1]) for edge in point_in: flow = flow_dict[edge[0]][edge[1]] if flow > 0: queu.append(edge[0]) S_c.add(edge[0]) return S_c def h_operator(self, S): """Given a set S of vertices in the flow network D, returns the operator h(S), a set of vertices in the undirected graph H1. See Smucler and Rotnitzky (2022), Journal of Causal Inference, for the full definition of this operator. :param S: set A set of vertices in the flow network D associated treatment, outcome, conditional and observable variables. :returns Z: set The set obtained from applying the h operator to S. """ Z = set() for node in self.graph._graph.nodes: nodep = node + "'" nodepp = node + "''" condition = nodep in S and nodepp not in S if condition: Z.add(node) return Z def optimal_adj_set(self): """Returns the optimal adjustment set with respect to treatment, outcome, conditional and observable variables. If the sufficient conditions for the existence of the optimal adjustment set outlined in Smucler, Sapienza and Rotnitzky (2021), Biometrika, do not hold, an error is raised. :returns: optimal: set The optimal adjustment set. """ H1 = self.build_H1() if self.graph.treatment_name[0] in H1.neighbors(self.graph.outcome_name[0]): raise ValueError(EXCEPTION_NO_ADJ) elif self.observed_nodes == set(self.graph._graph.nodes()) or self.observed_nodes.issubset( self.ancestors_all(self.conditional_node_names + [self.graph.treatment_name[0], self.graph.outcome_name[0]]) ): optimal = nx.node_boundary(H1, {self.graph.outcome_name[0]}) return optimal else: raise ValueError(EXCEPTION_COND_NO_OPT) def optimal_minimal_adj_set(self): """Returns the optimal minimal adjustment set with respect to treatment, outcome, conditional and observable variables. :returns: optimal_minimal: set The optimal minimal adjustment set. """ H1 = self.build_H1() if self.graph.treatment_name[0] in H1.neighbors(self.graph.outcome_name[0]): raise ValueError(EXCEPTION_NO_ADJ) else: optimal_minimal = self.unblocked(H1, nx.node_boundary(H1, [self.graph.outcome_name[0]])) return optimal_minimal def optimal_mincost_adj_set(self): """Returns the optimal minimum cost adjustment set with respect to treatment, outcome, conditional and observable variables. Note that when the costs are constant, this is the optimal adjustment set among those of minimum cardinality. :returns: optimal_mincost: set The optimal minimum cost adjustment set. """ H1 = self.build_H1() if self.graph.treatment_name[0] in H1.neighbors(self.graph.outcome_name[0]): raise ValueError(EXCEPTION_NO_ADJ) else: S_c = self.compute_smallest_mincut() optimal_mincost = self.h_operator(S_c) return optimal_mincost

from typing import List import networkx as nx import numpy as np EXCEPTION_NO_ADJ = "An adjustment set formed by observable variables does not exist" EXCEPTION_COND_NO_OPT = "Conditions to guarantee the existence of an optimal adjustment set are not satisfied" class EfficientBackdoor: """ Implements methods for finding optimal (efficient) backdoor sets. """ def __init__( self, graph: nx.DiGraph, action_nodes: List[str], outcome_nodes: List[str], observed_nodes: List[str], conditional_node_names=None, costs=None, ): """ :param graph: nx.DiGraph A causal graph. :param costs: list A list with non-negative costs associated with variables in the graph. Only used for estimatand_type='non-parametric-ate' and method_name='efficient-mincost-adjustment'. If not costs are provided by the user, and method_name='efficient-mincost-adjustment', costs are assumed to be equal to one for all variables in the graph. The structure of the list should be of the form [(node, {"cost": x}) for node in nodes]. :param conditional_node_names: list A list with variables that are used to determine treatment. If none are provided, it is assumed that the intervention sets the treatment to a constant. """ assert ( len(action_nodes) == 1 ), "The methods for computing efficient backdoor sets are only valid for one dimensional treatments" assert ( len(outcome_nodes) == 1 ), "The methods for computing efficient backdoor sets are only valid for one dimensional outcomes" self.graph = graph if costs is None: # If no costs are passed, use uniform costs costs = [(node, {"cost": 1}) for node in self.graph.nodes] assert all([tup["cost"] > 0 for _, tup in costs]), "All costs must be positive" self.graph.add_nodes_from(costs) self.observed_nodes = set([node for node in self.graph.nodes if node in set(observed_nodes)]) if conditional_node_names is None: conditional_node_names = [] assert set(conditional_node_names).issubset( self.observed_nodes ), "Some conditional variables are not marked as observed" self.conditional_node_names = conditional_node_names self.treatment_name = action_nodes[0] self.outcome_name = outcome_nodes[0] def ancestors_all(self, nodes): """Method to compute the set of all ancestors of a set of nodes. A node is always an ancestor of itself. :param nodes: list A list of nodes in the graph. :returns ancestors: set The set of nodes that are ancestors of nodes in nodes. """ ancestors = set() for node in nodes: ancestors_node = nx.ancestors(self.graph, node) ancestors = ancestors.union(ancestors_node) ancestors = ancestors.union(set(nodes)) return ancestors def backdoor_graph(self, G): """Method to compute the proper back-door graph associated with treatment and outcome. :param G: nx.DiGraph A directed acyclic graph. :returns Gbd: nx.DiGraph The proper backdoor graph of G. """ Gbd = G.copy() for path in nx.all_simple_edge_paths(G, self.treatment_name, self.outcome_name): first_edge = path[0] Gbd.remove_edge(first_edge[0], first_edge[1]) return Gbd def causal_vertices(self): """Method to compute the set of all vertices that lie in a causal path between treatment and outcome. :returns causal_vertices: set A set with vertices lying on some causal path between treatment and outcome. """ causal_vertices = set() causal_paths = list( nx.all_simple_paths( self.graph, source=self.treatment_name, target=self.outcome_name, ) ) for path in causal_paths: causal_vertices = causal_vertices.union(set(path)) causal_vertices.remove(self.treatment_name) return causal_vertices def forbidden(self): """Method to compute the forbidden set with respect to treatment and outcome. :returns forbidden: set The forbidden set. """ forbidden = set() for node in self.causal_vertices(): forbidden = forbidden.union(nx.descendants(self.graph, node).union({node})) return forbidden.union({self.treatment_name}) def ignore(self): """Method to compute the set of ignorable vertices with respect to treatment, outcome, conditional and observable variables. Used in the construction of the H0 and H1 graphs. See Smucler, Sapienza and Rotnitzky (2021), Biometrika, for the full definition of this set. :returns ignore: set The set of ignorable vertices. """ set1 = set(self.ancestors_all(self.conditional_node_names + [self.treatment_name, self.outcome_name])) set1.remove(self.treatment_name) set1.remove(self.outcome_name) set2 = set(self.graph.nodes()) - self.observed_nodes set2 = set2.union(self.forbidden()) ignore = set1.intersection(set2) return ignore def unblocked(self, H, Z): """Method to compute the unblocked set of Z with respect to treatment. See Smucler, Sapienza and Rotnitzky (2021), Biometrika, for the full definition of this set. :params H: nx.Graph An undirected graph. :param Z: list A list with nodes in the graph H. :returns unblocked: set The unblocked set. """ G2 = H.subgraph(H.nodes() - set(Z)) B = nx.node_connected_component(G2, self.treatment_name) unblocked = set(nx.node_boundary(H, B)) return unblocked def build_H0(self): """Returns the H0 graph associated with treatment, outcome, conditional and observable variables. See Smucler, Sapienza and Rotnitzky (2021), Biometrika, for the full definition of this graph. :returns H0: nx.Graph The H0 graph. """ # restriction to ancestors anc = self.ancestors_all(self.conditional_node_names + [self.treatment_name, self.outcome_name]) G2 = self.graph.subgraph(anc) # back-door graph G3 = self.backdoor_graph(G2) # moralization H0 = nx.moral_graph(G3) return H0 def build_H1(self): """Returns the H1 graph associated with treatment, outcome, conditional and observable variables. See Smucler, Sapienza and Rotnitzky (2021), Biometrika, for the full definition of this graph. :returns H1: nx.Graph The H1 graph. """ H0 = self.build_H0() ignore_nodes = self.ignore() H1 = H0.copy().subgraph(H0.nodes() - ignore_nodes) H1 = nx.Graph(H1) vertices_list = list(H1.nodes()) for i, node1 in enumerate(vertices_list): for node2 in vertices_list[(i + 1) :]: for path in nx.all_simple_paths(H0, source=node1, target=node2): if set(path).issubset(ignore_nodes.union({node1, node2})): H1.add_edge(node1, node2) break for node in self.conditional_node_names: H1.add_edge(self.treatment_name, node) H1.add_edge(node, self.outcome_name) return H1 def build_D(self): """Returns the D flow network associated with treatment, outcome, conditional and observable variables. If a node does not have a 'cost' attribute, this function will assume the cost is infinity. See Smucler and Rotnitzky (2022), Journal of Causa Inference, for the full definition of this flow network. :returns D: nx.DiGraph The D flow network. """ H1 = self.build_H1() D = nx.DiGraph() for node in H1.nodes.keys(): if "cost" in H1.nodes[node]: capacity = H1.nodes[node]["cost"] else: capacity = np.inf D.add_edge(node + "'", node + "''", capacity=capacity) for edge in H1.edges.keys(): D.add_edge(edge[0] + "''", edge[1] + "'", capacity=np.inf) D.add_edge(edge[1] + "''", edge[0] + "'", capacity=np.inf) return D def compute_smallest_mincut(self): """Returns a min-cut in the flow network D associated with treatment, outcome, conditional and observable variables that is contained in any other min-cut. :returns S_c: set The min-cut with the above property. """ D = self.build_D() _, flow_dict = nx.algorithms.flow.maximum_flow( flowG=D, _s=self.outcome_name + "''", _t=self.treatment_name + "'", ) queu = [self.outcome_name + "''"] S_c = set() visited = set() while len(queu) > 0: node = queu.pop() if node not in visited: visited.add(node) point_in = D.in_edges(node) point_out = D.out_edges(node) for edge in point_out: capacity = D.edges[edge]["capacity"] flow = flow_dict[edge[0]][edge[1]] if flow < capacity: queu.append(edge[1]) S_c.add(edge[1]) for edge in point_in: flow = flow_dict[edge[0]][edge[1]] if flow > 0: queu.append(edge[0]) S_c.add(edge[0]) return S_c def h_operator(self, S): """Given a set S of vertices in the flow network D, returns the operator h(S), a set of vertices in the undirected graph H1. See Smucler and Rotnitzky (2022), Journal of Causal Inference, for the full definition of this operator. :param S: set A set of vertices in the flow network D associated treatment, outcome, conditional and observable variables. :returns Z: set The set obtained from applying the h operator to S. """ Z = set() for node in self.graph.nodes: nodep = node + "'" nodepp = node + "''" condition = nodep in S and nodepp not in S if condition: Z.add(node) return Z def optimal_adj_set(self): """Returns the optimal adjustment set with respect to treatment, outcome, conditional and observable variables. If the sufficient conditions for the existence of the optimal adjustment set outlined in Smucler, Sapienza and Rotnitzky (2021), Biometrika, do not hold, an error is raised. :returns: optimal: set The optimal adjustment set. """ H1 = self.build_H1() if self.treatment_name in H1.neighbors(self.outcome_name): raise ValueError(EXCEPTION_NO_ADJ) elif self.observed_nodes == set(self.graph.nodes()) or self.observed_nodes.issubset( self.ancestors_all(self.conditional_node_names + [self.treatment_name, self.outcome_name]) ): optimal = nx.node_boundary(H1, {self.outcome_name}) return optimal else: raise ValueError(EXCEPTION_COND_NO_OPT) def optimal_minimal_adj_set(self): """Returns the optimal minimal adjustment set with respect to treatment, outcome, conditional and observable variables. :returns: optimal_minimal: set The optimal minimal adjustment set. """ H1 = self.build_H1() if self.treatment_name in H1.neighbors(self.outcome_name): raise ValueError(EXCEPTION_NO_ADJ) else: optimal_minimal = self.unblocked(H1, nx.node_boundary(H1, [self.outcome_name])) return optimal_minimal def optimal_mincost_adj_set(self): """Returns the optimal minimum cost adjustment set with respect to treatment, outcome, conditional and observable variables. Note that when the costs are constant, this is the optimal adjustment set among those of minimum cardinality. :returns: optimal_mincost: set The optimal minimum cost adjustment set. """ H1 = self.build_H1() if self.treatment_name in H1.neighbors(self.outcome_name): raise ValueError(EXCEPTION_NO_ADJ) else: S_c = self.compute_smallest_mincut() optimal_mincost = self.h_operator(S_c) return optimal_mincost

bloebp

4fd0a92bd2fabbacfe6f225ea9637d3e8f08407e

2a8e49a77eb43a74d7ee6fc0925a376cd60335f2

why not use `self.graph` in lines 48, 49? Not a strong preference since they are equivalent in the current code.

amit-sharma

py-why/dowhy

Proposal: Finalize functional API refactor - deprecate causal graph

- The graph should now be defined via a networkx graph. Most identification methods now expect an additional "observed_nodes" parameter accordingly. - CausalModel and CausalGraph still exist and should be compatible with the old API. Open task is still to replace the usage of CausalModel in the tests and notebooks. There are also some smaller details with the identification methods, which should be double checked.

2023-05-16 16:07:18+00:00

2023-11-27 17:48:56+00:00

dowhy/causal_identifier/id_identifier.py

from typing import Dict, List, Optional, Set, Union import networkx as nx import numpy as np from dowhy.causal_graph import CausalGraph from dowhy.utils.api import parse_state from dowhy.utils.graph_operations import find_ancestor, find_c_components, induced_graph from dowhy.utils.ordered_set import OrderedSet class IDExpression: """ Class for storing a causal estimand, as a result of the identification step using the ID algorithm. The object stores a list of estimators(self._product) whose porduct must be obtained and a list of variables (self._sum) over which the product must be marginalized. """ def __init__(self): self._product = [] self._sum = [] def add_product(self, element: Union[Dict, "IDExpression"]): """ Add an estimator to the list of product. :param element: Estimator to append to the product list. """ self._product.append(element) def add_sum(self, element: Set): """ Add variables to the list. :param element: Set of variables to append to the list self._sum. """ for el in element: self._sum.append(el) def get_val(self, return_type: str): """ Get either the list of estimators (for product) or list of variables (for the marginalization). :param return_type: "prod" to return the list of estimators or "sum" to return the list of variables. """ if return_type == "prod": return self._product elif return_type == "sum": return self._sum else: raise Exception("Provide correct return type.") def _print_estimator(self, prefix, estimator: Union[Dict, "IDExpression"] = None, start: bool = False): """ Print the IDExpression object. """ if estimator is None: return None string = "" if isinstance(estimator, IDExpression): s = True if len(estimator.get_val(return_type="sum")) > 0 else False if s: sum_vars = "{" + ",".join(estimator.get_val(return_type="sum")) + "}" string += prefix + "Sum over " + sum_vars + ":\n" prefix += "\t" for expression in estimator.get_val(return_type="prod"): add_string = self._print_estimator(prefix, expression) if add_string is None: return None else: string += add_string else: outcome_vars = list(estimator["outcome_vars"]) condition_vars = list(estimator["condition_vars"]) string += prefix + "Predictor: P(" + ",".join(outcome_vars) if len(condition_vars) > 0: string += "|" + ",".join(condition_vars) string += ")\n" if start: string = string[:-1] return string def __str__(self): string = self._print_estimator(prefix="", estimator=self, start=True) if string is None: return "The graph is not identifiable." else: return string class IDIdentifier: """ This class is for backwards compatibility with CausalModel Will be deprecated in the future in favor of function call id_identify_effect() """ def identify_effect( self, graph: CausalGraph, treatment_name: Union[str, List[str]], outcome_name: Union[str, List[str]], node_names: Optional[Union[str, List[str]]] = None, **kwargs, ): return identify_effect_id(graph, treatment_name, outcome_name, node_names, **kwargs) def identify_effect_id( graph: CausalGraph, treatment_name: Union[str, List[str]], outcome_name: Union[str, List[str]], node_names: Optional[Union[str, List[str]]] = None, **kwargs, ) -> IDExpression: """ Implementation of the ID algorithm. Link - https://ftp.cs.ucla.edu/pub/stat_ser/shpitser-thesis.pdf The pseudo code has been provided on Pg 40. :param treatment_names: OrderedSet comprising names of treatment variables. :param outcome_names:OrderedSet comprising names of outcome variables. :param node_names: OrderedSet comprising names of all nodes in the graph :returns: target estimand, an instance of the IDExpression class. """ if node_names is None: node_names = OrderedSet(graph._graph.nodes) adjacency_matrix = graph.get_adjacency_matrix() try: tsort_node_names = OrderedSet(list(nx.topological_sort(graph._graph))) # topological sorting of graph nodes except: raise Exception("The graph must be a directed acyclic graph (DAG).") return __adjacency_matrix_identify_effect( adjacency_matrix, OrderedSet(parse_state(treatment_name)), OrderedSet(parse_state(outcome_name)), tsort_node_names, node_names, ) def __adjacency_matrix_identify_effect( adjacency_matrix: np.matrix, treatment_name: OrderedSet, outcome_name: OrderedSet, tsort_node_names: OrderedSet, node_names: OrderedSet = None, ): node2idx, idx2node = __idx_node_mapping(node_names) # Estimators list for returning after identification estimators = IDExpression() # Line 1 # If no action has been taken, the effect on Y is just the marginal of the observational distribution P(v) on Y. if len(treatment_name) == 0: identifier = IDExpression() estimator = {} estimator["outcome_vars"] = node_names estimator["condition_vars"] = OrderedSet() identifier.add_product(estimator) identifier.add_sum(node_names.difference(outcome_name)) estimators.add_product(identifier) return estimators # Line 2 # If we are interested in the effect on Y, it is sufficient to restrict our attention on the parts of the model ancestral to Y. ancestors = find_ancestor(outcome_name, node_names, adjacency_matrix, node2idx, idx2node) if ( len(node_names.difference(ancestors)) != 0 ): # If there are elements which are not the ancestor of the outcome variables # Modify list of valid nodes treatment_name = treatment_name.intersection(ancestors) node_names = node_names.intersection(ancestors) adjacency_matrix = induced_graph(node_set=node_names, adjacency_matrix=adjacency_matrix, node2idx=node2idx) return __adjacency_matrix_identify_effect( treatment_name=treatment_name, outcome_name=outcome_name, adjacency_matrix=adjacency_matrix, tsort_node_names=tsort_node_names, node_names=node_names, ) # Line 3 - forces an action on any node where such an action would have no effect on Y – assuming we already acted on X. # Modify adjacency matrix to obtain that corresponding to do(X) adjacency_matrix_do_x = adjacency_matrix.copy() for x in treatment_name: x_idx = node2idx[x] for i in range(len(node_names)): adjacency_matrix_do_x[i, x_idx] = 0 ancestors = find_ancestor(outcome_name, node_names, adjacency_matrix_do_x, node2idx, idx2node) W = node_names.difference(treatment_name).difference(ancestors) if len(W) != 0: return __adjacency_matrix_identify_effect( treatment_name=treatment_name.union(W), outcome_name=outcome_name, adjacency_matrix=adjacency_matrix, tsort_node_names=tsort_node_names, node_names=node_names, ) # Line 4 - Decomposes the problem into a set of smaller problems using the key property of C-component factorization of causal models. # If the entire graph is a single C-component already, further problem decomposition is impossible, and we must provide base cases. # Modify adjacency matrix to remove treatment variables node_names_minus_x = node_names.difference(treatment_name) node2idx_minus_x, idx2node_minus_x = __idx_node_mapping(node_names_minus_x) adjacency_matrix_minus_x = induced_graph( node_set=node_names_minus_x, adjacency_matrix=adjacency_matrix, node2idx=node2idx ) c_components = find_c_components( adjacency_matrix=adjacency_matrix_minus_x, node_set=node_names_minus_x, idx2node=idx2node_minus_x ) if len(c_components) > 1: identifier = IDExpression() sum_over_set = node_names.difference(outcome_name.union(treatment_name)) for component in c_components: expressions = __adjacency_matrix_identify_effect( treatment_name=node_names.difference(component), outcome_name=OrderedSet(list(component)), adjacency_matrix=adjacency_matrix, tsort_node_names=tsort_node_names, node_names=node_names, ) for expression in expressions.get_val(return_type="prod"): identifier.add_product(expression) identifier.add_sum(sum_over_set) estimators.add_product(identifier) return estimators # Line 5 - The algorithms fails due to the presence of a hedge - the graph G, and a subgraph S that does not contain any X nodes. S = c_components[0] c_components_G = find_c_components(adjacency_matrix=adjacency_matrix, node_set=node_names, idx2node=idx2node) if len(c_components_G) == 1 and c_components_G[0] == node_names: return None # Line 6 - If there are no bidirected arcs from X to the other nodes in the current subproblem under consideration, then we can replace acting on X by conditioning, and thus solve the subproblem. if S in c_components_G: sum_over_set = S.difference(outcome_name) prev_nodes = [] for node in tsort_node_names: if node in S: identifier = IDExpression() estimator = {} estimator["outcome_vars"] = OrderedSet([node]) estimator["condition_vars"] = OrderedSet(prev_nodes) identifier.add_product(estimator) identifier.add_sum(sum_over_set) estimators.add_product(identifier) prev_nodes.append(node) return estimators # Line 7 - This is the most complicated case in the algorithm. Explain in the second last paragraph on Pg 41 of the link provided in the docstring above. for component in c_components_G: C = S.difference(component) if C.is_empty() is None: return __adjacency_matrix_identify_effect( treatment_name=treatment_name.intersection(component), outcome_name=outcome_name, adjacency_matrix=induced_graph( node_set=component, adjacency_matrix=adjacency_matrix, node2idx=node2idx ), tsort_node_names=tsort_node_names, node_names=node_names, ) def __idx_node_mapping(node_names: OrderedSet): """ Obtain the node name to index and index to node name mappings. :param node_names: Name of all nodes in the graph. :return: node to index and index to node mappings. """ node2idx = {} idx2node = {} for i, node in enumerate(node_names.get_all()): node2idx[node] = i idx2node[i] = node return node2idx, idx2node

from typing import Dict, List, Optional, Set, Union import networkx as nx import numpy as np from dowhy.graph import get_adjacency_matrix from dowhy.utils.api import parse_state from dowhy.utils.graph_operations import find_ancestor, find_c_components, induced_graph from dowhy.utils.ordered_set import OrderedSet class IDExpression: """ Class for storing a causal estimand, as a result of the identification step using the ID algorithm. The object stores a list of estimators(self._product) whose porduct must be obtained and a list of variables (self._sum) over which the product must be marginalized. """ def __init__(self): self._product = [] self._sum = [] def add_product(self, element: Union[Dict, "IDExpression"]): """ Add an estimator to the list of product. :param element: Estimator to append to the product list. """ self._product.append(element) def add_sum(self, element: Set): """ Add variables to the list. :param element: Set of variables to append to the list self._sum. """ for el in element: self._sum.append(el) def get_val(self, return_type: str): """ Get either the list of estimators (for product) or list of variables (for the marginalization). :param return_type: "prod" to return the list of estimators or "sum" to return the list of variables. """ if return_type == "prod": return self._product elif return_type == "sum": return self._sum else: raise Exception("Provide correct return type.") def _print_estimator(self, prefix, estimator: Union[Dict, "IDExpression"] = None, start: bool = False): """ Print the IDExpression object. """ if estimator is None: return None string = "" if isinstance(estimator, IDExpression): s = True if len(estimator.get_val(return_type="sum")) > 0 else False if s: sum_vars = "{" + ",".join(estimator.get_val(return_type="sum")) + "}" string += prefix + "Sum over " + sum_vars + ":\n" prefix += "\t" for expression in estimator.get_val(return_type="prod"): add_string = self._print_estimator(prefix, expression) if add_string is None: return None else: string += add_string else: outcome_vars = list(estimator["outcome_vars"]) condition_vars = list(estimator["condition_vars"]) string += prefix + "Predictor: P(" + ",".join(outcome_vars) if len(condition_vars) > 0: string += "|" + ",".join(condition_vars) string += ")\n" if start: string = string[:-1] return string def __str__(self): string = self._print_estimator(prefix="", estimator=self, start=True) if string is None: return "The graph is not identifiable." else: return string class IDIdentifier: """ This class is for backwards compatibility with CausalModel Will be deprecated in the future in favor of function call id_identify_effect() """ def identify_effect( self, graph: nx.DiGraph, action_nodes: Union[str, List[str]], outcome_nodes: Union[str, List[str]], observed_nodes: Union[str, List[str]], ): return identify_effect_id(graph, action_nodes, outcome_nodes) def identify_effect_id( graph: nx.DiGraph, action_nodes: Union[str, List[str]], outcome_nodes: Union[str, List[str]], ) -> IDExpression: """ Implementation of the ID algorithm. Link - https://ftp.cs.ucla.edu/pub/stat_ser/shpitser-thesis.pdf The pseudo code has been provided on Pg 40. :param treatment_names: OrderedSet comprising names of treatment variables. :param outcome_names:OrderedSet comprising names of outcome variables. :returns: target estimand, an instance of the IDExpression class. """ node_names = OrderedSet(graph.nodes) adjacency_matrix = np.asmatrix(get_adjacency_matrix(graph)) try: tsort_node_names = OrderedSet(list(nx.topological_sort(graph))) # topological sorting of graph nodes except: raise Exception("The graph must be a directed acyclic graph (DAG).") return __adjacency_matrix_identify_effect( adjacency_matrix, OrderedSet(parse_state(action_nodes)), OrderedSet(parse_state(outcome_nodes)), tsort_node_names, node_names, ) def __adjacency_matrix_identify_effect( adjacency_matrix: np.matrix, treatment_name: OrderedSet, outcome_name: OrderedSet, tsort_node_names: OrderedSet, node_names: OrderedSet = None, ): node2idx, idx2node = __idx_node_mapping(node_names) # Estimators list for returning after identification estimators = IDExpression() # Line 1 # If no action has been taken, the effect on Y is just the marginal of the observational distribution P(v) on Y. if len(treatment_name) == 0: identifier = IDExpression() estimator = {} estimator["outcome_vars"] = node_names estimator["condition_vars"] = OrderedSet() identifier.add_product(estimator) identifier.add_sum(node_names.difference(outcome_name)) estimators.add_product(identifier) return estimators # Line 2 # If we are interested in the effect on Y, it is sufficient to restrict our attention on the parts of the model ancestral to Y. ancestors = find_ancestor(outcome_name, node_names, adjacency_matrix, node2idx, idx2node) if ( len(node_names.difference(ancestors)) != 0 ): # If there are elements which are not the ancestor of the outcome variables # Modify list of valid nodes treatment_name = treatment_name.intersection(ancestors) node_names = node_names.intersection(ancestors) adjacency_matrix = induced_graph(node_set=node_names, adjacency_matrix=adjacency_matrix, node2idx=node2idx) return __adjacency_matrix_identify_effect( treatment_name=treatment_name, outcome_name=outcome_name, adjacency_matrix=adjacency_matrix, tsort_node_names=tsort_node_names, node_names=node_names, ) # Line 3 - forces an action on any node where such an action would have no effect on Y – assuming we already acted on X. # Modify adjacency matrix to obtain that corresponding to do(X) adjacency_matrix_do_x = adjacency_matrix.copy() for x in treatment_name: x_idx = node2idx[x] for i in range(len(node_names)): adjacency_matrix_do_x[i, x_idx] = 0 ancestors = find_ancestor(outcome_name, node_names, adjacency_matrix_do_x, node2idx, idx2node) W = node_names.difference(treatment_name).difference(ancestors) if len(W) != 0: return __adjacency_matrix_identify_effect( treatment_name=treatment_name.union(W), outcome_name=outcome_name, adjacency_matrix=adjacency_matrix, tsort_node_names=tsort_node_names, node_names=node_names, ) # Line 4 - Decomposes the problem into a set of smaller problems using the key property of C-component factorization of causal models. # If the entire graph is a single C-component already, further problem decomposition is impossible, and we must provide base cases. # Modify adjacency matrix to remove treatment variables node_names_minus_x = node_names.difference(treatment_name) node2idx_minus_x, idx2node_minus_x = __idx_node_mapping(node_names_minus_x) adjacency_matrix_minus_x = induced_graph( node_set=node_names_minus_x, adjacency_matrix=adjacency_matrix, node2idx=node2idx ) c_components = find_c_components( adjacency_matrix=adjacency_matrix_minus_x, node_set=node_names_minus_x, idx2node=idx2node_minus_x ) if len(c_components) > 1: identifier = IDExpression() sum_over_set = node_names.difference(outcome_name.union(treatment_name)) for component in c_components: expressions = __adjacency_matrix_identify_effect( treatment_name=node_names.difference(component), outcome_name=OrderedSet(list(component)), adjacency_matrix=adjacency_matrix, tsort_node_names=tsort_node_names, node_names=node_names, ) for expression in expressions.get_val(return_type="prod"): identifier.add_product(expression) identifier.add_sum(sum_over_set) estimators.add_product(identifier) return estimators # Line 5 - The algorithms fails due to the presence of a hedge - the graph G, and a subgraph S that does not contain any X nodes. S = c_components[0] c_components_G = find_c_components(adjacency_matrix=adjacency_matrix, node_set=node_names, idx2node=idx2node) if len(c_components_G) == 1 and c_components_G[0] == node_names: return None # Line 6 - If there are no bidirected arcs from X to the other nodes in the current subproblem under consideration, then we can replace acting on X by conditioning, and thus solve the subproblem. if S in c_components_G: sum_over_set = S.difference(outcome_name) prev_nodes = [] for node in tsort_node_names: if node in S: identifier = IDExpression() estimator = {} estimator["outcome_vars"] = OrderedSet([node]) estimator["condition_vars"] = OrderedSet(prev_nodes) identifier.add_product(estimator) identifier.add_sum(sum_over_set) estimators.add_product(identifier) prev_nodes.append(node) return estimators # Line 7 - This is the most complicated case in the algorithm. Explain in the second last paragraph on Pg 41 of the link provided in the docstring above. for component in c_components_G: C = S.difference(component) if C.is_empty() is None: return __adjacency_matrix_identify_effect( treatment_name=treatment_name.intersection(component), outcome_name=outcome_name, adjacency_matrix=induced_graph( node_set=component, adjacency_matrix=adjacency_matrix, node2idx=node2idx ), tsort_node_names=tsort_node_names, node_names=node_names, ) def __idx_node_mapping(node_names: OrderedSet): """ Obtain the node name to index and index to node name mappings. :param node_names: Name of all nodes in the graph. :return: node to index and index to node mappings. """ node2idx = {} idx2node = {} for i, node in enumerate(node_names.get_all()): node2idx[node] = i idx2node[i] = node return node2idx, idx2node

bloebp

4fd0a92bd2fabbacfe6f225ea9637d3e8f08407e

2a8e49a77eb43a74d7ee6fc0925a376cd60335f2

nice! good way to remove the node_names param

amit-sharma

py-why/dowhy

Proposal: Finalize functional API refactor - deprecate causal graph

- The graph should now be defined via a networkx graph. Most identification methods now expect an additional "observed_nodes" parameter accordingly. - CausalModel and CausalGraph still exist and should be compatible with the old API. Open task is still to replace the usage of CausalModel in the tests and notebooks. There are also some smaller details with the identification methods, which should be double checked.

2023-05-16 16:07:18+00:00

2023-11-27 17:48:56+00:00

dowhy/causal_identifier/identify_effect.py

from typing import List, Protocol, Union from dowhy.causal_graph import CausalGraph from dowhy.causal_identifier.auto_identifier import BackdoorAdjustment, EstimandType, identify_effect_auto from dowhy.causal_identifier.identified_estimand import IdentifiedEstimand class CausalIdentifier(Protocol): """ Protocol to define a CausalIdentifier, all CausalIdentifiers must conform to at least this list of methods. This class is for backwards compatibility with CausalModel Will be deprecated in the future in favor of function call auto_identify_effect() """ def identify_effect( self, graph: CausalGraph, treatment_name: Union[str, List[str]], outcome_name: Union[str, List[str]], **kwargs ): """Identify the causal effect to be estimated based on a CausalGraph :param graph: CausalGraph to be analyzed :param treatment_name: name of the treatment :param outcome_name: name of the outcome :param **kwargs: Additional parameters required by the identify_effect of a specific CausalIdentifier for example: conditional_node_names in AutoIdentifier or node_names in IDIdentifier :returns: a probability expression (estimand) for the causal effect if identified, else NULL """ ... def identify_effect( graph: CausalGraph, treatment_name: Union[str, List[str]], outcome_name: Union[str, List[str]], ) -> IdentifiedEstimand: """Identify the causal effect to be estimated based on a CausalGraph :param graph: CausalGraph to be analyzed :param treatment: name of the treatment :param outcome: name of the outcome :returns: a probability expression (estimand) for the causal effect if identified, else NULL """ return identify_effect_auto( graph, treatment_name, outcome_name, EstimandType.NONPARAMETRIC_ATE, backdoor_adjustment=BackdoorAdjustment.BACKDOOR_DEFAULT, proceed_when_unidentifiable=True, optimize_backdoor=False, )

from typing import List, Protocol, Union import networkx as nx from dowhy.causal_identifier.auto_identifier import BackdoorAdjustment, EstimandType, identify_effect_auto from dowhy.causal_identifier.identified_estimand import IdentifiedEstimand class CausalIdentifier(Protocol): """ Protocol to define a CausalIdentifier, all CausalIdentifiers must conform to at least this list of methods. This class is for backwards compatibility with CausalModel Will be deprecated in the future in favor of function call auto_identify_effect() """ def identify_effect( self, graph: nx.DiGraph, action_nodes: Union[str, List[str]], outcome_nodes: Union[str, List[str]], **kwargs ): """Identify the causal effect to be estimated based on a causal graph :param graph: Causal graph to be analyzed :param action_nodes: name of the treatment :param outcome_nodes: name of the outcome :param **kwargs: Additional parameters required by the identify_effect of a specific CausalIdentifier for example: conditional_node_names in AutoIdentifier or node_names in IDIdentifier :returns: a probability expression (estimand) for the causal effect if identified, else NULL """ ... def identify_effect( graph: nx.DiGraph, action_nodes: Union[str, List[str]], outcome_nodes: Union[str, List[str]], observed_nodes: Union[str, List[str]], ) -> IdentifiedEstimand: """Identify the causal effect to be estimated based on a causal graph :param graph: Causal graph to be analyzed :param treatment: name of the treatment :param outcome: name of the outcome :returns: a probability expression (estimand) for the causal effect if identified, else NULL """ return identify_effect_auto( graph, action_nodes, outcome_nodes, observed_nodes, EstimandType.NONPARAMETRIC_ATE, backdoor_adjustment=BackdoorAdjustment.BACKDOOR_DEFAULT, optimize_backdoor=False, )

bloebp

4fd0a92bd2fabbacfe6f225ea9637d3e8f08407e

2a8e49a77eb43a74d7ee6fc0925a376cd60335f2

can change "CausalGraph" to simply, "causal graph"

amit-sharma

py-why/dowhy

Proposal: Finalize functional API refactor - deprecate causal graph

- The graph should now be defined via a networkx graph. Most identification methods now expect an additional "observed_nodes" parameter accordingly. - CausalModel and CausalGraph still exist and should be compatible with the old API. Open task is still to replace the usage of CausalModel in the tests and notebooks. There are also some smaller details with the identification methods, which should be double checked.

2023-05-16 16:07:18+00:00

2023-11-27 17:48:56+00:00

dowhy/causal_identifier/identify_effect.py

from typing import List, Protocol, Union from dowhy.causal_graph import CausalGraph from dowhy.causal_identifier.auto_identifier import BackdoorAdjustment, EstimandType, identify_effect_auto from dowhy.causal_identifier.identified_estimand import IdentifiedEstimand class CausalIdentifier(Protocol): """ Protocol to define a CausalIdentifier, all CausalIdentifiers must conform to at least this list of methods. This class is for backwards compatibility with CausalModel Will be deprecated in the future in favor of function call auto_identify_effect() """ def identify_effect( self, graph: CausalGraph, treatment_name: Union[str, List[str]], outcome_name: Union[str, List[str]], **kwargs ): """Identify the causal effect to be estimated based on a CausalGraph :param graph: CausalGraph to be analyzed :param treatment_name: name of the treatment :param outcome_name: name of the outcome :param **kwargs: Additional parameters required by the identify_effect of a specific CausalIdentifier for example: conditional_node_names in AutoIdentifier or node_names in IDIdentifier :returns: a probability expression (estimand) for the causal effect if identified, else NULL """ ... def identify_effect( graph: CausalGraph, treatment_name: Union[str, List[str]], outcome_name: Union[str, List[str]], ) -> IdentifiedEstimand: """Identify the causal effect to be estimated based on a CausalGraph :param graph: CausalGraph to be analyzed :param treatment: name of the treatment :param outcome: name of the outcome :returns: a probability expression (estimand) for the causal effect if identified, else NULL """ return identify_effect_auto( graph, treatment_name, outcome_name, EstimandType.NONPARAMETRIC_ATE, backdoor_adjustment=BackdoorAdjustment.BACKDOOR_DEFAULT, proceed_when_unidentifiable=True, optimize_backdoor=False, )

from typing import List, Protocol, Union import networkx as nx from dowhy.causal_identifier.auto_identifier import BackdoorAdjustment, EstimandType, identify_effect_auto from dowhy.causal_identifier.identified_estimand import IdentifiedEstimand class CausalIdentifier(Protocol): """ Protocol to define a CausalIdentifier, all CausalIdentifiers must conform to at least this list of methods. This class is for backwards compatibility with CausalModel Will be deprecated in the future in favor of function call auto_identify_effect() """ def identify_effect( self, graph: nx.DiGraph, action_nodes: Union[str, List[str]], outcome_nodes: Union[str, List[str]], **kwargs ): """Identify the causal effect to be estimated based on a causal graph :param graph: Causal graph to be analyzed :param action_nodes: name of the treatment :param outcome_nodes: name of the outcome :param **kwargs: Additional parameters required by the identify_effect of a specific CausalIdentifier for example: conditional_node_names in AutoIdentifier or node_names in IDIdentifier :returns: a probability expression (estimand) for the causal effect if identified, else NULL """ ... def identify_effect( graph: nx.DiGraph, action_nodes: Union[str, List[str]], outcome_nodes: Union[str, List[str]], observed_nodes: Union[str, List[str]], ) -> IdentifiedEstimand: """Identify the causal effect to be estimated based on a causal graph :param graph: Causal graph to be analyzed :param treatment: name of the treatment :param outcome: name of the outcome :returns: a probability expression (estimand) for the causal effect if identified, else NULL """ return identify_effect_auto( graph, action_nodes, outcome_nodes, observed_nodes, EstimandType.NONPARAMETRIC_ATE, backdoor_adjustment=BackdoorAdjustment.BACKDOOR_DEFAULT, optimize_backdoor=False, )

bloebp

4fd0a92bd2fabbacfe6f225ea9637d3e8f08407e

2a8e49a77eb43a74d7ee6fc0925a376cd60335f2

good to change all mentions of CausalGraph in docs/comments to causal graph.

amit-sharma

py-why/dowhy

Proposal: Finalize functional API refactor - deprecate causal graph

- The graph should now be defined via a networkx graph. Most identification methods now expect an additional "observed_nodes" parameter accordingly. - CausalModel and CausalGraph still exist and should be compatible with the old API. Open task is still to replace the usage of CausalModel in the tests and notebooks. There are also some smaller details with the identification methods, which should be double checked.

2023-05-16 16:07:18+00:00

2023-11-27 17:48:56+00:00

dowhy/do_sampler.py

import logging import numpy as np import pandas as pd from dowhy.utils.api import parse_state class DoSampler: """Base class for a sampler from the interventional distribution.""" def __init__( self, data, params=None, variable_types=None, num_cores=1, causal_model=None, keep_original_treatment=False ): """ Initializes a do sampler with data and names of relevant variables. Do sampling implements the do() operation from Pearl (2000). This is an operation is defined on a causal bayesian network, an explicit implementation of which is the basis for the MCMC sampling method. We abstract the idea behind the three-step process to allow other methods, as well. The `disrupt_causes` method is the means to make treatment assignment ignorable. In the Pearlian framework, this is where we cut the edges pointing into the causal state. With other methods, this will typically be by using some approach which assumes conditional ignorability (e.g. weighting, or explicit conditioning with Robins G-formula.) Next, the `make_treatment_effective` method reflects the assumption that the intervention we impose is "effective". Most simply, we fix the causal state to some specific value. We skip this step there is no value specified for the causal state, and the original values are used instead. Finally, we sample from the resulting distribution. This can be either from a `point_sample` method, in the case that the inference method doesn't support batch sampling, or the `sample` method in the case that it does. For convenience, the `point_sample` method parallelizes with `multiprocessing` using the `num_cores` kwargs to set the number of cores to use for parallelization. While different methods will have their own class attributes, the `_df` method should be common to all methods. This is them temporary dataset which starts as a copy of the original data, and is modified to reflect the steps of the do operation. Read through the existing methods (weighting is likely the most minimal) to get an idea of how this works to implement one yourself. :param data: pandas.DataFrame containing the data :param identified_estimand: dowhy.causal_identifier.IdentifiedEstimand: and estimand using a backdoor method for effect identification. :param treatments: list or str: names of the treatment variables :param outcomes: list or str: names of the outcome variables :param variable_types: dict: A dictionary containing the variable's names and types. 'c' for continuous, 'o' for ordered, 'd' for discrete, and 'u' for unordered discrete. :param keep_original_treatment: bool: Whether to use `make_treatment_effective`, or to keep the original treatment assignments. :param params: (optional) additional method parameters """ self._data = data.copy() self._causal_model = causal_model self._target_estimand = self._causal_model.identify_effect() self._target_estimand.set_identifier_method("backdoor") self._treatment_names = parse_state(self._causal_model._treatment) self._outcome_names = parse_state(self._causal_model._outcome) self._estimate = None self._variable_types = variable_types self.num_cores = num_cores self.point_sampler = True self.sampler = None self.keep_original_treatment = keep_original_treatment if params is not None: for key, value in params.items(): setattr(self, key, value) self._df = self._data.copy() if not self._variable_types: self._infer_variable_types() self.dep_type = [self._variable_types[var] for var in self._outcome_names] self.indep_type = [ self._variable_types[var] for var in self._treatment_names + self._target_estimand.get_backdoor_variables() ] self.density_types = [self._variable_types[var] for var in self._target_estimand.get_backdoor_variables()] self.outcome_lower_support = self._data[self._outcome_names].min().values self.outcome_upper_support = self._data[self._outcome_names].max().values self.logger = logging.getLogger(__name__) def _sample_point(self, x_z): """ OVerride this if your sampling method only allows sampling a point at a time. :param : numpy.array: x_z is a numpy array containing the values of x and z in the order of the list given by self._treatment_names + self._target_estimand.get_backdoor_variables() :return: numpy.array: a sampled outcome point """ raise NotImplementedError def reset(self): """ If your `DoSampler` has more attributes that the `_df` attribute, you should reset them all to their initialization values by overriding this method. :return: """ self._df = self._data.copy() def make_treatment_effective(self, x): """ This is more likely the implementation you'd like to use, but some methods may require overriding this method to make the treatment effective. :param x: :return: """ if not self.keep_original_treatment: self._df[self._treatment_names] = x def disrupt_causes(self): """ Override this method to render treatment assignment conditionally ignorable :return: """ raise NotImplementedError def point_sample(self): if self.num_cores == 1: sampled_outcomes = self._df[self._treatment_names + self._target_estimand.get_backdoor_variables()].apply( self._sample_point, axis=1 ) else: from multiprocessing import Pool p = Pool(self.num_cores) sampled_outcomes = np.array( p.map( self.sampler.sample_point, self._df[self._treatment_names + self._target_estimand.get_backdoor_variables()].values, ) ) sampled_outcomes = pd.DataFrame(sampled_outcomes, columns=self._outcome_names) self._df[self._outcome_names] = sampled_outcomes def sample(self): """ By default, this expects a sampler to be built on class initialization which contains a `sample` method. Override this method if you want to use a different approach to sampling. :return: """ sampled_outcomes = self.sampler.sample( self._df[self._treatment_names + self._target_estimand.get_backdoor_variables()].values ) sampled_outcomes = pd.DataFrame(sampled_outcomes, columns=self._outcome_names) self._df[self._outcome_names] = sampled_outcomes def do_sample(self, x): self.reset() self.disrupt_causes() self.make_treatment_effective(x) if self.point_sampler: self.point_sample() else: self.sample() return self._df def _infer_variable_types(self): raise NotImplementedError("Variable type inference not implemented. Use the variable_types kwarg.")

import logging from typing import List import networkx as nx import numpy as np import pandas as pd from dowhy import EstimandType, identify_effect_auto from dowhy.utils.api import parse_state class DoSampler: """Base class for a sampler from the interventional distribution.""" def __init__( self, graph: nx.DiGraph, action_nodes: List[str], outcome_nodes: List[str], observed_nodes: List[str], data, params=None, variable_types=None, num_cores=1, keep_original_treatment=False, estimand_type=EstimandType.NONPARAMETRIC_ATE, ): """ Initializes a do sampler with data and names of relevant variables. Do sampling implements the do() operation from Pearl (2000). This is an operation is defined on a causal bayesian network, an explicit implementation of which is the basis for the MCMC sampling method. We abstract the idea behind the three-step process to allow other methods, as well. The `disrupt_causes` method is the means to make treatment assignment ignorable. In the Pearlian framework, this is where we cut the edges pointing into the causal state. With other methods, this will typically be by using some approach which assumes conditional ignorability (e.g. weighting, or explicit conditioning with Robins G-formula.) Next, the `make_treatment_effective` method reflects the assumption that the intervention we impose is "effective". Most simply, we fix the causal state to some specific value. We skip this step there is no value specified for the causal state, and the original values are used instead. Finally, we sample from the resulting distribution. This can be either from a `point_sample` method, in the case that the inference method doesn't support batch sampling, or the `sample` method in the case that it does. For convenience, the `point_sample` method parallelizes with `multiprocessing` using the `num_cores` kwargs to set the number of cores to use for parallelization. While different methods will have their own class attributes, the `_df` method should be common to all methods. This is them temporary dataset which starts as a copy of the original data, and is modified to reflect the steps of the do operation. Read through the existing methods (weighting is likely the most minimal) to get an idea of how this works to implement one yourself. :param data: pandas.DataFrame containing the data :param identified_estimand: dowhy.causal_identifier.IdentifiedEstimand: and estimand using a backdoor method for effect identification. :param treatments: list or str: names of the treatment variables :param outcomes: list or str: names of the outcome variables :param variable_types: dict: A dictionary containing the variable's names and types. 'c' for continuous, 'o' for ordered, 'd' for discrete, and 'u' for unordered discrete. :param keep_original_treatment: bool: Whether to use `make_treatment_effective`, or to keep the original treatment assignments. :param params: (optional) additional method parameters """ self._data = data.copy() self._target_estimand = identify_effect_auto( graph, action_nodes, outcome_nodes, observed_nodes, estimand_type=estimand_type ) self._target_estimand.set_identifier_method("backdoor") self._treatment_names = parse_state(action_nodes) self._outcome_names = parse_state(outcome_nodes) self._estimate = None self._variable_types = variable_types self.num_cores = num_cores self.point_sampler = True self.sampler = None self.keep_original_treatment = keep_original_treatment if params is not None: for key, value in params.items(): setattr(self, key, value) self._df = self._data.copy() if not self._variable_types: self._infer_variable_types() self.dep_type = [self._variable_types[var] for var in self._outcome_names] self.indep_type = [ self._variable_types[var] for var in self._treatment_names + self._target_estimand.get_backdoor_variables() ] self.density_types = [self._variable_types[var] for var in self._target_estimand.get_backdoor_variables()] self.outcome_lower_support = self._data[self._outcome_names].min().values self.outcome_upper_support = self._data[self._outcome_names].max().values self.logger = logging.getLogger(__name__) def _sample_point(self, x_z): """ OVerride this if your sampling method only allows sampling a point at a time. :param : numpy.array: x_z is a numpy array containing the values of x and z in the order of the list given by self._treatment_names + self._target_estimand.get_backdoor_variables() :return: numpy.array: a sampled outcome point """ raise NotImplementedError def reset(self): """ If your `DoSampler` has more attributes that the `_df` attribute, you should reset them all to their initialization values by overriding this method. :return: """ self._df = self._data.copy() def make_treatment_effective(self, x): """ This is more likely the implementation you'd like to use, but some methods may require overriding this method to make the treatment effective. :param x: :return: """ if not self.keep_original_treatment: self._df[self._treatment_names] = x def disrupt_causes(self): """ Override this method to render treatment assignment conditionally ignorable :return: """ raise NotImplementedError def point_sample(self): if self.num_cores == 1: sampled_outcomes = self._df[self._treatment_names + self._target_estimand.get_backdoor_variables()].apply( self._sample_point, axis=1 ) else: from multiprocessing import Pool p = Pool(self.num_cores) sampled_outcomes = np.array( p.map( self.sampler.sample_point, self._df[self._treatment_names + self._target_estimand.get_backdoor_variables()].values, ) ) sampled_outcomes = pd.DataFrame(sampled_outcomes, columns=self._outcome_names) self._df[self._outcome_names] = sampled_outcomes def sample(self): """ By default, this expects a sampler to be built on class initialization which contains a `sample` method. Override this method if you want to use a different approach to sampling. :return: """ sampled_outcomes = self.sampler.sample( self._df[self._treatment_names + self._target_estimand.get_backdoor_variables()].values ) sampled_outcomes = pd.DataFrame(sampled_outcomes, columns=self._outcome_names) self._df[self._outcome_names] = sampled_outcomes def do_sample(self, x): self.reset() self.disrupt_causes() self.make_treatment_effective(x) if self.point_sampler: self.point_sample() else: self.sample() return self._df def _infer_variable_types(self): raise NotImplementedError("Variable type inference not implemented. Use the variable_types kwarg.")

bloebp

4fd0a92bd2fabbacfe6f225ea9637d3e8f08407e

2a8e49a77eb43a74d7ee6fc0925a376cd60335f2

any specific reason you removed the `set_identifier_method` call?

amit-sharma

py-why/dowhy

Proposal: Finalize functional API refactor - deprecate causal graph

- The graph should now be defined via a networkx graph. Most identification methods now expect an additional "observed_nodes" parameter accordingly. - CausalModel and CausalGraph still exist and should be compatible with the old API. Open task is still to replace the usage of CausalModel in the tests and notebooks. There are also some smaller details with the identification methods, which should be double checked.

2023-05-16 16:07:18+00:00

2023-11-27 17:48:56+00:00

dowhy/do_sampler.py

import logging import numpy as np import pandas as pd from dowhy.utils.api import parse_state class DoSampler: """Base class for a sampler from the interventional distribution.""" def __init__( self, data, params=None, variable_types=None, num_cores=1, causal_model=None, keep_original_treatment=False ): """ Initializes a do sampler with data and names of relevant variables. Do sampling implements the do() operation from Pearl (2000). This is an operation is defined on a causal bayesian network, an explicit implementation of which is the basis for the MCMC sampling method. We abstract the idea behind the three-step process to allow other methods, as well. The `disrupt_causes` method is the means to make treatment assignment ignorable. In the Pearlian framework, this is where we cut the edges pointing into the causal state. With other methods, this will typically be by using some approach which assumes conditional ignorability (e.g. weighting, or explicit conditioning with Robins G-formula.) Next, the `make_treatment_effective` method reflects the assumption that the intervention we impose is "effective". Most simply, we fix the causal state to some specific value. We skip this step there is no value specified for the causal state, and the original values are used instead. Finally, we sample from the resulting distribution. This can be either from a `point_sample` method, in the case that the inference method doesn't support batch sampling, or the `sample` method in the case that it does. For convenience, the `point_sample` method parallelizes with `multiprocessing` using the `num_cores` kwargs to set the number of cores to use for parallelization. While different methods will have their own class attributes, the `_df` method should be common to all methods. This is them temporary dataset which starts as a copy of the original data, and is modified to reflect the steps of the do operation. Read through the existing methods (weighting is likely the most minimal) to get an idea of how this works to implement one yourself. :param data: pandas.DataFrame containing the data :param identified_estimand: dowhy.causal_identifier.IdentifiedEstimand: and estimand using a backdoor method for effect identification. :param treatments: list or str: names of the treatment variables :param outcomes: list or str: names of the outcome variables :param variable_types: dict: A dictionary containing the variable's names and types. 'c' for continuous, 'o' for ordered, 'd' for discrete, and 'u' for unordered discrete. :param keep_original_treatment: bool: Whether to use `make_treatment_effective`, or to keep the original treatment assignments. :param params: (optional) additional method parameters """ self._data = data.copy() self._causal_model = causal_model self._target_estimand = self._causal_model.identify_effect() self._target_estimand.set_identifier_method("backdoor") self._treatment_names = parse_state(self._causal_model._treatment) self._outcome_names = parse_state(self._causal_model._outcome) self._estimate = None self._variable_types = variable_types self.num_cores = num_cores self.point_sampler = True self.sampler = None self.keep_original_treatment = keep_original_treatment if params is not None: for key, value in params.items(): setattr(self, key, value) self._df = self._data.copy() if not self._variable_types: self._infer_variable_types() self.dep_type = [self._variable_types[var] for var in self._outcome_names] self.indep_type = [ self._variable_types[var] for var in self._treatment_names + self._target_estimand.get_backdoor_variables() ] self.density_types = [self._variable_types[var] for var in self._target_estimand.get_backdoor_variables()] self.outcome_lower_support = self._data[self._outcome_names].min().values self.outcome_upper_support = self._data[self._outcome_names].max().values self.logger = logging.getLogger(__name__) def _sample_point(self, x_z): """ OVerride this if your sampling method only allows sampling a point at a time. :param : numpy.array: x_z is a numpy array containing the values of x and z in the order of the list given by self._treatment_names + self._target_estimand.get_backdoor_variables() :return: numpy.array: a sampled outcome point """ raise NotImplementedError def reset(self): """ If your `DoSampler` has more attributes that the `_df` attribute, you should reset them all to their initialization values by overriding this method. :return: """ self._df = self._data.copy() def make_treatment_effective(self, x): """ This is more likely the implementation you'd like to use, but some methods may require overriding this method to make the treatment effective. :param x: :return: """ if not self.keep_original_treatment: self._df[self._treatment_names] = x def disrupt_causes(self): """ Override this method to render treatment assignment conditionally ignorable :return: """ raise NotImplementedError def point_sample(self): if self.num_cores == 1: sampled_outcomes = self._df[self._treatment_names + self._target_estimand.get_backdoor_variables()].apply( self._sample_point, axis=1 ) else: from multiprocessing import Pool p = Pool(self.num_cores) sampled_outcomes = np.array( p.map( self.sampler.sample_point, self._df[self._treatment_names + self._target_estimand.get_backdoor_variables()].values, ) ) sampled_outcomes = pd.DataFrame(sampled_outcomes, columns=self._outcome_names) self._df[self._outcome_names] = sampled_outcomes def sample(self): """ By default, this expects a sampler to be built on class initialization which contains a `sample` method. Override this method if you want to use a different approach to sampling. :return: """ sampled_outcomes = self.sampler.sample( self._df[self._treatment_names + self._target_estimand.get_backdoor_variables()].values ) sampled_outcomes = pd.DataFrame(sampled_outcomes, columns=self._outcome_names) self._df[self._outcome_names] = sampled_outcomes def do_sample(self, x): self.reset() self.disrupt_causes() self.make_treatment_effective(x) if self.point_sampler: self.point_sample() else: self.sample() return self._df def _infer_variable_types(self): raise NotImplementedError("Variable type inference not implemented. Use the variable_types kwarg.")

import logging from typing import List import networkx as nx import numpy as np import pandas as pd from dowhy import EstimandType, identify_effect_auto from dowhy.utils.api import parse_state class DoSampler: """Base class for a sampler from the interventional distribution.""" def __init__( self, graph: nx.DiGraph, action_nodes: List[str], outcome_nodes: List[str], observed_nodes: List[str], data, params=None, variable_types=None, num_cores=1, keep_original_treatment=False, estimand_type=EstimandType.NONPARAMETRIC_ATE, ): """ Initializes a do sampler with data and names of relevant variables. Do sampling implements the do() operation from Pearl (2000). This is an operation is defined on a causal bayesian network, an explicit implementation of which is the basis for the MCMC sampling method. We abstract the idea behind the three-step process to allow other methods, as well. The `disrupt_causes` method is the means to make treatment assignment ignorable. In the Pearlian framework, this is where we cut the edges pointing into the causal state. With other methods, this will typically be by using some approach which assumes conditional ignorability (e.g. weighting, or explicit conditioning with Robins G-formula.) Next, the `make_treatment_effective` method reflects the assumption that the intervention we impose is "effective". Most simply, we fix the causal state to some specific value. We skip this step there is no value specified for the causal state, and the original values are used instead. Finally, we sample from the resulting distribution. This can be either from a `point_sample` method, in the case that the inference method doesn't support batch sampling, or the `sample` method in the case that it does. For convenience, the `point_sample` method parallelizes with `multiprocessing` using the `num_cores` kwargs to set the number of cores to use for parallelization. While different methods will have their own class attributes, the `_df` method should be common to all methods. This is them temporary dataset which starts as a copy of the original data, and is modified to reflect the steps of the do operation. Read through the existing methods (weighting is likely the most minimal) to get an idea of how this works to implement one yourself. :param data: pandas.DataFrame containing the data :param identified_estimand: dowhy.causal_identifier.IdentifiedEstimand: and estimand using a backdoor method for effect identification. :param treatments: list or str: names of the treatment variables :param outcomes: list or str: names of the outcome variables :param variable_types: dict: A dictionary containing the variable's names and types. 'c' for continuous, 'o' for ordered, 'd' for discrete, and 'u' for unordered discrete. :param keep_original_treatment: bool: Whether to use `make_treatment_effective`, or to keep the original treatment assignments. :param params: (optional) additional method parameters """ self._data = data.copy() self._target_estimand = identify_effect_auto( graph, action_nodes, outcome_nodes, observed_nodes, estimand_type=estimand_type ) self._target_estimand.set_identifier_method("backdoor") self._treatment_names = parse_state(action_nodes) self._outcome_names = parse_state(outcome_nodes) self._estimate = None self._variable_types = variable_types self.num_cores = num_cores self.point_sampler = True self.sampler = None self.keep_original_treatment = keep_original_treatment if params is not None: for key, value in params.items(): setattr(self, key, value) self._df = self._data.copy() if not self._variable_types: self._infer_variable_types() self.dep_type = [self._variable_types[var] for var in self._outcome_names] self.indep_type = [ self._variable_types[var] for var in self._treatment_names + self._target_estimand.get_backdoor_variables() ] self.density_types = [self._variable_types[var] for var in self._target_estimand.get_backdoor_variables()] self.outcome_lower_support = self._data[self._outcome_names].min().values self.outcome_upper_support = self._data[self._outcome_names].max().values self.logger = logging.getLogger(__name__) def _sample_point(self, x_z): """ OVerride this if your sampling method only allows sampling a point at a time. :param : numpy.array: x_z is a numpy array containing the values of x and z in the order of the list given by self._treatment_names + self._target_estimand.get_backdoor_variables() :return: numpy.array: a sampled outcome point """ raise NotImplementedError def reset(self): """ If your `DoSampler` has more attributes that the `_df` attribute, you should reset them all to their initialization values by overriding this method. :return: """ self._df = self._data.copy() def make_treatment_effective(self, x): """ This is more likely the implementation you'd like to use, but some methods may require overriding this method to make the treatment effective. :param x: :return: """ if not self.keep_original_treatment: self._df[self._treatment_names] = x def disrupt_causes(self): """ Override this method to render treatment assignment conditionally ignorable :return: """ raise NotImplementedError def point_sample(self): if self.num_cores == 1: sampled_outcomes = self._df[self._treatment_names + self._target_estimand.get_backdoor_variables()].apply( self._sample_point, axis=1 ) else: from multiprocessing import Pool p = Pool(self.num_cores) sampled_outcomes = np.array( p.map( self.sampler.sample_point, self._df[self._treatment_names + self._target_estimand.get_backdoor_variables()].values, ) ) sampled_outcomes = pd.DataFrame(sampled_outcomes, columns=self._outcome_names) self._df[self._outcome_names] = sampled_outcomes def sample(self): """ By default, this expects a sampler to be built on class initialization which contains a `sample` method. Override this method if you want to use a different approach to sampling. :return: """ sampled_outcomes = self.sampler.sample( self._df[self._treatment_names + self._target_estimand.get_backdoor_variables()].values ) sampled_outcomes = pd.DataFrame(sampled_outcomes, columns=self._outcome_names) self._df[self._outcome_names] = sampled_outcomes def do_sample(self, x): self.reset() self.disrupt_causes() self.make_treatment_effective(x) if self.point_sampler: self.point_sample() else: self.sample() return self._df def _infer_variable_types(self): raise NotImplementedError("Variable type inference not implemented. Use the variable_types kwarg.")

bloebp

4fd0a92bd2fabbacfe6f225ea9637d3e8f08407e

2a8e49a77eb43a74d7ee6fc0925a376cd60335f2

I put it back in. I think my thought here was that the `identify_effect_auto` method should set the "identifier method", but I might mix something up here. Doesn't the `identify_effect_auto` select the identifier method (although it is currently by default the backdoor)?

bloebp

py-why/dowhy

Adding general version of CACM

This PR is the first step toward a general causal prediction API. The API supports _Causal, Independent, Confounded,_ and _Selected_ shifts (individual and multi-attribute settings) currently. The regularization has been implemented using `unconditional_reg` and `conditional_reg` functions, which can be used for the general _CACM_ API. Follow up: implement Phase I of _CACM_ for deriving conditional independence constraints given arbitrary graphs.

2023-04-18 10:36:38+00:00

2023-06-15 11:59:01+00:00

docs/source/example_notebooks/prediction/dowhy_causal_prediction_demo.ipynb

{ "cells": [ { "cell_type": "markdown", "id": "7a84e574", "metadata": {}, "source": [ "# Demo for DoWhy Causal Prediction on MNIST\n", "\n", "We're adding prediction functionality to DoWhy. The goal of this notebook is to demonstrate an example of causal prediction using *Causally Adaptive Constraint Minimization (CACM)* [1]. \n", "\n", "[1] Kaur, J.N., Kıcıman, E., & Sharma, A. (2022). Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization. ArXiv, abs/2206.07837." ] }, { "cell_type": "markdown", "id": "900de7ec", "metadata": {}, "source": [ "## Multi-attribute distribution shift datasets \n", "\n", "Domain generalization literature has largely focused on datasets with a single kind of distribution shift over one attribute. Using MNIST as an example, domains are created either by adding new values of a spurious attribute like rotation (e.g., Rotated-MNIST dataset [2]) or domains exhibit different values of correlation between the class label and a spurious attribute like color (e.g., Colored-MNIST [3]). However, real-world data often has multiple distribution shifts over different attributes. For example, satellite imagery data demonstrates distribution shifts over time as well as the region captured.\n", "\n", "[2] Ghifary, M., Kleijn, W., Zhang, M., & Balduzzi, D. (2015). Domain Generalization for Object Recognition with Multi-task Autoencoders. 2015 IEEE International Conference on Computer Vision (ICCV), 2551-2559.<br>\n", "[3] Arjovsky, M., Bottou, L., Gulrajani, I., & Lopez-Paz, D. (2019). Invariant Risk Minimization. ArXiv, abs/1907.02893.\n", "\n", "\n", "### Multi-attribute MNIST\n", "\n", "We create a *multi-attribute* shift variant of MNIST, where both the color and rotation angle of digits can shift across data distributions. Hence, we create three variants of MNIST -- `MNISTCausalAttribute` (single-attribute shift), `MNISTIndAttribute` (single-attribute shift), `MNISTCausalIndAttribute` (multi-attribute shift). To describe, `Causal`, `Ind`, and `CausalInd` datasets better, consider the causal graph for the data generating process below:\n", "\n" ] }, { "attachments": { "main_fig_mnist.drawio.png": { "image/png": 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" } }, "cell_type": "markdown", "id": "4d22f37d", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "id": "f2667794", "metadata": {}, "source": [ "Distribution shifts are characterized based on the relationship between spurious attributes ***A*** and the classification label *Y*.\n", "1. `Causal`: Attribute has a direct-*Causal* relationship with the class label i.e., *Y* causing attribute (e.g., *Color* here)\n", "2. `Ind`: Attribute is *Independent* of the class label (e.g., *Rotation* here)\n", "3. `CausalInd`: Different attributes having *Causal* and *Independent* relationships with *Y* co-exist in the data" ] }, { "cell_type": "markdown", "id": "56115bbd", "metadata": {}, "source": [ "### Domains in multi-attribute MNIST" ] }, { "cell_type": "markdown", "id": "403765c4", "metadata": {}, "source": [ "We describe the domains for our *multi-attribute* shift dataset `MNISTCausalIndAttribute`.<br> \n", "Each domain E_i has a specific *Rotation* angle r_i and a specific correlation corr_i between *Color C* and label *Y* . Our setup consists of 3 domains:\n", "E₁, E₂ are training domains, E₃ is the test domain. We define corr_i = P(*Y* = 1|*C* = 1) = P(*Y* = 0|*C* = 0) in E_i. In our setup, r₁ = 15◦, r₂ = 60◦, r₃ = 90◦ and corr₁ = 0.9, corr₂ = 0.8, corr₃ = 0.1. All environments have 25% label noise, as in [3]\n", "\n", "\n", "Other dataset-related details can be found in `dowhy.causal_prediction.datasets`." ] }, { "attachments": { "MNIST_visualize.drawio%20%283%29.png": { "image/png": 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4TWU5eHM3MDhVVHd6Qk9nOUJ3cDdoZGhYcmM5V1VkOVk5R2VmSmVGdVNaNmxoYkFZMU5wJTJCRVM5YUNndmN3bXoxNk1oMmpBdWZEcHZsNXZaOHgwWEVzYkM2NVRSM3BSWTFBUFJqOEtmNEowbnhjWk9vWHc5M2F3Nk4lMkZzT1FtNXE3STk1dlpYaVNvNm1zNGRYQjIzZTZiUHpRZXclMkZzZjZOTlZ1NURGJTJGenRSeDZpbGNicHppbFlWbjRvd012RU01MDVleVJSdGxyUUo1MmdHS1dOOUNIRHJ0eFN4NDdTQ3BQJTJCNG1heXYlMkJPU3R3bVdweTlqJTJGaFZFTTJjYnFIWEVVQ2EwcSUyRmxIeWVqcVY2WmZHOVlNRFdhN2NicE5lVU9yeE0zUzJUSVUzTVd2dms4N0RUdVNCJTJCTzFsUnFZZVd1VDRyWHRTOTJtdUVIR2g5WWQlMkIyYW5wJTJCRzhFd1JZMDFQdVpreUtoTmJaJTJCUFA1WG1zJTJGY1BacW5Vb3ZHcWp1WlNBMiUyQkZPV3lreU1yNHdVekRvalNDeVJ0NmxiWnVtVnZPeXlFbVlaWWZOTzlJVld0YzJlZktGVE1PTHJLbmlQbnFMYWY4RmlycDhJRHpYNFNLNTFwTGVqSk1SUzZwbSUyQkhJZjdrNXdrUlR4WUhkUEdiTFdjcTI4WU5Kd0hxWlp4STc5NVpFVnRHbzclMkZqUjFZOUVSTm8yM1hSWWZGTlRiczdpbk40WGhmJTJCd3Fld2JXQ0pwZTJHeEcyTUttOGRBSHFxVndPUEtzdHNZUG5sZThjdmUxajRvM3JNVE1pNjd6eFViSDJyRlZUNDNDcjRZb3ZYQjlXczFDNGNmQ3ZhciUyQm9EcUladFRib041VUNMenM2bnY5Y2JXa1klMkI5NFp0VGRiNHROYjVOdDFENzglMkJuVmZYOXpoUHQyU21DOVNEV0hnY05QaVJnemdYUyUyQm42dU9helBTYU9SUlVxRjhsVXNkTXJlJTJCVjhQWWZZdklWOHBVNTd1Rk9nZUNBeGNaSyUyQmZiMXB6QzclMkJSMCUyQjlBYzhjWUNZbTFibVdSaENwOUFHdkg4WjBLZGRaVFN4RVJ6UGl5T2NYaUxJRkZhd1VZQW9aMjRYN0lEaDNPTm5ldUJiUjVkVFdINHJwaUlMJTJGQ2UlMkY2bkhUZ0NlMmlqalloYU5WdWFKJTJCOW1YRXlCYUVhendJbU9Mc2tUblZjcE44UzRTTjZPVmFDU3Z1V1RSUlQ3M0dqYlpFU1dmbzR3WGE5S2xuMXFGUUhnOHNhRDNMMkw2bmJCUmtHJTJCN2xrYkxyWWZ2aGlkWjdESFMxbXVuMkFVNFZVVVM5VVhBUlRhSkJHdTNPRmlMU0RNN1QxMkhTJTJGQ0Y5UlNHeFhGOEU5RFlHbEJycFg4N05sTllBWkFGWHhROFJBZXJ4NHdCbE41JTJGa3dvdlNBb3lVeUFBZFdUOSUyQmppd2JEdmgxM2ZIcVRwbEhXcHZxbHRYc2ZjNjN5VzhKRXdkWEhwNGQzNjd2SlM5Qkd6YjJmR0hHbGhlMTFsUlhsMThqRyUyQmtzckd1WU0zJTJCUG1mc05IU1Q0aFdPTWZ6T3djNnZGRTZ6dGFha3U3YVlOTVI0SlNwWEhjZUFiU3l4UFhvUWQ4bG44S05EckVkSnJaQVI5VGV5M1YyOGZtZXhSMWMxVFZkZVFIVDFNcWFxR2IlMkJucklSc3RSM3pYaW5mdVFyUzBZb3hpajVnMXU0bSUyQmYwZnlISGFVUWphaXdBNEVwa0puNVI1bVFWJTJCVXdqbyUyQlJvV2RKUWFkM2I3bHJHMTM4bE5UUUVENU1ONjc5SjBVZVM5VHBPT3A4WHVMTU9Hcm5ub3NnUHYxZWZENnFadTZJckMyT09TcjclMkZYOFdSUWt0RnJOaGhwdVo2bWRqMW53S3FFcFQ4VGNFUHhWZVhXdmN0YjdwNVU2dmZiYWUlMkJOVFJFMHhGWmpZMjczJTJGU01VSHVVbU1TZmk3cVltMzVsdmlidEd5WEdENmRxS3lRUlFEJTJGUnhGVVd1MktwZlIlMkJxM0V0T0x6bFlkM1JqZ3pOSVZlJTJCNnk5NHFOTWU5MGhZRjc3Z3FDRVpLJTJCVHp0eGFkVzE0MyUyRnFLUXpvZDhLdnpJQjFOZEY0aTNJOWhVcGFSTkQ3WSUyRmszNU9ycVA4c3IzbCUyQm95d1dLUWRwRCUyQmdPakMzOUQlMkZ4WHRudmpnRTBPd2JxJTJCd2dXM0duTWFoZVRxVGYxeHRGdXlXSVIlMkJHVGVnZ3QxSXZCUGNvN2ZKOHIxcWIlMkJNN25VeTRVdXpxeHJZWXZZdU9EdXMxRWoyeU95cXh3Zjh0JTJCZ1hCVDVPUVk0dzJZMkpHWVJ2MDZ5akFnWXB4eEo2b0FBMmkxcGdvVGNQbHpwWTJwSHNZUXdvUmNHWlhGODVXWURpSzFuY3k2cjJ4UlhYYm1VSlpTRWE3JTJGQyUyQnIyR1JvM1FaSWQxWXNabnVqRE9oY21EQ0tGTmN6OE5naTdJMiUyRjZxOUxLWm00ZnhHWmVPWjRQS2s3TFlGUnhDZVlVTlJSMUk2eUhGMkFPJTJGc1pYOG9KWVhOSjB4ZEdBTjhkWDVwcDlyRU1aUVExVEpRbWIlMkJRNTI3OXRSUk5ZNEdXeEEzdDlVQWZ4cFdBR2IxYkM4Q1k4OE9ac3RleHgxNG1XcFVwSTc2RGd5VFRHNEQlMkJhR1ZpR3ZhNVRpZzYxZTZFcnZjb2NvYlVJemswYiUyRk1RSVFUWE5oVlVxSW9hU01hc3BjY2xZckUwQVY3SFhlbk82dHc5ZXllM2ZYREpDQkZzaU5hTGhRRk1HYnNDUDBLY1NZZkpJQWVzVXdnSUhwWE92VGs5cFdVOW8yMUpqRWI4OGJXZ1R5cFlPWlJ5bWIyTW9qVDFQOE1GT3dGOW04bWV4cGhkT2ZyQ29IcTJ5dyUyQmQ5d2hCVkR6eG53Z1E5NnJRcmM4YWV6N0ElMkJnMEV2WG11R3FUblJKREhid3BMTnp4JTJCOTF6NTUwczNBeVFCM1ZvM3VNV2NjS2J2TlVyRVBHcGE5dU5DSWhwRUpqVnJiU3VibFlRNCUyRlc1dWR1JTJCWG0xJTJCVER3Wmw2T1ZhOVhTNzRuJTJGTlJzVUVkUyUyQnNSS2ZGZVd6dnpFJTJCSUd0ME5wMHlQOXpjNGlaTWg3cTFZT0tyZGVYU284cDRxY00lMkJRJTJCJTJCekk0NWliZW5IOEZpSjA5UlZlV1ltZWhuYlU4ZmVUZ1dSWEZOZ0tGOVc0SWFzWVdpcXpmNWl4M2VYeWt4dndiQ05aSEJkQWFTRTZDeTJ6M2p0alJWYThWdXdmNyUyRlAzQ25GWVdXaWxKZXcwMjJ3WThMWXpTZ21EQk5oRXA4bGdVVnpZYUtaWUduS3czanV5JTJCREU2Zk1EcHVNU25qTVJzWDNnREZXVzN5JTJGRlc1aEpoTndzUWZKMmVGS2x0b0pCMWVvJTJGMVVuZTBIcG9CRnoxUkFqU2pDM1c5Q0R2YzhQM1ZXN1o4b3FUS2w0N3QxMVRmcHRFdzdYUXM3WmRkaU92UFU5bEpYWjVzNkl1YzY4NUt4QWwlMkJRblhwdXZNQnZVR3V0YmpCR0JWNUtydVI4bGpxTGs2ZzhmbEVZakFxVnFTQkMlMkZNS29nVUx6enI1YU1FVmFDVER3VUlNaEwxeEtLOERQNE9hOFYzVmNEc0R0MmJKam1EMmhTM1B5RUJyQkklMkZ4Qnd0UE44aE1YcEJRZ0xqZzFlTHZhR25mUW5rV2hNcDVtcHlCZTM0UGI3Y0VWdGN6Wk5lZ1ZNaTNNdFZNMEdYSHklMkZVRzZwNmc2ZWtUMXNNSzJNYUJuMFNRbCUyQmlrc1RPeWxEc3EwckVpMVpWQXNldUlrU1ZrZlJ5bmglMkZXT040JTJGVmJTeEN6JTJGeEh0R3BoWmo4QUtyYllPWlZ1cFpremQ4dzFTYnlmR3BkZyUyQmM5c1FJcEFoQ0hQOVNPdEtyb3hra1VPYlB6SWhGJTJCSG1ZUzRVZGd3WHR0bmVncCUyRmo0V0VmbTNzUFdibndvS3ptZk1tTnJMTE1NOEdlb3YzVVhzJTJGWmtVaEtGWU00S0VmN2JjanRuMFlqWWIwMVVDc25rS2RpdnJvc0xVeTREaCUyQnJJcThSNnBYdmdRRzNKOFA1YkRXUWk4SlNIY0FlV0pSUzUwWWElMkJITyUyRjc0aWZJdk83ZlhIJTJCS2kwQVBpaTdtSUtObWh0QW9FNVkzJTJGR3RqYzBUeXJqc1VUdTlaa0tpWDUxNHZhand2bkZyNFRpU2ElMkY3YmZpNUM2SkwwWmJPeEh1Sk80NyUyQkd6M2QzcDFkQyUyRjFlMmpwYU94bkhLSE50Y2dVdWZvc2Ftb0RHOWlQaG9nZURyOUplV0duYTJMM2pzdXcxOFFaWGQyJTJGRGdYeWdFZGhobTEzZk45SlM3VjliN1Z1OUdaNmNxaG0lMkJldG0zaXpkSG5RVGtOaTJkNzBrdUFPYWhXbDBoc0pqdlhBa3J3JTJCYUtBWGQxdlVjWUxZM1ppRU9JdXVoVExjdWpzZExLMDR1YUExNzBEQ3lQQUp2Q1Y2TjZlQ05HYWhaREpKQ25XWmVJJTJGeVhvQzklMkJpOVdkV1BTaHBCJTJCYXdDNVR1Q0NOQjJrM2FNT2dqNEclMkYzZURUOEpRTWNNJTJCV09haWEySG5KRXdmQmxqQzFaM3JHMmg0ZXVEY3VVNWlSU2F6SmVHMG81bkxZMVlFcldFYWJSekhJM2tPZFd1Q2ZyRTRKODhXT0R5JTJCTXlzRFclMkZRZnY2RFczOThTSlczdmdwTzdyblJqNXZiZk80Q3lUZXJpeUhQTWVLWDg4RDZBMk16VUNBa20xajJyUkoxUmozTEdxZDl4Zm84ZE1NZk55VyUyQnpjaGpHenFSUFRTJTJCJTJCeU9Dd215eEVSNWxSJTJGWG0weGk2MlZoWHdoMmpQMWpWQlVQemVDNGVKZkVVU0Z6TDJVRHlVJTJCbTRDYTNvMUxEVWdEbjVQaVZsdXhwRTRMR1JmYlY4c0VET2RuMjVIbnZhcG5aTHRPQTBQelhVaEs2YWM0SUduWjkxbWlBOVpZSDdFbWJqelFCVFFrak9ya0lFRkxmNFVYTm9ISjVPJTJCbzNkOUd0c2lFYlh0R3pxdjBuWXNrcmVQbm9Ib2E0dmI3cEZ5WVVIYzclMkY5aDdoZiUyRnFjUzFZOXVVbCUyRnpMdDlJTVg0Z2pWVlpLNnNzQ1k4ZnlFeG1zMnU2Q3BwdDM3VUIzZmwlMkJIZTBpUDMlMkZxek1GclppaERLNDNkWnh0alltVm53cTNJcFdQNnYlMkI1RWlQTEY0algxdHJIQVZhdzQ1bldzQk1BQ2hlVUlpNE1kRzN5U1hlMGtJcE5XZU9kellQRThBSUZnazVjcm5JQmZUcEtlTWVuWE5kUDdheEtjZUd4RmRjYks5cmxhaVhLeSUyQjNwcExjUkJLUFRSYVE4U0Z5bFB5R1VIN3dGcE1FVTZEdll4TGZWdllqY2R3U1QzQjN1VWs5YWI3V2I0aUl4Q01JdTBtdUtyd0czJTJGMTV5M1clMkZHM2MwY01VUkp3VDR5UGQyYmNWcWdvRE4yVFVGMDFmc3R0QzNJYyUyRlRjczRVUkJHcnlvUkRjMiUyQktWejhlZXRtcTQ0dVFtZGJmc3hNT3VhY3NrUmMwN2Rya3NsSmRxUHNnWWxZWGRuNmdQZ1VXRUJtTUJJbkFSU2dPZVVQM1pMbG5LWVVtUUR3RXFiQktqY3ZzUTd1JTJCTzRrWVQlMkY1QkVQQVFlV3hWaXBGQmV0OXclMkZOdUVwbXRZamd2JTJCckVVaDB1TTBOT09sS0cxbmJHcWJIQkkzZ0pTb28zNVhLVWZ0VFpmeGhwcEdidjZCQ0FYRFZOTEs5a0l3c0k0SWRpMSUyQnUyUiUyRkE4M1dOb0lidEw5aCUyQkRZU2c4N3kxMFRqYkVvNGFadllTWWxNJTJCbVJpRTcwS053TXpKV2pzMDYzNDM1andKcjZvQ0N2ZmtyVldxbExXVHZiNGFuTWppN0xqbDhmTlRvczRjTDFoWEpqc0tsWTd6bXJ2b3p6cXRYMmI0ZHFKMjZKJTJCVnFQOFh0aW12Yk16UnJ4RjViYkkzcXM1ME0lMkZ3YWJxVXRUN3JobVFmQ241MjdOVzJOcHp0aTZqcXJ1ZVR0aHlRYThmazgxSXM5OW9aanF6dUJmbCUyQkh0bjhCZ1JUenFobSUyRnhUQTZwRHc0JTJGUnE5TWtUS1NiS1VJRGJtMEhWMEluJTJGbFBDendyUUlLSDVkdTVDekRIcG95YU9USGNINEQlMkIlMkJIdW53NXJHMXVFJTJGREtjJTJGcnU5ZmtuTk1jUGZzJTJGbHg0SSUyQk10V0JkVGp6Rk5XeUE5R2Y4S1J1Q2ZPemY0U2Y1MXRzdXRPbWdnRElqbiUyQlk4SHA5UWZhak9mMDF3QlRIc1luWW1uMjVQN0pyYkklMkJqQ2xzZUxJZjBHU3c1OEslMkZaOHFNcklnakdTa3dHVTJGdHhKc1l4WjhncGQwdFlIck9IViUyQjZoZnRYSXMlMkJXMUZtdSUyRlFiMWpVREZnbUFNbVNoZU9zUkJkcVlMRFZpV1FuJTJCSTFWaWw3VW9ZVTBwbEVaNER6QUNCRzJjWW5YMkdURGwxVTQ2RVhpNnk1VmI4QVFRVWdLVm44U0JHVm4xN1VYaWlWVUF5YlFjc3BSQnFHUjlleTJEY0cwSE1zZ1ZMVUhrbGc4V0NFNHd4VzBSa3VZQjRCb2glMkJmJTJGa25WR1V4JTJGckZrd05KQW54aVMlMkY4V3pYdXlWOUJlVFg5R3Z2Qkl6S0wwQmtoa2xMejZkN3VNSzd5TkpkV1BadFZFSEpjeW5qR1Q2eE45VGE0Rm02RG5Ja2RjWHFIakYySXlNRiUyQjBCOFl5Z0lDTEFoUmNnSDR4JTJCVDFFbnVOQ1hiR0Y5SGlIWXl3WG1JQVVPRjNWelRiMWFVSTglMkZYMlVEV1pMQlglMkJFRkpYc052YVpYTTFERWRraUF2ek5ZUHN3WXM3JTJCcVBQdlJYZGkxR2dkU0RkRlh0WGk5JTJGcmdFcmhMRmxTWUdZak10ZGJOZ3ZlMkZnczRBSmlVS1hNM3pIZk1nNE13Q0hob1o2V2ElMkJIRmNKNlI4RU56d3lGOUxhOHRyMzJhbkgwT3NMcndQSW1Zem1qTENSUlRwUDdZJTJCViUyRmpiMHVDJTJCRTVuaTBkZGhoR0ZnaWR1U0ZveFNCUEF3QUQ1cHJ5ZkRTYkVGc1JwOWdxVCUyRlMlMkJIVTBseVhzT25wdHEwaGVJODBTcWc4Q0JtYlBZUlZMR1gyUExibHI4MENQSnU3WTl3SVpJdW9JeVFkU0N2UXhTZ2tIdGc2U2o4WGlhZEFzMWQ0TkVoZ0JRRHFTbElXT1F5TmRPV082a1ZBJTJGeWpmMjFTM0hOQ05ib0ZjSERHcFIlMkI4JTJCOU1DMUsxTUclMkI2c1VWM1k4aE4lMkJoZjBCZUxDcVNUaXJyak45c1ZNalR3Q21zZVNIeVFtJTJGbmdQN002Mjh0RkYwRzd5akxaJTJGTmFKYmJxY1Qyc2ZxNWJxUlNBQyUyRnMyVEROV1hEeEQ5NTBpd09EYkxES21GaVpPcW02ZHgyTXd2ZmE0NkFHbEtPNGxvblVjbWhqczQyWFR5YThOS2N2bnVsVEZsOEd4ZWJjNHVFNFNBbHRYZnglMkZ3a2tpTk1pbVZCY0N1WGlHOFdqQ3g5a09La1l1U29Fb29RJTJGWnAwTHZpRTFOMGJ6dHJ6Y1BWS3FYJTJGMlpBNW5tOGROcHJIUko4dVNEQTdKU3RkZTk2R3B5ZzNkaEdlZlFiRzNiMEJ4SiUyRkV5SWlFUXFrUmklMkJDQW02JTJCNGxTTjBQMkRueHdSN3A1UW5KTURzUlp4JTJGSmh3MUh0dXBJOUlZN2slMkJQcDVxVyUyQk5nT0YlMkZQeEZvJTJCMHJEWURBZFdxYWM3OU5ZU1cyU2o5U0hUeDl1MlBPdDUwRnpOQ0x1cmNjbGFYdDl1UFhnRnNUdzRZcmNCams1Y0daTUI2U05ZOXolMkZyUkFtMktBVGlRRFdWZTQxMG10cnFtRjdKMXpuUVJMWVJPYW1xWllSMEh2WEJsOSUyQk1ZSVRKRlF0VFBVJTJCblNyUTJHREclMkJtRTJ1cFlZcUdZNUU2VU1rQm1McXZrUUJuRHVhVTAxdFlWNW5EcXVqdW83aSUyQjc1JTJGSnQ3aGVBQUUybTVJVHBLdU5QeDZuaXBnNVolMkZ0UTlmdVRSd0JsWWslMkJiJTJGSUJ2aUR0S0FFZGNiQ1pjbXdRWVU2N3VCR0F3eWZ5ZW40SVA1dFZGS0w5aXZ6M01UQkl3NVBqSiUyQjFKVVAxV0ZhYVRaYmlJeGNNNWJTQTZ5OVVYUDdGZVI5UkxleWIlMkJ2RUlDWEF2MTVSJTJCSTFFNE1iZWZoS1NiZWFVSHI5eWJHSFVxNWVkMGxNYWtPSU8xcUVJaSUyQmNBNlkwSGhJZlN0R2I4Wm9sMk1jMkVicWZHN055cGVtekFVOEdTQXlhJTJGU3RNN0pLMnd2Mzk1RXJBUkl2eDB5a2tYRVhKaXJ4cGhzcmhYY1h2QmZvQ2hXMk1Pc3BxTU5rNGpwSXdmUDRqWTM4MEVMNXN6UGtjbTViVTBVTlY1eElzeXZaSlFCTGNrSzYlMkI5TlZ1VTZ5UGlpUFRURHpVUEU5eGFlS2I1SVluelVGZnVLeTY5ZCUyQmlCZDR3OHpLdFZDdGolMkIwcEF6RCUyQlo2RUxFVU9XVlVlcVRDMUdibGNrQldqbHNRdHMlMkZJTDBiaVd4WUtuejF0V1JtNzl0OSUyQjhQWWFuJTJGZTVLSzJXMnZReVJMWG5DaVdQd2FqMURRciUyRmFwSDlHNXFCSlZ2MFJ0VCUyRjFvNlp1ZE1MSWx2WG1VaWFiN2V6YWIlMkZKYWxGNTJ4cVM1JTJGRHNuMzlSMURvZWN0ODNBYkd0MWhRRDRLOW9qd2NPSWJtZVJqUlpDbjJwaHVzTkJGZUlWdWR6SlFrYzFVVDYlMkZscE1rUE5oeU1aMjNDWjVYUU43eGlOTFpwd2hHaHppUnJ6ZVBEZFlXRG41a2h3TzdJc0tOekRxektFd1NldnFSaWUweXBJcWhZQXhUMTNtR0pqMHk1cWpLYUpVSkpOSjhUWW9sTGpzaTNGWkpTYnlHS3VLMTZXQVBkUkJLSXYzTiUyQlJORVBoT2F1QSUyRnV3b1QxN3AlMkZkJTJGU3BjbklVR1N3VGJyayUyRktxNXNzWTVRZVJ0SnhOYWNYaGNZdHd2ZmtjU0EyWFd2S004VTF2QmJ3c1I0UUw0WVBDaUxEVGtXMCUyQlM5allXQUlxdmZra1pjZ0pndDdNZlVUczRVeWlkZWYxM2RYaGFSM3FjVnY0d1hHZml6VWdBSWdCeFMzeWs2U2JoU1NmUXpZdmR5dWRxY055ZE0zTGxuQUsxUlFDbW1RR21yZHRZS3RybFBoJTJGREZtcSUyRlZCN2w0dFlrRGxWRFElMkZPV0tuV2ZRbSUyQjlYNjllTEE2ajVkTEtqMEhYcyUyQiUyQjVoYXFGdyUyQm1TTiUyQnVTcyUyRkM2T1A3UTQ4c01nZGpyaDRrUDZCRmt2RnFKJTJGbmVMS256OHh0YUhrWjFVdTJpejNPTzg4WUFBVlh4Y2VGblNlUE1VRmJoJTJGSzEyZWxRMFBMcHZuUXNZMzhQSFp1OERyMFFyOFJqRE1qb0xjM0pwQmlVc2llck9FYXpVUVRQUlIyVGRma3JmaFM0MHM2MmkwQTJUZE5iSjV1Y3ZpUUhQdSUyRnpuVDJuVVp5UVIlMkZZYmx3VFVTZDMlMkZDbjR1cndFb3F4UkZoOW9qOFBOcGYySU5hODBZTFVmT2dKN1YlMkJTUGhCWFpJUjNqOUwyTjZBc1Q2NnE4d3RxN1ZKdjRSRWJOdlcxc21CSkNOY1B2dEI5RGRYUmJadE54ZVMxSHdMJTJCdUIxNGRtV0dQdVU5dUZoY0NlbFhhNE5vUkt3UDZ2aUtEN3FNcGpUU2xOdyUyRk5JbDNLcGVPNFd1SDg2c3g3OVk5VWYlMkJyMER1V3NJejZWdHIlMkJTaGFNZVFGOVc3RUZNdG9qWDVBNiUyQjgwc0lHUllRJTJGZnMwMVN4JTJCSmFFVkRFcnRmSjlGT2trelFDaWRRY1VIUUJaT1dMNG9Mc2JhTGxDZG1TNFVHdVIyaFRxbXIlMkZteUFocUlhU2klMkZRb0I4bGdaSlk2WFYzJTJGJTJCODl1U05OaGJKYk1laHZ4QVp4ckZCTkR3UWtJcXZFZDh6ZFNXbldicWFqZWY5JTJCNExGVHdLcGZhSVJ4Q254bU9DZGNMb0Z2V2RjRHlhdjdDZ24zYUtncmRvdnJDaE5scnZUNmFNdG1sWlU2eksxUiUyRmYxWndPc1dLdyUyQmUzWjhQQjFkYUVLOHVxJTJGa2htY3ZYTWFCd1pENUZOZUlLd2owcnJUJTJCeSUyRk8lMkJnbWNJaTZ6am85YTB4RkpSWDVuZ29FZ244JTJGVG1YUEhoWmElMkJTb0Exam9xMDZVcVFUS1ZIOXVHV2UxV3cxV09JYiUyRlA1U0FLbFVJUjN4aGNIQ1NYYU1tdkk1dHBSNEZHdFhwcGJjN0FaMzdhejFjdnM1YSUyRm5JUWNJeSUyRlNLZlQ4aE9EMFNDN0VMN0hDSkNYZXpoM05tT2tuNiUyQkNxSlNnZ2dWUEo2MTJ0UGM2dCUyQndwdjB3VlcxV3lwdmslMkJyY2MlMkZiVUhqYmJhd2hxU05nQVV0dmxMTUtaV3JWJTJGaXk2MThtU2IwSEJLbUVWRmJzTjBzN1hRZ1Y3Z2h3ZnpCTGxDdGpQR3NSNFpGNkpkejcxQVAyY21Md21PRjBBU21oY1h2MGppN0E1cXk2Um5KU2VQQ3V1aHowMjhRMyUyRjJUSlMwaWtrUzAlMkJTTDVHM3BNakFTckZJM0d1VklhdWZUOE9UdnB5T2pJZHZaVDJYJTJGcXViMGhWbHIwbnZqV3p1Z0g0NlB6eDgwY1NDd1VqN0pIcXdPa0VsQVBhdm50RG8yNHNmWXpwa2YlMkZ0NnQwZm1PdHFrUyUyRnZMR080bGJhc3lDVXFVOGNZbiUyQmk0M2c5YXQlMkJ4Z3FONzRnRXJxSXJTM1dXZ3BrNVBESDlaODJVMkRwd3F0NnRPdG4zNmpwMXVoeUtrcFJrRkZvRkUlMkZQVWEwd1dibTVDb0FweVZYWTdWaHZXUXMzMXkzWWxYcHRlaUtRTXFZclpPRE4wTklYRmxhbG5TMFRIZjQlMkZXUzdWNVB5VW5JSmE0bVROU3htTThDY292ZzREYk5NeklGVEYxcURVOXRRQ3glMkJMOGRKN1o4UENlRkxJdm10Yko0ViUyQjM1OGVmeVBlUHgxS2pKZW9nMjIwOVV3UkRXbWFabkNuWFpRcmZ2OVIwcUdoZDM3MW1YcDI1ZmFoSnNYc053Qm5sdlZoYzNsVnpXSnNvZGRYaDQweTRwJTJGU3dodzZUWXhaWTlQVkJBRmZNZDBhb3pteDFBcUx1V0pGdTVmTWtsZ0x0R01hamxFTjgzcFpnWG5jQU5WakVQVTR3RG5LVE5NZmZ4WHY5UGczeFZZWFJIYVlKZjMzSWwyaUVBSEFhd2tjcWRrZkRGZzJkY1VDOG9iTHlTaHZDdDl0eE1lY2Y2dE5rcjdBR25GQ1RicjBSMEVIUVQ1cU5BbUZ4MTg3RWFhNDBjJTJGRDElMkYlMkZHNGJQcGVyME5ybmNJblhDQU9xJTJGMFFpRlVBRHhCWmFITjElMkZHdmZMVUtJbTNnU2ZuNWk4N3pVWHloU2lVNCUyQjBJQ2RPRzlDWk0lMkZDRUpXUEZGa3V5eE5hejlGdCUyRkhwZmRrZUVSSlhTbHVIZDlRQUhhYmxTMHN5Uk55TGoxOHlFYlB3aDBEcVhjd2hvdFVxWGVZSkVxbWFOakxNbmVxV281QnczUFVuQm5IVkQ0RVhBdUk0V3VzQzFKQWUxODlSSE4xNXc2Z0RjZmYlMkJJcWdzY0d5RSUyQjBzc1N1JTJCZ1Q2bnBGS1dzY2FtTkVWTmI5cGFURm02YmklMkIxWFAxdiUyQmVxdXVWWFdvd2dvemNOQzJVUktQM1pHNEJFY3ZrTU5JNGlvS0VFTEszSjlmY0c2VGZXYllxaGw3RnFBdzZ5cURyQSUyRmFkbWg2RUY0d0pFYnFQTE9EbFdOcjFCM1hiUVdRQ0QzbXowaTJ2Z1I5Y1JCcFVOZHB1TWglMkJrUFFRTGMzNnBjYjFqYkFVU0RiVVJ3ZnlFcG84dTZhZUhWS1g1V3pEazNDbnBzcXZpaHVUQmRZNkJZVzVNRkl4RFFiUCUyRjdUbEE3Z3hwJTJGUFJJWHMzZjA4dmoxRmN6NDdoYmFFSVR3STR1blZxdndNSUFBUEtGaFphbVRPU25BM1czcm1hY3ZkVkNIREhCJTJGc2J6VnglMkJQNXNuV0VzVmpISG8lMkJkQWNPbU0zM2dWMm5iUWJQc3FFJTJGSEpFNVpQJTJCRHdMTzVqNWJKVEQlMkJudFlkVmJiOHBuWENvSU0wQ0N6Z3dMZlp1MlZxaEdTYVIzcXE5WHZsV0w4RmJLS0hmTEhMOVpNWjRoZVdQMjFHRmQ3VDF6JTJGeEs3T0wlMkY1NHZPOFpvYUUlMkJuWEVzVjM1QUZJSWlKJTJGdjcyMlpQRGxxaUlXQTVEUjhVY0tjczgxcHZyUXlZYXdNOHdnJTJCaEswRSUyQjdFWTE0eUFKcjEyd1JVVUtKciUyRmxWRTJkY0h5UEpjSWNjVmg4amxJeXVXOWJITWt6ZUdDN09rMjd2c01FVmpaZEVjbGVsUnNVSE9McTJaNm1nMWNwa2YzT2lmSXclMkYySFklMkJZVXFwelowODZ3WlQ0NklBbjBHSFJqbGpoQXNTTm8zTXJiUFNUNGM3V3VmQ1YwSWNNQ3hMM1doS2wlMkJIakExQVlmRkwlMkZyUGdwZ3AlMkJ3eTN3VG9OTlFOTEwlMkI3M3Z3TjV0SVdJWHZIWWJwdXVIeU9pUUVNaHJvclhKcXhhaFYyZWRxbGJpMjh0dVQ2VmUyM0tTck14VHplMGJvb1UzT2ZYQ2Zaa0Zka1ExNDElMkZ6TVVTM2pTbkt0bUZHdEJZUlptMSUyRk1oRzZaUFYzRUVMaHFlT0FQQjg4RDhsbWk5eXJFMzRXcCUyQm9KM0pyQ0IlMkY0MDdocFI5SGdzbFM1WUdkaFc4TWlKSzh4eVljWDY5YVZIYjRJOCUyRjhtRkNTeTI2MzFPY3RMNG9DS041R2xyU2Fad0VZS042b29SYk5WTHVWQjFKZTV2blB4dEdQQnJaZUhZS2pzcG81ZmpqeEtKJTJCanF4WFpmVWpDR0hZV2dFTWVxOHMlMkY2eUM4Q3NDdDFLZENmUHIwQ3JUa3BTY2NmQ0sxWW82NGVJQTVDZHNFclpKYkQ5NnpVdkNzQ2VuQUxVeDBYZUVBT3dBJTJGRExYVVJHUERqN3g0bDdlU0xUR2dxNjFoOXlDRkVHSzZMNXdwOG5rS2tDJTJCMG1GUSUyQnV6WHE4YnVBOVR1RUU2cHElMkZmcThUVk1vUUg3TWNXR2hiSmNhS2VvdEhxdjBwdVdhYkd6SU8yUEM4azE2VkowdWF6d0NBOVl5YjM1ZVZYayUyRiUyQm9IRm9WbG9Za3RoT1l4TVczVyUyQnhHQUNLTXpWbEdReWpxNDZaMU5KS2NRUW01WTVMVnIzNFY1amVFQjd1NmdmSjJGUGh6WEpaOEpUcmRzZHE2S1RSZVduTDFwZEg1NzJXQ08zZ2daaThJciUyRnlJRGNRd3Eza212MWY4dWJ0NGJ4WjNmRWtXQWl6eiUyQldxcUYlMkJBdkRqdFIzUjZtUGN2RWR1Z3hXSSUyQll2UkNxVmpqUTZ3RmtKeU9uVzdVdzVLNlFJMEY4Y0slMkZZbFdMcmdsdDUlMkI2RDE3QXc1JTJCeHRHSzFaT2lXWGslMkJMNDdWWDBEa3ZyNyUyRnFIbk8lMkZhJTJCOHdzcCUyQmR2MzhwQTdHQ3NoSkxSUExxd0I1aXlNbUd6N0RSWkVWSUh0YVlqUURXNlBHb0pOUGtXeHdtR3R0S3dIa3p3dTFrZHc5SDM0OUxYUDJQY0daeHZGODR6OVJtTVo4MWg3bnRnZlNKb3U3RGdlU0swWTBmYXBWa1ZESkw1cFFQRDQwQ0ExaFdVZ1VvcW9VcU1WU3NBVzBNNWpRVGx6WVRvOWw5YjZMUDhpYjAyS3dISHNMMDNKZUtJZUo0dXJ3R2pWUHE2d0p6TzFTUjNiWFVxSWRHWiUyRjNUV3BzaU94JTJCalpacXJFbDNLOTk4TSUyRlNOTVFiTWNHSUphbFdRSkRHeGhIdkxTWGdxa1ZRd2o2ZVExY01yMm1QNE96WmxpJTJGTm1ZakNUakNxWlZJRlo3RmQ0RGdaS2hramVRTmJ0cUZSRm5OYXlRTkM4dWtNVHBwJTJGQVVTNnpmN0N4TXdHaUdYZk9IYjNDQSUyQlJLZlJPZ2IwcWpDUzhPcW8lMkIzOE95eEhQQzVTOVgzVU85YmVwYVJ5ZjVuU3FCazYzNmUxdU1jNmVoS2xBS2hNJTJCMnYlMkJtNnd0cHJoMnVwcTlFZzQwJTJGSFNaYmxVYzdXanEyeDlxbiUyQlByOWF5ZnFxYldFUGUyYkx4dUpFS2gwZVpMUXRlZWpJNUlmRzZIVnI0MDNkNG5IZ0p1S3UzNCUyQmVVMzM5cWdHS0Jza2FWT2I2bkNwS05GQjZKV1lMcFF6SyUyRnQ3MFFvTFhoSXRXOVhtMWxOa2pGT0RBVjQwekpnSVdxQzNrUnVWYzd5VEZoJTJCMVFzNXU0aSUyQmdzbE13bk8lMkZmZ2IlMkJOUElyU3VrdG1sWW5raHZiNXhVcG9idmJSbjN4cHRhYmtQUkZmSVZwZDZnOFFFdWZCUUwlMkZxY2tReGk5NTRqenlNWko1WWZaRmZQSHUzOHozJTJGZjFjSG54b0ZDdmFFZFJ4cEMyQmJDbzVVMDEzNEpYZEpUNm15YlZWZ05PU1Y0NkYxZkk3bjd1dXFlUDVzc0xGTmtUQnVuQ1cyMHNNMERBSUl4MzhlWDFma2RUYjhweGZqZXRKR05tcDc5RU9XMDJSU3lUQkZ0a0RacXlQMzlpdWhXUXklMkYlMkZXYmowR3ZMdUNUNTFkYU9OMm9GZFlDTCUyQmNKSlZ4ZXRJMEN6WWczJTJCVGslMkZrMVlEQldhRk93NGlVaE40alFZN0E3bG53VWY1UU1taFhUZERqYVBzM203b3hid1lCUFN5R2V1OWVPWlFCMnl4VElsTFNnJTJGT1B6UllSTjkzck8yeCUyRmZSNmZ5cUhUaWVjWCUyQlVadlo4NVc5Q2RaaTFxVnZVa1Z6djVrNFl2dUNkM0FFYm1MZjZEcCUyQmtac0tvdEJnaWFzUXNuJTJGSlI1eWY5MGN1NjclMkZsSXBBNk9OOW5mMm5UdEtiMmxkeU0lMkJMdjZXdTRoNmRPRWNyR3ZZMzVxUldrZklBZVZ3YVpsWDJGcG5URHlNWTRoZ3FaWjNkMHI2UWZ2TlgwWDJWOW8xQkxVbEd1eWc2amlWanZzcyUyQmhVVHFEb2VJT1huNFlVZjBsSjVmU2VBNGpjQWIzTXhldVExUVNCN2hMOTdvdVklMkIzTnZ5YmtPQ200cVJ0YWNlVmxXRlRkbFpzJTJCTGdzVHNSTkVPZG5hNzU3ak1vM0ZwWkZpc0tOTEx5YXAlMkZlOURJT3ZoS0VmR29ZTmlXaHF0RCUyQkwzZ1NzSkFYSkRtaWp3b1R6JTJGSE5yN1BIeGE0bnc0SmYlMkZNWlNaWU5FeU1LbCUyRmYlMkJ3UkZvUXZZTWdqQTZkJTJGemJwQ0xqNFFpJTJGaiUyQlNOeGhNQ2dUUXBCeDBPWTB1MDglMkJPd0RINm9mZ1lidjROb3BPTGNHWGt1WnlDalMlMkZTS2JDR0JHVDUySlQ2bSUyQkZVWnJtY2JsTmJuY2pPT0k5VzNMOXNOenRBcERZbEJZRk9DZVM4S1VJR0xQWndvRkNid0VIeldxNnY0V2NCRWkyYmRBY3BneTV3bmlSbWpZTnNlQnYlMkZnejNkSjdJdHBVUiUyRnVqOTdKdTBEbzVSdWxIN1c2OWZVWDF5eDUlMkJIUUlzM3pMeHVGOXNNblFxZnZ1YTBYRmE3dXhHRCUyRmtGUWhweHl1YTVIeGRqWEFSSnJxSlVhVjhnZnV5VGY5NGZWYyUyRnNCJTJGd2d5WExxbXklMkJIRGhUbDBmUldHOGYlMkZCenNpeVdhWDdpcXVMdmEwTktxaFVOYXhlMjdIVjdWNVoyWFg3aVN3WndYZU5qVkV2SUdmcUdXdjlDQlN0Tmpna2dTJTJGMEtYM1laUHglMkZQMWlrYmtsQm1zMGpqckRsNiUyQmJNRUtYMW1LZnV0clN0Y0lqSGRmUHdTdWYwR0c5T3VpRlI2R3UxQjM1dlQwayUyQlRjcjduTE4lMkJwT2pnWTEwbDVlSzFHQkhCU3UzUkE1b2FVMk96c1g0V0Fjc21aRkZsZ3lTVUZxa2FaYlIwTmFHSlJ5dVpnclhkeHhHQ1J2NUUyU1BVbHJvc0pmUWdhMjJFbkEzN3VaSHJoZkJQeTZ2Ylk4ZUwyR1U4VlRrRkVrOFNJcGJ4ZHRXSmpZdjdVaU9TJTJCRnZjZnYlMkZnbVlhMzQwdU5odkMyOVVYa0JDbkdpMzNLbWxPbkgzYnlRaCUyQmlwZmsxWkQ4MjNTRDJtSFhjVjZka3NJeiUyQlVoblBTTXFwRUlhclp4ZFJ6NnZia1pIamVMbXNRbXRYJTJGR0pwWFZGYVEycjJWelF3aTVXbUc3aHFVV1h5YVRoUVdzTjRWVXpVOGxiekhkJTJGb0poVmV0Zlg1QnpxeUJXYWVmaiUyQjhyNzl6SFdQTDBOJTJCTk5qRVJjSm93M0dDMUdITnV1R1dPUWdocVY2VGM1S1kxMFhUJTJGcXBFVUFSa08lMkJ3c21ibFZ5Y0hMM1VKaW9HVllKUksyZXd1QUwxQlNHRkR1VWdjeU52QSUyQkVPRExmcDdkV2NRclpoQ2VmMHBZdWZDMXMyaXlhbVNsRVhxVXhybW04TTNsZSUyQnlnTmNrOVplYnlxZjREOGpLNzVjQU1rMW9XbnVCZ1VEREFFYWx6MGNOcEpjbUZ0ZE04ZEMxR0tldnBqRW5aNndQa2ZYa3BLOUo5amVETUtZbFZkRHM1OFRmNjUySWQxdXlvMzM4TGh5eVR1QWp3Sk5lbCUyRktranNWJTJGQ1NzcVhPYVp4dDBROW9RVGpXYXNwanA0TzY3SGN3Q2pyVjklMkJDczlERjVDeU1XTzZwT0g3ODB2MzlpV3kzbUV3TUdjMmk4Y3dZdFZPYWNZOXFJVXNqWldqMFdNNXlIMk95aDRBM2R2R1FjR29kbWxsYkN4REQwbzNJTEJpJTJCSE1sd1JiVDVvMTBtR2F1a1lranJjMmpqcjElMkI1UkFiWG1hYlh2ZmpGWjhyUGF1M3Y2JTJCdDNvbSUyQlBkM2lFSkgwWEhyd1lEZmhBbktJcDNzYyUyQkdJTXBUYjFNd3p5MVpnYkR6JTJCTUFJaG5PZXRwc2x1N2hzeEJyNElFcUwlMkZIRlZsaVhtZks3c0d5SVBXSmZMZ3M1T3JFSWR5RWxtUGVwNzROdGFpZG9ldkx2TTJEQzk2ZllkQnJla0t0YVIlMkZPS0RCOXg2OE1KQm1wRTAzRmJTTjcxaGtaMnRSRWdlSVg0JTJCMXV0Wmw3M0VqRVZRZm1Mb0psSnNickFUWGdGTGxnRE1WM2pjJTJGVTNZSHdLJTJGeHJrdWYxQTJrTlUydnRQemtkdW55T2Nvdm9LMXZXRlM1MURTTm51Qk83elo0SlhvUUZJWldYUDRRb1hpJTJCelM4cDZ4OEI2RjRONUtyakJTJTJCd1RlTDU4JTJGJTJCSU1za2NsOGhvZEUzJTJCanJKU1ZYYmtVUWtLRHhIN3VZJTJCWVJYTk1kVjgxbk5sREU3SWdvaVk1SE9reENyMzBWa3kxMlpXVmVwbFZtSzJkUlVGendscGxJSHdvNlU5VG5yRlEyakFoeWxLQXdMaW5CVU10WGF6bXpUOWsxZnNQMVk3NUYyNWclMkZZV0hFd3BKZWkxcjk3Znp4N0FrVDNzOU9obUhTOTRKSnk0b2MzY09LY1VJUFZlcnRCcW82dU9sMURWTm5DR3l4cmFIUUs3YlFScSUyQnRhenZ2THQzdTQ0TEZ2dXpJUldyRVQ0VklGNjZNZ3dLJTJGJTJCWFROUFdKdEZNa01uJTJGbmR5UDJtbnBQMzFMWGRwN0R5M3J4MGx5YnkxTHNLUzZCaGN1ekhWcDhVTVR6UjNrTWUlMkZHb0hZU0QzQWkyMm0wRDU0dGNLWCUyQkQ4bXJySkhJeUVmUEkxYVIzc3ZTcXRIOFRaQjFiTmd4UzloTUpVJTJCWWxBN2pTbkh1VkFnNXdHZVgwTVNXNWt6T3NRR1FVVTNhVldCNjlCQ0lmJTJCUXZUd3ZWJTJCbTR3d2JOZGRHTUhMNnFPNldSZkZWaW0yZzB2VkVCdWtyRGRnVndoMVNlcFBuZ1hRYWptb0NXTUdhREJZZGpZa2VIdVpEMTF4TUtyZjJ0R0hYJTJCQzFPTElyOUJ0TCUyRk1rJTJGbTgyJTJGcFRvaGZsUHY0Mlc1ZDZtYVpwZzZsSWVtbUZJeDBKRlkzYm9FZUg3dFppbFlNb0hyUGZqJTJGeE5QNTJiR0JQb0xkSTZFcUhWZENUMzM2SXhWbyUyQjZ0bXM2dGY2aUlyTVdqUEdOciUyQklkQWRrMzE1am5aNFBSRWRiNmdxUzNQOU9sQSUyRldIMFpiTzIlMkJ4VVN0ZnZxdUo5cGRUR2Y4YjJtb1Fva1hmQXVOd0lCSktCT0xIdEIlMkJFRjBzZ3QzTnNNZkRkSWdsbXNaVzQxOEI2eGtsMHVoJTJGNnN5SVZlRFFIMTlzcE1DVSUyRlp6cGIwcG0lMkJZVmVNTVpXWUZwdzdUZlUzRUIxa25ya3Q4RFVOOUxuekl5bSUyRmJFJTJCd3pHTlBOalRzciUyRng3ZzNsNExwTXd5cTlKc0N2QkFxbkxmN08xWVhFYmZ1NmtCcUxqSnozZ0VKeFp3QkY5UFplRDhjNU5Kek9zWVYzSEczV0k5bVhNYVhDJTJCV3RMcjdnNTVzVjFxdEFVT2lkcUpoOGpXMVRKUG1TYXJ3bzhodzhzQkpuT0ZpY09VMVM5a2h5d3l0a1Q5JTJGY0FtY1V0QnFXbEJIVlJ2S0d0NXlVSWhYJTJGYndYNWJabHlKTE9KY0RKbnl2VTB1SUxGTmNpaENpVTRKZTlXZjQyNTNIaDhaSnlPenp4Y1hsV0p5ZFZwRnphaHJLc1g0amRwcXZIemllMG5CaVY5RkJKSjJiVW1kZW91ZkRaQSUyQmFkckolMkJRUThtdWk2RkxNM1E2bzZ5b2JCVmw0ekZDVXd5cmcxTXZqdjRrQlhPalNLWUJDbUIlMkIlMkJsc3YlMkJ2MHAlMkIxakFXdkxlTHlRSDBwdUZ2emclMkZYbUtQNURQRlRoUjB6aTdWc1NMZFp2aEp3b1ByYkFOdlNoZ3Y0Tjdna1ZkN3ZNTmllNzBCQVlCMFNRUU9OV1FuVENLTklNMUYwbXpOeldEVktsdGZoQ1RPZEI5cmlKZ1pGc21FY2EyeDA4TkNZSWwxaHpGMjE0WFF1WTd1YjlJTWVwenR1NGN3VGs2SXNlUUtKa1VtMkVWZmpzTW9lYjJtVCUyRkZ0SmVZSSUyRk0lMkYzTXduOXRqams1UUY2TFB3WEkwSnVhU0ttZGY5OHlVVzdKMFYydVpVZHljMWNPNGxTd1M5Mkw4Q0ElMkI5S2FBRU5keiUyRlc4TGZKM0VHdW1JTTJ6RERYMW00VXU1UEwlMkZZMSUyRmxsWnVrVmhJR3R0MUVrbmczSWhraGZ5RGVTMjA2ZTBMTmlpTFZFTGtVQkc1RUYlMkZNZ3gwM0lrTXlXMUhHUWFTc0JtczMxcWhGVzhsJTJCQnBsdjZROXBQMktuaDVIS2dCZGJHM05MMkRvRmlVZDloUEhkdmljJTJCJTJCaDFzUmpwajZQc01xVmtsNUFSNWRNd2dRR0s4Mm56aG1yJTJGclR1eU1RWmFuR3czSmw0djNyekx5c09ZNVpmQXZBb1Jka294SyUyRkVGck1FJTJCMHMzNG50bTROdUZwdTNHc0lKTnVLcWZvUVhmQjZRZnRwZUh1Y0FNMVp0ODZ6RjZYRHlSMFRuZGhWUklPZFc4OGVmbmRSYnNtdElIYmloUUN2NkpxU3hLNWVnZUxjb1REczY4emglMkJGZGtBbnp4aWJuMU8lMkJaNjh0ZGl1cjkzcXR6SzUxJTJCT0M5SVI3NEFVZFp6QzNXdFBaMWJCSWQlMkIlMkJVUkJqJTJGY1lnd3F3NVhLaThNRUR3eW5DTGJJWWlZTFlSY2hIczJPemtPdmxrSUowVFhPRlBZUGhtWktIYWNRVkt5dFJSY1FWR3VmSGE5VmhNbXQ3ZVB3Q2FSaVdZT25nTmczM0Fudnk4dDZOUG5zWlAlMkYxYm5xQUhuRmtNRTJsU0tqZVNNc3FvamRzVDYyNkw2MVROdnpWdDBxUUJYbkJEcktzeDZNYzNEaHFZTkVRbWFLZXVvWkJNdjZmTGZvJTJGRlclMkJkT1hIdFJOMk85bFNpM3pzc0F0Z2ZkZnJMZDV5OWZ5aTFlcGJaalhIVFA4T0xYMHdLc2xJZFlUOHVwamptbnJYVTRnS0s4dUJVZUtLSUVPREJhWnBBa3ZUcmJKRCUyRkJuOUtnS3hNQXh0WmJQV0F1ZFBDb0ExQzQ5U2wlMkZ3b1N4SHYxV052UkdaVlAzQUtnWFRIT3JMeSUyQkdxOEhINDYwcnVCaE1wZWVrYW9vZHUyTEp5WnNVcFNXdkhCMnUlMkI0Z05mazZKeTFMZEE1MHZrMjFKWENDZkhVdldCM1oxNnVHcGZtQUpGWWJSc2YlMkZlaE1aJTJGZkwzYTlBYyUyRmJub1BHUUJaZ0xiZTczNnk5aEc1VjZJejQwQkszb3UlMkZjSmolMkJySHBYTW1TbEFjMVUyZUEwNSUyRnVZazA4ZzFNcnBScGl5NmdYalNzYTJtNEQwZUxVYTFEUTlRSjlGbVVGbThrWWlDSndRTldpSzlNJTJCb1g2djdHQWk0VSUyQmxxU0R5U3JFNk5GeTZzb3F3M1NOeDdLazM3ZyUyQmxjbnVRbkVyOUdhZCUyRjAwRUd2Q1lQaFV2bjdqMmdWeHplVUhSOXN2Z3pmWFl1ODV1cHZQMzclMkI5Z1dMa0dabE4lMkJDUlhIOCUyRktkNjlNWHRjV3FEUTlKYkNsenRnWEhJWkZTN2s1ZG5UUlYlMkZpOFdnbzNObUdqNGk3M3Y4JTJGaklHM2NOUHczVW42ZmN6bGtiVWVaR3QxT25MeXBOS1BPMjA2aVgwSURLaWdVeUswbW0lMkJ4bVpBOUgybVhabk1Db1lBbEpmSHZpTHB1SEtKVEFhS09KZkFaN1RuUUU5bDhxakxnRHhkVE5TVnRidmlqTGI5ODdQUUNXQUlkRkpQVEJyWWpQWXFTbXpXWjBsZ3h3WFFDSHdtSlZDTGdiWmFwOTJoaXN2UzNEdm8wUFpWQ2g2cmppdE1HdXlUck8lMkY4WFNkU3hKaXNYQVg4SkRIZkhlZTI1NDd6MWZ2OUN6RVJNVGMlMkJpZW91QkptU21saEgxTkZHbnllVzY3MTJZbTdvMXJTOFNKUWRxQjRMa1lNbjh3THVzQXVlRzFWVmFkRHI3bmtMY1Zrd2tTdVNsUG9ab2tOaWVweUtPRVBrR0dBZkJ5UTklMkJxNTFHMkhhR0d3MzIlMkI5eGFYcyUyQlRQVEpad205aDlqaTRJcUdDRHhLRzJnZ21KN0VkUHJVZVBGQkJQNVRwbE1IQSUyQmVPcW5EUzdETHNYc0pmbGZWJTJCZzU3V3B2WmJuS1B3ZVNsdWltNFdlUllBbFZ6YXdnJTJGZnNhQk5jUzZyek5jMnJzVk5oRGlhRW5DNiUyRnVCNVU3U216JTJCQkFlWGZQbXBHTmZDYyUyRndTMmp5V0Y4a0VNYVolMkZ2YyUyRlJnNWE1cGVNRGhOSTdpZUNhM0NHODU3NnliNkNOMkl0ckpPamdYTUFTdjVyb05nYnNIaiUyRjVxaTh3ejJweEIzcyUyQmQ4aXI3MzYxelN6RzJmTFgwS0VIdSUyQk0zazF6NHAzV2pCTkNDM1l1YUZnTEtuJTJCaXNTRiUyRmRZUGglMkJyJTJCaCUyQjBmbW1Wb0pLJTJGaXl6TnF5dG5ZaExIU1d1RWYxbVZ0cSUyQmxBbkNobElKZHNZNjJ5S1VWU3hEZCUyQlZ2OVBjUmxDJTJGWW5qSDRIQXhBVzlEYyUyQkEzU2N1T29vbmdXZVB2b3UydHUwbTV2UTBLcDZFa3VOWW5FdlRyOE9OdlJaM3NDOTRDY1VQJTJCTWVxZ0s0czJTZ0F5RmhqZmxqZGpSOWUlMkJKRlZvU2E0d3hhdU5XU1M5QXVxSFNaQzV2eFEwR0t5SFprYSUyRmlKVTA2ZDNabXVqMVRjYSUyQiUyRmtsQVhGS3QlMkZmY2FFS0hrbFo4MSUyQkF6alklMkJzSmlTWHdqeVJtdmlYM0xSMVl1JTJCa1FKV3RaOXlldFUlMkZQaHd0d3ZTV1pVNHJnakRCcVFOcHE1WkdEWnclMkY3UFZkTldMMjZVc0NkNUtSYSUyQllhUDNnUFFSMEk2T1pCR0FlV0luRzhNVWNZQ3k4SkZCMjlKMCUyRmZDRiUyRlNOMU52eHRPRiUyQkpNQTdlU2s3UHF2dDA0eUZpc1lGRHJaMVYyUHVURGVoR1k4R3I4Zmsxc0tESjZ6ckhuZzhXJTJGVlVFVUYyR1I5R2lVak85NGZ3TzZXSlJoMnl2MlZ1bEhoUVJ0TVNSRlc0OVElMkIzVUhmclkxWVd5YzU2RkVLWSUyRndvbW5CMVc0JTJGRnZweVF6Yzhma3R6Vm1tN05lRXIlMkJLeHhSVm1nSzg1Mk9adnIlMkZ2VkYlMkJjTFJwdlNjdHpWNGhTQmR3TFR6MVVFVTZBdUVjWUY5THRKUVkyQm5RaUJ6RzRRaHY3T3FOQjJqSHhJeFJVb3ZRajZqajRLdEJQQW01TnhLanRyWXZneVNHek0ySGw4RzB1RlBXTnZTZWZ3dWxNTEJMTW1pMm9GWDE2MXM4Z1FuY2NmZFVZYUZUZW1QcHdacndtVWJsY2R3NzRmbnJXcmIzdnR6dDdGRjYlMkZ1akw4T1NiYkMzenh3TlFTN2hWZERaMHpya2t4NUJQT1JFQzJRMDdhZzdqdnZhRHJZbUdGJTJGNEtPWDRPVXJrTWdDNzdHOUlyR1V3M21NZUZIcDUxVGdpeXZ2VlplJTJCWU9nRTlPcTFwb3k5T2pIOEl2QkJ3aXU3JTJCSyUyQklDUSUyRkZUUXZpNkduTUtDVEdlY3c3YjRENWJCJTJGYWJUUzM3cFl5N2t3NXZSRDM2ZyUyQmlVS1B0SmJpYWFNaWx2T2d5SFlsRThUb1FSb3JtUDlHTTgxeGRSciUyRlQyaVBtY0klMkY0WU94dHM5NlZqdlc4YkFGMjV0NFolMkJiSWNpdjc0eHVCVVd0Vzl6UkNuaVpkbkN1VWMyTFRnODlSS2E1V1NCdFlxOFZlQVolMkIlMkYwZDFaTVYyJTJGVFlKaEk0UTVTMVElMkZPNFFwVEJMMEpheVdZUkY4dmZoQWRjcjZiY0tzUmw1JTJGS3JLJTJCZ3BCWDclMkJuNzF1cGN0bFh1V3UxRk9yMThNQ0szMzU2dEhnelVTQXoxYWJlWG1sYzY2VHVMeFIlMkJzckJWdjJkSFFzTm4lMkZjMDRSWXgwJTJCZDB3eHpjQmZDWjFHVkNVSng4bTBqaTBlRUkyNkclMkJDajYlMkYxayUyRjBpQVZtckNPJTJCJTJCWXI2eHZRbEoxNnJ4TWJ0VmZoT1RaOEJxbEk1RW42Y2dpJTJCJTJGTjRuT3VFMkNzSTU4V1Vwc2lqWE5qTHNnMXlacmRhRTl0WXlyN0dhcSUyQko0Z3FwQlk0ZllqSmVVZXZVZ2tlZFVjTm1IUUx2VG9BVWRxV3FKTHZIaHFCeSUyRiUyRkhHRm5mblB3SXpLMlZFVm1Bd1lsME1qM1I5Y2UxNlFFSkFIOWdDTnJHbTliNHRydFVaR0lCNkY3eVA3aGNoJTJCQU9hWHRMaVp4ZTZSeSUyQk9VRnpGRklFQ2NkNHZXcmsxSUl2ajZpVWFnTjRkYnMlMkI4MERiNjJBc0NYOWRrTW5FdHpFVSUyQnhGNUJYbG4zWllDandSdUo4REFsVFMlMkZjMEh2dHI1YTlSWUJRRmtBSVBpNHE2aU1HJTJGZldZajlMSEFBdjkwUjNEMSUyQmY0TTNrb3J5VGFZcUVyV2hsWjR1ODVEb0lXN0MzN3hjbTRmUmlQMDI0eGlVJTJCNlVReDR0cWczM3M2cW94QjVmcVdma05sbiUyQm44aU45NjNqc05WanFVV01iTzVWJTJCdXFGNFNVTTBQc0clMkJxaW56R1V1a3RoMmpZZjlLcFJmTnQzclMlMkYzYkI1NWRTVWNyU2ozUExsT0QyJTJGTTMlMkJoSTZpdFdYZVBueFNTdnZNcDZjSyUyRnk2Y3NINmdzN1FHcXlqRXhOUkM3WmZMbHFNa0xNTVFiMlZMQ2E2WUVhQktQJTJGMHI3YjFIdjJnJTJCWG8yNFFEcGt5ZW5CWWNveUQ2eWdEQXFoQW5WJTJGcGduRTAlMkZhU1FOV3d4aVpjbGJSMkYwOFV1eHlZTm81Y2U5RXlqbW1XWWN2ajQlMkZZM3UwN2FMenRka2U1SGFpMHFYamtJJTJGa3Z4Wms3RUdMVjdUQng5eUh4ajE3VXFmODBhTGoyNEN6Vlc1SzdWQ1dXU3paUnVGTCUyRkxCR1ZvWHVOWVNueEQ0ZzJ2dFhNSWZoYTJEYmRRVFVVdm9IJTJCNkxLM1lKSkxRVXgzQ0tLSGU5S3Q4Z2NsQ21YaDNid0ltaTFBWWRYWnJqcHpuSkQzVzAyU1hjTVJOeXlYZ2g1UnlPYjV1S2FKYWZ6WGh2NFQ5QUdXUG93QnVOSXpQWGtGMVglMkJhb1dpWXp4MFU4YUhaWUklMkJiJTJCJTJGWGxnWUdKMlpBYSUyRmlXRGZmOE5QbU1RZnZUYnIxRURCNmNkZElvdXJMS2Z3bU5qdFJ1Y0tFQUlWZGI4c3FnTXA4VENlb2JYck5wNFpiWm0lMkJWdSUyQnd1ckU4SVZYOFpuQk9reW83YmJ3VUloVXNyZ0VvdUgzUEV1UXROd0tDZ1NHeWVraFZ3NW4xdXVkV0szJTJGSGFBN0RYSCUyQjk4dUd4aUdOWmZzVzJuSzYwRXhlJTJCdnJkVVEyJTJGJTJCSjZVdmRMS2QlMkZaVTdqODJqdVIlMkZKYmhzQzdMQ3lTMnNwYXJib0Nqbzk5a1VMV2pLYlVhQXRhYzZ4QmZqTGxxYlpzOFQlMkJzJTJCYVZyVkM1TktwWnkwVFZiTzlxRkRsSFFYN2xFQVB0VHViJTJCcXZiUzlMUU4zWjJpUmU5SGl3blhoJTJCU0Vjdjc2QlolMkZsM0Z4OXR1a3VHUXJ2akdzZElneDFOVjNubW5XcjFEOTFha1VQRUVia3B6RU9iYjJNRlVCdjFuUHclMkJjZFNzRyUyRnV5MiUyQktLeVFKcktqMW5yUmpCcHFFMVppcnhCaDZ1U0tHdlZrRmx5Q0hQQ2J0d2ppdFJ6SjE1NXZLZXdRZnZ1MHElMkZodFViYVZkTWplMHFwSmI1aHFSUndGJTJGWGQ3MDR3ZCUyQmNwN1R5YzZjUm01byUyQkxLV0IyWTNUaE8zaUZQRWZneGNicjVpbTExVVFSUSUyRnowYnUxb2h4OUMlMkYwaDRXUWZzRk9mZjVWOGxpJTJGTkJGZTNHbXNYSUhUNmElMkJ0YzMyNkU5dE10WmxPeGs3VTQ1aUxESThBS1ZJUW5PdjNuZHcwd0REc040dUklMkJBRyUyQnVDQU5CbzNxcTlwSFNyWExJVzNlcHk5Ykc1aG5BNU1LaWJiT00yZEp2NWRKQmF6c3llZlhPNU53M1d4VyUyQnFxelg4T3dneFc5Nk5MM1pRQ3AlMkJRY01QJTJCM2ZGa1pXdjd1bW45OTB3OFNmTDFWOVFkbEs3QUlieVduYmFITWM1MXRzdCUyQmtMVW5wamRXVmdIWXIlMkIlMkJhbTJoY01FMmV6bjdFTzdLUzlWVUdDM1NUYkkxblR4N2VHejlCb01KZFU5ZXV3WXFxaUIlMkJ0QXg0YjdhUEZhJTJGRDklMkJ1VHFTUVlobDJSME83enYlMkJLJTJGVlgyaGNIZkNkRndtSzRDVk45d3haQkg0bFlNbGtqVmU4QzF6NldaSG9RYzRESnhlWVhyV2tBZUtsSTlWeXJqY2RPTnhzSEJ2M3d2bmVJTHlZT2Z0S1hGMTl6QnM1eWdYcnAwNzl4WHg5UjNPRFdXbDhYN2hLb29EUyUyQlJZd0hMNjhFUHcwZlV5ekklMkZtc21rVXQxWWtCMlpCWnlndVFOVVE1cnJMbVdXanI5cFpiaCUyQm1vcm1jMzBwZGklMkZtbjBPOUJYMG5pbFElMkZsUXRYcCUyQjhXbHp3NURIJTJCT0FKRWJYZzAlMkIlMkJtTVZXRDdZMmpuVWlxankwMGVuZSUyRlpVbmZ2aWh5dmZIRnllNUF4OXRtdE91N2YlMkJOeFNrSCUyRmNKVjlEWFdOcTZ3T2JBcmd0MiUyQllmNlRWbGw0UEdOSU9tVElZZldWelQ1bXYlMkJKdWQ4TGxackhSRExiNEh2RFo2RUw4bnU3V2pyMmFUMkxsb0FxbUslMkIlMkJORlBYbmE3cHpybjNCenhaYyUyRkZ1R0tCbFJLcFZxRlpUJTJGREl6M3I5eGVLcGlhTmlHdlc0YUlibElRRkZ6Q2NyMWFQZmJXZzMzSjdYREtBYlJPajVic25YSlI1RjhrTEslMkY1MG45VUk2bFl4Tms5JTJGblJsa0NIbVA0VkNLJTJCNHBCeTF5JTJCbFQwYjlhbSUyRlFWN3NhMmtsVHVyNEQ5RXVZbkh3enlWZlg3V01LYjFmU0FWMFdQYURhakdZTVJheGR0cGNnVzFmZGsxaTVLQlhneVBybktEYSUyRjg1R0d4Nlg2emVHTCUyRnBuZUFYaUl5TUY3NVEwM2RsT3Jjd2VXSUpiMlNVb1J1U3JZYzIlMkZPRVgxQk9FbjBOcGlSMjJ2dWtoWk8lMkJQeFpCNFhIWFN2b1B0NFJTdUZJYUhoWmdUbW5VTkMzQmZjbTFONXk4dnB0V0RyNUljWXhyZ09VUUs4MzlHYlhseTBlT2hDZHQ1dHNJQiUyQmU3cHd2a3hHOHd0NFcyV3owZDk5azl2a2RjRFV2Qm90QWRRRHZGQ0doN0V6WjElMkZmNjJiJTJGWERTeTdsUXF6Z2dxMFZ5NDNVZU54czRXbklXVFlkUEliQXJScUtrZW4lMkJ0UzFBbTdmNFpqNFVDJTJGbE8weGglMkZwQjFndjc5ajFQcUF1RnU4TEpyQW0wckFOWk5qJTJCTkMlMkJ1SE5jTTFCZ3ZLdTk3S3FiVWFwNTBOdFFjdFk4UTU1dG9EVjljZjc4M09rSzdydyUyQmhhRkRQdEo0QmlHaTlVUlJ0WDRPMTN4U3d4RmlHTnh3N001Mzc3RGp4SiUyQlRJWHpvRWFtTzZVZGFsRXRYMkNhR2JHT1NCQ2VpJTJCbUxqY2VuWTZUbG80RCUyQkFJWGQ0VGJjaHNtejQyQk44V0VMcnVlcGRrV3dEclhmZnElMkJVVFVQd2dKYTZJbmtSeXElMkJkVmZZUlBxSnZjY3pXZU5KUXFZdmliJTJGWlV3VGhKWEpOTzdCZ2NXJTJCUlU3dTFsaXIzTW4lMkJmcFJpYjN5bk9YTWpINkFwOXduR3NJNSUyQkpmU1Y1T3UzbHZvcG5SaUJXSjl0JTJCUHFlZVF2UnRScllxU0tMbkx0OFZ6OENVZVFFYkZXa09JNDA2M1pHYWtLWmJvbCUyQjcyeTIyYlZWUHZNbTF6NUN4TmNyZGNhWGJoN0R6UDRieXdnUmNGeXAzMlVRRVFWR0pJb1R5UFlOSWRKbXlmS1NCb0UzZGRpMDBHMm1TTVN0dXlYRWJMMVlLNkxoSVp6MjJyOXl5WktRVnZSWWlNQ2FXcWwlMkI3MmpTRFZYbXhqNGcxTlNoRUt1S0xNSFlzWkFxTUZxMHNxbjRJMU84M2ZnbkFaRjBmb0VzYXBWQ2xUYUJqWTglMkJTUnlQSmZQN2pNa2F2VkViSGZIbmh4Y2dRTVliMWpoTFVWSzhrdG82bCUyRmwyU0t6T0Vhd2hiMDVEZzFyVVd2RDg4T1A1TENTWTR2MVZkYiUyQlEyQmNKc0ZZUlV0MHV4JTJGU1ElMkZNTlI1QW8lMkZxOEwwS2JmVlN0UVlTR2ZOQWhHTmFiNWhUJTJGVjhkYTZ4S2FuTmprM2lWWTdOT0pMWlU1eTczeGtqbEh0aTV1a1A2ZkZNRlolMkYwNVQzdmJSd0dMbWdrVCUyRmxOJTJGZGVPZEhFYSUyQktCZDRKUDFBZlVCOXlibVRKNjZJbnBBZEpydDc0MkZGcDRVZGRwQ2c4b3RhVXlzSEROOFlrT1lnNW85OUhCalFoS0dyeCUyQmM3Q3NPVVpONFRCYlhxYjh1bW0wWEdJb1BZdEZKTVhlaG84SUQyZ1l4NGhwZGg1WG1wUGJ3JTJCUnJHQm5rVVVCajI3eWk2WUJmQnVMTGVHQmxmenVsZ25paFhuM3BkaDZpSXFCVjJ0WGZQaEhaSkVtUnBDU0w1Vnd0WUQ1M1JERjZScG1ycUp2dlJoJTJGQkZRTVBCV3NWJTJCZVpuZE4wMXQ5aXdrZURRM0NEdGZGSktPb0VQTGk4RHJVZzE0ZDZtSmpoaXZVQ1VOTnFSVHpiUjl4QTllUDNpbVlwOVpock5JcDdrT3hEQ1QlMkJKc3RwUmpxRlRpSXZjMEgxJTJGWGV2a1c5dHJhbVVQb24zNDdNOVcxY093Yk5lWnNlMFhUWU04aXZXdUhyRUlZNWF1bjJjTVcwV2pSWCUyQmlGbDZ1T0lpWVU5N3p1SGE5MjJ6WnI2Zk1mVmQ0aVhUT21QV1VyOXREalVrUnFGbFkzTkElMkIxM21LVHJyM1M0U3ljVG9naCUyQmdtVXRjZHpENjByZkNPVW5mVklBbjRpZzJkJTJCZjN1TlRwb1NxUSUyRndSTjlMeXZRbFlWJTJCUEFpMVAlMkJ2JTJGRzdSZ0pZNDhLOU4wbGV5SlFtVFZ6SGRmNWI1aGFQdzFYOUZJRUI0Q1Y4SFZHUkhEZlpQR3NLSmNncnp6QmY3RzVSVm9LMjg0R1V0ekwlMkY1dkpKOHdjYUtXcTQ4dzBaZHFoJTJCWG9IWW4zdDhwdmRRN1g3MnlzTUVqZE9rYUszdjRRSEVUOGc2MnFpRzN5TyUyQmhxV056Qkd1eGlrNTZKeHZ1MSUyRjZYaW8wbThYTFNlQzJZUmUzVlBrb00lMkJXQkxiYnhWQ08wWXR3aXZWN3RUTzVSNjNtQ0szUjh4OWQwc2xYVDZoWWJiZW41REFWM2tWUmxXUFhvQ092UmlwUm93bFI0ZjdiMTJBSG5OJTJGWTFTa21FS3YwV3ZVcUp4dGI3aGxHTFglMkI2a3Y3aVB1andSZ3pFVGVzWkpDeVhycGVaazFMTDB2MFFxcjNQSzFWb1l0Tm5mT294JTJCbjZ1am9tJTJGd3RUOGNsTEk1RGFaQXVSZllpdndpOUtRSUxENlhEbG1pdXpjUnltZVB3N3dYdU1xMGJ4WWpyYnkyeVlva3h3U1M0WjFuRW1MT1VudVRTcHBFVk1Wa2hyUGZUVmZ4OHpCaEMzMUVJVmElMkJPJTJGV0RTbWdMbGUlMkZLQUI4Vjh4dnF0JTJGTHZ4Q0VxRWdyV1pxY1A3NmJLWSUyRjRabGttUFQ1c1JwUGNKSTY0aE0zJTJCV3BQczlEQyUyQlcyWFZaVTFkUlBYRyUyQnFKQkJQbUs3SlRBdUg4VmtvdGRJeXAyakJ5Z3lrM2hIMXppJTJCZWolMkIwb1ZMcVhTT1ZzaWhsUFF3ZjhFUVg0YkNHa3g5ZmVzcUtjaERTc09IdFVPZlhDcSUyQmR1ZWtTUWVUbVBleVFHa1RGeER3Z1phMHNaQnFiallPTlBNcmlMVU8xejYlMkZ0V3BzdGhJTVlGeDY5bHRJcVpYZnpnRHRPZ3Y3YXRtViUyQnprYnclMkJBdnVnN1Mwc2k4aFF1UlZNcEsxcnolMkZsUU9STGZOdjV2bG1yOVlFVE1aZkclMkZkbFpzOGlSall2NWs5cmNCTURiVnljSHRFQ2FEb2dtOUI5amQxa2lDbklpbmJUVW5lZ2d0JTJCS3piJTJGTlROTlhzMHo4VFZxZWlyZUdCYWEyR0wwMlpRbzkxeUZkZ3RWY3Y2SEJFaTJ2UzhFMnBiNEJaQlJkbGpGQW9MbDVDZEJJUGFZJTJGN3R2eEhBdWszenFGaVVEYUpVSldTUldzM3Y1VURrJTJCdTA2aGQ5TDVkdkZFU0tMNGZzSiUyRkJpN3pyekNtd29lN0lhVldZJTJCd3lwMzkzJTJCOWljcjd0JTJGMVBhN3VxbElDa0lxZkFaaDF1Z2olMkJ4eHBhckJCVTJ6Wjc2VHJIRTV4RkV4TEhrQSUyQlUyR1lHdVZIMFJrWGZ4TmhiY0NtJTJGS21kcE9hMyUyQktkQmF0MHZGYWo3NWlrQXBWbXhPVTZKS2VjUDZvZFlMTGpEQmZSSmVVUlRLTEdXaldNUzJRdEhPY1N2WDZ3eDlYVkhhR0lIYjZNM01vY3FJVjFINmZwJTJGbkpkdW14THpmSUxOdyUyQiUyQk1BbnUlMkJGM3VpSWFtd0xqTUp2dG1VT2hLdm9xekxubEdJN0J4RlFlQiUyQjZBRHVwOGZ1Z1pUZDVGVnlybW5idklLcHRsYXpZZmRCeDVWZExyc3g3TXZzeUg3THBWVGZoRkh1dWNvS3NkU0dNM0ltTSUyQnFOWGlkVFRxTnpqUjFEb3VwUVFnZlRYWVpsaGtiaHVjRXhNSklFJTJGRmJ4b244WWt3NyUyQkY4bjR4dDU0Um9Jajl4Zm5SJTJCZCUyRjhkZ2QzSzJlckp2Q3JtbGZhN2U0bDBTVzlHVkxtakMlMkJweDdwYm5sMFFwcThseG5LZHRCN1hiTVd2UXJ4M2NRV2M2cmRSJTJCQk5Va04lMkJqZWlVUUVZMXR3QTVVUyUyRmlLazElMkJDZjRmTjRiT293WFRFWlV1dlBjbzRvbFQ5RmRoZXZkaHhiZTZ1bWVabVhUWkIwM0N0M1dhbFMyNlN5SCUyRjVwMnBXNHFuQiUyQjFEdVYzVndKazBpJTJCSHVkNVcydjY4UTJSODc5Y0dpN3JxNEt5blU1ZVI0d01PRENPY1gwMGhKY1JFcE9sRmROTUtXQmtDOGEyYzNTb2h3U3d5M1ZxemEwaktydjBLUEp3NjBzTXVibjFRMSUyRkUlMkJpajdGSCUyQkxQcHlrNlY3SUo2dnpCbjQ0VFZidWszcEN5JTJGcnlxM1NzYmNYYnBteCUyRmU1c1NIVW11VVUlMkJrcnFFQmVrN3FlbTYzRjlNM1g0cmtybXJaUGkxY2hFdDJoJTJCdjkwM2hxZlRBZG9NYmFqWHBwUFBFZkxtZUtQRzZkWDl6RGUlMkZuT1c1b1EyM2RBdTAxN2hBMlAzcVVRMGp0eHQlMkZNR1RxUUVVdXFqSVB4aWtCMUw3YTBOdyUyQlVmJTJGaE0lMkZmeXM0eGpyYjY4eXRLbDV0UTRsNHd4S1AzRUF6dnNhdzJxeExhT01YRVhwenplMjNLbmlDRUR1em9wdjJzMiUyQjlGSjVMdElMSmEyJTJCJTJCUE9tazc3S3BGUjdxWVhYJTJGYlhLZU9oVSUyQjYlMkJuNWJwJTJCaTY0dHUwa3NLN1l5Y2ttVW1HSGlleEszcU9POFc0Q0RncXpqaktYdjlsYTdWa3RxMXFzZVAlMkZIZGhybTY3YkRmSExFOW1CVTVKJTJGTmxscHMxTzBTTTA4aHhTV1FKaEphNXdHVWdtYWJtS2pIbmE1aXhDVldtNk9zOUoyS2NWWXB2czJiTmNUVFJBM2VFQ1lNR0hYeDl0ZEFsbENHVjJGRTNiTXU2NU4lMkJPYkRqM01xNlRXVnBoRUlZdlh6UVNvdnpBcnFzQ0VjRlRxcHNtdUhJWGVaZzRacE84ZERXTTNVMW4xMGtjRklsM3llSWtsZGlieXJiYTltN3prMmJBaENvOGEwR2o2SFU5NWxnQlVQNDc3TmFMNTklMkJqb0YlMkJzNFdma2l6OTE2JTJCRWFwaEMzQTElMkJlOUdwN1M0NXA3aUJnbldGJTJGcmpzczhYbDZqVmd0cmxtSHElMkZnZWklMkIwRkdLNlJxODA3bHZkWEpnUnJ2anQlMkJDWHpMZGlTc3JoajNwVjBveFo1QnRITU1zUTVWZU9SZUVCVzFXdlR2M1ElMkJnTnU3aTklMkI5NmxNTnJ2VyUyQjNQJTJGJTJGeXltMjM0WFY4U2hMaUc5QkZudG1wWUFGMTBQSjc5bFJZTjJWRWJCTTdyUzY1U2dhSmFMRm1hdG9ZQSUyQnlwdWk1TFFvUG9xJTJCdTNHMXNVJTJGcjAlMkJoY09QTSUyRnVvU0poOXUxdHBZVE1Ja0pmJTJGZnh0TFFoaTBTNmNsSWh2UGQ5ViUyRjFvJTJCdjdTJTJGOExYWWlVcEFtNTN4Z25KeCUyRmRNbTI0JTJCR2t1bDI0eEpiQ3V2UE5QeEwxUG1rYWVDV0l4RE9OSFQlMkJRRXFDWVhQTGZzQ24zTlZ4YjdoVHBSRjhKN00ySG16ajlhb24yb3FwTDNGWHM1S0VvcWtBZWFtUU1pUkthVmdGZU8zSHNOZHY2SEl6ZVp3azdXZml2T1BmJTJCa1NiJTJCeERtZU1sTHJGejJrdTNxWThBWXc5dFRjU3dCbkp5Qk4wYm54Z1dka1JMVEJWclJ1aldGbWZ1SXExOW5hQW1CRm5zak1UYUpLdWtGVW93bDIyVFExRzZrNGhNbkVLVnpuelNZZkpMdWJmYmhqZjF3UjklMkZZMXNSVnRiUDc0MUczazd2SWtKbmE5WWE0Y3o5WkpISklWJTJCdlJuT3hIQnIlMkZlcElDMjlsNHdnbTUwRXlhdnIycXo0MjF5YTJxb1cxU3o3R3VldjQ4QTRWVm1PJTJCenBkMUI2eWxlV3pWZkxyR1B1YyUyRjMxWDQ5dFJEWDY4cWx4RFV2cGJCVld0d2dQJTJGYTYlMkYlMkJSaFd5b1l1N3pHcVVHOURaTlFCUHpHMmVVa1M1WGdYd3pTRDduVW5KbFMxUVZFWEh0RHBZeUxwWTJUY1lscjljYkdFT1VCTUFnTkNjSlc2cHNlSzNxT21vc090JTJGRjA3anY5SlpCSzRERnFYYXhaUzFlbDlDSXJWJTJGbk9zJTJCc1JFNVJxU3h1WkFuVmRQRTE0a2dkMiUyQmFYcGFaNHdjaW1YcmxTZm9TTkxCb01HdyUyQlolMkJleiUyRiUyRlV2OHhYeVprJTJCRG9GWm9EeGxzd0k5clptazlvYjBvJTJGRkNRbXRPTFJSeGJIaFdSSFVkTDkwNUZ1a1BuRmtoNTBSUlRzMmk3TWVFUnlYVFNpJTJCZHplZE50Vzh4QWFrSDl5TjlIdVBRR0M0VCUyQmxNbVM1aVViU3U1JTJGSzRVcEJuODVZVVd3SUVLUVglMkJNRzhFMm1xY0JEZ1UwZ2lWV0IlMkJoT0hMV25PM2hOQSUyQnBCaHNHZURzMVpVOHhndW9FZURMY005ZWthMTlTYiUyQiUyRkpoczVjQm5XMHh6THc0T205RUNEJTJCZkVVbjdxWW90VUhYOTNYdXJISXZ5YmNCJTJCJTJCY1dxNmxPeGV6aXdpTExFJTJGRDc3cFV4UFk0TEpaYiUyQkVwUVIyUHRiMUFnM3k5N21TYmJRcTRFYXRCTTU5VWRMVWRkU2JiVG1YU1Q4JTJCYVUzMnJEdlZaTXZzMkdFb3VtZzBuNWwzc1pVbFY3SmdzUDJtNno4eWIyJTJCRW9CTHFpclBKcEJ0TyUyRnppTWlYWWp1TjNaVENkWEhaWkl1aEs4RkFjRnhZMjBtYzBJSnZ1dEtBV3RCTHllb1VSamg5M1RLdThlZDFXQ083bXhiUHR3RGxZZ1d2M1E2YzBzR0pKWVNaYXA3bVJtTEpDNWdWZUhhbjQzQXpEMTFmd1dyUWRhNFRUdyUyRmdvJTJCcEUlMkJLZUlKUlhEbE51dEF4aWFtbXpQODJvZGNlanFWN3pzcTRTYlFpb25lT1YxWW5EdWZ0WEdCY3NXdk1idVNmYnRiZXgyc0ppdXduNmpBeGZmU3hTRyUyQmxjazZwb3hNdEFhcm5tTyUyRllSREJzeHg3NE1IUHZuSmNwbmFvaWtPbkNWViUyQk4lMkYxVGFOenMwVkdCbHRNMWs1eUtQZEZ6RWdLN1ZPZlg2VGFzUVpoSFJWY3FQMHNTODBKNG1ubCUyRm8weWZwOVNra29lYnFLOXp2YUlUSDNsOGRkOFVtaFZPNzY3Z1BzaFRMWXRrSEhPWGtWczFwTWt2YyUyQlE0ampaampZMU5CZGV1JTJGTHJrMU1SYWRySlh5WmtWQ2FIeENJMzU0cFRpZzVHM3loeUdDVVp5QXRsVmNKSWolMkZMUFU2WExEdVdscWs0QVU4bUt5Y3lyRDd0U28xMlE3dSUyRktKemo5NGFtckhCWW9DM0Q4WnpQaFNaZDdrc2lsVndYNUZ6JTJGNmh1RFMlMkZGdnd2MXVhUERlQm42NkRIYk1zempXVTJRcHZyNDgxUTREOHdJamVtb0pKSHNBaiUyQmpxRlpRa1dIOE1YJTJCcWYyaXh6dFRQeW02a0JQc3JodktSOUh4cjEwbUhnejJXQjFWSSUyRmNEVmxZNEUzMncwZW1YS1pXbkFGdkJvamdrakxVR0hzRnJWMXE0NVJNbjAlMkIxYVlHNm1DWHhaY1VkVWFBcG8xWjBuenhKWDZuMFVjMVQwcFA2ZWNlRTZvTlhmdVZyUnZvSFdtaHclMkJBdSUyQiUyQjFxdlk3c0pjbVBRZ25rVkpRajFRSCUyQmZyZ3NTcG5QSm5lbTJNek9KSCUyRnQlMkJRbVB6c0VpMnZIZkM3Y2lubFhsa0tpanRvNmNxTk9aanB6OWVIUmRXYmp0NXVwMDBTZEZhS0JGQTNEbiUyRnVjaWZyMyUyQjlsZWhYZE1wWlhONUpaS3V6VmZ4NXZGR1pLaDQ4UzUwRFg2STJjbWk2Rlcxc1hQaFV1VVYwQ3E5UkdxJTJGczZNJTJGWHowN2c5WW9EOE5QcGRoJTJCbkZTNiUyQjRtMDFhRmh2TVExbXhXbGowc294TU5vN0xobnlJT0gwS25ZbUdJOW5VbGV6eUdkajhGbzFWbWhUejFvMTlPeDNpanlKaCUyRjJlJTJCeGR4TEpiTzJ4OWs5UXRFM1VlSjEwRWhlQXBFZFU0SFpOMzExM3FVRnF5cXJJTVBpWEhpT2M4R1NmVk9hanBKcnhmeWNNaU0wODNxbjc2bno2YkRxZmRLJTJGWVUzUSUyRkhmWXdTZjRJODRHcjZreXF2JTJGeUh4UXVuN01ZWEhycjc1aEZwOTRFdXp4dTJnTUQxQVVKT2R6VGJnbGclMkZZcVR0YWZRQnpzTzIlMkZEQ28zelhaVzVLanZkc3gwYkxNdDd5VXIyTUlsWjBIZSUyRktFbiUyQk5seHJOc0YlMkZTV0lUS0tzcFo5NGolMkJlbWFZNWhHdW1LU2F5RGsyTUlrQVZDaVdSJTJGazNKcGFubHZnNnRucGlwUkl0TTNFa1hWVndyRlBkTVpkbVRtN3E5JTJCU0FKYiUyRlFWTkNtQ1kzYXk5d1NDYU9kVlVPdzZWU3JCSmdiM0x1Ym9MbHV5RjBVazZROFFrWmx1OWwybjZhc1pRRXolMkJmbWpRZ2R0UnNwNm9ZSlNQWUxyaklxU25QdmVxVm5UMUhlQjRQQzElMkJnOVJvOHV1WGw2MiUyRldmVlFOeVZ6azR1UkRqd2JHbURxN1pidm02UlZhZzJHSUxRcEUzWnYzTUhLVHclMkZzbWs1JTJCbkNtTkhnSjQ0bThVUDF1eFB5SGd0bWp2ZzMyU0Z5YjlaU3U5aWZIMzZNTnFlNmVOWiUyRm84N0RLJTJCOTE4OEFNM1ZiRllCJTJGbFVOWFpsZTlRaHdjallqQ1dTcmRkY3BYb1BXYkI5ZFljem5TYzFZVXVDMFklMkJ5TFRnZGloZyUyQm1ENHpDNzlXcWx5d0JqTzl2bjloTE56YlglMkZCbTFERzNTbWVPdWNlUGtkNWplMzVEdW1Mbzd6ME9uODl6b3FDdkg4d3pQTHBCeHFvYWpjcWNydERtQkh3YTNtYmtzbmo2ZEhtMFZZaTR3RGFFMUdhNUVtQXRESDNNWmFnSXFqRnFuS2N4cjJqVVZGQ3dQR3ZHSzZ6TENkemxBMm1wcUFiODVNTjYwNDdRRElGdFVrdERsbE0lMkIwRG5haFYlMkJneHoxNzFjJTJCZyUyRjM2b3RkYURyWEpnOExSV1R4U1VCRlRVa2wyck9aYk1QQ3lJWDRyUDBmTyUyQlpVd2M0RXNJMFFxYXQ0YnBkRFNqeXJkSnBhVFZ0RXM0JTJCdFc1aUJjSVl0clBOdHI2ZGJQM1A4OFh6bGIxRm0zQ1lubTllUDZHbiUyQktHbExWd1JxeGhuREdnbHpVcWptZWRreWc0TXVqQ1c0ZVdSUXlUN0wyVWhtUXhDODU2U25UZlV3ekhPeVhKaldGUFZONE1EVmc5JTJGQ0Z3Nk96d2pnaCUyQnJ1YXVEcXpzTzhGR1ZTVHdKQW1RQlpFQmRmQWwlMkJKN1lHYTJyNWRHWWdYWU9JdjVUV1UyNE5DaTkyMTVKdTNNanNYVWxyWDM5SUtGOGtTVm5ib1pqeUNaRzE5TCUyRndGRHA2ZWglMkY2M29jJTJCajhvU2VDbXhQc1VuNXZlVFVDMWJnJTJCdHJOdnlwbGdocE41OTdqYnFDV0VNZUVOSGtJRFFiemhVRExNbnFZJTJCVzdseHNJdXVmYzRLZlpYNkRCUk9sdUJHa1JKbktJMjclMkZreUptYnBVUkprc3FPeVUlMkJHWDgyRk85T2VyNUFFSnRMOU40VzI1S2FKcHJvTHhhdCUyRmF2NlclMkZKVmslMkY5ekV1ZDdsdzJNZWZVM3dnVjd6U1lveSUyRk1tSGk5cDN5dlNaUTBYUzF1WiUyRkpEJTJCZEZzcTlYVmk0dUxPdTdSTnFkUWc5VlRxRHdYdFFhT3MlMkIxSjNuSSUyQm9FZkd6S0lOOEJyNDU3V2MlMkYlMkZNTHBhOThEeU1hdUlrQ2xWc1oySmJKSEJnJTJGazljZjUzRG1mZDhCOE9tblJiTjRVak9jdUp6WHpxaCUyQkVjc3NaWjBQejc2QllYWnd6JTJGcm1qSEtheTd4MWtPM2JYbkpBY0ZEaWtXJTJGcHl2RyUyRktEVHpONHo3QkNZYXk4TTBBbmwlMkZINUpZJTJGaTVseGhHbkw4TnBlODAzcFFHQ3loM1VxMkM5TDNHalNTJTJGZlBvcnpucGdteFV6dHA0WWI4VSUyQmJoQiUyQklpVXFxb29jamZZdFglMkZXRVlaUyUyQndVUWNQVSUyQiUyQlZQbFB3YThzN1liQU01SnNDZkY1WkZZNWJTUVY0RXlSc2JTTHM5WSUyQjE3Vm0xRmk0NkpjU0M4ckFSaUtpSXNsN2ZqNlQ4T0xHMHM2JTJCZkNPWkkzWjQyOUtnSXU5TjN5Y3ZhNTdxbGJ3ZGozc0d4JTJGYWdndDAzbVFOZlBBJTJCdVFWZUdHdVplOUlCOU9xNWk5cXRPVHpkbFN6bllQc01WNjdrNm5tYmdPeXUzb0g2bkJZV0xHb0ZrdUNyakhUTUpVeGp0eEtNYUFIeVlMbHJFQWtBcjkxMjFjNkE1NmtGWjE5U2FZSzVSJTJGY2hQa0RYRFRIaGNxenpsZXIya1ltYnoxakRwQ24xSkVVM3VCVGY5clZIeHdiWnglMkJSSHVkTDJNdDlpMGNYdm40aDdJMWxXd2FtVWh6WVd0WWpHQUpyTXUxTGc0ZW9wJTJCUlF4NjJTTkd1VHdyT21QRFE0cjdFblBkWGhUTDdFQXlqbGpUdVV3Rk1EZXFia0daRmE1Q2tSRFd6VG9LcExGJTJCdXFybmpoa3F0MkNqM3l0ZDlmcmV1a0Nhc2g0Q0hWcDFjdDNkbnM5WXV4NmdxWmFYeDNCNEhpbTRMMlU1S1VycFpVU1JtZGJqN3Rrc0ZhekxJSG82aHA0bFMxOVlnclJld0RkbUo0WVVablp1dFpEOSUyQnpScjNRcGI4QnMlMkI4WFRhOFYlMkZWUDVNb3IzbUNPQnJtaDJ6cCUyRll2anpjWiUyQllBMjFVOXZ0TWdFUUZqWllGdnVBRGdXU2JoSFlEVHNmVDB5NmV6RFdCZm5UR1RkODhvYk5KRWVLMmxOcXhvVVZwM1FGVjZFMkJVcEZ2bHowV1VUWGRqUzVPNDhXRU05blR6ZVpRbVM1SWlzVGt6eGptZTZaYzd1VWVqT0IlMkI2NWFQMXclMkZ3UiUyQjNFSnRFdTRkU0hhY3g1OEl1U1pXV3YlMkJEbFFMSFRDcjVidGVlOUNWOFBZSmRKZ2UzbDU1V0hBJTJGOE1Jbmo4VXBPZFRnRDQ2ajBFVjVuR3A2bUJrd0FyT1JxOGZYRjB6MjBvMFolMkZGRWlGTmhVb0p4MHprTWxlYXNjSXVzTzdJbjVhd1Z5dmRqMHp6bWhnZDdaJTJGTjFlTkhEZG4xV2dnRVlEcjNnamo5TklJY0h6R2tNYW83cHRoTGFPJTJCeVJPenlyYVMlMkZFbFBraXJIU0cxTG5sYTEzMGUyNE85WEw1cjRhMFVqdlYxS3pQNVBZWUpVdHhUTVhFejlDb1NCWHA5clVLWTVFS21vM0JOZXU0S2ZuSXJKJTJGdzFaa1FZNmk2YXNpSzZrVFJWdkVVaVhNZXl2S1pJQ2tqOXAzd0dwNEh5RVBRZGFDbVRNSTZnWXlCbU9DQ3dwUDBBQ0xjRmRVZ3AydVRsJTJGUFRBNWJXVm05WlJrYWp6VSUyQm53dHhvZmNiQWowbzJ5Tmp5NTI4ck9sU2dqYllJcTFrMGVyR0ZUU0VzZENjVE9lUG8yOEFxVnhiZGdYQmFzRHNPR29iT3FuYU15aFJKUUxNdFdwb28ydnoxMFVqWGRzJTJGazJIdFVoUFZDaDRIJTJGVUI2VHpxQ2dRWm0wMXpNdHNIY2E1S2tySlJlcnBhY0p1SnclMkZJc1BmYWtCeno3OVpKTDk4SHFTN0VtS1BiVUZLNm9mRVhzdTcwNk42a1ozTFZaMCUyQlpxdmY0akVpTmZrdWFaZGx6S1FUNmZncTlVTjNDb1ZqcndLY1BUT1h1b2RkUGY3YkZPUm9tN3BQRzJhYlBrRjJldE1IZGk2MUJLNzdTWHNsWWVCRkI1VE1RYXhvR3VuTDBZcERIa3VIYUZhTGpjM1BpSWNpSWQlMkI3ekpxcG5ZYUJYV3VvemQ3ZHRaQVhuR2E3VE5YaUZGV2VSZ2s4N1RGM0ZCMlBpV01oRHVWNTlaVXdORjJ4OGVjaEl4eVJDM2YlMkZ0b2N6d1VreGRMJTJCelZLbXd0bzNva2k2Q1hRb3k1NlhPTTh2SDN6b3Z6Y2pvJTJCdllpWDZ4MGVuNGdPd0NhR1F3Y003aFlmM21RbGc0VHlrTHNSTjhvdVU5bGV4MXA3NG5yeE9kb3d6enZWWUxSS1Y2aHR6SWxNajJ3JTJGZDE3a0U0ZW9tVFpmNVlveGNyNlk3eGlGejVpWGZ2bVR6ayUyQlRaRldOTHNzQlJrTFlZQzIzVTVHUyUyRjFCRjdlNjdaaUhRVnd6JTJCOTEycTFvQ2RUSFNkRXBuek4xbzFIMGNIaktKQnNCSDdQVXlEUjBiVHp1c3QybXRRTWt2YnhHT0pZWHZhUmd0RWs0amY3U2QwSyUyRmhKbWIlMkZkd0NtN2tsOXQ3YlE5QXd5RzBaTTgxU1o4cWx5OFYySHUwTlNBQWJZaWhURzRHcmdjOEI2THJLdDdBYnBjTEhnRElveU1ST2RSUyUyQnJ1RXJVbXJ3aGdBbDdsOGE2NnhESWJPcExaNSUyRnZxZHhLQkpBa2hXYThJQUxNTE5QdHpXZTViWWZ3ekJYaGU3UFBPV2xqcFJqZGxaRjdYTlU1TkV2ZDFsNTVVeXdYViUyQmlXMmFkSElhU2w5aXlwRThkZ2NpT0Z0cTAwOVBUbnNvWkl0aVJQeWdjT01lZEhjUVdPcXVLYVpWJTJGUTZaZjBVNTRIRGJ3clE3SVBRdlRkQmRZWkVqbnhVdTB3SlVRVXhXeCUyQlJoUUNjUnN1cHklMkI5dEZWRSUyRkx1MmNuSmF5WU9KYVNFNkZaeFpXWndaUkdlJTJCcjVPJTJCWmFJUlBqb2E4MlNkM1VlTjl2WnBaSG8lMkJlYzlLQjVIOHl2TVBxWGttT1Y1QnJkJTJGckpRRnZJazByJTJGMVc3ZDElMkJpdlJNZnlGSzJOUktEbXhkVHZZUVplckhoJTJCWHoxOTNwMVBnbFBXM2RQZGZ1QzhJQ3lEeFVvOEw2JTJCM0U2JTJCMzRrcnZEbSUyRkVaTnRzSjlacSUyQmFaNXdkeUNCVCUyRjZTQmNGQ1JQc1VXR1U0eSUyQlFwejFIRGFBWldvOXd5b0JBZzJ4WXFQQjI5YnRrc3ROS0E0WkJ1cXFNZXlBVHRvcGxraElJeXBmJTJCJTJGN0xpdjElMkJ0Rzl0ekgydXJoMHBlQm1CUkJ0ZEhidG9ZbjVydHFoVFJtdWFkWnFqRnR0WkxzOUtYTXlIWXN4NzBvbG0yJTJCVUNQTjJON3VkVk5KUE5uWm1vWGY1eWpTb1BIT1A2S0czY0NIOElHUGR6eXhVY3hYM3FMJTJCY1JLampEdGRsakUyVHlHRXBGbWE5ajlCTk94VjJyS2VRdVhSYzBtR2FGTFNMcWtOTWdOOVY1anFvOW5pdnBRcyUyQk5pUXpXRFdrRXZhOVFJJTJCOHF2RWklMkZEZHV4YXY3QkhYc0FNWms5YW8zMThHYlc1ZzQ0dWZXenJqaFpJMDBwRFZ0N2pkJTJCbklpJTJGdldXZlgxd0ZmcTYlMkJLcXhYUnhabXFQYlFPVXRQVEt3OXBtdmdpdE9wNExoc3VsWkNENG5XeXFZWHU4RnQxTHlyU1RZOUNrU2JsVE1MUkhubW1TanFwbE0yUFpzeGdiOUJFYXZEOUxtWUVhMjB1TnVWa0d6NHZVMXBheEhqNXlCMHBjanA4V3VLQU95cDJHN2FiaTlzcEU5YWdlUSUyQmxTODdBc1ZUOXdZMHZaVFJTaDMwT25yYk50ZkZad3lwaGo5ZlMyaThNRnVsb1FpM1AlMkJlbkI0N01sZzZZMlhaRlFwUFVGWmE4JTJCdXJrTkdZeDU2N2Z4djlyM1VIWXVWV3dLMmM5VmE4eEc1MEdQQkhoRGI2b2RPaG50Wlh4OVQ4b0Y1Slp6OUl0R1ZVYnIxSWkwYm9EVnVOc1MzbWNXeklpU3BSaG5ZczdsUnZHejV5WlB2dm5hS0ZlJTJCVlJkakY1dUJBTXNFOXR4OWFpa292ZWl1V3gzYWt5YnMyUkhiZFdXSGk5RWtrQVBKTVU2UUFzMlNTYzFMZUxkWXVYSUZjbHRkSExiSGhBa1hCSk9nUURyZkQlMkZ4cXhybjMwSndycHVsTXhrVmZ1c2ZXcEFtamVYVmxDc3poOU5lZkxGWmRzS1Uzbkx0bDJTOGVMb3NETU9MVTg1dlBxTkZxTE5LaVd6ZDBqVTZicU1qT1Bab1J4OFRvZnhYTmVCSElCZzA3ek02U3Y1QXE3WlRXa2hWMyUyQiUyRlJYNXIxbklvVFRQenJ0Z3hibkRHbXQ4M1RaVnRPbDZrM1JtYkdCVXI3MWF4dXZaeWNuSnFGSnZabHhKU3RkcmU5T3ZQcDRJMmxSZDhURUIwNXpRdFBHVzE4azVudFZLZWVBT1dHVzNkMDdGVSUyQjNSQkQ0dWI2MElWMFNaNFd0WEFnMGZ2MWhVVEJqQmV6JTJCMFlZQkU2MDBoOFNYaGF6c1ZwOFFvcjd6TldrbSUyRmpaWWNrZlkzSnFvV1lxdmtTcjNzYUlsSEVZYUgzODJuaWolMkJIT3BXTGxGbTclMkZZakNXWHElMkZmb0pBZTVDOG1aSGRlUVZkeEQ1Vk5kZzNuZnZrcFJNejlhaXNKM1ZYcWFmViUyRlNLV0pJZ2FhbHhPaDI4a3JkJTJCbDQ3VFloUEFuV2ZUdjhxMkQ4cXpDS2tpaTFPJTJCMmgwdEVidGRVVWV0MVF6b0E1eU81anhlNE92eW1MMGp0c2tXN0hkUnlONTRWNzVWdmlKZ0lVZ2dmYWxlT2dSaHd6cCUyRmIzaWRPV2hYWGluQWJTRCUyQnZSMFJiNnJHRFZtcVJXbzFyYng0dkRReHZwMjE2Vk9VZmxBWXp1bSUyQjFLM0dNJTJCNXNaSjFpY2lkNmRWY1c5U0Z6R1gxN2R3VDhoME1XQmV5VnRUdFJybmpxRHVMMklyWXpPMlZFV05FU1hObFJTSXR4cUhYT2FZbWl3VGxwOVphekxpUkM1REN1aTE1TmhJdHpHcmt4RzlPaCUyQjJiY2xFYmpJdzQ0RkE2ZnpYY1oyQWI3QnJQZ0s5aXNGeG91WXByZDVNWlNQeUNCcXc0OHJTTjhmS3JpZkdYMjd2TWwlMkJqczFxRFZMdHRVRVFlVU1vTnU5T292bktOMnFpNjdCYUUxbmZZZFVnJTJGaEVmQ09lZnptdjd0c1RmbnBib3ZEb3JJYiUyRmUwbldZRDNZbWR2a2ljbW5QaUtrQ2E3JTJCaHhSbkdnJTJGWG40a050Y3g0c0NlRWxvNzYlMkJaSlQlMkJUNmNoSnZSRmZNN2ZTbEwlMkJLdWVvZXRBOWZzNXRtdGh0dXpWS3k5dEdhJTJGSkMzZUtDYmo3JTJCamFLUUlaRGhiYnZORzB6NXFPRUVIdlpLNWgxMFZQNzF0ZFlYdEZNMmF4eHZYT08lMkZBZ0ZsZHF3ckVtWSUyRmthTDFMQmlzNlZYd0ljdHBjU2hzbVZtMjNWUFAzUmtJMTQ4RFJFbHozcVB6YnYlMkY5cWZJYiUyQkp4RTNFV3QwaWFZaGUxQ3RjeFpKZSUyQmkwOVFia2QwS3Nnc3RWVm5ONjdSb3J2R0klMkZiaW9lVndEdnFnODcyeTVIbEZ6VXg3QXl2JTJGMTVwaTl5WmxUYklZUXJpbEFEWFdpa1FaTE5ZdE5EbFJKczMxZUZDYWJBcCUyQmtvb2RRNHBkcmFjTHJMTlhMenNTTEhsZkxLJTJCY2t3TEtsQlV1WjMlMkJLOXNLbDRXUWZoRmpveDVKekY5JTJCaVhDJTJCclNOMlRmWlZRdktOeFl5clY1dTlKN2RvVyUyQnFYODdUTGJJNXZaa2t0SXA2cnhBRk9LWjVaYmtMbHBMVFdwR29CdiUyQjU5UlhUZ2olMkZ4d3pRbXpBc09uTVFHJTJGU0dVMmk2eXJjUXlWZWpiODJzMmY4SDZGRGp5ZUVuZmV4YkFRTzZlMTdPbU5BWHBobiUyQnUyaCUyQms5Z2NkQTg1SmlEVko1MnlwVll6czNFNUhkaUhRUG9yeFFBVzVMZmZVbU1TRnNFOEtWYUhCNGphNkN3TENhNEwwNlFZM2ZwJTJCQTk0cDdhNnR5dVR5OXg2WGlKUEZYOE9xRjlBdURjJTJCZ2dXaWdMaSUyQmVGdmI1NVNqWWU2MGZxU1VYYkdxM1RNa1oxUHklMkY3TmNJMFRzRXQydWRUMFRVa2slMkJVaFZ3cVJHWjZWSEFPRUFOY042ck56RHQ4em1pWUolMkJtMFRrN3ZiQnV4NU9pbkh1N3ViVUYwTyUyQkp2NVFEMVJmQ1FZckU5Vkw1cm4lMkI3RjFCckcyQVN6c3dqMldGRlZURWdCRmRVazRYa09JUWh2ZzNyVTB1clhvQjNVY1VnN0FKaTVRJTJCcXREcEU1dW9BbHJuWiUyRnBIeWpxY2tzOENFaE5xalhBZWg5d2NEYlppSjluZEdRSktMNUt4UTJUbEc3djE3V0VJSGJaRGJZJTJGa3FQaHB1Vzh4MnI4eEJNWnlaMFkxN0JqNlY2ZFRtc3MwWHlxbUFoN045d0JmV1JuciUyQk81YVFTS3F4TXYxQ29ZcHg3dGMySFR2ZXNxZkNTV0F5JTJCS1M2UGJQeXgzZU9JcEZ0blBqWllSWjFqZkV5eGlxQk5IJTJGaHBYSEVsbXlENkFONml1UVBtMDRHNyUyRkdsNWE4UUVBNlU2U3Z5ZmFWRHlpM3JCYzlUQURMS0pJTlVWTCUyQmJpT3lnTml2bjlWTFB3WldVWXJpVlRtTExYWXpwMUZ5VWM3ZUElMkI3Y3JVNEU4dnYlMkJ1VDJ3bjRCWWRhZWZBYjIwc2FObDdVSDdkUkdkTTd4WjFMNGFoWCUyQjVaMGU1WTN4THlUJTJGbjBUVGNZc0hBZWNBclgzMXVGQjVBWCUyRlZoUmtpU0xFbTJZMDNQNGlVczJGNHVkOXNGTGo2dzRyMHFoOSUyQkxjQ2ltb1JVb2h6alJ2M1gxaFpMNjAlMkIyMVZxOHhMMnkyc2g2OXladkExcUFyUiUyQnZHSTBKeXFRR1JpVVU3T095N011TnpGJTJGUjdWUmdOaWNsWnBwUU1MMHh2M2piR3JpdmVBQ3BRVDRTVWhUS25KVVVVaiUyQjVxNG1YVEclMkYweUpwbjFmWWRqMTE0eWJjJTJCTXNUWWU1NkVIZjd5VXgyMHJnWlJrNWRMTDNrSXowNHlYSUtGZHFqN3pKNDRPUGN6TUlXTmxwa01lRmhYbmgyYW1DYWkwaG01TnVnJTJGTm40eGNFeDA0JTJGOHpCYTkzMEJYVVQ4SGglMkZSUWkyZTVGZFpYN2xxaEtwUUV0NlJOazZ4OTNzJTJGV2hpeDlwJTJCM0haMlAxRmlUJTJCbmNqJTJGJTJGeGlvSCUyRm1LbmpCdDhKM09xRmxkbmNaZEJUcU5aMXVERGliM29GSWNSVFpSZnhiOHdDWSUyRiUyRmFXM09lZW5WY09FNjVhZnIyeiUyRk5jblhLU0xkJTJCUEN6VlFwZm1jJTJCRk96aCUyRmNVb0s5Z3A4R2tQRXRLOTRpU2xYWFlaYU4lMkJBbzAzeWo3enpEa2hXRlg4U0xUS09KTDhlMWxveEtUNTQlMkZZeW9iQ2g2UEFOcDV1U1AxN2ZlZnhHNnptQmVxbDFkeUJaemg3JTJCU0hrdHVYeWJndXRyQm1yVE5xYWhDeGJIdG0zRXBUNFI4Y2IlMkIlMkJ6ekU4N056RWhyR2taN0J5NWhBUE4lMkJvbHVQUXdFRU5JRURNckR2SDJCVGs5c21rJTJCUFlJOUMwZzBkbEVSU2RnQXl0TlNzeGR6Tmk2bDNtYXhTOUx5djZmd2VTYjBIQSUyQk04aTVZSzhVTTIwZ1pjWkk0aktKc2o1R2YweDNIJTJGdGFYc3U5dGF1eDlFS2ZiTFVvckpXcHQ5UyUyRnJHNTBudVltOVhrbE5xdVFMM2xXWU9qMHpWWVhxTXNnUCUyQnNPSDA5N056WDFIRjJzYk1wYlhudGdKa2VmeDVGUUs1NVJQbjNReWE3RDhpUXJqdkpweG9PWExkU29XN0hZdCUyQmFnTnF3YVFuZXJoc2pBS3ZhTENhYkNYRGJWT2VJZSUyRkh5VkVySUFLZkEycERsRnQxbWJ1aGg3RzJsR3FVT2Y0USUyQjJTOGo0JTJCN29jem85WXgwQlB1JTJCMWg1UWxJdXNnOTlyRUpaa1VGWGwxc2x6bkJ0WldjdXJSaDgyOU1oQm9QR1ZOSWpzWHNUclNqdWElMkY4U1olMkZUY0NGS1JSd1pjeHQlMkJYVGpJeVhlaXI5JTJCcU9LWFhlOGhubmdyWG0lMkYlMkJLRUQ0N0xqTjlCMDJvUW55MXViZVlVcVhQZ2JUSlVSNU50T3oxN0cwVjNZRXlyMldha2YzRkdyaFNhZ2pRZTZBajBvNnhxcSUyRjZubSUyRjlaWmpCSXB2QUZCTzVzUmZMZXZyR1diNkpPZHNNc1dYbURNVW1aT2MzJTJCMnQlMkZ2WWYyVGRVOHZaMDBad2pBdWdoQ1R0bnlpTDAwcWRXdWt4c1VpJTJCTkV5d0pwZ1R6NzkxSGRBNTB3RXhOanB1dVNTbTVCdGk2MVFLTldxbWhDREdieVA4UnlYajVyY3V6dk11bW1FNzYzQyUyQmsxMUtZcGclMkJ2JTJGUXBvMm8xc1hMY05uaEtOS0taSnc1ODdBUnJzSGRhb01nVG9DS1haQzNaaXB2TnN0WDVIdjdpOGJJYWRwWjYlMkJXN2RiUW9ZWGgyd05UQnFXcER0JTJCTjVQTTQ5N3NMSUhVbFVHSkxma1pTajI0TzdCYmY3TWN2TUVqN2JyZmVScGclMkJyVjR5NzN1dTJ5MW1xdWhGY1VBakNqbSUyQjdyczRmTUdzRWolMkZSSDZuNTBzMGFVUk5EN2YzSk96eHZjSUUwcVNQZGJHT296aWhUVjdiZ0FyVXVlaG9qYmFVTDl3S216V2xlaXQ0YTNqS0NXT2JkMEx6VGFSY3RqN3hZNFVNMlNoUVZNUHk0N1l6N3Q3bE81eXBKSDBtVE9FNjBGZlBVWjBLd3hwTFlyQWF2T25uZjBEMzRaV1RWRFp6b0x2cWFZaCUyQnJ2a1FrNmFubDlPQWlsaGM0Z01Pd08xT3h6Wk8zVUMya3JtZDE2N3ljbXE2bGxOTTZNeVFhem16bzclMkIzUyUyQkZjeEl6WDlKVFl1SWNma3VDeXBSQ2YyUFlPJTJCRjIzV1Nza0RKQWl0RnVXMmkyOSUyQkZsV1dIbVhlTGpxcGlKN3E5NWJaendReU92ek5hblBOWmRRRERzNnNld2RjN2ZONXo3bFdjb3owYlhUNFc4dG5UbkhvU0EyVGVTJTJCRllwUVBhMWRSVng5SHlrQjhWS2V4YmFjOFZDZFVPdGNjdHl6R0R5c3FIT0Vnek44SWlGeUFWT2Z2emNUSTJQaGl4SjRFVmRrcTNiWU1JUlpPelNZNjdMSHUydEh1a05UVUVwdDFxJTJCJTJGa1FGVXpJN1RJbDF6S0k3YjBscXVBbEZ5blVGbUVWa1RSbSUyQkZiR1hMb2NpVEZiNEk3dGt1M2NZbzVKU2VKdnJzNkxRVHlnVFRSVWswdHZJV0hwOFJTb0oyTDRjUHFQWHVhVEkzWjNTcXlYcHRWbmU5bks2RnFRWkJYeFBDVGVUJTJGJTJCJTJCTldGV2RmQzcyJTJGaERuVUtmRXR5OXZxdG54aTlnc0oxazR4OGl2V0prNGE2REJ4TW1EWGg5RCUyQmVybUxaYmFiYlBzMmRpMkVvWnJad0pyVFlZbnI2WDMzeTFhM0tJTWxKYktsN3cxb2JmVGE1NVVOM2VWajI5ZEJ0ZnQ0dEUlMkJUTGRkTXpNUVRCdG4lMkJKTHloUUtCY0paelh1Z2ZLM2xMQ0h4NGRHYmNWcHA4R3owJTJCV3YxT3FDTllSaUxVZGFQQkxYcWhTOVo1T2VDR3Jwb1ZsUFolMkIwbmdEa01OZlRLaDZZaHhNNkUwTXc0TU1qRFBqMGF1UHQ4Q2NrTDgxallWRWJrWmI0S1oyQ3BzRTdheGUlMkZqbDlBbFYxTTNoMzIxRFFwVCUyQnl1YjdhdTdyNlVwbGZRRmhRaGsxMlhRUk8yJTJCc2IyUzAwSnhLZ2oyczlEUTFaSTRVTE9jVWVDMldlJTJGRk53bmhucjh4UENxYTRUell5VURRSG9UVUw1bEQ2RXZ2d1ozNmpHQURlZTJ3M1RnNmZtQ0IxNUhia29hYVh0cWQ4JTJCY2Z0RUFaeXl1TEFzTXQ5S3YyZWZzN0ozJTJCaDZieEVUVkFST2FYSXRsUlJqNWUlMkJMdWJaNUgzbXlVRyUyQiUyQnl0MHRybHU4eXRGZWZnZHc0RE43dlBRNzk2eHNDazY1bTN2TFBySUVIT3I2djQ0RkQzb0ZhZFE4SHIwcUMlMkJHMVVXZ2phbXdzR2JwOFBlUlFpd1J5aFVDVzd1REw0cUxtRXczUDR2ZUplbVZBTVhMNTh5a1YlMkZTWmQzSWpVNlFBOGdrVGRFbVRNNmklMkJLdkRscm1TMlEwWkZoU1MyQ0Y0ck0wM0pZM0dza1hqeUJaQWVnRUhxZHRNZ3BQWGJ5ZFV1YWlYYjBiaGFWR2lSRlpuTW5IM0psbFAlMkYlMkZOak15OEJ3VHJUS0lEWiUyQmVVN0pjOENUY3JxMk03NG5KN3EyU2JtR1dtc0QlMkJwUDNTaDFBNyUyRllIb0dMRiUyQlBoNXZiNjI3U1BOR1p2MCUyRmFlSEk0R1p1dW1uZGV2RXRSMlVJNjNUU0pWc1BUdmRWNHI5YWphMzZTZmZOaURLSWdKN2olMkZyd2pIOGhxdDZjMEpTWlVnS1MlMkIzMzVqZjlhSW5oT1hBREJXZVk4d0FMT01EJTJCNlY2YzhORDVpTnVlJTJGJTJCdkJhVUF5UmY5WjdLNFJKbUNrbm90T3JPNWxoemJWM3BqUldwRFh4JTJCU0pxQTR4OHRobkFWV2ZRa2Q2Q0F0Q21TSFBXNnR3eDlYMlZ6NEhWZGZQSWhZMjhKV2g1ckl6NDl2ckJEMCUyQkN0R2IxdDV3Y3olMkJlQVRucXRmYyUyQnJXZjlsQ0lUOGZpVTZnUWVXJTJGMklqMnozUFVWUUhTdE5aR0d6SiUyQnlrbyUyQlFYJTJCTkVyZDdBeGJpdnY4cWdBbVVlMmptWW9UeUpWelRsNzBTd28xaGtPJTJGWllXZFFMOGUzSFVDWmZIRFdoc25RWiUyRkZ4WG0lMkZPNnFyRVVFalFiSlRqT2JseHZxcERHcW9LWlZYbiUyQjVPYk5NTDg3OXR4SUYwJTJGMHJqVVFNWFk2NVJLV3lKTVVvd0VhVjN3bnlnVWRCMUdlVnRaWSUyQkh1djhWYXU5a2JwZXR3aTlnVHZmdkVOMTVlaTFVVjN2RGFIYlh4Qjlmc2FsQWcyNWhSeW5hSTl1WnVQQkxqYjklMkJSbTl0dUxaNGo3S0glMkZmeTNBOXglMkJKbERPamxMSFlTTmtqcjJ1WGcyJTJGc096Ujhxc2hVTUJJOVYlMkYxTWdzR2NOcVpNeVdWZzZpajNseVZ4a1kzUGxuWmNFVVM0ZmNYcnc2Q0d1b2trRDFQbEp0dFJJMWpYdnlDOFYlMkJyOFBIcXpsOFZaemZGU2trc3NXZmlQTDFGTzVzU21EcUJSbHdRZlNMJTJCM3pqJTJCckpzeDhJQXo2dUtIbWZyNUE2bGxlWWJnZXZaSTc2bTlza0ZodFBUdkpTTDdBViUyRktPNTZ3QkxNamtpcTkyaSUyQjB5d2UlMkI0UkZRU2ElMkIzekdkR095YjJ0TVN2Qm1JV1VXbWpNY1J3N2FYcFpQWEJnUjExQ2NQMDR1Y1RVczIzNGJvZnQ0eWxoMnBNSjhJd3IlMkZhN2FRbWs1dXk0RnlFZEFnVm9TWGFTN2RLM1dyNG9OUSUyQndVTEtQVDNZZ3hQdmFCREU3MDZRNnR4bVdmenUzVktzRzVUZyUyRjk1b1k3azdkVG14MUh1UmY4ZjZLZnNnMGltVkVDUUpCJTJGJTJCaEM3RURGNXpadWVnNEt5bG9HMG9rb0wyV1kzenNVOG5tS3MxJTJGT29rRVUyak9TNXVWZDh3blh1bDlhYjg5Q3dFMHduYjBhYkhkREdjOUxoMFZGWk1hUDh4UE5wTWx5JTJCRkNEaFRaSktKTGo1cXdFdWdVWExMd2Y4UHFFM1dHSWhkV1JCZjFCdmxyV0k5SzklMkJLMjRCQlZVb3JuQ3AzWWNkRkVDbjFmSUhrelJaaWNGY2NSUWx4eWxRWE40UFp5VFFBNlRJcXZUJTJGZ2ozY0FiUVVkN0M5MCUyQk1iTXBJJTJCZ0czaUNRcW56SyUyQiUyQnpGa3BkcjcyOGVVS2dTMERNYU5sekl3QWxpTGsyVlYxaiUyQiUyQnhHMk0wY3FvJTJCdVBIZnhOVktGeksybzh5SVZCZHBMbUZoOVd1Z3AzVDFibVNDTDdKJTJCVmc5T2RpZm5wcyUyRmYlMkZRJTJGJTJGeGJjUWNQQmpTUmhsSjltQTBWbGxkM0R4TjhxbkkwTVglMkIzJTJCdDVJUUxFaG1vUXdGOGxkVjRFdkI5bEdXS0Y0aiUyQjdzOTBBJTJGTjNCR0tKRTN4Y255S2pydDhlcUc3YzZxYTg3TkNENmxtalpVamFsaDl3M3V0YTVGRTRkb0VpOXN0azdnb2JOdVc0Y2M2TzdZYyUyRks0cGIzaGhRWWVpRUZOSFJHdXdxM3kwTzNWNmJCSmRKVVg4dFlhdTJudCUyRnNDdVo3OWFsMkNYOSUyQklKTThoZ2ZNSTNvczEyaWEyS1JpZFZ5ekFtQnQ1d2VQTHF0cDk4JTJGZkp2MWYlMkYwRElnWWF4YnBpRjhwWDR6RndiOVI2SEglMkJiOTc1RFpXZkxSZEtrTENzMm1INVF5UDBTZkZzbHBHam45M2pWRjk2dHBNckFXaWZXa2lNckR3VUQlMkJWendzaHdjMUwzc1BZckIyaUdnaW81YWZ1S0wlMkYlMkZjSHlkWmpjVlAzSEYyd2tWWTU5aGRRUjRVUm9WQ3ZTREV1cWolMkZqNCUyQm5vJTJCZ0RwUTVUTmloaGluNVhseGw0U0F4bWlZR0p3bmpTbkE5WlFpT1hQNSUyRmw1ajRtUUJzTWtSZVY1QlRib2lzWHAwbkRIekluTWhpJTJGJTJCQlFhaGdYMWRTelNYZzBRMVZtN3hzQjFQJTJCdmEwelVnaXIxV1c1cm01RWFmNTlVaU85aUZmbUU2JTJCR0VpcXRraUZ2b09wWElseWZqNjdOTEY0JTJCUk8zdjdTd24xbHNyUlhaNExxJTJGU0h1eG5KVWVhZ0lmUGhvUUJPVWtGYXlQbGU0bGVZQm0yQWdFVG9KdTZEZjdDc0VwdzNDQ2lhOWQ0V3dUczVtbXhRQmpUWXVpYW8lMkJCOWFYaTJXUktJNWh1VGJtcWZvSGhmNU94V2N0TVhqN1FPaTE1JTJCQ0YlMkI4ZHFIRXlZb1QyNEpoMGYxZmxrSEk5UXhFcjZ2b0MlMkJkJTJCeWZtSFB0Rk1yQUpxTEpRdWdFR0pJOFoyVGVIbENXQWNJJTJCOThuelBJME1WS3duaEY5Y2pmSVFCQnZ5SzFiYWJ2SzRZSWIwb2hDUk1oJTJCOHNtRnh6ZVpUUk4zYkxRMDRUZjBOJTJGaTZEQ0tyMjExOVNKeklaZVk5WEVwNGhjQmJIQjh6NEElMkZXQ05QeFR1Z1NXdXZLV2VjZCUyRmE1R0ppMmNnJTJCSnZ6dGx3SUx4VFFNOUlJS2pQQ0JUN3BXNGw2bnpPJTJCYVVnenVETzhIYiUyRlJ4ckFXQmJHaWlFWjcxc2xQZjgzJTJCT2Zsd2Jxekd1NXhibCUyQmFpZWtsQnJGYUNOYWpxYUU5a1UlMkZMZE10MWs2NkhWUVBRUGZINUwyWEM4ZjlMJTJGSnRxb3o0SjRTdjA3TGFCOE4zVW5zOGdIRFkzJTJGVXhqVmZSOFR2YXd3JTJGd3JWRklBUXBrRGIlMkZlWmlvS0VSaXNTcEVLUndVOXRKemVwQWJaNSUyQlFlJTJGa0kyQm9KN2NrZVYlMkJORlpCdEtQdmhIZmdicnNUQVhOY3hEZ3YlMkZZdzZIUDNYSktjNEFKTTY5SmdTbDBzZEpZZ3hUU3hYZmNTV2ZGRmhsY0VScUxyVm1VNUVYc0o4NmFjQlpCeGhDd2RYenk1UmFEWHQzZmlCVlJxRlFRT2Y4aUFhakdBaUlmUVglMkJNeVlIUHJ6M0F3RGI2JTJGWWc4YUt0V1NBVkpIQjBrd1k1eCUyRlVWMERlWXJ6MXJEYXdtcTZKekxSQTlPSHhyM1FmYjJVc1JhSTZQJTJCa2RBUFZtdHMlMkJwMWZ5OVV4UkNnRms2SzNhSGxZJTJCOGRLQ1llSHVDMXJkYSUyRkl2MThUSyUyQmxjJTJGTnZYUGo2UEljJTJGaVNKQkJVQzdFaXVUVlFtaXl0OE5rWWZzdk83Y3BLSUI2dG5KSTN5elFtZjVNenhjdmN6d0xqQmpHa04zb2kwamlrWkVLeUVOR0wlMkZwdkFEbE83bG9CWjRRSmw4U1dSZmhVSENPJTJGeUR0eFZKOGFPcHFYZFFGaDMwRGs2TnVrN2duV1lZdTAxWGh5Q3h6dE5hY2UxSW5YVGRqakYweXUyNHFYWlRVJTJCUW56bUx2VHJ3TE9HMjVudXM1UDM2VWFuM2ZnQ3B2bmJWRjdTaHIlMkJxVWFqJTJCZUdZaFU1OWZhQUFIa1p5bU5hR2gwVjdjMFJ1QlFRWCUyQjRydmNydnQlMkZkdWlyY1hUODBRcXVQZ28lMkJuVzNpZ1N0VUVoV2ZtdFpKbFNid0VYS3pPajJSdXR6N2tKRmxYUTNTWGM2ZnZGeUhHUlV0RDE2aTB3QzBNeExBZ2NqaFIlMkZ3SGZQaE5iWFN0cmRwQXQ5YkJnQlAzTEJZUyUyQlpvV3ViZzNwZjVGYWxpTnJvQ25ZQ2lzdGl3MCUyRlFaMm5McjdmcnVkeHdxbGFxWTN0d3JNZDhORllpMWZURURuRnd5JTJGdUpobm4lMkZkSXRSY2ZQeSUyQk5ocFZPbWVYOVNLJTJCTyUyQmU1NE11VDFBOTIlMkZUcnhsVVQzWnRWY3M0c2wxVSUyQnBjOXRCRGpkblU1RFZLNHFHUkw3U1FXNVRBdmZVSUhraVJZbTVleUZiczVvVDNiMVBROWdnc0MzWkMlMkYlMkJkMnM2bVc4SSUyQjc5VmFyOWNIUUNRZzExZzBIbFlYaU5yT1czWG51eFNjMWc1c21SS2p5dmdDM1hTSkZWaiUyRnl1ZzBFZTVmNFpZZlUxSXJjbkE3WFdHVFU5YjElMkZpbnlxUyUyQmdzVHclMkJoWHM3c1hQV0xVVDdlS3ZLdm1HSkZBNkVoY3JRR0l2VHFXSjE1aGJXcFZCU2FpbUxBM3dReDZ2NDltVXA4Mk5nVjdHeGx6bWgxJTJGNElqcnltOFNqZDNFJTJCJTJGSGZVc0hEck1rZk1tc0YwbXBSZTBSU1ZJMHYlMkJSVGtSViUyRmE1anV4RG5mTDElMkJ6bktXdWxqWFJxcXdJdWdqSEhEVGR1elNMN2dhNnh3dkM5T0hwSzklMkZRVmxRVjhpVSUyQmxGQk5Ta0dSWVhvd1FycXZ1M3YzbFU5ZDUySm9YUnBkdnE2bzBTMGlJRTE3QWRjWVAlMkJZQ1pyMERHQk9IQTQxWHpCRExZbWY5WXFwaDNEQkpFdWRSaXkwVkJQZVAyVG92SGZ4QldobEdJVGYxQ05GJTJCd2VSUlcwdGU3clRyUUY1TERNYmlCNmo1N3dQS09DTHBjVCUyQkIwVnowUDVuTXg4ODhscXhuM3YlMkJQN0k1NCUyQkhicWNhQUV1MnU3U3daVSUyQndCUWtsZVpRc2tYbDJoZ043YXRTWVVGdWlIeHlteVFJbEI3d3BVekprbER0NW02N0dVeGVOVEdjTzBESmZpYkh2MyUyQnN0dkxRa2l5SnU4bGVnSmNISUNscU96YUVWVUFmVmdFJTJGZXhDWVAzMGkyQ1hnbTF6d2FGdUNjNkpRbGNxSTE0M3FVUGQ3TlklMkZPVjhsVmQ5biUyRkZwV2NWWTlNJTJGYmFRRVpmU0E1c05HbXh6ckx6QyUyQkpKc3VpcWluNEdYZVRxVFJHcjd5QVUwRTRpaHBMNHZjaHh5WXc1NHZrUzY5OVlBUDFiYTZ5cTJURU16RiUyRjNROUZEMkpJWDJPazglMkZqMGhrQ3JnNEFIRVByN2ZLS2M0ckdkM3NYNDlyJTJGVzN0eTZxWDdidmxLend5dUJrVEtOWkp4NXYzeTlQUTZjNVlmUDY4czU4RTc2c1VzZUpjeVZhNHlBWDZmdjVRTUpZbnZFV3VDJTJGR0dYVXk3UXhRWDRTMlNIMGZYUXhIU25yQm9mZmVQNFVodlJMeXM5elplNks4OTRoanl2eFhxRjMydlVIT05ZZTl0OGtkSTJaZ3o2OTglMkYwVU1OVUYzODlwckRLWGh3NGZPJTJCT0tGRXJuek5kcSUyQjNGOUR5SSUyQnd0aSUyQkdRUSUyQndtdmtOckslMkJveUxDSzUlMkI1bjhHdFFhRk1nRzhuNEZTN1o0Q1lOUEpid24yVXVrZ1c2QUZtT3I1QThPS05rZjZKc1Y3NDElMkY3b2NsQVpBZVJkUiUyRjV2d0ZRakVQc2FDMm1nMGNKOVpGd1NQc3RxVWFwVyUyRlRuOHJ3cndOd1VGQTVGdHJMMTllelclMkJzZFpZQ3BBZGVjVWIxdG8wak9xSzRlRzR4UW5kU1hwMEY4WTZ2UG9EU0tzUXdJcWpvSVN3ejFsbXpuS053Vm54N0QwUThGSkxXcGFuaklMMU1rZmElMkJmNkd3S3k3biUyQkZlYjFEM3ZwdmYwZ3J3JTJGMlRkZUtaS2tlc0dYcE9DMm9YanA4NWs0V1NMMlRRUDliUTdHJTJGTFBTWmJEV09JeHRDMFpRUzRhNUM0N0pjMzlVQXNXMDRXVndZUkFybjglMkJvNzBmVjNmRnYzWDJiMWdmbEx4V1gzTWVvOW5KN3RGVGN3bTFsTzNlMExZTTRKaEhJczFkTExaMUclMkJ1SkdyOEdXa3RQcjZINXVMMG8lMkIwYWN3Y2lkZCUyRlAyT3dycCUyRlNZZDlBeU1nVWc3ZkxrJTJGUUhTOHczNWFQTkg2V0p0ZEl1VlI4ZzhBWnhsOUtoOXNTZ0xMSVZJUlZXRjlIWk1vVFlmc3dFSnZLSDlwMmIzTFJOZm5WaGVFdU9hYXF6dGdBNFNKTjNUaXElMkYyJTJGYjRGRDhUVVN0VXJ5RVQwamxsRUpJRG5uQnFhblNzMFllSiUyQlBRNHRJY0g4VzZ3WFBOc1VNUzZPanRMTyUyQnNxcTFNWiUyRnZLSXdHbTBJRXgzYXdkTEVweHRTczZMVElsbHQxZnhwaklueFA5eENHWVlFdmxscDFjU0xrQWRDMzI5aTZqMDNhQVczZ1JnQ01LMEJRSG5ZOG00MGh0N2EzTkltb0ZQS1BObGk4MEVQSjF0UFRWUkRZUGwzcm5GajF0UzExRmhjUExaWTI4MlZMaUpkZTlqbHptenBMa3liNHM0bWFhUE9QaHVjaXlKZEQ3SEZuT1dEZGZ6ZnJHeHEyJTJCTjhFdmZ3QjlnSXZUWEl1NGpwMXlrYTlDJTJCTDZFTmhGSmo2b3gxaFpXMVRrUzc1Wkhub0clMkJXR0gyaXRON2xqYyUyRmk3cjZZUGgzYUNla25vTkVHM0hLTmpjWk9UbWlBcGRWTGZKeWolMkZldmdXM25iJTJCc0JVZXltU2Zmc2IxeiUyQjFKNVBuQVRWMzYxUHF0cFpKJTJGUXJ5U1BmdUdQbEJHNURlY0FmMlJQNGtnRDdVc3VNbDRndDQlMkZUSEowR05WUDVpTm5ZZ0E3dFp0NGtQeDlKTGw2ZVZRUTJEaEV4QjRnT05EYmk5dlBXYm8lMkZoOGp5MFdxNm5ZSmNsT2tRV1hnczRxJTJGUFN1eCUyQkxTTGlkNEVRVmdSamtMZ3lDZWtQbEswbk5zJTJCNWpUQXpGN1pVaGpzbVlHRnElMkI2OTlsM3B2TzdPcjJKT1NHUXhveHZmWTFpNzBWTjdQb2kyMWliRSUyQkgwOE9mJTJCJTJCcHExdlZMYmwwN1BaSmJ6bXdSYUw3MGtlWCUyQlBrd3pIbkR2NHlncFRvTzVGekMyVnI2VGtDT0hWVHljcFFMdllpYkFGZzE0NFF4aWFhbWVYSCUyQlU5eFZWUXJ4VEFvaUpvUDIwb0FEenBrc2wzOWNMeHFUclk1ZUlXVWRPYmtGOWRrUlVRT0U3ZXNPcnZGTU5CcjJ5N0JjWUpJNWF6c0tydDAzJTJCU1I0TkNxUDQ5V3puOGpOS1p1VHBDd1NxWmNXJTJGYiUyQlVseGl0cGJFa3k0RkN2MEZGM1hFMSUyQkk1SSUyRm5aM3olMkJZajZWSmhRRjgzSU1qWE9ZdUJGaVQlMkY1QUY1RUdLeldqMEh0REdzN1hyeEVBZEhhaktudUVoWk0wRW5IT1FZdTZjZmJlbWljTExPVlQ4dGQlMkZWZlBnQ1hNJTJGdDVzRUZyU2JSb096MEYyRW4yYTBoYkI1WEVjNTJBU2FsT3B2Z3UzYXJhSWxxV043bmolMkZrZE5pVURlb1RwcGRVYXF0Zk16cm4yS1ltOG10eUhEMEI1QXA0Vm5xJTJGWkx0T3BhQUhZbXJScHZGa3F5Z3NuWUVqVUJnR0MzaGRXUGhkczJYVjhJdlNONmNNWCUyRlJkVHk5N0hJalN4aWVWSTZCVDdxS09KZ1h4QlJGRlM5UWc1TXM1WTVMYyUyQnA4ejh4JTJGJTJGRzB3MDNZNGhLQ1paaGx2ZlZ5bEdKS3RCdTBoTWxHZDZQTmRVdVdiY0lMbzZnZ1MlMkJhTTlaSmkyTiUyRnFKUjJkbnFGc2lyVklUM2VvdiUyRiUyQjF0Um02NU5lSDlzNWtvazAyUEpyYUxiV2wxaldvSTZ5WTNBb1JZSGRmd3RndU9mZHFVQVByb1Brd3hTN3FmcVBzQ2pSb2VueUYlMkI1UUxGam1Va25YNEVocVJxbjU1VzZST2Z6QWc2MVVrZmJUQ3h5d2dWRjZuTGI5cTNjR2w3JTJGTzR2UThmM211VDJyd1dHb0xkVDBpbXRWJTJGTjkydVgxVDJKJTJGd0pkd1lVcXhyUU03bzQ4WHphUDlpdiUyRjJydFRPbjF1TyUyQjNWWnJpQTFSd1BraWVScHFCeExYU2hHNVlxOGo2NktvJTJGbVBPcTY1WVhCeFNWWGolMkJ0eUg1MXZOWFdiTTlpWHdNTyUyRmElMkJQRzljNjB3cFZldmtObko3aFRSRWw3eXBZWEh5RzR2ZEJPbXpoRHRGenlmbE5uSGQzNnFEdFM1YyUyQk5iWkQlMkZ0WTVtOWliTyUyRmxzcmhDJTJGVlE3UGVrUDU4cFlNMTVjNm5zZTJrbiUyQk5VZjk5NjZEaDlWRjElMkZFNDl3cEthR0FuMDJkcVVrRTBCSmpDYjlZend2RnQ0a2g0SWQ0dmdIbVFraUZzNU5iandKaCUyRjQxZGslMkZvS1J6JTJGUjdHaSUyRkZxRHB6b1J2UVl4NU1GSzVLaHNQcEUyQVVNUzlMMzFibGJDNUlNY0ljS0J6JTJCUzM3akhSVUJiUE1ZQkYwdExvTFZKS3FOcjY3OTl2dHFKa0ViWG92bFJVSUJTN3dJWUtDWnplJTJGUDFwY3RFU2xDR1RjYmU0ZEdOMEp4cmlaUUJacmVvUWg5WHFlRnljTiUyQmxjdE1QNmRBZHQxbzVyVnclMkY4d1hjVGo5SXhhWEVBWjhqT2t2dHdMaDVMc2pFVDFuTUhaQldmMnJFVHVPdG1DMHk2dFh0eTAlMkJYTGVkM0ZTbmZWdyUyRnJsOWhDWGp4SSUyQmZpY3M4U1FZa2RLalVaRndxRlk1OVVVblBRb2QwUjdDJTJGeWp1cDA1U3NtaHc3UDY0c3ZscWNDWVFJaVBicXhCJTJCYTVjZDZuRTl5ZUh6cjFidiUyQnU4MGk3TmRFa0I2ZzlpRGVlV29aQ0lPUlFFOXk5JTJGSzY2VjFJZmwlMkI1YzJrVWlLWmVRMmsxZU9ZbERpRGlTRnpuc0Q2NFJvdVhDNU1JMyUyRlVydDh3Tlo2YzNlbDJ2dXo2S1gyQWFDd3dtNVVTWVN5dVhyeUNvU2pDZnlNZVlJejk2NXpSMExQRHNqOVI5bHQ5bEdiWDQzcG1pUXphdHJBVGhRYkFlNkp1eXh4cEtDeUNTNUhlSUdQSmtaV1AwSEpvdkRldkVRR3ZXZ3F2TDlXWnglMkJkTDNaaUEwY1hGdWRueDRmN1d2amIzZmptWjRoU3V0WkdFZGxBVXZxTDdoajVKZXRRUyUyQlhDbkNLZWJUQWQ3QzFtbVNDSk4ydzlCVzhWd3F4SnpQT0tWRGZLJTJCWmJmR1RWTmZYWUdqeW9LN2JoNTlTazdCelp0QjYlMkZkY2JtSDZodmxSUDFqS0xXZFNZc21YRFd2UjN5WlllQTA5RyUyQnlpdFoxRiUyRk4zaTJDbGVLczU2U2R2T0cycW9xaUZtcmUlMkJuSTVRQWUxM1pGRVhic2xwdE1IVDFSamZPN0FSanNMcEtFT0lQZ3FmMDhnTVB4OWdEczBzSUJ5bFlnNlpIc1NYNkFTd2lxTHBNTW5ualVuMU83cCUyRlR5TUkzb01RUHU1ZVRHMFJNdUI4dW5IVlM2diUyRmNVRnYzYll3YWtvek5LcEolMkIzNFRqR1NXSUVpZmxDSHA5WWFhQ3gzWnFQaU01YkVha01TRTRRcTkxbllVak1ZWElieSUyQnR6bkx3cHB5c3JpRG5aUGM1blU3UklFTE1POGFIN1RSeEkyQktaSlpiWmYzUUM1diUyQmFPQiUyQlBBRG1hbUxUWnNONzRQa2RnY0Z4U0M0ZzdEJTJCTXNiMWxKbGExQXB6MGNWdWhmM1N4N2lPVWFGUXBnT1hhMWtjS2hmY3lmYmYyVDI4OWNENyUyRiUyQkxvOXdGaDBxZGF2eHdEak53ZHAxeFhwTUdTdVNRTWM4V25KOVNJT3N2Yjc2ZDl3MG5sZmZ1bnNXYnVKRzNudnFiWjlRMGExUm5kNURESE5ROEI2cFA4RXRFJTJCOVpYNnRYVmsxVmw0YnA0aVp6ME83bXNFUiUyRnpid1pyNjRGSGtYRU94dGpla3RhMWtiVDhzMlh4a3hyYSUyRm5LU1NyQndBcFVMVEZ4WFpiclVsUEZXUlFZJTJCaWszVENHU1N5WHZ3WmlPaGs4U0VTdVA2YXgyaEVmd1Rsanp5MDQ2dEdYc0lxU1VvVmFEWHB3czllcVI4OWNsMFl2em1JM2JXTFoyRjI5QzNSWWhKRkVYZTNLek9XYVhBWGFiTFNXeEJ0UGo5dmZnOXNzSWlpeUR6dEZ6eEJWd1czSEY5c0RMNFQ2bDhGbmQ0JTJCWXVCcEglMkJTQU5QMWpTTnFDc3FvaU0wQ1htYnhzZFhIWmolMkYlMkI2dDFkQVhkNkhRS0tDa0R6VGx6VGk4V0FtaHFWMUxGeklJTFc5U2I5RmhTdjlpZ3hzQ0FnZGw3aDRmdjFXalIlMkZacHhhQXNFUGhGcE9Sdk1sQ3ltcWF6OUZpeG5XejN5QlVYQWJoZVQzRHRsbmk4diUyQng1QW05N2V2UkprN01icTEwT1gyNFZsazlXR2hsNmxKNWZrM1VmOXg1aVRHaWNZaGNGYmNuM1FTYmp0N0NrRnIlMkZxZ1l5S2NzTFkxbFZRTDNnWmF0NjhTOEZNV05RMyUyRklpbjU4SXZmd2NNQUVHTmFrQ1FWNzdseVdXNjQ2dm1YNSUyRkRvVXNpU3ptSXg5Y0dxWllGeGtvZHZFSGo2Y0FmWFRKYkx3OHJJMGdNalh5emFQN1VzbVJwemtkZDdWRiUyRlZIam5tSmVWMmpaVHhIWlBFUk5CcTVpbW15V0FQRGVtcENla0hCSjM1OEpReTducVV6eWRFUjRvajJpcUg1ZVdqRmwzUW1IM3N3QiUyRnh1SVZ4JTJCNGU4ZlZneGRDWlM5Rlk0eCUyRlRmNkdpT3pxaG9Id1VzVmclMkJSSyUyRklzbFdzYnhaRU9Cc21aa205QVhXVGxpRU5SS21wZVgyT283Q09RSTMwTzlxaGdNOHo0cUt6MVRUd0tPbyUyQiUyQlZPY0F6SFlZV2dEc25JMjZrV2dtaElJJTJCRHZFT0ZUdTAlMkZnck5rMkM1YVk4Rm81NnJ5RkxMcnZqUEdNOGo4U1VxMEpyZGdnczZmZGxUeVRwaE5rWHRtNVBrejVQb3hMbnBXU2ZLMzJjSXpsODhNc3pVNU1pRWNMcDJVTEtmT3FvNWYzdlpPNjYlMkZkUm02eGglMkZ5MW1LVG5GUkVxJTJGVERWVDlNV0FDUSUyRlRVaXlYJTJCNElWN0xHRDlFUnVKSElLRnJOeUpoaVVURThMdEpEWjNNOTV0QjhYdEltVkVkMCUyRkFNTkZwUlBYWGVCZTdJbnBGJTJGWDhVTSUyQll2SnZFSGYwWFlETmVURUpLVDZVUHVzJTJCaDljV3BEN1BDRmFQc0thcGdwbFZWS3NPUllBJTJGQ1ZmeGN1dEo4T1piVXJJd2REMWNtY2RoVUNPb1Z1TEpXMTJidHB0bHpqQ0VMMmJRT1dQeCUyRmN6QnREMGloTDA2WEhGVHg5QVJXdk1pbU9CRlZ5QnI5bHBKRUo4TWtNNVRLdTk4clhNSHNTN1ptcjhZMGtVb05MNE5PUGxGWmlzazZMNCUyRnhZM3VHVEI0ZWhtWUV3bDBKZiUyQkh0dVpWTUl3MWRibUViWU1xZkMlMkZuaDdOM1dSTXVaNzkyd2g3cEZJMkxsaHZIUUdUMUdlSmY0S0F5Yk8lMkJzbWs0Zmt5bUl1RnpwQlVVVXglMkYySjdTcTdrUDM5NUF1dE9XaUpIczJCNEFreGp6aXY4b3Z3SEYlMkZDT2V0d21KZkxNWjd2c29HWnlpVW5xUzhVdWJaZktOYU1OZE5DRiUyRnBOYnhBdktYSXlBSEY0ZEJSVWIwVDRVZzZqdjMzUUlyT3NGMnNiSHZuVWE0akhaZkFNc0xFTzVuNnRLR1Z6RElpbldLR0NvOEZ6VUw4WU52TXNRWWoxdTR2SXpWc1VkeU5sbkw3VWg3c3JBVGhSMHFWekpvOEpRdDJENVMlMkJrZHZZNHltRHFyWnk3RktmdTJpNmZiJTJCJTJGR04zZFdhOEw4T2tETFdXUVBkWFI1dlc1TzhaMVo2VlEwbGtSZkJsRXhZWCUyRjdpZGxQaUxmS2tJSUlUQ2tMNHgzc09ZbnEwZTZ5SEdSMERkJTJCc2pMZVJZZWdYUmx1dDYzdVhyYVRWbmJoJTJGd3J3OU55Wm45dEElMkJEUm1ydWFwd3ZaZlpCTUtYb3N6dnpIeGZZVEJaSmVNOW1vOERDZzQlMkZPb0h6WmQ5a2pqeUExUnpNYnczam1zUENyczZkbW5qNWJmNkNXeDJYR2JCWHZsM0EwZzl4OGVRYkE1aUp3blpoVjZqNmIyZDVSelVsQmFoWk1QUmtsNkdxUyUyRm9HR1QwckFBbzZPTjNyTW1Fa3E2bSUyRk5lRUFiSXBXJTJGcXNVckFiVXklMkJ3ZEM3cHdVJTJCRmxPelhWSjluanpzNlg3aDc4bzNEc0I2TENNeHBocmxwblNiQ2J0ajN5VmVUMGRmdURPSHN1YjJIOXN2ek1QRmVzc2I0cm1CNzg2RW9qZnlpY0p1aHZjWXlsRFlia285anNPYVFiaEVHRnpQZnJKZ2NLUmNsbklCbDBKN1kxNVgwJTJCeFMlMkJuYjdOaTFHbkNVNVU4RTUwbUJtSjE5eXFhV1d2TXZhb2luQkRkak1Gb0V6TTFsZnBGJTJGUXJNTEVEaEpjaEh4WGFHWSUyRkNOZE9UZiUyQmdvSGpVRGM0Sk0xNXg3QllnY3VsMmJqRjJOOHhQcUo0ZGluNnJnaWJjaWdvQkk5UTd1MDElMkJEUVM2RWdXWExFRDFzMlh2ekFZZU9PVzNyek56bjVPTWFMJTJGU3Rrc0F6c1dkQWwwYndRWkxmdGJ4RWN5dlkwWDRmUFJsdGZLSExkdGpYd1ZwZkc1NjhaZllBbHZZZGZ1MHpMJTJCJTJCalVra1B0ZUVxMXZLMUd0UkZFb1pEaVYwSXN1THJvVTglMkZrR1plJTJGVCUyRmtZbmhOUVAlMkJZMSUyRmlpN1QxJTJGRjk3JTJGUXNtNnM2Q29JcDQwa1pGUkVWQzUlMkZWUzJqOERWZFFsMTRSTGRXVHg2TyUyRkc4clJWRjQzMDRkVjcxT3daU0I0WFoyWUhjVEkwTDNNaExFU3ZrNXBPU1FpQzhLUnNIbGtsS2ZtVzQ0UzM2dXlSbFFPeGl4RmxoMWRqRjVLTXY4bkdpelFtR2tVdHFoWG5OeGw2MW5iSXgwY1dTVSUyQnFxRXNRc3JjVyUyRjZsYXZwQzJ2UXJqejZ4aXlHdkxpV3lLeG9VdDJaNlAyUjNhWHYyZGJVME5rQ2NGSk4xRFpHeHVwaEdDVHVCWUV3UldiemVGTWwwR01IWTRWZWZsM2VkeWZxdm5kTmpxM0JGSDVibFRzOG9jckc5M2FIWFpxaWFlWUI2ZU5zZzFIenh3azhyUkMzdzclMkZGUWk3bEdqM09YZEY5bFFyNXBMS2pjVFJHSnVveGVQbmxKQ3Y0eGclMkJxeFNRRHVDaXpkJTJGamxJem1KYkJEalclMkZ6THgya2pxMHJ0UEladGw0MzMlMkZYQnZ3JTJGR2FOQThKJTJCJTJCZkRyeExmTTFCcEI4YmZGekFrYUJwSGYzJTJCRyUyRjh5V2NwQiUyRmE2dGs1NWFqckNZTFZOYUQlMkZEeFVEUFFSb0lWTFdhRVlZNEdqeWNtRG5BWjJFMDVUMlo5Y0hsVk9JeGZyUGJGaFBPaVdqZWRxOUliUDNNUzJRcGxUa2U5UkdGb1VPSyUyQjFzVW0yWG16NWR1VVA4SkFISXJpVjNFNGdpWE5DQUxPTTN5OXBjWXhkZGdBbkNHaThmNnYyNmFKc2VFVGV3SzhoZEFLdHg4REFtRDYlMkJwV1Rhc3NZd2xwOVBUZGVxYnpSWEliWktodW5vOU1rWWxxQSUyQklnTWU2M1olMkZBVVd6M0F2Tm8lMkJGdjdIZ005bjBIUm4yNmVGVTVMJTJCTVI3NlZieVFSb2ElMkZ2cnBOcmxqTFZ3Tml6eWhkTTRQOUFzOHV4UyUyQnJjMGpUNTFkZWJ6Vlc3VnVRRldmNzNBWHZDQ1gyd3ZkamF1bWRFSzRBNnVxbFhHejRUTmtvM1RLWTVBSzZtV2dVaU5obUtxVXBKUTRlVWV6RmI1VnhobVlNYW9lUCUyRlYlMkJKaEVqUDMydnlWNHhrJTJGUERZYVBoOVZvJTJGQnh2dCUyRlQ3TmZFUCUyRkpjTkxoaUUzTWFCNm4xWHhHTW1rJTJGMEhNU0w1OERCV3c0UUQxbU4lMkZmVGJIMVFUdm82Y3VnMk9melFKNW9Sak5VMDc5eWlwM1dnakVpNzJJeXBDSnh4eTglMkJ5dHpncFJKbndmdXF6eVFMTFNCTSUyRjRhMzlJZkd2b2xDdFE5Szg3bFNlQWJiSmdJZkw4eTNDJTJGS1M2VnZFU2hKUGVHWk4yVTZuY1Z6SGhvalFBTDhVNk5mNDZra1o1MElkOTI0YXA5ZW1EZzRvelRCM0RtSWwyWlVXSlVEaHkzJTJGTlpiWWhBeUNKRHZsQno4Z0Iyd0RWcHRLMEJNT25Ud3pFbzdEQ0VOcEpZaHExWjM2OEJqWHc3JTJCSkM1aldtTUs4RVI3dkUyNFRJNWZNRFZ4TnV6U1RhdUFDNjNndHhNMzJvTmRaUXV2VHJNV2VKMlp3TTFTemJ3QXdCT3owc3lna3lTdGwlMkI1b0pEQm0lMkYyWUMlMkJvR1BHdjhXOG84NUdDcGluQUdVeEVxV09UN0xJSm9zWFdpNSUyQmJRU0hEVEtJWUFMaFJ1bVh1Y2JxRHhlJTJGT0VrUVdnVVo1V3hZeUV6UUZ2bDZ6YjJWbVV0TFMyN2s4cnAwJTJCRTlXY1hxNmM4WDN3S01ZRTc0eUROQkJBajVPNHo1eVoxRFhpOG8yNk9JJTJCWUc3NXBDVWRvd3Z1eXhoMTQ5VVJyN2s2djF4aFNkJTJGSEY3RVJyTFdobzMwOEZEJTJGNWhpNnIxd0d5MGZGZlhYVnVZZEdVWFBUemlnN2ZTc1ZGbFE3NzYwcTRrZnZTJTJGN0l1eHJWSlA3UmdUNklzVWZYZmdyOFlIbCUyQk9LcGtLQSUyQlUlMkZZYjZ5VzdzZUJYckJBOGdMZiUyRlhka0d5a2J1eHpWOE1pWHNWY2RYSEVWTktJdTBCMk0lMkJXRWlnUWd1ODRCSFh6ZnU0Y0swJTJGQ3A0MUxxektNWGhSVUNGZ3FoWGhHSjNBbTdqZnJSbWZWQmxwRW9vN3o5SyUyRlF0NTRTdCUyQnkwVThJMm9QUTR1dWZlMHBMOGtuMDMlMkZodmVJbllKc0JVcmU4YjZ1dEgxdzIxc284aTZpVXFIeDRnVHVpdFllQyUyRkZRQW9pJTJGMnlyY2paY3AlMkZqYjB5JTJCRXF2SFhIMFhQUU02VGJqdVVVNWFpNEpyYWUzVjkwUzViclBvUnJrWGlNa3BlaHpvVE1qbGNnRExHQ1BXRzJXZkRLQzVFd0d1N09lZG1TQ09FNlNEMXBFVHJFNjRWYzJpZjExQm9Pb0J2dCUyQk5JS3YlMkJpdmUzNk5tQnQ4eTVPNmQyQVc5JTJGUXU0RnYlMkJpOThkdVBqJTJCOUZjanZsUHlNbE56UEY3RWxqajM2OXU2Y2x0Zkp1Z0xMMWVIM3h2U0w0M2h5JTJCZWpQbFE3Qjh6Mk1DTFdkcFVES2JYOEY5dUo1cmhXM3lOR1lQRjZOZmR2RFdLbnhUUUFZUEtxWVpzSGRSNElCYkgyTW5IakFxM2MxbkhYUUN5YTVtWiUyQkh5Z1l0YldlYW9SOWk2Nm9peWR5TVBWJTJGUXlra01JWWFRa1BsZFpJTUpIMzZMJTJGNGpaVmI0T2NTb2M5aUJJVWt6UkhZZWl4OU5YTDhyU0lIN1JHMFE0RDElMkYxZXVxbSUyRm1EU0xoNThqaGlZQkF4OHc3RjE0c0NZYmE2Mnk0UndKemp5TVQ2VjNqaEVZMzlhbGhUalhKRG9wYWtteCUyQmIlMkZ6R0FTUEY4NUkyMXdzVWE1MHZUSW1yZlZlVmVRTU5ZeEo2JTJCaVA5QXYxSndBMlRJN09xaWw2WlVJT0VZR1lUN3ZoamZXaSUyQmFjNHZ5OGRjZ0RsWWZ1dXJmODN2cVMxWXFJJTJCc1p4T1VBMVklMkZPVjBGcVp2JTJGYkNVJTJGb2dSZ29RJTJCalQ2VGtsWlV5b3JvZUF6TlBmdGkzQiUyQjhZRTFQRjF0MDZsYU51ciUyQk1CanBITWZZalY2N2pkN2olMkJsc1UycDg5SlY0U3FSQlhOTnBqdFVsN2ZCZktJcVowVGJiYldyZk8lMkIzMktXc1VlNXpreCUyQlRxY2ZxY2J3OXpOM3BMblROeHVRak1NJTJGREF1Q2RSOGhiJTJGZUprbzFteGptUURwZk94RnhtRnRXRWRDR3ZlVmNxT1JHeGFDeTY4bk04V0t0Q2JCZCUyRlc3c1YlMkJOdUlrdTE5UHNqZzlMZEZsZXB4dWFHc1J4ajFhaUpIVzVnaDRHODZraE8xN2NKJTJGekE4T0xkQ0IwSHBkNURCTUhXQWF0cFdTbGZLbmdQbEFFNmV4OVhOVWpOMFhQYmV4bWZ4dHZLaTRDa0tvOUppT1hnTHVWWnR1ZVF6bFZsQUJFYjlTWE5Ra1FKMzAlMkJHenVWUE44UzZjQXRySTFqVXlJVVlacFhIcGo5S0xrQnVJJTJCRDElMkJzVCUyQnJ5N2dldjQ2alJSUnh1d21mcEZvalRka0QzQjBEQllhVXRNUklFZ3hxemdGRnV5d2VxMTR0M2pGSGNkMHllOEF2U3gxJTJCVHZpQjJMd2MlMkJoZHYlMkZ1cEprbWdYVjRqWVZqQWdvcTBMODBXaU85THZvQ0ZrY1p6WGF5cGlpTjZvRHJwckNtWk9BeVgycDlDSjFNbGdrUDY5NFMyWUg4MnVBJTJCN1pHaWh2SzQ3TkxBJTJCT05vU3pMSmJHTzVkV3VaTzNhdFgzSDFueEJWZXIxZlZva0VGMFhUOGpERW0zMDE1WHR0TTdwZ3g5cGdWVDc4WFBucG1XWHElMkZ0SiUyRnBsamVHWkFRRjlIRldFYUo3d2lkQzRlaXg2UmJmJTJCYjlyWWxjUWU3aXpPbjlNUzVySDl0dk9wa08lMkJRUGZ3MzdlNllmZyUyQk82dXl6UmUyYXB4cjllNjhkTkt2NXZJbGNBd05XT213ZkVyM2U0aXVSaUpKYVJMUVBYWHFLYml2Y1AwamJGYnpEYSUyRjQxaVp1bWlDQ1hkMHJlTmtuTVNmYzJZRkVDTzRZZXlGWFYwcEhIMmNpRFYwNjJvdGhQanpHTnI5RUlMR0U2RVlLOFhGR1RocVp0SyUyRmZwQ05HUnJCS05lZ1Iyb2NKdnolMkJ2RzR1UUl5YWRLRzRjWWJjayUyQk5OayUyRm9iYlowOUtMZjY5Tnpqblg5TWJxZmszeTRGNWl3TElYRmNDWkJ3SEZlJTJGYnpSb3pzWklQNnFaN1dVSnJod29ORGp2RjJKTjQ1eSUyRlE4aFNFc2VIS2U4UiUyRkc0NmEwc3VVTlklMkI3SGcyNVZ2VmJZRWlkSU9RJTJCdnpQZGY4QWFnQWI3djdLR20yM3UybnhmbTJFVG5RYSUyQktBRExoJTJCRVUlMkJZV2phS0FZWEpmWG9yeW03QTd6ck9nWDVVdTdmUlpzeXJKOFB1JTJCNTZMZiUyRm5qS3lwUzh0YWw2dXduTlBoSzYxRWdwczBMNGdVNDJLR04wSG14VXo5ZWl2S0pQUXhWcm5MT05pbWRDMTdSN3NleFlQMWJDeGdNb3AlMkYlMkZXcHA5Y251YllYYnFXQWRsJTJCUjVmZ0cxWXZmNjBMd1JvWCUyQlFwJTJGRUNUam56ekxIcTR3Nm1LTmo5a1hGM0t1NEdaTlpLZnhTWlA4a0RFZ2VCRHNOMXRvN2olMkJtME9IdFR1ZmFhT0RTMmRzOFprOWl6TzIyWjJCeGF6byUyQm9LaUgwazdCYW5kNG5sNTdYRWhPUFpKMzZtUzBiZWxZT2xhRWw0M3BQVjBsMERWRVY2SEt4M3dmZCUyQndKNXFSeWY3V2lKM2lhRiUyRjZRWjlKdGElMkJ5JTJGcXpMckRwNWlkTjlBY3ZXTmZ1ZHpJZHByVkpEcGU4eXEzSjFKcnVmRXR2eHBkcyUyRldLc3k5UDFxN09JczRpekFxSkpJSGZDVHZiNCUyQmFYY1g1bU1HUXVmQzNGZ0FQVWpmd3FLWDklMkJxbjQlMkZjYmlOTFRrOUZubEJLTHRlczF6b1gzZ3RuY04xUEFSZzBVOGRlZUpvdXR5JTJCOGElMkZvODJYc2lZMDdmT3ZzZ1o2WHc0cUhkbWR1WTdZbnBBMUo5akJmR0o2cDUzQXVoQkRibCUyRm95YSUyQjE4UUhoVnlzUmEwY2ZEWDRMZXY1VlpGRVZJa0k2JTJCNDRqQnA5ekVlM0VsZVU0cHJybGRnWEFHNkRVVGk5SGZVUTNLdSUyRkxUY1AyTFNoMGwzenZGWkxDNmRkVW1LSEI1MHZCbjVsWVFPV3BxV3htTjRyTGI4djFOQ1N0ODhtUlhDJTJGcUY0dWRHZmdUWVdzaUtmTnREdXh3eTZienJnMUx6OFRaUFBqaWwxNHZ5MVU2NzBaclE0VWNZbUFuM29iaml2cjNzbWR5MjZxS1hJQmVCODE0JTJCVGxOSXRRMFpGRUs0aEU1T0hUbE91Ulh2WWswUGtaMzI4TUFjdkRUb1YlMkZuNzU4Y3o1TlVqdW1XZ3VBTWNTUW4yeTUyUjRidzRnRHlGYktjJTJCbWE2MHgyalpaOWlKMnllMHBldDhyZnoxJTJCcUhGdkhyMkZjTUpsZ3NzZUgwUXVjdlV1alpvcWFaT0MxTUJpSUhFUDIlMkZYcEpjZ0gwbHlTN0FyVUxvZFFzbHIwS2NUJTJGM0tzS3FxNXVITm9wJTJCclBYNmpmUXp0WkJia2ZoZmpnSWJmNlZCJTJCZW5Sa2t3UzdMT2xVRGFyYTVaM3ZLSlZoUE1YM0U3cXQlMkJwRTRZaERXZkw2eW84c2dueEVneEpXd2R4aTFMT1BKSVBCQnRxNXVUaWV3NjQyTm5WcVh6MEQydjZsbE83RFZzcEcyQnElMkY0dDZMUktoSkhRZ3pmZHFKZ3FNcjVvNTNHdjNDV1NsSzlTcUNhRGl1bTkzY0hqWk0lMkZra3hzcG9NYTJFUGwlMkJVMktmaEZNJTJCMGdpaGV3aEh1RkQ2eDNUckM3NHh1U2RzbWpWJTJCbG1sa3dURkhaQUR5TE9WeWxINnF2WlklMkZObiUyRkhjVnNGU3MlMkZEMzhqbk9rU3olMkZSTjRnbTdLNEhhQ1BIVktnVyUyQjZ1JTJCc2lDSVRZQ1ZOQWJXMnRWV2k2ZmwxaVBBTU1PUyUyQm5BJTJCdWJnVkVNTVglMkZUZWVaU2ZuUkY1QUFsYnYxcmcwTmdpNFlRdjlqJTJCc241b05tUGFLSTB1N0hyUzlRSUh2R3BuZlVEcjE5M1pRMGJ0UGxtdkV6cURaaVFSY2F1dlpHcXN4TTlvSjJDQUZuM3VUblFOaXV4ZHZIVzNIUkE0YXplRmF2T1IlMkIwejByOENnUGRybUtWUiUyQlRhVjBLYU5qakU2eGglMkJOWnRMdSUyQm00TXVmV3VYdHppY1hSeGR0VXRBdDZoaFhoS2s3RFBXamVqM3RPUGozRENEZWNieFVQR0d5TnExdEdzT0M3NHZyZndpVm8yR1VJc01abkhGcSUyRnFpZEY1MDBqWCUyRmtpTUhyZ1oxJTJCRiUyRlo1Q2pqJTJGVzJyMjF6VzNUaWlQdzVFUzV1S1AlMkZZa2RyeW1VbjFQJTJGcXp1emNZaGJyVk5YRTA0andYbDV0OTIlMkZLSlBsSGhKc2l0NDlsTkt6S1I1SHpoJTJGVVIlMkIxM21qZ1A5VW1USk9HZkV6bk41M3JqMW10RiUyRmlxdjBobzRHbmh0djdobVFHYkp3N3E1S3Z1dU9GbDJWJTJCWCUyRjlkUW5vSDhQcG5Lc1NxQWUlMkJ2Z3BTcjV1MFozbXFjUVVQZ3hQanZXUCUyRlZNNEU4cW5PdEVlJTJCVWtwZG1zYzBkOXk3WlVtbFFJN3ZhT3F0U0pvQUNsbXJFeURoQjI0Um5pRXBSSzJpa05hZmNtQlZERTElMkJzUGk3SnFxWERUOFl1aDMlMkZGNSUyRkJ1RDZWN3ZNWFJaNjVSQXFxamM2UUtVWll2RGVrMk1MNXNzMG5JeXJlcmR5M1YxJTJGcG85eDgwMmlhT0hyWk9Gd2xqQzZlWGlMd2J4blB4Yno2OEpydTNFRWpNVGFiUFlOM2lraUJHSzVjcjYwSll5ZE1sJTJCREFFTSUyRkZMQVN2Q1FyQk1nb2xaMVk3aldjVTZaM045Y3lKdnBXUDgyVlk2YkVncnMlMkI3V0psYjV3OWglMkI1JTJGeU1EcnhUa0pQV2N6dXYxVnFvWHNSNWRPdTZXNU9JVCUyRmQ0VGQzc2VieTZRblhSZThkWjFDOTRhUmR0amIzcUdMb0M2RnpBaWpCOW5wYWh5MzV3JTJCVU1KMDZWZXFKcHh5SklzWTZRSTF4MjVLRHNFNWZxYnJ1NFg0dWxWJTJCYVhsbkJCWUxWSWE3TUtpZkxTcDlSdXFLNXFRNkdBajFxUktqRE1NTFNpTXhjZVJSSEVjRUhVUGMzWHg5Nzh6N2k0dHFYQzF4dkdXQk5ZaXBwaGJISUNmayUyQjUlMkZMM2NOaWRFeEZHQTNiZ3UyWFlGSkd5ZnZzODJYYkQzNWtOMm12NzgyS0NXZSUyQmlCNGJvSkpZcExMdVdIcDVVUXBXejglMkJlVk5FaFhZNVhGWG5rdmZSbVlRZnJsYnJ4dFBNbTJZTmolMkZ2bVVTWU9PNG1TelglMkJQa3EzU3ZmbHFMZ3BXNnhWTnNWc1ROcWZNclBHQUNKS3Fvb2ZqdWVNR2N1aFFtaVhOYWx0UVpYdiUyQlMlMkJJVWdIRXRXZ3BaeW9YQnQ0ZnUxVTFxOTVUTmlLNm9ncVBBcjlwY2NBNnNuemtBc1h6S0R2RkFmJTJGUnZVWnRyNVVGelgzWlVHTzQweUVJb1U4VEM2S1hLNGpEbkRkT3hFeXBnSTRkZFZZdFZiSmpKbnhhOHJFT3h2YjdTdUxmOEhnbkVKRGZtTHJHbzEzZzZVSlBrOFVvMnVNYWlLUVk2WnRYVDFwbzRsJTJGQ3ZFJTJCMEZzbnNSd2s4akszcUNkZCUyRkpZRmx1ZmYlMkJ1b0t4Um5mNmJJUEsyJTJGSmdhZHdEOEtTeW5lTE9BWk5JTWE4Zm9Mb1U0SUN6ZXJ5bVZTWWs4Z2QlMkJJaFhodnZVcCUyRiUyRnluRGFYJTJGZUxHN0Uya2swUGZhREdVVWdtbm9UdW9HZzJnd0xxOTVES3JESUdVc2RSamw1MHIzaEZVajhmdHJDdW5nOVZmbjBCSElnOW9YQWp0dFlUc3E4UnA2WTZBbzZTZFV5akdzUDNqYzJxJTJGUlZKTU5BbWRWSlBoMFdka29ibyUyQklEa0dyN2pWMUU0akt4c1Z4OFhRTlVNQW1YUjAlMkYwMlklMkZlWlYwV3cwcm9GVnVqampLQ1FndlFGdjIyJTJCNG43RFhiVXRMVURobFc3RyUyRm1zRiUyQk9LVnVWakJRNU1KYnJrMTFqSjl6OUFNVHNvJTJGQzRZTkQwRHNMMEpXSjROWSUyRnpaZkRWSjJXRWI4dWZJMU5uWlI4JTJCZVZKa3Z4dSUyQnY0OVFYRzVjeEM5Y1B6TDRPJTJCOUhwOGtFdDFiM25ONlYwWENBQ0tzc2tua3dSWEN2QWM1UFpSTzdacHZrSnFLNVhBY0FKYlN4T0lhWXZTbXJ2WXhIJTJCQWlNbFF5WWd2SVM0SSUyQkpHRExWT1R2ZyUyRk0zdGRucCUyQm5GU0FZYmxINW9qcTkxcDNMQnNycnR6ME4lMkYydExJTTF0WlA1aTdXcUc3a3FUb3NVallxUm5FUW50bHhqbmpoJTJCdVdEZjRZZkc3QkI4JTJCZkNKdktSbmRaYmdzZTlDRUhWbEdJelB4a3pwNk9kc2pVVnRKQjNMa201amRUN2pJcUl5N3RwbCUyQlk1Mm9LMHltYXNUb1FtQUFqVCUyRklRWGdHNTQ1djYyeWdJQ3QxaVJnZzdhOTNTUUxjczRabVJOZXpCJTJCaXYyVmh3TWRUd1clMkY2MEtFbG11VCUyRmlXVXdpWm1BUnJVS2NjQjBOdTV0M1B3YU54a0ZIdHI2cGs0S3EzZ2RCWENNVnk3enNXSDBrYTdhQUVPVktSdnVCTnNieHlURHg1aElaVm5wYlVRWVlJSHVIZzhENmY0WmV2SVBxYlFHSDNYWkp0WWdyMWpGZDZzWndLUENuZWNDQlhDSFY2JTJCcGtnaTRPSDFVSGZXOG9rWnZDdUxpY0tpM0Y4ZDBRaTA0UlBEdzVQRHBURFpPZkFaZ0ZHaWNvTE5rU1clMkJkQ3FjZ3J3QjVFSyUyQlJyaFVYUnVQa2J1UXZqMVh5d0xYMlNEdXJ0d0tMZUc0bGNQRjl1Q3NpOTFoelZ4a3FPdXZRUUFiSXFGUzZFTXk3YmlKbGglMkZMS0xGWFB1SkR6emt0ZUpkVTRpYjBuSWFzaTElMkZtNHBLVmRUVnBieEUlMkZTNlB1Y1kwMTMlMkZmN2lSV1VEJTJGUWVobElBTUFPcEFTSHk3SFFDNjRuR3loejN0TG9LWTN3ZnFiZWZWRGY0Q3hWSkpOQnBhTGtsRjdobnRSclpMSE1wJTJCQW5KSVlqMlFqV3pnNXFVU0l3WmFmTjQ2Y1FENTNJciUyQnU1YUxSdEg3dzZsbWVlQ1czQ0pNVzRTOGpJUVBvMldDWEpwYzE1Rk5HbjFVZzZJWFU5M1IlMkZvVmhTUHF6OHVzZVduRUI4NkklMkZlNCUyQlR4ZEZCcE5zSlFZbmJCMGJoOE9mJTJGRlk2TmZlJTJGVXI5MEZQbDNSbEJDZiUyQnVYZmdlaVc4MGN4MTZVZSUyRiUyRmRGQjclMkJWTTN6d204dVZqNkxlaGNQTms1cWhzSmt6d3ZXZDFyOUdEc2xxd1cxQkNCczRhZWlyRUtJSSUyQmd0RlBjeUc3eElCeE1hakFSeFl2V2dETld1ekh6NjBzT1BKSk1JVEF1dktHa25iJTJGTzRuVmZOMWJLWlBJWjY5JTJCWnpjaE9PMEZFZ2ZUMWhBWWJXUndSVVBvYjJHbkgxNWc0N3RIWHU0Z0lxbmhXSGQ2MmFNdE5ReUpyaXk5MCUyQlAxODVTNEhvR0RJYSUyRml1SnhDOTI2bnVTdzE0cTRIS3llRmwlMkZCenZCUVpoTXNTaU5wTlpGVWtjaWdWWGZITlJkRGlVeW9DNXRRMXJtZVg4aXh1cGk3ayUyRlNDJTJGUHgwcDIweCUyQktsdFRUbGhIazNIeVpOeHMlMkIxN2ozTGRvNDBJOXFUJTJGTFRIJTJCc0NwaXl5T3UlMkJvdHlMQzNBJTJCR3d4ZmR6R1F2Tk1HcDBjOFFCQ3UzZ0pYWnVEVFlQT2c4eiUyQm92V2p1ZTZQOSUyQldobmlhVjE5d081U2NUSSUyRnNLY3RrZ0llS2dpdWV1NW0xWHlkWmszZVRQN3hQdW5zU0hLRDhHbWYzdHkzdm9mUiUyRjB3d3AwVHdZUEtGR1h1eXJLVmV6cHlEMllwb1NOdnh3bnBSbEhCQ0lsRExxcGR1dERCajg0QkVYSXlJN1dJM1FPQyUyQnI5allUOGNRZ3dDJTJCMlZIc3owNnVUZ1dWdDNZWUs1S1ZSeVNEJTJCNjVRUXJxYk83SDJPdU5FVVI4VG5YWXYlMkZnWk5xdTklMkZYTDVpVzMxaWw0alRzNHQ2c2kzQWxTUnExT3RHMmc3VFNlUjBPVjEzM090dVBVelpMU21IaHZaeUtMVHhlV0ZoUHN3aXp0M0wlMkJ4WHc2eVclMkZjcEdJbyUyQnQ3SlNvYlRwY0d0YUEyQzNSVkhOckRZckE1ODVWNUdQdEZLSFNLYjkwb3hGWXpsRHlmcHRkNFB5d1NpWXZtaTFLUlVFVCUyRmNFdSUyQnIlMkZzd29jYWg2UnRBTjhWRUhWSDVuakE4S2tsbldUSmthbDFLWFlLNWRDOXllRlh5b1U5bHhWbzBUR2RmSzVJUTVRaXdMN0p3YmhxeEh5ZXBOeHU1N2dGNElNSG5CRnp5MTNMRkdaUjZEJTJGUHVKckxxUWczeVY2eSUyRmZETndZWDc5Q216SVUyM1NqNWs0YXZhQUhxWlQ0JTJCdElGZ2s4ZDY5ZmNiMWYlMkZLMzBYJTJGVXlIZiUyQjd6eWRXZ2ZqdWh0QkhMbEhWODlNVVJHbnZDV2p3RmdaeHpJbFBDczlLZE9UeHVuRE9mRW9ITzc4YXIlMkZxWGFvV2Z6aEM1ZSUyQjRmZlhFJTJCZyUyQlREZFhReXRYdTFpZ05GcDlYTG5VSlVTUm9BOUNLMDNOVFhpamFJVUVFVXZzZnZrUmthZDJDalVtZXEzVFM5SEZwMGZxb2U3eXl6VCUyQjdMNyUyQnF5ejglMkJIeElRVSUyRjIxa0t1SWlHY0xhMDVGWjZvZ2t0OUZQaU1vWCUyRmEzaXI1MmJkSWFGbndNa29wd2JuWTNzaE9HblpPeUMzc3BBQ0d0SlRjUFBUVUwxOGVuSUYzbkVrTHhzMGw2bzBWOWJKZXZzMUl1N1J1WjVvQUJSWFF1QWdQd01RVTZkeFBGcnBXNDRYRkZSc3ZXWkh4S00xR1RxVVMlMkJFcUYzdU85MkVYbjZ5R3VNdjJiSFZHVlRiVlJWeUhaN2VTaXBWZFpnWUdJaiUyQkdTdFJEMlRoTHhLc1YweERLbVdHTUNpNjR2b25OMTVFeE1US0laejFCTFNHdmprVUFVJTJGaDMyN3NTZTNvT1czY3ElMkJFUEg1VkE2TWxRNUN3MHlzNXpFZ1pxd2NVU2ZrbXpzdUljNGtONVlDeEJyWmI4dUg2Vmp6MjlQbmFlRkJnTkdrZVU3JTJGb1Rya2p5dzIyNTVEJTJGVTNVbEJnaDhReWRxQ0tPaXB3MmtwVnNSVTdadFNJVVVEMERGMnBXMzdEMmF6WkhSZGJsc0ZjUEh5R25Mbmt2JTJCeGRCVkxsbXBCOEpmZzRrdmMzZG5oN3M3WFAlMkJoNWk0bVlpTzYlMkJGNDVVWlpaazBmajhlcmNsc0FMbHh4YUJ0eDN2MHhKVzF1JTJCeiUyRjhPVTNOUWZXTWhHYWlHMlpZblJVbDFRcFJqTFF0cGFua2k0NXVQVHNqMjJDRng4NTJTdElKMUtiNnlBbk1HWktITlE1b0JucSUyQmdMVGdSZFNqQnBmV2hhaDBaWlpqV0NJMHIySVB4NDFyajduNzRqUmtBb3hhUGloRk5yJTJCWlVBUDdaMWRDOHpSTVVPJTJGSiUyRnROM3VKSXZTRVdVOUFVY2xOSGltTjhhOTdGUjNCdUpFN3RFd2g2VGxJQU9Hc3FyTVdHaWdOYjFCczNvN2glMkJSNjB0VnNMNDhDTjVYSzBjaE45UFRTVmpWOEE2c2hlZm5Jc0dYNXViT29ZN2FYJTJCYlRia3hoZVlnYUhyQkMzUUNFbEM4UnpSd0pyOHkxbCUyRnNMMDd6VGI2UzU2YVRtQyUyRndFQlBLeUY4NEhwaEhIT1hVd3VyaFFibExWMm40V1dpRlU4dCUyRjNJZURLZ0F6aXpXejJTaFdLSlJudjFwRE0zbm1zaHJTQXZPbmZzeHdFRm5SdmRBeTltRzVxRE9ybDNldFlzQ2h3allSUTRCQUVqeDdoZjZiVlJqRDJRSDRMRlZtbVpIMXJsWTJjRkVacUtsOU41NmQ1blRSYUg1RG5nd3Ztd3dnRGpXTWNNRUhERU9QZmVmQm1IM0w2JTJCZldFYnVjJTJCMTIwWFBYekslMkIxaEdTc1JpTndjTFlMMmNUUEJjVTlWTm5QeGxPZnhrbmJCUDN5VXpkSnYxOGJYWTBJcyUyQjQlMkZ1RyUyQnE0S2I2VEkxZXU0MnRubGloUzVDWUhUcVlMJTJCQW94cHhMVk55ZXpZWTFkd2tISzFhV1BjZzNxdGgyUHgyUDdYYWhPSUl5V2dPbThNVUVESyUyRml6ZE9tVlJZZ3FVbDJIbWpReUNBbjl0b08wNTVidGZBJTJCV2xGWTEyTCUyQlNOTjNXOG9TJTJGMXFGaHlpQlZ0TExzN0lYelZZc3ZrZ0swdlJQNkxKejVwbzFhJTJCQ2NkU0szQWJzVmRRZyUyRm9BJTJGMHJiRVZpTUcwaFRFbmpBSlE3cTVvT2hCVzRtbjlzaUFUdEltS1ZRNXBmSmhkMkxkWkF1T0VNdFN0SzJFNXJMc210bzZHYnNjOUw5U2glMkJvdXIlMkJzVEI1dFR6MEwlMkJsQSUyQmJrSXpETUk0ZlJGRDNzSnoyR2tFYUNlWEJrM3BNVDFhMXMxSzhsTWtHQ2VkMyUyQkJ6Tzd6ZjFhZVRscVJxJTJGVFNlYWFxRTZVT3RNQzdyYVR5cEh6eTZBWkMxSEhBWnNCYmVDOTNMJTJCc1lLbG5CMVBaTTBVd3l6RDdSdDRUaSUyQklEaWV2aWElMkJ4eW0lMkZKYiUyQjZXaHZNZngxU1pZcGZjd2E3OGZDZnQ0YWhPUmJWaWZud2NuOFloMkFNbEgzOTFVTlMxeHhiWUVHYmVUNlNPRzNQcGEzQjlzJTJCRWpyYUdxV3M1am1RRnRyczh2TUdnT3FmNDk5Tml1UUxHa2Zjek95b0wlMkZZN0NVRUViaE05RGNqUDJ2OFJUM1IzRWRvdVQ1aXVNOXpMbnNJOUI0JTJCSjB3U1RIM1JIJTJGRE1sUlFCUlBRQlAzeXlIR0VMa1pQdVZZekI5OVFUOXN5ZEZxcUJZZHBnblpQQmxwYjFyTFFNZzZDcElKTGFnZXk3endhMlEwZXlveHQzbjRTSXVBNUoxR1Q3dmw5U0Y0TzZkOWd3NHZ0RWRLUUklMkZSWDVQbGdSN09yZ3BCcmQ3UDM5Vk81N0l3NVJZY0tUd0dpelZlMG9paEY0JTJCa3VPVW1Pd3luRW8lMkIxQVRrMDlKendyWDNOenIlMkZGSGFTTzdzeFhHTnlJd0I0SHZ4THd5WWtOZm9tU0JOY1FPUjZyRkVXYmZ3YlE3aFNyMHElMkJsOFFTNDE0ViUyQlhIRkRBYnUyd1pZT0Y5SHNQeDElMkI0aDVtdzVUc3hyQmhWU2trRDBWYVVFMnlNSW03SkRzTEFueElZYnFQWHo3N1F3b1ZGYnNIYVVZNktKJTJGcG81MFNKQmtBVzdRcURhSlBENlJqbE5zSWMlMkJpJTJCcnc1UDA4JTJCa1VNNDBRNlJnNVYlMkJEeGcyeDMlMkZXcUYyOFVvMkRuNzJLckR6ZDB5M09kYXpwNTlaalJIbjBiOCUyQkRPajN3T3JEMWFmb2ZXJTJGN1BVck1UWHpGOVJaek44TDBBQ21OY3IlMkJCV1lKMmlLUlQ2NUtYWlRTS1BiVUVibEF2bWZjVjlYZkh4enJKJTJGYmtQbUtoODRYZ3JIV29taXZ2ZXlxdlhqalJjSUtkJTJGZFQyeCUyRmVGVHNCTmwydllGTUd2YkQzdEM3TXB0TUViZm5mJTJGVnJKN2d6YjYlMkJZZ29yZGV3b2dHbkJRM1RBJTJCOGYlMkY4QXA1V09ITHNNM1doTU1FcE5Xd3doZSUyQmJqVVE1RXJ6UjlnbTc1UiUyQmNsVThMRm53dzZwTGxLOTE5RHlmNHJ4d0RuViUyQnhKZnlrJTJGSSUyQlgxZDY3bEVWJTJGYW44SmN0NWxwNk5tU1Fwa3BSa3NaeTczVDBaZmtSS1gyMndqYUxJJTJCMzFCYzF1TkhTWjRVbVYlMkZ3YXRZWlolMkJIdkIwcDlWYjBzZzlnbnBLYTg1STE4ZFB6bEFxOXNzTXI2SmtVSHlkaWF4QlJhWVkxTkZiMEptZTBmSzFROVJycHFPdDFaOExQUTB6QnJnajlUNUNEOGtIMUpLem5aRVhmSjAxVG1iTmFMSHdGS2RxMFNWT0h2TXowJTJGZE12TUJReGZqUDh4WGE2YmZmQ01WVUYxVEV3SkMycTZoRE5nVFNsJTJCdnhKRHBIdHMwdlklMkYwNXE4QzBFTTU1M2dRdWYyTlRrRk5CRWpLaUgzNjhMb0J6JTJGR3dSR3ViUndNS0ZHQXJPc3hyVCUyRjJ4MnMlMkZLVlE5dURLaWNLZEtsTXNqJTJGYWElMkJCVVBHUzhudDNrNDY4SDkyRm5sUHFnbnNzQlUzZ0o4UGJhMU1VbmwlMkZBQTF5amxxOUduM2Nhck5RQ3p4N2dLM2FCaU5PSE43WHglMkZxbTcxR1JKZnNVM1g5RVlza3UyRCUyQk9HM0Jua2REeU9DQllxOWxEdkhHek1wVDdzc2hFSzBWWG92diUyQm9PUm1pdDBuUEU2dFVWSTNXOFJTVHRaUks2TVVNNWYzWnBkdDlJZTRnWHlZd1p0SjFGSmZPNTIxMk5rWVh1NiUyQndiUmN6VGNibHRtVVl0eWl6RFNMeGVvZkdsQ0hqYVNWR1ZSeUhUWDNjQlRoVGZKMldPejhMV3gxYmdGd2owNHk4dk4yQzg3RyUyRjlWNDdpMDk4eSUyRlJDMEJxSlZqcG93QWslMkZHOWx4TWVqdkJMc2FVa3Y5N3NXNlBlNVg5OHN4eDczVXpGQ0h2dFdhcHFoNmJMaVNrbHpESWtDalFhbnJsSkZkZWhKemo4JTJGenpYVHglMkJsaW1DeWNXOGN2ZXRFNzRobEZrQVgwUWQ2QkRNcEV2TEZRc3U5a2t1TGpXc3VHenRwWSUyRlNPSE9KOW9LSEVpN1Qxa1U3aktmSGNqNDVRQXhTZW52aGFOS3hxJTJGTFpNZyUyQnpGTUhyYXJ4SkRpaDdEdjFqaGRueXg2ajZGQ0Vxall1WGlJTW82SUhDMWtpVHlubWMxMFVubHRNZnZtVnYxN2IlMkZ5b0ZuNzZvbTBBdDRWVFA2ZDhpemJUUGV5TnlZQ3hIUnpudGJ3N3FpdHdCY2RzS0RXS0hKWkwwTEJUS2JNS0hTNVRkbE1HNVY5SjY3cCUyRmg2cnNlRk1vcFZoYTkyTmdOQ3lWRm1ybzRHJTJGbWxUNzhEUHVrTWFEVk1Ja3RqVVA4MDA3T09uY0VoVTZZQ3BjaGk2ZlRSUmdTNnN2SmVHc1RmUGlkclFuJTJCMG50MmZpRmxlWGNzbHYlMkI4bDJsU3QxUHlZZzk1Tk1NRHR5OFRzdzJlVmFYUDB2MEVZMmZTd0l2blNEWGFUJTJGdHp6WlEwc1R3M1Jud2lyQk9KUHpCQ0Z3ZFRnNDVaJTJGRFIlMkJmT1AlMkZpdVVrSE5nd0dzV1VZR012cUtTb2p2aHI4NmFXOUhYaFAzVDd0TkRWRFdQaUFUeDNNUVh3M3hwQ3ZQUVBLamhpeG1hMXljU2VvWXFGSiUyRmJGR1JodnFJbjFkVXRKYXZwWCUyRk5BTkU0OTZpTlZaY1B1ajJDNnpwWDR0UXowMXZ4a2tZanF0V2xzM0c3Z2klMkZNWVdVTlpibEhJREx2RmhUVml3WEl0SlhyU1NVWGdrMiUyQnpkbjFQQ1dueVcwVHpVczVKRTQ1WVNUdCUyQiUyRmZxRWg2WjRHYWFsJTJCQlhEM3ZxbzJJQ25ISHBmOEQ0Rk11UTJYNHBzViUyRnBQbFc4M3hWZWFMenRBbGl2M3J1ZVg1bWhXRjFCMUdKazRvc2JkYkg1MXB0MlFCeUV6cDZuck81eFJHSXNZMHhmNnBicHEwVzlYZ3ptNFR1bXMxMTl2Q0pWV29yTm9jT1M2WWI5eU9uZHloMWo4ZW1WYTN2ZFolMkZlaUQ5MlpvVjluVG9UJTJCN2FzbWM0QmpsQUduZTNFWWZHSnhSJTJCaXZmSmhZUkdic0J1NzYlMkJEdFRNUndjYVZUMmZsRjcwdm8wT3k2UDdzZHVrNnhkUDRPOUN5RVlCRyUyRmY4MWNWa3RxQXF0WnZhRyUyRmdIZFp6TFFDQ0hnc3Y4MTZIcWwxVHFTZDlkWTdPcUszUjlNWWhPU2tyWFZvakFMMFZsdFlsNXplNCUyQiUyQm1wYUhiYTVqc3hkT2s2aThkV0YwUFIwTWlENVdtVHlDMnZRbFp3UzUyenJMWiUyRnRZc0dycWhKN3dpRm9yVzM0MUolMkJhNlJNNUpCc2V1UERMJTJGYTFnR2RWcjNmZHNTTzZsUktxZlJmNVpvZ1hLeFlPZ0k2QkZ6VDVRSVFacUE2UElaaWw1VDRFWE1penF3VDhEaVNmQWswS2dxZXVNZnZlZTdJWHdSUTlDZTh3JTJGUFIxRDJxNXlKJTJCSm45V1V1aVUxR3p2MGRrSzRGaG5QYXBTcTY2RnV4VGJHV2thODBWRktUNzMzY3Z6SzgxcElhdDglMkJGTCUyRjRSbTElMkI1aUp3Q0FTQTdKUW5nVmZ2WWJsQnJMd01WaDFnUUl3cXdYMjlKS0w3SkNOcDI3d3pYRmhSaWwzU1A5dUZaU2RndnVtVmNpN1o4cmh6bjZwMjhyQXBiSThQd3RlZ2dvS3RBTUFCQXZrWmsxS3F6aFglMkZWTVpUWlIlMkJucnB5VTNuOVpqUWQxMEZEQURpcTZWWm9yc1JnWjljSDJsZjdrcmJWSWlWUElyUXQ0M09wb2NsUjgxU1V5aTVWSmxZRVUyRHpKeE9oWW5JTnFMaGtTVmtJUXY3VTRMVVlwdTRWcm94SEtLYlA0MHdPMmdBUzdZWk83QWZjbFUlMkJXZ1ViUjglMkZqVUJ4VUEyR2J3eTNrTncxUWZjdSUyRk5wQnZoVW5CbmUlMkYyeHFPbjc4JTJCZlFQTHU1VjVuYVB6N29idUZsQ2x5STYyZHVOeXJTRnVLS0U3MVRESlRaSW9Wck1IWUtTdjdxU1ZhZlZVWWk5cGNCYkh6MEtwb2xOZlhUWFQ5VGk4ZkpIWW80aXkwTUZ1ejhIQyUyQjdreUdiNDg1blZ0T3ZwTlRPUjRidkNMdiUyQnF0RmtCeHFyWmJUbjZvY3BYNlNSb1NtOFBpM1JVNnhLZlM1JTJGUHQ5ZWk1T1V1Q3hQdWIlMkJONkhVZUJIc2J0JTJCR3ZTRWNtJTJCZHRWUTNsOTNDWDQ4UXo3N0c4RVZOa2oxS2Y1TUZ1RmEwJTJCZjJ6WDlMMEo1dXVtMVJJMUtQTmRWVGtpT0prbmEwVzJ5UWwycHhVNnVRZWlTUVQwT1ElMkJpZFdGVHdEQzk0REd2S2lCbHpRVU1IQVNuWmc4aEhpczR5V1pyTERZSkMyVTA2YjNZYmd2diUyQk1tN3Z6eXRiVGh3YWFicG5kbjElMkZsaTZvTjBFWmpFc2t6R0RQT1dOTHVycTA4UmdPbyUyRld5QmU5dDJhbm1Rc282NkxwcXAzZjcyNFdNaXlUblIwN3g0N05aTm9hek9iaUx4dXZVeVBmTUwlMkZuT24zOUJBQTAyaTdUYU15dlI3VnJ6YlpKJTJCRXZMOGo5N0x1Y2M1SFJDcHJCM3NXUEtMZGZTdDBJSTVXZEpITDFOR0Vsa1VHTUlhJTJCZFh4cWoyRnpHTVR2ejlMeERWZk83aWw4RlNYZlZmQSUyQnJQNmR5ckV1UWlJRGlEeVJ5T0UzTFg4THlmdU5ZM2NpYXFjbDdSbjFleHA2WlVmanclMkIzNGYlMkI3TlVMeG94YXNDQ3c2OTVpWmxiemJPRktyUGN5dG5MOTc3enBieGdwSDIyMVI4dzdpeiUyQk1NSHRYUkdqSnF2N2VjJTJCc2FYNVBhZnFPblk3ZEM3QkNkdDlOc2FLUlNHNDFMWXpkd1ptbklWNldWbHBteDluVDF4ZDgyV3lUSG9RcUpUJTJGbURIbXFIJTJCYTdtVThHaHhLdjdXeURSb01leHl4UmhOa0hrWjZBOUdGbG5qMzFXeXVWT09VTlVmcXVTUG5CazhFSFNScHZyMG5WUk5FUyUyQlEwaEk2JTJCWWtVOHBucThad1hPd2pReGJ6VkNQZWVla3h4bXl2NTZBNzlTTnZmOXJuJTJGTWE1ZGNTQWxkODNZVDNFYnRlUWt0aUZKTkE2YXZ4Z2EwbE5PdFJQelhaT0swbE51ZW9VSlRoSyUyQnFCcnA1MU05Z1ZUUDFybWVNeTFkOTNUZDFqT0JxJTJCbHFFbVdlWmg5anM4TnBXcWVTcE1mJTJGJTJCWUo5OW5wY1hhdXhqJTJGck1oTFJvNzd5NyUyRnRvdXR5ZWx2RDFlS2JkYmh5ZmFPdXJRU2NNRkFSUGx5clluSXFDQmFYRXhKcjdQclRIJTJCRmxMajV1cHJsSnV6NVV1ZGZybWhCYTY2T0lTc3pmUnlUWmxmJTJGVjl6cVlhaCUyRmJ6cGRncTRWM2hjeFdIQVhTSXBob3BwMGlCYVBVVHBVJTJCRk1iSEtVNmVVU2c3WkNuRmQyaUFieXZQajFBaG8lMkJoczhLS0tPTiUyQjFoQTNhN25yVEZvRkY5TmF4YkgyZyUyQjVzN2tibGNsMzRUTk42ZCUyQnpLV2JHcjNadng1RTMzcXg4cndXTzc3dFVWQzBpJTJGRzklMkZMaW42ak9EaEcwVXZBeVpxSjY3YXk3OUhEVm1IUzRZaExTSWk1REE2MFY1U2tFSXFTJTJGdGxiN0kyT2daVnRVJTJGTGpRQnNmbHYySVNIdmcwbDZnQ3AxVzlZcFdhWmM2Y0laVDgzMDh0WERDZHY0NnBMRHQ3QXk3S1U0aFFPdDlNZElZdCUyRnFWVWJYV1lKViUyQkhaUDVyUmV3cjdMZjVnbGpMTUc0MkVIOU5Yb3pmSE9uOVlBZyUyRnlsS3Ewc0lnQ1pwJTJGeHZLRVJsazZTV3JuJTJCY3AxVUFPT3J1cnNZcEp0UGp6aXU2SHhlVDc0dzNtRUZvZmRZNG5PZzBLNFViRVpCcEFpTmVyenM1Q1k2MWN0MU1PVyUyRmpVU2pHWVFtRk16a3JSOThnVHY0aHQ2JTJCTW1mdHNJODhXd2I1V0k1Nks4WDVwUktUeHRXNVpqU3dpbkwzaDNCVE0wJTJCZnZHV0QyMFI5UnVSNVhvOVRTMHJjR0ZocElkV0F4NXpPenVhbWlranpvWlRIWVRaUENkYjIwSHFaQTFKbUp0VDVEM2dKRWZXMVE1ZkU4a1FaTWI0NG1zZGxkcVF4UXdyb3Z4eXY1S0UlMkJxZnJ6dllpaUFBTHFhVmxDVWN0aDAzazY1Wm9rNFVoUFJaOTNKT2FyMDM4NHU1ZHUzM2VwVyUyQmElMkJoZkZTNHA5QmFFa0pBbEhEWGozVnBYaU9rZUhhekU3YU9yZzh2TCUyRlZuVTlDMXdxbzUlMkZDMDlicE5yNjI1RmolMkZ1ckFBdFhRckdtJTJCcTdrTkRaVFZhNzRleG5lSHBCTGNrVE9iJTJGQXBNZmdROU1MVWxGMnFWVUxrR3I1dDRZNnZzODJlRyUyQkJvV3RoMzBrQ1BPZjBsZVhqZWZYWEI4R1h4Q1NPaUswRDR5dW1tY0dDNGFReExSdHczYWdxeHV3bWclMkZwdTFEVCUyQkZ1eTMwaWJFZGh5ekUzUVFzN01jTVdXYk1lMkRJblN0c2wxdXFpJTJCZ0VBeUZXSGVmT0JUenNIT1plMiUyRmNnUjM2U1pvNzJlQ0xVSHFxbGdpOWZMSkJGcE5JU1UyanJNZiUyQkRINEkxWEVzaHk5MXhIN1BYQyUyQjNwaWpBbnE2OUNQNmdUMUx1cyUyQkl2MlJYcUw3SzBYYWRsWFg0WG1DMU5BTXdScWF1R1BjZUFSSUQlMkZscU42U2NZWExVdnZScHFYY3hMQVZvMjBjVnY4WldBUGNmaUpBOFV6Y3I4NEM3c05KQko2UWN2NyUyRmM0a29FWDNaS1YlMkZNamNIRmJOaFg0Nkdia2NYRVpOT2ZSbjZkMFQ2SjJjVzl1cEtCTmVRUFZrOVZmMkhkcjBWM1JTRHNXJTJGZWg5UkdWMkJsSDNSbzhybyUyRlFMU0hWYXlNaXQyOG1Td0pibVJkYTl1WUJVeWRFT1NYbnByN01yNmQlMkZic2Z1JTJGVFZEZlg2NnpSeE9PQ2pKSDVSSG9VZnRNTSUyQlhlZklLY2hBNWQ0VU9HeXZ0MjNwdDdvbVRWT0pDZERMUm5vWXY3JTJCcHhqc1ZSd3BQaFFINGg0U2N6JTJCczRQRU1PM0F4MkxFSVQwWjFNc1JiR3Vta1o0alJvMVRudVBRdiUyQnpzaG9WT0xkZ3hEVll2V1M0RDBzdGl6WDNsc3hpYmZGYWNJNzBsazVJVmVmVmtHSlpIMWZ2T3RJeDNEd1dHcWpWMlBTb1JZVTBaQW1zMktwT3VPS01GSWkwaCUyRjBTeCUyRktaaWRDOSUyRjlMQVZvY1lHb1lmVlp5NHdwYk92UzdVQnM0VGJsWVBkQXlsRFdUNTBwQ1BtWE1iVHNMdFFzTElFbGJMVjlmTVNITnlwbSUyRmZycDg2OU0ycmwweHFOYU9ZeHNpTk9DSTJyb3hpcmo0VkRUdzZ4RmVJeW1TRnE5bUFWZUhwRzVWOEt0TktERXpoNTVmcXBRZFNYZ0UyY3diQnJKeDd0ZHBVY0dWVGRuNTlxVjR5UWhDSEFMdktmazdxZkZkcW9SQVBRZDFzM1loYTRwNnN2aFdGVjlsdHlBM2NuaHlVNnZmWGN4TVdCY1dVSDZka0RrQXBWVVNqRUwlMkZndnBXRER4UXB4OFA3YTh4aU1scnJybm1Zd3pCSUNwRVduMmNjViUyQm4xNmVuSDFKSFU0VFdaRm1jSEZCRlJMaHZwZTlhNVZXT3d0Z2NKQVhmJTJGTENJeTlRT0JLemlqQSUyRkVoSkVIY2c1bG5UUWRVazdydmxxUWpzYXRMVXZQR2FDSHFwWEFrdUlUcnZwY3lxem9obllMOEliNUhDNUlqUEZ1VHl4NXNoVjE1c0U1ZFp6RFV3ZCUyRndoMlI3SkxaczVLSzV4MVg3UTRHWmVtNmJpVzVLNzM4JTJCNkc1JTJGY2w3V3BKMG5MaDR3WjZSakNCWHclMkZ2TmJwRkd3RGcyTHF4SXA3ZHdYUlRDb0wlMkZoZ1NtRnJPNm84U2Nlb003UUdheFV2RDY3dW5uQjlHdXUlMkZFVUlhTEdLeWZWbmdERks3QkZNcTNaYWQ3dXZYeFRzdDJIcFdWRVB3Y09XTHdMSE1Ia3hVMkR5Smp5bnp6YjRSbUVNRENGQzhDYSUyQjNJeFZuSTI4NUNuMG0wOXlyemxtZEJvZVU1dk5QSmRUNk1rQ25WUVQlMkJ6cDFKdGx5WHZrWGk4ZlJqWm5kJTJCcDQzdnU5Y0JvRHVxaFVtOUp6RnFHN2MzY1NYJTJCc2dtcVRHaWRBYzE0TGkwWG9neHE0aVpuR0FzdkZMSTElMkZnOTVuJTJGbUJUMFNUOHBkYU1pMGoweEF6OFMlMkJsNEV4UjIlMkZBSXAyQVNNSVNYNThTZ1FweXVLSXFDUUxwVWRjRFN1dE03T2lzTDREMjFMYnVzJTJCaThOYVA5c1JJQWFlRnFUcnBUdlJsQVFtSkQ1JTJCendYaVRtdnoxRm1xMGZzbmxuayUyQnNEYiUyQjglMkJxVlFPUnZ2aWhLclZSeDd2JTJCa2JGRjVhTDVtdEp5VUtJM1BCbkFsazE1ZFNDbjloTTF6Z0pVNHJIMEhLWGJGcFFaUDBZMjkzZ0ZNMmVadXFYRTF3bGR1eEpBJTJCTlhldVV5NmU0R3NtTURKU3c1VVA3JTJCMWIybTBPSlpsVWdmeXlEc0hiV3ZtS1BVdmJGUlNRZGpuWmVvQ3RQUHZoUWtUMUxvNXRPZngyUnk3dWtTMGk2NEdhMmlhNEl6TDRjdHp3R1Y2d0VMMyUyQlpVVTZEeGowc2RjaXZvT1NOTXNtR29vaVdoWmlIcFo5JTJGNUpzZ0pReW1oSHpzeVplJTJCbkM4OWNsNHkyWHBMRyUyRmMlMkYxJTJCNkd6b3JvbVRvclExMWpUTDl2clZKN2lFdm4lMkJYMnkyNEdjWEh6ZUpiVUd6dVVWTUVqdWpCTjFsUnV0MmZjMiUyQnRjT3p5eGtqQ2pnbEtqaTJmQ2lRRWxSaURpTDBaZHd0TlJBd1dlSFdITVhpRzNzSkJCN3Z1ZVVOb3hWUVlLYlJpU2g3anBmeEpVbmVpbjkwOEdpdjhxWFRKJTJCTkhyYmpsVGVuMjl4T3hVZlN6MWw5WnQzV3pMWlltbXdrTkllWHpyOWIlMkJFdjY0NngwVnBYcmRoUENvNVZyeFZXV1AzNiUyRiUyRkpvUjhDVnZJNllvWTl1dnRWQjRlQ3hsVWdscjE1aVNaY1N3Rlc3V0FnWEVDR3IlMkJpZUlzRjdEb2xubiUyQlJiQUpOYmx0emZNM0txZ253JTJGYW0lMkZFVXNYc2M1JTJGYzFMQmRjcVI1S1pPN2ZKdEhDOXR6Tks2YkZaUEk3aUxPa0cxbzR4UzZDYmw1aFRoR1JtSmI3U1FkOGNNMVFkeWRvcWJ3UWZ2RVgxOWNiYUVrTHBVVGw5N0FNNWFFd1I3OU4yU3BOM1p5dTZ5S2RlJTJGVk96Z0huJTJGSm41U1UzdXZvSE53dFM4WVdpJTJGSml4WjAzNzBLZVQya3lSczFjQ1Q2eHBqVEVyMTc4JTJGSGxqOSUyRjNVZ1NOYm5Wc0lHdHUxNXVaNjJUYlpVbGFMaTJleFYyYVlCVXpuRFM3JTJCcFB4UkNnYWlPYU9lVyUyQlJGaE1ld0JwZWcya2FZbVZzTHRzYmZVWUslMkJjRVVaVnhVQUNZUCUyRlpJOVpKbTVtNWI5OTZ3QnFsJTJCSDdUU2VyMCUyRkZVWnBkNXpKbHpCbThMbFUwVFNBUlpNQ1d5YXk3WlBudTNBbUlqTlI0V05WYm1PQlA0cWZ1N0NHT2JOWlJUMTN5ejBWYlU5dWxQMWtVSGU2cVJvMzdsbE9ydEk3JTJGQVpZMjh3dUxhWGZuTUsydnBBdEI4dHNXdUptUHJEYW9YeklSVUdCVWd2RWpEdnFROWYyMDJuS3RTdGtzTCUyRmVuRGNFTmV2MzFrTEtzWkVXd0ZVa3Zzb3BqUnhYbFdkeGtjcWJxJTJGRVg1S3BwZnZvVzJlaG5YckdPQ1hEcDQ4JTJCbFF2cFVlYXJjQjg3eFN3amtiTTZ5UGduZEJaTVFHRkpkT1IzM25zRUxCbTFWT1JnYiUyQnJKUzl2JTJGU1QlMkJObFlPcUEzQnQ5eEgxaklibDl0TCUyRmcwSFg2WSUyRlpwa1lMa0cxNUM4Qm5WSTBFZU04RVBTVlM1TUNGSzdnU3k1VWV4aDR5ZnBuWWZnYzA3TWZsMHZINk5FSWdPd0xLcFNrZm5hQ3RvdktFZmZ2ZUhLZHUwRHdCTFlnOEZHZHVoR2hhdGkxZnFNeHl6MUZ1dHZvd3g5V1NFWlA3TWl0ZkxJcFBObUc3c0thMU11R3BodDZFZXAzaEx0QzlmJTJCY2c1cEclMkZtcDdGdlNvOVlpWG52MW9KeVVXSW9haWUzcm5CdnY5ZDBlc0k3dDdpJTJCRWJMRmI1ZmltT1BVM2JYSnJaN3ZLJTJGbU5udTglMkZkNEVvMURnVmJTZnpPc1FRbXRyVThrVmpFamJCeVElMkZzdHlSUHJrMW5qZGdpJTJGeGtCTDhYVCUyQjJXWm1HNm81T1FJd2pqc1hOeDFubkxsc2VlVW55RCUyQlFxOTNzejFqWnFOU00zNm5tendwTE5yZ2Y5VDRneUZZdGRkbUQ2WnJnTkIlMkZHTzNEZWl1TVlLQVZSU2hjNTBodGlpYzhVYTg1MTI1N3NHUFVrejZ4d1pTSGVrSm90d2lSTXpuUzlpWWQxU29pN25wT2psWFZCcnBaTVZqJTJCeEVjRzd0bTE0YkdGWEtuNkZQUU9XN1RzcWFoVzJDdEh2cmIyRlViWG5pU1pSSVdSWk83NE9HTSUyQkFxTmdxYmJZWWl2akxkM055d2NNZHYxajE1aXZVdVBuUlRSTGRXdG95cFhiTkx3U0l2UjM5eHQ0dGIwQnVOQU5vbU55S2FrR205WXR0QlNUTlFjdW5wdGhYRGN5d0JNWGFrYWwyM0FkUjdSWVppVVNZQ1QwJTJCQmhXR2NxTXZpVXR0ZzRyQ1dIVzZBVjkzU1pucGMxR3kxdmVPdXV6SXJpJTJGTGdRbk5qb05PcmJ4d3IwVlRpVTBTVFVvM01oc2prNndlZjlHQnBjd0M3RyUyRm1NZ3F4ckZtT09yUzEzYiUyRk15d1oxT1dsbWljaXNhaGx2YTBaejQ3M093NTVOUHRCRWMyaTBqd1EzN3J6eFA5d1VhVjlRQkxUYTdKMmM3MzY3NCUyQldTa3JEVGVZR3hISFJHcE5zblF5JTJCY0ZNbXpyZ2JTQ2o5dzh0cFJLMGEyRUs2ekp2SlRYN1JsTGs3ckwwQ3p1QnJmN3FscCUyRklvWFJUUmlwQmU3YVhPS1RMc2M2NWFGY21QSVBYa3dDd0xNZDlvYWdLdGFMJTJGTGZuN05LNXNGOFI5SGFhMWJ3aGgxNXo1enAyZGMyaVpsaTFFejV0ZnF1MjdrNFp5TFZDM2xMVjBCVzUxM2VxdDRKOU4zY0VkODRWVGpmaCUyQjlBNDNPbjRHRm1XVm9FYVJHbmtMVFpLalc3MVBqciUyQkxwTGJJcFdwNXJqRzVvY05YbWFuSnBpSEdDUjJVWUY3aTJkWDFUeE96MlpSJTJGaXU5elJFSEVMUlhuODVMTXh6UDNWVGZRcWFLa3BnbU5QajNJVTZxWkwydm8wQjYxU2d3dnpPJTJGUXVLUFlOWnE4bDVUTzh5JTJGdVFWc1BoSzBaMzNTJTJCVktmV3hHNURUZCUyRm5wejJXRnp4RjRkQTd4QlhpelBOUnZiUnBwbjNBMmJVVmt2alJNVXpYMVdzJTJCemZiTDhRQSUyQmJGcU9lJTJCZFZqcTRHZ1M2UjJZcnhlMjFHQ0xCaGJhNzJSZXIyMzZFczUyRkUwYjB1bUM5a0pVaXFlZTNsJTJGdzFvbU9CbEZ5NVU2dWhUcFBveTV0MERMaTBDT3V5OVBvR2l6SVdIQTBOOWRUJTJGdUswWmpFUklkVWpuVk5YVG5tZ0s0NFBoQmRhb2xodDdYRTl1YURWZmVwaUglMkZETDhkNUZIMEJFZWFsVnUyNWp1cUQ0ZFpVaTN5TkxibTJkOXg3ck1waTJ1QVhkUnRlZDBmdWEzZUxBeTQxZ3AwaUZaTXh4NDd3emhybmlVZG9oanR1RnUlMkJRdVhORzlPRUowMURaTW9wRmhVbVJUR0JHSTVmYWlITEZLTEJwaFB0OUVwSW9IMURkeWMzUERtVSUyRldScVpIbXV2UTd2Yk1kaHBTZXluVHhQWDlPcG1pNTlkVWlselh0T21Wa015NURHcnB4Um93clBlRGE5bU5hSE9DOGpHSHg0UGFWdlVJdTZzY1UzdSUyRmxmMHJLMG1iTDJKanptYlkzZTVQMXpia0FtWGNNREpuQ2RueXRDNWhObjVmRXROeFFGNnkwcGZOczJRSCUyQkd2TiUyQngwRGNHYkR2N2YwbyUyQiUyRnVNRGJ5WWY0NmpHMUNUeDU4VDl4MjAlMkJ5UXBHeFExR0Vyd3lFMnklMkJRaTh3RUdyU0MwZlhiTEw2OUJzZTBYdHJQWHlRYWxXdDUycXg5NEcycmxuaTNidHRuWnViVFpNY3JMTkw1aXgzWnJTcUlCcng5Ykw2YTBiN0lNclZoMHBLTFZ5RTEwU2N2V28yQ3lKQXhkejlFdVJWcTBtWEdkRkRXTWNFbjNTS09aYWd5OTR4bXN6M3ZzcGJLJTJGcUFJUWhtT2ZsdnQ5RVZzaCUyRmZrJTJCMzlrdHV1YVhKSVJLUjZHd1h6cTlSOHM3JTJCJTJGS2ozRklaUDg2QXdQU1VsJTJGNkdNYVRsMVdzbXF0dktmQk44RFhWUGllN1hOMWs3dCUyQjVXdGk4MlFoa1dtUU5ZU0ZrZU9MYmtQbU02JTJCQlE1TjFjbHkwS0xHUzJPeFNBZkVKbVFnJTJCT21TeDl5JTJGT3JqeDd4TlZDdzVBemVGcEFOSkthdll6N21SamI1TVhxTHd6cG5SMmxSVXN0ZXFnOXhyS2hIJTJCUksxaWU5b2J0Y0RuYlc4alklMkZiaWlMTWJhS3ZUV3ZNTGZYRXlJT1oyejNqbEZmV0JEZVowQzNMT0RZZ3gxMmRYQUclMkJrd3hCNUVHNGxhdmNKbHhjNDdYYTFMUXBzdUVxaWxhV3dXRjZTUTF5NUkyNm1oWVN5QjJIOWl4ZnJIQnFzRFhBMFlJRjdjRElCSSUyRlZudGJPbndaaW5GSiUyRlR0Yklqc3ZFRHR0VkttamxqRnBCTHVsMHQ4RkxpUXVIYUw5YWU0WlpiTmpzZHoxV251cjlzZFVYTWw3MCUyQnJLNVRWaFVpdDh6SzN5MU5tZzBmNXlwSCUyRk42b2xNYXhDZCUyRmQlMkY4U0cwcFJPSHNLJTJCOHZMTFVVeUxMZEclMkJmNUNHMFJ3Z3A5YmRUQWJ6NjN3ckg1V3dyOVNwJTJGSnJJck1KOVhCcXlDNHlLZXc0dnA1NlVtbnI2eW1KJTJGJTJGazVpT0p1aWkyemVhN2ZjbzBhTyUyRlJLMjY5bWNGYjJTUHZJYVZ3eXd2VFl1S01sVFpvaDZRUlF0aUFNaWd2N1R6RXFydlR6WE9KTzF2JTJGdEUlMkZSd05ZQk14bThnamVjJTJCakx3R1dscHJ3Smp4V2FBczlvNDRLMmxwV3hSeWNxdkgwdTR1Uk5HTGxUM0I3Q1NLUk1ad2xCYXRJNWZRdVZhTEY5SHVMZUJQYkZtRnJsJTJCUG91bkVHcWJBdDVQa3JIRTQ4MUZtazI5TXNnb3ZkbTdWQVY4bk1Qc2s1ZldUUnM5Vm1KbGo5cVJwQyUyQnVaZDBQVmJ4ZkxucUYlMkI2SmVQdnA1MUhXMkxlY3gyYThDaUFOdEU4eFV2VnAwZHczeDJheldhUmlNS3YyV0wzdVp0Wk9pNDNZcTRUQTgxZTFnZFJZbVBJUEJNSlp0V2x6TVRuMkpzcU8yT3h1RWttU29Zd3FleiUyQk1HV290bjFTbUpnT3lGR1l5dTNvTXFHNXluNUo1ZnJMWUNmVWJ0cXV5bWJRWE9BTDh0Tkc3SlBPSWROTGpvQ2VJZ0dCa01pd3AxdFY3dWpuQldMbkZiJTJGNyUyQjlVTjElMkJZcTJmT0xJeHZneTRJN3NwQ2FidjU5WjVUYlpFZEZnNnN6WWI3Z1dGJTJGNHBFcWRGdm53c2pwMkxSY09La0VvOEhFNSUyRkZyUEkxQ3hqNlA1S1pEbXJoeGhNZE5malJ1MGdNU2R0TngxJTJCT1NrNjJ5aWhrY0dENll4UHFhMGhuakJCdTV2MGloenM1NDFkQXJDa1RSMyUyRkxpQ0U5SVJ5VyUyQkYlMkJhVkxTZWxUYkV2SVVVSWlhaHBzdUJYWjFIcXY4SlZPdjJNbmgyQk9QZERmaU5yTm16WXFwMGNqdktxS1MwWkZoMFB2WTNJWldPNDE3ZTlGRGNPeGIzU0NrVEJ6MktkYkVsbiUyQmF6SzB1Zkc2NFJrUXU3YU8lMkJGVlExOEV4RXRXTmNkdDE0RjdsNjc2YldFWUdUOXJDVDFObmJPbTRKNWp1WGJOdmF5MzVncDBLVXU0RXRkaFd3UklBTFVkSXRDbUpwUlZKVzRmZTdIaWZPREhudzhvdlJRWk94WFBlblNvMTlOSm4lMkY5STdiZnllR2lDdXJjSFFCVUhpM1dweHJjbE96WG9aVlV0eU9ndHhqamo1azhGeDFLYWpkWjRuNnZSczV2YkYyQ3glMkZtZUVnMVlUSW9PbmcyNTZWNHRPZjh0SU5LRHhkVzRGYzlpOGd4bWgzakcwSnROTnlISml2bHhjUm1wWjJSQnFiUXVpV0xFdWx0ZGczdmRtVVhlUmRDRlkyNmdoVDN1dmdDMXlxSDJZaXhVSXZCZWY2VzdwTEVTTCUyRnViTHphb3hyJTJCJTJCczV4JTJGd29zR3FJWXB2OGtyVmUlMkJZbGRLb3NvV1pRNTJCSkF1JTJCcWhwZFVjaXJqZWxHdCUyRllYZUZ5cjBaWDZ1cEgxZWdlZGFpeE0yZ283RyUyRiUyQmhDeU5LckE4ZGpKMER6WEZQSSUyRnhiVGU0T3p4cnc1Wm1kSTQxOXUwZG15QUhTMVBJbTJualhOZUpzYVlDaDJkMFMydkhoSEplUG04JTJCMmoxWXNZczh1bUh0OVdsamx4NXV3SHZrN2F2MUxiSlZZdDN5dFRZcVE3YmQycGY2RTFFY1ZhM2lBTXJ5TFluYU1Nc3hiVnJObzNQRyUyRnUlMkZVc0hPMDJXclVqNXZ2ZW1WYThiU2dGVFB2VTViMU5qcW1VJTJGZnFsU1FwbzFVWXdIRUNlMk4ycVlUQzMybEZuSkFTNmZOR0p6YjU5WXlVUkVlY1JSZDR0dXN5TlVrV0o4OEJHZFZnZEs2NHNOWnJjTmZkQmJCYWVvdWxYOTVuZWtJWDA5aXptRXE1N01tMnpuU0pyTkJIYUplSWk3VDZXenNWRzR5aHlaQW9RWkZtM1BrdCUyQkxZS0xZVHVaYlhka3NlZlE1N3VmRGlWWmtTanNnU3Q4NnNzckMydTF2MW9idTJlVTR0TjVFeHlNYnV3b2F5YkkwcXhhZTIlMkJ5JTJCNkZ3RmZNQmlHR2I1d0hKdCUyRjdiVnhhbU9mcjVQWFhlMXF3SnFTNVgwYU9IempMWnZtZFVUd2FPN3MzTFZJcWxLaVA2VThGN2VPU2tEeEFzNTZhNFl1SHclMkJodkFzRFl0dHh0UG50MGFQVEQ3bHp1dXplWkJ2ZmVRelduZlE0a2REM0ZEeDhvcGVkOWlLMzdVJTJGM0UwNzVwdWNKN3VHZEwzWFVad0sxSGZDcUVCTFZucEZrcjIxamNaNFVUWTc4SHB1TFZBaDJOYSUyRjJDRnVQWFo5Wk9rWTdFMlVmbEpQMjVjdG5mbFlSQ0tGYXZoJTJGdDFTSmVpTHRTYndPQVMyT21DNnlMVWNWdTJ1ZE8yTW1pZW5WSkE5YktJc2VtTDRkVmVOQlVnamglMkJMTVZkb1BJdlpxT3ZjRHF0JTJCVjNWeG1HUHlZcXRseUJoYk85SW1rMjNDcFA0alVBbDNtYkNMeWQ0TiUyRjA5YzNPSHQlMkJzdHUzJTJGaGZzZHNlJTJCVUZhJTJGV2c5SE84SW1hb0owWmtXNjAlMkZxUHZEazFPRTNiWVVaZTJhazVjdjVKbyUyRmVsSXMlMkYxaHBGZW95ZmlpVG9mRTVqME5KNThoWkRic0pRYWN2aU9NQXQxMUpJN0JKZkVmWFFRaHk0U1ZHZHNiYXdHdkZwNlM3b2Vxa0c4QjluYzdtaTRUN2dBSHo2ZFpWUG50alVEdk5TaTMyMGtZaWU4aSUyRnpOUFhMWXElMkZwOFZGTnBYUlZkdDhjanFCeGJpTXpzZHQlMkJaOUdzZEV5TUVUUDFic21reVJaa2k4NU5qMHpsSlMyc1ozSzRVdHhVdWhoYTFRUXFMM2klMkZoc1pNeUQ5U0VQJTJCT0txTkZ0SHBQT2JrTEw4c2d4ZjR0dGtlYzVtcDVibHJ5NHJQdHFQNlRoU1R1d1ZHRzB0c2xnaUNuMUc1TWxleG0waTM3QjMzTXZNNmtSTXVaMSUyQnNSWFdNYnBmbks5ODJZdGEyJTJCaFNsSGRad3ElMkJMMTdmWXhGcnppY1lyS2llUncxeE5QdDFWciUyQjZYNkZTT2d3VTh4NjQ5Qng4RFBxJTJCSEo3em85RWZIcUpQYWpVbiUyRlBGSjJ0eElzeVFzdVkyWlBaaFFLcjVCVkhJbmM1OGpjNTh5WWx4WkZWeG9vOTdqS2t6N1lsbnF3dU9zZjJ1UzZCWXRJOTNGYzJxeXJKb3RtaUdnSnJ3JTJCemIyaG1DdlElMkJOYlhkNVo2eElDRno3OUl5Rk8wNXNmYVVBakVMVzhVbjg0M0NPcVNHOUlHNkRxRWpxanR2VGdlN05IVExBUXBzOWlWZHZXckV3ZmRRSHJkd2JjV2RTc3d0MVVGNkQlMkJEZTRST3BVdTUzakg4dXRBbmtGcCUyRjFKVThXS0dHbSUyQng3eGUxcFZlbkpacE5BV3RKeXpOS0VmRm9TJTJGR2xsZk8ycnpOY2l5Rm1PeCUyQlBFS3BxeXUybFhHZVhQYUEyY0R2YVFwd1RDdXlnU3FZN0JwVDd5VDN5WVZwUXN1MXlSUG9nJTJCJTJGMDN4a3dqbDNQWENNd1JYYjVWRkVwb01FVzFoc3RCaEswRjN1WVdtWkgxcDNzS2x2JTJCcXhJYkNtNmU0YTRmM1VlTklrdHloZ210OWRVeFd5VE5QTjRoSWJ0dXUyaTVjUyUyRmVHdVhoRW5BYUJVV0FtZFdKdzZpSXFrZ1ZlWGt6eThrZUNZYkx4QWl5QzlvemZRVUFUJTJCdWEwem5BTCUyRldSUW5YSEg1bWxCMWE0c0NCUEh0YzElMkJtMGxjekFSUCUyRmdVNDdud2VkUnROZlhyam9udXRLRDN6dlhLcGw5S0F0YUgwNGxPZGM4TU42UDhlJTJGTHpuMkJJSzY2bVp1dWVPbUpQc0dJcXhoMEJiZ2g5ODRWYzVuaW80Y1dwN2ZkeGdoR3RvMmtPa1IzejZHYnU2WW91bEpNOVgyaGZqd3pZZlRmcE42NWt1MzNIeWpLcEYwZ1R2dXB4Rk45cFhsdURnUlR4VHZmY28wN3FKM1RMTlpkMnVwVFh3ODZYJTJGZGNVRkNOZFdGNFQ4Mnl3UCUyRnpwUG9DeXo1SDE1TkpkTGx0VDJrdTJQYzY5OGJwcHdHd1pySkRDVXNxbTJYTjc1T0ZMdnZhR3pqbVlkamQlMkZ6bE53YnZkaExvNXVaSTVmTXJQN0k3diUyQmlseVRRN2JUYUYxRzBJQ1JlWTEybUZPaXVPaVc3cVElMkZxNjg2JTJCYVplMEZiMDB2cU5mNUhXNWZObjJXbFlsa05adGVsMWg5eVZBcyUyRm52UGczNlU5RjJJJTJGQm5QQXVSUVlMODlQdDlVeFI2VUlLY3ROM3JIUGclMkJCM1pnckQyeVA0ZUNHJTJGJTJCNmxDJTJCS21HclNuUkJ5NUg0ZElGd2QyOTRXQ2ZqaTRoUkRvRFVzRjJuM2FuUTU5aEVocWc2RWI2NlF4Mkp1azBqa2pPcTkzVjlzbGN3NUw3OGVsM1RGUHIwRWdtS2VOSVBCdDZKWmFDRFJFOHo5ZFUxZlAlMkZRQkp2QVN3aGVGTVJGODJ0YmVxcGx2a3ElMkY1cThjOEx2OHZFbDhiNU4lMkIyUlZRRDElMkZyNVFuWUZzT3VLbG9sekR6cVNEdjlmVHcxTDA2cjR4RGZiTmNEUnFqUkVmeWg5NG1zJTJGeHZ2ZFVqdmMySGFIUTFNVExjTlhMcVRGSlJWNGlwVzhpUVdtZE1IRFJHTiUyQjBJMEt6YlgxdUVHRG92d05FMVkwZTMwTFR3ciUyRmZnZ09jTUdONjBYd2Nla0NCdGhWMHZKWWxPYVp6OGlPQktMVGVEb0Z6Szl2eXlLVEZJYzVJcXklMkJVV1Q4U0pBTHFFaVhnc09UMzBWSXVvS1BCTm9EVnZwREF6bGhsODdnZ3dhaiUyQnV4VWNYbVl5clZrMFAzVXRiMGtkOUdMSDczYmFxUjhsQlhMM2FZR0RxRnJ4aTdqcVJWZk1JTTJQdmZKbFklMkJqbTQzTkV1Mnl6YUdaY0pEUm9Md0FMZklEVTNmWHViZmdCcE9ZRTBlOUw1U1JTb0p0dlRwbm12cDdKT3N4RHNjZEZEJTJGTWZudmElMkZRc2Z5U2pVbmxFQ1VUbUcyV2lWcUhYMERpYUFnMGY5U2gzZ2FQQ29kcUxBSk5QcGdobHhiNkc0QTZwcDF2VlJJbXBYd2NxMlJMc2NoUURIMlNCMVdPNWlhSG1iaE10JTJGS2tFWVVjTjc4cW5yWEV4U0VlYlYxVDNkeXJYQlR2VlV0dmZIQiUyRjdPNGRZUGowaW5BeW5DejlkckRsUHlyaWJlT2dWeksyMmx1MjZzdE5Ta2RYUzVMdnY5MHRqVUhFc1ozaDFIUnNKRmYxdjd5SU5WdXJoYjJLS0Q2MmhITDFZck9kSXU5ZFRvWkxFa1hSTlJHTUJoSnAlMkJYaHl3bzhsMG5uamRDdW5RSElYJTJGS2I4QWo2U2oyQUtMNGJoVVZPRnkwJTJCbSUyRnpPU3ZkbG1NdVltTiUyRndiRVgxVFpyOU1scXViY1JUYjNFeVJXJTJGa3JlanJ3cVZmanpDSTFYYzA3RjY4enAyWjY3JTJCTVU5MEhWRXVyOFBKUXN1b3J0cU9RcHU5TTFqcVpoU04lMkJoNUNNbFc1RG5DOUQzZXc4ZlYlMkJuV0llJTJGdEhEYTRTT2RRenVRNVVZa2VPVmpOc2I2Smo1TnhNRjl0cVBvZU0lMkZVdVpSb1ZzMyUyQmJJVlJMSXIzTVRJczBwdG8zbEYyWmclMkI4enI4clNxWHFyblBybmxBbk12ZUdyRjc1cTIzRHE5OEdnbHdVa2tmWExjMTl5OERtWlpjSFZDM3VkdlN5U2sxYlE4cmMwVXYxSDBqOGFXJTJGUlhTU04lMkJHTU5lS0p2ZGUlMkZlSXI1YnFvUlIwRFZSemU3eGxqZFpCVEwyanRBTXpXclJwam9LWEhmS3hHcE5wc1htRGg2bklsdnJkNDZCWWtzb3B1eHV3bXJKOEwlMkI2S2FlJTJCUFJLV3AlMkJTbEt6R20wS3ZScWJEZ1VyMEptJTJCbHpnTXp2QjFPc3A2MzllWTRHNmFtOXVMRzNxSUU3OXEwSWglMkZlSTViNFVSRG5lbktvSSUyQnhER1QlMkJPUnBwRlNQUFZuM3o1QnklMkJsZE5oN0dySGQydk9Bcm9vMXNzcll1TzZGTVBNJTJGQ1VVU0lNdFYyMzc1cmJycEQ1MmlFV1g5V211OEolMkZGazR2SyUyRnBxT0olMkZOYVJRdjBpWkk3ZGRGUFklMkZ5JTJCOFNwOGFLQm41ZVZQM3p3dWh5STFsTSUyQjMwYjl0ODRnZ2xMQVZRJTJGOHhybVU2c29NaVJOR3gyYSUyQjVtT1E2UldocHQ3M0VkNDlvMFdqZGJ1c2FQNTFqOWV1d0tWSnAlMkJtd095dWtZZWlQcG9RQSUyRmM0NE03NXJJdjhkRFhJWSUyRks4JTJGR2J2OURQNmRwczlpQlo1S0UyZGdpOUw1bTZpbTlmcmxGQ29lZ25qZ0dNUHQxUCUyQno2MzhqOGpSWERwRkUwJTJGZGNPUVVESXN6QU45cXJvQWpnaGhvVUppciUyRkFTbDc3R0Ixc3VTUDVNZHlJaldNTGlCTDkyTkhQNHFNTGlTMFdKcHZnZXpyOXRwd25tU0d0akclMkZmZlZRWUEzR1BLQWw2WkU4d2pPaXI1SUslMkZCcE0lMkZQUzV5aWQ3enZpc2ttV1VVYUZPNmY3dnZHWWh1bHVrNUdNdnNhTHl0OVBIU0dsZERCdXJEeG9pRTV1dHp2VE1Vbkw0ZHFYcHprYXZaUVNBcUE4ZVk0emhyNmpxdktwdllFem1NTVhrVU5UY1UlMkZqUmglMkI1Y0FoSGpKVW8zayUyQkElMkZwemIzMkJTOHFJUkNEOWpsajNrT3IwS2J6OWhsQ0Q4JTJGdE84cjk5M1NjTCUyRlFTVHoyaE9VOG54ZmxsbnJvWXVHb2ZzZWJ2Ulo1JTJCVmtEWlVmeTJyOUo5cXBPSFV2WmpoJTJGWWs3OCUyRnQlMkZJQSUyRmpZOXVBZ3NueFk5VUFQOTRQNFBGYiUyRk10NCUyQlZPRUt1aVBrbXZPN3RPb2tvdEhleW5vJTJGeW43SDd3R3d3ZTN4UWlhRUF3TkpPNyUyQk5MYVJaRiUyRlVkYzYlMkJLcCUyQlRnQnR6UlgyMUpodXhQS3JEdzVYTmxWYkg1ajB2bDB4VEVzcUFzVVVlb2U0ZnljUTV4a0syRk8wNndWM2hBJTJGZ3Z0dTZibDZPJTJGUElKdDliRnpFY2l4dnpoVkdtSFlqMThkMUpWZmx4dFB5MkFmVG1oV2RubFl6JTJCJTJCSWpZOWpFJTJCVTlkMkpSbkhWTElvNTElMkIyd0d2SHZFcGJWRXB2VXFwdVYxJTJGbmxVM3RtOEQzdXVzUTNVWk1MWW55RE9iOEhXZzFpeTFneHlpb3BUUUZSJTJCbXVCbzJuUGU5MVpTcjBtMTFOZURFakdxUlNDVzNkaEU0REZYdzVyaEg3dlF5MHNvWDBDJTJCRlJsZG5BQmdYT2pCN1lQMnY2d1ljQ2YzeFBHaHM0aiUyQlFYVWthVWQyOGdOdnV0OVluNjU2S3R0ZCUyQnlOMFBadyUyRkRJcnFhZXhJWDBBZVY0WjBhUjdpUFM5V0JldDMlMkZmYlZCajlXRVZlTWh2c2lPQ2lON1ZkMGNjWUFUR0hBTkh2OFIwVVBEOGtZYnl5YkJUeEhSYzkza3ZrRzdUc2U5SHllRjEwdjA2VFFlS2QwTkhiS3ZjSXQ0UjY1QTJSTE1mbVhiQ3pDNmtlMSUyRmJEJTJCRk5ndFh0QSUyQldYb3BBSjh0bUslMkJPZkNlTnlqbFMzJTJCRjl0eFp0dWNUbFM3WGFLVExVJTJGenNVZ0g0S2tUdFlnaW9hR2ZkTVRlSTZWVWN3WExCeVhtODFKWTRVdWdPTnVkRlhtSm5pWEZLWjhhbGFtTUVDSlJ0M3Z4MTBuYzc2cTlQcUtjVWI4RkclMkZCRm5tRkplajY4WVU1MyUyRkxNWlBaREZ3dmZXbVpybFlFaE1TTUl6WHEzeGVHWlpHWEFMUVgwJTJGVGxTa25nSDB0JTJCWGxMZUc0aTcxdEI0QmIlMkY4c09LakpOV3clMkZiWVpNbXBGTjZPUVk1cmQzRk1ubGJoZjJnM2k3SFJqaEw1QVVDNnlzNEpKZiUyRlVUam9JV240WXE2dmlaN3VLdnFhZ0Z2TndvWlBicUFXeDN3c3dqNlF4MW5xMFklMkJDV0NURSUyRlczbVkxdEVEUms2WDBOZUFaTHE0eGdpV2Z3UCUyRkFxbyUyRjJwY0pPTHBNdnlUbmJoVlI3M09UdGNpbktTQlNrUjBTVlY2bWx5eFM1TlZMbTNtdm1ZQ1F6OHFLcnB4SDkzdllmMjJ0SWkweTAlMkZ5RzBEcWxNb0NLMVZ6R1dyJTJGaThwJTJCRlZ2alpHdW43UWpVJTJGaTl2MktQejNHS0N5MFF1VWVuR1MlMkZUZkhnMnJlejRFc1VyZE9iaEpOZ1UwOHM1OVJNbm8zJTJGVUJyN2hEJTJGQkR3YkcwZm95bkU4JTJCS2RydWs2eiUyRnZEenR0QmFYTE9mbWp4ZTZmN3JwNVltV2RqcnUxbG8wMGptZFpQSUxhVk5MYXJacTFvdm1vNEIlMkJEMlhyeFZoJTJGWFFSTDBTR2FMTkQlMkJkcTU5Tzd5TUQlMkZzckV0UHYzeTJVYXF6Uk9IQ0pPbWxYNFYlMkZNN2g0UlQ1RU9xaSUyRkI3RXRLdnJUcEhOVHBCT05ZMEElMkJvN2JNeGNLTGFxQTl4UUdsUlFQQmEwTWtBQU53ck9vMEtjJTJGSk9UR1FyenNSZjRoN3JvaHhEcnUyTHpPNllsOG5EdE5iOE1HN0xURFhjN1dMdGhGJTJGbGtmTHBZJTJGWkZCQUVZViUyRklMaTNjQzlMZjIlMkZIc1I3NXo4MnRDaVlWMGMlMkZ1Z3p5ZTQ0azZRZmRCYkhack13VmFMUjdoa2xhNEJ6Mmo3Q29laWV4clpzeHQ1WiUyRk94RDBMdlNZYTFKWmdHNDRYMktwUFlXRzVJMyUyQnZ1aDNVbEtjJTJCcXN2WnlnWkVLNzlzTnFsZ3dlJTJCNTRyVkxTRkFORTRFYzdhYWpzbSUyQm4lMkJRbjFiM2dGQTZtJTJGM3RhejN0T0dYMU9OTHBHSTZjJTJCT3ByM0RZSiUyRkJLRGF5T3Blc1ZDYUtrM2pXekFHcmNpOVVVa3Z2RVJISHJwZmROciUyRmNMbVlSdkJQeXhFSm56VEdsZ2ZkTXYwSWtWRjlKRW9HbEVDNTZtYUx0UTBNeG5lNjRUWGZ4TEdRM1BuWmszTXlDRkpaYWw5aDR2OVFjRkFOaUt6dXFxZXhOS0xKOUZWa0R0aFZPcVVBbmJEdjhnaFJkNXBXJTJGJTJGcWYwSWEwVHU4NHNzaGt3U0dPMDhpNUglMkZNSVFBRzlpZVdFQTZFanNKSzNsdThNWUVjJTJGbmZrTEJJZmZFYk1uUmJRVU50MGVsS0l4anJNclViUFNUdnMlMkJtbkh3UUFBY0wxbldVVyUyRlUyUVNiR0UlMkJGY21mbzdZdm41R1pFR3hmaTJhdzFOOGk2JTJCM0QyQ0g1WHU3VEh3OHNCZDA1R1BtcjhPQWZ2SnV1MU1mMmRtS2FLUTRFNFBwZ2VkUWxGODU0Zm51RktTUGF0WHc3VVElMkZRRjZodGdNSktVU002SzliYUdwcGhVJTJCc2tCdmJXU1V3aVVYbnV6dSUyRjQzdU1LRjg5MmFlVVFiWDY0bGhod00zRVRBTlJaMGlNbiUyRk04dFZsUXRkSFFzSUN6bHh6U3dKbEp1aGZXckZCbjdnYlk4Y1AwNGVXRG53OGt1ZlMxZTM5Mms5TFB2dUdzRHZ6NEMyS0JMOEdNNDF3UEViQVZIUEZ2d28lMkJUMVAlMkJ2aHZ1M0MyRSUyQk5Ma3RHZHhPcmpMS3lwQjRScDFxdk1SNzdQNGtNRyUyQnBKJTJGdmJiemRFbDJ0TlZuRCUyQmE0Q1FnQXN4cWZyRktOWEV5QzIlMkZGJTJCWVVWTTRUNjUzTXZmWWhZSWdicHFGM0R4RWFhTWxKWFc1MCUyQjNxTE9yRVFMWTBiVng2b3hxOGVDMUplbURjSCUyQlBFZlMxZXhJQ2tTUkg4SmwyUGhMb1Z6dzUzQzdldVg3Tm5iVEhlWEFKbFBJaUlqTkJKZEY1eXhYdFg2SjB4b0RnVm5OSTU0bDclMkIlMkIlMkJ6cmpJR01McEZNZm1zY1NjNW1ZY25yOTFsalh0Z2xPVDNzbkxKV1VERHA1QzMzUHJuV0ZnRnBtUnpKdHdoQmxlNTAwT1Q2eCUyQlV0Ylk2cW5tciUyRkolMkYwYmJuSklwb01KTWZNOHRoaWxEOSUyQjMxVmFEdnI3S2FtTEZNem96MEZuTDlGYzZSVWdndkcyUDdYNno1SDFOVmtvZ21ldTNZNWdXcmNLY1F2OSUyRjBtSGIxTXFNeHNhOU1ESzU0cTdVWDc0cjFNTnVGd205ayUyRnZkUzJqTFhMaEVrQTk1UkVLV3BzMEQ5R01aZVR2alVDNUZIMlJQbHZ4aFFnQUYlMkJ5UzNCMWlFdnd5U1djcDdWa3YzdVY2R0FOZE4lMkJmZTk1aEs3TU5ZVlF2U3lNVTJYdkNiMEtpaGRrSVo2eUZvSHFDWTdsY2QzbjU1djFqcUZhdXI3UE4lMkIlMkJvbjUzR2hGRXRTTWlhNFdYOHFxbGppeEhCbzY5U0tTVm94ZGxrekZyMktrQjlQUE5OcXhUNXBvR05sbmhtUk15V3Rxa3pZakxFT0sweHBMbmRrNTl6am5WVkVwd213cFl4b2Y5R3VtNSUyRmlacndVaTlSZDdyaXFLT3d3JTJGVjdBOHVXeEREQXZCYXF3QVo5Q1lzWXRtdTVQcWFPOSUyRjFSUGllV3BhMkpzcGJ4Z09WeVlGNWFob1I0ZCUyQlplV0pjNk5DREIwM2FrJTJCSzZ5cjVrVTl3dmtKM0F0cDNiaEglMkZBcUJQMUJPVXpIZmJDTld0TFNvQXhjOFBRSmtveVZ1Smp1YzVuSGgxVDB4enlpVEt2TWJkZkJ6aEFQOTk1SFJQUmdYdzZFM2t4WEdiJTJGYlBnNThKVGolMkZacVVCZjBHRjBrNGVhVWdtMUY5WEE1NFFZcVc5MVdrSm9OcWtTaTI2NCUyRlhUNWM4dzNOdEJ0dWJmNGFLcnNEVFVoSzNYMGNWM09LTFNZYlhFUW00Q3VwdGx1TUJVaVVZb2ZuMkdxc2FvbGI5SU1NTlNtTE9tUGRWQlE5TVRkbllFa2dmVmUxa3hHaXY1Q2h5eTVRTkFLSXh4Ym83UFZPdkVEWnhiWTA0ZHc3MVJmQmJ6OXpkT3RONmRnMkpEaWg2SXJDemRIeW5sNjIlMkJyTVNUSW11VlcxNkhUbXMwSHpFU3VhbnRMaTdIJTJCSnJidExXNlVpSERHblZaSU5jUlNIWGw1MXhWeFRqaWVJVDdpTE4wSU5PeXRWdGR5NlZHa04zdW54aThBNk9VQ0E2UXlQS2lGVWdGUjVqZ09mMzhOakw0UEZZJTJGTXglMkZia1FoQVlvdXNRYjJhcGdienE4Q1kxVzBYYzZwSSUyRmZGbE9pZThKTnRVckxORzhWS095amloZTI3b0RoMkJKRDFtY1BOTVBOYldMcjJreW1RVmJNZENINGlwTTFCdDhxamtpTVBOSHREbGcycjglMkJRaDB6MjNVMm9qWDdTUGVwSTBHRlh0VWlGUHRXZ1Q1TjFiMjZUcWFKQUdyVzdybTZDcCUyQjVPZ1JtSW90dWdSTXFnbDdrNUt1aDMzalE2NGxlM0huY1lqS0tVQmUwYkJWWWNLcDB2cFVMNTVSWFg3SFhYNSUyQkklMkZ3ZGFDYWdaeDNBbUo4WFZzU3J1ZlNObjMlMkJ2OWJ4cFVvSVBGV2ZOdVVJOWlzTDFyQ0FtY05lOTU1cTklMkZlZWpjV0w1Nmt0SElYZ1ptNXdwZHpFSmE0VjZBVjE2ck5ieGd4Ym5hM0ZKMlVZZFlEWk1TVWU2Z0pXQ0lvJTJGeW1hUE1LdlFvT1ZqOGVoOE94STlMelpHVmYyTSUyRldmSDlHYlIlMkJ3VGptMVBZcnlmTnB6NmliT2pvVlhleGdzdWN6TXY2NUtyMEhsSHNkdFF0dWk3TDlaRnJDQWtJJTJCekJsdDIwbFNDMW5nOVd3a0xveVZvUU1HdUIyUGs1RWJaeTk4eHpxTTBYYUZ0anZEM3pJYlVQZ3c0dmkxSUF1cnUySVJueWZ3WGNjRDVOVVVKUUZVZ0NMM2dwcFY0cmtpUno4dDF1VmRWWjFEeW9pREgzeDMxc0ZUUEYlMkJEREJIcmJEbzM0UFU1YTRoV25XaHo2YW1mMWZaaTRmRTN5cHFjdiUyRk5seEE5ZTUwOXNtSnRXcmtSbEk2dkpxZmF4SE0zNUc2WnFzYmN1cVhhRkF5VUx0SjRHVW5WbHFYa0JHQndKTSUyQnlEc1l1MGZRYk8lMkZzb2RLVDllOWo0RVJuR3dPVEpaNEpLMFlralJwR1pNTGRFSVoxREFUbUdXRHRGblVOUDdic2Y2UjNLUUJsRUVSeW9NQUFzNFZ3MCUyRmtTaVVPT2UxNlJrcVRSanU0N2ZxSHAzQ2Ywc2FuN1BpSVdwQ255MSUyQlptVnczZXh1USUyQjVNVHpHUiUyQkprNkt2dzlHNzcxTEZ1b2VDdCUyRjdUMkNkaVdORHBpSDVZT0JLc1ZJbE9iU1NCem9TVDZia3RYWllacUJkWUNEQ0ZCQU1aaEN1R0tEOGtNUXBFNFhyMTFRVThhT1clMkZvR1o0Q2tZWW9KRU16YSUyRkR1JTJCWkxka3o3Z1ZOVHJEZVJTSVIlMkZkN2xDdkpkYVRQeW9rczhlJTJGV0ZvOXI3WGhBdDRwM3ZnZVpOMm5FNml1Q1hOZnp3azR3aFB4am42aG1yQkVaRjdqS29KdkFRZWZqMUJ5OWxISk9oSEdUZzBTdTZlVDVWcFJ3Q2lHSVAlMkJ5ZXVmbE1FTlNSQVJRajUwMEdyRGVObDdTV0VOd1RDWVUzNWU1czQ0bHF3VTBEWkIwMDh2QTBMaDBXRXJ4b1pxdW5DYyUyQiUyRmZMREUyODZzRXJydWowTUNRZmFvdXBWMUVrbWJzUnZiWFRTTXglMkJsNU5XdmYzQU1VV01OWlJPTlVmWHBMZjhFM0VJTXBFcU9SNlNPJTJGWGxvZnl0MVE5OTluaVd5OFpOemhmbHc0ZVJHZ1JUbDFmM3ExRjBlUnIlMkI2SWRDT0duV0VPMjRPejlvNyUyQjhIRWpTRmp2Q1ZkVSUyQnN2Smt0dzNnbGVtWUhqcUlGbGZ0TjZhaTRiM3JhUm5BVTBvdHRpZFVJN1RucUphbSUyQmtyYkRvMzJkJTJGWW5MTkx6cGJJZXB1RWNYbE5YbWw5eGZLNVREWms5YkIlMkZoelcwaENUOUN4RkFpYjlIVWJuRzFPaWIyU2xuTk9hRWppR2Y0N3F5YklwNklUQ2s1RXFuVE8wcSUyQm1seVRydkp6S3JKVlE0RlhxJTJCN1RpNDhtZzNnYngxSmdRRzd5ZTRGVmc3JTJGRkVqWFNkOFpSMFY1JTJGYTNLcGtmMVZOcE1GVmc3S1gxTm5GY25BbCUyRkZYZkpicWtGVU5aZzdsaTcwRUpzUU9FNWhOMU5wS1QwZzlTJTJGNlU2SktsYm01JTJGdGlBRlhvT1k0S1FHN053NGxieENoSTlmR3hHallXeFRUQTBkcnhJSnNxdFQlMkY4N1hVQ3BOSkl2RHdGeSUyRlZid1hybm9CRG1KMDU1QnZRV2xuN3pZclVHQXFlT3dlaExyQTBIcDA4SjJoWHEyclBYMnJFaDZJT1RDdDZZS0Zaa0Q3dDJaUmtNdDVFUEQlMkJwc005MG1vQTRiMDV3UUFPakV2SThXSjJWWDlhZzA1Wk8zNGtEQTBiMHlxd0k1ME9qclhTWG90JTJGbjNTbzZacVRkbCUyQm5lZU9GZzdoNzNOWjVKVHVxQTEwNXclMkJYS3UyRFNKOWRKJTJCT3V3cHJ6R0dnSHNVaVkyeTFNdlZ4cjUyN042M0dWbHZadVg4M3RmbjF2NlVDSzhlcSUyRlNvSjhUJTJGWkMlMkZEMXBaOSUyRjdFUjZNJTJGQVVrOFhESmprN2tBeDlkcTlrY2JncUN0Tk1iOTNsRDNtMzZEZFJmNEpVeXlFQ29zJTJCJTJGU0pIQnhMOTFHbmx5SVBJVWd1SDdKOUhweFMxQVJYSjMlMkJ2N2lEd3o4ekhlbXhKbUFycXFRWm84bWolMkZiSFJkQnZLdWJkRiUyQmJjRkNodlQ2aE80bCUyRnZuc01WZEs2RnpkJTJGanRtWVZOdGRTc3gzZmxDSU9td0dtWm9rU1JHMmtBemFnJTJGTHdpbjBHZSUyRnc2a2tEMW9UZzA3anRrc0g5eElCbjF3Z1kyQ0RaYTlmd0hBbTElMkY5MU5XUjRVTDBGZktPb3FHbENuR2Y2Q3B1U3oyVFElMkZmeUVFJTJGSzdZTEZocjFqYk1zaFAlMkJNcTU0ZXhFM2tIYjBLN1U2bkQ3V2tZMlVTZiUyQll2RWJKR0NpJTJCWkxqT0JKR0x1bldNS29wZ0dVeW9FVjdtUk90dmV4dTh5dHBIbkZYcUZEVUNTUVFjazB1WiUyQmpwV0szYUNFMVhVdm40TnZsU0xZbDNlVyUyQkx3b1psUmVCUDVuc2l6ckVzVWdzdWY3ZmpMOVRCUHFrNWd4SDFiamZkdiUyQjJSR2diZkpJd0NrakNFSWd6MjhlSEk2TDFjZGxUOEFGZzBzdERmTXlMZGlhVk5rTkJpcG1ZTWF6cDRtY3JkbUZ1OHYlMkZRViUyRnF0R2pEQVloY0I5cWdHSzhwZUVKZmxFSSUyRk5jM1FWOEo5ZjE4ZXhQdXViSVNOJTJCVWo2TFVWYXElMkJSbSUyRnFsbFJ3N1dCY3Y2dHFXM1pPTHNVeVB3RFglMkJGVGd6dWNoSTZxVDdDcnZwS1VJdEJmb2s1Vm1sYnlCRURhYnVSUW84MFZqRTV4aG9mZCUyRk1RcW9UNyUyQnElMkZKU2VkWFlaajRBd2JFOVZLdnAlMkIwc0ZIa0YxREQ1WUslMkJuZ3hXWnllbThxYjF2clhwRkEzVTZSVG1FOHlMY1lDeVlrUUhYYlVFMDRhM2xrZDR3eUZkdmdzaEJzMnhQbzJmWCUyRmhsRSUyRmpWV3dmU1lUbEphcXl6JTJGNUdlektWWGNwencwdnBIeXFxNkF3NlRIeGtzR1ZCdUdUJTJGJTJCeFVlcWdTVnloSE9hSlJianVsSmUlMkJhc0JLUnoxY3ZVc09EenAlMkI3Q1dYcVpOTWRoSnhKRnN0UGxqTyUyQjZBMmt1U2xGRE55cWlwRnVMRFFhN1ptUE5rM056MG80a2JxMjhUNnd5bUdmd0tmZzlIa203MXNvMFNSUkNzbTM2NW01UVhpWUVOZFh1Nmd4b1ZiaWYlMkJUNnJveVI1WW52U3JVaWZSSFB2VFJwV3dhWFhEbjFmek9rNWc1M21pUGwlMkJnUjBxWmR6bURiVThtM0tYMGlBMzVCU0Q5WDhTZ2JYVVQ1eWtUSnpUS1RLZ3RncW9FR2dHdkpVcnVYemhqMlB3SDdxTWdZOGp2JTJCRGZtcEIyMkxNOSUyRnpPZDF6cGNiWnFtRldoWUdZa0k3aGlXQzl1RjdFSnp2Mk5yNG0xOGdGc2hBbW1zRFBvN2FQMkclMkZhMk81T1o4N2FNUTBvaHAlMkJSZVE2ZFNreTNJdm5vTkIySmR1MUpjWndTNExmWDNmNVV5NXdMazJMRnlSRU8xM05pUHh0bFJ3Wm1GaEh6a2Fna0MlMkYxS1lsd1loa1ZLQnRFTWM0TzVxUXdYZWVGNG9NeTVXNDhKb0klMkJNSklhSTNuJTJCQ3JXWXclMkZER1JvN2pQN3lrcHZaanR5QWhuZGk1eGlBQjlGZFJBSHFnTTFWNEVOUnR1RHRDSGlCck1KOExwV0xLcmMzc1ZJNXJQN25OV0VaOTRqTTR6U3g5a2NFcERQcHRDZUxRanVJMzZFN2hoNVdsMUV0dWZYUnNBVHBFczZvRFQwNGVLRUZXaUozYjMxTE9pSW9Ob2h2U2sxVFhLd014OGQ5VFZxQVA5c1hCWWNqQ3gwRlFUN2xGeDRxNVFEd2R4Tk9kck91JTJCb0JWSWNDUm5SUVIwc3J4YTlQRTBWcCUyQnh6RkplJTJGSFlIMFJjJTJCeWxCSkR1UXJ2MVhhNTg0c3NiTmRmN2ppTGI1ZXRhcjRoZDhsdENnakJkRTclMkZhJTJCUEx1cDViVHA3eGtRa0ZQMUIyUjdNV1R2a0tZMU80RlR3WiUyRnVqbzA1OEJkVWl5c0Y5TzFxN0VZMnQ0VGhkR2duU2FlMEw4MHNjWDdERDV6TmM0TEZmODROd2NGY2h4N1QxaTFrNGd6dkJOMm40VmxBZnp0ZmoxeWxxU2hpZW8lMkJ6UDdVNFRXdzMlMkYxdEJQeWQzbjFUS2FXaUZ6SW54dEh0NWt6MnNpR3hia0VMT1JkemQ2ZVhtJTJGNkVUNTY4RFhTeXA1SHpLS1cwZkVTd2phRlBNdlBUb2x6TzNjSFVZOEs4eUpRcyUyRmxkZ29xUm5EdU9rRFUlMkZaVkhRb2ViSk9MVnY1aW50ZVNMQmwlMkZkODdQcjdvRUliUThvJTJCeFFwT3FzQ0o4RkFUWEMzV2p2biUyQjFVdThOU3RFTW9Fcno2SiUyRnJFTnU3QVJkWFFYTW15ME1iV2JSTEJZWGh2SzVONzMxbkhwcWY0clhNbUZ5QkZDRmxwamZxMGxRREZucHpuZndDbmFPbmpvNDZQVk9STTVVdTRrZGZTb3Q4U2tyJTJGWSUyQnFuZFB1Wjc3dGVjRlMwYXRpZVREaFBENVdZUlhLdjFiUFJlcm1uaUZyaGE1dzY3bDc1cThWMXM2JTJGQXRlY1ppUFJZWG0ybFVzRGRKRWFpOGZZYnpGMTlpVTNUVDRhaFdPTERSZSUyRkZXdldQWE5lV2Fzb1lXSU9zUnolMkJhSFNOV0dmbEMzcTJXaVByJTJGeDNvVGMwaUdNdWFsZzVBJTJCdnBaNTZlc3AlMkI4VWlESHdHcmY1emNGYktsT2wzNWVnMFp1S0dnN3g4VDl3WTJVa2hkYVNaVWZNb1pDek1LTzJ6SkJPQ1gyNGduemVQYUFvV2RSaHJZR0V3dzF5dkVudVcya2FkM3NGUnFMSUE4eSUyRiUyRlh6N3RHSmVDUDZVSW5jWVolMkZDJTJGaHB0Ukp5eU1EZU1uMHM1SEh2RG5UcTRYdDliJTJGelZUdFAxMU5kaDliblY3bkxnS1B2ODZPV2xBSFVuQmlzM3Z0cUJJWTh1ek9nZDdJaFB6cHlNNGZNJTJGJTJCUnB1Vk83WWt2emJKeW5MQTk4aFhDYmN1dzQlMkI4ZmoxbWV0U1hPZFVwJTJCMmJYRkZvZjFFNSUyRnUwJTJGR0FhYVFFUU9LOXclMkZEeHlvJTJCSGJZcENkRDNjb3lQOHpjR0N5VVhtckVPbTZaSlR2QlhnZTFPb2g2JTJGREZVU2JFS2VwWDU0MklMSVclMkZFcnVXSzZkUnZOTnlHeDhCYlAlMkZ2cWY0cGFWVGZtVmRFVXlqOUN5c1Z2cW9mSDRJSm9FaG9RS0FsZFJ2NHBiUVclMkJYeWpiUEM2N2x2U2olMkZsaSUyRnJsU2w1aEpFbHJ5QlVkSzBMJTJCdTVEQ2FKZWdsalUzbWZ5ZzhXS2lpMlluJTJGcFVHMGxIdnpFMk5mRmFBZndyU0lVcGMxWmdmWGtnVDJiQ0g0NlVpZEoyeGdKNDVqUHV2dFVrV2JzaEMlMkI1ckQ1MERsUWRadjElMkYlMkZRanBkSFpxJTJGZ04lMkZYNUV4UmxucU9HUE01VkhMZHc0T3dhdmVVUXZFJTJGdXNoUjRValIzZmxGUHlQY3JWZkdiSXo4YWd5dmtSaU53bTlvbmw2bmlYeXluenlqb0w0TXpiVWtnbXE4RjgzNGpsdW1sQ2w2OHFpWiUyRlEzNGEyS05wNXlHTVVxRVI1YlR3OFp4eXU2R2JpUGFZTmZTMmtqeGd2YVBtWmZaVzRkR0Y4SHZlOGcyNmJ1UnZKMHhxSHRoMVBCRmFVc2ZINGRZb21JJTJCTzZPNjVHcFN4T0JkczQxV2dsZzJxd1A3SU82ZGRRc1hzRmN2cE9lNGclMkZhcDJmdTAlMkIlMkZuSHhFJTJCbkppa29pbWYlMkZoJTJGNDRJMHVlZ0pjNEx3ZnAwWWFYaWRKR0FJZHJHYjh4YUNoaXFZNzdTdkdyM0ZaTEQwdUNCN1BFbWV3VkFkN2hQZkdqbDlHMHdCdFolMkZtcmdyOTczdkw4c3Q4R012QzEybnEzRWN4SlA2c0NwWHZwd29EYjFwcU1qNFczVSUyQiUyRk5jeHlVTjhaJTJCZ3U0WktGUyUyQlVKSGh0MzdPbE0xaWI2WTNsNFYlMkJRY3F5dlQ2RDAwRnJyVE5hT2pkTzlhT0hub1pFS3pUSk5weFZYMEllZTNucVVBeWMwcHNQUzZ4VXNTeVJlZkRCaVA1WTUxa05oNUZNZm5yR3dWZmZMdUVDb3hiOVFhZ1pTN3FQOU8lMkZrRFREUGk3UGFXMXd2JTJGN0dXRTJYTU03b05HM25PbkUlMkZybHBWbVNVWVRBeUM0ZFFPZFByaU9KZjlMWGZSSVRtN2xFWFdsRUdMeEF2dXBSNlgwJTJCN09mamVGJTJGRzExdk51JTJCdk9WREslMkZlbGRBZkl2SFdYNHpBd3NwcmZwTnpxODNEdGlWUThCck5IN1RUd3RsdnZwdmJxaXZqblZrRCUyRkc3YkY5SFRUd3RIY3VoeWJYVlZxZVB1Y3AzbEJ6clRXS1d4TG1SdHlxeEE4NzVpVWxHNmlPazNJN1YlMkZWaTBiNXNzSG9adiUyRjVPRHdWZXJXVUYzT1dMcDd2T0ZYRm96blRqM250MWd6NjVldk5kVnMxUEhiNiUyQmZmJTJGMHFIeU41MzhQOUVqQ3N3UmRDclRTRFNzZTNjYzN5alhZOVRVMzN1NlNlZCUyRlVTU2gzN3piOTF4VERPYmIzWDY3RTElMkJDJTJGWXVPbHM2MXFSUXh0dWxaSWhFMUNuVkVubEtVMlJkZXZFdHAzUHBNNlByWUt3R3VhWHVIV2hWJTJGVEpyQm8lMkZWdFVtZ01tNEVaNEt3elFxNERtTGpUVDI2anpvbGVpZTAwUVVkM1pOb1lIZVlrUG10dmlhRXVoT0ZYOTVnVEpLVWNoWVcyQ2Q4VndmTjhIQjclMkZPdmd6WDlOVWVqSnNoVGRGRzklMkI1RERLNTh1VkclMkJPcmZKNjJXWXpSTllkV3p2V1NqS2YlMkJJdHlXbUR0JTJCTk5UbkJQNk1iQUM3WVhjTUpxRmhJQXpURVVScUhDZXIlMkZDVEh2YTdRanlWaWglMkZ0TXRBZEIzOXJ4eEZxeWw4dm9QNGluTkpMbSUyRkRPbTdnRnBXVXZ2TDlpZVpZcERveU9pUnZ5VjJzenJpQ05sSXo1VHB4VFdIS1hhWTBiT0NaR0ZWZ3JGamRGRndSUEhQM3YlMkZGeXRwRWRUNnQlMkZYWDZMQ01LQWdEQUlqMG1FOVVrNUNDNFBTWCUyQlNmOG41dnpEZURxZlJmYjBJdVVwTVJjUlhvczdtMHQlMkJqYTNSekN6dmslMkJDVVUxbzVmYUxaZ0JRTkZ2OHYzck5ORVJJMDhEblc0WklpJTJGcDRwd0MlMkZ4dEdMTnpSZVVyNktvNlolMkJIWUdNSXZIcjBMZ3lnQjltUjNBSXhtdnNPcW1tQUklMkZzUWUlMkJvYiUyQlJCUm9Jcm1qSXI4U3Zic0JKaDAyOHoxOUtzb3Z1aFR4cGlZZTVSemVteiUyRmtDU2dLWUFrYSUyRnVXN0dQNExKeEV4QjFXYmNtSUd1d2hxVDJVYXlwMm53MTdWVDAlMkJaZHZWVGhad2lMdkg5UkhTMnZDM1lkdzcwSUNyc2lWN3BUSFZnVDdUWkhCMVJTbjY2TTI3JTJCWVJvaGZPeW9PM1l3akYyRHcyRXpvTDUwRm43UG5QcXgwM0FES091YjBZZEdnYmVJZUpsZTJoVGphRGF5elRBZ3hqY3h4TGNYTVI4MkxoeHNET3lNQ1MzNFolMkJGZzlFRUV3UnFCemVMUnNMYjQ4WHBZNVE0MDA1MjlIT0JJM0V1NjJxbWJSUGEzcGUlMkJYNEsxTUU2NDdKcWdQV1NHcnB0VnJwalV5bFZYQ1IwWTRyZm96Q3JyUiUyRjFyU1F0OWRTSkVXY2ZCYkp2NXF2VEtLYVVMTWNoVjZoeU91JTJGaE85a2VYbTlsbjYlMkYwa1JRMUVjSlgwMENTcyUyRiUyQmVnTFAxbU52bzdiWW04Sm1hdzlESDBoZUg1ZE1FSDlMR3Y3cndVVHZ5cm1vZkU0NXFuYmlKVm9sUFg2UUhSUkslMkJaaWZZdTRFUm9HYlhaTUVPZWEwN0RjeEVKQUs1emZGOGFScWgxNm9WOXBhbGt2UzFoeWtFa1o0Q0Z3cDNxYWZ6NCUyRmwxM3dJZGFnOXAzczVhUWZ1TjJHM2Z2NE94SzYlMkZTSm5yNk5PampXb1NIVjEzejBHUDdFWnlnOWlMRURrYVBYVkZmZjEwRmo1VHNLQ0NndFY3R204TlZrb2piOXJ2OWEzSElEclNoJTJGRWZ6R1h2TFROUkVFR3VOMnBERk9VZ2tWYzhqN3dQYmtWU1VIZTVTVzVFM1A0WEFaVmltOXo4c05kWkp4eFAwcnVjMEJlMmpMNnI2UGF1SGlCb2ZhWWdRT1BLU0dWZUZiaG9NRmZqMVF3eSUyRkdyVkl0dVhXdm9ZSUVFcGg5U3k0dHp5MXdneXV0V1lkWWUxNXJ6dyUyRnY2b2VvNlpYZmFoJTJCdkVVVnFGZ2R1aTAyWnY5VjBrdWE0WHpBUVRDTDk3dFZaQ2hLMnhVNjczZ0txa2pKZHRjSnplYU1yR2NDdXlRcjFWTmZ3RjF6YWdIZXRBZ0d4WjR3eHd0bnBZMHRpakM0R2NUU05rZEslMkIlMkZMdGZSRWZsWmphaE9qbU05WDZ3bmRMNGEwbWpqeFZXcndKb3NCcXJTWDNWZG1VYlpIZWJ6RkVMSTJEQUYlMkIxdnV1JTJCYm9tcjE2R0c5NEkyenNVRWhxZyUyRmpIN0lJNHdvcnYyTVhOWURPRzhQS2d6SEJCYW9zckNyMUlYTGEzUWVNQ21yZGIwRUJHTnZsNFpOR0MlMkJ3RTNCSXRvanA4RmhxTjRJOEUlMkZpVTRSYTJvVGxpZFVIJTJCVjFmc2ZtaGRqbHpMWnEwODRVVVE0djM4JTJCVkhVekNpNjBkaEdTQ1JFUXFJcXVLM1lNU1dvJTJCck9GekVFbkxneXZ4eEJFb2lka2hSNFd4a0o4NkJTSzlBeWRVZTFqQ0RMdGM5JTJCSEQ2R3dlR1pYTTdQUWJGYSUyQnl5ZjhCTXkwT3YlMkIlMkZJQlVrZHhnbmRrQ2thWWN2JTJGenlZJTJCRWNOOVM5V05NaTFvaUJCMHolMkJzYzZVY05QSU0xNHRzazVMMWNodHhNMEI4cXpybGs4JTJGZEFZSGclMkYlMkJNZHpjeE1RSGNHV1FXMU9sMUJYbzBoUmNLUVJIYlNOU29CMmNQV2o5bDFyWEJPcktsRm5NVTZ5emxkZ01vNVFKeklqRGxvQm1iZm9scGh3VWJOREkxQXElMkZwNzlhbmE5ZElIOTlTd0NFSTlqb2JhRDNkd3FyY1czcjJKNERIUlcwcVpxaHN3bVFIdjVzRXZZYXV4cEYlMkY4NTYlMkJ0bnlaUDQlMkZFSDhYVjg4UW96czZ5WjM2cVZuU2pHVXFPOEpMeHdQRGdVcGxjbWpoMXY4YUdKUnQzbCUyQjRpcSUyRlE1WmUyQ1hXcTRBWkslMkZJTms2T0JJWnhCOG9kTjZMSmRSVkFtNm13d0JWRnF3TnNtSkh0c3pWc0ZJTTVDa0VoZ2pQSG5OcEJMWE1ISngwREE5d2FRN1kzY1JHS2kxamk1bVVvcWk1MzJqREdCWSUyQlRVZXZGYlNTRlAzR1VobEhMRG9vdVRjSXUlMkJtVjJUSmVIVnp5S0RHbHJ6dEVCYUhCbUlJbCUyRnM3MHdtWVVRNG9oSGdIVTZ2Y2dKTXV4b0I4QXNkbkJjeXBnamZNdkgybkdlN1ptQ2hQSHNWaXc1TmVhQ0pOSTY5d3VWUkllJTJGbnJ5b3FHT2E3VnptemRaTVIycWZ0bGxFTlJzYWtBUW9FTGFSWHRsTlFqMnJxJTJGNlNFVmMyR0xobkNERjFPV0Uydk5nbGVUU1lDS1BNcVdYSWh0ZXd3VlhicmdWWFFpYW13ZVdZcjFWdTdPQyUyRnlsTXFRMDB5SFpEa2ElMkJ2NnltZ2hYTER2eFQ5eEVHaUtnSzVzeEp0d0hMdDJscmlOclBRS25XdkpmbkZLOFB3YWZPcGt3UDkyVjl0QiUyQmd3YkJua215dnZEcFZ2eiUyRnpsJTJCZUNEZUhEamhWeElNZEtveE1iZGdRcWRhMFlMJTJCZWNiVUZrM3FvcDJyMUIlMkJ4YzJxZkE1Zks5NlBBWSUyQnhwNmViOHFLOEpYMGZHQldJUVdsTlgzQ21nTWdCMWdYRWtWTCUyRjJLTEFBcXYzdTVKNThRVUo5TWRFJTJCcUo2dHc3SEtVRWVKOFhjQmxsTEVsWmJvMSUyRjhpJTJGeUZSMGU0am03T1RDME9nbGVRS0JCcXViOWNpdWJGMDhpTzVDWHBTR0l5YngyOEdvWWpNSWtORjNFbzBIOVFMZGgzcnlUaVVKY1hwUHExWExqTyUyRmpKZGVNR0hETmxMVEVKbmJrczIya1dodnJnWTl6N2JLNWJaSnJVUE5rcnVVUXF3bjZLbDM2a1FJZFcwVFVPUVBYNjNaT2ZyM2FQYXRmZm5LdUJYYUw1JTJCSVFsSzJLM0R5JTJGZW9WbTclMkY4dWpHQiUyRjhtNzljUDlPcW5wQkVZMWtGNFVKNENHOGNRNmZNS01TOWUxYjhNN3FFWCUyQnVOTHY3JTJCYUtLQm9UMnJJQ0IlMkZib0pTbURmc1NGdlgxU3FxSTZuJTJGbSUyRmtZaTR2amFySiUyQjN5OEIlMkJkdjA4U3BDdWtOeml4Y0d0M2t3NEYlMkJsZURmdHNuNWhRMFM4VHU3Y0E2Z2Y0R0YzSkZVUk5hV1JYOHB0RnFrciUyQkFPZ3ZjS0RjNVlnMG5DSyUyRlVyRG1lRGo3NFBreVI0QjNUWlhCSENlcmJkS0k4Q2ZtZDgwd2tsc1A1anI3Y2xFSmJiRTB5bjN5JTJCYWFmWGFMc0hUNWh2bjRscXNDVnFESjhYM05URldmUlRaWjBwWTZTR0hmV1MlMkJiTks0MXRxRWdIZXBSMFNFZjdmZkRDV1dOb1F4ZUtaY0t1UXp3bWZ3cURyc1lON1o2NU9ZYnZUZTdBJTJGdkw0U1pmeTA2S0RRSkkzRmkyeXZBdG1IYiUyRlBVTVJ3TVB4cU9na1kzQmJhUmJHRmhnNmoxSFBqdVpodlVPNnhBaVVEblJXbXJCSnprMjB0ZWZSbmRhRkVvVGpOazR3V1FWYU5GMUdWbnI3UTJKdUJobDl0czNHQlU2ZjBTMkRRNmhEZWt5JTJCSVJVOEklMkJmT3luUzQ3WWpKd1NhWUt1emVJSDM5YURVemM0UENFVUdmbElpSHk2NCUyRmJINEY3emRUS0tJVlolMkZYRCUyQndOa0FBUWtvZGxJaTRRdCUyRjI3anZqaWc4aE9nbUYyY3FmUGtRdDBheTd0Y1M3ayUyRjdjR3J1UnBZaFhnV2hmandQek9hMjI5JTJCdm1hUHpVYmpSeW44MldsTHJVQWlrcERXYkYwbWZINWdJTDN4MEdxVEM0a1B4SzBPTjM5WGJpaU9XZVpQOFE5NiUyRjdiOXJDamMlMkZXczFmQXcxaWtvenJubk9yOUw3Y3RrWEtoRiUyQkwyRzMzYzM2TzBxbUl6UTVyQXVZV3VCWE45UFYyZkcwN0R2SFZMMGY1SVlsaHBqUkdmRkNZUWowSzFLTDRUNEJJUHklMkZNTmpaNXZjazFnZE5MalBUN3FJTWRSTmhuWjQ4dVlPc2VHZFZOeFRyZjFmNXFmWHklMkIwV1hQMG1BSDZkZ1ZTekw0cDgxaHU4SGNLM1pXWXpzRjVWJTJCaTdndk5kcG5kWmJ0YVlNNFVFTDNLVnV6Vm1wZVVPNUVKUnVyQ2F6dkNaRlNpUERRdWhpNjExQU1nM2dDWW05cGJydVZ6OURURTcweCUyQmE1Q0IlMkJ6dXg5ZzF2c3lnbTRxR3QwUXUlMkY5dTgxVFM0UW4lMkJSR1REcjFLdWwzRVdYJTJCOWpDRjFJQVpUZUwxbDhNQUZRakk4ZnNhNmE3TU9mcWFCVjJCUUlnRzB4V2pGTnVWQjdrVzhXRlNTV1NxOHhKamtoVzM2TEglMkJ1V3ZBbXhuSVFSSzljQ1FyaktvVWJDV1RHNk93N3gzJTJCdDQlMkZqJTJGWm80RDN0JTJCY2RlJTJCemkyM3AlMkZ0OGNFUTBDem1qODFQS3hlOUJqS2Y5S0JIVlppSCUyRk85N1ZnWmsycHplZ0hwVXNmekVGc3RLZXJycWZJUFlxWVQlMkY0cUFNaW83cSUyQjZEMyUyRlpjdE1tcUNKYTFoTmM4RjZJMHlmMzlCSG9GYzB3dzNkQTduMFZTTWdHNCUyQm9YJTJCNDQwbWJIa0s0eW1LQmplWmQlMkZvZHo3VW9sTnBvaGlnMmolMkZLRjA5Y3VJVFRjcU9kaHclMkZUVjJieVZFUm5qVFh2dDc3a0RPNFpiUlVzUjZjQUpvQ1JpeDdUaHEzdSUyQkIxOFF2ZmxPTENWS3JTWHFaSFhSMkpzdU9HUTNWTyUyRllQT25ERmcwWm04eWhqdkhHN3EyelE5Qm1hTjZ1MDczS1lXTG1jTERjem9tSWlOMm9nYTlEYmJUYnMlMkJpMnFSazk1dlJkWE9GVXQ1REptRWhnWlglMkZvRU9PV2Ruam9QNFZjQ0Q0M1Y3VGU1U2p2aG91VUhPYjNhdDd2S2dlOUljUjFaRk9JYnFiVzBmY0hlckpXOWlDSVRyVUlzV0JzSTgweENoam56UHBCSnNSZktCbEowTWZ0R3hBdm1vZnhlOXZLUUczTWtrZDBpREFiSVdXQUdtOExkdGc0ZjJVOE92QkgxVnFXSm9GWU0zeE80bEtIelBGSmozSXRUakx4aVQ1UFc0eEYwZWxnY3NqVXNHV29vdlFwZ2hCeCUyQjBRVDF4WTJPc0tlYmw2VUp5QlpwOCUyRm0yZGNDVGFUMlNqYzhvekhLM2hKR0txS3VDQ1A3a2szdFpQeTJOb1hJUDJiMzdBMVZtVFF2N1ZQNWVzenFSVE9VRGtYJTJCZVlRamRvd3dYU00zVGF0ayUyRmkyQTdsazd1RyUyQnZpNmNFdDJXUzlMV2xBWUcyTHJRVkRLNGElMkZzT0htSWdmZ2lnUDVQWjJQcHp0USUyQmY1bjl1TkNtWjQxN3UlMkJubERFc01OUklPcnN1TEluSUZycU1KeWw4NFpKSXFiVG9KayUyQnFCdGFXR3RDVU9nVWhDbnBybWclMkZkJTJGbFU2d25DYXluUldlRnVsaVZyalZBS0RTazE0TkE0dlFYU0lkaExibTVSZDEyMzklMkIlMkZSVWR5OTFDJTJGJTJCS3BJSTdqZ0tVZ1VVb1B0ZTlOVk9OdkV6WjRjSVFVZjh0cGRyV2d5aEhTd2h0NFBrQlhOSTZmVDR5eG85UXg3dHFZMkclMkJQWU5jdjZJciUyQlhQVUJnVGRrbWhCQ2lmd0hyeG9NeFdEUGZ3NkxzTDZ5cFcxeXhjNnQwWmtCdlFqY0hsTVY3cnFoTFIlMkY1aGw1cnNrdTRucElFaXFEJTJCYVR6JTJCUHRISnBZQlZwekZLWGhDQjExdndYUmR4VElERUVrUk1Fa1lYckdXNlZZRzJWZWdaUm5rZ3htWVNTVEtxWlBpWHhORUdJRDA2JTJGd09FZ1AwVDBXdlc5alBORWh0V0NENVZkYjJJNU5RZWlXR2dMakFOMWRVZHA1YVpGdG0lMkZRdXN3b2VCWEYzS0RSRXdOSUl5SUFBQktBJTJCaDVjY1JyWjRrZmglMkZMeXJBbkRjY1RIa2pDU1E1ZjJ3MXc5NWM1MXgyYW1ENG1EayUyRlN6UGZzVVFMNzdSMyUyQmxWNERxZXQ1MUNYZyUyRmhoVnpqUjQlMkJJMGR6JTJGVUI1VkdhcThhdGxGSlhGdkdYJTJGUzlzanltaFMxWTB1bmZCWGEwd3prS05OV3lTS2tpdVY4MGNQR2oyeGtkQ2xPZWhIT3hTdkhOd3JrJTJGU0I1c0IlMkIlMkIlMkJuWm5aZUJYSFJGRFBLMkxCaTZ3Q2gxVWNKMEhPSSUyQkY4clVoQ2pacnhkRHhRUzl2ZWppT3VFZmJJMDlJQ3hoZ2FIeXdEd253RmUzT0Y4QnowWEM4U3JDZXZtelBSR0lsakZRQWlqaXBpZVk5eTVDdTQ4OXR6TXpMTWhxTDNyWWhidkpwdWR2SEhMQXpWN0VlU2xTTXM2aEl0ZWd1aVJ1Vzl6TDExWmlCUllxUEFrZm9lJTJGS0NRSzhyeTlPcWwlMkZvUHNQZEQ0ZnRyTDQlMkZJeVZoUXN4ZnFBQmhxYXVIMkZSMlh6ZCUyRklJWDZ0NXVvT0ZwZU5nWWt6VFRJU204WSUyRnFxdElKZDFyRGglMkJuWkNiZ2ZGTnVTQ1VWV3dsRlRHT29VZjFaTjl5YVZmdmNxJTJCaVlOUTlxT1dYZ3dIM2ducUxoTFJ4UmVpZldUclBCYzFVWFhIQTVrZ29ER3JQbFVlT2pjTjF3QVJDYm5pbXVSeFZ2dERmWXZON1hMblR4dFd6VHZQNlJ0RTY3TDBZODQwZ010dlRVWEZRS0dNS1VZRWlsNyUyRjZPQ3ZxMVdQVEFNSjF6WGlZbzU0MmJVNjRvb2pjRUNnWUlwVDBBTnVNOWhOcVlNdUdsejA5QUQlMkZnVnpUTHV3cSUyQjhLNDcyTEx0aEozWTRXelBINXBvY0p6RHE4Q1FKYUxRZnNDdjF5RkMwaiUyQkxpSEhnYk01SSUyRklvb1NGamJ4cm9udyUyQjlRakU1M2ZsWjI2cVo3cE9uOGNoeFd5U1FaaWlKZFJQbUZEN1M3U3VrQlNlVkt2T3VlY3NqY05ONHJjejFLWjVVeTU4VHV0WUxCRmYyaXpOR1A5S1Z6bWh4eXVRcDJnYk02S29mYUxsNEpPNDFLM2lxNTBLQnVPM1d4SmxhMjRiWkFKeDdvTHpERnVFdUhURHFoUnhIUEhSMEUzOEx6M3FaUENvbzRBWXJaOEg2R3clMkYyTXNaRU5ydmczcGJvQkp4QVVjMURMaVkwRjZ0SU16MnpzJTJGU3p5RnNTQUt3bXBhUnFIJTJCenpyJTJCSiUyRlolMkJhRXVFZ04zakpHVyUyRmcycFpxa1p4M2FRQUdBYXh6cWhGSTNlaTUlMkJZcWFNJTJGTlhxZGlmMm9hcTMyQklFJTJGNUZHJTJCNUFoZTVkckVka0JzQnlSQmtxUU91ZkN4THk5bTQlMkJSJTJGc3hrJTJGN3czaHBGaSUyQkp4T0RlbDZQdlg0M21wRVFTNFpvWDRyJTJGNVdKc0FScGxMRW43bnVPdGZBZ21MakhsR0ZiMGdKYVBzaHZXOGh0cEQ3NkJrZWVlcTczbVMzUHlJOWx0YWc0dmNYU0lTWURXZzZmU0ZzYzZHNyUyQkxaaXpCaVN1WDJDUDZmREN3VWM4UU1kTm5JeEl6T1dnNVNYYlV0ZlpvSTZRSkhjVmNUcUI0bW44bm1Da0dCSkdWZVhRWE1RMDh2V2doZElianNZMTRXaUNyN3o4NHB6NElCUkRwNU9ndXBrTjF3Rk15ekVWc3dkWGhLOFR6dHRZcWlDQXpHQjJuc0VkQnpCSkp0WDR4TEIlMkY2VDNNdjR2d3VpRjltRnRsNng0WlhFNUhjV2FFb29NaTR2Y2dLaVNXa0J4bHhNd3ZZeSUyRlZ5eXpHTjBnVU9BcFVld2wzSHF4STZ5ZFU1SkpSek5UT0hPVWpsSUVTenhtaGdzSkRzaHpCZHlyT2slMkZNaiUyQm1QN0pyWno2ZnJjYWRrQUVUWUclMkZaNDdsaEw5M3hqMkd4SXlUdjlnN3hoZXM3RHlUVDZWJTJGRFcwM292WUNBZGF0TWozN2MlMkZ4JTJGaDU4Qk41NTdDOCUyRk9HcTBLRXBYb3duM0NVQlllYnFFTmdJZXpaeEwzZSUyRjlqRjV5djJDRHdzdDBHaFBTQmFaQTNQenF0cW9vWUtRaTRvU0I0aTdJNU41UG1MdU0weUJzdTBpYndhZG4xY3B2NmJuWURKUDRHbWV5YWtQQkRweENubVNpdiUyRlNEZVI5V1o1YTlwTHklMkZJbUxQTkEzYzZ0RFluNzVpUnV6Q1J3bW10Sjg5QzlObU5JVFklMkJhJTJCU1JPaTdWa2lweklVYUsyNFpYZUc3cE5HVHJIZU91VWJCWjdPb3lwVyUyQnhyU1BMSDlHQ2RmbGYlMkZRJTJCZDdHUGQ3dDBCRzZPN21mZU0lMkJ2SUlFamMwVVQwUXZ1RTM5Q1BTWXQ5NXk4bUVrOFNsTlNSVE5zUG8zaUVuOXNCZUgyclE2YTlUN0pxTHBSb0xWWmZqdXNQYzNlWEYxSkFRNCUyRlJEOWhWQnlqODBXMVZGVWpNVWRKRlJaYjA1ZHklMkJrT1FZZEthRklLcHhKWUVMR1Nldjc1QlR0bFZsJTJCa0IxZzY0ODVnZzQzQXVoUlVmUDBoSnB1b21mUVM1VHp1JTJGRXg4OUUlMkJ3dGZKcGJBM3ZVVjFZVzIlMkZNdVVBU2llNkZIWjBCbWVCZzVDR2tBU0lwb3V6ZERmS1YyMDhlRTNjUVRib21aJTJGRmRMR2lzSkZpUXYzWHhnSnRLYmNteGJ3cW5UNndxa0dMbTFwQ0RjaUdUNUZ6VnFIMk9IM3Y1UUlaS29WdyUyQlJOelRyVEVDJTJGT28zU3Z5eTd0NjRsT0VIbHhpcFVNd3hsdmQlMkJnNFdqQTVRcEMlMkY3cG5TWVZVNVZWVXkxcU5reE5aVGlVYjNFQSUyRlJ6bzhTJTJCb3ZkM29kb3hHM1FyZGU5NzFTOVhZaiUyRjRuV1NTJTJCa3RteGlqWnhLODN4WDBReTRIQWhZVjhqRjFKWmxsUXJXME1lZDJOekp3JTJGcG41TiUyQjFwbjBvVXpYZm5EZGlzbDlCRnZ1Y1Zld0EwRlpaN0NPUXJwSG5YSFVIaTV2dU05SEJtS1NDQ2xYSXo5eDNKJTJGOWZPY0xTN1k0aE0lMkZ5OGJTeWhncE40VURwbEU4VHFQdjE2NiUyQkN5cm10dzI3dDdyVnU1UDQlMkJ2bDU0dVdGZ3pyU1VBV1dFUlRSWTIxMEFpYiUyQmJWOWtEYlFyV3ElMkJGTjhSNiUyRnBiMXNjcXJOVWhIY2JPT1ZYaDlrTWRiMnIxRzJoejNMeThTakVHTklhem4lMkJPZzI1NEdCN1FUODlUV2FJcTFoN0dkc3A4STduZnZ1Rm05M2prdEFIYmhleEJjQnhadiUyRkdLOERBbXRDMndUSGZmZkEwSkx5RG9GWTl4WmpuekFidWYlMkZCSUJiMzNMUjYzT0hOUiUyQlhOVzgyaHJLa0s1NjlUYUxoV0JQcTF5RlVySiUyRnlxcUlPVHZ2S05NOGdwZHV3WEJGYTlVUGtnTW01R1NSZElrMFhJTVBSM0hpY3Q2OTdTMFNBdnB3RnY5RERIYXZOMWxoODVWNlF5ZDNranFoTiUyRmJuakpYbEFiVjBqOHJ4M1F1WVVVVndzUGt2UjY0bTclMkJzSWpsTDZYR1Q5elQydVk4Q1NVZFBIR3NoMHlteFBXbmNGdEh6NkclMkIlMkJEdkwlMkY3RkNPYTd1NlhXSlA3M2hQSDhkYk9GdnhpcjRxcDg5bkI0TWFNajhLbVVKdUd1QVpGVERlRWIweDZDQjdlU2tSbiUyQkEydHpLekhlMUpxY2l6dFpWWDJWRmZmOVM3bXpGMUFLQjJoZGV4c3gwTUJ3cVJvbHZOaEZocWh3a2VVRXZxcUVwZk9NNWZuOVVSR3diQ2Y2SnVUYzBmQjZGeE1pMSUyQjhtSnd6SDdvME5pcWFBaXI4M1ltVlo2MUw4enVMNk1QS25SS1dkem9JT1dibDNxNkdBY250JTJGZkV0WCUyRk9FY3NOUjJQVTU4TTBnQ2VSZWJqUEZwbnZyVHdOeXFyMTE3S216bmFlWDFlMGZsOGE5OWdFNWJTY3NRT3VzZXZsMGZ0Qzc4WXhESG5JRjlIMWZ6OUZiMjlubFBNV1ZEJTJCVDhyclR0aXdjeTRHdmtQbzNKT0I5MlRYRWhpJTJGRG9CcjdaRW41T1VRTUN2RUlLV2EzaG5mVHpIYlJ1elYwRzJtaG04NWNBR3BxTE1SY0ExUDAzQU04QWdPMjJSbzh1V3lYJTJCckJnJTJGQ3hHZnUxc2pFTSUyQnpMNUtBeDB0NHNYUFRYSHRFbkx6RFNuZG92U0dYS1o0MmNkNFM4ZXJGRkZPZ3IzVk5tRGZ3Q3JIZUYlMkJsTlNjZ0clMkZsc0glMkZucFhqa0EzaFB5WWFibno1aGY0ZFQ5WDZwRFoxNUhZbW9ab3Y3Zm9LaFdmektDOFJKbllCNm4xdVJNQmx0clE0dHBsJTJGb2Y0WDZOVkw1N0c3UDdNWHIySFRxNE10bDlzdkVVaDJCczJoODZiZXhITlUzJTJGUmJXRjFNNzJHdXclMkZUSG1zbHN2TVQzc1hWcFBDYk5CQnNsbU5DU0k2OEo1YUMwQldROW1pTFgwOTBQNEVqMU5KWjIlMkZuekNHMnF4RHJab3F5ck9mJTJGaVNpZ0VrbCUyQkpjTUNYOGRTeDlQZEdWd3pmSkVRRHRHbHFHMGtmS0x1UzhUJTJGbGhNMG43bVhaWVdWaDdQSkVsdW5OJTJCOWNXWlFCRFVGa2c3dFFYSDV1MW5CJTJGaDhNR1NXUXpzUSUyRmRaWk9ha3ljUzd3Q2IyaDEzckd3OVZBZWclMkJRelJiSDJ5Yk1ZMDZtS3lmajRLU3lXanh0YWcwYzdQWEhRQVNKJTJCTmVPOEVXJTJCb3hpNm94NXQlMkZsZkprVW8xTktGdzZuZlVlQmswVGtMS21ic3pYNktkZmxsMmV0THJ2UXh5QnU3enRRanJtTFRnT0M4UGNlNVYydSUyQjRkJTJCN1VZeWFjZHNGYnNxaThLSmVlTUw5emFwWFJZcmxYNkhmMCUyQnVOckFsb1BqdWNxQXRNUyUyQm5vanBLMjlvTlk4d0dzdnZkdlZRVHRWZW50T3VOSGRENlBveEdpMUZVTERqbGJMTDBta3hMa3dEeVhzU1hscU1vNHRqWDlya2pVSVolMkZnWlUlMkY2WmhHaHJjb2JzRmNESG50NnpyemhoV281U2VwZ0hwMGhTb3V5TXlSS1Y1UmRnN3FkSnZWd0s5U1FRT1JJNkFhVDZISmcyWHYlMkZRVEMlMkZQaVYlMkZqZEx6WmYyRiUyQjVkOTA0NVklMkZjOVJVM0Z2b3ZDJTJGSGNGbjYlMkJkdXIzSlVIJTJCZHd6YWpQclVPVTVmaUNYJTJGQmJMb0ZxOGhvcUlldGFwaDRUczYzcWVaYVdNdHRHa00lMkZiR21QaGQ2bCUyRjY1SUtyQ1lRZmFtTUtXNjJaNFZ3eVBXMno4MjV3dnR3MXNtQ0ZrSThRZWJrT3psRm96aEs2cnlsdnFWNmwzN0pWU1ZETHp0ZVJxJTJCWEJkTkYlMkJqOTJ1bEZpR0NqdSUyRnIxTmYxc2VuV2ZEMzU0ZTQ0ZndCeENtY0xMU2pUWDh6bDY1VFZNYnlldUlZNXFCTHVhQ3haZVVSQnBkTHhwWlpxbTZoeXQ5Rng1Z25hWlJlWUxVSmRNbWFnUWI0a0lsTDF6eXlSOSUyRjRiMWlDbSUyQjl4ckNBNiUyRnJ2d1RtUUpzUVpaSmVRYVUlMkYybHE2c29KJTJGWU83Wmx4T2RBTWclMkJmQXk0dSUyRmtJWWh5JTJGem5EY0NlaDRlc2hrSUZuV3J2cHZPcGRreWt0aFRaJTJGQWpUOU4lMkZ5UlNmejE5YzhiTE1KRUMxbUlKMkxDdklmeWwxNWUxV21zN2E5S1dvbHE4ViUyQjlKbnRaSExtNVFlYXUlMkZ4SXZUSFpnMnhCRFR1OXg3JTJGaWFkYU9TJTJCaFdOVGI3NFdjQnRKSmVoZ1lmTHB6aE5OdUI4enF1ZXFlT2N2JTJGTW12MkduZiUyQlpla3E1MExQbFBCcGw1akprSjc2OGVPMFRmTmU4JTJGY3lrYlJHaXM0SnJzM2J1JTJCRzNKclRRaW5IUDg2Zmc3Sjd0dmJySjlhSE1NTGFpSDU5QndIa09PR0pvaG9kZEZTUFElMkY2JTJGbEslMkJreHFCMCUyRnZXRVlmcFlJZ29UbndrR05HYU13WmREcnVEdzVHYjJtbXUyTmYxdFhGaVBvcWl4U3htcCUyQlN2JTJCWXNlaGUlMkY2djFwYThsWGNkVU4zVmJlcm5oJTJGbXBSOFREaFg5TkhjbXZuTE9sOWNMQWpCZnFXdE0xVVJlSEIwcmxOVHJ6VnFVbUZETzZDQkhSTyUyQkpDenh0U2RQbXhqd3FTOXVCOUk0Zkt5a0Q2U0F3eU5PRTNOOEl4d3VrUHhqTkFJSkVDSFN5alZ4RGp2V0dZRHM2bU5yYnZHYTFoR3JJc0hPcW80MTZ0aTlGdHo1T1M3RHBuYjJLRTByRzBDMyUyRkdoZExxQ3JnRTVrcHBxZWJxRHEwWEZMJTJGREFxeGNXZGN2aSUyRjZvSFg3V2J5QzFDVTJwd0V1U2ptcnhoOENON0Y3JTJCcWlnNE5JYjdYOG9lazhNZFMlMkJ6eExFZDJTcmJsRmRtdUdiVTQlMkI3U1VNQkRXczl5U0tYJTJGbyUyRmtQMmZhZ2RNRDlPeWdWSTR3TCUyQlZBeXclMkJwTkxtSEp6JTJGNXpMY04lMkJQRiUyQlVIaUhYVXh4SER4eFdZUkdvZSUyQjhjSCUyQnAlMkZRUzJCZHhVRWdJc0J5aWxBVzBWR0JqY21YUSUyQlNtb2JxNjcxN0pMRUcxMWUlMkJneFBlZzdhU0lKNEolMkZad0RxSnJISm1GZ0FsendlU0lTckJYeThCMFlqd053RDdWSDk3amRtNkllSVpQd1BuZm5odyUyQldoRUxyeGgzQng5YkJsd015WlJwWEEwUUJHZlRiTXNDUDZ2b3JZc0NWNHU3Y0VTUjElMkZjU3FUbktSNGVRJTJGT3dOMm1zM05TanhnOUI5ekh5alhZeW0xeSUyQm4xQ21JTEpSRkczemJYd2lqME9zbjVrZFFDN29RbmdnR21XcnM5Mnp6WU15b0FDdEV4MEwzeW9ITHRxU1RLJTJCYnEyOVUyTVZmODJ1bHNncEZKTDN6MTJpejBmelhCN0tCMVRUbmVXaGU4eWs3Zk9hczQlMkJSWE5YOU42MU9xRHNyYjNZelVueDFLZGhkQnlKUG9TNHk5ZThYekN0MG1GRVhIbHkxUDFrSWF3UzZxbXIyOHIxVEM4amclMkY1cTZEM2k5YVNGSkF4T2t0Z0JDRDRFUjRqMkd2ZHFCckJaVWtrcTV3U1h1MjdVanVGN1NuJTJGVGljeCUyRndDTzlsSEVUQmZaSnlDc1NwU24zVnZYeDQlMkZ3TE9jdkg0MkM5dklCZVpjdEQ3alZSSzM5NjV0U0FPM0VlNkJCYkdrcGU4JTJGNmxxVDRQYk5JVW5nJTJGaW9nUEpVREZWc1c2Um1BTDlOeG9UNUdpNFclMkJQenoxSnhSS2NPd3N3aDhBaGJvTmNqODVXRnI3WXFEMmslMkZRWFF0bVl4MHNzQktqMG42V3lQOGVrJTJGZ2t4aHpjRWV0a3BZT1I1ZE9qTEJRajhqY2RoNHdlR09pREpHOW5SSndRbWVGbjFLUlhPQlphc2xrUWtkUSUyRnhUYXA0VUUxb3clMkY5ZjBhYktUZU5yRExUJTJGWjlhT2FWZTRkVjUlMkJmTWFPaGFSWUF6RU1pS0gzOW9QQUZsaTAlMkZjS1p1RiUyRlNVZWFBMGtLUW9GYkFwYVYzZ3N5U1NLTVJEZ29YR3M5WEh3RDZtOUlWeSUyQlh6MDNxSDFPdnBpenZXb2tyeEViSzRlUGdhNUhrbTd5JTJGUnduY2V3SDdqdzFJUk4xJTJCVGZqSzU4VFE5N051cXl4VjZIJTJGdnVqOWJSMU1RQWJ2a2J1RVB2MktDTEElMkZPMEZUYzElMkJDNEJiYXczZG9MVk8xYUk4NGVUZWtGMHRjMSUyQlROcTVnQUNURlhybjhQeXUyenc5OWZEekpXWCUyQnE3TVZGOWk2N3o2b0RDenNLVExiTXc2UVNQN01CN3lvdFV1cFhCb3ElMkZxJTJCJTJGZWdVVFhtNE1WY0daQmFVVHVaSldUZWFyUm93dHBiM3AwdWtVS3IwbG1MQ200TzdOblNraFBsWiUyQiUyQmFTZGhqa2RVeHFDNHhlMVo2cGclMkJJck5XcEhXZTdqeExOU2JCMXF1eThSYVJMRE1nb08zb0VCV0dBWUJCeWlmJTJCNzFZbWRKJTJGNjlENngwS2QxR0RFWFFQemRWSyUyRlMxUzJQN2w3U2tpMCUyRnduYTZZUkU2RyUyQkN2SzBva3dXRkklMkZidHV6dlF0aiUyRm5iMXg3ZGd5WUdGVnFlZnZ2eERuODlIJTJGakRLbHhjOEclMkZxaHJVckdEODVCWTVjVDIlMkI2RCUyRlRKNFhEaWNBRzlLWUxGM29ZWDBnR2gyJTJGakcycXZNJTJCMEMlMkYyTmdsN0pqJTJCR2QyNHd6aVFLWThlUkF5QUdjZ1R4T0wzN1JZMVBabVhjMXdabDVBWEpQYk5ybnN4TGkydXNSc2FpaUlxS3hDYWtFMUVHTnhJaDVoJTJCVTNSRlZiUG1rWnk3aVFNaCUyQjluek1uT0xnWjNhNkkxbGttWDV1M0Z2djNoazZuVWgzRjd4dWt0YjZOY1FUdWpZNjNJQWRmMm8yU0YlMkZaN1dLVkV4SXdFOUxaTWpGcGp2N0Jmam9ieDZlQSUyQnZnZE0zOHBsd21Wazh5RmxhVGpyQkZFeSUyQnZCeWI0dFQ3aHFGJTJGNWV5cHNsNUF5eWNTVnVxaWZJeWwwJTJCSlBHNnlhUTJCZWpsTk1jbURjRTFPb2w3ZjQ1elBuVGhFR1QydDdoSmlPc040c1FtOTdjWW1QZWU1RHlJaG01RWRPYTdaeldmaU41TXhDbml3RHNEcTR1Nm5uZHl1T2VnbDFhSzZVak5CelNVWm9ZbFpTOXJGQm9TZTYxSFVGJTJGJTJGUUkxeEk0MnNocXJoVkJFMktUNTVlQUI1VnZSYUFxYzh3dnRCZHh4RFZqTjlCVkFlWjFXN0Q5Smo5akNwelhIeW1pQWJjcTRYa3gwWWxlMjRBNENQbkxyVVNjakJKcTdaR2V3bnh5V3hlaDNOaEYzRGpYQkRwcnpLS3R3R0F2RHJDOVk3TCUyQnY5MUhJY3lKSXlPQWZMJTJGaEpvNUVNOTYzR0VLdkJSTVRjUThnMk8zNTZtTzV0UHJxMkVsSHlRdFdQdWxwZGFyUU9QQ3RSbmdZeGlsa1lHTWxwaEVzdWxkSU5qWiUyRmtCUGFDQkI4TW9oU21ubnBRWGFWeDRhSThjYlpZS2V6MWRyWFpZVFpxZkVYVXIlMkJWRFlrOVNmVEJHSlE3RmpjSnVhdGdjaXZhOXZta1dEMiUyRktFSyUyQllYTVd6a0Y1bEp3M0NmOWFUM21XUTJXbXp0b3U2aFZLSG9mVDhPbWhUd1hIejlEVXFheSUyQlgzamZTRXF4NVJ2JTJCJTJGT2ZSaHJHSzZwYnNHVlNjUVR5VEgxVkZqUXgxMTlhQyUyQm4lMkJKeVNCRU5EVGNkZnB6d0xmNnl0M0hPb1NlUFN5eWNNZ2ZienhDR1dsMks0aHhWVWNqbVpxZ1B2cm1DS3NBMmh3bjhoMzExZGpoSkJVUk1aT05jaGFETkEyJTJCWnhNODFDUVFZcklVcWpFNnZFSWdoZTdqOWNCTldybEZFTmxFREtSdlBWaG5ScWZXV0VCMFBReW14N0QlMkYlMkJhQ0tyWVg3Z2JNZHloRDRqJTJCc2lDUnZXZXJucVF0MWJHYUdMWnloMDRTWEk1bWJDZ2s0ckF3WXJ4UENsSWdnakdjTUg5VnZIUWo0ZnZ0QVlXMVN6ZXNWQUM5U3B6MFhPcW1zbXZNRXdtM3lQbXduOWNJUnE5WGptVmFyeSUyQnolMkJZNjNkRU9mazdweDVid245dXhDTFZDMDdjTmRkUG56Q1FvQURvelRESTRpZSUyQnhsM2klMkZTRSUyRjQ3cFdYeExOYmQlMkZQRk5IUk40UjM5bDJxS1JiQVdkR242aUZnd0RLcGJ5allha3NLQm00eiUyRkxLJTJGSmUzYzJQZFY3MG84T0xxdkVWbGhocUU2OHdPc2Y4ajZuM1dKSlVhYm9BbiUyQmJmanFGSmxtaXROVHUwMXBxbkg2aSUyQjM5aVVkWFZsQWtrQ0VlRiUyQmpzczlsSEhDaVRDdmlWcW9nekZuMmtWd3RmSUE0QjI1T2EzYWZxODNmTWdQR1hlUmVqbVpjeklnWEltN1FacjdQeTBSTTVKeFhFQmhDeXJLeUtNOVVleU5jc1JwMzNqT2hzd3k0dkt2clBkRmU0aDN2dm1EWWRVU3FTMlpRbjhuaU14WEhIZll6JTJGbHJKUVM5TE9aanRXaFJFWGttS2ZzOCUyQklHbnc5NnFRY2g1QSUyQk1HcUpCcFlCZDYxeSUyRnJGdGwyMXZUJTJCMVlrOFFqNlNUWXE2QUJ1S1dqdTZyeFpLUyUyRm0lMkIzaHEybU9Rd1Y0JTJGMGJkVjM2YWJadWpISmU0OTlEJTJGMlRJN2cxNVJEQ3c0Z2gzRXp5cCUyQmRiWmZQOUxPS0JHcjlSOGgwRXJHbEg0cVRSajQyblNuWVRVTktCM3hzSVViVzElMkZLS1BXdU1JWDhhdllWMDZOU2thZkVwMVdENmxaaSUyQnc5QkVOOUdMaiUyQiUyRmxDSEZGcVBCU0ZtRjd1bXE3R2VBJTJCazlpeUtqVzd5eXJnZ0NBUW5BcU1TY3dJRlJxUzh0eEpKYmUzcUpkR3NwRWxqbFZtRXZjWmFXS004QUpTcFdub2hLWDF3dTdkJTJCczRsSmxHSFVBJTJGSExSdmpENHJ5UTJ3alFNaGZqOTZ1MFUyQnZpM2FLbkZqMGZMdHJBalJDcyUyQnJ2MHhWVmVpZVUlMkZ1YmhvUTgyYW9ORU85SFhQZU5KZnloZXV2aVJqS3lYV1VidncwYW5icDAxZGtsSGFPbEpsSG5ZUGFBb3YxQmV2WCUyQnZzMXpWTSUyRiUyRkhOMUplRUdDTTN1RDZWNW5zWGp3NFZRVHdOZ1FnMm0yJTJCTGtFekt2alp4MiUyRlhpV0R1akR2JTJCNnNzJTJGeFRsJTJCY1ZxRThVelVLQnpEcTFobzlnRzNjM3o0dGlKMjJSUlRWU0lSY1ZIYkp5S2xCalhJTUoxWHlGZGRKT282NHM4THBzck85dnpLUjlJSkxSQmZ2Y1Z2ZjFwT0tLUWZGJTJCYXVrbVRvU0VIOXpRV1FEODBmJTJCSGtwdDJBJTJCRTY5NDRmeUtpU1k0R2ZtUWRkaEhMdE5XdEZ4Wk02MTZtZTZncnBVWW02ZDFZJTJCVXB5V0xJVDM0a0luVUY0Mkx5Y1dNMHZkNnZaY2hhTE9TMkVGdkJFdkJEd2l0OE1jeFRDWkpHYTQlMkZIdzJBdDRGdGxkT2FvJTJGSm0lMkZsODRyTHRrJTJGRndOMWdqNkxwRFJBN1RNcGxxSng3JTJCbk5vY3lvem9icSUyRkxSajdWMGtaVFpPeHpzVzZ1NUVuNmthSFdRS05RRTRDR01BdjNzYVdDRXp5alN0dXptaTY5ejRpVVlkemZzdmx4ZTZjUDJkTWRWb3hpJTJGU21sVE1XYldVcWl6bVJTUGlrM3lFYXV5RldtTHlWcFhPdGIwbjhhellhOXJXUk0lMkZFQWhRaG1IN1hpVWFBeE54cWFLYVE0UGdNUzR4OW9FNHYlMkY3UWtyWkN4Z0JGYWI3T1VHQnZ2MmlXdGV5JTJGUENiZGRrY25OOEVQcjg1cVRqY2hyRW1VbnEzWXMzYyUyQktzMlNXM1V5RHllT0xTQ1c0WklFbnRYZU9vSXYlMkJIRXpEcTZxamxMYWVuZUhUeWp6alZXelYlMkI2Z0J2YmRCVlhjTmpQUURyUW9VQ0xXaWZIczRMZnlWTklpOXVuNElVeHglMkZLdCUyQkxkaDJjTkNOTUFLODN5RVA2QnEzYnZQaXZVTWFaWktPS3VSVEpjcEFoZnFZU1Brc2JONkVOZFozeDBNbGRiR0x5TUl5bW0lMkJIYmtCdCUyRkZWZklMSjlKcjM1azhwZ2hUQmZLUk1yV09mRXYzdW5ydWdBdDd4Uk11ZTRNWndGRGNLOTlwJTJGU2kxWmFJbk5VTDl2WiUyRjFtVzJsTVhudlZKZ1k5V0lyUmJ6bm9jbm92bUt4RzVJOFlZWWpieVVoUkl4NlloRERHNmVFMXYzaDkxU1Y2clZVVXdnTmRpdFVWSmFCJTJGRWFXYm5iaSUyQk9KTFVRQjhJdEphVXglMkJLVHglMkJHM1h2WUhoeVpjcjZkTUQ4NVl3RHR3Q3FMTFE2OThuOGpLZVhKbDJ5ZTNxWGRZdmQ3QzFSQk9yVE1Ld2lFYSUyQmwyeXl5T2NvczdlZEVYNUdYVXhiVzlINHoxQWZYSzdYWiUyQkNlcWQ1eXZ6ZVRIamIwZ0JnaHR1RnRZWjMwdlppVFo4VGNyeUtqYU5lcGxQaUhyRUkzemluVmVrJTJGMyUyQlJoa0liSHFta2hRUmM4UzI3TldLVWJmZTk3cmxiSVRXY3lCZm9CSHFlaFNRNXlUVDAlMkZkdiUyRnRKdEFjWFh4S2dVbkxzSmxuZE9EJTJGbWJQNjY0YW1URzM1VG55SE41R0g0eVg2RllneXhlZk1sd1M5eFFhWXppSmlMWlh4VGtYMjk2RlkwYjR6JTJCd0tVTkU0TXFGM29wVXM3aUoyRXZvajJIYmtXZjdkWHBYR01oWUtQQnElMkI1JTJGNHpmb2tERm1xSnkyZHZkUlg4VXIxeWxqUnRMNEh6SGIyeWlJSFhrZXJSdm9YZjNqek5ld2Z0JTJGNDRGT1RzMWw4MUNURXE1Yk4lMkJjUWFyVm9KSmVTOXR3S0FqU2QxWFhVWGVHSkdBZUljS3ElMkZjWVNuJTJCbzkzVHNpZGZFMkNwSlBxMU5zdm9pOVJLMTVYTHdzczRkUkY3U0w4MU5qWDAxJTJCMXRGczRUdjZ5NE84bDZzWSUyQnE4ODhwV09qMUUxWGFvdXJkUkJ1MDBIeDVWOGwwJTJGOUR1N1I1STNFUk5mN09xVlBUQTJOQmQlMkJBNEkwSFdlSlN5djI1RHFMcWtNdE5DNmZ6N2ZFcUgwZTRzMkxNdGZTc0x4TlNoMzI5c1hpWlIzaXpVTlFNWCUyRldySlNtbUxwaiUyRk9qV21LaDlCNW5TQ0RqVHFNVnFiekpXbUVSbkUlMkZLWDdEUmM1bDgxY0hNVmxlS2o4VFk4OTgzaE0lMkZUMEFHcEhNdzQwdkd1bGp5THlDbUVJTVNOMSUyRjlSc01zYnU0RzRJblhjQTc4bUhDVXNQYllkOSUyQmI3SFdlMEhLVVJXWFJZSnRJN3JjYm9oV2kzampZdmlnUDNuVnFYd1V4cUJhWHhITlVmQWdKcWtjNWpCQ0t5SEExUTdiYjBwcHQlMkZtYjczcmZ2WlpTenBTMjVIeW1weGNSMnFCODYlMkJKcE85bWJVMyUyRnBTY282Qm84JTJCV3RFS3FEN0luNnZuSzEwdVNJJTJGbTd6em91WmQ0aVpReFN5SktxZ1BmJTJCWEVSdmNwUkE4aGpQMFYyWTglMkIwOU9sMGdpWElkSGdXOE9DckVCczMxTmphOFFuRUhmJTJGVTV2N282eXV1UCUyQnlRMSUyRkxHYlBUM0h4NVF3djgyYW56c2JKV3NIT2NycFNwbGNYRjhLd0pyYU1CaFAlMkJ1ZGgwQ1NkZ1dMbkVXVmtMQTFDWEZSUm9qTTRGZVVZTjJ1QSUyRjBUTEhGTTNHUTdpZElPclRpRWg1RkZnYXFhNVdUWDlGdVFGRWslMkZRcGVwVlQ1ek1iUzZmWkhJRVNMWnVJRHJ6T3NtU1NhZVBTMiUyQlBUUEtYemRzb3Z3VVNhSWxocUZ3d1hkT1hkVW9xdXVLbEZKekFJWSUyRmxrWmFoa21HU3I2ZXRzMFd6dnRidHU4c3JKJTJCeDRtUWNpa0EzSEtYSGY0Z3VzMUxscXBOMkpuV3FuNDIwR2NMczFZJTJGZXhKSVRIVFZjclIlMkJWZHNJQSUyRlNMRXRPc1U2cXNvRFJ5eFhLREdGT3V5WGFHTGJXdHo4VFo2MlpnWHgwVFZVNTRpRlNjMG0zS2VlR0wwcWZXandHUCUyQk1tRHhhZjRIQ015MXNrcjFicndEYjhta1ZURDRTVVZLM1ZkTDl2M3plR0IyMUpXU21ybUJZOGcyOUpuTzZZTndVcGJsZlRtZTgycWVuUHpURWc1UWw0RnA3OGNISldDeVBZZzZYU0M0anhVYzZvMmh0QVFlRXE1MXA3JTJCJTJGZGlWSU0lMkJ3VVozS1Z4bzN0WDNoVTVLS0ZkR20lMkZ2RXQ4NG93cmx2TUwyMnhHR0NsOFAyVFlzSDljZ0RUOHF5OWpqdHFteG1RYVFjYWFrRlNQakdDY1VaSTl0JTJCWlRzZkdHcERwMGhyNTB4eGhPWmNNM3hWUSUyQmZ1Q20wSUtVREZUb21JOHhON240bEowM0xETlpQc2syc3hqVmgyMUJ3MlZGNSUyQkFFRmFEVGVrNkY3eklCSEZmUWtpdkFSUUNVd3hPTE04WkROSUNPZCUyRnE5a3BLb2ZjQ2N6WDBJVlg5d0JsaTJtRk4zcks4em1iZlJYUmh5M1BmJTJCeSUyRndpWUZHaWVrekpqJTJGYURNYlpOSUlZYWNTNlRjcDk1bVFNT2ljeXl3b0hucXE0SFNzYzNKbW5YOXQlMkZOVGprbWNGaWxjS1lGJTJGV0d3cnJHWlhzdzdvUmRMNjBSMXlPU2tFTzNOWWdrdEREdXBVd1E3TnFBeXBadVNKRm5QekNwa1dHN3Z2YzdIVE8zMzlOcTBPd1gyOURiOUJIbnFpQWd3V2JtSDRXZ1FsMUVxTldRMVVlUlpQNHN4eElLdTBnYzBnQTZ1cEVWQjZkVzRPcXVYdmFvdFQlMkZ6a0hrUDYxc2t1MjQlMkJvTHAxRUV6UHd5Z3pRV1ZsM3p0aWFVRGMwaWFGdkNReDNVSVJNUzVhUFRreDZCeWE3OHY5UlQ4bHlhNWZLSjNucEdHcHklMkZibTByamNVcDl4dUR4dDRzOUNJUkI3YVg5RXV0R0I5Y002TEJ2OHFGYVhiSXJaV2RDYVdkcXpOamxmYzJJQzgzU2ZadCUyQjBRbHBjc3A3ejRmT25tYUxjbSUyRlBMWllsSjVScEQxbHE5RDBBU2dqa3pGdUt3Y1MlMkZFckRSTmpoMDRGS0dYTzFlZ0ppVDNMcUhIWmJ0RjkzVjBkY3BaemRDeXhIUm1YSVdUc1dydEpjUlJ4STBZTDc3NmZPY3E2a1diRlklMkZkazQ4NFFoclgxUXZaZnBhSU1XT2VnJTJGUTR6JTJCbHNtJTJGc2lZVVJ0UExvaTBiWER0bVU1bjIyc3diQ1FKJTJCSXNyTDElMkJTNVpOemhubmdRNUEyblJKaGtWTkxGUmF2RVJEMkJHREclMkJzYnNXWXFkclhiQ2wlMkZwYjN5VEtseFp4NSUyQjl5eFpCZEJDallTJTJCJTJGM0FlU25RVkRwbmNrYjNYVExNT3g1SnpObXRkRXNYckRLVXBTMHR1WE1KS1BGVlNicFBJVk1wZFhaNU5oS0olMkY1OHE3M2czMUJ4aXdpYXlENTZMMVhpRUtTdGIlMkJSZGdsemtwcHB2UzVJVWRteUo0NlMlMkJqWDIxaGc1TzV1NDB2TnFUSiUyQll0YUE5THBib2FjSEt4SGZlekNvUyUyRnZTQUNJdmp0JTJGMDAyTnFMZ2ZtaVlJa3YwUGF6MlNibFVpVFRBJTJCbG9uVEkzUnBXbVFEaFpGUXp1akFTZEN6QUpYd2RwZEtleUpDbUklMkY2a1E4d01mamdMUUZPbzNlMHlIOU9ZOGF6cEMyUWFGQXlvNUFzZERoWmtyQkxqMzVxTiUyQiUyRnVLTHdycmlvc0lYZnRXQWw2YjJMU2tabWRFdVZib25LSTFmbExsJTJCa1pUa0FuWnJ5MHM5NWFyN0h2QVolMkZUbkFkR3hSSWVYeW9TS1JRNDlmM2NiQk5mQXU5MVdqJTJCOXhPYnBkd050aGRxQW54ckpCRG1QSWlvT1V6OXhPTDVSYnNEOVZ3VXlERnpOOFprOXdBWHhjY01WblV0QmRUdjVkRGpxbkF1eCUyQiUyQjVBV0xnUUJUU2JsMFRIOFpXbjBMb1NDcWVQJTJGaGpSVHE2NSUyRkQlMkZIamdxUGQ5RUViU2xEZ1Y5U3gyUkFtcEJFVnd2Ym1PR2hFZ2xxdHFUUFZwMXVWMkRDWDNJYlZBMFA2c0NjbTNGS01XZW9Fb2RiOFlyU0J3NVo3NnhOcTJyNmRwN3dEViUyQmdGSzF5b25XUDA4OW9uUGVncFB6VXlvdTMydzVFQ2RrViUyRncwMTlMUm9oeCUyRkNTTVRqYjRTaFJRRGVDcUMwQ29ZT0NQZGQ0SnQ1N09KdENkYk05d2NsWVFDN2xUaXRuS3Z5S1dLb2I2N3BpRWxEeVloZllwSWg4emJ1Z2loS3JUUEJpa3lDcUhDREd2RThRTDQ1U2ZkTENYUEFleGglMkJaVjg1TktlUmYxU08wWXFMM2M1eHR1MVViSkx3R0FXNkZHc1NPc0huTUlWSzc1aEpyV21pRE5oYkpNQkZBbDMyclB6eEpNcVglMkJKVnJwRm9UalUwOWRERW8xcHdVa3hDbzJZMEolMkJNMzJJemk4clN1WUYzb2VBalI2NWk1VE1YWTNlQnFRVHVOWlFpUTlPbmJYT2YwVEwxUlJOMHpVYkw2ZnM1R2Vya2tTQiUyRkI5cXRIcUNtdmxhWXhzblcwWmhubWhFSlVIY01sS1pHSiUyQiUyQlJ1JTJCSjhxMkZiR1BFS3J2aTZRRGlHSVF1MmM2S3B2eXB5THpzdkFtTmNtOXRFVllCJTJCV1IlMkJDTVRXMUlWQ3Q3amxlamI4aVdYMXpjbjliOThBQ2dpVHpWeTBuRnF6OTB0SGhLOVJLdmRoZUMzdjA1Q3d4NXU2JTJCa1JFV285VlVmRGpmRUJ1QnRDUHF6NjJGNHVVZHYzeU16RTZRTVdQSTUwU2YlMkY3Q3E0Vk9qQ1ZOclZmRDRIVEZXdTAwQWV5WDU5T29SZ2I1JTJGM0NaMiUyQmEzJTJCZHBKR1NqOFZYdDZZTlphR2s3RFprMUQ3MkpLUVZIcVZ3bmVCQ1VwZWlFVXVSYXNYa3MzRUJNVExEeFQzWFo4WFJhS0oyQXBZT1pnJTJCMjdBOEdlcFFvUWFaJTJGYXRmYmZCZGg5cUt5MG84Z1lMTjhCY05EN1B6T1FuSTZTTnAlMkZEWEJrYnh3Zjlra1lLcTltZFlhY29PdGt5WmRjVkRqVFRvVWNvM3hOM2R2cDlWSkZZUGZNUXI1aUN5TWw5TjlSUCUyQnJ1VVZhbEp2N2tHMUVPYzJ3QWpFNG8ycEt6TWFuZThUSTZRYThNa0VqVG5lbTlCaiUyQkFhSGFrSUxtQmtubWklMkZLb2Q1VmttczglMkJCSHJVOVZFRCUyQm5hR1IlMkZWd1g3UTRVcjhqS2s2WWxIOHZycEVFbmxrcEt4UmMlMkJ5RVljcWo2WWduZmdkUjBHa0lndHVldlo2QjdjczdQazY3NFJLSjF5ciUyRmxIdUhud2U1VU53VEhWY1lMaDVCR2l0SlNuY0pUVVFIM1gyJTJCQ0k2MFhyVUkzVjBLenpCN0l4YkgzWkpTTDdCcWwxVGhHOTIlMkZHdjRUQnlEb2hLRll5ZmlOejNwS3lMNmZNdkZIcVlwYUtyVGVidCUyQmRsMXpxWmdQJTJGNmg2dyUyQjJDNlE5OHFTZXFHU0VOJTJCY0Nsd1ZVZmNmNkNxVEhGWiUyRlR2JTJCOHFFVmpLTVZyYW5XZ3JYU2RMbDV6RmRyMlNGeGZXUW1hbXNkblRPY2ZiU1hVV1B1QSUyQnZETUtvTnV6a2QxajBaSXBBTCUyRlMzWXlpaU9BNk8wQjglMkZ4bzFPbkVLSlhyT1J0clIlMkZyMzEyUUZ3WVJWQXhBVTBzUjVuZThDZUxLYVZENE5zeFZoWFZ5dzhSVCUyQm5PUHg4bmxsdHIyRSUyRnN0bXhNanUwRTZIM01zNDVsMVFHb3JLQVd6M3A4MiUyRjVCQmJEVVdna1JFbXRCaURLQUgwOWNEajRPUG40JTJCOUJWYVJIdFdEQVdnMzd5VHZoR01vQVhNTFBOZkhXaWZLZnZLRFN5dmprUnNHS2Vjb2YzNjY2UndHSlJ2NUNFemxpTnA0Wkl0TUoyQzhsYjljWno2YURoVk1ZNVR6MGk5TEVnOEdCV3p5SkU1dmZNTmVsUHZwTmZYMEElMkJyMWRzJTJCS09WaEM5czRMUE0wUWJOam80TXZTM3hFVVE0NUZTd1NCa0FxbFF4TFd6dnl6aCUyRjc2OFVEYm05RXpHbEFwTm5GOXlQZDQ0eW10Vk1XT05GWU5aWkFETlZVTUFMTXR0Y0R3NE9aJTJCZTNyVGVMZ2I5aFBEUU1hJTJGJTJCZ1Q0JTJGZEZqRDFzZk1TMFMxRiUyQmpuekFwbE4wWjhnbjBWTWklMkJITFlmUUI3ZXdxZEY4JTJCVXhLQnA0QTdkQkxXRkhXJTJCc1BYQ1U1aVQyWkRGWUx2ejJmTG5nTmtnWXdUcnlMUnBQeld2ZjhpQ3pleVhmVWxLUlpBN2VFQnVaVHNleTVReTVJcDhYbWRzQVI1SWpWJTJGSHR3bkJvaEphZmhlcFZpQjh0akcweGVtcWpXUXlwb2ZCZXFCYSUyQnNZUXl6JTJGTXV3dmJNVk1PbDduJTJCbXhrcHZTdkp2Y1g5T2trd3JGYm5iYjlOYlk2TEZOUHZySnZGTHVEdUtROEglMkJxYWVrWUVsOUMzRDUlMkJINWJaYmx5aHQwcnd6NnQ5V3lyU2VBc0RpUFBZenIlMkZyelFka0ZBVDklMkYlMkJKSUFzQnJOZVZFTSUyRjJiOGZBUiUyRm9UQlVWdFNxa3lxeCUyRk1QV0lDZzk2ekkxOFNlR05jVXN1elFyejZ4N0FZVm5jYkxwbjN2NkdjcSUyRmN0a1JHQTNMWW50bEk5R1lFaWJIWTM4MnVkcHFJQVJ3cWY1OTRyRjhLTWdsSXFXSDU5TE5rRzFVRXhsc1JESlhHbkR1SDNrMUxsJTJCYzhUYmgzMHFodGl0REV1VEZ4cmclMkZuNDBCJTJCUjNOdENMZ1BjWWpzZkVvdUl2NWtoJTJGN002JTJCWGpOWiUyQjR0TnBkcENOVkdNUjNUd0llS0pZQVJ3UWxiJTJCNm91WiUyRlpkNlNnaTd2ciUyRkhTSUR5d2tzWWpnNzBnMXo0a25SNnRneSUyQjNubldxNHZrVlZNUmJFM2xHd0FHVnZqVjJwQzVzYSUyQktvNkRNRlJNdmdDTW9iOHBSaVBqYWRodnFDejJhM3k0TU4xWm8wcVUlMkJPZ2VaOGQ4dVN1YmJBWnFjM0dLMG1ycG0lMkZmZnZzYlVMd1BuJTJGMWVPTVVLRFFNeFFiRnNONWZuM3dCZFowNWZ0UndYVWxqS1FpRHFFT2labnEwQnpPQXY5eDZLdjBlV0hPenpBUVdBb0s0REQ0YkJoZEZHRTJEUmlwOVZwWFB1QWhhd3ZVa2JDNFpqdzBKWjhrNnBFS0NiRzBmR1U1TE1yNlJBZGx2dGpGR2FlJTJGQjQ3M09QbzJ3T2lvQXhiY0k4bDl3czdZWHVNNFJwT21Cd2xWcSUyQm5aUDJDblo0TUgwWW9qJTJCN25VdzBsc01xTWFvcHlscmNLVkRDTmE4NCUyRmFSWHhHVllNeVpIMVlBVTlQQTJSWmRGVnRzQ285ZHpGUFlBVkFUdDg3T1JWbVFVckhkSXUxWkslMkJWeGU3SHllSmRhZ3IzQyUyRjRORGtmMlg0WnQ5eGlXV1oxTUo1dERCMDRXV1FiN0JCeGF0S3F2QzVhZnJGZm1ZNEp3Tmp1VSUyRkpGJTJGWDR1NFA3JTJGcEQ5c3lkUE9BWCUyRmlCZFhzWXpQSTE2dkg3JTJGeGU4UUJRM3RYSXdBbTJxMmY5MUNZSDVvMWg0bGxLWUpEJTJGRU1RcVY4bFU1U1QxdHhic1g4NGtjY0V4REREMHRUQXFXZmpwZUdBJTJGOFY3a3JNVW9FZHVhJTJGeUdOUlo4bEJFT2M0aXBablBzMUlDd20lMkZGaWF3QkVleSUyQjJUQUI1UWVKeWlYemVnJTJGelBmUXVpcWU1S0tEREthQnVtbm5MT3oyRVB4VDhGekI1MjRVZk53UnVEV256T014WkxoWnpYNmV6VzNmRm1Db1lCaEdIOVRGTXVVRWRzNTVZJTJCRHA1MjFVcjJnVnZpVHRET1FMdVNRdFE2M2dQVUVMWGV2Q2NtSUp4ck9MJTJCMXdhUXM1JTJCa0NQcmJ1NlRsRWdmMWkwQ2hZcVMxMTgzYiUyQkh1dUUwNklWRjhXd3IlMkJPc0dNRTlGSWdpaEF4RUtLVlAzJTJGZDVFaWpGNSUyQmQlMkJWT1RsJTJGa0RPMk0ybWdvbDhGJTJGT3kwT1kyZk1YVG1LTW5UdjIyaEN5a3FSUDZDdm1jWGVZZEYlMkJaUTkyeTVaOXJNQyUyQkR4YWlSRUglMkZYWjhpamdpMjB1TGJJbGRvV2RlVUxFckNNbWNyU1loTFoyekhDWnFPbDVJUGl5SiUyRlpJa3g3bkwyZ2dCaDgwSXRGaHNuczhLdTQyYU8yYzE5SVhrcVVORG55T285YXNvbzlXZmc3U1pMMCUyRkVUVUxWZ3lSbG5ZUEFtJTJGN3E5RTIyaUxoSEx5VUhVelNmdFh5WGxyMTlRQncxUCUyRmNudDZmOURtUFA2dGFQU3JBU2MzVTYlMkZvSVgyY3JqbUk1dU5sSzBlMnlsS2t0ZERNaVZNOVdiZ29PbWE0SXp1bkN6RFdPaTVuV1NPMjNtcmtPbGRRcGt5WWwlMkJ6MFh0TmZIRnFIR29kUXo5MzNCblBVaWI4eHpzcjN6NGhpT0x1RHJkak1XeDdqaXpsWDlGenBzTmFrNiUyQjBxZ3BsTWIwclNRMlFjT2RnQnE3YzFEVW5mVVg3dzZFMERSWiUyRlV1QXhuSk9GNXpBVmkxNyUyQlFzMHZCSW1hanR0MUVPOVUlMkY4NDVRaWZCQmlJUTFvVlRzY3l4TTFKMyUyQlFnWjdWc1IlMkZ1bTJmQmc2WW85T0tLRk5MJTJGVHNsdVhqJTJGWkJBV0dLSGsxQkMlMkZZMll4YnFJdnRHbm90ZVhoc0NVaFZuZGc2NnVpQmZ4OHMzbmxuNCUyRkVRUyUyRlA4ZjVTWTBaeHFSeE9wMDZsODZVNzBVd1FNVjdvbVIlMkZqc2o1Rkc3TTk5R1ZlY2xIaTZWejhvajhqWFVvJTJGcnRNY1p5dTBrYyUyQjg5OHF5VkpKRzFOQVVGRExQc05US2x6d0ZCblVMbDJtbEtwVm5VZUwlMkI1a2FUczNmRFk5cjFYWWU5TUFwajNVbTNEQmx3eFVzSUxjJTJCYk5XU2QxTzd6MERKU2QlMkZhbVc3UGM5VU5mVWlFQSUyQnJ5c0UxZmYweFJHWGhqWk5VcG5saU5Yb3BTQjVxSjdwMkRqWFE1MTNZZFcxeiUyRk5udkRaSkFFbTNiVjQ5aXY2NDlUS0lBSXdhNUEwa2FPNzVvY3ZtSUJPUHpDNXY4NXlITlhBbnpiRkNKeGJvS2N2TEtnWkZtUFBYbnp4NmUxSWFCc3RjdXo3SGxKS2FleG5CJTJGdDMxc2Ezdk1vTld6WmNtMllESTRXdWxCTHUzdiUyQjFkYlE5TEFLMVFNV0pvb0w5OHRHWFdCR2Mwb0clMkZOWHhZRGtBQWRWV2g1YU8xd2xQNVV4WHVqeDJQT3k4dm1EMmhhcHhHWEVuejB0UnBwcnFIcDFkSzQ3QUpJVHhYOHh3MmhCUk16QVZLNWx4eG0zbVBNajkxRWxTNkRlNmkwY01DejdxZFA2V1ZtVTFkbVNXTkZIWERUQlpneEVWc0tUeXVROXJ4dGEyR1ZDV1B1bnhLaTQlMkZ0WFZwU3pEM0ZLSGR6ZDJ0c1ZvSGh5NTBYQ2NldHJ3WjlSbTZRcFFSYm1QQ0wlMkJ1anhsYm51dU94M0tKNk51cUtCQ3JwMSUyRmM1QXQlMkZnenJzaWlGYnU4NGdTSlpzZVVDSzd3SXdxUjFGMEhxMFElMkJSd1dMMXp1U3hjOE42VHBLNkZXUTR0ZlA0akw4WU5PY2R2RThKNW9CMXhIWExjOGdvcFZiOE1Tc2RHZ3NGSXQ2MEVTdHh2S2lTJTJGMmdFandKV1A4TiUyRnZ3QU9yOGQwJTJCUEZTJTJCQU9RbVZ6dG1vMjZMNlNrJTJGRG5yT2hZSDZkNFNzRjhvZHdtTXV0TVUwZlpNUWRuJTJGWWdEMG45dXFJOGltVW81NDR0M3BtRDN4QXVvU1BuZXpSOE9aRUc3dyUyQkJXZEElMkZTR2tIQTYyMVBCeXZudU0wdThaRmJUcTZ0Q3N2WmpXa3VRRmxLTTJiSWk5b0s4b1NYNjFSYUczUk9DQ01JaXBVWVY1dzJGS0FVV2R4ZG43R1cwY1dXS1ozJTJCTWRPWENkRFo5JTJCMHNrbm1KS25hUkYlMkZRRkZiUHY4JTJCSEd2M3lKMER5eW1PQU1RTWNZJTJGM2puTk80JTJCN1FZRmZMUFduWXBzQ2RVRm9uUG5FVGhiYTE3dU0zdTYlMkZGOFJVYWJ5azE1JTJCRmdreEVzOUNKenElMkZKbjJJY1paYUpNZFlPRXJlY1pGdUlNd2FFWVVPbzhqZFFneW5pS2ppZmFwTlZsJTJGWDkzciUyRnlSMUdybTB4Tk1zOXBlaUJVbFdKNVg4bHJpJTJCMXFtdDJPUTFXbHc5bzByeiUyQmZtZGszeUZpcDkzZ0hJa25QbTloM0k0clpNN1BhY3dtV2pMSSUyQkpWTm9OSVcwTVdmT2RXaXpxQkNoS2ElMkZ6MVJNN1g2aWdocVpjYlFUQkp3MmY0ampWNEoxY1BkWlQ1ZmZkRXYyS3J4cDd2S3U2eEs5NUhkVFlmQ2JMcFMwcE1WbVBKQktJY3NUUnF6SGYlMkZUMG94JTJGM1l3RXRxRXNEeEIlMkJFeFRwVzJlbFAzSm1RNTAwQm9ONkJ2enBxSnUlMkJNWU4lMkY3R1JsR3owTERuS0xxUllDaGdQR2V1TFkzWmlHMVR0bUJxRzIlMkJxdSUyQiUyRmolMkZ4VWFSTDNpZGIwVUEwR2t3N3BiVE1XOERIaDduWUFXTHIyWXJId2UyTFppd3oyUUlOY2ZWWkQxUG5udiUyQnhyT3MwV3BXdEpMVyUyRjdLbWxHeTYlMkJRb0lqTGV5cmpTb3piSzRiVHlZU2p4ZEVuQmtHa3RONFJZYSUyRkR5OWpZaFl1UUJCNyUyRjZyYURLN3FWOElzZ2x1dGxsRk5LbDFSRldGWU05MSUyQjFCWXJmZ0o3bkY2MDFhaCUyQloyV2hNN0p1YVZzbm5RWE5JRldkMzhWZXlrSE4yVmtJc3lLNzBRYnUxT3JJczlJTDVYVmVyY2RIaXM1N3NCMkg4RmQ2aUNDanhRMjBtMWRsZ05Mdm1zN3d0QkJSJTJGVldndVBjek5COVl3YlBCYlRYeHFXSlRXWHBJWG4lMkJraDclMkZ3emd2MzhUUjJ1Q0dxM3FlZUJHcE1STXpaWHc2cjVxV3RhaVg3elh5eDhBb3NFTHZHR3RzbE4yTkRxN2Z4a0tVMnB1M1hCMW1SM3pKRXFLdllqek8wRyUyQlk3amd1dVpLZiUyQnBNUWxHTE5xWGFHUmt5MGxZZ0hhT2dXTlpISSUyRk9nemFrTDF0czB5OG5RalBRenlURXJhMTBNT1FMTm0wekdRaDVZJTJCdExkZUMxalRMODNtdGNDNzM3czd4dE10N0NrbjJtYzkweDNpUmt0V0k3VktGeEdxekRpRmVPUkZSayUyRnFpYUNaejg1aHhsNzRuVVpKWXQyOFpla3VFdzdsM3BmN1hzZXZPbzlwNHRPcHlyUiUyRll4ZkVIWXRFR25uZzZwNkxLbDlOUUI0blFuYSUyQlNPdCUyQk5WbVNUNEVweUZVRXJTTDBMb2FzOSUyQjExTUtXMko0NyUyRlk2VExPT0lSdnVJbGtNODZBQnhzNmVEZ2YyQWdBTnolMkJPdzRpUVpaRjJnakwlMkZubnRWdDlkZXozZVBXZzA5RSUyQlhGWXpPQWxFSkkyMGw5ZGZveW5zVmludUg0MXFLS2dNNEdpTiUyRlpLSnlLZVBsWFglMkJ5NUFUUkhmRCUyRnJZUzE2Q2t0Q3V2bkRQM2QyT05UMnJYcUhZaUc2RXQyRWhUREczWjQxQWFIbTJNUVpSN01vV0I2bnFtcEw5Y2tiRVYxeUI4cWRpYk9uOHRtNkxXV3RvdW11UngzbUhPdUpMaU0xTHlucjBtSDVhVUJXNldqVkxuUk1OdHdHUnFlTHVTaU1DallGaFNhd2pMUGc4QmtlMExwOU9OVTlJOVNRQmhzckxPMzF4MWJFOEVIQmZPdTVVclZMajdGRHNZTW5kdGpXMUw1VzRxNm9jZ1E2S3hOb3VaR0g2cTBySUhwJTJCUTRsQWZmSFNSZFJ3MUxpayUyRmc5VGhZY2FycnBMUElTNldvNnIyeTMybWo4VDNDRGNOSnpDOGJGd2sza2YxZ200QnlKclZYb1FTNDZWTmRrVkg5YlhBQ3klMkZpWXlDSE1aMllwTyUyRmhYRk05YldnQUkzJTJGRGNxMEhpR0laJTJCRnRMNlNuUmVEdmhSbXhVVHJmZlVmZXQlMkJGMTgxOXBqM0tvMzBLYm1aMSUyQjV0ZjFabldMdzRUMlhzSjhpWHRLZnpMbGFyMWhkNWo4VE5YY0JKVmw2WEFqRUxUQTRjWiUyQlFLUXNWOXR2YlAxTUZSd25DWDNBUmxLQWd4Mmh4JTJGM2hkZnhCWEpFRTNPUFNuZlN5M204aiUyQkRzM3E1Wjd0WjY0eWtjaFRCMTl3T3V1ZGZ3dGRXSHVGV3ZYdGpKRkxFVzBQSU52WkRlTlR1SUNJNyUyRlZuMURsb2UxaklIUEtDRjVIJTJCa0wlMkJ1cnIyRlRUNE5DSFdtUWVzdE9IRGpFSzB0ckxtNnRyYnZtcVp4OGpqTG81WnYwY2RQR3UlMkJKNVY1R2lJbkx6UnZUaGlkVm00ZjNxbjFObEFxazVrSkQlMkZydzI2SlUyYzFLc21zYVdQeXdqRlZIS04xYmVGcVdFalFlbG1hcEQ0MGR0QXlSclo3c2MzUFlPaFJqbFpJQ0ZyZjVSJTJCZWxNRzclMkJXemhQNVRYb2o2dU92YW55OTI2aG9maFRHUGkyTHRTV2dScjZnNlhzS1p4QjM0ZllMbU9iWU1lU1pvSmVSanpzVnpTQmpjVkt6Nk9xNkt3ZXI2N2oxSlVlcWp6VW9BMEdZclFiTWNUV1ZmVjdJM3ZhRHc4a0NydzZtN1poZyUyRjNwNUdwTDBTdFV2JTJGVldxRXI1RVhSYnZOUHl2RkFrc2JaQ1RHUFFLcW9EMVB0QnZHOUo3Tnl2cVJSVkpubE5IWDFNV24ySnN4RHdsb2VqWlBWclIwSm1sMFl1VDFKbXZXdkt6TlJ6UG5BZWkzMDVrVmhicDhxTjhzZWpoTHU5MEVNdkNZUTBmbzVWSFRQJTJCbWlYTG5jbGhWNjBEeHBtMkJraWVJbiUyQm5Wakt0MThnYkNsUWY3TkNXa2Q5VGV6R1RTQlNhUTlkbXd5VlF1NlZhVnFQbEwlMkY5MSUyRmxvU1dsJTJGczI2ZWhvSFZ6eTVYWlk3WFhad1lnUEJOdkpaRXVrb0tkTzVPaUtFRFc5NHRXelRBNWZRSUVLTXlSbzYlMkZXMVBVcnNpSmtPaWxvNmk5Tm51REtoTEFCSnhoYUxndU1wU2Y1WFpWWG15cTBhVHZYenBFSUU4NHFBQTljS3B1dyUyRjF3aFEwSmNQc3ZWWFdtbld6YnVNbHN6WEwzRk1jSE5NM0FDa29DQUtOZEQ5UWh4Mm90ZDV3WXBwWkx3azBhd1liR1FPSk5NWlZWUTdvVGNHczVxWmtadXJSTDRIbXNYV0l5cmx3ZTA0N0tKM05yT2l1cWh0MjhrWXVxM1R6JTJCRTMzeEV1dVhXYThYN2NYNURYQlZFbmdZR2YyalRQb3NoRW16eUdkVFdQcjlqVFd0Zm5HOXVieFVicXBXZTdnRmJmOVdRdSUyQkElMkJEaVFLd1Vha0VyYXdsNklkbzVTckpXRDZSeWFvenNKNGJRMGNlTU9UblBkUVlxUk1tUkZwYXMlMkJuUjFrQzklMkJPMkFqWUxtWVdqYjhhMmVQa0kyaEsyeUZ6NVBPR1BZSHNjZ2J0UU9WWEt2VU13N1NvelJIc0o0bVpiZkVLeWJxNzglMkZHbFo1c045VVVSQ1FNNUh1RUl0WDdSbXgzYnpPMnJSM0NpM1piUk9hVTVtbmdadEI5Z2d0M0kycE4lMkJmYWExQThuOTBaU2I5eUMxeFA5NTdKdnFBTjVxQW5mcFpKcXBCdklEWGhBVzhQdmVtJTJGS2hkJTJGblRzV3hqN004VHJWajNFJTJGVE9laXNicGp5eXl0dnkyV2Z1c3NTV3psWjlSY3owUjcyVE1nMTVZZFJzUExOY3RwNDlTUWRtbTFaZ3pjRyUyRkw2QTl4SXo4ZE9hRGN6WFE5MEU3SFNoZndyNnlKUnNjNnJZMUpkN012Y0ViWWpwM25zcHBVREJ1aWNNOTFmeEVWU2lpZWlXMlhqUTdtZnVkempMdGglMkZkV3NDJTJCVlIzUFZ6RnhYWnNNWnpFenIwc3ZkTG9XMUolMkZMZmt1dnhURzNwJTJGTWElMkJQc1RMeWtaVHA5TiUyQlZ2VjBhYlJ2VzZXZHlNeFV4eE9HNGJmaCUyQlAyS0dIcGN1cURpa1hQSjdpencyWVlmYWQ2ZUlLZEd2VVpHM1U2JTJGTlk4OWdXekFGSjlLNzZlUXVpVWZhZGM3VDF4M3k2Rk9lRmhNTm0zRWJrZ2VYVjN1cENDZU9XVktLcmUwRWMlMkZOWjBQanlrTExsWDU3MG9EdEZqbWlZNE9vc1ZlNW0zbmZlY2NyMElJSG9FUyUyQjVYOGxVR0hoMVJBYUNCTmNwNmFnY2h5OUd1bkpzQiUyQkMzT2ZTU0dWM1RaZldhbmdEdlluNW1hNVZwQWdrZmR5MUpiV3lTdDJzbmEwdE83dyUyQlFlbXVmNXQ5QiUyQlk5VzBXazJnV3RHMlpwZUhOMW5Qd3lLdmNuTXpXRVFsV2xnRWpZMENocyUyQlVlRUNmOEFCM0hrV1JjZUp2RXF2RzFNVURPNFRpT25QYnFsSDNmZzNGeXZ2WmxCeTFPR3U3SERjMEdaMXBRZlN6anlndUJSbXJUMGxkRUU5WGlybVZ1Q1dLN1RtZm43YXYlMkJhTlRkSUJoMng3ckowREdxMHB2RVJKeEhJZDF0WDMwM2NUYjlkMnl3QVZ0Q3lXR1o4QUtzYlQlMkI4V1ppRVVxZ1FIZTgyUWJodTU3c3J6RnNaZGF3dHJSYkw1MFAwb0dVbWFyY3VndCUyRldaaVNSdTRYSmlNb0tnanlQeG1uMUU3dWZheVRZYzd1SkhFdFhsWUFVcEZpbmhqaGQ4SDg5QUVkWTZGZUhUSWR0M1JZNzFSVk9GNk53YlQ1U2ElMkI3QTJVSW5sREJiOSUyRm5rSU1aMnRCczVRbHQ3ZE9iQXFvbmVObngzTm1Bc2k2T3l3RnR4VnhrZkk5VCUyRkJuOEE5dWxiQTZPOUhEOHpicm42c1hNazVQeHM2cEI2R29YcHN3NVglMkJWUzY2SnViJTJCbUozRk5XemtlaTBWcGdNYlJiWTdEd2lLSzZiTDRtOHA1V3F4T1lJWXMlMkZweG9ub2JhbmZhMDRTZGxlYll6cHRDWUMlMkZEcEZRUUJybHFTMDIySlolMkZFWGh6MVRocWZiUHhCcW5xbjJ2ZjFXa3NQS090Sms2SSUyQnNnbjE3MnREdW9QU2t5c1VGVXExJTJCTm91WllTRlRHcnhMSW1mVlQ2MSUyQnlLSmVna2Y4anBFek5YeTRVUU5LWmglMkZibE5sZFBuZUpYJTJGJTJGZ0tSJTJCeWxTV3N5T3l4VU5mM0l4d0R2S05yYXY4S0RoeHRiT0JNSVdFWjVhZnZyZDZZWVExSWZkWkdua3VvY0VkdzgwVzdoSG9rSjJnSUppeVNzMHozeTJpWGljMSUyQm1HWFBuMUlWZFQyazlpWUloT0tpJTJCdERsS0hMd2ZEJTJCWmc3eUdJN0x0YWp0cnNTek94UUpnaG5oUFolMkZRWUowb3d3VUlIUVJKNDduVXNLMTVweTh6bnJnVlAlMkZlazlOayUyRmRLNFhaMHFER3RmYU55WDNybTlwdFc4ZHBQYzFsUVo1OXJubjdWNVVPZGdMelBubyUyRmw4TlZaRSUyQk9pZkV2TFFKTjQ5QXliUVh4YSUyQmRiJTJGMW9XYjVURmclMkI3S2NLaW5JTk1IdVpDMHc1UVp3dGJodHUwbVdXQmhSV2FmZURBMmFFRUFjUUxLWTZ5Z1FvVmtYMjFHMnolMkJ2MmQ2VmVVUnMyclJXZ3h2em1mUzlBSkgyV1dmQ1ZFU1pPaklveWxFUG9pZGpqa25JalR6Zzl1MkZuOVh6U0pTenl5czROVnU2dHUyWGJsbmRzJTJGTXpaTDhYeEs0N1olMkJsUDRhM0NzZXF1ZnFqN3JOendYTUY1OFNDVyUyRk95VHNWYk9BWWUlMkZ3JTJGOERLRFNPYlNncHpmSkYlMkZrTDNMMjdZdHFITGpNSzVzME04N1h4bk00NVhGTjBydmlJWkpQM0pXWEVmanhSVzJMVTQ4NHAlMkIxNkg0NG5aMnM4NDFXZSUyRjYwTktsZFklMkZ2ZWdyY1ElMkZlRnA3aWZQQzdXbW96eDUlMkZEczElMkZhJTJCdU5PZSUyRnZNYktyejhuR2plNTROY2U1QVBIT2NpWHA0bk5IMFJKRWhzM08wNFZQVGxHMWZzR0kzJTJCdXZlQ0VPSzNMNWJFc1JDUGhiclBQSDBYS2xXaWVwZUJ6aGFuWUhMV3o2dWhaNTM3bmpBJTJGMFdxTFJiTktyayUyQjRnOFRueUFkWnA3ZjNTbHdHc0VqJTJGN3BzRHZMTTVsZ2hkR1BpMWYlMkZlY3c5MTJKMDA1VFYwZjlodHpTVjI5MVZPVGZOUE9lY2NkN0l4JTJCektWbHhEREJhT0pRVHFUaXlVODc2WHJiNWZOOW4xNFdoME9yOGhKaWZjM3lyRDkzOWVTRThpSnpaaEpWWTBHWTh1bllIdmR4Vm9JRWxXdGdZNm1pdVhIaFBKUGJMeFZMQXB2Qm85bUprYjlYMlNBYjZyanFGYlcxYlltNjczTE5BdDVGVmhEdGsxV0l6OSUyQjc4RVY3SW1jJTJCTGhiNmdjRDNyZFdUQmw4YjJucmF4TnRxMWRPR3JYSCUyRlA0NGpHaHNVU2hLclhEJTJCd1I5YnNNek1lRlBaMUtkQlpHJTJCVHM4S01vS3ZmYSUyRktOYUZOQzViQVY3dXNUM1pJOVBhZEQlMkJTRzQyRDJVbm4yTTBZdFhMczdSOWtJJTJGVmpsJTJGRDBKNUN3SzZDMWNabGNocnllNktRN3NaS0FER2dEVzNLNnhFOHZVM2I4cnA5OUZaU0JWSXdIYVBaQkN4ak5NTGdXeXZaQlNqNSUyQkdmWXNoazFnbzhXYWhJdDQ4NFRQbWZqTm9YdDA3S0hsUjFDZjlmYmxTVjRXY2ZRQmptZmNCVjdyJTJGTlYyQVJNanJzcmxsWHhUdlhVTXFLSDRWJTJCVUhPV1JzeDRvWmtWU1lTMnVJR3FuUXYzd0VaN2hZQ2lzNG9hVCUyQm55ZGJESWVadG4wYm1PbXRjcnR1bVdmTllEUjJPb0YyNE9LSDFvbGFBc3AxR2lXT1VvTHdzMk9FWkhTcDhDTVVzNjdwRWltN3E5eWRzU0ZITkZwTmJmQU91RGVTQzdtbFhWbXZYUFhjNGJ5ME5yd3pNWjY1ZGxiMFlXVTNaOTlGdHBZTE5yRFozYU92bFl0cnAyNDFhbjE1YkJQRmM0OUxrNjFBaGVpMUE4TmNKVXROZjczRnhPU09QWThmdGJJbCUyQllrb0xDVUZXVGVvT3l2QlJQTlZyMU1rN1YwbmRCbGZoNFFJdE5RbVhMU1BjME8xMGFCeTlGVUJRcTVEM1U1SHUwdlBoWkRhb1FnVDR0ek1TTzNNaHkwMyUyRnk0TjRJOUk5ejJGQ3NjcUxTZnBia2FUOUQ1NG1qbmtPd3pyWUlPMEhPaE9WcGRzTllad3lLMk5XeEtWMERuNW9aNWQlMkIwUk1PSVdnN0xCVEZsJTJGTzRuRzF5ZldVcGpTN0ZSYU10NFEyaVh1VmpCWE1mdmNOWjhDc3pkdTN3ODlTVERTdWpZWnNLRWlodmMlMkJqcEg0OFV5WTl6d2JyaWFYeVh1bloyaXNUQ3ZYSlVFRjk2UjNvc2VXc3g3JTJGNWslMkZobXRXaUpQOUFpSno3eEo1dUZlY0xsZFVtSGZYUmx2TldUekxzSSUyQjhWcUEwNWpGM2kzSXo5JTJCTVM1bGczcmxlZ3R3RThxTTgzbXZsaGlqZ2ZaQW05ZkhVOVY1azhBaG40TDBNTUh1RnNodW5uMENYRXB6bjh3aFJuN3VJVCUyRkxSdWsxUiUyRmdsU0g1NXZDM21PajdleVNPdlhydXh4TlBOQ1BldGFwSU1QQ2JwcG9RN2hjQ2NSdGRTR2ZJVHVYd3lkRlBjdWZzVTBpQ2JzUTBza3ZMUmpXWWlMV0ZFOEhhRnZnc3RuWHhQYnBMclpNZGxxZmpCSiUyQmY5blJRJTJCVnJxTDlvbyUyRm1ibUh1cCUyRmV4eGRQMFh5MTc0JTJGTmZhRyUyQmlUeEdqTjZKZHVvcGtUTkdiUW1sQ214elNSdjZyZUx6VFo0JTJCUE1VRWk3Q005RzNMZUY2RHJlekJjV0s2WkdaMzlHS3laeWp4b01Db0ptd3ZWVzdOc2JuZUxESElTV2VSbCUyQnV0OGJFSW45OUtiSTJGSnprZFlsJTJCQzRtbTYzR0t6cE5BUHBRRGV5OXJXJTJCNFEwMCUyRkxzaVdNVnhuM25pYnJvNTE5a3Ztdk52Y1QxOW5WUFlqb1B5elRLSjBwciUyRkZaSjRKYzhhSjVSVnJ1ZDVpR0RXcWFjZjV1dWZSOFVKdEQyWHEybjA3QzlqMmZ3MkNubFBJUHk1Rjg3JTJGMG94c2F3SCUyQlhxSnJ1aXBOWXllSnV0eTV5elpvR3VtZkJ0SmRlSmV2U0R0VE4lMkZKaTBOMXQ0SWtxZ2xFJTJCNHRpVnNDWkRWcGlOYk8xRjlJRUljdkdzRmIlMkJlY1pjOFpMRmV3S2dQQVMyMUowWGFWNDglMkIxV3hyeFFwY3RBNmRMS05yUncwVmk5JTJGNTdLaW5OczlWZiUyRlNUZnBrdWloQVZLTzBhZTJEZFludXRJYjRpYkM2R214eGVERXNMM1RCeGZoMVpWcEIlMkJRcjMyejFEZjVSb3pOcEd4OVRwJTJCdG5lbWRGdG9VYnlLN3JBYnFmTWJDbUFxbGglMkZnV1oxb01TMEowJTJGeDhsUVNxY2IyNVBMVlgzRTVYcVM4VVpWcjVKR3FxNVklMkZRMVh1SEVyYlVwZUplMDk2ekoza1BWSHZqYTBQdHJ2aGwya1hGbTdRMTJ2cHZTTEo4Wmo0cm5aQlhtS25Idld4MkdvSiUyRlR3NGtBSTRaNEhXbmU5MjNPUDFFUWxwaVgzYkVncXViVlpKNzJxSWdOalNaSGVwSkt3VzclMkJheUhDT2haYTFmWXg4WTl2ViUyQnBaa2hVYWhxM21vcXV4ankwdGIzOG5aYk5BaDc3dmFhMmM5ZUVDdWlmQ0tiUHZqeUZRcUs0SG9pOEpKMTZoNTdFVks2VERqSGpCYnAycDgwWnBURTM1RmVoNjk5R1M5eTR0UXJQZE9jWkF3U0NUTGdJTGh4UmZPbldlZDliY0hXUEglMkZpaTh2Y2FweDlaYlB6JTJCZWZVc3VsR21ueTZDckQzR0JsM2dzRFBWNTFHZ2ZxNEUlMkJzVHNJeUlsbmVMNHN1eEY0bWVXajNMVTR4TnhEbmlhN0olMkJGeXIlMkYyMlF1c3J6UHFnRXJaNThWVnpuakJQZ2NrR3hxV1MwUFNTJTJCbHRMZ3BGREJtJTJGZG5aTGU4aU54QzhoQkhLTmE3a1NMZmVJdDF5WGMzU3hSbDVVY3olMkJNN2RCVjlrYXNQVXNucTNxb0NkVkdxSXRuR2Y1bkZ6WnF2b0lSZVRRY3pEZVU3cXBqenVMcmhadXpkRWlqT1gyaWgyZjlueVlzSkU1ZnJSSnc2JTJCT3Y4aXE2cWQlMkJtZDEzcVNUc2xlNzJuQVNWeXNSMUtzdnk3Y2o4TE01NXklMkJKb2FDWTdERXhzJTJCWnMyc1NSTWNISlhZJTJCZXhsZWFjc1NibkglMkZxTWg4dTNYN2FmQ0hJJTJCTjBPY2pXUnpqUlhUcSUyQkxrWjV3NzV3N3NWNFlzN0ZxRlIyJTJGRiUyQmd6ellMY1ZvRm4zck1YNldxZU9jUTk3NkNYWkJuYWVCSjV4NEEyJTJGN0ZSJTJCc2lZTUg2VWRHOHNOZ2pycVJGcjF5TnlUWFhvTzdja3JQWHdaYXJCbVZBcjFlUEFLTmVGODBFcE9KcDhlOWtPUFdKOGh1V1A0emE3SGVUbkJlblVpMzBmQzIlMkJXaXA2NUN1TnQ1UlYlMkZHJTJGTnlMd21DSG5yTElrdGhpSmlCckN1SkZGSkFQdlBJeDBuVktxQXdUTFR4dWhtcmtmMnJTeUhZJTJGRTQlMkYzMG5abzdldDVzJTJCZ1ZrMnlZajNTMDh5aiUyQlhSMEVvN1VoUjVXVWcwQWNpMnNmMnQlMkZIMnk0RVp2SkxoMFBQaTNQOHVXUEVNOUE5UlNWa3c1a1FwJTJCcXFLS2tjWnB4NThXV2xNMWtmVloxMzhyVEZhamhrcDFqNUdqb0hrZGRXa1ZzJTJGRnRiR3N1NDBZaVdQcmRCcWd6VkU5JTJGRWVzOFZmZWFhVlh1Q1ZWaHl4a0NEcFh1JTJGRjZzcnBTOTc1SVkwWE9uc1FSYTQ5SDdLa051M1ZKdHR3OXUlMkJVUENvWmRCUzM4QWVoWmRQaGVia1BEUDhVUXpYRDN5NGN4ZGQwZ3dLaFAlMkZPNzhqZ2ZYOEJVTkclMkJNQXFYZ0pkTU9LdXR1TzYlMkJVOVBacDUydU1EbkFPZTdrUDBpaEFDY3JjV1ozZU5zZmViV21hb29tdkZrJTJCcXVaNlRzaXN5eG1uTXJOMWU1ZXJvb0J5UGZZa0tXcHhMbUJPeCUyQmdmdEVLeHQ0cnYzVlclMkJvWnBqaFFOdmslMkJEMm5KaG1wdTMyUGl5VE9pUHRMdjhZdWJsUDZjODNjZ0g3T3B2dkFyU2V5SXFiVG9iJTJCczFqMTNnY0MlMkZhSCUyQjJWM2llb0JkaXpNaG5hVWpRSVpaOGd0b21nZTIlMkZ2RURCVmRUdGZRNE5IeldraXJ5Q2ZxVlY4MGVhOEpPUnVFcFolMkZ1UFpnc2NCcUcyMnpLJTJGVkpHVzBEdTFvSTZDUm1YRW9NRXdpYWxObXVYV2lGSWNiQjF6bmYxb2R4QlREY0tnbXh6YUMxc280Zk1icmNvZ0E4d0olMkZVSFJEVno5SHRadndmSFBSMUdjM3BIS0JHOThIeSUyRjFzZXMwNFZLSTJQJTJCUG05N1lPdG1Hb1pyM3NYVWVUSUdtVFh4cm5MOWJZcjhSJTJCR2JNQ3ZaeW5QN1B3QlFqSlgwRTlTVThHd0dKTWxJNFhXVnlLcVZ1YSUyQk9XNFVEWEhIeUQwcnA3ZktRa20zU00yRnpla0NhRm50RW11bTEyJTJGZnpObWo2allKVTVVYXFGYmQwVlU4b1BGWnBtZW9tNzI0a0UzNW5raGRzRThmc0c3bjREcCUyRkFWSjJiT0xTWGVFc25hRElxR2ZaaVloZGU4WVZGOEZvbnJoTG5raXk4SHpYeEg5NUpJcGRFc0prSkt6cVhSbEFyaUZqWGxZWFdFWWl4eFRYZGZZa3diJTJCdDElMkJrb0w5S1N0dyUyRiUyRkQlMkZBJTJGMTdtcDhEOWQ0YVZnV0dZbUlWSTdMOTJONXd5bXE2dUJLVHY3OHlvaUNRU01qVnd2a00wa2E0dUZOSXBVVmVNa0RkNHlod3p6SHlXaHRUOTk3M1BtWkpJNnV1c3VxVjZiVVpRTlFneW1YNDVOSDhKek1KM2xxY2pGWUZaV2o0YkklMkJxdUJhNnBnRnRWcjVTdktaWDRZOUFua1RYeSUyRmRXM29VTFlOOG4zaDVJc0YyV1hWaXJMOHY5ZzV1JTJGZnU4eWclMkY0T0FJMSUyQjIlMkZQcTJRQkQ0YjlPciUyRmZOaCUyQiUyRjl0Z3RuJTJGZyUyQm4lMkI0dk94ejdmbEZXN0FmeDlBY2VEZlIlMkI1JTJGN3lINHYlMkZkbm5XM1YlMkY3WmglMkZ3JTJGNmIydVYxMlgxdnhOaiUyRngzNlRvcSUyRkRlWCUyRmQlMkZhJTJGSWlCJTJGMyUyRm1adlM4Njc3ciUyRlhjTGZhd2lvczMlMkJmZ2YlMkI3aDdqYjgzOWIlMkZtMVl0N3Y3YjhOYXhkUDNzdTdqOHYxTGZmZGJwM0dueEVuZUdlTmFiJTJGVTR2UHVUY2R2RyUyRmoyZyUyQjNaUWNkcVd5N2dQR1QxMjQlMkZMdXolMkZJaTNydnQlMkYzY0dzcXZMNzVQYk9MMWI0M1hLMCUyQiUyRmVpdnJLMzh1aiUyRnI2USUyRk45VzRIOWJ2bFBGVyUyRnglMkZNUG52N1ZkUTc2V0pkTzFSdW5VQ01sJTJCTzM0aHB0bHV4YnZtJTJCUXIlMkIzMGtTVG4wJTJCWldYTWFmTDR0ZEVDSmZ2QnVvM0Q2Zlp1ZkpiY0R3cGZQOG8wM3pWR3F4N3JmU3lEaHJTYm9VcElsR2VJOXd4ZEYlMkJiVk1MaGtjejRiJTJGVFczdTk1Y0lRWDclMkZuNHg1S2FieUhqVkp0ZkYlMkJHJTJGWEwlMkJQR3ZzQnhKJTJGazZTRkUwcTl6dWwlMkJxNERPc21mM3BUJTJCZSUyQmJqY3pmOFNPczlDMWJTVWgyMjVFU1MzejJjMzVFVnlTenZVZDk1U2Iya2hTOGc4bnU5dmtjYzcxJTJGMSUyRmV1ZjFIZSUyRnBWQ2RwRXZTVEQ3UXd4U3I1UFZkc3NsZzMlMkZIMWU3YnMlMkZaVkw2cnY5OE4zanZMJTJGJTJGN290OFdSSVpsWlR2dG8lMkYzYiUyQnNXNSUyQkM3QSUyRiUyRjI4dTh2OG41UCUyQjM0ZU9Ka0ZUcmY2dXh5bVBJZjNYTyUyRjEwVllkdkVTRElWOU9UaWZmWjVMMyUyQkw2azJPODFhbEx2cGRERiUyQiUyRnZlUFMyVlFjSnNXTHNyZEtYYnJaWDVITjJWdjY4bjlYdmY3SiUyRjdIeUZ6aUtCZEk5dVhGblNrclVYdnVOTmRzaW9vbWxkWll5b2RocG5DdXdQWW5Xd05CaUwlMkJTOWRvb2Q5MlIlMkJONlJ6RnhoQVdRU05wcTR6SiUyRiUyRmJXdDNCZ0x4NFFmRHdJa1E1NHcwSmp4WDEyMHoyUE9neWZEbUNjQUNLdDJNMVAxSHZkJTJCaENtaHZUUkRudDlybEJvUXI2Wk9jdDNUU0xXb2Q4NXhjMGd5b3VtMEw4aWpqZSUyRiUyQkk0cm9ycFoydjglMkJVMU8yV0lmdjVVOXFTSVFFR2N2M3clMkZlRFhFamxvWWVLbkx4T1JrekJCendYJTJCZkprdVAlMkYlMkZBJTJGJTJGSlZPTUxpejYlMkZuRnZWWHhPRjNVbTE4TXVKd0V3bUZNTiUyQkZGZUwlMkZTOU5WWkV1T0pNSFR6RjRNU3pFemF5ZG1UdUhwUiUyRm8xODJyVjFaV2s4REEzYzl5Y1FxS3RPNW1YZ2lCWWk0MXpjSFRhMTJEckYzVmFFakppaTVYU1lTZSUyQllQQklIbVJxQVglMkZyczNvOXFvWWlBalNLN2pKODNSYTBFdHo4OHdqM2hIYVJsVkF1biUyRnJQbjl1aHRLWlVQaFAxZnBST0tSMU05Q1FnVnVhSmFyQkYzVEx4d01EU3h6VjVhd2I0Y3ZydlhZalglMkJtT2Q3QjFYJTJCMHdwNktoYmlEeW84Q09tWWxFV21UNm1aNDV6enRBcUolMkI5Yk10dGdKNDNraUhRS2VRYVV6eU1DaVpJcWRYNFZiZW41VWdpWldFRmNXdjRPa2VwWU1qMmFVUGxYZEVBSUNkaTVldTZYY0pWUSUyQk5jaVJ5VGNuVkNlQnMzYnN0dXRSY1Y4NmNNcHpORVR1NDBMbHJyQyUyQjUyWUJJUzglMkZQcWVuSTMydGhWYmtXU0VraVVGRXQzanVITWF0TG5iWlFHTHBBRW1YMiUyRkhtb1ZBdyUyQm9tRWo1WVdoYU5vY25jYVhRb0xGYUVxTThueTNUMjVINTFBNyUyRjE5JTJCZjJGbFEwTVl3TTdPbjY1bEx6WDFBZDVoM1Q5WURWRHBxSm5rM3JVNG9tJTJCTW9lOWlZeEtDSjc5dW5rZm5XUk9hTzd1cXhjUUpnWndDSnp2YldNUlZOT29TT1hUUHRzRm45R1F2ekI1YTJEUXYybHdFZ2FkREk3JTJGWGNYQnRlYiUyRks5b2piVDFNZmwybSUyRkw3cWtRTTNONGF5RGE3R25JQVpkeXhVbktYRzd5SDZuQmQlMkZSWFBxUnF6SFhzc1ZHWVg0dFBSZE8wVmduRVFiRiUyQmRLc2xabVlNd21aWXpJaTROWEhYSDY1VFB6MTRtRUxFcDFQbXJjT0tnUnFxd0RqRWMwYTFyUGk2Z1c4YjhWWVROWXpZMUdORSUyQmxFT3BZREk5OUpuU3IyUkpWeW40V3oxcDE2ejg0UzNtMVFzYTh5OXhrYWhiWlJkNm9qVHM5aCUyRlpZWEo3QkdpSiUyRmpvaW1HM2d0N0I4cmZhMmptQzV5MTVaTXVPOURseDhtMkNqVGVyZnhrclBVRnI2QVNUSFJORm5GQ20ycnh5bFZWYmIzdjRBNEtNS3Jxb0ltOXRSTXJWaFl4SXdvVVpiMEZjS01MdUolMkY0b0JPY2VJVEpLOUtvVVB2dnZpaEFHN2VtZjYzbU9CbFRPVlg0SjJxZEMwQUVoSjlhWmVPS2pPRUFrQ0JIcUpobnN2NG8lMkJDdUMyN29XZXM0YXJab2dWSkVZcWpQcnc3RzBQb2ZZN2gzSmFtUXglMkZ0JTJGbFd5bTJISyUyRlRLelR2am1sWFUlMkJzcFBSOWtYMVJkS3hrMVdpQjZmYnA1MDFqS01rSnc0YlJwMUFyUnNqQ3dmYUFjalBmb1I1TTNPbVVVQnRmNUN4bDJKcmRIMmpQYVJNMVFRbWZXZjkxMmx0R3M3NnlySEwlMkY4cHJvQkNDeEFhQVBkMVhQbGRiQXpxZ2M2S3lFd0JVbmtOM2ZFa3pQT0hJaFdiand4N25vUHNkOE5jM1IzOEhmQUJmR1lkaWNsJTJGakJuMmdKM2YlMkZwclVlTDV0YVc3aXNqRmYyTSUyRkdzd1YyWSUyQjFIVUJEUHM0dDFkck4lMkJFWHR2WnQyaGliSXRGdllmdTNMa0ElMkZQSEJCekdqSFpIcWhGSVY4WkJlQzZGR2RqcUVSSjZwdXU4NTYwd1kzR1AwR3hFNlZscFpPZDQlMkZIcnZYMFU4SFdkOGFsd1llVDhYS3EzaEU5eHFYVzNENldRd0I0V082ZnpPZTdxdWN2NkliNmdsUGR6ajIwb3dkJTJGeXBNbXVQSjRmWkklMkY4VSUyQm1GMVlKQUw4NE90dmUySCUyRmk1OHZDd2plU0FWJTJCZDFoazVlN2JMRVRidDNFJTJCc2ZTN2hoZ3FTbmZreWVkWXJKQ2tUejV2Q1kxRiUyRmdIWDZMZEpvUTFDJTJGQ0wyNUN5NUtVS3NDUUFzTm5EYmVUalE5ODlzZnVaRGlwR01aUnN4NGpHNkNVM3Y0cVg0YmppWUNWUFNSZGhFbFh4eWtwYWIxME9Pck5JZklmSFhNbzFTNmN0TlVJelRHY3lkbjVHNyUyQndRbnpsbTQ3MkpYJTJGV0FabnNNcGpzdktNJTJCM1NYUEF4djEwd2ZCNSUyQnpTM21ReUJIYyUyQmJ6eTVFYUV5NlUlMkIyTG1iVnZqSXZTdjdPQ21DYVg4bDg4WUtZWWU2MmMyN3hlRWs4T0hpa29iTGJQakRMY2p0TXFSczFwM1B1aGpPR3BQOHd0ZElXc0Z1SE1sNGFud0JxZnZZNEpNVlRYeGxucjdXY0pzbDc0SG5aRG8wYVRqOEFNck9hUTFqaDBwR2taU05zWFIlMkJYeTJZOHI1Zm5EWmYlMkY3TVViJTJCZW9RS1lKdHBKciUyRkxIalRQeGFNJTJGM1c3SjF1ZmZWUXhOTDNveDclMkJ3R2tSUzBDelgyTXIzSm9remdmRk4zTWtmZ2lBWllpa0t3YktVaXFQWUElMkJWSWN2aXFUYXBlOHppMlVUbHh3Q3F0eURLSjd2RHByZlJEeDZyampaTWZDJTJGdXUlMkJCMHFWVjRTdkNwMFo1U2lId1VDMXg0MzhTRVVFZkJvbFR4WWw5MWxTeTFjaVdkaXR3WGhoYloyekUzJTJCUCUyQnZsR24xbERCemV4bU9HZ0VQa3FCejklMkYlMkZIRE1TQldPVzdlU28lMkJCdHglMkJ4V21zY2tSR3lGdjhpc0djRW5zd1FET1l2MENqcFAyaXRSeUhteUpJbG9ic0w5aFYwUmZNYkV6VHVzb1dUNTd0VklOJTJCTUNOY29zbjVScTE1TTY0dldxVmVGM0RIZ1V0SFZCZkZsQnBzYjhyJTJGSkdOQThRR0V5QWpDS1NZRHpIWWNQdGJIRGoyVXM1TnUxSHAwdUNINURuVGVydCUyQmpwbXFXNVIlMkZ2NnFwZE9ZdElRJTJCcVU4YUEzYURnU3VabFJxJTJGZFlrQ211WmRvaWdmMTdPWjlNdTAlMkJJN0lOWEY5bFZsOTA5bjZBUVolMkYwblBpMyUyRlMwdUNNRHdHT1MwZElwVTVRMk9hTVZ1WHVLc21PVE5xV0Noa1FkZHBRSjRaSEFNbDhQY1JMdmd3RVRJUHdUMGl1NDhzYnVZaHdXRFFjZU5MWmxxSlo5RyUyQnJ2OExOYzZQMmRzTDhwc3FDWHNkNG1zRjluZHJvcjNKTnhJazBiJTJCZEN4TTh5ciUyRjE5VHFEdGdWTTh5JTJCTjF2OGFJMWJmOHNEZWJ1JTJCYlhacGZxM0RYUjdha0dXVnFoV1pURiUyQkV4aHZYcCUyQlU5ZXZoTHIwTHV5aWZJRVB3Q1FZcGhiRHFrYVRtcDd1cTFzZnFlT0gxaU8zazRmUzB4alF3WW80MFZZJTJCdG5xSkYyNk85WCUyRmM4RUV1ajMzWFBjZFF2ZDEwUUtyJTJGaUhKSGNvRmFRTzI2SUY4WkI0d2h5cGFnSEg5WDJsb3d6bnRocHpGenhOTUhSSm5NVmJIdkhUaEN4QnlGa0Zmc0pSNHZ0QzFpdFJ1dXJLY2NCWFNXaXQzZmtDZmFLdlFYc0Fld3VEZkJ2R0QzU0ExJTJGWFNkSmhZUkdHZHFQczhqYmNZS0E1R3NQNWtLVlU0Mk9XdW1vWVZYN3Q4QjFYZ2wxOVNzY0haJTJCVXhpOGpOczhmekpZQTBRdzc5V3V2SnZMcm81UG9LZUZUMjF6NHo0U05tVSUyQkpTRFlQRGwlMkZNQmdJN055b3RjaCUyRjhuam5ocm9pUnZQaEJ2OWdPN3NnRjV1bExqT0tKT1o3N1JxNW1rdjNLeWglMkZjM1olMkJ0YWhxYzZ0d1QlMkJkOUs3dkRIbkdraVB4Y081d0Y1SU9ETFRVRUpuSTElMkZValIxeXJSOFVMTlFCZXlnb2l6QTNXSDVHcWx1VHY3TDM1dyUyQjRmZVZmd0NwbExadVBFNSUyRm5TaFdTRGZTRkFFQW9zJTJGJTJGRmp1c3ROTiUyQlhuM1VxaHRDQzVLQTg3eHh0R201am96WFZGdXNsMjJjc1NNOEVzdXA3UFBkSnFyZHVSVTZaRWRHUmdnJTJGTU5rekxjendzbzkwOVgwSkgxVWFieTUwTnBEeFZ0QkRRd1U3QyUyRnlaS3YwQXNQOUI2dXdmVCUyQnVxTEw4WXNHREpXR1FwVGM2NSUyQk5XNzFTcGU4cFlFZFI4c2RYZ2M5JTJGS2J6Q29YdjNieTdoajF2MzclMkJJJTJCWmRTc2NzTSUyQlUxWGxRVVI3RDJJdWZZUSUyRnFxaUdJRkhDQ0VOZlQlMkY3SzIzTU13dzklMkZ5ZXVjMnpRVnFIeVhiWjBJTEJKcmFiWExKbUJ2SjduOFFmNGZ3TFdYN1lTWUVmWm96ajEzT01BZDRWRENvSEFkaUZvVWRwR1BNMGhJVVd0UnNzR1E3UnZCSUl1JTJGJTJGQVZmTTFEOVpJR3Jyb3RiTXFMJTJGZkJ0TlZGeTl1cDN5MHNQSk01blg4V2RHZnpMUzE3dHQ0R1Q5a2NQeUpXeWhZZHdMUjF2VUhOZVMwdlhWUkVPeFFzbE1mWm5KWFRJY1kweWlyWnVFWEk4RnpBcnRMcSUyQnlod1d0dHp6M1JTMUR4aHVPUmIxUVMlMkJBV2Y1JTJCdnFGc0dwcE5wRE1ROGZZaWFpbFlldFRPOTl1SldocnFYNkJCUHI4b3hkRHZVTTkzVDV3WGYxM1lpMDJFTnBKRE1YdEViS28ySUZtWXRlTG5sUWtvZUhJN2xWbjB4RWh2RkgyYjhUYzRMJTJCRGtKcW9pQWhKVWs2QkVvTkhkbXA4d0JuRmE1WWpyT2JaTUZha3gzdiUyQlk4UjdpbnNTcVhyJTJCWXhlb1g1NXhEeEhkOWI5VklzaER3JTJGc21KN3IzQzlCT0lueHRBM3MwYmFSb3Y2aHNMbms4cHVUaTJiY0docUt2VW9sOGd4a0UlMkYlMkZnQmRPWHppREtmcHhJRkx6WkdRODZFNWlPMzYlMkJEMGlybWZzNzJvZHJmbyUyRkdybktQOHd1Qk1vcmtaMFM5MkY4JTJGYWlaWUZPTjQ5U3FrZ05WTmhNa3ZNeWdJcklheTBLdkY2SmRQUVFtb3YyU0xNMUdDWnpmeVNIY0Vnb1NJRTc5QjZpODJmJTJGNUhic1VicXVpdmd6SFlxRnhnMDQ2MXpEdkd2JTJCb1VLdzFFNXp4VSUyQlY3b1lvTmtiM3NHWE5nNEFqc3RRV3l4OHNrUzEwMGJPNGxDM05wRlNYdFJESUFzZG5SdkJZbWNVT1JCdzc5Ym5ZMmllRUV1T0FBM3lJQmphJTJCcmVMNGhLYlJQYzFmTTkyUkdVTzl3cnJDZE5ZanI0WVVKSE82d2RSJTJCY3Q4JTJGdnZJMEVBR0NTJTJGVG9nRlRUTUNlZHhRZmZ5SnpzWSUyQmcxciUyQkR2ZkpvTmNQV3ZOTGpHZ3hZJTJCeUt2WCUyRlVWbVVXTkEwWjNUaVYlMkZMRDN1dldVVjVWTkZ2UFVMS3YxTURIYTVOV214N3glMkJsdFdDeTZyd05OSHFkR0ltdENpaGhiTzZQMyUyQnd1aVo3aVlkMEdaeERhanJpUmkxZGR1ZlpLa3BmcU5kZEI3OCUyQkQ3YWlWTWxnUyUyRlNWVXZySDNaWUlIcHozV2gzdElJTmklMkJMM1RoTlFZa2dtUkdQV0slMkZYNUc1QzZzOWdscnFxOUhCSkhEanYlMkJyMGxoVnZ5Z2s2YlJXMVpXaE5tYng3aSUyRllBZlhaS1AlMkZpTzNjbUF0amlhR0k5NktjSmdxTVNYU1dHd2NWMEQwbzVuZXhGUUY5VXZlYkVOV3pGd01hYSUyQmlsWUklMkZscmJtMmhMcDR4bnBoR2RVM1pLYUVQUjB2dzZmbnk5R09Gclk3ZWo0ek03YWJSUGdmRG1udU9tcHlhaEFCSkR2eVNCVE9JOEJkVDMzZnRWV3JRWVpLV0ladGIwWjBYVzhPWVhYejc4QzglMkZNJTJCUkZpNElpbkxEcTdpRDV4MHhGOFklMkYxOElIN3BMVFhsYkxIYkEwa3hHcmppdENuclklMkY0UjVvd25wTkdMS3dlMWpPYzRKZ2VVWjclMkZYaGRybE05cTNZQVJnR0RBOUxhTnFrSGg1Zldnalp4S1d2M3pQajdNZThiZVNVTFRuM0xYbmMlMkY4ZEpyaXkzVUdOWk4zb2RyekJYOGNnQkxzWCUyRjJ6VVBrRWZJcnFDTGZycWZiMWVSRXhUOHNhNWxDZDV1SFVMbFF0JTJCJTJGVmNhYnRMeGkzVVBHckRwbkhYTENXeXZEZTZ2V05LdXhkYzdkYkFvRW9qVlRMdjVMWXRROXJmS0lMbkZpWGprRmlBRnB5dlBONnVtOWVzb0xzRnk4UDBCR1E3N2NVdHFYJTJGYTNaaVFYQXJhcCUyRmNzcHdTOE9SRE5IcVdzemIwR252V3ZtQXlpRzRYMzU0Q3ZxQmZGRWVxYVh6VGs0V3c5YURXU0NCbSUyRlhpc0klMkZkTHQlMkYlMkZhc09RVSUyQmZiUEhqOGhzdTIlMkZGSEMlMkZiZGZXVkgyanhyV2NHRkRpZXdHeFRRJTJGS3UxR21sVDRuajYxb054Zk1qR0wxZncycTFqcWNDQVJhdEtLdGRqTGV6YU9zRFg0ckFDZzVrU1dJbDAyRXlGempwR1pVd3olMkZ1UmFPUDQ1VVR4WSUyRkF0UmFBSjB3VmU0SUd2azl4RDZxekVUTXk5UE1KejZrc0FaV283M05BZVk1UVMzckhpMTFhTCUyRkFXbHRGcENSMzFTNzV2OHNnTDhmTW55cjNzUCUyRlJJa3RQZlloams4VGtKa3FRNzZDV1R1NXlLcmZWTkUxMEt2U2o4d212eGxFVllEYTRUTEF0SnBUb1FJQUwxOEhjbUM4cEpGdDRRdVdacXVwdWlJbjdNWWFpWXdaUnRoNFYlMkJsTjVLR00xWjd5cUZGY3BkYVVHd3V0dFhUdFBESGJONnZrcldoeUh4WGR1ZWNiS1RCVWFBbGwxYWdqZW1jdTk2JTJCc0VhVSUyRkFJOVd3VTZTR3VReHB1R0R6Y2xxbFhCRHNiVkRyYmpYT25BYWxsMUZKWWRpQllJZzdjcEZ2cDFRV2RMJTJGSUtLMVAlMkZ0blFVN3dTOG1TNFIxSUl2eUR1aHNwOEFFaHNycjlXRkRMaEljV1J5R1c5dHZYSXVJN1JnMzNPeEZodjJ5RGZSRUViTnUweDJ3UkxkQlBYSkpQQ3pDV0FxcVdzeFVWaG9WTEFYTEZPWG1OUGV1cHE1QmdRb21KZkFQeDRaMXhMcEpnNUtHSyUyQnp5WjA1ZjI3RVVnSGQxTmNhV0dJcTFKdFVGZkJFTDhhdnl6OVBsM21hajhkc0F1YzdxNTFJemZRZHdzQkFJNiUyQmpDOVZJcFFsN0ZmQ0xtNjR1bkxkRnc2aFFOa0dqTmd1SXZjd0xyenhQVTFmcjVKY3JhR2JWZmJNZHFTNnV1YiUyQlJEZ0JXWGdnVFZETVV3SDk3aU8lMkJiQ0t6ZlRqQnZ1cWJ3TExHeHVWTXdKWG94NkQ2MTNmbnhCMzVaSDN4cHl6VW9wSlJOTjZBUjBGWmxoS2ElMkZnVDVVOUk1V05kN2pQb1ZwcmVwcU05ZXFuQUVUd290cDhzS2tXWUw2S0t1bm9Nd0JacXZmWDRwd3d3cmFrOUlybWJGajFNaDIlMkYwRnJPdzdvZ1VOeFhPQVFuRjhGU0xLb0ZEWjNWUCUyQjB2YWltVUtuSU1QYVZ2aWxVZEhDJTJCcEpreHFzeTA1TEpsS0NOTzFvU2N6V2ZpbEpBJTJGQ1BtTUpidG1jS0YlMkZTSHVOZWxEVmRrVlc0SyUyQmlFNkdVbHNacWJMQ0lJY3EzQnA4YWpGSXA5JTJGclB3U0VJQllGZVJSeTJkbUZtVWNHeVRzcjRMVVNZbXVlWlJwZWElMkZGYyUyRmkyWEcwdDV5eXhvRUJQJTJGQlpkQVE5U2olMkZvRGZ2UmdPOFJvcVJnMSUyQk9XMVA0R0NEcHROWk03VDRyN0tkZ3R5ZUd6bWhIUTA2cmFpcDZ4ektPS0ZiU2J2ekZuZlZ1cjZJU1olMkZzUjVKNnFnMWEyY3NsbENyblpiQmdvTmxwU015bVNvMVcyYTNXWEtWNzdieUZJa1M4QjlwY0hVYmZ4eVhLamtBclBsTHRsWmZPU1dFQ2ZVZ0VJNU9TdGg5cjFVWXp1WTJ5OFcyUWU5YUElMkZiVllsVkZCJTJCNXp4TCUyRmJzM3V5cGJhaWYzaGltUEN6eGFMTnBYMkdCSTFZNERtSk9zTHRxRWhSRGNMd0NpbTlDNTBoNlhpSUV2c2UzcE5KRTFRTyUyQnMzalo4OUpjZk5JNWZrOTZOSmZ6YW5mTUFxZlNZUDBteVpSNkJZTWtVJTJCNTc4aHY5SkFwdFAyQ2VDVHp4T0FlTnBuVDV2amN2N0olMkZPRU81dlVMMlglMkZ4REZVa1JhMXg5cGQ2QzAlMkZ1cjdjcVFYa2pyZ3BvN1N2WXJGR2lzcTFreiUyRlZ4UVdVS0pHNURPJTJCTWs2JTJCQUFmY3VEZWM4ajJrd0RCckNCJTJGUUNoY0VoSURkTXBaZWo2eUdmN2FhbGhiV29LZWkyMVdPN2VpMmMwZkFhU251NEFNVjlEaHgwcUFacEtRRnZadjEzTGRIQlBmMGtSJTJCdVhIT283cEhRU2tkaFZEZjglMkZKTzJUcmhDekMlMkZyRVhCaUFWeWR5amlYN3BQakRvWDJxRjltTEtYRnJtR1g5NHBWdXhjRnFkczJtN2cwY1RZakgxaTFaZDQzQWx3aXNRTTR2eEN0Y1d0QUklMkYlMkJBRVZlTUE2Z1hJeTZIMnN1anBSdDlubmxEVVltSDJ5SlRaMU5LcDhHWTJabWRJbE1ibVRtMzA3NG1SYXYlMkZ5VktTZGRUbGdtSVJuJTJGTSUyQmxNUWowekFPb0lKT1l2cFdEdiUyRk0zWDZuRGZURTFnRGVMOSUyQkZhakM0ZEJ2UkxEV0dud0Z3Q2dnRG50emZlYnBxcVUxSnU5emVkUEdqVWdvM1ozMWV2UkFmZm9HTnlhcnhpcnU2eUE3V2FNNHFsN1prYnclMkI3WE5YaHBXa09NOUpmWFFjb0FtRlVwS3d6czlYb2ZtUnZPcGlkb0ViVGhzdCUyQkpFV3RjdnprYTZZdDFvYWxlT0FlbkNyZ3JMJTJCNE9HWGc3UzN0Ym9sejdJNnhEMjJETlFwVXA3Q0k1OWM0VmxObXRJdSUyQkxuSGdiVlBrRzhxNHVhWWxPTTh4UDRUWUVrSlhBR0dTY3hQRnlvJTJCRmNhVFZjTHUzNnI1RDRYS28wMVBoWFolMkIlMkZPUjdvQWhKamxVd0hyZHNoQ01mZzk0NlN5dnlZZXdsdzNPcHdUamhhWFdJOWlZZGNrYVI5OGFaS0F2WjB0M1ZZRDg3cWRDV0k1MXBnUXAxS2RsbWJoQmZBV0VnOXQzbG45JTJCSzlMajFwdlc5dUp4TktLTVFFTXRaSDVlRnlpRmhoeSUyQlRrWWZialpPaTVmaUU4MVNVYWMlMkZmejdFN2tISjdFUWdzQ0JsRFVaTlU2emd2cnk1dmFrTUVIczduelBIeTAlMkZGV1pEMjI3MU1nNiUyRiUyQmNYcm5TOG1vUzZlZzN0cEFSblJ0N2JTaDlrdTRpUFgzVFNxU1hZTmVvaklCUUhoUjY3WjZHYzE3am5iNVNCcmhaRHR2ZVdYVDZqWDdDTWhNTWJJRWZ3TTR4UDdUZXQ0RTlLJTJGeWV2SDF3ZTl2QnFEdWdydndnM1A1OWlCbGpGQ3RNY1JqNUlBMVZOTXNOMkZaWnFjZ0FDbGg3TU8lMkZGUyUyQnl1WldYNE50dHczYk16VXZBTHpya3ZnMzZ4aW1RYUw1d0h4Z2JRT2h5UVh3NE83T1ZVUGt5cmx3RVZOOFViamJ0eVF3WE9USEo0MDYwNGRvVXdFc0JXVmhLdmhSQzZNWEx6NnRmaEZQdlpqYnlaUndwZXhrRWtkdXpjUFVKcW1OZlolMkZsczZmTEo5NFl6ZkNQZFgwb1N6VkpDMkJlRTl3UHJCRG80NWhLSGJrWjJaRiUyQllneXp6c0F0RmRHS2xtY0RwN0l2TG83d0UlMkJ4VXZpQU0lMkZqaEl0NFZUeU1YWnByY1VxbEZRSW9JR2ZvcWdDRjFzUWVzVGpJSGJ0NzRPck1RbzFlN0pVN3FBUTdOQ1JHMUE3NlVXZmh1a3BaYWxpVkk0cjNybWRNYWc0MVF1OFZBMTNFJTJGJTJGbXdLV21MMmQzJTJCa1ZxOVRCVWt4ODJ6Yk5mM3lyb3h2dnZKNGloa2UyNjVZbmo3NzZRYm9peWgxcHpKdzc4b1dROHBWSnVjWUFrMm1xJTJCYWY0JTJGcFhQdElnRGJTampGQkIxUTB0cmp3b093YlBkdDhPNWNQVDZnOFpBbVV4WmdkT05uelhpRkUzRFBmSmhwaXlsMjVreHFNQXA4enlmNUhtYmNvJTJGWHZORkYwTUpUQU9lMSUyQkFQbEJsQ2gyNkFSVHpYJTJCU0w1NHh2V29lT3dpcWRFc0NjQ1YwcERBYnljWmZvQnV3TzBZSU54U0UwS2NzSE9jOXlBU2dHUXVGU3olMkJNVFMzeXVqNTlYa2llRFhudzh2cVY0dnpYYWpaVDdrdTFkS3BYOXIxVEJWeWdFMSUyQkRaeFlXSTNTd1JPYUdJNFRQVnpWelg0RGlVQTElMkJ2YjVGeDRHV2VIcnZRSHE2a2ttRjZUNEhVbFhEWFJBc3NtQ3lHZjdsOTElMkJDbkk2Qjl1NDRCS05ranNPR2htYWZWd1NaJTJGSlZBWThuTSUyRml0NGFUQ3h0OER5d0FZMVh2cFVhdEMzUG1uRU9WT2dzcjVma1VVd3FBbnIlMkZMVlJMZjYlMkJtcG9sR3RDbG9vNDh4WmV4dVpUZzdBYWVJbEpkbExNJTJGTE90MFE3RzBnTEZ1aEtGQkJYYXBHVFpPZVMxSVZHNVgxSmdsZ1d6MEF6M1owUWJMYlhrdnlqMnloN2ElMkI5Rjd6YzVXcGFXb0FKa3BoJTJGcDdzWW44aSUyQnBXMzR5MnpVb2VxN3dGaGpveGtLTnQzMlNTWXNpalBRRlNrMDQlMkI5M052TEZBdm5HMkEzUXoybHI2MlB3OUl1STlCM0lPTDYyMURTUzc1VmpTTzYyRkJDQmE3UThZTzZwcGsyNUhTSHV4Y2IxYkdBMzVLczNTN2t3NURMc3QwTEZkS3B3bCUyQlJnZUlacnB2SFpQTEExSEFHVUVDSjBtOGhJc25ReVZ0Z25QY1Vja3VQTXZLQU9sOHFjRDh3eVdqSk1MdGpFM1lLWHo4djRRTjV2U2l5cVloNGJnOW5yVnEzQlpGck1YdEVlZTl0enBZajhVQ1BSWkRmRnFDZjJnT0FkV3d2b2xodEZCJTJCbFlJNmZUOHluaW1BTWhheUIlMkZLVHVTTjlHb1hQM3ZUdlREZXk5R0I3RERhSklNWHhmZG5FRCUyQiUyRmZseWVPSE42eXNIWk5mRk44QTUlMkJyNWk3dlRxJTJCajBVWHZZMlNneDNmeGJoUjBiT2Y4bDlNM3gxMFFGV0pVNXkwJTJGMTliMDN0Vzd2d293Mmt6bUx4bVYlMkZwemNpbk96NmlYczl6dHh3TWl5ZzlOMnJyd3lrWDFiODF3WnJOU29IaDdld1VxT2tUVW5IVUd3WHRGUDFaUVBpd1QlMkIlMkJ0T0lYYjBYYkw3RWg3b2o3TiUyRjBRdEdDcUFDcHIlMkZ5UWI0SnBqVmdGVG5YazN4Z1VFRjAzUDhDdFBJdmRzdlg1b1M0eDVNWFdpQnpKYWc1S0JWdiUyQjJVUEtDdDVpNCUyRml2M1I3MXdGWTMlMkJxanVHaWpJNmIxN3o0M05XaGlYb1EzS0FDb21KUDI4TVZ3TTlGZGJJOWNYallWJTJGOFdHS0JQWDZZS2VwZHlBOHQzWnU0VXZCbGozVlg5c09kUzFOOVphVUNCZmJYRFFSSzN3eVFiJTJGd2w3eWpTRiUyRjI3cTY3Qm41ajVKZFVwVzR4U3FyaTJ3Wmtzc3REMnpMS294MTViaDIyM3JxTTFjMGdLRng0dHpMdWI1WUpkYjhpY3lic3VndWh2SmM0WHlZMjRDU1dDRnRCNGVLJTJCZkZhaUVtVXdUQ3l5MU1paVM1bmpzbm5DdnI5QVhhbFRyd0F6SjBDT1g0WDNrTGFWMkFZVlp2WE8lMkJGTjd5bHdTZ2FHajRka0xTaUJqWGw3MyUyQkZhS1dzQTVuS0pVJTJGYUV1SjJZWWElMkZWb1o2WUJEeFl2aThGd1lIbDAzbFhtYjJBMTJKNEJPUkVTc3RJVWgwSGpTNSUyRkMzM2NTdHY0MXdodyUyQkMlMkZyajRsbFlSYzZrUEslMkI4WlRCQWcyVVklMkJuVzdRQU5oY3llbXhsbnFZWmdxQjhlbktiZjhhWndFOHhwZGpHWk1IJTJCNHczVU8wek9EQXdEczBvOWFZNk1iWXFvb0xJcTZtc3N0T2xuc1daS3F4N3pXdURVRHBaJTJCZ3R1UncwckREZGVnSWMwbEdtNGJVdjBHODI2JTJCd2hTWm53WlJ6YkxNT0wycHklMkJjY3NENHRic0JHMzIlMkJic0lGbHdpR1ZwQ0sxdUx1eWhFUDVwVXRNZGg0RzhHNk1zNmgyR21HN0EzJTJGdnRjcTNRMXM2VyUyRkpnQ0RUNmdyRHQxQzVucGhEJTJGZk16TWVKbXZpSVRyNTRudjhPSGxnTW9iU3VzTDNvN25TJTJGZ0s5aFVSNTNEbTEzZHZVb2wwYVJFZkEwODlYNmUyWVY4T2dTTTl1S2xjTE5SRE9SQ0hsZVR0MlNQUTExaU1sb2Q1YXlheVg2elZNejJCazk3ckdqSmdVMWwwQmhMbUdsS3BrUlpLeWhUbzVHZldRQiUyRnBTYWg1TklZZnZ0TE41djNRekZVTDFiR2J3MmZDT0JFV2ZrcURUUlRzUjNvYjR6bGJ1eU1aMlZaNVhKZzdRQWw4Wk5TTVZXWEVEcWI4MkpGNWxjOFljMDFoN1NaVWxwZFFScGpaVmFwek1aJTJCJTJGUWYlMkZ0TlBHem9Yd0ZHRnpMUGslMkJBWmVOV0tPNDRZJTJCTkhwckQlMkJOdEltd2t1NzJ0eTczZ3p6V3JMZUJNMjIxUEM0dyUyQkxGeEVDaSUyRnlSJTJCRTBWRXFHJTJCY20yZ3RURE8lMkZwV0ElMkZSQ3hUV05kZDV4em9HVkpnaTU1T1VZdE5FNkoyRlBXb2RUWGs0djJsbnhWV1p3c29Udk1WRUpzJTJCcWpEUUNHMVc3Y1FTNkp1R0ZSN2tsbWtiNGFGWTV5ZmE0TEVrRGVyRFFPJTJGelU0Y3BWckg1SWR1djBSMlBNN05qeDhQUGJ2NzZqMGNzMHklMkIzWXNKRnVVbTExRkxMbUVhVDdCZmhqNTFINUgxZWxFWEF1RkgzelEyVkNEcFglMkZBRURUJTJCaUhWSiUyRlBkTEZVViUyQmxtaWJwcFVoVW5ZYnFZbWpqRU5OYVpaY2pPWnJkZnVlZU1ER09QZnp0UnNUT0F2R1BpcHJOam1ZV2FjNUh1aDI0UEd2MUwlMkJhZm82TGYwMXJUWWRWWk5WZDlPN25VUnN5UXJ0eVdYcG1SYjA2ODhsRkVHUE5yVzY0ZjgwOU1kYUlYUXVKZm5WczhYYkhnVTlEY3BmRENiajJJcDdTOTZTQkpxNWU1WkglMkJqTnlNbFFPT2wwQWhHelFScVpsTEJZYTVCdzR6cjRWUFdHSmxKcGN3aTRud3RQYks2NlNpbEVDVXdSQTJRRmxRNmEwR0dvWExYNjFXdVlVc3hJUktMd3RGS2YlMkZoJTJGa1RQZUNIa29wM2lybDdqQkhncXBtUEw0azAwdlExRGNtYnF0dFZaYjRNSkhRY0hqMjF5c0wlMkZCNHc0aDNmeG40RXVvdCUyQlBxYkYwbThoTEJEQ1N6bzZlRFd6djN6TTh6VE9pTHhGVGZSZ29lbCUyQjklMkZTNERsUkRlNFFLSG84TEZ5T00lMkIzUVJiZXFMUDRrTm9wdDNiUzV1Yjhod1pZSm92a29PN2pXWWRlVDJFaTcwbmZZc1E2WHdiazlPT2VPa0pNa0RKalFnbHlRYjVuazM0ZE1wVVhNbEcxdERTVzBuVEtkY3hNc0lBYWlDMHZhZXFCUUI5UHU2SEVBSlBERDlqbEFuOXFtZ2hlanJSMjFqd1BkMFdQc1lVTm9POGgzeG9GTWtKTkF5eSUyRnQlMkZ6bndMaGVSOURnTThGTFc1aklFdTRvcFR4c29YMUc0MCUyQnN3ckFaSFJldVA1bVpJSEI1cWNCJTJGSmhkbk1pTlluNzZVWks0Y21zSkVMNjhPRXBrU1NtcE1iMEpUajRrRjN4djNsMENRR0lGWks2dVFJSG5PaFlGcEYlMkZNdW1wUldoYVoyU0lXM3ZmNWVkY2pNdWIyZU1QYUFxdk1LdkVPeDR6Y0NVNkpQenhscVFuOHpGNjNoJTJGV0liT2kwTFNHczklMkZWcmNudnZNenE0MWdxb3lNQWx1elhtOGp1OVJhdU4xVGJBTGJPc1gxdzlVdk8lMkYzYXhTdFpvZ1NyQkl2aGlhdjUlMkZlMWxtSUxCN01iJTJCeSUyQmJoRU5kYzdaSG52NXF0RngwdFFUeiUyRlJrMEJwU3NHS2UyOWJpYkpRSDZyTFFTVDVlRUl3UDdJYlNlNzUzQmx2aDQlMkIwOEhQJTJGUlZYdVRSUXZTRno2QzNNUFFSYTQlMkZNa05HaW95T2k0TkhHQncwYTZYZ1ZiMTR3N3lpJTJCamNFUFFwQVhEaGw3TVhWYnRLRERiSVlWb09QeHZ2UFRKekljJTJGWWo0M0RwOVgyR0Q2eVZWdXBnJTJCVm81ZzJjenJhZUtWUDBTMGZRdEMwejBmVUxNZWlZR0lPRDRncEVmU3YzNVlmOFl0UVA4S200R0xBYlVsa1VLZyUyQmZJRWp0TW9XaTVWNE1OUGlZRk03dWdwOGljcU1BdTlOdzhMQVBkbjY3ZjRwTmgxUUw4SjNJM0RFWE1Wa1hlNTd5WUVVR3J2WEdWNUluVTVhWEJSMzIwQnVGWXU2elZtcFZOcjlLcW5qVDdDdlpFdmIzWEpJJTJCeDNTSFZWelhnWnJOdDRTa1V0MFh1cU5RczhNczdWWmFxYVR1cmI5OGl4R3d5TE94ZVJMYWJxUGlSblVaMUVYU3JJamVvS0VmWGh5R29FbkxFVjFXSERxYTltWG1ZcHh5eHV1NFpDTE9sNmlSV1B0SSUyRmJTODNxMGJRdTFMUEh3WjVpTWM1dSUyQjZuUEs4ZXozaWJYQ1FFQlFUSVBWelFKRnVnVEp5cCUyQllEM2UlMkZCZlQ3dUolMkZ2WVhuNElpaklNYkFsT2gyYkF0JTJCcDFuRGdUb2kwSTYlMkZSUEgyRms5N0o5V2lEZnRWS3VBOUpFNENWN2tsMzh6VTYxcVQ0Ull4ZlRPUyUyQklJS1E5MGQwaFNDUGElMkJsV0owS0toaXlkUk5WQSUyQnF1RjdjZTBHcDVDdmh5N2Y3MCUyQiUyRnZSbjJKVVJEbWRzUHRxUURGNHZ1bmFZenJaTjZISCUyRmN0TW1DNDZVMEw2M3k4Rm5vMmV6STJKZnp0UkVIaDJVJTJGSUZSbFVtanRPTWVua0NFUDFYdHVZbGVaR3lvVWtsS3kweEFRRCUyRnlLaUFJRDZNelZsdzFDUGY5UFhROTNiTVdZN0VGYU16Nzl4ejFYcU1lSlNUblpXNTR4M0I5OVlMUG5IJTJGUk9xVFhqWXFubXY3V3YzWDJTc2RnaXlWWTlNSlZpU2tDckxPb1dmd3dlZGx3bWY3cXZsd3lCc2RtbFFPYUN2Qkgza3kzZVhxa1g3bTNsdVowZXUzN09YTjdxOTZWREMlMkYzZnR5N0kzZURKMnM0JTJCRDEwUGJjVHlZd2pabTVDUFg2TjhjR3BmSTZBR0JBeXI1ZFV6NWFGdGdVaCUyQmJpRVE2cXFGT1dYJTJCcWppMmFxbklwSndvOCUyQiUyRlY1eVNSdlI2WE9iV1czY2s5V1BBT2xQeEhwbkZVZTMlMkJ2TzNydEJLUTBiSzlINjRndnNZcXhSbkFjeDlNeENaNTMzQjJjdlh0SXhYeVgxR3VNZVlHNW56UnAlMkZ0dXZvOFc2eWw4dlFvM3ZKWWpvblE4b0h3Qm1oNWZZZWkzZlB5ciUyRnJTV3Baa0xvZ3Q3VGJBRWJlUCUyRjZpYSUyRmNLNzg4SjFLUyUyRlZpNDN3VUUwQTZQNndBM0RQbFM3aFNUOWhWUzBzaUp5dzdBRThsMkFMeiUyQmtWWXl5dDZkWFNJNlJJM0E5Q3NhTHA0RiUyRldYR3NZdnglMkJyMVhzZlhYUlNWbDdZOFQxZmZzZDVsZXh4TVc5U1NjQiUyRjhKYUUwbFVyUzBmUmYlMkZoJTJCRG1uQ3luNzFHd2FnZGNWTWNHbldmNDFjQ1RZTzVRNEVTM2ZlOUNDdm1vdDB2QyUyRmFsWllsank1QlQlMkZVVFolMkZUaTkyS3NOMEN0aUpWR3A3Z2NVVGZPbEtCVjRrSXhUZFNUWmZxVFg0Wm1vRndPOTB1bHF4RHZpSlJUZSUyRm9zRFNKUWNhRFlxVnNxSk1ucng3T25lODRpU0R1Q0JsUGg0dThwN2VRVmExelE1emRDeTRrQno4OGJLSEJLNnclMkZhb2d0ejU5dDlSRmdDQyUyQjFlZyUyQmg1bW9VdjRkQ2VIbXE5MjNXT21ZaTN0ejFHdmx5RkZVdEJSaSUyQmRaeGRDOTZ1RDVvWTlnRGJYSlU5bW8xSDA0VHNlTzFCTHBtYUVCb0NnN2hqQyUyRmNBS0xuTCUyQlhiRHVueHhnYlFXOXRZMzVsMERUYmJBaktGRjlSRSUyQjF0cFFQWEhZRkc3dkVqcVVoU3FyQnBXWnBFOW1OMHprek11U0hXUDBzYXdEdWxUazh3M09xWkp5aWtZNGg2Qm1nVmFuWXJGdEVuU2h4VGZkN0IwOHFTajVlZWp6aGxWU3VjTFJya2dyMjclMkZNaTZjamlFJTJCMUpMYnB0RmJKRFZ5Y3R2SGFiQ3VvbHFQTDdVazViRlpGRFZFc3U4YVgzdFpaOW02dDZKQnlxV1RPTzk1QXVmQ1ZKczlqU0JrQ0NQV0ZrTEI2eVFSY2hLYjA2alR4aktmTndmTWFPNWthaFVRbHFzRTRTUXY0Mk9wb1F2czBNTyUyQllpaSUyQlBIWCUyRldjcU5KMHNsZUlIU1VDMUYxaHgxa2NNalpXbHVUdG96YXdUM21mTSUyRmg1T1BlQkZ1Nm9udFFxMXV6a28wNzg3YnZxYUI2aSUyRjYxRlVaNHRyMUlraHZuWk50WllYTzk0TE56T0olMkZTTzdsRzJTYXdlUDNWbURueUNwYkNiTGVNUm92JTJCcWNXcyUyQmRkblFCYklEWThIWTQ2Wm1aSU5WZTM1VlZKeDY2S1o4ZGg5MEZuV1o4VFpYYlJneUp4TjdFYW5BZGNSYUdQa0w0MkxlODU1aXAyVlF0bXZTZCUyQkpPU09QR0hVZmgxREtSUmh0RzYzJTJCMjNNQmJmbTVkdWNiZ2RMeERoU3gzb0xSaGJYdE1lNEtidjdOZGgzU0ZWUzE1SjBUZWxaYU16VVVXbXg0d0ZTcE1xWVJLMXhWbFhVZHolMkJWNmJJdnpoOHlDSmxxdWpmMjFPOEJ2b0NTcFdVMXZUV28zeGptc3FPSEw0MUU3U3Q5cXNiSnFta1dJSnk1UlZtM1psdnJoNFFESWwlMkJLN1J4aUowQmVWSmRFckhXdnZ4a3BsSFV1cEJOUFZwYTJMek1aOWo5cWwlMkZsS3Y3R250Y3klMkZEMHZvTkF2d3pKaXJpTVRjMHNVN2lyZ2xiaWZyMGlkUGlrT21Mam9pc3NFdVZwVk54R1lWY1BwM08weHRwcUt1WHBIOWM1VEU1cE51cXJ1enpTYldKZmllbXhwVXlPTnhsMVFTciUyRmtIeURYc0hlTElGb3JneW1WRW0xdG84V0tUV2FPSnBhNHNhSDh0U01PaE96cHI5aGxLJTJCNFZMZnF1U1pZdkkwTlcwdmVrMXNlcE5SbWo2UTJMVHFGMnhreFhzWjQlMkJvTjVUSkpYbTlLTDclMkJ3YXJXYThQYU1va3FRejNpeiUyQjM1MGNObzBFWEt4Sktkd1B3SndEU21lMG9WTnp0clZkNkx4R1JaWmxoalRwWXp3OXo3WkxXMGRwS0s2NjRIeDBRbW52eHNqSThTWmwlMkI1WXpzUzNCN1ZPTjdFRm81aE1pOE1DaU5PbkJra2FRemxHd2FKYUY0OTVEZW0lMkZMeWhiWEgzQmQxNGUyJTJCbjhRSWM4aHVPc0glMkJKNHBQeXg4dXF2allVREFzQUF3alNKUDJNb3glMkY2Q0xndGYlMkJCOTNXd0hUTWdmdHA0Q2IxMkxQOWk0dCUyRkFZZkljMWpIcThkZDl1c09yWlZhb253NnM3S21vNGlXcGZ0OG9wTnZEY0E5Q3FaSE44NVF0cDJBMU90bFpVVFZEZWxWeFlUM3hYQnltZ3p6eUU1ek5ENVhRMHhicExiVm9Sek1zdjRyTUtBcmdlZjc5JTJCR1lRcFZlTWxld0l2cnpWSHE5ZnI3dXFJS2dTV0tmQlJjMXNoRHRldERWUmRhQWVEVFM5V2IlMkZ2ajNRZldFdGZCc05ZMmhXNjg3YmdBQzdlYWhqNXNWS25hVXZnelpUaTJtTVEzQjF1NnM3ZmhNSXZkZHM0cGllcGZWMzklMkIyOW5vVElKMzJFRDNOYkJFSXlVMEhORzU3JTJCYVlZbEZEOUUwWExtejcxV2paOXBoR1IlMkZUUENOSlZMYXBOTkJzRFVYWjRhb3k1eHNtelhCbHcxMDlaT1FFRHJQMXFlWnJMd3ZZajFYamhueXgwa3NTYXAyZDV0WksyaVZLbm52ajZtTGY3UlN6RXQzZjhLNVZlOVFScHAlMkZBbVVabmdZNjRHRVNUa0pYcDFrMmtQRHhJYXgzUWZ2TXdyTGo3JTJGYWk1QlNsJTJGS0tYUXRTaTc0TVVPRG5qRzgzSDJHZVBJOXA5QnNPck5zSUxyTWZSYm01JTJCanZYcTIlMkJNWlM0dTN3VFR0N1NFakJKS0l3ZnU4aGJiT2RkcUlHMnRnUFBJZk96TmRtM0NES3BjeG8lMkY3d29zS3g0bnlsUEp3Q0hsaiUyRmdlNTdVcnB4SEpYRElMVkl6SFFWdUlabzJVTUF5OW5nUFdaUkE3R3JXSSUyQmxIekM3dGMlMkJKdFR6M1ZUSlZiUHBxM2xUZ0ZhbHNxYWluWUhHajBzcHdvUmFIU1JHSjVwa21LdjVGMjZ3Q2c4OGMzRmxpemQydmFMaDIxTktSU1haOSUyRmIlMkYzazdCdGowN3BKUFZyJTJGbWIlMkJPSzlGSVQzYWslMkJkZVI1dEQzS3olMkJJJTJGcyUyRmZualYyMWg3VzZpZ2pLOFdpdEtMUE14R1JiWVQ0NldHOEprJTJCVFhjMFFBZCUyQnc5NXUyc08lMkJaZ1JLQ2daWmlMNmh0V0xseGNaZnN1VlUwUXVLSnRhNnMxOUZSSFUxVEJ2dHE2Zm5yamRrRTUlMkJVcHRlaHl6MzA3MkxZV2pCU1U5QWNZTmY2WGZWVlFNVWNDanBPbUpxc29jb3d4MVJaRm1mMldBQiUyRlJQbndiM1BUUnlycEw2UlNIUDdJVzUlMkJFZlJjbDFkUk81SkxnMDhDRjk1eTdVWmJqZlJGOEI4eVFyWFB4V1lGQnFVaTBlUlN2RHlmeDFwMzZxRjZWbGxFVnJBRkZBYTVmNEU4TXd5YUF3WTFnUCUyRnMwaHAlMkYxQkdBVnZOOXZ6ZlJGN2ZiR092MFpNWENSblpjU2ppb0VUc2l3U1FSRXpaemIzY25UeGZKTU5rM25aWVNZOGJSbzQ4RjRndW9GV1VWbllQSmtkVHEybUpDSnVXejclMkY2dTVYeXBzZlh6OHclMkIwNGlWa2V0blFqT1VoWnc1ViUyQmVINFlRRUkzeU50U1VlMlM2aGFseXNEblc4MksxZGVKb3hUJTJCc0RXSVlCZFRhc2V1YURodGZuczIlMkIzWDVHU1FtbGxsOVAzNmlSZWQ5bDI3bjhITVZYbXhnZiUyRkM4M1FIbXM1VzFSRmF0ciUyRmxDS25DRVFJU3pTZk95VzNqQm9vNWtIR1RrOW55QWJCS0kyZjRiRnc1a0s4VVZheTJxc0YlMkZObFE3T3VkJTJCQUx5NXdRU3FMemJMeiUyRkt3MER0RGVMUU9zbXJlZmh2dGM2QW9NUXluazNqdEZTMGwlMkJYY1clMkJqckFuN2dEUVRCZyUyQndaNWtXTFRGQ2pVOUE0cjJJV1NTcFBKU3pIVTVFJTJGY29Nc0preGdyMiUyRmdwQXM0ajNoRmpYWmtTNDRxblhQOXI3S0xkbUpsYkRNQXVyRk1FcnNzUFl3TDJyYnhkZUYzcWtBQjVIWEhIYzJnWDRZUTMyS0tmMFdxa3B5eU11TSUyQlZlZnh1Y1pCb3hhdjFneGs4NFh0dkowV3h2dm1jNEVsdWM5UlVSY1dFTUxtS0swJTJCMXpSUWRoWHBJdEw0WG5wMWslMkIxWHpsSyUyQjYlMkI2VThWcjhBNHNYczRyaTBDMXlCWEQ5RWVlZTVSUkU3RzJud24xV3g3M0NDZDFqN2tBMiUyRmw3MjZ6VHNiRDF0ZVg3MVJWUlZsaW8xSENuSU9mRFZ6YW5MVjkxUnEwaDU4eXNrOVgxSUdWMFlCQ0xQV2lQem4yYmRVVHhUcEd5ejNMMWoxNFZ4NWZTUyUyRkQ4UXFybDBUcmIzOXhLYzV0c3g3R1hpSVg1bnQ3RDlkaWpYaXRVNHZnVjRxMTVaSDNoTyUyRlg1MjFnd2liJTJCUTk3JTJCb2lDZDloYzdIQnMxRUpUVTBLbjM1MUtSY0xXb0lYeHh3U3d5dXF3cUtDaTlDM2Nrc0ZOTm45WUxhNnVlS21kSzhyMHBqayUyRnAySmtEUnc4amdqUHN1cGhPbEpmem9FVEE1bWJuM2FsYjBxSyUyQiUyQkpPajRHc041cDlxTDI4VWdhJTJCdnMxME55ZTBoSjNiRWxYVjM2NGtKTUw1MllZJTJGOVFKVnFtNDkwJTJGMVE1MSUyRkpCZHdXTmd1TG1uM0c3NUkzejU2YjZFYjFRRVlmMUZYc2RRUlYlMkY1RDkyWm5aR2JpdGF6TVRQSVclMkY4Nmt5c3oydUsxR1FONU5kbTQ3eSUyQlZyckF0Nkh5eFNOM2RBZHYlMkI4WkRSSkE0V0hjUW94eHpaUzlYMEJLWUJpcG9MaE9FODVzSSUyQm0lMkJLbkdqRENXTHphVXVXSDVwakVza0VOSVVCa0FwZk1CODFRZWRBY2xYckg3dm1pSEJZUlpzOE9qalclMkZLMjZiNVVjbkZxQlNpc2lWTXVWYzZIUWxqY2J2OEw4MGM2OGlZRjhuUkxIbnRPMUIlMkJzQUp2emklMkZRWW9kdmF4amw1UjVkSkFmdDdsUTAwdzc4eUI5eEo4aCUyRkJSM3I4SGpSbzRNUzJ2U0hnYnglMkZlSGk1S1RVNHN5OFlnR1BhM1l5SFVrSlJ6dHFnNHhFeGFMUmJOY0ROdDN5eDVmNDlQMFhEQ1hPaWlmUVhaalJOOU5sNU1nM2p1NEVFcXVQeFQ0UkZpUkhYNExyVEklMkZKbWE5cHZPakZhRDNzbW5BOXN5cnRHVVV2Zkhudm9ReVB6eElONjlrYzQ1d0ZIb05Hd0dkWE9SQW9yJTJCejNiQnE1MTFPMGZQemFyJTJGdnZieU9mYmlONGhXaVpUM3YxRmVrZDYlMkJMaDM3Q1MwNjJaT3lac1JSM1V0aUVkN3ZBQVRQR2xDRGhMeVliR2lDSzJpSkVaT21hNVd1dmo2MzUyUXVzUTFzdzd4ekJlM0hKYnVtTm5XZ3hFZUtSd2FhNDdtOFFlcW5ONkxwWlkwVlV0ZWlZRjdhYWJaSVRFck9pVTJnRmZUT1BFSDZ1M3ZGVUs0dnN3a3RzNFZONEd4NWg3ZWVuRjRhelAwWHRFeWhPRDc2S2RFdTc4Vk1lSDl6TjclMkJ4U3FxdGpCZjVIZXpmJTJGYjNlUFA0aVl3RiUyRnp6ayUyRnU2cVplMSUyQmRVeU1RJTJCc2dia1h0eWE3UlNWMDZOQ0xJajI3enFzckx5UDhnWU1hZzZOYm1zaXZtdEl4Mlc2WkxrJTJGNXU4cmpZZGhKdUkzJTJCdms0M0RiS2ZOVm4wQXNiTVNsN1JzTUM4b2txcE9GS1ZIVDh5RTVzTVV0VnAyMXQ5ZUhlVyUyQmJtJTJGODdZQ01kOCUyRkFtMlkyeEF4ZHQwNWFJamdIJTJCOXdjV1hTTUc2bUlrYVRacG05Z2pLYmdoT3RmRW5VMElHaUpWWm1BbXBWd0VielZiZlolMkZxVWFhUGM3VkJwTkRTOHlFbCUyQmhRRE5EM2xjdzhTM2VYRDVEejRLM0dxaUprT0Z3SFVLaTBOJTJCbSUyQmNFTks1WENab040VVczWHNvJTJCaVRncjNqVGVmTG5oZ3ZZVUtETmNrNUg5ayUyRjByZGpiNjljTURxbTVhZ1U5dmpnMGNxWmx3TzczVUV5OEkwZEl4cTdxczJQM0d0SHkybjlDemdibllva1pTank1OGxQMVJzZUQwcXpsJTJGTkh0aWd3RmVYU3Q5ZkxuJTJCbyUyQmJBWkJmam8wWnMlMkJLN1RBWmRlNTBOeGlYJTJCZkIlMkIzdTBHaFl1RTRLVWRwT0VmWGxPcUlJNGxNMW1TcklZZ01adWFPNXY3cFhoRmZXaWw4d0doelVtUEhYSFROUUpwdlJDUFMxWDVxVTclMkZGZUVJSUpxbiUyRk5XWHVpczBwUFElMkJkV3RWc214dmZjRjhqVVRXWjlMSnElMkI2bWMyUyUyQnBXY0ZVQSUyRlh4cEhYZTF3NDZ2TWdzeVdiaHNJVWklMkZ5RWhJMGlnTVhMMEhoNjA3M0tXNThhUVliYiUyRlpVREViNklXZGxHUGVRWVdUYkdwRGlmNUxDJTJCRHhzRyUyQnIzMUJlTSUyRjFOdzVlWmRlZElXVkk0bHRUUCUyQnhzbkZxUzZ3QnBYS2NqMmN2JTJCMnJZdWdNZVlwYno3SWhWU3I1JTJGdnN0N1JnNlp2JTJGS2FzdUw0dXowJTJCMEFHbFZaJTJGR2UyVG5vZ0ZMbkZ5RGFvMks1UXFMb1ZwR3hSS2N3VTFNNUUwZHNJQ1JtSXNaUkUzMVI2ckRmT2dCS2dUTTElMkZZeDQ4Q2kxeVc5YjNFZmJpQnJodnE2cDNXRFdhdnF4QzhiSDAwNk5mYldkbjZFZ2hXc1FqbGNTcGRCJTJGZE5MdTZDQmVSVjdMaVZVamh0aiUyQllSVWplaXNkUzdsZFRkTVJkNzhrSkt4MjRWbXFHVHZzQzJxaERkUWdSMkhGb09yMWpyeiUyQkl2U2ZCOWNSdkZUciUyQkx5RVZPaWtuTWRFTzZEc25iMiUyRkkwUkRZdDhDUWpLamNKaW1KbkN5VHh5NnVVUiUyQm04JTJCQ3klMkJwRWxMS2N3UEt2M3EwOVAlMkJrcVVQTGw1cktUJTJCTFVMdmdETGNMaCUyRjFWMVBxVXVVbzZ0JTJCYUVaMEo1VWd4VTBIc3hUdklVRkwwZUZrJTJCZ1lJSm55SDFWNUg1ZDdka2R5UHNvM0Y3RlBFMnhGTFQlMkJpazZRNFNCTGZnTUNSSUNtdjI1cmxmMGo0eFF3MkxYUVVDZzBRbzd2c2RoVmlUZm0lMkI0QXkxbjIzN3lJT3hGOTZmOHNJayUyQnhDWHN1a1R1NHhPbXMwS2ZNU3FtVyUyRm5yNXhBRWFJbWdIWiUyQmprNmtYOUpkejNEckYlMkJicks4ODVwTlZZcm80U0ZidSUyRlVwcCUyQk4zMHNKZW9XdjF1VERMNXJZdDBmalYyVk5LYnJNamU3YlNvMm1MeHQzVFdFJTJGUkt5UGh4SyUyRklnbXJXalpjcU5ud2o0Q2E0ZG9kbG9IT2RDa1hUeVZMVXROaWVERHFjdExRblBXY0lNRG5XbURqcTdqdG1yaiUyQnElMkZUbk5SU3dzR3klMkJxMlYlMkZmTSUyQnYwVHdRcnI4JTJGOEFCV0Z1N0l0RlFCRkpmaU9wZmhZT1NMbllvZHVqUCUyRkc0V2ZpWFVVT0RIc3lndWpiT3NMa3lWSWd3aExvNWNNOEIlMkYwT3FBY0FXSUJzTDJYWFJHdnYzeGhPOTlpREk1NmtZencxNnZicmd1bjUwcUNXQ2olMkZzVWpCVzVMZ2I3eTVZRDVBSyUyQm1TbThhcTMwWSUyRlBtbGFsZnN6dHZkJTJCaDZRYSUyQiUyRmxZZjN4VmglMkI5b2EyZzlEZUl0Sk9IczhXT2Y1QVRBTEtXQTZPa0pUcmZtRTljJTJCcSUyRmNvJTJGMmNxZXJRb1FqTDc5TnJYU0clMkY3WGpyZmVxQndIZFZZcVVZYkZOQXMlMkZrSGtoaGpuWUVJQ1N5emlIMERaJTJCSDFGZEM2S2VIMElKSDMlMkZuUlNoNUZLcUZ4TlN1YU5pWTF3VDVsYnluZzRiVkc1ZEVCUzB4WUElMkJmSndob05SdG5hOHM3MGhPOWxZWHp3RjUxdG84VCUyQlY4ZnpSV1MyeCUyQlN5R3dWYTV4SmhLVHhVZWZGeE04UUVQMU5UQ2EyRlEzaDYwWHolMkJycjVweXE3RTY0dzZPTzNkNkpkJTJGeGVpOGZRJTJGSGQzRDFxJTJGcEVkZXpYdzE2T0g3c3J4TEd2cXZuVFhWdW1mZDYzeDltSENycnVZMXBHeCUyQmxUTFNTczBjcVk5MTBJa1BFVGNaMjhMdHdMYVZBYVpGdTBzTjBCamZSTUJQVTZ5VSUyQnh3UGZLejFMMDhpSmliaDJqY2dDbzRzY0pZa1dEJTJCM2doS1JQUTdyVzdIUmU4WHJYZmswempsNlVSdURKRGVSaFNSN3Z3S0FpY2lxNzhoU2xMR2VidldpeERDeW9Ob2JrSCUyQkkxeUhSZHpwZCUyQnhkUGdtRUIzaTRLYUExcVVYS1YzMTVzZ0dRaFFTbCUyQjNyYlZBWGUzVTdjNUUlMkJ1NW93d290ZlJvZWllbTdjS2ZjZWVDcHF4TGtmNWxYVHFPc2hXdFdHMXMxc21GZUFTQzlSZGolMkJ3MSUyRk1MbXoxNmZtMzFPNUdBT2xqNjQlMkY1M05ENVFJMU81JTJCakJVdG9DZkZuU3lUbHp4Q1ZuJTJGWml2VTdjZExtcDhncm9NYkc2OThOY1hwWGdSYUxnOThSb0hQJTJGSjU2cXBqbFpWbVNrJTJCMlhEanNkNWlxY2s3dlJFcFNIU2NkM3ZMJTJCM0d5dm9NJTJGcXJGdmpxTm5BU1UwanklMkZWZyUyQlRKVFdqbk53R0NHOGg0c3ZXSDclMkZDZWlqTENUMWIlMkZSNGM3QWx2cmhWMUVTYWYlMkZBcGJ6STJjVzlTZkRmcGpCNVdQY2MwJTJCdVRKRTV6bmZBemM4cHIyZjFtNmlpMjdsUmo0UzRacldwcVoyVHN6TSUyRnZybnoxNWk1eE1jbVk4MTkwdHFVcXFsbWlnOTBRNVdrMkptQVdFJTJGSFhOelE0cXVTWWhVT0ZnVnUxUW5UNlRPcUtpWjNLbW9wUUxyN2RTeFpzaDRLRmJaZGFOMlpPWmJ1NTE5YngyZnJEYlhrN08xQ1clMkYlMkZoT1NVRjVyT3VPZFJnOEFGSDdTOHZldjdjV0xodzFFSHF3b09NWlZ3ZHZyVGc5ZVA3dzdEV2w2TiUyQkdId2NlckFJejluMHZTTmRXR2ZhQVFFb0gwNG1NWklLdGVQNlkybGZxZGhZSFhaSVolMkYzZXk2TGpiVlM1MEFxQm9lMFprVHFQVG5WUk1lUFNpSGg1dWVyOGd5TnpHRXdSJTJCJTJGdSUyRjJsclZHaUk2eldidkkzc0l4SjZnJTJGZjVVQXEyU3FiSDUlMkI2V3ZHYnBLVlB0NFN3YkNVYXJMU1RkN2lBZVBDR1hLcXFiUExpbzJhc1Y5WVduNHhtJTJGMiUyRllJQXQ4Q2xNM1RneTNla2JmZHViJTJCRDJ6dTFrdnVPVkZ3V3BTenpTUWpNMmEycXlVVkpTVXFmJTJGTGYwRkZNNkVTJTJGSUIlMkY5Q1RiREM2SVliZnI3MVJ5cWhORDlxbVh3MDNsc05xdyUyRllJRjl1TDhPWXJhdWxiU2ZNeVYlMkJ1Rk16VkQlMkZDeTBVeDlOc1hiYlJzY3BTTUtzcHZyUHp4RnB5cHNnMlVOWiUyRkklMkJQTkElMkZoNmdldGRXSlNDc2tWdThUUE82ZCUyRnVnaFZCT1loUDNmRXEwMXNKSSUyQkJmUWZscEx2eW8xMzBLaDZQYnhOS2MlMkJRWjlLbyUyQmVOQW1TYnNwMGMyVW1ja04wYmE1MXI2T0dCZGNsMnpVOTdERmx4VjFYZmdzeHlialRuNTFsZUk3ZVMybzdtJTJGWE5hcHBIYzByaHA4T3NyTml3cTVHQ2w4TEQ0Z2M4bGJYNnFqeSUyQnpSV1FXYmZicTlDUE1TUkQzZHpPT0t3bTBQZmtkdExhJTJGaCUyRjNRUUZDVEJ5OHVrJTJGZTAlMkJlQnBxdVpyUENNYXdrT3RTMVpoUTR2U0JROUZTMVR0R1puT25VZzZub2VjRzk5SWUlMkJyWElTbHZZblExMTVibiUyQm9hS2xqTmQ4UzV6TXlLcjBJWXNZaGFPYWQlMkJaZWtZMlE4WWlESCUyQjNJbzZVSXdNbUd5Z2h3S2lMSVBrVlpPYlpKMThaQ2ZRRDJ4d210MnhLZlZpQWU0JTJCeU9jYkEzMUNveWNuaTZpN1BDWThhU2wxdGM2dnBhTmpuTFlwOHNoUG9GT3FlRkVLT0dtaFA2WmFnSlNpaWk3SEpoJTJGJTJGdXFLZm5JN3N5VFhvbVBOYmN1NTYzcG1XVVcwT295N3Npa1ZFdkk4OSUyRmI3eWdxVEYlMkZ3UThDMmUzT3F1OWVWV0JHQ2dySWhpb1d1c3hPVGhUV2ZmSUJ6UnhGaFIxeWslMkZzOFlLNm5CM1pRdjNCY24lMkZkQWVmUVBRZkJBOHgwQ2NDOSUyRkNVZE1kSlJpdlZzZjVEbXVGRnV0UG90bVU3NmYzY1FITzlJd04zajZPVyUyQkVueUc4JTJCZmhWekhYT2VOejhuSjIlMkZibiUyQjBzOEFHVVFHUVlKSEE4N0dIRWhQQnlmdnVnJTJGMXlMRk00ZzFpMFZUM1RwcUpsNXJsJTJGdkpUaVkxVEVJR0g1cjVXQSUyRlg2JTJGZkN5d1dlbEdkQ1gzeDE5VnVtWFI4aHRHaDZUYmJ2UG5yYWxHU3d1UFZHOEx2T1JrSVZKakx5RkwzTWdoVXZhJTJGJTJGTDc1NSUyRkZGdk9yYzB1dlJhNFZBJTJGQ3NMT0JmVFZQWHJOMzVKNUpiZEhHJTJGU0ZEOW1VU2c0R25ZQ0tWeTk2aFhNdEdSaE5XaW5UZVZxdjlNSzJTY283dmhsNnRiaFhobGZlUzJxbXBLOW1sc2JudTlJNkVQayUyRnNaMm1ZTU8lMkJUUng4OEpsUlAlMkJkaWxLdXZPaEtkaiUyRkpvRExkUlpmOWs0SGFiT1VmbSUyQmFWZ29peWphNTZXNHUlMkJhMFlMT3Vza3RIQ3c0YnJqWlVyWTJ1M3czb3NzYVJvZEdMeFNUajhOelNRN0NzbTRIOG1TTkJEJTJCTkRzdEMlMkZqbXklMkJPMVRWVmowM1kzM09FcDhRTiUyRjNNemg3WlppN0pXUGo0cVJseTBxMDhSS1hMTGo1b0NrZiUyQm5XM3lCeVR6RWg5b05UNDEwWmRydkYlMkZhNFlnT25YcDFzdTJsbWxhOURPUEpHNFY5cUlXdkl6cFFQYnhoTFpPSW1pJTJGd1l6bkNYZnhIUzJIOCUyRlFzaWZUVU4zSjM1bTVRYkQ2OVhha2d1QnZ1Snd5OHlEamlSbUYlMkZoVGRiQ3FaNTJuR1FXcHVLTjFpeDU4UnRmSUMlMkJ1dGt5Z05kS3NZMHlsajR0cEZ1TGl2c1I1eXB0R3ZISHBRam9JVDh3cTJGRUMlMkIlMkZwbEV1bVVlNGJuTnIydDFjcDIxVDNFUU50MWI1RGxCaEVCM3JFTDBnWElHM0oxTzhwZTA0WDlqM2xCb2Jsb0x5ZyUyQmE5dW11VWxxaVY4M3A0SnlaNHk1SUlyOUVSa3pVJTJCJTJCQnNKdHRlS2pBRUglMkZqcjNaTEFMcENsNWN5NDNZZ3RqQmZka2xkcnlVSkFGbU1vTiUyQkxEeTZpYkFxRXpCNiUyRk1OYSUyRkNaYVY3WGZ1WTFrVWNrUm11JTJGN2FGYVk4SDAlMkJlOHJjaGNxZnNQdlViT3ZrNG9HJTJCOU5LMEdJSTNLSTZVV29UVE9DRE5BTmslMkZqelo3MyUyQlphMFAlMkJURXFTNWkxYnFxT3ZyZWdPR244ejNreGglMkJqTGR5ZWZvWlZQak5KSlROcm95TUkyQ0k0UlAxeVpUUjEzJTJGMSUyRmFQa2l4JTJCdVBrSDZBcTlGZGRCZ3NSc05PVVdvJTJGc1lsTDNwbTRuNU1yQWR0aTlVTGRUVm5YN3pvamxXYW5IWUlnTHZQd0lwTDdsZ2RTTEE1YjFUN3JpWUw5MldXVkw2amM4WEtmWVRVYXFBN3dqZkd4OEE5djZQSVJWYSUyQmtLWTclMkY1c3oxTEJvd2d1ZjBybTd6MkY4aXE3bnhtWnowU05hJTJGWGJXbHBBcUxRNW1KcG11aktRJTJCVEpvNjBzaER5JTJCbEpHWVc0VWNpbTVsR29nSzFtaVVLYjRWakNhR2tYOHU0NkNWd25ONnYwbCUyRjZnbmpQWElkSkE1c1A3QUl2VEwxZmdHYVZqTmhiRXBtJTJCZEZibXhjazhmJTJCOWV2TiUyRlElMkZsaUpQRDc5UzZlaHpSWEh6RndLRGp1dnZHbSUyQjZKYlhndGtDcUJ1N0ZKZ2RNTDZOR0FWZE5SdXZyNU4wWlVRSWVpb0doJTJCRmI0ZmNaQzVsVTdrbDM5VkI3OW5qN095Z05qZm12QmRxdk1yTDVoZkJxYmxNd1B3cEJWJTJCJTJCZjUzMzN3VnFPenhnUVVyV2w1OW1yZ3RkaFBaMiUyRnkzQmhZUUE1WDRZQzV2WGdjZWhDR2FIUkpxWFpKWjlKOGxjUU5ybm9tbUdJTlZ0aUslMkI5TlRrS0p1aSUyRlY5Ykw1M2xJdjN6YWNvJTJCTTNTaFNpbzA3MzFkcG5mZDhnUG5tZm15cEdKNVludlZIb3FIRmlabyUyRmpYeTdkdGdLR3IxdmtiOHFjSE5nbDY1RE5nSENwMmQ2NnRFYU9sJTJCR29zc21UNENlSWZEdFJPYXV5UGJyYU14blpoRnRmQlVhMlN0Mm1OZTRLZVg3MlNaMnAlMkJ2T0ZMNVlhM0FORUUxSkl2ekhUOSUyRnpyc3hLN3BCekMlMkJrR3d1Mm84NEMlMkZsYUlaazdRT1dqSWlWQlNRN21KJTJGMXA0JTJCODRac056UXJmRVRXUlZBUDhaQkx4bUtFdiUyQnVCR1pBcG9JS2R5RDNYMklZbDhaVHR0UU5DNU1tdWYza2ozTmVUbE54N1hnaXZDT2xZdnpHdXBlWENPU3lDM1lPaiUyRnBmaFRMNE5UMWplajBpSDE5c3hCJTJGcnZzQzkweTN6STFDUm8lMkY5OTVCelh4aVVqYWJ3aHNMdGk4NHVJRWJDRDJuUjY1WW1LNVEyOHpOcmg5bmpXdUxxRTkyRmZqUUNnJTJGd1NjZlFiaXhiVlJ1cVZFbXIzJTJGYmxNJTJGMHlkOXZxQjVhdHJVV01QbTR3Q1lqUGNRS0VVY0dwS3duS09Cb3FOT3JzRENKWlc2MkJRSnBlVk5KbFVwOFI5UlJra2JZMXF5WW4lMkJsZ0ZrVkUlMkZmVVFvSnglMkZkJTJCVG5KalJSb3JEJTJCdDd5SFprSGwlMkY0MUJ3WndicDJZQXBQcm5hUVRxTTJsRExRT0FWSFVEVjh6bUc2cnlFWUJVJTJGWVU2N3lyNzFwVmdud0ZJRzBTOGlYWTBGQkVQejBZRTE2dkNHUWpSWiUyQkF6cWlQNmlzNjdRN0dFUE9neCUyRmhuRlpEMllEdjZaUFhGcWtVdVNBVWM0STRaJTJCN21ySTMyd2g0VlpSTWw3YWk5NjN3JTJGbVFVYzB0JTJCM3RMTDRLJTJGN1pOdW9waFJlazZMWjE0TVBIVkFVZUxiWVIlMkJlakttbVdKbUtheFZSdTl1WU1NMnVsOXZWSTBOOHdVaHRhMWIxSGw3dmhqMHF2dnBvV2Yxa0JTN1YlMkZhaVg3bUwlMkY4bFdYVlg1eTNLVGJKQUxod1E4Ym9KWU1DSnpscFhIMW44YTlabVZRODNQN2JFRGUwUHpKdmg4dUlzRTJLN1JpRCUyQmlGOXpwVWdRWGhxUmdhdE5uOFQzJTJGaEZyVTJ5VDJ0VHElMkJLdll3NWhHNzVOVFhUMmRhNnFFb0NaNUQwS1d4b3Q2aDUxTzJVOGVwd0JISlpwZGJrZyUyQlJkemlsekNTQzhsZGN0SXVjSlYlMkZ1dnQ4OG11WG55MEFocmw1alN3YmFWbms2clF2S0NYaDNZTk55enlaWWxxdlNXJTJGTGpaRTAzM0N4NXFTRUdwa3IlMkZneEwwZHhWQnNzRG9Wc2Z0JTJGdGozbUVYVUh0ZiUyQkZmejdaZEg2YVhEV3doTXpGclV4RDFBbnJDJTJCdUxvbjY3aHNmYkpGcGZ4SlZyWU1kdVhFYXpETHRqVU5qa21obm1zJTJCR2xFaG5HV0JWOG12ODR0YWZ5JTJCTUZ0YyUyRnJtYndSajVzYUxjUmRxYjhXbTF0Mk5CcTQlMkZ5c0hvRFNSd2Fzd2NxTDBiSkU4d2d3eXM1MjRoME9PakwlMkZ6bFBIYmJ2bjluJTJGdXdtRzBCRzZpWkhidXAweFpBN3p3ZlpOODFHeGsxTVVSNWpMa3VRQ1ZTJTJCcjZtWGVVekQxc090T2JDbTU2VjlhNEFBc3lFZmlHNmcwZnI1eHozVGFHdVQ1SkI1bG0lMkY2Um1DMHg3Z0J5ZWZUMUN0aFdrRWt4U3Y5YzJRM3lQeG5MQ0pPUFlodktSTVg2RWklMkZ3eG82aUROSkVvRjRUeGFYeW5aVSUyRmclMkZNRTVxanlPRXdxb2xFSjBvQkpIQ0Z1SGY1dGtKSnV2b0prWURIekxHc2pOOCUyRjEwdXZ3clhoNUdJRnVZVEFYaXZWM2ZWazBrNW9QRXVsNjUyc1JMWDROMWxuT25jRk8zJTJCMTRhZWV5RmxWM01KbiUyQkNsTXZQYUhxbTBHSUJKUG0xVGNacHN1dlllU1c5QzB3Ymt6anhZdURRYm1sckxSNyUyRml5OEdGbzlhVWhLSSUyRlZra1dsRkwxYW9NeFNBUTZKbSUyQjNuekNmQmF1bHdIRmgwRG1oS0FGdkdWYm1GblVmSFV4T3BkVXA2JTJCcTZkdUZKbmVwcXVIc1pQWFQ5aktpeXk4Y0lOekg3d1RmODFoWEo1ODBRMEJLYjd4QW4ydVgwWDNyOFhCdHV4STBLQ1IwZkFocGtIbVBwa0tINXJjdDUlMkZ5cjZZZU9jQktMSDVkRzF0NmJ1anV1VWpORmpUN21Qamkzemw2TFNoU0d2YiUyRmFvckFjV2RvV3UxZ0NmdzAxOXMyckpNbkY3dHBtdDFDMEVMVzgwZDhHR0k3NVNzaUtqck5rVm5yaXZzWCUyQlMyU3Nobm9JVmtqWWZSMlVpVWRWOEo0N2FOV0pXYXhSS1g0Z2IlMkZMeko5eU5uUmVLQlpCem0lMkZLdXFSaHJ6ZWtkeGFWVmllbCUyQmp0UjEwczMyamJZQyUyQkp2JTJGbWFGSmZKSGhISEc3ZTFqQkdTMFUyQ0FYYjVrVUxHNjYwbW9yNTloOGxEeEVOQkdtdzZnMkRKeFE3YmVCdWp1VW5QNXZIc0kxSlpiWEplNnJxVDdjVm5ZbzlmMGxtaUZEUlBWcTJ0Z2ZEN3MyakclMkJNMEY0Nks2TFl3Tk16QmQ1Y2xPZlA1NUNUTCUyQmlzeHJrZ1NmTUlkRiUyQlU3QlhIeWlKNHpBVTlmV011Rm0lMkJFZ3psRUVyM1pwZFpGMVhsWDIlMkI2UmlOR2RMWW5YU3NIY09kQiUyQnQwUUtJOFVIJTJCdU5OQ0l4JTJCRHJ6bCUyRjNDQk4lMkJ2ekU5NTBwR0RNWmNBNmIxNG53R3k2Q3Z1QUMzWk82ZnhVcmdQOFhBY3VHcERMTFA2UEZEbktRbmUza3AlMkIxSmRoZTJ3Tkd1Q29xY0J0ZGxlS2Y4Q2dxUGc0bTFFM1ZEdlhmR3JEYmtLVEZCdE5hT29XT0hZdlpySTZGekdaNXNGdHM5dnFya2lycHhodWNMQVdEOEQ0enVSMjglMkJ2a0s1N0xzdzA0S2J1ZWtpeWluZ2FaTkQ1REg4bmN2QTZqakQ3NlIlMkJmSUxrREwydFBYQlZKc0VIOURUTWtKM3hVU1UlMkJrVjdhc25SRkVPbGN3NVpLczlpc0g5SzclMkJWbFBmSGlTYmJ4Q0ROMWVKNVg1bXFzaU5Rc0ozTiUyRnNhdjNCQ3ZLVGFNa29ic21aTVdTZSUyQlpSdEIyVDZzWXNxVE9wbVhSZ3F0WWJ6dnpHZVVXJTJCU0lZS1Z1bWE5Nm0yVTVDTWclMkJXUEF4V28lMkJHaEtId2RsUW1ielpYQTdaaWpSMjlmcFJqOVpieFY0aGpJUnhyNGpqQ1hLU0tqdnliUiUyQk9ZY3BmSSUyQndOZiUyRlV1dklSdjNLY1ZXejl5SHB0QmxtM3I1UXF6Q2x2eGYxT2pFQ3M2JTJCQ1o5dWlCM3EwdHhydnppOVR4amtJc2ZCQlE5TThUV0dmZm9KY1d1a0xwejl5VW9la0tPVzJkWmZSNlJZc2pCenZYc2VjM0dSNzNBYWlaVmw4eTRHUmxBYkZYVjY2VCUyRjNzZVFodVlPSEQzSzIzJTJGZE43NGNCb0xNa3pJUloydyUyRjgzNWdhM0N4SEwyMnp3NDE0MFFtazdTYVJ0ZyUyRjFBVlEzSFYxZDB0REZyb29mamdqbCUyRiUyRnFPblJCSGNTVEdGbWRMdFpwTXFGcmplWG5nbEpxRFlTVDdWR1ltWm5xVmszakxlWVk5RXlRempJd1pKdFl0YXFyQzhqU005bGp1QXJRVkJkcCUyRm9PSXNQT01vNHFTJTJCMUc1JTJCUllzSVUzRUZLb01TV202dXdaJTJCY2Zkbzl1SE5rJTJGdXJPcGdnR2puc2xFeXolMkYlMkZWQVpRVktyVUtENnNIbGVuNXNEUVVtckVpeXElMkZPaG5FayUyRlQ1Umo2TnUlMkIlMkZDRDJDaFZiQnZKM0xaOWtEUVFPS0ZiaG5BeXlUcXA1bkFyOVR2UXR4U296MXNodUdpZnQ5RmxlUmZ6TGNvRnVnSDNZOEZoRlgxTGxMckFITTBmdm9jVTMlMkY5bkZ1JTJCa1E0RmF2cGhrSDVZJTJCUEpIQ0FaV0ZmJTJGRU9LYUVkSFk0a29CZXFYMTRnaXpvJTJCbCUyRldhcktiQUxWdkZaZUdXMEJkWUlwJTJGSDFUNGZCZ0F0R3Fyd0tZejhGJTJCZ2puTXNITG9QcG11bUVJazR5eDZETHlCZElMR0J0ZUxJcUV4UCUyRlp5RyUyQlBSTmQ5eUNINmlJSEo5c1dZVmZXWkd6U20ySEpjZ3hhYWVsJTJCRzdVdHVZUWl0RGlPNVo5R1I5b3haJTJGR0JPZXpYWmtXMU42cSUyRlRobWFmRiUyRjdIcmlrMjRJYTFUOSUyRm5xQ1VZQkxWdVl2ZWo1cHlCUFBWakVuJTJGU2dsRnh2SXhxOFZzN2slMkZMUE9NTk8xamJkN3oxNUowYUhmWXdScjdsT2lTaTBRcEY4ZnFNeGJLVVU3WHlWaVZiMzluV1EwTHdWV0dVREFJMmJRJTJCR1pPWlpsdlI5RzFEREpzWUtudjdCTWhxQjQxJTJGJTJGSE5KQW95dUFQR3ZEOUx4S1plSXdpU3glMkIwV2xHUHBJcmZzTmN1Sk9xQ0E4dkhtUEQwTjhFVTA0VnFsMGowVlJSOHNpNlFrbm5vMThlUmtPUWxaR2Mzdzg1OW5uZFJpJTJCYXpFRiUyQmd3UUZnRXFvZ2tRWEh2enMlMkZseGdYQzI5ak50T0NrN2JwZkpKdUFtSElxemp2eGY1SmtTTlQzdWlHTDU3OGVzM05rMTZQNDNoYzJYN295NE1ua08wMWFUaXVsVEZEYkx2ZG5ZNE56WmNWMXU4YnElMkJwZGI3SEJJN3M3cSUyQnJtTkVCJTJGT2hzZ0ZuOXVJNjVUaURFV2haejlOVDZuZDdwQml6c1RaTEwlMkZxZWdjUWxQJTJCRUMwY1VBMiUyRkpDZGM2JTJGREdEaHdrSUd4MUt4V0N0WUJvM0NKOUx5RmtWM2RwTm40SXgyQkoxN1hBckp0V1clMkZnT0xLSHo0elR6SDAlMkZCakZGTlAwUmxIYXN6U0wlMkZUc3kyekVtWCUyRkRURlFnOGY4eGFUSllyOGVOaHFjcHppZ3Y3OVVCaFdKalp5NVpLZHlnUzJybUo3dEdVJTJGUVVWNzB1cDlrZTlydzZlZmJHWGRnR2ppTyUyQkVBTG5OTWQ1TFVyOTZnTXZuS0N3WUUxMkQ5eng2VElOVzklMkJlczVNWHpnSUVkRERvbDE4cVFtRjFaTnpLV3lieHpFeXp1JTJGTUN3N1NkT0lvRmI5WkJ2RWVSdTFpb1JYaXQzcGxxclg4d3NMcEFISG5SYm1jJTJGJTJCZXhSajcydVJ6cnBVbERFJTJGdjZuWDBURUw5N29xTVZSQTVzVGpYWnJDUFFmcXRlVTJ0RkhocndoTXR1SGFFUzg2czNhSkVYc2NNNFZwSk1Pb1F1JTJCJTJGcW5FcjBWM0xsSFhPTHZFJTJCb0U3Yk5vJTJGWHBUUmIzNWJESXVTOTlaJTJGJTJGQjhPTHRqamxrZGU1U2ZuT3IzWWxWQ2klMkJ0bXFOU0wxTE0xNWdGelo4eTlwRHYzdkhaOFgybjc0empRako4elJUV3c3ZzRwN2c3aWVFQkFrJTJGZ0lLMnJ1TmNQJTJGdTRaTyUyQmF1QWk0dFdVMEVGcGdSYUp2M2JVSjRMJTJCYzVhM1RlRlBaSXlYUk9paGlYSjBmOEljMXVpblRTUVklMkZjc2RPZ21JamlmdmwlMkJTWVVINkQ5WW84Z3pmNHVhSDBWMWU1SEkzbmFqSDlka0FDJTJCa3pRTXMyMXFyYzFISWJDTlFEcCUyRlhHYjZLbSUyQlclMkJBYUZhYmNRUHNHOCUyRjZheCUyRjlMUVJLUkpMTEFPbnRxYjNFdWNJUXV3SEowWjUwRDN1VHNibSUyRnVLQlI0ZWo1b2tIR1VaSXclMkI2TmhrZ1c5YSUyRmZsR3ZpM3QlMkJETXNhM0lNaFRsYjFVMHBuMm9SalQ4elZiUzBqcEx2S3UxcEsxYXp5N3VheDh3V3Y0ZnJ1aGxXcFdiJTJCMW5QSE8xSUF2b055SERCaTMlMkZIY05kRE4lMkJ6QlVISTIlMkI4MGJPUEV4SWs3YjUlMkZlQTFJaEtYVnBRMTRUVDF5ZjJ5VUJiJTJGWXpzVmJSR1pJOVltcUxaVFolMkJ0U1NidTVNTDE1dDd0WnBnZVMzc3loeDIyYXUwdE4zS2FZTnJ1eUV5OVpPRmphd0x3VGo0SG1lZHlBQ0ZabWxDWFNhdkNJNHBCd1Vma3IzWTdxMlRaSWpZYk9xdnhxaXRMJTJCNHZBUmlqcCUyQkg0N3lBWnhzUm5RJTJGSEY1aFdMZFhFdlNQTyUyRlMlMkZ5bDh6eDlBNHVZbUtDeENmOWl2cm1FVG1pUTJ4dzdCMGQ1aGFtajNJdGV2RGc4bW1FT1BpbWVhRmZ5SmVqTlBVOW9laUlDbDBqakdEQXJ6OEhMNHhQdkdOTXI3VXU5VGZadEpPSjloYXIzcFdzcmxCYktYSFBqUEh4N3pxQmdPZUZaVFpzeWNMaVFzeVRCMXU2VFElMkY5MUxmblg1ZmVQeWpvdFBKWEZQRUdId1lBZ0o4b2FQeWlTN0k2dm5yQWJJJTJCUWZjc1B6RGlaanJYYko5SXNKQ1d5YUhVTmk5MXdoNFF4QXczQzlOM2cyTFR0VUdwZHVuU3hrJTJCN1BXaEdXa0FYRHFOR3hwZDhpZG5vc0Fkb3lUbHRzMmRXTkFtT3U4cDUxMGM5NGJ0NVo3ZTd6ellqWHBObWZ2RlhFa0MlMkZOTU9mTms3VU9DRTNzT1NWZWhINEc4cEpXMmtjdlZKcHU3VlpFbmpldXIlMkY3MFYlMkJpMDRuNWV0a0NhWlpMZ2ZYRUhiVSUyRmN2eDhhV3NYNkNqNnZZJTJGeiUyQld2Wmx0U2ZSMDNISXE1MGpMSENvcnRjeUE0UHYlMkJPeVBPVjZxRVROVU5WS1QwRnMlMkJqd1NaazE1V0MxMnlBcUFXN3BvMHlIaEI2Z2M3bnRkc2h0UU1zdDBQM3RJeDNEdGFGRU1DV0RoS044aUhwWllFSyUyRkY4JTJGelhOcGZMOTlHR2RiQlNROTRJQ09SdkpWZmhMbGYlMkJHOVRuSklBQXJoSDB4WUlpaWJTdiUyQnV2djdraFJicjk5bmdDcnNveFhoNWdYMFV1UXJSSVlRQjB6S3IlMkJKbWU5NHY0a252bjNhREwzNlUlMkJOak81SEJzSGhlV0RpaDJiNSUyQnA2VTl4Uk8zenJYclM1MFozODBwUVRCNnFRcUlTeTNxQ2J4MTczTGJ6YkJ2dkRVVEhQZUFZQ2Z0a2t2WHk0cHc3JTJGM08lMkJHbVhWTjZWNGw0VUZtWWh6S2FaOXVUNUM1QW00eDU5eDVYQU55RGc4dDUxN094NzQwQ0hrNjl1ZkdYbm80R1lyVE80NXNlTlpqSGpMQ3B3ZVJoWU9LT2VMZlklMkZkdHElMkZTS0xKdzV1NWF5cUpRNFlMTVJtVEwlMkJVUHNGM3hGczlrM1VZaCUyRk1DMyUyRjYyVDVmYWJNdk1XT2ZFUjMxenNWYmNQWVNZJTJGMHkyMngwV0YxVGszelRzUjUxcXlzOHFRJTJGYzdDSCUyQjRCbiUyQkV3d2VXQjZmNURDbzZETUpRQUhQZ0xUZjdDS1BWZyUyRjViMFBxYnFCNiUyRmVZUHVjcGptTXpudm1Od2ZZeEtKVXlrQ2c4dWNWZ0JzbnIlMkJveFdEaUFSd3dLSSUyRjd1bEw3Y2FPeTJiZEo4c3d4Z2pSRjM5eWxXM2xvQiUyRmhXcmt2NiUyRnRFQ1ZpUkZTWVE4VDJ2YzFpTFhicm16JTJGNzhRZkw0MUUyNXBIbWM5JTJCNEpBcDNwM3FiNmx0QkhTZHRNZ2FVSXJWciUyQklNYkZXSDNpYTBuRGxxaW80Y1pyTTRMUGdZdkx6c213d2tRR0hCQUFIVkNJSUE2N2RpOHBPcThZMzJ3bzh1WEluand6TVJhblhPRjFETzN3VE55TkNkUUR1JTJCWDlRMTRxdGhlZ2EwMlU2NzMxTlhDMGlhNDljNkNIR1R2Vlhxb1B2a0lnMzVBeFF1NkgxamJyOGp1VVhxWklRM2lWeUd6dlROeEZMdmxqZzJSUjdZaCUyQmR1dVBqSlVCQWlZOVRXRCUyRjlEb0E5aDE5Y00welg1dUxteVp2bjJDaSUyQmVNJTJGUGs4QTNHRVJBNWJDcWVPWXhyTHF6SGJrcE1oJTJGbkRQMk00V3hUZmpoQXEwQSUyRnNrS0ozOUpWcyUyQmNSVSUyRjclMkZOMkRra1RkSURQTWV6dWJaJTJCYnVIVnhPaThlUXZ5eWk2JTJGNmttSFUzelptdEhhU1lBbnF4UWgwY3VsNlBNekxZYzUlMkZCZGtNNkp5aXZ0STZPUWlJZWFIMm9yUW1pRERKV0E2JTJGJTJCVW5ESDA3UThYaE5WSTZia216Q29BeEhTcWVFMEdLYUlDNUhHV1p3WDdJa290WGF6ZDNLQzlzTGY2TFpOVlNZOTJzZUdzcXR2UE5GJTJGbTIxT3dVZzZlOURaRTAlMkIlMkJiN0IzMXZlQyUyRkM5TWIlMkZQSzlCeGNIWVRENGRqMThERlRmMmV6Z2FyNTk4NmIxaWZiS2l3YkJoRERZOHVNRWNtZ2E2ZGFwYXBoNHExTEc3UWxwV3JOZDUwekdGREtQRDdjZEUzeHJsTzBqWjdWN2xnYVFRMXRENFk2SGh3YUs1YSUyQlRydXp6a2g5M2RMc29NdjR0cGZWRnJUeHpOJTJCWUg0bTJXR1hqNzgyT3NJWFlEbWpZekR5czNlcFFwWGNFSnJydzB5Y2lDb1ZlWHViVm1RcUtRU0tlOVNOJTJGSW4ydnVEbmZ0Z28lMkIlMkZiNnFSUzNZVExsWll2ZWclMkY4cmg5TmlTNjdyOGozOGFBTHNVaHRTc1hHb1RRbDJEU1dvbDQzMUphbmF0bEQ0eFlHY2llJTJGdmdqejBVcXk3bFNWaG9oNnlhRUUlMkZRMm1mdnl5dlZqSiUyQjFQelZmSThCJTJCVkFIYUZXWDBWTVNtVzhMSUU2a1BoZSUyRiUyQm5RTlBabnMzWEYwTjNYaTN1VXRiUyUyRjZ5YWFLQW82dGtGQ2ZuOTJJM2xjOGNQSE41S01yZ3h6eHlBd3hjcHUlMkJzZ2Z2eCUyQkF0OEFFMEJXUU5NdTlGdDVURUJhbEVaSFBUWlNoaml6M2Y5eXlzbCUyQjBZazU3OVZrRG5YUXlmNDZvTnhIVjBaZHElMkJySTlUdEc1blMzZFZ0ZUhyZWwwZzk2RVhzVldTJTJCYkdqd1ZzZFhRcWN1NkIlMkZYdXB0RnlWYzlaeFdYT1UySThhcG5uVkZvSzNheFYlMkZLZ1pMc1V3bUtWV2xKRnNuUFJ6eFlrNkJrczdFN2o4dyUyQlN1OXZKdmUxcGVNd0FSdlgyajN1Z0UzbFhZQ3hxWFlxbWV0V0d3b0JqYUE1VEp5NTFxYjlJYk53c2oyNlNuYWQzciUyRkcwZ2tRMkFDTjUxOXFrWk1EYkJ4TFYzR1pHTnJQVGVSQmFWSzZnaGNrRWJFS2JsMEJ4bFRFWDI3MUFkV1d6bUtzNDAxcTl1MklaRVIlMkZrdWd1aU5qMkRXWU1NUkFIcTh2R3VYM3VmVmtIcUhvUHh3Q0JuNnlodkxHclFxJTJCSmhGQ3d0QW5iWSUyRnhESmpCejV6VjFXWWdIYlZ2UFhGeW5Cb1BYUlYwVjFkd0c0VFp4JTJCYlhFJTJCY3B0STh2aGhHSHE3Vk1STDYlMkJWdjE3YVVOeVdGQmlMa2lxYjdrempaQnc1UVFnd3MwTlAyOXJQaFh0RTg1N3hMSGFWNVYlMkZYZnkxMVolMkJXZWdxdSUyQk84c3ZBNlpESWVqOURJWWt1aiUyQmNtaFA5WUNXS2olMkY5d0h4MVdYZzJvcEZwVDBuSTFOM0s5ek5XVUZ6UUJLdmprJTJCVTcwcGxOaTNKV01mYlV5YVhmb2I2d0VPJTJGZm5qWTg2eWh6aUUlMkJGYXZsRDJvR1UxbUl2SThEejNYMXhVZFpOc1JhOFQ3RUI4a3I4NXRaQzkzdG1FVllNSlFNRkc3cFhQb2E0MkU3aHNBM0V2a0FEcHgxMDBOSTJDUzh2JTJGbHhRJTJGd1MxZUhZaWcxWiUyQk1icW13ZWI4N3RFbzRHSkZadzZZNzdmdmtMUkhMbnljdUo1SDlURG1XQnJTeUJhdkM3TzRyUmtWVWkzZ25VUFFkUSUyQnI4d3MxZkhZQzFpRnRsOFlpbXZsOW1Ubm5Jc3lkekRhYVhrZE80MHZEaEt5SUh2a2lyeUhOMWZyRnhiVmRGTDRoRmo5NzhNdjJEZmxnQ2olMkJobDl5WEhVUDFPa1d0ZVZMU0FMU1V4ZFREaWk1VCUyRm9uMzh1MVgyTlNyNG5BcXFxeW1vRSUyQlo4eThGUEJKUHpCY0tsUiUyRkZtak94SkQ2ckJad2hXWVNpQ0xYMTVSTnlsVUxQZEJIWnlZdTFtVG9UMlhRV3Nid2xuV3laaW9tNWFESDZ6eGM1NVdoYWRSdFN0UlFGZkZmcE9qNkhkbGk4MENmWXYlMkI1STJ1TUdjZDBzakJ2QkR6TzZPMUhtaTFzYkh0c1FIMTZYdWwlMkJlMlBmdk1vVmtpaVplZlZ1YzhhYjRKWWlmZGVTY2glMkJJSEhoU3BFc0xyam1Tc2paRDM1Y0k3dnZtNm52aE16a3J6YUs1MzIlMkZlM1RlVFRMQjJvVmxHSE8zR2dCaU1RclZjOWNVbmNzdzVUUlRjbWxJNG1CJTJGSDNTU3pidHJhRlhZZExkYUl5UHpxcSUyQnk5Y3IlMkZKN0I4eEtCSm1MR01mUTh3SzBDVEV3ZkRackpRZnJtSzh3NFlPWG9RJTJCbExERyUyQk41N3FXJTJCZGg1T0Z2bDRWNmk3Sk9kJTJCWWFEeVFseVNWWDczR3NzbDR5cjBxeWhNMjY5S0pGSnYwT3FoYnRidjhWTU5RM1RJNEd5SWJROTNPZWRSOVBBTGpmVVJ6ZXVmd2U3STN3UWpId1BMSVlNWGpINXp2VG90JTJCbzNac3ltcnptYVM5RjJ2TWlwM0VwamhkNWs5V2slMkIzcyUyRjZuY3JMVmZ1UFRmeHFLd3E4WFA2VGNEQmdqOXlLbVoyY1ZucnZla1N2emQlMkZhSUVIaGFCZWRpR1lNWjdZMlZ6d21iZEVUSGNnQTB6NnRpcFdjUmFOJTJGdW4xVnRoOEJsRUNJcmpyTnBhRnV6UXdIJTJGeEJ6WVhSM0puNGRHYm0wNTNvd1duWTJJdlRkOWlTMHY5T1d5aE1XMFo3dlcxZU11ViUyQkdQb2ZDQk9Yd05xTFQwSEpMQkhOUDhZZDFFeTVZUUdUejdrM1h2d1BxamxGWlpkSGdWTiUyQkE4SnpGUG90SmlyU2kzYVpNRSUyQmx6NGxrb1ZkMXBNY1pnVXUyZlVrZXpZSEtSQ3FUbVFEUVQ3Tk1qdFpiVmhoJTJGMmljd0tvJTJGcWs1R0FxRGYyTzhoVzRQeHVtZyUyQjNseXYzZGFmcHNTTWpOd0glMkZqUTFIcTg3b2J2TFBKcnp6M2N6QVFhRjE2NUJpMjc3MHhBazRENFpxbjhiZTFNYnRidFRrY0h3ZVBGeEY2TGNocE1kVWppNkdXZiUyQjdPV2NQOXExaDN0cHFidiUyRnI3ZURkUWlzdzFMbUM2WlNGdEhjV3ZCJTJCSWhmQW8xRGhycDFJcThtREVGa0E4VWxacnRsMUVrSDd2cjQ0R00lMkJzV0F2N2dzWVFReEZMd2Q2dlpzYldvZGlYOGxaZ0VVaEx6UUFhMUU5R2FMZzIwS29hMyUyQm5hSGpReGx2eFFhZUdneEFQSWwzV3FtS01pOU43ZVI1cHRVcWlJMUhYYUFrdXZ0M2klMkZiZ3dsTk9rbFhmOW43ejFYOFJSdnk2JTJGOG9XQzJGMWV0NzF6RGw4SmdLTyUyQkRIejV2bm5yeXdUMlFidSUyQlJCTmdaUEV0eWE4cER4dFhvakdpUDVlOTRIayUyQjh2U091SkYzSzd4SWRyRkE3UEFKNG9BckVpeHdWRDlQU2FmeDJ3bG1GcjFvNGEwSnJRY1YlMkJ2TDJ3bVhoM0JDOCUyRkR4YlM1cmQ1QXdPOFNVZ3h6UmdIVDRjSEpjbnk4bzhmRjVJaSUyRkFiOWRmeG9QbFkzWHRmeXJ1NE11TCUyRmw1dTBmeDBwb3lURWd5bzNIU25leTYxaXhzZ2czdTIlMkJmODdHa2RHTmo0cklMMTZSOGZyNzFsUWxHQzF4SnlwVko3c2d2MVBqYzZ0diUyQktaQmdiVlBjS0NoTjNGU3AzWXY4T0Q1ZkRzZzhIOFBKRmVyMndrYnNTRiUyQkJhYnZWU2tVTGYwV2lQQjZxSUF2RktZZk1tSmVudGlVOGZvUzl3M3J5UDFSbElNTDJwdjhqYVRCeTFvbDl5RlpDWWxWdSUyRkZUR3JlMlBRNk8wWiUyRnZ1c2UlMkJBYkxFUGxpRUg4Zk1aTWR3ZjZ6S2Jta3p1SGZyS2poS1h1NFZ3TGhGNm90M3lqR3dqNTk5REhQbHoxclZ6bFN1d2VRTjMwcVpaWlklMkZWZFBHUG1KckdrT1olMkZoMnhaRjdtWE9tUjlaRW94UGREWXFzUHBJRkVlajYzazdnRVJEeEE3UzBtNmYxVjMwb2dBR0lGJTJCcTVnS2dkcjE5RnNTaWp2UXpzWmd2VlhjNWZxaHZuR3lzWW0lMkI5Nm1HR0pYWlpIJTJGN1hXb0wyJTJCUFZ2RFBhMFVoRyUyQnhrbkU2ZXh3YzdWd1pjZ0JmaCUyRnVpWUhKJTJCMlN2SkNuM3FpWDg5eUVZNGkxcjdUeFplanl6Q2tiYW9yJTJGZWslMkZoNiUyQkVrblVMUXM1OXBpeERneXdlZVMlMkZRcyUyRjdyV0FGJTJGTTFFZnEwelBTWTBmeiUyRmQlMkZFTkN3WU93NGRaQjclMkZCdWRMQVViVkQ0UkFaeFkzaklaZGJJQVJFbzNabk9UYUI3SWl1T2pjSHpZeENhWHhCODNLQ0JrZmRwS29vaWtzYXE1alBMWHljMzhQWDZ4TmFJbno4dERvazZIMHNKR1p0ZHQ3dHYwRlpkdWklMkZlZzB4ZEo0c3V5S3JqSlpCaW5DOFhoM2hRR2FldjglMkJtTEtUMFFBZjBkQ045UXlHVGtiRnI4SElXOTQ1TVdEVUxsWDAyWEZMdmJhQkV5cVI5dGRPUEVpSWlGbGtpYiUyRnJRc0pQVmgxbmNGMDJwbzlMUXglMkY1YXZ0RjdPUmJnaHFTNXZmSWx0eG9yNUVoMFpGbEo2dEpjQmpjUVVUZG9QTGglMkJpaTgzWSUyRkVOM2h2ZEJTNHZ6emZhYzZNdTB6U2ZxVndOODVFRllzRHIlMkYwd2txMmR5dHFRdnJZdzglMkJ1ZUZVJTJCQzgxSXJXSk5oWVJKbEUyMU5yNXRzQjZHZnFOV3Y3MWdYNzFPeHJZRm5nJTJGZlBIeFFyaHdNZm91U3Y1OVdiamF0bCUyRmZZTiUyRm5LTWZaRjhVQ3RMdXpFUXVQYWZrd0hpVlgyeFVhenNZUnZPaGZuQkswTzJkaE82bzhtN3NOMlZwZ0Vwa01YSiUyQjc1ZEFJYll2JTJCU2dPYjFuUiUyQmJ6UTBpekY0Q1NHMUlLamlXQzBibkZVRXlIR0xmRzN2aGdEcmN4dDVSUW54b2lvZEo1WFUwJTJCY3FFbVQxZWU3RDhrZ2ElMkZib2dGdGVmWGFpUFdFMnlJYUNReTNHcWJXd2llOUJwNXNLcXd1SEhQdnI1M01ybyUyQnY0M1ZmUnZvSE1CeSUyQmdiN0UlMkZyTXdHZ3lxQkFRZVdGeXc1UmNpbTJNZFRyV1clMkYlMkJmc0syQk9RSjA4JTJCSVd0ZHUlMkZPc3FlOXZSUXJmYVBYRlA3VHJ2U2p4UnBKNWZnYkFZWUY3cU44anolMkYlMkZvSmJ2UDRnQXlneVhVMnRiZkNaJTJGUFhmSUxxenpSWEFXWUhUYTZkSDRnWDlQQXNTYjcxY2lsbUowYTVUOW44T3BTN1BZTFVWQXVLWEtEZnhiMHlhM21SaHRRaU1NYjl6WEphOHBGMjhjYmslMkZpWWZoZUl1YzNLNW1pZmo2YVlzOWVkdkhnQlFVNUVCSTVocFJzMEFoSmZHOTdSNVh6aXo0U0Jud1p2ZmI0aDYyc21DJTJCd3NqTUpzbCUyRm9rbkdMazl4VnBGM0pBYmYybVdpRWd0STRXTkNYazliSGZGJTJCY3NJbyUyQnpTQkVLaW90SWpreDNic1JYNTB6bTRNdE5NZmZncndVVFpOSzliNFZaOVpoTWRCNlNDWmNlM1JOSWR5aGVTeVZ2d3d4aTViVmNtOENsV3JmbkhZJTJCQVc4VjhldVQzYURnT1JpMzdxWFlybEJka29hWXRaZGpQRU93JTJCc0ZnY0FMck4wSGhZNFB5OVkwWCUyQlVmQUtySHMlMkJEaGRxVE52UDdzSDgySTFhTnVGUXNzSCUyQnAxJTJGQjF5dUZPbWRTV0p4UFNiMkFrb24ybWpEY1k2ZWJ4aThMUmJkSGhRSEk0T2MlMkJicWhBYzlRWlQwR0FrYnBWRDloUWtZVzEzRDUlMkIwbjdDd3lwWjZDeWY2TmNiTkw4cVdIVnZUTDAlMkZ3azhJMHJ6RHdlbDJKVGJnWTZTOUs3Y2pBYmNGNFpRME43TTRPWDI0d1BZN2NtSEVpeGZnY2FPYUpOTTNSbEJWVTMlMkJtU2hNJTJGa1ZGRGFJbG1OZEYlMkJpd2NPeDQlMkZUbnhTVXM5YmJ3M2J3a1FaZEQzY1JldjliTFJFclZ2SnRLWU1tb1Nvd2NUV3NZS2FpdVF2enhyOU9KRjBiRFNmYnNzOXJhR3JONkdJbmFsV3BMRUJvU0ttNmhZOUZjTERTaWVvdkFUU2xlWEhveGJabkt4ZEN0cm9kb2lJN0lMVmdqZjcyMTlDS05leUdaWG9makxhVFF4dVpZUXBBbUlvdjRtcnI3YnNDTWJ2YmlGOThJWGs0VzglMkI4aW54OXBnQ2Y1SUVCUTQ2THVCRnlxYk9RTXR2SFhqZVNEcnYzJTJCR3VMOXM5QlRNM2x4em4lMkJZY25QZGZRelZNb2ZobFh6bFpoJTJGRXlsUmdTbFFmYjglMkZKaW95S3pkMjdmdVdGTlFqUEdBUUtOUVltVDdrNHpPb24lMkZ0YXdsMUFzTDFXZnFLMzExV1RsQTRMZ1ZmWUhOMzR3Q0lhSXlOJTJCdHdiaFdGVTNUcXJyVXpIeEw3ZnZJUDZ0JTJCWTVKSGN1STlrVXhibWklMkZQWFNlbnVGQzdmTWFDb1VuS1lzU1daaFN4NVhPSk1mcmF5OVZRYnRpMm0zJTJGUWhkQTZyMDMyUyUyQjZlaExFYWhTalpXemk2Yjg1VXZNYnJseXdQbTl5Q0dpSFpIRFppOXUlMkZ1TmlWM2lEODIySmhlNUVtM2NxcUVreUZ3RUtIWDVTV1o0amdUbCUyRjFiVEU5Y2JOeG9oU010bkJQVWdvQm14cEJIJTJGbzBxdHJFQU1EdFNYbG5KZVQlMkYlMkJUUGJNRXF1TmVqYU83Rkp6SFh2VSUyRmFtWkc1N1VPNURxM2lXSndPS21zVUlmR2VOelZCYWxnYzNxUlMlMkZHNjhvT0c0Z3pmc0xLZVNhS2lTQkZZTVlJU3lQd1FpZGN2WjBWYmFzMUdiaU9iMkVQNmw4M0VpdmlQdERvUXZ5V0FvSWswWTBHUGNWeCUyRllWTlFhZzJJTzh4TnRkbzExJTJCJTJGVzhYUHZQMUNER2k0S3RDVzlNbjRFM2RQaHRKU1JQcmQ1eUVuMG14RE12dGNYNWRtSmpYem1pSks1UXRQZE9jVURFbXNqdE1rZTRkdlNkJTJCMEpkczlsQXB2UklVJTJGNUpJUXYlMkJiZXZnU201bWI0WkczSDNDZFBydklOalM0S0NhRiUyRk1QUU9jWkJiRkxsS2hOQmtobzdjNk1nbHRQT2lYdXJrY0ZxSzFVJTJCJTJGUmphYzZoMlNCdzlBSmVFMG5Ld2YlMkI4UmczTlJmR1ZYa0E3MDcwcXJDYWM1R3I1ZXhKTU16SW1ndiUyQkhFNmxWM2tTYTRqdGVneG1RSUIlMkZTaWdSbXI1amVqeERsWG85blRrQkU1Tmg5WmhZWkRZbXZSeTk4S09MOGVwQm9LQ3ZDQW5DUlJPUTFCdGtQbEE5S0R5U0JDazFFWUkyTzdBSWk0c0c4VldldVF6cXlGa0V5WTVZQVZ0V2J6NjQzR2FDbGxkWiUyQlU0V3AlMkYwWGREdGkyUFRpQjJtMkdjMnR2dlk0SFVId25mdTlhUzY4cWxDMVBES09qckZFTlNaRFRGaUo3aTc2R09nbk9QblI5Y2tURzBtWkJqSjVoYXczR3FLYnpKSTFhZzh2TVFseTlOM093UXZPOWtuUHRaQjJRaXhqMiUyQlU1V3lYMHNOZE9nSWV3TDg4TG12YzM4Wm15NWV0cFVpT0hobVJ4SFdLWmlMeG9uY0QlMkIyTExsNE1BQXhsU0FwVFU3SHB5eUphdnM2U2l6WTlxNkdUR0tlU3VJbXYwSXF1WHFtbDF5TER0b1dSZlBzbmNTYkpkZnd6eFpQMm9kT0tRSHJ4cEpBMDdpWmdLMzdxdlI3WmJmbDFXVzQxRDBKdTlBTGdzS2duOWdEc2NWblhBSzBEZXlad2JZZkF2V3pjNyUyRkhKOWRlajdFZmlvSVVOUUslMkZGNXNZV1pWcHpzU0xCT1klMkY5bGhFaDJFT3Z1dU96diUyRmJTNzBnTjJBS3pDU0NqWndWeFZPQVF6OSUyRnVzV0R6SXBWJTJGQ1k5bnBEWWZhUXJGdWgxb1MwbjZScDJtMCUyRkUlMkIyQXI2OEpsSnNjWHBMWDFnWk0zVGJPUkFqUXVBcTJmeXpwOVJndiUyQmhrOSUyQlFmZVZPWlVlRXYyNDNvdjM3OUtmbENWYlVWeVU4ZldYT0RENm9ocExXMHFPQld5JTJCbDZTcnUlMkIlMkJOdFhicnlKamhiZ1d6d1gwJTJGMGFwWCUyRkhwc2I0TEI2cE5iNFFtelRFazJyTEt5VUpzdmp5M3FnUVVpbzh3dzM1dFd6OG14ekNWZDNOZ29CRUFxVTZPZWlhb0lpUk9rMU0zbzdraVRCM3pMaG9NOGRSbDMlMkYza1VFVlNTelgzT2dxYnA5S014M251c09yJTJCSnRkdlVNdnEyejZYZklodzRSN0duN1JFNCUyQnBOZUpmOTVZWGpJcFZjN0xueHJocmtPdmRXbHc2M2NWV3BjMmxJY0lOVFlyeHVyWTQ2U2hxS0l2Rk5QTTE0elFKQlJHOXBQWjNQRkM5U2ZObTE0VXJsYXpOOGdEWU8lMkZWZmVsb0VabXBrWFFTJTJGOVBrWnhXckRhNU9jYUk5ZXpxMzBWNmh3NiUyQjFUbGViY3BYMk16SE1aUDJMTHJheUR4Z0x6dEJXaHI0UFpDVmlyT3FsaGlLOHJqWE8xSVZyUWhJOWtTdFpNZ0pjbjRvUlJ5cER5JTJGZFVZSnJxbEo5a2xrdlNaRnRQbndUZVNDdzlkWmIlMkZhbXolMkZ0S0FtYnZCQ0RFOHhmcTRES3dxVEZWem81dUxBM28ydEQ1Z2thZWxyZlJBbUdyMjFMb2M1ZVpiSjl3bHllSk1tU1pEdlc5Q3o3Q2hQZkVXcjRxdzNQN2k5bk80JTJGYVJpNEFrU2YyYmJsdGNMZ0t5dzFQVDdHbUlUT1F2c2FpTHNpTFRLbllybmhJRXFTdTRWcCUyRm5hSmRpZzdSMWVONUFJTHN6QW9QVU5XdUZrNFo4ZkQ3R3huMkVDJTJCdTU2V0lPMGhnTlY1VHpIZUo3aklHTVElMkJUVktxWlJaMXRTJTJCeUZ1RVRRVGZqd0hvRXk1RXRkdnR2U3hPS1NrbEUwSVdaOEZhUWElMkZGWmhKTnNJN0VuaCUyQnglMkY5c3ZMVHF1eHdsU0xQQXNLZEFjVUxhZ1ptTVc5ek9UdHhRdkw2ZkhhZUtCbUpkVWFhZlVFcFFLZ1BQWkdheGczYkd3NFJ1bTEwdnpEdzI0Sk9JZHZMcVlYOEJCWFFiSWlucktJZ2dPREVNZlhWZFJrNTJlZXE4NjhFZEpvQVpuVDNUM1dRZ2JBRnpqYmF5aHY1aXl0aVZ1MjhMdmlHSnJXRzBIN3ZaTkxza2plUVBOaFgyYlJtaEglMkJPcFQlMkJQQTB3SVpPNmtDYU9xckxrNlZaaUE4dWJNZlAlMkJoS0w1MXZ2RWhndWlMa2dKYkRTM1QzNSUyRnlJbjQ5TVVDWUpONDhkR3ZRJTJCVDFSZmhNejNaeks1bW9oJTJGRFZSdzNzYXFadTAlMkJVSmpiVjJNaW5RYUpSOUxxWnRoSlp0JTJCZnJGWVZvTFR1NUlaaVVxRFJVanZ3Q0l3dnpHWG55VTZTSmVIdERmcmMzTTdTaThEWE5oVDhrajZrRDM0NzFyelFvOUlXTlZGYmZpRUZ1TVNhVXpDblFOa2pYUkRxNiUyRlhXanhBM1hpZmVVQngwYWtNZVV3aDBKcjVxeXQ4NUZ6Mk52aVNXMFFpWndyV0kyV3IxRUdaRWttQkllMjFTSmdmYURZWTA5d01UbzhwYTRWamRDTzdGc09KZFBkc3RhMWxMRGxvZ1lvdEl6ZFV0N2dVWFVPVzZrMEFtOXglMkJWbWx2YiUyRjZSJTJCdDBVc3pXTE1nJTJCcDhoTjhyVFlEQVhuN1ppOWtlTG1iMXVzQWtQcWNUVXFzYmg2VVg2ZjZzcXl5dlB3WFBEbFpkZDBRbWZieGdZJTJCZTdPTDVKOEw1MFo2JTJCbUNMSzUlMkZrWDhFMSUyQmhNa3BOdlFZUVdLd1Bzc2tDNkxvTU9iRk9vQWppaTZWd3o5eTJEOTIlMkI2VWR1YWElMkJQOGtTRmRPR0xiM2h5cWZ1eG5XTEJ5UERVNzVLYVNhb3d4SE95d1ZzU3JaVCUyRnBKbEY2dXE3TWlGa3owTHc0cCUyQlJhWHQ0TzU5UndQS3glMkZPRkolMkZJTFElMkZzUFpBZnlmUUNMSmVOOFpzZjc2eVVBOTBzZmFGM3FReSUyQnRhU25ybWhjd0RmbXZyd21UOUtMOWNsQVNQeVU2TVZUT0NiVlc2emV2ekZ0dHgwZ3BZRnFXSSUyQlAyUkV2ZHV0N0htbER5aHVueWpHWUU3TTdxYW15VmFKQ2VydmxoNlJ2NUJrV2QwZVEwY202a3lCMUJDVDN6bVVOJTJCRXdXWDZ2bjJES1BFWnhtcnZHeDRFTlJjTVBnUHJTS1BrRWs5WlhFYTFURlFmN0Z3YkRMQVlWWVNGa05lNlpicmc3V1UlMkJnTjNjRlJxYWdMcjNuY3dWbzl6dDZrYzNMbnJjUzhRbU9VbnE0cUJobTdLN0VCV0JvMiUyRkJPTXUyZUQxJTJGdGFFQlh4cTZGS3k2WDRlTW4lMkJxbXlXaFdxcjhrM1dTSlBwVUQlMkZyU3BEZHNDQSUyQm1IQyUyRng2V1djR0dTYnVKYiUyQnJzdVBmUXpCRzRQJTJCNW0yYUJQQUZmSXg1akolMkZzZ2x2QWZBREZkSWRtYzdyWjBPMzRYc243aFVlREFWQU9MeHNiOXZBQjE5SXglMkJDa20yQWVabDh4Q3JzWG1tTFFKcHFzeFpDODFlRUZpVk45OGVWUE1yYXhZcURQT3hVNU9YanlMQmNwdFVtc2RxR1JwazJGN0NSa3ZGR3BHQ1NZc0p0clNyUDJYR3VDMjF2T1NCJTJCS1ZGV1RSdWRTTFRQQ1M2anh1MFZTRHhObkxMMzJjUWRlaDRsUEFUbjZTcHlWT05TaWY0dDVkWkV1bzZ6YjVzNFpyZ2hjWWxwbEgwcUlxJTJGTXUwSXM0UThYeDNJVlozeDVSR1JFbGZzMzlYJTJGNEVZanh3aEcwMUIlMkJXSzZaMTBmb3p1SmFDbmN6SVRlNCUyRmVkZVJBelRkMjIzTDhrJTJCJTJCQjhZYkZxN3N5ZUdISXBOcFczJTJGTHVhV2x1d25GMzZqVXhFYlVRdGJLNFhhcllLbWRCWFNuSTlGZkh3U0VkdktRUDNiaUpzd3dzcFNTWVpLdTVFbXI0YyUyRmF6SnRzcjB5UVhHWUUyWUY0NHBDMnZrWVN6OXFyTlVDMWslMkJPamtwT1F0bDNWRWxCZVZMVFRlZXpCVzVlU1ZmUjh6NWpWJTJGN0diZE56VGpkeThOUUxIMFpLWHIyWmthbXpjWCUyQkJERmxyNFZqMUlIaTNOJTJCbTJHQWp2aTVuRTAyaURXWmZuZXpDQkY1TCUyQnVYTmFTUll5MTNoOWRJUEg2bXZRN2dxNDc3STUyWHlwaUJtTDBsTVN1THUxcCUyQnJWWEJZNE5YczRhdVdLd3RJejNnbmpkYzExWWRiRVFTenlMNGp2cWI5aHNjS3V5TXllQnk2WlQwJTJGTnY1MFdmeGZ0MyUyRlJIRVFSU1NzSnlvdVN6VmV5WDROV1JGaWhwZ2RWYUR5dVZkOEhmR2N3ZGxQZUgwY3lMcFNRVXRUM1lJbnI5VFNBNVA0U3FWWWl0dENoeFhzTlQ2MGJxMnVSYjBkYmo0WW80NnNEMDE5UzBMU1FNZlRjS0JLc0hxQkNLdjB0VTV6eFFXZWF4ZGVFS3AlMkJVSEhPMXhPUnZ3cHl6ciUyQkFlNlY1VENycG9lTDU3T1VvRUpwR2JONm5rcE54bzllM0RJZTU1bTRiUGlVRWtBNkx2dTU2anRyU2hSQWttMDJLazNvcWNQN1l3M0tUU1M2ZTlDcTBCY3kwN084Z094dDhrVnQ2U251M0NBWkhYbktKV1NsV015aXN4QlRKNHQ1YnNXTERVV2hhR1JTbXhzS00lMkJ6ZVpudFh6N0hTNmY5WXJZOVBRNEFNWGtxN2JGeGVHVGZxNTZ5NmJuWiUyRjdMeWJSZUdXeVFzNWNyUExxanRMVTNOQ1I5YjI1Skg1ejlncHhpUVNFRDRsYWZjMEE3cHVHQSUyQjVWQiUyQjFPY0Y0eDBTWlh6NGZQdzhBUTN4Qmd6YnVKWUVRSmg3S2JtaVh0aVEzZ09HVkRKUWNTTDdHOGlPd1pzNFdkdmpTcXpTclFVQkk5bDlsJTJCMCUyQlNqUmZmN29ySVc5QzVIZkdPYUVoWHdKaFNlJTJGcXYxWFFRWVZ6RXR3WUdnJTJCYVhxenBpWnVRVXlmM3BoWFY1T2RMNjRvJTJGQTJhMGloWGZkMjM2TzFtMUw0WTFLQklNNWNnbk9NWnR3UmVCOCUyQjVvZ2kyWm9FM1ZHT1RnNlN4Rk1yUVhXZWZmaHV5SmVrMlVyc041bzRDJTJCcDV1UFRsNGtMJTJCdEJoOElTQnd2Q3RxcXMxUHpza01UamNmUEJQeFZvaHA1OU9zYUg0c2ZjQlVmJTJGZWFIUjRiREZvc0YzMDFzUkFJd0FMOGxocFFvJTJCeDRKYmt6TloxQXp0UHJGZ1UlMkZWcE5JZ1BvVlMyQ1V0VCUyRlBHd09mNkZJYVh0YnolMkI0TEJ6N2pUbWtNWk5EbEhITEd6Uk50WWJXRWhGZ0pIMGNkdDglMkY2ZmtVbVZRdGFQJTJGZEElMkZPM1hqd1FKZVlJTXZPWSUyQk93WFBBRlN1MDlpOVg1bkl4VVdMREU4SDI4YURYbHZxYTNna3BBczNwOWViTW5tbTY1UkhMeFJtcXY1UmtSdGdhdTViU294WUtIWWQlMkJZbGlHYk9MRE1UUTVxOTN1cHFSOEFBTDNuYzZRS0NPJTJGR29nRTQ1Q056blNuZm5jbmFnYzN0N2gzV1FQUkRmaE9mJTJGellLSVNRJTJCSm1OSGRMN2t0WlNYN04wRGtoR2NzbjJoUksyMkpkbnVBSWR0OWtTeU5kMzN4UVdTZWZ0ZEJwRmJZUVp4dGx1MUZ5TmxxZnVCTCUyRlFtUHVHbnNKQjdYZ2x0M1NVSVVYV25HWFFIajFaR3doYnNjZnhIMDFYdFRJcHUwVmZDNVJKM2QlMkI1d2R5ams2USUyRjhQU2ZwWkpLZXFtcjRaTzIxdGthMW5sJTJCVXJHRGhRVGZqV0pFOE50clFRYkxSd0gxU2ZhYkwzJTJCOFQ3VHo5MDZEVkZkcVhISGt0UDFlMEsyN0dmUTJIb3hIMUpBRExhREZQd3VBZlMlMkZ6NVQzZiUyRmhtSSUyQmRDZmlDNXhxUFRnQTJMdExWT1ZvcXFsOGtzWWZzbExza0hKVGlWV0h5S2ZNaEhNdmJwJTJCVkslMkJrJTJCMyUyRk1BOVRMRmJuQVRhRlE3SGlFMXVVVWdOUGpMeWdYTUVIWmU1JTJGdXl4REgwd0R0OEJsQ1ZEenppTUh5NSUyRiUyQmRENElVdkM4MjUlMkJIbGlvNDF0MTM0VXNMVUJqWWZEdHhHN0xXMmM0WXFrNjhIeDBEUXlvdDVkc1ZpOGZQem5vTTFveGNLTFFhTXJmVkhPQnpYdWpNTlZMeWFhZ0dLY1pObjJ6RXRyN1JlSTAxOU1JTWFyN1VTR0xBcGhPRVV4M2tuJTJGQ3N5aTVIclo3czBlUG40dFlWMEpHWmpYYXYlMkJ3VTZrcFJPeFA0a0R2ZGVlUWtSWDVYMDFnVm9pbzhkV2FjOWEwQWhFWFhLN1VzUDRYSCUyQnA1NWlNazhqVXVheDk3TSUyQnFFUFZ2MVFKY3FmZU1RUjNuNzQyS1dFTGhUYXJDVkM3bUdkNjVHdjAwbDYxZTBzRXRQZXRScmFWZmRxU1AlMkJGRE9HQ1dwRjlES1ZBNUpSQUtMJTJCRlhmdmhUdWwlMkJSV1d1QUlESXU0WjNqdyUyRmhTbjUzMWdrJTJGTmE0UmZFZ3NpeSUyQko2bTNnRUw2bzEwNEh4YkNRakklMkZBcHFSVVFVVVFxZ0NneXpHZm9BRmlaZEV5a1IzQ3VNTnI0cTNBbTRwcUZBbVNxb1Q4NmolMkJIVEQlMkZyMHNtVlJsV3JMJTJCZkNNWEdWbndyWXJUMk1xRFRhQUtTODZtbHc3ajAlMkJKZ2U4SHpUJTJCOVpQWGRUYWlXNTdqcTJYWGhVV0ViVWprSXRiaFZUYiUyRmVnd0pKTTNuNjhuODllTUwwZ29WMjVoVkpVOFNsUWF0cUYwaTF4aUd1ciUyQjVTNk52bU94M1dSVGJBZVYwbjFNVCUyQlQlMkJuSGhDMnR2YWZ3TkZIaGNsdUlwY0ElMkZrT1FvV3FlRDVGSyUyQkpxTTFZU1o4amZsT2ZXUE5sdXMwQ0pTTVUlMkJ1czVvQyUyRmF3aWlNTzZDdSUyQm8lMkI0YWhvNHBMOW4yTzV4bCUyRiUyRndKWFlScW16WUN5a1VMRWF6TXg2MzBsY3BtZE91MUdOVE1OaHA2bkMyejc4dDZSRm8lMkZObHlXMmttRE5hZjY4bnAzSkxXcFo3Y3NrQTN5JTJGcGN6RUxqRlFqekFuUnQxOXJWc0VaQWw1ZFV0b3NFODlkaUJhYmNYckw4RFN0ZWptd3FERUhKT2VwU2FsdmpwZ3VwJTJCQUE5TyUyRjJPcVFIWGZ0WENWMkhzdGMlMkZOVkVKMk85TDZxbkZCR2RjY2RsbEdkVWIweWd1NWFSaXdqNyUyRnk2Y0wlMkIlMkZKMVBZJTJCbmhVMG5TNElPazY4czFuNWFrQ2txaVhjcmxnUFgxSFJXUnZZQzhkbjVtOEFBeGlDWHZNeHI2T3B3R2VqT0FMUkJMZVg5R3Q5TXVydmlzMDJaOHg0TVlLbE9TZTVQZ2pZJTJCUE9HNEo4VHJRWTZIM0RRQ2d2SnZrOGNwemZDJTJCQ2YlMkJUbnFvS2FFcm16aWdxSWUlMkJZY0RJQlRZJTJCYWlxSzRaVUN3VGxrZyUyRm16TlRaRkRQRlhHQk50MHpsWHdCeEltb2J5TDVwVzZNelQlMkY0V0NUTDYzMXN4RzloRExpc2NKeXVCbjAwTEdLNmd1RTdEQklkRmElMkZ0bkkzeFpOU1J3dWRTUnRKYnFmQjFrZ0twbURSaVVIcEhOUlpxeFpnT1g0UmNYQXA3TXROalpTNDh1UkdTYnolMkJSbHVreEExWXNNTkJUdlZOU2VXOU1WWjdMY3V4U3k4bDhCM1FPcEVyamhTOThUekVEZUhGbnZWdjlxTml0bDl3b2tTcDMxY3FodDZWamYwS2FtVkpZSVJmdlh6NEpmWTVaczZ5dm9WYndJaFpKcDQ4WTk5cUZLeDJVZUR3N3k1SkNGUnJ1b25SZjhIYzFzRXI3JTJGMUE1T1MwZWhacXBhbnJkT1pCMyUyQlZZYlFhb3dNNjE5U0NEUm5zNDRQVG1kMzdKeXB4M3U3VGZuTUtGNnlhRms4YXNNU29CZE1INXVheGhmbVp3b1RjSzlLUzYlMkZXUGFPUmZjRCUyRjhxOXFwMDYlMkI0WHVRVGJOeHZTaWt3ejlDWEI5d09wUkkwV2RJdmM1MzA2emRBRG1td3hRWXpuQ0FTeEl3cUhtRFJtUVNreUhFeTd6V0x3QzQ0OW0yQkladFdNYWNhWVEzTTExVGFGSDUxREJiUjNlQjhUZnNkNVRac1YyOFhiSWptY0g1ME96MWV3Nk5pbDB1YmtLS09VbmpONHZ2OXd5QSUyQmFMZG9DTEYzTFMlMkZiTTRFRFhJbG00amE0dTd6bnRxenVhUXlVViUyRlFkczNVU3dZU2M2ZHdlYTkzNXdab3VyMjU2bExEc2hqU1BxdzBTOTdXMnJnM2s2cXYwY3N5V3czY1pVVHFieVNwNDZ5ZDhlbmxxWE9QdElOYTZkaDQ1dXVaOTR4Y3V2N2tzalJMdGJ1b2pZN1olMkJFNk8yNnVVRzJmTkdXTXF1dkdHVG1RbyUyQkpObjhRT0k0RWpscXU5Y2pqWFgwaVZYcnY0TFRsV0VDUUw1eWZkbVQ1NTNFVVJXY3BhQ2ZqbHpvdklKdlRKWnhFdXAwRkMlMkJxVkNpQnkwdFZPYWphTVYyR0NyRUZRTlNWcmdBSDVZalJFMnVnNUFzc1VhcVpiTThPSTZqU25qM09hVGxJJTJCcTg1V1FyMm5sTWpYUzMyazk1YlpsaUJ4JTJGUUFXb2pTbnM4WTdqb0ZRRGd2N3B6bFNjWUtlMGVla3lXcHJvVk1kcTglMkIwVERlN3RuM3ZFOG5IRXBjZkF2bVJyeEtxS2ZtRkp1OVJLcGNrZzlFZnVTeVdKS2c4SVpnJTJGNUdCVjZWNXNrcTFsYWhmaE5hbDRSR1U1ZGxyZFg1R0hMNWxJZlAlMkJCV29idGNsRXMlMkZUMUh5QzJKNGRhV3QzJTJGdGRQTnQ3OXFhM1NWV1Bzd2RqUU9DQnFGbjlGOExPMzkwdncxTEM0OWNPR0JGaUxtQXNYNGhzNE1vbW5TWEZYSkVTdXMlMkZkUUp3U0k5ZDFrOHAlMkY0NEw5cUQ1Um1TYjljRW1ORW5NUk41ZHh0bmF0bFlqSW1UMGRveHNjUVNrWXpuQmxuRjdGdnJtRHhaYXc3ZzJSVnFLenpYUXVwN0lTd1FLWDRpVEVxdEklMkY1N2xLY1hWd2ZFWEIzczliTHAydSUyQmQ1Z2NiajElMkZkZXdGNGJ1TTRlcTE0U2cwRkdLMFNZcldWdUlDZ1Rwam81a1l5YzFSWkczcUN6MVk5dE1hRWRBanJpVjBuR1VNeG1zdGhLJTJCZ2pkOXU4eFpXVXhQd01YUlNYcWxuTjlsT2U2Rmt5blZmYml1OUdLZEhWdDAwTkpRUm1NZGlEamNzRjFQWXBOdkxrSzlOejVZcDZpTFNIdm5SM0pkMXRSNzZrakdhQk5wWSUyRiUyQnNoajRBTEpqMDJ5VlhwWkhLczRwcDJnQTB2RjFCd3Jza2I4a2xHM1VGJTJGdmRvTGVuUzBReXpESzJaV3NQM1Z2UkU1WHlVSGU1Zm80Mm9SZTRuckowWTc4bHFnZmZISzViVGxDRFlvbm5wUXUxSWNsdXNwYm5LSDRtY09EQTJxMm1IUndUb0ZGaW10Z2hqSkp4VHdyTkZMOEJtelpaMnQ5VDR4TTZNcGFnR3cyU0xwOWVxdU5OUkRncTlJMk9XNGQ1Mmtrc3VkV2lsJTJGanUzM2h1M3VHUlJZWUtoQnlFaCUyQmlkNzglMkZtQ3Z5SFMlMkZ0cEZpaURDZThWazR4VUVQeXRZMjI5a0t3N0FhOWhXYlRNM0d2Ynp0TjNhJTJCbUN1Z1d0VXZqVkg1eGtNZ3pLTG1kaUVWNVpjQk5xM0ZJVWJSYU9ZVXclMkZUNno1Z3I1cnRNRyUyQkV0RXVYblV6WW9ENG81VWpIZVYwTGJnMHJkaGRtdGhGTEZ4SFNyekU1MzhtZFptanVjeiUyQjdFcGFLNjBQdE8xNXpzT3pPQiUyRmRnTXNZNlhMdHd5YjJKVmkxM0slMkZZY0JXd0poT1VYWkcwZmpYRnF3Zzg5am5xdDE0STBXT0RkRE5HMXBFaGg4YmxyY3k5bml2V0xpdGhzWXIyRzhpSGFvejdqVHI4V2h2UVZCOWtwRThWcGw4SkxQJTJGQmV0UCUyQmZETDZBcmJoWk1LZDBuY0h2dCUyQk9kdDMycE50cDU1eVdXYlklMkI1dHFkeVVpajJpTmFybk5Ec2VvZVdvRiUyRmZlcGtOaHhJd25leUlpVU1GTHZDSWhlTTBKOTVmc2tnRXVreUolMkZnSEVZN0ZqbUpPZ0VFaWcwb0glMkJzVzBSd1RHMGI2OGU4ZVpsQUxFWCUyRjhxb1k2eWFGR284aHFCSTcyaSUyRlNGY0cwVXRCS2JqdElFUzYzZWFHallzS0hycTBteGM5a0FYd3AwZkphTkU5dFV6b2ZJd0lrUE96TG9ZZzAzUiUyRjRsWDd2TkRPWjZUNXNWRU9iUlZyNWExTzZrS1N1eHZ2UDE5U3Q5MHJHV2VLa05YbWZ1VWZjZEZGQUlCUGJLUnFuWkV6MXFiTUlmQiUyRmlSOXpEJTJGVEM5SkV1bSUyQlZGcVBwMnZkU2t6QUtBcXFuN0olMkJ2NEEwbFYwUDRNbjQ1TE1vWTNQRU9BejhNMktYNGNUcXBDQ0hwVXVTVGdIZ1lFb1ZZVEp4dGtxZnBtd1VEYiUyQk5ZcktKRiUyRmF4azVUd3RBVEclMkJwcWtiT2xUJTJCOUYlMkJIWmZ0UkMybFBTVXNoN2NRZ0lSWW1aZ3NRN3NmemIySjBvbkNYRDVLZWp1MFJGSXIzTkVvYnBsTUVKTjVyNG1WZVNmVnk3MGZRVnBHcWladndzdjRnbTNkNUJweVg2YW5UWGlMRVp4UlNrbCUyQnZHamFkVDk4NWtRczRiMWIyd09TaDJTN0ZrSG05RU9jJTJGR2xqWXVDYVNFYms5RzNwNWpMTFZVWDhENnBxRkZRWVN1amRWdVkwVGRCS091V2lOMjdrTVhRS1hYYk9ObVZmQldJSmVBeDE2V2RZRkFTOXRVNXBld2QwZHZROFBGRmpaTHVlbkh3SlI0bHczcTNkMmZ2V1VkdTlXd3A2ODZvZVhZaFJrTWdzV2hzJTJGbHJTa0h5alZESlFBJTJGV3dZbmRBWkhXQm43M0ZlUHV1NjdOU3NZMzFxazJSODNVSU9RV0k5ciUyQml2N0p6ekU0RXJNZm9yRG9KR1BLJTJCN3Z1dlBYQTFDYWVUMW5VNkJUOFBFVHNGWWR3WUQlMkY0bW5LYWtxN2ZjbFlHJTJGdWJ0WTdISiUyRlZDZW9TWFJsSWg2eXFXZ1h3TE8wWHNHcm5Mb3ZaOEtzQmk2eW1TUVc1d3p5SXZ3eVlyMmR6S1pza29pcjgwR1lMbFQlMkZQR2YwNG1LMVJGZU9oRHVNeHd6YVlCZGhqb3Q1JTJGNW9KYyUyQmNyaG5jZiUyQkYyNkQ5UGFZZnNaVGl5JTJCZUVoMzlXQ2JiUlZGWGFQQWgyWW9FR1J2OWZnODBaZmtlWmdLNkVOJTJGWVdLaiUyRmJLaWtiNWFpVlRTY2hwQlhEJTJCcWR3SlZvJTJGSlFQVCUyQjRtWmo0UnN2U1JaU1ZQVE84VHdCJTJGZ2JPQ25YeldPQjlaNXlma2hFSllUSkhsbWJuOGlwbWR0eDlPYTdoQktDblVCekJ1VGNlN0ZvSTZ3YklvVzduRHdoY1VQSWUlMkZLTXhkdlZRanIlMkZGdko1T3ElMkJldUFTTzV1eTJNMzZSVDFzQ0duUG4zSVFWOG1GN3hZcG4wb3NsQ2dOcmwxUDJtSnBUdHlRUVdFTDZmMFZUdXoxUFBQcXlvUE9HTTZ1Zk5XMjklMkJXOVdkUTdLdiUyQll6dE1ib1FQbUZYbXc5YXR5Uzgxd3ZsZWIzJTJCZjJHV1ZvViUyQnk0QXRJQWVSN21pdjg3SmdxckFxcWV2QVBJV3ZnbmhWVnI5cEMzdFcySVZCcVVXY2tLclNUOUxXN2QzQzd6N2k2JTJCbkladiUyRmgxaXlIRTJmNzY3Z2RpYXZsQXVodnIwUXdVczFWM0VUMEV4JTJCMFByRFI1SFZDY0lMdE4wRXJRc2gycG9PM1hoNlhiM1UxQnp0JTJGd2dReGtYN2dKV2FsN2RuZW5PVTFKJTJGRkJlUnVnSElQbVd6WTVmWmxXYkNwJTJCNjNraVNvbEs1RUVHMWxoUlJ2dDlEblRtWVFUR1dpWUsxJTJGUGtVcHlJbnE0MmpmNzdlMGNPT2dxdWglMkJFbnhZNldBMmw4VkZLJTJCeEg4VmJiMkd6N2U5U3RuYlRYOFRWcG9MWFZrMXElMkJxVzBTb3czJTJGU3pldWlaOXolMkZkTzlPYm1sdUV2NjNIRSUyRkxaY0RycVlzdzRNTkY2U3VxSVZGYWFqbXdMbDJCZGx5YTF3aWFmNFhjMzdnZFdaNjQ4STAzZlN2WGJLMXRNTFBHVDIlMkZmaVJ5V2FzN1NxbHk0M0RBTkwzNHh4Y0pXdVNnQjBvdDhKTGhTZGlSRGY4Qk5VZ2kzaUJ5VnJPUEZ4TGszRGEyUDdtNkJhUjluSWNpSmRFdEpLZGZvV2lkN0c2UjZ4JTJCNXY2OFd3ckJCT1ptczRZJTJGMDNrUUVWU1ZIV2dvckxqYmRUNEpwc1olMkZCOExZWnB1WXZnWVZMMHFJJTJCd2plRXp3N05KT0xHeUFnVDBXOWFtcHZSRnloOSUyQlRFWGs2M2N3RXZwZHF3ZjEzbUNaeGlKR3BZR2Z0MCUyRlRtclJXaDdaamtOQk5XJTJCcVpkQ2ZUNjBoeFpjM1A5ckhIMzMlMkJpM0dmSFdpY21heTJ2N2dGSzRPeXdrS29TTCUyRlZYeGVTTm9Icm5zdkswakV2VmQlMkYlMkI0SFEyY2pPdEFFMEdmTHdMRjRxV3BZbnk0VFcxcmpSY1BNVXFOTXFheUNIRUFGS1VZR1dXS2NFU2lvcSUyRmROWWd5SEg5aWxpMDJmMHkzeVlTbTdpVUhTZEpzWHNDOTRLNEw0eTNBQWZkRUFLQXplM0ElMkZ5QVRUU3lOa0ZEaTdNZCUyQmJPa01UYkRWN25ZeUwwamo1dVNKJTJCMEdidjdGOHZwQ1BiTEg1UGF4YURSNWlZbXBUZGlsMHVQTDFkams1ZEgyY3c5QkszU2psQU56UWxmZkRIdmg2JTJCdGppWDIwUjBlbWUlMkI4TFRCTGlyU0ZJdVExVVREVEVmU1Z3NURCaFp5eHh1WkJaYXAwSFBwVlBVYjJHbzBSQ3paMVc0ZExJcDRHbndTYUV2VnFIc0ZZNE45eXN3UWFMN1NweXcwYzdUaTJrZjJYcEM5QWRWNjJVSGdHRjhrOHpCeGZRJTJGRklNZlclMkZEbHlXM3U2YWV1Vjk2YkpPZXBqS1E5elM1UzE0RUtTd3E5VDhOc3prYmx4b0JsVXVhdDJUQUdYT0p6WmZMbCUyQm15c0ZmZk8lMkZRUmZHJTJGSFFBQjltJTJCaDdPMlB0dmQ3ZEU3TWdwUSUyRnhWNGU4S1Y5WW00b3BSRktWU0g1UG96R0VQT1NRUzZnZFdHNWtOUGZiVnlQJTJGQlhCVHI3NUhOJTJCcktlUjFucEIlMkJlMTY5QUVENTRSMGhQWExIZlQxektQWXl6blA1bVl1SmZ0S0oweUlsSGNjcGdZVEowMzZ1ZlAlMkZKZnZ2WHhpaFlVWjY1ZlZEJTJCWHc1d213c1I0RDBGWGR4QWZCTXA4dWhBbXM4ZnhtUVhvVm9mM3RRZGFBZ20yWElyOE9YN0pTcXJYeHo2dmFtdjhjbXhodTNEeEs0bTVia3diQjdSUlllTGxBNXZqVTg5UVJkNlJkNEdwJTJCYUVSQVBhWlNyYVVhSyUyQlNaODY1RHlsVDJrNlpSWVRKcjI0TyUyQjRFaUYyZUxra2xSdnRXOUN3b1VJMU53eGJMTSUyQkN6ZWEwJTJCTUM4SXV4aCUyRjBlaiUyQmt2MnFCVXklMkYxajNQNUUxOGRoR3BlejU2WDRiZmpHUnkxdnVmeWxEJTJGNGtqcDh5SGtaTW9nM3BIMWphQVBVRHJyY3lhb2VtY01FQU1zTlRTcDkwMG40eUw3RWxQb1NxZXJaeTBPZU92Szk1MEdodGdiNkw2T214VHFpZHdHJTJGRzAyUm8lMkI4ZWx2eDZPNzlpQlZhOG5TcHc3TmVrZzJuZTNQcnZkaGl6WDFMdGolMkJ3QlFFRDVMJTJCd3hkNUtJdDcwVEx1VU13QjBUVEc0THBqQyUyRkNvZWhYU0pxZU1xaU9xaUdoN01lcHpwaU5tZ3F2VXB6RlFpT1BzVHp4dlZwT1lEcVh1Tkt2V1NrYzhLQmF3NktxMFdCcjdRUHo2YXhEb2VRNktsTUx4V0t0THBuTmRBNGhkZ2RNRiUyRjh1ckhHRWxtODQ1Zmdiblp3RUVFVDBuZTR4dnUlMkZrcHg5SUdGJTJCdWV6QmRMVzBGSlBrUVpHNW5PMmVhbm94QUxoRCUyRldYM0Q1SnJla0lkMFl0SmJkRThEdEE3dUR6bXFmOVNENmVIbnZDJTJCVTdvdnI4WG9uJTJCcyUyQkVkbVMxdjEzTHBhVWNHd1hVZlh6JTJCVFU4VExtb09MYnFEVnNndGRPWTl0cjZ3azZzNTZubWVyZVdMb29GU3VnaWJtb3l1dE1PdVdVNjI0VUN6U29sWUZ1OW44RElqVThNaHMwJTJGblY4ajUwWUdvU0IwcjVnSHk1SlJkTEJ6SFNBS2xDZGRYUnV0ZUJPaFpKSkZZWWZKbkRDSGFrRiUyQkdvNlV4ZG9XUllyJTJGcm9FMzVmRU5yTmVPQnRmN2l4OGxDUzUxN016cG84cUl4MEY3Vkk1cnIwRUdNaHRFZ3RnclAySDhSU3hnMTcwc0NjdTJyOTUxJTJGQVFCbiUyRnhTVldZMEdOWTYlMkJyZSUyRjhLVDYzQWZvJTJCOFZUcjh4QzE0S0l2TUdDcUhRbzhxaWlicVAlMkYlMkJlaUQlMkJwVzNoUmNtRElNc3RacmtkanhoeEszd0dxSGdybVg0UnhsT2FzRlBBdG9EUzRhUHdIYWdtcWdTRTRxTFI5b3RlUm9VTFElMkZLWUozeTNYN1dRNVdFWEVkRXF1aThPWXJpYktoa2hqaDVBQUhZd1NzZUp5dkRyUTJaTDBXSEJkTHFLNTNEeTlQTDBLYVJkd3NNRmhSYnRNWUt4UlRjcFp1ODg4djR4ZCUyQm5rb1VKcHhObGhYbE02TWI2d0UyJTJCYjZoJTJGJTJCUDNscmZUMlRadnVXM01wTnFiMWZtMXNoTzBVUGgwSG1kdTBlclNuTFpZZzlWRHAlMkJzS0VNdTJwM0VxVWR2Yjc0eW1Qd3dJV1pFd0xmMUp6TE8lMkZ6ZTZkWiUyQjRKN0J3Qk9qTVFLMDJzUVclMkZVTXFmWEg4V2U0bjVJUSUyRmNJVmFtamQwbkZJd28zc3Rma0hrdzl3R1B6bHA2cUJMS3dTa1JINzZKbENNa2Z2M2p4Qk45RkRyR2NvTjlMUEdRMTFoa2VrNW42RnFxVHA2cTg4MWwxRmZNa2NyeGRBYmM3RCUyQnk5NE9oNlZIZnNLUFZUdng3R2VrZFA0QzliR2dQZmo0S216ZEduNkRnRkVPWjM3TFl4Z0pYZ2tHZFo1YnBOa2c3Tmg4ZktRJTJGczJTajJJSjc0ZmxJa0thTm1zdk15eE1zZG9NQ2VrNm9YSnF0dU5mSHduTWcyUjBLZ2FqWHA5aXVTYlJQZ0liVkZESlNnUTZvQXZyZCUyRjlMY0MxQUslMkI2eFJuTnRIVmZlc3lWU1lCblR0SjlTZk5VU29aMnNDcFBBdDdjRWpuRzlwNVA1TElUODVJb2N1OWR4ZU41Rlp1Yjd3WXAxdWltJTJGRFQ2YkpSb3UwYmhmS0Z1UnZyNUVqNWtwRnExcWJFUHgxak9oU2hjc3Q4dzhNSmZvV1g0TGxOSEpJRzFWOVlWZEszanVXTjJnOUUyVnElMkZtb0FteWdFZGpoekZhNVhqWmJNcTNSeTJ1NmR6OXVlOUZ6UmtoOTBWJTJCU0t2a212OHg5T1E2USUyQldSZjRTZHMlMkI5T1V2JTJGTEd1ZEFGdnBTSjhqS2R3NmlBblhyWGolMkZKMUZCRVJQM1NIVmpjTnVoM0ZQVkNIS2U3JTJGakNiM3hWN1NrRGdXWUszWnI4c0J0UzczWHFSaXpJU3BTY2tPM3RlWmRhaUhkOVlZR0VMc29pRTNmVzFmQW9lTSUyRk5YQllnZWtITnV3eEhFaEpJdXhxUTZHSCUyRjBvN0ZUeDhxeWlmRDRFTk1aWFhSazZJWDVhNFZlbE5Ka0ZuZFFINW1kYnVsbHJOenppSFdWaEoxaEZlTTJOV0hwNG9TRXFmcVVpUmRrRFRuVHJhRiUyRlhDSGRUSEIwUzFhJTJCJTJGTEF4S1A0RllHYTJXT2NFSkticCUyRjdReEFXVGdTc25Jbnh1TDJYaVltJTJGWXFRQk9aY3VKSXQ5WlQ5MGxCUXc3YWdNQnExNzRCYUZ1alhZSU9TSGo4U3pDc1VkVFc5N1JQN0lYMFM5V1l2MnBkRmYzV0xjZWo4MG9tN1VVVWZSNXk4MU9zemtsT1RtNzluTm96UDRVdFNIUnlrMWNtazhsMCUyQkQ2dERoaGZ4R3FSd2VYcjB4YlVySW9IbTN3d2N1Z3lEYUY5Z2ZLaVZhODllMW9SRzlMWlVwRnhFeGxjOXFzak5acXJMNDhWOHFId1hQdmZxelRuOE1CcUtWM0RTWENBTTJPUDhXWE9mMWZ1WDU0Y3ElMkJGSjNsUGxDdnJDWmIxQUdpTlJQUnVGVDZkNDdWRkZ5JTJGc1h1NyUyRm1zWnFJWHUyWDB0b24xNXVDWFZPY2NnZHBJdDB5dHZzcUpsQXdxWnJTSndwdzZpd09zJTJGTXNDVnl4JTJGcWUlMkZQT3klMkZ2SDlVUTZmZmc3NkE5SHZoR09haW1IR3R2RENLM3c0TDg4cmoxMSUyQlVnYVE3ZXdiTHN3SDJPbXN6NGRvUG1NeXd6amFnMG5sWTNxQkxYckpORVY0R1hyVHN2eDFiWmUwdHo3bHNOQ0hVNHQlMkZEcnBZUHd0d1lhYkslMkZsQzNac1NyMWZobllSVUd1VzBFVjRGa1V4Rk9WNE51Mkxta21yU0JqQjBzdVVpNmdIOHdreVpZbUFRNDFqd0NMUTVwdVk3dUNiY2V3S1IwUURuOHAlMkJuajlCMlNYM2hiSXNQa2tVa3pXR1pqaldsMTJISUZMTlFoZlZEOTRBRXdnNzYlMkZZY0lKU21IZmJoZTh5eWRKTUU4ancxTHUlMkZ5RFlKJTJCZzZCUmdkcHpKcnRjenclMkJyRlNHaHlvQiUyQkt4eHJNUGM1dzlwTGRTTU40MW1HWVZnVnZibFlZQ0VWYktTUkpFS0MlMkZpbzIxR3RuZGNhY0FnNXdCcE5WeEtQbk91YlFKUFl1WDF0SGRJRXd5dWRna3ZPTFNzOTdpdnd3b1NXTGw0WmIzT1hqT01IQTZpMEFNWnJYUnNzS3ZQMzV5WVlFUGV1JTJCWkhmdGNOZmh0YmhUWlolMkY0eTglMkI5VnUwWUYydFVlZGx3SUZzMXhjbGZtaVNKbHQlMkJCVSUyQkdGaEppb2NSTllWaDhReTRCM1A3NE5YUEVlQ1lKdTRaN2FXNnNxM0xvclBTYlFicmVQQXIzcXZvMmolMkJsYzkyN0JDQUFpclljMDQ2dXVDMGtQRVN5WU1WOUhQcWhjbzRUbHE0VXVwYU5sd3ZHVWc3YTRqcXdGN2RFUFU0MUR0aSUyQnVxZFVuJTJCZk1tdHdnYnBMQTY2ZmkzTnFOZVhKN0U5R0w2bGFxJTJCSFhUd2xUNXpnOEpxUjZDOHkwN0h1dHI5YTVmTlk0Ym9nckpiTG5nWXlKM1ltNExSM0ZpTGRCNGN2c21HaXR5eFpGMXI3U1RsVUhqaVl5d2F6SWQ5VnhSUUNMRlJFQTZzMVdPVjRLUFRQaFlZYjVvczZnaTNoJTJCVjh1clREb2tuTHVMbVVOZ3MzaGVpaktqa1dnSm1ROXM4WHppYjJrbHNzQXdpeUR6eFV4QUswaFRCQUZOYVRWcnpRUEFKbHhEWUk5RExCNHFmJTJCWGl5V3hHS05hOXJPVmxTMGp0dHlxSmt0YmVYemhIRmdVcTlMcUV3T2dDUWFMc0hyblkyUWt6M2hjZXNlbHZLRmklMkJFdFd6SUhCRDh3ZVElMkZXclU5UTYzeUpmaXpWSzZRWFdleGpxVzgwT21pbmlZbiUyRk1SWlNXV3RjZGZicVQlMkZieDJtcExmV3lZQTBhY3ZPZXRhSjYxUHJwZVc0NEElMkIxTWxrV01nMEpCaGlCckZXdTdrUXpMMU5kUTFQZHpiS1J0VXU0ciUyQmZ3azNrdHMzUkZjc3hiczB2SDlKWFZOVmcyUWc2YXJCOTR0VTlvdUJ6TEhlNEtCV3hSUHJOY3FjWnF3WUdhbVZGeFppc21taDdvbXM4TnBaaWtmRUtTMWNXa2Vib3BSNGQzN3NPYXdHUEZVVGJmcEFUcXZ4OHNtbHNOOHY4UHRHZE1rRDdQbkxJWkolMkJ6d1ZDWVg3Q05lSXFzdyUyRlhlQSUyQiUyRkNnN3pGbU5sYzdHaHhOSVZWWm4xZFY0WnNUNFB4MEdPajhWSEhYMDFFbHc2ZUxJU2dSY1ZTc083TDRLYXJyYkFLTXZZbkM3R2VMNE5xOWt4NzRxMnlUaHRza256JTJGTDdFQmQwNFhiUkRLMjFXcmg3JTJGNmhKZVglMkY4MUZXSmZjYmh5ZTdReFRuVFRGVDVmQm5MVWZJT1JtNHd3aXExUFNBSFQ5dzNVRGNTTkF4YUpUNDU4czY5RjNwQlhucTVjRlRhSmpkdE9VTEZ4a2hnM2RWV1Z1TGV4aERJOEpGR2FZNENHSWs1WE53YXJJZSUyRjJSQXdWakZEV2lPTXVwQWhYZ0VWUFdoZ09leVluJTJGNlhrZSUyRndnc2tDWmY2NW1YcFdXOWk5SWdCUTBhSWl4SmRLeWZ0a1ZXNVVYeDdGcHVJZE02V3pVYXBuOFJRdmNnU2RFSyUyQjJZOGczR0lMZllPJTJGd2pPQngzeVlFbFg2WFpnb2FkeXVYWVlYOFAlMkZoaVpOMllBZEw0cjFYQUZLN001M3VMZFkxNFQ2RkpZVzNydnBhOVJkbDFReVNLb2xFOEZaN0tTOURDJTJGT2tzQ0FESmgyVFltNkxjeWlYbU91SUZ3VlljYUdXUmU2RlFqdExhYWxUWGNUUjclMkI0M1NJcUtYWWpoc1pra25BcFNQNTZEN293UVVTNmF1UGslMkZRS0FtSk4xSzlJNUpWSFVqZjN1dSUyQmI4WFpSS25vUTVjNnpwRGR6NWRlYVNOVThpZzBiMmFFSHhNckpmQlZpTWpLSFlDSVdQbHIlMkJGWUFTTjg1RmVIdE94R05UMTdjM25EZWdLWXFyYklPTTB6UnFadkpHYjdwZEljbU05VzBVVGYycXZPVlFFZTlvM2R4VTYlMkZRcDB5VyUyRnRQaG9BUSUyQjglMkYwbjA0RlhKSGNmeHJVQWtXT3dxcDBwQmJ5WWJ2dTh0YjE1VGRpYWdTbjVzZ3BVdSUyRkVFY0xZUHl6MENkQ3R4TCUyRnVWam9XTHg3Q3BjJTJCU0s3Y2RRTmJiTkMlMkJXTFR6MXdOUWExbnE1ZXlhS3E5QVJJOFNpR1ZxVnBqYldDMmJhNGJnOUJLRXglMkJoZVdTUDZRejFBSDVFYmNyQyUyRnBzTVFLTWE2Ym1uYlBSTDhUWVQyWEolMkJmdVR6VVNld2Y4aEI5c1lLZVNWc2NwNGlkalBQQXRYVmxqRElORHo1MnBsTDNiajVOeXJDdW1aUXJ4VmxRODdwOGtNU1R1ViUyQmVaOGlwJTJGV1Z5OTA5eGVCZHZVdktuOUcybDJCRkUxdWZ1MUpDU0hBSzV1UmZwM2dMNWclMkJXOXJTcVBwTnY4NWVLa1pObiUyQmlPcTRVNlY4YmNjN0pCS3kzRFdvMCUyQjlpcjR5WkpIN1JYVWROOU5LWUlqdUgxd1VwaUREckk3WGEwUzV4c0NybDVGWFZsNjUwWHowU1VNeXJNYjFYczA5Vko3R2VZUHVIN1dqTm9ZT3RLSjBjNnAlMkJCbGI5c3pqa3FyZVBLbHFmRWJ5TmVGdlR0d1NuY2NrNEFjUW5sSEhXTVA4cUZqS1drcyUyRm9YUEpXeldNYkwySWNSWG5IeU9JVXVXY3hhJTJGaWdxbXNZM2lHTzg5Z0RtQVBxSkw5bGVoV2FvNklYZjdtME11ak15TzdQTTZPV0lVQk9jQjZSSGdwSlVRdkZ5UnNWc3VLT3I3OUJkNCUyRmZQbkdXamxQVjFMUWlvb3dJblloaWY0aElUaTl3eThIcVB0N01INW5OeUdMb3BoNlJLZk1PMkxYZVR0VUhRM0syajFleDJ0eUE2RVFLdmpCZHNqWkUlMkZONlhNRFZQcUozclhnSTVNVlNXT3Q4Q0h3a0I5M1F2eGtVMUNFTnh3cEslMkI1JTJCSVJJcERXY0VzZjlnYXQ3NVdmd255NjB0ckNYNUJwZ1VOYWpYcDFycFVkTXc5Sk1PbEpXMCUyRlpqZzdWQ2tjWkV6ZkZ6am9VM2MwUVNMSzVVblRFOWJSJTJCcWZpaGZUTXcyVDBqNHJxRzgyRWU2RnVKOHV2WW1GR0kzdFcwNFV6aGVCQyUyRjdDdm9EZWVqOEVhaGNDbjUzek1BcnBYSTI2ZndGJTJGRlhYJTJGTzJNb1Z0Q1VMM1hQV3NyQ2xTbDNrb2xNMFJPRzM1Q1kzbXRtTzBEOW9JZ25ZZ3hqd3R0bHlmUDlnNUZPdzl0clFxcXJwJTJCQ3d2QlNCSTVYb3U1bDNWbzVPYWRYQ2h5amJLZVFSRVN0bGJGSEgyNEcyVkVDVUZrSFlVRCUyRnlkYWxhUlgyOXdxUjFkbGNySk5PNWJ6NUwlMkZ6bEFFeHl1UlRUS2JRMVhxJTJGRXAzaUtlZEZKTzZ3OVNUdmJFaUowU0RxQWkxa0tPVkhoODFNNiUyRnF0VGNkUms0TUE2TEg5R2sydERtRzdhd25WbiUyQktqT25YU2FDTVB2TkdjblBSR1ZTQTIlMkJiU3dhMUYlMkJHOW5hRTJUUjR3MkN5bkdTWHglMkZhN2xMQWhyYmlFZDl4OGhTQ1YlMkZQVHlVY3VNZGRXdHF6ZlViNEc5Mmw4STNzVkdFRXlXMG9SWVF1YU5UaWJza2hBNUtibUdxb3hpVHV1YkwwMGFVT3JIcHRiSmUlMkIyR2NzbWRQaEJyQyUyRjZwOTVjJTJCS2U3ZWolMkI5ZlVNY2p6ZndqRFQlMkYxUm9RR2slMkJNYm5jTUFDQVJXam5RdFJKeXlsa0hXZDA5ZUFSb0xTTnJQa0ZYVmlnYTN1YmpYUjZwOCUyQkNxdEcwZWw4bzZwdEloaUxvNUxxbE1TSVBIMXZON3MlMkJuZ0dBWlVpekoxaVlDSzZKUExhT0I0Z1J3ck5wJTJCUHQ5SmVlYXAydmd6QlJPVVcwczJmUjklMkJmeXRKNGR0ZzBVeFB2WDRwNUV1eGptVFZScTRjV2hIdHVVbmNvRXVIM1hWZDVCdVkwa3FzSjdYczFRb2ExZCUyQnNzc1U3NUk2R2NhUmU0JTJCeXljejRzMjFnRTFpZmowbWJDRDVTdzhicFklMkJtblZVNGxURk5nMUJYNkJSU1VvczklMkJoRTh1cG8lMkZmMDFnWHdsd2cwdCUyQlJpYkl6bEE1Z1FwdURFZyUyQjlhc29MY3hsT3ljZ0lxQ1glMkJlVW5qblRpcEdmYzdXVHFmYkxSRnRRcmJEUzk3MTRGNEI0WmVXMG1UVFptSnE5YjJyZCUyRk02N2oxbmd2bjB6JTJCUmFoeXl5YnpKSW5vTGxZQXNQeXJDdURnUFY0dk5QUVBST1hCdFA4bUlhWGI2WjVPT3dPOExCemZ4a2FEN0R5dlJmdVljeHBtWlVUUyUyQmJaMWl3RHl5eXV4QWVRJTJCRnJMb0laRmhxUFhwek1CMWdaS3Q1cXVQVGFmR2JSWmx6TzNIUXMzSnZBaEI2ZElxJTJGQTNFMHMlMkZaUUtVMGgzT1FNdnVHZDFaMWVPM2VsSUs2U0RQU2M5Z0clMkZYNkZJQ2JLYXpkdzVJdlVCJTJGTk1lS2c3dk9wZnlUT2p4cmVqZUt6M2JIcE50b3BpNlZVdnJaOWU5cSUyQkdMMG50JTJGT0licWhwRkdwOFBqdlNNY0F2JTJCUlBHOWp1MUI5ZFE5ODBuVExSVXRqWHFETG5aOXp5JTJGVWtvcU5LamklMkJBekglMkZCT0ZYemNURlZCWW5nSHBMMDg2aDRERWdzVlFVMHlYeDN1Vjhpc2U3UFFWWUlpQTVIWUEzanVBNDIlMkJjdjdBblRWSVFEUCUyRlhIZWl2JTJCb3cyTEFTdVolMkJEMERZaFJsbFJJQmc3aHh2RHZTU3NtcXVtRUZJdjh0RFRZS0klMkJmUml4STl0S2JZMElLSnhOUlFzWVZHRkRCZU5KUU5MMUhjeUtaeFpsSTdGeGVVSG0lMkZCU2xxbVVObU5VRzh1RnZ1elkzUmdDZXNYciUyRmh0QXBQQnJGbDNEU0hWMGxQbG15aFQ2ZXFCJTJGeUpFajB3SUExUk9iRU92V21KdFBndnZGcnoyeHdleHc0dWtibEJnUDlEayUyQm42RnphMVd2MTRhcGhaSnpIYWllc2FyR2xyRGo0RCUyRnVwb0NaeE84bkI5JTJCTGh4amtiR3FYOVdpU0QlMkZwZUJWNVZoRzRVRFk4b2d4VnJDM1hrZ2Fic21JUWpvbzNLV3ZuTEwlMkI4dkRqYXJMS2RLbnkxWndiN3drdThmWVk4Nkh0TnFjQmlXYjZQYVpLajhoOGVLZSUyRnh2cjhNcjdTRmYlMkZZeGlSbmhDZmsxVDdKWEFOd1glMkJyQiUyQkV1ZTlmT09QVGxVdk1uejFlJTJCODFhY2FwSzdPdlo0V0Ewa3h0UzVMNXNqa2V5WkpxQmwlMkJUU0YlMkZQOHh2OFk0N09FZGMlMkJkd3prd0piUWRVVTV2aGFpemRFQ2lKdXdWWGdtM0lHbFhjMHRBc1JSMFBxWlhIelRXd1BIYWQyb1VrTWVvN3RhcGZkOW9oNVYlMkZSMTJtT3BxJTJCeENLOVlzUGNSZU4lMkZTV3hoZGJmYmhGN2dYJTJGZTFoZWJTNnp1WTJmamZQSEozd0FPNjVGY0xlVjQ2QW1HMCUyQm1XWFBCJTJGT3c3Um5yNGs0WVF2Z3ZGTnUlMkZzYlNySyUyQnZGUE51cFZrdUZRTkZFZHlRRHBXVWtpNEtmejRZb0ozMWYyMTJwMVNRb2lYYmRyWjN1a3hmaFZhYVRsMXljTjBQMGhHdDJPcyUyQnd0RDBRRXJKOWVPc042UldIbyUyQmElMkJBcm0yM2RzY1c1Q2U2ZUM5dUtFZVp6dnB6eWlJWUVJbUtnMTd2MGZQOTNic2E0WDhxSjhhU2VuNnpEekFvanduMjN4biUyQk9LNmZSM3lOMkRsRHQlMkJ5Zmc2WlZuQUF2RTJXMm53UGZjJTJGbnNmN3dmJTJGdjhFbzRLellrTG9PbW9UNDJiSzhDYVFLUUpLWXRIMkIyYUllSWxncU5aS2VGajRHUWU1Y2dRd1BsV0NQQmJxOFpMdnZ2d1MxbDM2MnhyOXpiOTJVZVh4cDgxd0h5YVVUVzNKM1BpOExBQkkxMyUyQld1NkZGSCUyQlhxVUpFWHJLOEptZE55eSUyRkFib1ppckI3emtVRmZrVzh5anRzclQ0JTJGdXNhaWolMkZIZVRrdVRtN0YwVFRVQjlYcmFOcGQ4WVJoMXo3aURhZ3NQMldSVlFGQkFKc1VySldKeDQ2aTRDaFVxcGp2M1phODVhU041JTJGaXF6M21FaSUyRms2JTJCTmhFU1Brdk9sWVd3czdKOTZ1OXJVVDFlUk0yV254TkFYc3lEMzJaSyUyRjZLaDVvUXkzN2swWnFHUzlrZ3VVNHNFVzhMNllOMXlmQ3UlMkZHRVQ2SDlueXNudHIzN0liaXpwaFVGS1Z3QVBHMFI2Y011OWRxMmJxOEpzeE56UURvOGElMkZ5eGlSMTFCWmUlMkJZcUZOSjIlMkJPRFFBYkFEMmpkUnFxSyUyQjJWdmx3Rm5CelJxUnpwSzVVc3BMRjFlSmJuclpTSG1LbjYyYzA2MGYxcFFPclEySXBNVlpjMnhZUGpLNUtuWjRvaEZKaGtkOCUyQlRVb1AzT0JGS01YUVhMVGw0bnFicUxQdWRWZXhwY0h0ZVhIQ0g1ZXFjYm9UeERndG8lMkY3dkFUMDNoWW90MmIyVTV4dE4wTWo0UTJFNFlPajI3dEI0eVRMTU5oMFFkekxQMWxYbE05MmhlczZyTjZuUmQlMkYyeDZKb1RXbEhFb2h6NGpjeFNXOUYlMkJWTG10WjczSzhjQ0tmbTNQNDBHTnRWRkVRMHBIN29JOEFtVkpJZmpxQTZETlZXQmoyVTRvdSUyQlN6NEZYZkJwVjIyeHZwNHY0SmZUOHdYcnUweiUyRk84YTh2MUNrWXM3ZGRJejJ2d1I3Uk9hNm8ydXpjdnFtJTJGcEJrN3FEdEklMkJsRlUyZExycGZUTHdKQVpISndIRXFvV3VFb2VqejluMTNnWDFCT2xldWFCRVFzTWVGT00wJTJCZVpWdmw1OWJqdWp4b045cmxWTkxOR0xGRDhDelo3c0dVeTZlUlVvazJtd08lMkZJV0puVWdMV2twR1YlMkJjNGFOd0xhNTVRYlp2bEJ2MDl1ZkNGckVzNDZwM29QUDJhTlFSN2pURzZ2SkVKMHpxSldZOFI0QkVyVlZlS24zNVFjbXEzcVVYU2I4bmQ5TVcySG5YOEVKNE1QNDIwblJMM0k0QjNvUnB3TDdwQWpXQ0M1M2pudTQlMkZKZFRYaFRxMk0lMkI4T0lFUFR4WTlIRVBsJTJCeVJYdTRPVFFxJTJCZnNKJTJGOHl5THFnSVFXdGNVbzAwc1NDTUNPJTJCNCUyQmVGNEFNM0RTMzROOTJZcnUxcVVVWmh2cXkyS3hmSjNERyUyQlF1Z2U0dzNPcnkzMmxIcFdya00yME1vam5manFORkYzSE50TENVd0IwY3pkViUyQmpoYlh0V2FMMmNic2xwZGtDJTJCJTJGUUxxR0NaSUllVTFCWiUyQjFrRUJma2JtTE5KYVZ3eERwJTJCbWMlMkZlaENySDhSN2owbklPcGdpSkZlOVoySmpMJTJGQWdPNVVibGFjWVg0dUZQUUppZm9qQzFBaDJkNiUyRjd2elhIdk5IVSUyRmV3ZzNiZDZ3Q0wzMUFpMmZjeGFVZE9XYkJJa3FqRFhSa3c5emRWOVJ1SFY4YUdJSCUyRlVHeFlzdyUyRiUyQklMWU5SZHpJWlJSeHp3aE01dnN1bGY2ajFqamlyblcwQkFuMzFleXdRJTJCblhWJTJGNmR4Y1JXOUFRJTJGUUFYVnR6MUZrZXRWSUF1bG1tcGdGSnlnNWFWakdYd0NObzlRU3VtQXA1NnoxdEMwWTJQSXI3JTJCVWNrQXFiMyUyRnpyNE4lMkJJekliRnAzV3ZzWllVZ29tZ0ZlM0tJeXFRbnNVdVF5NWx6VEh3Z29CMG9rR3AzSHR3eGpJTW5OODJBVk5CZEFyYWV5Y3p1OHV2NDN1QktpWjJLMko4YVlwRTVZJTJCVEljc2JmbTVobHY5RXB1VmNjeVQ0cG5KdEtpQnBodSUyRiUyRkNQeXB2Z1AlMkZDTDNYUHp0WjFQU1Y1WWhqNVYyTDJWTG1pMklEb0dIOWhUckVIdlh3bm5uUUVaVU5Jbmg0RFpwbDl6QU9TZG0lMkZiNXZnWElGbDhnNTh1TmpiVmJMWVBBU0ZTJTJGaG5BUks3MHJpVjhLa3hQTTV1TnN3VVFCelZIa1lhMzl4cFpjMGthQWhhMlBtYTFKUDJIWDhxRnY4VVk1c3M0byUyQjR6NFRqUXNjbjlHb0xqMDZhTUJzS2xMQ2lIZEI0dFlmUzltUUd5RmQ5NEh6dG8lMkJ6UGNuUUcwMWJsMExyM2I2RnY3RkZkMWpZTGpwZkxPRTVXVzg2Q0kzaXNPemdqdk5hRnJJQmpmU3ZqZmJFaDY1VmRMd0hoQzByOURranprWTNJc2h2T3ZKQzJwbmV5S3pxdG15WGc4amxpbll0JTJCV29UUHNxcktkUmdVU1dnZlBIVm9UQmY5aGRLSlladklhYiUyRlJHZkVyWGdubmc3JTJCNHJhODclMkJvJTJCJTJCY0diZTFPdWFlWjBvTU53JTJCN3Q3ZUl6cFVaTW9ZQW1Yb2x5enowT1NISzdNTVVsSmF6JTJGdjkxNENMJTJGZGhEMUlLWDl2a1ElMkZ1aGk3NlRZTVVweDNDajVEZHZOdmR6MUY5UUdvdHE1S1VhMHZEeXc1d3Exc1JOQXdSZkUwdHllbTBqWThaelpXVXNlVlVKYThGeWdnNUV1MDRPYkx0dXZoRHFTM3MlMkZkWU9oUG5XRWxtNDlYN3A3M1hyTlElMkZXaGhDUlFWVnZEbDRyYW5HdGd1ZFN6TWZ3M095RHVQdEFoNk5SJTJGdkdwckdzOTlQRXFJRmtFRlRmJTJCWFRFejBlMWU1clJMUEQxNVFlRCUyRjdKZlVLQzdaQTdDTE8xaEk3NEtvSkxDeXFJUUh0RExsJTJGcExUb3FZbFdYQzhHb0R6RlY1RlM0a1dyQTFneHZVRG9aUEtZdnpKV3FXUEVtaDZ3RzVuUTN2dWN1dUIzeHV3YnZRVSUyRkFSd01Nb3dGbjhXSlFjOVdlWSUyQkNRUlJ4T2luSFkzJTJCTWdWeVk1Ymo4JTJGcWtyZHY0MmtzRWhwV2xhY0wxM3FQTjEyS1B0am5Ic1pud0VLNFA1NHBYOGlEcEQwaSUyRm0ycDRwODhqa3czd0NPVjQ2Sjh4WiUyQnFaRjUzc25FWVJmZ2w0Y0RLMUk4Q1VkSkUxcENlRTE2VnUlMkZzQmNTNzAwVXExbUZJdGxGM0EyYzJIU0FlWVNSYk1IV2dMQ0tzJTJCT2thN09XV1l3JTJGQnFac2EzVE1QRmpBdWlBc0diUEM1V0NPYWNhQmY5dDJET0s2MjA5Zk1VOVFOWjlCU2RPWTlVTXZqQUtEZnZuUUslMkI5TlcwNSUyRm9pbE1jVW45WTNVTjZPRVl6REtjU1NlWWdRbnMzalVWZFgwbU9ZVVF5RnVncDRRJTJGdzlZcFF6UFJIbjZMeDMwb2hLZFR0cmtzbUlFS3czY05GQWFNcVZvZTlXeFVMRXZ6U0I4dTljWXdPSlpBNmI5R21BTE9JU0g0MHkwU1ljUlgwUVJSSWwyOHQza2sydVo4emJmUmNxQnhNUE5WUCUyQnE4cFNTRWhmNTFXWCUyQkYxQzZqYkllTlpDNXlmcUpwWnl3Skk3eWk1JTJCOXNCcFJuWVpmWjBCZWJzYnNMd285OHJCRnpjakdEMlN4N25pUVFNYW1LSVRwVmRlTFhxZDNXaXpUQnRCQUdQR0UxbGtJdXlpd2FPV1Q4S1VHZ01BdiUyQjZ4RUk5MFAzUyUyRktCY2J4QyUyQnJYNEcydVhTclUzWXpQWXZNJTJCSzgwM21OeDNHTnk1MllEeHhXTVcxcUthUjZlRGNtc3VCMFExN2ZhdTg5UERGZU1PTExvMGVTZDVsSnE1NXo5M05vdWdPTWtLZDM5aHIzeVFESEtoa1JkTGl6R0Z4Vnd5d2E0b2h1bGxTU1Rkc1FSamxmVCUyQlVoSHc0eGZDeWgxTGNJWjc0UXIlMkI5bVcwZjluMHE5a3Y0aFZNaXh5Rk1yOFBzdVBmZiUyQjAxQ1VRJTJGc1phQXJDb1N3TUxHYSUyRkxINGhuM1d4dkhOdyUyRjRiUUJUalBORE1PSCUyRiUyQjJ1UFhiY09mNDRRbTI2Zm5wJTJCMDl6SWlEa3NmZjNBRGRHREFjT3h5OTJkSDBGJTJCZlJ4R3pTYTQ1WFpMaHBTTk9NSmhZZ3FZRVhpclZwdk8lMkIlMkJFUzF3WCUyRlhCamJVdVUlMkJtT3FRQiUyQkFvWDZwVzNDYUdXbHdxUXZsMVAybTZZVmxjb1ZZckJEU1p2WGNlOTYxQk5MME5WSCUyRjVkZHVhbVIlMkJDdkhuZnpZRHhvWGRWSlIzNVhCR0NYOVZ1YktZN0lZZmtMOE1TdU1PNkVUUVglMkZnbEwwSGcxakdJejZ1Z21abU5qOFJWaXVDTlJqcWdvVHlkcjNxMTdKJTJGRlVqWlR1YjYlMkJvSyUyQjRuR294R2d6YVNJOHE4VVpWJTJCNjAxbWNOUkVZN1ZyeExMbUI5OG9IaUdCT0lMQ1d2ckw5SnJiNHVwd05nMEwlMkJYb2VWYW90JTJGQ0dCNHdjOXRyVlhiZ0J6QTU3cTdjNmN1JTJGS2lCM1VBZG9CQ1UlMkJFYkFQcnZJd0g4TlhYeDE0JTJCYm5MOEQwbSUyQmdIMHVyJTJCZFglMkYlMkZiUDRUSHpXbWglMkJMJTJGQjlqNGllcUhsTyUyRm5vaCUyRmd5d2xCMXgwUDNOeVRtTlRJa2drbmdaNSUyQlpUUnVYMGdRV3FXOEhVZXlTMUc0JTJGWEpWeUlOaUNIJTJCOGltV284QzBVa3lzS0NyYm9KbVolMkZsT1pERFVEb2ZYeHglMkZIOUhZdmttYWZBMjJCSlF3N0RkOVVIeXpsRzV6Y1BpYmR6RWdQZVFiYlpweEJVUyUyRnJ1dmR1SXZ2dmtTWlVlZFBBU3N4b0FIUExNN2phNlBtYjclMkYlMkIzdWR6MFN2cVp3VlVJb0JTWXNXOE1JZjg4UDN6OFdtdiUyQkZWN3VmWmxpRmJPU25BMTZvZVo3NUxZOGNvUm5GREZ2Q1lldENYbEF1NEpFZWY0MlBqNmkwTGVCc1kzSHFlQmx2Wk92dThlRSUyRlBzYTM5TTdtdnZQeFdzenpSTlgyRXMlMkZRcnVRejdFZW5mRGFVVTY5RExmbTdPdkRWJTJCb1I1TDVYbFJFV2wzJTJCSlF4JTJGZnR2Zm1NS0lUY09KOVl5cEpkNG9XN0VOY2R0VjVNck5jZlRralNmRFIxJTJGamFKZzBWa2E1SFBNZnpoWlVOQ0tDZzJvJTJCUU9HZGxxZTVDdDY1c1NPWnRybWdYNVhveVBhWEljWmZnbmdFTGNqRDR1enBYVmRpRFpod0dtaFJjU2tQMVlUOGkxZEM4eUV2eW95cFNGanEzJTJGMGRFZHBTNUJBcTRYVjhlUHZiSE96VDc0UjBYcCUyRnU0VCUyQlM4MlUlMkJGNDJoODEweUs1WmczY3RtUUpmUG1yM2Y2bmNrSVgwMURVWnB6YXR0bE4lMkY4UlBKemdDamJEWDE1Vk81JTJCd00ybEczWkYlMkJWSVB6VnJEbjNubkV1NGNBTXlTU2dUdERVRkRMT1dodCUyRnNJSzhBRUJ6MGQ2NDhXbkQ2ODEzYVhxMzlweW9MeGVjU1ltUWNTaUg0VWhvbFBWYnJNVWN1eWRyT0xsMW12elYlMkJIUG5zS2ZsanBUZjUzM3ElMkZQU2ZjdFVqdjVaJTJGSVhUa2lpNjkzeGhTcjlLOXVaV09VU0lYSVNsUWMlMkI5OGRNa2RaTFB4enV1RjBqTVUyYkc2bVhzcm9MOGNWeE8xWm1YNktGRjZVRENwVEpRaVlKaEhiaUlxJTJGWmNaS1NURjNDa205QURwdVhQOHpvaTg2R0U1YkY2SGdBdGZSeGkxTXlNMSUyQk82YUFIRVVzZkQlMkI4MWtwdVQ3NEIxdiUyQjYlMkZ1TTFxYlI4UHpvMm9obkYwZjBXNGR4dm44RnJOMDZ5RXJsdUdzT0t0JTJCeFVyNmhmRG5DT3oxZHVrUlFEMEpvUmJKWUFlY1BxQng0aXRJVDcyJTJGN0sxRW83NXRYU1NOaHlnZTZaYTl5Qm96R3VEYjM1M2NYdzdMQzhiNzNBRlc3NTZzZmRWa0owWnd4JTJGZGd1eTcwdHVPbmswQmNhWVR2aDRKMks5MHY5a2F1NHFUa25rJTJGUjZwZzQ3em5nJTJGUkV5d0FvTE5pRm9IeSUyRkFuVmdvTnFuWG42eG9vR1d2T1E3R1dISFFrM0Q2eUlwemE4eFpXJTJGbFBqYUttWk9KVk40YjdJNVZRYW02MDNsSVBEbVcwTjA5JTJCNTdZbTVUTzkxbklJTERpR2I5aTZ1ZExuNzRpTGFCNjEzQ3ptTGsyVUlsOE5aQUNxOHFqWHZwNHlyUTNabE5hUmdTakxUUkRONnYlMkZKZXhweGNwaVhUOHZBdVdabjllbDZIc1FvMHpCR2VTdVZMMXZjcENYM20wbFdzb1A1emw0VlY2eng2TTNqMXdvVTdTc3VPY2VTNXl6QkN2ZiUyRkp1JTJCZ1R5UGh3ZUFQNFQ5cFlub1RGZ2RweDVzSSUyRjI0TWVqc2VqdGwwJTJCJTJGOUV6dUdpTjIlMkJLUzdDNmJDV0RjYXczTTl0anlxQ3JsNDNRZDNEOENsajQ4dG5NVHJmd2xHTExYR2ZQYSUyRlpRZjNXZVFubmdBJTJGRDV6RmJmJTJCJTJGMzNYRmN0WTVmTE83clFldTh0MmJuYyUyQjFwcU4xdzU5d21uUVZrRmZDcnM2RkUwNnFsMndPOEdjVllyczQza1N3c00xR2tjdjlCalI3cVdVTThMdGVKanRidm1tbTh3cWI2eTNQRk8xNld3WU0wYWE5VzV3czJmaDRMTEYzeTFEU040YXpQWXR2dFZUOVB0ZTRKRkx6M3BKTUl0YTElMkZmbEoyJTJCbkFFU3ZwbklCaHElMkZlZGpaTDk4NmI4NVR3SUlmbVZlJTJCaUFqY3lWWGhuSDNsTkR6SFZOczc3c2prUlZjN3UlMkJrblR1d3FNVyUyRnNKZUhiVDBmaGt5JTJGSkpTbk9RWSUyRjVYZE5yOXR2VW5xR3cxb1ZwVWdXcVdiSEprJTJCVWRrWVJZZVBQYmtIU3E3RkJmNmVDU2Yyb3UzR3c4bkpZaWhWTHlqZnVtUmNBV3F0UklCbW1mSE5MSlhrUyUyRk5VMjgwOUc4dlRFSUlYNExzbmRlMHBvVmt6bkVTM1dNdnclMkJleDFhbkxzVVhHaU5WenJGdE1NM1AyZmNYRHRVVlVBdnZwV1hwSUF3JTJCeE1MJTJCNiUyQkRpWEY5eCUyRmIlMkI2OVhkaTFqNTN0djkyZkg4T2NtQTI2a2NWRTBZb0J3bXQ3cVlJdmVPbUJtOU5NOW5hWXhISlg3c1NDbk9hdTNMd3VmSVVOS0c1VzlpS1BYaDlqSGtGaW5WR0NnYnFXRkNLbXZaSXo5UUNKayUyQkVIUyUyQmpyZnVvam9VbVpYcmNFbE1HZnZNR0hxTTRRYmc0WGJnN2paZ2dlaXpvZjdxSW5MbTEwckQyTzV3Nkp4RkJGUmRjNDhmU3BrMW1tVDV0RGklMkZMMGFOa2x3d2x4aWxYbklNNnJ1aWpqMDA4MmRIRHN2cDNJJTJCVEVFZnlQNWF1WTBsT0pRaiUyQkVuN2dpUGZlYzhNTjNnJTJGMjZ4JTJCOWVpZEZLS1JkYU1wa1psVlhyZkp1NmFCZ1JoWkhjUjclMkJCTzYzSmNrTHEzaWg0Mks5NjQzbFVYWERGUTd5ZnZHSUVReFd4YiUyQk9WZnltclY5dlNvbHJ6ZWZ2aWRvbFFSampVRW44cHFvVjVBeGtrQlRtTHdNMXltMGc4SkVGSmcxckt5OG5hbG1JTW41VjFSc0xPa25yTHl6eXlPSlB6aEg2QTNUdm05SURlayUyRk1FWlRjclM5OU00SlFKeDJRT3VJMU1vNE11Y0xzYyUyRnJYMHBYRW5jeFNrcERNdHFzSzdTdDFvQ3pFUEdTZEFWSzhUQkNxNENKRFJZcDQ2JTJCSzFKeHF5R1REMXg3bHdvdEt2JTJCOWNvbkRXaUlsVkdNQUd3ZDdzREp4QyUyQkJ6b1lLZkhROE8zQk9WQXh0eUtvbnhkUjNkTEZQSkR6NWhFWkU5Wm5QSjl3NzBqVU9QNHVmbElPUHR2TUhMaUIlMkJhVXpwWkZRR1dqallGdFJqUlQ0ajN6Qm4lMkI1RXF0NU9oUFdkVjRvYVFCMHFEJTJGdExoJTJCWnYyR3lyJTJCS2JRZ0xsZUtqRXJibHE0SHZOeW5HbWFkTlhtY05uV3U4OHVFOGlaJTJGODJqbjlPeXBBdE5sVmxmRzlNVmxoMXkxJTJCNTg5NmdZaGElMkZWTUxzczdERVdFcEpvWHUlMkJYTk43WW9ZRkFCa1prQ2YlMkI4WGtvSlp5MmlBQTVma1BTbTJQZkpIaDgyUE1FVzU2M2xWZGYwWDFJRDFKZE5GSFNLUnA4V3MxOVVwWDJYblBSbHkxQlhuYlY3TTh3NXZWJTJCeHJ3Y1Z6R1BBTnZrQ3JCUGJTUjV1MG5JJTJCTiUyQjQ3NDVlb0QwQiUyQnB3MDE5RGl6ZzRLRCUyQlJzTFlVNHlUV1VxaG5JazZGUkViV3Blb0NYJTJCbmpnOWJKYyUyRjk4MlFieHJGYkFFbDZRSG9BZ3VTSXJCZnpDVEd3cTJkcXZwd25XYmFPRTFmRnBBUU12VEJpZ3RzaEJJSzFJQnIlMkY5QWdHbVB3U2kyZjdRM0F4ZU5iaSUyQmU2ZHhHJTJCTm4xJTJGUVFZOFF4dWVWUUlkdkNCdkdpYWNzazZXdHl4T2RQVjRjTCUyQkhiMjA1UUx4SjRhJTJCdCUyQmhvNGxLY1ludkt3TUNCOVNpM1pTTzNJQldqNDR1QWFiTkdHR1phVExpREFTQWI1c1hDOGxWRmRNcDVjdXBHaW04MlhFZVdvOFFqdnQ4V2FYZEJhU0p4WlBFNjcydE1KMnpHSXRweGNIdUsxQkoxUm52cnM5OWIlMkJmYk42JTJCJTJGN1NuekYzYVZ0JTJGT0tOVGV1YjlZRVNKdlY0WFFkOHhrJTJGaWZDQTMxcElTSFM0ZGZicXRDSHlYek9sZk5IMFpZQ3RWJTJCcmhIY0Z2dkJLUnhCU3BpWHhmaDEwRiUyQlZxM2hrNGp3JTJCcE11cXFCV3JmT3NPSXc4OTJkeXFHZ0pINmVtcThFTTI0RSUyQjlRcFdydW1Wa1ZNNVJGbSUyRldyVVRIb25OQ1BXMmFsbWxHY1hqQkp5c0dkczZBVnFkTVh6Sk5lajFQOFFuZnVyOHFEUGpUZXk2NXgyTjNOdnhna3BuJTJCZGpheCUyQkVLa0xLaUREZnZNU1pQSk9LJTJCM2dJSnQ4aFJSQ04lMkZJd2dYUTZMQWVZZlpxQWRraTl1QkpnSDRvM0FJRyUyRnRrJTJGSTdmenNmNlo0UDJjWHNoJTJCRVBLd1VpSm9iWjRlZEpucFdUam1CdSUyQlZKcWFocXJPJTJGZkk1TjIyT2hMeTVyR2w1USUyQmFOdiUyRkdEY3ZRME0xSjBoJTJCUXVKamtvTDlEblR3YURJRWVuYWF2emc2dkVMcUUzc2hQcklWNmxBUmtMSWMzVm03eTBqNnFtWFZaajA0VWk2a09tUyUyRmdiMEFBcU44WlgyZkdtWFE2OGpsWnNhWTdxbDR6cVRFcXRjd3E5NzQxeUJ3NXFDNnhpMXZnbDBiY29WaGFURVJ5Z09ZanBqT2pibjB1d1hGd0NoMHYxNmdINTl4JTJCZHJ1NThkWDd5R0Z1TW5wMVBYbWUyWiUyQnd6V2xFekdpZyUyRkVqZTdhWnFhRU1hZCUyQkMyYkolMkZlMUElMkJWazRKSFVuOW5kZjRLWjdxWktQN1dQelRIJTJCUGgwJTJCSWtpd3lXbzlYUWJ5SkRDallNV3dXWSUyRldieTdTVzVYdUoyVGpGQ0x6UmxiNmwlMkZTaks0WU02STY1MHR4RXFqcHpwOSUyQmlFQ2V5emlxeUklMkJoaE5RZzFkZW1NZmVoR2lsUVZxc2tZOVB0RW1GUXhoJTJCMVlNMWVIMWNLWjNma3dZOElUTGpLcHNCeTJEZ3NJMUVsZ2d4ZHdkbU16aU5HOGNZUG1tTjQ4blNJaEg3dnJCYU1MeFJZaGZMbVo1WFR4R3JCZUNwcEFCRFhYa3NOeUxIOU05Ym5WYlJhbDdINUprbzI2aE9jM3N6RzNjdG82b25IU2xWVWN4QnZWbVp4MjZLdnJGZWVObzJiaHNDSHdnZzJzaEZlSUJwYzd0a29WVGFDYiUyRllsN3BSNUZZN0d2ZGI0N05FWVdFS3JqTkJ6UlZ3eHd6dXR6WDFNbkVtSUNKTXpnM05WJTJCb0RYVVBlSFF6V0tuQ3IlMkJrblIwbnExTVRJZ01QM1JjalFYaGRXcUl5clBEJTJCUmFkWExMdm9FMldJS1pvSUxqa3dXYWcwNVQ5NzNjMk9tbWZKWmthdm44JTJGdlJxR3FnZWRLMlpwOW5BJTJGS3plc2FtMWp5U2JWODZqOHklMkJqRHJIQ2d0SHYlMkYxOVJaM2NuSkpMNGhWJTJCY1Vvck5Icjc4b2NpNlY3QTFzemNWWEhKMFA3ZDlkOU1tNSUyQlpIWXNWJTJGcHVMZHQ5N0NtZjBqZTdJMU45JTJCYmhlUkZNbzRCVHNhZ2RvblVublBlNkxnSndTeEZnVXhFVSUyQkdjTjMxODdZbkI4ampaWXl3eHFtMGZPYnVZZERUUiUyRnhhbWVBS2pwU3c0ZEk1dUR0a01yekw0VHBzMjJ5Qzc4MFRKMW93eWdHRnZPaWU2UUJUdVNlMGVMOXhLaHJ3TkRqYTBLUVolMkZnWGY0aUolMkJCTllMJTJGT1Y3ejJ4MWN3dzMxUWNla2FLdHRNYlBmMlBTc1M3MlJOeE5aJTJCMzBZYXhKUnB1MjBCa0ROZ1RDa3RBMUZFZkZxRXlTJTJGSkx5MTlwUHhSYVZxemhobWgxNFAlMkZYVGZ2aWJtMWUzTGVqekYwYVdIT1dLNGY5YXFBbUIyNiUyQlNiZEFTMm5nZ0NMaTJNTGszUVJQeiUyRk5UWllNellXWHdTRGNUQW1JbDB3aEdJcm1qJTJCbHYwNFlqSk5rTndoJTJCdSUyRnNkTk43OHJLZlU2U3ZpS3RvRHo0cGZ5TklwQVlhcnN6ckNsTjJVTlclMkZmRTdyNlRYeTB5JTJCRjlwaU1QT3pMZ2cySTljTWJCbmNvZjBMJTJGM20zb2lSbzNpQm5xb1ZtV0RMZnNGWHBWMyUyQm9OSjB3NnBvaVlNQW1wSFB6V1VPajNpUXF4YnIzd0VneDlGNEFwYW93aHhVRVV0b1hpJTJGRkxER2R5ZlZtcVR2JTJCaXRjRms4aG5rWDlaWGdTJTJGZEM0Q3VxYThkRUxrUHBtMVE5RU1hUGFjOWdWUmlxeVo4JTJGTHAxMUlsVmMzd3AlMkZHVU1hejZRcWVuVktUbVplVkRNazNiaTllR0pZOTFIMWNwODRHZWRmbzJsJTJGWVpRc0RmbXNRV21NVUJhRHhqbHlSJTJGUkRxJTJCUGxLcUFKWXVRTWF1eFQlMkJhTTRkSjQ4ejhvQzdtUkZMRWpLb3dmMEhVSXIlMkZhYlFEdkdpSlVrYUglMkZIR25vJTJGN0hWVXhIRkhaalMlMkY4Y1hzNkg4YWExQWh5JTJGSzF1RzFGaXR4R3Y5ZjBTOEJ4WiUyQkVKYzJ1Rk1MY1YlMkJLd0gyam9GM3pKM3NqY2FyQmtzTGRMU0N1SzA5WG56UzYlMkJIOUYlMkJXR1FUamUlMkZQTjN2VFNzblNjOUklMkJWbmQxUk8zcm1sZ09iOEI0UTdjMDE1eW9KRVB4MzklMkZzSSUyRkRtazYwUXp6Rnc2dFN5TTdpQjNEbkhWZVNiZVRkdk42JTJCVjAlMkJ4d05LVGthT2NJdWg3U0hpdGNHUWkyNkZlOSUyQiUyRmxocll3JTJCR0x6aGNud0VhcG5udEV6RVN6NTM0TVlrUE1yWGtkYzN1a1ZJU3lYa3lTUzRQaWl3SmFpSWlBQmdpZFVidzlvaVVaM0JZWWt5MWdJNUp6NlIzZ1pOaldkcVUyd3pyRlRKdGNsb2RHRjRUOWhMWk1leCUyRnFZM1gwSnNpaThFWGdsMk9IYWlKZ0RJbHFQUXRSWVUlMkZmcFRueCUyRmpVTGUlMkJKdnpGSko0QkMlMkJxSGJMdElDUEwlMkZaZE1oMnRkQ2ZQYk5ZbUpOc0RQVTNDUW51cUlJZHd6NnklMkJlVjJIYmpsNEUxT21wSDFhTUlIRXY0ZGRRUTNhNUh4biUyQlFtdUdPbHhMd295RW9iVUtObXVEVVpaTUtjVTdpJTJCOVN2MFptZXJ2S09aQ1c1VDVzQ2thcXJWMG1EQkFlUk9nUktjTzFVOSUyRnpjdUFUUFRFSzhVcDQ3R1RpaGQlMkZvciUyQlZMeCUyQjZVeFVsZncwdnB5RWpCWHU5JTJGczRQNU1OcWJnS1pUR095ZXVPSkRHQ0V6TzgxdEolMkZuM0tEZlF3dzBsWCUyRnBQZWViM3Z0eHJlbDc1UDhHWmw0VUFBMFBGNk5ITkJpJTJGRkQ3YU9UbTlPQ1dWendvbWFiaksxNjY3WE1hN082OHVwRCUyRng1bSUyRkJlN2ZUSDZPQTZ2Rnk3cGxwVkM3QU9iWkw1N0tjV3R1V2Q4JTJCUk5jUGhTaGd2OCUyQjRZZXNKOE5rSTN0QTBhVlNXUTd5anZHNkRBSHpUVVdISWhkS0JqSWZEcHBkNU9QakFXOXVmTTNMSCUyRnBMbzYwZEF6eGRYb3U5YTIzQWN4Q1lIeEE5cFNKY1NvJTJCYnAzSTFyMDBwenZzQXlBYmR0ZlZkNXE3T1pTT1JQNnlWak5qS01jRkdZSiUyQjcwRGdXcVdkeFlka0tZRWwyWmtEY21ETDdQVlclMkY1eGZJZ3BTOVdRaHhkYnVWa3IlMkY3NE80WmElMkZ0dFc3QzdvZERGJTJCeVV2b3JaQ0dobFlmRzg0MWVlJTJGWkc0VU4lMkZFJTJCekp5b2lTbHlET3dmVlFzMU5PVERSJTJCRkNLaGRHWmdmVW1RcTQ2UmolMkY0NXFpeEt0aTJ4NVJzUlQzT2dTNEpLTWRXcHFUc2VsTiUyRlhRMnEzdG81dGRiM3F4aXBwUXN3dkIzTnViJTJCRmF4SWdGTkZ1N2Y4SmFybzI2VkMyczB1bk9FQTdPSk00NlltJTJCYiUyRmROb1h2bDRacmVTUXFtSERtd3p1R3l4aTloTHMzTThWNUxJdzNoeEdkY0pzd1U3OWMyTzFybFQxb0cyMU8zUDZnOGFZSlljbEU4SmNxbk15JTJGbzBxRzQlMkJoUWIlMkJRaTR0bXJCNiUyRmNyUnk4ckx0aEhkeSUyQlVPQmx5cFNRaU8ybGZsZHBNbVc5RTA2JTJCSHFNOExMbTBzdiUyRnR3RVo0bHhLYXRFbnMxVllSa3JDRiUyQjIlMkJHQWoyR2FQSm5xZU1XSUtrRHJPMzBCcDM1eDFWSFF5ZjJJNElBS3JHa1RNJTJCbUVKUzJsdzNuWHZHRUUlMkZkb2xpd2hFbmUxNyUyQjZmNzZCZ25IQ1RhN0V4TzFQVnI0ZmJFY0NaMkEwNmE2dFMlMkZieFROSWVTMG1TWnRSRW4lMkYydGJhamdQd1NBYUtodjVrS0dOMDF3VWM4blpsV2V1T3FURHUlMkJmRWIwcHV1Rkp6NnR0NzhBMlZlVjJOZFJDcDFLUDIzVnBoVmNnRSUyRnZYTlBtNnFxcU9BUnpvcUZoNGtYb1pQSDYlMkZpRHI1TFF2dXZUeUhodGUlMkZHRktiRWdYMW5tRHc0WHpSJTJGa2RpUGZ3YzVtM2ZwbVZJMHQ4JTJGN3BCWHg2ZlQlMkJRc2lmZ0V1M2pETyUyRkJsWEtxMTJDOERCTk1nU1ByMVJLR2VjZXhOSHlzSlJKYmlTOWJZV05yeGhyYlFoNjNqOENWJTJGdWZScFdwSGM5UGFoRzJxOHp3JTJCRzBlQ2NkUTNwb1NRVHRsODl1M1Jpb3JOJTJCMjRBbHBkaUZXM0VxTGhPUDA2NlYxJTJGb2p6Vk5OWjNEVWdIdHVBdHpmVERMVVZLTFMlMkJzQVZHMnQlMkZLSjFKd3lLN2ozajlXOWg0aHFKckFyR3ZrWE83dnM0Ymh6R29qRnZUUHVNZWNRdW1kYnNtdlA2R0NRWXB1VHgyQXNtNDBRQzRaJTJGRUJERnNPa1BmN2NmSFJNQ1I0b1JYMzJYZHVIUzhhdGZwMmo3TUlWJTJGbVlab3p2RWNDRVJFbWhmclBZaVNZOWZmdUxldm1GTjd0MnpDQmdDZDFBWEZYRXF1MFQ5eGZkZEVHVHNFbnB4NElnVWVvSzM4TFdzOExYamhQaE45dlglMkJCSCUyRmR2TiUyQkdtMk00U1lqNTFsWCUyRkNzRWRRcHEyOFlPYTNORDBwWjhNbkNzRXpSRkx2MjV2Ymx3MjUyU1R2UVZOQUZteGZDUGxPMDhleFY4ZDBWMyUyRjBiRyUyQnhPQ2ZzR0VNUWUxYmNpU0RoYzBJRzFSWTU1TyUyRmxUaTFVcTFsbENrWTBpMWFrOTZOZGszSGpHcEUxM2NMWTVhWkN0bm1XdDJVWDlsbmZOSUVHeHlKdlZ4MHRrdGc0Ulp2NG5rT01TWXFFTnJKMXMlMkZoOFJmM0lrNkZKQm9HY0hhWTZuVHdmJTJCQ1A4VWlRZGdUNmpRSnQ4elhWM29ZNVBldHNwSmtlTjJEQ2hKQm9VZFJUZk00TTg2eGhVSHNvN1IlMkJPSkY5eTBQWWl6JTJCalolMkJqcmd4S2ZhM0hrcyUyRm9vRXNzR3ZqalFoNjRzMVBCVVpYakEwYlo3eDVjZWxncloyQ2JPZFFmaCUyQkxTWmd0VVZKWWx6JTJGSiUyRlNDVjhpZURSdkdtaFpmQko2U3F5MWFXYTAxSGN2ZWxxeGclMkJVSG8yd3h3U0h6eHc4bzFvcmdxeSUyRnNvWFRtNjc4YjF1eDJXRlQ4azMzeU44dlN0MUtFNjZOWkZBS3ZpVTBBZkdGc243dHREZmhCWW5wUHhPTU5WViUyRjZ0QzVPYlJnaExvRFM5bWQlMkJRdWhHbjBxSEJ1cCUyQlhQSVBBRThPV0lMSlhKVlkySFpGSkg0RWt4TG9UbmtKa3RiMnREcDJMaTMzS1NhdSUyRlRqdEVTNSUyRnBPRjdacVRTT09yTmE5OGxjY29NQlR0SHlVcUw4U1JkWGx1bVlRMzNHVTE3NFh5TFIlMkZSYWR0ckhJNmxjVWhuNmR2d1BpNXB3WFRNOSUyRk5LTyUyQmZEckxFNTQ2WkdKVHlicVlySjlQYWg3S2lCMDdmYnNHYlRyaFBUNVdHbnhRb0RQRHFqQ3JwQ2ZiOHVlbFR4ayUyQldDbm1vclFVTlclMkZKT0hEc1dPJTJCcG84cVBwSnFydVJLbDkzZnRrWnVUdld0N3BFSEMyQm1aaWFIeE52UnFhd1c2NTdFaXFoT0hlaUF2aE01WkR2eFJWbVhvWlh4bmNsYVh1TzJsdVEyaXlNdEt4aGFuVDg0dzlRMW9WY1BMSmt2bHhxdmdLVUJZeW5rc1NpTGhqenB0RnlrUiUyRkZLa0k0dk5YUlN6TkpUcndxaDcycTREdnglMkIlMkZ0VjB0R1dnTE1LM2dMam9zd2NrV2pKTmduSmZjaFVaRHgxQW1HRzFVczBMZUZIZGclMkJBTlZ3S0JSYURyWW1nTFVSOERIdjZyOW1IYiUyQiUyRlN5SDNwd2FNblNleUprRVJYQmJMMmtET0FvRHVycFolMkJYcyUyRmtWYmYlMkYlMkIlMkJ0T0hrMkpxbDV2ZTQ3cnpPQlAxWnN3RzNjZXh2NHpXUTdZJTJGNmxmZlN2ZUdkVzJpRkJVS0hKREhVWUNrdVdXQzYlMkZjRnEyZm8lMkJGZU9zWVU2ZWtEelV6eUZkMHJWbHdINGo5aHVvQ3dDS0pHSiUyRjB2SkJYJTJGZnA0M1lvTnFaUGpLS0RiMldUU1NzWTViSHAyNVJpZkIzY1pGcnJSdzJtcmc1aFklMkZyc3lKYlZnSGVVUEJxeXlHTWltMDFhQ3I2am1TbmNNNkkyMThXRnRzUEZrJTJCVSUyRjNCa0pZVnMlMkI3R0dEY2R0SVRRQWNhWldnNHd6YXlCOVIyMUtveFIlMkZmRWhsVzN4MWxYMUZ3RmFVUFMlMkJuQ3loc0pRa2RXJTJGSExZTEhsNGlYZFoyb0dHM0ZRU1JnOW56RjRRV2VETkdtWVBpbWZNYlZRY1RQSzJoJTJCOTFNWGszUmh6NW83bCUyQlozZHRoYWtXaGVtcXlseCUyRjBBbHZzNkdZYmpJSXRITFJ1Q1M3c3FESGZzVzVNVmxUcjVHQk1waUM4dGl2SDJtJTJGR3J5VSUyQmx1VzhrSDJsSVU5M1RMejAlMkY4WGNtRk02WGhyUlFXVkp3dmJVOUJNcXBwb3BLZ1dVSDBKMDlBYXgwJTJCNkZQVVhHZm1lbnRNeDFNelh6RVVWbDhGWDB3MWxQS2xVZXB1WHltdHU2VmZPZnF1VmZNOGY1YllGb2lwZGpuWjU3dDYzJTJCN2l5a1laY2VZeU1rRWVTeHJrUk1ITThNQSUyQmFuTDlHSmZNMFhzOU9IVkV6UnRiRHFYZjh2S0FIMWZBVGtubCUyRkd6UU52YkZSNW9mMHUzVGw3MU1jdjVvVk5jdm9lRDBicFBPWGJIV3lEckVrczdsaFU4bHZxNmNrTnVRRTdudWE3RzdpU1l3MzdsZmxyNDY1UkFzRGM0RTBCeXhrdDlVVWxSJTJCd2Frd0l1JTJGYmtGRHBqWUhxVzJ3TUlocTRZS0tQWXFpZWpNR2J5TTVyVVZhWkVQamFxM2Zja0lXWkF4REE4eGZEYU5uMVRZM3VFb1RQdWN1RFdLNUt1Yms4U2wxZlVKd0doWkNpOXpGaGpsMEFqRnhPVlNlVml1dDc0ZlBvNU5vTEhEQ0dnODBQUDNCNVpubmkwT3I0bU8lMkJQV0loMHd4JTJGVnNzNHRiTzhzeWlLTWROdDR6UVd2SWxtbVJCUnZTem5qQ0x4ZzB1WHkwYm5yJTJGZERCNllyc0c5N3RTaSUyRlV1eXpSbHNmbEYxQlFtdHBESjZvVGxWdXJLTFkzVHE5Ukg2bmcxbkN0QnNYemtqZ3owdE00a0Rnb0xacWx5JTJGMVB6VGJURGx5Q05FMlFtUUJLUUZybHlqSHhoRURaJTJCa3RoNVp2eGtvQmkyNlJsV3Z3JTJGMHZGejNHc0NzV0g1V0xkQ0Y2dU9uaTdtM0FXVzQlMkJOUHJwJTJGbEt5NXZoN1lpZDcxdkdoMEg4TmklMkZTYnMxUWhtclRBZFdFM0R1SmNydjdkQmNkM2oweEFTdUViU0k3ZUtKTVQ3aVBmTFpDMFNoZTJCTWlMNEFSdzR0R1dMNHAyUlhUJTJCSkpJN3ZZeVMlMkJ3UjFRdTNtOGVuaWJ4eGlCVEtPNkZ4SVdWbUhGY1FRekFCSWNuJTJGT0RWMEV5WURiNHo1Qm9KUGQxVmF1RiUyQlVkS2YzeDhkc2FycEJQTWh1aFc3bFRObzV6bSUyRjM3ek9SJTJCRlczYnUyRzR4bkglMkZBYXowT3VzTktQS0ZrJTJCUG53MU9jaFNXYVV2JTJCME40Z09jdTRtMlgyZUNBOGQ5OHFrem5paSUyRkU5MjNjT2RGaWZnRE44V3ZVenVrcE9yYyUyRkV3R0tST05PMmVDOVdIVjdmTkZMblBudVJUcE5uNGtPbmd6RGR2T3l5OFNSVUtYa2lQQk15amMxc3JkWTRDMmRPMkpsUjVOQnFDdnRyOWpkWHZheG00Q0hpQnJvTTN2Nm1CS3Q3R3ZJNnh5TG0xYjklMkJUOWM1RE9HTmVtVFp1dVBIQ3NzcG5RJTJCOUNkUHRJcmVYJTJGVUw3ZENDQ1hMOTB1JTJCdmR0Z1BtOWNmSkNVaHMlMkYlMkZFdnYySlVaNW4xT09JdXZLcHNPaVk3ZEJRRmRYcnclMkJFeFlqSHN4N082SUdqS2NtU3dlcGgycEkyZEkyWXNPeG1mTXRudmh6YUI1MFduUSUyRmJKUVU1NCUyQiUyRmRYaGM2Q21FbXJka1NleUhuUjB0ellmTUlOTEdBbGVGRUdQVGdWYnZBekxic0trSlZTJTJCJTJCVGYyekI5SDlqams5enRjbGc1b2V4R1lOM1olMkZqQ2RIM2RQckF1STVpbGsydzE1NXJhQzhSdWgxbVJoYXZuYlVBblFMQUlJQ28zWmlvWUQ5bkNKMW9jJTJCQSUyQmxHUENVV1lxeGpDM1dPajJWSjNGZlZlZEZoM1g3Y1oyQWFYTHo5ZmR5eU1Vd1lsSkZYYnBWbWFlN2tZWENsQjdTYXJCU3JFdFNtU1Rmd2V1cSUyRmZTUzdKQUZxJTJCZWZCWmpNNXdjeFhSOU1xVXdoczQzMTlEOXhOSDBtald3YjZzMWNyaSUyRmVkVDhYZE1WRjFkTzlldmpMSHFCVTVvOFF4WW5ZWDB6UWJNQjBBZjAzZSUyRjRCYXF4eFJmblZZOUMwenNSUjFsJTJCUDE4c3FvYVlhVmJ4V3UwV1NuVENRN3RZV1BQNjVBR0EzQ3NiVDZVajFibFoxYzB0M3BBTHBUWFdVMThRZXdSMHZ4b2tNM01hdGhwZ0ZaZHJqWFI4OGU0cnR5NEJRd2ExNk9qclRqd0x4V2xsR0ZMWmVQMUFBMkZQd2RFQloyMzRWT1luUVhldGJWb2VUcDclMkJ2M2xKS0VZR0lkTDhaaE95QTMwdDZGTkZDSzhLcVMlMkZPeTdYMmh2Z0J2SFM5TnRRWGpQZGtHMlhaMGZFZndlb2xnV3FGZFc0cnBzTG43OVBTNSUyQlNiTUExQjZTUGpmWWZTVWlLVTEwUHk5RmhtOE9aZHRUZFJIUjJEQWM2TzZxNkhSMWZHV0glMkI4dUNsTFplWW4xY3IlMkZJcGN5NHB3Tnh6JTJGS3ZublpVWUxwekRjM3lCclpmVlVjRzhPSzE4YUVSaWs5akVVZTJnSyUyRnpqR2hKRGV4MXROSmJyRnk4d2klMkJORmhKNTIyc3c1JTJCUXZMR0FyS0NrY1JWcXJsSHpKQmdGQllKTGg1WmxNYVd6OWFOOHdxMXdrUTJ1bHQ3R1R2dGlad3NBQUMxc1ZpT2lJYXliSU1HZXQyOXdGNzhtMWpVeW5SRzRTNUVCMGxQeXN0WjEzeU1QdWI0d2NKZ0dOZVBlSzdHM2pMTkFSJTJCa1IlMkI4aWlYS1RrZXpvJTJGYXVlUUo1OWNKVm03dTJ6MDJBbUVwaFg3V1BiJTJGaTFOTDM2WiUyRkllandoZDV5T2ZmZURBbDVpQVdGR0xhajZ0cUJGJTJGdFh3JTJGbDVmZ0F0SFNTQiUyRlZEWVc5b0JKUHhKaTFPb0ZrZDZZNjd3bSUyQk1qelpQcEtrYUtsa21qS3djUHBVT2pPUnZGeTJPdHdoZSUyRlBxWHdPWkhRYTBjRndnVGhyTld2TFhXcU8yJTJGUWxPaDZaS2tzOFglMkZhbkNFcFFjZTFseDZDMXBJdm5SOHVyYlFtV1pFNjdJVWV2SVFuM1VCMUx5UXBYNlVGWUtKYkQ0d1RWV3ZPJTJGZDhvTVNHQUxONWZrWXNSdHpkS2dsdm0lMkJzdFlCdUdHJTJGZ1NlMDRmMnFJcm9qN29zY3dRVXJDVUZET3kwQklkNXBET0R2NG9EVFNJVEFNZ0pvcW1JJTJCc2F0VFZXaEdERGlQaTNIT3JDcm5QJTJCYldXTyUyRmlBS3BXQVJxaUNpUnNJNmZVTHhvQm5PNnlFT2pIVThVSU16aEE2MkVUV3o2djYyMGxGSjY5cHQxRlh6b3llZFBtRm1CT01YeGZOSG9RbVNHQmJ2Rjl1MSUyQlNWVVlkaCUyQjlYYmZlcFhJTG1vbUh0cHB3clJQMDNuSU1PZ3hzamJWM2RTR3ptSzltYjRzZ2l4NGVYbFJ4VFRJOVBKQnp4eWVjSXJ2ZmJaOG11MmN1cmRzdnh5cGpaMFZTYVlGOWVNVSUyRm1sd25qaDdYNENKUlE1VTVZdEdpTjVJRFdSYVljdjNEdSUyRlg1WGJkbU9pUzRrMWRIdzVnVGx0N3piNXNQS0lUTm41eEw2cU5vMjh3NlIlMkY0M2liOUJ6bmZGJTJGS1hPUWRaU3kyUnZOMWhBa0pHTUFjRjVQMHBrbWhVRlU1bFp4dGRQc0tuZmhHbEg5V1hFN05maU4yJTJCME9SWXB0VEdaclMwWWYxeEllalhLbmlXYk5mMHZBeHRlMU5tdlhLdkRRcGlvNHp0UndXWFFvYkw0ZWlabmRTZkhKTmIlMkJDYUVsJTJGdVI5ZmpNcGl2SGtvUzJYRGN3clU3YWlaNEhNajdYSHdRZnUxQXhpaU41Z1hXYVlsOUV2MVlkYVE1cnFWcmhqQ3ZWZWN3OGYweGlKa3hSaDltWGo5YXQlMkJjTXZQcURKZFphZXIxQ1ZmMDBKblEwT2tFdVlXSkVhbG5RdkV1Ymk4ZjdSZ3NGV0RPdTdDSjFYc1EzUHd1TkpoQWEzSGRZYTNldXVxemVKaExjY2RvM2RLaXVudVVRbjNIakkwN3JadzBLRzltJTJGSTBwZVhGYUJpNUN2MWNuS3VDamtoZnFza3pyTHlueVlHWGNpJTJCdEFyOUdaU05ESmFnVEpJUGJRSHliVFZzZndxaSUyQktOcDNMZG4yUWFPV1NuOElyZ25mVk9YQm00dG1KNyUyRnl4WlhaeHJQT3JTUkZYZmNOSm04UVVSTnpqc1I0RnVjSXZaTG5nJTJCclFZcW03UkFvMFhrYngzNHhmamhvQWVldXo2MjVUZGo3ejRiUGhMRXNmcEZVR01MUmh5bVRnSkVhZ3RjTG0xVUNlMzlxbktzYVBaY3FWclJ5c2ZuYjAlMkJCYXo3QlFkNWNuMlRITzlCZU5vM09aTDNWTWE4VHpJT3RuMHRaaVdyUUNrJTJCZWl4Uzd1RUZzSVVHaklxUlJsV2lhTjdMTTRUTnVQJTJGbGt3SUxmZ3B6WXZ5JTJGZDQzT010RkY3RzNMaVVLdVhINzJSckxOcDJSZzVrcTdEUEU3N253Rk1rSm1oUVphNHp2JTJCSWtQN3lNc2x5MExMaCUyQjZoVWFLOUJKZnZTQWJ0WHFKclg0aVBQSkdYUFNxbGo0JTJCTFJ4d2dSUkRZS3VIc29NaElzREslMkZ1Q2dZcE5UUzEyTGhsdVhKTTN2MVNXZkJkSHR0dVJ6eWdVcmpPUVg2aEpOWCUyRkllV0lRbnk2MzdGUGhiQ1U1VUVYJTJCYlNKMklQVFpFdUIwSnYwM1Q2QkpSa3M3TW1SJTJCTXoyZ05WVkdRV0tEeVZzcEZVWG80VThsb3V5Z0pJTjJmYVF3MVpRRzJBNnZxJTJCRGJqN2MlMkZZSFJVUFpiWVNkVWRZNDJOZmVaOHRGVkUzdnBGVFJqZmhRRyUyQjB4WlVJZFk3amFtdTNLJTJCMnJRcnRpeWpUaSUyRjNnd0J5eEglMkZ0WTBwM05yQzB2Rzh5UnpmaSUyQmY5WHlrbXVJRWk2SVBlanEyWlRLMHclMkJoWVdHVnM2SFhMbEFKcSUyRiUyRjY3THNVUTdhWDc1SzUlMkJqSWtyZUdTeXFRZCUyQlhQS3JqWTIzdEpMR3ZTTSUyQnd2OXliMHpyNXZJRGlOVGtETjFmTyUyRmxnT3VnR3BJOWlwS0lnNVppV2hrYmtMUkRjcElVVUVoVUJqcVR3WlRmN1FVN0ZlODVURU9QMnFvUDZwdCUyRlglMkJhSVJZTnA4QnIxSGdBVk43dlBVZXB3ZUVkZVI2JTJGbEdUTTJUclNZSUZKTmw0QSUyRmY5V0tjV3N0NkZXdWJCa0dxZHh2UXRIbEw3NWdxY2xwdWpqZVBGODBqcm5kbGVWZDVHVWJTZTNTbnlhWCUyQkhWdUl1R1h1Nkg2MnpWNlptSnolMkZvS0xzRER6aTk2dmJXJTJGQjRkUll0cEc2elMybUw0T3dDOGVWUjZvdG5NdXFqTDVHZTRTaWc1SXhhclE0a1N6cjQ1dnFhdEcyR0JZeUpnblVnOVZPcCUyQmloJTJGZmxyaUhocHpyTllHOGZyaXlNdUlzT0xySnhIMzJXZmlIMWlkY2U5JTJCMVp4UjdBMzhLVDB0Qk5ISThqWE5xbm5iYlREN202YSUyRmJOblJKWjUyakZlREtqc2lxQU1xMTM2VmlvJTJCb3I4MFgyOFRzenB4TTlvJTJCRnhRTk45TDBaTHYyVzZteUk4cXBzTG4zSkdwZTlhZU8xeXFXaWdTa3ElMkIlMkZmUmlWaE5SWUl6S050WW1NM0ZOVGVERSUyQjhoZDZLM3ZmT0Y2TTJCZFRWNEkyeXhsd3Nrcnp0WG1pN1M3MnFDWDlYSXlSR3BSWGRIY2lDTjJ2JTJGYTNTTTBibDBJSE8lMkIwN3RpSW9kZUh3ZSUyQjk2bkVPNVBqNElZcVptYUUlMkZGeWlPS3IwTSUyRnhGZUgwZ2J5RHU5NkE1d1JVWHR4SGNpUWhJQ1ZhRUNZVHo3Y2YzZERmbnRHdHBNSGpVN0lWTVJFYUwzdXolMkI1SUglMkJqcFQ1a05FTWdvV1lUN1BsMUtxOTg3WlhSTXB0Q1hwcXgwUGF5aHl2VGpFbTg3QVkyRzRYek1KUDVvM21EU3F3TUM5U3hLTlVaUTV3NGVMU1hxYlZsYlElMkZJMXoydjAzdWVQMzBqdlE3eVM5S2tWOFclMkZYUk10ZTA1T01WUnYzdmN1TTZyM05POGhlOXdwSVVSQVRwZFBiRCUyRlZEJTJCV05TRTJCekx1MTV3YiUyQmlmc3NjaVBXOVFiWTFCQmZSc0k5TEJjOE5uWkFlZ05yaXZwM0QlMkJYalkxaGlpM3YwJTJGTXpTNWxhbk9rdmFqQ0glMkJuVk02NWVRaXRHNERISDc4T1VjWEV0QVNreDRSVHpTaG56aXlvYk9UNVhtdTVGbkJxcnlYRzhxWWh5SmpzYWRjVWpENzAlMkJybmxrdHNDQVJwRlcyR2tCR05NOXYyZDhZSXd4NGVoaEZGTDlJMnUlMkZLN1lIYnlEZ1N3MkVhbk82OFdHUCUyRjFTbzNsdzVwcGNkYXpWbnU5M3RpWnZDVXhzQ2d5MXM3eER6TFQxTUpMV0xpZWdJMTFhSiUyRmZPMW1SNTl6cks4RzZIRkJ3c2VacGl1Z0R1aDhPN1QwdCUyQkxNTnZ1ZnZJaldic2w1cUVqVFZQdWxCbWFZTEpIQkdCbEFUa2w3OFVKTzFhdkxiNXo2dTBaYzFpQkw2YXkyYW0yJTJCYTJHSWx3MnlBS29tVm9OdDNlRCUyQmdsSXA3bmVkUmNQdnVVcTNwZHhLRUQ4d0pwdEpva1hod3dDZ2NzWUZpb3FWJTJGYXdlZ21HcEhMakVsTkNYZ1pvJTJCOFVDZ3l3UllROHdDUWF2ZFVCc2Z3aDdPZlUwdVpYcllqVzM1dTJXUk84d2MlMkI0S2twTXluMGNXQnpQRENodkd4NUZjTVVlTExYd3NEeFYwbFh5ajRqRHZybEE4SU1MNW5TQ2pReTZXTFIlMkZkbDBoT2d2MkFuYk5BUVpSMG5idWRESGxKNjQlMkJuM3dONUJ0a0ZRbUl2MnpqaGNOcVQwcXFzMjdKUDZwRSUyQkJqZUExa0JHY2llJTJCTTlJMkN6eU9sU3E4V1lBWjAwazBjYiUyRnJHWERoaHI5ZERQUGdUUFlEdTZlRmNCTUQyUDBHaGY0bzA1ZGttMHlBOG5vYXM0R0FoVTJKV0NzZWwxZFNHJTJCJTJGU1cxdGd4aVBkTW9XbFE0THcwSEJ6UiUyRmlWZE5rZXdxb1BXWWJZYVR6U01IdFNjd0hHRTh1SjN1TXlPSW11aGY1TXV6b3JSd1pkJTJGSHB0Mk9Ya0VkUTU2c2YwUFhMbVdtSXlJS3FMQzcyT0xKcUYxbHVyJTJGJTJGRnpXeE9rWG1mUWpGaSUyRlpZTUJGSE9HRFF4OGpPR203NG9Fb3UyYTA1SFpsZk5RWnJWaW53N0VkMTRob3glMkJORzNycDNRcEozcklCR084WVVORTdtVkZUdGxLdGxhZGFsS1VHQUwyU1VTVlREUSUyRjhDU3dIM0YxanBxR0p3Tk1aZlhWZXNSZHlJVSUyRlpjVDQwVXBzOHklMkJIeUlENjk2UmpUS1VkQ1ZjQldjZ0ttdGIlMkZ6ZGtrbko3eEpiNXdlNVV5eWczdmY2QmExJTJGSTdqJTJCQjNPTzdxVzNOVyUyRnJoS1VvY0QzbEV5SGtrNUZVJTJCZzJkUFNlTyUyQkY4MjNRMDVyT3JuNG80NjFsTW4zR0syblFlNjdIcldnbGx5M2klMkZSa1kwRGJUaWdib0k1bUl3QWtlZk1hQ3loNW91c0NYdEFNNjZCZ3dWMHVkd1UzWE1tM0REN0QxRUdwbEg2bFplRk1iR0REZ3Q4cTZIb1RqaXNOZGVlclQ2a3l2V2FnMFhlTjIlMkZJdThVV2llSSUyRkI1Z21CRiUyRkxYSFZJNFhtRDI1b1BvSUo2OWdzJTJGcDElMkJKQ1VHJTJGMUJlNTU4V2JUMFRNNUs2T0V4cHpNR2FjRnRMN2lzdzQ0UFRFSkN4QzZFSjhYeWYxa3JHWGliOHNtTHU3VXQ0UTBWSU1acXRUenVqdER2YWZtVSUyRnFHS1BCSFlQQzUyMU1UYjg4ZDIlMkZYdHA2aW5zb0dqTHZhMWlrMmMlMkJjUmI0ekV4c3RpWGlhWFhlbWdkNTVaY1JRZ2todXJpc1NFZDY0ZHdKQ0JTdWNIaU9Fbms0ejRnTGM1TEp4eG9JWTBQJTJCYzNSODRhNSUyRlFHYjFFZUt6TURZVUw4eE5ERDE0YXNMeHF0SHFBc3d6eWc0JTJGbE9jbWVVeGhxdjIxeGp6UFJMNE52SDRxQmVkZEdhdUs2Ulp2em4yVUlxTzRSN1ZIMjIwZEJHR3Z6UXVIQ3V2aTlsOEx3U2pQa1clMkJqOVIlMkIxVGhEVTVGbkVuNlBkZHNvcU1ZdlNyZXIwWXlQSldlMFBrU3NCanU3cGpwR0ZHTUJMU3V6JTJCenNUcjJEZDlnZjR4eUslMkJUYjdPSUl2T3ZYOUhjdiUyQkc4ZSUyQjJNeVZ5NW9iUEVSWHVUWFZEd3lsQXUxc2ZJSlFJazNURmMzM3ZCMDRoODZZQjIwTllnT0JiZ0dNYXJ1RTU0aXpkeXBYd2RZZjZRaWZYZjhlelhYdXBBMHd1JTJGMENsbXgwelBQVDVmU2g4ajRPbXRoaUl2aHV2cFlCeTRLTSUyQms2V05rbDB6MzR5cVZ4dHRBWU5ZV1ZvenRRUEZJWVIlMkZIN0M3dGNnNVVrbkhkV2FoWVhUd05URWNRQUZla2FvOWpiUXZ6T1p4OCUyRkJ2UUVId0tIRGJlVktad2kzVnQxZ01tJTJGaEc4T2I0SDJjekhHaUgzYng0Z1JDeTJ2SzcyJTJGOHpRJTJCJTJCa2I5aUZCWHFySjFRTTYlMkJtdldyejdIZGNuWEdzRWJ6VGVBR01aQlFvVXdxWTFHeWpCJTJCNkhWS3NodkQ2VTVnUU91aEs3UDJZamRDZnQ3eCUyQjhPd01MalBya2NKdHEzT1prdXpkdlNSODhaeWVteVp2TEZWbUZzJTJGcU50a2FDUkl1WDFqQmJ3NHAlMkZJd2FrY2VzJTJGYmYlMkY4VEk0Q2daJTJCZWFGMDRTa0FhYjBITFQlMkZGVEJaSnBscU9CT2tUMDRjS2ZZdHRwQjJRMUpKZG5PMms2YVFhYXF0QmVVaDJ0SEVhYmNTenJhekFiVmp4cjU0UGJMcUs1bDBVblVjQ1lka3doU2hFdVd4VHNOM1J2U0ptc0UzOXJZNGFjNW5yYmkzaEoxMnZwR2NTbzVNbFIzZjJpdGRpdm5aWHhHMDNPTnNWbG1YdTZYUHRCcnVvbGRmZm41dFJPd0p3VWslMkJ2UFYzdXRURTl6UjdSQmFtc1pQTE1EV0VUMDdiY3FvTTRKUGdTVVljdUxWdzR2cEZZYWxRMGMlMkJUWXNIaVdKMmN1ZXo0RXNhMjZtTElsZ3JXQUo0dGt3UFRuRkNXYTcyJTJGZjJsNnVONXp1T1F1YXBGWlMlMkJqJTJCWDd2Vm1uWDlNV2hVSzRMJTJCOEhpZUhReG1FRnlvdXRpZjQlMkJ0M2pyVW9GJTJCdFZGeFNUTXZ5MlpFU2EwVW5pQTRrWTMzVkZ3Zld6dnEwcmlya0FHWUhZYkVka1EwYXpIcTMlMkJ0bDVHeHhWZUZtZkpla3BoNm5ac0JNVmxXOFBuWVZxTUtRJTJCSlJKUzdSY2NmNUxnM0dlaXZYV0ZwTWdsUjdvJTJGZno1NEoybXM3ellDMzVSNkxOVnR5MU9la0pud0RobjMlMkZoZDUzRm1nVzZMV1RSQVZxTUV6eG5ZY2loejVqeTc5ZnpONjJkdCUyRkMlMkJPOWFobmFSTkI1VVpRQ2RaWkIlMkJ2dkt4M25LZkFEN3l5YVp6YzQ1bFNqZ2tvT1RqMVBidWdUVSUyRjdNTjdFSmdnbURiOUJVdWhZSyUyRlJrZTd1TmFLMHc4aDglMkJZYWlvJTJGT3l0dTVEeEFKM2tnMHQ5TXFhYXV3eTN2eE9yJTJCN0lLOEw5MXYwJTJGbnc2RTVDR0VneW1TZlhRQ2xidWN3cm5zMEFhSjl0djlXSTIlMkZjVjh6QmJPV2VBalBQY3klMkJodWhYYVhPNkZ6SmlNd0ZGNkxzeElxR1Q5Yk5CTUU5VVdjUU9wU1JiMmpGJTJCRFpjemwybm40UkNQMm50R0hmbUJMNGU2c05KMzdQY3h4TU8lMkIlMkJzWThnNWFENERlV3Z0YXJjcG10MVBwalB6UkswbUd1d2YzaXBacG5FMUE0c0ZiRTNtWEo4VHRka0hMVUl6SHZ5Tm5FUGwzSCUyRjJMVyUyRmNLUDVpYUZjQ2RsJTJCZGF5WXpZbHlLVWloNmxlYTFWSTZvY2pRU3FCVUN1UE5pQURKZnZFRG9EWnZUd0lJMnZkWGt2c1dsYjElMkZBMDdueGtybWJQdVo5NjA4Qklqdm1SWkQxMGNBUGI3ZVNtSzVBV28yUGJzVCUyQkJjZHk5d1lOcE1CSW5IbE9WMDU5bU9KcmtaRjFTdktBSTBrbiUyQm93NFdFbWN5TUttZGlCNXRXJTJCJTJCbjJkYzBzUFpyNWRNSHpVbWEzQlA2WnlacGhXVGpmWFEwVTlFU0FoeGl2Mmg2RFZGOFNrODVoVWwwMWRhdFpxWWhlQ1Bzcmh1VTJuaVBxZnVGMXRPWEE1eERLR05VWDB6QUpCdmZBWnBiMUZDWW9wbk15djZTV2twaCUyRm0xV0F2UCUyQm5FNXlpYTRpbWV6RW04S1ZSUEMlMkZHcE80Yk0lMkYwdklEQUs4SERFaEVNb3V2UHl0TDRxSXJsd2laV09MeUszNnRhTGk2aFowN1ZtcVRodTV2UUtxSUczUnk1Wm9lQlBCNzRxNE5halVjeThFWTVyQlJlZlhjeTZyV1NhJTJCZGUzdlpqTDhuNWNuWjRtcWNCaTNLRENLT0xCWEdYMkxXWXZtY1Q4WmRFcG9oYXlIYyUyRmxaWVdmTDBjWUlTbXglMkIyeTBRdDFyY21jczBmNTYzcXBsMnNMbkVuamt0MnV3NmglMkZwbXZ4ajlzell3MHM1eUtaR1UlMkJXTzdQOUxjbkNZNEhxcyUyQmh0MyUyQlQ5VFl0aVBONmpWMmhjMHVkMFA1Mm96JTJGTVpIeTB6b3NoamglMkZSMGtndzVvZk8xRGU0NmpOTXZJbDRHQmpOYzhFM1RjemcxTWFlaldBWjhHSGl1cG40U3NHUFFTJTJCaFBOQU5ycHFBSkdPJTJGaUpOUWFwYTZVY3B6cWo3WkZmSkYlMkJNRlBLU25qbUg4ajVpMmU1SXlKM0FEaiUyQjBmRU1WRDltdlIyYlFWYTYzNjAxQkFDWGZTeFJDblI1RlhEYjZyc3cyVkNlS20zSndFVUFNaEM0RThrZXZuRVZSTGRTNDVQb09KaHdJYXBCVGpPelglMkZ2YiUyRm81OHRyNXNBZ291V29NbjZ1SXc2N1kwUHpndVNhcWRnRFR2QVRRc2dtRWw1d3lGJTJCclo4SnJabkpqVGs0eCUyRnJRem5PMEE1ZFkxeU9wY05zYzRkQ2pra0JmdFNIVnIxdE02OFJMQ2xpTDZjNUZIWWdoRWRvMjRkbFE4bkY5N2l4SzkwMSUyRkZBRWJHZVIlMkJzYjViJTJCRm5BVzBQY0tFaDlpWlJSVGRLTHBiSGFRYWhsaXlocGc5N3VhT3gxaW9EbEo4bkV1c2lEWkFScW9XSzJzaGQ1Y3oxUURtVHVMOUhoaVQ5RE5CWHkyeVV5SlVNJTJGUEFtN25XdFBKQkNyVlFiVE5LU3hEd1dOZVgxMWtTSTZBQWNsMnBBZ1dUbDk0QkVhQmtBSEcwdE5paXkzNTRrVFRNelg4dFFtRHZiWG9pc3NxYkk4c2I5WHlLOW8wTkkyaFB4TSUyRlJ1UVN1bkQlMkYxSkpwTGZMRmZmY1djQnBwN2tISTBUWXlMV0ttT3RxbjR1M1Y1NjgzdGhsOWIyZ1dVMkFQWHY5cmwwSll3NEIlMkY5WExuTEpGMVE2QVM0RmpqcVJmQ0t0eW9wR1lhQkRrTnhycyUyRjlkeHFVcjRCd20zVHoweHVIV25mM1piaVJTNExEUnpmaFF2WHVUcGpPdXI5JTJCM2xINFNIJTJCTWo3aHd4aVVsbFJLc3ZwSmdtZFg3RFdJNnJneGVkbEx4ZEdyREMzMHBmUHhRYlhpbUMxMjlaMVglMkJpSlBid3lpdVROczc2WTYzWG9wR1BnR2lIbzlidzY4bGxJVWklMkY2SjF0WUhlaFhNemloVzZLMTRYJTJCRktUNm5IY1hmRldEZGNhbWNBRUFaWXVqazc1OVZTSWRrRWVIanFZamtaMkRKbDg5a3ptS3FsVk5BVzhUekZ3QyUyRiUyRm40Yk45RmNsV1U0emZHcDFTcnlxU3lKWk56VDFiOGg3eUJ2RXFrSCUyQkNqeHE4MGZ5SzFYVW11dDU5SVFFOWdTeHZJWWJMR20xJTJGaTRMNUNacHVOWXZ6OTZuMFFNR1NMUTdWeTMxanU1eTJsN08zYiUyRkFuYlZ0bnlyJTJCZEtqRG5qc0pDajhvb0tDUnVxOXJ4Nno2aFJ5eVRFcGpIdWZCbDJrRGNyTUltcyUyQkc5RDRYcnl3NDBLdG0zJTJGbldRd3plMzhlZiUyRmJUUG9OYWFUOWNZTGYxM0RXeWVFaGtlVmRrOVByWEhNSUF0ZUoyWkp2Z1Q0U3g3RnZqbVYwYm1DS0pOTk9OTVltNk00U285YnMxVEslMkI3dUJCOHBRd0lOJTJCOUoyOSUyRm1MMTlzWk9YVngwS0UlMkZ1aWdaVzVmUzlzdGdPUldtTmZPampqODZqcXdtNG8lMkYxdyUyQk01bThaUzhSQXg4cmVkRXpKRE5DTHcxUDJ2M3hiUjljN1g5d1IzN2pSazZucTNGd2xiYWQzd1BrdFBXUWlySlN6dUNhdEdqWmY1NUhPMElhelJybW5NZ3UyaHdXdWo1cm1paVkzeUVwVmM0Nkdjek5OTmxzbnB2YzZ1TWhhSWpmdHM2YUg5elVDS1RYaWY1QUpTbXN3eGZtTGs2SFdNdVJ5UDB0T29wNlRjR29UeDVaN3JHYTBQTU5qJTJGYWklMkJWd3poSnpnJTJGaDclMkZKTVF4UUolMkZYOXRESmRkbXRMS0IxbUNDRHBLNW1qendvd2p6SmhOTCUyRlRDSnJJM05SamtybUdZOVMlMkYlMkJ2ckwlMkJtN0dPQUtTSTcwRE0lMkZzT3gybm9IQWU3ZDlyeWtoZnJySUs3YVp3dFEzJTJCVDNSS0t0M0pxciUyRlZvQTJPT3RjWWJiNmJpU1QxMkd2JTJGMm9pY1ZSdmFUaldlajNDN1UlMkYzdkZGdmJEcDg4WGo3WlpTWGs5YzUlMkZqRW1MM0Z6dzVMb3RyYmxWdTVpV2ZzM3J4cDhPTTMlMkJjY05SVk80dVpzWkxUUERpMERLSE5mZnhEVG5wcWRveVlRR1NOOHFnVVZmWERIMnhlWVhMYUF4VVdDZTV4WlhnTkcxbEtxcW1GVHNNNmxPR1Nka1AzdTRzNzVYTlp2eDJoVFkyelBnYjhGRjdkRU1iM0JwVWFRVExwTnRaMzhsd0lyQkdMYmtaRCUyRiUyQm1aTkdqTDJPQXAlMkJwaVE5dnBmTUxja0ZNMXJUT3ZkQ3p1WkFYZW9Ddm1YTFd3NGZDJTJCMVNudXBHcVVHNmptUXJmODZrUkZncEp3cmxhTVUlMkJlSEZHeXkzTlh3RDF6dEVKaG81RmJoV0Z3cTBGTUxZbXVidmFHWGp0MEtub3ElMkZPRW9LMkkyYzElMkI3M01rT3d1R216aWdWZG04eCUyQmxSS3V6UzNDVzBXUmRKODM2dkVUVk4zdThEZEVISnJla0IwMDdJWEhLUndWbFYlMkZoQ0thNE9pN0JyM2VKblhuRDUweUhmeVBQa3h3U2JzN281Z1hjSWFJUnQlMkYzNjdmN3J0RjdEbDQxOGFpdHRsckU3bGIlMkJsOW9zdGlubXlCM2hTMmslMkIwSjd4NlZEZzM5WFBFY1QyeFp3djhpM01ETmpaaHJ5clNQM3o4czhwVmtaQjFIYmlxN0pPZloyYjQlMkJXJTJCTExsMkVuYm43ekpDJTJCSE9KazV1WDNSNkJ4aGpVU0lMZ0tNcU9iMEN5bGxBRXVhNGt0Tld2Vm1aYjZSMUYlMkIlMkZYSng1UFh3UTJWM2FqTXdMSWQ3MzJnZEV4Um43N0pXNUUxd3hSNDB1eWdKN1NqdUV1VWRrc25teFByMDRqJTJCS21HU3JSdDFZUEZNVTUxZmU5NU9DcVFTYjFWRDNqTG1kOFRjaklHQkVUJTJCd0VtZCUyRnZHbVptT2s5S253WVQwWlQzYUY2Mmt4QXQxeFhEOFdsRzB6N1V6MWlnS2NId2pYZm5FT09QVnl4cEJ2VkF2NWhva0UlMkJSOGV3VWZKZGR0N2pscFg5VE1ETE9BaHFrbDJjUEp6TDRRTiUyRmxiT0RTd20lMkIlMkI4R09YTmhyNmFiNyUyQkNuUHFwQXdiYkV2SjlUdSUyRlglMkZld2E3UDBraUNPJTJGY284eTc5TmxxekxtNzlEUGtXNVh5SFd4d0FqRXJyVWl3ZnZtbG5ITlg2blVEMHpUUUJLT2l5WGFoWkVZdTdvaUthN3RubHhERlhONERId2kwRWVKZHhjTzZxeWNJaUxsJTJGMiUyRnVDMVZqR0RMbzE1cEUlMkJSdUZTTzJSRnJwa0x1Zkh1ak43eFlpZWtadTBLZSUyRjlaYXVSZXVObno5Z1gyQ20xUVVOMktNQWZteGVYbGIlMkZNblJpJTJCTmZaakdEZUJsbkhMMmxyZ0VYOG9vdjZZdTZENWpSc0w4SXZzTXIlMkJJeWhjV2lQYjFHcFF5OTM2bTFFdmVCMEhZS3dWOFRlOGFQYVN2bTI5SWsySCUyRlNGVTg1ZmVHYWlzNzglMkJWTE12R3VUSmJqdFNaWXRuZ2tWZlJDa2o3Q3pJNEwzUUo2VTdPaGVhTHFBUHN3dVhOUFRVVXRSTWQ5bkdWcE95dTJrbkVsdlZLclRUMzNoaXN1czVXTHVEdDViViUyQlNZaW4xZ25qTHRqNlRTS2VFbVclMkJtQnIlMkZ4SllvdGVhJTJCMVR0aTZMNyUyQjhPTlRvckpvZFJDbVJWTEt2RWIwVlR6RVFuTkREcWIzNmtrY3hZSU5RdHBlQiUyQnJYeEh5MGhqWFk4UkRBeUd4R1o3VjBSbDhQRVFYTkY3JTJGM0RFQWJncHhuVGFlMXlGJTJCRDNjT3p3SXcyd0x1ZlR4JTJGalhZMHZsU0YlMkZYOHozcmJRSW82SUE5c2Q2S1dlbks4OUcwUiUyQkJFOGRaTzdla3pmcjNYR1gxdzVnaVRLJTJCJTJGQ0dJVzN0dER4JTJGZWRBcyUyRnI1dmFVOFJoN043QnRVSTBucVMzYVFueVdTcU5JVlRRTUoyUUd1WmV2MlBtUTU4OVpiJTJCaEhENktKY2FmZ2ZIQnNmeVBobWRLY1dYNE9XQjU5dUpkRVR0NyUyQnV1dFNGV2lrTHdMcVdKdDdnWVdtVGVTaTYzZk0lMkJwJTJGNExBMXBoJTJCaFdWMlN0TzZtc3N0TFVkWmI5UkcwT0tDVnpSJTJGVDVFSlA0MWJxTGlvd1Y1elFxeSUyRmt2bFY3ekRLWnNibVglMkZyOWdJaWtzUjhqZnVzMFZrJTJCJTJCbjkzQnoxUTZBYWRqUnJwWFFKVnRjblpic0dMTDFra3E3T0V2VDFjekRxaHpuclRHNmV6Rjg4UXhKVGxRNFklMkZLYWV6RjNyM3RjUnglMkJ4cTdKa1ZRVXlHelJJTDFWY1JBMmhDM1YwTXZ5R0tvYWI5emRnOU13STNoQk83TFVsWWNHeGdtZGhOb3ZpJTJGYVVNdlc1THNSWkplSSUyQk9uV2h3eWlYckRNMkNKWjIzZiUyQk1IJTJCTWxIa2dsMFlaVjkxaTZMOGZlclpUUW1Ib3QzeGIyQTdFS0ZzMXZjdk1Nbm81U0EzaW1KdTNkcHc5V1J3MDBpZkh5QWVTWkMwUlBMbVJmJTJGWGYwdFVvc0kwM0lOT1gxa2dZYUFDSlZlUGMxWjlCTWJkVVg2Y3p6Wk1lYWZZZGZQQThNdFMxU3ZhQ1dyVmYzQXdGMkJXM0FsOXo1NWVuRERNU2Iyb3htVndRSFJ4U0htdUJPZEZCajdTYWlsUGlDcjlZVjRFMXRUd2J1V01MTUNVWUZRaWFibCUyQkREc3VrN0Q2ZkJ4OFZqSHpyTVlkZUJ4d3AxdGxOY0dEVGhMa1NwejQ1MW1KNTVHMnMxV0FYZENIYWR2UnY3JTJGM29STFZyTkRoUjh5VG1DQyUyQnFRWGxqOUloajFudGhWdkVrMlhiMDRKU3ZqMHQ4aHNYT0tQTHR0UEpWaWgwcUkzRTVCV3pkZVN0M3RjNm5tMGtJQnJaeXR3MndNOVBoSVRYRzJUV1RVZUN4eGtWc29pJTJGJTJCWEdYUiUyQkc0UER0bVZBOEtqYmNQNnBnTU02MCUyQnhDdU50REtSSlFzTWFnUzBmcHNmVmUlMkJhbjIxRE9takFUcUw0V2lxSFR1ZDFtJTJGb0duNjJacnBXTCUyRjRZa1hKbjNSWGJlYlRyOURTJTJGWXN1SEZySHl1NmNVcVhyd1BZZiUyRmdVYkolMkIlMkZVNVJUVHIxYmkzeEl1OWFOd1VlcGxuOGFEeDBIMnpEY256YWduN1hIdjJXUkp6MEdVc004RGtOVSUyQjRmNjNIRnBGdmZUdUdhVWNJSlRRSEhieTVWd3lkeUVra2hmdVFrV3laTDFCd1Y3ODJ2MG1ERGVybmJWMDUwMW5EUDF3QWlmN09POENHSmllZ21XVFNkZ2FBdkNVSUlCdFkxczAyVGRBcXhUT2kzSFBNbmN6ZGg2YVNUcndhdTRrS3o2bTVBSXVlWDZwSkpld2I5RXNPSGZPRkJnR3BiWFc4blNJcWYlMkZ4aDdyMjFYbFZoYjlHdjI2MjFnRXglMkZKMmVUNFJzNDU4JTJGV0hHblB0MDg3alhXM01aUnVNb1VBbDlTNnBwSXlTMkFkQUQxcVA3YkF3RCUyQm1sMENCRWQlMkJkUjRJMzhDN3UwUEl1cnZHZEhDZWFVTDQ2SmFUT0JDdjUwV0puazBNNWFmVlhpOWYyNFg4azlMMG4xZUNQS3lRbnBScjd3VjJsJTJCNGQydE1zVXhmTG92enBkMXlCS2l2d0wxdWs5a2tFNlpxVVhGdVAlMkJNU1U3dWZWMFMlMkZVYUY0M1pObDJBTnVtMEd3M29ad2FjbkFCV1JPSW9aWGoxTEtoaktyWkJ2Tnl3JTJCQjczM0tpU3pxTE9XdUhoUHN2TzklMkZhSXZrTDlsdW9WbSUyQkRrdWNhdGNSNTdxRkplUk5Vc2hhbmZ1SkFGem9ORE1TanJ3VUdhWHhTb1NzeWFjdnc3WmVRMjdDMmtZdlBBdlNTZFozZkJsZldpV21OVXJTVG9HRXVkSnZZdEV5NnYlMkI5WSUyQmZKdUZSa0dGd2laUERmRVBXSmxYUkwlMkJHWUVFYXJxOFI0MXZnb3ZQaXVxSCUyQjFEUGpVa0xPMHNGRVUlMkJMZSUyQjRFUjNabHdrUkNKaGhOQWtVQUE2QTB0TGFUcFRUSGlKNWpGRkxRdGdSVDNZUjB2UGFUc1RFdWQwTjRZS1F6JTJGOVFCbWVQZ0JlM0dwaGhmNXRwU2xJbyUyQlMlMkJuQ1FoS00wWW0wS09sdFM5dzF3T28wNm9qUVdLdWltelk1TlQlMkIlMkYyb2pVMXFTcVhoN25ubkVSdGY1RXh4T2Y3RGhoRjczeXZnVWw5ZzhjciUyRjhnWGxhM21lJTJGY2xIY09ycDM5b3BzSnZrdEZlaWI3cFZuNWI2VVZuVE92b2twcUhBVXpJT0dnJTJGVFRpbSUyQk01Qm9odDAlMkJTOFkwUEI3JTJGSUsxTyUyRmZvUGM2eFBXWExBZEQyJTJGNHA3Tm1GSmx6UVJWWEtZVU54TGt6c2dTUk1lbnolMkZ2YllPdmYlMkJrYUpxZ29DMDBTVHZWSGklMkZycnpTeEdZeTNNOUVvdHAzWHhtcEgxNTA3eGg4UXNYbHJ5WmMxMFJrNWVnd3hOVW5sUWZ4ME54JTJGRHhHYSUyQnB5ZTFFZkx0cDhtendjdWRWcSUyQjROTVpVcXZueFIzYTNiaklWVks2NlFKN1FxaU1sQno0dnFRQUZ5QXZWZUlIODdTaE9Yb0dPZDZ1OUE0Y0dDbG1lUUclMkZWOHg4JTJGNjlobUUlMkZIcDQySkp0U2I5UjA5NDFWQyUyRkxIRXNrRUhJZ3B0cWNzM05sODZrNVg2UnFpU21TWllUdVBERHgxaGptRUR3alJvd20wajVOa2tFbjNxMWx3WVBWVDVReUdjVW4zOWdXU0dEMTJPb2xOdTVlRHp0OVhUNzFjSjYzMXFabWxROUZpaVFwRkFxVXlMeEJGcVNlS1Z0TGlYZEFqQ01LNXZoYVRPZ2Q2UVAlMkJBbUhybm5SVklnTnFwVHU3OUk3cUpyVENXWm5NOHhSSkpDUm1iMnNLdEZINGN6M2NYZWR4cm16MHlHbk5JcGZWdE5rSzglMkJBWGFyUnJ2M0RteGJpaDVRJTJCazVST3RmQno4SXpONjU1R2Z1MnF2ZGdLVmRMTmk4ckY0MVJWdnZjUTRuUDU0bkVrb2JxYzElMkJaNXcyR3hCUzlSZEx2eURmbyUyQlJ1VXhSM2xkJTJGeDF2JTJCYWYlMkZDV0g2NUgyakFrV3k3S0RESXhhQVhrS08wb3luYXp5SDZCWW94WkdEZEVaQXAydU5PTTFnanMlMkJUcVViczN0dVVjaE5yUmZnRDZXZTR2eXJxbEtzYXJXcERNUG1NOXFuME1NT2pRUGdBSVZEOVRMeFBkZWdKdVZlM3olMkJOWE1ZRHdqTUpJQUg0aDZFRVdSWEVSMzhiOGFQOFdMdEV3JTJCcjNtMVFlVTlPQk9hQUwxNiUyRjZQZFZnUTNHJTJCaUZ2VVB4ZW5pJTJGN3dNZzhWQVExREZST21mREFjbVpaaVcwMVRqJTJCNmJKayUyRmZOZHVQUDVOQ0xVdzJNRWg0OGNoRmxoRm8lMkJienR5SVhDQW93OHQwQm5LOUZQJTJCSm5qZkloaUJhNEdKaUp3QU91bEFBaHRwbktBNFZvV25DcXNVbXdxcXBMYklKSXpOQnpUaUUwN2xVS01XdXoxeDdWeUhZTGs4WlpnQ3kwc3VpenY3VktHOGp1b0l1Tjd5d1VCUnR3QUpySVdEMDhaQ1MlMkJzZWNVMzVjWWg3d2k5NFNxUDlQbk1BTUU1eW9JSTZONElRcFRXWmhwUnplRmxhNGFNSnRScGI5WThjd1hpTEVHUGllMXNsdGdwZFFFMyUyQlpaZUZreWNHc0Z6WEE3QyUyQkoxSGpTaHFyJTJCZjRzVklUVmZNU1hIOGRYbmtFTHF2S1MlMkZvYkZGdEhFTGZlcnN2aWpydlgzN3BGTmk5Uk5xd2g1RTJIdUVCN3N6aTZsaE8xR05jZ1BzeEZQcTZidTVjOVk5TTdDWGVqWE9qd0NsbCUyRmVXWmpmSXZ5QzdsbHlvYm5JMFc2MloyclJrRmR2cWdkaVpnJTJCNHJZJTJCVXl5cmhybTQ5bjNVVTFCemtiTUFOazg0UEUwT0NndHpQJTJGOTFNSzhLdmRnYkhIYm9PSDhsdHlCdmloZHE1TlpyN01LTiUyQkFhWDRZeXpkZCUyRmg4N1FPUjZ5NiUyQkc3b0tIZmNyV3RCaSUyRnZWWCUyRmk4UzhVUHRmdDlSelJSVUVsTjZEM2g1UFQzdm1HbWV2Nkp1MEVUVERHTTE1M1VTQ2xMJTJGMVRuRVgzUEJTcllnTnh6OGlFVjBhYXVzcHpyN080VyUyRjhlbHRpYjU5bG9QY0l1JTJCSGJvbkJzTmFkZFlJWkpIUEJSU0szbmE3VjclMkJ3RmpBWkF0d0lkVjY3Y0EycENCQXltQVZBSDlydyUyRlIlMkZWMiUyQjJCbG53TWRIMElQdG1JZUNhaiUyRk5zdDY3eSUyRkVEb2NiJTJCb2wlMkZCUDJVczVFbTE4MVhHd2olMkJIM2o3Q2wwYk9NUVVHY2JoTCUyQlRTbGdyJTJGMlV0ZnY5YzZRM1dSVnB4WHZySEElMkZLckc5WVI2anV5eWg5NWNReXBSWHg1SGVWbDVuT0J4YlUlMkZOVEZzZGJvODVKR3RQY3lzWkgwcE1OUHI3YmMwaXdYUkFkVzRzMUpmUGhsSWRtcndnbllQTU8lMkZ3c0JOeUJBZk1DNzM2NHFOOXdHdFNxeGM4ck1OZzJFdWtEaEp0SXNzdWt3VDV0c1pzZnJEd2xacHFxWHNtWmElMkZ0YUhNNW0yZXRsUktST1E3VVEzOCUyRnVFY1lFU0pFNGtDTmJQZkliUEgwR2IlMkJ4cG84N1MxJTJGRkNSSERnRkFEN044SnZxakx5R3FaSXVlaiUyQk1WZ2N1U0JZOWx2aCUyQkxqWVJhYWp3cldzN3poemxjcmhMVXYlMkZ0ZWdhVXRmejAwOEhkczRIeFlXN3V2ZWYlMkI5MXRFVnBZQndVMXN4dSUyRmdQazR3czNPTHJHckxZSUdSOEYxR3VaMTJYdldNRzRUN3FJJTJGZVVMTEtzMEpNMm1qOFhONkxKWTlzWHhVUXNMTXJKTEg5TDFFZXVDOE5idjBrRyUyRkxmJTJCamFINDdtTSUyQlJTbXV2JTJGbzFnOXNTJTJGWVFlclVQRkhaZjlYS0dUSyUyQmg0T2ZTUVNFRWh6anNiSnFqN1glMkZBdzljM0FZdVVPJTJGJTJGYjlZaTlFSzZZd1VHM2ZBU2Q3M1JlOHhTaGVvZVdkSlpMcjc2VlhwU3B1d0FKREl0Q3VRQmtQMWw1ajhyanNkTCUyQkU4S3ZSUWdIMllCYSUyRldvbWFmeWZJcE1qbnpZVm9XOE9za0cyYiUyRmFLb2V2SyUyRnljJTJGeWN1eVVmYW9jREIlMkJDNlZMSGx4THJOa25xVFBEOGwydmhQQUduOCUyRnZCc3UxcVFwdW55TDBzbU5GakMxaU9OemVEaUVtWnJSNVNYdXd2ZzdUZzVMSWYzRFpCdVN1YTMzMFVMQzdHNUZLMjZrcHk1ZjJ6ZVBOVFdDUUtIaFJ1aTk0byUyRnVRNXVVWkxTYVdXanRoN3NidSUyRmFoVyUyRkZZZ0ZiQk9COU1JQURrcjQ0bnhpSE9veTlPVm9xbTU0YkpMUSUyRkM4a1BhSVRoRWY2U3pJOW9IQjlkUXV3RSUyRkVTWmJiVldjQ3V3ZG9NJTJGVE9tWXRHblhhJTJGUiUyQnZEN2dVckFQYTlVMElHNnNpemUwZldoUHlydiUyRmxpOFF5QVFQWmFWcmlkcFZTdlhJajJ5VWpYMEpacGZ0cmVlZktDZVMlMkJWU2xxVDFUdWx1JTJGSHpvSTdjNFhwRTFWZUp0RzVBTXljcWU0b2RRQ1VzeTd6ZzlDVHk1aUVXNTc0elpzejd2NXpGOGZlNkI1b0M2WVZtaFZQNXUlMkZ2cXo1SjFkOUVualJTZlJUeFdQZ3FRcGUwWXJRVGRRdUFLck1TNFZSME1XUWc0M1ZSYlk3JTJGUGMlMkZtaGVnN29GakNWcXVpYXE0R0F1MVFxaiUyQjdNeTZRUXU1UVlPQ3BuTzJ5Wkd6YW9LTDZYODlyU0NMJTJCTEJIbGI4bDhzb2NaN0pXa3lVSTd5Mkd2ZEFaYkxzZFd0TXZWT29EakclMkZtcE4wY1J1VjhqdjBxYm9HMlcwQ1Rud2ZKRnJ4UG9aVW12ZkttVFpUc3RvQ2tLc1laUiUyQmh6VWtkdEVNbWF2OW9ETjRISnNzJTJCJTJCc0FEMmxWQlRGRGhXc0dYMUFXcVAzJTJCTkdHazJmanpIMHFDTkE3SWZCNCUyRkdoQzRHbTZ3JTJGV1JCJTJCcFlNc3QlMkJpNThHSkp4bUpzbDlac1JYRTZtQ0kycmVOV0x4cm5STW5ZODZBWjRZJTJCQlBiVEJRQkZrOXJ6NmRjeVN6OWppNkFKeXFHWHFIVmlDMWJ6SGVDZ2h6UzklMkIlMkZVMVhIbkZmVEtHRTUyVmNBck1jc09LRzd1RlolMkY3eE9YVnZ6TDlaMzYlMkJOQzFIQWdxdFBkdlU3dlk4NGVKQ3JCVzN0N2FRRzE2c3M1VTV2eWd5SHNtcHEyTW8lMkJ3YXFFb2tseHAzYWNhVjc5QkQ1MVRLUVRmQTJwOUE0Tm90NHBDSnZaVnExUGJ4VlJ4UkJ4M3ZtSGg3ZDQlMkJBWDBqbDZtRk51c210VTdFekVXJTJCTm8wUUhvelJ5YURQeHluTm1NUiUyRmNZRVBHOHZXY2xKaElYeGhWbzJmZnR1SjJwQXV4YXdMOFJYYmQxJTJGZCUyRkFpWmdwQkUxQjRvY2lsWVZUb1JmeVg4UlhYcHBDS1A4QlY4OFJ4OG5TSiUyRmtsSWQwWXZacFVyUnhFSVdZZ1hlRzdkck8zNHAydk9VdXdqbHhNamVPSmZkZzclMkIyUjlyd1pUU0R0YWxHN00xN0RRc2VYNFJCV25HbUluQ1FwWmpyeFZPa1lEciUyQkFBN3ZtZUJHWm1jRG1UUzNpUVZvSGNZN3lDcTBPOGhIS2xxUzluUW9zYkhIWnFqMlFKZUNIUFZ4NWpnQ2xvM29jajd0TWY4Rmw0N3lqNnZxOGRPRWlNZlpkVmltOVFYREpNV1JjJTJGakNwekpkdFR4bkJZN3RxVDE4VlJENmhZdnhxQ1BmRDZZbnJETkxGVmhsYkRDNmtSSEg4V09rZGo2aTVPbmRud3VoaDhEc3hUczNtTWM2ZGJBSiUyQjFlU2wlMkIlMkJNUDJ6NkJVUFJSS25BcHg4MkpxU2k4ME5XJTJGNDF2ZXcwdUJ1d1V2aldLbDR1UzY0eEdINUxHSiUyRk95S3dZOVJ2QjB4ZFJXejQlMkJCY28xOHZmNGN0dkZ4ZU4lMkZKb3dvcyUyQk5qVzQ5TDd6UnJmc1NMZ2RGMnBycFJPb1FOaFBaZCUyQmRjbkI3UGklMkJaVENJeEhLdTFtblVuUDZRYWptaFlTeG1kdmVzdmNGdVB2Nmc2dXdzRlN5TjJicFI2SFpCUFdsNkh2SUpzSXRIWVFmWmhmcTUzRyUyRllKRm51Q3dDM3VsSzJlcU9nSXViUXZrcHNqVjVSZFdSU2Q2JTJGMEwzc05zelVtazJaVG1pN2dzcyUyRmt5eXVtJTJCa2h0MDRnQVBOcHNWNFBZMlhQbUslMkJWOFN1WTA0WndIOGs1bVVEOUdEajFxR1JJVDhVd1NBbEdjeXNMRUw2UFN5dE1sUkJHeUVnTmZLVEF1bGxFTXdmU3pSM2lnMXVxbkVPYk13ODN6YTdzNW5qMlFrS0N4S3Q4aXI5SnB6YUV3b3glMkZ4ViUyQk1kbDh5alZZZlNmY1VYT09FMldOSmFaajd5WWpjJTJGZ2tOWUthVXR0R1JSam1zNmt1RDVHcVk3b1M3JTJGT25RNlh4TlFGQ1R3UVJUZHpTdG1iSVVTSDBVY2FYS000NFZmazViWnZSVWd4eEhiJTJCcHZJckJ3alhTTWVNM0Y4dURIVWJFdXpqZDNzVDJ3Y3NCbXRuOSUyRnU1ZVNMY3JWMkRnZkJodlQ2VnJxc2F3eXpBallkZ1FFTW93OGglMkJ4VmFmNmkxVSUyRjBRVFNxZjl0ZTVSUTRHeE1KRlZlaG1raWFibklkb1RuVUlXOHZTcEExemZxT2wlMkZldGFNdnlOZmF4UU1JdlUxU2doMHMyJTJGNkhHVkw1TUh1QU9iTFplNWtJZjk1ajVOVlpGS1V3aDFnMWo1ZlNaUjhYVU96QWlHRDB4OTJuNDBkdjNNc1VpUGJXMXlQZmpqMXY0UlhFb2pvTVk1Q1B0TkQxZVl4aSUyQmJuSkVjOUJleHYwVXdIWSUyQiUyRnA1dHV4aHZSR2E0QnBROU9BbTlLelJRR3p1S1ZDOHlORzhRaGpEWEFVRWdIenR3enZpcHolMkZzekJIdSUyRkRkbngxaHQ2Q2h4MDRvaVdUelk3YktibmJrN2ZKVzBoNHpqejBUME1TJTJGeFhwOWVwYzlNdVZEJTJGdE8lMkZrUTJkMDNGQVUwb2lIVHh3Z1NMQ3VWcCUyQkc1WVlBVmRpc05kRmFKcDlnOFU3TnRaWXglMkZkNndRSEx1SURhV3NjRUNmOEp6UkpwRm9vb0xSYzd3ZnJ4YXBGSm8yQ1JURlRXN2V4TTAyZ2RpT3R0ZlJuJTJCbk9yVjRPZnFyTEg2clB3eGFJTGk3VExRNXFjeWpnZDNUWTZrZFd1Z3hJekFtSExpY1ZhTGdNd0Y4YmFhRFB0a0lLJTJCJTJCd2M2TWV6VElJa1d0S3BINloyTXBNN2dzJTJGY2hoYm5OeWFMWUREOVI0SlZoNUNQVWx4cE5uNXc2UyUyRjRhZTVTUXhOMXgyNXIyZDZOS2hrQXBPUTBLcnI2aUZ4V2F3UGxCVERwdkVxeDc5SE1PRWN6TDB5VUJUT0pBc1ZPJTJCbFpIY1JYeVpMcHkwQ2JWTHRtT2JjcVR2M1kyb01MM29RSWdlNjVFb2ltbzAyN1ZvVHpaakxtUEFRbWhRTElDYUlOa0NFNnclMkJYbnZUeGFzVWNpZ1NWUyUyQmNWR0F6MXR0YU9kMzJJNGZzQlFETiUyQmlIOUJaYXlYYm9KVTJtUlprQlB1WlVIUXBhbzFZMW9KR3BoaFIlMkJ2SyUyQk5BTGpHJTJGVEFXMmxiYzVyMzNzM0w5alFSOXolMkJSTCUyQk56cTFsMzBScnNXSG5MY0paR0tUT3pLWjMlMkZ6Z3JQWXYlMkJBM2hsSzlJRjNtZklPcVBpMGM0dG8zNFNVVE1ja0JYZyUyRmMyUlVTNXolMkJkUVA4YzZIVkJYWk1zNHhFVXNyeDhCSkFNeHNHemo5Ukp5clRhQjFvMGtjSkdTNFE0MFdCOSUyQlZDZmNzUDhrc1JmRSUyQjElMkJUaHRmd29RM21SRXRuekU3aGlVUWZyWm5MOGVDR013M1Rjb2l3eTR0YmlaZ1BaRDg1YURFWjBPcGJFQ0RqV2NjVEFhcjBDeXk0a0FHbXpTWTVEblQlMkJSWklCTUJ6JTJGdDdjVSUyRmhmelJIUHU4MWVsMTloa1FsREphWnRuR2gwNmklMkJyJTJCMXNSZEU1aG42QURUZW1jOWY0QlNPVGhlbjJFVWg2VGlHNUF2M0E0d2hqbE9wSU5tSGxnZ0szJTJCNDF6aTVhMjVzOGF5cVpzQ2klMkIlMkYyQkhUbTVobHVhU3htQ0ZzS0ZydmlqcXg3MEY3NEZkOFNnZVZZZmNIWk40N1NGRkNEdDVzU3IyeFhmbE1MQmd3YkxIaGVPdmZpTk5VMG9rbmVDcEx1aWVBclhCWVMzMllLSUIlMkI4OCUyRkt1ZTVBNVhYR1lmaW0lMkZobFlhdGh6ODNaemVTWHBBdTRWdFVHelJTSEc0THM3JTJGS1pwS00lMkZlMnlXalclMkZJTTlnU0pJek5sY1FpZk4lMkJIOWF4NXY0alNGSEZRYW9Ra1Q4bE5PeVklMkJ1dEJIZ1o2bTVycWJEUUtWQjZHMlBhZlVTRHUzeDI0YyUyRjFZaUpqQUVTY25mR2hNJTJCazhNeTdzZndsdGl0SXZYSnNBc1djb093JTJCbEprOGNDNmxXbThUcnQ5cVFzNUhzUXVaSjBDTTJSdkoyWkVKbmJ6SVF5NG1BN2tiVWxJVDdhVXZGS3VrNHlPcUJCRGhCQmxBZ01iVHVDR21rWm1ZY2g4V2htJTJGbXlmamwxVmtLM1VGNThBT010QVA3SG9WZFRiQm1DR3cxTjhRbUJsaU1SU0pmTURwOWVkMkc3NWhTa2F3VzIxTFpLNW90b1d5SGFVZE9rdkNYbnRvSFAxJTJGZE1KUmFpZVVKRXFyVFNjZUY3a2dtZmlCJTJGODFzQ1YzU2lxbVhPM0NhRzJRbjBOOG9GdjdFeDUyWXBLd1FsaDJDSmp3aDhSbHFOb1pVd29uSDhVWGRqdFdyJTJGM0dSMjVnaVFEYVZDMyUyRmVvRGFOcG56ZCUyQjJGakZhSDdDQmZsWHJ6QjElMkZ0WGNPNWU5TiUyRnJVcVolMkJiYVA4ZHNxNkFoYkxUUzI2dW5uUHhHRFRvJTJGbnpwJTJGWFVQWlVDOFdGUlJQOFNOSUhOelJKRjNUOU9sWFMzakNwY091b0lrRFBMN1ZNJTJGNHNGNTlkWkhvZ1dhb2theEFGWHh2MzZLJTJCellXdlREMDFmckVWNktJMGFjeWxLTFIwdDV6aE8lMkJjZUxkYWdPJTJCODl6NUwzU1hOVTZ2cmtwRUVtd3glMkJrNTJwTDlzWU1JdHpEZ1NQYjlSJTJCRUw0NW51UTMwMWpnJTJGNnZnQWtDNnJEUTJYVXpVQkVBN1JBTnppdnE4bGkxZCUyRkM3VUNxRmpPeTliVXYxQWNVeDZoMnJtT0dCNVB5ZFAzZHl1TXhrRzVVUmhSWXJmbDM2aVIlMkZUVW1qRmxFZmRHRWVObDUlMkZYQ25LQURyNW52bDVNbFNJNUtTYnNPcTk0M1ZyWkZobVpTJTJGdHRteE94N0glMkJYWEEzYTJhcTFING1qTzFEYUs2MU9wY1BOemkyVlcxcnBsb1IwUVYlMkJNcDZhWXdJMzExOXhXVldBUkZGU1p2bCUyRlBNR3NMUUNFdEV2a2lNYWxibkR0Sk53Z2tlUXFiRGpldWZXMXJyQlphMTl6JTJCNnFyejZ0RW9pWiUyQlpiZHJwWEVxMyUyQldJOTN1OGhVWm9aUUdQbGxKZDVESjJ6UmM2Qjk5WFRoUGlnTXVkMldzenIlMkJTc1ExWnBuUDQlMkJHVnVUTlk5cTZKeHdlemJETGtGbUxxV1FsVDlFTnozc1RVUVptUlJpMG83Z2VjRmczdmJNYnJJekt6UnRqMWozM3lYUEtGNCUyQkdHMThRSHEwM1Z6TDZ6VjhNMlVwZnNtajZBODk0ek1sR1N1WnNIUGE4JTJGWTMxcXphTFY0JTJGMXd1WmZWWWhOa3ZGZlJCbFhQOXRmTXdUU0pvYkdsUm1wUndhNVolMkJROEpaQ1B4QjFzREdKaWNhdjVQYTdQdVgxYmcxTVl2S2tEbDNKbVNYQzUxMmZvYXNpMU1FdXQ4VGpLZGkyVEJ4bkFmdkZwYUFvc3lIU2xtWE90SVNTalZlVTNmS1NLbWlNdSUyQlNZUzFvcUtpVHI0U0ZrWGNIdHdRN2RibGtOeW90ZXpSVXdaZG82T1pwVFJLdlJPNVdSQ1czbiUyRmxieWdvN210b0d2a1lLejBrdUI1UFliQmxuZXJrRHR6ZFdqZjBRTlljSWVBNkNzV1BBMzd4MXclMkZocUVJZk5ZbkgyOGIxN1NkN0ZhJTJCanFUZ3BKSVBTSzVkZjhvSFdOa3o2SVFra2UxJTJGOXlpUG85OW5ZcjhBdUo4V2UlMkZFREg0OE5lWmVsanNyeER3QUdIZzQlMkJRU3pRRHAzbUpFMzFxeTU5NndtTzN0dUZXTlpMZnVoNnI3UHhBVDUlMkY5aFllY1J0bG5OT3JYMURIb2Q0dCUyRkl0akM1UUpnNk9MMWpibE1FNlFGUDJ5Z0sza09CQkhjNDlNbjdJSHBQNEo2emh5YTh0R0RMQjlJOEFpcENrTFlYMk5mbW1tWHcwc1YlMkZMVmVWY0Rtb1FOV3JaeWRZMURCaHJ0RjdUQUw1NlNQQ0hVTEw1MHg5eUtGUUI0TEo5cnZ5ODhxJTJCdFZXSkZxUGlYUG5HMXNhZFAyNVhmRUVIWXBjWkk1akNPNndabzYzVHYyUEM3WmswV2ZUNXgwZGxrQk9PS3ZmMjJYWGZNWEtUNSUyQmFmZ0lwZlBuWjltNW1hWncwdjFSbjczQzFWJTJCVkNyWmhKUUxTM0NzJTJGQjVBYUlDeGwlMkZuU25HM1hMZFhvcFBScU1lZ3hac1pNY3pZV0ZpYXJ3TTdaeDVXZjd2RW1kOTgyNjRDakZRN1ZkbHhJaXZRd21sTnJkM2w5NFBNcG4zbzclMkJHaFVJa3JVcHVyVDNZMVlTZWI2aUc4UCUyRlh1MXVYM0RBYjdIeU5CSWZLaVYzaTdKM1BzNVg4YU1QNWtOJTJGcTU3TlV1SyUyRndsMlNVJTJGJTJGbjJrOGttSE4xZHIxME9RWXBHdXpIbUczUW1wTCUyQjZjYmMyanRWZFpReTVRcUp6YklBa0lyQWM5Qjl1cXYlMkZPJTJGUklITmxxUHJyUEZKJTJGSjc5WHZ5SDczY2RZeHBmRnM0Qm1JU3dOb0V3RWE0REowcW9FVzNMejdBRXAlMkJsTUV1OVFEODBJMElsUk9BSjEzcWklMkZiUjNPelFMNFpWeXI2ZkNPRiUyQjNWak5DVVFnQlhBSTlOdkVTJTJGZHFVaG1STVM4Wk9rVk95ekdyRCUyQlFFMWlFVFBTYiUyQkwyajkwTDRwJTJCeVglMkZWNWFlJTJCWWY0NGpJcE9ReElyQ1QydUklMkY5aGIzUFFhUzhvdjVPbHZtSGU1djFvNTloSTVLTnRwSVBabzVIMXpWQ1NHS3U5VW9Xd0g3a0xzOU45NEh3MEhIWVc2cm5OZDlQWWQxVWMzSVh6Tlc1bTB5Rkp1VUxYV2xtYkw2cjdSTTRYSEVGZzU2c1JVSjdlZ211Z2FiTE1NWmtWM2l4d3RybG9ZaVpDTVQ5V0lxU0tYb3k3NVN1bHJFelE2SFVidzh0aTRsdFFMNEpSYldVMExIeUtRaDc3ZEdWJTJCViUyQkVINSUyRmdQJTJGNFpZd3hkbXRGTCUyQkVDTURld0J0JTJCQ1FrcjlGeXZRd1NnSk5IeE5EblQlMkJLYk4wUEs1bGh6TkZ6U0wzdjNEYU1GY1lBRnhIUlpIZTFqRnVDSzZCdnR3dzUwT3VyTFZrS1lqJTJCdSUyRjA0M2tBc0FqckwlMkIlMkZ4ejFQJTJGOFdkd0J5a2ExWTMlMkJUYVNJSCUyRjNzbnNvdHpTeW1WWiUyRnMlMkJYJTJGZnVqaTNIWSUyQkxpdnUlMkZ2OTdPVkxGZyUyRnh1JTJGbCUyRlBwJTJGJTJGJTJCVUJIdnJ6SUNYeng4NEglMkZiWnJpSlIlMkIyJTJGMmZUbCUyRnVmTDlOZlFqNzIlMkJiYThLQjM2NzRBdjlPJTJCSSUyQjklMkZIJTJGJTJGdjVyTE90JTJCdDl0MlAlMkJIJTJGdHRhNVhWWiUyRmUlMkZ2WXY5OU5WNyUyRmJTaiUyRjc0OERCJTJCdSUyRlV3SUVlREg1Q3hmJTJGdTRLJTJGOXglMkJvenY0ZGclMkZ3M2hMamI4MzliJTJGbTFZdDd2N2I4TmF4Uk40VyUyRmR4JTJCYjdTWUxoMUduZHFuT1NkTWE3MVZvJTJGRHV6OFp0MjNzM3k5MFlBZjlndmx5R2ZjaFk4WnVYTjc5V1Y3RWU3ZjlQNzlBZFhVSmp0ekc2ZDBhcjFPZWdyRVY5WlclMkZsMGYlMkZuWkQ2MzYzUSUyRjI0QlB4VnY3JTJCMm4lMkZuMzg4TlB3eWh6ekNwOXVuWkFpbEg4UDhtZTdGZWVXN3pzVWZKUmZDWHRsbFdMWG5JRWZzSVVKYU1rUDNtMDB6cndmODdQa2QwZ0V1Z1pJZ2VqUm1zZTU0QWdjZkJ2TVRDQXo3eTRvRVV3UVVUSXBBZXpGcyUyQkYlMkYlMkZtdTVCZjdJZjklMkZtVDhwNXY5c0VYWElSQlVmeEZQdDVkeEIlMkZJbVpTdVVoUHluc043ejlLWjZqbXI0SXVZQU4xMk0zdjl2UDk1ZmQ0Tm4zM0M4enBmbjhPbGJ5ZmgwOWFnbjN2TmRMQnU2OTZYelVMNGthWlVadjNPRFlmbUF5Y2d5elpFN3dtSlNNVWdVUmhKa1dwSlRXOXg0SiUyRjhudmNGJTJCdzMzMiUyQko3eXZ5N3ElMkZmOTd2SmVHRDdSZEVWZUIzZjdRRkYlMkY0MmNvcUMlMkZHMFZSSDVNMVRseDdOUU96am1pb3N5VVlMb3RpWkVka3dwVDVIdmNPbFEwZjlmYWFrS2NNRnFoY3ZZbiUyRm5rVlpNaTE0N2Q2bk03JTJCJTJGcHIlMkZuQUU4cU01a0piSTlLaWxsTzhmZU9ad0NmNzNmJTJGcjZUUEdDeFVwemNsUnJ4ejhQJTJCZlJkb0lsWDlJeGpXYSUyQiUyQmZDaWJvNGN1T2dKNDNnZVFyWm42blVITmNJZjdnV0NrSlloblpVeGQ4RXUlMkY5VFIwSkdvSElVJTJCQXJjMjNLNlFUTmFUYmJ0UmZzeWVST0VaejZlS1pOREJITkdVMk1vMEpETFRzJTJGMzcxamFSem1GdGN6NDVnbDI3cmklMkYlMkJuRGRNZG12JTJCYTlMcUZVSjRPTnVJNDUlMkY2ZGZ4MGxuR0hIOHVwZkdTdEdHdVVKWGxlekFsS2hRemElMkJiMCUyRmo2VjFqVmJtYkhBVUQ5ZW90SlJrMGFMYVNHRVVsdUFVRE0lMkJQJTJGNXk4SmxzQmczcWVTRCUyQiUyRlVHazREeG4lMkI1bCUyRnhudVVVZiUyQjd1dTh5Q3BUV3pBTXkzWVdveFJ3bG91ZmpxWCUyQkpUS3FDQVBQRmwyaUJzeUR5SjF1Q21hQlF4aHk5JTJCSnk3VU1nN2JGSk9tNUVJd1A4V2MzZ1AyMzd1cSUyQmNpJTJCbGhmSlRlSjBUd0t0RFE5U2Y0c2lIU3JSYlJWbHEwJTJGbnFrcGxpTjh5Q0U0bmZiejZReW03ZGtmVDUyUlRQZDBzeFcwN2lTeFN5aDJxTDAzbEc1YklSUjBtT0JRZ2ZsS29RZTVqbVZxZ3VtcTVqZiUyRjJWSko0JTJCdFBpVzExSEFWR2luOWpYREE3WjNKb3JzT0M1UEVwQzc3dVpVMzVGVjRNM05EeGt5cDFMciUyRlh4MG83UFNhTnZyMFRGaFY4bUplZ2NCV1lNVm10NEFBSkNGNEl6Z3J4b2VzZWZsMkdXcFZ2Y2xFa2tFZWhHN0R3UEVmcTlxbnhKaFozZDRIbjFtbTF4UTZ6Ukc1RGYyUGk1eGpYU2RMaTJNOEdRZU13eFR4ZHpxQWF4dkxKU0ljV1FEcnBCM0hxMk90R2NFV3FhZ1BRNGNDOEtTUUZXMW5HJTJGWW9JN2Y3NkplZGZRa1l3MnJGOVJyRmk0QW9jSUMlMkJzbnozRkpaZ2ZIJTJCajBYZmpyRWVTOTV3Wm9HaDR5N0huUHJudzBja2dUM0ZnYmRoaXc1R25qRENpMHp2Y3g0RzBuVUtPTE9pVXJiWjdiS0pTWXFFNFY4Z2Z4OEVWYkg2TWZnRmdybG5YeWRsdTNYOU5BMm1WJTJCJTJCdWd2d1EySXlBVlExTEVPYnJKVXhKY1lnSlFWaHc2UmhnT244cjhKUmVNS1J4eEliYnFYMFVRNFFHcVViQ2R0Y2xNdVZRSlFFemJzaEdNS01ybTIydEphOWJjT2NMcU5ZOER4bDBYTUlQd0JPQzRjbTB5WHJ2anZUTCUyRjJpMVQlMkYwSm5xaWc0WXk3RGZnSmNINSUyRndaNWVMalV0QUVjNlk0V2ZGMWlVWFJqSTNjQ1dCQ0lqV3V6b3lVNnRENUJYQnBkQjFTenFQdU5rNUtjZHR2WVdCRk1oTmVydUlFak8xc2llaGR1bFA4YUFOQ1RzUzdvaGVRT0RGaWg4UDRWa3VnQm5GWVZBazJBSGVRTTJMVGMzVTQ5VXZKVTlraERxUmtCTHNlZU5FeFl5SFVNUm5MbERLYXBBMUJNYTFFSTI1SGYlMkJ6cEVEV3FPdTVsUjFIT3NNYXdha3d1JTJCaU9Dbld0Ujl3aExGNSUyRnVEWWJGaXUxUjl5YTV2OW5jRzRqdmIyYkdGaDdSTlcxOEpKczJmaWluaDV5UlkyUnU3QVZVQ1piRTNRVnBVN085VUFtbkU0ZDk1OVVJdjlMTFNMWTMwciUyRmRoTGFYUGlKWTBMcVltYjJTaWl2SWhpQnpKRyUyRlNYOUJEb2lmdU5sR1VzUG1YeUczbkh4WmZ0Ynh0YjVRcUZERnNTNmZsMGtFbk9KNFhsNTclMkJPTkpUT3paNGFnRk1wc244NVM4R0lnOGY4M09WSXlGOCUyQk1nSWxvZXp6WDJKN2JNVWdNd01PU1ZPRVowQ3l1WDBvSVN4eW9SYU0wVk1nQ0JrYjA3RkVuJTJCMmhVQlRwN1BvenllaVpxclRtOUg0aFFqS2pQUWRtYllrRWxSVm40OGNIJTJGY0RuR0pkMG1wenRkeklibFMzcTN5RldKYTNQa0NwOXc1VGpPdjRvVnQ4QXFXaVdYb1FiM1Q4U0pUSDZLQ0dqN0drdTVLNkZUM1NTTFEySjZadjAlMkZNeE9hSFBhaFAlMkZZNWJ2bjBHQjVvUW1QbUslMkJzVTdIUiUyRnM1MmQ3aG9Nbkowc0c1MHp0eXElMkYwdUlaSDklMkJhOVdXMW56MjF3MmhRS2NuZlZmNVJWYmVXZGRtRlpmOFVHSm5ZRkNyUjFwUXA1JTJGZm16TEZaTExXUTgwOGdGSm9rTmFoYjJsdk9aYkhKaDhtN1o0M3ZKenhnVGdXUEN3dkdyWmltNzhSTndFSDVOcG1WakkzTXdaRWoxWW15UnBmZXlBUk1EU2lGdldBM0hUSTJMQXpJR0FVOEk1VVNyUDlNQ1VZMWg5S0Z1R3gyQkxTQzRLY2dIZDFJOHNOSWMzaVVZd1JhOTRxYUlDWlUwOEM2MiUyQjNrU2lNcCUyQlJJYzFvNW81V3lyJTJCNzUlMkI3bGlXNkxENmVuTUR4SElwTnI5OU9hMnk5YUtsbVhtSWFwc0JaUiUyQjV2bEFnWmhnV3UyaEtqMU5vUXVmQ2Y5Uk9KM2UxUyUyQkxHUlZRVFljUnBvWU1hQWxTdmZscTlCWHlzVzY2aFRDZ013aHQyUHRkejZROE9abW4wTWtpT0ozYXNVSEx6OGclMkZHNUw1aUJWUmNoUVdZdWJLUDVGQlJjV0hEa3pwYkZHelpkbjB1MXR1VXglMkZ2NGdETmJpY01CcEFTQTMyMGxpdW5QSUZLdEw4Ym1zTzcydHU0R2NreVBmOWprSGlXeGhkeDE5cmxLbDV0YllTWk8lMkI4TWpYNUZ0ZmFDME5pcTE3RzdiTVNFdWpITXVxNkYxdTZhSjVvYVlveTlwM0pveEhFN21jQksxNCUyRnR1dWJ4JTJGeFJKU2tLcUxiU0pFbGZhS2xSYzUxeWszV0w1czJmSVdpdUk0bUZoY2NtUW0yZSUyQiUyRndIR1RGbjNGdTZ3U1RmSGwlMkY4YiUyRms5eVU0Wkc4Y2FGOXZYeHZHZmliV2t4blZOY3FZc0F6YW9pQ0sxMjlKd0l4UW9jJTJGajl4TlZUT0hDV2U4cElocEc2THlNNUdkN2tmOHNkaDJWQ1lvZHZmJTJCNDFxVHElMkJ3OXZIRDNLOUFWSjglMkJCcGtUMnI1UVg4WlBuRjZPNG5mTnFVSFhQdHQ3SXNmZXhQRzlFYTlpbzllUWNKNEpmWjVTWHY2UHFJbzhaNklNdExuTjFHODFPSk8lMkJZbVVJbCUyQjZuV0tCRiUyRkhDJTJCOHlKdE1hNklzNENOczY5ZiUyRnRTMk5ic2ZYUGZYTnJtVjQwMm0lMkZuSDh0b2NxQm9aNGtoNk5JJTJGTW5MJTJCNVEyMjFQYkdOTFJ3V09RM3Q1QUt3dWdVNVFscWxlQjVLZmI0Ym1Ua3VMTHBKWmhMWkJ1b1l4WnlnOWUlMkZzd2padlRXTkxmcWVKbmUlMkYlMkJsSiUyRjVWWE1YeXk0aVpLd1lqZnhtVyUyRm42eFhoNGdSMHNUUEUlMkJxNG8zSnEwMmFjV1diVzQ3VE9qbmRCeEtaRWFoYlhSMUolMkZycTV2eUpIcTVnUG1NT21odE14TXRWSzBTdzlLbGxvYzNJN3BxMkxkUzNnOFlyNGNNNmpwNW8lMkZwYWNYTmxSaXduM0V4cmYzVGFrWWRQTTN0TXdKclRIeDJGTVBaVkxUVm85QUtHNlAlMkY0ZyUyQnp1Mmtxb3hvdHpwTG5Wa21HTlo0N1RVMjBLMlZ2MG8wallvdnJoek1sWGtwOUolMkJkVU5oVVhaSVJwNHp0ZmxuaXlWTVJqZE1nVFdjSWJFcUhBdHFYZWpoV2dPdGNGMEZ6OFB6Q2xGNU44SFVCVnQ1ZlhkSG10QTE4NnQ3bk5pUjJEd21CSENsVkR2Ulg2eThlRFo5MFV2Y0VUZGJmdjVTWlJPcHNxJTJCd2RNSXd0VjlnSVgyM1NIM2ZhMGQ1T1VpNE52NHNaTlNyMmxVcUlvN2hKZzZsS3NsNnNaZDhsaVBVZ0pDUU1ESUkxemRnTmRKRDdKRDlTZTUxQnJFUTlkbjdoRiUyRnYzNUY5SSUyQjJ3aFhSUjduMGdtTGxyQ0dBY1hoUHlrZFJmZlV2MXRTaGZPb0txWHlzSGUlMkJBbFY5MmVVZHVLOGglMkZMWXpmaHlEQjhncXhvJTJGS0RhJTJCNiUyQkRyaUlFYnVnSXVqQiUyRnRBbWR4MzFzd3olMkZrb1lqJTJGQllkZmh1c2IlMkZEMEF0ZSUyRiUyRmNxaDVsYWhoWnRNNG9GdlJ3djNBT1FJQ0N5R0lCMW8xdjU5SDZ0OWR2WWVaSTEwNkR0dyUyRiUyQjRNZVV4OXBYZEh1emlkaW9ZYTc2Nm9kMUE4cmN1WmpMbk92JTJGeWpFZnpITDM3b3FHTHZ5QzR5ZGdmbDRVVjY5WHdxS21naCUyRkQ2SHJzOW5FVHNKaGlmSGk4U0psVmIlMkI3SXpxWlhyV2YxQzV2WGJkQU03U2hqcXpJWHZPTCUyRjhzcWM3eWRPdzhLJTJGYjJXOSUyQmU0c29FRFhkVSUyQlhCSlJQeUVRJTJCWkx0WENTQXRDTnc1aHFMdiUyQllSYTVac0M5MGRQT3UlMkI4RnAyckV1TCUyRlpYOWVIZ3g4b1Jxc3BZVXM1cDA5bzVLcVRUZ2VUa0ZGTVN6aTM0Q1ZpeGltVmNqTDg0ckFiM1Y2eXhlazBFJTJGZ0FEWk1EdzlLWCUyRkpTTVo5TUtmUSUyRks4RjhEY0RvMDZNdFBydFltcFRzUHhwdFVvZmNqMGJQT0I1ZWpVNllONDZlMEppcExwWVlIMFdwd0klMkJReXU2RmZCc1lOb25qaERuWk5Pa2JDb0FWJTJGT2l1eHdUdEJHcmNuaVMyRlo0c2YybnNkb2VjelRmbENUV1pYNHZpUHVrZkVkQWJDdmJVYjFUVE1TMXV2OURUSXNsMXlyZ2k4ZllsVnByZk1IUUxJa210V1ZRem9KSkFZcjZ5RFR5bEdnQU94T2pyJTJGUmYwRWZZU3BiMzc0TUhjZm1YZk9SSDhLdEFjM2p1cjJIeHIyaHZRN0olMkZtRjk4S0pHOUJWQmQ0cHpZaDlLZGRCQU9RQ28weGZ6WkRKeUxYQ3J2RzY0JTJGQzdXaWtiejZvT3dFeWphdHZOMGJrcFZTVjljNjY3V005SmNJcncxcEwybGE1SmFDaldBZWglMkZJSnNua0xKZEsxWFhKMjhRdjZ3UE5WNmVaUm4xcmtMdVA3Mzl3RU5xeE8wcjUlMkJMQ25MZTRxcmQzUCUyRnJ1dWNjSzN5UWdEJTJCWldKQmRrZkNxT1YlMkJhcUlhWkJGeWFaZ2t1aGZqOThYQTUyaFdVMWp0MXYycnYzRXI3R3FOYlBCSFZIR1NmN2hZQ05ra3JjWTF2anBqQm5xdFVaemZpSGpZa2YwazVvOEhxMVA3czBNbE9xJTJCR2tXa0lZOU53aDVMSkMwMGxycGdraU1odGJ4RDRsY25nRTJWZEY0VHBCU1BSYzJ1ZVUlMkZnJTJGeVlxb3F4NXh4WjZnaWdMc2h0WnUyOThkTGRLV1I5JTJCdDNrNUlnaVJmOWFCa1Vpb0U2bCUyQnp4dU5pVFZ1WjNWWiUyRnlqJTJGelRUSms3dlclMkZIb0NCMDhoWVQ2eCUyRnklMkZybGZuNlV2NmEydGdGN0pBUElydGNuTkxwNGF2b3o5bHNQOWRVTHRiWDNtYXdZMUhZQ3lzQjI5UW1lUU9ta21tamRucFdFWk5xbTVmeTh6ODZkcHYwYSUyRlVMemhXamU3V0JOSnpNMnZ4aXU0eWR1dkNpNmFPVyUyRm5DTXU5TXBkZ21zRHFxOUN2T0VKTTFRY2tMSkMzZDlwd3c3UkoxT2U2b09LJTJCSTNraG8yUWJnN3R4NEw5TmNMVHYlMkZLWmlodWUzM1NCWGY2SVAyRkxlMlduT1BRcXBQWXBYcnU1ZHNWcUtFcmcyWDE1SlBWblYwSjZkJTJGQkV1eG5IMG1lRWlvZVRFWDZuNlc1ZE1tcVVINE5TSGViQ2t3VkVOUkpmVGVkelBrVUtqVmN6Y2cwJTJCalVYJTJGU1RmcWM5RCUyQlhMRE45c3JkY0RISURsR2hndDliUm9yelhsJTJCaFNBT3M1ejBBNWVKZlZxRlI5UlczVmd6a3Z4S0JXODlsQnFjRU5KWWhBRVhFcmRHZXN2UnIwVTRNQlFDVWx3Um5CcjQzSXRBQ2dCJTJGYUVvbyUyQjZUN2JzJTJGY3dGcldyMVVRdjBROGc0aHVYS3N2ZGZsZkhhbk5OVkFPQ0MxaUJDZ3VGWEFRT1hlJTJGdFdzbFFoeEpXZiUyRmElMkZic0JJN0ZiRmQlMkZPS2U0ZFdScUtGTm1mRkxpS2hPYUphVGFCSXViWnAzUVNzbHFONmEyS1hrakdqazIzVnFMblZ1c1hkNGVVTHFveHRscGExMUd5SU55bTE0T3NJclBHTHg5RVlObVQlMkJHaiUyQjYzJTJCanNibjRERUZ2OHNRZ0c2akI5MU5kNjhxOWxzNDc0VXByTzB4ZTVZbkl3Y1lTZDRSbGtVMVd0UlM5UWlBTWExcHRMZWtwQktTbUt4a3VlJTJGOHB2bWxKdE9kdHM5WGNiZkVUcldDRmFjVFdjZ2o0NGhhdCUyRjFGJTJCRmtPcnNoVHJVYXZwVkZ3NG91MFVmSmJqOXBseSUyRnR1bVU3VmQzUzBFWE9pM3Q4amZyMTJYMkRlNFBWakxQWGpRTVI5JTJCU3k3T3F0Smc4dURHekh2UmZMeDVMSk1GTDJpNFFsNUo2T0ZsJTJCcmEzR3JxUHJISWpPckglMkZwSiUyRmN0RFAxWnVHS3J2b2dBTEczWnF2a1FNaHRacXhrazgxMk5oamRYbUwlMkJZY0pPUUJQRmVLZG9idHRUOFlDWUNjdTc4cTNEc2IlMkZRaGRMWlU1ejNXSldoUm42cGxYREI1OFFYaHE4NjZGdnVwZUxxOHclMkZwZWxQSHllb0ZpejRnSnE1cDRVTldMOE10ams1V3ROQ0tqalpOa2xlYnFJJTJCZzNZOXJIYWUlMkY5ek8yVmsyWFZTaXdvOHIyTjU1OVpNQkFNYzRnZHJNZzNPT2JRazBCYkVFTGhkNEpoZFRac2s2JTJCc2JUZnVRTm0zR3MzeiUyRmt6TVY2VEpEaGxDSTN4TW5GaThmQSUyQlVTekklMkZnc2d0VXN2S3RQV2lFWlBwS0FxV0xwbXFiZzI0VllTTGxoU3ExV3NWaDR6Nk1LJTJGcFIzZVJaJTJCM0dWUUdNT01nZnNsSlZHRUhpa3JPZ294eTgxOTVwOWMycmNnMWMxbiUyRnRXMG54QmF1MkNvTFBkOHclMkZ6bCUyQkYyTEJxRGNFM2xWYzZmMWRERk5rNVNERnI1eldhd3c2dDclMkJ5ZThsOTclMkZxdWc0MUNUZDFnJTJCY3BSZnFyJTJCbCUyRklmNFRodjYwMGJ0NEZIS2FyM3lPUk4xcnk0Wms0U3BSU1olMkZKSHMlMkY2V3NORzlnalQzbnUlMkIwN1FpNVRlSU9qbEN0YTNrY3pySHFMTFJQTG9MRldYUHYxMDNSJTJCV1g4R01sUzRpc0gzbG80RGtoaG82bFBOakVoSlBqMkc2cE93YXJEJTJGSUJRWHhPSU84cVROQ2ZxajNTcWtvYVlLcG44YXZWV1lmaGhsVW9LOXlmMjhZa0FmNmFSeUdSd3dkaFJXSEozQmxCc1R2UTQxcW83Qm5hRmhqMG5PeGFDS0pMU3NVQ3dXVG9WWFZ6UWRlZVFFMEUwcm8xMGVHN3NWZzFQNDNXM2VIWkolMkZLT0U5SzZiZ1hRb0FIb3I3UW1MM1ptTFRXelVQSUhDNiUyRjJvTEolMkJVaVhqWk9xSkV4RmJQY0p6VnZrWklOOHVUSXAyTSUyQnN5VTJtQ1BFcW5FNElWZUlqUUNSdUVXbkFQOVZtSHVwbm14SnhXWmxGSXoxJTJCRWtESWY5ZyUyRmVQWUNjenhRVUpqcFY0alVlVGNpVlV5RGd5elhYUlN6SWVNbUN2cThnbjQ2WkVJMTc4bnlkd0NXYUZPY2t2eEx1bVY2UmN4NUtGZG5VNWJzcThJYVVFbG5sZWpRRmpEJTJCSFFSVHcweW41ZFI3NVdxbWdQOUNpdFl1V1M0WmpoTTZDQUZlQ3M1RmxmbkhrV2pzJTJGTm5zRlpmOE0wREQ3NEUwYlJ5V211TGlIMXlQZ240NVp3JTJCeDd2eTFndlpncEEyNHQ5VVQwWk15TkRZQW80SXlkdThKbzJhVFdHcjQ2enNjVDR6TEkwV2RWS1lGQnlJeUJueHNvcmhKMElNJTJGOUslMkI5eUUlMkJ4cXVlamh6UTdKUlU5eUJsMjNORUIwc1pVTm03ekFORmtsTXBiMVJ2TVYwMmFXdkpaeGQlMkIzOGZkUk1EOHExaEtDR1VYOFZkWU84RENFYmJqdUp4Y3hOR3RzJTJCb3VkcSUyRjhhWmZQbEZaR2xadWY2ZzREMjU2YW14b3Rmd3RadjdxJTJCSkVYOFExUnBiaERWbnA2JTJCMUc5Z1JpaUFvMDYzeDV4Sk5YYWRyOWNBSmZEJTJCYXNaZ29CbVU1NEhZNjNWR1FrVkxNalZscGYxbFl2cHFPTXRhTG5ZZWM0ciUyQiUyQjk2TG5iaW84aWpHeHlzUUVYS0lxN3BSdU83WjFEbVVyVnV2JTJCVWk4OVZJVzBuYVVzRnQlMkJMbHk0ZktrblFqWHBhTDVEMXVwZUh0SXIzZ3VDalEzam9CZFozTCUyRndvR2pYRENBcjJoVnVUVDhGQVd1eFVYdzBOM3dHcTZvOE1oUVBMUHZTWTJZSUI2VjJvazdxd0NDdnh3MXc0ZUxocWF4JTJCZjBJQ2xrQWF5MkdZVHRVSnRYY0pJbGV3UWZxOUEzeTlKSVVEcWdwMEN4RDkxb1dtOHFoJTJGaTRQUVMzRXhkN1BKZ09obzVvdEdFY1FkUEwxMkp0SGhTQWpFeEk3amFQWCUyRjY3QyUyRnFlM1Y0Y3BrakVkRnVyOVZlYVJlJTJCOUtKUVV4ZU9GMk1pbmEzZ1I1QXVoNWdmWHRPMGxQblhhWXkzVks2anh2WEFubUFRJTJCZkRXeFI1dmZnckp3TDhuUDcyMDFYYVR2MXFwenJ6dUNia21sbWJvUTMyY2N0c0pVOHdoNiUyRnhjcUJSNEZaJTJGN0pudWZyeldtcjBPNWZXWnpNYlNrd0lveSUyRmgwVmFXMFgzQVZaMXNsUnEzMGd3ZjhMJTJCZEhUJTJGT1Z2akZycDJlNHgyMkpObWppNUJmMVZrQUxSclBrcHp6MExNM2NJeHhPT0FMT1QwQmF4Smk1R3N3M0xmOCUyRk8lMkZmNTgwYWEwMmxHdUp6UTlXalcwS3pncWN0a2tST3k2N2dxa3RxREFLa00wN0NHZ3FFcnBZY08xeGVDTHYwZ3AlMkJCZTQzT3E2WllJQ0ZsZlFmUllFWjVaY2ZmbHlHTnRueG94VFJOR0cxaDNYNSUyQldiYllUd2duRlp2eVFPcFFVNFdHbUJFc1dVWXolMkY0U3huMHVzYXBzSVBEdzQlMkYwMzhiOWVudGFNMHFRSkY3eSUyQmhMcmdhbyUyQjYyVmtXNlB1cDlLVGdsWG1wSjNRYnhxSm9ESnBiM0dsMWwwVEkwMDNvNzVPSll4enRXWXMzWmQwVUlha1h1VDFUWHFjNktVcUx4WXU1SHE4M3NuUEVKaHB1ZDBXaFF5N1IlMkJ0T3MxM3VXaUV2ckRKQTBFQ29LYkZpT0ZpaTNWZ2g0SDdrWWxaZ0ZiMGpmU3hQMXpKYVAzOXRjJTJCN3djckZKczAxc1hDWmU0bjRnb2k3UjNJV1B1Q1J1cnh5ZFJOUlFiVjRpdGNSNmJETnBWUmZScU1iWXhNS0VKZkZlcFJBeHNXUkNSRCUyRlNxVCUyQm9JRE9jdHJDdUpObXdIa3AlMkZnSyUyRkVRaCUyQlFtN24lMkJ5TTBKQ3hoS0JGQndoYmROWFFZeDM5Qm01ZkNpcDV6dlBYV3J2S1htb2Z6aXQ5T1doRSUyQmZmdE9lbGo1akpiVlhrJTJGbFh4WGN3eUdOSzBWOHh1RkgydzF0Sk9FM1pVVDdPUXRaeVN4M09vME9GJTJCWmtvZEJWTWhzbThsJTJCTUZtVXRGeEs1WHI5NTBTciUyQlF1RTMwS2NwT01NV0Y2NVRENkZrcmF1cnNDMmtmM0xKUDJ5dmdXbEVnRm1YcHpiTDJocVBURmtQN3VIJTJCVjN0aWlyNGtnQW5HMVFybkhsSmN3eDJLeGNqMDlFODZITW5rbCUyQjZtJTJCMHk4WGFPbWIzRlNMcEslMkJCYlNyUkVDYjlMRUxyRjV0c2tnVTFjSkw4M08lMkZwaUszQkRBYjdLUGRIJTJCN0R2dFBDYnBQMmQ5aTRsRFFFQUFQN1hyQnUybmRjUTZMcHU1dkxuRUVINkhEOUdxZkRiVFc5aXElMkJmWXRUREg2c01YUXhIRnlaTTV0R3h3a2ElMkJnSHNRNkhIakdON00lMkJjUmlQaEdzYXFEdGRnbFhEMTUxNkwxYjRyJTJGQlNRakxhVnlCNFI1YTFrOWhuVDZubjM0RkdFUUxYWUkySTVBUzUycEc1MkowT2NBSzlxdGFrUjF6VU1tREJFQlJENk9NNGMwRVZqS2pzVjcyUVp5YU5PRzg3djVGVjEwV3dzTFlrTWgxWUNXTmd6ZjZxdDNlemM0JTJCTjlMaEolMkI5Y003VU1vOVdWbUw1Yk55UDZ2VDFwRzF2WG5hd2RlTGYxVkxjeEtrSEtFSU1HTHBoWUJTVktUTHlodTN2bUI1UyUyRnoya09ldXR1ZFVXWkl3dFElMkZZb3RlJTJCc3ZNRnMlMkJVYzQlMkZ6Tlc3U2VyTnF4VyUyRndCNjVyeFI5UlJUQk1LZFlBTFRrJTJCMUZjNUJWVnQ4RVdhcTgybVdNNXIyZk9LMElweXpZdGJxOFBFc3VpbWZzdzdQN1N2V24wdWltNzdrSDlodnlrcExic2k1MjhqMVpsRFV4VmJHWmtQVHhDa0VNRjRMb1F3QkpSU04lMkJqNEFsWGtjTFJiSnMlMkJIUXRsNVF3b1g0cEdrVFNRaVdLcUVhZ1ZVcDE4eUpYdmh6TUVYS3dSRTJuRmFpT0tqN05EMVN4a2picXZGNWtvVVlHQklRVUFGdHQxbjlOZGRXJTJCcXdJQmJ1NzR2OGF1djFFeG54VFN4Uk5VeVppRHlmelIlMkJ6b1hOZmM2R1piU1hyWVk2M1ZPJTJGMWFiMXdHMG93WUNhVnRXaEZBUWl5U3ZaWURnbDU0YTh5YlhuZmtWeVkwaWZFeko2ODc4aUFiNEVBZlRUS3FlbmFPWGZPTkdiQ05zVXo5eVF2amR3SG8lMkJrWXBVcnNvMHdwOTJIZHZzMU55MmxmS3YlMkZTVTFGQUklMkZtRVc3cTh5cnVOY3JqUkVvcXE3U0JKRmlCYUQ5ZEg5eEsxM3k0S3NGZHdKZmVUOSUyRmZ1dUw2V2x0VmRMZyUyQmIyNVUycjI2cEtkcVd5SlJnb25NYWZhdG95T2RqeEVRejhNS094NzVIV2ZPdzdNWUIlMkZ4SUk2bENuODVWaGxsbGpVd29oeXV5NGNCS1dlc0NDOXRYU2ZUVmtCUWRUeEhxbzZ1aElMTjBlWFFjNlE3M1lpSlVaY2NvbmdLZCUyRmdwMjhEUENpNTk1bVVicHJocElRSkxidEVGSERYS1M5MTlWVThzczVIS3FkV3JrSXI1YTRJa29hb3VWMlhQSHpRa3NhVFBMZ1lJUmNEVEpGdnpUR2kwV2hmVUZwNTNnT2tXTlBuODAzdGhqSmF4c3c5VWJaRXQzN2VhMDVlc2VDM3dwOTRRR3dwTiUyRmFFeTZHdE9vemklMkJwZEVyaElHbXFLNjc4YWdEZVIwV2NheWRZT1lvaEFJT1hlRmxpN2FhYnBlQ0hkWGN3anhmVkNwclo0V1pYM051V2VsYWMwS2dLJTJGc09DcFBIeXMxR3cxZCUyQmxCbkFnWnVqVlAlMkIlMkZGaXFxbm41aGNIU3RjN0s0eVFoS2hMWnZUajFRd2NNbDBucW5FYWNUU3BEaVZhaVZ0N1V4NTNnR1Fham1HJTJCNTczVU5CeUU3NDB3Z3ElMkZ0JTJCWXp5UiUyQnpGV2tqczdFUU9rVUdod0xLTjlKWVdpY3BnS0FWbGJRQjM0c3NZTSUyRmR1cWIlMkZZSDVTc0h2UFFXUCUyQkZlRlRvSDczNThkY0VKVm1kQ0JCdWhQMDdsT1lNNm8lMkJxUFFLTDZ2RVBGTDZTemk2Y0d1eVMyVDdjT0tXbGJ6R1JkJTJCVzFoJTJCJTJGJTJGa2piZ2hmMHpMOWhyQ2RSNGIlMkJlRVNidXF1cUslMkZ0SUNFMUpVTHBsOHFob3lWRnBYckhaV3hTYUxYODE5MlNuZThWRiUyQmZ0JTJCdzFLNjNKVEtDbmVMZTdPNlcxaG40Umk4a292dHBJNVVOJTJCck5qNGhMakRzNSUyQmZBRlk3MGolMkI3VVdRSEw1ZSUyRlJYczhvZ0dIeWMwNmY1bkN4clN1MzVZUldmJTJGQ3BxeXFVRFg5cTdNd3pFYjAyZzB4dUxlY2lTTTI2eWwydnE2WHpyQ3d2UFJQdXY0cTRWeE1YWFdTcjQ4ZHY3a1ZCeXpGT085OU9QbERJMkZLUiUyQlV1eEJhTmxEJTJGYXdmb3h6Vk9XMzJ0MjgwdUwxUVolMkJxY05CN05DY1ZOUWwlMkZsSWNiUEdHSjRJczJlYzNHZlg5SXZHaGwxNlVxJTJGYU1TcWolMkJZTG5ZakFSZEFzdE5qdSUyRmZBJTJGSDMwN0t1MGREem53VWlIUE5EemNPJTJCTSUyRmlOcjhHdGxSbWZySFhWbk0yekUwTDBlRDhDZmJ6MHc1OWhPa1l2SldqdjM4MyUyQnpIcDc3bWF4R1RqUnJWR21kWlBBMDVtdmh5bmFic3JLdlFNWU5ObFViaE42dGRHQ3p4biUyRlhiVHJGVFAlMkZrZWolMkJGOEdGZmtZY3B4ck9xeWFSencyV0tMRE1EeVhKWEslMkZPVklsMTZsUnQ0dGc1aGd4Wms4REtsemx6TGoxZE1ZN3llaDJXOWIzOWlFNzNHZVdjWWVJT2hGRDE5diUyRkQzbmRzUzhyMFFMNFNIbXFKOTk2enczdnZlZnFCMjklMkZNRTh6eTMlMkZYcFU3ZFVKTXBRaEZJcHdhWG00R3diJTJCYU42Ujd0eFJPaXF3SHpxd0pNTkt2ZDN3b2JoUDlscmt4STY5Y043dGtEJTJGOXBaMmhjVFVYa2JUdkpKRmFJVUlEY3U3RExUdVZ5UXdqVXJrZmw5SEczN1JoWkl0c2FhJTJCakVhUnNBemRkSSUyQjU5YUZwM3NtOWRsSWltOVR0aTh2WEdQY05UQXlBYUlma2NDMkxrdDNTeWtUanZnQ0MlMkJnR1hreFBMb3RXTCUyQnlEQnd5bjZUS1hOU3JhY0dseElwbHNjczdPYnFmS1hCWnRKVkRFVXRleTkzMlBKY3BoZzFmemQ2WElxZGgwS0oyeWJQQzMlMkIzUGEwU1Z6WFpiSVlmeHJOV09KMEUlMkJUNGhvYWFqSlFnWlhCc0ZHOEplaHlUdkRjREZwdGJvTnFjSnpOR2IzOW10NzFnNU9SZTd4WDlMam9jbFlFajE5OUdZWkZSJTJCOGFpJTJCUllCSFpzSVN0V1ZsNjZYYiUyRlNwZXZ2bEVGOTIyM1pLbmxzWUxZenByUUZlWUpmYjZ2NEN4NUklMkZxVEF6U2x6YnpCdUglMkJPN20wWW1idFU4cyUyRnNDY3EzNUtxVHBpZmdPYU5UcVpVVlFKTk52eWZ3Vm1qZ2p3cW0lMkZXeW4wY3RlOEd2eWQzRjMlMkZ5VDJEajVZQkJpME9YWUNlUlhsenpxJTJCMVo2bGNwTUN2QWV3anhESThxczlqSDMweXY5ZmtSOEMlMkJxMlFCNWJuQW50T3k0JTJGSmVWeHhkNUZjVkFncjVCVDVxWGlzdjd4UnJWMzNGRHZmR0RaYkdmY0R1MW1MaU4lMkJzYjFKNkZhUFc1U09Ud2pLamVsS1FNcmFEMFpiV1MwYjFnNGxWUmtqUUVOaHVTdk1GT285Vzg0UzFzU3h2cVhaNHl6MzlCM3FQOXpxTmV6ZzFOMm5lU1BUaG0yaXhoYTV2eHE4WVd6VnI4Rm43S1BpdUd2dk8xT3RwZ0FZZTdyemg0WVFBUldyZlNPNVNZVkdDajVyMndEZHhuZ2xDUUFvNUV5enRlRXNlb3clMkZzY3ByMzMzVnEyYlVvJTJGdWpyWXUyVzFUR0RMRU85Y05nVTg0OXUlMkJXWDdBVE1WYVJWZnlzTDZIcEdMQTMzalR6JTJCbVFGa3RVVmtTUU13bkdZNjBzRVU2JTJCV0NlRlZHTjNUUVFKeG9DZTJYbVNFdkNtJTJCeG9aJTJGUFVRRjlFaEZlSjJZdkxYNXYwcjhob0ZMR1c5ZTZBTUxwSmhmYXZxSlFKVFJOcnA4JTJCZHBYTjVKS3lNdUhTYlpHTVpXUlRUSFNvSUFoaFdPSnFCc3B2ZnF2NDY5cHNuYVRvTUlvTWt4RSUyRnZXTHNTJTJCMUx0ZGZxWnlzcUFqcXVDT3VKakpDRnFKZk4zSjNhdFl5aHhpUjZLZW5DemZnNFVRamNSN3RSNHhaYTR6WVF5akpWb2JqMHMyQnB2VzhhZ2dycUFkZGVWZVRYMTVvdEV4ZSUyRkRsa0ZxNW5CaXM2JTJCdkttTjZ6ektSS1lNZjBrV0VTbEQlMkZ0cTNVOVJtSnB3MWtndXZySSUyQnRMWGpkMVF1eXJyUUVwTVBtJTJGSmYxdE5naFMlMkZEOUR1YnB0WFdpUEtZcVhONjV1c01HcGxrUHZDQUhnUExTTVd2TU5tRFBiMmFaajVmUkNoZHczd2loa1FBWFV1em00b0hQYk5mQ1JtWnNFaUtNVHZCVXJvRDNscE5qOVRUYkJSdyUyQkpEdUpkdVpqcmlXdThubFRFRDJOdmRVWDVwVWRobjlIaW0xc1RuTnBQQ0NGQW1od3NoVzNFUWFwYUlhTkt6d2lQZXZSSUxNSlY3NjYwNFhUb2pBYTVmZ004WkxtY09HdEZ2N1lDa3NIZ3JqJTJGcHJhZnRtT2FKZzAlMkJUc1Z3a0s5bURTaVZpdm5lMDElMkZSVUpmbGtaS1htaTE1MnpKOGh4TEJBUEhxR0NUTVVNdk9QSERVV2pkWk1PM1dXRzl4ZXNKVlRacTNGQmNya0lka3pZRTh3dHV1SUNVUzlTNkNqbW1xVGp6a28xcWRzWTJLaDR1Rmt4TDdoZGx2Z09LOGI3U3FieWtGYzR6JTJGeENmaVVUV3NaWUhwUyUyRndhNkpoc2xYcnFMR1c2cml4SHpSU2J1JTJCSWxjZ0F1MzV4Smp1bm1uUzY3YnVIUldXRGZaOFR1JTJGWWdkTzdnJTJGSUt6ZjdoU283dmZzeWkxcktZd3habXBab0ViJTJGWFlTUDcyYiUyQkFtd2ZhUlhWemx6JTJCJTJGSklHRWxINlFQcEdTZjM1Y0VCbEtOMEhBNVo5ZTZFViUyQkNZUnhEbzloQml4QmcydDVLNklBZ1FZdXBQZHpGQUolMkZhcW9YU1FGanBkMFg1alhoNnFaWGFOdnF3WlFtJTJGTGx3QWYlMkJrRnZpTjdDNE14cFJDMkVnWjgwR0N2dzZ0aDJsJTJCVDJsd1ZIJTJGUnpjZEVpUzlLdSUyRnZ5dlRhdWt4QnZINFl6RWhKMVRRVSUyQlF6ajQ4diUyQnFJS2ZaUTYzdSUyQjZjM3NNdk1pSUxKR1NQYjNGY0tnV0ZxZWtYd2hSdCUyRkdnMlRMd1QlMkZlS2VQSHI4RVY1STlERGJEYjlwdWFsam9GS3FOempSbCUyRmVYczYlMkZweTh6JTJCdVc0WXJPMTM1blZsJTJGVllGcjFManRYNG1CWlNLQyUyQk1NWElmSnp2ejdSSU44YWJINEZkUGlmeDhieDZYcXFyS2JPSk4yZGZSWGtmWkJCOWhwcElSJTJGZ0tLdFBHWCUyQk0xbXVoRHNXSTd3bEVFaFNoTW5BcWx2Ynhxdnk4RUpwR25WYlVOaTNJWkg2R0lHYmJBbHp5T1Y5UER1NTZleGhLQmhxbEhScFFlM2NkQmp4SnpWckJ4YWJ5cGxOTDRzbFEzYiUyQmlkRW1La0lmSWY3UldzcWdMNWZmJTJGaWVWek40a3lmS3Z0cExhUm5veGIlMkJKa1Z4UnFxMWZ6SiUyQmJLU3RndmhFOVh3R3FIMDBYZWNqbzRURndsY0dWTXJlWFJHckFOYUl3dzVKMnZlSkFHMUpFM1VlczFFY2kxcHJnaCUyQmlMaE9PMFdwS0RxTHg3bjduelZGUjZCbEFHc1dRdSUyQkFla3podVBjUzk2UDRRc0p2RUpKSGtYT1dwcm9xVFFvWmJ3bnRySTdkTFM5SWhQYTlOJTJCdDl3aHZLNFBnNm5FdVpmRXlqOEpQT1lodVR5ZzdzVXRJMTRHcG5UUGQ3T0cyTFViamNJa0JYJTJGZEFESnhTJTJGUklCY3pZcjAzOEFBekdZYXdFU2U5ZU90M1Nla3Z6VVVEZTZMSkFLM2J1NGllJTJGbFVKTTRNTkV4MWRzNkhRbEpueDF2V1NOTVp1MXpubFV5NUtsc0hjYSUyQk9JSVR2MFlvSVN0ak1yempMRmxNZDdBUzQ1MHdHMGZDUVFzOFJQR2tCZ2M1M3JJVVlvMUclMkI1dXBVbno5c3lmRHJmUTQwdCUyQnJNczkyaWp5anNvV1F1THZxR29MOEFqS0s0Q25CUm1ENGhoVExUMTg2Yzl3djZ3cEppZ05vQ0RtaVZxRWhSWEhzZFclMkZJenpGSHZlRWtkdFlLWURFUTNPUDBYdHF4Y2RkQzVNMjdUaFBBclQ5UkN0MiUyQlZFaTlaanppQWRtYlhWMzMzSTNhV0JMVVpmVTdPRDhsZmtPRHBGZzBmTHd6JTJGeDJqUyUyRmFWWWlWTm9VdHIyTVBsVDBEQVB6RVh1cldJTVk2WEFiOWlxb1UyM3VRWmZucVlpVSUyQmp2V2dWVkdNVkp3UE91Umt4SmZtaUdISGRxOEc4TTI1cmN6YUcyempwN0ZSMXBDVkVvbzNGYzFRT3B1NjJTYmFPWjJLaVl2Q2Zva0ZZUGZPZ0xkaSUyQnBHJTJGOXdHJTJGSWR0Z2tENHc1Z2NYWm9vZ3M1UmZXeEwza0EzREFuTjlHUkxJc0VXSFpaMlRnSzR4OGNDNzVzVUtwTzJ5a1BoRVZOJTJCQWFIQkpZVGRoUjlLVU1UUWxrUzhqcUEweGo1JTJGMVdUaXZ4UEwxaGxTSFNFdzVLS0pIYUJJVTkzdm5BWnpsVVZ6eU4lMkYwJTJCSEFOc3NBQzJlVkVNNnA4YmY5VVpETE96SWR2ZzdSTnRTJTJGJTJCU01FQ2w4d3ZqQmVHcng2YXlDUmdCQ252dU0xd1gyN0R3bGFMRENEQUN3TFZ4SlhRcUVQQVRyYk9aSzFGalU3ckZuJTJCQVJxVzRLaWtTSU5RbFNnWGo4VFolMkYlMkJCSHRHSURiTUl5ZjlCc2xKRXRsWEFjZ1lLYWJ2ZjljdHczNWx0SXclMkZDUG10JTJCQVg3c3JiN1hxYXZ5dDFmMjlJVGQwUERTZG41cGlGUnNIUkQ0elVwaGFMWHBlcjczYzJXTWJaUTI4NHBwWmRRViUyRmR1JTJGa1JvR2JvWXpGOWhXUyUyQmZoRVVCWVN1Y3dBaUdiMG0zNFZxVlpxNGlHVSUyRlRDQkZtN2NCUGFMemJOM0Ztbzh5UjFwNTNkeEN6WHFTSTVZclFWTXA3REYlMkZNOHI2SlJmWXNYMlclMkZoSnQweEg4S2dqUDJBN3VKUSUyRmtGU0FzOGxjWmZFbyUyRlNkYk5aYVZqOGZoTnY0ajNkR2h1WjFwYnRXQVFjckg3djBDMHh2dHVoM2h2dkdEeTF5eWplQTMwVnRuUW0yQkduV2dVczY4MzlyeFhmZDVsZ21qdjByd3pnV0dqbjhyN1dENzdvbWNQTXlVcDA4TTdIcTYlMkZoaDFiWVpmNWdYdTM2VyUyQkczSjRFend3SGxPZjNIWTAlMkZNZVpXdmk5OVU0a2NPQ0x1d2x5cllnZWxOamFrb2dJSGJWTkxqUUFMbWlUSm5nUmFseFhQbURIamtxMXdzN2E4QjdJSldqTUxPWU1NazVMVUxhYXF1WmtXaCUyRlJDM0ZEMHlEQjhMSVNZdjdVJTJCZFZiN2labVdWZmtrdW50cmlhaHlNV1U1eW9JUmtFZ2haM0xERE9KRHhNNEh2UUZhTlVmbmhWbE1tWWxXeHlZd2dEMiUyRjRJdHI3Wm5neDh4dlZjdTM3OW8zUSUyRms3YjBNRDhXTHV1UWUyNTRiaUM1RUlPVnMlMkJtaHphZ1hLTENhbmFTMmlEUlJKTzI5VDFHTmklMkZDNzNwRVlOV0lmTjFkT0tHTUNBakdQdld4ZXhGZFRTdHNSS3Q1eWFabXBlV1ZaWDRmZndsRlg2OTdoRlNQVjVNd2VtRzhjUSUyQkljQUI3TWVNRGM0Z1V4RkcyT2I0MUFLbjRRbWpWOHNYWDRSbEVCTzdMOW5zaXViN2kxVWpveFE4emsyNHU1SldNWDZDblhIMW1IbXlodlFpNFc0NDg3VmI0R1ByS1B2UElDaSUyRnp3a2JzSkQxRDlGNDFOZWtyYVAyZExEblJrQ1N3dllRcWxPQzJ6bUtBU1UzU2J2NDREdVpSNmFqb092MjdnNFMlMkZ1NDI3OVdCRDQ2enAzODBwdjBSb1AyZk5Gemk5d3A4ZHJqVlo1TzUlMkI2R0VSSWs4SjYzaHlNV2lPbmMyeTJoJTJGcFpVbHRTOVgzWXBXM1FQSmZwYmM1TzElMkJCOGVhZ1YlMkJ5STdiakJYdkk5TnZFeVhPcHklMkZKZSUyQnIlMkZvWFZKenpPenpqSWlKWkd4YTVrSENWZyUyQndIVmw4ZHd6SjdWR2ZEcUVWS1RJMEd5VUJYUEx1YnBVY3VveFVVUiUyRkQxc0pjR1NMJTJCOXdsMElwVnBJUzBJMHFycWpQRWlVUGtKU2toUDdtdzlZMmpXVlRwY1pzU1BqNG9JRkp6SFkyR3ZWM21vT2JIRFpnaWhleW83N0RFZCUyQmRJZ2Fib1BsTXE3SHM0dHpObHVJSXNlbFpFZHJNN2klMkZ3N1dPdUpld2tHVCUyRjBqUDZCWXdVdE9jV0Fjb2p5ZDVmekYwZ1lyZnc3b3BNS3JwYTNhZmY0YlJLTyUyRlR2YXh0RyUyQmVZcEVSSmJjVGJWVmIyWkJrNUc1c3VVcEQ0b2J1UFdGWHozQ2hQZlk2U3VaNjF0a1BoZjFVbFQycCUyQjNUZ2Jub3V2Y29IRWtiYjQ2cFVVenI3TWZyR3cwUjk5c2x2c1NQSVdiQWFRNTJDZ0ZQVTdVMXRpMWolMkIwSWJld0NpNXpEZUU1OFRqNXFXJTJCdkgzUkdLeTRhdzVTMVJMb3NLZ1JsNTg2ZTlPVkZYUFplbXNnOWYlMkZwcmZoRFI2dFNzOSUyRkp4OVVubEo4dmtjdUtISktYaW1rTGxjZ0p3MXh4YVJuJTJCOUZIYXJkYWp5Y0NSRUNkdVRyaTd4NTRjdVMxeDE2ZmhWWElHJTJGZW1ZVUZnbEtGJTJCTmYxek5WdiUyRlFkb0xRTmlmZ3pLZnElMkJmM0pXa1BXZnZRdkhLMlVTNnFhRHBMTGdBNmRueHYlMkJoa3JKRXVkYUFwcTg0aThaZ2Y1dkxONjlWRiUyRjRPeGpwOFpzajNrVTVBNjZ5ZEFYVU9YQkh2T3hHV2RGenBpeVJXRG5oSkNYJTJGUnFUTUh0N3lqR1JnTDU3ZDRRTGolMkJUMXc2cTMlMkIyQUYlMkJlZmJhRWNoJTJGcEFzZ2VNcThVQjNES3k4cnVqMGcxQzVkbk0lMkIxeEtMcSUyQiUyRklCV3NyWFlQVHVwNjVpMTQ3bm56UlJ1SWdDYnY2ZDRUJTJGMzVhSTZNMktYMEJnbkhYRTV6Z2JDUWxLbmxmcU5YT1lYSmNoYW5iUWh1JTJCb1dWUTdxTzRTRTNNcVlDTGUlMkYlMkIzYVpQMGVoajFZWjVaYkphNWdEJTJCekVxa2Vtc3FYS3BPNDZMeiUyQnp6U3cyZlhvbzhZNSUyQkVWWURoS1Vxb1pONjl0ckQwbjZVVXIxJTJGdHcyeXhvJTJCdE1xMFhMa2dONTNXaDIza2tDVEN0V20ydEwlMkJleTN6ZjlyZWlzRW53WWtjWXBWcTlXdmRXeEdoVktSVEslMkJxODVUVXRySSUyQnVHdyUyQkZ2JTJGYzlyR2xsR1FKZlhGT2NDY08wR2xxN2JEcVJYNmZXS21IRzB3VEpTZ2o3OTRZOWY3OTA5a1dHdDIlMkJHc1F1WktkalBQQmJOREszUm1KZVRHZVRPZE05RWRoaFhpJTJCQnNmN3ZxclZDdjlXcjBIMUpUMFpTTVBnOXJYdlZQNmpqd21FakxFZHNnZThYZ1p3N1VlOEZkemwlMkZJJTJGZWVQWXklMkI3MWZDUWNsYW9UMHY3YmlSZFJlNzFya0llS0pDdWM3bk9BJTJCVjlHVENnNkpUYTEyWk9jNFpMaCUyQmNVd3Z2VExaZmRxbTc1ZGd6R0JHdiUyRk82cWVybHNXbHZGVW9Fak5ScEhsTEtsakhqTllPJTJCakx2Vnh4QkVxT0RGM0ZXclIxa2VYZjlNSjBSVTEzYW9mU1lRVFdZUERGTDRsSzdSVWpyR2xRejVka2xhd1owSlE2SFFIV2RyOGhKYkpBTUI2R09YODJzOFRNZmlvTWdab0JhMjhoQzNTNWw4NW9MMVA2Y1M2UmslMkZtUlVFMVh2cFY3NE1sWmZHZndyT2REbk82cHZTbnRpeGh5VHVHZldhak9ndmo0ZW5LamNPdmpYdnlES1R0Mll2WXZZeHVDTkVVM3JscUx0VlJFUXNXaGMlMkIlMkYlMkY1NzRHNzZCT2VSMzVwRTZFYUN2YnE4RWFzSzRUb01zczUxd3d5SkhkeVh1MlN2blJLWEV2MXF3UCUyQkZxdndFSFglMkI3aVZja2VxQjhMQ2JsR3Y0Zk53eHI3VTgwYjNiTTUlMkJwU0N4a0lEdUdhSHRSOUVmJTJCOTN0RjF0bVVBb1VPNGhVaTBPTnQxZmJVMndQdnElMkZBSUhxN1hDWXlUJTJCQmMwJTJCRm1DV0JmbCUyRkNBbElJa0M4SiUyRiUyQjB4dHcwRDM1bk5QeHV6SGxQbGZUOTRjRlJ6TnBhS0hCWk9pNyUyQnhrd29ScmJDUjhNTXg0blFMNnJIMGhFbyUyQlRmYUhQYiUyRkMxeSUyRkQ0T0R3emVyM2ZQc0cySjdjUlpCJTJCYmQzdnlKYiUyRnpGRHJKMGFYWHdlWW4lMkJUbzFmaGglMkZuMjlCeThubW9KajNGS0lYek4lMkZpaVNjcFozMDAxc3FyRDBvRHBTRUElMkJWU3V6dEs3R0k4VFZsQk9sWlh3OHZxeUo2JTJCMSUyQk40U25vVFVPYWdmbkZSYWxzbnNFZW4lMkY1THVnQSUyQmUxRTFJZ3kxWkJ5WVhQUDY0aUFXZllDakZwa1BaVzgwYkpiQ1dDYlZNejVDdGdxRDU5OWtQaEdEbE5NamdZUTFMelJJNmV6Q3FpTzFnS1plUzElMkZkaGhHZzl5Vm5UUW9xNmhXWkZxVzhaTTZWZlB3aElaVXFESHpMMUxCQVU2OUF2dzRVNHJlM3BCUHRrSno4NklVZjI3Rm9vTVBZJTJCN0RlbXk3MXVIREVyVmc4TVIzZTZrMjN4WHFtZzQxNzVmakFkNjlxJTJGUlNBa0xrUDdUZ2NQdWNwM3M1S3V3cTJDd0I3T1RnZnF3eUc1eXlLalglMkZuSzlGaTUwdlZYaXJSRE51b2hzS0tzV3E3QzlDWDg2RkJWdjhIVExhUGFWWHZyQ3A1TXZaMFNvYTlaZTA3JTJCM3p2dVdmNVF4bTBCYm1oUWNKcTV0WExhd2NGU2FzczhPOXc3VGJyWnBJeWJMNUFaR1I4NzYlMkI2NjdRJTJGa0JaakFqSGo1V1YyMXdRTnZraDQ2VHFrd0NXWDU0MTFZV29zS0lPNzJ5MSUyRkFyY09mZ21haTFtbkszblVWNTBsSmE2ZzVHZVBVVjd0NG1veHN1U2l3UkhzYXplbzVDM0JiMWFYWHhhaUtTS3glMkZlcnZVa21tbDNpZE5lVWx2Z0ZrZmhzJTJGa1lmUnVVWkJkeUNzMEpQeGF4ZE0wNlBJa1hIZjA0Vk9BN1YzTEV4MEJSdHpnVjRrZmR1eEVReXI1T24xeSUyRlRTVktjSjA4ZkRsdDFrVXlBdklQUU5neG81S1lVTXVjTEpkdWFxZjRtVTZhaXA0dFI1czZ5Q2RBbUxPWUF2QXFpMnB1dXlVYzBvR0prMWgzVmNjMHFabFhHRVdkaXZPbFZaeXB1UGZQUlgyJTJCbmtqYmFlMG9yeURQcnh4JTJCSmFldTZMdG9CdU5jaXpaWVRrYTJmRzNxamN2UTYzaEVIWXRHMU5Fb0ROOVhXJTJCa3FxJTJGbmN6UGd5OFdJU2clMkZKVUtoWWNwb3MlMkZJM2JJbUE0Y3NvJTJGcnJKQ2R1JTJCT1UzS1RsdGZORnVIRktFa2pxNiUyQndVYmsybUNIRXpSeDhBcVVYejFmUCUyRlRQZXVwZ1gxSDBiOFQ3ak95OXRzaUE1ODNjcUFaWTlMSVU5QVFFRkg5R2lqUFo0aDBQanF0M2dRJTJCOEJmaTAlMkJxQmxVbjhHNzBwMmptJTJCeUxzYk9MciUyRk5mYWgyaCUyQkdMY2JTUjJpdEg2cHV1QlJRWGNJYVpaSnRNRzQlMkZNSnZhQkV3NkU5a2g1eDElMkI2JTJGQlpmdHZTSERra2NWJTJCNVNDbXVFdHkwV3dkVkF4QURLbzRPNjVucXlQWXVzczZvWE5EWENuQjI5Y1lKQmRZWjVWNiUyQlczN2FCc0RSeTdyc1ZuZGxoQ3NFQ2FjUmIzOFFuVXc3Q0djMDhubFZRc1NQeGhPMmsyZExEcEk1RjZTSWVXZ2FHaWtMMWslMkJHeENnJTJGdGZMemFWRjhINlNFbjlYbVFOWiUyRjdwUllpQjBHQjJ2UFQ0aVl1bkRJRzdKNWlTSVhidTIyJTJCQngxQnR4bUIwTGVEdzZsbTMxbFo5QmolMkZ4WnlOcDNVeWNINklwRVNwa2xoMkRuMm16dnhGMlJ0V3p3WjcxQjcwdWtsMWtvNXU0aUtWd1ByWEZHNiUyQmZKdEtVaWkzWFpRSDg1aVh6RWw1dGk5Z24zSjJ0T09kb1Z6WVVZd2dhOXFLNnJOUG5ab3FOTThmOFdGTTFHQ0hnNllTQlBkRllpOGtmaVlyWGY5NnRtbkRHRmJxckJ2akt4aEp2ekNvOUE0Z2U5MmVxbUNYOHBlV1ZjNGYzVThGJTJGb1IyUllWSkx1UkMlMkZrNWNTMlR3eU1DJTJGZTJUaVczQTFtJTJCOUtuJTJGanVnRnFJRUJab05qOU82T3I2cSUyRmtIWFYwcjVOQ292bnVud1JuN29pT0c2OHBWSWhlSjBhRkcwa2UwM0ZHMVFrOGk3OUNmbkh1VEJzZVlLdDUxQUJlSGVCRjhsYlclMkZPZTZETXhDZmp5dSUyRkpDMHdoTWVIWSUyQnFZVU5lb2s1RmJCM3ZDUDJaYm1rMjIyZFN6TGlPeWFpdlZVZjVTRkljJTJCMnRodnR6bUxLRGdEWVlzVzhZZm5ncGlDZ2RMZ0hOc2hYN2x4ZCUyRkJBZmtWMXdqVGNXdjZROXllR0hrRUR6WjJpU1F0UjFXdjRGWkFJTHBKZHQ5akliVk5MT2lpenF4Q0dhbWFXZkZmbWNnQW1iRnE4cE5kSFJvWUNWNlNzdyUyQkxwcSUyQkllbmoxU0xFOUJGcU1QcHk1Z1dSSXJiaW5tR1dFdTRzblZXJTJGOUhTSXVQVllUQjRtY01LbXFUWWhERFpYM1k5a2lnOXNXR1gzOFhEYXolMkYyNjMlMkYlMkJ3QnR2aUp4T21FJTJGQVd5OUxWbGxZUTdJWnE2eFY1V2F0TUoxMHBWTzFRU0U3Y1RrSUFHc001ejBOTnJDSFIlMkJlSlFxelc3TzMlMkJTWmZybVZNRkw3b2s3OTZpSyUyRjFTeVpVbXF4OGtTZDczSlglMkYxMXA4Yk1IbFVqWXNPbk12bUNFJTJGODYwZzVJY2FqWDBnem92RlNzc0ZkZndaQjFVaE83Y0VCNlN2dlJNOFQ0JTJGTDBPMURUem95VHRpek1HJTJCZ0xvVmR3cDBoS0EzSzVHWTRzdUlmTDJRSFJPaHFnSUhoRjkxNnFRdlBaYmFSNWhOcGRYaXptMHBkdEszWXczNVNVN3Yya1V5N1JxbXIzTGtxRWhuZTNtV0w2M09ESHFoV1h4bG9oOGwlMkZ2aEwlMkZVckZmJTJCZWgwNnhUcnQ5Mml5RjF6Nm53ajBqUEI2cmkwUHBYVnNPc3NJMXFDOHVpN0UxT0d4d1ZCWDkzOVJieTZNRXJEbnJzRDY3TXBKJTJCaFZUdkxMc1pYVXYwcCUyRmklMkZnUHRudmI2Q0xSeEVGZk4yTUNGT0RyWkQ3UGlZblhhN1clMkZFUzhYJTJGMDhycWM5QnV2emRqJTJCaFc4eDBKUW9DNlNzUkMlMkJrOEY4TU45aUxzJTJGYTJ3MEh2REpIeiUyQjc3Z2dZY2owbFNmUWVhMmNmVFd2JTJCdnQxayUyRlViaEhzSFklMkZMR3RrblQlMkZlREI0Mnczc1pJM0xpS1ZhekZsMFRmWVNYcEFpMTFST0FNUTAxJTJGekNCRVlRN1dBRlh0OUpHYXFCZ1diVEZWJTJCQ1hWbTZxMnRqVDg5Y0YxUjZVZHhQekQ2eksyeFZPJTJGOEclMkJYSzdVR0hHeCUyRjV5RGRhUEZHYWpGck14Qlk2JTJGOGJ0Y3NhY2JEUEpUSXJ5ZFZ1aG9IZSUyRkdtR3o1a3U2blRiNTQ4eCUyQlRkZjVYWiUyRnd1NG1BRDBGRk1kTiUyQmRlQVhUVndOMEc2d1RDazlJRlhhcGZLNW9tdGZwRlE0akZ6VSUyQmVxeWVtZlZpTktFUmdkY1FBMGlYbU9lcUcxNTI2VFJ6akZPSjN4djhjS0tBTEpQbFB6TGZsWGV1eGVQUVZUQTZoWmc2Wno1akYlMkJ5JTJGMEM0VVNGSG5kYjZPR1VUOWJQNmxSJTJGazU5YVBPYWdKajVlWkFHTU03QnRiYllLYU5jOTQ5QkwlMkIlMkZSTVRDZ2p1cyUyQkIlMkZiOTRQYXhPV3dnU2REQzdhd2wlMkZNTk1xOWVxSjRrWlE4JTJGMnRMb3hHMU9UUVlaRWIybElQZHJ5OFglMkZSYkY2b2x6cndmUDMzcXl4RlYlMkJjZWVTTUljY3BqMiUyQnhiZ1lxMUh4OUd0ZlolMkZQRlpKNDFYaG03diUyRnJ2Qm5iOXgxMWU3czAlMkZIcWwlMkJ3em5SUFU5R1RGckNQUGFLZmh6eU8wVkFTYndwVHJ0NEpWaldtUnhZOWd6VlpWJTJGSVJCWVZzdjV5VWpxR3d1ZkJTTDNYNFQ1T3RWdUlzbU9MZjlVQ1g0MlRraVNhR1RIWHk3dUs2OVhXNVRNSkFwTEtWWDM3cjk3USUyRjZiSXE3ekZCMXNvMnV0dGkzN2h6Z2NRUm1iZENjbGZOMnBmRU4zNEdyWXlkbHo3cFdFNUJ6UDdGS1lPUTdqQXlKbjRxTnp3QVZEVllPTWU3Rm5nNiUyRjRKSUxNSTlrUnZwTm1wcWtTTjRCZTZKUkVMVHVhTDhKOFVvNHFXRXRNMW1iZGhGUHNWUWxCS3RzJTJGdjFvVHNQMklsTHQ4TXIlMkZTYlBSbGtCb3pCVUNLNWF0JTJGR1gyNk9JUWVKVkN0SlVYTGw3MzJYTzdJbEttOGp6WWFVSmpMeUVGek1rUWtjaDBnJTJGaFclMkJKakRwJTJGeVpxekVaM3BMMmVmOGlZWiUyQnYyS0V3WCUyRnlJM1B4ZHNzTVZ0RDdHYkElMkYzV3B0RnlWOTc1RkRpY1lleG5OJTJGZEU1JTJGNzVleVBqRXBQd1hMNyUyRnVrUkhmOXNzTW1MdU1lY1FGeU5WZ3plYnY0N1BGJTJCMWYlMkZkS2Q5MDIzZ2ElMkZGMmFMMGolMkZmYSUyRlNmUWZUOTNUdVZYb2FyMEQ5bmUwTkE4R1AlMkYlMkJ2OVNyMlczU093NWdqMFpBRU1KSU90MWJlREFMSHBZRXYzUDMlMkJmZkgzJTJCRiUyQnlsVE9ROE9kaWl6YmI5ZmRXSjEzUU9PZmZKeWlxWnpmNTBPMUs2TDRid0J3ZnYycjZlJTJGV3NVOVJmSzV2dmF3YWxjWnZ4cDNJdjdDNWJsZk0lMkZ5VU9OUiUyQlpiOTNPRCUyQktTJTJCSUhXenpsQUxFaWhuUXYyOWl1cyUyQk5kTDdObVRiTW1waFVxSEtoN2d3TEZpSiUyRjVVZkg3dUolMkJFTm5CUG1qSUJjSVFrZExOSGRKWkJLJTJCb3pncXRuckttY1JrJTJGaVYlMkJhejkwTWVnN1lsMUhiY2tHTEg5aG0yUFo3MGV2NWgwbGlrSVExUHRpU0UwJTJCNUQzNWVEcDFaNFZPc0h2eXFnSkQlMkZpdVBIcmtFUm40YzlWd1VTeThqOEZVTTM3U1lxJTJCVG9VJTJCS1lMJTJGeER6alBnY01kWGJaRjBiVnROOFBmQzlDTVZRMkJXZE9MNXIwTFFQVENHYkZmUnVtS2VRS1ZoJTJCUDNhMlVRZmtFMmI1N3pBemJwbVRYdGE3REVvempFV2lraXk0VWROQ3FPc3p4azBOc3VWeGU1SlFVbGg4dXFzeDlxMU9ZSG03dEFVR0NMYXl2QjNrUktyZzZ1JTJGZGFYM3A5dXlqNTlURlNiaUlWRWh4SUFtZjBjazMxWDJMa0QwcVFpS24lMkZkNGx2QjVRTkQwZDZkMUN2SnRxcDFtcXh6OWlzNmolMkJpb0FTaUZXdVM1R1lzQmIlMkJsWkFCQmRzRlJPdmlNTTM3bloxVzJRSGNzWU5sZG5ZdTd5THVMRTh2TXpGVXJKbHd5MjJPMWRhV1dKY2RFOEg1c3VWcmQ2UEpkWFlLblJhRTFybVhkajVlZWh3WG9kYnlMNFRyViUyRktSalR4VjNIVCUyRmhVMURjJTJGbiUyRjhxOUQ4WkhrUHlCcGhXSmclMkJMaXk3czdQJTJGU3k5ZmFyTDhMJTJCUXZESDMzRHFCVkQyRklJSUxUT3g5M25uY1dPeVNKbXZ0MVNYTnIlMkZ6UWZMdmw3MExaZHNDN05iNlpubWlWYndvOGkyUk0wclB6TkVsOGVzejRwT3hBY1diaDNvWGE2VXZFQXIlMkYxcmxPayUyQjlkSzhSeWx1NFVHelMlMkJ3V2RFMFMlMkJrcWVPQVA3YUJEJTJGWDR5cGlITU1yTWZhSkF1TiUyRllPMUZzM3g2RzVhaXk1ZktIY1B0Q2NoV1RvSVBGdjglMkZQWUlpSnRZM1c5MlVvMWVJaTVWcGpudkdEcSUyRkQ5RTR0JTJGNk52M0xmeHJ6dzZlVml2STN0MXE1YyUyQjlva0U0JTJGeG1vMjclMkZtQUc4OGp6eiUyQllHNCUyRiUyQk0lMkZFJTJCNzk0VzR1NlAzZmg2elU1VXQlMkJvcmxCTGdKUnRnM2xmcklUZmZTRlhGdnZVMEd2bUl3cWZpRDN0TnBDRCUyQiUyRlhDMTFENEdvSmklMkJ6VjB0MSUyRlp6aHUxNWNsaWp3WnlBJTJGdzFGUHdaV3NwSzNGOURJaURiJTJCN3RYVVZtaWxPUXpoSHM0QkZNdEsxV1hldFY5JTJGbWRHSlR6UzdvTFpPVm52SzJCenI3UVRTaXkwdnlGRndPZU5WTXRKMDZWdURaUUd1T1ZNWDNZUkpSYXlrM2QlMkY3MThFTVBmMU5STno5RXM5NnZiTHdIaTQlMkY5Q3RTNVhYQ2xaOWJuJTJGJTJGbHdJZWVYZnV6Q0NDcDBKZnBvOHZaOU9TSnVVQjNQZlRIclZjUkZkQlJJQlp6JTJGWm5CSmxmSTV1djEySzdoJTJCdnEzRWdNNnFJaTd0MzNoUm5pTTY4UmF6M1g3dDJWZCUyRkg1TDNWMzZjU0VocWY2MFVWb2Z5YkU2UXN6Z012UXZXY3RKOUYxJTJGeG41WkI0cTFrdzNUNzVPczhNdXJydFRuJTJGR1hJQmYlMkZCb0wlMkZUV1BaVG8lMkJoUjlkU2tlMDFWTndHbUxJVGQzZEVSZjBaa2w1RCUyRlFzcmxqb3B4dnRaZTNZdEJmbDFId0s5aHJ6WDBCOVd2R1pzalc2WFF5UjJ1elp0NlAlMkJaQWZQZjluOTlPSnlHMThoamNDWTdlVmhMVUZ4aWRHb2dXWWplczdWdEVadGt2SiUyQjE0OVJSd3Q5ZnBUVGdZaDl4Mmd2NWxPeWxXNlphWWxwbGVjMzRsVmwlMkZlMWljUHF3RmMzQTlKNDRNMVVRTG82SDkzaTZzY2FZNmVmSVgwU2pRMGJxMFVLeFJibGs3dG9rZnA3NmZOMk9pa01McyUyQkV5MUtmNW5LcGRmbkZtOFpxd2xwVmNVbHRqNXphbGZuSnElMkJ6QWFjYyUyQlg3aDJ5aUtsSVVDZXZ3MzA1T3AwQTJJUVowdUQ0dG1IOW01TDlReHBIbjFMa3hZVWpSJTJGelAwaGZIZEZrczBucjJtckNTOFZwUVdXZm10cUduYm1Od0RmMlBQT1hPczglMkZMNDE4eXJkd05LdHRLcGVHWGZaNlIlMkJqVVRUYThTUjdZZmdUR0R1MkMlMkZrRzVGViUyRkJucCUyRm94OHlGdSUyRlJ0ekduR1Q4TnBSMmJObnQlMkJJeHN3Wkc4WE1Qc0JmYmg1UWg5alR5WFM4bGVQaG1rNVQlMkJvODBKeCUyRnZKNHNXMEx4WFlJM25UN2diMGhJN3FLamRPN0YxenU5UVVYTk1aZXBvclMyUHYxM2VpSzYxSExsdkVMWHQ0ald5YlA4SSUyRlBSNmhrJTJGQjdMWldUUEh2JTJCSFhmJTJGRHJ2OWgxJTJGJTJCdzYlMkY4dmRxRXV5WHYweDUlMkJtdnlFNmpWOWl4UUxnNE1PZTdwYzZyRkRsJTJCVEp4RFZIQWcwUmNtVFY4SlIlMkIlMkJ2WDFybyUyQmUyR3hrZjViT0tFUjBIaGclMkJ2eDU0JTJCeFpMbVF0cnl1dkVUSiUyQmlyUFByVWtVZXElMkZmZHZ2a2Y4R2ZNJTJGRW5vUUJSNEFrRTFJS2ZJVkREaFJnU2tubWhmNFFtQTRFJTJGWUhRdVdCYkdGODRVc2VRWXQlMkZCVmpwYnVMWkFBZlJORkIxdFp0a1M3Q0I5S3V5c1haNUIlMkZkaTBra1psQ1RsOEhzR2dmYlRUJTJGT002OVk5QTRwJTJCVFBzRzJQQ1hWa1ZVd0VRYnBRUXAyJTJCV0lpQlhxeTNaNiUyRnZVaWNXOW9mJTJGSFVIcG5XS3hCZyUyQkNhd2NmJTJGRTdoMVlla0FNRlZGR1lYZCUyRkIxaVdRYm1HTEZsaTAlMkJHTTh1RmQlMkYzb3daeWVWeWRjMUwzejNkM2RIMEppSXNYaERlUUpZJTJCdXR1T242RW1UcUlCdnhxTGFsMEs0eTElMkI5SmtYMjRvdlF4QkZMOXZxVWUlMkJvYm5xazFXeFcweGVBdnhPa2clMkJCNnFYZUhtTHlaSmNkUVlGOVo0cEZKRzc0OFQyM09YMG5xVFNpRHQyQWZPMEJ1ZUw4ZVAyNk5SenJnMTh1OGRlNUxZM3k1QldKNURkQWg4Tmk5a0lWTXNBTG1DU2p4djVUcEsyV2dZbElUNXpOJTJGdE5SUFd4d1FWTW1QNFJYZXhmUCUyRjd4JTJCelFqNCUyQndFNU01cXNyYkNhYmhGQTFaZ2VMUHk0byUyRlA1YiUyRndlMVJPcVJWYjdpWXNxSFB4QjJlc0VLak94YVU2JTJGU0ZBaWNZRyUyQk81ZzNZdk5TQiUyQiUyQnZLJTJCSUdrdWY3SEtFcmx6VyUyQktES1dsbnFyc04lMkZUbFliakptZHZKdSUyQjdJJTJGNlUlMkJWOW4wZyUyRjFVTktSWjhZZVNxblkxN0lXbUt3NCUyQmNTdERCcFRVSFhOTGx2cHRjZUVUQks2bUs2dm1Ic2ZaNTRUcTUlMkJ6ZiUyRmtlcGtmeDlhbENLQ0tmN01XQWFKbFAlMkJKYm9tTjNnaENBVVpXNDNyS1g2TjU1em9HeDY2M0hydktMY2ExUzFnZFpiWkdUbGI2QjlmOG5DJTJCbVhBbXlRcGZzM2dXYzZkcFdUc294JTJGM0xzbTVibU9zVUkwTnRnRnNtQ2ElMkZHMXQ4ZFVJVjVkQ3U3N3I4NElyeHc1OXo3Y2N2V1ZTUnFtOWp4Z3VyMTlYN3ZXdEg2MlltZFVMdWZDT3JHJTJCTE5rUmdiM0xvb2E5JTJCV2o4TENvUFNjWmRaRmNldk1zN2QlMkJDYlA5SUFUanRCanZDSk4lMkZpWWVOREFXMWlIT2hRJTJCTWdqeGRmQkpSNkN0VWE0UTh0RlFqYmRIMHBzcUNzbHlkWXQlMkJiODF2dThsZWpnZGVDY0JaVE92V3BCbWJIdG1hRlclMkZMSHFHY21CRldzMVdRZ1ZNdXZFOEtKWm1Pc0Ztbm50ajJocml2VEVndjkwJTJCUU9tM3lKRndxbTNkQ3gzcUUlMkZDZWdYb2k0dUMzNkJaYXZLJTJCTm44QU1uV1VUUVh1Sll3WHhJaFUzZGhoVzVMckJzZktWZCUyRksyWHozSTUlMkJTaEZFNWxBUFFsOFQyWm9hb09XRUh0a09oMVh1U0tJdktaclBBQyUyQjlJRSUyRmNaeWExSG56eVZ0MXhGTGFHSU1ZJTJGNUZNVFhXNkk3cmJxdHpTY1FGa1F2S01sM2pUYndyMGRSY01Ya0dRTGNaQVRTNTRORmhDSEJzc2UxZHd0Y2xsRDclMkZmNDB2bU9COGo3U2g5a0c3Z2FYeUdTbG83T1Q5bG9WOWxJdGtIMmNhTkc4JTJCSTZ1cDZrbHJiUzVaT2JGTGV2ZTNhcDFGOHdzRm40VWFrdERiamlzV2h4eFV6WkVFRHZ0NEJqelZtdnpiVlJLdjZ2d2IyJTJGT2wlMkZBWE9GM2tlOEJwZnBOaU9NelJybm9xZjlHM3olMkZmVnZ2SFpPSHNORSUyQjI0Y0lOcWNiTGF6QXdCZ0hsUSUyQjI2YkZIQ1BMcFZ1cG8lMkY2R09ueEdObiUyQndjMDVtelhoZkNsaW82bHgyaVhyQUM3YmtFeHQweEY3WFRkSHRaaDVra2RCQzh6b0dFZUtxcjVyTkFNRkpCQU9hTE5SNXZYaDAybXRhaVk1RlRJTlNIU3BrMEdCdGxWYTdkdXo1ajJSZm5talcxJTJCJTJCT0NSYnQycXF1eFZRc2ZXQzNsUUhSOHZ6blY1YU5kbkE2eFF0RyUyQlBtUWgzdkVGQTVNWXNFUDNBSHZWSVZuSWkwTGRSMU9EbXVMZzBKMHhMbDNVVFZrYnoyWXMyYk4zY2gzT0dqVzBpejlmZTdnZGRTTm1MayUyQko2TzFZMEtoUDJ5NWozcEQzTVdxenp0QTQ4UzZFcVp1MXRJcEFRQW9FeU9tT1RoR0pXYXBNZ1R0Y0UlMkI4TE5oYUs4RUJXSVU3ZnRqSkhLb3E3ZEE1cHpnNmhwd1lyelFJYjhyOG0lMkIlMkJyc0ZjdkZHRG11TkNHd1B4ZVpBbFVyNWc2aThHdmZxdWJFR01WM2pEVSUyRnpyakVvSkRYaDRXTUxubnMxJTJCM2xlR2tidVk1UTVQa2ZVTSUyQiUyRjdSUVd2WEloTGtWbkpkREF2UmhyYWtMSzdlcW02eGRIWTJ4VnRIa2xXSXlGJTJGZXRWbzFPenl0TmwlMkJaUnpHZ2xvV2NSbWJBZFU5VGZkY1k1V00wT2RjdnklMkI5SUhMQVFQREJyenFKWWlQU0JRMENEaUVTSkdibTNaaVhiZU1sRDFkcGFYR3olMkI2bDEwSkdvSFFONEZoJTJCZEFpTFRrdjZrRjFYeVJkb3VSdWlVS3BtNTY3M2Z5R2R1Y3h2MVRlU2Z3dGZDc09pc0RSN0V5Znh1a1YlMkZtZ0ZoUCUyRkw4NG9zcEJKTXFaN2xBUnYxYTF4OHBRcFBLR3JiTXA4cmEzcHB3ZzElMkJ6a2lQQnBzVm5Qd1lINkQlMkYlMkJIMkxXOHdhMjNjRTVVVUJ1JTJCZ3VmJTJGclVTanBvWk5ZSk9kTFhrR0NxTHhRbzlsNjZjZkRxdHhYcXVCbnVmWmFEY3VPJTJCRXNQNVQ3YlVEUmNmOFIzQzltbWQ1MW1rNU4xJTJGZW5pd1VsU0o5SzZNV0VjU01hcWhDNE84NnZYQU9mZVl2N2VjZ04lMkZZNnhGQ2hBOGc5d2hCdzVieWxlRm1hQnk2QWlkdmFIZE9rUll0MmZXYVNBMXElMkZDcTVzYk41VyUyRmF1OEcxR1Z6QXNydVJlZFhnTzRKMmx1OVZLcmhQNTNjS2hUSlFqZzV1a2glMkZYYiUyQjVQRGt3RHdleSUyRjNsazFDNm04WDExQk95cW1lVHk4a2Vab3VGbUtwcUFtZ2d4aHE4Mm1nT2JDTjM5ZExmTlhuREUzTlZqSEVFaFZIYkdsZWlOTjJaSkoxNUNEQ05zQWhRbVVhSXQ2TUFKSjBVV1o3M21uZnFObDJMb1pDNUVyMmxCNDNaOFNxWnM4UXJhRyUyQmlNZENhJTJCR1M2amFwNCUyRnkyeEx6Z1ZHdHdTWmw1NjRMbDdWSm9xRlMlMkZ4NDZlSXZGaUlCV3NHY2tDZng5ZHhPU0RmTWR0NHd1aXlscnNUUHBXVzRZdUJFYmlGOUU4cSUyQnlZV2FlaWY1bFhQdEVhb05XM0xkcldWQ2JwTTBnTmtJSUFvNThjVWFzeVElMkJjU1dGdzJyTWh4cXk2V1NoMHNKOWczZW1Bb3hURTg3VEdTZXJJUGxMTGcxOTJVUXBPOUV1RDA2Yzh4dXY1cXVLQW9PcGI4RU14TUFCU05kY2pMaUdVcWpOOTI0cGplWEpSRGRhMUhnJTJCZnNiakNKbFFvQzVSNTQ0JTJCQldSUG1KSTZ1MDRYRDh6MTBLNElJZkpYVldRZHo2b3N5QzZ1dDd0NEYweDR0cWpzdUFQQmRvR3JsTmEyMlVBN1RaR3AlMkJPQnloTGkxUkh6SHdBSDQwYyUyRjJvZWh2RTBMVzl0Sjl0RGR2cWJtS0JMT2gxUFZKdDRTcEZWcE5GU29RY0slMkIlMkZSTHNPa3ZpNSUyQlJIQUU3WTJtUDMzSTZ1YjlzJTJGZHJtbHVmNXJLNDFrMHV2SGxhSHd3JTJGWGxlanM3YVNxU3h3MDJqMndockZTSEJMdjg1blNaR3dIMXdiWDJiMzhJR0huQ3hUdU5zV3lVaEcybiUyQjUlMkJQZEx2NVp1TGoyUnlkeTZLbHdPb3ZzU3daZWhEODVOVGx3Z0EwRmNxN1Z6V0MyejQ5Mno0YXJKb1F4TDhpNW9KU2hac1lSJTJGNyUyQlUlMkJrTSUyQjNUVEdJTmNvTDk0NWJXJTJGaUoxbmpPV05oR2ElMkJQRXlqMkVJc0NXV0dLQWpCbHl5RE55elRHN1NxckZwS0N2cEp2dlNGN0NSeXdSUTBIYXFWbDVVT3JFMyUyRlZCNmJqJTJCY0Z0cWJNMUhKM2h0OVRWajU3N29MWmVGSXNuMFB1aXlIbjgzQzRiNTVVYWozbERFdjRpaVElMkZ6b2xIblh6QnI4ZDd6bEc0WFdkTXo4RWxQd2ZZM29YMU9RQXY3NnJZQkJ4VXR3Nkp2UTN4RVBHc2p6JTJGcHRnUjdNam9OSnZENDVnN1NJaUlmdXpwdlFRTWN0cnpUZVFHOFc0YUg0cUlId1FNT0FBRCUyQiUyQmpjMmcyYzNUaFNNbzlQYnk2NDEwbXZnTCUyRm5mYkV0Z2hocGVxJTJGTVNkUUhZczR4UDJRZ2V1bG8zS3lPdFd6NGpyb3hTMmhGUUNmaUlUNnFQQzNRRndjZnBlRjVqcTRwZXhuVE9tb0xuTGdPWUJ3ZFRQNmZJMHVPV285UVE1VWFSbzFIbzJ6ZWolMkJmUktWNFNKOVBUMWRWV29icjh6N2FsaEVBd2dTVFpqbTFqWEFUZ1FrQ2RYRW9yc2t6Q2dhJTJCWmtMc0FUNlFZOUhrOEV5M3c1d3pJVlFOcE52ZXI3JTJGbXdvYnRnNkpUZDhWckhHMHdseHEyNDVXSmFxcjlZb3ZVJTJGTXFLTGdrWUxpNUtLNjJsTG15b2F2WFNDQiUyQjZLNlZSNkN3aE5sRUNNdkVUOVFVY0VQcXVxVmlYQUNyZGNwVmNEenpubTV5ZExScFUlMkJ4NzR4a1h4amJrTVJaTkRlVEpSMEZXamdwdlNUY21ZMVklMkJXcTJYYUlocnY4M1UwQkhqa1M0ZEpkdUliRlRZMEc2eVI5bnk4UXE3Z1hVUWRXSHBsQklKWXJVekVqVFJXQ29CQndhMzU5cDRxRXY5R2t6MWdKWk5FeDVLYU1OUFJmRlZsVWJJazlhZ1ZXMG9RUkNlVnJKR3l4WmRTaVVwdWtPcncxeFo5UDJDWFQybjNmUUlEN0clMkIxeXIzT1JZYzNxUHhWZ3JwM3hCeksyUW1TR3M0UjBadjBSSlUyWTFvY0Q1aXZXYkxWVnQ2OFo4NDZrRUYzdGJ3dVQzU3VXcnVVSHJXdCUyQllXa0hiS21iRWY4TWdtJTJCZ3NZd0U0YmZIcCUyRlpjRzNYZGNZT3VhVVpUZG1QdGxOM01ST3lpRXo3OWVWSW9POEFxcDU4YWJiNTZtOGtFbkdvRlQ1dTRHZkslMkJET3pKWWMzNUtjekZUTFdvUFJWeWVxOEEzc2I4YkxZbTA4RXBmNGRpd3NxdHh3Q1hvaUU1bXhzaUN0RXFjNzk2b3B4T2klMkY1WlZQZEhRQVdYUE10b0M0UEs4dlhkekM0aUVLQTg3d2xjZU5aVWY4TmZQUEElMkYxMDEzRU9PYlNoMGZXWHhCaGZHMFVaSThtUyUyRmFqcXJCWGhNWmx1RWpHTnZxWmlheUhIekFiTmVtbm9zd1JaMW00JTJGelpmdEpyQWgzcEpGSGRXYUxkU0clMkJ3MGlwVUxUc3RNbjRDcG5nbVdXRUg4a0hwJTJGVCUyQk5PVkgyJTJGSWpQVzk0WjdYNk5QUTRLRHZlc2xZd1RremloQVJURyUyRlJobUloZ29YUWY1MEFZV01MS0pxZzA4MU1ES3dMd1owNGdNVFV2QXpvMjNEYjhCZWQzb2lrUFc2WE5lQWFFU2oydHpGNjBkYmYlMkZobzA0ek12azliWGpLVExGQlN1cmpialZhUG9YekhBdVZFemtuUFJ3ZkdzeDQ4a1RidzJoNTE4dXNaZ1p1aTBKV0Z0ZkZodmF2T1liNDR0MEtzUmFZQzJlcTBpQ08zSXMyQTdFUyUyQm84bE9jRVQ2TzlhaFljWVlWWGx2b2V4UzBHVGp1ZGp0TGp2QktUaHUxJTJCdE5xaVVZdSUyQmx2Q1Z4S1I0TnVRTSUyQmtBVFBla3JBeHEyQmN4OHgzcDZhMUlQVU1nTnNqeWpQZnFsJTJCYXFBN2JsdWNqekhWN2tWYzZNNncxZGprOHNFcmhmcDlmS0RrbUIlMkZLTDdCMiUyQjV6MzhJZm9PSlU3b1hyQTZ5bUdyZTNYS3F4YWR5WTRZMWZlN0MlMkZNbmdaWFVDSVdUUCUyQldQQmRVYTRvUGFaaGpVdnRXZW0yTmh4NzVUcGcyNTNpVXBlcTAzSmFkeXQ0YXNkRXAwQkhEVjVtYlBFNWVmMTZheWRNN3V1VXR2RSUyRmxiRmNNdjVGUkxtUzNJUlFNcGxJT3psb1BDblFzS0hhaWslMkZ0TmpySlgzcGtrVk1XajE0Y1liJTJCOHRpandQOHF0bCUyRnlLQUJiJTJCTWslMkZVSzNLeWx5bU9vVnFwWnRyR1NOTEdiYk5SVmxxV3FSNTlOJTJGdjM0NVRwRkpKdWxNUlAzVmpBZ0dYZjlYeVoxSmNWWW9UQkZ5STZ2MnVUY29kb29kWUhvbmxHNUk1N3p5UUdHRVF4VkVjc0FDMkpKbkdwRzFtQ2phUlYxWjBQR1dzdjRqNzBjRWFHYkd6U3JWNCUyRlNNYjVXVXY3aFJObWFFTjJsNkpTdGtFZTElMkZXdndoQiUyRmlYNEMwS2Fqdmc2R0ZEQXU0RmJWbmFpUlFUSkZsQzJqZHQyZE1NRGNqeXZnWWZvanlKdERqZnJHVEd2Vm1seDRVblhBZzF6VTFxNTlCQ3MlMkIwQUl6amRsRGpyJTJCTm55eUF4SWFGN3NpeSUyRnFKbW9mSW5ZSExxODJndmxjUzNkUmpuOXJURXVRQ01GJTJGazVMYkpQNEpYeTRsJTJGaXpQOHhoVUNWdCUyQkl5TWpKWVVRZDZQbDhLRXBGV1p0WFklMkJLS1VicWxjT3dEUzBiY3BySXE2eVY4VGpWVW9aS1E4Um13SHV4Y2owV3VJSzZlWmROUGYydlNLSmVNemZweHFnREtaTjMxbjBhN0klMkJhJTJCUHFsQWhBdTNOZmJlS1JhcVRNbGI4aWJINWZMZmx2bDgwZ29Gckk3cldxRG9LbkpkdHUxODNqRWhNV0NPZVBnOHRqZzdONFM0cW1EeDVJMW5qYVVwTjVraHBHU3Rpc0F0UTRUVFJ0R09lJTJCTUFtbUY4eG1uWUE3eWM5aU51VzlxcmtWOGtJQldKS0JDU2YlMkJ2dVFpakNOanVLSFducDJoVVV4MThIZmU0JTJCR0pwUmo0d2tLQVpwNk1icGRqb2UxYzlGT1AxY2loT1Q1bTFEeko0WDZKcGVUVnF0TjNsdkhNVDdud29NcEdPTU85MVcyOSUyQndTZWNBQXRhTk0zVXVHcFA1bU5KWFlxcCUyRmxaaHVIZlBmc1hiRFNwM00yaEFkcFNkNmtxODVleE1BJTJGVFRWbjM3ZmFpN1hLSWxMcnhSVkdIa3o0ZUNZTGZSbFp0eDZRaDJtVmtubjFCbUVtRTk1JTJCR2NUaFZyNzJpTHJ6amFMQ04lMkJwY1hKcURrRyUyQkdNOWRTRUJPRTRwU2puZko1d0Q3R3FBNnMyJTJGd3pqcUk1NzB1dkRSbFpYbXJCMVExcWtkaXZSJTJCYWdTd1NYMk5LciUyQjJuUUw5c04lMkJjQ3hyYnhJJTJGcUM5SHVTdGF1d2RTY0o5OSUyQmpZV1F2V09IemZCRHFGUWc3JTJGWG1MOUxKOW5lcXZPaEtNMVRpdGtmaUtzM3NKdDhiQWYlMkIwMDNuYnczVUJQOUM5cDBjcTdWVEY0eXhYc0FBMFY1Y09mSVZTcXZRQkp1a1FSV3AzbGlscGVVQlJQd2xTSzJKeU5SRzFzVWJyV1pyMDB5UjVzQm5lY0lHMm01SG1qdE9ncGV1RlJsUTZyR0dxUU1uOGdMY0IlMkJsYXpLdDFXV2t2bEFUN2tScWpybENOVnF2YUVMNm5DTVBwWkM3cm1PZmJ6Qkt4bTBOeVloTlZacGxFc1pmeVJNcEJVVXBrViUyQmZnV3JuRjZoeTVGYjYwb0ViaGVFSnBONlIycFh2WmlsdkdmR2E4cDdrcnolMkJuY20lMkJCeVVkN0NVM2slMkIlMkJaTWZqRjZ0VGRyWWFVWmxGVHpYaE9kYzZzM3VyakNyc3V1ektadHRpWHBNSEVETDdJbUhZRW1TbTZzTVc2OWZ0Uk5LZjc2b0hsaTFuVkc5Z2xuS0VOJTJCQjNPMElQZGtVUUtHNTh5MDBxd1AlMkJ5JTJGU2xvSnRYNmJmRkdhNEdJcnpoclM4cFdOT0Zma25BQ1NvMER4JTJCQmpkN3VwZ2NzZUYzUFJXY0UxRUElMkIzSUo5WTJ2N1A1TlR3RFlERWJJR3I5QTU2JTJCQlQ3WGpLT21FZngwa2tqdFUlMkY0YVhEbXl2akp6b0NIdzhMcU03QmRRUXowSm1iVUVDQ2FuJTJCc2Z5OXJwZFJDOWVLRGJxUEdXeWtlNElxbU0lMkZxbDJNTmdzZ2RmTEpvbjJKc2JDN3hTZGZBNTU5JTJCJTJGYnhqWnElMkJ5YnU0YTF2SXpEeWd3T2dhV2tFekp2TEZHYWRpNyUyQmpGT0tkekNMY0ZiJTJCYWg2Tm1ZS3lwUkpjVDBmVXh0NHpYRWdHeCUyRjZ5bnBacmRjWW1nbnlvQmRjMUozOHJXR0xwbFZ6NGlhYkFYeklwTVpTJTJGa290NjFHUVd4dW5VVW9BeThxVEtJSGJsOG5SeXMyd1AlMkY5aUN4Njc2azFVUm02JTJCbW5qOHZRUnozcXAwN3FldHlFTHBlc0FCYSUyQjB2akh5bklQT0RLUmxsSHQzbWdjQVZoZjZqdGR4MXhkT2RTeU1kcWFZYzVZbGEzYjA5WVFVeXhLRHdBdVpITDZtWVoya0FnVVdxRVJvVWxHRXglMkJrRURIRkwzWCUyRmx3STlsTVI2RWtNQVZIeTRDQWRuS01mNlFPeVhlJTJCRENIa0xhV3hvTWNYYVhXWFl3SGJWUTIlMkZxWnpORFNGN01oNk1YWW04bEJhcDNEekd2V1NpTFQzbU9TTiUyQkVFcUhmTWZtc0oweFdqZSUyQjFKTyUyQnJDWSUyRmJTbFp5WWklMkI3ZWdJYjNSbVJidFlDQUgxSDBvWlMlMkZ1bFdITlBhVFJSMmxkdDhJNUw2azRMb3Jta2V6aXZVRERZMnhUVCUyQm9uVHlYYTE3ZVV6VWJ0RnFyTVp0QUk0MkpwNXFiVnB5czUzTzdlczFhWXRZUmNaaTh6cE1EQ3JIOGU5MlJjQzgwZHVOJTJGJTJCYWslMkZPZXVDcjJTUG1Od3dQYzNLRllOYUVZTTJKTXBCOVUydlZKQTJxZWswSlVJRkZyQVppMjlTcDdtRjBTR1BDMm9QM1BXekhEUXVjNG9RMWc0QVczcyUyQmpWbG5NNlVVdEVQcFB3QndRTjRVclhvaUpqTmtjakZSUWl5aCUyQjlUaFpHczZ0SlRDOTVhYjVpOXJvVGNPUUV3eUZiU3U2cVNvWiUyQnpPaWxLN1JYY0dqTjFCNnlPUCUyRkRWQ3NlWmN1UHczTE4xeW10emElMkJFbURzRDdhb2lIc1p0bGFlbWhoRjYlMkZZM0pwbkE1SzVLM3VJc05lczElMkZYZFpKaldsbUdYcm9zUUdxdXNreXVnRjg3eGV6TXVXUFJnMGdnYm96U1FzdXVaZmNXNXI5aGZGZnRpazE5WmV6MlU2WHE3WDFwalpXYTBBcUluSmlFcjZ6eGF3STJEd1M1QmNUS2JsQTFoMzJJamN6cnNzUTAya25iNkFmOUUyMVBkM3RIZUtBT3JVTzRYQXNqcHVKV3JjbWxvazdCNWlyRVppV05jTXdIUFN4QkxtZXlwc29NJTJGNyUyRjBQWWVhNUpDTzlib0slMkJFaGhuanZQVE5NNENIdzd1a3ZPJTJCdDBuNTc5b3p1cXJ6SXpEQ0F0TFdsTFN6OFpqUlBwcFViTTkzeHVIVWZLVFRNSlJrN0ZodDFsVjV6TVgxenJPYXF4TTAlMkZSdU5tSkU2YTE5SFg2cVloZDAwYVJhaDJPd3pUVW5OeG5LN2tUNHZLclB2VyUyQlZuTTJ3UVdhb001QUNjcmdGcEVjcVZpTEpzMkpLbDlpUEw0MTZ5TyUyRjhBaDAwUFJBR0s2U2duT1V1eDNGejJKZEJoRTNGRU5BJTJCZGlBZ25HMEJ5Q1ZKSGhRT3N1OUthMGVGM09qUUxrQ1l0Y0tsSW05YjIxemR5U1ZSQ3VpaHpFN25pR25BeDlqejR4TUJjZHZKS0wxJTJGdGE0SURNc2hIbUlvZ2pva09CRElDZ0JOZDVObFd6UW93SU0xTGtYcDBKQiUyQkZEdFI4OVg4QmlhUiUyRkt6VGlIcUg5VmNpM1JQN0U5WHk1aDdHVnNIR1dEOU9GT09EajJ0ZyUyRjI2VnMxOTg1azB3RlR4N0ZGZUp4WWpBeVJvbFk0dHN3dk9GTWRZcHNvVkFPRUgydmRPQ25ETDhoZmloRGRid2NUU21ITlFoTnJMSHZaUDAlMkJXUkJIbFpMaGI2T2swa2drVFBkcllleXc5bDVPT3JGNFA3ZWpZWFozeUJCTkFGU0IybTdOanpOJTJGM0N4S1B2bmt1a3k2ejlVUVdpTXJ2cTVobjNmZnQlMkJPY2U2dVZwM2lHa2JqMGVCZVpLaUU2dzM3MDZVenJpUGpUYVYlMkI2cSUyQk8lMkZyVm1uc1FFazhVMVZpUU5vS1ZIU25ZTTRUYjZ3aVFvUGZ4anR3eWQ2NmtTZEVEdVhPZzFDTlZFeVg3JTJGbXpnRFhXd1ludXp1SHhUTDRXeEd2NFFxbzBmUHpESGk3bHlsb3o0Z2QzZm5UMjAlMkJJM1pHbG56Z0tpYXhma1FEUXBvbEQ3Wmp4JTJCVzg3V294T3VUNyUyQmZvM1o5aXlXVTlpZE01eWRadmZqRlB1ZnNuYWZpbUpacXVrRE90OEclMkJLbWkwJTJGOVJJNTU1Tm5vJTJGeFZETDJoNnA2NW5UYWN3JTJCJTJCS3JZcUF1Y2xxUzc5MEM2b1g2RVp3cmtEQVk5R1BTVSUyQnd2VXZERGFvYSUyQlRnenZLdmt1aFJYJTJGNEtIbHdqdiUyRmF1bnpKdk01NiUyQk1vRCUyRjZCZWNQSTlJNmhGcEg4YVpnampkVlpYSWhmYlRFZGt6cGFZRVFTN1dJVUJRRmVUSVFnWG91UUNUNjRUZFhDa2d3QlZTVHVQU1ExVDdDZjIzRzglMkZLNWU4QjBSYnVFV2pnS3g0WVgxJTJGemtwY0c2eVJ6ZnZXN2RqY3NiQ2pETXZ1bWZmcHM4ZUhhRWIlMkZ1WkE3SlJ2bnZ0SHBrUVI5dDBrV3I0MmZqNUpTVGNZcTM1Wjk1QTZ5bFQ4RHdiN3pIb2JQZ3VndWRYeFVBajlKdmtuWSUyRkNaSTRJVnZzSk5aRXNsY1N2QXppYkV6YURaaUE1N1dsSFVKSHZSYnhVQng0VktCUTFsZiUyRmJCZ0EyTnpFc1g3VVBYUTZoOTJOTVIyTHlWaGFNcnJVJTJGMDRGMG5kRnhraVFudmx5YzhaZk8yeDUzelgzZFh3OWtNdENGM01iVjdLZjJyeHBmY3NyUTMzR20lMkJZYnJLMER4QU8xSldPVHBMJTJGSk5pRDRtYVVIYktDZnBvcFBHcUNuNlBlTkxSWUZRelY1YWpwUDZqUzJTS0xPZFE5dUFvUUxnRGRlblI0ZDRvRWZUWDJXUnZiSEJndERsWHBtR3U4STNEUmprZEtMbG5wbjBOWlJxeXVqWTdzR1ZWdWo0bk9HMG9ZbkZhZElpZSUyRnhpR1FRa2ZwbWZwUVlkcDQ0WUZRa1QyRFVMRGlDVlpVTUkwQkRIJTJCQUJOTnI3MFhzcDJHZlYlMkJHcDB4ellWSWVOJTJCJTJCcTdXNkRNS0Fkbjk1VGpDJTJCazNKQ2o5Q1NzdlVEM1prakpJeFZ3dkVURzN1Nk0lMkZWMW9OVWhBdEVuMSUyQjMlMkZtU0xWT3lQTTRpeFgzNHhLVlM3dVNkanpWJTJCbEpWemxUQVZhY0M4NGY1b1ZDT1VKNm1PVVZmckxkbXpMVGt4U0U5SjBMOVhUUjVzUEtuRHFJVnBRQUlsYWNqVyUyRjlWR2s4eFR1SWNEbXRNaEMlMkJoQU5SV0liTHlMRExBcU81cGNDZXpkZ3BGN3FTREtMWTRoTUR5T0NvbXJHSkZqVSUyRndXaHhCcXhjQ2plRGpBMjN6NFlnbGpjdmhKbmJ6RVBodVQ1TFZ3REpiYk55MmdrdG5QdW5OJTJCNE9Gc014REpaUkpSQllFYXFldURZTzZwVUcyZG1mJTJCVEVKS0N3JTJCRmpSWXRUJTJGRkZjOExnbFhQQjMlMkJzZ1lhc29PT2ZRWlFiRzNzeDFPU28wWk9uM2x6RkNwTnJsbnRqcXh6dWNWdCUyQlRTVWdTU3h4Ylc5RWJvRzNOM3dtbVVFOGFJU3hhRk1HZnlKUFh0YXh3dnRVRUpVVHVrJTJCOUdzMmZxRzk3b2Z0dSUyRllUT1NhRnZEaEVWVnRqWDF6Qkg0ZTVhdzlsY0ZEMjUlMkIzVG54T1hxam1lTnMybHVZbmlUVkVTYSUyRklCYXN0ckF3eklzeHFYaWFhYTRyVE1EQWRpOHlRUHVZNjVyS2dIc2QyazVJeXJaSlAxOCUyQng4ZUpYeTJ4dmRuRWFielMlMkIxVm12akVWUHNLUXFHWVNDejZFNHIlMkJZVWk5JTJCWHlaTng1cW9NUWVjJTJCQ1JmREZ6OTdzYkY1V1V0ciUyQlNsM1FFbU1MZU9oSzZKb25QS2swZGtMR240TldjWGZZRFpoVVkwJTJGSkpzaVljS0IyVzhDdG1xZmVyNEFuVSUyRkpYNExIamxsb3FTNDVrYmR4bzg3M3AyV3NFb0U3TmhwczM4JTJGdlNtekd2V2dIYnZpMjd3OXBVUjNyZG04JTJCN3BmR29SZG1DNiUyRkFmUllzeThxU3ZrZG1ubDBWbW43Wk1EdzJBbkhTQUI5RmFxeG9FUk5lZ0xPc3JQU1hmOVpzV3BWZUV1T0p1SUVkajlMQyUyRm5QdGRvanJ0MnpERUwlMkZTS0dqSkVZbmNqaFBDaWZtNTZVSDhQR2dTeVBSaW96ZW51TGk5RndSeG5UT1NkbmE4VXl4dXM4Y3pjb01xMHBCeWVOVEZuSlNiZU9zNjB4c1VHSmRuMG1MNWUlMkZzRzBjMk1MbHBlN21ONGZZNnp1VlZLSyUyRkJTcnk2ckg4MFBYa1pLTkk1NVI5TW4lMkZuSUJKM09weGZ3UXZmNlh2WDhJbURya0NOb00wR0pMRVRhbXhSS1NQZDclMkI5UmE4JTJGWERRS20xMmpmOHplcWgxbVhGcmVuNUlJMHYyUnJQNXpkNWhRbW5HMUslMkYyYTdrUXVFQ0FYd1pUQnBhOCUyRjRqbXpmUnljUHpkM0R5MyUyQnBtVDIxQWc5cGU2UVVLYkclMkYlMkZ5ZVdxV3M4UmVsJTJGRjZpb3RTYWdJSnhIWCUyRjB0UVljRTM2ZHllUHZORldOd0U1TW9vUFN4WmFHYTF1N1glMkY4QTlucFltdkhMdSUyRkFqaERYUHNsTE4yYWpBTGwlMkJMY3htSHclMkZKVmRkWG1lYzVzcGM2N2JNc3gwTXMlMkZXckdhbjclMkJEOXdBUHJya1FkVkZkeE9FZUZHNjF4S043N21oOTdYejd3eVlpZ1dxcE9sUEFUN0gyT2M3WFBJSE8lMkJmdE0ySktPWUJ0NTkzQjIyS0J0RVl1NnlzSG5XYlNqc1JkUDhHV3FkM09XTXBvZ29RUjh6UUhFeGxzb1JyNUp5WkhEUHQ5WFlHWWV6Rjc1ZDIlMkJ2VUZ4eEFTU1IxNHpRJTJGQ00lMkJtaW9IS1FpQVVaaXZqQ1oxd1UwdWxOdUZaY0xSaGtqT3hxR3hWWjIwMWlhaWJpJTJCS2k4a1k2Tll2NDRLalJUcUxOaXFJVlpnazhmRTdlZHpGd2hzNGRqYWRtc28lMkI4MUlyTzZYS3NXVTE0REFockRyR2dKWWZGSDdwJTJCdEhrTjJZU2I5TnR0d0pxaGRLTUFmaG51OEpRYXBEeEN1U2o3eWR3aDg0Qkd3MlVma1hVQTkwbGFKSGJRbXFWJTJGR2VwWTJKME4lMkJsNEVIWTFBT2E5V1NGMFF5a3lISXc4ZCUyQkVtSlFlV25FTWZOJTJCN2U1d09xazZtZjlOakNQOXFhZnVHNkxOcWZGNDEwOWlaRUg5R1BiaCUyRjFNdWZrRGN2Mk1pJTJCZFE3Wk5Ra2Z2WXQlMkZkWnVRSW5EcHl3NVZ0U3F6QmE4UzNDNnolMkIxVSUyQkYwUGlVMHhXSHZOJTJCdU0zb1hpcU1YME45UFFmZnklMkJzZXhmUjJuJTJCa1J1b1BZJTJCbjQzcVNjMSUyQnZIRyUyQlJvN25oUnRUNVI2akpYdkV2ZUliV1RKczcxUDZRQXZzZHF2emlZamYxWkNSaEpzS2hnMHAzVXFmTE5KSDJldWhIczZtdjk4aUt5b1VoSVlVdVVWa3pYUEhyZTg1Mk9oOHRrdFRQVGFiVDJTNWR0T3dONk1oVjJZM2R5bURiSGh6REFPOHZNZ2YlMkZmSFplbHFiMGVPUE9laklxZmhDYm9TZnBkR2lnalpTZjliMWdrRGJwMmdER2hYaFpZJTJGNExteVJwWnp2MWRLdUZmeTFRTSUyRmRnUEN5NnYyNXRybHZGWHh3MUhFM2h3N2xrQWZlUUxLQWthalRJUkRpTnZzcmNkeXZibEQlMkJWMktTUEFwYnhDZVg0d3pUUyUyQmJyTUhtejElMkJpZGN6S0gxRmNVOGprdFNibVNpeCUyQnJEYk1JdWZBZ3hDRHByUmVlRjhHaWNLSWVGcXY5VlBJUzFpY2Y0dXU1eUF2SkZWTmhObG1xZ0lpbnJsRHZsZWpad01vZkMxeGN6Nmlvb1BBZ3U0dVluNDBnT2l3T2l2emV1THRCdnBEV0llQ21BNEVGd2FhTlBVbCUyQjRyMGZPaFgzSDVLJTJCUk91Qnk4N0hMQ28wNkNVS0tPTWx4aDVXV0Z0czlYY2FQSW1QbUhqUjhxNE9yVlNUeXpmaSUyRlZkUG9MR0JmVXglMkJMU29TMiUyQnMwODlxRng0TFJlMkhzQ0haR2JYck5GZzJQUFh2NVNiWlFJcXlLbjclMkZMN2R2aVZyQnl5U3pMS1lXOG95em5PMkNXQ3JpQldoR1hJbFA1MHJId2ZqNUc5QUFrYnRKa2tWUUklMkYlMkJkaHZrSUxJJTJGM1FSd28lMkZvNWhuRE5vdFpVTnJVTzFNNWpqamNFVDI3ZkIwMHhyMVZGRGZsVjJoQVJIeEVnUkU0OTlXc29HTTI1WE1NZXBtRGszJTJGWFdHdDFrNE1pOUs5YTlIS1pMeG1yU0p4a3FRSGFEeHFmN25pN2hZWTRFb3BoaEpCTU5vTlpwV01tQndYeiUyRmZ5WmlPU2k2MGQzbldPbVJUeWpsZnUyQSUyRm05RiUyRlBRZDIlMkJBSVp4dEgzdldOcnVMU0lOOWZsSjFWSTZIaUtJM0NrczJzNFM0RnM1MURCTE5heGxkJTJGYUI0VzFzRVhjYnlxTSUyRk1pSk5wMXJzUmJjc2htUlpCb0IlMkJKdGdWdFNadFZtM1k2eUdmeTh1TmNkMTBhbk54WkJId2RFNk1kU2pUcnNBVE9paDZoQ2poNVpXUW9abERFc0N2cEF2ZTBLTjdVJTJGVWNUSmNIa2l2Mm1aUXZwY2xiM2wlMkZObTE4RElORWFWREs3WGdzcyUyQmlkJTJGOG9sTzZHVUVhb2wlMkI2VXQyT0ttQmVqekhqQkFJWlo5dnF1MjZ6OXE4dlY5UlNRYk5sWGQ5UzVlQ3o5d1oyb0hHdnFTaHkwS3dPSk9mRVRXUjYlMkJsSmxtTDRuVHZ4cEhaTm1JY2loYmxMMXhFNEZ1cWdKejlmamJSOUJ4T0J4RFlXUk1icEMwT1AlMkJ5NUpqeUNUTzZCZWc0Umw2c3VQNEV5Y3dZc0kzVXYlMkJKbEU1RjB2RVdtTTdsOWNiRGx5dXdtUDFDa2F5elVvJTJCa21kU3pPdmZCZERkN3piRlFSJTJGZHhYYWtWOXlQVXRDWVBrdGczYmpxa0Fnc3JoYkVGck5iTUtaRk1pODBtSnV1SHVsak02T1NiQ2NWbU10TnFnSThFRnVJbVpFWVNlNm9Sc3RmR3Rib2swVU1icjR4UEclMkJjbWFYdjlWZlBWVjclMkZHMW9kJTJGNFNPem1KMlZiYzhoeXFPSll6bXBQWlh4TTI1OSUyRnVhd0FiWCUyRmViN0ZJUmJLTVdKRnd2a1lNZ25lMkh6emtyVFdwUmFWazhva2xrSHB3djNyNkNvNXdjeUklMkZKTmU0ZnlaaFdhZlFWa21XWG9FMFRmUnRMRkMlMkZwbWE1ZTIlMkZqTjd0bjlpTjhGYnhHN2p2UDkzUCUyQkhhWmpnZmlWcDNHakNzWmdHUFElMkZaa1AlMkZyQ2JFNUxqJTJCcVZNOUlmNEt1UEtnTFZOREdVaTNUVnA1Q0dYREhaSmpCNVFwYyUyRjRyVWlGejB4QUprb1VzOTI4b2VLcnZBN0NSbUVldEhnS0p3OU4weFhOOTd3ZGVOVnVkQ3V0RDVEWEZBQmloSjhHJTJCa0tKTVVyTUM4MlNYJTJGNmZtV1R6SWFGZFl0bDhsRldZcjN2SEtlWWk3QmZYYSUyRkV5VUxEeGJtYlZKSE16MHZNTzFwdXN6cEtSTFRaRWFrZjZVRCUyRjBoNDd1NjRpY3VMNnpKdEN5aTBOTlhlOE5ZMzZtcnVBTFZDSVM2OU8lMkZDUUZMZm0zdmM0NmV1QllSZ1h1bmRmQlYzYzhmUmZUUXZWUlI5TWMlMkZid3E0dk01ZXJ0JTJGOFFHSUhjNlRwZkxrZk05eVIlMkJtUGE2Mlc2MDJla1IwMW1XR2V1U3g5QkolMkJRckpqSWFtRmtqZ29NbjlKUmNJY094M080JTJGZ0luclRhS1glMkIlMkJ0cHFBbTFYWElTZWhNSjRTUFI4MW1pcXpOek5nT0JiSjE2Vm9GNU04JTJCUVZ1MjF6ZnFHTmxxayUyQmpacTE5NnZXOTNxOUI5dEV2Ymw0UWVpZGI5SEJsVEQ5anFURmtDT203OFpqOExhWSUyRmJ5MmdySExHRmltY2lBM2VvcnlMRWtjV3RlaDElMkJsdXVWY2FBZHpLZHpoSlIyaEhlOENIRWFScWRiaEozNzBEdXhMVzk2b25FWklHR0pySGVBM2Zod1FFJTJGdXRTWWRDaEo0UnFMY0hneU43ME5ZWWg1YTJTaWR3cDZueVpuaXY3QnVYYThrenFaNDFsTXFWTCUyRjAzSDZXRlpUbk9sY0d6NFAlMkJHMW9ISU1CZ0JZTXJpNEhPNVVuWGglMkJ5Z0JTaEhwOWt1NlRBVTlJVnNJUVlXU0IyVjVJdVl4bnBKU0JiRWdiODR2clFtVEV6eFlxVzlHQ3dtbCUyQnA1M2tRVFprcHhlRG1LdXRjekIxVllxeWU2QnQyVG0wN3VDdWJpJTJGN2dPSThHdkRmVnNCN1p2SThCSU95SE5MbUYwVDJmc0NPZXlmaFB0MUx3M0ZpOSUyRjd4TDY4UmhKbkI2V2pON2JhOHFkd3YxUiUyRkUlMkJyZW5Cc3FjTlQxRjZjUVF2dnZMZmVTNnN0Tjh0Q0hjVll0Q2c2WGF5ODNpTEpDSkYlMkZER2VJbldHbEtZYUNVUjB2TFdwJTJCSlU2aURwQVJoSVV2M2lHYktYJTJCdGtPJTJGRm5zZ0t1bGk0Y2FUbE5KMmZhZk53MWlNaUkxRGkxTzZraiUyRlFrbW43NXh2YnVOcGVIRnJ6OVI3Q3BwUnZuRFljc2E3M1ZqZUNwUFlmbk4xdGRPbGpBT2Z3amJYbjhoJTJCNjhEckg0a3J4YVJYSWIlMkZoV0kyR0hYYlZZY3NOdExBeWJHQW9xbE81cWhoaFd3MU16c1dwOG5SWkprejJHYU00biUyQklrMXd2WmxyVnl2d3RCTHdXaG5oRCUyQktwNTlJUldCcmJObGhxS3F2UEF0dFlOTDBkNThMME9GcTJPU1dXWnBOSEFienJ6MzF3d1JCMVloJTJCRFkxMDB6bks0V0tyV0tVdXlvNDJ6TUZERVl3JTJGWEJZZzFEUXc2c28zN3lyRzNpbjBxNEh0bzNFOCUyRnZkJTJCTUZWbzFqcUc2UjA1SUs3UDRqMnAwSGMxVFhkNjRRRVVyRGtTZE9wNTJ4OG5LaU1aMVI0OVY1VHplMEhmYkpUVVduJTJGVk9pV1FCeDg4TW5BcVFCUDYwdUFZc3JKenBNY1VxRVVYc2J4VyUyQm1kd1pCN2hnbDhjRlQyVncwNTcydFIwWjl5M1VCZDJlU2R6VWNwdDJwUUtiNTA4dlVlVDBBa0hZczhkZHYlMkJSJTJGbTAxZjJHeTBkRkNLUXFicWpsMnk2NGNXMWxBeGZJQWFNdTBPWVFWUEdPTlkxZ1dTcTdqNUVXZ2lJSzUzcllmQVJRT2M0ZUpDRGo5dCUyRjhZSjhRZXJKV1hDTTVjQTBaMkM3NWh3Y04zem9meDhwYUpWaFp5M28wV3NVYXlpY0VldFQ1JTJGR0hRaGJiTnEzN2k1N1RYZmxpallhS24lMkJxbmRHTWdqaGs0TklkbnU3UVJ4OWx1VHFCMiUyRk44SFM0TW02VUJxWkxIdnlQcWJkdktKNHlaJTJGaENtZ1RKNUJIUlZ0UDZPeGZ5UFRUTVBGbVlPU1VPbHhxMiUyRjRxQUl0aWFpeSUyQmRQRUIyU25aZzBOUzJyVFU3WEIwYTFzJTJCZkZBciUyQjJvaVZ1VmRYSkZLUjBKN1VyYjFEVmVxOHBCamhKMEt5cnVzTGNUalNHWVZIN3QlMkJjZ0VKZ1BLUiUyQmRUS1ZJQWFsOE5LbHJtNXJGJTJCYzBmR004RnQ5NTBmeU1oMzdORHp4RFg3VDNEWmtJdmF0VlZDM0xPZHF3ZHVrWWw0SFJLWmtnY0k5Z0ZDYjlVd21LMFJ4MUxtc2xXOVFic1pvdjZCWURyZVlKUEdXWmwyRldiNDBYbFhpVGphdiUyQllXZzhReGVqSEl3cEpWYmF2WjF0VTMyVkk2OUdyY1NydThpcmElMkZ2NU8lMkZQNDdQN1JMemN6eTlreGQlMkJJQTVYWFpOV1RSVWRsOXFCcnowN1dvaVE5ZEZ1MG1lZ1lGSlhXVGRXZ2l4ZnY3Z1NicFhoVzJ0WEsxOVV1YzJ3U2lUaSUyRmozMEE5NFQyWUclMkZ3VyUyRmh6cmxlNnVrWlB4ZFdJaUp2QUN4MnNwaXZ1TTUlMkJVWWYxelZWQUhMcTJLejQxSnluY21jQ1ZzN3B6cUVuNGdZcTdMYndoTUU3OE9VQUFmZ3NKNkgxeHRGaGpUS2E2cDc3bnhiODlEdHdTYmJFdzlpaEhyUnZKUVYlMkJweTg0dkN2MlZwdnBVOTVubVZnbDZvcUVFUnZIdXlYZEdMZ1FqcEpCUzlqY3clMkJXZXhjWTdiU2FKaFVjTnJZNU85Y2Z6MWQlMkJMODY0MFpHYnFPSmFka1p1bkJsOEdzVkZWMktTZGp4U2l6WTkyMXV6anZDTzFiVHhjJTJCbHJWOElSVEtUd2NrSnhCRkFtZFpsM3NsdHNnUG5qNEVIbCUyQkVPRExIeCUyQjlsZkxsemM4UFZLRExWOWl4aTVxeXE3NVQxZDI2R01qZ2o2OUtxNTI3QnRKNDNoTEQ3UyUyQk13OTIyM3lsb0ZrbmpTUmlrT2I2VHZubkZoUjNkJTJGclFGdlFOWVVuaGxlQkdKbE04OHJHaFRzNjRxJTJCajBpUUhXeVl2aDVZWXRwdXBORFFsbEVFeUZhclVaRGdPQUJySXhReWFVYTlqTXZrV2tiSlQxcmhDSjVoTHklMkZRcmw4aGQ3VFdpNE9VbHMzZlNtY1lIREdJa3pUd1cycG40bCUyQkwwVDVveWlQSDhxMlJDVUJ5b1hxbXVVWlNPZiUyQmVvNDZqejhSJTJCaGRYNU9aJTJGckcwekZqTkxGRTZuTzBJN2RJRTJISlU3MDlzSERQJTJCblBaVXlLa003VWgwT2Rud3gwa0Y0MiUyRnpJMHAlMkZtYTN0YzdZMFFSWjdPNnhldllmT3RPNkYxRDFuUFFHJTJGdmFsdDk3Z1pmZXZ5Q3BjYUs5NGpxR2JwYUwwbjAwTXNWZW0lMkJ1QWZZN3d0SVZqbXRFN1Y4Vk14JTJCJTJCSTZaR0UlMkZoYk1GbG5ZeTV0eTJOUlMlMkJENkZBaVRWbzNLdHdBNXJ6UkdkMEdqY2lLUm9zcnI3cUdRb2lvT2xTU1R0Mm9rejZDc01iN2I5WXJNRE5MekFuYzIlMkY4V0hCWExsVW9EcWlIbzVlWG9PTHZoZTVnTloxeG52d0NnVU1LMDYlMkJVR25EOGdZYXU5eFRYeFh3NiUyQjg0Rlp1Rk8xNmlwQTVRb3E3ajVCUnhOeWUlMkJUcWF3TkI5ZmxQcFE1dFRNNWRMVmJHcmQ2eURKcDE3QW9EOUlMTDU5ejBYMU5nemEzbWVHeGdHODZvYnh1TzB2V1Z1ZlFoUnhxVUtoOVNUU045VFp5Ulc4TWUxMSUyRk1tdVJJY216akFFUVFCUnRkc1ZtYlBvem9tTlJNTjZJNnRHV1NwRm01NnJUYTdBMG1hWGZCNjlVRFloMU1ZaXFqblhSQnVHMnlmNHp4M2FMWW1SVm16bHZoOTBRMUxiJTJCVHVEQzN3enZlbSUyQndRa2NIU3VCVzJUaE1Da2clMkJJYVI3SndGWncwUTJ4SlNuVDl2dEtPWkJPZ0xlTENtRWg5Skk2YUNwUk1WbEtISlJvSFFUJTJCM3plQ2tHc3FMNzg0dUNUbmRjbmF2ZWY3dWwwNzgwVmhqS28xYnRYOXpxZFNOTVN2MzdCTjAzJTJCa1Q5SERWUVgxbE5VYjFjV3dYUERwVHNBSFd5RExjak5sejhMQkxHdkQlMkZ1R2V2dXZ1YlRtcW9kSHVzdGpGWWJFTTVRWUo5SGZPbnhWbSUyRjNUJTJGSGQ0b01OVVBTZ1BVUEs1cDVqS1h4NVo5bG96aGRndmY4eWU2RW1lbVpHZFdPbjVWaXhDdDBnNXpRZ21WOXRVbm9lRGllVFVoVThDclVOejdPcnhkNmwwTTZSUG9wVUx3U1ElMkJjT2VSSzJ3cWF6MFBZOGNna0JtRE5rZXpIOXprUTBGaWFnOFQ4RzlMaWdjeHh0bGRaWjVaQjYlMkIxb3Y5aFhScEVkUVVuOVdLejR0dzF5eERtNjVzTVhmY2RONnNtQnpVVnNvJTJCckdoSiUyQmYwandla3NrWDElMkZKUDRnUlltZ2NtNlZNb2R6NDZoQjRZemNVdlJtdGQ4blZtcGFzVVgxOGt1NlZRT1ZjUGR2JTJGMHdjY2MxaHlqUXV1enh6ZElxR2I0dXhPNGVtSWtRbE9LQVZ6UHZhQWh2UTk1ZlBkNmJKJTJGNG1WdnN5WFRFVEhhRm5oQjJITmdHOXE1NW84ZzAlMkZkaVRNWEczejZYbkclMkZleDRnaXhzUk1QeG05Nzl3MVZNYlZNSFJoYWxjcmM4QjclMkZxS0MlMkZXbjZPJTJGRlUzUzB5R3NsVSUyRldnb1M5OTlFYWY1WnR2YTFWNVVVTVpTcGk0V2dIVXI0VGQ4ajlzSUFOaHc3U3dNZjlZNkpxR2ZZJTJGMFhadHp1cXR2bzlSR2xPSlRnaGs0YU1lNHBqQmR4aHVCbTAwT2lQSTFEM1gwWkVGOWtuUE8yb2drbmNseGpWMEJna0pSZVNYYnpSRFVvbUhqcGFOWVZEYzNndFZDSTVCM2tsZnVkSGhyUWRKN1F5RTJWJTJCJTJCM2VOT2ElMkJLaGR6TDVoWTlEa2M5M24zaW5MbXdCcmMyJTJCUDh5Y0pEVyUyQlplNjJiVjF6Njh6S2k1MDBEd0R5U0FGJTJGJTJCZ1BST2QlMkJmTnRheSUyRlAxR3YlMkZVZkdmbTM2ZHYxNWNOYWo2QUpJSlRINEVIZm0lMkIzUmNpbTIzOTlib3BlRHZjeEY1ZGdiS0t3RkVoeDdUdWJvZ21rekVkeW9XMExlV0t5U3NIYldlNmUxNzJRWTA4VGUxU0hBalRxaGZ2R1I5MzM3a0NmU3c0dG1mYzAybWQ1Rmo5NFBjYyUyRlhGYjFUSyUyRlJMRTNnc0I5dUNaQ1dYYWYxUHZLeTMzQiUyRmY4Nk4ySmxZNE91RSUyRiUyRnVvVjlKMyUyQlRoc0h3UEo5STBNZGh0aFdncGYyZk5jYnczTHc4dWNMME5ISVNNV1U4WFhTUHhJbTJEeWtiWVZRbkdXaWtwTyUyRkphckdyaW4xZk9RWWc5Y2t3ZHltZEJZRTRUJTJCOWhGMllVYnZEd1pwcDJEZUpqdG4wbDMyQkdMUW5Yd1pjdDB5VlUwJTJGV2FHYkxzcm9ldFB6VHZGbWY2UUFseUslMkI1RklCYkM1JTJGZmZxJTJGcmRlWU1WUngzSWxTeXk0bkkxcERTSDRjcjBtdkVOU1RSZXdpTCUyRm51a1d2SmFvaSUyRmJla0ZBY2JGaVlyZE1lY25MNCUyRnEyb0tWa05ObmdMRXRYWWRWSUdnWEZiTjA3cnZOSnk2ZFlFd3A0Y0J2eVUlMkI0bXNnZXdiWXRxa0tWdDAlMkYyYTdGJTJCQUZLSjlBVzhKOE5KUzlyNmtBREVVTiUyRmk1T0dPQjl4UE42czlORzB6ZXdPWHo3T1VzU3glMkJ1aDNzaGN6ZUltUlU2WmZxcmJ4QWgyZ1pxRzVUV04yTUtRZW5QJTJCMm5DMmpYSUEybUl0T0tQMmxwV3I2dnlvUFczVHpsOUplWCUyQlVsUVB3R3ZjUUtOV2M4UHdwWFNBJTJCd0djeUFFZXpCRkduN1pYTUo5JTJGOEVHN0RTd0Vwc1hPZVUzblp5JTJGUzU2c2s0djVvSFlSYmElMkJjUjROblRDOXRiVCUyQnoxTlZTdmVXWVBUQUZONE13V3dpQmJmc2xJV3Y4R0olMkZRYTAzSE9hYWQ3b2NBdDhFUGk2YXVzSUp0ZGxDS01MZlRWa2gzU1M3dnZGMSUyQm4lMkZ1dnclMkZKdmhTa0VDQW9xbVdlSlgxcjhUazFzOCUyRkdUZmh5RWZiU2JveXh5WHgxN2F6QjRxdWM1UjFHZm9SMVFQRlFZdkRVaGw0anlWcTZpNFFkQWprcEpKSWtxQk4lMkY1OFA2MTRrQTdZUFRVSHBjODhjZ3p2UzYlMkZtWjI4bEUxWXJFZ3gyeThMZjlmaWR1dGFZeDRaTUlWWHJWakg4JTJGZ21xVnpqcTBhMDRWVEFVTXJheHFINWJERExFOW9zd2JoNllhNERBaklTUWxTaURtQzhqSnNkdUdpZTBwZXhzSWxPSTFVYldWS3p5OElnWjExeE9jMTBkN3F4V0h6cnZYdkFwc3dxaDFCckl6YXVpNThIMjl2UHUlMkZhVzVhRURYbjM1dGpEOG9BTWJEN1dOJTJGZ3ZTWHZyNlhiN2tIeFQ5M2glMkZKQ09kS09lb1d3SFZmM092MWh2cXJ4T05IWCUyQnpIaXprdSUyRjczelRPV0NLa2JzZHY4SDk0ODc3MUc4dlZIeEozNzF2N0F1cHlVaG1nc1N1YkljajIzaWRNN2MxaiUyRkhidnYlMkI4cE5tb1lDR1R3Q0FVQ09reVloaTJObjVPV2pqNDVqeUJJZjlxTHpuWXBzcXRqMXQ2SkFHZ0FrVGc3VzU4TTk4RTMxc0FCWGJEejhYMDZIOGM1RTRaNTQ5WTZaJTJGYzhnTmk2S2p5bmN2N2Z3azI3JTJCVnNpbnJqZ3dzem0lMkZSSnptemFzM3lkVlZzaDVRRVdmWHFrVU4lMkJ2SEolMkJXMW05UjBvc0slMkY4ZiUyRmV1JTJGUlh4JTJGN1lCem80dlV6Y21QVkZqS0dRbXBrJTJGJTJGbiUyQnNNYkRQUkdCaDN0JTJGVWg3NEpHMXZ3WTFFOW11MVFyUndnT2FyUk1MbVltNElkNzcxRW9HSUlPJTJCdzFXZXFjTE1UZG5mVHJNdHg3WVpKUFRKdjhHczFFekhYMVpuYXpqS2YlMkJ3UFJRZlhsUEdmb2pIU2g3ZUNoQU5GSGc5clY5YiUyQkcwJTJGMTBhQ3k2dEVScjNHd1JQZFBVSzBBS3B1RHYxelIlMkZzZloyOGRqejQlMkZmQ09sd0tuT1NmZHMzNWNNJTJGNVZmeWVUbnIxOE16MnYzcmtpaVhoUjg0cSUyRlN4UGVTd3Q0aSUyQiUyRldlUm1PVWZVcXRVTnByb093eEo0UnFzSGZUSGglMkJQMXlPemJrTmFqY2VNbHNLTlAzTkdBb0dxTXZrdzhqbEl2a254cU9IQSUyRkhuRWVwN2NiT2N5a29TUkVacCUyRnJOSFc1S25FNm1EN2NQZnZmQiUyQmVQNzM0U0lwZHBkJTJCVnliaHh1VDl6eEloWExGRVRFRjd5azgyNDh3dng0RURqdWw4R25MY2V6N1NQVjdNYzdEenlyUzlhMk9YalBpUE0ySiUyRkgyME1pdElrUFZOQTFZWEMlMkZ5eHhhc0xTcUx4RCUyQjYxUHFuV3ZmWSUyRnphOSUyRnlqJTJGazFmZTk4NGZ5S2lscXVjRHV1Z0ttRUklMkJTcFB2aG82UDNvM3M5eTElMkJWYmt5TW5wam5tJTJGN0ZDejElMkYlMkZyTEQ5dnNGdWhDVGx0WEhydlRUUnRyMkExOVNCbXFmaElGbUh2aXVZZmptMXZKWCUyRjQxNiUyRmYlMkI3MXEzb2xqRVhVbFlSUyUyQkI4YkhQRFh3V3FXMnUlMkZIJTJGSm5SYXdEcEQyalNWTWdzaDRLQ0I1NVJJdjVBOTh4T3Z3NW1wMGRnVEF4d3NCZFZSY2ZlSEVGejExUGZPeE00V1BZZks3elIxd29GcklDSDNNNFB3JTJGZmZ5enZmeTl0ZTkwcTlmJTJCN0ZySk5PcTE3JTJCejcxcTJKd3psJTJGdXZoNmVOR2I0ZWxiek81Vk1vJTJGTThPJTJCJTJCVzFRJTJCc2tGeG1yOERUN1AlMkJoUng5M3JYRGxNSlc3TyUyQkFOR0ElMkZ5YURSJTJGdEl2bCUyRnZSdjI0MVgxJTJCSWVnc1FrJTJGam45V3FIbXZGV1lWR2dZWDAwRG8lMkYwRVA3bEplNE1JWCUyRk80dzluVXRHamRNMFglMkJCNiUyRiUyQkppZUdvVENmSzdOVFJjTUolMkZIT3NQTXhxeFlVUGdXRSUyQmZRTHdMVmlLb3VmRUxzaiUyRmclMkJoJTJCdkRwejllaDFMJTJGM0Y3TjVqVVB4dmNUTmU3ZHJiQVMwSE12UHklMkZpRUVidlAlMkI2RmJJTTZyTXFuU1J6SFU5bHIxdjlIOEQ2OCUyQm8yYVY3SFN1eW9rUFhpODg4SzAlMkZnRnJKZ25iMExUa1FiJTJGTDJiUXFUd2xmclJGJTJCajhzOWtWWmVVMUZtSUJiJTJGZGVuUlpUdmF2a0IlMkJSMWh4d3Z4endZN1dBTTJpUFpRbUh5WSUyQjdYdSUyRjJBRyUyRndMV1BSMEFzUDZEeFQlMkZEQnBDbFJmJTJGald2JTJCODJwaGZBaFVNelBaQ1ZuVDlzOEVYc29BTkh0cTZQalBYdnclMkYlMkZQNWhodklCVnY0RGxQJTJGOVEyTTRxQ2N5N0xMQ1gydiUyRjFhczEzWEdqUk9XM2ltbVA0cndWdSUyRnl6d0t4WEp5RXZhRSUyRndQYW1TdmE0bmRuMnY5QjRmJTJGWEt2dzVnR3ElMkZ1dlpMeUhvSnpjV285ZTFMT1B6endMeEZGZ2daUkszQ1FJWjhWJTJGVWtFUGxkYXdYc01SJTJGT0V4Ykx3NFRiV0Rnd3YyJTJGWGwwRW9zYSUyQmdQViUyQnRjcFV2eG41endaZnlIcHRFRFBnUHJmanpZRCUyQml4czI1YjZRdFFQSHl2JTJGaE1BMXd1SDlCNjliNiUyRiUyRlZyWWpaZXYyNlNqUzc4OVUxZCUyRjZ5d24lMkI0ME44N3MwTERxJTJCbzliJTJGVU9OJTJCa1dOUDdmNkR3N1RlVE55cVJDaUFMVCUyQng2dGYwSHE5bW45VW1weVU5dmhuaGE5YmRWUldqZCUyRm9jZ1lJJTJGVCUyQll3YjJZOGY4N0h4ZzQ3Q0NIenNLeSUyRnRvUHowSmxod0ZWaDg5SU5DVVclMkZDTjFIVzZBbGpnazZhazVaT1VJbWwlMkJuNDVPNFN6bHIlMkJzeEh2eHJCbTVHRG1LYmNFZFcxZ05tZXQ0MXZ4dkdOV3c0VUQ3RFpJTUY3NFd4a2VYZHRIN2lzMW9lM1hIcmw5SHhnTXdMeXpUTHlPQjI1aXVJRzVuN1pteUd6WlhVMTklMkZDaktFWjElMkZYclVPVndINTd4JTJGZSUyRnY2bXdPVkV4d0g0bFZzZ0Z6dWNiakx2NjlNSkRmR2xvbkttOTYlMkZIOGpYWjdLQUtzQWRETlI3MzlXc2U3UEM0RSUyQmZOaXNLJTJGRXhZNEpoaUI1WTlMS0M4dHhOTFNTSjN6RFliUklENkYlMkZrOThnMVFYb1lLVnRrN1FjVXclMkIlMkZydWFRQiUyQldrRHRSWGE4SzN5V3FWWHVHS3FURWZCdXJ4QkdMVWVOUjRlYXRxZ2tBVVgzakhzcHFzeiUyRmFZSjV5anlxRkNBWWwxRlM2VWhoZGFsaUVKQ21CaFglMkZhS0JBUTh2OWtrYiUyRnRZSXZwUHptRnB4c1FJWkVYbUg1TjBIQXluMjh0bjh6ZHc5eDZSWm5rMUIwRjhUQUpWMG1DNkZCNFVXWiUyRmRqdVRkZXVONTN1d3F0U09vRzB4YlhBOWRSZW4zSVlDZHV0NWhYWnAxT1EzZFJodGx0NDZaV3JLJTJGTGclMkZsOVZMJTJGdm5uQ3JJb1FpVm9iTDFjSkslMkZScUNQQmU0Q0JOOHVIWUdjb0ZSOEElMkJHUEJ6UjlkQ1BUS2ZHODd5UmQ5M2J3OURCbXZZaldpUmRvc2ZxJTJCOEE0MWxzN0lMeVJwdEl0NlFkbkVVYW1rdlZsU1BuUldoJTJGSEVpRmdkVDRwc25zdVluMFpZNG80VG9lRFQyRmklMkJFR2l4dlRrc04yWGdNQ251cm9MNzlqJTJGYkp0dm5mV3doNUI5eEMxdEhtOW5mWlk3TzlmWEdlaiUyQmtiNVpVcnBvanRRUmhSWEdkSUNibkg1WFdReWZYJTJGcXE1JTJCUGNoTW41JTJCNDdyakl3ckRGdTdoM01TVTVvRDZxJTJGbSUyQjMlMkZldWRUQVNmUEluN2FZemNraSUyRnp6Vmt4Mk8lMkJjdlNpSDRUcVNSelFrMTF5a0NWJTJCTWpubHVCR1kxd0hKJTJGdTlUQlAzbmFiR2JsZk53RGg2Mm01bDRXUkVLd2ZVWnpUNGZjOGxMWkZYZUdFOEdlVWFXV2YlMkZRTllIQjR1OFRjclpJUVgwYmx1RHdnMkhsN1BBM0RidHc3WHRFVDJIRno4UTZSZTlScElJUWh3JTJGMjhFeGI4SU9wZ0hmMGN6MVN5QkdvVEgwJTJGMlF4ZXhpb3lERGpNVzM2ZXh1JTJGb21ueEVzVFUlMkZ2VmFVUzJaSE1tZXo5JTJGdnpoT3BEb1llanNNR2RURmZkVG5nVE5tQzc5YzJmeiUyRlNWdUFaMTJmZVZXNiUyQjV1MWpUc0FLVXlvZXNvdUtET0dPOVB1c3cwam9qbXVla0R5WGdBc2UlMkZGaVVHYVRrNzFIb0xDNU91cmhIVHNQSFVjV1pxVk9yM0dZYkJsaUhYaW9zQlolMkYlMkJJUk9ubGhJVFByJTJGTlZUYlJSbDhjMHNUUm54dSUyRmRvTWVSR3NqbnlPc1clMkZYV1pKVURTZ1hQUmh0Vldyd2pBU3YlMkZrJTJGUzdRMTM1WUphd0xSMWZyVnpoN0p1Rko3bE9oN2hYbmxvUVJZU3U5NFFQNzRjdjVhVzZ1JTJGc0l2VTZiRWFxOXFKRkZhJTJGcnQlMkZHY3Jlbm84MFhIS3NVbGZsdVZ5Q2tXRzRSS0VrTUxFTnk3YmlhUzM3czVZZmhVaVAlMkZwMzgxMllqJTJCd2VGV0x3dUR2dTZRMDZhWjF4SmVPbEhnSXpRc0lqRTMydGQlMkJqR0FrZ0VmaWtLaSUyRnpabEtvNzVRQ3pmb0txZU9WOHd5VjU1M1VtNUhPMjMxUnJHOU5vSHl3cVdTTFR5V00lMkIzMmNZdyUyRkNsJTJCZ1RFNGtXMmxqT3dQRHRQcEwyazhqWTVUeWd1YmVzTnBYWHAzSzEzMGpET2hWd05ObWgxM3k0TDdqWFY5a3p0NVpOM3VYUGpyQWhPJTJGRnNkZlpVbG5QUXdLRlJrT3Z4OEs2OENTQk1aOHB0TUh4NHh1RTNMJTJCSUdiaUxKWU1odFhEWGQlMkZRYkJ3c0lyYTBKNzk4a0F5c1VEJTJGU1I2Mlg2MU5HQU82WVNzN3lzVWNCMU9hcVlvMllrUFprZk0lMkJhTkRoJTJGazlQOUxubER3U01KVDdZZFp5NnV4WTJad1lxWkVNeEN5YlJMa2YwS2JpR1hFdjJVdnIwWXlwVGZyMDlQdzNlTTM2aU1yMjlrSm91bWtoUUNLV0Q2VHJ0MjhGdHNLRzkyNmElMkZlSmtITkx6N2xxdlBJJTJGWlF2bjBpUDVkd3JTZThza1NMVjBOY0R5WXZqZlBiQnVkSTJYdjA2U3JJeVFxakxvR21uQVAyOWduRUV2RFhyNWdxMUQ1OE9Jc09uS1RpZUxOSTgweHRXJTJGa2J6bno0b0tyMGV3NWl1TkxwUmp3aDk2bGE0REJ3aU9NazF1dFpla1g3SVBuREJvRXpQbjhoQ1BsQUxFMWRnc2dHallsRU1aT1pGWm5wVWtNOUklMkJ4VUkxMzNwOGR4d0syeEdXRE5mbzVSdnpFQlNpRzc3WjEzcyUyQmlKVHhCaSUyRmRsczczSEJRT1FTT1I5U0NTOEl4SmUlMkJTeURHJTJCekx4R0RQc05jVXdFNXd5V3BvekR0UjBiajVlR0x5cjBjbkpUa2hLbnJrdFFZQVJ5TllLZG40bzBiMk1jSkp3ZVEycVhwRzFSSEUlMkZDMTBpNUtrWFJBUnY4QmU0Y3JHQzV1ZnNSODExejBPM3pLVlViQkphb0RSMWZPd2lYenZTVlVOOHc0UCUyQkwzQk4yVkRldWN4UnNrUlQydEM0NG1XOERwTVpPeVBxcDI5UGpwUzNJNGhUa0NUYUtKbWQwUWFmNzJDVFVYVEdrSWFlTktCYWJob1ZzVnBqSXBZV1N1Uk5hS3ZWYkxWY2Z1dGFKdExDSnF6ZlVWeFFwMXlCNHBZNW5OYmh4WGpHbW5HZmNxMzlMazQlMkJGd012aEVWc2VkcWNuMXFzWjJyTDZUd29PaE90UyUyQnZ4Z0JBemRTNnNBQXZvRVklMkJ1aTlLeEkwb3h5TW53cE1vVG41WXVIWW1pZGhBaXFIJTJGbGN5S0hPVU9OY2lhVzhYQTFkZnRBTWRMOEFKMk5tVE1HNVI0WGxORSUyQkdpJTJGT1lmVWx1ZUJuT2hWRGklMkJ2N3N1M0MxMWFQSXRFUUp3UE5zJTJGczlTdGQyODJyc01MTmZGYjhwNWFZRHFndTBrVE1lOTk1TDE2VjMxMnIydTlQdEZvWnklMkIxUkc3N0dMWkl4WFBOYnM0bkJnJTJCNU54WkFEOWlOb1JOOGx1V2c1ZTlNTVQzc0F3b0JQWnJ0cjhYd2xKVHRIbk1iQ1VIM2dlMWF6UzlweEZVRnRuQXNaOExyODhUdDlBTjM1M3BHUUJOSHglMkJqJTJGZ1piT0hqenVTME5PbnR6JTJGJTJCZDN6VGJYaGtkQVZaYlRYRkNDdnczMVVUM2xmTEJkd3BFbTVrejRUU0RockhvRVdLbGNGbHp3ZU11VmZnM1N3UlUwZmtvZDYydSUyRnJlcmwzc1BCVENUTmVGU2pOODg2QkxHJTJCJTJGUnAlMkI0VzdvWiUyQkVQOFZrMEt5YUNJY3FkNGJaMDY3cmdEbVd0ME1tOHJuSGd3VTFXMHNkTnI5dml2MUZBR0RjNXE1TjFrMnkweTdVSFQlMkJtTVFNWVd2eWdKaiUyQmtXRENHVXdCRTVxYk1RVGtpVyUyQm9heFUlMkJ6eWZIQ2F2SlF1cE43Q20lMkJHdWhTNkd5dTVFVCUyQjV0WXc0WERZcldPVFZmQk5jU0FreVR4QlBOeVY0OWUxbFNUdzhSMnB1S3haVHB6ZmdaMWVFczZHT1ZsSCUyQmpJNFpNcm9TdUdGOUhzRUc5Tkd1eEx6U0F3Y0tDTDVRZ2slMkJmdnR6YWY5d21BdkpNTW5TcGlJbUtnOVViUEtEQ1MlMkZiZEx5NFFJMGFBRkpTQTJsWGQ2bDdCWUQlMkYlMkJhcXpubHh4dUJYZDEwTUdCalJBZEtjcmVUMExKcnNzYnVmYTQ4RXVTYnpzVGFUQ3BXUEpDJTJGVnVIdlp3cEFnSlh6RHhFdU03RnptV3NWN1lBa0pjRmpHMDhpajhJWXBsbUdZbjNPMjA0bUo0VlZMNWNZWEcxWUQ1ZUtMb1c1RHZVbG1YVHc4amclMkZLNHl6NlA2cXl4eUF4JTJGMDRXVHVFSVhxSWFiQWZHZVc4WjJjcTZUYTVyaUpjQ3dKblFMVTVmck5VckRqbFolMkJxZ0NKMUh5b1BMOVZpaWdYWiUyQjh5bSUyRm9Gd3l6aFNIR0RmQXFvOTFCMXE0MFJuVmtzWXc4dXNZbjZjTjZ0dXhnM2RTbTNadmJNekdhQjZ0THprdmgxSWxva09WVVlJVTNWenFPaXAzRHM5VFlPcDdkbmI3VkczbGpiUm85ZUNNak9uMDdnVlVMaUZOcXNraGhOaFFzYURvZ3F5Q3d3RTAwOVBYRTExU2RMSjF0WXllS0xrenAwZHZOVW5IZkZPUWZWRm5HSnMwQ1ltOGk5NUVxY3F2UkxsaWVOaiUyRk5rUlpzNDU2RTIxbzVTbW4lMkIwM014Q0o1SzZtdGRCb3FCJTJCJTJGOHc5RnZVS08wbzcwcCUyRmFkT3FtYlFvRUtlaTkxTyUyQkpad01aVmNOREFDeEpUY3NzR2haelMzYUQ3M09lblhUdHlmbGJCcDBWV2pJTFlpSXI4UndpREkwJTJCcXNyNHoxSFd1ekV4MEdQT3hSaUluSkZCT3doWFd1YzJ5NFl0OFpvaWU4d0VHM1ZjN2tyZCUyRklodEc1ak5ZcXpRMnBKejZodXZvYVVObFJCWlBJb25ITzB2VXgydzVIJTJGQUFrUUtTZnlZcVZka3lwM2RSR1hsU0pyaiUyQlJLR1IlMkZuNiUyRmh0d005S1dEeEJNdVpydXJMSklxdFJoamlWUVk2TnlVbmRiUld2RkRvellXVGdlSDB3YzZaZzd6ZklLWVZDQ2VMZUR3MyUyQjIzTGNMWkc3a2slMkJDMEZpUGRNWnRDJTJCJTJCZjh2ajMxM2RkUHIwJTJGWHZjeGJQOUx0dG81UEczdnU3TllMZnpZeEtvVWJmYXZQUTJGMmxWQ0Q5V1lidVI5N3gybVVCMVZnZHc5dU4wbFRDY1NPb252WEVpeGRzb3U1OUVCSDF2bllHMzI4cCUyQjRNWkVKeDE1Wkp3c1VPUEtDVHVUSiUyRnBSJTJCbU9Wbzk3RkpXZ2VueG5DeiUyRmdUbFVzZGxSTXMlMkI3YjglMkJRWmp0ckxFVjR4VXNOS08xQjQwVXRUcUtaMzlicHBJUFQ2b3AlMkJUeG5XaXFYU0NnaExPaGlJNFpON3d1d3VNb3g0Y3lyJTJGVUtScDlhOE5XbXNwY2xKSUhFamtSVkZJNjZWR3VOUTFueFQ5M0NqTVNiT3JRJTJGVmUxWU1VN1p4RUt5OFpOMnZFaUlhZlVkUVNEQmswS3c1VW9waEozczBUbmxtSzc0RzdDeGZ0VWJhWHp6Rm11NERsaWQ3dlFpdXNqdnZFd2dNUmRsTGtkJTJCaSUyQjFhSmR0Sm9BaUJCREZOc3pUdCUyQmc0VHFENGlNWklGa245VHcwRGlIOFBLejVVNXBHVUZvSXluT25UJTJGTFh5TXllV1Y1bTFtZ3J1WXBqNFY3VFpuenpOdmx1dlY1ekdmc0N6Q21mSkI1R2VTRnY4Nkl1MkdiRXhtUzZBQmMwb3h1em8wMHpYVmxlRGxFbjZnNyUyQkVRbjV3SyUyQll5VW12UGZvYW9EblFaVzhmQ25heFVrZzdGUDVkSW52JTJCeHRib3VvMkNMdnk3cUtyTHpQQ1VFbHM0bWZJeHMlMkJoNTRFekdRZjBOaDlka2dUJTJGa3ByNlZObDZvOSUyRm9DdWhRUiUyQjNnQmVkU3lmZUU2U3A1MkljRkZnJTJGY0NyNTAwUFBwOFdjdGVzQnU5ODhDbHZUYTN1a081VnVoYXZySXR3MEk5Rk4wR1NTSndEd2JEZzc5QUtjZEUwTXo4Q3NBRGlLeGhrYzZWZElDU1hlZmlWTSUyRjJ6akcyMlY4RzhSUkZsWks2T2c1RlR1a2IlMkZlVU5GQnhhQVVWWjl5eTNnaFA3Q3VsWkZtSzFJbmMxWUM2dUNFWWM4OEd5SG0lMkJNa0dsOSUyQnF0R1cwQ1kzbkc4eVBvSm43ZDJvJTJCdSUyQmo5JTJCVDJ2ZGIxWkRWN1V3eVhxMHVpOXlLTlBwTXlraFhQRFVzZTE1MHZQZDgwM3h1ZEh6UTIlMkZmakw0VkxqaldCNXlYUXdJbkpUYURHNDU4U2QzZnVya1VFekRHYTFvVmF1OUNaJTJGVnRiWDBScUF0cFNYJTJCSjNXZzRUaGRJZk9wN1ZKcWVHR0Zib0VaaDY4WWhoNFRoVmVUNlk4QmFXVTBnd0pWJTJGRWdudWUlMkJhZTRzdmE0cVVubXdnY0paVjRBNkJMWjlZaWdpbTViZ3NTWlQxQVM5d1lid1E3dDhTYjZLdjM2YktYd2JBSzlqcHgxZ05RSmZUODhrVzBGekE1VDE3bk1nV29ldTNDakJxQ1RhOWd5Y3BvT29NRWszeXZSSW5SZFZpdG1POUpWV1o2Y3BIZU9xZnMxQUtFc2tGMzNLaEpITndUcW4zb3R6N0xjaiUyQnVpbVFibTcyMTVWVENOVyUyQjhIZVE4YkEzMGtncUdxTmY5dHdxdDRPUXpwOTNINWxoYXc4TEplS3hnWVllYmxzV0tOJTJGbVIzUG9EQTRORzgycUpJc29zMzh4N2R0TWNUa3FHZDhnbkxwN1BtdzlEak9URHpIWFpZVGphV1RxMzhSRldDUE5Va285MEI0SHpvc0hFMmxMR2trd0RycmtLZThQdXdSMnpOJTJCa2FiVjA5MEoxNTZyRjYlMkZTT3djSnclMkJBWVgwZ05iVEg2OXk2TkVseWslMkYzeSUyQjg5MUptck5JNjVrRW9tcnZSQ2dYdDk4TFhxbkdlVmdPaFM4RGM0RElzRUJMYjRzMzZ6TndrT3VkUDNvOUQ0VEtQZDBIVjkzYUdkVjZ4c0x5U1RscldCSGtLcG8zckM0d3VSJTJGdk1meVFZUTJsdXdRSjFCOHlNSHFFbGYyM1JveHYwVGRMemlKbiUyQlpicGt0WGNobWt1ekdKYU81QXB5VXBmbUdJNVg1Q0FKR3o2eUEySDFHUnJCakVKcyUyRm1VOHV5dDhGSGNCYjc4eCUyRm5kdWwwaUFySGd0Tnhqd3I4MkVRRzB4JTJCMEVDN3JhR0Izb3N5QXhvUEczemU4MSUyRiUyQnEwRWZGWiUyRldDa2pVVDFNNXpiMUVYNHI0RDBmZVhDN0RMSlNEdlZCR3hsM0xFREZ6cEx2VXhBZzI0WXlhRjElMkJjVDdJdHFqSVBsRGZzejIlMkI2JTJGUTJ3TiUyQkFhVDhZcUVMUmpZakxzJTJGT21aZG1kSzklMkZ3NVNlZjc0VGg1OXBZU3B5Z2Y5MCUyQnVEM2JaN1JZUUR1YUdaSVlzSFB0Rm9kSTZoRHVkQUNmRU1Zcnl5dFNuZ2dhRmdlTDFuVDY5bk5LeWtBQkEzb2pTMjAyd2tDZzNsTG9DMmxleFdHdHJJJTJCMFpLMlFDYyUyQnp0MUdHSTdtSTY4S1BpM3hiQiUyRjdwQiUyRjFIekh1WXBOMFZCUmZVaFFnJTJCRSUyRjBuY0JnZXA0OWFwNG5YQ3pRU09oUFJQbVlzcjNYZk9ZelElMkJ5UnI2JTJCUVZ3JTJGMTJwS0FkUEhveGNmdjgxT1hGSE5CNE5KJTJCenJhcHBpTkdVNDZKUkRQZjg5YWdUcUoxQWZ0ZlpiRklCcFJIMEVtMnlsa1NGZVBqTTFsM3JkN1VHQTVQQ3Y3WjlSZ01ObnB4NWdjNGRnVmJrYXBQS0slMkI3MFclMkJGaGV0UktPbTlSejdGQiUyRnpscVZIS3A3RTUxS1E5RnhvdW41elVHa01DQWlBdXl4aExwcmZxMGF1a2JmYXY5T2dpU1RPeEhGejdqRFdwblU1OEVuNFklMkJWJTJCSFJsQWZ5MDglMkJDY0FIRnhNMEJTbnNnOWozcEdnakdTSWVlQXQxJTJGNWNUWFk5dHZlbm1MemNWcFRqODZSSXpjWnVFMyUyQmsxYjFZMUlTNHpTN2dEbGNKaXBkNnZDYnRLajVhRE8lMkJJZEhEQm5VRUk5aXdlemlSYW02c2VQaGpUV0JuVmRSYWdlS3lNOGMzUTlGaTY2c2JmWkN3MWpiQ1pUMHJWMFZuVWI4S04zTmlKcnNidWI3Mmk3JTJCMkxVTFE0bnV1bUZpb09uZWdkcUVuVzJPV0pRUVZTU0VKbjFucFloUTVyenFJV2tqTk1UM2NVQkVFS1I2OHFOcDklMkYzMXhiUHpSNU5KOFVyRXBmYUxtOWZ2WXg3anZ4aU02QjZobXIlMkJqYmxYdkd3eUJiTkZUSG8wZE10bkNMYTVjeDkwTDN5UDdQYk41SU5PRk9DOXRQaDNCSjR6TXpQa2hpclNERUN3MDdvJTJGV09tRGolMkI1ZFkxRzk4M0FXOHl3Z1VONkNObWthMTVicEtXVzN5NUgxWVVWclE3UUh6bk5TVHVjYThlSTFlZiUyRnJQQjhjSHAxOWdHMFBibFVYOERkR09DMjBjS0laWktWM3lMR3NaSTQlMkI3SXUzcHQ1MXF5Y2c3UmtVejJwYTh6T0FYRXpvJTJGVjNBQWg3d3RuMFNpZGolMkY1emMweDhzVG9UVFVIVEVjTzN4OW9TMTNObXdPM0tVSUNIaW1Lb01vcm5MeHBobmVUdUtrZTB6ZEQ5ayUyRlo4R1c2dzIwZXk3NGZ6c2RDZ0RQTXE2Z3czUzBwOVBSeWhiZ25GZEZTRnFVem0zbVpPVFV3ZSUyRjM4cTVCNm9rc2hVR0t2aCUyQnFmMkpXaml6OEh4Y3ZUNWZobGdQREhQamhpRzdXTTBydUlsS252dW80Mk9JeVg5SG5oOTZXdDM1eEIlMkZ3TGNXOWlYMFlMdjJ5UjNydjlzJTJGT3MyME44d3BWYTNZZ0RLTFh4S0x2NlBINUVsT2RVbDR1RnpUbTJVQzN6NHk3ZEdmeVhjenlDcnBGdVJUTURaM3ZLU3ltTVN6cW5FN3FBTWEzNGVzYW1rbnBtVEwyZGZIQTJHQURmdDlUNE9vOFNFZ1d5dUtqcjIlMkZiOHR5bUp0bzNKd3F2ZGswcGl4USUyRlFrRGdHazhSNmIwSW9vYU1KV1UweG9URlNVWnBLem9xUE9idVRIVHAyaGlpczlwOFdvdnhFNkh0VWl2dXV1Wm1nRm1ONXlHM3U2UjRWN3p1SE9PN1RYTWYxOWNOMlZlMzhLMG5tVWdWdUFUYkIyUkRqelRlWjltZEFpJTJCcHFQdEN6bCUyRmp2TVdsJTJCeGhINGFyZjVkNENqbjk2ZVVSYTlMeW0wNWpBeU9BYmdjWTd4SlNZYXNSQnNiUzN2cmN2eWRVcGoydiUyRk85TVVja3YlMkZrZDJmNzE3JTJCbXVrOVBPbFdWenJVaHZQS0IlMkI3aTJYVWFZT3h0MHp4QmRVQmQzelclMkZTc1JjWDFkZG01NXV3dVJ3OHYyd0RseXVSUTlhQkE2SVROZjNwRGU0OFZZd3d2M04lMkJwREh3U08ybXpiMjQxSCUyQnROdjE5SWZraVJrV0VWJTJCSkhlMlBMYUZmUVhISFF3Zk1NVm5SRklPSnoyRkJobTBHbnc5cjh0JTJCUk9GYlM3V2MyYVBmT0tQQU9lb0hnamd5RHZlWUtuZFVGJTJCTEFvQ2JRdjVVb3hwVzNJdXNPRGRQa2JhSEdxMFo4OFZVTEY3OXFSTXV6eDhvJTJCUHdDYmNIeG9jZjY3dW8lMkZ3UUFaVVd2WCUyRmV4U2d6T0FvaWxOTVFMQllFRnNXSjgyczVMUlVQSTBadyUyRjM2ZUlydm5ieVhSN1hhR3c2NXZoTyUyRjNhaXdpJTJCcmpUanZzM2Jrc0tXJTJCOWU2eHRCaGZPek82ZE43MDRUVTdvU2QzODhScXNyUG0lMkYxOTc3N1gwT3JOa2lUM05pWkF1WmdMZVhNSURoQWNJZTZPQXQ0UW4zTk1MeFgzJTJCTTlOOU9ucWtVV3RHb1poOXNja1BKSUVDS2l0enJhdzBQWHN5RDM1N080ZDR1YmF3OFMlMkJ1ZnhlODA1clY2VDFQbzN1JTJCSmVVUDglMkJnMDRmcjZjQTUyRlZtbmFieEt1blVNV2xPajRjbllNMXp4dVlXVEF4JTJGdkNpZm9lS1JmTGkwN2xkR3BiZFJSY3NPdklVVTRFeG1sdmxnSU5zbUJaWjVmMTF3eHBpZ1hRWUJPMmpJU1pzdTUyaGxrMlBhMVZiaGVzSjMzTDV4RkJ6bFpaTlJVR3IlMkJUcDVXV3oxUzlrSVFxS2o0NkY3VERnUTV5eXZLRGx3TmVQS1JPTXdJbW9xJTJGVmRyYUxXOGxIM0RPSUxzNXY0blJ0dXBpMzR1cFhiSFo2SmRzbnJtVGY1JTJGVTkzb1Fxclp1NkYzd3FmMXVrT3ltNWRZV2g4cDQxTGZGSzBZRWFZNEhkc0FCOUFNMDFvQVZGNG85TzYlMkZMZzBnVHRWJTJCUUg1MTUzMFo1bzN1ZTF5cGdqNHpLOWdnQjMxJTJGRU9BajdpdnpDV1FqVndHNTlYZVBJVlBGbkxlYkcyY2VGblJhJTJGUUdXNmNlQUlRMXJHTmE4RDRoeUZ2N2xsQk5vaXB6S1R2ZzlwOEpxelVualBaJTJCRHpQMXdrS2dCJTJCb2ZtZXR4ZzBtVHk3dHBLTXBta1JYaWx2bEYzVkZDbEx6RlUxQnB0QzdGUmRRZzZJSVlHU3clMkJDa1lnRWlPSWs4cU5mRDQ0alNIJTJCWGxqNnFHWGNFTGNxblJlNTRmNUFwd1dvbEtlbHlGMGdEakN1THBoQXljS1RVM0p5amoxd1haakVmSnBwMTNDc0tWT21UbWZMWUlLZ21hU3ZaaFpORThnU2RiUThlY0QzamdUV0IlMkYlMkZrenQzVm9FdFVmSzdWMG40YTBYUHZTMGg5bmJFSHNhNDd3NTlEOFElMkJDJTJCdnBaeThZMGJVMEhORVMxamtZTjl4U1JwM0M2Q0pHeG54VG9xMiUyQk1OcjhBandreXl3ZDVCS3NJdnQ2dmVCeEF3WkhMRmZkcjY0alhaRThQU3ZUJTJGSEwyWXY4eVdXOVRrTGFYQXVzQmVub2UzZlklMkIxc3V5QTdZYzJaaElwN1ppb1BpaXZxbzY0bXE2eDdaREw4ZTh2TEdXbllhQVkwN29NZHl0NExzZ2Q5Z3NRTUJYdldhU3lJbmdUamY3NVFyT3N1UmgxWW9UVEZCZ2E0JTJCWWhCcHhqa1NIMDFvcGhmdGxUSzRJbWJQZGJwSVNSZUhuNmd0aFRJWDMzVFBiZ2hvT0lYeCUyRlhlS1dCNThCRUwlMkJ2WlA1cHZXRW85bFlLYzZ6ZDFnampQTlUxcFlFVll1RUw5bVFFWWhsbyUyQjVJY2R2VHE5dTNURWNSdkVzNUVNMyUyRk9oOW8xV2JWaGxudzBJJTJCJTJCQnlxVG5tc3dIa1Fma3h0NWlKM1BVa2lJeGJhY2YwJTJGUWd0V05EQ0NpbWZtRHFGeEl2NzBCekVRblhRYUlXeGdrS2t6aU5hN1hlY2NpeEFwa1BtJTJCUyUyQmhPcXplSm5EUVFRM2lwVHVTWXpWczF1JTJGOGR4NVhoWFJwRm9jSTJ5OWNiR0ZQYXQ0dGpoVDFmWmhQTTZwbFNuMGlqZEZyYyUyQmlkR0xzVjBIbDAxNXRmamlWQ2Q4R0duN1ElMkZZVzhGS2Z2cmxEU2M3NndiNm5MbDc2WkNiMkw1eU1velUwaHElMkZ0bmRNRzBpTERTaTVMeHk1MExNYUZtdkFvRiUyRnREOWJYUzRXQWVhMzc5JTJCbFp3QiUyRmVxWXg3aCUyRkdTVmJ0MVlob2VxU2xoemxkblRtQyUyRjN3bUs2Yk1xVWxiSkZqY2RKZDYlMkJQalRvRlJ2JTJCNFhFMG1ZeGRPS3klMkZqS1o4R1hwcUNOa01Gb2NxRjM3YUslMkZxQW5iUlB1ck5YbWs3NXk1bW5ncnVHdGhHdERqakw4NnIlMkJPeU5YMFdkTENPVk5MRFJvem5yV2YlMkIlMkZFbUclMkJzdCUyQnV3UFpTaDg2RG56cndLTlNidlQ2WHQ5cWExRDVFS051eFAlMkZhS2FPM251dTJnVWY4YTdEcjZ0dyUyRkljcTF5WHolMkJPcjB4Wm1oSTcyZUtPMHpkSHV2TWdjaUNPVWdLbnR6ZVp1dGdENnhoQTNMOEd0WEZCMElaMnBsVmFYSUV1MkZ2UzN1c1Y4Rlk2YzgwSDdUJTJCdFhOWlBjaDQ4U3ptZXMlMkJXM2JvOFZUR3Y5bFclMkIzSjRYUm9xWjdLJTJCOURBMjJZRzJKNWRSUzMwakJDTmxqcERKVk1idUFWaCUyQmRMTHFaZUV6S01ZZ2phdktXNzhXdHVZNVh6T2R0M1o3dHFCcEw5eEswVk1LQjhnaGZHNnV6R3NObU1VN0EwMFJoYjNxbWpwUlRXazN4STM4T0xDOXZDJTJCTFgyeEdmOHN5ME9lM0lkT29FeVRMa3hza0RWdEJVVzAwS1dVeHcybGxCSEFhJTJCZyUyQjJsJTJCcWpFRlBXYzgxMEk1WXBSWjVzUlJwSEJjNHRJSElrZ2RzRnduYURQJTJCb05YMUZTMDBIZzFBdHVydENpRkhoYU9CSSUyRjFpRXNjalFJdGpJU1dBSTNUWVJBQ0pIOWZrcSUyQnhVUGF4RmFFZE9tS3FYT0h0UFdER1R0NTlXM2MyTk9JaUo3MDBRN1B3VU00bkglMkJPRlMlMkJHTiUyRnVLJTJGUVQlMkZwRlZ5U0lwbGJHNW5JY2JjcHhTZU0yS3d1RVZ4QXElMkY3VlBZWmFOWHZjODltSWd5RFM0eVlSR0tuZ1laVktmeFVkTExCRVFGYzJuSDF6T254TFh6Z29oZ1AwYUlBdTV1eGhjMW1jdGs4JTJGbnJSTEI5TkM1NiUyRnFHQ29aJTJCOFQzNlZhRTJiYTJOcVZLdTVndCUyQjhOb1VTbkdLdjFiSjI5MDVCdnBjNUZNTGdhbE16OThZSGFuZUFCYmxKYzJoRiUyRlExMElsMHlqUmM2Q3JqVXQlMkJqUEczRkZjOFRFV21zQ2V0czBtU29YNzhScCUyRjclMkJDVUJoWW1wJTJGQ1NzRTRJemgyam10dklxRkhwYXFwbzl5M2N3Y0FjMk8lMkJUV1A2QUw0RE52d1Vvb0NFb2Q5SWgzTk1kVDhvOGVTc3lranN3Y1E3JTJGZVc0eEFSallEclQxZmdHMktUMyUyQkdsdFpUQ1lQTE00cXN5Q002RXpleDBTU0QxZWFDMURBY09CZjJLOXNUc3Blczh4WEFpOEEyMGZXdGRIdG02Tkw4eHFjNWgxTjdHYSUyQlE1NVh2NktEMGwwRTVydmlWWVBuQVg3VHY4b3ZaamlsTFZTNWRxYmRBeExTTWMzVUxmS05hcEJDUml3MDEyWTdGWmRBUVduN28yajAlMkZQenc0UVYzc1Nwa0U0WUl3TkFCMmlSenhPaSUyQkIlMkZjYVpLUEZSJTJCcTQlMkJhJTJCbVJwbDZqclFKWnQlMkZ3cWRIJTJGNlhOS1lRYXk5Y3JoaGJSMkYyTklyaEo3MXV1RURKSmZ5OFp4TWJBdTQyeVVucmlwWnkyJTJCY1lYRFJLU1gyNWRqSWc1eURBSlkwcmVqcnp4VzVaRm0xb1FFbUx5NVg2OElEdmFKQjR6VnRTOW52Y3ROUyUyRmJHdkNZTUlsQnFWbDBFRWJ4SHZXMVA1VnElMkZya1VmUHE2b0tTVkxTSXl2aktwYjhGSmFRJTJCJTJCMEN5aFd1NEU1anNWNWpHVHcxcWRxSHYyN1AlMkJLYmw4UG8lMkJxbThidyUyRjE1YjNCdiUyRjlQY1JIYVM2OXIlMkZ3dndYczVVY1JkT0x1eGk3c2xtM3p6YWhOMEpyRXV6eFloUGtBWDNWYTBUMU80TWZObGFLMzJHQnh6VG0lMkZTUjVIWnY1OXQlMkZpJTJGaFg2NXBiZUhhVUh3ZHlZSk1na1lkNERaTmZ3SWhuMDdMOXV5OU53S0ZCdW8wd3N1V21pTUxFRmR1bW5EcWVESHEyYjUxbEQ5WHdTVW0yS0g2WVJ2STZISHFxSGU0JTJCaXNQMUlyNk5EZFNQRDlTOERiSFpXT0dCVEZTWTJESDEwejM5YVZoS05uMjIlMkZqQVhKTkNCMUR3WW9BNWtwRmI1WVNlVjNVY1FyaEhBZEIlMkZDMTlNSFF2dlJWelhFcEdLcXp1dDdOdGJ5Q1Mxd2RPNnBSZSUyRk1XU3pYTE0lMkZQbDYwckt2RjElMkJBWTg1dXR6cnZKQlolMkZJTE9pMWZHQjBSJTJGZkduNTQwMENRQTIwbmx4U3BaWWlwNGpSaSUyQiUyRkZWM3VaZGU0JTJGV05peiUyRiUyQlI0UmlDaFJVSVZQU1JlVHVKWHRyaTRYMGJDUmNaUnZxZCUyRmsydndpdE9nQ0RpbHFyeXM5YnA4ZU9pd3ZPTElaNncwVjQzYTMlMkJLYmVoOEFZNWhoRlRPOEVEMGhWcmJKcU9sOVNuYjJuVmtGNUlTa0E3dkp1NUFCTUppdGF0WWdQVHdUT2RTMzFucW0zMGdiRCUyRk1ieVZueHEySGtJbGNoV1lkWHlncDdWNExrdmo4MDdvMHh1STViQzlHdGN2Z3I1U1Zrb1dUc05mdVk0N0loUW4zZ1kzaVRKcHcwRnBia0x4a1YlMkJNQnZsTFRyQlBCZ3hOTnRJQ0ozOGNyMFB4eFFpdllKTFcxejdrbzAzVUxDeFJoakRnMVpYdm9TMVlzZFVRMkdLTHBrdjd5UFZ0bEhQTXBzNm1SS3dmQmRyUmVPeEdCYzlsbFZaaGtQSFNaZ044cGEybCUyRnp5STh5WFNxZGlNJTJCYTJoTjQ1SGl4TGNQR1A3OE5kanAweHpOTTM1SzliZm1aV3E0a1ROZTlNQ3ZNUXhLQWMzJTJGRlhXNHlpbUZFWlJIM3poY1JwQ0hIayUyQksxSHN2aTdmaWJWNmRZZG1INnRPZGglMkZGd3AzZnpxSlJhWW1rRmk5NzUlMkZrYlZYN2NZWW9lVW11OHMlMkJFcmdQc3VOUHpBdkhvQWNRNmNXNktwR3FVQ0sxS04zZEwzYnVaT2tGMmVKcTdENFU1MzZQRjV5RVJ3MlBsaSUyRnNraExqRmZUUW93MmNvTmlqclVSWEl0RFRsbjJEek1pdk84YlUlMkJWbEVuc0psVlltejZPWGdteHlveGFMWGZTUmdJRzJCMUpSdjJGTjdxTzBFSk15WVVUa0olMkZpQyUyRlZTJTJCVFlGdW45ZGMlMkZpMzFZNFlFdHQyZHpMODVXaUklMkJlQ1RnYnNYT3V2SzZUWXdSR2ludE9ObWppU3pCVzc3RGNHOCUyRndUVEVNazhTSVVKNEtsa0V1V3clMkJLZEdXNVJMZlBtUmxrUGFoa3MlMkZ4NTdUbDZ3SUtoNzZ5OE55Q1dJSWFDTUdiYkpaMks1a2klMkZXVXF5Nm0ybCUyQjNhTzNHJTJGN2g1ZEUweiUyQmYxYzg5dEpRTkhBcFRJbjBtUWolMkZxUWMwJTJGcFRTUTNxWm5tNHlMbjYxWkZWcXBTbnBON0dROGNhaGZhT214TXpUWHc3M0FtajJ0M0ZWU3NWZm1pVXdseFIwbnh5cmlXdlVtYkUwajdrS0JOV0luJTJCQmhLVjQ5SUE1QlpTTUx0SW0wSGw3Wk9IJTJGMkxIYUtta2h3T2k2c3kwMmVMSHo4dUNidnRjZUd1a3gzQXA1RnZydFdabFhiamRVOGclMkZhVHVOaTklMkJyeGRGU0lsNDM5VUxJZDM4b2FsJTJCWDclMkJOciUyRmdsdjRHZHltenlXdXRtSFVOY3J4JTJGJTJCdHhoeGtPWWNSemd5VThUNlpSTzlJT0FQTWFkQVhFRm5OZmE3NDRxNkVpJTJCaXBVeWJDb3k0RHlvQmY0M01wY0NTaEdSQnRZY2ZXQkozWHVpWnUwTmRQOU9GVSUyRkFZbkduNXZ0TFpUOHpVc0RlempUUjBOYXA3aWlTaUptQ1NiM1NtdVBaYXlZWUptZDZoZCUyRm1GdWtSRkhnM1ROVjkwVDluNkx0Q3cxZHNhMUdNWmpqNFZNQkU5RUVZYmpGZkhvQ1RyOU56QjA4Vm5wY0lkSmRNOVl5cGJRdWdYUWIlMkJZYTBwN2taZDFySWRINVVRNjBncHpST2JmclFQVXEzaWlYWGU5TkRMeVBsJTJGaXZyOG1VQWR3JTJGSXpQNkw5ZnAzSm5FayUyQmI1cGpxM2liOEJjcjFqM1Awc1A5TDkwaVJkUjlqb1hKeDIyT0xDJTJGNGxzWXFMbDFhWXprV3ZMTFI0ZFl5bzkyTlF5czZhSlhSRzJlNVZRNGU5N0t6MGZqR2N3eUhXOHdDMGkzbWJVNTlIeTlINzBhSWVheXlNSEVUZ2d1NHFyaWclMkYlMkJLZHJFOHRDdlpJZUs5N2xsWTdoJTJCNTU1Z1BNNjRveDF0bGNYUXdrclZJVjhWa0x5WnJvZjAlMkZoMU92MU1qa2Zpd1h0aVgyeWlTV0pPSTBxTVlya1RjeiUyRjNRTEJJS3pGRyUyRmkyMnQxN1lPamJkajlIWDRCdExNa2Y2TGpzQ204QzlPbnRTWFlzbjFYNjVGU0txb1l2TXRpZ3R6MGpNdlZ4ektWU3NETEZsTkxvaXI3cHJNVHBmd05XSUNObHN5NGVzN096Z1B1cWVlZXlSUDBnbFZNYWQ1eG91UGFJRzNSJTJCU29sYU1Ta1hUajVSUGRYRzhsc09hOCUyQlFxbFhvOFVxQjVnczdzN3FWQkwySXhOWTRKcnl0RnIyeWJ6ZENYYmtXVzh2ZWloQmMlMkJ3RWx4aUtLNEs4SzAyUFhISVF6JTJGSWo0N1B6bXBiSmZQa2s3bmxlMU00dnZ3VGh1ZWl0NzVKdm16cXFHUHFEaGE4QjVjJTJGWkdKNXp2USUyQmxYaWczczkxcldJaklpWGh5blc2OFRGTWYlMkZRZHFTNjVFY0Zwck1VdXlNakRJcWVES3pXNFNCaWhGcHVyV011RzdxZTAlMkZkTDgwTHYzQiUyRjg2QmdVeWswbFF2RHE4UFdoa1ppZkglMkIzTHRPM3g3Z0NRaU9xUEtncnk3NzNENkJlTHI1ZEtIJTJCV2o5eiUyQmJ2c2p2MThVZjRkVXVnaEhuJTJCNHZxMzZWeEZ3dkF5ZDVTb29XMEl4YlNURnhnQ2xyRENIcGlmTiUyQkJRa1dQM2VVakpQTUZwWE5kRFRoVXk5TGZZc1N6UyUyRmVDdkFwNFY0NlVmdGh4OGhZWTVNQ3o0REVsZ1daV2tJM2c3TjFjJTJGcFpoakNTOWJUdk5wbENETHVCTkpFYlA4YjhYa3ZmTjZzcEVPck1VTWxraElYYjZiTEF6dzBzdmhZREtZbkE1NlcwNlBldVpFc09ESUlHNlBoM2UlMkJqQiUyQkttM1JvUFBVUjI3R1F0amZuaEZhYjc2VEhCYXBXbGtKQURrT3dpeFBjOVZaTVZqNTlBYzhRWVdBSWcycjFLaDdtcHpPRjd0N2VTTTJ4ejIlMkJvWmtnZkhzbDREOU9kSHhhUSUyRmNpRWxGY1BiTSUyRkZrUFdOTTAlMkJVN0F1bFFndFFpZEFtTjlta2Fod2hmdmdoTHJiaGtCaTBWeDdtN3I3JTJGYmhvZTMyc0IwUG1tRjZFN3RVZkUlMkY5NnJzVzNNYiUyQjNWYnpXSm43UkkyJTJGalJFMnZFVDlwdTVac0lidWNEWUpOMUlOY0duOElXdDhzOSUyRkNzaVdCMzFwUTY1S3NFVzBZaHk3bkJwRFc5UXNlZHkyaFhhRmVWN3pRajBFd3NDRlBnNzJ6TzlBZGhCMGYycVNqN2xmclZKODBaczNOd3Q0U2hMMWxsQzJXNUN5TDRzSmkzajkyQTBFJTJGZlFNZjk0ckpsdUxrTG0lMkJUeVRjbHVScWNOODR4OEhqMVJOcUFjUklMakxQOGFsSXRldXNjJTJCSGQ4SFZHUlQlMkZSbEV3ZFEwdUxMVzBCc01kNkkwJTJCSk9DJTJCcXpwU2h4M2QyVDdyZk5YNjNpJTJGekpLdldVeXpVeUFqY2xucVJka1VQS1NkMkQ0SW9DJTJGeUNuJTJCYjgzc1dJT09pRE9vbUpVSERBbDY1Y3p2dVJqMEZjUmZWNiUyQmcxZllONExPM3MxJTJGRHFKQnpJJTJGa0kzVWZSSldrNDZyNmw5MUR6bFNQRnJZYUdJbSUyRlJ5VUElMkZaNVpUMFYzaHIlMkIlMkI2JTJCQlEzQkFuTVRYU09TOFhwa0pOdUhEYTFrVCUyQlJ4MU1uZkozeVFMR1JkSEozVnJYeUJUdWV3SE5LM3JLNWc2YXpXYlR1NTM1UUhLRDRING1ZdGUxaDhVVG9mRTc1VkElMkZtbVV3VTRtaVFVU3NzWFlWYUxqNTM5ZVo2SzNSbGIwZkMyd2dxMzBzVjNrV2N4OXpEUUVoWiUyRm9USyUyRk5xVk53SkROc2ZuOWxyc3RFMzdrUjdPYXRoclhOcHo0NFVtJTJCbFZLdXRVQjNoVGNKRjFSdDJUTlJYZVFoZ2hxaTZkcFhEJTJGb1ZUS0xmUUlLTEdQYXhDN25CcmVqVHJqendleGd2eVlnRTY3S0w2OG9YRzdpTDQ5Zjduc1QxUlM1QktOS2J5NU5RTXBTb2EzdDlBdnRPYXM5MmU4Ym4zQnlHcXo1YyUyQmh6YzR0ZEY2VXlaN2RLNGw1MlYwajQ4YSUyQnVoYjd6SG0wNVZkTERpQVg3YldPc1I0JTJGZkQxclBIOW5TWFhoSHNhMCUyRmI0OUhVekltMzU0QUg1azAySU9DSUx5MkZjR0QwU21mUk9xZ0JBN3ZNWEwlMkJZVmMzU0Q5VXhMMUREUWpROHJvMjZSV2lwazZ4aFRyMkQxNXhwQU9peW5rd1NIQlhWbG9lQ3dOZldsTms1SnVvdER3V3Rncm1QJTJGRE5paXBJTHVKcFJ0VmZsJTJCU3kxZFolMkJsN1djNll4cmolMkZBREpIa1NlQUxVajdRZTltWkl2YUp0bHdpcXZmJTJGb2NoZlFyN0ZOcmVuVEQ3cDd2U05XcG56NmRiMWFOSGk3d2dCZzN5WXFFak9TSGxvJTJGNk0zVVN5NEVLZFN6VWN1b0h0eGxORFNNV042cXY5VHFFbDFIQVdUaXRxZTQzTFduTnBScUVqNzJaQVNIaVFZek5zekJXdW50VzNNNVFKWmZwZTl5V2lJS2lXS3NuWFg5NnQ0JTJCbUtGTng4YWtrSGFlYUVlbElHMGQ0QXQ3U2hyYldOTyUyQlZ0c2FwbXk3M05iRjZ4QlJtVXNOSm90YXNEQkV5aXNRckR3NjJHNDNZR2xmajNnJTJCeFhWYlNjdWdYd3ZIT3dIVk1zemFQTFFMejllYmJwcno1em5zWDBtazl1dEJsYUJWQ3A2QU9ybmZrMVhlNWhVZlJTUW9EJTJCcER5cDJ4bm94JTJGT011WjhlZVV0cjBmU2QwekVkb0FmVHhibVlUblhjbSUyRm14VzN3Z3ZHZHBUY0tqN3F4dFBGOU53MlklMkZtcFhpSjVUMkJIR3dYQ3F3TWMybSUyQjRsJTJCTlZkaWxQQXRVckl6T0U5TUolMkZweDYzcjFUVUlBejMlMkJXaDkxbFdkbko5V0haSmp1cjBBZWZBVHh1NFJ3JTJCZk93Q0JEVWd5TE1YSEIzc1RIeVJENFF2STd5dFFnUURuN2I5WHFabmZGbElpcjRSZFVyRUlmbyUyQkZYQjlkZWg1elBtUDNVbEtBcU1XVFdaOHN4SHRRUUZDdyUyQm02bTZqMDF5Y1h4cEZXT1RBT0ROSWJqZ3gxS1ZyOFZFSWRhYllpTjlUWmpIOGJGVEpHNUdzbkxZQ3BhcXJCNVdWdmdkRFdYMk5CNk5oMEU0JTJGNmtwdm8zY2sxSENFam9RS0xUTHhsUlNDbCUyRnRTJTJGanpRN1VCWG5OZW03QVJBcUJMMHQlMkI3cVh6djJlekglMkI5U09XQSUyQkRzZ0lhSFdoUXlMZTB5JTJGb1pPd21yM0NGbVR4a3NGVTkwUiUyRnpOWDEydUI4VHp5UWtjNnRWckVaeWMxWDNCWkxsemhWJTJGcjd3MFFQZUVkSkZNazExJTJCJTJCVSUyQnV2dW1Jc3JCRDFORWdhOG42M09oekFqbnQybmZUdkFoVEY0THlJTXpVV2pZMVJDdFlkdlJKRE5xc0JkYWhsJTJCQXp0dDRBWHduSjFSWmQ1eDNic1dibkJhTU8xWmxVS016YWpXRWJFUHRHS09xenJSeTI5c0czJTJCNXk0ZW5XVEl3WVJhZlRRZUFKdzdIcDFra1ZlclhKSWhYaDJEODZGcFZrSGQ5YkZIVHgyaWY4RlMybUhkMmxCWVZKenUzOUozN2ZmVjVEbFdISiUyQkZIUFh3Q0plJTJGbGFPdTJNbDhrSSUyRlFpQktlM1MzNTFjcWFBR0NyMkN2bUdjRktkWEkyMkxmWHEzZ0JlTFV0ajFHT2hMWHU3ZzZZUUE5VzB4TzdjZ28xS3BPTktwdEpQc1pjUTRxWGZMYW91RmExaFRwUEJucVR2dzJDR1dGdTBSZFQ0SEJPYkNETFhKaGlBTUhkYklDcFNkYnpITmtuSUFQcWJaWHlzam5yaUhtWlRoS09wWlIzellxT3JlZFJkNDN4Y0xTJTJCd0dwcWU1JTJCWVFONnNnRllRcTFyYmF1RkpMWVlMbDZzb20lMkJySG5HSzVpMWZHZyUyQnJKeFViQWIlMkZ2TUpqSiUyQkgySmVDJTJCa3dXWGU3WVFxME83SXh0eTh1VDAlMkZZZUxTVHRkM09pVXlhV1FlTjc3bHVIY2R6ZGFqZmIxeG8lMkJCakI4ZUt2JTJGSUJ3dXE5aElhQ3NxZzBCUGdnJTJGSHFzeWtyRldKSlRvbk5MU25PckdYbllZZzZCOXFVWWtVZ1lvNSUyQnVWMkpQSjhLYXhldSUyRklnc3N0QzI1NmxHQjMxRWZvN0ppS2RjeDl5d0l6TU4lMkZtUWkxNk9tciUyQlU0bU9IaVl6V0lkQTExY3lnT0dEYUVxTyUyQktnUkxaZGNENUhxTU1XODVISFJxMlpkdE9UeFQ3VGl0RFBqcyUyQm9vSkYlMkZpY0Q5JTJCOXJHaCUyRjdKN3N4TXFoWEh5bmYlMkJoVUk1SURoTjRXQWxhNjJ4OFBHbyUyRjBMJTJCYlJzaFQ5bGp0Mk5oQnhabTdkZmRTdCUyRlVKQzRYaXhSeThVRHd1RXlRbVpPT3pyVCUyQnduNFNqbVluZXM0UEN6VER2aGpmcU5RWHlzQnk5TXNSUDdyZjZCNzR1UUgxWnlKJTJGUnNEaHFxUGV5MDdtN3J3c2ZCUHRRM1EzRzJYTnM0RDBNNjUzR0V1SjVuV0Q4NiUyQlpwNDdMRmVtS2RUJTJCY0V3MG9DJTJGSEVIMHVrbGRQaU9WTTJPSnZTcHdGdzQ2WGF6QU5LelhWbGRHaUFyWXUzbXMwcU1CNXRwVm9vZTZCZzhCRHlPTU51VUF0b2d2SXFkbFclMkZuM05lc0hjZE9raW53bHpYaWwlMkJBdE9rd3hLbE01c1RUSHoyaUtaM05MeGtwbHlUUXBJNyUyRlhZcU84WEYzS2pOZUtnekJVcUU3aVBjMjd1amolMkJTJTJCT0IxT01QT0hXeUs1OFhvRmwlMkZpYlhSbWM4MzE4bWpyWnVYREIzc3BxczZ0VFlmaFhHRmdHMzF4WHVkbmM2aTRDQTZnUWh2YnZxWnh6c1Q4a3F6NDVseDI2d3NpVlJvJTJGMGlrRHolMkJROHc5clFSVUprbjAzQnQlMkZ2T2liV2k1eGV5dlpabFpOeXdhcFlvUUVSbWtKVVk0VmlnbWNvRFlCYWVUZ0JsSjZWWHg5TWdyc1ZueVp3alQ1YW83cGRyNmVmeTRpZ3VUQ2dIaGJuRGtObkQ2VzRHOFk4aGF3elFjSnpGZk9iN0pzZ3RLdFBzd1dwZXh4THZFWUp1NTBKR2RtMSUyQm9TTjlwMEx2YjZ0dVhQWnlQT1d5ZXR1VzBsZW5ldnc1a3U1YjIxbWUlMkJUNW9ldm02eGhlU2NnMzFoQnUxdWIzbXJ2SU1nN3pIcFpqZmxwcUptZHlzc0xIbjBKUklPeVBoSWNwMDljOVFoRVF4VEk4R2Y5MXE3VXAlMkJ0bzdyeklURWFldnNOMG5rem9VM2M4aXUyS3NlQXBwSmN2V1FhMHFaV2dURWVhWlJES0h4SVRPY0s4bWRSWjB2dkFTcDIxcVpDTVFFJTJGJTJCcWVxUjRoYiUyRlhGdXZRQ3VnSzUlMkJ0NXo1NVZ2NSUyQmxxUm5SOUEwNkpJZGd3d1VaUlg2U3RoTWdLS1BZak1rWlQ2WE9TeE9NTWdyJTJGYTZCdUFtSkRQZTlNZlRVMGFWTUhpRGp2YVZEU3kxdkRKanRiJTJCWkk5eUElMkZZTTIlMkZZYXEwQnFVQVNxbElrM1BrY1BhViUyRjJDJTJGRWg2VmVzMXYlMkJWNWh4TVJyN0JKczEwaCUyRjNINTliUThJcEV0aHRwRiUyRlhNakJqOUc0WUVhRk5EYUlySmRkWk1MTGw0VTFoNzRXblpTQzV4UW9kVlpBMiUyRlU0TjVrYk42dmFVM29lTFhtSU9NT1hHa2wwUVJhUVIzcVVVanliNWVkNWpPc3M4akNvcldnYkc2Sjl1RWRzeXR0WjVtMWMzT2JIdXVaSzBFNldkQVZMJTJCS2dEQUE1RzQ5akQycjliZUN0ejdURHJmQzY2aDZQMWlmSGZwUW1GRFEyV3g0QlNXZmRQQ0xNUzczVjFvNnRmVlk3eUxnWUpTSVdaOFUzbHRlYzBSQWtjTHMzc0VYdCUyQlNlRlZOckd2VGdJdURheGxDaFdMd3JkeVg4b2ZXQkdZSkswVmNIOGYwbiUyRjlwck0lMkZNWmF0NktoemlXQkM5bG0zVCUyQkpDYTk4UERGTm5KT0hSaWp0M2VNSElPREVVd25vbkViV1JOZiUyRlVVVmpWSHRWR2FwbFM2NVV6MTZ3dlhpS2Y4YlVpYW9lb0R3Yk1ic0U0RUthY1ZONG11ZXFtdGlsRzYzSU95MkcwRGxGR3RuT20xcW5QeiUyQm5OTWo1MVozVHk5dGtjanZDa1UzQXpvTmtsS3dSV2FNUHJDQVZaTWp1ZWV4bEhFbTFsM0hxdzQlMkJ6R3RJdXlzSlJEQkxGSnZhcGF5MnlEYlAlMkJhZ2p2dkxaU1lyNzdMQ29iMkhoWUJtZXN0VWxNeHNPQWg1c3R3V0NBOSUyRllwWEJCR1ZjZzJZV2d2NHBrWXR6OVlkeE1tRGpIV1hnZlpxcnQ5YmFmaDFoaiUyRm12Ym9vQmpzbFk3SFhCUHJ3aTIydThjc3A0eHMzYThzbGhYajJRbkE1U1h1ZGR5eE54RHVwOFo3bjc1NnBQT09UYml4anc3dWQwUVAxYWNuaTAzalIlMkZVNkdKb2xieVVSSWUlMkZuNTRNc2xYdGRMNjFlYlg3VEtZSWRsa2VtbzJORm5jY3BvM0Y0VXh4TjZoVE02OFJwcnVNYmticHlIMHpOUERVanYlMkI4bFVSdUVSVHZOU0hQQXZNdEdJMzM1VUpDdHhCc3hOaDFTS2MzWkJLT2U5NHYwRkh5QUdzSDZhV2djcUZITXclMkZzc20lMkIlMkY1OVBUV1lVcXpEMTE3ckxxc3Jlckp4NmtnelptanVCazZQdnJpSmVLMU1vMW82SkNISEoyT0U2UW5PcENZQ0FhZU9TQTV3Y1F5QzVVMVBwN05WTWwxaGVIdTJaSFBWekVhYlJyMjVkS3dJSFhJRGNuOGh2anRUMTZXQXhaYnR2WW1XUUxUdEElMkZEQWIwTmtkcWFldnI3ODFlMURUNFRGSW90bSUyQndrdWZ6N0tlVFhPQSUyQmtaVEMyMWlsMWJDcUZ0QWZDeXlPRCUyQmtCQkpsNkl2MSUyQm01S2hIWkI3cnRmVXRETTFLaldHTkZ1a1Z4aFNxOTkzRHVyaFgxeENhQTBGdmJGd0JlTDFBQmJLZU9aVHUlMkJQdXczQkZkdmg4VWR0dTBWZEhyRDVyWXFLZTVkbDBEMkVNbFFkeGVMTGM1encxU3BwS3R2T3hOTU5qaU0lMkJ3ZVglMkZGUGs4JTJCJTJGY0JjTnVLRjgxZDF0VUpramxod3hQbVo4M3E4V3EyUUUwVkoxeXY3emdPaE0wanV5RUthaVRZVWV5SkhMUkwlMkZ5b0tjMHV4ZndQOEQ1OVNqcmFucCUyRlJuYTYlMkZYbG52c2E1OWNSdkFVVDhmd1FwU2dzRmg3cDdqYk5zeCUyQmM3OURWOUN5JTJGNlU0SWtzMTBLU0VxZzJyc2JoV1FVZ2JlREFKc3lOWXB2dVJJcE5RZzh1YUxQRSUyRm4lMkIzM002OE1nUVl1NE1JOCUyQkE0ZEZIdXklMkJCckU2SmdJS2g3MlRwTGU4aTR5S3FLZng1WlRFWEI0VXIyakxRcFRQZWhBZjB5RWk2R3VpcVk4TlRiZHBmVzNqUHRjUGU5S0YlMkJCWWNtQ3RzYnl5U0VIZjdBdDlMS0FKa3AlMkJ4MzRzdWhqTG9aeTRUSE9Jb0ZJTCUyRjFHVVdENVpSckg5SDR2c2hhazE4d0RPTGpHM1Fkdm03MEFkRldielVOMGMlMkJ2b2wlMkYlMkZWMFclMkZ2c3Z3aDdaYllnOUFNY0FtRG1LZHI4dWs3MmM2VmRRczVicTlUS0dDWGFxcldzWlhGaWhGWWNYbzZiVnQxWVh1ZTdhMEZobmpnT25zT2dKTEtlZzZ6NXhPRGNwWlR0SWpYdGhPdFVneVVvZlFjTEN3d1NtUGElMkZrTCUyQndMZGFMZ3RaVlpMcFNoMTZZNm5Yalg3NzY1ZGg3bWFCNTFRdyUyQlc4Nlc4NmtJYldmQ09BU2lqUlBpWjFqR2VBV2N4RjFhaXNiY0VlYllrZXlpJTJGVmk5Qkw0JTJGWW9taHNCSXp1ZFRDZDg4OVVsSWl1RkhFSFNuNkpwZEZDVGE3T0VKdmpsYlozZ1lSbEx2ZzN6biUyQkFxdjlCVnR4TFYlMkJYWDlxcjBBVnFsUVlGZTdaOWZYY3dkOWc1SVphdDBlaUFMbFJ3JTJCOTJZNG5mT2toJTJCZjJEU0olMkZmNE9RMnZ5dFhNY1BnR0EyUXpKQW93Qk1OWEU5VHNlMGdpR1YlMkZ0SmJxemxMeXNlNzVhbjVYd1E3QjFUNE94WUZpaWExOFo3QmwyYk5sclY3SW0lMkYyN2lhWGFkdnJqQzhZVUQlMkJQWWdwcE4ydnRVZXZCN0lndXRPMEhUbU5oM09HeTVibVRkdWNrJTJCJTJCN0tmbDZXJTJGUU9tWGxBTUJHaTRlQVlDUEdqY0ZQN1NURUEzcSUyQkxVOTZKMHFsaWFiQmR0dDM0Sk1PZ3hVTUlKWGxZJTJCdFQlMkZNTUNOMlJKRnhTdkNTZmxjOTFjJTJGMlpXb3BZeCUyQmNLcGc2cU1uVWxVd1VwT1o0VWFyeHZTRzF2VWhTUGUlMkZLd2olMkZPWXh6alRsaDJKQ1ZVQk5kUWFpSEpmRWZEbERnOElRVFlFS1JhUzlyRm5saGx2SG9mWFFwRFBITiUyQmNxM0FnVWNIcXZvRldibmYybCUyRnpVNzFLWGJHdnlJc21aUFNZcHllR0xUQ2FyVkhpZWNoWHl4c2Y2R2NnejhROGRCMkZXJTJGNTRVeGZMJTJGSFNsaXNuMnluSEFocGIzYVh3MFFhYkJIQ1VKbFQwQ0swViUyQkp4OGwzejh4RnF5N1lDZ0I4UkttdVBJV2ZnMCUyRkZQRU9lbSUyRlByUzAyVkg1QlhtV2RlVTE3JTJCREx1YlJvSHIxTSUyQjBMclV6Um5KaCUyQkF5ekRsa3VEdGxvdkxpRTdTOUcwNjRWeFl3RmdFdnUybGZZRlo4aklPSExwMVUxeGlKclREelR0RUZkSmZiaE1wWWsxYnlrJTJGSkxtVG9ic1JNcXhMZjZEcyUyQk9wREw5SWRRMGw0dmJSd1E4cXp6aldSVWxNbDlqR2NhZUQlMkZzMFJSTnVWMDFrZm1LZHlHM3JHZE1TdkpTRiUyRmpjNmNYMkJCaUtBJTJGNE5NaHJBZm94UjBxSVpNRGUwVFBMT2IxVWFJJTJCNEVPbDRFOEJVODZ2VGRPZHVwOHUlMkY4d1ZzTGZHUnBwNzFTcXA4V1d3TDBsREFzOTJGbXBuJTJCMklKakl6bno3cXlONlhpSGZ2UjJHNThaRmpJeEdDQmtlTkswdjNLczU2RmdISkkxbVFKbElCTEhLcEVQdVM5d1E5c1Q3UFJ3bkNWQ3FLZTFSSm4xSjk3SUJOZ28lMkZGVmlEa2VSakJQV0VNZmZEMTdlNGhrRkZvbFhDYXdDR3BZRWMlMkY0RzBHZTNIZHp1UUtYdFh6U2RLb2F0ciUyRno3OGk2Nzl1bFBDWlVJcjd5YUlQejR4d1h1RU1ST3dMJTJGOEY1TXB5Zjk1MGdaSVZMc3BReGhSSzJ1cjBoWlB6NW9wbURBdGNXd0lmOHIwdiUyQiUyQkxacDRUQnJnRHdBWURNVzgzdnV2RnRadWw3Y1pmSDhaZnZDdlpEWnU1QjVwWXI2ViUyRmJvalNhR0dkUmZSbGloJTJCRVpaR2lUaEtXMUQlMkJWZHZmRnNvWFhNQzNwbElkY2kyNEZ5QVhqJTJCNnRtOWZ6QURBQWVoRWFSQTREMiUyRk4lMkIlMkZ5clRvcDZZbHZCYmMzYThoT28zVk1LSGZVRGozeVZmeEtMWGclMkJKS282aW95ejclMkJITVhiSXBEJTJCdkFXUUg4UzhnTyUyRjdFYXFEZ1pQZlc0QkJxUzJ4SG43YUZNUzhsQzB0blpKZGdYbzFwcWY5cXJKVUxXNWdtTjZCbFclMkIxWlRiMUlVbE9sdUVXcjhHS1MyamNFdlFuaHRTRWJyZHZ6VkcxeU1CM1hsM0MlMkZSMCUyQlZsU0JvaUhqN29ON3p3S1hzZzVyS0x0ZGxBanJqUUx6ViUyQjUxaU5FcEVBWG94czh2S1UlMkJ5MGpHdGFqak5RQmJvbGNCTmlBRkY2eDNVJTJCRkpMbUlmVkJHelNFJTJGRU8lMkJEUFlJT2h5YzElMkZUMXJKNWZxQmVCUHA4cSUyQmxwNFlOT0FOajFTJTJGeXV3WU1PUURyOVBXQ2hzenFkMmlGdjRnTkolMkJFdkFUOUM3V1B4Z1NLayUyRno0RVBTRXIwTkhGZW9KR3U4WU9jY05nUEZVUlYlMkZCWkZsbjNIdUdzR0JpZnhPRkZtQ1FRU2klMkJ2V0ZpdFl3VU9mZWglMkZDMFJiTUpVclNHRXglMkZTdEwzUGwwUzV2blA0QXZjQiUyRldjUGdPUHBzNmclMkZhMEpab2U5YWYlMkYyTlZvRzN2RnZQNVM1NWI1djJpSmdjQSUyRmlHd0VoYzBUV1RkOE1xU2pFenBzZFhhOFBDZ005dzVveEd5Q0pBUGFlaWczNiUyQnZQRkxjJTJCUzUlMkI0VXpLJTJGMXl4JTJGcHNmRlY4SzhEQ08yeXk1ZElQd0NxS0d0a2NYJTJCR3AwU0IzaFYlMkZLeUFEZGRpS2g5VWdxMUYzcDh3R1ExazdyWmN1Mm1UaGRuT1RmalBUM0tXdUVFUkxleHZXZm5yRTdJZkFTNFA5JTJCa0NkaCUyQkZvMWIlMkJkckhGJTJGeEYyVWF5cEkzSDBtT1U5R3FsRUVZNHJ1cTJMb0Y5ODZ2UDNyYmtqbzZmMGlRZ1ZGZlo2JTJCcUNEb3BwN0ZVdTk5aTklMkZ3TWZqSUIxa29sT2hPalVxYUNXZ0gwckhNUU1QSUE3aWIlMkJkJTJGdTI4TDZIczFvcjBiZGxza0g5b0Mzd1BIY3FwaFNLSmpuNlhsR2NVenIyNDNSZU1nTmklMkJqUU1hWWk2T0huZGVXS2FWSTJRVEIlMkZjYXR2bTRvR3RqdTlZRjZJbXAwRUtwbnBkSm84WkNKaWp6M01pcnlzUzd3Q0VhZm1yN3A5VUZ6NGNSQTIlMkZwRDdDVWpvMk5MdGxnU1NBVEh6TDhlQWF5Z1NXWWNOZXIyNUxwZDJhMEVWV254QkJKZlB6V2JNa0dUbFRyRUJGUyUyRkhWWUhNeXRwUjN4RmtETlBMYU56YW93NmU5MkJIQ0xieVgxZjZCcEZ1SWdGUHBDTHpUcUs0aDY4NiUyQlpkV0xiWmg5NUh0WFNMTE1wbTdmRWh6cGdaWENqbSUyQlBQZXJsWXdkeUNRNEhlbkN5ekF3MWphUUxzdlZtNnQ5YlZibHFHMWxhaVg1aElkQTdZYjllUllvWlNXSVlzam5WUU1EZG0zQXBuc1U3eEZnS0g1Y2dqZzUzUHd0cVRlbTRhZTcwY0hQZlQlMkZ2Y3VKbVZaQXJFaGIwSVlyQiUyRmZzMDJvT0Ezb0pQUWlBbSUyQnhObVlXJTJCQmJuQzRDUnBzc0ZnaDRFd05IMmt4S2RUd1dMeDVZMGdFYmlvMERVOXZ5MSUyRjFlNTQ0VHdpWHdqWiUyRldTcjIlMkJ2Nll2ODBDd3NXUkNVNjVadlJWNWZaNDJFRUpZJTJGVlpjakl3MHdZbCUyQmlxOUtIRU5kcTglMkYlMkJwS2RFZnIzZ3RkZmxXNG01M2txUUM0Rk1IclIyMHowWUlZN25aeEM3TzdVJTJGJTJCalp2dVhub1kzVnZ2QXNnNkZvJTJGVXlwNFRIUDQlMkZQWkZzdlliTGpCQXA3WUtrNDBtdmpnTWdQV21RMlBHR1NQbSUyQjRsc3M0ckh1bkxxWVU2ciUyQkNWcCUyQlkxNnFkczRtWFVra1FXSlR6WCUyQjZBazR3NjRrVlVXWm1jbHhnVEhGdWhuaVRhOWkzWUtzY1Z0c2Fnc1JETHF1M1UlMkZldm51ZlpvTnNrcmlRJTJGRlRXR1JkVW9WWmVMclY2UVQyTVBYMzFyJTJCaU43aXl2NW5WOVNyUWJiZkVWbFlheHRqdG05akpKU09GS2hHYmpkYUpTWWZ6OU9Zbm1yTyUyQnl2UiUyQmkwY3J2OFkzTGVOZk8lMkJyTTBEakF4Z2h6dng2eTNmZVBHb3VkYyUyRkZjbWZFJTJCZXdMMmlPaWQ0cm5qWmtOWGYwVEh2aWF5cHE4OEo1NFpaVXglMkZXcDJYQW4yTkglMkJ1UEVpbm92VndXazlQNW1saXBIRlNNQVlVN0s4WVpVbmNVMk9ncnFYb3h6NDE4V3MxYzNLc0hadWslMkZDWU5YTkgzNWo3YnhLM1diY3NtY2hVQldzWkFGZTFRc3VwQWNseHYyQzZTTUR6aEV6dkxhS05ad1ZUZ04yWDdEWDAzdEhZVFI5a0REaTJMWnZERlFlVUkwMGZONzJtTEU4NSUyQlZ3UG40c0FGeENRbDhRQ3VXdDRHTEpBaVBIUGdKazh1WmxjV2E3cU5ST1VZY2VqN0VpTnZvZEFIbDBocmRTVU8wR1h3R1psNEtydXRBVFJpVFclMkZjYSUyQlBtaGp1Zm5FY2N5dGllYzdrc3FqRVRkMkNsVjJ0alFKM0NpRlhuSGMlMkJjWXhQcUJmWHh4Tm8lMkZNUWcwTjR5Z01Sd2ZxemVzZDNwa1N3Tnc3RmI1SjhwblBpZjR5dzB6JTJGWlB6dXQ2T0tDcmxJWnIzMUVWU2xhanp2ZjJtVlZITWV0Nm5pZ2dYYmdmbWVieHFpNUxRNFZ6M0p5c1hUWlZ1RHhlZXB6aTJJSUREMUY2MWp2dWsxdzBmNDNKOGwlMkJ2Vmt0SU0wdXRwQnVUJTJCMVo0cnp5SG1NdjRncW5qTGNZTzBycVBvdmp2NkV3a3VlNTQxSnpVcDRXZVE1bWdqTk9xdHJMaVlubzNUaiUyRmZSRkx5cjlHY1ZqOE1paTlxdDFCTnRPSnlTZmxuN3R2blVkUCUyQldNQnFkUTMlMkJwSXpVRmlIS2o5RCUyQlNUbHRXa1JEVDZLdTVxdiUyQldYQWFJZjVxU3M1UnRoSTNHa3o5TGtqM2RQS2NlSkx6V3k3WkFjRWdCS1U4Y0pBeHM0UEJ3aCUyQlRBbVRWazVFMyUyRjloOSUyQmhiN3pma0NMOHdDUiUyRjZjaERhMHl5RlV6QVdqVUFjeG8ycGJRcnF2WlVXaUZrJTJCaXdoT28wbVpCaUNsVnlMd2x4QlY3eHEzbThINUxXRzRDTHBiVkx2ZW9mYkdUcnA3MDNYYjFJSzdhOEVxJTJGQzc3MWRrSkZLWDl0bE94QklJaHhYYzdTMEE0ejV6TWtDRWs0VXJQVkJ1Um9iQUk3MDhTMXppVFclMkZ3TXd1MmNQVEUyYnA3enlzM0NxJTJGWDhkYzU5M3hVTklsJTJCSFZaJTJGUUpQRmZ6MGxDaVFwOG9GWFliYXBOSHVidjJiUDdvSHp3WXE5QjBLSmZuM1dIcUxiMVd6bUJkMzd3NzMxaXhtdzF1b0tyMW9UNklNOFZzWFhIWEZrdTNzV2JVTU5HUWpacE5EMElYaHZYY2t3eFVoMDFHJTJCZkI5MlJhb2xNUU5vT3ZHejJiZ1pzdEVXNGslMkJRJTJGcnZSSEdZT2NXcmFrVW1XSE43dHY0R1hyZ0ZTTTR1NEhYYnJJMTJRaUlZb3JMRUxyVVFMR3ZxYkJIWUIlMkJ1S3hLV3ozb0JpMXlEVUZnVm1EY3V3eXpCTkRtSHlIRWtIY0xvVGtBSVYlMkZqdlBZeGY4aTZWVGQ4MHNtejE5M0xCY0lVZVcwR3A5TDE5U2JwcWYxT3dFS0p5eFVMSTFnd0doanBZY2twYWhXbHMlMkJwbWNSNUVVOFBTR0ZZcVZ0dXh2VWc3Q1d3eWZDM0JvRzROOXY3azhJdWdUQVVBMDNlS3A0RDlvSW9XclgyUGNSS3JqTEZ3NXdGYUhqVCUyQkJqc2ZKeCUyQkJqVFZyRlVwMU5ScEc2eVA0d3VHRHR2QXFVekQ1MHdqYkhnamVsVHVUY2RFTEl5UlRwdHJNSG40YkdBM1BOUkNLaHprJTJCckMlMkJoNVpJeFhPcmxKY2d6RlRMMUVPaiUyQmZGSFV5MTd4NGhhSThFWCUyQlVrVXdlTFU1OUVTNVI2R1JwZFNKcUt5RWZseVFzYTJObSUyRm5lTURqUER2ZFVwcEhCTiUyQkclMkY2TExtQ21vUFZHTjJpOGJJJTJGU0k3dmlEcDVZRGpQbVBmVVg1Y2YyJTJGZU9UNnNNM2pJV1FsMDZOdHJ0OERtb1RwJTJCaUpYSTZjJTJGNmVlTlpWVXNtUG0lMkZkOWVMbkRpanJtU1ZhRUtoaklpVTN4NzllZWh5NkFTN0xLUW5ZMmVCamdCYjAyVjNrQ2cwcXUxWmVpQ0xuWU1uJTJGZWoweTVSJTJCNVdwN1ZtY0R6R2gyWnA0NlpYdElBTXl6RkVIVFo3RnVGUUtmVzMzdCUyQm9PSnB2Z0VnUmx1ZlJ0dGZwQkZBa2glMkZIYnFzaVVRTG9BZW9BQVdrNDBJZ2pMcjlaWjY3OFU4QjIxbnlYWGhuJTJCTmxTQXJ5OG9LUUQ0TVpTeTFBcWdRdnNNRDFEeGtUVkh2cWMlMkJyaVFDN1pHczRBZFRCdkpkeGR0VG15cEg5SVlJNzc1R3dZNmVLQTJnYmozNVo0QlJjeXlSTlFxN21lY1doWktZQUIyVEtsWTllNmpzOG9QVXNEeWtDNUxPbXl2aU5ESFBWTGo4UEpWZTZvZSUyQko5QTFqTVZ4QmVaUm04ckM1eXhTVnR2YWFpMWZjJTJGRFlkTEg2WFdwSERFJTJCeTJyJTJGUjJrS3M2SkslMkYyZTZXYW9zNm1IRmFCcVBQOXF3Vnl3c1p4UjQlMkJwZGNSYjJ2NGtjSUJTWnJTRE1qMnl4T1liTDRLR2IwR0d0aGhwYkpjaWxhWVl0eE9uZllOeG4lMkJyVXJuWmpyNmZ6JTJGb0FrM1lPd25CQ0F5bHZTYkdQSVc3cHBWaVJJVm5YclF1MWlMY1k5dHhQTU43N1lJYk81NElwcG5uZ3dUeUtvMnZ0c3h5R0J1T2huZElyZk9HaFZyS0QxaklWUUhaT3ppVElZJTJCaGRZT1JmSFhHU2MxelkyJTJGZTI1bFk3UHl2cGJMV3IlMkJxRk5aSUZYYVJDeGNkVHVmWHY5OEphSG0lMkZ4MkEzbU1XbTZyVVhndnFuNzhxdkhyV2FFdlpCeWhkdUxyM3c1RTg3c1JlWENPZ1F5MzZFZUhBdlJCQjVsMVVEZU5jSXFIYnNFVnloRU1FMFg4YTRhb1BmaEFtazhXM1JZTWt0Z0RuYXc4Y3hwSzJaNUZEYndZVVp3a05PYlFnTGR2SCUyRmhqZVJnSjZJZjB1dHVxa1ExbE1kVDJFN1FBa1hGZUIyZXVFTE1KcWNzRk1qWHRZQ1dhNUdVbW1YJTJGM0VVUkVYQmx4MEJqdXdJbDVZQzdOVUQlMkI1WVpleTB1OEx2Y3ptaUtCbVBKeFRIcUhCNlBpOCUyQlhKQ3RkRWZkayUyRmZ4NWlKamVwN0dSSk0wQjc1WkhzYkdrbUF2aUlzOUtqamgxdFhTMWI4MHJkWDdWSFVxYTQ2Y1N5bXJ6VTZJV201TDZoV1g5WEQ5bjhsaElLenZudVlvenVWQlR3VjJXWTBoSXd5TnZPb29LS0pvUGxvTGVpYUxQbzBBeTd3NSUyQm1ZMnM1Z1d0M2FjSXA3V1l1R0UlMkI1N014cFRDOU9ybXBYWHFYUlFxbXdrNU56b1FRWFRGSFglMkJ2SGVwNWUxY3VSWndoYTRuMGY0cU50Tkh3V1RpY1U2QXhtZk5rUTUyTWd6YTVmNlhlWWJYS01OVHA3ejUlMkJrb0FvbUQ1RUhHR3dOcWtwRmh0eEpLRFNQdW9JYVNscXo4VUV1SEFpUHdZNzRRMW80bnBjYXhOS0JaeFBIWmU3WndFc01GZ3E4eHdGMzYlMkJvR0tpdUZKUFlqd3RmS0tFcXVjTHBwWXg2UTFMbG8lMkJYcHR5bSUyRjlVTWQ3OFo5bnM4VmxqVkRncVNRNDF2TkU3b1kzd0FlTUZnT3ZualhuY1dRbWwzR1drTk00aEtxUjBnS3dFaVUxYjlma3JQNVhEWmozVFZuWXBrN3dyMTFEU0Nkb2tTeiUyRjRoT1VOdFl4cGYlMkJBNXlTdGJkZk9nU2UlMkJOTmo3ZHdqVklQVXl1OWlBNGZDdjQ4c3U5angxYXFXYnVQRlJPOGplR0Y2enVNMjRuRDk5anU5S09xSlIydHlhJTJCRDFJNkFFVFlheVo3TGZkZTk3SXprWFN2UXozSHpyJTJCZDRzZFZiRklUdmd4a0MyMFRVNCUyQlUyTnhvRjUycHg5UlZFVyUyQkMwZmpxNGVCdWFtZ1h0N2R3RHFYb0g1OUtKaGZuTThpNFR4eXFuWXZXUEdXNmZHZDUzelJHVWhvTVJ4YVMzdHlpYnBmZXNrVVhleVJRaDg5SVc2UTZISiUyRnk4MmhIS3Z5Y1p5RHNoYnBjSzdBU2ZLY0JieDFlTmFOeiUyQjJwJTJCSWtSZkR4M2YzY2dDNThYOUpUY0l6WXdrdjMxazVDeVpFeVZzcUtXVTFDZ29namVlVWQzTExzNTZUNWMxZzUlMkJIem44NyUyRjlNNnRPWFBKcW02eHZ6aWwydktHMzFQdmRPNWhFMiUyRlRsSTElMkZYaDlabkZTRnQlMkZ5cm1YWDhTTUVhU0EySGk1MlAwSG1VNGZkJTJCVk15RWVTWjNpT3JYVERXYkNDbVZPcWRQVDlQZVZDYVBOSzNZZG9BZWViOG10T0pCRnR2bnI3blZZJTJCTHRZSGppOVVJbFRhZERya3NFNGRmY2w1ZkklMkZBcXVCM0Q2QzFEZTE5WmN5MFQzNktSZUElMkJ2Qk5zc2I3SVE4TmxGSkIzSG9jakFmeVliTnBrTXUwUHBkZk8zbnJ6eiUyQmtwbW9FUGxteldGZjFwamtEelkwSXhDWlVSdWZPZXdmbzFRYzZTZWluUllGbUJHejJNZHdQVnJydUFEQ2NDS2pIWTEzMjRPd0pYYk92SUpVRWNQTXBtbkg2eE0zdUg5N2pTeTZjUHZXdnpIUDBzWU11bGU1R08zUUNVUGFNN05ZUk85S2RNbWcxTjRud0k2WCUyQjNlbk1wTSUyQmdLdlNEN2J6ejZodnd5VXlZTjB5QUptd3NMN296SWE5aXFTNEgzb3RTWGtOOTRQdmFKamt6dkpMcXpia1ZmTDVQbjBQNUVPUThESXklMkY5Nm5CJTJGUHUlMkZtQ0Q4dThOSTNid25NNUhPd3RjWGZrSFRKbSUyRjhuUzQ3ajhHcVJHZGNUUjQ1cENhaWpIZnJPalp0ZnRudTBBeHhRZW9xUXprOFh4dm43NmdVSVR4dTNvTFkzeXpaeVg0ckdLdkVXc2FRRDF5QWZZWSUyRm5JSWlqM0wxZXFaNk1HaGowcDIzdURlekNwUkdSNGY2WUxCSHBybCUyRmpxc1dhY3NPOVJWbkRSaE1lQ2YwSXZ2enYzYUg0NzdHJTJGcE15Z09Vb0wxWXR1TDhHM0FYQXk4b09EUWxEMTdiJTJGcXREcVBBM2xQdWNVakYlMkJpZzAwdVlEJTJCJTJGZ01VZ3Y3ODVQcnpONEwlMkIlMkZlJTJCanliZjZyMlBFZjhiJTJGZnVhNmFLcjZyek1UZiUyRiUyRnVReTElMkJCNnAlMkZuUDduZVA1ZEZNREJreXY2JTJGcTh4JTJGTjRqVUpQJTJGJTJCUTMlMkI5NXRJJTJCbSUyRng1OGpmRUtKJTJGcnNDVzQzTUxDTFJ1ViUyRiUyRm5FMkwlMkJqbjk5OEolMkZXNW42T01zOFhZSG82Znc4RCUyQnE3RjhwJTJGV29pJTJCeTdjOUh5WGNiZnglMkY5OWRQblhmWDMxMyUyRiUyRkd2OVhUZ1dWU2ZZdmYlMkZWTzZoRzBidnJYbCUyQk9mbzgyd2d0dCUyQkVCWDAxOVdCRlA0RzhQJTJGcW9MUnYxdVRQb0NCdUhOYnhPZVUlMkZEVTgzRlBmTkpkODE2WlVoWjdadGFkTHZWdnczaHZsUGg1RiUyRk1XamtFVXB3dk40JTJCejl6ejhQTTI2WnRxZU41bmozQVd5M01BaUc2VEpUM3o5dzglMkJUWjZEbno5MDQ1bmZKUDJkQ25yJTJCbnNabTJINVNock4lMkZ3M2x3cnVmVyUyRjhqQTc5VHJ0b3hkd1kzOSUyQkp5WEg4WUJuS1ZzJTJCdjVmSGZvUFdETFBldmpQQ1BFdlZnMzJiNndhbEliJTJCZWNsZyUyRndFcmh2aCUyRnZtS3clMkY1NFY4N0EwYkZzZU9mNGIlMkZpZ2ZKdiUyRko5SiUyRjM4SiUyQlglMkYwMzRQJTJCRCUyRiUyRmM4WCUyRnhwU3V2eDNTJTJGTCUyRkQ2UU5qRmhNUGswUDVNUXZsandaa3Y4WUljVCUyQnBkckdvSDlMYmY4YkFvaiUyQkJ3Z2clMkJmODlBVVQlMkJJWURJJTJGeExBJTJGeUVDaU1MJTJGRXlXUSUyQnA4cGdjVzYlMkZiTUFQcEtIJTJGaSUyRkolMkJ4OGllVGlOJTJGN2NsRCUyRnFQa1R4QTAwWXdLZiUyRjRURnFTcWRiSHZBRGYlMkJEOEIlM0MlMkZkaWFncmFtJTNFJTNDJTJGbXhmaWxlJTNF+X1mawAAIABJREFUeF7snQm4XFWV71fdzCBkAiEJsy2YCaS1FbFR4oAIaPtMaFEbx2c3qB0UJAlKEugWWsBnt9qvHQIICSTIKCQyEwZNCJnTrYAMr1sBCTKoQObcW+/77bPXqV279pnq1r1VFWrz8d2bW3XO2WcPa/3/a9qlG66/vvzob34jndYZgc4ItPYIfPWrX5Vhw4a1RCeXLVsm999/f0v0pdOJzgh0RiB5BE499VTZf//9W2KIOnKjJaah04nOCGSOwKc+9SnZb7/9zPdK06ZOLV9/ww2ZF3W+0BmBzgg0dwReeOF5GT16r+Z2wj79ggsukHPPPbcl+tLpRGcEOiOQPAIPPPCAHHPMMS0xRBdeeKF84xvfaIm+dDrRGYHOCCSPAKT+6KOPrhCFDevXyMMr7+iM2S42Al8//9syeNAgOe/rZ+xib/baep0vfe08+fFPFkmrEYW5c+fI5o2PvLYm4zXwtqvXbZAvfW2uPHTPz14Db7vrvuKS25fK1FNPl1YjCrNnz5EnNr5iBr5cLkupVMr3e7ksZaybpVK/XEffjDW14PNeq9etW7tK5px9hixZ+qD09PQkzmtXV5eZP3dsdR1krQf/uiLz435Xd715XvRBzZrSfrp9y/s8t5/Roo2emPZ+NePSw3qP9kdDx6Us8X11HMwzesqmn3f+/BY57bOnSIco7Lq6oerNZs69WIYNHSLnndMhCu085R2i0M6z13597xCF9puzUI/bgSgYkuAAlwi4lQxgyQJHNde1KOGQfiY4zXre+rWrZfbZ02XxPctrlqOC3RiYSsmAVQXRLlkMAfoQmVTg7T8sBtYAc4PRnWcp8TOfRK2mbxaYu5/7IF/HOPRsl4Qk9S3x2YZMVIhF34xLNB6VcbGjxHtLWe689RY57TM5iUIVI7JvFf2tBPmKXiYiYnFLviaajHhdFLnGzFBl4NzFoYOY/LdK/zL7Fr+jJX9WUOlC8ie/9tnuuFSsJHn6W9dY2gWVNv6z5l4kQ4cMNR6Faibez3NnBjF6Zt/OQ+W9ojHNOw/1z11frmldcx2i4Ivbzr/7cgQ6RKEvR7f/7t26RGG2PPncplgXoBOwphorNP91lXJZpBtyXQnZX8fzOteZOWMOdB7WrVkls2dMl8V31xKF0KpXLKAYyyWGSd/XdeLjMsUWvhfAvY/2U/vsYrjEXWlJHp8XvbdLgos8O8ZIAcLc1+PC/Tsehf6T0S3xpJlzLpJhQyOi0GntOwIdotC+c9eOPe8QhXactdo+ty5RmCNPPvdqFVF49dUoFKmr1CU9ZQhDZFzs8UKTuqy1FzuQfrd310XP2H3318VA0LXidnfvlK1bthhbZfXzbN8ixFobtuL205KgqJ/FrgOcDh06rOrZ9G/nzp2ydesWM2YGhDboeZC1PP0cOHCgDBu6m7FAK3gn9GjuzK/IknsejOdWV2XIMs5n8fO6uuJ3cUmAb1gMfV89UDoWLvEIEQrtU5l3tYTPvzbUt4jIipRy9tXfkUlhU/psDYFq3LhULPlpz477aT17qUThVytul64ux9TvvOWOHTutWb//BGip1CUDBw6oeSCW4p07d/RfR6qeVJJBgwYGn92MMTIbuqtLBgyoHSc+c4mCsvUTpn1eVq/9z34fv8MnvknuXrwg+NxDj3yv/OnPL/d7n3jgXbcskCMmvanm2eP/6jh58cU/9nufIHa/ffgXsdBEwHaIQr9Pw2v6gR2isGtMfysTBXIU1MoK4B1/0KgocqAZrSyyfP3jMmZsVOHFbdctmi8zvvIPzeiVeeaU9x0vl199U83z16x8UKZ9+D1NG7PD3jRRbr9/dVW/jEdh5nRZfOdyE0GA7jr98x+XX/3n+iiiwI0MCf2e93N3neh96YnCV3ufyxbcKIceNr6GjNx8wzXy7YvOj/quz+zNs13YnNU3EVl8x3IZPmJEHPLEPti+fZu8/11/GR6j3vQtMC5V72w///ndK2TPPYebIbnj5zfnDz1yV8ABE4+R5/7wQr9ulpOOnyI3LPiPmmdu/MMLcuDE5lRwIDH4ld+HQfbBk98tv9/4h34dIx72D5/9uHzv4jnB54Y8Cscc/zFZsWp9v/fzyMMnyMp7awUeHXn9G94mf/zTn/u9Tzxw5dIb5cgjJtY8e+xh75DnX3ip3/s0bNhQefnpDfFzESJfPvv8TjJzv8/Ea/eBHaKwa8x9qxMFg1FKJWMZH3/gKJHImN3/rSTyy7WPydhxtUTh+msWyIzpTSQKx0VEwbWkoxPWrnpIpp00pXlEYcJEufXelWauMFbSlCjccueyOJF56olTZN2qh5oyp0vuWSETJk42HgAdPzqyaMFlcu7Xpvd/n8yCF1n18O/i6oWaH4HXasLBo5u2B9Y9/nsZMWKkGae7blucjyh0d3dXWan3G//OJhCF98hNV/8gnkxegAW58bnnZf8Jf92UScabsHnjr6tAnLrUDpr0Lnnm2ef6vV+nfe4T8v1L5iYThd2GyXmzKpuiWUThzZPHy0NLbwp6rVqBKOj60oFsFaJAf9rRo7D3IW+Rl195NXM/DBIR/qdtzvx29heOmDxeVi6trdaDTHvduEnS090cNHLyR06Qq+b9a80LXL7gWjn9zNnZL9YH34gMH7+qubNPFH65fJV85O9O64MeZN9y0vhD5f5bF8U5ce4Vh731vfLiS3/KvkngGwPfKrLjb3pk4Owu6a7jDnf9bL4ceXitgWH8X71fnn+x/w0M5Ko9suou2WvUqPhtWp0oaAz31m1bZPwBTfQolPAoPCH77DumJvzo2kXzZWaTicKlC26IwLgNDWGC165aIdNOaqJHYcJEue3elXEYDvpzw7o1kUfhruWxtbzZRGHi5CMM+FUyw9hdfeW85hEFEVn9yO9k1Oi9qpKoIcsTDhzdNOK37rFnZMTIaA8aj0JS1aNfP3R7zAJ9udkoovB3IvIvIoLd/XYR+ab1/GwNCOoTjjtWbl70o5pPWoko0DlCoRDSrUAU/GSgVvIoQBRW3Rcut9hrojBCRP5SRDg38JliWt94FFD4XtTdvm98e90gpFgPqr/tehR0Pk8/c45ceuVP26o8al6iwNTta73GjzYgwDGNKOwxbrJAGJrR2pko3PeLFfKBj366GcMmh096k6y+7+YgURh72FHy4kt1hAdeKCLHRYqodGIUX160LbvzOnnrkYfXXLbfhKPl+edfLHq7hnz/qUeWy+v3Gt0GRGG2PLGxYkTYtnVr5FEoPg0NGTdkP6FH+44ZZ+7nJp62ikchXqM2DGXNyhVy8oeaSBTGT5TbH1gdg/AkojDtxCnG+9HvrSRiPAqToj2q4wdhuPqKeXLu2U3yKIjImkefMkRBS8jSN/bAhIOaRxTW/OZpGQlRyJvMHMo87w1R2F1EdhORz4jIx0TkSBEhwwA70O9E5PcicouIXO6tpJOOr/Yo6MfNJQqDZPPGWgscfWs2UQjN28w5F8uwYdXlUZvlUdDQI90cbsWBuojCEGuKZnFNEJFPicjdIrJKRDZaxLkpG3m6oUfuGGZ6FAbbhdwb5cYhyyBl2rPRDz/0iL/tyh4F4Od5uK1FhOPbHu6lRkkiCiT/jTjgCIlyibLbGy2BIcWyEYF6bUcUzporD1nPTD1E4XUikmP7ZU5EnxCFH4vINBEhmna8SD0uhQ5RyJy64Bc4cM2co/DcK5GFnPjsbdvyEQVkLg5BuH5v5G6gZ8s3RDkKJFJXEqpLkkgUMCzhCqU/IZFC2iC6aWAp+s6f6+vwlOOOl8uuujEuZxkVwikJRKHZoUe33bfKJpxHYxbnKNwVVT1Cx3/0hGObG3rkEAX1KjTbo6BEQckLP/EoTDxor4av67y7dN1jv5cRI0ea/VioPCoP0BCk3hCFqSKC0n23iLxZRCAOj9l9zn6/HjeaiCz13ujE446Vny36UVxNQEFcc4lCFHoUAuWFiQJAFwHSy7xsQo/IUQiV+jJEgXMUqHpk3Zbv+uApTclRaLhH4a9E5BAReYeIfEBEDnXcU6eLCG6qxfZnym6BKLz58Ak147fvGzOslUQdsJB7M39TSDKxigZ322uQKBwlIh8mT0VEDragnIwbQGY9rRGhR3da/Ejk7f8TEYJucFbloxjhXjeCKIBLWG4HiAjBLdmBXekjmBp61AuiQLkH7HXABfr4pIhE9VmKt4YTBYwKOKrJ2/upiOBdqAPDdYhC8bnkCkMU5syWJ5/dFNXRF5Ft27Zmhx7tLyIn2E34uIg8Ya2M9XWj5qpl6x+rTWYui1z/06tkxvS/r33KJBF5i7V44gpFQCAw3iMikIS9rAFrT6uDADgQU8BOAaemEoXqDmiOQvM9CvTLAN6SyPo1q+PQI+1v0zwKIrJkqfUo0D1K7pajsNNF85uYo2A9CiNHVTx/YLfNmzc1mSg8I3uSYC2l9PKofuiRG9dVL1GAUP9QRMgoQHnwbxQHBh0IOYr3u3bf+HjL9SgoMOcnSdVpOQo8Q41EOIDxXOxjn8E+3SYi2+sULoMG1elR4GUjr2bUALGgIiwkxGGh9fGgIzcLKq3UHIXAgWupHgWEGxPFIDE5CD4QHL9j8ebvCME6LDp4FB5aemPNKYMMR2GPwhEiQj77P9rJBYkQyzbLjh+TTp9RKICB+0Qk7AiKk5n9Mmw1HgWIydmW9cJ8mdOviMhllvDVs6Y+LiKErdNXm0fnehR0D37xrLky74prdsnQI/ToQSLyZRH5W4vhFokIujcUkpg1zGlEYc/9JsvOndlamqWidbA4dxoi8z/Ft2ZVVxtBFBAXeGapNQLwpk/1AnA612iiAG461oqNv7HeIebw3yzhypq70OcNJQooB/YuNTKwWKGIautl5OpmYaIALviolff3NIDlBXrZPqFHlfKoJpl5i01mTtN9p1ghgdxdISK/FJH7LXOOKqv2qi3f8ITsO2Zs1T3o27UL58vMM7xkZjbiJ+z/gAoYMUQA3YlO4nPiKQmfGGmTrwibgExAUgEmOVOlqoiCrfBDv0zVo2bmKIyfKLfdj/ve6sKyiDlwzeYotAxRmHy4Ab/qKaJf9XoUGH7skkw9U4n8xaBFVAz/zgvdNEfBGTrZsmVzIlEgIGiyiLzNLpv/a5c9SyjvM7M2x9rHnpYRI0YZfJar6pFrLS/qUQBX7i0iuJ15uXdayxJ7BvWMde5mEXFr9LC3Qpa6Ez8wRX628Ic11nvfo0D0BkQAjHugJfGEMjCB7F8I/Il2r95rlRd1i36LNzBr9LzPQ0RBx6vKowCY5sXwGmD+Y0CIq9AyV+Q8v1dEkEusMEJmbrVKpFLwJlfvXI+CAkvt06y5F8vQIZFHQf+WShSoyoll/ud29dNvBB+CDYs3coE+6w7J1cPoSw2tesREEnqIiRUk9192EWBx0gYBQzkDBK4TkWXhziZ6FA49qrY8KqhWQ6KZS9AtYCOn0K/pARIHFMXmGBN9+loLPeKd0avoVPYxvIm9i1OIfQoQLmLJTw496pbh++cjCoRBYXgmufopEbmNUsMF1nroq70hCogTbJqftOIEbILSusCKD3ASWzKpsQ0wjvh4qtFE4SwRuchTXjzzsxZH1YPnGkoUUEQQ8ktsyN98u+DqmNvCRAEl9GOR0s0iZZLzQBcNbu1EFCiPqiUztxKfnZWjgDwHdGtDh5LwiCJHd9Yrg+39NPTINUoCmkx5VD+Z+Q0iMsMiRgAI8luFFDo/XGU+Mlb9HwuCcoKPsEehBZKZlSg45UkJPZoz6wyTzKwn/zbbozB+0uToED976jDzW69HAQj0HSs69LUxcKMvKCcD/8/TVj/6Oxk1aq94/TNW27aEcxSwbXzJLnVdViy3n0B4ROSBPA/M8R2IwsiRo3OEHj10e3yMsR9ak9ejwB75XxawY9V/v2U+7HEUPvjuigylpu/kexT4O5PtE4W/sN6+o21+GoQBoAHGjYp2Rc8GtyNLsFQyoVeJSEFMLrk9CoA+UA+sibAYNDUx9G4DcDLze1iCAKN50ALHAmbCNI/CrLmXyNChg+W8c6ID15jXOPQIyxpWGbfhjgeVsPpV8CII+R1XEJYc3ondgjWnQOIwoUeUR9VTMN0qBLk9CkwoBIvjGLAI0o877OTyk/FEAQPiP28RKF4GPCEQHtzVXvNzFHSdBXMUGk0U2DC421iMtANFhg3YNcqj5k1mNuNtp5Pf11gnFmnvLK9rLWHIIevMVxoRegSOgy8T7QAmgIey7HrT6iUKcGGW+smW84KPECfIM3g9HJ4IOHK82LbkfbkNMfQha5Fi+7JNtCZPI4kChiEw0KkeUUCsIepwvN1Qh9O0oUTh7dY9hGxA9n3VMsE6JrYQUWCB8/LHiwhJOQDGrMx9ZAOyGFkGy8t2hEnbEAVCjzZWTmbOVfGFhY13W48MYlwYE/TSPxNPYhdaEauCM+/LNpDMPNbE2rvJ7cEcBQxmhL1GZeerm0sSNH9BQxjQX/yOgwK3ILl0/DuF5ChR0DKa5mElkbUmR8ELPeLZPIP/ERC48xplcvZe8zDXo2Cx2drVK2OioP2cdkLfJzMDoZCRLIfnnX7GoUclkXJPdDAcc7tw/qV1VT3Cpve/ReTfrREaHYH6xvvMULMEMZRkNXIURo4aJSXpisPvkvYAOgiDt1+4F0xNPh9GrIuzHpjjc3IUONuBlitHQUmCWyI1jSiwHvUIMhTYfzt7mYkDooLrisbThjwKodCjr4nIR6wiZLFgbUMmE8YAG2PvkHOPwtQwdixbfA5xySF/42FWouCOTY1HgQfiPcAkykO1AVbdTctqU2bF9xCAIBKALR3LGfsOUfjuRbPjGHs3V2HWeZfI0MGDaz0Kr6wXwZXrV2Vk8BBarBfMvAwOpkreCQbGv/k7riFYFowLq1yO5nsU3DHMQxRKe4iUcd1Drw+zY8muxKWLwIXS807QexALk84OxnqIJYpA84BXQYmCT477hSgwbiAs4lrJefgnMSdpu+co8JVdOZnZXzp4GwmvYZmxxPAwgOnyhiGlexQONyeaZrW32nBoCuOALeGXWPSjY/Dqa/USBcYBuUqUG0t4od2K9Ms6oUwOwLcsd0f+0jRCEJc117LEcKyxZbQmT6OIAkuYHDRIndsQF4rr2JJEXXCsYpGaQA0lCgwESgCrErL26/1EFBgUkqeJw4XVIfsJifSVDwAP8Mn/Gi+L7GUB5iju1DZEYXZ16NEWashneRRAQyQ84qH3G5ZBjGwQBioPYGgrCJAfJPRo7Lg43t48ghyFawI5CixiwEZWI1yXaup3WfCO1QGDIZsa9Pc9DvKxXpGEewWJQlJ5VPQcwgLrKR73/yiJbDLJiQ1vLlFQ459bHlW9RVkeBZY3qu9dVp4BNzBkqJSm6wyX5o0ztVyDx5cp4HdwHlCK63AuYRChxUTBe3uXKCi3whAE/IGPauSyexnPIBqGiGc8C0Qzs00xiBNuCZ5km2KPrBTQDw97RBRGVx0Ql0QUWO4sFTdyXe+KUwqH2rctxu7NJFeFHi1JKY/68Mo74vAUv+5sElFgXX7O4mKtha4uGbDbEmv09a1ceV4olKPAdb5HAdyI0ZsB/aANNaK6En9jkfF3FgHMDKschgD+jqWLs/lYWHn3ke9RcMFlHHoExcRiBcpQiwOrG03tYhQGBQHHINJhkjXUCrBQpPT3UenVrJbqUYAoDKl4FLjXMR/8mKw4eL3I9+2zia3QRv/YDVBj9YqoxR7XOTFdmoPDbuR7KL0cjYThVfbANf+cjlxEgYIAX7Rxa5Aunk3sBWZnrDJY+yE4fIZZAcIDmoIoHCFSOl2kDMrymht65OYpjDn0HfKCXw+dxc0i4tk0JMsXLLHz4yqQYiRTM6+EcjG2WMTY3fTRbcz70IhNV4Ue2VC1XTlHIbR0SCYmHhNhDCflJ3wwT0stj7rfZOnOkaPAc7AcEeoDaaBhRGQL19vqJQpEyxAXSwQdywTxgkLEKEloJ45BQi7hwGApyAxihb+DiRlHKgdjdbvROiz1HRpFFBAlbE01jGqNBpY69gXwL5gJnYARJ4/VTfvYUKJA5jwTC9CjikZUnKWuVtijgCsdhIEMhfUCEn05gC7A4EEmOLKGRYdcwBrGpshobUMU5syWJ56NTIem6tH2bdnJzCh62Hrt0RXRwmNM2RjzRAR3ZBZa88Zy2YbHZMyYyG6rep2QleuuCYQeAXoIGwU1qpU0NDd4jtCf6CkYM6AEyymxeMwruhb2zs+EBlHQcxRi/UTVo1WBHAWEAkiVcAqeBbjB1bihJPJqudfhWW4XlSiop4OfftUj1nCaRwF1Dach9Q+iwL+1SIPLodUYjQpVCAU8YQi1IWNQwfBFPKg0iAKhR66XiH66OQrAGUA+Q0eoKYYW1DX2Rn5XAxXGEPQQNkq8pnA/uDt9A1PyN2QgqZLcK63iG6FHo0fvXSmPSuiRCb+rLY8KhIT/Mq1mbdpoa0gJYwdhwqtwpnVSZsmIpM85R2H4iCi2L5dHIXSjNI8ChVuIAIHAgtcgBwwuXgSiU3xZmPdFTvrAFLnJy1FISmbmHANAtaYFIDfc6B3wGQNNDC3Mk/4hr8GQRVrIo6CkKiYKWLIJuIbqaWgPg4CiQPD7ViQYFqSC6htUUqDhTVBfWkYHQzkKesk5510iQ0IehZfWizBxoAmbjxQ/BvMkcf9a1xYigcsGJUc/+TsanwkGeIcPqq7pda+qHiERNNuVjEnGB2sgWT1pC0yJArubXQxK8Vrhk5m5J6gVBERDiTMWkAZMHygy5hFTLoSR8SGehjHFosTh1FgzE0zkr8UcBX9OCP9F8CIINWqD7YG71XUth7ZG+jkKk6Q754FrLBnCKMGVGBvYzljNCf0p4oXUPtZLFFhOOMowRGJVwkmGAmProqDgrXB4lhO4BEKBskXxkrJDShE2CdzUiKNrnEFrBFFAuSNHwT4oMc0NY/6IoYVogVvASeBfcBFbJW9rKFEg/gm5B6LQikd5O+J9rxBR4Fp0AuEmNJQPShNkosYgBghFBVFg0aHEGEwIAsYG9ErGwmsbouB7FDZvzq4hz5igA5CtoCTcjchRxgwPEX9Hl2Kh5zM8N+iyFBDuTinnKIwZt19NlcVgMjObj7hqGDhsXMs56g2ZV+YKoA5pUVcbwoy+kvDExoBJg/LABgl6tCZHwYbRrFvNycxe6BEbDH3EpudZCAoMestLIg+Xo/XE/+j0PMlfgCVM5nyfvjqGTj/0CGxW5VEwY1GWaSe+J3iOAlOFAZdhBKhXp5HXbkonFSL+0P0bcplusjQw8NCMR8EmM2tlJkKQ3JOZmUrEAmqdaYNfIi9R13A87omRA9iDIQY5hwyF52vZbD5jCcBR4WcYlJ5OCQohmXn0XntX5d8mVT1inCA+37DLDXGBl51ljihDR7HM8dgG4I2RvRiJmO60oyn1wLXc5VGx9hLL5YawpBEFDKpYlHghiAJGVMh2toM/XULn9SjkkfNgOnAlZB75i+sbS13RlsujwE0p48ImI1GZFlrl7sMx+51jVwTAF+ECMsiBSNI8Cuec920ZMnhQVB7Vz1GgfyANdkRWgxCohwTqjnBjV0C9GVAGGBSX0jT0KFRaNtWjgALFfMCYoGxpmE1JZGMnpzWAOSFgjC9eAO5hzyvQy/zQI+1f0KPARRAAghVBjcwrCp+dS24Epl3iQ3geVB8lRsOCCYrjPdggSBjWBmPP+DneCCUK7ji9lkKPGC74Fu5U+KnmCWJ/vNSGr/CdJPnSqHMU2Booj5MsUGeKwGuA4qJhlPS3XqKAkIcc4DFguYEvCG9H1qI0UBQQCXASoB1jCdYvPud/LFzgUwzokB23NYIosIRRtjSWMkQOI+Y/WSCAIRhshLghAY9+sH3z5p42lCgQEvo+kdIPS1K+qVy/FYv5KHrgGvEJgH6NdQDN8G9NGIERElqjtYHxlGImZMEhK1BggN+U1jZEwclRQM7lqiHPRsSCgFcITxCM8wdW17IB8Dag1DXWDSUPmmIMQZFpmfQlkV+ue0zGjrWR4I6+TjxHAWRLvh5uPeYNtx+LnKZn+bDY2bgaD8hn9A8dQZ8RMJAdctQAUQGhFhMFp0+JVY+wDGA5oEIfpmhFiWxSjFWMAesNNIy+5n/GCSGLedzHG2phxZTO9Q7SVKJg9FRXSUplkXVe1SP6OfWEY4NEQfPBkfHIK2QZ0CIpD5yh07QUfue7WiSSYSNaGys/RAFYQoMocDIzfeT/0DkKqHNqydAHlgkwga2I7MSmB3/Du0BjWIFCTDtwjahr5foMNeGfOJswkjBkSSGWfjIzhu4o/C584JpGw0AGkJssN+6PMYgwXfqDzEW+uo134l0wekFc0ANJRprcOQr1nszMmif6BHxLmABuF9w/btQMewKL1mMlkf3LkcEEbwOEO6n19sA1wtlZTFTSROlD/mF7GBvIFGfQizYlCsQ5DxxY7XOsqnrEg2h5kpIRLiQ8s8qwTsD48TWxOvlfFUlCZ5Uo+OE8fP2c878tQwZViAJ/yzxwjZ2KBR/mp6+IoFELOjdh8FByqrjoNxOMpyEBAdR4FLQCVFZ5VOg6O5R8CO7NbsY1hLJww6aSJpMFQBlSlAi73HPhFz5wDRKIUsJfygLDvIASB4UR+3alE55FnxDAzDFjqtn1CGzeB48IyAoSYTdMyKOwK5/MHJo2ypGSPoMRC689nAuQjq5i6nEogQFCkXmNSGZ2+wQ3VWMuwhnBjH7NERVY9Wr1EgWIATKWJazpQSgilgxKCr5Kfiz/q+Jkm+DZRZFRtTEJIzWCKIB3keeERDFO4DfmShtAAG8QZAJFxVZAgWmoEkQmrTWUKLBPAZzIVKy8OfOrQv0rTBQOEin9m0gZGYFcRYOjMN1a3biMYFEgA4wNADcGlL/BvDJieNuGKHgeBYjC+AMyTmZG7hKzhnkVNIgcx3pOI2ICUy8mauQ8BhkWGRuBBYasBfWB3BI80Jqj4B4ICrhA+08PAAAgAElEQVS8btGC2vKo7oKAyYPEMEtrRAC5KAgL0CvP9IUFBiW8I2wcGmgUweISCvuRhh4Bxk2z5yGtW7UyfOAawfQgSPrEOoKMAIC4HAECNuF3+oVeR5DgjqSvGNEwavE/wpcS7rwHQgRzunMapht6pMOhoUe33LXMVBqiJeUocDu2ItCBR7El4TZ0n24q3+MeGsqIvFOgy/c0FYW/MfyAfNdRr0RBoz7MOQplqfIosFxwxGBK5blwUUI02WohGU9kCv/TZ8QJMg19RH/Zsshd8iyw8wbPoy6JrHmkNkchrTyqOhu1TgsOSd4XWwLTzJbAsEZDtCh0QyeAveGyTB3QBNkc2gKao8A98oUemcMzqnldkkeBvQgxoEoIeIf1hOdUMSUTS6fZE2BKLGFMDHsCsE6OQJISq5coaPIfAINBY+9iJyCEnX0AwWchsP4JbaBfTHoexe96FPw8jsIHrunuosPEOkJR8SVhrmRAlShkZP5pMrNbRUhv/fXzv21qpatHgb9nEgUECQKXWDJoP43V5gYE8jdWGyAY4YaVnDwLdhsILrASXaLA6biDBg2MD/RL9SgQ98ECAiGyWFj9SBWsbBleDCOrkEb0DamC5QaG6jQ/9Egt+YknM6OESEzEfYzFBUHL7zBQfI7sRiVYSDikEO5bCAs/6RNoCQLENXhmcD1bUqlEwSV+rzWPAnIF/gUuQCEA1jEk4qZmygHN4Aamk+F060mn5yhMlO6deXZ6tECYJixFbAWsTliu0J30oajHtF6iAEfGKQY5oIF3UFTwZXgnhmqUEpGBWiAHpQGXZ2ywOyT1tbdEAQ8GDjXEFjKWrQ8Xh/9qQ6GxBZlTxAV9Zlz5LjiYraNxtyEF1lCigLUIcEYnIAr+KZ/VoiH1X4WJAmAW9w+AHxMgngIAJaY+GgPIZGJZ1vgCXGgsPHQEpkqUV4orpt2IAsANQJnrwDWs5aA4LPcsHjYCi89tgGIIGId44MbSmDf0BZ4FCJduXtd6XhJZtu4xk8xMPDvNWKGlLDeYZGbvHAV/ZTC3IDCs+cwVQBuwg7XjFyWRbQGZw4YFAEEaQLoYnwKJWBCFy6/mZSuNvpGjcPJJ700FLqWhXVJ+f0+k/9BJADP0F4RUEaVWSWJtQVjoNwQC3UTf8MKDmqkQxrjbBlG4/YEolk51phKFJXdjKo5a0snM2NVY8ux5DM0ECKAegR4YF/gd7IgMwx7Ik1CtAHH+xrUA5TTPpFv1CLxGG9A1oCpHAf1C2BA2A4YGjAiGxTuhtV386YaTsiK4lr4phgUOAMoZUrYrEck1rSSy2hAFqh6V4jKyhB5NOnjv4HzC84hihpiwbNGDjEcIO/MZ8pitQJQ2PJY3Z8zAuSzJ0NExmqOQ+xwF98W2b98hgwcPkiSiwMAC+omfQiGBbWE94CWNV2U98j0GlZfUamDgYTyH3yqJdAf2UYgoJOUouH0+oCRydjkKf4qKPUUNpskE6h7GmIMVjLWPqwrvRloMF/fIXR41tEDS/gYSwcRGHJfW2WIlFgg96jHlv6oJnksUOGhq4MAB2USBfkJaQGOhxDE+15Wnj3PLm0BxCZrz5rTuHAWQGdVKIAtIFV3pebw1LDrMmVBudj4oxis8rETB98gkEgXen0WNNCGuAjMIpIWAbBYYdJ/G+9NflBW/I6SRiDBW3glTCoIZCeWYMDo5CtHyASOhn9Dt/GTIcLrBY5lOpp/hRxhCKjQdKD306M2yY0fOcmJ2GjWFCH3JsldygjEzw9lXtePrJQp4RnErs7R4d8YC+YqixZqPAkGOYZTR77DsSNLLyg/rLVFArhMXi1KH92q4tQtrmD84sjpZGRSNokBssF3g2niLID++4m8oUUA2MWg8GOMGFS3qbIWJAoYWFjGDA8tlUgG6KCJeGh3AZCP3mUgs4chhlBP/xggD/kqZ1HYjCuhzyMKO7duzPQqao0CsCI1FA8IL8X6sg2wSgDvIje+AkiBdIE3kLkDYuVY9CkZ0E07DKi2JXLdwvszwD1zz1wwLenJJ5LxyxIrVHE5kwHyR0rNdUt7irWzWAPoAZEw/bhApfaFLylurv6ehR+6BYVHoEeVRp+SzcNr+lgaXpIxg+MtytCnpK2SAClsudMCyAApl3YE0+YknzPHGK1FwQ2RdomCAeUnk5IQcBeSYVvJBBWIHVHlKF4nmxUjCd5AtAHLyAnxumLZ9Fy99UCZOOiIOo9f1ds2Cy2vKo2J8QZYiy8CwvCr6JuDkMY/E601uGOpcG1scWwRRaegJCEWorXn0aRk1enR8CBxzm3SOAtczHgB/RAGiC0gRSnFkCuk3S19Ds/BuoCsgMXjmCcRAX/jbhtCjPYbvacKz7vz5LXLaZ06RZcuWydFHw7hFStOmTi1vWL9GqHqU1JKIAp3BwEvINp3nJTgMF3lMp/k3HYKkatI/2A2DCoQBOf2+hJeu16OAERwCwqRp7CzvhSEBpQ9eA9sBNFBeYD5AANZLyA7RMxCGEEZvOFFgkFgFbEJCWQCPeBCUYudQYmk5CnV5FHgm7hi8CAizUCMREAHDrsCC4jYEOqDZO/5aiQLJRLEb1V6X6FFASLFAsLLB+gD5p4qUXspXEcogSpTtG0VKXxYpY0X0dghEAXDJBiHRdcCAyKKUShRYzCAcJQpc4JInUBvIh3hjl5hwa4gOY6u1ejENwGpZC0++Ng9c85cYxjl0FXIFg6vGr7Jf2S4sB4YSDzs/GWJyFjn7LjX0aOxEgVAXbehRODzkQMv0oVDUGJznfvUSBdzZKDGWDA2jNJEUyC9iVTFEgp0wToMjUSYQLf6W5fXoLVFg/BlzIhyQoyhxnKHIUW1waJyCfM4SR76C05D9eogdodzMH3LYV4ANJQogD0xuaH88i7049KwwUdABwViBe4U8JSy3xCegIJGnuM2YVEyCDBxWOMJSkYPIEwYONpXQ2okocOCa5kHmCj1C5mIKxRKPzmGR4RHCdOtb+FhofB+dgQVQc9sUkGA0wtxLWBeNHIW1vzHJzP45Chy4NvMMgv8yGl4FIgMARMQD8m82IAgXNu2f2EgfMX5hkAMFQhjRF1pRzz5OPQoaZ8+Y9epkZs2R0TEC1aKzWWcAM9x9/FQxyfcgDaxL50SxtNAjPApKIJJyFBgWHs1+B+6gTtX2xyORs+RmwZvxqPIZMoPhyit3IQqTJsMmRbp7uo03gRY6mRn5hCMKBz/gH3MS8ggoA7f0G3fycyrQE+gsPCIsL7ZvqEEU8ChoYz63pCT0Mx58W8OuGAtkr9ZG4D70H0gPUdBUGTwwEBaMNfBRxhjDGmIPme22tVQ9Gj5curoGyO1Lfianf/bjyUQhlGjKzdLKozKR4Eq3sR+1aAN4ETxFaBKdo6Ncg9LHOAKYD53bVW8yM5OFmx7mBE7UeuIsMvrEmTdMJMSG9Q9bReZgwKEvWDKhTAymb91yiYKGz6ilvq7QI2YUQYe2h+Hjm8fqwc7Iaa7sC6JQ2lOkjPCiH+wedqyGQGnVCVxCsD58gMhRgqn5DJCMEkT4OMl3dXkUmBAUA0KWCYXyQ5HzYj2kDcoFywnkBiHsBQ4mnaMw5rB3yAsvJEwCEoIQAsbHdVvpJkBxYebGCuijNcgVrmAWJ4yaXQ8AQBA/2yEK4Dg4M1OFHvcTh5EbTCeCEVcsQhRlw/4224iD/ZYi4j2Z1N0te+w3Sbp35k2jrVyPkKYyB1YujSpDx4M3WI557lgvUcAKxNJA0GMcZVuBOxgXotogEMg4IgNYdsgu/s11WSGVvSUKkBbCnLTkPxY4wo7w2moDk2MV1BKp5HmC3VC+mlzNFkGHQAB9d3pDiQIsk73HQ4jJZoCQwZjbmEgWUp7JrCeZWQcE5UcsAd4DrNqYU5H5yFLIAnFkgE0GDNmKp4GFjQmWidfMygACaSei8ORzr8ZgMhdR4H2PK4n8sBwBCXQACwcdg9JmUYE+XYchY8iYwrYZQ/QTa4B5ZrPAWmG314g8uO4J2WfMWBMKomQhd+gRfbPAu7RHl5RP64mQHMQPgIFO1OIfkELAECAJYISRiO+x/ghF8vJm/HMUzJkF5R5ZtypQ9SgBmKb+WVE5go01CCllzbFG6R+omfElhhALhG1pRIGTmbVsalKOgnoUmEYALUYY/6gQusR2Ra7QLXgKcjhHoIXp5RI8CpPfXFVdiL9ffcU8OfdspGd1Q7cAtgkW4KfmRKCewZTI3DToQVg9Mgx8ydbW0tr+c7TqEX/XkrdZhw6CV9E3cF5EFmOBDOVv5KbzOTYIvByaCon9GTEHzkYGozcxKCH2wMHYHnS74FEYMTIqj3rHz2/O51Fw0xT4ff8J75Tn/lCbL020DHINQ4029i6GW0LeUArgS0C3GphZl4QbEm1BQ0YS8u1HkihR0FwA7ZN/jkKevaGubr7LusfCBeknJJDPcBHhpcSAjrGCNyUckoF39YYShRChqosoAMI1iZnOsbroDLPnJA6lvaNPFNxE67qSmfVhoA0Gg93q7hD+7gaFq0LDpQ7Z0aBxiIbj2tdzFApVPQJMIzwJgWJnsAvykgTegyQUsqZQEN8QKd0kUvaAe+HyqNyXMWCB4C+N9lZ1o4+gJcwfjqOuNECkzO5m8UFiGDtc6GwkiFC5QxQIY0Q/IdTSIkMYOmxFGPFQMBjkCD9qVNUjd0LVugX/VXkA34QrIr/yYMt6iQK6G8sb+JICKTyXd0VE4Gzj78g0foJJ0OnkesLdXct+SIb0ligQesT2xJWNzGTOXJKA+EAsgINQZGwX5D76gD6DOxhb3pEQUAiRbzBvKFHA0AD45n86ipLCBQ1mwCABiANAZsVs9YYoMBHkJCnDA0BiVGBQAIu4qtDgyFEIDMoU+YfMQVfAVhNa2xAF5xwFAOXWLVtkfNaBa7wzlm6C2clT0JgUNgJzhhzFW4sOdQ83ZdyGipT27ZLyp3siwILMJuSAjcvCe7/I8lWPyz77jo1P74Uk0K5fdFV6MrM/F8wbxiP6hzDDUs9iB52hR38pUjq3JOXflStVCnifFKJAjoKW96wkMzeIKLj9V28DSJTNyzghaBlf9otDwvxzFFiv69esltkzp4smMxO+NfXEcNUjN/QIMEtYUeisHLAh+VbIGrYIhcuwkudp7jkKfF/ndOEVlwaJAt/h1YFlGEGAG/yb5UQfOTASi32Sp9YlCugiDcH3+wpRGDW6Uh2GPZBUHlWvZRrQA9g96Rd4GvmOrGcJQwpYcm6IJ30lwpLPmUL4MWNJ2BQwBBs1y5K25jdPyahRexkSeteti7OJQgjIJXkUsGLhXtFQdvYdHUIOe5En8VihzOggRJ5OguNCR1D7oUfKvCAs+09ANTW2QRQIKcQAAWlhj0PuXWXrehTUk6C9qIsoMGPEPBGGQgMZQZmTAuMCrxydozDXzz8333TPUdBLM5OZ6xlWdohmh2usI1YcFJ5tIY+CrrXE0CMELGgI9IOQYtVrpYg8/VSUskOk9I8i5UBNbffANTf0aN83HiUvvpRwHCrvyD5nsaC4IEXuSZ0sIPyCSDnl15qRC1oiVpnGzmXcQEg25mJXy1EABDKNGO+yGt8DB2H0w8KEEMxq6Fi2i94/verRROkJJURlPcT2idAesBtTjV7HCk5cPn6nrMyHeokCXUNnY1lDuLsrkuWF0YWG7EKZMG6EVoIpsTSlWd96SxR02DQf0lWgYCW8CxjOIQO45AlTQj8QBso7gdnI6WU8CT9Cn/gVpRpKFDB6YOkFlQDIGRzMhpgracgXJtU/WyawPuoOPeJegFQmB0VJn5AXmPrIp4JRAXrR+ChJvoO6w61F3/hbgnJtG6JQz8nMjBtjhRzFXYZ10s0xBkyAiCALGNl8yyP5e4eWI/RGaBd6hbHmurERUeDANcCkGhYBcIQeZSYzh+QHCJPkdOYOVyDGIRpMmHgWCA16g76wFhFgAA5CZZ3mhh7pCcj8XLuKHAXvHIUccqxRX4nLo1oPDAbddWtXydxZXzFEgYZnJin0yCUK8HNkmx+dxT2QcXgtldsDlCELeZp7jgLfV7wRCj1y74f+YYnhhMLIgQEZnsSyQo7pWQuIDm3wUaYYmYd9ElKB1znU1KOgFbb4TlrVI/ceBCRwNIYecMwyRyfoORQETmDQguzgeMN5ijghYgY/u5ZwxYDPOyo8gSiMHDnaeILuyDqZWTukGeJaSSctRwFjLcoK1kWsMAOKgk8y/IKRyOMiBImXwvIVUmYnHjdFfrbohzVuo3o8CnkWFYsBaxd7FiWG4gKf4trRpkTBr3jE54WJAsoCizlmSYgCg8DDoItucxKNSlgk9hApswpYiRglPvcJ+f4lOMsqG0E3hE8U+Pu7PniKrFilR4XkGZmM7yBV2RmQAgXLCEOs6Zg/LQCu52Rmc5AewhQFDuLkfnmPxwXRaQ1NQDtDFPDSJJ2jkJqjYIekNEikDMMkMBvSoI3dSbC4noWBpIM4MZdIPJAl5lTMDpikIQu2uURB57Fdqx5tfuVVMywYEpANmlMYWlEsIzzeYDRct3y/nvMK0ojCnvtNFgh+PQ1MQQgxuh/HGeAYvU7EgxZTSbtvb4hC6L7gEEQA/WL5IEsxaqCs4MMo06RSeHq/RhGFUP9wuDFW4CDwHQoWMqMeBxQdRnX6yDsQZoZh3y/y1lCiQEcBbiSxEU6JhmWfkuDMAkSuooVzhH32iigwKMTYIR8wlW4RKS0TKSMLiLnDlU3sg6tEQSUEPoOUErzNbUMUrEdBcxSyrKk16wvZDmFA7mKwAaUhW0E9hG0xh0nCA/kLYIHBoneZ98MgCk/ImDHjIqJQKsUW/OsWZpRHTdv0WOQBPOgsvFl4r+gruSnkoNB3dCeGNhg0ORfoE6dp6JGGRJk1QYnNlYGTmesRbHVeo8nMbpJ1VXlUO4ZJyczIKPQCoBzey5DAk1HVLHXwJDKOaYIfEwXCduX3FKda1dtUnaOghKbcI4vmX1aTzOwPA0YrnEHIU+y5WOuBMoBt+op3F4ID1GGpsSSxAWIER54RdRyqLsRz3NAjfS5EIekcBbdvLFdwKTiV6DCWPZWaIC/0gS1B33CQYhNhiWPAR7QAowheQC7TuFYLR1L1aM8RIwy5yyQKoco53DDtwDUNa0P9MmB0KmTwQDHAzpCNAAG+Q4wx8Wmhph4F18ORp+pRneveLEQ8wIAVPf0c4k/kiFrxQgeuERJFtaFCRAEmgmWcXYBCgBZC+/ALQa0reS7R7KrFC9MpAoWZRpl9WOS090ZEIeQJmnXeJTJ0yGCZO2t6nDjWcI8CK5fVSL8Rivyb2AhWKvG0djHUlaPASoZMIRlAZnmIAguNHQOaY6eyu6HT7KDAaSNu6JG7/vMQBfOu7FhQDsAj3vUW8WIipcE6NeOTf4OGWVh4PFAYjvUr5FH44llzZd4V18gLLzwvox2XZb1rvRHXXXDBBTJ37hzZvDF8Yt/eh7xFxr3yqrFaMBUsDV4TI2DIiMBQkoCLosAqDlYqcoKvvlNy6FG3DN+/fqLA/ekj7wK/Q4nxb0KksYSxxNK8Co0mCvSHJUc+B25ntggueSxahAAxluQAJBwAboarL4kChhfGBBxEKg/iwT3rkLEDI9NHfsfVj3jjf9eL23CiwMNgLgh5TYoFqGE+1IFEEWSEH/WKKFBQZ6RIGW8kB1m+3hIFZD0TCAgGSaFMCbehv1jXCFkCBCdY4dqGKMyeIyQza8uKzw7KK+YO4M1CB5mhE7EuQPzIAUjL5MegBUjHCEW7QWT52sdNjkK0z9UtLpJ44FoRIYpOgixQvY/+8mzN7tf7gI7xMnjBEuYchauiM3e7pMsQGf5bu+ohObnJHgVTHtWUkY0aRGHOrDPEJDNDuMoiU0+cEjxwDU6OTRT7GYSB7Qb/JeGWZGU8tswGsgzLPECdv2NcCJ5PEJiPuDyq/UwJ4ML5l2YSBS6B0wFtZtnlBalB7iNTyVtAhbMd8Y4QBksaB58zlZALx/5X1Ts/9IgP83oU9EbYYYGPLGH8N4gNciLIkQCL43ngf+AT+W0YaHBaIVq0ICOYl61i5s4lCmk5Cnrgmht+oZ1KIwpsKZiNxvfjig81Jh03EvHHgAZwLosCj2uoVSUzO5VyGu1RMFbrcmQMR1lRbhkwAENDX+DpVWXbsKpHkASyY/Q4VVYamhSzGztGT4NjcJlprSzEbDOzaFx20kyR0074hHz3otk1VXsY01lzL5GhQwfLeedEJzPTGkYUWHG4hdg9AHrYHyuPiaX/HBPonHOARwFQTmPDuussMfQIJcn7Y3ZkDFjp0GnGSkmTu3iId6A8DTsGyQJK1e+TAxBQHm7oEbdSwjXm0KPkhRcTQo/cZ8KUeXfoO7sWpslORFpo3Wk+05J59IHdjFWTd/AC3H2iQH++fPb58uOfLGo7orDplVcNv2Pvw6XAZkwDggzVR9ynFotC1xOjyrARzohlOU/svy87+ir0SJ/D+yAbkGHoengwyx1rWFrF3kYTBfqB4YWKSDYFx0QuYqhGaUC2WPpJIaC8T18SBUgM2BaRAIEh1N7NOUGhYt3CAocyhtODm/3a5Q0nCu6CYRBZZMgwZAoTiNuDhZnRekUUuDfPRrYxUKAMzJeYKDEcsVGweoOgYFkAS1xXyIyUE+ralSjkOkchbT6Qrch79EPagk+5x7L1j8uYsePisqga6twQomCUniWFCDf0EzoT3aGchBAo1h1hqE5zPQpKYPi5xoQeFSuPmrWmi3zuehTMci51GaJAjsJiDlyzHoVpCeVRuQZViXMNTy3hkjoUQBw8pGwLZBuygoI3cGc8lXmbEgXtiyaC5/Eo+M9gqohqg+8BS7DlutHGqHwM5cgvHH94e5OOeVrzqD1wzeZNdJkchc0y8aC9iuVg5hgIyAKkhbow8GrGGSgFTifKTek6RGHEyFH5cxRCz04jCgwWygqGyFoH/MP4XKMHhJpBJsEfxcG+BuvCiJwzPKoefeIHpsjPFlZCj+rNUXhdVA7ZnHkCe0XmMpmr7KqcXY48BvyTsEUGFqyOkvVLbStRcEGuAstCHgWoKAqB1UcDHeHDgjCwK0AAbgOZaCk/zJZ8F4X2e5HTTq2EHvlzN2vuxTJ0yBBz4JqGS+UiCjYBzASXM5EIXwaGxspiBzB5uEmx3qPY+EnoFOCXvAvovwPMj4Qo3Ft9cIz2N5EoqLeCxYMA5d9IEczNlDdkIjVYj5vRB8ZFQ7noJ8TBO43ZHSf1KPjetFweBb0R/UL400fGhDHTPAS+g9cAdAzRYx4hUQnHklcOXKuUam3X0KOXX6n2/+NIAfvood4AWq3xrDnf/Buvo5sUm0Mexl9J9SgcMFl27qgv9Mjvgx4YxHZgGRLMhyEkqTWaKPAc8jNwWoE1Efosd4yqYElyGrIwU18SBaIu2Apgb0QC4ahungqRl0Rd4nhk+6BDkLe+Ib9PiAJKAE1PJzAN8jsdRK5ivnTKQCbNZ6+JAjfGkzFSpESoOUail0RKs23pZ0QlcfgwKMJlcL+DnlJOZ24bojBntjy5cVNslCkcelREIOT87rL1j8nYseQoRE3BZcOIgtsPNi4sGYGhRkAMf4An75Quv+pR1DlCj1Y03aNw2/2rokgF61VQjwJVj7QlVT3Sz1naGFrAXsAhPWSNz5kL/sfRAo8nnymrQIM7zHGOQjmqFKWhbnk9CqGlQ7oJ9kAcgRAZLUUKLMOyD9djCsHCSYYuJQoGgtrDjdPKo+ZcwplfQ9xBzIBH2F7dplWPciUzh2LvuVkaUYBdETeGGwkgAOPjLAU6A1YCc+JtgEiDNRlQPsd9j0tHs679t6z3HAW9D9Y+jAyUgcLQDDFB7tInnolewKhD7jCghN+JIkFXECqI0deLCqk5cM0N9ylEFAhqxhqvq8x9eXAMoTtQP/5Hy2KIB4nozmEFYn51chTMgWFdXWbh6TzOnHuxDBs6xHgUtK+5iAKhTgSXQ/lZzZh32aU8n8kDjTCw7Bbd0Wh4yA+KjPAozz2uRKFQ1SMdFxQ5ORD0izFhEYGGGAclM3yXnYBrlzEEvdFPFC2SJqEZj8LkCfHZDtq/QkSBYeoSKUPwQGj0DwQEgWIcyFHQSlawaMYzAa8qUdB+tLNHwScKGElxg+K5Y2goGMBywrjG7+xRACM6s1443xflUUNLB0MC062HSSJTWG7Ik1DrC6KAUxKLPFuAJDWMNqTisE1QwGlhR/SRwzRfeYaYx+q2et0G+dJZc+UhW2b2vl+skA98lODvYg0vEYrdj5Shf3BqdAWWLkQcdgdCD/r0wDXtPvFQhKpgHsQVhHGBfxMaAmPNkXnfEKJglKuVs3gOcAehgFCkGFn0pCU8Cbi2U85Q4FZtQxTiZGbAW1f+qkfFll+hby/f8ITxKLhyt1fJzGlPBxQx7wg+rWWJ/qS8qxdvqUTBvR1hPetWrWwJj4IacE2C9eqVJvTIlEe1pw5nEQXeC/mFHCDQAimjsAhDB1uRympE3RVtldCjKLFDiUJWMnOe54B7iSbWKkNMn3uuQdo9Yo+C4VjR4X5FQ4/y9LHId+KTmU151FvktM828MA1OgLeBUMy0WBXTdpHCcC4IMyQBqxd4CMsTFQPAYw7RXFq3sn3KOgX8oYeoTQBJbi1mFD2IPtSs8O5n1Y3cB+OsRdGCJZPOkfBLUGqJ/oWIgr40MiSQWPizsAsaZOUjHanAyAQLOT466HTeBUCLap6NCfeBO5XZs65SIYNHWo8CtpyEQV8VKA5GCCMCyblHkHIzfib0ns9iIJsnoTYUEMUlt5UdQKkjmOiR0E7rVlNUHmUKcxTq4VggoAg0FCkxFywyEBtfmZkYPySzlFIrXqUtAPp00CRUo9IWZPY+K5Wbcqxc3e1qkfuK7P/ODoCGcHvkHWcaUwfS50lBYCslyTwrL4OPdL3gQdiyUf+YQ1j2bP8mPZQCHlfEAU8pIwXUYCQLMQJ5AXoT1RLVk0YiYwAACAASURBVJXPviYK7tzr4USMDbwfI5FKJYzn/BvC4I9dn3gUtP4h5AA3BiAdWcFA5jyErWFEAcTB/+gDPT8BmUbMBQqKRUX+BLH3Ga2diAI5Chrdvn3btuyTmbNevjefl0RM6NEYPSu4crPrFy3IPpm5N8/OuNbkKCy4IS7bGuGWkqxe9WDTPQq33r/SbFiK3gB416+NyqPmOUfBlw2oTryQyC2WP/CHSDyMtRiT6vEwazKz8SY4eScLr0wuj5p3KuF6vvcjyzCj9179qC2PWo5KthK21R8ehbR3w6MwfMQIM4933ZZWHvWhO6qRcyWfJ9WjQHIi1newIl5clWcYaTS5GdZF7D8KQU8Hzzo1tLceBZ6J7KXYAMocIAJZoE9ubBlkxVVOuGSSCEwomVlZaiGiQAwW6AJUAQFQo57NBC8tECnbcwxKm9JPIdaqR74lhEXhEoVCHgVQHN4CWBa/w/Z0F6Dx6RuZOmTy0EB7xJPwHgmJdm7VI3/BZhIFvQBpAkJjMdEv8mghMBrmgylaT+HMueP9cxSU+BX1KOR8XObXXKKgROqLZ86ReVf+tO1yFHyPgr48qSPwPf7Xoh/kKsGfU3I1M8cujSgwliMOOEI4KLERDVAOX4X4gDuJZuGE0SQ81xdEARFN6BHVgtgONEQIFjjc+VmEqz+JAtsUjy3iAxEBSdBQLXQFYxcqNtQnRAGlxcM1mZUAaOQwf89hXGCcG0YUdDGykEhkRcZxlg4NuYtVjbqyKV5RvUX7EIXZ8uRzUegR/23fui3fOQqN2LgJ99Achbi6EN8ri1x3zQKZMR2m1pwWPHCNUqSr++AchQKvqDkK7iVu1SPALwA9qepRgUfV/VU/mZkbgdca4VGou1NO1aNyOcrQY81t27JVJhw0uuE5Cnn7GXsUKI+a98A1s0dwidj4qbTQIyItqLihJYk1KxxXPEwQtYwSxTWeEipe8z5+1SPtT16Pgt6Q0HGwJQnKlIjCYKRFB+gbCjVv0qQShR07dpgwJLcVIgpciGsDkyQx99SzqrOlncw8Y85FspvjUUgtjxpyr0AIcAuRgc7/NNxDhB0RkEf8WM4WylHQccxNFJxnlTBD7xApZ6GhjP65RMFd83V5FHKORdrXdmWPgvveYCIi27AcsRUoO4fjrMh5eqFxTPcoTJKe7ry7PXsy6TfWcQAwhghyLpJaXxAFnsW2JNwZroyrnkJjyGPGMutN+4soIFrwdGCEIZoGPk9+vzYcl3wWKh3QF0ShtFtJyu8tRyWjUFJ4bdMmLzCpDScK+gyYFO55FCq/oxtymirbhyjYqkcmfzD7sKnsndj7bxB6tK9WPbK4h7vWfY5C77tk7uCGHrlJua1QHpUcBQz1hDoP6BoQJzObqkdUiIGPJ1Q9atDwpN6mqjwqeJazNEhxTDlwrT/6peVRozM7oj5t2vyqTDpo794rwDpfoBBRCMWP89w0osDnWLUI86FKEEoT4wx5V73BcL31KPjjhXGG/vSmT65Hwc/nKEwU6pxQ/zKIglY98j+bMedbstuw3eS8c6an5yiwVkkgwdcXyBYyJwoPFiltjbwbWimqyCtAFFbcE7lQ/VYPUSjy7LTv+h4FnddmexTcvbirJDM3as7S7pOeozBRunf2lorUPh3OinxJc4/3FVGAbFHVk3wFjDSE2eeIUjEv0V9EASM5ZQ3xHBCaTdMzNfA0QB6Syt73BVEwHcCVRU4VicsoLw7FKND6jChoHwg9wmNaQGG1D1GIPApmHZTLTY/Pph+/XPeYjNtv/xjgaljUDddc3XSPAiczd/d0mxAVJQuUR22Fqke6XP3QI/17M4nC4qUPyqTJb67JO7n6inmJJzMXEAF1f3XNo0/LiFEjzXyqV23b1q25zlGo+6EZF0ZVj0ZKd0+P3H3bkuyTmUP3yyIKhBIQXxadx9eYpkTBB+RFPQqN6U10l1B51LqqHjWwU6kehdkQhWH5chTQ1iQhJyimesiB+5p+6JELhFuFKLh9ajZRcMeuQxTyb5jkqkeNDT3K36Pom31FFLQfRAqSDEjUSt7KIP1FFOgjtRFI1dEDaukj508Q3UjUTVKf+4woFJ1A7/t9ThTq6F/7EIU58uRzrxrwiyWacxTGHzCqadZUhvrB/3xC9tl3bFWuH6EzNyy6quk5ChAFmmIhxm39apKZm3syM+co6By6ROGWO6PyqLRmEoXYo2At90q2elP1qI5tWXMJycyjRo2WHtsvxqr5OQpReVRaauiRnqMQqnyURRQaMXj+PRrtUWhEHxt2jkIjOmPvoUTBTbDW28+AKOy+m5w3iyjmqOVKZm5g//RWDclR6IN+4VE4YtJ46RoQeTpaxaPgvmqHKOSf+DSPwu5jJ0q5p/EehTy962uiQB9C0YNpfetPokA/CPPEeE8kDRVCSDWiGFjajHSIQp7VFX2nnYgCycxqHa/rwLX8w5L9TZKZ1z0m+44dF1t5TUhIidCj5ucoqEcBUqX6ifKozfYoEHoEmaJfNJOjMGO63HLXMhM5QF85FA7vRzOae46CPh+ycM2Cy3MduNYnfS6JrHr4tzJ69N7x+mcfbNr0qkw6uHmhR2sfe0ZGjhxlPBwQhdM/+3FZtmyZHH00bleR0rSpU8sb1q+Rh1dqJfMKWNKBaiZRaHWPgo5RM0OPOJk51AxRsB4FHcfrbrpVnvtDPWfe9m7b7DV6pJwyjZoGte2y+dfKli05A3F7142aqz829STZe6+ISbsehXlXXiPbtmZVom9wZ8hlHDhATvv8J82Ndc46RCH/OPdXedT8PYq+2R9EoWif+psoUCedMCQajss8no8OUcg/q+1EFPAoRPLWwBDZvr3/Za07shgBXflv0qyJsy2LdHeTzehUd8k/Jb3+JodxDfTyIekXuqF5/YryV+kXhhd+5/81qx6SubO+IkvuIQM/alNPOFbWNYso3PuQvGnipDgPwC2POvtrFeNpryepwA3KnIHxCAeujTKlgdUjY4jCIc0jClr1CIJcKJlZ351F+dQzz5qTdPuz7TZsqOzzetKOndKhKJjubvnd0xQD7P+GUDvoAI2yrX7+U08/Kzu7CwSUNqj7e+6xu4weReBXNdDl326OQoMet8vfJilPp5kvfvqZc+TSNqt69MKLL5l8loq9WO3drt27yO/5vjtg4AAZNYLDNmrb8y9oXZ34WKVA/9zr8j2z1iZee92QIUNk+J6vs2NSecbWrdvklVeJ0C/6rAaMZ0lk79ERSXZbo85RaMSe6RCF/KPYTkRBy6Oq5V4TYk3ByLxOP2fLFAqNDVxXXe3IJuI5Q6/Jufyp0LOce9RzXcViX5YeW3OfMQJkMmYKfuPHJImRrGVU4Do94ZgYe8Ud/NTyqCQza0lS8FpSK+oB1fvkvW7AgAHx+DBWjFtX1wDZ2b1TShEHzNXyPs+/WdJ19EtzYLQ6FD+JCjHrq86iHr25rmtA5BVibu9YcnPyOQoaeqQTX7MAcw1pY7+kpSq1T2zWUDJsY5+afTfDom0GfSuNl9/zUHnUVgHCrdiPVumTzqOGk7WjRyF7F732vlEPUGjGKPlE4dVXN8mT/5NyJHAfdpJzYA79C/eY88rDHn70Mdmxs/+NM/TgjW84yHhr/dbMPk0a/0YZMEB9NiJLbl8qU089XR544AE55phj+nCW8t/6wgsvlNn2wDWuUq9p/LPcY8J/DLCzVZGqLP22eo1a+zXEhe8mXxeRD3R20nWxHjflKqOylTEwdQ4xjQBndHZAah9LUdlLPWeg3uu0D0pUYlxma/Ab0G4JQyOel6ufFmKbQ9VsdUz3wDVDFHqYRybQjrkBv854UBrUnsEQjyPftR4KF1fVc52e7VBFAJ1l2h9zlzSW+m4ullVipfPZ32tMk6qZszuW5DhwzQdLO3fslIGDKsInv0jo3Te1H35/enogC81xA6a9UbNAZtJp2vSV8qi7Dxsmc8+ZLu644R0aYGPyEwWRWc0ZHldHWMVj4xprnQHT8TH9MALEG80czzJXONeZe9pY0sS58e7rClyjPHhJpxxeVT+tYqkizTn6acqe+feso596jw5R6J0saZWr25UotMr4dfpRbARalyjMlic2VupcqZyrAcPFXrfub+tzXR2u+RNNNU5a/ap98PsZ99eQp9pqgnUPSJ4Lbd9cQO96FPTvLtnKc9tGfUcJIWrXJTM+MW3U8/LeJ17jAa9Bs4zzPoYsHHrkg1/DOiyuiphPhAoj4hghOJdFRQTSXsP3LPDSe0TXRoyYFjMsB4xV9cH5u/9y7j1cN2HiM7y+hq/hgZGbz8F98XsqazVuU+u6cccoOjbcjgns2YJzE/poD9uA9LiLumh/fUCq1/PTP5k5RBDyLvC++F6zyFXSu9SS0p6me7DoE0Rh3hXXtNWBa32xXtr9nh2i0O4z2F79b12iEJ2jEKUnRPrPBeT67zTg5MrqpN9DsxX6rhqczPOsvvYNUNo/30rskpyk32Pvg+OJSPyu8URE4FavU1zkE6lqo1clkCZxPFzgFRic5OtqDYYhArV29UqZM/MMWXz38pq7670bOW8uSfEf6BO92OviRKX4mM/cIwF7JuHQ+Lk5rwMDuyFj8TgGiFfN+4WMlM6cFpm/GMg7a03H485bb0kvjxoCbjt3dpsEy2aAOn1mu3gRmjFGOjahZ8+cc7GQ64FHoTKWEfhVopVn48bfybEZgvcrSxSzaJOeXHnF+lLvRo3w7MXz1POQ9H4u0awSwPaZIU9NlkIqNJaOZMu6b8ej0F4ALam3HaKwa8xju7xF6xKFysnMaigsBqYr9ecLXdfVZUJbTDiKhg4B3om1x/KM4dN6rJUoxAC3h2KWTshRHtBvnlffda7lW0FviDjpZwoqi4yHq2/zXqdr3zUM67Ub1q0xVY9comC+54BgE65kE8XzPtOAac4cyHmdGnBD+zSJQPigPI0Y8g6KZ4pep7kJ7jhWrTvPcO6TikaQ0Ur4WtQbHw9mEgV/YH3wWVm8EXvFGq4vwsPdnAL9nXtUBj3fdXTcHxD3Pjw76VkAZ4CnLxAUpNZ7nT8WSeRc8xew3jM++calx4TQwDaLXufnbLj9nDn3Yhk2ZEh8jkIziIy7ppr9/KIKvtn91ed3iELRmWvN73eIQmvOy67aq9YlChWPQiTjIhAYA2LraYgsvE5oaOh3zXzWzaWu/wLXAeY1ZMCX+VUgXYszFH6WNVUXuM7Yq+z3TRKzH9IQWLQKyg0yL/Cs6Lv5+qjejlAkA6FHc2adIeQoMG9usrCOq3qR8j4vDuWIT3rN7meVUdTmSagnKFpalg3ae+o41zzLXX9mndmQ6ay1lXCdC/rj6fM8AiYsW9d/oedkj4u/l9y5dAl7ajIz5VGTgFHYSlzZwEUsqVnW0zzAshHPy7pHnn72QAbicCJH0AVi3kPMs7JgozWY1Sf/HpEgja4NWcBnzb1Yhg4dak5mDo1rFSnBsj8wynx3vUhKvFyXa9HrlKSlKeSoqhYkj1rRUciXMt0QyavuYxT6VfS6WGgExjC0F9i3PRwSRNWCKgLsEr1u02/6XOljynXWyxJVhyjV5I7Qxy+eOUfmtVnVo10VfPXmvTpEoTej17m26Ai0OlHQ93Gtt5oA64aK+Bb12MpvSYJrVa+QjQjUhXQqf6PFlucYA9oE5ISB1uuM3rCGPdWLSX1UwKzVbVTn6L3c6/y/+ZZx18vhdtH3Kuhnqjez+xiFiIfG0e2v3s8dB/dZ5hyFmdMNUfBBsXtv9TDkeV4V5nFyIkPjF+GhiKBUYY6YsUS/6D8Vf/lrzX1mvFaw9Hvrzfc6ha5z3zEUQua+h17vv5s/B1Vj4lS7LHpdaA3R30I5Cs2KZ9f4fddS3mzLrivQfE+H66HQ7zVv7KotD7PmXiJDhwyuOpk5JhRWOEaLK8ql8Mc5/ncoNs5ZZYnzE7guNF55FKCrHHxrhi+kk+6Xto6yQo38e4aJRNjyk7V+0+7FZ18++3z58U8WdXIU8iyUFv5Ohyi08OTsgl1rXaJQCT0KyVUFRnmmxLe0h0BsnvskyWglMe49YkBmw2jUkm/AdtT5+OtJoNr5QhwWHwNYtf85FYMUHPpj41qC/fj6JIBp/u4U18jsowPA9VrXX6D61yQzz4AoLDfGaw3xcgFwKALCnx8NT/KBbGgO9G/mfbSykv2jmTvPWxWcR2e+Qs8OrUd/zLKu0++bdWbydSvh337+hBsWnmftFt0DLoZ114jO0123Lk4uj/qrFbdnVhPSsBoftKmHxI+ri9mUU5os7qRXYlQ76b+03lPBt7uhE59nGWX1d8PVdvxYff/dNAcgdPKxTmISiHU3rg9Ea8cwApk8z08Oj2PINJbSYbb0wQ3zchf1rPMsUTjnjLjqURZo1XdKet8QoK6MQ5WMjNc4c8c7qZCINkZtgjgXZFVn4js7duyUQYFKXEkkJA2I+/kvPsP3BYu/PqM+F096LnJNJ/Qoj7hs/e90iELrz9Gu1MPWJQpzRA9ci4GeB4pdAFNlebbgXD93QZar+6os2BYYK7hNAkox2LNVhFwd7fZHdYTr8fb1BJ4RDRnyDVtBHeMZ1ZKe4b+Xr9v083x97KrKHYzfweYUunPjA8rQPtGqR7fcGZ3MrLqRn/6/XeOe71XRd6qcFBPlBPhroqpPjuGTsVevQhVAj3MkKwa9CLhXqjC6BMCdtySvTGhcIuJYoVIRYYn6lNTn2ANSZcD1vq/Xe16RPHvAX8t+v/Vdc3sUKIdKCIqCVsBXCJT1pUDlpQCXJFG3UsubVF0EBPbV++3YsUPmXvhdGTxoUOxR8IF0IsFxOhWHBnpxkknXpt0zNC4h0O8Lv8RnWbKZ9P3M8q72PZPIU1XfAonVtc/1iFLAo5LYV38hOALji2fNbbsD1/pqXbfzfTtEoZ1nr/363i5EwQfSjLQBVyQDO0Yx36in36sB6fY6N7TEvZfeW4Gs+/z4ADiMW1qR0AG66FC+r/cL9dNN3nXBtpvP6QLU+O8OCQqB4iqCEQCUeg0/QzmiPkFxQbFe6/bR1dehUCJ9N71P1YFr9myHGIRb8qHj5h8Opp4RBcyqJ2vHzEZbW2LpWvLduXAJU8U4Tah4hCld4G/WUcBwndSneBwtwQiNoz82ulZdkmrGwhqGIZVuaBLfc+cwtJZ90ug+Mx5nHSd7RoUfjqfv6K6dO3+ecY5CmtW8YlmvrYLkL0C3w9oRt+MK4ABitIEDB9aUH+XvGuNdg6EsaGWy+c7gwYPMV9zFVfU8ewMFfj5g1b+7II7fXYHi9sG/Xi3kUSnVarej+2/Ae81R8V7cu7uQ3Gv9MU0Ct24/Z8y+yFQ9Ou/rZ+TKf0gaZ//vSfOi71dUpSa9Sz0hXEnX5OmbrwRC45/0bkljsn37jnh9utemhWD513Q8CkVXVGt+v0MUWnNedtVetTJRMCczO7kCWGAVLPl62J0fVz/6OtoFTj6IqtKr1iJr9LsXe666PZb91lLmAtaQLlZMYwCwjaCoAt3d3QakukYvBWdJf9PnqIUakORiD5KwNYRF76UVeXzQqWNlgLit2hPMAQS8Ogm/LkGI+qEl76vLt3L/kEcBPadYzH9eiJS4INz1APmE0bWA+9jPnXufbMZrxklyNmMVmOeqsdakeQdnutjWJbEx6NZ8CSdErfJ8e6q1ixm96pDue/nzp4Dfx94h8uCTJv23/qTvAxgDi11vX3KznP7ZU2TZsmVy9NFHm2VYmjZ1annD+jVCMrN2LH5REXFBSxqRcF+qRvBmxLhnCeokIpJ4XS+f5963qrqRd9+kEJis9ynyeRohyIr3P3v2t8yBaxAFmpYiTQPASd6QPMQkiWjkvtaxkhQB6WlrL+vZ+nloLNOuDVm33PcvMo7u+g49s0MUiuyY1v1uhyi07tzsij1rZaLw+LMvxwDSB/gKLl2g7MpT/dzVEa6Rh+tqAG5CiG6SjA9Z012AFfQCuOEvTugNz9Bzluib9tUFmvq7ftf0y8tVCD3fxWo+mTE6zfSjkqTtkhI3HKjquQlhYDomru5TAEs//nP9WpPMrKFHfn9UzxmCZsfHjIVNDHfJnAGvztlUquNDBChojLaLxyeTPsatEBNKuFfIjz9nOm/aJ/VImNK6gdyImMg4Xgf+pjjDJVCGvLmWf0tK8DZQLCdpLbvVQbV/bg6IPk/v7+4ndx34z04tj/qrFbelHi6VBHz04du3b5fBgwfXyNskS27W/ZIEt7uxQ5u8nuclCYs8eQk+SNVrQv1Iek63U3HIfe/t27bL4CG1Y5pFEPQeZ5/7Ldl9twpRaAdlWO+6aMS7Za2tRjyjnnt0iEI9o9Z613SIQuvNya7co9YlCpWTmRXM6Dy4FeUUwLjx/i7IDBkQfR3rglusxljgXWDkgvY0y6v2TwGeAtkkI4/fjzSDlOtNUTyh9/eBZUwMTMB7dRy8Cwh5XqiP/kFbLtHQa1wA6gJKd6+4QF7/rlWPIAq+oc/11LhEI0QI4mvd8xestTs05zUhOk6YU4isuADarXxl5j8QFeKPUfDfbmJ7QKi468HHOG4f/XXiYj2fPOpjQrjFJ0j6DJ8guPOk19x12+L0A9fc99MbugSgr0FcFlBLAtr9Key3bdsmQ4YMSX1kf/bTJTM+OfGJQtF+6Xz7G1Gfo2vDXyv6ufYtz3N9oqnP9IlX/CxLovw+6PeT8jF8Ypenb6EN6f+tx7qWQ3tEn+F+liY4/MV1+ldny6Xzr+1UPerPjd4Hz+oQhT4Y1M4tE0egdYmCPUfBsaL62MMcBdBVqXfv13x3AZVaUdWy7IIf83uCpzoqSS22tHYlWTY6hqBihdf7ud93vR76jLI9VJTHucCYZ9QAPjeMyAl74buutR3iFFndo2pKrvXcLTNq+mvj7BPJhQ2bccdayZFLTEJjEoVUVUriu2Os4+J7FCJgWz2O/ryFgKqfT+ICc3cudGxccmnGz7Hy6/Nccujqbj53802q1g7zxhkbzpzofRRHVHIuKgnRLhHyyaf7PPe9zDU2HMrf0C5+8KtqufcIrTEF/+47KNnyyZ4+J7M8qg80k7wEIclU1JIPoFM3js8+88r+UP+SyEySdyCp36FEEu1X2rjoc/x+JF2T1K80ABt/lhBipZ+7oUf6t29//1L5798+bcPRSBar3gi8o7rVfGBT2WyV63QBVllu7EC587r/uDEy8yt/XzW1bj+3bNkaH12vlhFd0Lr5qXygcY56o0rcY3WfKn2Nwg9dl5y+IwJl5lf/XvYbu6+5nbvRZs69SF7dtKWqv/6z3U3vChB3TKqFsoZCVvJY3IoLjCEk9DsXnGMu0/HpeBTySoTW/l6HKLT2/OxqvWtdolB9MvO2rVvl5A+/L3ioWNLBppF8DFfZc+fRCS0PlgA395GyXH71jbLXXq+P5a7e/O47fi7//q8XJT6rUuwj/Ywzt79+v9Mipd/ytqNkzj9fUrM0H334v2TmV78Y/72mHzZ/Ie25ecdQH+L288CDDpHv/ejKqpwJ36Og+tEFwUnGMtV1/DQA1sbMK9B19aviAxej5cFLLjD2+8E7+kTTJSWhXIc4DMqe9q3fd8mXf52Opf8+rr6Px1tDkPxzP7zVYPrtnOml3gPzTs4p0i5pcfMyDB5ycmgyk5mrN1glMfc9J31SXnzpT1Un7UXxc7a0lHNhTS1gp5RT1PGY4Fe9buVMjOi57/7rt8v3Lp5TU9v/hRdfkvd+6NTg4XC6kON7ec/r7d8hNmt/cYuzOSuHlBz3kU/Lc8+/WPV+7lhEAqnS3HHI8/eksfvbj54o3/jaF4MlOt1kZn3yMcd/TFasWt/vOvHIwyfIyntvCs7b69/wNvnjn/7c733igSuX3ihHHjGxpl9jD3uHPP/CS/3ep2HDhsrLT2+oWmPteI7CMcefLJs2b47fo0KNdCVHHyUdOOoqeHfnuHulakPZ2x76hkPkmp98v2beEJ5vnfJhiSJKa1voeanPqn4NW8U7+d3ef+wx8i/nz6x58I2Lb5dvXvz9KpCUOCZVQqTSu6wx0Yf6c0A1uxX3/KymT6vXbZAvnTVXHlpa+1m/b4jOA+segdYlClF5VLOcy2XZsnmzTDxkL5HIZlVpfmmcukci+8JfrvuNjB23v5VJpfhAzRt+erXMmG4NXD54qdp4KX3XjZfn++47l0SmvO94uezqG2teYO2qFTLtQ++pHTMVQSEwlD0MFYCW8d3DJkyUW+9dWVWZyk1m5nLA6Oc/NVV+tW5dhH6MYCMuyAKzGC35c26/546be5pyLG1NbBIuhOi+HpCa/9Mlctj4ibUy99qr5aJvzq4gsrKehkwfNdPd/qRIEv3lGWZ92oHtsu8Rj7crXbFM6vftNbFnqyy3379GRowYWYU5tm7dIsceNanyDBRVdAZr1CoJK5X3MeyQLlF7vlz9fb2wxLjg7bDjo9Yq12pVFrnjF2tkxMhRpk+5cxRcdsbv+0/4a3nuDy/kWWYN+85Jx79Hbrr6BzX32/jc86Y/zWhULNq88VcS8kIcNOld8syzz/V7t0773Cfk+5fMjZ/rzp0hCrtxMjPnKETW+GYRhTcfPkFW3XtT1fjoOLYCUfAnbsyh7xBIaX83lyi0s0dh70PeIi+/EoGB/mxHTB4vKwMAF+vTHvtNku6dPhrpn96d/JET5Kp5/1rzsMsXXCunn4nS6v8GUXj197+uebBPFNZt+LWc9Y1vGhNuFJKB3omUoUkAZEhJusPjZxV3VPavZHSU6O91XPeGQw6US/89suj67SOnfEFeeeUVKZtKLjY0oqsU6UQTjx4p655SdPCSdPdIT5cNTejpqeM67leWnpLID75zgbzp0DfU9Omjn/wH+fPLLwfGBWVuyzAmjosdw6rxzH/djVf9SEaOGB73qXWJwmx5cuOrBjiil7Zv2ybjDxoVBr39tC2WrX9cxo7bL9aTUdiMyHXXXFUhCv3UF/cxU95/vFy+8KaoX3o2+sPsVgAAIABJREFUg5RkzaoVcjJEIY189GF/IQq33786JlRgi7VrVsqcGWfIknseNE9mDKedNEXWrnyoD3uScOuSyJJ7VsiESYfXYKOF8y+Vc782vf/7ZJ+4+tGnZPTovWKXGOO0dcsWmXDw6KbtgXWPPSPDR4w0Mjsz9Ij38OOr+Nt+49/ZNKLgu5RagSgwJn7IULOJQsj1NmPORbLb0Kg8qm7cd33wlOZ5FJbeFDNkt7+tSBRaxaPAvJ1+5py2O0eh1YgC+3X4/oebyl/NaO1MFO77xQr5wEc/3Yxhk8MnvUlW33dzkCiMPewoefGlPzalX8vuvE7eemQFhGgn9ptwtDz//ItN6dNTjyyX1+81ug2IwhzRqkfgjW3btsqEg5oHkmAED254QvbZd2zFxm0Nx9e3AFG49KobakJn16x8UKad1FyicNt9q8xa05AW9SgsvouTmSNDwbQTp8jaVU0gCiKyZGmFKLhh0FdfMU/OPbt5RGHNo0/JyFGVfcoY4lGYcODophG/tRCF4SPMnGWHHiUEyzWTKPgSt1WIgt+vZhOFkGaaOediGTZsiPEoqMesWR6FRoYeHSUie4oIKeXY/DeKyJN1qmZCj7BC+zkMYw49Sl54sQAIOVhEdhORp6yrsk5juh96xGu1Y45CqxEFPAq7j5so5e7mmOA6RKG+DdohCvnHrZ2IAucoqPFq+/ZtMv6AUU0DSfTjl+sek3H77V91wBtgtxWIAh4Fk6/n4DPjUWgmURg/UW67PyIKmougOQoQBSUQU084tqlEYfzEyVVleCGmi+Zf1jyiUBJZ/chTMmrU6KrE9M2bN8nEg/Ay5N/vjfwmHgVCjxifQlWP3E7sakSB8K9RsDgRKYLnNPQoqk88QLp7uuOE7MJEYYqIvEVECIt8WUT+H4WIRWRNsenX0CNl8FUJuRCFoUNijwJ3bjZR0LeLk5YGDJCiHgVO/PhLERlBjoGIkDXyPTufRfeZ5ij4o57LozBQRDjrjzjGL4vIQSJytYgQmk+aAZ9v85JTMqZXiYI7jx2ikH9PJIUe4UkYfsBk2bkj3aOwu11bfxCR522YaCOyZxpJFJBdLDHkV29a3tCjXcWj8FYR+ZPdlm8UkbEi8gsRebiOQex4FOoYNBG58MILZfZsW/XI1pgnR8F4FIoK7/q6ELwKj8Lr9x1TczrutYvmy8zp/9DAJxW7lQk9uvqmuGynFvgwOQotQBQ0AZjQPhN6NOsMWXznchP2B+iEzPgehf1EhBiHPURkk4jcLSIrRKSAWS7XIOJRmDj5CBPiYziWrbLV9NCjR34nowg9ctqWLZvrIgpAj0NE5EgReZe9H3rhlyLyqIg8KyJ59JcJPRpO6JHIHWknM//6odvNQIZq9OclCsT0gZu255rG9C81MkfhOBGhngEOYTA5IaBYpVG2d4nIby0gyOq2EgX3exqqVZgoULDgEyLCwXdoL7TVVSLyw6xeVH/u5yi4n86ae7EMJfTonOmxpaSZoUcPLb0xWNkiL1Fgfb3ObgJOl7jV/hvo9w2L09kk/23XYJ5odCUKfsjdvm/MCGsAZXxSRP6XiEwWkWE24Qhi8LSI/EREjhWRL1gvQ04l6HoUdC9+8ay5Mu+Ka9qqPGqreRQgCnvun56jgEwAhJP+tsOuo/+y8uJ/im3Lmm83iigAdpFbZEOhWPOs8aSuvxaIAvqIeX2HiEwSkd+LCLLjTSKyj4iQrv3PdcxtYaKA8MJwMNK6QXfW8dCMS9rJo0Ays1bXo+pRXxEFxSQMHXsladiXb3hc9h0zzoyw5idw5sJ1ixb0LkeBDoDmWIiamEon+D/H5oUoXHbVDTWlUZseemQ9Clr+FOzoehQ093bqiVNknRd6hD3t2yLydhEho4Y9uMBisUbuigpRqJw4wdw21aOALdgJPYqWREkgCvXsASIrCAoFSmI8pQFBsDdfJyJLLWHIGtd1j/3ehB5Bqe66dbGclnUyc+iGSUSBKCuiLRQDwRA/bIUxoSCkybE/wMFF5aISBb/MaN7QIwYPJgWIvNAa7lESeBAoegnOQ1FQT+DHIkIdoEqNlvCwukTBLUfFtwsRBQTHeSIyVUQwbbFaoNb/bhFv1qw6n8dEwbol3dwJQxSG9NKjwAQa6Wknsc5KFKFkZvWC5CUKKPgTReRHInKZiHzTdo0QJIjgNBH53yLyIRF5zG6YrKF0PQruWkv1KDB/F9sdiuKnscBZbHyGy4r/YS10GLNlztD4TuhR1oylf56WzJwVenSWiBBBhmzAQkNYG9YuhDFrDpkWBUwUb70lCmNE5KMiMt7KU5TBFZaDFu9NdEWfEwX2QA4wlNb/3oYeoZ+wXlJvCtHFnCLSIIS0a0Tkn0SkKBEsRBR48HsiS1XpTyUpP10urhBzTHK7EQW1jpOj0FehR9htcfbScNjfnqDnSWYeM2acsYRrv/hZVfUoxxxUfYV5Z6HB7s+0Vi32Ay7x2/A6l0R2pluQDFG4+sa4T1qie93qh5ruUbj1vpVVhj+IAh6FJXc9aAobJOUoDLVDArhlW2BYpsxJpSRL0YEOf19zFKrKtHZ1ydVXzut1MnOeQlZJb1FNFCgI0WNyFIqGHmE0JUvqImsE+bld22BwsPm9IvIflohljeja3zxt8iYyqx49vPKOYOlKHpBEFD5gLTOKgQBrZ1tr1322gwhqimNhtQeIozsAfFluc9ej4Ca+5iEKuE8eL4tQp4lFCJDEAofRFxCAolhiXTUTLD7HOk2uPgo4qYU8CvrdQkQBc9b/FZHDLMBUsNkbouAkomufZs29xOQozJ0VeRTYuIVCj2B+7F5kGQMI7WeX83vWBHqDSI6CehT8xOs8RIGQkOMtOPq6FfiP22dgqPsISb/WiP82EXkip8stKfQoKUfBVBVjXFhckAJtoA200AetlwgJSHtERAgzw52Vw6tQCT3iCPuomGcn9ChLzFU+Tw492inDDzhCdu5INlkQBfhmEeEUC2QZghiBSwgSzj5IA3KNhschx3TGHestUcDx+IDdjjix9rayltA7DCL1uO37lCgweOwPBO5fiAibFV94wdYbosDj3ysinxWRd1o9xLY9RkSoVwTpo0I9bvr7C/YrN1FgHHgYjAT3NWyUxdMHrZ2IguYooJM2Ux61D0KPIPgMOfqAhncQEBUihZqjUFWDvlSS6xbNlxn1hB4BMtio/M9D0ZuIc4QG/2NFJaqAxZfSqqoedXUZLwxjliv0CFca3m0spL0k7H4XKTuqOQp8FnsUZkyXxXcvj8N80pKZGRb+R+YSsVtbE653m0SJQhweZcdv0YLL6iYKTCOhU9GJG5FaR6ZgfM4yNOvbrLahR0pgyj09smXLFpl4cLEcBeAjRtE5Nk8T4sV0Qw6IomHKgZkYyrMMXHgU9hw+3ITe3bHk5mSPwq9W3FYVn7djx04ZPDgyJ/tEAeDNmseq+zFvLlV58vN3No6cRXClja6BOKDksnTGSR94j9y08Ac15wPkIQp0Cb2Ei4vBwruAIuBtsMhhLfw/1suw2E402I89DaFJao0gCqUhImVmDz8RuUBoeGYVUyauDSolFojdSg09Ou8SGTp4cHqOAtRY6TGWDjQq7hVM8sRpnWYHUQUcg7NQROZb5JJzL2syM1/3w3yyiAJrjbABonmI8gHQuYVoIQqnisgsq5OpsUC3UQxZzSUK3d0VcF7jUcBHCuDBDXWENUPSsesdlxQWQ0ogglBYUJTfJq4BCfJdkdJlIuW0BQaZ9c5RoP+d0KOsWax83huPgt4F+UAQwvsgac6joRgoNQwjyLYC21R6SxTYlliIIMOsawwc4AyU1A1WFucfpeibfUoUAMfsF6xJeE4xgvybHbwCoKVeooBCZ5si5wlxwNp2vvXAEN2JrEe5I4pRtGkGotC45iYKoIq/sR5klBDavMjCganiBs/RwXYiCoQeAZRMacg6rKlpa529gRGfPQMQjY7TjAz5n7O42b+eHIV9x46LDWrqVUhMZkZn7m49AqE8NIDGVywrJYgcw5omI2OxRO+jRwEgeJwT7BcxUejuiU+qpm+pHgXWzF+JyN85VgQqfhCHkpSQwyChp9D5OZoSBReEuweu6UFjaUQBAgf0Ya6IDlid47lFvrJk6YMycTISu7rV61FArTOtbGdCGcEda62BGTyCxyqP/ZRk5tF77RWTPnpXT9UjDgn4W2sIgSDQL3Ava/xbFnsz5XhTs07OwstBMrOpenTrLXLaZ06RZcuWydFHY6ISKU2bOrW8Yf0awaOQ1FyigPH7O9YNznrXCAv2CvIPxQVGwk3PurvZ6ov3iwhuQAaUcB9eJK31xqPAfRESKAZAI2EDKPZQA6+D9egnRuE0y5wShe3bd8QkCkKFws3jUTDW6H+0JktMHb8SkTtt3OoFdqDIzCV+JmdziYJvqT8HojBUPQrEXvbIu0/4eKU8KswJuolQwcyWt6G4SApA+edskUfhpuABW1lEgUfs3SUyqyeSvURrUVxIDXMoBlYvGxdPEV6tPzPWOUy+/oFrSmKCRIHkc7wG6klgsc+wgh5B7JoU2ByXisgpdoDOFiktFCnzvZSmREEVFRt3V/IooF/xKGqkVs7lk/traUThdeMmSk/OqkesM1JQwLt4s9yD2nD3Il/g+XiuWIfIvrSovN4QBQwzJOuzTVFQyFoi2ljnWIrusc6+okU5+4woMBBsSoQqBhD+jX8cVE4caoEKEvUSBZy2iFfmjfmhK3iKnrFjhQeSRD/UIHNH8nqRlpso6IKnuAHB2OcWiMOF7WBsgCHyMhnyrH2Iwmx5YmNEFACU1JAff2Bjqh4x71+1YajgED26EvJ/rcXuoblevv5x2WcM7q/ooDDVA9cuvFJmnoHZyWnoTTzH/BlgAVNHn7vtM1a3ogcA58S/sRAxOEGcAUSQSD5jcX6nJLKtdoIhCm55VNXxqTkK6CnwBO40BC2bAFYMEkd/MRAKiBAuYBL6pBYQXKd8jmBJWHNKFGKreLksWh51yd0PxiA4jSgAbCFyFCXBnuaDWf6OQw69TjfoHvgxr0MuMfSozvKorC2GDvyBDGZIGT70GbgRssMyABYQSZ4Ubcw5Clr1SPMQN216VSYdvLfsYc9Q4x35Py1kH24H0cLwwfe+Zvt3grXJwBeBJBiUCAZJa4XPUeBmTL6CYYCKSxSQe3QMCy6DhKJicCiIhRGaEB68qzAZLF+8LGF6J1v2g6JbJCKfz+g4ROHGq/6jJvk1r0dBbz8I0m+Pe3cfyVk8g8tRnC+LFRcO8jjN2NUIj4JBHpADBgXFQWgPypSYd35iYcgaHOdF0g5cO+f8b8uQQYNqPQpr1ke+M9xBsCN2Iqs96dhaf64YJPqOmwhknqO5HgUVdnlzFNCXsGRkHxvy3dbVxtrCOwVooyoyHi7WX5EwjNxVjzB7gDRAGNpAkgh5pAZjws7le0gItNTlNt6N7yN88XljVWSzJARF78o5CiwvVDHVGVDg6MgcXC7H6qp8JYkoQOhHHniE8DNPQ7axRfHek3iHF0sdb7+xnJ5QRSw4kFRibAl1AAeHCENviAL9Ramy3XA3EwpF3+D4RLhhxUKMgFeKtD4hCgwc7hhMbYAkOocbGUslFk6ASoFJr4coIM4Qs7jgsbqhh/AqsC2RJYg8cBFrcZ7FcAW6ZIY4N1EADCInkK+AxH/JmYWOMYKFRseIOSNGisWWkuvUPkTBVj2yVWhCOQq6jELrGfAD1wzxTewyeN3AGRBn9gT5Peh4bHCAptCxqCZHYew4sUf3mQPzaMEchQPsWib5gRAiDEI/9XrKZ6BfWCjA4tOWCCAk2MwsQIA8+2KZXYQwV69pjoKeA5Ar9AihRZ8wf6u3g/UHS8a6QeQCwosBZBOwUQiPpYFy0VWEK11WEnmuHETmEIXbH4gOXKNPVIFct3aVzJ45XSAKaujKSxTYk0rqNO8fna98CqlNl7BPYpzJs1+rQo+6u801AwYMqCtHgT5hmef56DHWEgnD2EOoWUKgAX/HQI5NBIiXdPzumkfJBxgV6RN74rRJZj5wtHykHMkoPAQYQ/nfDeZwlwciAq8pwR2H2ihnjPiIGzAR92HcWPNAkbQxU6LA/Tlw7fTPfrx3HgXkPOsQHETDYg+TiSrnJjeAOPgXkIchOuss0lDVI5Q8B+z09mRmBhiLHEXPWIgYaxhcwsnTmksUfOt9Ho+CubdLFDBtoOEREBAFlArMhdWYs6WFHn0dojBksMlR0GZyFB5dH2XBwOgQWm6jT+xIbSg3BJ0mAiDoaKAjQDJB0iShZDSXKPhfzfIogDewEpH/zSORwWxCrA24L8HuDB0hBMxlkQZRmDzxMBk4kBestH3+4u3y0h+BfbbhesIqBNihsQNJmmBsaJidseSwkGgI5k85N0S6wWBg0/zOYoMsIKydhRfyKJz+1dly6fxr277qETIDwQduRI6gt1AOLCUsI4C2AlEpwWlO8yjsNnZC4QdgPEQJIIjV5+rn6AJEmVK2FIAEJxPrla2OZQl81xuigLxCTCBnsbJp5Ar9wGtKyB2ACHmWpxSeDlyfEAVd+2xUOos8Q87AbEjoJIkTppOz1UMU2IaMFcYplDEGhQPt9mMssa4RegSQRCfB3TF45QEf2u3cRIELWETEsCE7MLD8wD48bQxgptSMxBLCROOJYJIp6ZbAdduHKMyWJ5/bFIPMHdu3V3kUkBNgXLA08wc3cm1YyBA4FGqHuWWPMSToCXAIwSaoVPAuS4/9iAbkmqQcBXPgmvUoaBlNdPx11yyQWb5Hgc0NI0EnYHUnZAFLgdtIpGNzMl9YsECP2tChCDzmlBclwx7vBOjTW4QQhXkLrjcAV6tE4fFY/dByOflD7w0vWvQSsXY0rDEMIHHY6CgGChT7Xbvw0VfE9NIQvgZN238zcOxXdJa35tzQI30tE3o0Y7o5mbkoUcDooZXh2avY5HgNtoE2usAcYjEHSGc1iIKeo6DfpV8Lr7y08DkKiC+4FbZcQDfcilAjCC32QQA7QwoOYbhJScKpFGqE+VAeVatrMeQaerSxHG15oALGdqLT0JEso1BDxCL/8S6HGn1hGbIE04IZTNWjEdGBa5knM/sVhvTBrkeBzYlVC7cLGxPFREcXRKfbJ74MQhtFitLF3UvpJvZbUmtkeVT/GeyDj1tiTS4R+wPwibJPaw3xKIBuoc8nRZaE0jdEyviyIApgVQSQZulm7QTky+c+Id+/JFwvAKIwOORReHZ9JIUJ4FVJrAiNCWaF0rRILyU+6TeSl3hL+snAYTXEIpKjXAFE4cG7b4gTdLdt225IDC2LKAAseQTCHvkLg8aqxIYiURGAhCcBL31RoJnbo0C8NegCiyAN7WTn0IwhVlQWOfOYpyHx6DSLDguP7fiu7FFAT/Ha8FAEHE4t5IjKEvQmw4DHMU+sZ2iYU0OP9pskPTuLrpAoZBLOp7reJwrqJgYPgoHh1gh6tgmuavTtRz9yglw1rzZd7/IF18rpZ2aZTSKZy1pHZxMqqaHEKFSUBJECYHLIFkszT+sTooCWBzhp2Q3YPV5SQhuJ50Lw9zFRAGiS0wQuB3+hlghrQF5A9hBhGFD5OyINQFK0FSIK3BwkoSW0SLRiItMaY6iJgLwIuQ1qDU5wHbUPUah4FBgCQJJb9Qgip/aYtCECOOJswdNMmD+kD2MRHgR4KgYkCD4yh5aWzOyWR1WiwM9geVR0AJZPQD6oke3rozAUFR4FjEDk5mDhd1ppcEnK3yxHpfroMPHYXyiJbK4GUepRcPP6IAprVnKOwpRsokAQO8Y83KKYvhFmeA2w8MKiELbEZdEQKgA6F52DjgkfAZk7MT/qUeAyJQXuycw6hkU9CshMeDT8BSIPeQBqgDXZAqhhOBDBEFlpO1oeVcN7+MnYhc5RAMMiN1R9u3VKeEe4HZFbyHas9fTLde6pdR9bCH3DmANeIdrGD3sn9GjkyFFVB8HpWSILyxGMUGKMAYOITcKa0JN+47lEwdF/vGihxl7AG83PpMbJzHvuGSUzp+YoVCczxx4Rc1+XKLAhYex42iCpeK5Y46y3R7oi3PnXPRH+4SXAT6xVrDzoDhgYaxWZx6AmJZz2NVFgMgCcTAK4GKtSVh5PI4iCSXZlMHBfYvaALrJCsTAg6bAqAOBzNojC9y6eE4do4TG13ixJ9ChsWx/F8hAmAwrHfcrE0jDJJ7mHmDhoM4PH5LJyUXhawDelzyGPQt7QIwAYnh/kGnyFTQvDB3DyGd1SvI2SKWIZjIlCWaoO0KvxKKgvnAXDwkdKgHyZK8wMSDL+TvKm3/gunho6iysEaQhq4XoQDErFltPxiUJPT1n+ccb58uOfLGp7jwKu0P/P3p2AW3ZWdcJfpypkICFDBTQTYVDBTAQaGwEb2qDNbLct0K1+n1+LPRFoAzJkUFIJ+oEEtG1bv7YbIjKGeY4MQRCRhCSVEZkFB0BECbOZU3W+57f3Xue+d9+9z9nn3FN1q8J989RTlXv38O53WGv91/qv9VIC9KJlx16y9DPHj87lABNt4EGZpQi6ltuyqEfls02V7UJYz1Px0/okY4DcJ60TKPAlAFLGRb4CJ6RGtjKGk7U4T1Gd3QoU8MtoMHKFtgQUyDuWuonmLR/QFokoZOl6YDTpDOYwowtEly1IfLHHUWnn8M1UvZ4bKABQDEcTJqrAemU8co+XTSe5KClH+gB3yjU81uSIDlO4HW3fAQp1RMHGV7e9TT3iUHSWCbDXbmwPdA+iVjMkAAP2DnvXnHOYq1LmTwkU2POgeheQRj065tjjJkZvvteBa2siCui2XsbTwRufZWfKzvIsEBgWG8uWF6vd3M8i5jhkiGfUqLiujCiUlF05CoMiCoC5dUTX2INqLLNkGf10EgOOUEPzgLq4yf2eUwy4JZzpJt9izzZCOYFCaYR//LprKurRuy4xG3XrOnAtf5c5CuaZ84NNabiIB8b47zU+BstfF3XHEMmTBe6ZHtOqjpc5CsZOVEZ/3/DaV6ypekREGQrPh6esm2yGwFAxe7g2OZnpKS4nYIbfI2m1HOEcSkw8AIEzPP2u+TzUo23btlWGWkaJUI/kKDxwXNs6/LhAEf3IPONjoEuIjXaOBlMSy8ee4bsk13yPJWrMgBZ7Ydp5MRlRME6DTmYuE3Xzw0qgwPAnqwyEzhOwDhU2WDYxMOojU8kn17ckdhh4H4t5QZd00bWf+JjT4u0X1aePzVsetUuI5s8MqkUoLGRf/MEo4pvN6ejT7vO7Eii0x2kw9ajrJQaHJUBxGEwIfmDriiikt743R+Fz19UTZSLyFKIECtPea9XKQufaNLELAIXbb7+9ovkk59LrZkUUXEN/HjKK+D/jejNg7BAYbG+fgSQkwCEgs1tyFHJcbACCVKw0yZTlmKXVmz+jxYQ/KHfWCiOBW8LCZwTYQOC+WOYt3VWP7kzJzBSBpFzK2tKjKyl3ulbjqTGElAS7kpCckf+9asUum3pkijkOTVFZP4PsoywoF07BSk413wXM5uHcIgp08HqoR56NxsTQIfjZmmQmGQqfWkpC1MAydhy5NiThb+lAgZbk1uJBtQkZt7SmkB+ZRuboKI/swKjCIkAhFwSxykai/IFU2670FBJfOL28iHPkV1ePnxsokJ00NyoHZwEXIG3eFlYWnEUlPATl6JwEC9EY1gqLigzpsJD2HaCwPVQ9Sg9vu+JLFuLLnKByg+fv2L2YCSJq9Lm9l15YBpyh4ki3/ziP3Id2JLjV1S697nNx9DE1UNjC09YYcZ05CtYvJZTZrXQBQVU2JdPklRBwkAud2dUkGUH3NjbQ0QouZtWjSr40eRN11aMr+yMK+Lj0CYFFGPhwC9zi12dRPoAhPWq8Do8a1Zb37eMKRY8O2hLjS3bV/WId29OszYZm3EU9umrHFXH+Oc+a5Cjo5zSgAA+bQ7KeHENFJTfJW/MLs7AA2ZsaPQE/2zrmFfd+mnNX1aMTTz61cKLWJeK7qh4ZLnLckNiqzJuykafkiXVEdPl/68y/rTU/Z4/YzqK/nGGGzTPbrLRMZi6ff9NNN1ZAQQfIKlFPS8w0mQLvYxpaanleQk6fda9vbCFOVD/PgpVMFWKH2O2jQumHHIVDDzu8Lo/6x++cXvUoK/i0+fft8qhQEuEL3VDiBsoA0ROUPwdzucktgFRs9o0/kj6sWYvER2R4MAcvIwrtvsybzJzPy3J5AA7EaFIZJ0DyUA90O6JQ9m1dQMGq5CYBFFgf6wQK+c1l1aP82VznKHQJNhUSCBq7FhpU53bBiEI+fghQcK0NAWUTFl7LScOTCjHzUBMyBAgHPdk35IwzEQWHwVUJWVtXzIjeA9fsfga/zKbMFMpD6ZLjmRrLwiL47VqLPFMeLHweJ0TqfKV7D4k4aMuB8Z0vpx+0HqHTn709LnzVG/f5iEJ7OYkKWfqGM52kPIVkh60AKPi5qOPQPToNKNztuOknM3ctd32hb0U5RK5ME6HNO0N2WI/WG0Hte6xLsoUtKMLPLqSE1gsUvFvQkd1N+WA00P3e6+fGiXcLw4BHbghYXjZQGB08itHJEbt+f1xvTFmlOsY7abOKMtgHBmwg+lsPUDCfxoenTRMMTC8dfWA74u9SwANrMkyWyNxAIe/Ek9Ep2p8Rh35YlnvJwx9YS4+OGD1nFOObxzVgIG8IuA4uu8fvS0DBOQpZWvPWW2+NE+esekTEMi7JDrJfEBwQzEb2Z7XP+rzlmi3UN88l9ai6uHH4OEdhTdWjEiig8KBTWFxlk6hsM1pwwoCMjgwDltfhwAEKkjdF81v8kaQe5S0JFqaWR7V25MQINZZAwUPoLxaod3JD43gywvSVwMpmgH9oFPE/xnXFBJQk3+jPDRHlOQppB5XUo3zMNOqRPcg/SpXCxoxc4IGsy4N6edGzGqXr4S94RZVxQzpNziX1KBPAkw7VRT3ibBE1lrvq3QI80g/JcTpAlMvtHVrgAAAgAElEQVRU2bqGzB/rL/2ArvH/SaNFGcIQRAVtY0jlUbcdiV9TN+MnqiaZORWdNc1MsBz0jbHPASUgSwQI8sw6mgn1ieOUn4HYnWZWZkRBfy7543fNdzJzes3bQIHi5NigGK073kFrDGDwOx9ZNh/FA2fwyEcfzZkC4ZCL9IawU5ZMpIyXTT2y+OwbYR0DJyI3hz1efU4ChTKXI09CXhdQAJ8Z26Azl6ryqCXvwiosD2sprCYRhd+94NzJGRgJ9vT37CHnKLTmaub/gvEQlnWe+Q2kdFZN6HkA6tHlH3zrpJ/lZUOBQt5jzZGDusEJI1HHWRnChYaJzCXPCJhZXPf+A9ceFjd8vUuyN70gMexiOzfdD4wiwEBHLDiNpNPKZLCMRBDYroeuRRYeGHHQfjVQKEHonSmi0LU8yABOBs0wENTm0TARdpS77ZBCe1qWwdQD1+75gLjjjiHwcXUvKTHg1B/TyLCkO1GLKDc2sSlndFqHmm1c9nO9QMEYMYTIzXTIg5OMXQqT8rQkOfyyLPQscLVsoFBl8gEDNibPj1AKbWt9S6Tg3iLouYH76lW3Fsh6gUL5OIENnj7GANobwAfI0V98H0kxmCUzPHNhoMD9bWETXJDwyyJG7xjFmCfXIs9DYxiYFBbKB08bgjbLITnvHSGQfQko5DkK9YFrN3aeSkvOE49kguTQruMnDKXlJSBuf5AVDDYRuKQnmS/OBjqjL3B+mXMUjjqm9j5XpbXr3dN5jgIFw2gBBoAA3BKLq2wJFETRhDaA5TaX0oa1GDOpmQu9Lls/aavKoxbR6qtQj574qG4PCmEklEZg+eCMKORTrS1hSd4Olq39yrtQ1O6oLrUWgQnKlEXPkGP5vnMFKGR5VJfnOQrv/sCwA9fcY844OogOgB5zl6Olzl5cKXfOsaw7QB/sw/wwx9MCkwkUci4TmPYduAYoCAKJKHACEVkpx00vbz5jPZ1AQCr5n05xf6fMBQ7Qo4DV9rSLKBzZJDP7RpW2qj3QceCatYydJk2U3ewdRKhIKKcUWdWnD6139w4BCuhQhx9xRGwZNRGFp/aco/CJy98XW9QMbVF9/H/XycyQnbVPSVpDQiXVvc1HWHOZQM+Q66j8VYEEScTArEXAaSJMY1+1IwppOC0aUQAQrHcgBkVFtTKUo3naMnIUOt9ntQl12LgGwuqXiaV/Zhn6spNYJoTJCgVwajLz2ee9pDrAa+GTmfsGB9rjkbAK2V2yfFoCrn1rO0ehNIS//wcfEt/45jz1Wmqh4iA/jklReorEMPK2SqOoDtcZRXxnxhwDCqeecuJk7We/eyMK5YcRopREAgUvhT6HFLRnGcty5XWlMLhIToo4aOvaiMKdHSiUQ5reP+FVxlw2zjZOabYSGlDv0jzlhLjyQ7gZqxuaw8HHnBTjvooLPQ+E4elUEQ5LncICZJJyP1R+rBco5Hu8W9CRIhWWpsgoWl9sqfOwUWAU2iz60dKBAuAsUkarIvJSAoACC4/By3XIHbZBQCHHUO0AmMUYaQJ+uqtx7g8Jdlz2gTfHgx/Iz7i6HXfiw+NrXxsgACxoypM3939HbPn2KHaVCgmBmyNBVZ3MjEeSPiJi9PRRjP9urWDbl4BCdTJzk1BX5SiIKLQsHh5k0ThURXYBn0u74Ah1yYiSKsaAw9DitBTw5p1WQJBtQtRybvZUpQ5A4fuPOnpCU0lqbGcyM/4yS42OZmgDC6xKiZfUmO8gNCB6lrCf/8zaROVKYdGhokw8EKJLKErF1HZRj/RtKlAgRIVPrC0fzGKlX9JZxfPBTW0j6D+Pmn+3gYKlTdDYs2iyNoZ//3oNFJRH1drJzHIU8iyKadSjcuekkc34h3OYE+aWLUitJnjXHcNE1JB7nCV9tMEyopD98c4+oAAgcBiQ7RwKfBlyuUWTAU35dYYt6VBAQJvOmN/EVGPGdZkfqh4dse3IVfTrqjyq08k7rH7fzFzQN0DKv+khfTM2Aoxdp0KXEQVBr2kFAq7+rLyJOspRUY8AhY9OOXCNx22//Wo+RHqnu4CC39svJsseAVI132mS2x61LoVqcfhw3n0GPKAgMxua64ooUPZfu+EbC5VH5egidEwcIwTPbd7WBgrlWK0roqAjoCMhATVxG2bZUisBLcnqsGtIO3zBppU5Ck4WBvZSyAEKBx5wwPSTmecdBNcvASiUr10UKNi4hseQQe3WEAALQPg5p8os+JERhV3GbuvKQTtH3++hccPXZxA4GPiUhLmBhLmzeGdm2QlAnwiMncxNQNKRAr8XcdD+a4HC05+9PV5+J6Qe9S09QpqSMIei6Nk4qTPC34705zX9EYWdcdjxp8Qdt/dHFAh93uXMMWDfAgrAJ4dbynD6vasCxbSttCygwFb0h41JUVAAcKql5N/0ueVHRMwq+blUoFBVsWhi3RBdO8kTeOA5zdJXUE6X96g1iMuMKOSj6Ssh+bVnttbGKFBorZVMjPbcrhcojAir50aMGXS8zqwPHrVMbqaCdebjEaO/GsX4v45rT8iU00r3NaCQeqrPSDIElgm6neVlfQMEpXglgqlLgRoBeV7fXFYMLHY4u9i9gERfRbA8R6GcZwbwW9742jjrjNXhgtHhoxj/xnglV48Vy3Mv2uNvHeTFtw/8TtkeSLQdEiHo6HwhQJvVhhZhKFr7HIU0yq/ecXl/RCETRbnHWZSPHUV8edwPFOgei74LKLBLcGg4LFnOPHIXrACFZFf4+/prr56co5Cf8KTH/3hcswM/aznNfNsq2Ch8lezFPvCX5VGrszGqtJPaEf66KQeuYcnw9cGAxJn1wjHDz5E/p5esM+bYIu2az345Dj8c4q9tNXNa5en0AAXv0HP1EOwFS8TapoMAGKuzTW9yzzxAQZ+AF32pqEe/NAModH14H1Bw7YGjiDPGNb9LZIEazvJRQwoRQpC8OOQmsMozJrRSAoWS6rNoRAFQEMkVTfjvWyJ2DelcazB2W0TBe2xIFgAPMw5XnhMOzjIkRRJ4HkBoQqVp08qjnrW9jiicf07rHIUdsw70nrH820Dh2oiRKOiUuoz1ycxvW3OAnjctAhQ4bNA/UAey7Bc8LH+GAGGzY3ANAQqMSx6Hsg2KKHB9kIFcEZloKO+gK0aeD8cTsRhFIySdibPa8TLunhNx0Ph7O6JQzoFcJh50AtrSzwbsJ4WAPi62Q0yjHh16z1Ni5x27KiFr+PmD4TyKx7aynlQUMi08mPQ+BgFvZKafEMyciX7f5cXp2znLAgr5fM4VRhSvFQOIB0t/6XyEOcaS75t+0vx+8U9fWatirrr2+njGc86LK5rIzIf//PJ4zM8kqarnC20fsotLl0eTYcHazr0gFIS2wIskabJEgFPEze4ACsZFwQ24pd3IC0ERNts0vL9eoFC9l/VqnCwu9E2l94AFDXVRqMP4WaD0gTEm8HqyEvc1oJB5AE5mPvE+3d5URr49yQGENy56YMWyv/PATWvd0PCYzrsvc/7lKGQy86ryqG94TZz5y6tLFo0O3BLjZ+yqLTRKh/FCUPHYM14Y1OY1TwaloFZU8MqSExaHZCBXH8QidV/RMqIwqXi0axyjLaPp5VHtLe8zWJz+TxhFfKOo2FJGFAgx70di78oMZjlTpvhdzIYGSWdEobTNkno09MC1GVZG56/JbkE2EQVd5hBh03W1jCj4XUk/mgYU8jk+lywlrkRoy0bVszPgp/mJrBFlRCHndVpEoXw3wIBSJFUUtY4uZCJieTJFSl2IrWN50WezIgqrgMJ7mhyFaRGFrgGfBhRc/19GEc8Y10BBGOQXRxG3DKD1pBNKdDp5XuRlVe7vsY+Kt7+Oz3ilWZT/+LWvzx1R4HGj7EVACBKhq0XaUg5c63sxSYd+JOxBqdJS4qdCLl+MGH07YtxhmZTlUUU4gOZMyj1r+wVx0IEHTiIKFuUjH/ezcfl6gQJBw0JjrTN6ecGEOXkbWC0duyepR+3kdMMxL1Aglyl5CkJBiUyEZ69z0IkWwSyYDgzAaWChL0fhqPs9NL4+K6JAiki+scBoMbuxK1RlHmm1TFRkc4kocF3rnBCachyv2gQK5fZAPSBGUAcIbvYn/WcoDSnuslDwEKBQUY+OPSnusnNc2au2GOe2SJR/Wy8iCqbROuJNtt3oSZ6c9CtYczxNaOPzKIllAwXjoR/YKQJa6HYUBcDAw2aPiKjxtnIUdvV1qRGFnDihZYaRfYCWl84Dg8zla8+gMMh16itBUyyC3QEU0P1FiYgw4orD1x9lbOF2Dis5H/RSHxhcClDwnRa5RSxSDOERbCaQYEsdCihAy9yYJWJu6ZJ9Dig0/e+jHvm1dayKt+XkbwwajAC2K9COWmTI2A+oivMeuJlDWB64ljqq9xyFSmk11iqeCh3NOC+djzrEwYdaIcpc0IUn02ZOLUYoCLgwtyJxLerRH75utYNN/6ZGFHg87C1rC/hs5yiUQOFbEaPjtsT41l3dZHf7Vt8JXMYZ3ueFa6lH+lRGFCrD3DERTzxtqREFQMEnoaP52561d7ta+8A1ffKnj3pUPoM5xlHFr8eLn5RY15geLHHG+ZQsxl4Tc3LgGm91EVGochQGOLBFVZhdbB2MHiKXr5HzjMVsPOhEgMLysof4IadV3V914NrF71x+RMFo6AzvrUgCdEfudUWx2hNBKOOK+gALAKCmY1D7TmuAQk5uenwXiSjYh5Qmp73n20PTwsp9M5xAAcVn69YtVWJk0rTWTT0i7A6NGFOkwssWDC+Fws+UiCzFDm0/LaJw5vYL4uC7HrT2ZOb1AgUDxHqSpGLy9MvKtHtJ8ilAodpo47pMWbZFgAJnDsOOPs1jHzzxbqOIt47r3HBGJVtlGsMBUGCMlBWP9GtQRAH30+IXC+T5w0XlcfmriNEnI8a3RowOixhDqZQGYEDgmmOKhQdHaFjY1323ry6Pmuvs6c85L17+yjfc6aoeDQXrcFiegMlryHGHjWep8fqUNKCpyczHPyC23L6zwrOcBvSoUnbZTBEZUZbQbNcQsA2FpRmYA2T65NnLBgq5BSkKDAaOTIDZOKlgjJrEHueNNU5dwb7dAhR0rMzqKyeZtuLK55JjJA0oybw7gAIbD1AwNpykFCoageRAXjrzb1sSw30U1aUBBeNDkFnQSPXclCwRnPEmcjC6PmKsY/h2U4qh70tAYXUy801xUk9EoVw+cJK5wsCyhNjUnEEalg0Kqp8v0sqIQt5fAYWOiMKq528d1fN2dnOkroVl/dMHwn4W0dqUqfoR9gCXrwRKC5FBzwNRtDKikDkd+jX1wDV0VnQmjkcg0+C0M/RFrOglFjCh2ufYBRR4GxhMfxAxelUNKsochdTpVdWjM8+Id/9JrZH1c9nUowQK9LtEd8Pbd1BuO0ch+9lV9ahrzdAFWcIVTRFLzFY1vd4LoM46N7Hrue2qR66ZRT1qP4cMMzWcRQK5ppOdw4ZmE+mfXHanWPM7EL2cH31tMFAoD1xrP2xWRMH1vHMQfeZ28szZ2GzcdI67JssLUm68veX6xBsVWaWwl131KCvhGFzv5H2TE8EjIcIgjEU5TFP+u416xGLBWzcAYKINTsi8NmJ0ScR4SlZiAgUHc9mYhf0dZ557QdwV9ehXn1mV/wS21l0eNRdHGyiYNEKnh7zddeBaPmpeoOA+w2SDkMF0qgY587CaV7gFLZQQmVbFZOGqR16Y9dlYHIQzC0NcnBfJorLwGQB+z3VN8gB/yuawSkRibBQAgzB2yUGb1KMp8mzyK15/rYwm+P+pJzMfe1Ls2jmuthf6CZAAMMB5bFa2mOVLRgACmmWejWzADJH7xyDZaKDQN04UGeekvcDQtbzo+bYtsNuAQk/HRk8axfinx7VDgfCH8vClprTdARSIWg4qcsL2y4qtlK61gGhiDEXH+whXSwUKXIScCP6gn+KLoTCyiHnTyBKaH42Ekupp+xpQ8Bn0UlUacgo/u/xcYlYwimNSs6ZVwcHMmkUznbbOyhyFpB65vrPq0fQlW2+8PMhh2rVAMyOdZ5UbGHBoebX6chTw/ntPZp7Vv3l/T8+xNn1TE2Lroh5dfdUVcd5Zz4qLP4i4V8/t0GTmIV2iXu1dQXyyOivitc8szGeV1KPsDxuo6xyFWe+nunnx+QXRUZEHUFAHpFmteXRV9Ug+QPGbafS7vr5xYnEM2ROoWPwK/I90IkoqVhw9yTEOVE8rAQ0oqHqkDUpm7urUEKAAWB8zrp0eSQcQXZD4wXbCkxIiSeezD5TX4P8pXPvknFHEtU3OTQkU8vQ/fVskouA+uRDeRxkYWBEPYUr/FvWTAqDi2bRyWyVQcHCY/8+2cETB6rMRJSCxXvK0R3vtsxGjL9XFIfratIjC8859cRxy8F33TESBZcUjkscl8qgUltQygQK9Kg8BruKIZ7yRX3AKnYpTiIphI89KOi2BQrnOBkUUGBRbI8aquFDwWbAdsOPOtZhYpdmAKR1FLwPv8URatdM2gcIskT3990OAgifIUZBiYopQHTlyORM49Dj44DhTw3NFSbjWEseJhfvKardDerw7Igp972XkYvlwxqBpkMNkcDuqsKeBQsXjYuEJGTOGs4TwlAHcHUCBkSEYyhYXhUlmBh1Bf5l/4osIBhq6PJZLBQrl95s8HfFS7kAIto2Ge8ZrXwIKqh4lb/y2227trHrUtyxEy1DvUJGoRswYlTtnVfmatk+riMLRx06oIK4dFFHoeiiLNpOaprx0tP8oxoTOvxnXCsuct3R9RhTKXACPnBpRGCKQ5r2mDK1ysjRVj/KMggn16Mwz4l0fqHlWSm0+6QnLS2am9zlxAEOefR5zjkIR066WJzOXOSf6O4R61PU88hSri1EuT8KfaQe+9Q3xVZ/+Yhx59zrzIffAvBGF8tnEKpEBLBMZ9kZGxdm56Egc9tMyU/PANc+tTmaeVfWo7MBtt90W+++/f2d51K5BoFChfUYcRUoP6BxFxRZOGh97iRJmMzHkXMtu8jGSC7V2RCGNuEWBgoUFrJho1AE0Pf3V9AMybNIBerdQO6JQgoXBQIHbWwQhmTc0kew5Vq7ZhVqeqNLFdICQnVyJKNQRg7I97/kvrqhHIgrZlhZRsGvwHpAEgRxkPVaW0DjyKJhfGMElUFgv9ci64g1E80cZwCVmCGH18KSKSom45lqaJhPXFVHIByec5440t1xbvIGaeba4KAAbwYbQ4dVnqk26mEDhe+kchWnzM+/v+oDC7XfcEUccf2pVyW1WQ1XkuBUQIicIYXQetEXLfZ7chHzXngQK3smFQXEQLxyB9gtnSNn2NFDYcsCW2PXUXTVQIOtoejHxdqHxopO7CyhwZnk9PA8kstNEvClV0QagkNMBO6PLU71bgQJlRcMLZ3EmDLSA9yWggHqU7aYbb4yT7juMn+0ech71Qu4SsSpAy8aeJ8LXlgEf+7jyqM05CgU9dib1aJYwmfb7PEGUYcT67ag8sOochcKwvNo5Cj/1E8NPolxPPzvunUY9yogCHTbtwLV5u5RFKKhPbTBQaKoepU5dJKJQ9lXgj8gauC3XfGYChUmC+ngcN99001x7oP1QZgaTQ4CKtUeW+RmZLwKe9Oy+MQcUDj8C5BgYUehKNh0SUcgObN0S8fhdNQBgeJN5vHGMNuFvkQR2JTFBX7Avqe52zkAJFMpcgEWBgv4BCrhm2CCoKzxLcnAJGp4le3WaB7ormTnBwmCgYCa5rLKIN4kHsRgA0k68WxhmYJsWUXju838z7nbIwbsnoqB/VqbEFLGt5CUniZsGLiyqBAptz4jHzEs9grWEHVHd8hwPGxeLwdDO08ochTw8z/1DIwqTd0EvXJM6xc3BEtEseETaaZWQig53RRRO/5Vz48JXv+l7NkdhnvmcFlE4+JgTY7yANWF7stks6Vm5V3193dNAIfsh/YWHCdBpU6UXAgr2uTVtww0oWLFmPHC4m5Pdt7x/FLuePa7dcj3P2h1Agf7hyAWiiGGvpw/IFXRuQAHTAoAQZe5quw0ozLPYW9fuS0AhT2bmALz9ttvihHuvPUdhHUMx960iCoBC6Wyjq976xtfFmWesrno098PXcUMVUXjd26pIRzY22jU7Lo8n9x24to73Db21DRTcd83VO2L7WWdEVj1ado4CoJDefHaaiKCIKfpgV2tTj/KaIVWPho7DItcBCtuOvPskT3Ne+t2sd6JGJcUeW3FIGdcECvoyd0Th1ltujQMOPGBwRKH9AZzkwkXCIahFHM2Ag1xOvPFEhl0fnkAhIwk779gZW/fbOjf1KE949Q7Obn3hDLftMq8CaEnAMm0SlpKjoBMyq4E3nQP5hJcNzg0RWy6er3TrVKDwa79ZUY8yomAsf/wJP7/+qkc5SDQrKwT5DSGOFYUop15XC263qUe33npbHHBAff7ivEBh1kaZ5/d9EYW5gcI8L51y7Sb1aH0DOZV6dNxJseuORazb9fXJ3RsFFKbLswXKo2b8nyUtFDsv8OIOFnUHNpLgS/juQaBgTPg4UFdEiXwS3jOWoO7pGqeR3AUVsrraJlBYbE+86EUvinPP3R5lMjPaRdeBa4u9YbG7Mpm5otKwD7ZsqWghzlFol0dd7A2L3TWhHu3cGVu2bq14/wzwPZqj0NF1QOG9f7ZjVVGSqjzqmWfsthwF1oLqscABzr0cWFHfvhhxu+rR3gIUsuqR/phPbZ48ncVW0vS75o4odD1unojCsj5iWcnMohoEP4evEt8UAdtcMgomXd9hHV3fkUChNHLzusERBTfwwstNkLQhgpAHASwweAkUbrvt9th//5WcCY967q/9ZtztbrsxopAaV1hG9QbgB+rqMB6m5Sh83w88JL75rfWkoi0wcM0tCRTakbRNoDB9TF/4whfGeedtj5u+2p2Neo/7Pji+892+MzMXn69Zd/ZSj26/I4641zDq0ax3LPL7Ow1QyI8nauzzRXhYBDLZ13VKUGtwd0dEwSsksaOgSvYW1NVIIMxB3UNrQEfqa5de8ub4kQet42TmRRbRjHv2pYjCqqpHqEdKQ24Mhq9GdVUysx80HvzOk5l3w9z1PbI8mdk1WQXy2quu3HPJzB2dmwCFhtbjkhIocEqKziyTejTvsGeOgjHTl3Q6r5d6NG8/2tdPzlEoKFE33XRjnHyfe2zYHgAUDnMIXEM9Ov2pPxeXXjrlZOauQdhIoFAmmOIXf/0b35zrHAXObhEMHiSeAmWAOfQXkUlLiSjkAGckcZGOFJNURhTGzWEs+WvUo0PuuhJR8POl5SjkS0RFRBPE8KGunu/pAgppnG84UHjASSs5I8137S1AQTWrXz7zBfGyP3r9JvVogISeFlG46zEnzu8BH/DOIZfc6YDCkI9ewjW7CyjoGtGlMpp8JsFRDVjwb2zBTaCwhAlsPSIjCkk9ogOqcxSO37aYUl5SF5N6VK2LLVsmVQLfeNGr4uxnKnewMa0ECpW+zHS3vYB6lBEFNpr28euuqU5mfvcHaka8/i6z6tG8M/DuD34sTn7AAyfl2JeVozBvP9rXV9SjbXevDs6bHLh200115a912oOL9i2BgmhVVfXoF392GFAoPawbCRTywzNPYd4cBRhJyCrtcty2RX3XCRS6vPdzRRQWnc2O+6ZWPXr+i+OQQ+qqRzmfSwcKA79l2oFrGw4UTs1yRSsfc/T9HhY3fH2R41QGDkjPZZvJzOsbv2lA4W7HnVydzLwRbRMoLDbquxMo6BEHkriAanjqpAMHrxiQgrEZUVhsPkug4AmjGMXNt9wcJ95r44HCUaoeNY3BtNfkKFz09lVnD1UHru0Fyczv+fCVq0DVVTuuiPPPeVaVo5BAYW+IKFhj41gBWRe96sJ4/vO6jspebE3Pe5fyqNu2HVnRtvYWoHDN5/4ujjh8W2UozwUUfHwaxBsJFPLQqZyMeYHCvJM47fqlRhSW1LFZ5VEPPmh11aMd13w8vv3tgfX2ltRHj0GB+tEfkUoeVeUZiZS5ST7851eEROKNaA/5kVOrhG+bNsOl/v1nH70yJKrv6cab9ah/KWXLONUleJ/x3PM3IwoDJ2Ja1aPD73nqhq2zTaAwcAJbl+1uoCCqgEWFcpSJzQhzsxx7m0BhsfkscxTSmLz1llvqHIVZg77YKwfdddn1qh4dPUlmTt20t1CPkstenZUUo7iqAgqP2rAxQz0CFCqwV53fNKqpR01EIcdvo4HCCSedsiqPQn83GihkjkKyZcytcxQ2kn7nHIVDD5NB3JRH7YsofPKK900GNA253GEf/+Rn9riCPfyww+K+91afYnW77fY74hOfUqNoz0sVm+FBDzipMuCc5FtWSPiLT30ubr99YGmbQaJr2EV3P3JbHH+cIo51OdX2gWsHH7z6ZOZhT909V5UVj7qqa+2et+6bTy3H6vRnb48LX/XGfYp69I6LLwklSfd0O+KwQ+MnT+MfXt2st7e9+32ByrUR7fjjjo4f/RGntqxuf/O3X4od16pksOcb+fWkf+148dXtqmuvj2c857y44kN1naQP//nl8Zif6Tt6bPf2e3cDhUV7vwkUFhu5VdSjxtPLYProRz5U1d3fNZZMXBue1b8bhVbxzCsPLM3v35KNd1VWwOz76sTkvvvc/6MP/xdxwAEHTpKF8+u++vdfib/8bJ2HpT+a67NlHfzKO9zYJPpf/j7f23VP/TV1y/vKZ6qOc9Ipp64Z7O9+59txzdVXTrzS9XisfGPZ1+ljuth9d7vbofGgBz+ksoGyv9XJzC3q0RUf+2h86xt7PjJvwH7skT8edzv0sFWee3390hf/Jj758Z765Ist67nuetSjH1c5AHPsKiflHXfEJe+7eCPM2qrvP/EYfaqLy1zynnfNph61PfhzjcCSLy6Npa6ymkt+3WKPs8tX5MJiz9hNd525/YK464H1ycxame+xm14512MB0v3227oG8c/1kCVcnGvL2qeX2udRLOEVCz0iy9FMoPUAACAASURBVLXuixGFhT5486a9YgTaQOHb3/lufPwTM45Q3k09P+SQgyvnTFe7fMe1GxL10xd90rd2u+Kqa6tI/Ea0h/7zf1ZFarNd/L4PxZN+4fT4yEc+Eo94xCM2oktr3tmmHrkgwYB/rzK8cfILoFD9uwEK5enJXf+e977saPu+7E8mxLqu9O6XDq+uf+f9mffg/vx3Xx8BIopoMhb1INUR+MbYqH5XXdYYH1PGpbqioLnkOM8az2n35bdWuhNwclhuE1EoqUeTcW1AYc53e57bP1/GfQk02+OclaN631mO/YB/l2tn1nflOpqslQrc1W3Wmi4305Bry2tm9rFYI4OAguvLevKrFtUeEjXluQltgLA3eKJzfDa6L7PG5qztF8RBqEfnrOQo5BRKfE5Bk8lIoiTZ6p+NYuvWxuNQreZaUEnCYVT7/7X3RPWzLgFbvrO9lMrrS2FaKpKkB9lZSuXm2uQlTgO/FOJ575rvG0fs3LXyfdOWtXvJZIAmldR631d7tWuEWeuEteNZgrpNoLCHBM/ma6oRaAOFzWHZN0dgbwYKk6pHsRI5KI2oSnYXxuVMQ2eVYb3i3Z91X74zdWlpRE4M80ZGp44qjbo1xn5xWNs0UDPtvjW6M0FAcu0L8DTr+1YZlB08jEEGZ/u+hrlQabBm3PX5+muvrsuj/slllc5kX8wNnlqHo5X251TQ1bqvhk8N2GrmL+e2ilY1ZWbb9m32d5rBP2TuBoHXQqz0vbcChjPGcei7pn1bFZFq7LsqR8HJzPNWPRosJhfxrrfvKf5/FWptnTpcgQmG7bze/CnvG/ydPRd2Rj0aA3sVJ2jIi5bQz7O2vyQOOvCAVSczD3n1Hrkmv2+RNbMbOriiKJRSq/MVShC0G17Z+cgSJKcQe8Zzzo+Xv+oN+xT1aE+N1+Z7lj8Cm0Bh+WO6EU/cq4HCV79buUpKQzKBwVAvfRpIU4314h1t4899E6Oxtv5XVcgpaTUr1zWJseW97ahH8868vwQjq97ZcV+5Tsp3dtk5bWDV9X19hnCXAT50PEtDO8GUT7kW9ag5R6EdDam+q+RG578TUVTlnAru9Kx/z7hvAuqK57QBQh1RmfHO0n6bdW35jT33jROgFGAl53HVGO2mcembh3LtvP/iKUChzFHwsDJPYU97zsv3pQHXLv25EYJ3zTs32MidRcc6+zxA4cA4T0ShVTrVt5TJu2UUqYyYeAeDOT31WY/Y3zbakPtKKk/XWlr2+3zbkH7OWkOl0X4HmlSTgL2MMenqY19/NiMKs2Zq8/fLHIFNoLDM0dy4Z+3NQEF51NQFSYGZ2+m3pKFte1r7DPuJHmvoIn2ApgkWd3rTpxnu+TldkYuJx7flDc97JhSkJY1J32NqYFCDqvTKZ3+riEKRo1B+TwnmOmlYDVNhmqe/976OfJZyPMrxTL07K6KQ8zSIMtYCvOX6yfetAnG76lySBI35rq779kQ/vbeM1gyuejQvMJjlAZi66Gag6va9E+DQ8Bd7N2vHS5fRz/Zjy2dmfkfljd6yEu2Yp48pZIYIlPZGbM8boHCgiMI5dY5C+5m7WaZMHr835b0M+eZ51/+QZw69pmusNoHC0NHbvG4ZI7AJFJYxihv/jL0ZKKAelfqoNDp7Ofzpqa8Mw5rZPY8hl0nP5X2TdxUHh6XnuR1RSIOqNPCWOculMdk2Hv1/l+1Tvr809krH3ixjc9H7Sg+9f1991RVx3tnPqs5RKOkw02ymVbq2y/HawTBZY8e07lsVGWl+1853nQqsisjBfLbbSnSifV/XWuqjfQ211ZZhz5bgJIHw1IjCp658/yoE3GVUtpH3ZEKaTdaVfLwKSTWbe9p95aKah/5RbiLP6ORtLdrPBsyU39fO4+jy7neNR3vDd/V1nvvaY1QunnPOf2kcsP/+FfVoTf8GbMBOg3me+1obeJoBPktgDNoUHYJm5kZvl4oqFmDJaZwlnNq/733vjD527bt9serRMhXo5rP27AhsAoU9O9676217NVBoqEdpsJWGbdsD3DU+qd8ncnZodJ8tVxx01WU3lD9LQypBQhkdzz63dXi7v/mNZZ/La0rDtn1Nvm+V7dVENPJ3JRBIR+M0Q71zvTXjN+2+NhVqcj5BQ9nKqkdVMnPTx3J+91SRkAmgYwc2lPVcU23DeBYrY1l7swTC7fVQAoPysL9lvXvWc9ogfTpQuOL98/P9Z/Vgyb9vG5pZZ37Jr1n4cWm0t0HEwg+c48auBV8BhQP2XxVRKB/ZHs9SGLVDd6Uw6zP4l+GJ7+rD0Pe17+37nmljUP1uDmO+753l5l/l4eip7NFeM+Xa3owozLERNi9d9whsAoV1D+Fe8YC9Gyj804R+UQ5W2xPelSfQjiJM7m+SfkvjPo34VcZ04Uic5fkuDbc0QPOZ5d+duqblhCqvmfSxoQQnNbjU4xODt8mHWPOOljGev581hiUQq2lftSe89z7sjeK06nIcyvm59podsf2sZ8a7Lrl0VdnUac66NYCv0L+L3lfSnKpxzkTd4hvLa6oIUaP227ZGV/+67JFp9+U6KSMwq8BBUxa4LJe6Zr329K8arq7qYC0J1Nfnqm8t2tfgqkdtI2eW1BtqyM16TvujV10/wGOwDEO179unGZ2MPDz+0rjuAwvL7OOsiMs5L/itOOAud1mdzNwzjsucwxzDadWhhizuIetl6DWDxr1HsA99x6x9M6sP7d/n/28ChXlnYPP69YzAJlBYz+jtPffuzUBBjkI2epMuS+576YXuraDXlApVvS7z5UoDuGsW0mgsq8iUer3sT1sWtw34tFXaRqDSpjubvD6/y/tSV7eN9Pz/NgOij2I9ub/RVWUCeAlc8t9rvi8jB0VFqVnGcRrRpX2WRm8ZJWgfuFYavuV8lGNZHjpWRk+65qLrvrYdkffltV1OumodFJGGEgy0PfrtecgxyPeWz+9j1ORYubek1XXe28NwyGe331FGJ9rfXq6/IfflN73v4nfG6bOqHu0NXvpZBtVGiuJZfZv1+2X3PTeaedtvv/1qsNIIg199wW/F/m2gMKMDt912W+y/f33wRtmWBXymjc+0tdf3u76fd4KoKTSjacOSfe4bm+53TT9foyzZOiv8efqvnBsXvvpNm1WPlr15Np/XOQKbQOHOsTD2XqBwbnzhH26sBrltOO/auTO2NGW61/xuV3PgWVEBscugT4OsyzgtDfO2YeV3bYdgXp/FMcpKeF26rOv57Z/l/0/uL6hE5fvphRIotY3nyf93lNAsDdqSolQZ70Vhkza9tryvPY5935bGagkUsm9toFJGIPL7ygh+BRibCEr5DP+eBiBLJ20JbPK+1NFlAZe28d2moU36VyykNf1rolhd/WuPQb3gV47Ym4Ct5hyKcj2V/W2Ph8dMgFCRW5M2RRsEde2DcrxKEOvnM3MU8oFpEE01eufgn08MoZahlkbetPeURlhpFDvZLu+79dZb44ADDpiMRxcK9Mvbb7s97rL/yn2zvrdtAN52662xf/GePsOxnJi24Mnf9X37NKOx/btZoKQPKCyDGjXr3b6z7z05buYNKCkXrfvaC7ettvvePaRP057VdX/fHC/yriHmR9dzNyMKQ0Zu85pljcAmUFjWSG7sc/ZeoLA9Juco5IFhxVDt2rkrtmxde/rxKr26qz6VOQ3pUjeWHuK8Zyg/vm3Y5v0Z9fC+NDbTOEzKTlI40pDzrHaJ7bzX7/qoJm0veBqYq+rpt84paNsZpUHcNm4Z0pLBs39dACC/od2X0lhve7PLA9eyPyWFpnxPm/pT2dBFDmtpZJdGeLkGyrFs2xD5/122RAUIm3Okcg5yPsr+dkUj1vSlRf8q+93uUwmCy/kpv70EJ+V4rLILWnPfN3b580k/OtZMrl3X5lodVPVob4godInX3WWYbawoX/zt5QbIRd5+WhsotA33eQBDH81pCFjKfvWBoFlzOw089d3b+20DKGzzzkrfuyoAu/8Ba3J/uihZZUSofd8mUJh3RjavX88IbAKF9Yze3nPv3gwUVpVHra2vycBVhlzDuy696m0jtTTE0qhK46grtyH1ZPu+0kjMyEHpla3+PR5XlKL8eQlGJgbzFAO8BBddAAMIaIOdtpG7Cgw0h4lJzJ4AlSKXoTRoJzqypxBJ29DsG0tAKE87LgFU/vuaq3fE9jPPiHd94NLqR20wUIKjtsHdti+6DOlcF23w1dXfciyr72+tsXb0IPtTPqvL657fmuCrXCd5b7mG2qA1/z9P3y7nrs+OK+2nyf1NVK0aN/8u9k+7bwl+yuf3AUs/l6Nw+lN/rvvAtU9c/t7JJhgi6toTWd7Ta/jNMNK6jM5Zp0RPNTKXaBR2RQC82zig/PS1LiS45to58wamzU/5vs4chdb5Cfms9ji2F9KQNdG+ppy7rnnqAxllOHRovxbpb9+zy+/QR9GrtnLp9PosMkite7rGZBMoLGFgNx8xeAQ2gcLgodqrL9x7gcJq6lElW1uHWdXFT+uWv6/UZOtQtC653NZppRFayvzSKCzvkfyaBBHPbxvirm17nv3MYVppsLX7md9RGai+oSj1Wp0g3NTV77Kj0kBO8LMKPLUqOVbX7hpXEZkub3qfAT6JbhRUlhyfNpDKn6/ymI8duLZjco5C+/tz3EvDOQ1qP8s/k2+1JpqxLw1c17VBxbR+lmAl5y2jUNmX/HmZ6zL55p07q7malgfTtiO65n7V/Df0L3OeP8/3TaIkDSgFTvMbasAqGlT/rA1kPKsrclbvn5X7yrlon/mRQOFpv7iOk5mrF9RftlcIyHb0Yypo2EM97vO8t4XXeroz9DtVPXKOwnlnn1F5aIS+tnaAmnxeGwzlz3fe4b6ta7o8tB954zwRjPJlbSpZX39T2cyag/aHDP0O1/mGkvbW+64pILUrvDlRiB0L4xnPOS9e9srNk5nXs2c27x0+AptAYfhY7c1X7r1AYYV6VBpvlbHd0E9WycOkdzSDnSCiNPRK46w0kEsDLY2rNcChsWnoR9V9VjmBCk559q3k/K8ylls8+jKq0QYAbdpRGnklKCmNyAQs5X2lgVz2uTSmS6O+yk2ogNau6juTflOOY1sPtY3w8nltr7yTmbefdUZc/IGP1Z9bRElSx3axA/r0YfWMQo+2DeM8q6rU39Wzmio+uZ6mHdZbfXszLvXr6jFq2x/5s3Z1pHLs+gBDMxQrdnOzpkoQmknOOf7ttV0DzC0rc1ecI9Ze42v6UUWfmjmfQu/K+y7543fF02YlM7cFXx+XfpWBNMUoype3O79q4TTocYgBN8uo66u0016off/fJfjbC7GN2qb1qStfYtaYDFE+fV6S8t7yHIX8+a/9+m/H5z7/12vOzBjyzvVcc5973TNe8htnTR5RCoz//MvnxLe+vVIFYz3vmffeC379rLjvve9Zy6SizNgvPeOc+M53vrMiNNoZUvO+aOD1++9/l3jdhb9TXZ1reTOiMHDwNi9byghsAoWlDOOGP2TvBQrnxue/Wh+4lkZOCRDyZ23vaWm0tu9LKkclw7OUZJO0W54k7L4uA35iaLfKUJb9Kmkm1fuLJOKub3FvGxCkQdimRpW6pwsslTZDubDaHvq2Hiv/f/KOBniVY9gFurL/kwTcJupT/bxIwM3+VOcotKhH+c7Mi5i8ZxyTPJQuR2DZ77Yx3p6HVWCoGPOucZysrSIqlOsDYGh7+fPb+gBaHuK3Zm0UBwnnt5RgoFqHhd1bjkFGhBI0ZoRLXkkf+CyBZBvAVfuhKXHbtYfa62lqjsInr3jfGiSVD3jsz/xifP0b31ol+NoodBGpmPiiGrCG35fPfeTDHxK//aJfXfPYG77xrdCfKnxXeB8akVMFO9oLpH2dxbB1tCV2Qlmtc+P7vAnEwl322xqX/clbqle1veOPf/J/iq/dcEMt+PKZTV/yffVpksuxOLOfT/7px8VZz/qv9fi1qkKcff5L46AmopAD+YjH/vu4fMd1i0zXuu550ANOjCv/9O2TkG05zt/3Aw+Jb37r2+t6/qI3X/mht8UDH3DiZO0nADvm/g+Lr93wjUUfu/B9Bx10YHzny9evWl/74oFrL/uj18dtt9++8DgseuO2Iw6Ln3/Kv1lzu73xf15xUcUz3oj2g/e9dzz2Jx+55tWf/Mzn4k8/cvlGdKnitp7+n/7vNe/eBAobMh1Lf+neCxTqiEKZSHnF5R+t9Galq1NFpmO3OfC21p41TWeVnq3+b4WsVF43ORRsMrordkOlg3iQI+JBD/7RqiBKSXt1yz/+w1fjC1/43Mp7Sw9388yVH9UdTzWfdsAaEIBVU9w7OSStMujS8Vz37dC7HRYnnXLqxKbJZ/3Td78bn/iLWo/nd3hOsrhyDEtWVzVC5TWtFVc+Z5VZVDqCm/sPuuvBceqpD57MhX6hHk1OZm5A1Mte/jvxpS/+TYy34L60ra2I8dZxjHYm9ar+/sD8Kq4djyoXfGlZ1TPe3Ft+RklZe8bpZ8bRRx9TD29jL+rn1Tsuj3f98RubCkTN3aNxjMb9TJmMNFTrU0rArpo+Vvcqqu+rutn8cGXd1ZNd/3xc3Xf2OS+Mgw85ZNWc7tx5R7zg1587sR1XfdvWcYx3Nn0rxrFeu/n8+t/5s3JcJ6utujd7vNJ3//rVX/3NOPDAg6ofDjpHob2oDfBxJ/xY/MM/1kbwnmpPfOyj4u2v+4Pqdbtw7rbUA/XVf/ha3PPEf7GnurHqPSgnN331E6s8z4ne7n3yI+Pv/v4f9ni/nvZLPx//8yXbW2c4yJvYGmef95I48IADqnMUcl43Gih0DdBGA4UHnXrSmm4dfb+Hxg1f/+Yen88ECuWL98WIwj3u++D4zndXvIZ7aiAfeMoJccWH3rHmdYD9Yfd8QNxxx865uoJwdwjHQETUBR0Xa0/56cfHa19eR4rK9orXvClOf/a5iz10nXeJXn337z6x5iltoHDdxz8VZ27/zXW+bbHbf+A+x8cf/M4LO29+8i+cviFrTGd+76UviPv/0H3X9Osp/+Hp8e0Nio6+6ZW/H4cfftikT3svUGhyFBoKxq233BIn3GdbbcS0fWmLsJyn5SauSn5ohmoUcdm1fxlHHX1s9YOajlLnG7zlDa+JM5/5X0sc0hiyRfnr9vvyHXWqw+oDPLv61nV/892nPfqx8YrXvL3ipZfRiKuuuCye8q9/oh6zVahjyj4rAVg5zuX9bZDmcR19vv8JJ8X7PrxjJddiy5Yoy6NOHJlPPC2uufKKxTb/eu4aRVz8wcvjxJMfUD2ljO5c9KoL4/nPO2M9T1/83lHE1Z/+Uhx+xLY6z6BJELcHTrz3kavX2eJvmfvOaz775TjiiCMroDETKOzcqW7vSlmyfNuGAIXHnBZvv+h/r/pgkw2wbDRQyE6VoGojgcLvvfS8NfWf9bECCgceGOefs7IpNgoo8Nrv+NO317kSRX1om2WjgUIZUci53eiIQrnwN4HCcLl36iknxJUdQEGk6JBjT4pdO0tLYfpzt0WEGMALIuILEfEzw7ux1oDch4HCh//88njMz/yHdXz94rc+4OQfjqs+/M6Jp6580jH3f2h8/Rt7Hszrw6WXvDl+5EG1EVK24058eHzta19f/IPXceeXPn1ZfN/dj5w8Ye8FCtvjC1+tT2bWbrn55jjxPkc23s4BA8CYrbzOqwIJA27sv+TS6/4yjj6mBgpaGrpveeNr4sxfriP2G9FO+1ePjT983dtWvMVNiOCqHZfHU574qA0zLO9/4knx3g/vqCMUTeWl66+9epLMnLbRk55wWly7Y36gcHREnBAR91B8JSLE9q+OiHl4Bxd/6PJJNGbC4xhHXPSaC+P5z90goOA7PvOl2Hbk3WubrV5tcfPNN+0VQMGEvn+eHIWSA78hQKGJKLT5a3tDRMHUZohwb4goAAply7k7a/tLKurRZkShX8SjHmVEoQR+R93vofH1vSSi8PTnnBcv38eSmTcqotAHFKqIwvGnxh23iw0Ma4+LCH70UyNCrPCoYbd1XrUvRxQ2gcLaKd0ECotthhe96EVx7rnbI09mJnOdT3TCvbYNN3r/r4h4orBORFy2WD/adyVQKCmx+lZFFM7YC4BCAxCSp371lR+LJ28wUHjPn165EuUYbVmpenTJZRV40J70+B+PaxYACsijz42IhzfR3Osj4tkRgaQJOAxx9wAKIgplrgXL/HWvfPnGRRQaoJARhbQ5brnl5jjxXhscUdhWOxkG5SiUSaYZYdgQoLDEiMIPRMQREXFgs8g4IjgkPhkR35lDziT1KG/ZmyIKXZ9RAYWDDojzz3nm5Nd7Q0SBMEYBEb3aGyIKDzj5hKovKG4amtveElGwxv7b814QOP833PC1OPLIu8+xYnffpS984QvjvPO2x01f/XTnS+YCCneJiB/lph2oAaZ8Vi9Q2LkzDj325CDThrZ/FhG/EhGIjpTU24fe2HHdJlBYbPA2IwrDx23fiSislEf1dTffNKc39ZyI2B4R6l/8WUT8+ya6MHyo1lx52fUiCsdNzjLIXIU3v+E1cdYGA4VXXFRLnrI60DU7Lt9YoHDCSfHeP9sxYTLQ4+0D1+guUY9FgALSOSx4TDNTt0XEZyPiORFx1cDIQhlR8JgEDK971cs3PKJwRGOU50KsomobTT3admQ1RjOpR337bEOAwmMfFW977f+qDMnypMZFIgpnNnYIvCS98paIYJuowfPxOWyTrhyFHLONph51zd2Z2y+Iu6Ie/epAoJAVUOejcQ8Sz5nMXIKrvHGjqUdLyVGAPEvWHlT6ksYd8oqI+FREkHYzWuYolON0p6Ue/duI+OGI+P6I+LmI+LVG4V/UbNJZg9Xx++nUo5MrWTKkYXpzXP5WM4Xvi4h/N+TGnmvWCxSAll+OiP8UEQ+LiM9HBKIEksvfRYRz6efNCBmao7BbIgocjngF/zh9UDeBwvBFt+8AhdXJzLfeest83tTHRoQcfDLjSxHx2xHx8sVlhhEWUTjq6GMmlKOsYvPm1796wyMKgELqgwQLewNQeN9HrloFrJJ69K5LLp1U53nyE05bCCggnT+hAApceCS3PLE/joj/ZwA2BBROOOmUSdSjygkYjfaKiEIChQq8jLbEzbfcNN8eGC4WBl15zef+Lg4/nDt9QDJz6eUtn94HFBwzxr/5fY2S+kpzE0N8va1MZs5nLZKjQIG+NSKkq/5tAxAoWEr11U30Un+HhLLaEQX9ykTrjQQKZTLz7ber818fAHfmuRfEXQ+aAyi8sUFSik19cb0zuPr+BAr502rcGAt7QY4C43JSJ7mpwDV3ROH8iDg9IhDbSbNrGoK7hXVTRDyjkXAzKNVlMnMqh33xHIVBEYWfj4ifjIh/3hBS/zwiFBUDqnYsYPmiCfXkKKAeHX78qWF/DGkcCU+NiP8TEX/VGOnvGXLjkoHC3SLi+Ij4wYj4xYh4WUTsHxE/1Cwzy8uS+khjJ83D2N9QoGCAWQJ/UnxIx9htAoXhi25fAwpZjRBQOOH4OahHDA4RyAsi4v4RofgP4AA9L1ho7dLrPhdHV8nMownnnk54yxteG2ee8V+GT8KSr8wchdQFqac2nHrURBR8bnrqy2TmNICf9IT5qEdknVkQNDLF7fg5effliIAV/7JhiPQNeTuikGO3N1CPjpDM3JyuDPyJKJx0n7sPM0RnrTGOt5+KiI9GxN8MA9DXAgpHbFs8omDCJQ93VT2ScPK0iHhWQxXkEGT7sZGGGN7TvvcJjzkt3tFKZnb9vBEF6WaSEREkeAd5Cn88IqTmoRRYjPhvQ8yHLqCQ37CRQKHKUSgqEyR9rAQKM6sesUhYQiwQHpoPNAabAXJ+ypABmjKhbaBQXjp3RIFigAA/1BgaspwY5wvkEJY5CiUgPfaHHz68PKqNqTALg/fDEXFcRPxYRFwYEdCzhYdWY3M8LyKu7R+oTqDw3PPvfNQjYJTwsO6UFcqWc/jfG28hDjKEP7BNjSgcc9KEXjbrcfITyAisBvLswbNumPH7RSMK/yoiXtw43w0Tp4bA1OHNlqzqkzdggfODtw11e4ajvurthgEFkTeCWHa4xI+X9kfb9ghQyMTYOeZ4GTkKArjpKZ3j1b2X7jtAoT5HIUs73nTjjfMbSeS/TfnaiLhPRHBB/3pE/P1iI5nUI8mHpe3y5te/Js5S9WiDGqCQ1KM0ylVkuvbqKxenHlnvmd07LMC65uurqkcfQQKqgYLa/9dfVyczX/wn9YFr7JAh1CM+A8HF/xYRj2rUJ/YHd+fa0jq1KfI/mumeVokugULdyeYAuJ074/WvfcUGU4++HNu2basrRjlIbevWbvod3Whgakd/xM0NGJ7FTDB4HJdCz+wPulQ+uft7GqBw2OFHVA7TqTkKn7j8vZ3HP3tuX0TBMVVC4c9vFBjlxPsG7S0I7CefkRGFshLTPBEFjuH9x7Uc4WXD8ntv83QLUL//v4bqaBynjOGkT11AIQ3wjQQKv3vBuZ1z97xzXxyHHHzX6mTmbL05CiwPA/TAiPhM80eplxOhs8ZlefFiXl7vzqpH/p1Rjxy7qUDhro3XyKK3YSgFIWeCDufCH5mmJhB6TuN8oADsAgr6OFdEAZEdjYbR87qI4Bm3wSXBkGSnNWgaQKDMjGtP+54oj/rkiPjPjVbIcSA1oXmhP3NrLQIN74qIzzXjSLjMaFOBwnEnx647hi0MMowH//GNLfL0WS+e8ftFgILgFFsaUFgpernyonZVRCAChv7DJlo660t3O1BgCXfRGCEeHi/2F63v3z0CeCGg4L3CLlkVp0uxskDyOv2Zs7L1MoAC/SnYuIB/o3O17TtAYXUy88LeVGCB7H1RQ1z/hUZWLOCl/Oi1n61yFMrI8t6SzAwoKFCS1BmTj/f/5CeeNp9HlsAwZgxJa/72UcSXxgsZa4BC5ihkZOjj111TAYV5qUeipY9p5O29GllXcyK6G5GCCkp8jC24bQAAIABJREFUsDn7mNIXf+hjceLJ9RkUk4TmiLjo1d1Vj+xHflGiQ2NaUDnYbctsOz71t3Hk3RkIddO3NfQ7cwQI82pnooaST69qHI3T1rhBdN3Jjd5E5aVHmwOzu77l2s99JQ4/4oiqL6oenT7vycwe2gcUfqmZXJnp1iB5zPtG+P3bUcSNeU7GAqNcUo/KCkxDIwrGiiOXZ1Ck4y8aJZpHaFkM7E7hK1RyIf1ZXrh5IwoJ2ukjc8aOpbtuGUU8cFyH1STo/PU6IjDOUciqR+3j0Z/3/BfHwXc9aFiOgk5amITtrU0tMgtUEoeQkUnmwRfe/Z/NLppjXkug0L5tKlCg0MUiTaBJI+hYTiSJkBApc2iT2OZ3nBwMD4twQFsKUMBRQZq0eRHJUWfKjSwT9uymjMObpocCEyiUc3n6r5wbF776TXeeZGZzCYCmJWusCA/CzVhyMVW0tAZ4Xdl4Rt42e0L7gMLtd9wRRxz/wHDS/JBmudsKmFFYY5cMuWnKNYsABdtR9Q8JfKm88hWGhoLMYUrPG7vb8D50QBBwtwGFu0SM7jaKMSEHNLeHnJHypEZAG2jAuQfVLAQUnlKjpRE+1rcixmvPlKsFMiToWmeJslQ0CmKWx25J5VHf2QQaOcPnKarRt8z2NaCQjqKZ1KPM/8rwSylbyQrOGH8zSN68mDML9eiYY5mKK40MfusbX7d7qEd950O0DMCMKKQxnkCmOkfhp35iPqDA28Dhxtv8oMYByMHFaOtrZT+LvmVEoezXGurRli2Dqh6JIlCRbLUhTTeYJXLIGPLMla6WVY/Kw4GrHIWOZGZHjT2k8V9JL/QOfj0EAX93iYQ8IqNts09Ksfb0C1C4+93vseoguDVVj9g1ZOR/jAjVeLyEbMLJ4ozMRm6Sr2UnJLQBBig/mqg8Wrk8Htd2yFo5Coceelhs3bp1ekThU1e+vwofZVmr8hv7gIL9S5EC9PeLiIOL/jLSlRacp+5t+c715igwwpXXYreJJryyYam05874OXLIGHKcT2slUGiXbW1HFDiX0Z5EM7B5jJUIBlu23HvsbrRsQMU6GBLZKPsIKPRGFJ7/4jjkkIERBQ9leFOqIgq4w3YPNyVKjQWq45Qqy8lOnaOzbaBQGsK9QMGgWey4zDzxKCg8fx9scigIPgsP1Yf7lece/LfjB2peQOGUk364OqCubN//gw+Jb3xzwOo10ZAoQ0MEgbIqieLCh6+JiJ9ouCzvnh5uu7NHFEYHjmL8kXFtqFHumrlCd2Plyo2hyJBV8/evjwhnkn1tNgCcFlE4+JgTYzzLzd50SW6CKWVrovJImVhPWwQowFKwuqH4/WI4bAteaAE+y5/OpywtNX9L/bBFZh0Qt3SgQAHcL2L0rIixkIxQM1oeAJhuOaA/+VRcgvYMbb9soECwq4rD46yYQLtZf5QwWUbJutYe/UrE6BujGM84b2O9EYU3RMQjmmGBfzmr1tv2NaDgexlujKTeHAVynRJnSVongB2lmY3YVh0cmrbOfmMx+hHq0fcfVbtuq2OwUBJGEW+66FVx1jNxJJfY7t149Mk3kXzeYx5Lzi1/Fw1QePlr3lIZcNkY53IU5gYKB40ifnxcU/0cUmDP0Ud9B8S4flt9EnHVyOlGJZbUo+xXBRTOPCMu/uDHJqduD6Ee8bGRb8THPI3KYNfxy3W1VdSj5gJO5zd0UI/sR7ZssnzyeY4ZVlir6x1+B0RgPpd5ucwRZkqfS+qqz3wp5ChIYs4Tn6tzFMryqOwtZZ94fMqGX2oeknPKyULOssfIME0HeB/Yc5RC5Z1uHDIovx3JdiIKdzv00IqZgnp0+lN/Li699NJ4+MN5ciJGT37Sk8a4ZYBCuyXin1b1iA35LyMCFscxy8b5jOOLMrhAJDC6gIJnD40oAApsC7pIMrMFhXbfbmwQdh1QMyvENE9EATpmYPD6QahanSq10uw5SBWNTCgNep3Xc1lGFNrfdua5L467HnRQXR61efHU8qgsDCj2/REjJ5xblJLFhFoIYadPoQIp/6KzGTNPg04HenYHoHDFB9+6iiI1k3pk4LhVgQRRA8hYQon20YjRX0eMCV2WVNawBGT0c4BR6THrjijw0KA7oUTxZrXPpeINYG1KwCOQkcinGKt3aqBA4fxIk/TNSjJm6ZYBEgg1fytHxoBLj4iNKTHrfzWFtDv2cf5oGlC423GnxM6BJzNzdHA4w6brzU/Qt0WAgvuIaTXLyFjB6iztjF7EJkKJsh2xsyg6DYMLXkVDmpZatHSg8OCI0b+LGOtwenrNubkU5qDUKDOVriCv322E9JQqawtHFGh+awt/QP4QT0w2DpCfbvYtGUOJuv7RtXNk9JhRjK8fTx289QIFQ8K2pdPNHf1EnP7RlLU961f7ElD4wj/UNbp27toZt992Wz9QYERjzvL6mStca+DSxrS4yQU/4+WjIxgkFv6crap6dMwxtfHmkKRq+YzjrVUy8wI5CgAO3QAoa/Tvv24cWnQCqzgjp37vW6B8TjoNKLog4rRjHxt/+Nq3TaoeVY8ajSqgsNA5CpxrhAbjyPv/JGL0b7fE+JYOpYSfDYT5Fo1HuDkkPYGC+du6ZWs1jx+/tqEefeDS2OJ063HEkKpHVCaSQpmqNmT6GOhS/hjyXQ1Q+OGTTp7MaVay6oooWI3EwjyN/cYHiLpeptHxHTI/+sQaoHCkUqS5NEbNgWslUGDr4EHNarxBOgFplZXKCRcGOdsoWzrk/t+1Dx2co1BFFJwU11R9KR81DSgwyA0MBw1jO/sFUbGL7NlFqiCt5Ciot1+jaWjwazd8Y9DJzPrFvqQwTSSwYGGVzade3tCj6BG6YhpjZWiOgjxWi5/DqmzYJxkm4y1k07LDgUb6TBGHt4zqw9yGtmkRhbnLowIKvH2MWXHxxuivznphkAtnCYmRm1yWOk1442jgeglh4pZ3RBoeeMqJsePD3ZXoeyMKBhLtiZUknKbcQVcjYQg/VYcsQOPHo6ifM2pG9gGFo35o9qmv1bhAmgwMYAoX0KLLRjkQtgwioSR9FDac0qeuZObTn709LnzVG+8c1KM8VdVi5ylkSDIeCTqRBPrKWMkiJtA4+cwn3p7x60L7xZAvo+oRTGfKyGnLmWyAT3UddlnE+bEoUMCwY0xyvMDo+mY45MZzlusLWpJlz+/J4er3tu+fNkuSnOlqSwUKBC5DgleewLdvKStRPlqUQkvjxLwzoEQKzeuUthBQAEKtHc4DMsw7GG00u/VlIiWf/E7TDyFlFgcjSI4MOUb58t70JBCsByjwWpobc4cpqUu8qsRp0i/SOThUD7huXwQK+i1HoffANfKB151yhtgZ2JRoImC6xnqjn5gJBpK3bZ6ck1HEpdd+Lo5qTmbeEqOJETc4mRmKxxPEKafcgRZyzAa2IX0HryA9oZ+ZUMwtDSlyhlgIrvE76/Z9Eacd/9h4xevePjlDIWk0C5VH3Tqq+/WkcQ3eUR4Yj4SFv9uW7SnNvklPJ92mMgy7pUlm7qIevesDl9aJ6jEMKJgyTtUSKBAXukXm2ROGst2GAIWTTqlzFLQcu4tedeGaA9e8z2daNqJ7wAeR8bNNXZK+fWj5cTTzgXAszRBnley56lNfjCOPbHIURjVYdujgqoiCfALMCGub8cqDIBJEAZC1hH56nQkL3maUFJTLN22J8XN21XmAmd+QH0BxcKS22tWf/VJs21bXmJqazLxoRMGDTaIcWHoAzYYytZ8ZxuIUwDEDfGDUv+psGVEoAcw8EQVrmg3L4ctubB8kvt8o4p3j+uRVzmD7YFp5waERBeDEAgOgfHfa/RwKaUMzQixO70bLFpHhMBUJFEUa2sqIQhvonbX9gmB4iijMrHrkhQw1wgO8FzEom0VpcTKKWVGsD9YL/gOBQvBYyAznDrTVFVHIx3cCBYJfmMyg8RqlBdQ3MAQyDzXLjtVkQhmWM9hDJVAo6VCDkplxPkQL0hXxjmbBlUAJN0SfbFieVmBiAFDI+fL3ne7ANQLCWJgfY5fUD8ZjGgCkNgEJSYs08ITxsgiz8rplslFrPUynHp1U0StntXSCy7m2lLEeOEJgaLaAvUt+kxV9/Nj2OxYFCp7D8ScKTaaKJOgfeQ8HJ1PB1qR6LDf2rf5xVFh6fUbn0oCCwUFlAIoZ57xDPxkxAhLuGzEGGuxNwo2yYMgDD4zyGW0hoEDDkx0mikeaAUQZkE2cDYwxzgchGREu1gE5Zi9TzjhnqJc4oT0n/64HKFja5o8DkJjCtrPGBFoMnS7SRbNX6urB29eAQsq4mx02NetkZsaNuQQuedb61g5vJRkL/M3RLrv+83F0AxRKw3LmOQqUvE1nTQPJ0B8k6A9jiMEvTwdHBeg02aUhZKPSUXRCm9z+jxGnPawGClrZr96IAmtbX6x5e40DL1lL/p+MFaYU1TCe+gK82BPtBedZvoMA0T8eV+WGOoCCn113zVV1MnMDFBjmXSczY1bAfoaKGO+iHmHzUqc884ZVAJDvqGzULFMFBakLzyf1aFfDN03w0pXMnBEFw8GXB5fan4aKmiJSNIErflTmz30b08dQmmZ7Vwoi0GDK+xw0V32mBgp5JoZxWkM9yoiCeeGg5fiAQgh/jhYdInd5kTJ3RwcpJEpCwReRoLJsFGXGGOZhajVAQXlUz/rAe94dT+tLZv7kFe/rjCZ43pAD1+hy+4LeJ5PJYX0EjIAhIH8Wtafs+xMf86h4+0V/sCrKMU9EgVJFk6Xc2RhsDnK/3TgAcEQ5NNENplHb1wAFSLWJwJQ5CqKfbCEL7SujFdvn1h7JDzE7iZCiBxJ+bIvSYsOkHKBQnqNQ3lUBheLANYLmkY/72bh8R09s1oRBoSwMgyJpuN0IF9EEi1QjkBhujAI7o6ffXcnMatvvt99+0RtRsBlsCpJCdKHkprb6VU3DvSLGFDzhaMfOARQSJOTfR9/vYXHD13us0fLdNL1sLGiYhGkEaXWJPjFOJIwxgIEeYzel3ampR/ndNAXD0abLKkfWDUONkZaeLQCU98EmprQoLInN8mh44jraNKBw6HGnVCeCD2kS5UyXrl7esB04yHWXTBO8UohgaLR0PUDBMrL8DRdnJb1OMYpa5k5OHC/gxzlOT+DVUq4cll1taUCBd14HGShAOnQCCKJ5HxAxprjIh1RakA9Xup/NaAsBBbKJHGNUJr3t8ojRM0cx/otxbRTxbJEPZJ3/54ww6RAZq11UgdLg1uxo6wEKnILEgnXl8VhZ6GOcWnQHcWpuzdtANVD1cN8BCnV51KxEs8ZImrYmyALOKoCUEcsYWikgUw+sA5IgsTnCMlV51KOPC0YlikpVJHUcs89R4Okjs1iyJpYxTc/z3DOCOGlNqCRI+oulOQcCPO3RDfUoxpPciSpHYcflVenRNc/ikGKAsVwZY6zYBAp58BUn3LSWkQ2WOJ1ugbLqLdaGoZ5Vj8rHtIGC3yX1KANDpkvkk2jHICO/dEtwLyMKhoffEXb3WqDCdoX9S5HhOnYTUA1QtFt14NrJp6wat74D10rqEYeyapnEFjuSOZH+QMPKnjUUfB2WI0cvkE+zkM18rJlS2TXMqEfbth05iXDo00033Rgn3bs4R0GOArnK2SFaQOmQqUwS60ynzC2B4ZiPspYsx4d1l/OexWl8FABiPlvt6s8q2cq4WyCikM8aAhRcS1nZH8JIPOr6CSxzJPlZXXV3WFtvRCHfYhyNGTuOYi8b43L7uFb6ToQ3jkOoR2ngetZ6y6MaI7pJNMHYGaOf3CIcNWycpkYUzmuAghyFpk3NUWAF0VYmkgHcmrDR/g0FCdSHCi04UQRKlVabIgAXqnpkF+Kr8/oBDOgAfc3GMZAMdVKJS2AOoNB+7KCIgpt8OylBquCoFJtwtDVinMpKtAaQwC+Y0sqqR1kO7053MvN+EdXYkKaMOeOXa4cmgLApKJKXsATErAHGKHcra7jH3u+nHu2Mw+45HChQTph4GFJ5FAaMwgjXONDJXDbxkIPY1gMUvI9ni5eKWKArbE2OoRIE2LYUGxuZY5Xu8A3C6F1MjKUABRofx5TxZg/i1JAh2YQ6KDTGN/RibzJAeH3PbC6aUhRhIaDgsd7JO5WlIJWW834cstujLtpx73GM9ZciZWCyBkQuoSva3n7lMOmQxYsCBcYQFiVdg0JsbuhIW8DSt+4EZkSr6FAGkMjzkGNs9h2gUJ/MrDHMb7vl1n7qUZesBBAYS/ce1W7dM8c1QLW2yAfeNh58m4SumtUa6lGWR63AwqhGtTMjCjaYKKe1L1oAwJhQE2ZviH7QSz73JaOIT4z7yesd/QQULnztW6v+ZL/8fe0O5yh0lEfl2eBi79LFFheDg2AgwIyhflr3FlzKVN4PeovBRF9BtDwTRf5hST2q82rHcd3VO2L72c+Kiz9wWeVAZRslUNAtBASvtiVF1bwSUBAvIUKo+2xUqXzcTEnMHFipKmVLoNBVsTKrHrm+rHzURT3KFCVOZeIAWABMyHyNz5Q5od9UE1lMpbMrTTOw4I9hN9Wupzu6piEjCglGPX9N1SMODuvGQ72AjPRSdBXC3ED6g5tFN0pQoxC6Dp4AVMlbaMzcdsizsjzqJe95VzztF3+2O5l5vRGFnDyLhqwF6jPHlSxmE1iPQ9sTH3NavN2Ba0Z6VB/eAekPpR7Neg+QcOi4VvZCRWhSfdnz+ax2RKGk+ix6jgIQg+JgfOxNCl50aGibFlE4+7yXxIEHHFCVRx1EPcIBtfCEflJDGf4tEWM71S61aC1MO4ZQFqaBfmdUnXTg2hUfetskaqU/5lT+ycwcBeVOeTLsvIYKUOUHlLsQJwRH2gZjnPA48+6ILEwhDqIeMS4rL1KRo3PU/R4aX//6gHNuKQnfzoOVtYFNHoFsnCx6/TwnYvT6iPGM0lpdEYWnP+e8ePkr33DnyFEoFzbXDAOTwsrGVUMgMtxI/2MjRo+IGNsk5piVjMrCCOyIN089R+HYk2LXjGo2ZfeygidDjUEnHM6BqKEyWmIUTEdu2Jrtu16gwEnO2YLtZmnR3yIMaEYZaPNzHjsKV6Ef8pedxDEuet1mvC0FKPBmcX+nK1yHEgCUo5CVaxh5rGWhWyFfzb6hvDoU2MJAgeEoQ5icyqRAE4rYTNGKIuiLQSXzgND3RYxujBhz7wP8lCsHwBKBAuYHmW+YGCJSbkqRwAnMvhWh57xiiOjKAEm0D0UUaqCQ8naNN3Wo8nMdY4mxYW7LGsLkhLnOiFEaSDyBHU6GLI/KCE+KCh0jR2FqMjMvI2eZd9tgot8MbO/BbTbhjxjX68i+EFkYUH43hwBQqHIUWvSZ3hwFa5n7vav8qj2BfmTd4/ugGeunUCkhkjxK/TN+Fl+P829yjkIR6cjyqBd/4GO1zTbeFU95wqOqMx/gqPYxOF7jZ0ABFcAGyybaIDCSNEpyF80oKUB53VCgkGutrzyqYCggIDBkKn0+B5D9SdaTv8wPQSJLjuznkKGGDDlfluuyUfv8EkBFO+o8AQqNvWGcbr3lltU5CgaE3WP9kl9kkPCKcAzvVDkv0A3nGVABqOpsOf/kGF0LqfW0TGb266lAIXMU0HsYTWVS89CIQvaBZ9ygsTuBWFETdidbjwIbEvwvIwold3xRoGDcKE6LwB97yd41xiaarWvBTispODSZeaicI0PY3pwS/k1fsoWSEug59nZGkKwV/bVuyBtt1TkKO53ytwIpK6Bw4AF11aMm+jGVepTIrm304ztQqIwBu4R3kPtLBMLumebpb/pZRhR2tfrZCxTwhcUg7TibxEAQZrx83HIaY5Kws1Eoey03iRqXrKkpCLCdzJwCZXBEweKxoHlmyoVtGkhHLl9WJqkiVjkjUvQ9QT3KDSJ5FPC0ISnYbDxY3KqfjRjdJWLMO0yCs8hpF95CGxagaCmyqRGF40+JO24fIn3qjljenMvkGGchRZYpL5YgL7Cptbtm6f71AgXGJZqkrWCoRA44FeD0zGmyPdgAaI+MTVuVESpaKa1otwAFESEggFCCpLysq/H8Ev40sr0C5GVFGL8jeEuvZvOMhYFCui45DoB4jQyDsKCmDC8DLfreLjeHVfrBiDFXZ4dSWDSioBscvl5LdLaNJ6ILewTtTVKldafozwzGYvV5+05EoaYepRdw5jkKPUtq1Y+tp6zOU/6CbuAWRi0je62BDtrix67/fHz/0ces8jx7zFveMAMo4A2bIB5dooWQ4OQoQ3g2K2I7ipS1NqPARtn9pB5VKq3x0vNEX1sduNZBPZo1VhKaydLnj2ugwNjwDWTqUB5lk6PgwLUVT/04rrumPpn53R+4bFL288mPP20CFORTlUXITQd5ausxqMvaUhzoyAFsXHtEiorIQ+ZV+0yin7iRatRHPTrx5Aes2LLVqdvjuOjVf7jmZGbq2p4TCeAT9R7PpIpgQWaGZGVqR1oAc4RpwvY27cSMf2ehPt/Dxmf7tqO5V3/mS3H4tm2rKFHod6uoRwQE3ciZglVBnhkUzlCdbAM4A2s+6U1Rh7KiFjQFKLSr+RRrZeGqR+UhZ/MChXw/o5cwxFIhazl3Vduawb6obp9EFMqFP4746j9+bVDVo/Z+Ee6yl/0R0WFvWrjsXQuWY4zyt2/6GDTLjihYA1Azg4QniYPeWQsqkSa4B/Szti/7Xf8sUkBHv9dEFJoIjO8/+/yXxIH71xGFbFOpR31CJrO9xePQQghavHsdIyQzPjdFSLUjCuWlvUDBpEkUAwpMkN1ooEQI6gT9WrgxPAwepMUYBzBAf/SpAUABiEmBlwB5MFDo+2aeGygQqPInF9cMQf49BRTMJ++H+bKezDWNaA4pdBuBxKVgbVx8Db+3EYwpAdgCXlPLox57cjjpfZ6mi2SYbliC2eA+wRBdlUYx60iR9QIFiszS57wEXjhj5FSJaGRFJluffuDgp0DpDkqKbrEN2m1dEQXzYN5o8vR2AQK8+O1G8+Js8YYZSCiGQcUSYNz5vY7/VMToMxFj1zQCcGGgoA/JxfLerOPNAsD5YXVQSq7hzdJvaywtEQRpDpCuxLZ1HLhGFwICbEnzwn7EiiqbLrELAAoMEfNO7tsOutNHQ9p3gEJ9MjPPPcNtDe0iB8Oizzry/rZGGDxdPBNeViCUUZ6ncBk8m8KApfOLzDDoaA5FE1FAPdKq2vbNWQpvuujVcdYzp5RHtX7pR/LdphSNZ/gkZ8YD6QJRULQBfBUeyYHHcU+Awmg0oR5xZi10MrO+HDaKeMa4NsQsMguLIONtWAAo5BAas+sLoJD6NKlHRDiRzva13cszEwQXdQM9qWzAsW1K3Zta9nLZ3GOPkG1dEbekHlUUH6ycpvRt38nM+gQUcNxyApk29hfRhT7PSQssZH4FezZtWaaQ/88cCm8kn+3ZsmqpfgAKKgxlvzpzFFJ+WZLWq6IeeFrokAayz0iFqCAn8i5LRbHPhJYpi7KOazGYgIJkZmvrkj9+V38yc0YUSvpFevIXBQrCRPYEh7TvsncMnBDSrNaOKCRfe9GIAptTBIFdYi/n/kgnOkBNCZMffSc0J1AoIxzryVEAFBghmc9pjLLaW44PHZYxAr+3ViBfvGltWo7COee/NA44YP9JRMH1CwEFYV3uTHE4q54Hws8IFtZTz+Ir5zgjCl0leGeezAwUELAEMYWh0oRNQDpw5dKsBsZGEPOzm4XhxA4HAIUHnXrSJAyehw6uCyjom4UG9ZGKpAflNYBovAYoqGT3vPPjZX/0+jsf9ahakE20wBqyAVm33ODcNNw3eBc2AZI+DiaNYxxRkUjvVptOPTo5RLPmaekIZ3OkA9z99i5sA7QLdMya2vUCBXLK8kc3NRyWPBAAEKAW+SpLzNITtmeMkhuGUuTBUmy3dQEFrjNzJapjrsgGWrariazR8rxdjG8A2t71UZwP3IoUG4tYYijXXDNN6wIK2RcGEWucHCFECU+Dpk8GjAUgauVnOcnkCWuetu9o64ko0IPmUQBmSn2G6q2JX3SZLQy/9B3+t68BhTTIVT066d5HrjV+WG4cQDyhosXmEchk7KMblY37l5IHVv3BJ3aPOS2btSrCxALNwhNVjsJfxjHHNkCh8TwDC2+pzlEoXQQdi8EaIo8YN9aX/jLMhP0BAvsEZxBt1vegtqnKNMAwT+pRVjzKyki9ycyzhBtQQ84KS+qrXJyfHkXcNJ4Z7S4fXVKPMp9j5WTmSxvG+GiSo2Crk6VSzgTpBFZWH3G6tuMAgsCf4eVDalf6ZIoA26a4q/pceTJzmd/x+lf94ZryqPl26oUfAZXI+8h3NiyxZbj0BTbkYyidyqiCcirkFWmzgMIR1TkKNSD19xrqUXbIgzlj6T9OGfpwVllM4WM2UGaHU04Gy/0+pqMcUwIFr527PGr2dVGgACAYPLadRZFJIOhxsw7OfcJjTot3yFFotXmBAnDP8WWhiSAABNggbDdoFMXc4mB8K5gDrFG0Xa2MKLQN3nlzFNAXTx/XpbunnUhozoEHC4/iF4rGqElA2Y4olInWFVDYf/8qopARooWAAg6jwdLQRAg6f3SI0s9DYqYIKRGFK/909TkKM6se5fMoCFIGgiOzLSjuBxuGwZEWkcnlhrDAuHxZSZQGzdpTJQf1KCMKJQAcco5C5+fuHzG6Z8SYFcC1S1FwS886Jrd52PdURKEcQAKCpctQSw3COkpvCAPBZqDgCD6VOxIoFs9ZxjkKZbcw2uj/rIKYxCV7lnTigBZiniXP1gsUsk8YFJxLbNu008k1oEGuGgDBDuAVyzKq7KqSP5vPWjdQYGhxkcsF4lHtqwQBzaAoscMMJnmSLkCoByhQycpHXRMxun4U46/WEm4pQMGgmEihZJaKaCNjkSFnMAlh3F4eLHKGNWCNcTRAgx1tUaDAQDJPRBmc0i6w0X4VXe+PYBqxh7PNbmAYtZ3S+xpQyG/tpR4BjkAjg5qhRPf4N0M7f+XDAAAgAElEQVSC4UP3iCJrST1iCPEIMtbdLxqNwyVfLOVKAoXUaWx4JzOjHhXkbs7JmRGF/AgyXuSKg8p7yTJAkzFB93C08fQCCEAFIDOl+Ec+NpOZ06DMSMdVTmbuox4RTjjuZaiKoAISGER0KK8DIn5ydwb0pVybq89RqH3jGVFQHjW99+0D10whf4K1z+Cm2rvSKaaYEtWvLAMefFuYr6GLUFoCBfckWOg6cK18H7q3ofIH/uR/nFFlvaKvyzni29KMB/2gf2Vwye9EFACFZC+wJddQj8oOHTyKuOu4nq8hzFlrn/e5PAiXPGM8UlYdycKSmQ897LBqMqryqH3JzOs5R6FvUilX3ixg2uATeFAi41w0ZZrttCqiUHDahwIFsp4O4IRE07FPoE/2hsUqmIjKxYFloQIKFgbOMZDNtmzTCYaeozBrkfu9ohv/cVzrTJsFcOTwyvM1XAPAYM+IgDBEKH5/l0AmIwpdnvpzXvBbccBd7jKhHkn+feTj/n1/edQhHSeAGOl4ycjbrBJabEZbqOpRzzNH+0WMCWZwv4/vCSjwBiIJ2uldlIiOk5mz4u3CEQWTCJlCc1wSlJrFvx6g8Jzz42WvvJNGFMo5Vk7zHRFjoXCbNJ3/NiXPNcGRzUYR1RICLHJqpkYUjjm5SqCfp9G5DDSOBt7cLPEMBzL6OJ4lnc5qywIKEu7Y5mxeWIns8ocDE7MBb5atUjZyjxxuOzD3v8td4rtfWVtw/qprr49nPOe8uOJDdcmYD//55fGYn2kdOU5wGRgRMwYXTkyfsQHgSczzOIiFImuHYEQokofLVbcM6lE5CJQmgWrgGJiMOYrX34xOABXfwCDhEdivEBjjzsS32qJAwaNVYYV/jfy0Knv5SnLfnEpS103dlnaF8la2fQkoVMnMjUe1N5nZnLEqLXBAgefRRiQb8hhrRo/5YYyzPMldzqGkwwKiHEf4epnszHIjN9A4mgYoHHV0TX6pDLhGEcysepQPAHJFxfBUbFB7wfrh7KADhCRFTyE9NKS+w0Nbc1pSj/wqjd3ecxQYOkAvoydrePp/uoiBo48El0i8pFHAiod0zgYovOfPrpxUh2J/ZHlUOQrZuk5mhtlhFcQEW66rUM+07pDg8CMVzwToq6NSAYVTHrAil0zrrnG8/jVrcxTm/Pw1lwMKpr7UBfpJRUmJKlsChfpnDjnesvYchfV0CEjllGmHbBiPPOEdCmtwMvOyqh61vw8Dw5q135NuSOfPOsXu8Y8+Ld75+sUjCiYOiGcf0j+cCKKVBC07JA8QJXeyWj6nmL1MjylQ0EaCCRRuv/2OuMtdPHWlzRtRcKe5ZO8CNQA+G9ziesUoYsu4/v88LbCvJPRU6lEBFAZVPRqyOAltyVB5ABq0NeDUqQeeckLs+PA7qjr2+23dWocmG1fCVOrRkD51XZNAgedS3LCtVZt76ojCSVVfSrB19P0fFjfcMOAchfa7KSNeSZ4ai4lhhHIxIMzsUd+zEYUcR5vTfLGoUoPQBFkX3HW8KpT/NyO2/Okodt2xYqFOzVE47uTYecd8QMHr2CuZR59vYn9wQOsmp0NSAfuW67KAAnnAJhElYPOmN46HmhOGU6Z0JMHx5FpXaep1AQW4IT2XshFl/XUNLQCgExQBo4SHaABVMcdxKRGFclIQjPWdUsLntS8NaipV1jtPrGgkY5QR1eKze9yiQMG9hkIQdhZdrew2G5mTkE6DuQAFuBlHmrGk7WtAodrKu3bG7bfeNrs8KqcUzgp9wxlD/ZID1h1ZQGnblBlRaOfNMUASKBgwHv1M7hxFXHbd6ohCevAHUY9yoqwhwoChQ7FzEHknBU9w8OiKqDFEBqqWpB55RVZkqpOZe8qjbhtFnDOu0eiLIkYfGsX4vCZx2V7kkSRLrXFgv11BZ6C+rahHH9lRGd7+27plS1y948o4T3nUD35scjhcF1Ag1skoQUaOWtvR0KXTY1oXMuWEZUilTzsqI89RSGpUZZaPJTNfuCaZeeBn917GfjOt9mY2fRU8alfoverTX4wjjjxyEr0yfuh3J5fnKKynQxQDJylPUqkMzDvUgg3Salke1RqbGVFoe6XTIF6UepR9AWAJNca5PQNvitL1UXzcV0YUqk0yZ3lUOgy9SBSSvAcUKHNRBjLHmAH1UG1Gcyw8EyvM5N8cmWVbZkShax3YQMZnVmJkeW8ChZI2k7//1Rf8VjAG1p3MXL7QwFKeBjPrjA9Y1AkUui7drUCB54RwbhdgbjrSrnqU/Vs4osBjA0TJH7eQhHrFRgfap9/zQIHxxpIyX7zVNkVdqHvFM0QCU/S8dK22bOpR39JmlJNf2AUDAmqxLKCgP8L2bCXLLBMA20o2l5tiCXRDF611XUAhB4aGnxYOJ/RFFCAZFCWG+JRyxe3xXjpQ2Bqx5bBR7LrfuBb+jE/7FLhXl3pHxOiyUYy/PZ5qya8HKAwQl72XmG9bQ/UrjkP6CqYx3/sMUNh+bnz+7+uIAs/9bbfdFiccX58KO7MJj8k9IBt4A8vEITfbjKzIrHQ184H1BdWBa5nMXNTdngsolO8CWoAF+TloTyIenEYLHLiWJzPn4+tk5su7qx7Rz8KKNn7W8SQsyFFGD6celPnWiNHvb4nxLQMVU2scV52j0Hj8JuVR/6QGCn0nM+ejdInTQ7DH0SvEOYO7j4qkp5ziZJnrmSDTet+mHnlvBRRedWFvjsLA5bLmMt9h6WE2lk1EoQ0USurR5HTym26KE7vydBbtECFhHQCr2QwYwYG/2mrXfO7v4rDDDq/mbFB51C4Ky3qBAiYGp6qiEvYNT7113HPoZfUJZUQhE0z9fCj1SKSNHUumeA/6OqEqggA4iBi0ebtZfpAnX4SmXXZ2dwOFRdZEGVFo398GCjNPZh7SASuf4ofAcJSHFPjmwDvlhOocBWKAXLEgM29itwAFkoTLl9sO1LdxOhTRusujtsdMJlSetCLEPM+hGK2IQo7Pne7AtWnrjHeXFwRnHXdR9IortajmVSk8G7njqPfp1KOTYteuIdbI7I3AQ8++tKK7EoXbT1gmUMCwoEw5higmNGz2UuIp705li3WAxt1VvnUpQGHWUAkdQzY8mGg8jJRZhN/imUsHCp6NA3vXiNEREWPEYhY37xGloJ8DIqQbBRR0nx6T54DRgk2SxtI+AxTOraseZbvp5pvm96YmZSBpYaV1KYmDAh/aigPXKq/9aDTh2C8MFIa+e8Z1GVEQedm6ZevEAL/qisviKT/1E2t1mnFg9ADlwovluEiIYQi9JWL03i0xvnUxkKDLGVGoIi8NKKiAwplnVBGFbE96/I9XFZr6GrBgKmEZ3ngUSWzArkZsECECztb+rIjcuz/0sTj5lAdOnMz5zFk5CotMXRdQYJdLCWhXXm7nKHjfXKeTD+kg/aiMKspenlw+BShkRMGj33fxO+L0p/5c94FrXTkKbrII7nniv4h/+MeebNABncbH/5VxjRwVl+DJt4+n2VAZUUhP+bwRBRMnCkhmZPRFpI0znMceMu0yGcp91f79PgkUVD062yjUbaFk5rxZVQmhVNCZJ1HYklE8oHUlM+dtuwUomEguCqBGSEvoueCk5rsTKCibubU4g2LhiIKyncYkcycYuULdA+3T7/mIQk4M8iqDTkSG0KMVgAjz2pTS7Fp2u4N6lO/xaqWqsaOk6IgmcDZkPuW0bbBMoJDvER01NByWnOPUPtsAs0dkUn/x2vsSZvcIUMCxMV+42ewHnekr29MxgLsFKLTfY6B4qPF4hiQLrpN6NEBcTr2ESMEuIIoZTtn2NaBQOYtQj+aJKJQjY12JQEpOZxCR89aX5PTVdTNmDvml14koNDkKTSUaEY+3vP41cdYz8R82pk0OXGtyq5xxZcyuu2pH98nMurk1YnTQlhj/m111Er+cjVsjRreOYoxqd5tIzvq+JyMKCWD8/fFrr6nOUXjXJZdWYGtWRKHsAXNCUI9jYzWpe+Uq+J0IkfoxpIkonHTKqRVQyMRhQPANr3nF0qlHGF0YlpzN0psMLyIDE2lVZbNRxNWf/nIcsa0+9KMibomq3Xrr6gPXhnzgtGvoTqWbeOc5QTSZ2fITOsLLV3/2S3H44UdU+RKqHs0ECnUZrtrrm229EYV8jmicPqP1kMtAQ19rU49cx8P6tRu+MegcBf2v8tccnNO8RCRhHlpPu2/7JFBoqEdVbsB+W9cHFMB/O4IQ5smRODYwSbfMUWCQ7/aIgsljSUm0BHBgpQ4DpR1RSC/+QkDB+ECjkDyppuQB4DDQ+NDlTaDQ2nW8IT8QMToqYpwWuY2NetTRdgf1SDRfqWfGOJtExBHdV4LpENqRbu4OoJCfL1xPJ+D7spXQn+UwYPugYfclzO4RoHD3iNHtoxgfM67pF/7MYaTsEaBgICmJOfq1kRGFkmZW+oT3JaBQnsx8y803z85R6DMUoKU8SdXfHAp00oCoUPlIQOGoY46ZRBLyHICZB66t16CbcX+Zo8A2ywTwXupRPs8iMTZZIMDPre851vi0riVQcM2uXTsrAzOTmS9uqEd+15WjsJuHbPL4BArt9+2OiAKgw7fFxk2gkIddtyMfOz79t3HkkfdYdXbT0iMKPlqnKKs8iFBHROE7FAKgcMQRdSWmmeVR2zz39Z6j0J6gFHBZEmvaXu4CCp43lHpU7pf893r3SAKFrGlcnl69SDLzMjYM6tHvXrA9tgjbtFq76pFfryui4AEmj0uLwh9ycl7Tpz2eo+C9di4rCu/Cru2okFSWRy2jCgslM1NU6AvoDLjYwEl/1LVz+jeBQsewoCDh1rSrC8wBFADAQ45ZjHokNM5hKT9dVyx7XRGsWgmyT9/NuxMoeDMqKhADwKQ3iwNpmszbI0BhnUJujwGFOfu5kUChr6v7KlDorXo055wsfHlDPTrm2HtOqgpldaE3v97JzDPOUVj4xbNvzKpHlc2BstscBNdb9Wj2I5dyRZnMnHZQAgXlUbPM7N4AFCqANR6HaMzuylEwqPyE/syiRO349Bfj7ne/xwT0WWuAwsn3vsfSgNy8kwwo5CFwl1w89MC1ekVO2rIiCvN0vg0Ukgc3L1CY552zrl32ycyz3jfk99NyFMpzFBLcPPJxP7u+8qhDOtVxTXkyc5tOtluoR/qgJA1w0Fc/raM8Kg470LVQRAGIUmf30REjxMT3R4wHRlxyyEqgkGteqcqXvfINd84D1xZcT323TaMeHXrcKVXVrUWawAYqO9okT738AGcXDG27GygM7Ud53SZQWGTU6ns2gcJiY/eiF70ozm1yFNJr33uOwmKvWOiuj173uTj22HtO+PYVUIgt8eY3vDrOPGPKycwLvW34Te3yqBlVuHbHFd3JzMMfva4r88A1D0k9VQIFlYaM4VOe8KipOQrr6sSMm8uqR1kxyi27o+rRPN9Rncx85N0nVazcK6q21GTmeTok8twABbbZB9474xyF3R1RmKfvT3j0afGOpjxqLkR/y5WQM7ERrYt6lF7ojYwo/M+XbK9CRuaPoYtipC3tZOYlDHY7olCutd0GFAb0u4wolIn8CwEF7xPmQ8+aowRk2c12REGf/tvzXnDnPZl5wBzNc0k/9WhnHHbPxYGCPmREVKEcZ3ENqYGffd8ECvPM4sq1mxGF4eO270QUzo0vfPXGuuoRltAttyxOPRo+PFOvvOz6z1c5CiVbgHH51upk5o0HCmXn6/KoewdQKFkVWfUoIwrz5CgsaRpXPSarHlVgpjmzQ59e98qXL73q0Tz9v+ozX6y895VOGdXJ4BsNFJyjcOhhh1dRl5nUIx3vKrO5kRGFEiQY1L0polAujo0ECr/30vM61+nZ5780DmxOZs5x/N3//cr42y/OwRmaZwdMufbYY46KZz/jl+qNIbloy8oxK7/2G78dN9888KCBJfUnH6NPxx2LsFG3HKdzX/g7ceON85iCy+mY8zkueIEyMSvte6rq0TqHcU9UPZIojM02D5VxEygsNrGbQGH4uO07QGF7yFFIeXvLLTcvN5Fz+JBNrnSOwtHHHlcDBYSK5qCfwQeuLfDOIbdkRKF97dU7Lu8/mXnIg9d5TR64RghW5xSMIq656orYfvazwoFraZhvNPXoxJMfMDHGk360O3IU5hlOQEGOQq5/f288UPhKHHr4YdVcvv/id8bTnvqz/VWPukqj+og/fPWb4qab1pMGPM8w1tfe+173jJ96nBS91Qdh6Yf+bESDtp7xX35hFZjKMXvFa98SN/7TnDyTJXzEKSfdP/7lv/jRVYkx+dizz3tpHHjg/nH+OYr6rx7HJbx6XY/ISgSlR2JdD1zHzX3rfh2PXOqtT3/OefHyfYx69EvPOHNDwN+9jz8ufvN8iSKrm8jffzj9ObFzQerReif0YQ/5Z3HG09QKXd0+9JHLqrndiObgw9e8XKHN1a19MvM/3XhT/PXfdtSi3QOdPujAA+IH7+vkoLXt05/9fNyxczEq2Xq7/oP3uVdVeKDdPv25L8Qdd8xiKa/37d33n3C/H4j99lupGXPx+z4UT/qF0+MjH/lIPOIRTn/c+FZSj7I3qh496/T2eeJ7sK+jiBe+9Pfi8CNWKtHUpzNHXH7pR+I1r1C1Y2Payac+ME4/43krh4E2Sfd//YW/jN960fkb0ynntxx3z/i1X79gEoHRkfJk5orhMN4V//1FL4i/+sLQOkX/f3vXASZFsXUvICAiJgSRqGIAAyKggoI554SRp/jjM6BiRiSqGMGMGXPCrGBCQVAEJUhGwYDyRCUpOcf/OzV9e+/UVIfZndmZHW7z8e3uTFc6VV19Tt17qzLbnNt63UP1GuySVEeUMPLrYTTgFezvnoOrHNH9jzxNW28Nt4OiK9fPQN/HnqGtqm4dL5gZ1XZZFHIApynSrku+E7pc4STLlRh16dWHtqxc2Ry4lmvsuPx8Gl/ATeJiW69y1Z82RmXRopAr7LTckiNgC4WS56g55AKBvBUKPT3XI+9gM3a/4J8ctCsDeP252RzS5rlsyN9FsG+66ezy/XpYnSY/d9XV0xbB9ROuJrKNJi9Hu5KK9wKZpa+9+d4TDil5WNjGKS+6fcn15IBvdteC61HPLtcZi4K8gtqX1f4Wh+bZzx6wxsp5uuMkI+PSq4wZKzyOrWDrrOIS9vx4dYs8cC2ITOaC3OUjgeMBZ5Ne6ddY2i+EMHJ7a68+hBU5tigwMbYf4o0bNiaOYa+QiG3AhV1iMGB5pwDzoXfoFcdDoGw7DW7DZ4wJ109aDngLUi6Lx5dMg+9QNr4z86G3Xy9vq+oqJ24axoHzlOPbtnSgrlwWp+N6xqmfbCPXD+nl+GY8kZ/rUqFQ2k/V5l2eCoXC6P+8FQoIZp63LMl3j1egfQInSbXoDieZte51Ef2gdHZPS9FgPNv5necdLMbvIvnO5985L3MImeeYGKe+gek8UsffszuP3MrXkF4qn5nybHER1QdiS2G0c8L3YxNCYWhCKJh3nNgVJw4W6fRdWJ8yEU/qX6u+fh863Ehj1bU46TzBlzRmxGdxyvXHixffEDYm5VgMSifFJ8bTkE8jgpmRkU3igsicLJTJnCQ68rOUB8lrID4PSmOXGzR122XLsuxBZwZtuYSlgkmb/xDixFbvxGBXfeNaN+ThHoHlmZPro8uz62jyxr/yiQAYHiB+G7wTEvnvW3t6QqFrwvUol1eQoMmFCGUc5FjPZT3s/pN1AW5lMZg5l2NNyy4ZAioUSoZfvqTOX6HQg2bOS+w2Lxd++X3NhNgnxiIY1RBQLzA16Peo75PSeeRRppH9Z3OIdPJO515ZJxl4i8/9lXuv3T4/cVgZXGX6osUTMEzeo+oXJ53EynY9YitJVDmpbTfMJrKfg9JxnWzxKUWg5HhBmCU2/ix+PYLaHVQ/e96Iwi1O/8QaDzgcT5ywHRnMHLY6XZqTn8uyIetmE+Rs183GRdaPyWbOiKa34uHCwAiFKgmLggs/1B1CjVfp2TrAbUIatAufS3EVO115HBGZWjNX/+Iuv9yNsHAUWRRQP7uO8AOGP25QHaVlQAoCmY4tCrI+tnWBhWwmynNhmWLBWL+BKni7VjFyalHI9hOu+UsEVCgUxnjIX6FQZFFwEXR/Xo4QBC5yHUdIJKXzFteY1DK3YHJuVlvx/tlUjjaVS6yQRxG4jAgYz/Wa3322m48/QlF/zxJdVK8EhUy/rvHT2Xjh7wnfj6Ne7HqE9/5GV92k0OPtINLFNDqd32+CAG8y9UleZE2nL9O5N3Bs8ngzCnmTOahOLvom1TuN8V/cZ6GoH4uI5BefBJyjMHDQQKpft3bo7Mj7y+fVFBpCkvOqnlmoTBDZlkX9u3Cx+bP6DjgvMP+ujRs2UfkKDiWRg6ryJJKDoiOLxGnkS5ctz7tzFLr36E4Nd8FRX3oVEgJr1qwljLm6dXC8nF5lFYEVK1bR3PkL8i6YuVv3btSgYUNsl5MKLSz7rs+z0QkpZQlC4RFd8lxes1F8aJ42tym/iWhDOSL83OTh5nlB5KyOsgEbN5ltbv/9ZwHVrle/6KTzXLze4/DC0hxnFk4p4xtbbHGflvpA8wpkPDZuopUrVtCCOXNTdz364MMPacfqOJ1Kr0JCYPnyFWY1pGpVnC2rV1lFYNmy5bRy1er8Ewrdu9NONRP7QetVOAisW7feCNN8XWAoHKSz2xIIvsVLluafUOjWjWrUxFHrehUSAuvWraPly5bS9jtUL6RmbXZtWbNmDS1dsjh4e9TNDpECb3DXOx4gnL6KXY/0KrsIqOtR2e27slhzdT0qi72WWue8dj2au8x4pW70ghTkxhD8u8s11MQ1eqf+osXZTOdb7bH5RJplmboXM529WYbfRkfQq8TIhYXByMMrEE+xaUhSHnHSbdroB6VPnjieet7SiQYhmNmzJCS56yS53aTbd9huNeFylFafo22oo7xgtCqfCAA3ngSe1Uh6aXD8rHS59n83W7+mn87lUodqGYw8o4LpKxFDK2Ne7fqVZIyFjc3Ik5kLY3rUVjACKhQKYyyoUCiMfiwrrVChUFZ6KryeeS0UzK5HHgGXxJ/JqbfxiNw5xxAqScDTTOcT8JB09imKKDNJmKRRZsbSCeLIPuUyyJpxidM+F54lSWcOW/Ou8ePGUK/brqdBX4wysQAcIFvUbwlSHAsXJtDenrPh6Tzi7qmToPJ8Eg4PLq/eRiyI3ZlKOsYix7SHlS+gWFDZAliMMz8IO6J9UhTi9+Kmi9z1KGXHI8vXK2cBuzmet5PiAeL4v+WovnbcQrGEQnHb50hXVsdLSr0ziEmsoWGVd9WNPem5l9/KO9ejXr160sq502M1SW8qOwioUCg7fRVW03wWCnwyc74hLXfD4RXgfKijWRX3VsKTzlLwrAz5UEeco9Cjcyd/e1SuUz7hiDq5tlwvDfzssWVbCFgMlUZd7DLk+Io8mTluBZlI4bTTChXKm91qePec9es30BZbYB99dMgGf099BEPj3kQnYX/+cs50cevguq9o15yESiwfo7ykem40+tIzbeFnySJxErhUSIk1SuzUw3gkot+BH/BEmXiwGEe0k39PZztPlNHtzgeN61Gv2zr55rWS4Bs3rWxf3DS5vi9fRQ2w1O1Rcz06Nq/yjVC4uReN+fLDzavhBdbafBYKv85d5u/24nQlSSyJBh6IaVZPHS4z5o1dwnTy7CB7t0D77BynO4g4yNN2IwqrH6ruKo+Hpf1doqkJjMLcpOS2oLKMoPIkkY7TPt4Z0AiFWzulHLiWVGaIO5ZsT4o7UDHT2fgzllHubdJiU9I+53aFTS9y9Z9dpdLqN5cFIsClKqltXjruQ/R9pOsRE/+wwcS+W9meU4sGvrePcPny9PPMWfToky+aLaX63tWFtqxcyVTjtbc/ptHjvqftt92Wene/IWOk+J9/F1G33g/SD9N/pmrVqtENV19Gxx3ZypTpeoC8oxGcW4JmCq8o64b83hy45p3MLEUIH5wW1I7AzxNHCYa0v8hvr2hyS/yWsBgnn/1wa8/7afmKlbTrLvXopms6eFugbqRbetxLCMRr364tHXjAvoHlhY3TIL/Dr0eNp1cGvEPtLzqX2rRqljJWXGdshOHEWEZNqHYe6aRT16PMPD2PP/sS/fL7LKq+3fZmGz/ew73rHffTytWr6bKLz6d9G++VmcKIaPDQr6nfMy/R8pUrqcnejeihe7pTxYoVM5Z/tjIqC0Lhrj6P0b+LFlH9enXoho6X+VDcenti7jj/rNOo5YEHZBSiceMn07Mvv0nnnnkSHXtkm4zmnY3MyoJQYFLOvEJuFx3mn52NdK4V32efeYT+mPU71a5Tjzpec3NiAbJ8eerdqzOtXrOa/nvFdVS/wa6hfu7wgy9agPS2WyW4yyR+l++Gzz8bRK+99CytWrWCmjRrTr1uf5A2wZrgbRGeOIOtSBjwe4e3Qk3C0TtzKRRHbM/p5eeT5zjp4OPvTaCmTBy4NuF76ukJBT5DCm2zy89G36UzVhB03fv2zoSDZjtccS3tsktD0wdjxoykjwe+S9tuuz3d0uV2X4BJUYH+SqcsOZ773t+T5s+dRz3u6EPVqm3jizzuf8aFx4SfNmCsJKVLY4w503njAN9FnszMk5XtgmS7tMiBbZM/Ow+pgplnMtjcAUEn0tqT57r16+mIEy+gsROm0CknHEUfvP6U6bTt6h9Aq1atpqGDXqPDDz3Qf/DMUe/lEx2Lyy7PnD4M64ZneZArCXPn/UMtDj+d5v/zL1WqVJHWrl1nLCJvvfgYnX7ysX7VolaiXdglKugFsWBFQBz2BkKPuSNhYUhYZGySKcsMzJ+IuvTqQ1taJzNzWv5pp98g9vFP7ruiw/FsixLqJ08wDsrb7s8ZP8+kJoecbB7I36Z8TfXq1KI77+9Hvfs8Ts3235dGfv6mT6y4LoyJjY2cIH11DCw9C83sv+fT40+/RC+8+jYtXrqMnut3D/ZvnBwAACAASURBVF1y4dl+lZw4ei5APibe33yvjSUmHowlHudFlqCE5Y37saiuRVarhJDDmRZFFiQuR4VCZmjUpKk/0sFHnWksnn/PGEPbbluNnuj/Ct3Y9W4jVid+8zFV2XLLjBT2/KtvUccbe5qXQeUtK9HqVWuo1k416MexX1DVrfJ7F7KyIBT+mP0n7X3Q8YR3Avpt70Z7+H3ZeK/dadzwDzMmyhYuWkQPP/E8vfTG+zR/wb90/52d6fqrOmRknGQzk7wVCj170My5OHDNrLgl1kwcp8wGLfbIOIVMpnP1xbjRo+i804+lSpUr07gfZlG1atvSU/0eoL539aRdd9udPvtqHFXeckvnwmHSO9469ZiJtHzHPv/MY3R3zy6Gk2xRqSKtW7OWdqpbm74ZO8OcGRR1ubBAmnRWptPBU+aN3+WBa7KuSeVL3hOBSRLvyWA65HvxeafSyOFfUsvDDqc33v3McMRjWu9Ps36dSd3v6kOX/vfqRMBzSh2DLVZBvPjFF5+kcd+OpC8+GkQbNm6gMdNm0Y471vDjbbDwLQ8C5nGfFGPgLbSmWloEjywhnqY/qRyZA9cuPd+969EPYwabioedlyDVETKNIslRA9v1vSxDPkQ8KKf88BM1P+w0Q95/nvAlPfrUS/TwEy/QkYe1os/efcEn2TLvRYuXmheK69pum2omL/t6oN/zdNvtfahli6b01adv0KdffE1ntbvKEO/Ff0zyiR+nSxlQYv9lkEB0p4v0p42R8F8PWsXmPI1QqFyZena51h/00l8wTGQE1au4abj/bJFzctvLaMjwkXTYoQfTx289S7vtfwTBkjPs49epTasW3rukyFKB9P9450NswknV3mEziUFOVGPHHZImbW7HN2Om0lEnneM3i4WC7DeMfT4EJWr77CAcMvV5ouGJRnW8qRf1f+lNjVFI+2FJToC+ueK6rvTygPep0Z670aSRn9DOexxMi5YspeEfv0GHHNw8pQQI0oWLlgSWXAPbSluDZfXqNVS3cStatnwFjfjsTWredD+q26glLVqyjD566zk67qj8Xo0uC0IBHXLhZdfRewMH0yEHHUCff/AK7dXiGPp7zrxQjBf8u9DfpUV2Klw+q+/g3iL8p19nUpNWJ/m3q1Ao/oN4zz33UI8ePUm6Htk+7K73ftC7zvW5+SzxwnET+ACPAFer8I657sr29PGH79IBBx1M7340jBrX34HWrllDHw8bTXvv2yTlHYUVa2wv6Vvui84HM59Vr1EjSSAhg9WrV1GLvevTqhUr6f3BX1Ojvfc1f69Ytpze/XQ4NWtxcCzQJXY2qUzgYoIa0sIlKB1XiAktXI96egeuSXGA9PZ70dmflhgIFYqyD9NMh3ovXbKEDmvRyPx84a0Pafb/ZlGvztfTPk2a0qAho1Lc2lBf9Cn6Vnpk8O9VqmxFVbbaKkmUMQat9m9I8+bMScBVjmjstN9pxxru7YFlmw0FEDtFxRF8Mo0twm08pSiUgyvwwLXJk8bTj2M/dw7EIDHgGpDIAPdD2PAKKlc88RMr5UUPbxjpDFuVvqXHffTIky/S7rs1oF9/+5+p9+zpI6lWzRp+G2Tex5/ZnoaN+M7Zvi8Gvk6HH9Lcr1difilH513aid4f9Dl1ufEq6t3terOSVKfRISaPpX9OpipVEiuPSQLBO/mPcYA5LugwFNk+LhOTkjExeg8zV9jGwsfeKy9IwLksCrK+fj0F2Y4s0xu4QYKSPw8TTrIjVq5aY8jaylWr6NCWLWjU6O/p0ovOoWcfuztlAkY7cYhc7b0S7l/2td1229CCmeOS0sl6Iv3J53SgoV9/S/373UPthUXBmaH40LYepPS92C4t6JkJ7Ecf04RVQV7AUWMUonon/vfYV37nPQ8289QF55xKA979iM45/QR6rf8jTpfF6T/9Sk1bn+wsAGRk0R9FcwHf9PPM32m/lifQNttsTQtmjjcfd7yxBz3/6tv03/bn0+N974hf4RzcWVaEwtq1a6nBvq2NkGvdsgWNHP09nXPGifR6/0ecqKHPt955H9/CLG9CX833+ioI8gs7XEfvDRpsLAo3dOzgu67loItiFZnvFgWbLNqNSlgOEiumfLlERUnT2eQM+UkSNX/eXGrVpKFZlDrt7HPpw7cH0FnnXUQP9OufRIDZ/WfypO/pzBMOcx86Vo5oxqxFVLlyEX9Auh9/mEwnH9WStt++Ok346U+T7w0d/48GvvsmXXrFNdTzrr7JpNFbR/LryoevWWAE4cVCSr7HZNKkdKIPkM7XPcIKhLS2RYE9R/CTsXEN3LA+lcLG2c/iQ+YcBhMhiOx28X2PP3w/PXTvHdRwz71oxfLlNH/uHHr7ky+pWfOEKLOJdbu2J9GoEcOdz95/r76euva81/9O8mMur+ketWnZsiU0ZurvVLNmraRxHfZAB43PWH0nOWqiUaYoiZUsG59HxigggXTTsTOwH1b/QRe+63aDw1ZYTXfyIYNyC7AiV/gk4LnzFi5aTLs2OYJWrlxlvu/d7QbqcuOVvoXDFjFDho+iOXPnO/vimCNbU+1aNVIsKSwuHry7K3W68hL6d9FiqrV7YgAt+mMibR1wiJnrhF+bOMa1xLjuCxJo3DiZpsvtfU0cx+23Jc5RCFP1Ehy73HTKxCxijpvnY+Ud+w3b+T/05Et0a4/EQ1Zt66o0bcxgql2rZoqaxvdYrX37g0+dfQnL0Plnn5LSTlnecWe0p+HffEf9H7uH2l90tueHmCxgo04ht/s4UDR7rkgpz4QQlEHPh6yzuh6FTaPpf/fGOwPp0o6dTUKI+f/9MJJq1nAfHAdh8dFnXwYWcmHb03xLId805YfpdOARZ9CO1Xegv2YkFijueuBx6n1/Pzr5+CPp/deeTr/SpZiirAgFQNL/5Tfpmpt7GXSqblWFJo78hBrUq+NEC8/aG28P9Pdjlzdh04fzzj4lFOULL7ue3hv4GfW5swtd3/FSFQrFGJNsUcCuRy6y4lrldM2RciHKXr3masVNZzeDyTDSs/vJ6y/1px6dE+9RuLNO/mUObV1tGycCCxf+Q8OHuBdekeDMtuebTU7kNX7cd9T25KNp59p16dvJvxhs7u/djZ7p9zCdePqZ9ET/1/3bk9yDPP916SbjqlQYDzNEXlhfZL+40rmIK8qUQkGKhDjDJKh+KX1jiZM46ewxJf8+rnUz+vXnGaaYthdeTPc/8rQfh8I4cBnffDWU5s+b52zOno0a0377NzPfybHJf6PM/XffOSEUYFHYsabZolWK4KC2xMHSbmMULpwn6mePndAYBXY9kijI4GYbHZ8c2avoaaxOhw0gaA8E8dgBHlzu+x99Tue172S+X/bXlBT3IQnUtOk/05Ily5KCjBnY/fbei7aptrWpilzxPbtdR/po8DC6vesN1O2mK2nBPwv9lewlsyfRVltV8e8HsUwYD5IDk5xEPwmvonT24GJs/DyMqk81ULC7jI0T0t92e1+qXKmSOXBNklkz2YmJIWoFXA52WU+ZLkzUyH4OSlN/nzZGzD18bw+65vJ2SUMjEcOREJU4NXbshMmJ+htXoaK2wPf84BZN/bRyDHC5x51xCQ3/ZnRKjAInihZFibgC+76ivxN95Hp2cA+/fCSm9vixrRcqFOK8auLfs379eqq+WzMTN3BXz5uoc6fLA0kfTsSePO3HwMwPbt40xQr00y+IvTmJtt9uW5r7y1iT9pYe99BjT79MF513Or3weJ/4lc3BnWVJKACeJoecSD/98hv16HwNdb/l2kDE8OyNGT/JSVC3qFCBDmy2fyjaLBTuv6Mz3XC1WhSKMzSNUPBiFMKEgk2GzXxpHXKQdI9FIM17wTgg2amSrQWuNriIGVyNmjTciWDFuv2+h+ji/7vST2qTtBUrltP0H6cm3hGIkfPO+9oEY/EmMivWcEP2PQGoHE2dPIFOO+5Q45IyZupvZk7pess1NOCl5+nci9vTvX2fSLF42qv0YYRYvidlm6MwDErH7zFuA+5zWRRcfczlhxHgpLZ4sabJ78yiQPCg9tify7py/sOHDqYOF55l8DYEvkZNk0zeyxjMmDGNli9N7Na1qZznxuUtku+0c22qV7eBU3AxL2zSsBYtW7qExv4wyy/HVXeXWJaYSUxdnNO20PCzwE+CFMCusmIFMwcaBzx7U9RqccqDZ+0J75M36/OiINWiwE87L0ky4XLU+MDjCBP88jlT/W1YOX9JhKNcjw5r1Swl5gC7HfV55Fk66bgjaOCAZ2jKtBnU/PDTTZVWzv3BDCze7lW6i9h+8y7CaH8mCW2IccaHI0hU4AaZtxEKwqIg8YxSnMV5CXCa5PaAHCfIs+wTvpf7fb+DTqAZM3+nd199gk4/6RjfwmPXE8HldfZKuIDZF4jZ/Jljk3YrsNOzUIBF4eILzkwJWDJ5irEZJpZLglFUWmnV0BiFKLTS/75Oo5YmFubdV56g0046xh+jvBMS5xjmeoR7Fjtcj5YuXUo77XGwGfOzpn5DNXasTnsfdCz9Nms2Pfvo3dT+onPyejW6rAmFI04+n74bO5Geerg3/V+7cwMHA7sebfCs1/JGLBYt+C3hJhZ0+UJBXY/Sf+C8FDJGAR9JshhGkOIUWNL0dhl2fs0a1aHFCxfRgA8HU8tDD08WnOLFPXmi53rkqjRcj/5YRJUqVS5a7CpHtGjRQjqwcX3j3jR66kzaYfvq1ObAxvT37Nn05AsD6IRTTnfG3tkLfkwSUwSA59YchWMUhiZ/LwDd7r8UoeCdvI00+GdzR66LLDOQl3j42hyCuVBQOv5eLqRKoj3n77/o0KZ7UMVKleinPxf78Nj4Ye64+NxTAl2PLu94A3XplXCXDsLQWBSMUPidqsOiYAVLu4RMVH+4+K6hMGJHKikcWFwGiUzca4KZ2wcEMyNGwV4VlucjyAFmE0GXgrEHZGCaJFJWdO6CS1FKgsxCoWLFLYxFgQOFXQ/CpCk/0uKlS53PSJN9G9MO221rvvMVPrb6mjyNWh9/nlm1PuPU42jIsJG0ZOkyuubyS+jhe7sm5WWvLst85I02nnKFOYq4B+Fnrz7L8qRFgQeP60EL6j9Xu+ytwoImHt4BKGjc2On2O+h4mjFzFr33aoK4uS6QdpzNMWqM+4WOnSHgq+y6pkz/jbrdfh+NHT+Z4Lq23957Up3aO1O/vj1pl/p1/SR2m1152ZjbaVxiL/WeRIA255UIeE+N7cE9alGIer2l/71LKLhyWbFyJX0/Yaq7gHJEbVodmGJRwM1X39SDnnvlbRNcf8ShB9M7Az8zCwv/+2EU1TQB9+nXubRSlCWhgDXjwz2h8OTDvalDiFDAMzji23GJYDnrwvujtbd5gv3dX3/PpY439aCJU36kefP/oUZ7NqRd6tWhPr270F57JLZWzMcrb2MUevSgmfNW+As6YfzBRZSCxIUkhfy+k/0i0+Fzf/61XEDkO1KSymaN69DifyEUPqeDWrX2V/jZVYnbATI4dfJEY3lOWPyTz3xoeehhqdaBcuXoxqs70Advv0G1atemfQ44gL785BOqsMUWNHXmPBMoy20Kqh+fhMw7PUbhKnmK304vHoHjK23yat5XsN44BrwRCp070cdDv/PtOIF1dViAuE9skhtGlm2ibFfLJ8bC2wNpmKTP/fsvOqTpHlSpUiWaMXux8TqR72W0l/vvx6mTaenSJUXWBmgm401SjnauU5ca7LJbUvFc7xuv7UCLFi6kUcO/JFizWx5+OO3ftDnd2vWuRHli+1OXaDKfOWJQJNm3+WMULnIsJfXRxk2J7VGDdj2CUAhbqUbGcnVVigoXyeUtJYPSIL8glxeJdhCB/uPPv+m08y+nihUq0OhhH/i7CvHA5sEQdwJ31WXI8G/p5u5309Kly6lipYp0ROuDzYpgAmS3i4ld35TTrr0KudoVh6Rye2R66TIlJ4fb7niAKleq6McoyMFh4xJEZIPEQhCuLnEZJJyQB28heuaFV9KsP/6ih+/tRke0aRlrbLjqwP0i+xN1mvTDTOrQ8caUJO+8/Dg13LW+k+zJ+tm4++M6yGLmqFzQwxzkpsR9efVNt9OzLw3QXY/iPswx7jv61Ito0ZIl5myDI9u0jCTu3kJajJwT88LqNWvpiuu60XfjJtC6tetou+22pZef6kv77dPIO1MkVlY5uaksCQUAdGnHW2jytOnUo/O1dOYpx2ccs1mzZ9NZF3WUYZzm95eeeoCa7NMo4+VlKsP8FQqJXY9w+SSF34vez6QVXblTj2/wTezeY94tJm6gvG8Jlgtw/jvPSidXxWU97HlekqhzTj2Kli9eSvc/9jTtf0CLxDtKHFiV4q5ibUrC87/LrQWfwb2p83VXEHYP2rB+HW23/Q70xPOv064N90hxhQniSFIsSZJryrYCfOXfPl8S7zOb4zFOdv0Zy8kTxycduCbPduB3mU1uWXSY72XQdEggu4vvuESPLTCShKRX1j8L5lO7c06iShUr06ChoxKuwd4mNP772ltY2OgJDB97xw6UEhsur905J9PCBQsSvNFzH8cZGfc99FSSa70/bsWY4s98/CyB5fM9ry48JhPbfiUvfqeIQS9MgJ8h3kgndHtU165HUcIhakJzkWF7NdYm0kGnD9vEz2VBCCuPewl+WkFKO45wiWqzPzGVS5w8HWbpkOXFKTtINMky5T1d73jAnMyMGAU7/zChEqeNLAiQb1AbXW2KwsRVNvLhiUz2XdGD7O2NGqfiIfcEid8UrCxxUMJiE0PTC/p24aMWhUwgnJoHk/84IiDOPUG1tNOWJK/sIJGca1kTCgkC4zQUZAwu13bJ+WwVQsPzVij07EG/zlmetHJrk68UMmr1JL8PfDIM66xYKQ56VyaIYGLfer5cZcn0Pon2EkiiynnwPTZnssWELVDsAcrkVpbpKk+mS5DshAe6XVebG3C7XCRanjNlE1S7nUxCZZoJ2B6VD1yzzsWQGPOKPfIwbttiAxSfzAb0t3QLDhoj9qo81zEFM++DJFEqD1Rj64oVd2uLAclJEqv/iJ8oOhvBF7KiAtwP3N6gMSZxD+o77mO+Vx5o5+JL/BkfBMjpjINY+RiuR0ggD86yB7F0J4kk7iFkyiaQxSGPdt1cB3Gl85aIFBkiM1nfqLpHfR9HIKTTDnmvEQqIUejSKa0swuocVV8X4Q1LA1McBm6YoEqr8tZkzmM6rmALGldx6xCEXdDnciWEywAm8oAdFQpx0c/efZkkopnMKxstLotCIRs4JBOL7AqRbNQ/f4VCT5o5N7HrERMl+zDUIMIInCTR9MmmByDn6doFiFetbZEgd+jj/H3y5fnYsDuPf5gnrAnCx5zT2fWWgsYuxydoglTbAsEmfz7Js84mcAkLFlFsObDFgSSh9rbctjgJEhZy3MIS0uPWTjToi1FO1yrca3MBe7E0aBGX+9xVT3s7ebsPnWPMqPyi07E5f/wMW0i2t5V13ZsihBKDyQjZFFERsq2633/WKoUUGRIPvp+fCZeQ4vLlgr0cY/h8yKcfhbseZWOySidPSZCiCLb83iZWsswoYssTggtA1xafnLdLLMQpKx080r3XxkxaFOLmFYalnQcP2KA0LjyCVnqQN9ffZf2QDz9+5zI5jSvfQNLuOH06Lj5B97m2xZUTcdDkE3YPt0mFQkl7p+TpM0nuM5lXyVuWmoMKhVRM8r3PXOMgn4XCr3MSrkc2kQ4jqExWJVlzvettawDfg3RsdZCkiomVTfLlnB30bg+zfLhEQhzyzj7rXC+/jd7KuySzfp3F6rdvZRGuJa7YQpNW7Cgk08l+kCRa4m2TdxYKJkYhxNwWRwzJ9P4qfciOmvI96qqj/XzYIi+FP3gWGhcRT9q8xmonj00XaZd94OJS/JldpusUb38MeGNCpo0SefK5w+9SNKDsyGBmSdZcEw+DaQ/EoJeVi6hxpaKEQHFfgLLD0yXu9mCxA3JtQhy4Shywf35x2iTLDCPZrrylUAhy6fLzdASVc572QIpqRzp9axP+qLzxfTrCJGxMxxnPHGsj6xUmaIIEQcrY8lak+KENS6cHrsUZFdm9J5NEMZN5ZaPVKhRSUc33PnONg3wXCnJxLokkCV91SbaZHNmLMjYpcxF+e65nEm6v2vpixFvl53SSFPu8wguIkyu8QcJH9o9tRZbpXSSO68rf2e+MsBVw2U7XfZK026SRSa/TEmG2fi2fdHghxyi4hIKPn+ciJttUJJ5S93/n+rGbjHQj8om347RpxjtQlFgB7PbzE4QLYy/f54yPHCsSS/k7j80UTMVOUhIbu89TRIpIJ61yUsC6+k8+I9JKhvJKZFEIIqnpknHZIa480yGZcV+SrjpGrZzLNGEEPVZ9HUEvsdL5oz3YB1/WM8qiENRX6QoQrlZabYjRWbZ1IKxtLuJuP5BJE2OG4woixVPM8uyVG/kS4H5Ri0KMwZPlWzJJFDOZVzaarUJBhUI2xhXyTJyjkHA9kvO7nE+ZKAWRKknmk/iE2AKU537kYZP3sLbJ+dgmfjKdfZ/0C7dX7+13hdzBRpYRVFcphGyyn0JSPXcaWVe7PpIkuoQN0vqiTKyY2yLK9sJgi8JHQ75NgtjVXylcRGwVaAs9ro/EJ4mIW5YWKQrNoWbeeRZ2P0StvLssSq66yfqZ3xMfFB1enMbBatwu1zMgRQMDbI+HItGVCPYPswZJDCU2sU5mth+iFDIooqu5MUHEMdCi4EWV+zzY8t2SZMn1cOIzO+8wFxRZDneq3U57ECYNRO9mGb8hyXXSoA8giLK+UcTc9b2cNMMmOpnW7HrkBTMHlWl/zn/bP7nMIILM9we208PFLg/3YyILWhWRbY0SJkG4Ba2kyAnMXiXiMYjPEd8QZ3yF9Yv8zh5rst6uNqhQiIts9u7LJLnPZF7ZaLEKhVRU873PXOMgfy0KPWjm3BVFB5xy8KfHspKInhUHkMQNzM6DiajkqD36nau+luVCviecBF7wFl7hloLEFgs2R5DkL5DAee5R5l6vPLk6zPxFvp8TsCWIoYvH2PdyHklE2XNBssmzXZ5dFyk05K5HNpY2pzPfC05gvx8D4z8MVyzv97ctOCSRlyvpXE8ZDC3rxLjZ/cnE29QH4zQkLkWOMblK78eIELaVTQhXiY90ieNYBu5TPiSN7/ctM149uF1Bz4wsK8qqExnM/P4HH5iThl0X7xEc5zv73rC0xXnByfyyXVZQe83gKk7lc5Bm7Zq1ZvLAHsGldaXb5+ncn869mWqvv0d2Dvt9zZo15kTqf/5ZQNWr75ipppUon7vvvpu6d+9O1bauWqJ8NHH+IYADyVavWUNVq7jfCflXY62RCwFsPb1q9WoaMWIEtWnTJi9AgkWhW7duVLVataSdh0zl7EU2+aIVuxQl3cdpXGk5TRzrLpcVlCbO90F1lG2TdQmrcxJzjrGrXxB2rheYrKcLdy7bhZvEm+/jrTjXbyC8q6pUTZz54BMliWmCARddUf1m32+P4qi+lf0Whr1sM5dp97nrCQrDKG4fugilPQ6j2hE0PoPwCfl8w4b1tHrlKho1ahQdckjicNtyAz98f9PUaT/mxSSilVAEFAE3Ajis6/rrrqMtq3iTcI6B+vbbUTTi669pg3egUI6ro8UrAoqAA4EK5cvRRe3+Q/Xq1csLfL4bPYaGDRuGk6ryoj5aCUVAEXAgUH4LuuTidlS3Tp2EUNgU5HSl6CkCioAioAgoAoqAIqAIKAKKwGaLgAqFzbbrteGKgCKgCCgCioAioAgoAopAMAIqFHR0KAKKgCKgCCgCioAioAgoAopACgIqFHRQKAKKgCKgCCgCioAioAgoAoqACgUdA4qAIqAIKAKKgCKgCCgCioAiEI2AWhSiMdI7FAFFQBFQBBQBRUARUAQUgc0OARUKm12Xa4MVAUVAEVAEFAFFQBFQBBSBaARUKERjpHcoAoqAIqAIKAKKgCKgCCgCmx0CKhQ2uy7XBisCioAioAgoAoqAIqAIKALRCKhQiMZI71AEFAFFQBFQBBQBRUARUAQ2OwRUKGx2Xa4NVgQUAUVAEVAEFAFFQBFQBKIRUKEQjZHeoQgoAoqAIqAIKAKKgCKgCGx2CKhQ2Oy6XBusCCgCioAioAgoAoqAIqAIRCOgQiEaI71DEVAEFAFFQBFQBBQBRUAR2OwQUKGw2XW5NlgRUAQUAUVAEVAEFAFFQBGIRkCFQjRGeocioAgoAoqAIqAIKAKKgCKw2SGgQmGz63JtsCKgCCgCioAioAgoAoqAIhCNgAqFaIz0DkVAEVAEFAFFQBFQBBQBRWCzQ0CFwmbX5dpgRUARUAQUAUVAEVAEFAFFIBoBFQrRGOkdeYLA4MGD6cQTT6RNmzZlpUZHHnkk/ec//6H/+7//y0r+mqkioAiUPgI6b5Q+5lqiIpCPCHzxxRd0/PHHK4dIs3NUKKQJ2MyZM+m1116jXr16pZky+vajjz6ahg0bZm7MFhmOroX7jmeffZZat25Ne++9d3GzSEq3ePFievTRR2PjOHr0aGrVqhXh58EHH5yU15lnnknffPMN7bzzzub/unXr/O/LlSvnY4k+O+KIIwLrv2bNGtpyyy3pww8/pNNPPz0j7dRMFAEgkM1545hjjqEvv/yy4OeNZcuW0ccff0yLFi0yc8EBBxwQObh03oiESG/YjBG444476PbbbzcIDB8+PPT9WNowvfPOO/Tjjz/G5ghR9fv222/pggsuMO3cbbfdkm4/99xz6Z9//nHOoZJD3H///XTQQQdtdhyiIIUCOn2fffbJ2ACTowKrzl999RXNnTuXdtppp6ixmfb3eHD79+9Pf/75Z9pps5kAD0vHjh3piSeeKFExV199NS1dupSmTZtGkyZNii2IunTpQlWqVAns0wkTJlDz5s3pqquuoieffNKv47x582jAgAF0ww030E8//UR77rlnaP3ff/99euSRR2jEiBElaqcmLnsIH0HNdQAAIABJREFU6LyR+T7L1LyBebdly5Z07LHHmgWGoUOHUufOnSPneJ03Mt+nmmPJEMjWPANiDf6Ad2s6F/gMnq9ffvmFdt9993SSZvVe1GnFihU0duzYjJQDbwEsMl5zzTXO/MaNG2dEQBiHiLOAW4gcoiCFAl5OUMnZWPXHCJs1axbtsssuGRm8diZ4OFjdZ6WAYma6YMECWr9+vVmxL8mFyWybbbYxlgH0UZwHb86cOdSiRQuaMmUKVa9e3Vk8rDyYCF555RXz076w+jhx4sRYVe/QoYOxKJx22mmx7tebCgMBnTcy34+ZmjfgLnD55ZfT2WefbSp52WWX0fPPP09wJYB4cF06b2S+PzXHkiOQrXkGAgTPG1bM07luu+02uu+++2K9i9PJNxP3/vzzz5GLe3HKGTNmDB122GH0999/B3KIHj160F133RVoWdl1113p999/j1McFRqHKDih8Oqrr9LFF1+cd+o4zujC6netWrUI5i2slhXyxSbPOEIB90Ltw+0g6ILFAJYAiIn99tvP3Lbvvvv6qytbbLGFETpxrn79+pnx89hjj8W5Xe8pAAR03sjfTuQVT7gNMgnCggOI0cMPP0zXX3+9s/I6b+Rvn26uNcvmPIMFOKyGgz+kcx166KG07bbb0qeffppOsjJ1L+aC77//nj766KPAeh944IE0ffp0Wr58uX+PcogEFAUjFPDSqFq1qlnpB9EDMcRDA6sCCOapp57qWxngpwaf3hkzZtAOO+zgD4o333zT+LDxCwkvo7Zt25rvBw0aZFyCYJqC8uQLyvG3334zabDi1b17d3rhhRfMywur0mE+8ciDX2YNGzY0K2OoJwY03Gj4gpn9gw8+MP56+PyBBx4wX8HvGabCk046iT755BMTOwFffahwlIu2d+rUyXzHZvv27dsnPShLliwhtPuZZ54xqwr4G25bHIvAhB6k+dprr/XTwvJRt25dwmrE2rVrTZvh43fKKafQhRdeGDmJpCMUUBb8sLt16xaYLywG6HMIClwgFwh8XrVqlf93VF9w5jAdIiYDQZB6FTYCOm/k/7wB94Onn36aateubeZnfr4xLwRZEHGPzhuF/eyWpdaFzTPcjpEjR5p3MN7XuOxFNLz/33rrLUN2TzjhBLrnnnvMew6utnCDfvzxx/14gzBvCvAa8ImVK1dS165dCbGRyEu+3+Gm+9RTT5n3epMmTejmm2+mM844I0Eay5Wjpk2bmmcSrjy33nqrcQ9izvH111/73gLgRC4+gHbgP9px3HHHGS8Nfj9ng2vFmQsqV65MzZo1o++++86fY9AXffv23ew5RMEIBTwwGMAQB3vttZchyPgbg++ss84yJBPuKyDBL7/8svm+UqVK/grVpZdeanzm4Z7CJiiQ6hdffJEWLlxoCDoCjREEg8/w3Q8//GBWrd977z1jEkeAHR48kGWo+/POO8+Ii6ALZPnzzz8nBNmA4ILwY7DK+ARMHCD/8MuFKJAEGw8pVtJB8jGpoEzcP3/+fPOSbNeunXGfAQaIDYCggQjhCw/BTTfdZNJgEuC8n3vuOWM6Q1AvhA8mIEx0yBcXHiYIKJj9q1WrZrDBZIA2XHTRRc6AYxuDdIQC+hGT0hVXXOGEEpNao0aN/IkG5le09ZJLLjGTXboXzJToz3yLE0m3HXp/NAI6b5SteYN7FPNHnz59COQqKKhZ543o8a93lA4CPM8gzg9xcpKfoAbgHCDvb7/9tlmku+WWW2jHHXc0JBwXRALewXg3Y9EP4x/X4YcfbngO8gfXgMUCF7sw261jSxy4C8oBf8HviPHj5wiWBfAKcA4QeU4Da97kyZMNDwIHwEJanTp1qGfPnkZMsEsVysS7999//zX1wCo97sMFSz24CNpw5513+nmDT6Dt2eJaUXMB6gg8WKzARemPP/4wi6jF2dik0DhEwQgFDMK//vrLrHJDcYOs48IDBIXbpk0bo5wx2GF1gDBgBQzzNfxd8dLZbrvtTDoMLJDMK6+80tyLtFj1P+SQQwgrzthpBw8uBjh24QCJ5YcP6aG4MSFg8LsutnLINPvvv795ACFEcOEntuq0LQyoGwJ0sboGgQDfOQgXXkVgUz3KZosILBwQNLNnzzZ5I6AYbUE9YYnAxRMC7gGOEFKwFgCfd999l0aNGuVjg3LteAqsCqAuCHjGhBh2pSsUUD77J9v5woKEiRf1RHtwQSBhtZFXQWQa1BPlM852fjyO4ApWs2bN0Hbol2UfAZ03Eju4lYV5A/W0LcRBIxDzZCbnDVhVQZLOP/9881/njbL/7JdmC8aPH28WKyU/QfmwXoM/QCjw+2qPPfYwxJ+JK6wGCMwfMmSIWcUHOQfZxuIiLvAR7CQYFp/AMTt333234Qu48N787LPPDIHHBa4B7gQSD2sAX3jXQ1Rg0fDXX381vvx4j8ry7NgL3Id2yE1EbrzxRuPNAcHBF9KBm2BBNxtcC+VEzQWwnGBxVLYH3h1Y/MQipLwwT0JM4Tv2HCn0uaCghAJUHB4eqEE76BYWBZjXbHMeFCwIOuIa8ADhgnqH5QEPEBQ1X4gbAJmGa0/FihX9z11BRCDaeLCDIuxBZOGqA2sCLiYrmBwQqIcLKh1WClgM+OLJhsk4zGQg/LBMwISH68EHHzSmQtlWWBfKly9vyDMuJuoDBw70g3bRDqyic53kJAFc77333qTnASseEFMsRiCgQOZdW5jaD1K6QiFs6zZMlphA0e81atQwRUEYYkKG8ENZW221lVmlgZjC9qloS1h8BCaWfNsuzsZQ/84MAjpvlJ15A3MmLJjYCAGkIuyKeoZd8wa2gIYQsecNlNW7d2/jmgmfblhbXa6MUWVmZsRqLmURAV7QsvkJ3lVYpJQXVrExxviCiACRxgXiCiLP7118tv322xvyj8WyoIvfubJ8kH8IDyyU4oJgAceQngf4HBYBjHte+KxXr57hKbw4ybxEvjPRXrgms88/uBZW7UHImWux6LcX5TLJtVD/qOcSwh+uUHDHwu6KuGCtAa/ClunADhwImypcd911ZpEYeITlG1VmWRrDBSUUpCuP3Qkg3HjA7FXk119/3bjoQOUeddRRJhl80jBQ4WrDxBN+snio8HDC5C0vPDR4IHgVHWrz5JNPNqYrfGdf2FoVQgZuPxxvgAcVDx5bGHh1Hgoepjq+uG58HyYG3IN4Bb7gBoTAXX6o4YqDtmPlglfl8RPEHg8xYjtwQTDBcvLQQw/5efEqoy2a4KIF6wdbH5AA+OM+CIWoKx2hAMGECSnIooAHEoIOZfPF8SVYzUDfT5061Td/cpuChAKLNhUKUb1YGN/rvJHox7Iwb2CugvsFrKkgHiAjQf7YmZw35Gop5mPMr3IBB/jpvFEY80G2WhG0EAerNcZU1C6N4CpY2ANXqVChgtmBB88DL2zalgq7HbBmwEuAFwJ5xZ9djXE/LAAQ4nC54Yvfl3wfL05KbwhwC7gps3sy0kKIQ9jz9qYuroW4CCzUyC1QM8m1uA0NGjQwvMbFIWCJgUs5+I+0KMATBWIBHAIu7XzGFRYN4JWC/sIujPDIQDykvAptLigooQAij1V1DAgoVahcfvgw0XNsgexQmPTwkErSCDPb6tWrDdGGpQGDh91yoMaxYg/zHFaU+KGBKQ0vJlwYSBgo8OGXh3VwuexTL4koRAIINvZARhr492EQQnTwIITaxYoXYjDgeoQLkw92SpI79MCaAd9GDk5CG2EiwwOIekOg4KGXW6nBLAnXKsRvQBzhezwAME1CFCBOArjwg4QXNMpkkyXqwkIK25PaZjx70kpHKCDuAVubuWIUGEsILrTLviDeNm7caGIc7IkvSChg0sIqiwqFbL0y8ytfnTcS/ZHv84Y9l+LFjfmRF1vsUZXJeUNu04h5EKSLgxy5XJ038uu5zrfaYNEQC402P8FKNd7xUijgXQ1BAM8DvMuxOMnvK7yPwQPAORC3yIuMbClwcQ5gYbsoczqQe3AWeCHACwLkWO6chHc53I5B6LE7EhYn4XoEjsIXFluxcQhciHCxeAGvgfs2+Bh4hc21wNcw/yImIhtci+sHqw3q6OIQsNzAdStot0nUGeIJbuDywqIq5kxwJvsqtLmgoIQCm3oaN25s1CEGLcxD2DUIOwpBgdvnH/DKOKtjFgQwbWPFCrsi4QEGsYWyBnnES4JNc3ho4HcnTVa8+oRBhC07sZptX1DuIOEg81wmhAdcYvBwQTBgYMPkhwGMiQMBT3gA4T7EvvOYfEDa2bcR24Oi7VJgyFgCnkTgkwfBg3YjT7QPwgiTEe7BqgNW4vE7Podwgk8eCxIEamOnBf6bVx2QH9qB1QW0L+hioYAXMLAIuzApwW0KgeL2xf6dNqnHpAlXAQgE4I/JKK5QYDMv3AwQIK5XYSOg8waZbYXzed7gjSnkSMTmE5ibg7ZHzfS8gbJBKuDCCFdH+12i80ZhzxMlbV3QPIP3NzgKrAW4ePGRz4KyD2iTXAT3y7+xig8C61o0A5/AhQVADo7G+xqLmhARa9asMVwCC6oIbsb1xhtvGJdtcBB4SeBCfeAtIT0HsKgJ3sICg/kAXKRBtFEm3I7ghcBcizkAVvnBc7LFtVBnWEvhTu7aOZHrEcQhwH+wICp3yERfQvABA7gs2VehzQUFJRSgukEcQaqxow8GJT9IGJxBgcUgzfD5A9nnrUjxkOKBkIMAg4MDb6BA+aGBDxv7/nN5UOtwywlatUb9UF9+2dWvX98QYQxIGYQMpY28EBOB+6HubcIrBzgeUDwMyJ8Ds/EZTybynAE81BAmwAmxEXgBYoDD6sD7DfNDhMAmaWoHDlKMLFu2zJj18ALF6kjQnszID2ZNuFZBfOBC0DfyDtq+FH0A1S7zRD7oaz5pGa4ACLyW/2GehVBk/8u4QgGuXLAGYZs2vQofgdKYN4CiDKzF3FKW5g0ZA1Xa8wbPX66RCKFQWvMGrxJiJVWSBq6XzhuFP1eUpIVB8wzyxMKb5CfyIMERI0aYVX6+4CcP4o0YQVx4l2JlHkQfq95BLkxwTwbPAVcBqQe3wP2wFMCiAB6EC+MY4gBeBv/973+NxU4umLksIOAD0vWJ+QAW7FAfjqdgroXv8VzD/dnmWmg72pipORP5wFqC7fDlWUw2hwB3QPyi/AkOAT4TFPsBPoa5wLYuFtpcUFBCoSQPsaZNDwG4dcmzHtJLnd7dWL2AqRWrHDw5ppdD8t1RMQogHohR4R0lSlKWplUEFIEiBMrqvAHCg1VPXvjB6ihWZeWl84aOdEUgPxGAQEAwOMQPrB/FvZAe57nw4ix4Aly47djXQpsLVCgUd8RoulJFAMFEMB0G7SIVtzIQCbjgjgWzKPwm5e4RMLUipsPehSJu/nqfIqAI5A8CmZo3sGIKCxCCR/Efrp7YZ54vzBvYAQ6xXHopAopA/iEAqwtiPkrKIbDLJDwxECANTwt4Nkg3xEKcC1Qo5N941hoFIICXtX2mRLpgcZA7p8PKIAsFuEIh5gMm4qitF9MtV+9XBBSB3CCQiXmDFxhkC9jlSeeN3PSrlqoIpIMAb+GKWEvEmRb3wi5J2EgB1gW4HmEBga9CnQtUKBR3tGi6nCAQtKNDJiqDnZWwg1RQLEsmytA8FAFFoPQRgI81DpnMxqXzRjZQ1TwVgcwjgI1jsAuT3LEpk6UU6lygQiGTo0TzUgQUAUVAEVAEFAFFQBFQBAoEARUKBdKR2gxFQBFQBBQBRUARUAQUAUUgkwioUMgkmpqXIqAIKAKKgCKgCCgCioAiUCAIqFAokI7UZigCioAioAgoAoqAIqAIKAKZRECFQibR1LwUAUVAEVAEFAFFQBFQBBSBAkFAhUKBdKQ2QxFQBBQBRUARUAQUAUVAEcgkAioUMomm5qUIKAKKgCKgCCgCioAioAgUCAIqFAqkI7UZioAioAgoAoqAIqAIKAKKQCYRUKGQSTQ1L0VAEVAEFAFFQBFQBBQBRaBAEFChUCAdqc1QBBQBRUARUAQUAUVAEVAEMomACoVMoql5KQKKgCKgCCgCioAioAgoAgWCgAqFAulIbYYioAgoAoqAIqAIKAKKgCKQSQRUKGQSTc1LEVAEFAFFQBFQBBQBRUARKBAEVCgUSEdqMxQBRUARUAQUAUVAEVAEFIFMIqBCIZNoal6KgCKgCCgCioAioAgoAopAgSCgQqFAOlKboQgoAoqAIqAIKAKKgCKgCGQSARUKmURT81IEFAFFQBFQBBQBRUARUAQKBAEVCgXSkdoMRUARUAQKBYFRo0ZR69atU5ozcuRIOvTQQ/Oima+//jq1a9eO8qlO2Qbm33//pYsuuoh22WUXevjhh6lKlSrZLlLzVwQUgRwjoEIhxx2gxZdtBFyEpkGDBrT//vvTaaedRm3btqVtttkmrxvJhOe1114zJKA0LiYcKAvlV69evTSK1TLKCAIqFHLXUTwfXHHFFSliQIVC7vpFS1YEcoWACoVcIa/lFgQCTGiwsrjvvvuaNuFlOnToUJo4cSLttdde9PTTT9Phhx9O5cqVy8s2Dxw4kJ588knq2LEjnX766aVSx0WLFlHnzp1NWX369KHtt9++VMrVQsomAvm4ep+PdcpE76pQyASKmociUDgIqFAonL7UluQAARYK9mr8xo0b6csvv6RbbrmFFi9ebFbN88VlIgcwlUqRYQSH+wmWnrfeeot23HFH33piWzRWrVpFN9xwA9WpU4d69Ohh6s55BzXkzjvv9O/le1yr4scff3ySBSUqX1mey+LTu3dv6tmzZ1K1XHUplQ7IYiFhpDwKw2zhESUU+Puo8tGH7733nhmXWFgIu3766Sc677zz6Oyzz04Zb1mE38+6NCwKpVFGaWClZSgChYKACoVC6UltR04QCBIKXJkpU6bQ+eefTwceeCD169cv792QcgJihgplYsZiQJIuJtS2UPj888/JJnKZEAph5FW6dESR3CChEOSaw/cXmt98WRMKTHYxvmxxaA93FQrJiKhQyNCEqNkoAhlCQIVChoDUbDZPBKKEwoYNG+iOO+6g5557jj766CNq3ry5AWrq1KnG/xduPwsXLqSjjz7auP4grmGLLbYw93DeX331Fc2ZM4fuuece+uuvv+jSSy+lW2+9lbbeemvq37+/cRtasGABXXnllXTTTTfRDjvsYNL/8ssvJs13331HWInEBRcoCJdLLrnED0TkcphcMlHG/Z06dTJ1f//99016uFjdeOONfhnr16+nQYMGmTrAgoKy0ZYOHTqYn9wWe3TIMjgoEoQJdWFcXnrpJdMuXj2tV69e6CADmXzllVdo3rx5SSuuTDxg0eGVW7YogMjhksTaJRRkwVF9zulnzZqVEn+BugCrm2++2RkIGpaW68CrypMnT04RObgHOCDYVFqwXJYHW0wErVZH1cmVt8u/neuGMRQkgII6OGr1ntNF9Y1NQjGuZX2CVv8l5igL1h1+HlyiDPW4+uqrzTMNLGxrkJ2fq92yLi6MZZogcczjHffCEoExg0v2jxQ1Uf1i4/fAAw/4Fi1Xnwf1m2usxRHNLqxd9S80oRw68emXikCWEVChkGWANfvCRiCKmKD1INLw/Wey8PXXXxuivuWWWxphUK1aNRoyZAh98803dN999xmyD4LNeR9yyCG0bNkycy9IMFwUQPZB0n/99Vc69thjafr06fTxxx/T9ddfT7169aIKFSoYgQBB0bJlSz9YGEQB9912220mRgD3BQkF1LtSpUp0wAEHGIvItGnTTNrLL7+c7r77bvPdY489Zlwg0B7Ub8mSJfTyyy/TunXrQoOUg4TCI488QlWrVqU999zTiBoIo3feeceIjmeeeSY0lgFEAxgieByWHHYpQvtAtK666iqDjXQ9atKkCX3xxRcGIxYsJRUKJQnUjiLlGE9MGuMEn3N+wM6+bHJZHKEQRmBtspbOvXZdsyEUME769u2bgouNa3GsN2grxm737t3psssuS9klqDSFAosV2dCSCgWOKXrzzTeT8LPFQraFQhiOKhYK+92rrSs9BFQolB7WWlIBIhBHKPA9WCG85pprzGo7YhieeOIJ4wePa82aNSao96mnnqIPP/yQDjroIJ/Ad+nShbp27WoEBSwUIOaPP/64IfoQFdiiMA7BRDmbNm0yFg7U6Y033qAaNWoECoVJkyYZd6kWLVqYQGwIk27dupnVd6QF0frPf/5DO++8Mz366KO01VZbmbbgvsGDB5sV7aAg5SCh8PzzzxtcTjzxRCpfvrypL4LBQbykRcY1lFgowLLSvn17k0+zZs38eIOjjjrKrPJKoYD6//bbb2ZVlAliSYWCJPOuld6wxyCqH9N1y0B+WPUFWZQ7SzFpl2QqXaEQVheUu3LlyqQyIcROOumkJD/8uDtuZVoouCxJ/JwGkWiJlRQPQZYZxCdhF7Eo16Ko7+V4SSdGQQoz2zoBERO0vWnYnBbkUiUFqcQjHaHA7Yw7xmVdXONYiv8CfPVokxSBUkNAhUKpQa0FFSIC6QoFrP4fd9xxhuhffPHFSZBgFfycc84xrkVY8bdX+vlmuNc89NBDKcGPLsKxdOlSAin69NNP6X//+59xPYCrkySwYa5HNpmQZdeqVcuslsLagfbsvvvusbs4zPXIDi4eMWKEsS5ErRCyUIC1A4IGIuzcc881LhcQDbhcQuGEE04whG7u3LkG0/r166cEM8uGxelzKRY4bRzRECUU0iGKYZ3hInDpCgVJDqMCdoPqEhfLTAsF1MceZy6CKkU+B7ZzW4LqhM9fffXVJIsWzoQIwijbQiHqubH7Jo5QcJ2j4MIqm0IhrJ7sxmj3cewJSm9UBBQBHwEVCjoYFIESIBCH6MCtCOIgyq+ZiUrDhg3pwQcfpPHjx5tDp+wXPV5+cJmwd0mxCQcINtyE4OcP152mTZtS48aN6bPPPqOxY8f66dMRCnbZcAvCCj7EB1bw2rRpQ8ccc4z5GXYYUzpCIUgw2d3GQgHiBr7nwAi4QyzhswkTJjiFAkSCJDmIH7B3PSqOUOA0tutKWHBrtoRCkP93SSwKaJ/L9SPMJSrIjSfKjSrTQsFFdF1CIU4QdVR8S5QrWjaFQtzdlOKO77DV/rC4g7jxMKhHXItCVExDHGFegqlfkyoCmw0CKhQ2m67WhmYDgSihYAczz5gxI/A010wKBV7tnz17tglG5jMeeKVbEoiSCAXkBzcGxC4MGzaMxowZYywXZ555pokpgGuT68q2UPjjjz/84E0moRxgarseQSjI1XHELAAfuT1qXCIVNsbirMBHCYUo0mmXH3R/JiwKsixXLISLQI8ePTpJ4EY9P1Gr9+mshkeR0EwIheLGM8Ql9OlYlNIRIHHHtwqFbLxFNE9FIL8RUKGQ3/2jtctzBKKIDoKML7jgAj9YFq4/WOXGajd8oeUV1/UojkUB+Qbtt24TiJIKBdkGxFogpgBB1djRCQHOuRAKsGa42ulyPeLTqJmEob477bSTsebY7ib4LqrPw4Zs1GpplFBgoYeYijjuPkFjJR2hkK44cfn6B1mF4mKZK4tCWP1cdYranci1M1A6hD6fhYILq6B+C3PpinpG+PmKO3by/BWi1VME8h4BFQp530VawXxGIOhlhWDlcePGmWBjuOVgdxDssIMTiRHMvHbtWkOo69ata5oXFsxcHNcjBD6DAMPfXp7fgMBdbHn6559/ltj1qGbNmmbHoDPOOIMqV67sdxPHFOA7xGTkSijY5YZZFPhe6c4QRMSjCAoTHQRKswix8w/KO45QkO4+Ljcm5AHssdOWa4ckmV66/PDnsEaxb3dYWcABvvh2HAunkcGkjKtstwxGjRI9uRIKLkzQl3Kc8PMZ1u8s8FyWg3R2sQqqj+sZS0eAyPRxYhTs80ekyJYuka5+j3rGGEeOGQo6hC4omFkKCfyuB13m8xtU61YWEFChUBZ6SeuYtwjwSxV7sbN7D15gQ4cOpYkTJ5qgYexk1KpVK78NvD0qPmjbtq3ZPShse9TiCAUEFiPgGTsjnXLKKaZ8uOMgqBkXzjvgF3pxLQp8FgGEEAKwEf8AC8qLL75oBFAuXY9c8RFxhEIQeY3yhw4iwK6BG+Y7HUcoIM8oFxeuT9R9yItXufE7YjPsrVSxew/iPOxzIaLydsU/8D7+LlzCYjfixAoETRKuvokbo8AE3z79Gn3IlkFuZ5D1RhJXWKnibr/qsj6EbXfr2o42rktTcYSCC++450VgjOE8kaATpoMsM3Gx47qlG8idty8arZgikEMEVCjkEHwtuuwj4CJLDRo0MAIBbjcsBGRLseUn3IywpWicA9eKIxSwCgdS8eyzz5oVX5B5xA0gUBfbr2YiRmGPPfYwMQkDBgwwZ0UgNgHlYmtSWE2C4hOARbZjFIorFCQJlwQzHaHAfe0iO1Er53GFQjpl2GOUyRbXTxJ0OzgZ9eXgbtcBcq5g5iAh5Mobrl2MbZiAyqVQsMUC44UtgLFAgOeTt+HFvUHbjoa5cLnGV9ChdUEHpJWmUIC1DGILwoevoIB0u22oJxYZglwj5fxgi1ZXGXHxKPtvG22BIpAbBFQo5AZ3LVURUAQUAUVAEVAEFAFFQBHIawRUKOR192jlFAFFQBFQBBQBRUARUAQUgdwgoEIhN7hrqYqAIqAIKAKKgCKgCCgCikBeI6BCIa+7RyunCCgCioAioAgoAoqAIqAI5AYBFQq5wV1LVQQUAUVAEVAEFAFFQBFQBPIaARUKed09WjlFQBFQBBQBRUARUAQUAUUgNwioUMgN7lqqIqAIKAKKgCKgCCgCioAikNcIqFDI6+7RyikCioAioAgoAoqAIqAIKAK5QUCFQm5w11IVAUVAEVAEFAFFQBFQBBSBvEZAhUJed49WThFQBBQBRUARUAQUAUVAEcgNAioUcoO7lqoIKAKKgCKgCCgCioAioAjkNQIqFPK6e7RyioAioAgoAoqAIqAIKAKKQG7iB4nDAAAAbElEQVQQUKGQG9y1VEVAEVAEFAFFQBFQBBQBRSCvEVChkNfdo5VTBBQBRUARUAQUAUVAEVAEcoOACoXc4K6lKgKKgCKgCCgCioAioAgoAnmNgAqFvO4erZwioAgoAoqAIqAIKAKKgCKQGwT+H70NuwC+pAveAAAAAElFTkSuQmCC" } }, "cell_type": "markdown", "id": "9ee1890a", "metadata": {}, "source": [ "" ] }, { "cell_type": "code", "execution_count": null, "id": "8c441b37", "metadata": { "scrolled": true }, "outputs": [], "source": [ "import torch\n", "import pytorch_lightning as pl" ] }, { "cell_type": "markdown", "id": "1bca038c", "metadata": {}, "source": [ "## Initialize dataset" ] }, { "cell_type": "code", "execution_count": null, "id": "23c5c320", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.datasets.mnist import MNISTCausalAttribute\n", "\n", "# dataset class initialization requires mandatory param `data_dir`\n", "# `download` is passed to torchvision.datasets.MNIST and downloads data if not present \n", "data_dir = 'data'\n", "dataset = MNISTCausalAttribute(data_dir, download=True)" ] }, { "cell_type": "markdown", "id": "eb49277b", "metadata": {}, "source": [ "## Initialize data loaders\n", "\n", "`get_loaders` returns data loaders for training, validation, and test. `loaders` returned is a dictionary of `train_loaders`, `val_loaders`, `test_loaders`. There are two scenarios supported currently to initialize validation domains:\n", "\n", "**Method 1**: When a domain(s) from the dataset is explicitly specified as the validation domain <br>\n", "**Method 2**: When no specific validation domain is present, a subset of the training domain(s) is used to create the validation set\n", "\n", "Run either cell below Method 1 or Method 2 as required." ] }, { "cell_type": "code", "execution_count": null, "id": "64dd7f3c", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.dataloaders.get_data_loader import get_loaders" ] }, { "cell_type": "markdown", "id": "51cbb17d", "metadata": {}, "source": [ "### Method 1: Provide validation domain explicitly\n", "Provide index of validation domains as `val_envs`. `test_envs` is an optional parameter." ] }, { "cell_type": "code", "execution_count": null, "id": "a75b74fa", "metadata": {}, "outputs": [], "source": [ "loaders = get_loaders(dataset, train_envs=[0, 1], batch_size=64,\n", " val_envs=[2], test_envs=[3])" ] }, { "cell_type": "markdown", "id": "9da42119", "metadata": {}, "source": [ "### Method 2: Validation set using subset of training data\n", "\n", "`val_envs`, `test_envs` are optional parameters. If `val_envs` is not provided, a subset of training data is used for creating the validation set. The fraction of training data used is determined by `holdout_fraction`." ] }, { "cell_type": "code", "execution_count": null, "id": "5201cd08", "metadata": {}, "outputs": [], "source": [ "loaders = get_loaders(dataset, train_envs=[0, 1], batch_size=64,\n", " holdout_fraction=0.2, test_envs=[3])" ] }, { "cell_type": "markdown", "id": "2b599691", "metadata": {}, "source": [ "The code below handles more than one validation or test domains, if present. Run the cell below irrespective of Method 1 or 2 used above." ] }, { "cell_type": "code", "execution_count": null, "id": "afbd74ef", "metadata": {}, "outputs": [], "source": [ "# handle multiple validation and test domains if present \n", "from pytorch_lightning.trainer.supporters import CombinedLoader\n", "\n", "if len(loaders['val_loaders']) > 1:\n", " val_loaders = loaders['val_loaders']\n", " loaders['val_loaders'] = CombinedLoader(val_loaders)\n", " \n", "if len(loaders['test_loaders']) > 1:\n", " test_loaders = loaders['test_loaders']\n", " loaders['test_loaders'] = CombinedLoader(test_loaders)" ] }, { "cell_type": "markdown", "id": "106cea0d", "metadata": {}, "source": [ "## Initialize model and algorithm " ] }, { "cell_type": "code", "execution_count": null, "id": "5b977c7f", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.models.networks import MNIST_MLP, Classifier" ] }, { "cell_type": "markdown", "id": "cbe00718", "metadata": {}, "source": [ "`model` below is expected to be of type `torch.nn.Sequential` with two `torch.nn.Module` elements (feature extractor and classifier). We provide sample networks (MLP, ResNet) in `dowhy.causal_prediction.models.networks` but the user can flexibly use any model.\n" ] }, { "cell_type": "code", "execution_count": null, "id": "9a663fe7", "metadata": {}, "outputs": [], "source": [ "featurizer = MNIST_MLP(dataset.input_shape)\n", "classifier = Classifier(\n", " featurizer.n_outputs,\n", " dataset.num_classes)\n", "\n", "model = torch.nn.Sequential(featurizer, classifier)" ] }, { "cell_type": "markdown", "id": "adf11498", "metadata": {}, "source": [ "### Initialize algorithm class: ERM" ] }, { "cell_type": "code", "execution_count": null, "id": "51fadf20", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.algorithms.erm import ERM" ] }, { "cell_type": "code", "execution_count": null, "id": "633da7eb", "metadata": {}, "outputs": [], "source": [ "algorithm = ERM(model, lr=1e-3)" ] }, { "cell_type": "markdown", "id": "94828541", "metadata": {}, "source": [ "## Fit predictor and start training\n", "\n", "Note: The optimal accuracy for `MNISTCausalAttribute` (and other MNIST variants introduced) is **75%** as we introduce 25% noise following previous work." ] }, { "cell_type": "code", "execution_count": null, "id": "27f43e54", "metadata": {}, "outputs": [], "source": [ "trainer = pl.Trainer(devices=1, max_epochs=5) \n", "\n", "# val_loaders is optional param\n", "trainer.fit(algorithm, loaders['train_loaders'], loaders['val_loaders'])" ] }, { "cell_type": "markdown", "id": "8635befc", "metadata": {}, "source": [ "## Evaluate on test domain\n", "\n", "Perform an evaluation epoch over the test set using `trainer.test`. `ckpt_path` determines the model to be used for evaluation -- 'best', 'last', or path to a specific checkpoint. If `ckpt_path` is not passed, best model checkpoint from the previous `trainer.fit` is loaded (https://pytorch-lightning.readthedocs.io/en/stable/_modules/pytorch_lightning/trainer/trainer.html#Trainer.test).\n", "\n", "We report accuracy (`test_acc`) and cross-entropy loss (`test_loss`) on the test domains/test set." ] }, { "cell_type": "code", "execution_count": null, "id": "4f382191", "metadata": { "scrolled": true }, "outputs": [], "source": [ "if 'test_loaders' in loaders:\n", " trainer.test(dataloaders=loaders['test_loaders'], ckpt_path='best')" ] }, { "cell_type": "markdown", "id": "bb318eea", "metadata": {}, "source": [ "## Prediction with CACM\n", "\n", "We now train and evaluate the above dataset with CACM. We specify the type of shifts present using list `attr_types` provided as input to CACM. Further instructions regarding using CACM with multi-attribute shifts is provided in the next section." ] }, { "cell_type": "code", "execution_count": null, "id": "08881e8d", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.algorithms.cacm import CACM" ] }, { "cell_type": "code", "execution_count": null, "id": "48c87949", "metadata": {}, "outputs": [], "source": [ "# `attr_types` list contains type of attributes present (supports 'causal' and 'ind' currently)\n", "algorithm = CACM(model, lr=1e-3, gamma=1e-2, attr_types=['causal'], lambda_causal=100.)" ] }, { "cell_type": "code", "execution_count": null, "id": "2a9b1e8a", "metadata": {}, "outputs": [], "source": [ "trainer = pl.Trainer(devices=1, max_epochs=5) \n", "\n", "trainer.fit(algorithm, loaders['train_loaders'], loaders['val_loaders'])" ] }, { "cell_type": "code", "execution_count": null, "id": "cf1640b9", "metadata": {}, "outputs": [], "source": [ "if 'test_loaders' in loaders:\n", " trainer.test(dataloaders=loaders['test_loaders'], ckpt_path='best')" ] }, { "cell_type": "markdown", "id": "72066ad0", "metadata": {}, "source": [ "## Extending to different datasets and algorithms" ] }, { "cell_type": "markdown", "id": "a249be81", "metadata": {}, "source": [ "### MNIST Independent and Causal+Independent datasets\n", "\n", "We show how to perform the above evaluation for `MNISTIndAttribute` and`MNISTCausalIndAttribute` datasets. Additional `attr_types` should be provided to CACM algorithm for handling multiple shifts. We currently support `Causal` and `Independent` distribution shifts in the data." ] }, { "cell_type": "markdown", "id": "9a025cb3", "metadata": {}, "source": [ "#### `MNISTIndAttribute`: Single-attribute *Independent* shift" ] }, { "cell_type": "code", "execution_count": null, "id": "8c5cd482", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.datasets.mnist import MNISTIndAttribute\n", "\n", "data_dir = 'data'\n", "dataset = MNISTIndAttribute(data_dir)" ] }, { "cell_type": "code", "execution_count": null, "id": "4b28707a", "metadata": {}, "outputs": [], "source": [ "algorithm = CACM(model, lr=1e-3, gamma=1e-2, attr_types=['ind'], lambda_ind=10.)" ] }, { "cell_type": "markdown", "id": "aa460184", "metadata": {}, "source": [ "#### `MNISTCausalIndAttribute`: Multi-attribute *Causal*+*Independent* shift" ] }, { "cell_type": "code", "execution_count": null, "id": "17b446be", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.datasets.mnist import MNISTCausalIndAttribute\n", "\n", "data_dir = 'data'\n", "dataset = MNISTCausalIndAttribute(data_dir)" ] }, { "cell_type": "code", "execution_count": null, "id": "9448cc87", "metadata": {}, "outputs": [], "source": [ "# `attr_types` can be provided in any order\n", "algorithm = CACM(model, lr=1e-3, gamma=1e-2, attr_types=['ind', 'causal'], lambda_causal=100., lambda_ind=10.)" ] }, { "cell_type": "markdown", "id": "829479bc", "metadata": {}, "source": [ "### Additional datasets and algorithms\n", "\n", "We provide our demo on MNIST using ERM and *CACM* algorithms. It is possible to extend the evaluation to new datasets and algorithms for evaluation.\n", "\n", "\n", "New datasets can be added to `dowhy.causal_prediction.datasets` and imported here, as we did for MNIST. We provide description of the MNIST dataset (and variants) in `dowhy.causal_prediction.datasets.mnist` that will be helpful in creating new dataset classes. We currently support `Causal` and `Independent` distribution shifts in the data.\n", "\n", "We have implemented ERM in `dowhy.causal_prediction.algorithms` as a baseline. Additional algorithms can be added by overriding the `training_step` function in base class `PredictionAlgorithm`." ] }, { "cell_type": "code", "execution_count": null, "id": "6206a050", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.11" } }, "nbformat": 4, "nbformat_minor": 5 }

{ "cells": [ { "cell_type": "markdown", "id": "7a84e574", "metadata": {}, "source": [ "# Demo for DoWhy Causal Prediction on MNIST\n", "\n", "The goal of this notebook is to demonstrate an example of causal prediction using *Causally Adaptive Constraint Minimization (CACM)* (https://arxiv.org/abs/2206.07837) [1]. " ] }, { "cell_type": "markdown", "id": "900de7ec", "metadata": {}, "source": [ "## Multi-attribute distribution shift datasets \n", "\n", "Domain generalization literature has largely focused on datasets with a single kind of distribution shift over one attribute. Using MNIST as an example, domains are created either by adding new values of a spurious attribute like rotation (e.g., Rotated-MNIST dataset [2]) or domains exhibit different values of correlation between the class label and a spurious attribute like color (e.g., Colored-MNIST [3]). However, real-world data often has multiple distribution shifts over different attributes. For example, satellite imagery data demonstrates distribution shifts over time as well as the region captured.\n", "\n", "\n", "### Multi-attribute MNIST\n", "\n", "We create a *multi-attribute* shift variant of MNIST, where both the color and rotation angle of digits can shift across data distributions. Hence, we create three variants of MNIST -- `MNISTCausalAttribute` (single-attribute shift), `MNISTIndAttribute` (single-attribute shift), `MNISTCausalIndAttribute` (multi-attribute shift). To describe, `Causal`, `Ind`, and `CausalInd` datasets better, consider the causal graph for the data generating process below:\n", "\n" ] }, { "attachments": { "main_fig_mnist.drawio.png": { "image/png": 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" } }, "cell_type": "markdown", "id": "4d22f37d", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "id": "f2667794", "metadata": {}, "source": [ "Distribution shifts are characterized based on the relationship between spurious attributes ***A*** and the classification label *Y*.\n", "1. `Causal`: Attribute has a direct-*Causal* relationship with the class label i.e., *Y* causing attribute (e.g., *Color* here)\n", "2. `Ind`: Attribute is *Independent* of the class label (e.g., *Rotation* here)\n", "3. `CausalInd`: Different attributes having *Causal* and *Independent* relationships with *Y* co-exist in the data" ] }, { "cell_type": "markdown", "id": "56115bbd", "metadata": {}, "source": [ "### Domains in multi-attribute MNIST" ] }, { "cell_type": "markdown", "id": "403765c4", "metadata": {}, "source": [ "We describe the domains for our *multi-attribute* shift dataset `MNISTCausalIndAttribute`.<br> \n", "Each domain E_i has a specific *Rotation* angle r_i and a specific correlation corr_i between *Color C* and label *Y* . Our setup consists of 3 domains:\n", "E₁, E₂ are training domains, E₃ is the test domain. We define corr_i = P(*Y* = 1|*C* = 1) = P(*Y* = 0|*C* = 0) in E_i. In our setup, r₁ = 15◦, r₂ = 60◦, r₃ = 90◦ and corr₁ = 0.9, corr₂ = 0.8, corr₃ = 0.1. All environments have 25% label noise, as in [3]\n", "\n", "\n", "Other dataset-related details can be found in `dowhy.causal_prediction.datasets`." ] }, { "attachments": { "MNIST_visualize.drawio%20%283%29.png": { "image/png": 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" } }, "cell_type": "markdown", "id": "9ee1890a", "metadata": {}, "source": [ "" ] }, { "cell_type": "code", "execution_count": null, "id": "8c441b37", "metadata": { "scrolled": true }, "outputs": [], "source": [ "import torch\n", "import pytorch_lightning as pl" ] }, { "cell_type": "markdown", "id": "1bca038c", "metadata": {}, "source": [ "## Initialize dataset" ] }, { "cell_type": "code", "execution_count": null, "id": "23c5c320", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.datasets.mnist import MNISTCausalAttribute\n", "\n", "# dataset class initialization requires mandatory param `data_dir`\n", "# `download` is passed to torchvision.datasets.MNIST and downloads data if not present \n", "data_dir = 'data'\n", "dataset = MNISTCausalAttribute(data_dir, download=True)" ] }, { "cell_type": "markdown", "id": "eb49277b", "metadata": {}, "source": [ "## Initialize data loaders\n", "\n", "`get_loaders` returns data loaders for training, validation, and test. `loaders` returned is a dictionary of `train_loaders`, `val_loaders`, `test_loaders`. There are two scenarios supported currently to initialize validation domains:\n", "\n", "**Method 1**: When a domain(s) from the dataset is explicitly specified as the validation domain <br>\n", "**Method 2**: When no specific validation domain is present, a subset of the training domain(s) is used to create the validation set\n", "\n", "Run either cell below Method 1 or Method 2 as required." ] }, { "cell_type": "code", "execution_count": null, "id": "64dd7f3c", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.dataloaders.get_data_loader import get_loaders" ] }, { "cell_type": "markdown", "id": "51cbb17d", "metadata": {}, "source": [ "### Method 1: Provide validation domain explicitly\n", "Provide index of validation domains as `val_envs`. `test_envs` is an optional parameter." ] }, { "cell_type": "code", "execution_count": null, "id": "a75b74fa", "metadata": {}, "outputs": [], "source": [ "loaders = get_loaders(dataset, train_envs=[0, 1], batch_size=64,\n", " val_envs=[2], test_envs=[3])" ] }, { "cell_type": "markdown", "id": "9da42119", "metadata": {}, "source": [ "### Method 2: Validation set using subset of training data\n", "\n", "`val_envs`, `test_envs` are optional parameters. If `val_envs` is not provided, a subset of training data is used for creating the validation set. The fraction of training data used is determined by `holdout_fraction`." ] }, { "cell_type": "code", "execution_count": null, "id": "5201cd08", "metadata": {}, "outputs": [], "source": [ "loaders = get_loaders(dataset, train_envs=[0, 1], batch_size=64,\n", " holdout_fraction=0.2, test_envs=[3])" ] }, { "cell_type": "markdown", "id": "2b599691", "metadata": {}, "source": [ "The code below handles more than one validation or test domains, if present. Run the cell below irrespective of Method 1 or 2 used above." ] }, { "cell_type": "code", "execution_count": null, "id": "afbd74ef", "metadata": {}, "outputs": [], "source": [ "# handle multiple validation and test domains if present \n", "from pytorch_lightning.trainer.supporters import CombinedLoader\n", "\n", "if len(loaders['val_loaders']) > 1:\n", " val_loaders = loaders['val_loaders']\n", " loaders['val_loaders'] = CombinedLoader(val_loaders)\n", " \n", "if len(loaders['test_loaders']) > 1:\n", " test_loaders = loaders['test_loaders']\n", " loaders['test_loaders'] = CombinedLoader(test_loaders)" ] }, { "cell_type": "markdown", "id": "106cea0d", "metadata": {}, "source": [ "## Initialize model and algorithm " ] }, { "cell_type": "code", "execution_count": null, "id": "5b977c7f", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.models.networks import MNIST_MLP, Classifier" ] }, { "cell_type": "markdown", "id": "cbe00718", "metadata": {}, "source": [ "`model` below is expected to be of type `torch.nn.Sequential` with two `torch.nn.Module` elements (feature extractor and classifier). We provide sample networks (MLP, ResNet) in `dowhy.causal_prediction.models.networks` but the user can flexibly use any model.\n" ] }, { "cell_type": "code", "execution_count": null, "id": "9a663fe7", "metadata": {}, "outputs": [], "source": [ "featurizer = MNIST_MLP(dataset.input_shape)\n", "classifier = Classifier(\n", " featurizer.n_outputs,\n", " dataset.num_classes)\n", "\n", "model = torch.nn.Sequential(featurizer, classifier)" ] }, { "cell_type": "markdown", "id": "adf11498", "metadata": {}, "source": [ "### Initialize algorithm class: ERM\n", "\n", "We have implemented Empirical Risk Minimization (ERM) in `dowhy.causal_prediction.algorithms` as a baseline." ] }, { "cell_type": "code", "execution_count": null, "id": "51fadf20", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.algorithms.erm import ERM" ] }, { "cell_type": "code", "execution_count": null, "id": "633da7eb", "metadata": {}, "outputs": [], "source": [ "algorithm = ERM(model, lr=1e-3)" ] }, { "cell_type": "markdown", "id": "94828541", "metadata": {}, "source": [ "## Fit predictor and start training\n", "\n", "Note: The optimal accuracy for `MNISTCausalAttribute` (and other MNIST variants introduced) is **75%** as we introduce 25% noise following previous work." ] }, { "cell_type": "code", "execution_count": null, "id": "27f43e54", "metadata": {}, "outputs": [], "source": [ "trainer = pl.Trainer(devices=1, max_epochs=5) \n", "\n", "# val_loaders is optional param\n", "trainer.fit(algorithm, loaders['train_loaders'], loaders['val_loaders'])" ] }, { "cell_type": "markdown", "id": "8635befc", "metadata": {}, "source": [ "## Evaluate on test domain\n", "\n", "Perform an evaluation epoch over the test set using `trainer.test`. `ckpt_path` determines the model to be used for evaluation -- 'best', 'last', or path to a specific checkpoint. If `ckpt_path` is not passed, best model checkpoint from the previous `trainer.fit` is loaded (https://pytorch-lightning.readthedocs.io/en/stable/_modules/pytorch_lightning/trainer/trainer.html#Trainer.test).\n", "\n", "We report accuracy (`test_acc`) and cross-entropy loss (`test_loss`) on the test domains/test set." ] }, { "cell_type": "code", "execution_count": null, "id": "4f382191", "metadata": { "scrolled": true }, "outputs": [], "source": [ "if 'test_loaders' in loaders:\n", " trainer.test(dataloaders=loaders['test_loaders'], ckpt_path='best')" ] }, { "cell_type": "markdown", "id": "bb318eea", "metadata": {}, "source": [ "## Prediction with *CACM*\n", "\n", "We now train and evaluate the above dataset with *CACM*. We specify the type of shifts present using list `attr_types` provided as input to *CACM*. Further instructions regarding using *CACM* with multi-attribute shifts is provided in the next section." ] }, { "cell_type": "code", "execution_count": null, "id": "08881e8d", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.algorithms.cacm import CACM" ] }, { "cell_type": "code", "execution_count": null, "id": "48c87949", "metadata": {}, "outputs": [], "source": [ "# `attr_types` list contains type of attributes present (supports 'causal', 'conf', ind', and 'sel' currently)\n", "algorithm = CACM(model, lr=1e-3, gamma=1e-2, attr_types=['causal'], lambda_causal=100.)" ] }, { "cell_type": "code", "execution_count": null, "id": "2a9b1e8a", "metadata": {}, "outputs": [], "source": [ "trainer = pl.Trainer(devices=1, max_epochs=5) \n", "\n", "trainer.fit(algorithm, loaders['train_loaders'], loaders['val_loaders'])" ] }, { "cell_type": "code", "execution_count": null, "id": "cf1640b9", "metadata": {}, "outputs": [], "source": [ "if 'test_loaders' in loaders:\n", " trainer.test(dataloaders=loaders['test_loaders'], ckpt_path='best')" ] }, { "cell_type": "markdown", "id": "72066ad0", "metadata": {}, "source": [ "## Extending to different datasets and algorithms" ] }, { "cell_type": "markdown", "id": "a249be81", "metadata": {}, "source": [ "### MNIST Independent and Causal+Independent datasets\n", "\n", "We show how to perform the above evaluation for `MNISTIndAttribute` and`MNISTCausalIndAttribute` datasets. Additional `attr_types` should be provided to *CACM* algorithm for handling multiple shifts. We currently support *Causal*, *Confounded*, *Independent*, and *Selected* distribution shifts in the data." ] }, { "cell_type": "markdown", "id": "9a025cb3", "metadata": {}, "source": [ "#### `MNISTIndAttribute`: Single-attribute *Independent* shift" ] }, { "cell_type": "code", "execution_count": null, "id": "8c5cd482", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.datasets.mnist import MNISTIndAttribute\n", "\n", "data_dir = 'data'\n", "dataset = MNISTIndAttribute(data_dir)" ] }, { "cell_type": "code", "execution_count": null, "id": "4b28707a", "metadata": {}, "outputs": [], "source": [ "algorithm = CACM(model, lr=1e-3, gamma=1e-2, attr_types=['ind'], lambda_ind=10., E_eq_A=[0])" ] }, { "cell_type": "markdown", "id": "aa460184", "metadata": {}, "source": [ "#### `MNISTCausalIndAttribute`: Multi-attribute *Causal*+*Independent* shift" ] }, { "cell_type": "code", "execution_count": null, "id": "17b446be", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.datasets.mnist import MNISTCausalIndAttribute\n", "\n", "data_dir = 'data'\n", "dataset = MNISTCausalIndAttribute(data_dir)" ] }, { "cell_type": "code", "execution_count": null, "id": "9448cc87", "metadata": {}, "outputs": [], "source": [ "# `attr_types` should be ordered consistent with the attribute order in dataset class\n", "algorithm = CACM(model, lr=1e-3, gamma=1e-2, attr_types=['causal', 'ind'], lambda_causal=100., lambda_ind=10., E_eq_A=[1])" ] }, { "cell_type": "markdown", "id": "829479bc", "metadata": {}, "source": [ "### Additional datasets and algorithms\n", "\n", "We provide our demo on MNIST using ERM and *CACM* algorithms. It is possible to extend the evaluation to new datasets and algorithms for evaluation.\n", "\n", "\n", "New datasets can be added to `dowhy.causal_prediction.datasets` and imported here, as we did for MNIST. We provide description of the MNIST dataset (and variants) in `dowhy.causal_prediction.datasets.mnist` that will be helpful in creating new dataset classes. We currently support *Causal*, *Confounded*, *Independent*, and *Selected* distribution shifts in the data. \n", "\n", "We have implemented ERM in `dowhy.causal_prediction.algorithms` as a baseline. Additional algorithms can be added by overriding the `training_step` function in base class `PredictionAlgorithm`." ] }, { "cell_type": "markdown", "id": "3b9e1b05", "metadata": {}, "source": [ "## References\n", "\n", "[1] Kaur, J.N., Kıcıman, E., & Sharma, A. (2022). Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization. ArXiv, abs/2206.07837.\n", "\n", "[2] Ghifary, M., Kleijn, W., Zhang, M., & Balduzzi, D. (2015). Domain Generalization for Object Recognition with Multi-task Autoencoders. 2015 IEEE International Conference on Computer Vision (ICCV), 2551-2559.<br>\n", "\n", "[3] Arjovsky, M., Bottou, L., Gulrajani, I., & Lopez-Paz, D. (2019). Invariant Risk Minimization. ArXiv, abs/1907.02893.\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.16" } }, "nbformat": 4, "nbformat_minor": 5 }

jivatneet

c87b511bfd95caec2bc50e4686ed21ca448e9c37

0fb1314c5ddaeb6805109453433ac6248063f3c5

can remove the first sentence: we're adding.. to dowhy"

amit-sharma

py-why/dowhy

Adding general version of CACM

This PR is the first step toward a general causal prediction API. The API supports _Causal, Independent, Confounded,_ and _Selected_ shifts (individual and multi-attribute settings) currently. The regularization has been implemented using `unconditional_reg` and `conditional_reg` functions, which can be used for the general _CACM_ API. Follow up: implement Phase I of _CACM_ for deriving conditional independence constraints given arbitrary graphs.

2023-04-18 10:36:38+00:00

2023-06-15 11:59:01+00:00

docs/source/example_notebooks/prediction/dowhy_causal_prediction_demo.ipynb

{ "cells": [ { "cell_type": "markdown", "id": "7a84e574", "metadata": {}, "source": [ "# Demo for DoWhy Causal Prediction on MNIST\n", "\n", "We're adding prediction functionality to DoWhy. The goal of this notebook is to demonstrate an example of causal prediction using *Causally Adaptive Constraint Minimization (CACM)* [1]. \n", "\n", "[1] Kaur, J.N., Kıcıman, E., & Sharma, A. (2022). Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization. ArXiv, abs/2206.07837." ] }, { "cell_type": "markdown", "id": "900de7ec", "metadata": {}, "source": [ "## Multi-attribute distribution shift datasets \n", "\n", "Domain generalization literature has largely focused on datasets with a single kind of distribution shift over one attribute. Using MNIST as an example, domains are created either by adding new values of a spurious attribute like rotation (e.g., Rotated-MNIST dataset [2]) or domains exhibit different values of correlation between the class label and a spurious attribute like color (e.g., Colored-MNIST [3]). However, real-world data often has multiple distribution shifts over different attributes. For example, satellite imagery data demonstrates distribution shifts over time as well as the region captured.\n", "\n", "[2] Ghifary, M., Kleijn, W., Zhang, M., & Balduzzi, D. (2015). Domain Generalization for Object Recognition with Multi-task Autoencoders. 2015 IEEE International Conference on Computer Vision (ICCV), 2551-2559.<br>\n", "[3] Arjovsky, M., Bottou, L., Gulrajani, I., & Lopez-Paz, D. (2019). Invariant Risk Minimization. ArXiv, abs/1907.02893.\n", "\n", "\n", "### Multi-attribute MNIST\n", "\n", "We create a *multi-attribute* shift variant of MNIST, where both the color and rotation angle of digits can shift across data distributions. Hence, we create three variants of MNIST -- `MNISTCausalAttribute` (single-attribute shift), `MNISTIndAttribute` (single-attribute shift), `MNISTCausalIndAttribute` (multi-attribute shift). To describe, `Causal`, `Ind`, and `CausalInd` datasets better, consider the causal graph for the data generating process below:\n", "\n" ] }, { "attachments": { "main_fig_mnist.drawio.png": { "image/png": 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" } }, "cell_type": "markdown", "id": "4d22f37d", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "id": "f2667794", "metadata": {}, "source": [ "Distribution shifts are characterized based on the relationship between spurious attributes ***A*** and the classification label *Y*.\n", "1. `Causal`: Attribute has a direct-*Causal* relationship with the class label i.e., *Y* causing attribute (e.g., *Color* here)\n", "2. `Ind`: Attribute is *Independent* of the class label (e.g., *Rotation* here)\n", "3. `CausalInd`: Different attributes having *Causal* and *Independent* relationships with *Y* co-exist in the data" ] }, { "cell_type": "markdown", "id": "56115bbd", "metadata": {}, "source": [ "### Domains in multi-attribute MNIST" ] }, { "cell_type": "markdown", "id": "403765c4", "metadata": {}, "source": [ "We describe the domains for our *multi-attribute* shift dataset `MNISTCausalIndAttribute`.<br> \n", "Each domain E_i has a specific *Rotation* angle r_i and a specific correlation corr_i between *Color C* and label *Y* . Our setup consists of 3 domains:\n", "E₁, E₂ are training domains, E₃ is the test domain. We define corr_i = P(*Y* = 1|*C* = 1) = P(*Y* = 0|*C* = 0) in E_i. In our setup, r₁ = 15◦, r₂ = 60◦, r₃ = 90◦ and corr₁ = 0.9, corr₂ = 0.8, corr₃ = 0.1. All environments have 25% label noise, as in [3]\n", "\n", "\n", "Other dataset-related details can be found in `dowhy.causal_prediction.datasets`." ] }, { "attachments": { "MNIST_visualize.drawio%20%283%29.png": { "image/png": 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4TWU5eHM3MDhVVHd6Qk9nOUJ3cDdoZGhYcmM5V1VkOVk5R2VmSmVGdVNaNmxoYkFZMU5wJTJCRVM5YUNndmN3bXoxNk1oMmpBdWZEcHZsNXZaOHgwWEVzYkM2NVRSM3BSWTFBUFJqOEtmNEowbnhjWk9vWHc5M2F3Nk4lMkZzT1FtNXE3STk1dlpYaVNvNm1zNGRYQjIzZTZiUHpRZXclMkZzZjZOTlZ1NURGJTJGenRSeDZpbGNicHppbFlWbjRvd012RU01MDVleVJSdGxyUUo1MmdHS1dOOUNIRHJ0eFN4NDdTQ3BQJTJCNG1heXYlMkJPU3R3bVdweTlqJTJGaFZFTTJjYnFIWEVVQ2EwcSUyRmxIeWVqcVY2WmZHOVlNRFdhN2NicE5lVU9yeE0zUzJUSVUzTVd2dms4N0RUdVNCJTJCTzFsUnFZZVd1VDRyWHRTOTJtdUVIR2g5WWQlMkIyYW5wJTJCRzhFd1JZMDFQdVpreUtoTmJaJTJCUFA1WG1zJTJGY1BacW5Vb3ZHcWp1WlNBMiUyQkZPV3lreU1yNHdVekRvalNDeVJ0NmxiWnVtVnZPeXlFbVlaWWZOTzlJVld0YzJlZktGVE1PTHJLbmlQbnFMYWY4RmlycDhJRHpYNFNLNTFwTGVqSk1SUzZwbSUyQkhJZjdrNXdrUlR4WUhkUEdiTFdjcTI4WU5Kd0hxWlp4STc5NVpFVnRHbzclMkZqUjFZOUVSTm8yM1hSWWZGTlRiczdpbk40WGhmJTJCd3Fld2JXQ0pwZTJHeEcyTUttOGRBSHFxVndPUEtzdHNZUG5sZThjdmUxajRvM3JNVE1pNjd6eFViSDJyRlZUNDNDcjRZb3ZYQjlXczFDNGNmQ3ZhciUyQm9EcUladFRib041VUNMenM2bnY5Y2JXa1klMkI5NFp0VGRiNHROYjVOdDFENzglMkJuVmZYOXpoUHQyU21DOVNEV0hnY05QaVJnemdYUyUyQm42dU9helBTYU9SUlVxRjhsVXNkTXJlJTJCVjhQWWZZdklWOHBVNTd1Rk9nZUNBeGNaSyUyQmZiMXB6QzclMkJSMCUyQjlBYzhjWUNZbTFibVdSaENwOUFHdkg4WjBLZGRaVFN4RVJ6UGl5T2NYaUxJRkZhd1VZQW9aMjRYN0lEaDNPTm5ldUJiUjVkVFdINHJwaUlMJTJGQ2UlMkY2bkhUZ0NlMmlqalloYU5WdWFKJTJCOW1YRXlCYUVhendJbU9Mc2tUblZjcE44UzRTTjZPVmFDU3Z1V1RSUlQ3M0dqYlpFU1dmbzR3WGE5S2xuMXFGUUhnOHNhRDNMMkw2bmJCUmtHJTJCN2xrYkxyWWZ2aGlkWjdESFMxbXVuMkFVNFZVVVM5VVhBUlRhSkJHdTNPRmlMU0RNN1QxMkhTJTJGQ0Y5UlNHeFhGOEU5RFlHbEJycFg4N05sTllBWkFGWHhROFJBZXJ4NHdCbE41JTJGa3dvdlNBb3lVeUFBZFdUOSUyQmppd2JEdmgxM2ZIcVRwbEhXcHZxbHRYc2ZjNjN5VzhKRXdkWEhwNGQzNjd2SlM5Qkd6YjJmR0hHbGhlMTFsUlhsMThqRyUyQmtzckd1WU0zJTJCUG1mc05IU1Q0aFdPTWZ6T3djNnZGRTZ6dGFha3U3YVlOTVI0SlNwWEhjZUFiU3l4UFhvUWQ4bG44S05EckVkSnJaQVI5VGV5M1YyOGZtZXhSMWMxVFZkZVFIVDFNcWFxR2IlMkJucklSc3RSM3pYaW5mdVFyUzBZb3hpajVnMXU0bSUyQmYwZnlISGFVUWphaXdBNEVwa0puNVI1bVFWJTJCVXdqbyUyQlJvV2RKUWFkM2I3bHJHMTM4bE5UUUVENU1ONjc5SjBVZVM5VHBPT3A4WHVMTU9Hcm5ub3NnUHYxZWZENnFadTZJckMyT09TcjclMkZYOFdSUWt0RnJOaGhwdVo2bWRqMW53S3FFcFQ4VGNFUHhWZVhXdmN0YjdwNVU2dmZiYWUlMkJOVFJFMHhGWmpZMjczJTJGU01VSHVVbU1TZmk3cVltMzVsdmlidEd5WEdENmRxS3lRUlFEJTJGUnhGVVd1MktwZlIlMkJxM0V0T0x6bFlkM1JqZ3pOSVZlJTJCNnk5NHFOTWU5MGhZRjc3Z3FDRVpLJTJCVHp0eGFkVzE0MyUyRnFLUXpvZDhLdnpJQjFOZEY0aTNJOWhVcGFSTkQ3WSUyRmszNU9ycVA4c3IzbCUyQm95d1dLUWRwRCUyQmdPakMzOUQlMkZ4WHRudmpnRTBPd2JxJTJCd2dXM0duTWFoZVRxVGYxeHRGdXlXSVIlMkJHVGVnZ3QxSXZCUGNvN2ZKOHIxcWIlMkJNN25VeTRVdXpxeHJZWXZZdU9EdXMxRWoyeU95cXh3Zjh0JTJCZ1hCVDVPUVk0dzJZMkpHWVJ2MDZ5akFnWXB4eEo2b0FBMmkxcGdvVGNQbHpwWTJwSHNZUXdvUmNHWlhGODVXWURpSzFuY3k2cjJ4UlhYYm1VSlpTRWE3JTJGQyUyQnIyR1JvM1FaSWQxWXNabnVqRE9oY21EQ0tGTmN6OE5naTdJMiUyRjZxOUxLWm00ZnhHWmVPWjRQS2s3TFlGUnhDZVlVTlJSMUk2eUhGMkFPJTJGc1pYOG9KWVhOSjB4ZEdBTjhkWDVwcDlyRU1aUVExVEpRbWIlMkJRNTI3OXRSUk5ZNEdXeEEzdDlVQWZ4cFdBR2IxYkM4Q1k4OE9ac3RleHgxNG1XcFVwSTc2RGd5VFRHNEQlMkJhR1ZpR3ZhNVRpZzYxZTZFcnZjb2NvYlVJemswYiUyRk1RSVFUWE5oVlVxSW9hU01hc3BjY2xZckUwQVY3SFhlbk82dHc5ZXllM2ZYREpDQkZzaU5hTGhRRk1HYnNDUDBLY1NZZkpJQWVzVXdnSUhwWE92VGs5cFdVOW8yMUpqRWI4OGJXZ1R5cFlPWlJ5bWIyTW9qVDFQOE1GT3dGOW04bWV4cGhkT2ZyQ29IcTJ5dyUyQmQ5d2hCVkR6eG53Z1E5NnJRcmM4YWV6N0ElMkJnMEV2WG11R3FUblJKREhid3BMTnp4JTJCOTF6NTUwczNBeVFCM1ZvM3VNV2NjS2J2TlVyRVBHcGE5dU5DSWhwRUpqVnJiU3VibFlRNCUyRlc1dWR1JTJCWG0xJTJCVER3Wmw2T1ZhOVhTNzRuJTJGTlJzVUVkUyUyQnNSS2ZGZVd6dnpFJTJCSUd0ME5wMHlQOXpjNGlaTWg3cTFZT0tyZGVYU284cDRxY00lMkJRJTJCJTJCekk0NWliZW5IOEZpSjA5UlZlV1ltZWhuYlU4ZmVUZ1dSWEZOZ0tGOVc0SWFzWVdpcXpmNWl4M2VYeWt4dndiQ05aSEJkQWFTRTZDeTJ6M2p0alJWYThWdXdmNyUyRlAzQ25GWVdXaWxKZXcwMjJ3WThMWXpTZ21EQk5oRXA4bGdVVnpZYUtaWUduS3czanV5JTJCREU2Zk1EcHVNU25qTVJzWDNnREZXVzN5JTJGRlc1aEpoTndzUWZKMmVGS2x0b0pCMWVvJTJGMVVuZTBIcG9CRnoxUkFqU2pDM1c5Q0R2YzhQM1ZXN1o4b3FUS2w0N3QxMVRmcHRFdzdYUXM3WmRkaU92UFU5bEpYWjVzNkl1YzY4NUt4QWwlMkJRblhwdXZNQnZVR3V0YmpCR0JWNUtydVI4bGpxTGs2ZzhmbEVZakFxVnFTQkMlMkZNS29nVUx6enI1YU1FVmFDVER3VUlNaEwxeEtLOERQNE9hOFYzVmNEc0R0MmJKam1EMmhTM1B5RUJyQkklMkZ4Qnd0UE44aE1YcEJRZ0xqZzFlTHZhR25mUW5rV2hNcDVtcHlCZTM0UGI3Y0VWdGN6Wk5lZ1ZNaTNNdFZNMEdYSHklMkZVRzZwNmc2ZWtUMXNNSzJNYUJuMFNRbCUyQmlrc1RPeWxEc3EwckVpMVpWQXNldUlrU1ZrZlJ5bmglMkZXT040JTJGVmJTeEN6JTJGeEh0R3BoWmo4QUtyYllPWlZ1cFpremQ4dzFTYnlmR3BkZyUyQmM5c1FJcEFoQ0hQOVNPdEtyb3hra1VPYlB6SWhGJTJCSG1ZUzRVZGd3WHR0bmVncCUyRmo0V0VmbTNzUFdibndvS3ptZk1tTnJMTE1NOEdlb3YzVVhzJTJGWmtVaEtGWU00S0VmN2JjanRuMFlqWWIwMVVDc25rS2RpdnJvc0xVeTREaCUyQnJJcThSNnBYdmdRRzNKOFA1YkRXUWk4SlNIY0FlV0pSUzUwWWElMkJITyUyRjc0aWZJdk83ZlhIJTJCS2kwQVBpaTdtSUtObWh0QW9FNVkzJTJGR3RqYzBUeXJqc1VUdTlaa0tpWDUxNHZhand2bkZyNFRpU2ElMkY3YmZpNUM2SkwwWmJPeEh1Sk80NyUyQkd6M2QzcDFkQyUyRjFlMmpwYU94bkhLSE50Y2dVdWZvc2Ftb0RHOWlQaG9nZURyOUplV0duYTJMM2pzdXcxOFFaWGQyJTJGRGdYeWdFZGhobTEzZk45SlM3VjliN1Z1OUdaNmNxaG0lMkJldG0zaXpkSG5RVGtOaTJkNzBrdUFPYWhXbDBoc0pqdlhBa3J3JTJCYUtBWGQxdlVjWUxZM1ppRU9JdXVoVExjdWpzZExLMDR1YUExNzBEQ3lQQUp2Q1Y2TjZlQ05HYWhaREpKQ25XWmVJJTJGeVhvQzklMkJpOVdkV1BTaHBCJTJCYXdDNVR1Q0NOQjJrM2FNT2dqNEclMkYzZURUOEpRTWNNJTJCV09haWEySG5KRXdmQmxqQzFaM3JHMmg0ZXVEY3VVNWlSU2F6SmVHMG81bkxZMVlFcldFYWJSekhJM2tPZFd1Q2ZyRTRKODhXT0R5JTJCTXlzRFclMkZRZnY2RFczOThTSlczdmdwTzdyblJqNXZiZk80Q3lUZXJpeUhQTWVLWDg4RDZBMk16VUNBa20xajJyUkoxUmozTEdxZDl4Zm84ZE1NZk55VyUyQnpjaGpHenFSUFRTJTJCJTJCeU9Dd215eEVSNWxSJTJGWG0weGk2MlZoWHdoMmpQMWpWQlVQemVDNGVKZkVVU0Z6TDJVRHlVJTJCbTRDYTNvMUxEVWdEbjVQaVZsdXhwRTRMR1JmYlY4c0VET2RuMjVIbnZhcG5aTHRPQTBQelhVaEs2YWM0SUduWjkxbWlBOVpZSDdFbWJqelFCVFFrak9ya0lFRkxmNFVYTm9ISjVPJTJCbzNkOUd0c2lFYlh0R3pxdjBuWXNrcmVQbm9Ib2E0dmI3cEZ5WVVIYzclMkY5aDdoZiUyRnFjUzFZOXVVbCUyRnpMdDlJTVg0Z2pWVlpLNnNzQ1k4ZnlFeG1zMnU2Q3BwdDM3VUIzZmwlMkJIZTBpUDMlMkZxek1GclppaERLNDNkWnh0alltVm53cTNJcFdQNnYlMkI1RWlQTEY0algxdHJIQVZhdzQ1bldzQk1BQ2hlVUlpNE1kRzN5U1hlMGtJcE5XZU9kellQRThBSUZnazVjcm5JQmZUcEtlTWVuWE5kUDdheEtjZUd4RmRjYks5cmxhaVhLeSUyQjNwcExjUkJLUFRSYVE4U0Z5bFB5R1VIN3dGcE1FVTZEdll4TGZWdllqY2R3U1QzQjN1VWs5YWI3V2I0aUl4Q01JdTBtdUtyd0czJTJGMTV5M1clMkZHM2MwY01VUkp3VDR5UGQyYmNWcWdvRE4yVFVGMDFmc3R0QzNJYyUyRlRjczRVUkJHcnlvUkRjMiUyQktWejhlZXRtcTQ0dVFtZGJmc3hNT3VhY3NrUmMwN2Rya3NsSmRxUHNnWWxZWGRuNmdQZ1VXRUJtTUJJbkFSU2dPZVVQM1pMbG5LWVVtUUR3RXFiQktqY3ZzUTd1JTJCTzRrWVQlMkY1QkVQQVFlV3hWaXBGQmV0OXclMkZOdUVwbXRZamd2JTJCckVVaDB1TTBOT09sS0cxbmJHcWJIQkkzZ0pTb28zNVhLVWZ0VFpmeGhwcEdidjZCQ0FYRFZOTEs5a0l3c0k0SWRpMSUyQnUyUiUyRkE4M1dOb0lidEw5aCUyQkRZU2c4N3kxMFRqYkVvNGFadllTWWxNJTJCbVJpRTcwS053TXpKV2pzMDYzNDM1andKcjZvQ0N2ZmtyVldxbExXVHZiNGFuTWppN0xqbDhmTlRvczRjTDFoWEpqc0tsWTd6bXJ2b3p6cXRYMmI0ZHFKMjZKJTJCVnFQOFh0aW12Yk16UnJ4RjViYkkzcXM1ME0lMkZ3YWJxVXRUN3JobVFmQ241MjdOVzJOcHp0aTZqcXJ1ZVR0aHlRYThmazgxSXM5OW9aanF6dUJmbCUyQkh0bjhCZ1JUenFobSUyRnhUQTZwRHc0JTJGUnE5TWtUS1NiS1VJRGJtMEhWMEluJTJGbFBDendyUUlLSDVkdTVDekRIcG95YU9USGNINEQlMkIlMkJIdW53NXJHMXVFJTJGREtjJTJGcnU5ZmtuTk1jUGZzJTJGbHg0SSUyQk10V0JkVGp6Rk5XeUE5R2Y4S1J1Q2ZPemY0U2Y1MXRzdXRPbWdnRElqbiUyQlk4SHA5UWZhak9mMDF3QlRIc1luWW1uMjVQN0pyYkklMkJqQ2xzZUxJZjBHU3c1OEslMkZaOHFNcklnakdTa3dHVTJGdHhKc1l4WjhncGQwdFlIck9IViUyQjZoZnRYSXMlMkJXMUZtdSUyRlFiMWpVREZnbUFNbVNoZU9zUkJkcVlMRFZpV1FuJTJCSTFWaWw3VW9ZVTBwbEVaNER6QUNCRzJjWW5YMkdURGwxVTQ2RVhpNnk1VmI4QVFRVWdLVm44U0JHVm4xN1VYaWlWVUF5YlFjc3BSQnFHUjlleTJEY0cwSE1zZ1ZMVUhrbGc4V0NFNHd4VzBSa3VZQjRCb2glMkJmJTJGa25WR1V4JTJGckZrd05KQW54aVMlMkY4V3pYdXlWOUJlVFg5R3Z2Qkl6S0wwQmtoa2xMejZkN3VNSzd5TkpkV1BadFZFSEpjeW5qR1Q2eE45VGE0Rm02RG5Ja2RjWHFIakYySXlNRiUyQjBCOFl5Z0lDTEFoUmNnSDR4JTJCVDFFbnVOQ1hiR0Y5SGlIWXl3WG1JQVVPRjNWelRiMWFVSTglMkZYMlVEV1pMQlglMkJFRkpYc052YVpYTTFERWRraUF2ek5ZUHN3WXM3JTJCcVBQdlJYZGkxR2dkU0RkRlh0WGk5JTJGcmdFcmhMRmxTWUdZak10ZGJOZ3ZlMkZnczRBSmlVS1hNM3pIZk1nNE13Q0hob1o2V2ElMkJIRmNKNlI4RU56d3lGOUxhOHRyMzJhbkgwT3NMcndQSW1Zem1qTENSUlRwUDdZJTJCViUyRmpiMHVDJTJCRTVuaTBkZGhoR0ZnaWR1U0ZveFNCUEF3QUQ1cHJ5ZkRTYkVGc1JwOWdxVCUyRlMlMkJIVTBseVhzT25wdHEwaGVJODBTcWc4Q0JtYlBZUlZMR1gyUExibHI4MENQSnU3WTl3SVpJdW9JeVFkU0N2UXhTZ2tIdGc2U2o4WGlhZEFzMWQ0TkVoZ0JRRHFTbElXT1F5TmRPV082a1ZBJTJGeWpmMjFTM0hOQ05ib0ZjSERHcFIlMkI4JTJCOU1DMUsxTUclMkI2c1VWM1k4aE4lMkJoZjBCZUxDcVNUaXJyak45c1ZNalR3Q21zZVNIeVFtJTJGbmdQN002Mjh0RkYwRzd5akxaJTJGTmFKYmJxY1Qyc2ZxNWJxUlNBQyUyRnMyVEROV1hEeEQ5NTBpd09EYkxES21GaVpPcW02ZHgyTXd2ZmE0NkFHbEtPNGxvblVjbWhqczQyWFR5YThOS2N2bnVsVEZsOEd4ZWJjNHVFNFNBbHRYZnglMkZ3a2tpTk1pbVZCY0N1WGlHOFdqQ3g5a09La1l1U29Fb29RJTJGWnAwTHZpRTFOMGJ6dHJ6Y1BWS3FYJTJGMlpBNW5tOGROcHJIUko4dVNEQTdKU3RkZTk2R3B5ZzNkaEdlZlFiRzNiMEJ4SiUyRkV5SWlFUXFrUmklMkJDQW02JTJCNGxTTjBQMkRueHdSN3A1UW5KTURzUlp4JTJGSmh3MUh0dXBJOUlZN2slMkJQcDVxVyUyQk5nT0YlMkZQeEZvJTJCMHJEWURBZFdxYWM3OU5ZU1cyU2o5U0hUeDl1MlBPdDUwRnpOQ0x1cmNjbGFYdDl1UFhnRnNUdzRZcmNCams1Y0daTUI2U05ZOXolMkZyUkFtMktBVGlRRFdWZTQxMG10cnFtRjdKMXpuUVJMWVJPYW1xWllSMEh2WEJsOSUyQk1ZSVRKRlF0VFBVJTJCblNyUTJHREclMkJtRTJ1cFlZcUdZNUU2VU1rQm1McXZrUUJuRHVhVTAxdFlWNW5EcXVqdW83aSUyQjc1JTJGSnQ3aGVBQUUybTVJVHBLdU5QeDZuaXBnNVolMkZ0UTlmdVRSd0JsWWslMkJiJTJGSUJ2aUR0S0FFZGNiQ1pjbXdRWVU2N3VCR0F3eWZ5ZW40SVA1dFZGS0w5aXZ6M01UQkl3NVBqSiUyQjFKVVAxV0ZhYVRaYmlJeGNNNWJTQTZ5OVVYUDdGZVI5UkxleWIlMkJ2RUlDWEF2MTVSJTJCSTFFNE1iZWZoS1NiZWFVSHI5eWJHSFVxNWVkMGxNYWtPSU8xcUVJaSUyQmNBNlkwSGhJZlN0R2I4Wm9sMk1jMkVicWZHN055cGVtekFVOEdTQXlhJTJGU3RNN0pLMnd2Mzk1RXJBUkl2eDB5a2tYRVhKaXJ4cGhzcmhYY1h2QmZvQ2hXMk1Pc3BxTU5rNGpwSXdmUDRqWTM4MEVMNXN6UGtjbTViVTBVTlY1eElzeXZaSlFCTGNrSzYlMkI5TlZ1VTZ5UGlpUFRURHpVUEU5eGFlS2I1SVluelVGZnVLeTY5ZCUyQmlCZDR3OHpLdFZDdGolMkIwcEF6RCUyQlo2RUxFVU9XVlVlcVRDMUdibGNrQldqbHNRdHMlMkZJTDBiaVd4WUtuejF0V1JtNzl0OSUyQjhQWWFuJTJGZTVLSzJXMnZReVJMWG5DaVdQd2FqMURRciUyRmFwSDlHNXFCSlZ2MFJ0VCUyRjFvNlp1ZE1MSWx2WG1VaWFiN2V6YWIlMkZKYWxGNTJ4cVM1JTJGRHNuMzlSMURvZWN0ODNBYkd0MWhRRDRLOW9qd2NPSWJtZVJqUlpDbjJwaHVzTkJGZUlWdWR6SlFrYzFVVDYlMkZscE1rUE5oeU1aMjNDWjVYUU43eGlOTFpwd2hHaHppUnJ6ZVBEZFlXRG41a2h3TzdJc0tOekRxektFd1NldnFSaWUweXBJcWhZQXhUMTNtR0pqMHk1cWpLYUpVSkpOSjhUWW9sTGpzaTNGWkpTYnlHS3VLMTZXQVBkUkJLSXYzTiUyQlJORVBoT2F1QSUyRnV3b1QxN3AlMkZkJTJGU3BjbklVR1N3VGJyayUyRktxNXNzWTVRZVJ0SnhOYWNYaGNZdHd2ZmtjU0EyWFd2S004VTF2QmJ3c1I0UUw0WVBDaUxEVGtXMCUyQlM5allXQUlxdmZra1pjZ0pndDdNZlVUczRVeWlkZWYxM2RYaGFSM3FjVnY0d1hHZml6VWdBSWdCeFMzeWs2U2JoU1NmUXpZdmR5dWRxY055ZE0zTGxuQUsxUlFDbW1RR21yZHRZS3RybFBoJTJGREZtcSUyRlZCN2w0dFlrRGxWRFElMkZPV0tuV2ZRbSUyQjlYNjllTEE2ajVkTEtqMEhYcyUyQiUyQjVoYXFGdyUyQm1TTiUyQnVTcyUyRkM2T1A3UTQ4c01nZGpyaDRrUDZCRmt2RnFKJTJGbmVMS256OHh0YUhrWjFVdTJpejNPTzg4WUFBVlh4Y2VGblNlUE1VRmJoJTJGSzEyZWxRMFBMcHZuUXNZMzhQSFp1OERyMFFyOFJqRE1qb0xjM0pwQmlVc2llck9FYXpVUVRQUlIyVGRma3JmaFM0MHM2MmkwQTJUZE5iSjV1Y3ZpUUhQdSUyRnpuVDJuVVp5UVIlMkZZYmx3VFVTZDMlMkZDbjR1cndFb3F4UkZoOW9qOFBOcGYySU5hODBZTFVmT2dKN1YlMkJTUGhCWFpJUjNqOUwyTjZBc1Q2NnE4d3RxN1ZKdjRSRWJOdlcxc21CSkNOY1B2dEI5RGRYUmJadE54ZVMxSHdMJTJCdUIxNGRtV0dQdVU5dUZoY0NlbFhhNE5vUkt3UDZ2aUtEN3FNcGpUU2xOdyUyRk5JbDNLcGVPNFd1SDg2c3g3OVk5VWYlMkJyMER1V3NJejZWdHIlMkJTaGFNZVFGOVc3RUZNdG9qWDVBNiUyQjgwc0lHUllRJTJGZnMwMVN4JTJCSmFFVkRFcnRmSjlGT2trelFDaWRRY1VIUUJaT1dMNG9Mc2JhTGxDZG1TNFVHdVIyaFRxbXIlMkZteUFocUlhU2klMkZRb0I4bGdaSlk2WFYzJTJGJTJCODl1U05OaGJKYk1laHZ4QVp4ckZCTkR3UWtJcXZFZDh6ZFNXbldicWFqZWY5JTJCNExGVHdLcGZhSVJ4Q254bU9DZGNMb0Z2V2RjRHlhdjdDZ24zYUtncmRvdnJDaE5scnZUNmFNdG1sWlU2eksxUiUyRmYxWndPc1dLdyUyQmUzWjhQQjFkYUVLOHVxJTJGa2htY3ZYTWFCd1pENUZOZUlLd2owcnJUJTJCeSUyRk8lMkJnbWNJaTZ6am85YTB4RkpSWDVuZ29FZ244JTJGVG1YUEhoWmElMkJTb0Exam9xMDZVcVFUS1ZIOXVHV2UxV3cxV09JYiUyRlA1U0FLbFVJUjN4aGNIQ1NYYU1tdkk1dHBSNEZHdFhwcGJjN0FaMzdhejFjdnM1YSUyRm5JUWNJeSUyRlNLZlQ4aE9EMFNDN0VMN0hDSkNYZXpoM05tT2tuNiUyQkNxSlNnZ2dWUEo2MTJ0UGM2dCUyQndwdjB3VlcxV3lwdmslMkJyY2MlMkZiVUhqYmJhd2hxU05nQVV0dmxMTUtaV3JWJTJGaXk2MThtU2IwSEJLbUVWRmJzTjBzN1hRZ1Y3Z2h3ZnpCTGxDdGpQR3NSNFpGNkpkejcxQVAyY21Md21PRjBBU21oY1h2MGppN0E1cXk2Um5KU2VQQ3V1aHowMjhRMyUyRjJUSlMwaWtrUzAlMkJTTDVHM3BNakFTckZJM0d1VklhdWZUOE9UdnB5T2pJZHZaVDJYJTJGcXViMGhWbHIwbnZqV3p1Z0g0NlB6eDgwY1NDd1VqN0pIcXdPa0VsQVBhdm50RG8yNHNmWXpwa2YlMkZ0NnQwZm1PdHFrUyUyRnZMR080bGJhc3lDVXFVOGNZbiUyQmk0M2c5YXQlMkJ4Z3FONzRnRXJxSXJTM1dXZ3BrNVBESDlaODJVMkRwd3F0NnRPdG4zNmpwMXVoeUtrcFJrRkZvRkUlMkZQVWEwd1dibTVDb0FweVZYWTdWaHZXUXMzMXkzWWxYcHRlaUtRTXFZclpPRE4wTklYRmxhbG5TMFRIZjQlMkZXUzdWNVB5VW5JSmE0bVROU3htTThDY292ZzREYk5NeklGVEYxcURVOXRRQ3glMkJMOGRKN1o4UENlRkxJdm10Yko0ViUyQjM1OGVmeVBlUHgxS2pKZW9nMjIwOVV3UkRXbWFabkNuWFpRcmZ2OVIwcUdoZDM3MW1YcDI1ZmFoSnNYc053Qm5sdlZoYzNsVnpXSnNvZGRYaDQweTRwJTJGU3dodzZUWXhaWTlQVkJBRmZNZDBhb3pteDFBcUx1V0pGdTVmTWtsZ0x0R01hamxFTjgzcFpnWG5jQU5WakVQVTR3RG5LVE5NZmZ4WHY5UGczeFZZWFJIYVlKZjMzSWwyaUVBSEFhd2tjcWRrZkRGZzJkY1VDOG9iTHlTaHZDdDl0eE1lY2Y2dE5rcjdBR25GQ1RicjBSMEVIUVQ1cU5BbUZ4MTg3RWFhNDBjJTJGRDElMkYlMkZHNGJQcGVyME5ybmNJblhDQU9xJTJGMFFpRlVBRHhCWmFITjElMkZHdmZMVUtJbTNnU2ZuNWk4N3pVWHloU2lVNCUyQjBJQ2RPRzlDWk0lMkZDRUpXUEZGa3V5eE5hejlGdCUyRkhwZmRrZUVSSlhTbHVIZDlRQUhhYmxTMHN5Uk55TGoxOHlFYlB3aDBEcVhjd2hvdFVxWGVZSkVxbWFOakxNbmVxV281QnczUFVuQm5IVkQ0RVhBdUk0V3VzQzFKQWUxODlSSE4xNXc2Z0RjZmYlMkJJcWdzY0d5RSUyQjBzc1N1JTJCZ1Q2bnBGS1dzY2FtTkVWTmI5cGFURm02YmklMkIxWFAxdiUyQmVxdXVWWFdvd2dvemNOQzJVUktQM1pHNEJFY3ZrTU5JNGlvS0VFTEszSjlmY0c2VGZXYllxaGw3RnFBdzZ5cURyQSUyRmFkbWg2RUY0d0pFYnFQTE9EbFdOcjFCM1hiUVdRQ0QzbXowaTJ2Z1I5Y1JCcFVOZHB1TWglMkJrUFFRTGMzNnBjYjFqYkFVU0RiVVJ3ZnlFcG84dTZhZUhWS1g1V3pEazNDbnBzcXZpaHVUQmRZNkJZVzVNRkl4RFFiUCUyRjdUbEE3Z3hwJTJGUFJJWHMzZjA4dmoxRmN6NDdoYmFFSVR3STR1blZxdndNSUFBUEtGaFphbVRPU25BM1czcm1hY3ZkVkNIREhCJTJGc2J6VnglMkJQNXNuV0VzVmpISG8lMkJkQWNPbU0zM2dWMm5iUWJQc3FFJTJGSEpFNVpQJTJCRHdMTzVqNWJKVEQlMkJudFlkVmJiOHBuWENvSU0wQ0N6Z3dMZlp1MlZxaEdTYVIzcXE5WHZsV0w4RmJLS0hmTEhMOVpNWjRoZVdQMjFHRmQ3VDF6JTJGeEs3T0wlMkY1NHZPOFpvYUUlMkJuWEVzVjM1QUZJSWlKJTJGdjcyMlpQRGxxaUlXQTVEUjhVY0tjczgxcHZyUXlZYXdNOHdnJTJCaEswRSUyQjdFWTE0eUFKcjEyd1JVVUtKciUyRmxWRTJkY0h5UEpjSWNjVmg4amxJeXVXOWJITWt6ZUdDN09rMjd2c01FVmpaZEVjbGVsUnNVSE9McTJaNm1nMWNwa2YzT2lmSXclMkYySFklMkJZVXFwelowODZ3WlQ0NklBbjBHSFJqbGpoQXNTTm8zTXJiUFNUNGM3V3VmQ1YwSWNNQ3hMM1doS2wlMkJIakExQVlmRkwlMkZyUGdwZ3AlMkJ3eTN3VG9OTlFOTEwlMkI3M3Z3TjV0SVdJWHZIWWJwdXVIeU9pUUVNaHJvclhKcXhhaFYyZWRxbGJpMjh0dVQ2VmUyM0tTck14VHplMGJvb1UzT2ZYQ2Zaa0Zka1ExNDElMkZ6TVVTM2pTbkt0bUZHdEJZUlptMSUyRk1oRzZaUFYzRUVMaHFlT0FQQjg4RDhsbWk5eXJFMzRXcCUyQm9KM0pyQ0IlMkY0MDdocFI5SGdzbFM1WUdkaFc4TWlKSzh4eVljWDY5YVZIYjRJOCUyRjhtRkNTeTI2MzFPY3RMNG9DS041R2xyU2Fad0VZS042b29SYk5WTHVWQjFKZTV2blB4dEdQQnJaZUhZS2pzcG81ZmpqeEtKJTJCanF4WFpmVWpDR0hZV2dFTWVxOHMlMkY2eUM4Q3NDdDFLZENmUHIwQ3JUa3BTY2NmQ0sxWW82NGVJQTVDZHNFclpKYkQ5NnpVdkNzQ2VuQUxVeDBYZUVBT3dBJTJGRExYVVJHUERqN3g0bDdlU0xUR2dxNjFoOXlDRkVHSzZMNXdwOG5rS2tDJTJCMG1GUSUyQnV6WHE4YnVBOVR1RUU2cHElMkZmcThUVk1vUUg3TWNXR2hiSmNhS2VvdEhxdjBwdVdhYkd6SU8yUEM4azE2VkowdWF6d0NBOVl5YjM1ZVZYayUyRiUyQm9IRm9WbG9Za3RoT1l4TVczVyUyQnhHQUNLTXpWbEdReWpxNDZaMU5KS2NRUW01WTVMVnIzNFY1amVFQjd1NmdmSjJGUGh6WEpaOEpUcmRzZHE2S1RSZVduTDFwZEg1NzJXQ08zZ2daaThJciUyRnlJRGNRd3Eza212MWY4dWJ0NGJ4WjNmRWtXQWl6eiUyQldxcUYlMkJBdkRqdFIzUjZtUGN2RWR1Z3hXSSUyQll2UkNxVmpqUTZ3RmtKeU9uVzdVdzVLNlFJMEY4Y0slMkZZbFdMcmdsdDUlMkI2RDE3QXc1JTJCeHRHSzFaT2lXWGslMkJMNDdWWDBEa3ZyNyUyRnFIbk8lMkZhJTJCOHdzcCUyQmR2MzhwQTdHQ3NoSkxSUExxd0I1aXlNbUd6N0RSWkVWSUh0YVlqUURXNlBHb0pOUGtXeHdtR3R0S3dIa3p3dTFrZHc5SDM0OUxYUDJQY0daeHZGODR6OVJtTVo4MWg3bnRnZlNKb3U3RGdlU0swWTBmYXBWa1ZESkw1cFFQRDQwQ0ExaFdVZ1VvcW9VcU1WU3NBVzBNNWpRVGx6WVRvOWw5YjZMUDhpYjAyS3dISHNMMDNKZUtJZUo0dXJ3R2pWUHE2d0p6TzFTUjNiWFVxSWRHWiUyRjNUV3BzaU94JTJCalpacXJFbDNLOTk4TSUyRlNOTVFiTWNHSUphbFdRSkRHeGhIdkxTWGdxa1ZRd2o2ZVExY01yMm1QNE96WmxpJTJGTm1ZakNUakNxWlZJRlo3RmQ0RGdaS2hramVRTmJ0cUZSRm5OYXlRTkM4dWtNVHBwJTJGQVVTNnpmN0N4TXdHaUdYZk9IYjNDQSUyQlJLZlJPZ2IwcWpDUzhPcW8lMkIzOE95eEhQQzVTOVgzVU85YmVwYVJ5ZjVuU3FCazYzNmUxdU1jNmVoS2xBS2hNJTJCMnYlMkJtNnd0cHJoMnVwcTlFZzQwJTJGSFNaYmxVYzdXanEyeDlxbiUyQlByOWF5ZnFxYldFUGUyYkx4dUpFS2gwZVpMUXRlZWpJNUlmRzZIVnI0MDNkNG5IZ0p1S3UzNCUyQmVVMzM5cWdHS0Jza2FWT2I2bkNwS05GQjZKV1lMcFF6SyUyRnQ3MFFvTFhoSXRXOVhtMWxOa2pGT0RBVjQwekpnSVdxQzNrUnVWYzd5VEZoJTJCMVFzNXU0aSUyQmdzbE13bk8lMkZmZ2IlMkJOUElyU3VrdG1sWW5raHZiNXhVcG9idmJSbjN4cHRhYmtQUkZmSVZwZDZnOFFFdWZCUUwlMkZxY2tReGk5NTRqenlNWko1WWZaRmZQSHUzOHozJTJGZjFjSG54b0ZDdmFFZFJ4cEMyQmJDbzVVMDEzNEpYZEpUNm15YlZWZ05PU1Y0NkYxZkk3bjd1dXFlUDVzc0xGTmtUQnVuQ1cyMHNNMERBSUl4MzhlWDFma2RUYjhweGZqZXRKR05tcDc5RU9XMDJSU3lUQkZ0a0RacXlQMzlpdWhXUXklMkYlMkZXYmowR3ZMdUNUNTFkYU9OMm9GZFlDTCUyQmNKSlZ4ZXRJMEN6WWczJTJCVGslMkZrMVlEQldhRk93NGlVaE40alFZN0E3bG53VWY1UU1taFhUZERqYVBzM203b3hid1lCUFN5R2V1OWVPWlFCMnl4VElsTFNnJTJGT1B6UllSTjkzck8yeCUyRmZSNmZ5cUhUaWVjWCUyQlVadlo4NVc5Q2RaaTFxVnZVa1Z6djVrNFl2dUNkM0FFYm1MZjZEcCUyQmtac0tvdEJnaWFzUXNuJTJGSlI1eWY5MGN1NjclMkZsSXBBNk9OOW5mMm5UdEtiMmxkeU0lMkJMdjZXdTRoNmRPRWNyR3ZZMzVxUldrZklBZVZ3YVpsWDJGcG5URHlNWTRoZ3FaWjNkMHI2UWZ2TlgwWDJWOW8xQkxVbEd1eWc2amlWanZzcyUyQmhVVHFEb2VJT1huNFlVZjBsSjVmU2VBNGpjQWIzTXhldVExUVNCN2hMOTdvdVklMkIzTnZ5YmtPQ200cVJ0YWNlVmxXRlRkbFpzJTJCTGdzVHNSTkVPZG5hNzU3ak1vM0ZwWkZpc0tOTEx5YXAlMkZlOURJT3ZoS0VmR29ZTmlXaHF0RCUyQkwzZ1NzSkFYSkRtaWp3b1R6JTJGSE5yN1BIeGE0bnc0SmYlMkZNWlNaWU5FeU1LbCUyRmYlMkJ3UkZvUXZZTWdqQTZkJTJGemJwQ0xqNFFpJTJGaiUyQlNOeGhNQ2dUUXBCeDBPWTB1MDglMkJPd0RINm9mZ1lidjROb3BPTGNHWGt1WnlDalMlMkZTS2JDR0JHVDUySlQ2bSUyQkZVWnJtY2JsTmJuY2pPT0k5VzNMOXNOenRBcERZbEJZRk9DZVM4S1VJR0xQWndvRkNid0VIeldxNnY0V2NCRWkyYmRBY3BneTV3bmlSbWpZTnNlQnYlMkZnejNkSjdJdHBVUiUyRnVqOTdKdTBEbzVSdWxIN1c2OWZVWDF5eDUlMkJIUUlzM3pMeHVGOXNNblFxZnZ1YTBYRmE3dXhHRCUyRmtGUWhweHl1YTVIeGRqWEFSSnJxSlVhVjhnZnV5VGY5NGZWYyUyRnNCJTJGd2d5WExxbXklMkJIRGhUbDBmUldHOGYlMkZCenNpeVdhWDdpcXVMdmEwTktxaFVOYXhlMjdIVjdWNVoyWFg3aVN3WndYZU5qVkV2SUdmcUdXdjlDQlN0Tmpna2dTJTJGMEtYM1laUHglMkZQMWlrYmtsQm1zMGpqckRsNiUyQmJNRUtYMW1LZnV0clN0Y0lqSGRmUHdTdWYwR0c5T3VpRlI2R3UxQjM1dlQwayUyQlRjcjduTE4lMkJwT2pnWTEwbDVlSzFHQkhCU3UzUkE1b2FVMk96c1g0V0Fjc21aRkZsZ3lTVUZxa2FaYlIwTmFHSlJ5dVpnclhkeHhHQ1J2NUUyU1BVbHJvc0pmUWdhMjJFbkEzN3VaSHJoZkJQeTZ2Ylk4ZUwyR1U4VlRrRkVrOFNJcGJ4ZHRXSmpZdjdVaU9TJTJCRnZjZnYlMkZnbVlhMzQwdU5odkMyOVVYa0JDbkdpMzNLbWxPbkgzYnlRaCUyQmlwZmsxWkQ4MjNTRDJtSFhjVjZka3NJeiUyQlVoblBTTXFwRUlhclp4ZFJ6NnZia1pIamVMbXNRbXRYJTJGR0pwWFZGYVEycjJWelF3aTVXbUc3aHFVV1h5YVRoUVdzTjRWVXpVOGxiekhkJTJGb0poVmV0Zlg1QnpxeUJXYWVmaiUyQjhyNzl6SFdQTDBOJTJCTk5qRVJjSm93M0dDMUdITnV1R1dPUWdocVY2VGM1S1kxMFhUJTJGcXBFVUFSa08lMkJ3c21ibFZ5Y0hMM1VKaW9HVllKUksyZXd1QUwxQlNHRkR1VWdjeU52QSUyQkVPRExmcDdkV2NRclpoQ2VmMHBZdWZDMXMyaXlhbVNsRVhxVXhybW04TTNsZSUyQnlnTmNrOVplYnlxZjREOGpLNzVjQU1rMW9XbnVCZ1VEREFFYWx6MGNOcEpjbUZ0ZE04ZEMxR0tldnBqRW5aNndQa2ZYa3BLOUo5amVETUtZbFZkRHM1OFRmNjUySWQxdXlvMzM4TGh5eVR1QWp3Sk5lbCUyRktranNWJTJGQ1NzcVhPYVp4dDBROW9RVGpXYXNwanA0TzY3SGN3Q2pyVjklMkJDczlERjVDeU1XTzZwT0g3ODB2MzlpV3kzbUV3TUdjMmk4Y3dZdFZPYWNZOXFJVXNqWldqMFdNNXlIMk95aDRBM2R2R1FjR29kbWxsYkN4REQwbzNJTEJpJTJCSE1sd1JiVDVvMTBtR2F1a1lranJjMmpqcjElMkI1UkFiWG1hYlh2ZmpGWjhyUGF1M3Y2JTJCdDNvbSUyQlBkM2lFSkgwWEhyd1lEZmhBbktJcDNzYyUyQkdJTXBUYjFNd3p5MVpnYkR6JTJCTUFJaG5PZXRwc2x1N2hzeEJyNElFcUwlMkZIRlZsaVhtZks3c0d5SVBXSmZMZ3M1T3JFSWR5RWxtUGVwNzROdGFpZG9ldkx2TTJEQzk2ZllkQnJla0t0YVIlMkZPS0RCOXg2OE1KQm1wRTAzRmJTTjcxaGtaMnRSRWdlSVg0JTJCMXV0Wmw3M0VqRVZRZm1Mb0psSnNickFUWGdGTGxnRE1WM2pjJTJGVTNZSHdLJTJGeHJrdWYxQTJrTlUydnRQemtkdW55T2Nvdm9LMXZXRlM1MURTTm51Qk83elo0SlhvUUZJWldYUDRRb1hpJTJCelM4cDZ4OEI2RjRONUtyakJTJTJCd1RlTDU4JTJGJTJCSU1za2NsOGhvZEUzJTJCanJKU1ZYYmtVUWtLRHhIN3VZJTJCWVJYTk1kVjgxbk5sREU3SWdvaVk1SE9reENyMzBWa3kxMlpXVmVwbFZtSzJkUlVGendscGxJSHdvNlU5VG5yRlEyakFoeWxLQXdMaW5CVU10WGF6bXpUOWsxZnNQMVk3NUYyNWclMkZZV0hFd3BKZWkxcjk3Znp4N0FrVDNzOU9obUhTOTRKSnk0b2MzY09LY1VJUFZlcnRCcW82dU9sMURWTm5DR3l4cmFIUUs3YlFScSUyQnRhenZ2THQzdTQ0TEZ2dXpJUldyRVQ0VklGNjZNZ3dLJTJGJTJCWFROUFdKdEZNa01uJTJGbmR5UDJtbnBQMzFMWGRwN0R5M3J4MGx5YnkxTHNLUzZCaGN1ekhWcDhVTVR6UjNrTWUlMkZHb0hZU0QzQWkyMm0wRDU0dGNLWCUyQkQ4bXJySkhJeUVmUEkxYVIzc3ZTcXRIOFRaQjFiTmd4UzloTUpVJTJCWWxBN2pTbkh1VkFnNXdHZVgwTVNXNWt6T3NRR1FVVTNhVldCNjlCQ0lmJTJCUXZUd3ZWJTJCbTR3d2JOZGRHTUhMNnFPNldSZkZWaW0yZzB2VkVCdWtyRGRnVndoMVNlcFBuZ1hRYWptb0NXTUdhREJZZGpZa2VIdVpEMTF4TUtyZjJ0R0hYJTJCQzFPTElyOUJ0TCUyRk1rJTJGbTgyJTJGcFRvaGZsUHY0Mlc1ZDZtYVpwZzZsSWVtbUZJeDBKRlkzYm9FZUg3dFppbFlNb0hyUGZqJTJGeE5QNTJiR0JQb0xkSTZFcUhWZENUMzM2SXhWbyUyQjZ0bXM2dGY2aUlyTVdqUEdOciUyQklkQWRrMzE1am5aNFBSRWRiNmdxUzNQOU9sQSUyRldIMFpiTzIlMkJ4VVN0ZnZxdUo5cGRUR2Y4YjJtb1Fva1hmQXVOd0lCSktCT0xIdEIlMkJFRjBzZ3QzTnNNZkRkSWdsbXNaVzQxOEI2eGtsMHVoJTJGNnN5SVZlRFFIMTlzcE1DVSUyRlp6cGIwcG0lMkJZVmVNTVpXWUZwdzdUZlUzRUIxa25ya3Q4RFVOOUxuekl5bSUyRmJFJTJCd3pHTlBOalRzciUyRng3ZzNsNExwTXd5cTlKc0N2QkFxbkxmN08xWVhFYmZ1NmtCcUxqSnozZ0VKeFp3QkY5UFplRDhjNU5Kek9zWVYzSEczV0k5bVhNYVhDJTJCV3RMcjdnNTVzVjFxdEFVT2lkcUpoOGpXMVRKUG1TYXJ3bzhodzhzQkpuT0ZpY09VMVM5a2h5d3l0a1Q5JTJGY0FtY1V0QnFXbEJIVlJ2S0d0NXlVSWhYJTJGYndYNWJabHlKTE9KY0RKbnl2VTB1SUxGTmNpaENpVTRKZTlXZjQyNTNIaDhaSnlPenp4Y1hsV0p5ZFZwRnphaHJLc1g0amRwcXZIemllMG5CaVY5RkJKSjJiVW1kZW91ZkRaQSUyQmFkckolMkJRUThtdWk2RkxNM1E2bzZ5b2JCVmw0ekZDVXd5cmcxTXZqdjRrQlhPalNLWUJDbUIlMkIlMkJsc3YlMkJ2MHAlMkIxakFXdkxlTHlRSDBwdUZ2emclMkZYbUtQNURQRlRoUjB6aTdWc1NMZFp2aEp3b1ByYkFOdlNoZ3Y0Tjdna1ZkN3ZNTmllNzBCQVlCMFNRUU9OV1FuVENLTklNMUYwbXpOeldEVktsdGZoQ1RPZEI5cmlKZ1pGc21FY2EyeDA4TkNZSWwxaHpGMjE0WFF1WTd1YjlJTWVwenR1NGN3VGs2SXNlUUtKa1VtMkVWZmpzTW9lYjJtVCUyRkZ0SmVZSSUyRk0lMkYzTXduOXRqams1UUY2TFB3WEkwSnVhU0ttZGY5OHlVVzdKMFYydVpVZHljMWNPNGxTd1M5Mkw4Q0ElMkI5S2FBRU5keiUyRlc4TGZKM0VHdW1JTTJ6RERYMW00VXU1UEwlMkZZMSUyRmxsWnVrVmhJR3R0MUVrbmczSWhraGZ5RGVTMjA2ZTBMTmlpTFZFTGtVQkc1RUYlMkZNZ3gwM0lrTXlXMUhHUWFTc0JtczMxcWhGVzhsJTJCQnBsdjZROXBQMktuaDVIS2dCZGJHM05MMkRvRmlVZDloUEhkdmljJTJCJTJCaDFzUmpwajZQc01xVmtsNUFSNWRNd2dRR0s4Mm56aG1yJTJGclR1eU1RWmFuR3czSmw0djNyekx5c09ZNVpmQXZBb1Jka294SyUyRkVGck1FJTJCMHMzNG50bTROdUZwdTNHc0lKTnVLcWZvUVhmQjZRZnRwZUh1Y0FNMVp0ODZ6RjZYRHlSMFRuZGhWUklPZFc4OGVmbmRSYnNtdElIYmloUUN2NkpxU3hLNWVnZUxjb1REczY4emglMkJGZGtBbnp4aWJuMU8lMkJaNjh0ZGl1cjkzcXR6SzUxJTJCT0M5SVI3NEFVZFp6QzNXdFBaMWJCSWQlMkIlMkJVUkJqJTJGY1lnd3F3NVhLaThNRUR3eW5DTGJJWWlZTFlSY2hIczJPemtPdmxrSUowVFhPRlBZUGhtWktIYWNRVkt5dFJSY1FWR3VmSGE5VmhNbXQ3ZVB3Q2FSaVdZT25nTmczM0Fudnk4dDZOUG5zWlAlMkYxYm5xQUhuRmtNRTJsU0tqZVNNc3FvamRzVDYyNkw2MVROdnpWdDBxUUJYbkJEcktzeDZNYzNEaHFZTkVRbWFLZXVvWkJNdjZmTGZvJTJGRlclMkJkT1hIdFJOMk85bFNpM3pzc0F0Z2ZkZnJMZDV5OWZ5aTFlcGJaalhIVFA4T0xYMHdLc2xJZFlUOHVwamptbnJYVTRnS0s4dUJVZUtLSUVPREJhWnBBa3ZUcmJKRCUyRkJuOUtnS3hNQXh0WmJQV0F1ZFBDb0ExQzQ5U2wlMkZ3b1N4SHYxV052UkdaVlAzQUtnWFRIT3JMeSUyQkdxOEhINDYwcnVCaE1wZWVrYW9vZHUyTEp5WnNVcFNXdkhCMnUlMkI0Z05mazZKeTFMZEE1MHZrMjFKWENDZkhVdldCM1oxNnVHcGZtQUpGWWJSc2YlMkZlaE1aJTJGZkwzYTlBYyUyRmJub1BHUUJaZ0xiZTczNnk5aEc1VjZJejQwQkszb3UlMkZjSmolMkJySHBYTW1TbEFjMVUyZUEwNSUyRnVZazA4ZzFNcnBScGl5NmdYalNzYTJtNEQwZUxVYTFEUTlRSjlGbVVGbThrWWlDSndRTldpSzlNJTJCb1g2djdHQWk0VSUyQmxxU0R5U3JFNk5GeTZzb3F3M1NOeDdLazM3ZyUyQmxjbnVRbkVyOUdhZCUyRjAwRUd2Q1lQaFV2bjdqMmdWeHplVUhSOXN2Z3pmWFl1ODV1cHZQMzclMkI5Z1dMa0dabE4lMkJDUlhIOCUyRktkNjlNWHRjV3FEUTlKYkNsenRnWEhJWkZTN2s1ZG5UUlYlMkZpOFdnbzNObUdqNGk3M3Y4JTJGaklHM2NOUHczVW42ZmN6bGtiVWVaR3QxT25MeXBOS1BPMjA2aVgwSURLaWdVeUswbW0lMkJ4bVpBOUgybVhabk1Db1lBbEpmSHZpTHB1SEtKVEFhS09KZkFaN1RuUUU5bDhxakxnRHhkVE5TVnRidmlqTGI5ODdQUUNXQUlkRkpQVEJyWWpQWXFTbXpXWjBsZ3h3WFFDSHdtSlZDTGdiWmFwOTJoaXN2UzNEdm8wUFpWQ2g2cmppdE1HdXlUck8lMkY4WFNkU3hKaXNYQVg4SkRIZkhlZTI1NDd6MWZ2OUN6RVJNVGMlMkJpZW91QkptU21saEgxTkZHbnllVzY3MTJZbTdvMXJTOFNKUWRxQjRMa1lNbjh3THVzQXVlRzFWVmFkRHI3bmtMY1Zrd2tTdVNsUG9ab2tOaWVweUtPRVBrR0dBZkJ5UTklMkJxNTFHMkhhR0d3MzIlMkI5eGFYcyUyQlRQVEpad205aDlqaTRJcUdDRHhLRzJnZ21KN0VkUHJVZVBGQkJQNVRwbE1IQSUyQmVPcW5EUzdETHNYc0pmbGZWJTJCZzU3V3B2WmJuS1B3ZVNsdWltNFdlUllBbFZ6YXdnJTJGZnNhQk5jUzZyek5jMnJzVk5oRGlhRW5DNiUyRnVCNVU3U216JTJCQkFlWGZQbXBHTmZDYyUyRndTMmp5V0Y4a0VNYVolMkZ2YyUyRlJnNWE1cGVNRGhOSTdpZUNhM0NHODU3NnliNkNOMkl0ckpPamdYTUFTdjVyb05nYnNIaiUyRjVxaTh3ejJweEIzcyUyQmQ4aXI3MzYxelN6RzJmTFgwS0VIdSUyQk0zazF6NHAzV2pCTkNDM1l1YUZnTEtuJTJCaXNTRiUyRmRZUGglMkJyJTJCaCUyQjBmbW1Wb0pLJTJGaXl6TnF5dG5ZaExIU1d1RWYxbVZ0cSUyQmxBbkNobElKZHNZNjJ5S1VWU3hEZCUyQlZ2OVBjUmxDJTJGWW5qSDRIQXhBVzlEYyUyQkEzU2N1T29vbmdXZVB2b3UydHUwbTV2UTBLcDZFa3VOWW5FdlRyOE9OdlJaM3NDOTRDY1VQJTJCTWVxZ0s0czJTZ0F5RmhqZmxqZGpSOWUlMkJKRlZvU2E0d3hhdU5XU1M5QXVxSFNaQzV2eFEwR0t5SFprYSUyRmlKVTA2ZDNabXVqMVRjYSUyQiUyRmtsQVhGS3QlMkZmY2FFS0hrbFo4MSUyQkF6alklMkJzSmlTWHdqeVJtdmlYM0xSMVl1JTJCa1FKV3RaOXlldFUlMkZQaHd0d3ZTV1pVNHJnakRCcVFOcHE1WkdEWnclMkY3UFZkTldMMjZVc0NkNUtSYSUyQllhUDNnUFFSMEk2T1pCR0FlV0luRzhNVWNZQ3k4SkZCMjlKMCUyRmZDRiUyRlNOMU52eHRPRiUyQkpNQTdlU2s3UHF2dDA0eUZpc1lGRHJaMVYyUHVURGVoR1k4R3I4Zmsxc0tESjZ6ckhuZzhXJTJGVlVFVUYyR1I5R2lVak85NGZ3TzZXSlJoMnl2MlZ1bEhoUVJ0TVNSRlc0OVElMkIzVUhmclkxWVd5YzU2RkVLWSUyRndvbW5CMVc0JTJGRnZweVF6Yzhma3R6Vm1tN05lRXIlMkJLeHhSVm1nSzg1Mk9adnIlMkZ2VkYlMkJjTFJwdlNjdHpWNGhTQmR3TFR6MVVFVTZBdUVjWUY5THRKUVkyQm5RaUJ6RzRRaHY3T3FOQjJqSHhJeFJVb3ZRajZqajRLdEJQQW01TnhLanRyWXZneVNHek0ySGw4RzB1RlBXTnZTZWZ3dWxNTEJMTW1pMm9GWDE2MXM4Z1FuY2NmZFVZYUZUZW1QcHdacndtVWJsY2R3NzRmbnJXcmIzdnR6dDdGRjYlMkZ1akw4T1NiYkMzenh3TlFTN2hWZERaMHpya2t4NUJQT1JFQzJRMDdhZzdqdnZhRHJZbUdGJTJGNEtPWDRPVXJrTWdDNzdHOUlyR1V3M21NZUZIcDUxVGdpeXZ2VlplJTJCWU9nRTlPcTFwb3k5T2pIOEl2QkJ3aXU3JTJCSyUyQklDUSUyRkZUUXZpNkduTUtDVEdlY3c3YjRENWJCJTJGYWJUUzM3cFl5N2t3NXZSRDM2ZyUyQmlVS1B0SmJpYWFNaWx2T2d5SFlsRThUb1FSb3JtUDlHTTgxeGRSciUyRlQyaVBtY0klMkY0WU94dHM5NlZqdlc4YkFGMjV0NFolMkJiSWNpdjc0eHVCVVd0Vzl6UkNuaVpkbkN1VWMyTFRnODlSS2E1V1NCdFlxOFZlQVolMkIlMkYwZDFaTVYyJTJGVFlKaEk0UTVTMVElMkZPNFFwVEJMMEpheVdZUkY4dmZoQWRjcjZiY0tzUmw1JTJGS3JLJTJCZ3BCWDclMkJuNzF1cGN0bFh1V3UxRk9yMThNQ0szMzU2dEhnelVTQXoxYWJlWG1sYzY2VHVMeFIlMkJzckJWdjJkSFFzTm4lMkZjMDRSWXgwJTJCZDB3eHpjQmZDWjFHVkNVSng4bTBqaTBlRUkyNkclMkJDajYlMkYxayUyRjBpQVZtckNPJTJCJTJCWXI2eHZRbEoxNnJ4TWJ0VmZoT1RaOEJxbEk1RW42Y2dpJTJCJTJGTjRuT3VFMkNzSTU4V1Vwc2lqWE5qTHNnMXlacmRhRTl0WXlyN0dhcSUyQko0Z3FwQlk0ZllqSmVVZXZVZ2tlZFVjTm1IUUx2VG9BVWRxV3FKTHZIaHFCeSUyRiUyRkhHRm5mblB3SXpLMlZFVm1Bd1lsME1qM1I5Y2UxNlFFSkFIOWdDTnJHbTliNHRydFVaR0lCNkY3eVA3aGNoJTJCQU9hWHRMaVp4ZTZSeSUyQk9VRnpGRklFQ2NkNHZXcmsxSUl2ajZpVWFnTjRkYnMlMkI4MERiNjJBc0NYOWRrTW5FdHpFVSUyQnhGNUJYbG4zWllDandSdUo4REFsVFMlMkZjMEh2dHI1YTlSWUJRRmtBSVBpNHE2aU1HJTJGZldZajlMSEFBdjkwUjNEMSUyQmY0TTNrb3J5VGFZcUVyV2hsWjR1ODVEb0lXN0MzN3hjbTRmUmlQMDI0eGlVJTJCNlVReDR0cWczM3M2cW94QjVmcVdma05sbiUyQm44aU45NjNqc05WanFVV01iTzVWJTJCdXFGNFNVTTBQc0clMkJxaW56R1V1a3RoMmpZZjlLcFJmTnQzclMlMkYzYkI1NWRTVWNyU2ozUExsT0QyJTJGTTMlMkJoSTZpdFdYZVBueFNTdnZNcDZjSyUyRnk2Y3NINmdzN1FHcXlqRXhOUkM3WmZMbHFNa0xNTVFiMlZMQ2E2WUVhQktQJTJGMHI3YjFIdjJnJTJCWG8yNFFEcGt5ZW5CWWNveUQ2eWdEQXFoQW5WJTJGcGduRTAlMkZhU1FOV3d4aVpjbGJSMkYwOFV1eHlZTm81Y2U5RXlqbW1XWWN2ajQlMkZZM3UwN2FMenRka2U1SGFpMHFYamtJJTJGa3Z4Wms3RUdMVjdUQng5eUh4ajE3VXFmODBhTGoyNEN6Vlc1SzdWQ1dXU3paUnVGTCUyRkxCR1ZvWHVOWVNueEQ0ZzJ2dFhNSWZoYTJEYmRRVFVVdm9IJTJCNkxLM1lKSkxRVXgzQ0tLSGU5S3Q4Z2NsQ21YaDNid0ltaTFBWWRYWnJqcHpuSkQzVzAyU1hjTVJOeXlYZ2g1UnlPYjV1S2FKYWZ6WGh2NFQ5QUdXUG93QnVOSXpQWGtGMVglMkJhb1dpWXp4MFU4YUhaWUklMkJiJTJCJTJGWGxnWUdKMlpBYSUyRmlXRGZmOE5QbU1RZnZUYnIxRURCNmNkZElvdXJMS2Z3bU5qdFJ1Y0tFQUlWZGI4c3FnTXA4VENlb2JYck5wNFpiWm0lMkJWdSUyQnd1ckU4SVZYOFpuQk9reW83YmJ3VUloVXNyZ0VvdUgzUEV1UXROd0tDZ1NHeWVraFZ3NW4xdXVkV0szJTJGSGFBN0RYSCUyQjk4dUd4aUdOWmZzVzJuSzYwRXhlJTJCdnJkVVEyJTJGJTJCSjZVdmRMS2QlMkZaVTdqODJqdVIlMkZKYmhzQzdMQ3lTMnNwYXJib0Nqbzk5a1VMV2pLYlVhQXRhYzZ4QmZqTGxxYlpzOFQlMkJzJTJCYVZyVkM1TktwWnkwVFZiTzlxRkRsSFFYN2xFQVB0VHViJTJCcXZiUzlMUU4zWjJpUmU5SGl3blhoJTJCU0Vjdjc2QlolMkZsM0Z4OXR1a3VHUXJ2akdzZElneDFOVjNubW5XcjFEOTFha1VQRUVia3B6RU9iYjJNRlVCdjFuUHclMkJjZFNzRyUyRnV5MiUyQktLeVFKcktqMW5yUmpCcHFFMVppcnhCaDZ1U0tHdlZrRmx5Q0hQQ2J0d2ppdFJ6SjE1NXZLZXdRZnZ1MHElMkZodFViYVZkTWplMHFwSmI1aHFSUndGJTJGWGQ3MDR3ZCUyQmNwN1R5YzZjUm01byUyQkxLV0IyWTNUaE8zaUZQRWZneGNicjVpbTExVVFSUSUyRnowYnUxb2h4OUMlMkYwaDRXUWZzRk9mZjVWOGxpJTJGTkJGZTNHbXNYSUhUNmElMkJ0YzMyNkU5dE10WmxPeGs3VTQ1aUxESThBS1ZJUW5PdjNuZHcwd0REc040dUklMkJBRyUyQnVDQU5CbzNxcTlwSFNyWExJVzNlcHk5Ykc1aG5BNU1LaWJiT00yZEp2NWRKQmF6c3llZlhPNU53M1d4VyUyQnFxelg4T3dneFc5Nk5MM1pRQ3AlMkJRY01QJTJCM2ZGa1pXdjd1bW45OTB3OFNmTDFWOVFkbEs3QUlieVduYmFITWM1MXRzdCUyQmtMVW5wamRXVmdIWXIlMkIlMkJhbTJoY01FMmV6bjdFTzdLUzlWVUdDM1NUYkkxblR4N2VHejlCb01KZFU5ZXV3WXFxaUIlMkJ0QXg0YjdhUEZhJTJGRDklMkJ1VHFTUVlobDJSME83enYlMkJLJTJGVlgyaGNIZkNkRndtSzRDVk45d3haQkg0bFlNbGtqVmU4QzF6NldaSG9RYzRESnhlWVhyV2tBZUtsSTlWeXJqY2RPTnhzSEJ2M3d2bmVJTHlZT2Z0S1hGMTl6QnM1eWdYcnAwNzl4WHg5UjNPRFdXbDhYN2hLb29EUyUyQlJZd0hMNjhFUHcwZlV5ekklMkZtc21rVXQxWWtCMlpCWnlndVFOVVE1cnJMbVdXanI5cFpiaCUyQm1vcm1jMzBwZGklMkZtbjBPOUJYMG5pbFElMkZsUXRYcCUyQjhXbHp3NURIJTJCT0FKRWJYZzAlMkIlMkJtTVZXRDdZMmpuVWlxankwMGVuZSUyRlpVbmZ2aWh5dmZIRnllNUF4OXRtdE91N2YlMkJOeFNrSCUyRmNKVjlEWFdOcTZ3T2JBcmd0MiUyQllmNlRWbGw0UEdOSU9tVElZZldWelQ1bXYlMkJKdWQ4TGxackhSRExiNEh2RFo2RUw4bnU3V2pyMmFUMkxsb0FxbUslMkIlMkJORlBYbmE3cHpybjNCenhaYyUyRkZ1R0tCbFJLcFZxRlpUJTJGREl6M3I5eGVLcGlhTmlHdlc0YUlibElRRkZ6Q2NyMWFQZmJXZzMzSjdYREtBYlJPajVic25YSlI1RjhrTEslMkY1MG45VUk2bFl4Tms5JTJGblJsa0NIbVA0VkNLJTJCNHBCeTF5JTJCbFQwYjlhbSUyRlFWN3NhMmtsVHVyNEQ5RXVZbkh3enlWZlg3V01LYjFmU0FWMFdQYURhakdZTVJheGR0cGNnVzFmZGsxaTVLQlhneVBybktEYSUyRjg1R0d4Nlg2emVHTCUyRnBuZUFYaUl5TUY3NVEwM2RsT3Jjd2VXSUpiMlNVb1J1U3JZYzIlMkZPRVgxQk9FbjBOcGlSMjJ2dWtoWk8lMkJQeFpCNFhIWFN2b1B0NFJTdUZJYUhoWmdUbW5VTkMzQmZjbTFONXk4dnB0V0RyNUljWXhyZ09VUUs4MzlHYlhseTBlT2hDZHQ1dHNJQiUyQmU3cHd2a3hHOHd0NFcyV3owZDk5azl2a2RjRFV2Qm90QWRRRHZGQ0doN0V6WjElMkZmNjJiJTJGWERTeTdsUXF6Z2dxMFZ5NDNVZU54czRXbklXVFlkUEliQXJScUtrZW4lMkJ0UzFBbTdmNFpqNFVDJTJGbE8weGglMkZwQjFndjc5ajFQcUF1RnU4TEpyQW0wckFOWk5qJTJCTkMlMkJ1SE5jTTFCZ3ZLdTk3S3FiVWFwNTBOdFFjdFk4UTU1dG9EVjljZjc4M09rSzdydyUyQmhhRkRQdEo0QmlHaTlVUlJ0WDRPMTN4U3d4RmlHTnh3N001Mzc3RGp4SiUyQlRJWHpvRWFtTzZVZGFsRXRYMkNhR2JHT1NCQ2VpJTJCbUxqY2VuWTZUbG80RCUyQkFJWGQ0VGJjaHNtejQyQk44V0VMcnVlcGRrV3dEclhmZnElMkJVVFVQd2dKYTZJbmtSeXElMkJkVmZZUlBxSnZjY3pXZU5KUXFZdmliJTJGWlV3VGhKWEpOTzdCZ2NXJTJCUlU3dTFsaXIzTW4lMkJmcFJpYjN5bk9YTWpINkFwOXduR3NJNSUyQkpmU1Y1T3UzbHZvcG5SaUJXSjl0JTJCUHFlZVF2UnRScllxU0tMbkx0OFZ6OENVZVFFYkZXa09JNDA2M1pHYWtLWmJvbCUyQjcyeTIyYlZWUHZNbTF6NUN4TmNyZGNhWGJoN0R6UDRieXdnUmNGeXAzMlVRRVFWR0pJb1R5UFlOSWRKbXlmS1NCb0UzZGRpMDBHMm1TTVN0dXlYRWJMMVlLNkxoSVp6MjJyOXl5WktRVnZSWWlNQ2FXcWwlMkI3MmpTRFZYbXhqNGcxTlNoRUt1S0xNSFlzWkFxTUZxMHNxbjRJMU84M2ZnbkFaRjBmb0VzYXBWQ2xUYUJqWTglMkJTUnlQSmZQN2pNa2F2VkViSGZIbmh4Y2dRTVliMWpoTFVWSzhrdG82bCUyRmwyU0t6T0Vhd2hiMDVEZzFyVVd2RDg4T1A1TENTWTR2MVZkYiUyQlEyQmNKc0ZZUlV0MHV4JTJGU1ElMkZNTlI1QW8lMkZxOEwwS2JmVlN0UVlTR2ZOQWhHTmFiNWhUJTJGVjhkYTZ4S2FuTmprM2lWWTdOT0pMWlU1eTczeGtqbEh0aTV1a1A2ZkZNRlolMkYwNVQzdmJSd0dMbWdrVCUyRmxOJTJGZGVPZEhFYSUyQktCZDRKUDFBZlVCOXlibVRKNjZJbnBBZEpydDc0MkZGcDRVZGRwQ2c4b3RhVXlzSEROOFlrT1lnNW85OUhCalFoS0dyeCUyQmM3Q3NPVVpONFRCYlhxYjh1bW0wWEdJb1BZdEZKTVhlaG84SUQyZ1l4NGhwZGg1WG1wUGJ3JTJCUnJHQm5rVVVCajI3eWk2WUJmQnVMTGVHQmxmenVsZ25paFhuM3BkaDZpSXFCVjJ0WGZQaEhaSkVtUnBDU0w1Vnd0WUQ1M1JERjZScG1ycUp2dlJoJTJGQkZRTVBCV3NWJTJCZVpuZE4wMXQ5aXdrZURRM0NEdGZGSktPb0VQTGk4RHJVZzE0ZDZtSmpoaXZVQ1VOTnFSVHpiUjl4QTllUDNpbVlwOVpock5JcDdrT3hEQ1QlMkJKc3RwUmpxRlRpSXZjMEgxJTJGWGV2a1c5dHJhbVVQb24zNDdNOVcxY093Yk5lWnNlMFhUWU04aXZXdUhyRUlZNWF1bjJjTVcwV2pSWCUyQmlGbDZ1T0lpWVU5N3p1SGE5MjJ6WnI2Zk1mVmQ0aVhUT21QV1VyOXREalVrUnFGbFkzTkElMkIxM21LVHJyM1M0U3ljVG9naCUyQmdtVXRjZHpENjByZkNPVW5mVklBbjRpZzJkJTJCZjN1TlRwb1NxUSUyRndSTjlMeXZRbFlWJTJCUEFpMVAlMkJ2JTJGRzdSZ0pZNDhLOU4wbGV5SlFtVFZ6SGRmNWI1aGFQdzFYOUZJRUI0Q1Y4SFZHUkhEZlpQR3NLSmNncnp6QmY3RzVSVm9LMjg0R1V0ekwlMkY1dkpKOHdjYUtXcTQ4dzBaZHFoJTJCWG9IWW4zdDhwdmRRN1g3MnlzTUVqZE9rYUszdjRRSEVUOGc2MnFpRzN5TyUyQmhxV056Qkd1eGlrNTZKeHZ1MSUyRjZYaW8wbThYTFNlQzJZUmUzVlBrb00lMkJXQkxiYnhWQ08wWXR3aXZWN3RUTzVSNjNtQ0szUjh4OWQwc2xYVDZoWWJiZW41REFWM2tWUmxXUFhvQ092UmlwUm93bFI0ZjdiMTJBSG5OJTJGWTFTa21FS3YwV3ZVcUp4dGI3aGxHTFglMkI2a3Y3aVB1andSZ3pFVGVzWkpDeVhycGVaazFMTDB2MFFxcjNQSzFWb1l0Tm5mT294JTJCbjZ1am9tJTJGd3RUOGNsTEk1RGFaQXVSZllpdndpOUtRSUxENlhEbG1pdXpjUnltZVB3N3dYdU1xMGJ4WWpyYnkyeVlva3h3U1M0WjFuRW1MT1VudVRTcHBFVk1Wa2hyUGZUVmZ4OHpCaEMzMUVJVmElMkJPJTJGV0RTbWdMbGUlMkZLQUI4Vjh4dnF0JTJGTHZ4Q0VxRWdyV1pxY1A3NmJLWSUyRjRabGttUFQ1c1JwUGNKSTY0aE0zJTJCV3BQczlEQyUyQlcyWFZaVTFkUlBYRyUyQnFKQkJQbUs3SlRBdUg4VmtvdGRJeXAyakJ5Z3lrM2hIMXppJTJCZWolMkIwb1ZMcVhTT1ZzaWhsUFF3ZjhFUVg0YkNHa3g5ZmVzcUtjaERTc09IdFVPZlhDcSUyQmR1ZWtTUWVUbVBleVFHa1RGeER3Z1phMHNaQnFiallPTlBNcmlMVU8xejYlMkZ0V3BzdGhJTVlGeDY5bHRJcVpYZnpnRHRPZ3Y3YXRtViUyQnprYnclMkJBdnVnN1Mwc2k4aFF1UlZNcEsxcnolMkZsUU9STGZOdjV2bG1yOVlFVE1aZkclMkZkbFpzOGlSall2NWs5cmNCTURiVnljSHRFQ2FEb2dtOUI5amQxa2lDbklpbmJUVW5lZ2d0JTJCS3piJTJGTlROTlhzMHo4VFZxZWlyZUdCYWEyR0wwMlpRbzkxeUZkZ3RWY3Y2SEJFaTJ2UzhFMnBiNEJaQlJkbGpGQW9MbDVDZEJJUGFZJTJGN3R2eEhBdWszenFGaVVEYUpVSldTUldzM3Y1VURrJTJCdTA2aGQ5TDVkdkZFU0tMNGZzSiUyRkJpN3pyekNtd29lN0lhVldZJTJCd3lwMzkzJTJCOWljcjd0JTJGMVBhN3VxbElDa0lxZkFaaDF1Z2olMkJ4eHBhckJCVTJ6Wjc2VHJIRTV4RkV4TEhrQSUyQlUyR1lHdVZIMFJrWGZ4TmhiY0NtJTJGS21kcE9hMyUyQktkQmF0MHZGYWo3NWlrQXBWbXhPVTZKS2VjUDZvZFlMTGpEQmZSSmVVUlRLTEdXaldNUzJRdEhPY1N2WDZ3eDlYVkhhR0lIYjZNM01vY3FJVjFINmZwJTJGbkpkdW14THpmSUxOdyUyQiUyQk1BbnUlMkJGM3VpSWFtd0xqTUp2dG1VT2hLdm9xekxubEdJN0J4RlFlQiUyQjZBRHVwOGZ1Z1pUZDVGVnlybW5idklLcHRsYXpZZmRCeDVWZExyc3g3TXZzeUg3THBWVGZoRkh1dWNvS3NkU0dNM0ltTSUyQnFOWGlkVFRxTnpqUjFEb3VwUVFnZlRYWVpsaGtiaHVjRXhNSklFJTJGRmJ4b244WWt3NyUyQkY4bjR4dDU0Um9Jajl4Zm5SJTJCZCUyRjhkZ2QzSzJlckp2Q3JtbGZhN2U0bDBTVzlHVkxtakMlMkJweDdwYm5sMFFwcThseG5LZHRCN1hiTVd2UXJ4M2NRV2M2cmRSJTJCQk5Va04lMkJqZWlVUUVZMXR3QTVVUyUyRmlLazElMkJDZjRmTjRiT293WFRFWlV1dlBjbzRvbFQ5RmRoZXZkaHhiZTZ1bWVabVhUWkIwM0N0M1dhbFMyNlN5SCUyRjVwMnBXNHFuQiUyQjFEdVYzVndKazBpJTJCSHVkNVcydjY4UTJSODc5Y0dpN3JxNEt5blU1ZVI0d01PRENPY1gwMGhKY1JFcE9sRmROTUtXQmtDOGEyYzNTb2h3U3d5M1ZxemEwaktydjBLUEp3NjBzTXVibjFRMSUyRkUlMkJpajdGSCUyQkxQcHlrNlY3SUo2dnpCbjQ0VFZidWszcEN5JTJGcnlxM1NzYmNYYnBteCUyRmU1c1NIVW11VVUlMkJrcnFFQmVrN3FlbTYzRjlNM1g0cmtybXJaUGkxY2hFdDJoJTJCdjkwM2hxZlRBZG9NYmFqWHBwUFBFZkxtZUtQRzZkWDl6RGUlMkZuT1c1b1EyM2RBdTAxN2hBMlAzcVVRMGp0eHQlMkZNR1RxUUVVdXFqSVB4aWtCMUw3YTBOdyUyQlVmJTJGaE0lMkZmeXM0eGpyYjY4eXRLbDV0UTRsNHd4S1AzRUF6dnNhdzJxeExhT01YRVhwenplMjNLbmlDRUR1em9wdjJzMiUyQjlGSjVMdElMSmEyJTJCJTJCUE9tazc3S3BGUjdxWVhYJTJGYlhLZU9oVSUyQjYlMkJuNWJwJTJCaTY0dHUwa3NLN1l5Y2ttVW1HSGlleEszcU9POFc0Q0RncXpqaktYdjlsYTdWa3RxMXFzZVAlMkZIZGhybTY3YkRmSExFOW1CVTVKJTJGTmxscHMxTzBTTTA4aHhTV1FKaEphNXdHVWdtYWJtS2pIbmE1aXhDVldtNk9zOUoyS2NWWXB2czJiTmNUVFJBM2VFQ1lNR0hYeDl0ZEFsbENHVjJGRTNiTXU2NU4lMkJPYkRqM01xNlRXVnBoRUlZdlh6UVNvdnpBcnFzQ0VjRlRxcHNtdUhJWGVaZzRacE84ZERXTTNVMW4xMGtjRklsM3llSWtsZGlieXJiYTltN3prMmJBaENvOGEwR2o2SFU5NWxnQlVQNDc3TmFMNTklMkJqb0YlMkJzNFdma2l6OTE2JTJCRWFwaEMzQTElMkJlOUdwN1M0NXA3aUJnbldGJTJGcmpzczhYbDZqVmd0cmxtSHElMkZnZWklMkIwRkdLNlJxODA3bHZkWEpnUnJ2anQlMkJDWHpMZGlTc3JoajNwVjBveFo1QnRITU1zUTVWZU9SZUVCVzFXdlR2M1ElMkJnTnU3aTklMkI5NmxNTnJ2VyUyQjNQJTJGJTJGeXltMjM0WFY4U2hMaUc5QkZudG1wWUFGMTBQSjc5bFJZTjJWRWJCTTdyUzY1U2dhSmFMRm1hdG9ZQSUyQnlwdWk1TFFvUG9xJTJCdTNHMXNVJTJGcjAlMkJoY09QTSUyRnVvU0poOXUxdHBZVE1Ja0pmJTJGZnh0TFFoaTBTNmNsSWh2UGQ5ViUyRjFvJTJCdjdTJTJGOExYWWlVcEFtNTN4Z25KeCUyRmRNbTI0JTJCR2t1bDI0eEpiQ3V2UE5QeEwxUG1rYWVDV0l4RE9OSFQlMkJRRXFDWVhQTGZzQ24zTlZ4YjdoVHBSRjhKN00ySG16ajlhb24yb3FwTDNGWHM1S0VvcWtBZWFtUU1pUkthVmdGZU8zSHNOZHY2SEl6ZVp3azdXZml2T1BmJTJCa1NiJTJCeERtZU1sTHJGejJrdTNxWThBWXc5dFRjU3dCbkp5Qk4wYm54Z1dka1JMVEJWclJ1aldGbWZ1SXExOW5hQW1CRm5zak1UYUpLdWtGVW93bDIyVFExRzZrNGhNbkVLVnpuelNZZkpMdWJmYmhqZjF3UjklMkZZMXNSVnRiUDc0MUczazd2SWtKbmE5WWE0Y3o5WkpISklWJTJCdlJuT3hIQnIlMkZlcElDMjlsNHdnbTUwRXlhdnIycXo0MjF5YTJxb1cxU3o3R3VldjQ4QTRWVm1PJTJCenBkMUI2eWxlV3pWZkxyR1B1YyUyRjMxWDQ5dFJEWDY4cWx4RFV2cGJCVld0d2dQJTJGYTYlMkYlMkJSaFd5b1l1N3pHcVVHOURaTlFCUHpHMmVVa1M1WGdYd3pTRDduVW5KbFMxUVZFWEh0RHBZeUxwWTJUY1lscjljYkdFT1VCTUFnTkNjSlc2cHNlSzNxT21vc090JTJGRjA3anY5SlpCSzRERnFYYXhaUzFlbDlDSXJWJTJGbk9zJTJCc1JFNVJxU3h1WkFuVmRQRTE0a2dkMiUyQmFYcGFaNHdjaW1YcmxTZm9TTkxCb01HdyUyQlolMkJleiUyRiUyRlV2OHhYeVprJTJCRG9GWm9EeGxzd0k5clptazlvYjBvJTJGRkNRbXRPTFJSeGJIaFdSSFVkTDkwNUZ1a1BuRmtoNTBSUlRzMmk3TWVFUnlYVFNpJTJCZHplZE50Vzh4QWFrSDl5TjlIdVBRR0M0VCUyQmxNbVM1aVViU3U1JTJGSzRVcEJuODVZVVd3SUVLUVglMkJNRzhFMm1xY0JEZ1UwZ2lWV0IlMkJoT0hMV25PM2hOQSUyQnBCaHNHZURzMVpVOHhndW9FZURMY005ZWthMTlTYiUyQiUyRkpoczVjQm5XMHh6THc0T205RUNEJTJCZkVVbjdxWW90VUhYOTNYdXJISXZ5YmNCJTJCJTJCY1dxNmxPeGV6aXdpTExFJTJGRDc3cFV4UFk0TEpaYiUyQkVwUVIyUHRiMUFnM3k5N21TYmJRcTRFYXRCTTU5VWRMVWRkU2JiVG1YU1Q4JTJCYVUzMnJEdlZaTXZzMkdFb3VtZzBuNWwzc1pVbFY3SmdzUDJtNno4eWIyJTJCRW9CTHFpclBKcEJ0TyUyRnppTWlYWWp1TjNaVENkWEhaWkl1aEs4RkFjRnhZMjBtYzBJSnZ1dEtBV3RCTHllb1VSamg5M1RLdThlZDFXQ083bXhiUHR3RGxZZ1d2M1E2YzBzR0pKWVNaYXA3bVJtTEpDNWdWZUhhbjQzQXpEMTFmd1dyUWRhNFRUdyUyRmdvJTJCcEUlMkJLZUlKUlhEbE51dEF4aWFtbXpQODJvZGNlanFWN3pzcTRTYlFpb25lT1YxWW5EdWZ0WEdCY3NXdk1idVNmYnRiZXgyc0ppdXduNmpBeGZmU3hTRyUyQmxjazZwb3hNdEFhcm5tTyUyRllSREJzeHg3NE1IUHZuSmNwbmFvaWtPbkNWViUyQk4lMkYxVGFOenMwVkdCbHRNMWs1eUtQZEZ6RWdLN1ZPZlg2VGFzUVpoSFJWY3FQMHNTODBKNG1ubCUyRm8weWZwOVNra29lYnFLOXp2YUlUSDNsOGRkOFVtaFZPNzY3Z1BzaFRMWXRrSEhPWGtWczFwTWt2YyUyQlE0ampaampZMU5CZGV1JTJGTHJrMU1SYWRySlh5WmtWQ2FIeENJMzU0cFRpZzVHM3loeUdDVVp5QXRsVmNKSWolMkZMUFU2WExEdVdscWs0QVU4bUt5Y3lyRDd0U28xMlE3dSUyRktKemo5NGFtckhCWW9DM0Q4WnpQaFNaZDdrc2lsVndYNUZ6JTJGNmh1RFMlMkZGdnd2MXVhUERlQm42NkRIYk1zempXVTJRcHZyNDgxUTREOHdJamVtb0pKSHNBaiUyQmpxRlpRa1dIOE1YJTJCcWYyaXh6dFRQeW02a0JQc3JodktSOUh4cjEwbUhnejJXQjFWSSUyRmNEVmxZNEUzMncwZW1YS1pXbkFGdkJvamdrakxVR0hzRnJWMXE0NVJNbjAlMkIxYVlHNm1DWHhaY1VkVWFBcG8xWjBuenhKWDZuMFVjMVQwcFA2ZWNlRTZvTlhmdVZyUnZvSFdtaHclMkJBdSUyQiUyQjFxdlk3c0pjbVBRZ25rVkpRajFRSCUyQmZyZ3NTcG5QSm5lbTJNek9KSCUyRnQlMkJRbVB6c0VpMnZIZkM3Y2lubFhsa0tpanRvNmNxTk9aanB6OWVIUmRXYmp0NXVwMDBTZEZhS0JGQTNEbiUyRnVjaWZyMyUyQjlsZWhYZE1wWlhONUpaS3V6VmZ4NXZGR1pLaDQ4UzUwRFg2STJjbWk2Rlcxc1hQaFV1VVYwQ3E5UkdxJTJGczZNJTJGWHowN2c5WW9EOE5QcGRoJTJCbkZTNiUyQjRtMDFhRmh2TVExbXhXbGowc294TU5vN0xobnlJT0gwS25ZbUdJOW5VbGV6eUdkajhGbzFWbWhUejFvMTlPeDNpanlKaCUyRjJlJTJCeGR4TEpiTzJ4OWs5UXRFM1VlSjEwRWhlQXBFZFU0SFpOMzExM3FVRnF5cXJJTVBpWEhpT2M4R1NmVk9hanBKcnhmeWNNaU0wODNxbjc2bno2YkRxZmRLJTJGWVUzUSUyRkhmWXdTZjRJODRHcjZreXF2JTJGeUh4UXVuN01ZWEhycjc1aEZwOTRFdXp4dTJnTUQxQVVKT2R6VGJnbGclMkZZcVR0YWZRQnpzTzIlMkZEQ28zelhaVzVLanZkc3gwYkxNdDd5VXIyTUlsWjBIZSUyRktFbiUyQk5seHJOc0YlMkZTV0lUS0tzcFo5NGolMkJlbWFZNWhHdW1LU2F5RGsyTUlrQVZDaVdSJTJGazNKcGFubHZnNnRucGlwUkl0TTNFa1hWVndyRlBkTVpkbVRtN3E5JTJCU0FKYiUyRlFWTkNtQ1kzYXk5d1NDYU9kVlVPdzZWU3JCSmdiM0x1Ym9MbHV5RjBVazZROFFrWmx1OWwybjZhc1pRRXolMkJmbWpRZ2R0UnNwNm9ZSlNQWUxyaklxU25QdmVxVm5UMUhlQjRQQzElMkJnOVJvOHV1WGw2MiUyRldmVlFOeVZ6azR1UkRqd2JHbURxN1pidm02UlZhZzJHSUxRcEUzWnYzTUhLVHclMkZzbWs1JTJCbkNtTkhnSjQ0bThVUDF1eFB5SGd0bWp2ZzMyU0Z5YjlaU3U5aWZIMzZNTnFlNmVOWiUyRm84N0RLJTJCOTE4OEFNM1ZiRllCJTJGbFVOWFpsZTlRaHdjallqQ1dTcmRkY3BYb1BXYkI5ZFljem5TYzFZVXVDMFklMkJ5TFRnZGloZyUyQm1ENHpDNzlXcWx5d0JqTzl2bjloTE56YlglMkZCbTFERzNTbWVPdWNlUGtkNWplMzVEdW1Mbzd6ME9uODl6b3FDdkg4d3pQTHBCeHFvYWpjcWNydERtQkh3YTNtYmtzbmo2ZEhtMFZZaTR3RGFFMUdhNUVtQXRESDNNWmFnSXFqRnFuS2N4cjJqVVZGQ3dQR3ZHSzZ6TENkemxBMm1wcUFiODVNTjYwNDdRRElGdFVrdERsbE0lMkIwRG5haFYlMkJneHoxNzFjJTJCZyUyRjM2b3RkYURyWEpnOExSV1R4U1VCRlRVa2wyck9aYk1QQ3lJWDRyUDBmTyUyQlpVd2M0RXNJMFFxYXQ0YnBkRFNqeXJkSnBhVFZ0RXM0JTJCdFc1aUJjSVl0clBOdHI2ZGJQM1A4OFh6bGIxRm0zQ1lubTllUDZHbiUyQktHbExWd1JxeGhuREdnbHpVcWptZWRreWc0TXVqQ1c0ZVdSUXlUN0wyVWhtUXhDODU2U25UZlV3ekhPeVhKaldGUFZONE1EVmc5JTJGQ0Z3Nk96d2pnaCUyQnJ1YXVEcXpzTzhGR1ZTVHdKQW1RQlpFQmRmQWwlMkJKN1lHYTJyNWRHWWdYWU9JdjVUV1UyNE5DaTkyMTVKdTNNanNYVWxyWDM5SUtGOGtTVm5ib1pqeUNaRzE5TCUyRndGRHA2ZWglMkY2M29jJTJCajhvU2VDbXhQc1VuNXZlVFVDMWJnJTJCdHJOdnlwbGdocE41OTdqYnFDV0VNZUVOSGtJRFFiemhVRExNbnFZJTJCVzdseHNJdXVmYzRLZlpYNkRCUk9sdUJHa1JKbktJMjclMkZreUptYnBVUkprc3FPeVUlMkJHWDgyRk85T2VyNUFFSnRMOU40VzI1S2FKcHJvTHhhdCUyRmF2NlclMkZKVmslMkY5ekV1ZDdsdzJNZWZVM3dnVjd6U1lveSUyRk1tSGk5cDN5dlNaUTBYUzF1WiUyRkpEJTJCZEZzcTlYVmk0dUxPdTdSTnFkUWc5VlRxRHdYdFFhT3MlMkIxSjNuSSUyQm9FZkd6S0lOOEJyNDU3V2MlMkYlMkZNTHBhOThEeU1hdUlrQ2xWc1oySmJKSEJnJTJGazljZjUzRG1mZDhCOE9tblJiTjRVak9jdUp6WHpxaCUyQkVjc3NaWjBQejc2QllYWnd6JTJGcm1qSEtheTd4MWtPM2JYbkpBY0ZEaWtXJTJGcHl2RyUyRktEVHpONHo3QkNZYXk4TTBBbmwlMkZINUpZJTJGaTVseGhHbkw4TnBlODAzcFFHQ3loM1VxMkM5TDNHalNTJTJGZlBvcnpucGdteFV6dHA0WWI4VSUyQmJoQiUyQklpVXFxb29jamZZdFglMkZXRVlaUyUyQndVUWNQVSUyQiUyQlZQbFB3YThzN1liQU01SnNDZkY1WkZZNWJTUVY0RXlSc2JTTHM5WSUyQjE3Vm0xRmk0NkpjU0M4ckFSaUtpSXNsN2ZqNlQ4T0xHMHM2JTJCZkNPWkkzWjQyOUtnSXU5TjN5Y3ZhNTdxbGJ3ZGozc0d4JTJGYWdndDAzbVFOZlBBJTJCdVFWZUdHdVplOUlCOU9xNWk5cXRPVHpkbFN6bllQc01WNjdrNm5tYmdPeXUzb0g2bkJZV0xHb0ZrdUNyakhUTUpVeGp0eEtNYUFIeVlMbHJFQWtBcjkxMjFjNkE1NmtGWjE5U2FZSzVSJTJGY2hQa0RYRFRIaGNxenpsZXIya1ltYnoxakRwQ24xSkVVM3VCVGY5clZIeHdiWnglMkJSSHVkTDJNdDlpMGNYdm40aDdJMWxXd2FtVWh6WVd0WWpHQUpyTXUxTGc0ZW9wJTJCUlF4NjJTTkd1VHdyT21QRFE0cjdFblBkWGhUTDdFQXlqbGpUdVV3Rk1EZXFia0daRmE1Q2tSRFd6VG9LcExGJTJCdXFybmpoa3F0MkNqM3l0ZDlmcmV1a0Nhc2g0Q0hWcDFjdDNkbnM5WXV4NmdxWmFYeDNCNEhpbTRMMlU1S1VycFpVU1JtZGJqN3Rrc0ZhekxJSG82aHA0bFMxOVlnclJld0RkbUo0WVVablp1dFpEOSUyQnpScjNRcGI4QnMlMkI4WFRhOFYlMkZWUDVNb3IzbUNPQnJtaDJ6cCUyRll2anpjWiUyQllBMjFVOXZ0TWdFUUZqWllGdnVBRGdXU2JoSFlEVHNmVDB5NmV6RFdCZm5UR1RkODhvYk5KRWVLMmxOcXhvVVZwM1FGVjZFMkJVcEZ2bHowV1VUWGRqUzVPNDhXRU05blR6ZVpRbVM1SWlzVGt6eGptZTZaYzd1VWVqT0IlMkI2NWFQMXclMkZ3UiUyQjNFSnRFdTRkU0hhY3g1OEl1U1pXV3YlMkJEbFFMSFRDcjVidGVlOUNWOFBZSmRKZ2UzbDU1V0hBJTJGOE1Jbmo4VXBPZFRnRDQ2ajBFVjVuR3A2bUJrd0FyT1JxOGZYRjB6MjBvMFolMkZGRWlGTmhVb0p4MHprTWxlYXNjSXVzTzdJbjVhd1Z5dmRqMHp6bWhnZDdaJTJGTjFlTkhEZG4xV2dnRVlEcjNnamo5TklJY0h6R2tNYW83cHRoTGFPJTJCeVJPenlyYVMlMkZFbFBraXJIU0cxTG5sYTEzMGUyNE85WEw1cjRhMFVqdlYxS3pQNVBZWUpVdHhUTVhFejlDb1NCWHA5clVLWTVFS21vM0JOZXU0S2ZuSXJKJTJGdzFaa1FZNmk2YXNpSzZrVFJWdkVVaVhNZXl2S1pJQ2tqOXAzd0dwNEh5RVBRZGFDbVRNSTZnWXlCbU9DQ3dwUDBBQ0xjRmRVZ3AydVRsJTJGUFRBNWJXVm05WlJrYWp6VSUyQm53dHhvZmNiQWowbzJ5Tmp5NTI4ck9sU2dqYllJcTFrMGVyR0ZUU0VzZENjVE9lUG8yOEFxVnhiZGdYQmFzRHNPR29iT3FuYU15aFJKUUxNdFdwb28ydnoxMFVqWGRzJTJGazJIdFVoUFZDaDRIJTJGVUI2VHpxQ2dRWm0wMXpNdHNIY2E1S2tySlJlcnBhY0p1SnclMkZJc1BmYWtCeno3OVpKTDk4SHFTN0VtS1BiVUZLNm9mRVhzdTcwNk42a1ozTFZaMCUyQlpxdmY0akVpTmZrdWFaZGx6S1FUNmZncTlVTjNDb1ZqcndLY1BUT1h1b2RkUGY3YkZPUm9tN3BQRzJhYlBrRjJldE1IZGk2MUJLNzdTWHNsWWVCRkI1VE1RYXhvR3VuTDBZcERIa3VIYUZhTGpjM1BpSWNpSWQlMkI3ekpxcG5ZYUJYV3VvemQ3ZHRaQVhuR2E3VE5YaUZGV2VSZ2s4N1RGM0ZCMlBpV01oRHVWNTlaVXdORjJ4OGVjaEl4eVJDM2YlMkZ0b2N6d1VreGRMJTJCelZLbXd0bzNva2k2Q1hRb3k1NlhPTTh2SDN6b3Z6Y2pvJTJCdllpWDZ4MGVuNGdPd0NhR1F3Y003aFlmM21RbGc0VHlrTHNSTjhvdVU5bGV4MXA3NG5yeE9kb3d6enZWWUxSS1Y2aHR6SWxNajJ3JTJGZDE3a0U0ZW9tVFpmNVlveGNyNlk3eGlGejVpWGZ2bVR6ayUyQlRaRldOTHNzQlJrTFlZQzIzVTVHUyUyRjFCRjdlNjdaaUhRVnd6JTJCOTEycTFvQ2RUSFNkRXBuek4xbzFIMGNIaktKQnNCSDdQVXlEUjBiVHp1c3QybXRRTWt2YnhHT0pZWHZhUmd0RWs0amY3U2QwSyUyRmhKbWIlMkZkd0NtN2tsOXQ3YlE5QXd5RzBaTTgxU1o4cWx5OFYySHUwTlNBQWJZaWhURzRHcmdjOEI2THJLdDdBYnBjTEhnRElveU1ST2RSUyUyQnJ1RXJVbXJ3aGdBbDdsOGE2NnhESWJPcExaNSUyRnZxZHhLQkpBa2hXYThJQUxNTE5QdHpXZTViWWZ3ekJYaGU3UFBPV2xqcFJqZGxaRjdYTlU1TkV2ZDFsNTVVeXdYViUyQmlXMmFkSElhU2w5aXlwRThkZ2NpT0Z0cTAwOVBUbnNvWkl0aVJQeWdjT01lZEhjUVdPcXVLYVpWJTJGUTZaZjBVNTRIRGJ3clE3SVBRdlRkQmRZWkVqbnhVdTB3SlVRVXhXeCUyQlJoUUNjUnN1cHklMkI5dEZWRSUyRkx1MmNuSmF5WU9KYVNFNkZaeFpXWndaUkdlJTJCcjVPJTJCWmFJUlBqb2E4MlNkM1VlTjl2WnBaSG8lMkJlYzlLQjVIOHl2TVBxWGttT1Y1QnJkJTJGckpRRnZJazByJTJGMVc3ZDElMkJpdlJNZnlGSzJOUktEbXhkVHZZUVplckhoJTJCWHoxOTNwMVBnbFBXM2RQZGZ1QzhJQ3lEeFVvOEw2JTJCM0U2JTJCMzRrcnZEbSUyRkVaTnRzSjlacSUyQmFaNXdkeUNCVCUyRjZTQmNGQ1JQc1VXR1U0eSUyQlFwejFIRGFBWldvOXd5b0JBZzJ4WXFQQjI5YnRrc3ROS0E0WkJ1cXFNZXlBVHRvcGxraElJeXBmJTJCJTJGN0xpdjElMkJ0Rzl0ekgydXJoMHBlQm1CUkJ0ZEhidG9ZbjVydHFoVFJtdWFkWnFqRnR0WkxzOUtYTXlIWXN4NzBvbG0yJTJCVUNQTjJON3VkVk5KUE5uWm1vWGY1eWpTb1BIT1A2S0czY0NIOElHUGR6eXhVY3hYM3FMJTJCY1JLampEdGRsakUyVHlHRXBGbWE5ajlCTk94VjJyS2VRdVhSYzBtR2FGTFNMcWtOTWdOOVY1anFvOW5pdnBRcyUyQk5pUXpXRFdrRXZhOVFJJTJCOHF2RWklMkZEZHV4YXY3QkhYc0FNWms5YW8zMThHYlc1ZzQ0dWZXenJqaFpJMDBwRFZ0N2pkJTJCbklpJTJGdldXZlgxd0ZmcTYlMkJLcXhYUnhabXFQYlFPVXRQVEt3OXBtdmdpdE9wNExoc3VsWkNENG5XeXFZWHU4RnQxTHlyU1RZOUNrU2JsVE1MUkhubW1TanFwbE0yUFpzeGdiOUJFYXZEOUxtWUVhMjB1TnVWa0d6NHZVMXBheEhqNXlCMHBjanA4V3VLQU95cDJHN2FiaTlzcEU5YWdlUSUyQmxTODdBc1ZUOXdZMHZaVFJTaDMwT25yYk50ZkZad3lwaGo5ZlMyaThNRnVsb1FpM1AlMkJlbkI0N01sZzZZMlhaRlFwUFVGWmE4JTJCdXJrTkdZeDU2N2Z4djlyM1VIWXVWV3dLMmM5VmE4eEc1MEdQQkhoRGI2b2RPaG50Wlh4OVQ4b0Y1Slp6OUl0R1ZVYnIxSWkwYm9EVnVOc1MzbWNXeklpU3BSaG5ZczdsUnZHejV5WlB2dm5hS0ZlJTJCVlJkakY1dUJBTXNFOXR4OWFpa292ZWl1V3gzYWt5YnMyUkhiZFdXSGk5RWtrQVBKTVU2UUFzMlNTYzFMZUxkWXVYSUZjbHRkSExiSGhBa1hCSk9nUURyZkQlMkZ4cXhybjMwSndycHVsTXhrVmZ1c2ZXcEFtamVYVmxDc3poOU5lZkxGWmRzS1Uzbkx0bDJTOGVMb3NETU9MVTg1dlBxTkZxTE5LaVd6ZDBqVTZicU1qT1Bab1J4OFRvZnhYTmVCSElCZzA3ek02U3Y1QXE3WlRXa2hWMyUyQiUyRlJYNXIxbklvVFRQenJ0Z3hibkRHbXQ4M1RaVnRPbDZrM1JtYkdCVXI3MWF4dXZaeWNuSnFGSnZabHhKU3RkcmU5T3ZQcDRJMmxSZDhURUIwNXpRdFBHVzE4azVudFZLZWVBT1dHVzNkMDdGVSUyQjNSQkQ0dWI2MElWMFNaNFd0WEFnMGZ2MWhVVEJqQmV6JTJCMFlZQkU2MDBoOFNYaGF6c1ZwOFFvcjd6TldrbSUyRmpaWWNrZlkzSnFvV1lxdmtTcjNzYUlsSEVZYUgzODJuaWolMkJIT3BXTGxGbTclMkZZakNXWHElMkZmb0pBZTVDOG1aSGRlUVZkeEQ1Vk5kZzNuZnZrcFJNejlhaXNKM1ZYcWFmViUyRlNLV0pJZ2FhbHhPaDI4a3JkJTJCbDQ3VFloUEFuV2ZUdjhxMkQ4cXpDS2tpaTFPJTJCMmgwdEVidGRVVWV0MVF6b0E1eU81anhlNE92eW1MMGp0c2tXN0hkUnlONTRWNzVWdmlKZ0lVZ2dmYWxlT2dSaHd6cCUyRmIzaWRPV2hYWGluQWJTRCUyQnZSMFJiNnJHRFZtcVJXbzFyYng0dkRReHZwMjE2Vk9VZmxBWXp1bSUyQjFLM0dNJTJCNXNaSjFpY2lkNmRWY1c5U0Z6R1gxN2R3VDhoME1XQmV5VnRUdFJybmpxRHVMMklyWXpPMlZFV05FU1hObFJTSXR4cUhYT2FZbWl3VGxwOVphekxpUkM1REN1aTE1TmhJdHpHcmt4RzlPaCUyQjJiY2xFYmpJdzQ0RkE2ZnpYY1oyQWI3QnJQZ0s5aXNGeG91WXByZDVNWlNQeUNCcXc0OHJTTjhmS3JpZkdYMjd2TWwlMkJqczFxRFZMdHRVRVFlVU1vTnU5T292bktOMnFpNjdCYUUxbmZZZFVnJTJGaEVmQ09lZnptdjd0c1RmbnBib3ZEb3JJYiUyRmUwbldZRDNZbWR2a2ljbW5QaUtrQ2E3JTJCaHhSbkdnJTJGWG40a050Y3g0c0NlRWxvNzYlMkJaSlQlMkJUNmNoSnZSRmZNN2ZTbEwlMkJLdWVvZXRBOWZzNXRtdGh0dXpWS3k5dEdhJTJGSkMzZUtDYmo3JTJCamFLUUlaRGhiYnZORzB6NXFPRUVIdlpLNWgxMFZQNzF0ZFlYdEZNMmF4eHZYT08lMkZBZ0ZsZHF3ckVtWSUyRmthTDFMQmlzNlZYd0ljdHBjU2hzbVZtMjNWUFAzUmtJMTQ4RFJFbHozcVB6YnYlMkY5cWZJYiUyQkp4RTNFV3QwaWFZaGUxQ3RjeFpKZSUyQmkwOVFia2QwS3Nnc3RWVm5ONjdSb3J2R0klMkZiaW9lVndEdnFnODcyeTVIbEZ6VXg3QXl2JTJGMTVwaTl5WmxUYklZUXJpbEFEWFdpa1FaTE5ZdE5EbFJKczMxZUZDYWJBcCUyQmtvb2RRNHBkcmFjTHJMTlhMenNTTEhsZkxLJTJCY2t3TEtsQlV1WjMlMkJLOXNLbDRXUWZoRmpveDVKekY5JTJCaVhDJTJCclNOMlRmWlZRdktOeFl5clY1dTlKN2RvVyUyQnFYODdUTGJJNXZaa2t0SXA2cnhBRk9LWjVaYmtMbHBMVFdwR29CdiUyQjU5UlhUZ2olMkZ4d3pRbXpBc09uTVFHJTJGU0dVMmk2eXJjUXlWZWpiODJzMmY4SDZGRGp5ZUVuZmV4YkFRTzZlMTdPbU5BWHBobiUyQnUyaCUyQms5Z2NkQTg1SmlEVko1MnlwVll6czNFNUhkaUhRUG9yeFFBVzVMZmZVbU1TRnNFOEtWYUhCNGphNkN3TENhNEwwNlFZM2ZwJTJCQTk0cDdhNnR5dVR5OXg2WGlKUEZYOE9xRjlBdURjJTJCZ2dXaWdMaSUyQmVGdmI1NVNqWWU2MGZxU1VYYkdxM1RNa1oxUHklMkY3TmNJMFRzRXQydWRUMFRVa2slMkJVaFZ3cVJHWjZWSEFPRUFOY042ck56RHQ4em1pWUolMkJtMFRrN3ZiQnV4NU9pbkh1N3ViVUYwTyUyQkp2NVFEMVJmQ1FZckU5Vkw1cm4lMkI3RjFCckcyQVN6c3dqMldGRlZURWdCRmRVazRYa09JUWh2ZzNyVTB1clhvQjNVY1VnN0FKaTVRJTJCcXREcEU1dW9BbHJuWiUyRnBIeWpxY2tzOENFaE5xalhBZWg5d2NEYlppSjluZEdRSktMNUt4UTJUbEc3djE3V0VJSGJaRGJZJTJGa3FQaHB1Vzh4MnI4eEJNWnlaMFkxN0JqNlY2ZFRtc3MwWHlxbUFoN045d0JmV1JuciUyQk81YVFTS3F4TXYxQ29ZcHg3dGMySFR2ZXNxZkNTV0F5JTJCS1M2UGJQeXgzZU9JcEZ0blBqWllSWjFqZkV5eGlxQk5IJTJGaHBYSEVsbXlENkFONml1UVBtMDRHNyUyRkdsNWE4UUVBNlU2U3Z5ZmFWRHlpM3JCYzlUQURMS0pJTlVWTCUyQmJpT3lnTml2bjlWTFB3WldVWXJpVlRtTExYWXpwMUZ5VWM3ZUElMkI3Y3JVNEU4dnYlMkJ1VDJ3bjRCWWRhZWZBYjIwc2FObDdVSDdkUkdkTTd4WjFMNGFoWCUyQjVaMGU1WTN4THlUJTJGbjBUVGNZc0hBZWNBclgzMXVGQjVBWCUyRlZoUmtpU0xFbTJZMDNQNGlVczJGNHVkOXNGTGo2dzRyMHFoOSUyQkxjQ2ltb1JVb2h6alJ2M1gxaFpMNjAlMkIyMVZxOHhMMnkyc2g2OXladkExcUFyUiUyQnZHSTBKeXFRR1JpVVU3T095N011TnpGJTJGUjdWUmdOaWNsWnBwUU1MMHh2M2piR3JpdmVBQ3BRVDRTVWhUS25KVVVVaiUyQjVxNG1YVEclMkYweUpwbjFmWWRqMTE0eWJjJTJCTXNUWWU1NkVIZjd5VXgyMHJnWlJrNWRMTDNrSXowNHlYSUtGZHFqN3pKNDRPUGN6TUlXTmxwa01lRmhYbmgyYW1DYWkwaG01TnVnJTJGTm40eGNFeDA0JTJGOHpCYTkzMEJYVVQ4SGglMkZSUWkyZTVGZFpYN2xxaEtwUUV0NlJOazZ4OTNzJTJGV2hpeDlwJTJCM0haMlAxRmlUJTJCbmNqJTJGJTJGeGlvSCUyRm1LbmpCdDhKM09xRmxkbmNaZEJUcU5aMXVERGliM29GSWNSVFpSZnhiOHdDWSUyRiUyRmFXM09lZW5WY09FNjVhZnIyeiUyRk5jblhLU0xkJTJCUEN6VlFwZm1jJTJCRk96aCUyRmNVb0s5Z3A4R2tQRXRLOTRpU2xYWFlaYU4lMkJBbzAzeWo3enpEa2hXRlg4U0xUS09KTDhlMWxveEtUNTQlMkZZeW9iQ2g2UEFOcDV1U1AxN2ZlZnhHNnptQmVxbDFkeUJaemg3JTJCU0hrdHVYeWJndXRyQm1yVE5xYWhDeGJIdG0zRXBUNFI4Y2IlMkIlMkJ6ekU4N056RWhyR2taN0J5NWhBUE4lMkJvbHVQUXdFRU5JRURNckR2SDJCVGs5c21rJTJCUFlJOUMwZzBkbEVSU2RnQXl0TlNzeGR6Tmk2bDNtYXhTOUx5djZmd2VTYjBIQSUyQk04aTVZSzhVTTIwZ1pjWkk0aktKc2o1R2YweDNIJTJGdGFYc3U5dGF1eDlFS2ZiTFVvckpXcHQ5UyUyRnJHNTBudVltOVhrbE5xdVFMM2xXWU9qMHpWWVhxTXNnUCUyQnNPSDA5N056WDFIRjJzYk1wYlhudGdKa2VmeDVGUUs1NVJQbjNReWE3RDhpUXJqdkpweG9PWExkU29XN0hZdCUyQmFnTnF3YVFuZXJoc2pBS3ZhTENhYkNYRGJWT2VJZSUyRkh5VkVySUFLZkEycERsRnQxbWJ1aGg3RzJsR3FVT2Y0USUyQjJTOGo0JTJCN29jem85WXgwQlB1JTJCMWg1UWxJdXNnOTlyRUpaa1VGWGwxc2x6bkJ0WldjdXJSaDgyOU1oQm9QR1ZOSWpzWHNUclNqdWElMkY4U1olMkZUY0NGS1JSd1pjeHQlMkJYVGpJeVhlaXI5JTJCcU9LWFhlOGhubmdyWG0lMkYlMkJLRUQ0N0xqTjlCMDJvUW55MXViZVlVcVhQZ2JUSlVSNU50T3oxN0cwVjNZRXlyMldha2YzRkdyaFNhZ2pRZTZBajBvNnhxcSUyRjZubSUyRjlaWmpCSXB2QUZCTzVzUmZMZXZyR1diNkpPZHNNc1dYbURNVW1aT2MzJTJCMnQlMkZ2WWYyVGRVOHZaMDBad2pBdWdoQ1R0bnlpTDAwcWRXdWt4c1VpJTJCTkV5d0pwZ1R6NzkxSGRBNTB3RXhOanB1dVNTbTVCdGk2MVFLTldxbWhDREdieVA4UnlYajVyY3V6dk11bW1FNzYzQyUyQmsxMUtZcGclMkJ2JTJGUXBvMm8xc1hMY05uaEtOS0taSnc1ODdBUnJzSGRhb01nVG9DS1haQzNaaXB2TnN0WDVIdjdpOGJJYWRwWjYlMkJXN2RiUW9ZWGgyd05UQnFXcER0JTJCTjVQTTQ5N3NMSUhVbFVHSkxma1pTajI0TzdCYmY3TWN2TUVqN2JyZmVScGclMkJyVjR5NzN1dTJ5MW1xdWhGY1VBakNqbSUyQjdyczRmTUdzRWolMkZSSDZuNTBzMGFVUk5EN2YzSk96eHZjSUUwcVNQZGJHT296aWhUVjdiZ0FyVXVlaG9qYmFVTDl3S216V2xlaXQ0YTNqS0NXT2JkMEx6VGFSY3RqN3hZNFVNMlNoUVZNUHk0N1l6N3Q3bE81eXBKSDBtVE9FNjBGZlBVWjBLd3hwTFlyQWF2T25uZjBEMzRaV1RWRFp6b0x2cWFZaCUyQnJ2a1FrNmFubDlPQWlsaGM0Z01Pd08xT3h6Wk8zVUMya3JtZDE2N3ljbXE2bGxOTTZNeVFhem16bzclMkIzUyUyQkZjeEl6WDlKVFl1SWNma3VDeXBSQ2YyUFlPJTJCRjIzV1Nza0RKQWl0RnVXMmkyOSUyQkZsV1dIbVhlTGpxcGlKN3E5NWJaendReU92ek5hblBOWmRRRERzNnNld2RjN2ZONXo3bFdjb3owYlhUNFc4dG5UbkhvU0EyVGVTJTJCRllwUVBhMWRSVng5SHlrQjhWS2V4YmFjOFZDZFVPdGNjdHl6R0R5c3FIT0Vnek44SWlGeUFWT2Z2emNUSTJQaGl4SjRFVmRrcTNiWU1JUlpPelNZNjdMSHUydEh1a05UVUVwdDFxJTJCJTJGa1FGVXpJN1RJbDF6S0k3YjBscXVBbEZ5blVGbUVWa1RSbSUyQkZiR1hMb2NpVEZiNEk3dGt1M2NZbzVKU2VKdnJzNkxRVHlnVFRSVWswdHZJV0hwOFJTb0oyTDRjUHFQWHVhVEkzWjNTcXlYcHRWbmU5bks2RnFRWkJYeFBDVGVUJTJGJTJCJTJCTldGV2RmQzcyJTJGaERuVUtmRXR5OXZxdG54aTlnc0oxazR4OGl2V0prNGE2REJ4TW1EWGg5RCUyQmVybUxaYmFiYlBzMmRpMkVvWnJad0pyVFlZbnI2WDMzeTFhM0tJTWxKYktsN3cxb2JmVGE1NVVOM2VWajI5ZEJ0ZnQ0dEUlMkJUTGRkTXpNUVRCdG4lMkJKTHloUUtCY0paelh1Z2ZLM2xMQ0h4NGRHYmNWcHA4R3owJTJCV3YxT3FDTllSaUxVZGFQQkxYcWhTOVo1T2VDR3Jwb1ZsUFolMkIwbmdEa01OZlRLaDZZaHhNNkUwTXc0TU1qRFBqMGF1UHQ4Q2NrTDgxallWRWJrWmI0S1oyQ3BzRTdheGUlMkZqbDlBbFYxTTNoMzIxRFFwVCUyQnl1YjdhdTdyNlVwbGZRRmhRaGsxMlhRUk8yJTJCc2IyUzAwSnhLZ2oyczlEUTFaSTRVTE9jVWVDMldlJTJGRk53bmhucjh4UENxYTRUell5VURRSG9UVUw1bEQ2RXZ2d1ozNmpHQURlZTJ3M1RnNmZtQ0IxNUhia29hYVh0cWQ4JTJCY2Z0RUFaeXl1TEFzTXQ5S3YyZWZzN0ozJTJCaDZieEVUVkFST2FYSXRsUlJqNWUlMkJMdWJaNUgzbXlVRyUyQiUyQnl0MHRybHU4eXRGZWZnZHc0RE43dlBRNzk2eHNDazY1bTN2TFBySUVIT3I2djQ0RkQzb0ZhZFE4SHIwcUMlMkJHMVVXZ2phbXdzR2JwOFBlUlFpd1J5aFVDVzd1REw0cUxtRXczUDR2ZUplbVZBTVhMNTh5a1YlMkZTWmQzSWpVNlFBOGdrVGRFbVRNNmklMkJLdkRscm1TMlEwWkZoU1MyQ0Y0ck0wM0pZM0dza1hqeUJaQWVnRUhxZHRNZ3BQWGJ5ZFV1YWlYYjBiaGFWR2lSRlpuTW5IM0psbFAlMkYlMkZOak15OEJ3VHJUS0lEWiUyQmVVN0pjOENUY3JxMk03NG5KN3EyU2JtR1dtc0QlMkJwUDNTaDFBNyUyRllIb0dMRiUyQlBoNXZiNjI3U1BOR1p2MCUyRmFlSEk0R1p1dW1uZGV2RXRSMlVJNjNUU0pWc1BUdmRWNHI5YWphMzZTZmZOaURLSWdKN2olMkZyd2pIOGhxdDZjMEpTWlVnS1MlMkIzMzVqZjlhSW5oT1hBREJXZVk4d0FMT01EJTJCNlY2YzhORDVpTnVlJTJGJTJCdkJhVUF5UmY5WjdLNFJKbUNrbm90T3JPNWxoemJWM3BqUldwRFh4JTJCU0pxQTR4OHRobkFWV2ZRa2Q2Q0F0Q21TSFBXNnR3eDlYMlZ6NEhWZGZQSWhZMjhKV2g1ckl6NDl2ckJEMCUyQkN0R2IxdDV3Y3olMkJlQVRucXRmYyUyQnJXZjlsQ0lUOGZpVTZnUWVXJTJGMklqMnozUFVWUUhTdE5aR0d6SiUyQnlrbyUyQlFYJTJCTkVyZDdBeGJpdnY4cWdBbVVlMmptWW9UeUpWelRsNzBTd28xaGtPJTJGWllXZFFMOGUzSFVDWmZIRFdoc25RWiUyRkZ4WG0lMkZPNnFyRVVFalFiSlRqT2JseHZxcERHcW9LWlZYbiUyQjVPYk5NTDg3OXR4SUYwJTJGMHJqVVFNWFk2NVJLV3lKTVVvd0VhVjN3bnlnVWRCMUdlVnRaWSUyQkh1djhWYXU5a2JwZXR3aTlnVHZmdkVOMTVlaTFVVjN2RGFIYlh4Qjlmc2FsQWcyNWhSeW5hSTl1WnVQQkxqYjklMkJSbTl0dUxaNGo3S0glMkZmeTNBOXglMkJKbERPamxMSFlTTmtqcjJ1WGcyJTJGc096Ujhxc2hVTUJJOVYlMkYxTWdzR2NOcVpNeVdWZzZpajNseVZ4a1kzUGxuWmNFVVM0ZmNYcnc2Q0d1b2trRDFQbEp0dFJJMWpYdnlDOFYlMkJyOFBIcXpsOFZaemZGU2trc3NXZmlQTDFGTzVzU21EcUJSbHdRZlNMJTJCM3pqJTJCckpzeDhJQXo2dUtIbWZyNUE2bGxlWWJnZXZaSTc2bTlza0ZodFBUdkpTTDdBViUyRktPNTZ3QkxNamtpcTkyaSUyQjB5d2UlMkI0UkZRU2ElMkIzekdkR095YjJ0TVN2Qm1JV1VXbWpNY1J3N2FYcFpQWEJnUjExQ2NQMDR1Y1RVczIzNGJvZnQ0eWxoMnBNSjhJd3IlMkZhN2FRbWs1dXk0RnlFZEFnVm9TWGFTN2RLM1dyNG9OUSUyQndVTEtQVDNZZ3hQdmFCREU3MDZRNnR4bVdmenUzVktzRzVUZyUyRjk1b1k3azdkVG14MUh1UmY4ZjZLZnNnMGltVkVDUUpCJTJGJTJCaEM3RURGNXpadWVnNEt5bG9HMG9rb0wyV1kzenNVOG5tS3MxJTJGT29rRVUyak9TNXVWZDh3blh1bDlhYjg5Q3dFMHduYjBhYkhkREdjOUxoMFZGWk1hUDh4UE5wTWx5JTJCRkNEaFRaSktKTGo1cXdFdWdVWExMd2Y4UHFFM1dHSWhkV1JCZjFCdmxyV0k5SzklMkJLMjRCQlZVb3JuQ3AzWWNkRkVDbjFmSUhrelJaaWNGY2NSUWx4eWxRWE40UFp5VFFBNlRJcXZUJTJGZ2ozY0FiUVVkN0M5MCUyQk1iTXBJJTJCZ0czaUNRcW56SyUyQiUyQnpGa3BkcjcyOGVVS2dTMERNYU5sekl3QWxpTGsyVlYxaiUyQiUyQnhHMk0wY3FvJTJCdVBIZnhOVktGeksybzh5SVZCZHBMbUZoOVd1Z3AzVDFibVNDTDdKJTJCVmc5T2RpZm5wcyUyRmYlMkZRJTJGJTJGeGJjUWNQQmpTUmhsSjltQTBWbGxkM0R4TjhxbkkwTVglMkIzJTJCdDVJUUxFaG1vUXdGOGxkVjRFdkI5bEdXS0Y0aiUyQjdzOTBBJTJGTjNCR0tKRTN4Y255S2pydDhlcUc3YzZxYTg3TkNENmxtalpVamFsaDl3M3V0YTVGRTRkb0VpOXN0azdnb2JOdVc0Y2M2TzdZYyUyRks0cGIzaGhRWWVpRUZOSFJHdXdxM3kwTzNWNmJCSmRKVVg4dFlhdTJudCUyRnNDdVo3OWFsMkNYOSUyQklKTThoZ2ZNSTNvczEyaWEyS1JpZFZ5ekFtQnQ1d2VQTHF0cDk4JTJGZkp2MWYlMkYwRElnWWF4YnBpRjhwWDR6RndiOVI2SEglMkJiOTc1RFpXZkxSZEtrTENzMm1INVF5UDBTZkZzbHBHam45M2pWRjk2dHBNckFXaWZXa2lNckR3VUQlMkJWendzaHdjMUwzc1BZckIyaUdnaW81YWZ1S0wlMkYlMkZjSHlkWmpjVlAzSEYyd2tWWTU5aGRRUjRVUm9WQ3ZTREV1cWolMkZqNCUyQm5vJTJCZ0RwUTVUTmloaGluNVhseGw0U0F4bWlZR0p3bmpTbkE5WlFpT1hQNSUyRmw1ajRtUUJzTWtSZVY1QlRib2lzWHAwbkRIekluTWhpJTJGJTJCQlFhaGdYMWRTelNYZzBRMVZtN3hzQjFQJTJCdmEwelVnaXIxV1c1cm01RWFmNTlVaU85aUZmbUU2JTJCR0VpcXRraUZ2b09wWElseWZqNjdOTEY0JTJCUk8zdjdTd24xbHNyUlhaNExxJTJGU0h1eG5KVWVhZ0lmUGhvUUJPVWtGYXlQbGU0bGVZQm0yQWdFVG9KdTZEZjdDc0VwdzNDQ2lhOWQ0V3dUczVtbXhRQmpUWXVpYW8lMkJCOWFYaTJXUktJNWh1VGJtcWZvSGhmNU94V2N0TVhqN1FPaTE1JTJCQ0YlMkI4ZHFIRXlZb1QyNEpoMGYxZmxrSEk5UXhFcjZ2b0MlMkJkJTJCeWZtSFB0Rk1yQUpxTEpRdWdFR0pJOFoyVGVIbENXQWNJJTJCOThuelBJME1WS3duaEY5Y2pmSVFCQnZ5SzFiYWJ2SzRZSWIwb2hDUk1oJTJCOHNtRnh6ZVpUUk4zYkxRMDRUZjBOJTJGaTZEQ0tyMjExOVNKeklaZVk5WEVwNGhjQmJIQjh6NEElMkZXQ05QeFR1Z1NXdXZLV2VjZCUyRmE1R0ppMmNnJTJCSnZ6dGx3SUx4VFFNOUlJS2pQQ0JUN3BXNGw2bnpPJTJCYVVnenVETzhIYiUyRlJ4ckFXQmJHaWlFWjcxc2xQZjgzJTJCT2Zsd2Jxekd1NXhibCUyQmFpZWtsQnJGYUNOYWpxYUU5a1UlMkZMZE10MWs2NkhWUVBRUGZINUwyWEM4ZjlMJTJGSnRxb3o0SjRTdjA3TGFCOE4zVW5zOGdIRFkzJTJGVXhqVmZSOFR2YXd3JTJGd3JWRklBUXBrRGIlMkZlWmlvS0VSaXNTcEVLUndVOXRKemVwQWJaNSUyQlFlJTJGa0kyQm9KN2NrZVYlMkJORlpCdEtQdmhIZmdicnNUQVhOY3hEZ3YlMkZZdzZIUDNYSktjNEFKTTY5SmdTbDBzZEpZZ3hUU3hYZmNTV2ZGRmhsY0VScUxyVm1VNUVYc0o4NmFjQlpCeGhDd2RYenk1UmFEWHQzZmlCVlJxRlFRT2Y4aUFhakdBaUlmUVglMkJNeVlIUHJ6M0F3RGI2JTJGWWc4YUt0V1NBVkpIQjBrd1k1eCUyRlVWMERlWXJ6MXJEYXdtcTZKekxSQTlPSHhyM1FmYjJVc1JhSTZQJTJCa2RBUFZtdHMlMkJwMWZ5OVV4UkNnRms2SzNhSGxZJTJCOGRLQ1llSHVDMXJkYSUyRkl2MThUSyUyQmxjJTJGTnZYUGo2UEljJTJGaVNKQkJVQzdFaXVUVlFtaXl0OE5rWWZzdk83Y3BLSUI2dG5KSTN5elFtZjVNenhjdmN6d0xqQmpHa04zb2kwamlrWkVLeUVOR0wlMkZwdkFEbE83bG9CWjRRSmw4U1dSZmhVSENPJTJGeUR0eFZKOGFPcHFYZFFGaDMwRGs2TnVrN2duV1lZdTAxWGh5Q3h6dE5hY2UxSW5YVGRqakYweXUyNHFYWlRVJTJCUW56bUx2VHJ3TE9HMjVudXM1UDM2VWFuM2ZnQ3B2bmJWRjdTaHIlMkJxVWFqJTJCZUdZaFU1OWZhQUFIa1p5bU5hR2gwVjdjMFJ1QlFRWCUyQjRydmNydnQlMkZkdWlyY1hUODBRcXVQZ28lMkJuVzNpZ1N0VUVoV2ZtdFpKbFNid0VYS3pPajJSdXR6N2tKRmxYUTNTWGM2ZnZGeUhHUlV0RDE2aTB3QzBNeExBZ2NqaFIlMkZ3SGZQaE5iWFN0cmRwQXQ5YkJnQlAzTEJZUyUyQlpvV3ViZzNwZjVGYWxpTnJvQ25ZQ2lzdGl3MCUyRlFaMm5McjdmcnVkeHdxbGFxWTN0d3JNZDhORllpMWZURURuRnd5JTJGdUpobm4lMkZkSXRSY2ZQeSUyQk5ocFZPbWVYOVNLJTJCTyUyQmU1NE11VDFBOTIlMkZUcnhsVVQzWnRWY3M0c2wxVSUyQnBjOXRCRGpkblU1RFZLNHFHUkw3U1FXNVRBdmZVSUhraVJZbTVleUZiczVvVDNiMVBROWdnc0MzWkMlMkYlMkJkMnM2bVc4SSUyQjc5VmFyOWNIUUNRZzExZzBIbFlYaU5yT1czWG51eFNjMWc1c21SS2p5dmdDM1hTSkZWaiUyRnl1ZzBFZTVmNFpZZlUxSXJjbkE3WFdHVFU5YjElMkZpbnlxUyUyQmdzVHclMkJoWHM3c1hQV0xVVDdlS3ZLdm1HSkZBNkVoY3JRR0l2VHFXSjE1aGJXcFZCU2FpbUxBM3dReDZ2NDltVXA4Mk5nVjdHeGx6bWgxJTJGNElqcnltOFNqZDNFJTJCJTJGSGZVc0hEck1rZk1tc0YwbXBSZTBSU1ZJMHYlMkJSVGtSViUyRmE1anV4RG5mTDElMkJ6bktXdWxqWFJxcXdJdWdqSEhEVGR1elNMN2dhNnh3dkM5T0hwSzklMkZRVmxRVjhpVSUyQmxGQk5Ta0dSWVhvd1FycXZ1M3YzbFU5ZDUySm9YUnBkdnE2bzBTMGlJRTE3QWRjWVAlMkJZQ1pyMERHQk9IQTQxWHpCRExZbWY5WXFwaDNEQkpFdWRSaXkwVkJQZVAyVG92SGZ4QldobEdJVGYxQ05GJTJCd2VSUlcwdGU3clRyUUY1TERNYmlCNmo1N3dQS09DTHBjVCUyQkIwVnowUDVuTXg4ODhscXhuM3YlMkJQN0k1NCUyQkhicWNhQUV1MnU3U3daVSUyQndCUWtsZVpRc2tYbDJoZ043YXRTWVVGdWlIeHlteVFJbEI3d3BVekprbER0NW02N0dVeGVOVEdjTzBESmZpYkh2MyUyQnN0dkxRa2l5SnU4bGVnSmNISUNscU96YUVWVUFmVmdFJTJGZXhDWVAzMGkyQ1hnbTF6d2FGdUNjNkpRbGNxSTE0M3FVUGQ3TlklMkZPVjhsVmQ5biUyRkZwV2NWWTlNJTJGYmFRRVpmU0E1c05HbXh6ckx6QyUyQkpKc3VpcWluNEdYZVRxVFJHcjd5QVUwRTRpaHBMNHZjaHh5WXc1NHZrUzY5OVlBUDFiYTZ5cTJURU16RiUyRjNROUZEMkpJWDJPazglMkZqMGhrQ3JnNEFIRVByN2ZLS2M0ckdkM3NYNDlyJTJGVzN0eTZxWDdidmxLend5dUJrVEtOWkp4NXYzeTlQUTZjNVlmUDY4czU4RTc2c1VzZUpjeVZhNHlBWDZmdjVRTUpZbnZFV3VDJTJGR0dYVXk3UXhRWDRTMlNIMGZYUXhIU25yQm9mZmVQNFVodlJMeXM5elplNks4OTRoanl2eFhxRjMydlVIT05ZZTl0OGtkSTJaZ3o2OTglMkYwVU1OVUYzODlwckRLWGh3NGZPJTJCT0tGRXJuek5kcSUyQjNGOUR5SSUyQnd0aSUyQkdRUSUyQndtdmtOckslMkJveUxDSzUlMkI1bjhHdFFhRk1nRzhuNEZTN1o0Q1lOUEpid24yVXVrZ1c2QUZtT3I1QThPS05rZjZKc1Y3NDElMkY3b2NsQVpBZVJkUiUyRjV2d0ZRakVQc2FDMm1nMGNKOVpGd1NQc3RxVWFwVyUyRlRuOHJ3cndOd1VGQTVGdHJMMTllelclMkJzZFpZQ3BBZGVjVWIxdG8wak9xSzRlRzR4UW5kU1hwMEY4WTZ2UG9EU0tzUXdJcWpvSVN3ejFsbXpuS053Vm54N0QwUThGSkxXcGFuaklMMU1rZmElMkJmNkd3S3k3biUyQkZlYjFEM3ZwdmYwZ3J3JTJGMlRkZUtaS2tlc0dYcE9DMm9YanA4NWs0V1NMMlRRUDliUTdHJTJGTFBTWmJEV09JeHRDMFpRUzRhNUM0N0pjMzlVQXNXMDRXVndZUkFybjglMkJvNzBmVjNmRnYzWDJiMWdmbEx4V1gzTWVvOW5KN3RGVGN3bTFsTzNlMExZTTRKaEhJczFkTExaMUclMkJ1SkdyOEdXa3RQcjZINXVMMG8lMkIwYWN3Y2lkZCUyRlAyT3dycCUyRlNZZDlBeU1nVWc3ZkxrJTJGUUhTOHczNWFQTkg2V0p0ZEl1VlI4ZzhBWnhsOUtoOXNTZ0xMSVZJUlZXRjlIWk1vVFlmc3dFSnZLSDlwMmIzTFJOZm5WaGVFdU9hYXF6dGdBNFNKTjNUaXElMkYyJTJGYjRGRDhUVVN0VXJ5RVQwamxsRUpJRG5uQnFhblNzMFllSiUyQlBRNHRJY0g4VzZ3WFBOc1VNUzZPanRMTyUyQnNxcTFNWiUyRnZLSXdHbTBJRXgzYXdkTEVweHRTczZMVElsbHQxZnhwaklueFA5eENHWVlFdmxscDFjU0xrQWRDMzI5aTZqMDNhQVczZ1JnQ01LMEJRSG5ZOG00MGh0N2EzTkltb0ZQS1BObGk4MEVQSjF0UFRWUkRZUGwzcm5GajF0UzExRmhjUExaWTI4MlZMaUpkZTlqbHptenBMa3liNHM0bWFhUE9QaHVjaXlKZEQ3SEZuT1dEZGZ6ZnJHeHEyJTJCTjhFdmZ3QjlnSXZUWEl1NGpwMXlrYTlDJTJCTDZFTmhGSmo2b3gxaFpXMVRrUzc1Wkhub0clMkJXR0gyaXRON2xqYyUyRmk3cjZZUGgzYUNla25vTkVHM0hLTmpjWk9UbWlBcGRWTGZKeWolMkZldmdXM25iJTJCc0JVZXltU2Zmc2IxeiUyQjFKNVBuQVRWMzYxUHF0cFpKJTJGUXJ5U1BmdUdQbEJHNURlY0FmMlJQNGtnRDdVc3VNbDRndDQlMkZUSEowR05WUDVpTm5ZZ0E3dFp0NGtQeDlKTGw2ZVZRUTJEaEV4QjRnT05EYmk5dlBXYm8lMkZoOGp5MFdxNm5ZSmNsT2tRV1hnczRxJTJGUFN1eCUyQkxTTGlkNEVRVmdSamtMZ3lDZWtQbEswbk5zJTJCNWpUQXpGN1pVaGpzbVlHRnElMkI2OTlsM3B2TzdPcjJKT1NHUXhveHZmWTFpNzBWTjdQb2kyMWliRSUyQkgwOE9mJTJCJTJCcHExdlZMYmwwN1BaSmJ6bXdSYUw3MGtlWCUyQlBrd3pIbkR2NHlncFRvTzVGekMyVnI2VGtDT0hWVHljcFFMdllpYkFGZzE0NFF4aWFhbWVYSCUyQlU5eFZWUXJ4VEFvaUpvUDIwb0FEenBrc2wzOWNMeHFUclk1ZUlXVWRPYmtGOWRrUlVRT0U3ZXNPcnZGTU5CcjJ5N0JjWUpJNWF6c0tydDAzJTJCU1I0TkNxUDQ5V3puOGpOS1p1VHBDd1NxWmNXJTJGYiUyQlVseGl0cGJFa3k0RkN2MEZGM1hFMSUyQkk1SSUyRm5aM3olMkJZajZWSmhRRjgzSU1qWE9ZdUJGaVQlMkY1QUY1RUdLeldqMEh0REdzN1hyeEVBZEhhaktudUVoWk0wRW5IT1FZdTZjZmJlbWljTExPVlQ4dGQlMkZWZlBnQ1hNJTJGdDVzRUZyU2JSb096MEYyRW4yYTBoYkI1WEVjNTJBU2FsT3B2Z3UzYXJhSWxxV043bmolMkZrZE5pVURlb1RwcGRVYXF0Zk16cm4yS1ltOG10eUhEMEI1QXA0Vm5xJTJGWkx0T3BhQUhZbXJScHZGa3F5Z3NuWUVqVUJnR0MzaGRXUGhkczJYVjhJdlNONmNNWCUyRlJkVHk5N0hJalN4aWVWSTZCVDdxS09KZ1h4QlJGRlM5UWc1TXM1WTVMYyUyQnA4ejh4JTJGJTJGRzB3MDNZNGhLQ1paaGx2ZlZ5bEdKS3RCdTBoTWxHZDZQTmRVdVdiY0lMbzZnZ1MlMkJhTTlaSmkyTiUyRnFKUjJkbnFGc2lyVklUM2VvdiUyRiUyQjF0Um02NU5lSDlzNWtvazAyUEpyYUxiV2wxaldvSTZ5WTNBb1JZSGRmd3RndU9mZHFVQVByb1Brd3hTN3FmcVBzQ2pSb2VueUYlMkI1UUxGam1Va25YNEVocVJxbjU1VzZST2Z6QWc2MVVrZmJUQ3h5d2dWRjZuTGI5cTNjR2w3JTJGTzR2UThmM211VDJyd1dHb0xkVDBpbXRWJTJGTjkydVgxVDJKJTJGd0pkd1lVcXhyUU03bzQ4WHphUDlpdiUyRjJydFRPbjF1TyUyQjNWWnJpQTFSd1BraWVScHFCeExYU2hHNVlxOGo2NktvJTJGbVBPcTY1WVhCeFNWWGolMkJ0eUg1MXZOWFdiTTlpWHdNTyUyRmElMkJQRzljNjB3cFZldmtObko3aFRSRWw3eXBZWEh5RzR2ZEJPbXpoRHRGenlmbE5uSGQzNnFEdFM1YyUyQk5iWkQlMkZ0WTVtOWliTyUyRmxzcmhDJTJGVlE3UGVrUDU4cFlNMTVjNm5zZTJrbiUyQk5VZjk5NjZEaDlWRjElMkZFNDl3cEthR0FuMDJkcVVrRTBCSmpDYjlZend2RnQ0a2g0SWQ0dmdIbVFraUZzNU5iandKaCUyRjQxZGslMkZvS1J6JTJGUjdHaSUyRkZxRHB6b1J2UVl4NU1GSzVLaHNQcEUyQVVNUzlMMzFibGJDNUlNY0ljS0J6JTJCUzM3akhSVUJiUE1ZQkYwdExvTFZKS3FOcjY3OTl2dHFKa0ViWG92bFJVSUJTN3dJWUtDWnplJTJGUDFwY3RFU2xDR1RjYmU0ZEdOMEp4cmlaUUJacmVvUWg5WHFlRnljTiUyQmxjdE1QNmRBZHQxbzVyVnclMkY4d1hjVGo5SXhhWEVBWjhqT2t2dHdMaDVMc2pFVDFuTUhaQldmMnJFVHVPdG1DMHk2dFh0eTAlMkJYTGVkM0ZTbmZWdyUyRnJsOWhDWGp4SSUyQmZpY3M4U1FZa2RLalVaRndxRlk1OVVVblBRb2QwUjdDJTJGeWp1cDA1U3NtaHc3UDY0c3ZscWNDWVFJaVBicXhCJTJCYTVjZDZuRTl5ZUh6cjFidiUyQnU4MGk3TmRFa0I2ZzlpRGVlV29aQ0lPUlFFOXk5JTJGSzY2VjFJZmwlMkI1YzJrVWlLWmVRMmsxZU9ZbERpRGlTRnpuc0Q2NFJvdVhDNU1JMyUyRlVydDh3Tlo2YzNlbDJ2dXo2S1gyQWFDd3dtNVVTWVN5dVhyeUNvU2pDZnlNZVlJejk2NXpSMExQRHNqOVI5bHQ5bEdiWDQzcG1pUXphdHJBVGhRYkFlNkp1eXh4cEtDeUNTNUhlSUdQSmtaV1AwSEpvdkRldkVRR3ZXZ3F2TDlXWnglMkJkTDNaaUEwY1hGdWRueDRmN1d2amIzZmptWjRoU3V0WkdFZGxBVXZxTDdoajVKZXRRUyUyQlhDbkNLZWJUQWQ3QzFtbVNDSk4ydzlCVzhWd3F4SnpQT0tWRGZLJTJCWmJmR1RWTmZYWUdqeW9LN2JoNTlTazdCelp0QjYlMkZkY2JtSDZodmxSUDFqS0xXZFNZc21YRFd2UjN5WlllQTA5RyUyQnlpdFoxRiUyRk4zaTJDbGVLczU2U2R2T0cycW9xaUZtcmUlMkJuSTVRQWUxM1pGRVhic2xwdE1IVDFSamZPN0FSanNMcEtFT0lQZ3FmMDhnTVB4OWdEczBzSUJ5bFlnNlpIc1NYNkFTd2lxTHBNTW5ualVuMU83cCUyRlR5TUkzb01RUHU1ZVRHMFJNdUI4dW5IVlM2diUyRmNVRnYzYll3YWtvek5LcEolMkIzNFRqR1NXSUVpZmxDSHA5WWFhQ3gzWnFQaU01YkVha01TRTRRcTkxbllVak1ZWElieSUyQnR6bkx3cHB5c3JpRG5aUGM1blU3UklFTE1POGFIN1RSeEkyQktaSlpiWmYzUUM1diUyQmFPQiUyQlBBRG1hbUxUWnNONzRQa2RnY0Z4U0M0ZzdEJTJCTXNiMWxKbGExQXB6MGNWdWhmM1N4N2lPVWFGUXBnT1hhMWtjS2hmY3lmYmYyVDI4OWNENyUyRiUyQkxvOXdGaDBxZGF2eHdEak53ZHAxeFhwTUdTdVNRTWM4V25KOVNJT3N2Yjc2ZDl3MG5sZmZ1bnNXYnVKRzNudnFiWjlRMGExUm5kNURESE5ROEI2cFA4RXRFJTJCOVpYNnRYVmsxVmw0YnA0aVp6ME83bXNFUiUyRnpid1pyNjRGSGtYRU94dGpla3RhMWtiVDhzMlh4a3hyYSUyRm5LU1NyQndBcFVMVEZ4WFpiclVsUEZXUlFZJTJCaWszVENHU1N5WHZ3WmlPaGs4U0VTdVA2YXgyaEVmd1Rsanp5MDQ2dEdYc0lxU1VvVmFEWHB3czllcVI4OWNsMFl2em1JM2JXTFoyRjI5QzNSWWhKRkVYZTNLek9XYVhBWGFiTFNXeEJ0UGo5dmZnOXNzSWlpeUR6dEZ6eEJWd1czSEY5c0RMNFQ2bDhGbmQ0JTJCWXVCcEglMkJTQU5QMWpTTnFDc3FvaU0wQ1htYnhzZFhIWmolMkYlMkI2dDFkQVhkNkhRS0tDa0R6VGx6VGk4V0FtaHFWMUxGeklJTFc5U2I5RmhTdjlpZ3hzQ0FnZGw3aDRmdjFXalIlMkZacHhhQXNFUGhGcE9Sdk1sQ3ltcWF6OUZpeG5XejN5QlVYQWJoZVQzRHRsbmk4diUyQng1QW05N2V2UkprN01icTEwT1gyNFZsazlXR2hsNmxKNWZrM1VmOXg1aVRHaWNZaGNGYmNuM1FTYmp0N0NrRnIlMkZxZ1l5S2NzTFkxbFZRTDNnWmF0NjhTOEZNV05RMyUyRklpbjU4SXZmd2NNQUVHTmFrQ1FWNzdseVdXNjQ2dm1YNSUyRkRvVXNpU3ptSXg5Y0dxWllGeGtvZHZFSGo2Y0FmWFRKYkx3OHJJMGdNalh5emFQN1VzbVJwemtkZDdWRiUyRlZIam5tSmVWMmpaVHhIWlBFUk5CcTVpbW15V0FQRGVtcENla0hCSjM1OEpReTducVV6eWRFUjRvajJpcUg1ZVdqRmwzUW1IM3N3QiUyRnh1SVZ4JTJCNGU4ZlZneGRDWlM5Rlk0eCUyRlRmNkdpT3pxaG9Id1VzVmclMkJSSyUyRklzbFdzYnhaRU9Cc21aa205QVhXVGxpRU5SS21wZVgyT283Q09RSTMwTzlxaGdNOHo0cUt6MVRUd0tPbyUyQiUyQlZPY0F6SFlZV2dEc25JMjZrV2dtaElJJTJCRHZFT0ZUdTAlMkZnck5rMkM1YVk4Rm81NnJ5RkxMcnZqUEdNOGo4U1VxMEpyZGdnczZmZGxUeVRwaE5rWHRtNVBrejVQb3hMbnBXU2ZLMzJjSXpsODhNc3pVNU1pRWNMcDJVTEtmT3FvNWYzdlpPNjYlMkZkUm02eGglMkZ5MW1LVG5GUkVxJTJGVERWVDlNV0FDUSUyRlRVaXlYJTJCNElWN0xHRDlFUnVKSElLRnJOeUpoaVVURThMdEpEWjNNOTV0QjhYdEltVkVkMCUyRkFNTkZwUlBYWGVCZTdJbnBGJTJGWDhVTSUyQll2SnZFSGYwWFlETmVURUpLVDZVUHVzJTJCaDljV3BEN1BDRmFQc0thcGdwbFZWS3NPUllBJTJGQ1ZmeGN1dEo4T1piVXJJd2REMWNtY2RoVUNPb1Z1TEpXMTJidHB0bHpqQ0VMMmJRT1dQeCUyRmN6QnREMGloTDA2WEhGVHg5QVJXdk1pbU9CRlZ5QnI5bHBKRUo4TWtNNVRLdTk4clhNSHNTN1ptcjhZMGtVb05MNE5PUGxGWmlzazZMNCUyRnhZM3VHVEI0ZWhtWUV3bDBKZiUyQkh0dVpWTUl3MWRibUViWU1xZkMlMkZuaDdOM1dSTXVaNzkyd2g3cEZJMkxsaHZIUUdUMUdlSmY0S0F5Yk8lMkJzbWs0Zmt5bUl1RnpwQlVVVXglMkYySjdTcTdrUDM5NUF1dE9XaUpIczJCNEFreGp6aXY4b3Z3SEYlMkZDT2V0d21KZkxNWjd2c29HWnlpVW5xUzhVdWJaZktOYU1OZE5DRiUyRnBOYnhBdktYSXlBSEY0ZEJSVWIwVDRVZzZqdjMzUUlyT3NGMnNiSHZuVWE0akhaZkFNc0xFTzVuNnRLR1Z6RElpbldLR0NvOEZ6VUw4WU52TXNRWWoxdTR2SXpWc1VkeU5sbkw3VWg3c3JBVGhSMHFWekpvOEpRdDJENVMlMkJrZHZZNHltRHFyWnk3RktmdTJpNmZiJTJCJTJGR04zZFdhOEw4T2tETFdXUVBkWFI1dlc1TzhaMVo2VlEwbGtSZkJsRXhZWCUyRjdpZGxQaUxmS2tJSUlUQ2tMNHgzc09ZbnEwZTZ5SEdSMERkJTJCc2pMZVJZZWdYUmx1dDYzdVhyYVRWbmJoJTJGd3J3OU55Wm45dEElMkJEUm1ydWFwd3ZaZlpCTUtYb3N6dnpIeGZZVEJaSmVNOW1vOERDZzQlMkZPb0h6WmQ5a2pqeUExUnpNYnczam1zUENyczZkbW5qNWJmNkNXeDJYR2JCWHZsM0EwZzl4OGVRYkE1aUp3blpoVjZqNmIyZDVSelVsQmFoWk1QUmtsNkdxUyUyRm9HR1QwckFBbzZPTjNyTW1Fa3E2bSUyRk5lRUFiSXBXJTJGcXNVckFiVXklMkJ3ZEM3cHdVJTJCRmxPelhWSjluanpzNlg3aDc4bzNEc0I2TENNeHBocmxwblNiQ2J0ajN5VmVUMGRmdURPSHN1YjJIOXN2ek1QRmVzc2I0cm1CNzg2RW9qZnlpY0p1aHZjWXlsRFlia285anNPYVFiaEVHRnpQZnJKZ2NLUmNsbklCbDBKN1kxNVgwJTJCeFMlMkJuYjdOaTFHbkNVNVU4RTUwbUJtSjE5eXFhV1d2TXZhb2luQkRkak1Gb0V6TTFsZnBGJTJGUXJNTEVEaEpjaEh4WGFHWSUyRkNOZE9UZiUyQmdvSGpVRGM0Sk0xNXg3QllnY3VsMmJqRjJOOHhQcUo0ZGluNnJnaWJjaWdvQkk5UTd1MDElMkJEUVM2RWdXWExFRDFzMlh2ekFZZU9PVzNyek56bjVPTWFMJTJGU3Rrc0F6c1dkQWwwYndRWkxmdGJ4RWN5dlkwWDRmUFJsdGZLSExkdGpYd1ZwZkc1NjhaZllBbHZZZGZ1MHpMJTJCJTJCalVra1B0ZUVxMXZLMUd0UkZFb1pEaVYwSXN1THJvVTglMkZrR1plJTJGVCUyRmtZbmhOUVAlMkJZMSUyRmlpN1QxJTJGRjk3JTJGUXNtNnM2Q29JcDQwa1pGUkVWQzUlMkZWUzJqOERWZFFsMTRSTGRXVHg2TyUyRkc4clJWRjQzMDRkVjcxT3daU0I0WFoyWUhjVEkwTDNNaExFU3ZrNXBPU1FpQzhLUnNIbGtsS2ZtVzQ0UzM2dXlSbFFPeGl4RmxoMWRqRjVLTXY4bkdpelFtR2tVdHFoWG5OeGw2MW5iSXgwY1dTVSUyQnFxRXNRc3JjVyUyRjZsYXZwQzJ2UXJqejZ4aXlHdkxpV3lLeG9VdDJaNlAyUjNhWHYyZGJVME5rQ2NGSk4xRFpHeHVwaEdDVHVCWUV3UldiemVGTWwwR01IWTRWZWZsM2VkeWZxdm5kTmpxM0JGSDVibFRzOG9jckc5M2FIWFpxaWFlWUI2ZU5zZzFIenh3azhyUkMzdzclMkZGUWk3bEdqM09YZEY5bFFyNXBMS2pjVFJHSnVveGVQbmxKQ3Y0eGclMkJxeFNRRHVDaXpkJTJGamxJem1KYkJEalclMkZ6THgya2pxMHJ0UEladGw0MzMlMkZYQnZ3JTJGR2FOQThKJTJCJTJCZkRyeExmTTFCcEI4YmZGekFrYUJwSGYzJTJCRyUyRjh5V2NwQiUyRmE2dGs1NWFqckNZTFZOYUQlMkZEeFVEUFFSb0lWTFdhRVlZNEdqeWNtRG5BWjJFMDVUMlo5Y0hsVk9JeGZyUGJGaFBPaVdqZWRxOUliUDNNUzJRcGxUa2U5UkdGb1VPSyUyQjFzVW0yWG16NWR1VVA4SkFISXJpVjNFNGdpWE5DQUxPTTN5OXBjWXhkZGdBbkNHaThmNnYyNmFKc2VFVGV3SzhoZEFLdHg4REFtRDYlMkJwV1Rhc3NZd2xwOVBUZGVxYnpSWEliWktodW5vOU1rWWxxQSUyQklnTWU2M1olMkZBVVd6M0F2Tm8lMkJGdjdIZ005bjBIUm4yNmVGVTVMJTJCTVI3NlZieVFSb2ElMkZ2cnBOcmxqTFZ3Tml6eWhkTTRQOUFzOHV4UyUyQnJjMGpUNTFkZWJ6Vlc3VnVRRldmNzNBWHZDQ1gyd3ZkamF1bWRFSzRBNnVxbFhHejRUTmtvM1RLWTVBSzZtV2dVaU5obUtxVXBKUTRlVWV6RmI1VnhobVlNYW9lUCUyRlYlMkJKaEVqUDMydnlWNHhrJTJGUERZYVBoOVZvJTJGQnh2dCUyRlQ3TmZFUCUyRkpjTkxoaUUzTWFCNm4xWHhHTW1rJTJGMEhNU0w1OERCV3c0UUQxbU4lMkZmVGJIMVFUdm82Y3VnMk9melFKNW9Sak5VMDc5eWlwM1dnakVpNzJJeXBDSnh4eTglMkJ5dHpncFJKbndmdXF6eVFMTFNCTSUyRjRhMzlJZkd2b2xDdFE5Szg3bFNlQWJiSmdJZkw4eTNDJTJGS1M2VnZFU2hKUGVHWk4yVTZuY1Z6SGhvalFBTDhVNk5mNDZra1o1MElkOTI0YXA5ZW1EZzRvelRCM0RtSWwyWlVXSlVEaHkzJTJGTlpiWWhBeUNKRHZsQno4Z0Iyd0RWcHRLMEJNT25Ud3pFbzdEQ0VOcEpZaHExWjM2OEJqWHc3JTJCSkM1aldtTUs4RVI3dkUyNFRJNWZNRFZ4TnV6U1RhdUFDNjNndHhNMzJvTmRaUXV2VHJNV2VKMlp3TTFTemJ3QXdCT3owc3lna3lTdGwlMkI1b0pEQm0lMkYyWUMlMkJvR1BHdjhXOG84NUdDcGluQUdVeEVxV09UN0xJSm9zWFdpNSUyQmJRU0hEVEtJWUFMaFJ1bVh1Y2JxRHhlJTJGT0VrUVdnVVo1V3hZeUV6UUZ2bDZ6YjJWbVV0TFMyN2s4cnAwJTJCRTlXY1hxNmM4WDN3S01ZRTc0eUROQkJBajVPNHo1eVoxRFhpOG8yNk9JJTJCWUc3NXBDVWRvd3Z1eXhoMTQ5VVJyN2s2djF4aFNkJTJGSEY3RVJyTFdobzMwOEZEJTJGNWhpNnIxd0d5MGZGZlhYVnVZZEdVWFBUemlnN2ZTc1ZGbFE3NzYwcTRrZnZTJTJGN0l1eHJWSlA3UmdUNklzVWZYZmdyOFlIbCUyQk9LcGtLQSUyQlUlMkZZYjZ5VzdzZUJYckJBOGdMZiUyRlhka0d5a2J1eHpWOE1pWHNWY2RYSEVWTktJdTBCMk0lMkJXRWlnUWd1ODRCSFh6ZnU0Y0swJTJGQ3A0MUxxektNWGhSVUNGZ3FoWGhHSjNBbTdqZnJSbWZWQmxwRW9vN3o5SyUyRlF0NTRTdCUyQnkwVThJMm9QUTR1dWZlMHBMOGtuMDMlMkZodmVJbllKc0JVcmU4YjZ1dEgxdzIxc284aTZpVXFIeDRnVHVpdFllQyUyRkZRQW9pJTJGMnlyY2paY3AlMkZqYjB5JTJCRXF2SFhIMFhQUU02VGJqdVVVNWFpNEpyYWUzVjkwUzViclBvUnJrWGlNa3BlaHpvVE1qbGNnRExHQ1BXRzJXZkRLQzVFd0d1N09lZG1TQ09FNlNEMXBFVHJFNjRWYzJpZjExQm9Pb0J2dCUyQk5JS3YlMkJpdmUzNk5tQnQ4eTVPNmQyQVc5JTJGUXU0RnYlMkJpOThkdVBqJTJCOUZjanZsUHlNbE56UEY3RWxqajM2OXU2Y2x0Zkp1Z0xMMWVIM3h2U0w0M2h5JTJCZWpQbFE3Qjh6Mk1DTFdkcFVES2JYOEY5dUo1cmhXM3lOR1lQRjZOZmR2RFdLbnhUUUFZUEtxWVpzSGRSNElCYkgyTW5IakFxM2MxbkhYUUN5YTVtWiUyQkh5Z1l0YldlYW9SOWk2Nm9peWR5TVBWJTJGUXlra01JWWFRa1BsZFpJTUpIMzZMJTJGNGpaVmI0T2NTb2M5aUJJVWt6UkhZZWl4OU5YTDhyU0lIN1JHMFE0RDElMkYxZXVxbSUyRm1EU0xoNThqaGlZQkF4OHc3RjE0c0NZYmE2Mnk0UndKemp5TVQ2VjNqaEVZMzlhbGhUalhKRG9wYWtteCUyQmIlMkZ6R0FTUEY4NUkyMXdzVWE1MHZUSW1yZlZlVmVRTU5ZeEo2JTJCaVA5QXYxSndBMlRJN09xaWw2WlVJT0VZR1lUN3ZoamZXaSUyQmFjNHZ5OGRjZ0RsWWZ1dXJmODN2cVMxWXFJJTJCc1p4T1VBMVklMkZPVjBGcVp2JTJGYkNVJTJGb2dSZ29RJTJCalQ2VGtsWlV5b3JvZUF6TlBmdGkzQiUyQjhZRTFQRjF0MDZsYU51ciUyQk1CanBITWZZalY2N2pkN2olMkJsc1UycDg5SlY0U3FSQlhOTnBqdFVsN2ZCZktJcVowVGJiYldyZk8lMkIzMktXc1VlNXpreCUyQlRxY2ZxY2J3OXpOM3BMblROeHVRak1NJTJGREF1Q2RSOGhiJTJGZUprbzFteGptUURwZk94RnhtRnRXRWRDR3ZlVmNxT1JHeGFDeTY4bk04V0t0Q2JCZCUyRlc3c1YlMkJOdUlrdTE5UHNqZzlMZEZsZXB4dWFHc1J4ajFhaUpIVzVnaDRHODZraE8xN2NKJTJGekE4T0xkQ0IwSHBkNURCTUhXQWF0cFdTbGZLbmdQbEFFNmV4OVhOVWpOMFhQYmV4bWZ4dHZLaTRDa0tvOUppT1hnTHVWWnR1ZVF6bFZsQUJFYjlTWE5Ra1FKMzAlMkJHenVWUE44UzZjQXRySTFqVXlJVVlacFhIcGo5S0xrQnVJJTJCRDElMkJzVCUyQnJ5N2dldjQ2alJSUnh1d21mcEZvalRka0QzQjBEQllhVXRNUklFZ3hxemdGRnV5d2VxMTR0M2pGSGNkMHllOEF2U3gxJTJCVHZpQjJMd2MlMkJoZHYlMkZ1cEprbWdYVjRqWVZqQWdvcTBMODBXaU85THZvQ0ZrY1p6WGF5cGlpTjZvRHJwckNtWk9BeVgycDlDSjFNbGdrUDY5NFMyWUg4MnVBJTJCN1pHaWh2SzQ3TkxBJTJCT05vU3pMSmJHTzVkV3VaTzNhdFgzSDFueEJWZXIxZlZva0VGMFhUOGpERW0zMDE1WHR0TTdwZ3g5cGdWVDc4WFBucG1XWHElMkZ0SiUyRnBsamVHWkFRRjlIRldFYUo3d2lkQzRlaXg2UmJmJTJCYjlyWWxjUWU3aXpPbjlNUzVySDl0dk9wa08lMkJRUGZ3MzdlNllmZyUyQk82dXl6UmUyYXB4cjllNjhkTkt2NXZJbGNBd05XT213ZkVyM2U0aXVSaUpKYVJMUVBYWHFLYml2Y1AwamJGYnpEYSUyRjQxaVp1bWlDQ1hkMHJlTmtuTVNmYzJZRkVDTzRZZXlGWFYwcEhIMmNpRFYwNjJvdGhQanpHTnI5RUlMR0U2RVlLOFhGR1RocVp0SyUyRmZwQ05HUnJCS05lZ1Iyb2NKdnolMkJ2RzR1UUl5YWRLRzRjWWJjayUyQk5OayUyRm9iYlowOUtMZjY5Tnpqblg5TWJxZmszeTRGNWl3TElYRmNDWkJ3SEZlJTJGYnpSb3pzWklQNnFaN1dVSnJod29ORGp2RjJKTjQ1eSUyRlE4aFNFc2VIS2U4UiUyRkc0NmEwc3VVTlklMkI3SGcyNVZ2VmJZRWlkSU9RJTJCdnpQZGY4QWFnQWI3djdLR20yM3UybnhmbTJFVG5RYSUyQktBRExoJTJCRVUlMkJZV2phS0FZWEpmWG9yeW03QTd6ck9nWDVVdTdmUlpzeXJKOFB1JTJCNTZMZiUyRm5qS3lwUzh0YWw2dXduTlBoSzYxRWdwczBMNGdVNDJLR04wSG14VXo5ZWl2S0pQUXhWcm5MT05pbWRDMTdSN3NleFlQMWJDeGdNb3AlMkYlMkZXcHA5Y251YllYYnFXQWRsJTJCUjVmZ0cxWXZmNjBMd1JvWCUyQlFwJTJGRUNUam56ekxIcTR3Nm1LTmo5a1hGM0t1NEdaTlpLZnhTWlA4a0RFZ2VCRHNOMXRvN2olMkJtME9IdFR1ZmFhT0RTMmRzOFprOWl6TzIyWjJCeGF6byUyQm9LaUgwazdCYW5kNG5sNTdYRWhPUFpKMzZtUzBiZWxZT2xhRWw0M3BQVjBsMERWRVY2SEt4M3dmZCUyQndKNXFSeWY3V2lKM2lhRiUyRjZRWjlKdGElMkJ5JTJGcXpMckRwNWlkTjlBY3ZXTmZ1ZHpJZHByVkpEcGU4eXEzSjFKcnVmRXR2eHBkcyUyRldLc3k5UDFxN09JczRpekFxSkpJSGZDVHZiNCUyQmFYY1g1bU1HUXVmQzNGZ0FQVWpmd3FLWDklMkJxbjQlMkZjYmlOTFRrOUZubEJLTHRlczF6b1gzZ3RuY04xUEFSZzBVOGRlZUpvdXR5JTJCOGElMkZvODJYc2lZMDdmT3ZzZ1o2WHc0cUhkbWR1WTdZbnBBMUo5akJmR0o2cDUzQXVoQkRibCUyRm95YSUyQjE4UUhoVnlzUmEwY2ZEWDRMZXY1VlpGRVZJa0k2JTJCNDRqQnA5ekVlM0VsZVU0cHJybGRnWEFHNkRVVGk5SGZVUTNLdSUyRkxUY1AyTFNoMGwzenZGWkxDNmRkVW1LSEI1MHZCbjVsWVFPV3BxV3htTjRyTGI4djFOQ1N0ODhtUlhDJTJGcUY0dWRHZmdUWVdzaUtmTnREdXh3eTZienJnMUx6OFRaUFBqaWwxNHZ5MVU2NzBaclE0VWNZbUFuM29iaml2cjNzbWR5MjZxS1hJQmVCODE0JTJCVGxOSXRRMFpGRUs0aEU1T0hUbE91Ulh2WWswUGtaMzI4TUFjdkRUb1YlMkZuNzU4Y3o1TlVqdW1XZ3VBTWNTUW4yeTUyUjRidzRnRHlGYktjJTJCbWE2MHgyalpaOWlKMnllMHBldDhyZnoxJTJCcUhGdkhyMkZjTUpsZ3NzZUgwUXVjdlV1alpvcWFaT0MxTUJpSUhFUDIlMkZYcEpjZ0gwbHlTN0FyVUxvZFFzbHIwS2NUJTJGM0tzS3FxNXVITm9wJTJCclBYNmpmUXp0WkJia2ZoZmpnSWJmNlZCJTJCZW5Sa2t3UzdMT2xVRGFyYTVaM3ZLSlZoUE1YM0U3cXQlMkJwRTRZaERXZkw2eW84c2dueEVneEpXd2R4aTFMT1BKSVBCQnRxNXVUaWV3NjQyTm5WcVh6MEQydjZsbE83RFZzcEcyQnElMkY0dDZMUktoSkhRZ3pmZHFKZ3FNcjVvNTNHdjNDV1NsSzlTcUNhRGl1bTkzY0hqWk0lMkZra3hzcG9NYTJFUGwlMkJVMktmaEZNJTJCMGdpaGV3aEh1RkQ2eDNUckM3NHh1U2RzbWpWJTJCbG1sa3dURkhaQUR5TE9WeWxINnF2WlklMkZObiUyRkhjVnNGU3MlMkZEMzhqbk9rU3olMkZSTjRnbTdLNEhhQ1BIVktnVyUyQjZ1JTJCc2lDSVRZQ1ZOQWJXMnRWV2k2ZmwxaVBBTU1PUyUyQm5BJTJCdWJnVkVNTVglMkZUZWVaU2ZuUkY1QUFsYnYxcmcwTmdpNFlRdjlqJTJCc241b05tUGFLSTB1N0hyUzlRSUh2R3BuZlVEcjE5M1pRMGJ0UGxtdkV6cURaaVFSY2F1dlpHcXN4TTlvSjJDQUZuM3VUblFOaXV4ZHZIVzNIUkE0YXplRmF2T1IlMkIwejByOENnUGRybUtWUiUyQlRhVjBLYU5qakU2eGglMkJOWnRMdSUyQm00TXVmV3VYdHppY1hSeGR0VXRBdDZoaFhoS2s3RFBXamVqM3RPUGozRENEZWNieFVQR0d5TnExdEdzT0M3NHZyZndpVm8yR1VJc01abkhGcSUyRnFpZEY1MDBqWCUyRmtpTUhyZ1oxJTJCRiUyRlo1Q2pqJTJGVzJyMjF6VzNUaWlQdzVFUzV1S1AlMkZZa2RyeW1VbjFQJTJGcXp1emNZaGJyVk5YRTA0andYbDV0OTIlMkZLSlBsSGhKc2l0NDlsTkt6S1I1SHpoJTJGVVIlMkIxM21qZ1A5VW1USk9HZkV6bk41M3JqMW10RiUyRmlxdjBobzRHbmh0djdobVFHYkp3N3E1S3Z1dU9GbDJWJTJCWCUyRjlkUW5vSDhQcG5Lc1NxQWUlMkJ2Z3BTcjV1MFozbXFjUVVQZ3hQanZXUCUyRlZNNEU4cW5PdEVlJTJCVWtwZG1zYzBkOXk3WlVtbFFJN3ZhT3F0U0pvQUNsbXJFeURoQjI0Um5pRXBSSzJpa05hZmNtQlZERTElMkJzUGk3SnFxWERUOFl1aDMlMkZGNSUyRkJ1RDZWN3ZNWFJaNjVSQXFxamM2UUtVWll2RGVrMk1MNXNzMG5JeXJlcmR5M1YxJTJGcG85eDgwMmlhT0hyWk9Gd2xqQzZlWGlMd2J4blB4Yno2OEpydTNFRWpNVGFiUFlOM2lraUJHSzVjcjYwSll5ZE1sJTJCREFFTSUyRkZMQVN2Q1FyQk1nb2xaMVk3aldjVTZaM045Y3lKdnBXUDgyVlk2YkVncnMlMkI3V0psYjV3OWglMkI1JTJGeU1EcnhUa0pQV2N6dXYxVnFvWHNSNWRPdTZXNU9JVCUyRmQ0VGQzc2VieTZRblhSZThkWjFDOTRhUmR0amIzcUdMb0M2RnpBaWpCOW5wYWh5MzV3JTJCVU1KMDZWZXFKcHh5SklzWTZRSTF4MjVLRHNFNWZxYnJ1NFg0dWxWJTJCYVhsbkJCWUxWSWE3TUtpZkxTcDlSdXFLNXFRNkdBajFxUktqRE1NTFNpTXhjZVJSSEVjRUhVUGMzWHg5Nzh6N2k0dHFYQzF4dkdXQk5ZaXBwaGJISUNmayUyQjUlMkZMM2NOaWRFeEZHQTNiZ3UyWFlGSkd5ZnZzODJYYkQzNWtOMm12NzgyS0NXZSUyQmlCNGJvSkpZcExMdVdIcDVVUXBXejglMkJlVk5FaFhZNVhGWG5rdmZSbVlRZnJsYnJ4dFBNbTJZTmolMkZ2bVVTWU9PNG1TelglMkJQa3EzU3ZmbHFMZ3BXNnhWTnNWc1ROcWZNclBHQUNKS3Fvb2ZqdWVNR2N1aFFtaVhOYWx0UVpYdiUyQlMlMkJJVWdIRXRXZ3BaeW9YQnQ0ZnUxVTFxOTVUTmlLNm9ncVBBcjlwY2NBNnNuemtBc1h6S0R2RkFmJTJGUnZVWnRyNVVGelgzWlVHTzQweUVJb1U4VEM2S1hLNGpEbkRkT3hFeXBnSTRkZFZZdFZiSmpKbnhhOHJFT3h2YjdTdUxmOEhnbkVKRGZtTHJHbzEzZzZVSlBrOFVvMnVNYWlLUVk2WnRYVDFwbzRsJTJGQ3ZFJTJCMEZzbnNSd2s4akszcUNkZCUyRkpZRmx1ZmYlMkJ1b0t4Um5mNmJJUEsyJTJGSmdhZHdEOEtTeW5lTE9BWk5JTWE4Zm9Mb1U0SUN6ZXJ5bVZTWWs4Z2QlMkJJaFhodnZVcCUyRiUyRnluRGFYJTJGZUxHN0Uya2swUGZhREdVVWdtbm9UdW9HZzJnd0xxOTVES3JESUdVc2RSamw1MHIzaEZVajhmdHJDdW5nOVZmbjBCSElnOW9YQWp0dFlUc3E4UnA2WTZBbzZTZFV5akdzUDNqYzJxJTJGUlZKTU5BbWRWSlBoMFdka29ibyUyQklEa0dyN2pWMUU0akt4c1Z4OFhRTlVNQW1YUjAlMkYwMlklMkZlWlYwV3cwcm9GVnVqampLQ1FndlFGdjIyJTJCNG43RFhiVXRMVURobFc3RyUyRm1zRiUyQk9LVnVWakJRNU1KYnJrMTFqSjl6OUFNVHNvJTJGQzRZTkQwRHNMMEpXSjROWSUyRnpaZkRWSjJXRWI4dWZJMU5uWlI4JTJCZVZKa3Z4dSUyQnY0OVFYRzVjeEM5Y1B6TDRPJTJCOUhwOGtFdDFiM25ONlYwWENBQ0tzc2tua3dSWEN2QWM1UFpSTzdacHZrSnFLNVhBY0FKYlN4T0lhWXZTbXJ2WXhIJTJCQWlNbFF5WWd2SVM0SSUyQkpHRExWT1R2ZyUyRk0zdGRucCUyQm5GU0FZYmxINW9qcTkxcDNMQnNycnR6ME4lMkYydExJTTF0WlA1aTdXcUc3a3FUb3NVallxUm5FUW50bHhqbmpoJTJCdVdEZjRZZkc3QkI4JTJCZkNKdktSbmRaYmdzZTlDRUhWbEdJelB4a3pwNk9kc2pVVnRKQjNMa201amRUN2pJcUl5N3RwbCUyQlk1Mm9LMHltYXNUb1FtQUFqVCUyRklRWGdHNTQ1djYyeWdJQ3QxaVJnZzdhOTNTUUxjczRabVJOZXpCJTJCaXYyVmh3TWRUd1clMkY2MEtFbG11VCUyRmlXVXdpWm1BUnJVS2NjQjBOdTV0M1B3YU54a0ZIdHI2cGs0S3EzZ2RCWENNVnk3enNXSDBrYTdhQUVPVktSdnVCTnNieHlURHg1aElaVm5wYlVRWVlJSHVIZzhENmY0WmV2SVBxYlFHSDNYWkp0WWdyMWpGZDZzWndLUENuZWNDQlhDSFY2JTJCcGtnaTRPSDFVSGZXOG9rWnZDdUxpY0tpM0Y4ZDBRaTA0UlBEdzVQRHBURFpPZkFaZ0ZHaWNvTE5rU1clMkJkQ3FjZ3J3QjVFSyUyQlJyaFVYUnVQa2J1UXZqMVh5d0xYMlNEdXJ0d0tMZUc0bGNQRjl1Q3NpOTFoelZ4a3FPdXZRUUFiSXFGUzZFTXk3YmlKbGglMkZMS0xGWFB1SkR6emt0ZUpkVTRpYjBuSWFzaTElMkZtNHBLVmRUVnBieEUlMkZTNlB1Y1kwMTMlMkZmN2lSV1VEJTJGUWVobElBTUFPcEFTSHk3SFFDNjRuR3loejN0TG9LWTN3ZnFiZWZWRGY0Q3hWSkpOQnBhTGtsRjdobnRSclpMSE1wJTJCQW5KSVlqMlFqV3pnNXFVU0l3WmFmTjQ2Y1FENTNJciUyQnU1YUxSdEg3dzZsbWVlQ1czQ0pNVzRTOGpJUVBvMldDWEpwYzE1Rk5HbjFVZzZJWFU5M1IlMkZvVmhTUHF6OHVzZVduRUI4NkklMkZlNCUyQlR4ZEZCcE5zSlFZbmJCMGJoOE9mJTJGRlk2TmZlJTJGVXI5MEZQbDNSbEJDZiUyQnVYZmdlaVc4MGN4MTZVZSUyRiUyRmRGQjclMkJWTTN6d204dVZqNkxlaGNQTms1cWhzSmt6d3ZXZDFyOUdEc2xxd1cxQkNCczRhZWlyRUtJSSUyQmd0RlBjeUc3eElCeE1hakFSeFl2V2dETld1ekh6NjBzT1BKSk1JVEF1dktHa25iJTJGTzRuVmZOMWJLWlBJWjY5JTJCWnpjaE9PMEZFZ2ZUMWhBWWJXUndSVVBvYjJHbkgxNWc0N3RIWHU0Z0lxbmhXSGQ2MmFNdE5ReUpyaXk5MCUyQlAxODVTNEhvR0RJYSUyRml1SnhDOTI2bnVTdzE0cTRIS3llRmwlMkZCenZCUVpoTXNTaU5wTlpGVWtjaWdWWGZITlJkRGlVeW9DNXRRMXJtZVg4aXh1cGk3ayUyRlNDJTJGUHgwcDIweCUyQktsdFRUbGhIazNIeVpOeHMlMkIxN2ozTGRvNDBJOXFUJTJGTFRIJTJCc0NwaXl5T3UlMkJvdHlMQzNBJTJCR3d4ZmR6R1F2Tk1HcDBjOFFCQ3UzZ0pYWnVEVFlQT2c4eiUyQm92V2p1ZTZQOSUyQldobmlhVjE5d081U2NUSSUyRnNLY3RrZ0llS2dpdWV1NW0xWHlkWmszZVRQN3hQdW5zU0hLRDhHbWYzdHkzdm9mUiUyRjB3d3AwVHdZUEtGR1h1eXJLVmV6cHlEMllwb1NOdnh3bnBSbEhCQ0lsRExxcGR1dERCajg0QkVYSXlJN1dJM1FPQyUyQnI5allUOGNRZ3dDJTJCMlZIc3owNnVUZ1dWdDNZWUs1S1ZSeVNEJTJCNjVRUXJxYk83SDJPdU5FVVI4VG5YWXYlMkZnWk5xdTklMkZYTDVpVzMxaWw0alRzNHQ2c2kzQWxTUnExT3RHMmc3VFNlUjBPVjEzM090dVBVelpMU21IaHZaeUtMVHhlV0ZoUHN3aXp0M0wlMkJ4WHc2eVclMkZjcEdJbyUyQnQ3SlNvYlRwY0d0YUEyQzNSVkhOckRZckE1ODVWNUdQdEZLSFNLYjkwb3hGWXpsRHlmcHRkNFB5d1NpWXZtaTFLUlVFVCUyRmNFdSUyQnIlMkZzd29jYWg2UnRBTjhWRUhWSDVuakE4S2tsbldUSmthbDFLWFlLNWRDOXllRlh5b1U5bHhWbzBUR2RmSzVJUTVRaXdMN0p3YmhxeEh5ZXBOeHU1N2dGNElNSG5CRnp5MTNMRkdaUjZEJTJGUHVKckxxUWczeVY2eSUyRmZETndZWDc5Q216SVUyM1NqNWs0YXZhQUhxWlQ0JTJCdElGZ2s4ZDY5ZmNiMWYlMkZLMzBYJTJGVXlIZiUyQjd6eWRXZ2ZqdWh0QkhMbEhWODlNVVJHbnZDV2p3RmdaeHpJbFBDczlLZE9UeHVuRE9mRW9ITzc4YXIlMkZxWGFvV2Z6aEM1ZSUyQjRmZlhFJTJCZyUyQlREZFhReXRYdTFpZ05GcDlYTG5VSlVTUm9BOUNLMDNOVFhpamFJVUVFVXZzZnZrUmthZDJDalVtZXEzVFM5SEZwMGZxb2U3eXl6VCUyQjdMNyUyQnF5ejglMkJIeElRVSUyRjIxa0t1SWlHY0xhMDVGWjZvZ2t0OUZQaU1vWCUyRmEzaXI1MmJkSWFGbndNa29wd2JuWTNzaE9HblpPeUMzc3BBQ0d0SlRjUFBUVUwxOGVuSUYzbkVrTHhzMGw2bzBWOWJKZXZzMUl1N1J1WjVvQUJSWFF1QWdQd01RVTZkeFBGcnBXNDRYRkZSc3ZXWkh4S00xR1RxVVMlMkJFcUYzdU85MkVYbjZ5R3VNdjJiSFZHVlRiVlJWeUhaN2VTaXBWZFpnWUdJaiUyQkdTdFJEMlRoTHhLc1YweERLbVdHTUNpNjR2b25OMTVFeE1US0laejFCTFNHdmprVUFVJTJGaDMyN3NTZTNvT1czY3ElMkJFUEg1VkE2TWxRNUN3MHlzNXpFZ1pxd2NVU2ZrbXpzdUljNGtONVlDeEJyWmI4dUg2Vmp6MjlQbmFlRkJnTkdrZVU3JTJGb1Rya2p5dzIyNTVEJTJGVTNVbEJnaDhReWRxQ0tPaXB3MmtwVnNSVTdadFNJVVVEMERGMnBXMzdEMmF6WkhSZGJsc0ZjUEh5R25Mbmt2JTJCeGRCVkxsbXBCOEpmZzRrdmMzZG5oN3M3WFAlMkJoNWk0bVlpTzYlMkJGNDVVWlpaazBmajhlcmNsc0FMbHh4YUJ0eDN2MHhKVzF1JTJCeiUyRjhPVTNOUWZXTWhHYWlHMlpZblJVbDFRcFJqTFF0cGFua2k0NXVQVHNqMjJDRng4NTJTdElKMUtiNnlBbk1HWktITlE1b0JucSUyQmdMVGdSZFNqQnBmV2hhaDBaWlpqV0NJMHIySVB4NDFyajduNzRqUmtBb3hhUGloRk5yJTJCWlVBUDdaMWRDOHpSTVVPJTJGSiUyRnROM3VKSXZTRVdVOUFVY2xOSGltTjhhOTdGUjNCdUpFN3RFd2g2VGxJQU9Hc3FyTVdHaWdOYjFCczNvN2glMkJSNjB0VnNMNDhDTjVYSzBjaE45UFRTVmpWOEE2c2hlZm5Jc0dYNXViT29ZN2FYJTJCYlRia3hoZVlnYUhyQkMzUUNFbEM4UnpSd0pyOHkxbCUyRnNMMDd6VGI2UzU2YVRtQyUyRndFQlBLeUY4NEhwaEhIT1hVd3VyaFFibExWMm40V1dpRlU4dCUyRjNJZURLZ0F6aXpXejJTaFdLSlJudjFwRE0zbm1zaHJTQXZPbmZzeHdFRm5SdmRBeTltRzVxRE9ybDNldFlzQ2h3allSUTRCQUVqeDdoZjZiVlJqRDJRSDRMRlZtbVpIMXJsWTJjRkVacUtsOU41NmQ1blRSYUg1RG5nd3Ztd3dnRGpXTWNNRUhERU9QZmVmQm1IM0w2JTJCZldFYnVjJTJCMTIwWFBYekslMkIxaEdTc1JpTndjTFlMMmNUUEJjVTlWTm5QeGxPZnhrbmJCUDN5VXpkSnYxOGJYWTBJcyUyQjQlMkZ1RyUyQnE0S2I2VEkxZXU0MnRubGloUzVDWUhUcVlMJTJCQW94cHhMVk55ZXpZWTFkd2tISzFhV1BjZzNxdGgyUHgyUDdYYWhPSUl5V2dPbThNVUVESyUyRml6ZE9tVlJZZ3FVbDJIbWpReUNBbjl0b08wNTVidGZBJTJCV2xGWTEyTCUyQlNOTjNXOG9TJTJGMXFGaHlpQlZ0TExzN0lYelZZc3ZrZ0swdlJQNkxKejVwbzFhJTJCQ2NkU0szQWJzVmRRZyUyRm9BJTJGMHJiRVZpTUcwaFRFbmpBSlE3cTVvT2hCVzRtbjlzaUFUdEltS1ZRNXBmSmhkMkxkWkF1T0VNdFN0SzJFNXJMc210bzZHYnNjOUw5U2glMkJvdXIlMkJzVEI1dFR6MEwlMkJsQSUyQmJrSXpETUk0ZlJGRDNzSnoyR2tFYUNlWEJrM3BNVDFhMXMxSzhsTWtHQ2VkMyUyQkJ6Tzd6ZjFhZVRscVJxJTJGVFNlYWFxRTZVT3RNQzdyYVR5cEh6eTZBWkMxSEhBWnNCYmVDOTNMJTJCc1lLbG5CMVBaTTBVd3l6RDdSdDRUaSUyQklEaWV2aWElMkJ4eW0lMkZKYiUyQjZXaHZNZngxU1pZcGZjd2E3OGZDZnQ0YWhPUmJWaWZud2NuOFloMkFNbEgzOTFVTlMxeHhiWUVHYmVUNlNPRzNQcGEzQjlzJTJCRWpyYUdxV3M1am1RRnRyczh2TUdnT3FmNDk5Tml1UUxHa2Zjek95b0wlMkZZN0NVRUViaE05RGNqUDJ2OFJUM1IzRWRvdVQ1aXVNOXpMbnNJOUI0JTJCSjB3U1RIM1JIJTJGRE1sUlFCUlBRQlAzeXlIR0VMa1pQdVZZekI5OVFUOXN5ZEZxcUJZZHBnblpQQmxwYjFyTFFNZzZDcElKTGFnZXk3endhMlEwZXlveHQzbjRTSXVBNUoxR1Q3dmw5U0Y0TzZkOWd3NHZ0RWRLUUklMkZSWDVQbGdSN09yZ3BCcmQ3UDM5Vk81N0l3NVJZY0tUd0dpelZlMG9paEY0JTJCa3VPVW1Pd3luRW8lMkIxQVRrMDlKendyWDNOenIlMkZGSGFTTzdzeFhHTnlJd0I0SHZ4THd5WWtOZm9tU0JOY1FPUjZyRkVXYmZ3YlE3aFNyMHElMkJsOFFTNDE0ViUyQlhIRkRBYnUyd1pZT0Y5SHNQeDElMkI0aDVtdzVUc3hyQmhWU2trRDBWYVVFMnlNSW03SkRzTEFueElZYnFQWHo3N1F3b1ZGYnNIYVVZNktKJTJGcG81MFNKQmtBVzdRcURhSlBENlJqbE5zSWMlMkJpJTJCcnc1UDA4JTJCa1VNNDBRNlJnNVYlMkJEeGcyeDMlMkZXcUYyOFVvMkRuNzJLckR6ZDB5M09kYXpwNTlaalJIbjBiOCUyQkRPajN3T3JEMWFmb2ZXJTJGN1BVck1UWHpGOVJaek44TDBBQ21OY3IlMkJCV1lKMmlLUlQ2NUtYWlRTS1BiVUVibEF2bWZjVjlYZkh4enJKJTJGYmtQbUtoODRYZ3JIV29taXZ2ZXlxdlhqalJjSUtkJTJGZFQyeCUyRmVGVHNCTmwydllGTUd2YkQzdEM3TXB0TUViZm5mJTJGVnJKN2d6YjYlMkJZZ29yZGV3b2dHbkJRM1RBJTJCOGYlMkY4QXA1V09ITHNNM1doTU1FcE5Xd3doZSUyQmJqVVE1RXJ6UjlnbTc1UiUyQmNsVThMRm53dzZwTGxLOTE5RHlmNHJ4d0RuViUyQnhKZnlrJTJGSSUyQlgxZDY3bEVWJTJGYW44SmN0NWxwNk5tU1Fwa3BSa3NaeTczVDBaZmtSS1gyMndqYUxJJTJCMzFCYzF1TkhTWjRVbVYlMkZ3YXRZWlolMkJIdkIwcDlWYjBzZzlnbnBLYTg1STE4ZFB6bEFxOXNzTXI2SmtVSHlkaWF4QlJhWVkxTkZiMEptZTBmSzFROVJycHFPdDFaOExQUTB6QnJnajlUNUNEOGtIMUpLem5aRVhmSjAxVG1iTmFMSHdGS2RxMFNWT0h2TXowJTJGZE12TUJReGZqUDh4WGE2YmZmQ01WVUYxVEV3SkMycTZoRE5nVFNsJTJCdnhKRHBIdHMwdlklMkYwNXE4QzBFTTU1M2dRdWYyTlRrRk5CRWpLaUgzNjhMb0J6JTJGR3dSR3ViUndNS0ZHQXJPc3hyVCUyRjJ4MnMlMkZLVlE5dURLaWNLZEtsTXNqJTJGYWElMkJCVVBHUzhudDNrNDY4SDkyRm5sUHFnbnNzQlUzZ0o4UGJhMU1VbmwlMkZBQTF5amxxOUduM2Nhck5RQ3p4N2dLM2FCaU5PSE43WHglMkZxbTcxR1JKZnNVM1g5RVlza3UyRCUyQk9HM0Jua2REeU9DQllxOWxEdkhHek1wVDdzc2hFSzBWWG92diUyQm9PUm1pdDBuUEU2dFVWSTNXOFJTVHRaUks2TVVNNWYzWnBkdDlJZTRnWHlZd1p0SjFGSmZPNTIxMk5rWVh1NiUyQndiUmN6VGNibHRtVVl0eWl6RFNMeGVvZkdsQ0hqYVNWR1ZSeUhUWDNjQlRoVGZKMldPejhMV3gxYmdGd2owNHk4dk4yQzg3RyUyRjlWNDdpMDk4eSUyRlJDMEJxSlZqcG93QWslMkZHOWx4TWVqdkJMc2FVa3Y5N3NXNlBlNVg5OHN4eDczVXpGQ0h2dFdhcHFoNmJMaVNrbHpESWtDalFhbnJsSkZkZWhKemo4JTJGenpYVHglMkJsaW1DeWNXOGN2ZXRFNzRobEZrQVgwUWQ2QkRNcEV2TEZRc3U5a2t1TGpXc3VHenRwWSUyRlNPSE9KOW9LSEVpN1Qxa1U3aktmSGNqNDVRQXhTZW52aGFOS3hxJTJGTFpNZyUyQnpGTUhyYXJ4SkRpaDdEdjFqaGRueXg2ajZGQ0Vxall1WGlJTW82SUhDMWtpVHlubWMxMFVubHRNZnZtVnYxN2IlMkZ5b0ZuNzZvbTBBdDRWVFA2ZDhpemJUUGV5TnlZQ3hIUnpudGJ3N3FpdHdCY2RzS0RXS0hKWkwwTEJUS2JNS0hTNVRkbE1HNVY5SjY3cCUyRmg2cnNlRk1vcFZoYTkyTmdOQ3lWRm1ybzRHJTJGbWxUNzhEUHVrTWFEVk1Ja3RqVVA4MDA3T09uY0VoVTZZQ3BjaGk2ZlRSUmdTNnN2SmVHc1RmUGlkclFuJTJCMG50MmZpRmxlWGNzbHYlMkI4bDJsU3QxUHlZZzk1Tk1NRHR5OFRzdzJlVmFYUDB2MEVZMmZTd0l2blNEWGFUJTJGdHp6WlEwc1R3M1Jud2lyQk9KUHpCQ0Z3ZFRnNDVaJTJGRFIlMkJmT1AlMkZpdVVrSE5nd0dzV1VZR012cUtTb2p2aHI4NmFXOUhYaFAzVDd0TkRWRFdQaUFUeDNNUVh3M3hwQ3ZQUVBLamhpeG1hMXljU2VvWXFGSiUyRmJGR1JodnFJbjFkVXRKYXZwWCUyRk5BTkU0OTZpTlZaY1B1ajJDNnpwWDR0UXowMXZ4a2tZanF0V2xzM0c3Z2klMkZNWVdVTlpibEhJREx2RmhUVml3WEl0SlhyU1NVWGdrMiUyQnpkbjFQQ1dueVcwVHpVczVKRTQ1WVNUdCUyQiUyRmZxRWg2WjRHYWFsJTJCQlhEM3ZxbzJJQ25ISHBmOEQ0Rk11UTJYNHBzViUyRnBQbFc4M3hWZWFMenRBbGl2M3J1ZVg1bWhXRjFCMUdKazRvc2JkYkg1MXB0MlFCeUV6cDZuck81eFJHSXNZMHhmNnBicHEwVzlYZ3ptNFR1bXMxMTl2Q0pWV29yTm9jT1M2WWI5eU9uZHloMWo4ZW1WYTN2ZFolMkZlaUQ5MlpvVjluVG9UJTJCN2FzbWM0QmpsQUduZTNFWWZHSnhSJTJCaXZmSmhZUkdic0J1NzYlMkJEdFRNUndjYVZUMmZsRjcwdm8wT3k2UDdzZHVrNnhkUDRPOUN5RVlCRyUyRmY4MWNWa3RxQXF0WnZhRyUyRmdIZFp6TFFDQ0hnc3Y4MTZIcWwxVHFTZDlkWTdPcUszUjlNWWhPU2tyWFZvakFMMFZsdFlsNXplNCUyQiUyQm1wYUhiYTVqc3hkT2s2aThkV0YwUFIwTWlENVdtVHlDMnZRbFp3UzUyenJMWiUyRnRZc0dycWhKN3dpRm9yVzM0MUolMkJhNlJNNUpCc2V1UERMJTJGYTFnR2RWcjNmZHNTTzZsUktxZlJmNVpvZ1hLeFlPZ0k2QkZ6VDVRSVFacUE2UElaaWw1VDRFWE1penF3VDhEaVNmQWswS2dxZXVNZnZlZTdJWHdSUTlDZTh3JTJGUFIxRDJxNXlKJTJCSm45V1V1aVUxR3p2MGRrSzRGaG5QYXBTcTY2RnV4VGJHV2thODBWRktUNzMzY3Z6SzgxcElhdDglMkJGTCUyRjRSbTElMkI1aUp3Q0FTQTdKUW5nVmZ2WWJsQnJMd01WaDFnUUl3cXdYMjlKS0w3SkNOcDI3d3pYRmhSaWwzU1A5dUZaU2RndnVtVmNpN1o4cmh6bjZwMjhyQXBiSThQd3RlZ2dvS3RBTUFCQXZrWmsxS3F6aFglMkZWTVpUWlIlMkJucnB5VTNuOVpqUWQxMEZEQURpcTZWWm9yc1JnWjljSDJsZjdrcmJWSWlWUElyUXQ0M09wb2NsUjgxU1V5aTVWSmxZRVUyRHpKeE9oWW5JTnFMaGtTVmtJUXY3VTRMVVlwdTRWcm94SEtLYlA0MHdPMmdBUzdZWk83QWZjbFUlMkJXZ1ViUjglMkZqVUJ4VUEyR2J3eTNrTncxUWZjdSUyRk5wQnZoVW5CbmUlMkYyeHFPbjc4JTJCZlFQTHU1VjVuYVB6N29idUZsQ2x5STYyZHVOeXJTRnVLS0U3MVRESlRaSW9Wck1IWUtTdjdxU1ZhZlZVWWk5cGNCYkh6MEtwb2xOZlhUWFQ5VGk4ZkpIWW80aXkwTUZ1ejhIQyUyQjdreUdiNDg1blZ0T3ZwTlRPUjRidkNMdiUyQnF0RmtCeHFyWmJUbjZvY3BYNlNSb1NtOFBpM1JVNnhLZlM1JTJGUHQ5ZWk1T1V1Q3hQdWIlMkJONkhVZUJIc2J0JTJCR3ZTRWNtJTJCZHRWUTNsOTNDWDQ4UXo3N0c4RVZOa2oxS2Y1TUZ1RmEwJTJCZjJ6WDlMMEo1dXVtMVJJMUtQTmRWVGtpT0prbmEwVzJ5UWwycHhVNnVRZWlTUVQwT1ElMkJpZFdGVHdEQzk0REd2S2lCbHpRVU1IQVNuWmc4aEhpczR5V1pyTERZSkMyVTA2YjNZYmd2diUyQk1tN3Z6eXRiVGh3YWFicG5kbjElMkZsaTZvTjBFWmpFc2t6R0RQT1dOTHVycTA4UmdPbyUyRld5QmU5dDJhbm1Rc282NkxwcXAzZjcyNFdNaXlUblIwN3g0N05aTm9hek9iaUx4dXZVeVBmTUwlMkZuT24zOUJBQTAyaTdUYU15dlI3VnJ6YlpKJTJCRXZMOGo5N0x1Y2M1SFJDcHJCM3NXUEtMZGZTdDBJSTVXZEpITDFOR0Vsa1VHTUlhJTJCZFh4cWoyRnpHTVR2ejlMeERWZk83aWw4RlNYZlZmQSUyQnJQNmR5ckV1UWlJRGlEeVJ5T0UzTFg4THlmdU5ZM2NpYXFjbDdSbjFleHA2WlVmanclMkIzNGYlMkI3TlVMeG94YXNDQ3c2OTVpWmxiemJPRktyUGN5dG5MOTc3enBieGdwSDIyMVI4dzdpeiUyQk1NSHRYUkdqSnF2N2VjJTJCc2FYNVBhZnFPblk3ZEM3QkNkdDlOc2FLUlNHNDFMWXpkd1ptbklWNldWbHBteDluVDF4ZDgyV3lUSG9RcUpUJTJGbURIbXFIJTJCYTdtVThHaHhLdjdXeURSb01leHl4UmhOa0hrWjZBOUdGbG5qMzFXeXVWT09VTlVmcXVTUG5CazhFSFNScHZyMG5WUk5FUyUyQlEwaEk2JTJCWWtVOHBucThad1hPd2pReGJ6VkNQZWVla3h4bXl2NTZBNzlTTnZmOXJuJTJGTWE1ZGNTQWxkODNZVDNFYnRlUWt0aUZKTkE2YXZ4Z2EwbE5PdFJQelhaT0swbE51ZW9VSlRoSyUyQnFCcnA1MU05Z1ZUUDFybWVNeTFkOTNUZDFqT0JxJTJCbHFFbVdlWmg5anM4TnBXcWVTcE1mJTJGJTJCWUo5OW5wY1hhdXhqJTJGck1oTFJvNzd5NyUyRnRvdXR5ZWx2RDFlS2JkYmh5ZmFPdXJRU2NNRkFSUGx5clluSXFDQmFYRXhKcjdQclRIJTJCRmxMajV1cHJsSnV6NVV1ZGZybWhCYTY2T0lTc3pmUnlUWmxmJTJGVjl6cVlhaCUyRmJ6cGRncTRWM2hjeFdIQVhTSXBob3BwMGlCYVBVVHBVJTJCRk1iSEtVNmVVU2c3WkNuRmQyaUFieXZQajFBaG8lMkJoczhLS0tPTiUyQjFoQTNhN25yVEZvRkY5TmF4YkgyZyUyQjVzN2tibGNsMzRUTk42ZCUyQnpLV2JHcjNadng1RTMzcXg4cndXTzc3dFVWQzBpJTJGRzklMkZMaW42ak9EaEcwVXZBeVpxSjY3YXk3OUhEVm1IUzRZaExTSWk1REE2MFY1U2tFSXFTJTJGdGxiN0kyT2daVnRVJTJGTGpRQnNmbHYySVNIdmcwbDZnQ3AxVzlZcFdhWmM2Y0laVDgzMDh0WERDZHY0NnBMRHQ3QXk3S1U0aFFPdDlNZElZdCUyRnFWVWJYV1lKViUyQkhaUDVyUmV3cjdMZjVnbGpMTUc0MkVIOU5Yb3pmSE9uOVlBZyUyRnlsS3Ewc0lnQ1pwJTJGeHZLRVJsazZTV3JuJTJCY3AxVUFPT3J1cnNZcEp0UGp6aXU2SHhlVDc0dzNtRUZvZmRZNG5PZzBLNFViRVpCcEFpTmVyenM1Q1k2MWN0MU1PVyUyRmpVU2pHWVFtRk16a3JSOThnVHY0aHQ2JTJCTW1mdHNJODhXd2I1V0k1Nks4WDVwUktUeHRXNVpqU3dpbkwzaDNCVE0wJTJCZnZHV0QyMFI5UnVSNVhvOVRTMHJjR0ZocElkV0F4NXpPenVhbWlranpvWlRIWVRaUENkYjIwSHFaQTFKbUp0VDVEM2dKRWZXMVE1ZkU4a1FaTWI0NG1zZGxkcVF4UXdyb3Z4eXY1S0UlMkJxZnJ6dllpaUFBTHFhVmxDVWN0aDAzazY1Wm9rNFVoUFJaOTNKT2FyMDM4NHU1ZHUzM2VwVyUyQmElMkJoZkZTNHA5QmFFa0pBbEhEWGozVnBYaU9rZUhhekU3YU9yZzh2TCUyRlZuVTlDMXdxbzUlMkZDMDlicE5yNjI1RmolMkZ1ckFBdFhRckdtJTJCcTdrTkRaVFZhNzRleG5lSHBCTGNrVE9iJTJGQXBNZmdROU1MVWxGMnFWVUxrR3I1dDRZNnZzODJlRyUyQkJvV3RoMzBrQ1BPZjBsZVhqZWZYWEI4R1h4Q1NPaUswRDR5dW1tY0dDNGFReExSdHczYWdxeHV3bWclMkZwdTFEVCUyQkZ1eTMwaWJFZGh5ekUzUVFzN01jTVdXYk1lMkRJblN0c2wxdXFpJTJCZ0VBeUZXSGVmT0JUenNIT1plMiUyRmNnUjM2U1pvNzJlQ0xVSHFxbGdpOWZMSkJGcE5JU1UyanJNZiUyQkRINEkxWEVzaHk5MXhIN1BYQyUyQjNwaWpBbnE2OUNQNmdUMUx1cyUyQkl2MlJYcUw3SzBYYWRsWFg0WG1DMU5BTXdScWF1R1BjZUFSSUQlMkZscU42U2NZWExVdnZScHFYY3hMQVZvMjBjVnY4WldBUGNmaUpBOFV6Y3I4NEM3c05KQko2UWN2NyUyRmM0a29FWDNaS1YlMkZNamNIRmJOaFg0Nkdia2NYRVpOT2ZSbjZkMFQ2SjJjVzl1cEtCTmVRUFZrOVZmMkhkcjBWM1JTRHNXJTJGZWg5UkdWMkJsSDNSbzhybyUyRlFMU0hWYXlNaXQyOG1Td0pibVJkYTl1WUJVeWRFT1NYbnByN01yNmQlMkZic2Z1JTJGVFZEZlg2NnpSeE9PQ2pKSDVSSG9VZnRNTSUyQlhlZklLY2hBNWQ0VU9HeXZ0MjNwdDdvbVRWT0pDZERMUm5vWXY3JTJCcHhqc1ZSd3BQaFFINGg0U2N6JTJCczRQRU1PM0F4MkxFSVQwWjFNc1JiR3Vta1o0alJvMVRudVBRdiUyQnpzaG9WT0xkZ3hEVll2V1M0RDBzdGl6WDNsc3hpYmZGYWNJNzBsazVJVmVmVmtHSlpIMWZ2T3RJeDNEd1dHcWpWMlBTb1JZVTBaQW1zMktwT3VPS01GSWkwaCUyRjBTeCUyRktaaWRDOSUyRjlMQVZvY1lHb1lmVlp5NHdwYk92UzdVQnM0VGJsWVBkQXlsRFdUNTBwQ1BtWE1iVHNMdFFzTElFbGJMVjlmTVNITnlwbSUyRmZycDg2OU0ycmwweHFOYU9ZeHNpTk9DSTJyb3hpcmo0VkRUdzZ4RmVJeW1TRnE5bUFWZUhwRzVWOEt0TktERXpoNTVmcXBRZFNYZ0UyY3diQnJKeDd0ZHBVY0dWVGRuNTlxVjR5UWhDSEFMdktmazdxZkZkcW9SQVBRZDFzM1loYTRwNnN2aFdGVjlsdHlBM2NuaHlVNnZmWGN4TVdCY1dVSDZka0RrQXBWVVNqRUwlMkZndnBXRER4UXB4OFA3YTh4aU1scnJybm1Zd3pCSUNwRVduMmNjViUyQm4xNmVuSDFKSFU0VFdaRm1jSEZCRlJMaHZwZTlhNVZXT3d0Z2NKQVhmJTJGTENJeTlRT0JLemlqQSUyRkVoSkVIY2c1bG5UUWRVazdydmxxUWpzYXRMVXZQR2FDSHFwWEFrdUlUcnZwY3lxem9obllMOEliNUhDNUlqUEZ1VHl4NXNoVjE1c0U1ZFp6RFV3ZCUyRndoMlI3SkxaczVLSzV4MVg3UTRHWmVtNmJpVzVLNzM4JTJCNkc1JTJGY2w3V3BKMG5MaDR3WjZSakNCWHclMkZ2TmJwRkd3RGcyTHF4SXA3ZHdYUlRDb0wlMkZoZ1NtRnJPNm84U2Nlb003UUdheFV2RDY3dW5uQjlHdXUlMkZFVUlhTEdLeWZWbmdERks3QkZNcTNaYWQ3dXZYeFRzdDJIcFdWRVB3Y09XTHdMSE1Ia3hVMkR5Smp5bnp6YjRSbUVNRENGQzhDYSUyQjNJeFZuSTI4NUNuMG0wOXlyemxtZEJvZVU1dk5QSmRUNk1rQ25WUVQlMkJ6cDFKdGx5WHZrWGk4ZlJqWm5kJTJCcDQzdnU5Y0JvRHVxaFVtOUp6RnFHN2MzY1NYJTJCc2dtcVRHaWRBYzE0TGkwWG9neHE0aVpuR0FzdkZMSTElMkZnOTVuJTJGbUJUMFNUOHBkYU1pMGoweEF6OFMlMkJsNEV4UjIlMkZBSXAyQVNNSVNYNThTZ1FweXVLSXFDUUxwVWRjRFN1dE03T2lzTDREMjFMYnVzJTJCaThOYVA5c1JJQWFlRnFUcnBUdlJsQVFtSkQ1JTJCendYaVRtdnoxRm1xMGZzbmxuayUyQnNEYiUyQjglMkJxVlFPUnZ2aWhLclZSeDd2JTJCa2JGRjVhTDVtdEp5VUtJM1BCbkFsazE1ZFNDbjloTTF6Z0pVNHJIMEhLWGJGcFFaUDBZMjkzZ0ZNMmVadXFYRTF3bGR1eEpBJTJCTlhldVV5NmU0R3NtTURKU3c1VVA3JTJCMWIybTBPSlpsVWdmeXlEc0hiV3ZtS1BVdmJGUlNRZGpuWmVvQ3RQUHZoUWtUMUxvNXRPZngyUnk3dWtTMGk2NEdhMmlhNEl6TDRjdHp3R1Y2d0VMMyUyQlpVVTZEeGowc2RjaXZvT1NOTXNtR29vaVdoWmlIcFo5JTJGNUpzZ0pReW1oSHpzeVplJTJCbkM4OWNsNHkyWHBMRyUyRmMlMkYxJTJCNkd6b3JvbVRvclExMWpUTDl2clZKN2lFdm4lMkJYMnkyNEdjWEh6ZUpiVUd6dVVWTUVqdWpCTjFsUnV0MmZjMiUyQnRjT3p5eGtqQ2pnbEtqaTJmQ2lRRWxSaURpTDBaZHd0TlJBd1dlSFdITVhpRzNzSkJCN3Z1ZVVOb3hWUVlLYlJpU2g3anBmeEpVbmVpbjkwOEdpdjhxWFRKJTJCTkhyYmpsVGVuMjl4T3hVZlN6MWw5WnQzV3pMWlltbXdrTkllWHpyOWIlMkJFdjY0NngwVnBYcmRoUENvNVZyeFZXV1AzNiUyRiUyRkpvUjhDVnZJNllvWTl1dnRWQjRlQ3hsVWdscjE1aVNaY1N3Rlc3V0FnWEVDR3IlMkJpZUlzRjdEb2xubiUyQlJiQUpOYmx0emZNM0txZ253JTJGYW0lMkZFVXNYc2M1JTJGYzFMQmRjcVI1S1pPN2ZKdEhDOXR6Tks2YkZaUEk3aUxPa0cxbzR4UzZDYmw1aFRoR1JtSmI3U1FkOGNNMVFkeWRvcWJ3UWZ2RVgxOWNiYUVrTHBVVGw5N0FNNWFFd1I3OU4yU3BOM1p5dTZ5S2RlJTJGVk96Z0huJTJGSm41U1UzdXZvSE53dFM4WVdpJTJGSml4WjAzNzBLZVQya3lSczFjQ1Q2eHBqVEVyMTc4JTJGSGxqOSUyRjNVZ1NOYm5Wc0lHdHUxNXVaNjJUYlpVbGFMaTJleFYyYVlCVXpuRFM3JTJCcFB4UkNnYWlPYU9lVyUyQlJGaE1ld0JwZWcya2FZbVZzTHRzYmZVWUslMkJjRVVaVnhVQUNZUCUyRlpJOVpKbTVtNWI5OTZ3QnFsJTJCSDdUU2VyMCUyRkZVWnBkNXpKbHpCbThMbFUwVFNBUlpNQ1d5YXk3WlBudTNBbUlqTlI0V05WYm1PQlA0cWZ1N0NHT2JOWlJUMTN5ejBWYlU5dWxQMWtVSGU2cVJvMzdsbE9ydEk3JTJGQVpZMjh3dUxhWGZuTUsydnBBdEI4dHNXdUptUHJEYW9YeklSVUdCVWd2RWpEdnFROWYyMDJuS3RTdGtzTCUyRmVuRGNFTmV2MzFrTEtzWkVXd0ZVa3Zzb3BqUnhYbFdkeGtjcWJxJTJGRVg1S3BwZnZvVzJlaG5YckdPQ1hEcDQ4JTJCbFF2cFVlYXJjQjg3eFN3amtiTTZ5UGduZEJaTVFHRkpkT1IzM25zRUxCbTFWT1JnYiUyQnJKUzl2JTJGU1QlMkJObFlPcUEzQnQ5eEgxaklibDl0TCUyRmcwSFg2WSUyRlpwa1lMa0cxNUM4Qm5WSTBFZU04RVBTVlM1TUNGSzdnU3k1VWV4aDR5ZnBuWWZnYzA3TWZsMHZINk5FSWdPd0xLcFNrZm5hQ3RvdktFZmZ2ZUhLZHUwRHdCTFlnOEZHZHVoR2hhdGkxZnFNeHl6MUZ1dHZvd3g5V1NFWlA3TWl0ZkxJcFBObUc3c0thMU11R3BodDZFZXAzaEx0QzlmJTJCY2c1cEclMkZtcDdGdlNvOVlpWG52MW9KeVVXSW9haWUzcm5CdnY5ZDBlc0k3dDdpJTJCRWJMRmI1ZmltT1BVM2JYSnJaN3ZLJTJGbU5udTglMkZkNEVvMURnVmJTZnpPc1FRbXRyVThrVmpFamJCeVElMkZzdHlSUHJrMW5qZGdpJTJGeGtCTDhYVCUyQjJXWm1HNm81T1FJd2pqc1hOeDFubkxsc2VlVW55RCUyQlFxOTNzejFqWnFOU00zNm5tendwTE5yZ2Y5VDRneUZZdGRkbUQ2WnJnTkIlMkZHTzNEZWl1TVlLQVZSU2hjNTBodGlpYzhVYTg1MTI1N3NHUFVrejZ4d1pTSGVrSm90d2lSTXpuUzlpWWQxU29pN25wT2psWFZCcnBaTVZqJTJCeEVjRzd0bTE0YkdGWEtuNkZQUU9XN1RzcWFoVzJDdEh2cmIyRlViWG5pU1pSSVdSWk83NE9HTSUyQkFxTmdxYmJZWWl2akxkM055d2NNZHYxajE1aXZVdVBuUlRSTGRXdG95cFhiTkx3U0l2UjM5eHQ0dGIwQnVOQU5vbU55S2FrR205WXR0QlNUTlFjdW5wdGhYRGN5d0JNWGFrYWwyM0FkUjdSWVppVVNZQ1QwJTJCQmhXR2NxTXZpVXR0ZzRyQ1dIVzZBVjkzU1pucGMxR3kxdmVPdXV6SXJpJTJGTGdRbk5qb05PcmJ4d3IwVlRpVTBTVFVvM01oc2prNndlZjlHQnBjd0M3RyUyRm1NZ3F4ckZtT09yUzEzYiUyRk15d1oxT1dsbWljaXNhaGx2YTBaejQ3M093NTVOUHRCRWMyaTBqd1EzN3J6eFA5d1VhVjlRQkxUYTdKMmM3MzY3NCUyQldTa3JEVGVZR3hISFJHcE5zblF5JTJCY0ZNbXpyZ2JTQ2o5dzh0cFJLMGEyRUs2ekp2SlRYN1JsTGs3ckwwQ3p1QnJmN3FscCUyRklvWFJUUmlwQmU3YVhPS1RMc2M2NWFGY21QSVBYa3dDd0xNZDlvYWdLdGFMJTJGTGZuN05LNXNGOFI5SGFhMWJ3aGgxNXo1enAyZGMyaVpsaTFFejV0ZnF1MjdrNFp5TFZDM2xMVjBCVzUxM2VxdDRKOU4zY0VkODRWVGpmaCUyQjlBNDNPbjRHRm1XVm9FYVJHbmtMVFpLalc3MVBqciUyQkxwTGJJcFdwNXJqRzVvY05YbWFuSnBpSEdDUjJVWUY3aTJkWDFUeE96MlpSJTJGaXU5elJFSEVMUlhuODVMTXh6UDNWVGZRcWFLa3BnbU5QajNJVTZxWkwydm8wQjYxU2d3dnpPJTJGUXVLUFlOWnE4bDVUTzh5JTJGdVFWc1BoSzBaMzNTJTJCVktmV3hHNURUZCUyRm5wejJXRnp4RjRkQTd4QlhpelBOUnZiUnBwbjNBMmJVVmt2alJNVXpYMVdzJTJCemZiTDhRQSUyQmJGcU9lJTJCZFZqcTRHZ1M2UjJZcnhlMjFHQ0xCaGJhNzJSZXIyMzZFczUyRkUwYjB1bUM5a0pVaXFlZTNsJTJGdzFvbU9CbEZ5NVU2dWhUcFBveTV0MERMaTBDT3V5OVBvR2l6SVdIQTBOOWRUJTJGdUswWmpFUklkVWpuVk5YVG5tZ0s0NFBoQmRhb2xodDdYRTl1YURWZmVwaUglMkZETDhkNUZIMEJFZWFsVnUyNWp1cUQ0ZFpVaTN5TkxibTJkOXg3ck1waTJ1QVhkUnRlZDBmdWEzZUxBeTQxZ3AwaUZaTXh4NDd3emhybmlVZG9oanR1RnUlMkJRdVhORzlPRUowMURaTW9wRmhVbVJUR0JHSTVmYWlITEZLTEJwaFB0OUVwSW9IMURkeWMzUERtVSUyRldScVpIbXV2UTd2Yk1kaHBTZXluVHhQWDlPcG1pNTlkVWlselh0T21Wa015NURHcnB4Um93clBlRGE5bU5hSE9DOGpHSHg0UGFWdlVJdTZzY1UzdSUyRmxmMHJLMG1iTDJKanptYlkzZTVQMXpia0FtWGNNREpuQ2RueXRDNWhObjVmRXROeFFGNnkwcGZOczJRSCUyQkd2TiUyQngwRGNHYkR2N2YwbyUyQiUyRnVNRGJ5WWY0NmpHMUNUeDU4VDl4MjAlMkJ5UXBHeFExR0Vyd3lFMnklMkJRaTh3RUdyU0MwZlhiTEw2OUJzZTBYdHJQWHlRYWxXdDUycXg5NEcycmxuaTNidHRuWnViVFpNY3JMTkw1aXgzWnJTcUlCcng5Ykw2YTBiN0lNclZoMHBLTFZ5RTEwU2N2V28yQ3lKQXhkejlFdVJWcTBtWEdkRkRXTWNFbjNTS09aYWd5OTR4bXN6M3ZzcGJLJTJGcUFJUWhtT2ZsdnQ5RVZzaCUyRmZrJTJCMzlrdHV1YVhKSVJLUjZHd1h6cTlSOHM3JTJCJTJGS2ozRklaUDg2QXdQU1VsJTJGNkdNYVRsMVdzbXF0dktmQk44RFhWUGllN1hOMWs3dCUyQjVXdGk4MlFoa1dtUU5ZU0ZrZU9MYmtQbU02JTJCQlE1TjFjbHkwS0xHUzJPeFNBZkVKbVFnJTJCT21TeDl5JTJGT3JqeDd4TlZDdzVBemVGcEFOSkthdll6N21SamI1TVhxTHd6cG5SMmxSVXN0ZXFnOXhyS2hIJTJCUksxaWU5b2J0Y0RuYlc4alklMkZiaWlMTWJhS3ZUV3ZNTGZYRXlJT1oyejNqbEZmV0JEZVowQzNMT0RZZ3gxMmRYQUclMkJrd3hCNUVHNGxhdmNKbHhjNDdYYTFMUXBzdUVxaWxhV3dXRjZTUTF5NUkyNm1oWVN5QjJIOWl4ZnJIQnFzRFhBMFlJRjdjRElCSSUyRlZudGJPbndaaW5GSiUyRlR0Yklqc3ZFRHR0VkttamxqRnBCTHVsMHQ4RkxpUXVIYUw5YWU0WlpiTmpzZHoxV251cjlzZFVYTWw3MCUyQnJLNVRWaFVpdDh6SzN5MU5tZzBmNXlwSCUyRk42b2xNYXhDZCUyRmQlMkY4U0cwcFJPSHNLJTJCOHZMTFVVeUxMZEclMkJmNUNHMFJ3Z3A5YmRUQWJ6NjN3ckg1V3dyOVNwJTJGSnJJck1KOVhCcXlDNHlLZXc0dnA1NlVtbnI2eW1KJTJGJTJGazVpT0p1aWkyemVhN2ZjbzBhTyUyRlJLMjY5bWNGYjJTUHZJYVZ3eXd2VFl1S01sVFpvaDZRUlF0aUFNaWd2N1R6RXFydlR6WE9KTzF2JTJGdEUlMkZSd05ZQk14bThnamVjJTJCakx3R1dscHJ3Smp4V2FBczlvNDRLMmxwV3hSeWNxdkgwdTR1Uk5HTGxUM0I3Q1NLUk1ad2xCYXRJNWZRdVZhTEY5SHVMZUJQYkZtRnJsJTJCUG91bkVHcWJBdDVQa3JIRTQ4MUZtazI5TXNnb3ZkbTdWQVY4bk1Qc2s1ZldUUnM5Vm1KbGo5cVJwQyUyQnVaZDBQVmJ4ZkxucUYlMkI2SmVQdnA1MUhXMkxlY3gyYThDaUFOdEU4eFV2VnAwZHczeDJheldhUmlNS3YyV0wzdVp0Wk9pNDNZcTRUQTgxZTFnZFJZbVBJUEJNSlp0V2x6TVRuMkpzcU8yT3h1RWttU29Zd3FleiUyQk1HV290bjFTbUpnT3lGR1l5dTNvTXFHNXluNUo1ZnJMWUNmVWJ0cXV5bWJRWE9BTDh0Tkc3SlBPSWROTGpvQ2VJZ0dCa01pd3AxdFY3dWpuQldMbkZiJTJGNyUyQjlVTjElMkJZcTJmT0xJeHZneTRJN3NwQ2FidjU5WjVUYlpFZEZnNnN6WWI3Z1dGJTJGNHBFcWRGdm53c2pwMkxSY09La0VvOEhFNSUyRkZyUEkxQ3hqNlA1S1pEbXJoeGhNZE5malJ1MGdNU2R0TngxJTJCT1NrNjJ5aWhrY0dENll4UHFhMGhuakJCdTV2MGloenM1NDFkQXJDa1RSMyUyRkxpQ0U5SVJ5VyUyQkYlMkJhVkxTZWxUYkV2SVVVSWlhaHBzdUJYWjFIcXY4SlZPdjJNbmgyQk9QZERmaU5yTm16WXFwMGNqdktxS1MwWkZoMFB2WTNJWldPNDE3ZTlGRGNPeGIzU0NrVEJ6MktkYkVsbiUyQmF6SzB1Zkc2NFJrUXU3YU8lMkJGVlExOEV4RXRXTmNkdDE0RjdsNjc2YldFWUdUOXJDVDFObmJPbTRKNWp1WGJOdmF5MzVncDBLVXU0RXRkaFd3UklBTFVkSXRDbUpwUlZKVzRmZTdIaWZPREhudzhvdlJRWk94WFBlblNvMTlOSm4lMkY5STdiZnllR2lDdXJjSFFCVUhpM1dweHJjbE96WG9aVlV0eU9ndHhqamo1azhGeDFLYWpkWjRuNnZSczV2YkYyQ3glMkZtZUVnMVlUSW9PbmcyNTZWNHRPZjh0SU5LRHhkVzRGYzlpOGd4bWgzakcwSnROTnlISml2bHhjUm1wWjJSQnFiUXVpV0xFdWx0ZGczdmRtVVhlUmRDRlkyNmdoVDN1dmdDMXlxSDJZaXhVSXZCZWY2VzdwTEVTTCUyRnViTHphb3hyJTJCJTJCczV4JTJGd29zR3FJWXB2OGtyVmUlMkJZbGRLb3NvV1pRNTJCSkF1JTJCcWhwZFVjaXJqZWxHdCUyRllYZUZ5cjBaWDZ1cEgxZWdlZGFpeE0yZ283RyUyRiUyQmhDeU5LckE4ZGpKMER6WEZQSSUyRnhiVGU0T3p4cnc1Wm1kSTQxOXUwZG15QUhTMVBJbTJualhOZUpzYVlDaDJkMFMydkhoSEplUG04JTJCMmoxWXNZczh1bUh0OVdsamx4NXV3SHZrN2F2MUxiSlZZdDN5dFRZcVE3YmQycGY2RTFFY1ZhM2lBTXJ5TFluYU1Nc3hiVnJObzNQRyUyRnUlMkZVc0hPMDJXclVqNXZ2ZW1WYThiU2dGVFB2VTViMU5qcW1VJTJGZnFsU1FwbzFVWXdIRUNlMk4ycVlUQzMybEZuSkFTNmZOR0p6YjU5WXlVUkVlY1JSZDR0dXN5TlVrV0o4OEJHZFZnZEs2NHNOWnJjTmZkQmJCYWVvdWxYOTVuZWtJWDA5aXptRXE1N01tMnpuU0pyTkJIYUplSWk3VDZXenNWRzR5aHlaQW9RWkZtM1BrdCUyQkxZS0xZVHVaYlhka3NlZlE1N3VmRGlWWmtTanNnU3Q4NnNzckMydTF2MW9idTJlVTR0TjVFeHlNYnV3b2F5YkkwcXhhZTIlMkJ5JTJCNkZ3RmZNQmlHR2I1d0hKdCUyRjdiVnhhbU9mcjVQWFhlMXF3SnFTNVgwYU9IempMWnZtZFVUd2FPN3MzTFZJcWxLaVA2VThGN2VPU2tEeEFzNTZhNFl1SHclMkJodkFzRFl0dHh0UG50MGFQVEQ3bHp1dXplWkJ2ZmVRelduZlE0a2REM0ZEeDhvcGVkOWlLMzdVJTJGM0UwNzVwdWNKN3VHZEwzWFVad0sxSGZDcUVCTFZucEZrcjIxamNaNFVUWTc4SHB1TFZBaDJOYSUyRjJDRnVQWFo5Wk9rWTdFMlVmbEpQMjVjdG5mbFlSQ0tGYXZoJTJGdDFTSmVpTHRTYndPQVMyT21DNnlMVWNWdTJ1ZE8yTW1pZW5WSkE5YktJc2VtTDRkVmVOQlVnamglMkJMTVZkb1BJdlpxT3ZjRHF0JTJCVjNWeG1HUHlZcXRseUJoYk85SW1rMjNDcFA0alVBbDNtYkNMeWQ0TiUyRjA5YzNPSHQlMkJzdHUzJTJGaGZzZHNlJTJCVUZhJTJGV2c5SE84SW1hb0owWmtXNjAlMkZxUHZEazFPRTNiWVVaZTJhazVjdjVKbyUyRmVsSXMlMkYxaHBGZW95ZmlpVG9mRTVqME5KNThoWkRic0pRYWN2aU9NQXQxMUpJN0JKZkVmWFFRaHk0U1ZHZHNiYXdHdkZwNlM3b2Vxa0c4QjluYzdtaTRUN2dBSHo2ZFpWUG50alVEdk5TaTMyMGtZaWU4aSUyRnpOUFhMWXElMkZwOFZGTnBYUlZkdDhjanFCeGJpTXpzZHQlMkJaOUdzZEV5TUVUUDFic21reVJaa2k4NU5qMHpsSlMyc1ozSzRVdHhVdWhoYTFRUXFMM2klMkZoc1pNeUQ5U0VQJTJCT0txTkZ0SHBQT2JrTEw4c2d4ZjR0dGtlYzVtcDVibHJ5NHJQdHFQNlRoU1R1d1ZHRzB0c2xnaUNuMUc1TWxleG0waTM3QjMzTXZNNmtSTXVaMSUyQnNSWFdNYnBmbks5ODJZdGEyJTJCaFNsSGRad3ElMkJMMTdmWXhGcnppY1lyS2llUncxeE5QdDFWciUyQjZYNkZTT2d3VTh4NjQ5Qng4RFBxJTJCSEo3em85RWZIcUpQYWpVbiUyRlBGSjJ0eElzeVFzdVkyWlBaaFFLcjVCVkhJbmM1OGpjNTh5WWx4WkZWeG9vOTdqS2t6N1lsbnF3dU9zZjJ1UzZCWXRJOTNGYzJxeXJKb3RtaUdnSnJ3JTJCemIyaG1DdlElMkJOYlhkNVo2eElDRno3OUl5Rk8wNXNmYVVBakVMVzhVbjg0M0NPcVNHOUlHNkRxRWpxanR2VGdlN05IVExBUXBzOWlWZHZXckV3ZmRRSHJkd2JjV2RTc3d0MVVGNkQlMkJEZTRST3BVdTUzakg4dXRBbmtGcCUyRjFKVThXS0dHbSUyQng3eGUxcFZlbkpacE5BV3RKeXpOS0VmRm9TJTJGR2xsZk8ycnpOY2l5Rm1PeCUyQlBFS3BxeXUybFhHZVhQYUEyY0R2YVFwd1RDdXlnU3FZN0JwVDd5VDN5WVZwUXN1MXlSUG9nJTJCJTJGMDN4a3dqbDNQWENNd1JYYjVWRkVwb01FVzFoc3RCaEswRjN1WVdtWkgxcDNzS2x2JTJCcXhJYkNtNmU0YTRmM1VlTklrdHloZ210OWRVeFd5VE5QTjRoSWJ0dXUyaTVjUyUyRmVHdVhoRW5BYUJVV0FtZFdKdzZpSXFrZ1ZlWGt6eThrZUNZYkx4QWl5QzlvemZRVUFUJTJCdWEwem5BTCUyRldSUW5YSEg1bWxCMWE0c0NCUEh0YzElMkJtMGxjekFSUCUyRmdVNDdud2VkUnROZlhyam9udXRLRDN6dlhLcGw5S0F0YUgwNGxPZGM4TU42UDhlJTJGTHpuMkJJSzY2bVp1dWVPbUpQc0dJcXhoMEJiZ2g5ODRWYzVuaW80Y1dwN2ZkeGdoR3RvMmtPa1IzejZHYnU2WW91bEpNOVgyaGZqd3pZZlRmcE42NWt1MzNIeWpLcEYwZ1R2dXB4Rk45cFhsdURnUlR4VHZmY28wN3FKM1RMTlpkMnVwVFh3ODZYJTJGZGNVRkNOZFdGNFQ4Mnl3UCUyRnpwUG9DeXo1SDE1TkpkTGx0VDJrdTJQYzY5OGJwcHdHd1pySkRDVXNxbTJYTjc1T0ZMdnZhR3pqbVlkamQlMkZ6bE53YnZkaExvNXVaSTVmTXJQN0k3diUyQmlseVRRN2JUYUYxRzBJQ1JlWTEybUZPaXVPaVc3cVElMkZxNjg2JTJCYVplMEZiMDB2cU5mNUhXNWZObjJXbFlsa05adGVsMWg5eVZBcyUyRm52UGczNlU5RjJJJTJGQm5QQXVSUVlMODlQdDlVeFI2VUlLY3ROM3JIUGclMkJCM1pnckQyeVA0ZUNHJTJGJTJCNmxDJTJCS21HclNuUkJ5NUg0ZElGd2QyOTRXQ2ZqaTRoUkRvRFVzRjJuM2FuUTU5aEVocWc2RWI2NlF4Mkp1azBqa2pPcTkzVjlzbGN3NUw3OGVsM1RGUHIwRWdtS2VOSVBCdDZKWmFDRFJFOHo5ZFUxZlAlMkZRQkp2QVN3aGVGTVJGODJ0YmVxcGx2a3ElMkY1cThjOEx2OHZFbDhiNU4lMkIyUlZRRDElMkZyNVFuWUZzT3VLbG9sekR6cVNEdjlmVHcxTDA2cjR4RGZiTmNEUnFqUkVmeWg5NG1zJTJGeHZ2ZFVqdmMySGFIUTFNVExjTlhMcVRGSlJWNGlwVzhpUVdtZE1IRFJHTiUyQjBJMEt6YlgxdUVHRG92d05FMVkwZTMwTFR3ciUyRmZnZ09jTUdONjBYd2Nla0NCdGhWMHZKWWxPYVp6OGlPQktMVGVEb0Z6Szl2eXlLVEZJYzVJcXklMkJVV1Q4U0pBTHFFaVhnc09UMzBWSXVvS1BCTm9EVnZwREF6bGhsODdnZ3dhaiUyQnV4VWNYbVl5clZrMFAzVXRiMGtkOUdMSDczYmFxUjhsQlhMM2FZR0RxRnJ4aTdqcVJWZk1JTTJQdmZKbFklMkJqbTQzTkV1Mnl6YUdaY0pEUm9Md0FMZklEVTNmWHViZmdCcE9ZRTBlOUw1U1JTb0p0dlRwbm12cDdKT3N4RHNjZEZEJTJGTWZudmElMkZRc2Z5U2pVbmxFQ1VUbUcyV2lWcUhYMERpYUFnMGY5U2gzZ2FQQ29kcUxBSk5QcGdobHhiNkc0QTZwcDF2VlJJbXBYd2NxMlJMc2NoUURIMlNCMVdPNWlhSG1iaE10JTJGS2tFWVVjTjc4cW5yWEV4U0VlYlYxVDNkeXJYQlR2VlV0dmZIQiUyRjdPNGRZUGowaW5BeW5DejlkckRsUHlyaWJlT2dWeksyMmx1MjZzdE5Ta2RYUzVMdnY5MHRqVUhFc1ozaDFIUnNKRmYxdjd5SU5WdXJoYjJLS0Q2MmhITDFZck9kSXU5ZFRvWkxFa1hSTlJHTUJoSnAlMkJYaHl3bzhsMG5uamRDdW5RSElYJTJGS2I4QWo2U2oyQUtMNGJoVVZPRnkwJTJCbSUyRnpPU3ZkbG1NdVltTiUyRndiRVgxVFpyOU1scXViY1JUYjNFeVJXJTJGa3JlanJ3cVZmanpDSTFYYzA3RjY4enAyWjY3JTJCTVU5MEhWRXVyOFBKUXN1b3J0cU9RcHU5TTFqcVpoU04lMkJoNUNNbFc1RG5DOUQzZXc4ZlYlMkJuV0llJTJGdEhEYTRTT2RRenVRNVVZa2VPVmpOc2I2Smo1TnhNRjl0cVBvZU0lMkZVdVpSb1ZzMyUyQmJJVlJMSXIzTVRJczBwdG8zbEYyWmclMkI4enI4clNxWHFyblBybmxBbk12ZUdyRjc1cTIzRHE5OEdnbHdVa2tmWExjMTl5OERtWlpjSFZDM3VkdlN5U2sxYlE4cmMwVXYxSDBqOGFXJTJGUlhTU04lMkJHTU5lS0p2ZGUlMkZlSXI1YnFvUlIwRFZSemU3eGxqZFpCVEwyanRBTXpXclJwam9LWEhmS3hHcE5wc1htRGg2bklsdnJkNDZCWWtzb3B1eHV3bXJKOEwlMkI2S2FlJTJCUFJLV3AlMkJTbEt6R20wS3ZScWJEZ1VyMEptJTJCbHpnTXp2QjFPc3A2MzllWTRHNmFtOXVMRzNxSUU3OXEwSWglMkZlSTViNFVSRG5lbktvSSUyQnhER1QlMkJPUnBwRlNQUFZuM3o1QnklMkJsZE5oN0dySGQydk9Bcm9vMXNzcll1TzZGTVBNJTJGQ1VVU0lNdFYyMzc1cmJycEQ1MmlFV1g5V211OEolMkZGazR2SyUyRnBxT0olMkZOYVJRdjBpWkk3ZGRGUFklMkZ5JTJCOFNwOGFLQm41ZVZQM3p3dWh5STFsTSUyQjMwYjl0ODRnZ2xMQVZRJTJGOHhybVU2c29NaVJOR3gyYSUyQjVtT1E2UldocHQ3M0VkNDlvMFdqZGJ1c2FQNTFqOWV1d0tWSnAlMkJtd095dWtZZWlQcG9RQSUyRmM0NE03NXJJdjhkRFhJWSUyRks4JTJGR2J2OURQNmRwczlpQlo1S0UyZGdpOUw1bTZpbTlmcmxGQ29lZ25qZ0dNUHQxUCUyQno2MzhqOGpSWERwRkUwJTJGZGNPUVVESXN6QU45cXJvQWpnaGhvVUppciUyRkFTbDc3R0Ixc3VTUDVNZHlJaldNTGlCTDkyTkhQNHFNTGlTMFdKcHZnZXpyOXRwd25tU0d0akclMkZmZlZRWUEzR1BLQWw2WkU4d2pPaXI1SUslMkZCcE0lMkZQUzV5aWQ3enZpc2ttV1VVYUZPNmY3dnZHWWh1bHVrNUdNdnNhTHl0OVBIU0dsZERCdXJEeG9pRTV1dHp2VE1Vbkw0ZHFYcHprYXZaUVNBcUE4ZVk0emhyNmpxdktwdllFem1NTVhrVU5UY1UlMkZqUmglMkI1Y0FoSGpKVW8zayUyQkElMkZwemIzMkJTOHFJUkNEOWpsajNrT3IwS2J6OWhsQ0Q4JTJGdE84cjk5M1NjTCUyRlFTVHoyaE9VOG54ZmxsbnJvWXVHb2ZzZWJ2Ulo1JTJCVmtEWlVmeTJyOUo5cXBPSFV2WmpoJTJGWWs3OCUyRnQlMkZJQSUyRmpZOXVBZ3NueFk5VUFQOTRQNFBGYiUyRk10NCUyQlZPRUt1aVBrbXZPN3RPb2tvdEhleW5vJTJGeW43SDd3R3d3ZTN4UWlhRUF3TkpPNyUyQk5MYVJaRiUyRlVkYzYlMkJLcCUyQlRnQnR6UlgyMUpodXhQS3JEdzVYTmxWYkg1ajB2bDB4VEVzcUFzVVVlb2U0ZnljUTV4a0syRk8wNndWM2hBJTJGZ3Z0dTZibDZPJTJGUElKdDliRnpFY2l4dnpoVkdtSFlqMThkMUpWZmx4dFB5MkFmVG1oV2RubFl6JTJCJTJCSWpZOWpFJTJCVTlkMkpSbkhWTElvNTElMkIyd0d2SHZFcGJWRXB2VXFwdVYxJTJGbmxVM3RtOEQzdXVzUTNVWk1MWW55RE9iOEhXZzFpeTFneHlpb3BUUUZSJTJCbXVCbzJuUGU5MVpTcjBtMTFOZURFakdxUlNDVzNkaEU0REZYdzVyaEg3dlF5MHNvWDBDJTJCRlJsZG5BQmdYT2pCN1lQMnY2d1ljQ2YzeFBHaHM0aiUyQlFYVWthVWQyOGdOdnV0OVluNjU2S3R0ZCUyQnlOMFBadyUyRkRJcnFhZXhJWDBBZVY0WjBhUjdpUFM5V0JldDMlMkZmYlZCajlXRVZlTWh2c2lPQ2lON1ZkMGNjWUFUR0hBTkh2OFIwVVBEOGtZYnl5YkJUeEhSYzkza3ZrRzdUc2U5SHllRjEwdjA2VFFlS2QwTkhiS3ZjSXQ0UjY1QTJSTE1mbVhiQ3pDNmtlMSUyRmJEJTJCRk5ndFh0QSUyQldYb3BBSjh0bUslMkJPZkNlTnlqbFMzJTJCRjl0eFp0dWNUbFM3WGFLVExVJTJGenNVZ0g0S2tUdFlnaW9hR2ZkTVRlSTZWVWN3WExCeVhtODFKWTRVdWdPTnVkRlhtSm5pWEZLWjhhbGFtTUVDSlJ0M3Z4MTBuYzc2cTlQcUtjVWI4RkclMkZCRm5tRkplajY4WVU1MyUyRkxNWlBaREZ3dmZXbVpybFlFaE1TTUl6WHEzeGVHWlpHWEFMUVgwJTJGVGxTa25nSDB0JTJCWGxMZUc0aTcxdEI0QmIlMkY4c09LakpOV3clMkZiWVpNbXBGTjZPUVk1cmQzRk1ubGJoZjJnM2k3SFJqaEw1QVVDNnlzNEpKZiUyRlVUam9JV240WXE2dmlaN3VLdnFhZ0Z2TndvWlBicUFXeDN3c3dqNlF4MW5xMFklMkJDV0NURSUyRlczbVkxdEVEUms2WDBOZUFaTHE0eGdpV2Z3UCUyRkFxbyUyRjJwY0pPTHBNdnlUbmJoVlI3M09UdGNpbktTQlNrUjBTVlY2bWx5eFM1TlZMbTNtdm1ZQ1F6OHFLcnB4SDkzdllmMjJ0SWkweTAlMkZ5RzBEcWxNb0NLMVZ6R1dyJTJGaThwJTJCRlZ2alpHdW43UWpVJTJGaTl2MktQejNHS0N5MFF1VWVuR1MlMkZUZkhnMnJlejRFc1VyZE9iaEpOZ1UwOHM1OVJNbm8zJTJGVUJyN2hEJTJGQkR3YkcwZm95bkU4JTJCS2RydWs2eiUyRnZEenR0QmFYTE9mbWp4ZTZmN3JwNVltV2RqcnUxbG8wMGptZFpQSUxhVk5MYXJacTFvdm1vNEIlMkJEMlhyeFZoJTJGWFFSTDBTR2FMTkQlMkJkcTU5Tzd5TUQlMkZzckV0UHYzeTJVYXF6Uk9IQ0pPbWxYNFYlMkZNN2g0UlQ1RU9xaSUyRkI3RXRLdnJUcEhOVHBCT05ZMEElMkJvN2JNeGNLTGFxQTl4UUdsUlFQQmEwTWtBQU53ck9vMEtjJTJGSk9UR1FyenNSZjRoN3JvaHhEcnUyTHpPNllsOG5EdE5iOE1HN0xURFhjN1dMdGhGJTJGbGtmTHBZJTJGWkZCQUVZViUyRklMaTNjQzlMZjIlMkZIc1I3NXo4MnRDaVlWMGMlMkZ1Z3p5ZTQ0azZRZmRCYkhack13VmFMUjdoa2xhNEJ6Mmo3Q29laWV4clpzeHQ1WiUyRk94RDBMdlNZYTFKWmdHNDRYMktwUFlXRzVJMyUyQnZ1aDNVbEtjJTJCcXN2WnlnWkVLNzlzTnFsZ3dlJTJCNTRyVkxTRkFORTRFYzdhYWpzbSUyQm4lMkJRbjFiM2dGQTZtJTJGM3RhejN0T0dYMU9OTHBHSTZjJTJCT3ByM0RZSiUyRkJLRGF5T3Blc1ZDYUtrM2pXekFHcmNpOVVVa3Z2RVJISHJwZmROciUyRmNMbVlSdkJQeXhFSm56VEdsZ2ZkTXYwSWtWRjlKRW9HbEVDNTZtYUx0UTBNeG5lNjRUWGZ4TEdRM1BuWmszTXlDRkpaYWw5aDR2OVFjRkFOaUt6dXFxZXhOS0xKOUZWa0R0aFZPcVVBbmJEdjhnaFJkNXBXJTJGJTJGcWYwSWEwVHU4NHNzaGt3U0dPMDhpNUglMkZNSVFBRzlpZVdFQTZFanNKSzNsdThNWUVjJTJGbmZrTEJJZmZFYk1uUmJRVU50MGVsS0l4anJNclViUFNUdnMlMkJtbkh3UUFBY0wxbldVVyUyRlUyUVNiR0UlMkJGY21mbzdZdm41R1pFR3hmaTJhdzFOOGk2JTJCM0QyQ0g1WHU3VEh3OHNCZDA1R1BtcjhPQWZ2SnV1MU1mMmRtS2FLUTRFNFBwZ2VkUWxGODU0Zm51RktTUGF0WHc3VVElMkZRRjZodGdNSktVU002SzliYUdwcGhVJTJCc2tCdmJXU1V3aVVYbnV6dSUyRjQzdU1LRjg5MmFlVVFiWDY0bGhod00zRVRBTlJaMGlNbiUyRk04dFZsUXRkSFFzSUN6bHh6U3dKbEp1aGZXckZCbjdnYlk4Y1AwNGVXRG53OGt1ZlMxZTM5Mms5TFB2dUdzRHZ6NEMyS0JMOEdNNDF3UEViQVZIUEZ2d28lMkJUMVAlMkJ2aHZ1M0MyRSUyQk5Ma3RHZHhPcmpMS3lwQjRScDFxdk1SNzdQNGtNRyUyQnBKJTJGdmJiemRFbDJ0TlZuRCUyQmE0Q1FnQXN4cWZyRktOWEV5QzIlMkZGJTJCWVVWTTRUNjUzTXZmWWhZSWdicHFGM0R4RWFhTWxKWFc1MCUyQjNxTE9yRVFMWTBiVng2b3hxOGVDMUplbURjSCUyQlBFZlMxZXhJQ2tTUkg4SmwyUGhMb1Z6dzUzQzdldVg3Tm5iVEhlWEFKbFBJaUlqTkJKZEY1eXhYdFg2SjB4b0RnVm5OSTU0bDclMkIlMkIlMkJ6cmpJR01McEZNZm1zY1NjNW1ZY25yOTFsalh0Z2xPVDNzbkxKV1VERHA1QzMzUHJuV0ZnRnBtUnpKdHdoQmxlNTAwT1Q2eCUyQlV0Ylk2cW5tciUyRkolMkYwYmJuSklwb01KTWZNOHRoaWxEOSUyQjMxVmFEdnI3S2FtTEZNem96MEZuTDlGYzZSVWdndkcyUDdYNno1SDFOVmtvZ21ldTNZNWdXcmNLY1F2OSUyRjBtSGIxTXFNeHNhOU1ESzU0cTdVWDc0cjFNTnVGd205ayUyRnZkUzJqTFhMaEVrQTk1UkVLV3BzMEQ5R01aZVR2alVDNUZIMlJQbHZ4aFFnQUYlMkJ5UzNCMWlFdnd5U1djcDdWa3YzdVY2R0FOZE4lMkJmZTk1aEs3TU5ZVlF2U3lNVTJYdkNiMEtpaGRrSVo2eUZvSHFDWTdsY2QzbjU1djFqcUZhdXI3UE4lMkIlMkJvbjUzR2hGRXRTTWlhNFdYOHFxbGppeEhCbzY5U0tTVm94ZGxrekZyMktrQjlQUE5OcXhUNXBvR05sbmhtUk15V3Rxa3pZakxFT0sweHBMbmRrNTl6am5WVkVwd213cFl4b2Y5R3VtNSUyRmlacndVaTlSZDdyaXFLT3d3JTJGVjdBOHVXeEREQXZCYXF3QVo5Q1lzWXRtdTVQcWFPOSUyRjFSUGllV3BhMkpzcGJ4Z09WeVlGNWFob1I0ZCUyQlplV0pjNk5DREIwM2FrJTJCSzZ5cjVrVTl3dmtKM0F0cDNiaEglMkZBcUJQMUJPVXpIZmJDTld0TFNvQXhjOFBRSmtveVZ1Smp1YzVuSGgxVDB4enlpVEt2TWJkZkJ6aEFQOTk1SFJQUmdYdzZFM2t4WEdiJTJGYlBnNThKVGolMkZacVVCZjBHRjBrNGVhVWdtMUY5WEE1NFFZcVc5MVdrSm9OcWtTaTI2NCUyRlhUNWM4dzNOdEJ0dWJmNGFLcnNEVFVoSzNYMGNWM09LTFNZYlhFUW00Q3VwdGx1TUJVaVVZb2ZuMkdxc2FvbGI5SU1NTlNtTE9tUGRWQlE5TVRkbllFa2dmVmUxa3hHaXY1Q2h5eTVRTkFLSXh4Ym83UFZPdkVEWnhiWTA0ZHc3MVJmQmJ6OXpkT3RONmRnMkpEaWg2SXJDemRIeW5sNjIlMkJyTVNUSW11VlcxNkhUbXMwSHpFU3VhbnRMaTdIJTJCSnJidExXNlVpSERHblZaSU5jUlNIWGw1MXhWeFRqaWVJVDdpTE4wSU5PeXRWdGR5NlZHa04zdW54aThBNk9VQ0E2UXlQS2lGVWdGUjVqZ09mMzhOakw0UEZZJTJGTXglMkZia1FoQVlvdXNRYjJhcGdienE4Q1kxVzBYYzZwSSUyRmZGbE9pZThKTnRVckxORzhWS095amloZTI3b0RoMkJKRDFtY1BOTVBOYldMcjJreW1RVmJNZENINGlwTTFCdDhxamtpTVBOSHREbGcycjglMkJRaDB6MjNVMm9qWDdTUGVwSTBHRlh0VWlGUHRXZ1Q1TjFiMjZUcWFKQUdyVzdybTZDcCUyQjVPZ1JtSW90dWdSTXFnbDdrNUt1aDMzalE2NGxlM0huY1lqS0tVQmUwYkJWWWNLcDB2cFVMNTVSWFg3SFhYNSUyQkklMkZ3ZGFDYWdaeDNBbUo4WFZzU3J1ZlNObjMlMkJ2OWJ4cFVvSVBGV2ZOdVVJOWlzTDFyQ0FtY05lOTU1cTklMkZlZWpjV0w1Nmt0SElYZ1ptNXdwZHpFSmE0VjZBVjE2ck5ieGd4Ym5hM0ZKMlVZZFlEWk1TVWU2Z0pXQ0lvJTJGeW1hUE1LdlFvT1ZqOGVoOE94STlMelpHVmYyTSUyRldmSDlHYlIlMkJ3VGptMVBZcnlmTnB6NmliT2pvVlhleGdzdWN6TXY2NUtyMEhsSHNkdFF0dWk3TDlaRnJDQWtJJTJCekJsdDIwbFNDMW5nOVd3a0xveVZvUU1HdUIyUGs1RWJaeTk4eHpxTTBYYUZ0anZEM3pJYlVQZ3c0dmkxSUF1cnUySVJueWZ3WGNjRDVOVVVKUUZVZ0NMM2dwcFY0cmtpUno4dDF1VmRWWjFEeW9pREgzeDMxc0ZUUEYlMkJEREJIcmJEbzM0UFU1YTRoV25XaHo2YW1mMWZaaTRmRTN5cHFjdiUyRk5seEE5ZTUwOXNtSnRXcmtSbEk2dkpxZmF4SE0zNUc2WnFzYmN1cVhhRkF5VUx0SjRHVW5WbHFYa0JHQndKTSUyQnlEc1l1MGZRYk8lMkZzb2RLVDllOWo0RVJuR3dPVEpaNEpLMFlralJwR1pNTGRFSVoxREFUbUdXRHRGblVOUDdic2Y2UjNLUUJsRUVSeW9NQUFzNFZ3MCUyRmtTaVVPT2UxNlJrcVRSanU0N2ZxSHAzQ2Ywc2FuN1BpSVdwQ255MSUyQlptVnczZXh1USUyQjVNVHpHUiUyQkprNkt2dzlHNzcxTEZ1b2VDdCUyRjdUMkNkaVdORHBpSDVZT0JLc1ZJbE9iU1NCem9TVDZia3RYWllacUJkWUNEQ0ZCQU1aaEN1R0tEOGtNUXBFNFhyMTFRVThhT1clMkZvR1o0Q2tZWW9KRU16YSUyRkR1JTJCWkxka3o3Z1ZOVHJEZVJTSVIlMkZkN2xDdkpkYVRQeW9rczhlJTJGV0ZvOXI3WGhBdDRwM3ZnZVpOMm5FNml1Q1hOZnp3azR3aFB4am42aG1yQkVaRjdqS29KdkFRZWZqMUJ5OWxISk9oSEdUZzBTdTZlVDVWcFJ3Q2lHSVAlMkJ5ZXVmbE1FTlNSQVJRajUwMEdyRGVObDdTV0VOd1RDWVUzNWU1czQ0bHF3VTBEWkIwMDh2QTBMaDBXRXJ4b1pxdW5DYyUyQiUyRmZMREUyODZzRXJydWowTUNRZmFvdXBWMUVrbWJzUnZiWFRTTXglMkJsNU5XdmYzQU1VV01OWlJPTlVmWHBMZjhFM0VJTXBFcU9SNlNPJTJGWGxvZnl0MVE5OTluaVd5OFpOemhmbHc0ZVJHZ1JUbDFmM3ExRjBlUnIlMkI2SWRDT0duV0VPMjRPejlvNyUyQjhIRWpTRmp2Q1ZkVSUyQnN2Smt0dzNnbGVtWUhqcUlGbGZ0TjZhaTRiM3JhUm5BVTBvdHRpZFVJN1RucUphbSUyQmtyYkRvMzJkJTJGWW5MTkx6cGJJZXB1RWNYbE5YbWw5eGZLNVREWms5YkIlMkZoelcwaENUOUN4RkFpYjlIVWJuRzFPaWIyU2xuTk9hRWppR2Y0N3F5YklwNklUQ2s1RXFuVE8wcSUyQm1seVRydkp6S3JKVlE0RlhxJTJCN1RpNDhtZzNnYngxSmdRRzd5ZTRGVmc3JTJGRkVqWFNkOFpSMFY1JTJGYTNLcGtmMVZOcE1GVmc3S1gxTm5GY25BbCUyRkZYZkpicWtGVU5aZzdsaTcwRUpzUU9FNWhOMU5wS1QwZzlTJTJGNlU2SktsYm01JTJGdGlBRlhvT1k0S1FHN053NGxieENoSTlmR3hHallXeFRUQTBkcnhJSnNxdFQlMkY4N1hVQ3BOSkl2RHdGeSUyRlZid1hybm9CRG1KMDU1QnZRV2xuN3pZclVHQXFlT3dlaExyQTBIcDA4SjJoWHEyclBYMnJFaDZJT1RDdDZZS0Zaa0Q3dDJaUmtNdDVFUEQlMkJwc005MG1vQTRiMDV3UUFPakV2SThXSjJWWDlhZzA1Wk8zNGtEQTBiMHlxd0k1ME9qclhTWG90JTJGbjNTbzZacVRkbCUyQm5lZU9GZzdoNzNOWjVKVHVxQTEwNXclMkJYS3UyRFNKOWRKJTJCT3V3cHJ6R0dnSHNVaVkyeTFNdlZ4cjUyN042M0dWbHZadVg4M3RmbjF2NlVDSzhlcSUyRlNvSjhUJTJGWkMlMkZEMXBaOSUyRjdFUjZNJTJGQVVrOFhESmprN2tBeDlkcTlrY2JncUN0Tk1iOTNsRDNtMzZEZFJmNEpVeXlFQ29zJTJCJTJGU0pIQnhMOTFHbmx5SVBJVWd1SDdKOUhweFMxQVJYSjMlMkJ2N2lEd3o4ekhlbXhKbUFycXFRWm84bWolMkZiSFJkQnZLdWJkRiUyQmJjRkNodlQ2aE80bCUyRnZuc01WZEs2RnpkJTJGanRtWVZOdGRTc3gzZmxDSU9td0dtWm9rU1JHMmtBemFnJTJGTHdpbjBHZSUyRnc2a2tEMW9UZzA3anRrc0g5eElCbjF3Z1kyQ0RaYTlmd0hBbTElMkY5MU5XUjRVTDBGZktPb3FHbENuR2Y2Q3B1U3oyVFElMkZmeUVFJTJGSzdZTEZocjFqYk1zaFAlMkJNcTU0ZXhFM2tIYjBLN1U2bkQ3V2tZMlVTZiUyQll2RWJKR0NpJTJCWkxqT0JKR0x1bldNS29wZ0dVeW9FVjdtUk90dmV4dTh5dHBIbkZYcUZEVUNTUVFjazB1WiUyQmpwV0szYUNFMVhVdm40TnZsU0xZbDNlVyUyQkx3b1psUmVCUDVuc2l6ckVzVWdzdWY3ZmpMOVRCUHFrNWd4SDFiamZkdiUyQjJSR2diZkpJd0NrakNFSWd6MjhlSEk2TDFjZGxUOEFGZzBzdERmTXlMZGlhVk5rTkJpcG1ZTWF6cDRtY3JkbUZ1OHYlMkZRViUyRnF0R2pEQVloY0I5cWdHSzhwZUVKZmxFSSUyRk5jM1FWOEo5ZjE4ZXhQdXViSVNOJTJCVWo2TFVWYXElMkJSbSUyRnFsbFJ3N1dCY3Y2dHFXM1pPTHNVeVB3RFglMkJGVGd6dWNoSTZxVDdDcnZwS1VJdEJmb2s1Vm1sYnlCRURhYnVSUW84MFZqRTV4aG9mZCUyRk1RcW9UNyUyQnElMkZKU2VkWFlaajRBd2JFOVZLdnAlMkIwc0ZIa0YxREQ1WUslMkJuZ3hXWnllbThxYjF2clhwRkEzVTZSVG1FOHlMY1lDeVlrUUhYYlVFMDRhM2xrZDR3eUZkdmdzaEJzMnhQbzJmWCUyRmhsRSUyRmpWV3dmU1lUbEphcXl6JTJGNUdlektWWGNwencwdnBIeXFxNkF3NlRIeGtzR1ZCdUdUJTJGJTJCeFVlcWdTVnloSE9hSlJianVsSmUlMkJhc0JLUnoxY3ZVc09EenAlMkI3Q1dYcVpOTWRoSnhKRnN0UGxqTyUyQjZBMmt1U2xGRE55cWlwRnVMRFFhN1ptUE5rM056MG80a2JxMjhUNnd5bUdmd0tmZzlIa203MXNvMFNSUkNzbTM2NW01UVhpWUVOZFh1Nmd4b1ZiaWYlMkJUNnJveVI1WW52U3JVaWZSSFB2VFJwV3dhWFhEbjFmek9rNWc1M21pUGwlMkJnUjBxWmR6bURiVThtM0tYMGlBMzVCU0Q5WDhTZ2JYVVQ1eWtUSnpUS1RLZ3RncW9FR2dHdkpVcnVYemhqMlB3SDdxTWdZOGp2JTJCRGZtcEIyMkxNOSUyRnpPZDF6cGNiWnFtRldoWUdZa0k3aGlXQzl1RjdFSnp2Mk5yNG0xOGdGc2hBbW1zRFBvN2FQMkclMkZhMk81T1o4N2FNUTBvaHAlMkJSZVE2ZFNreTNJdm5vTkIySmR1MUpjWndTNExmWDNmNVV5NXdMazJMRnlSRU8xM05pUHh0bFJ3Wm1GaEh6a2Fna0MlMkYxS1lsd1loa1ZLQnRFTWM0TzVxUXdYZWVGNG9NeTVXNDhKb0klMkJNSklhSTNuJTJCQ3JXWXclMkZER1JvN2pQN3lrcHZaanR5QWhuZGk1eGlBQjlGZFJBSHFnTTFWNEVOUnR1RHRDSGlCck1KOExwV0xLcmMzc1ZJNXJQN25OV0VaOTRqTTR6U3g5a2NFcERQcHRDZUxRanVJMzZFN2hoNVdsMUV0dWZYUnNBVHBFczZvRFQwNGVLRUZXaUozYjMxTE9pSW9Ob2h2U2sxVFhLd014OGQ5VFZxQVA5c1hCWWNqQ3gwRlFUN2xGeDRxNVFEd2R4Tk9kck91JTJCb0JWSWNDUm5SUVIwc3J4YTlQRTBWcCUyQnh6RkplJTJGSFlIMFJjJTJCeWxCSkR1UXJ2MVhhNTg0c3NiTmRmN2ppTGI1ZXRhcjRoZDhsdENnakJkRTclMkZhJTJCUEx1cDViVHA3eGtRa0ZQMUIyUjdNV1R2a0tZMU80RlR3WiUyRnVqbzA1OEJkVWl5c0Y5TzFxN0VZMnQ0VGhkR2duU2FlMEw4MHNjWDdERDV6TmM0TEZmODROd2NGY2h4N1QxaTFrNGd6dkJOMm40VmxBZnp0ZmoxeWxxU2hpZW8lMkJ6UDdVNFRXdzMlMkYxdEJQeWQzbjFUS2FXaUZ6SW54dEh0NWt6MnNpR3hia0VMT1JkemQ2ZVhtJTJGNkVUNTY4RFhTeXA1SHpLS1cwZkVTd2phRlBNdlBUb2x6TzNjSFVZOEs4eUpRcyUyRmxkZ29xUm5EdU9rRFUlMkZaVkhRb2ViSk9MVnY1aW50ZVNMQmwlMkZkODdQcjdvRUliUThvJTJCeFFwT3FzQ0o4RkFUWEMzV2p2biUyQjFVdThOU3RFTW9Fcno2SiUyRnJFTnU3QVJkWFFYTW15ME1iV2JSTEJZWGh2SzVONzMxbkhwcWY0clhNbUZ5QkZDRmxwamZxMGxRREZucHpuZndDbmFPbmpvNDZQVk9STTVVdTRrZGZTb3Q4U2tyJTJGWSUyQnFuZFB1Wjc3dGVjRlMwYXRpZVREaFBENVdZUlhLdjFiUFJlcm1uaUZyaGE1dzY3bDc1cThWMXM2JTJGQXRlY1ppUFJZWG0ybFVzRGRKRWFpOGZZYnpGMTlpVTNUVDRhaFdPTERSZSUyRkZXdldQWE5lV2Fzb1lXSU9zUnolMkJhSFNOV0dmbEMzcTJXaVByJTJGeDNvVGMwaUdNdWFsZzVBJTJCdnBaNTZlc3AlMkI4VWlESHdHcmY1emNGYktsT2wzNWVnMFp1S0dnN3g4VDl3WTJVa2hkYVNaVWZNb1pDek1LTzJ6SkJPQ1gyNGduemVQYUFvV2RSaHJZR0V3dzF5dkVudVcya2FkM3NGUnFMSUE4eSUyRiUyRlh6N3RHSmVDUDZVSW5jWVolMkZDJTJGaHB0Ukp5eU1EZU1uMHM1SEh2RG5UcTRYdDliJTJGelZUdFAxMU5kaDliblY3bkxnS1B2ODZPV2xBSFVuQmlzM3Z0cUJJWTh1ek9nZDdJaFB6cHlNNGZNJTJGJTJCUnB1Vk83WWt2emJKeW5MQTk4aFhDYmN1dzQlMkI4ZmoxbWV0U1hPZFVwJTJCMmJYRkZvZjFFNSUyRnUwJTJGR0FhYVFFUU9LOXclMkZEeHlvJTJCSGJZcENkRDNjb3lQOHpjR0N5VVhtckVPbTZaSlR2QlhnZTFPb2g2JTJGREZVU2JFS2VwWDU0MklMSVclMkZFcnVXSzZkUnZOTnlHeDhCYlAlMkZ2cWY0cGFWVGZtVmRFVXlqOUN5c1Z2cW9mSDRJSm9FaG9RS0FsZFJ2NHBiUVclMkJYeWpiUEM2N2x2U2olMkZsaSUyRnJsU2w1aEpFbHJ5QlVkSzBMJTJCdTVEQ2FKZWdsalUzbWZ5ZzhXS2lpMlluJTJGcFVHMGxIdnpFMk5mRmFBZndyU0lVcGMxWmdmWGtnVDJiQ0g0NlVpZEoyeGdKNDVqUHV2dFVrV2JzaEMlMkI1ckQ1MERsUWRadjElMkYlMkZRanBkSFpxJTJGZ04lMkZYNUV4UmxucU9HUE01VkhMZHc0T3dhdmVVUXZFJTJGdXNoUjRValIzZmxGUHlQY3JWZkdiSXo4YWd5dmtSaU53bTlvbmw2bmlYeXluenlqb0w0TXpiVWtnbXE4RjgzNGpsdW1sQ2w2OHFpWiUyRlEzNGEyS05wNXlHTVVxRVI1YlR3OFp4eXU2R2JpUGFZTmZTMmtqeGd2YVBtWmZaVzRkR0Y4SHZlOGcyNmJ1UnZKMHhxSHRoMVBCRmFVc2ZINGRZb21JJTJCTzZPNjVHcFN4T0JkczQxV2dsZzJxd1A3SU82ZGRRc1hzRmN2cE9lNGclMkZhcDJmdTAlMkIlMkZuSHhFJTJCbkppa29pbWYlMkZoJTJGNDRJMHVlZ0pjNEx3ZnAwWWFYaWRKR0FJZHJHYjh4YUNoaXFZNzdTdkdyM0ZaTEQwdUNCN1BFbWV3VkFkN2hQZkdqbDlHMHdCdFolMkZtcmdyOTczdkw4c3Q4R012QzEybnEzRWN4SlA2c0NwWHZwd29EYjFwcU1qNFczVSUyQiUyRk5jeHlVTjhaJTJCZ3U0WktGUyUyQlVKSGh0MzdPbE0xaWI2WTNsNFYlMkJRY3F5dlQ2RDAwRnJyVE5hT2pkTzlhT0hub1pFS3pUSk5weFZYMEllZTNucVVBeWMwcHNQUzZ4VXNTeVJlZkRCaVA1WTUxa05oNUZNZm5yR3dWZmZMdUVDb3hiOVFhZ1pTN3FQOU8lMkZrRFREUGk3UGFXMXd2JTJGN0dXRTJYTU03b05HM25PbkUlMkZybHBWbVNVWVRBeUM0ZFFPZFByaU9KZjlMWGZSSVRtN2xFWFdsRUdMeEF2dXBSNlgwJTJCN09mamVGJTJGRzExdk51JTJCdk9WREslMkZlbGRBZkl2SFdYNHpBd3NwcmZwTnpxODNEdGlWUThCck5IN1RUd3RsdnZwdmJxaXZqblZrRCUyRkc3YkY5SFRUd3RIY3VoeWJYVlZxZVB1Y3AzbEJ6clRXS1d4TG1SdHlxeEE4NzVpVWxHNmlPazNJN1YlMkZWaTBiNXNzSG9adiUyRjVPRHdWZXJXVUYzT1dMcDd2T0ZYRm96blRqM250MWd6NjVldk5kVnMxUEhiNiUyQmZmJTJGMHFIeU41MzhQOUVqQ3N3UmRDclRTRFNzZTNjYzN5alhZOVRVMzN1NlNlZCUyRlVTU2gzN3piOTF4VERPYmIzWDY3RTElMkJDJTJGWXVPbHM2MXFSUXh0dWxaSWhFMUNuVkVubEtVMlJkZXZFdHAzUHBNNlByWUt3R3VhWHVIV2hWJTJGVEpyQm8lMkZWdFVtZ01tNEVaNEt3elFxNERtTGpUVDI2anpvbGVpZTAwUVVkM1pOb1lIZVlrUG10dmlhRXVoT0ZYOTVnVEpLVWNoWVcyQ2Q4VndmTjhIQjclMkZPdmd6WDlOVWVqSnNoVGRGRzklMkI1RERLNTh1VkclMkJPcmZKNjJXWXpSTllkV3p2V1NqS2YlMkJJdHlXbUR0JTJCTk5UbkJQNk1iQUM3WVhjTUpxRmhJQXpURVVScUhDZXIlMkZDVEh2YTdRanlWaWglMkZ0TXRBZEIzOXJ4eEZxeWw4dm9QNGluTkpMbSUyRkRPbTdnRnBXVXZ2TDlpZVpZcERveU9pUnZ5VjJzenJpQ05sSXo1VHB4VFdIS1hhWTBiT0NaR0ZWZ3JGamRGRndSUEhQM3YlMkZGeXRwRWRUNnQlMkZYWDZMQ01LQWdEQUlqMG1FOVVrNUNDNFBTWCUyQlNmOG41dnpEZURxZlJmYjBJdVVwTVJjUlhvczdtMHQlMkJqYTNSekN6dmslMkJDVVUxbzVmYUxaZ0JRTkZ2OHYzck5ORVJJMDhEblc0WklpJTJGcDRwd0MlMkZ4dEdMTnpSZVVyNktvNlolMkJIWUdNSXZIcjBMZ3lnQjltUjNBSXhtdnNPcW1tQUklMkZzUWUlMkJvYiUyQlJCUm9Jcm1qSXI4U3Zic0JKaDAyOHoxOUtzb3Z1aFR4cGlZZTVSemVteiUyRmtDU2dLWUFrYSUyRnVXN0dQNExKeEV4QjFXYmNtSUd1d2hxVDJVYXlwMm53MTdWVDAlMkJaZHZWVGhad2lMdkg5UkhTMnZDM1lkdzcwSUNyc2lWN3BUSFZnVDdUWkhCMVJTbjY2TTI3JTJCWVJvaGZPeW9PM1l3akYyRHcyRXpvTDUwRm43UG5QcXgwM0FES091YjBZZEdnYmVJZUpsZTJoVGphRGF5elRBZ3hqY3h4TGNYTVI4MkxoeHNET3lNQ1MzNFolMkJGZzlFRUV3UnFCemVMUnNMYjQ4WHBZNVE0MDA1MjlIT0JJM0V1NjJxbWJSUGEzcGUlMkJYNEsxTUU2NDdKcWdQV1NHcnB0VnJwalV5bFZYQ1IwWTRyZm96Q3JyUiUyRjFyU1F0OWRTSkVXY2ZCYkp2NXF2VEtLYVVMTWNoVjZoeU91JTJGaE85a2VYbTlsbjYlMkYwa1JRMUVjSlgwMENTcyUyRiUyQmVnTFAxbU52bzdiWW04Sm1hdzlESDBoZUg1ZE1FSDlMR3Y3cndVVHZ5cm1vZkU0NXFuYmlKVm9sUFg2UUhSUkslMkJaaWZZdTRFUm9HYlhaTUVPZWEwN0RjeEVKQUs1emZGOGFScWgxNm9WOXBhbGt2UzFoeWtFa1o0Q0Z3cDNxYWZ6NCUyRmwxM3dJZGFnOXAzczVhUWZ1TjJHM2Z2NE94SzYlMkZTSm5yNk5PampXb1NIVjEzejBHUDdFWnlnOWlMRURrYVBYVkZmZjEwRmo1VHNLQ0NndFY3R204TlZrb2piOXJ2OWEzSElEclNoJTJGRWZ6R1h2TFROUkVFR3VOMnBERk9VZ2tWYzhqN3dQYmtWU1VIZTVTVzVFM1A0WEFaVmltOXo4c05kWkp4eFAwcnVjMEJlMmpMNnI2UGF1SGlCb2ZhWWdRT1BLU0dWZUZiaG9NRmZqMVF3eSUyRkdyVkl0dVhXdm9ZSUVFcGg5U3k0dHp5MXdneXV0V1lkWWUxNXJ6dyUyRnY2b2VvNlpYZmFoJTJCdkVVVnFGZ2R1aTAyWnY5VjBrdWE0WHpBUVRDTDk3dFZaQ2hLMnhVNjczZ0txa2pKZHRjSnplYU1yR2NDdXlRcjFWTmZ3RjF6YWdIZXRBZ0d4WjR3eHd0bnBZMHRpakM0R2NUU05rZEslMkIlMkZMdGZSRWZsWmphaE9qbU05WDZ3bmRMNGEwbWpqeFZXcndKb3NCcXJTWDNWZG1VYlpIZWJ6RkVMSTJEQUYlMkIxdnV1JTJCYm9tcjE2R0c5NEkyenNVRWhxZyUyRmpIN0lJNHdvcnYyTVhOWURPRzhQS2d6SEJCYW9zckNyMUlYTGEzUWVNQ21yZGIwRUJHTnZsNFpOR0MlMkJ3RTNCSXRvanA4RmhxTjRJOEUlMkZpVTRSYTJvVGxpZFVIJTJCVjFmc2ZtaGRqbHpMWnEwODRVVVE0djM4JTJCVkhVekNpNjBkaEdTQ1JFUXFJcXVLM1lNU1dvJTJCck9GekVFbkxneXZ4eEJFb2lka2hSNFd4a0o4NkJTSzlBeWRVZTFqQ0RMdGM5JTJCSEQ2R3dlR1pYTTdQUWJGYSUyQnl5ZjhCTXkwT3YlMkIlMkZJQlVrZHhnbmRrQ2thWWN2JTJGenlZJTJCRWNOOVM5V05NaTFvaUJCMHolMkJzYzZVY05QSU0xNHRzazVMMWNodHhNMEI4cXpybGs4JTJGZEFZSGclMkYlMkJNZHpjeE1RSGNHV1FXMU9sMUJYbzBoUmNLUVJIYlNOU29CMmNQV2o5bDFyWEJPcktsRm5NVTZ5emxkZ01vNVFKeklqRGxvQm1iZm9scGh3VWJOREkxQXElMkZwNzlhbmE5ZElIOTlTd0NFSTlqb2JhRDNkd3FyY1czcjJKNERIUlcwcVpxaHN3bVFIdjVzRXZZYXV4cEYlMkY4NTYlMkJ0bnlaUDQlMkZFSDhYVjg4UW96czZ5WjM2cVZuU2pHVXFPOEpMeHdQRGdVcGxjbWpoMXY4YUdKUnQzbCUyQjRpcSUyRlE1WmUyQ1hXcTRBWkslMkZJTms2T0JJWnhCOG9kTjZMSmRSVkFtNm13d0JWRnF3TnNtSkh0c3pWc0ZJTTVDa0VoZ2pQSG5OcEJMWE1ISngwREE5d2FRN1kzY1JHS2kxamk1bVVvcWk1MzJqREdCWSUyQlRVZXZGYlNTRlAzR1VobEhMRG9vdVRjSXUlMkJtVjJUSmVIVnp5S0RHbHJ6dEVCYUhCbUlJbCUyRnM3MHdtWVVRNG9oSGdIVTZ2Y2dKTXV4b0I4QXNkbkJjeXBnamZNdkgybkdlN1ptQ2hQSHNWaXc1TmVhQ0pOSTY5d3VWUkllJTJGbnJ5b3FHT2E3VnptemRaTVIycWZ0bGxFTlJzYWtBUW9FTGFSWHRsTlFqMnJxJTJGNlNFVmMyR0xobkNERjFPV0Uydk5nbGVUU1lDS1BNcVdYSWh0ZXd3VlhicmdWWFFpYW13ZVdZcjFWdTdPQyUyRnlsTXFRMDB5SFpEa2ElMkJ2NnltZ2hYTER2eFQ5eEVHaUtnSzVzeEp0d0hMdDJscmlOclBRS25XdkpmbkZLOFB3YWZPcGt3UDkyVjl0QiUyQmd3YkJua215dnZEcFZ2eiUyRnpsJTJCZUNEZUhEamhWeElNZEtveE1iZGdRcWRhMFlMJTJCZWNiVUZrM3FvcDJyMUIlMkJ4YzJxZkE1Zks5NlBBWSUyQnhwNmViOHFLOEpYMGZHQldJUVdsTlgzQ21nTWdCMWdYRWtWTCUyRjJLTEFBcXYzdTVKNThRVUo5TWRFJTJCcUo2dHc3SEtVRWVKOFhjQmxsTEVsWmJvMSUyRjhpJTJGeUZSMGU0am03T1RDME9nbGVRS0JCcXViOWNpdWJGMDhpTzVDWHBTR0l5YngyOEdvWWpNSWtORjNFbzBIOVFMZGgzcnlUaVVKY1hwUHExWExqTyUyRmpKZGVNR0hETmxMVEVKbmJrczIya1dodnJnWTl6N2JLNWJaSnJVUE5rcnVVUXF3bjZLbDM2a1FJZFcwVFVPUVBYNjNaT2ZyM2FQYXRmZm5LdUJYYUw1JTJCSVFsSzJLM0R5JTJGZW9WbTclMkY4dWpHQiUyRjhtNzljUDlPcW5wQkVZMWtGNFVKNENHOGNRNmZNS01TOWUxYjhNN3FFWCUyQnVOTHY3JTJCYUtLQm9UMnJJQ0IlMkZib0pTbURmc1NGdlgxU3FxSTZuJTJGbSUyRmtZaTR2amFySiUyQjN5OEIlMkJkdjA4U3BDdWtOeml4Y0d0M2t3NEYlMkJsZURmdHNuNWhRMFM4VHU3Y0E2Z2Y0R0YzSkZVUk5hV1JYOHB0RnFrciUyQkFPZ3ZjS0RjNVlnMG5DSyUyRlVyRG1lRGo3NFBreVI0QjNUWlhCSENlcmJkS0k4Q2ZtZDgwd2tsc1A1anI3Y2xFSmJiRTB5bjN5JTJCYWFmWGFMc0hUNWh2bjRscXNDVnFESjhYM05URldmUlRaWjBwWTZTR0hmV1MlMkJiTks0MXRxRWdIZXBSMFNFZjdmZkRDV1dOb1F4ZUtaY0t1UXp3bWZ3cURyc1lON1o2NU9ZYnZUZTdBJTJGdkw0U1pmeTA2S0RRSkkzRmkyeXZBdG1IYiUyRlBVTVJ3TVB4cU9na1kzQmJhUmJHRmhnNmoxSFBqdVpodlVPNnhBaVVEblJXbXJCSnprMjB0ZWZSbmRhRkVvVGpOazR3V1FWYU5GMUdWbnI3UTJKdUJobDl0czNHQlU2ZjBTMkRRNmhEZWt5JTJCSVJVOEklMkJmT3luUzQ3WWpKd1NhWUt1emVJSDM5YURVemM0UENFVUdmbElpSHk2NCUyRmJINEY3emRUS0tJVlolMkZYRCUyQndOa0FBUWtvZGxJaTRRdCUyRjI3anZqaWc4aE9nbUYyY3FmUGtRdDBheTd0Y1M3ayUyRjdjR3J1UnBZaFhnV2hmandQek9hMjI5JTJCdm1hUHpVYmpSeW44MldsTHJVQWlrcERXYkYwbWZINWdJTDN4MEdxVEM0a1B4SzBPTjM5WGJpaU9XZVpQOFE5NiUyRjdiOXJDamMlMkZXczFmQXcxaWtvenJubk9yOUw3Y3RrWEtoRiUyQkwyRzMzYzM2TzBxbUl6UTVyQXVZV3VCWE45UFYyZkcwN0R2SFZMMGY1SVlsaHBqUkdmRkNZUWowSzFLTDRUNEJJUHklMkZNTmpaNXZjazFnZE5MalBUN3FJTWRSTmhuWjQ4dVlPc2VHZFZOeFRyZjFmNXFmWHklMkIwV1hQMG1BSDZkZ1ZTekw0cDgxaHU4SGNLM1pXWXpzRjVWJTJCaTdndk5kcG5kWmJ0YVlNNFVFTDNLVnV6Vm1wZVVPNUVKUnVyQ2F6dkNaRlNpUERRdWhpNjExQU1nM2dDWW05cGJydVZ6OURURTcweCUyQmE1Q0IlMkJ6dXg5ZzF2c3lnbTRxR3QwUXUlMkY5dTgxVFM0UW4lMkJSR1REcjFLdWwzRVdYJTJCOWpDRjFJQVpUZUwxbDhNQUZRakk4ZnNhNmE3TU9mcWFCVjJCUUlnRzB4V2pGTnVWQjdrVzhXRlNTV1NxOHhKamtoVzM2TEglMkJ1V3ZBbXhuSVFSSzljQ1FyaktvVWJDV1RHNk93N3gzJTJCdDQlMkZqJTJGWm80RDN0JTJCY2RlJTJCemkyM3AlMkZ0OGNFUTBDem1qODFQS3hlOUJqS2Y5S0JIVlppSCUyRk85N1ZnWmsycHplZ0hwVXNmekVGc3RLZXJycWZJUFlxWVQlMkY0cUFNaW83cSUyQjZEMyUyRlpjdE1tcUNKYTFoTmM4RjZJMHlmMzlCSG9GYzB3dzNkQTduMFZTTWdHNCUyQm9YJTJCNDQwbWJIa0s0eW1LQmplWmQlMkZvZHo3VW9sTnBvaGlnMmolMkZLRjA5Y3VJVFRjcU9kaHclMkZUVjJieVZFUm5qVFh2dDc3a0RPNFpiUlVzUjZjQUpvQ1JpeDdUaHEzdSUyQkIxOFF2ZmxPTENWS3JTWHFaSFhSMkpzdU9HUTNWTyUyRllQT25ERmcwWm04eWhqdkhHN3EyelE5Qm1hTjZ1MDczS1lXTG1jTERjem9tSWlOMm9nYTlEYmJUYnMlMkJpMnFSazk1dlJkWE9GVXQ1REptRWhnWlglMkZvRU9PV2Ruam9QNFZjQ0Q0M1Y3VGU1U2p2aG91VUhPYjNhdDd2S2dlOUljUjFaRk9JYnFiVzBmY0hlckpXOWlDSVRyVUlzV0JzSTgweENoam56UHBCSnNSZktCbEowTWZ0R3hBdm1vZnhlOXZLUUczTWtrZDBpREFiSVdXQUdtOExkdGc0ZjJVOE92QkgxVnFXSm9GWU0zeE80bEtIelBGSmozSXRUakx4aVQ1UFc0eEYwZWxnY3NqVXNHV29vdlFwZ2hCeCUyQjBRVDF4WTJPc0tlYmw2VUp5QlpwOCUyRm0yZGNDVGFUMlNqYzhvekhLM2hKR0txS3VDQ1A3a2szdFpQeTJOb1hJUDJiMzdBMVZtVFF2N1ZQNWVzenFSVE9VRGtYJTJCZVlRamRvd3dYU00zVGF0ayUyRmkyQTdsazd1RyUyQnZpNmNFdDJXUzlMV2xBWUcyTHJRVkRLNGElMkZzT0htSWdmZ2lnUDVQWjJQcHp0USUyQmY1bjl1TkNtWjQxN3UlMkJubERFc01OUklPcnN1TEluSUZycU1KeWw4NFpKSXFiVG9KayUyQnFCdGFXR3RDVU9nVWhDbnBybWclMkZkJTJGbFU2d25DYXluUldlRnVsaVZyalZBS0RTazE0TkE0dlFYU0lkaExibTVSZDEyMzklMkIlMkZSVWR5OTFDJTJGJTJCS3BJSTdqZ0tVZ1VVb1B0ZTlOVk9OdkV6WjRjSVFVZjh0cGRyV2d5aEhTd2h0NFBrQlhOSTZmVDR5eG85UXg3dHFZMkclMkJQWU5jdjZJciUyQlhQVUJnVGRrbWhCQ2lmd0hyeG9NeFdEUGZ3NkxzTDZ5cFcxeXhjNnQwWmtCdlFqY0hsTVY3cnFoTFIlMkY1aGw1cnNrdTRucElFaXFEJTJCYVR6JTJCUHRISnBZQlZwekZLWGhDQjExdndYUmR4VElERUVrUk1Fa1lYckdXNlZZRzJWZWdaUm5rZ3htWVNTVEtxWlBpWHhORUdJRDA2JTJGd09FZ1AwVDBXdlc5alBORWh0V0NENVZkYjJJNU5RZWlXR2dMakFOMWRVZHA1YVpGdG0lMkZRdXN3b2VCWEYzS0RSRXdOSUl5SUFBQktBJTJCaDVjY1JyWjRrZmglMkZMeXJBbkRjY1RIa2pDU1E1ZjJ3MXc5NWM1MXgyYW1ENG1EayUyRlN6UGZzVVFMNzdSMyUyQmxWNERxZXQ1MUNYZyUyRmhoVnpqUjQlMkJJMGR6JTJGVUI1VkdhcThhdGxGSlhGdkdYJTJGUzlzanltaFMxWTB1bmZCWGEwd3prS05OV3lTS2tpdVY4MGNQR2oyeGtkQ2xPZWhIT3hTdkhOd3JrJTJGU0I1c0IlMkIlMkIlMkJuWm5aZUJYSFJGRFBLMkxCaTZ3Q2gxVWNKMEhPSSUyQkY4clVoQ2pacnhkRHhRUzl2ZWppT3VFZmJJMDlJQ3hoZ2FIeXdEd253RmUzT0Y4QnowWEM4U3JDZXZtelBSR0lsakZRQWlqaXBpZVk5eTVDdTQ4OXR6TXpMTWhxTDNyWWhidkpwdWR2SEhMQXpWN0VlU2xTTXM2aEl0ZWd1aVJ1Vzl6TDExWmlCUllxUEFrZm9lJTJGS0NRSzhyeTlPcWwlMkZvUHNQZEQ0ZnRyTDQlMkZJeVZoUXN4ZnFBQmhxYXVIMkZSMlh6ZCUyRklJWDZ0NXVvT0ZwZU5nWWt6VFRJU204WSUyRnFxdElKZDFyRGglMkJuWkNiZ2ZGTnVTQ1VWV3dsRlRHT29VZjFaTjl5YVZmdmNxJTJCaVlOUTlxT1dYZ3dIM2ducUxoTFJ4UmVpZldUclBCYzFVWFhIQTVrZ29ER3JQbFVlT2pjTjF3QVJDYm5pbXVSeFZ2dERmWXZON1hMblR4dFd6VHZQNlJ0RTY3TDBZODQwZ010dlRVWEZRS0dNS1VZRWlsNyUyRjZPQ3ZxMVdQVEFNSjF6WGlZbzU0MmJVNjRvb2pjRUNnWUlwVDBBTnVNOWhOcVlNdUdsejA5QUQlMkZnVnpUTHV3cSUyQjhLNDcyTEx0aEozWTRXelBINXBvY0p6RHE4Q1FKYUxRZnNDdjF5RkMwaiUyQkxpSEhnYk01SSUyRklvb1NGamJ4cm9udyUyQjlRakU1M2ZsWjI2cVo3cE9uOGNoeFd5U1FaaWlKZFJQbUZEN1M3U3VrQlNlVkt2T3VlY3NqY05ONHJjejFLWjVVeTU4VHV0WUxCRmYyaXpOR1A5S1Z6bWh4eXVRcDJnYk02S29mYUxsNEpPNDFLM2lxNTBLQnVPM1d4SmxhMjRiWkFKeDdvTHpERnVFdUhURHFoUnhIUEhSMEUzOEx6M3FaUENvbzRBWXJaOEg2R3clMkYyTXNaRU5ydmczcGJvQkp4QVVjMURMaVkwRjZ0SU16MnpzJTJGU3p5RnNTQUt3bXBhUnFIJTJCenpyJTJCSiUyRlolMkJhRXVFZ04zakpHVyUyRmcycFpxa1p4M2FRQUdBYXh6cWhGSTNlaTUlMkJZcWFNJTJGTlhxZGlmMm9hcTMyQklFJTJGNUZHJTJCNUFoZTVkckVka0JzQnlSQmtxUU91ZkN4THk5bTQlMkJSJTJGc3hrJTJGN3czaHBGaSUyQkp4T0RlbDZQdlg0M21wRVFTNFpvWDRyJTJGNVdKc0FScGxMRW43bnVPdGZBZ21MakhsR0ZiMGdKYVBzaHZXOGh0cEQ3NkJrZWVlcTczbVMzUHlJOWx0YWc0dmNYU0lTWURXZzZmU0ZzYzZHNyUyQkxaaXpCaVN1WDJDUDZmREN3VWM4UU1kTm5JeEl6T1dnNVNYYlV0ZlpvSTZRSkhjVmNUcUI0bW44bm1Da0dCSkdWZVhRWE1RMDh2V2doZElianNZMTRXaUNyN3o4NHB6NElCUkRwNU9ndXBrTjF3Rk15ekVWc3dkWGhLOFR6dHRZcWlDQXpHQjJuc0VkQnpCSkp0WDR4TEIlMkY2VDNNdjR2d3VpRjltRnRsNng0WlhFNUhjV2FFb29NaTR2Y2dLaVNXa0J4bHhNd3ZZeSUyRlZ5eXpHTjBnVU9BcFVld2wzSHF4STZ5ZFU1SkpSek5UT0hPVWpsSUVTenhtaGdzSkRzaHpCZHlyT2slMkZNaiUyQm1QN0pyWno2ZnJjYWRrQUVUWUclMkZaNDdsaEw5M3hqMkd4SXlUdjlnN3hoZXM3RHlUVDZWJTJGRFcwM292WUNBZGF0TWozN2MlMkZ4JTJGaDU4Qk41NTdDOCUyRk9HcTBLRXBYb3duM0NVQlllYnFFTmdJZXpaeEwzZSUyRjlqRjV5djJDRHdzdDBHaFBTQmFaQTNQenF0cW9vWUtRaTRvU0I0aTdJNU41UG1MdU0weUJzdTBpYndhZG4xY3B2NmJuWURKUDRHbWV5YWtQQkRweENubVNpdiUyRlNEZVI5V1o1YTlwTHklMkZJbUxQTkEzYzZ0RFluNzVpUnV6Q1J3bW10Sjg5QzlObU5JVFklMkJhJTJCU1JPaTdWa2lweklVYUsyNFpYZUc3cE5HVHJIZU91VWJCWjdPb3lwVyUyQnhyU1BMSDlHQ2RmbGYlMkZRJTJCZDdHUGQ3dDBCRzZPN21mZU0lMkJ2SUlFamMwVVQwUXZ1RTM5Q1BTWXQ5NXk4bUVrOFNsTlNSVE5zUG8zaUVuOXNCZUgyclE2YTlUN0pxTHBSb0xWWmZqdXNQYzNlWEYxSkFRNCUyRlJEOWhWQnlqODBXMVZGVWpNVWRKRlJaYjA1ZHklMkJrT1FZZEthRklLcHhKWUVMR1Nldjc1QlR0bFZsJTJCa0IxZzY0ODVnZzQzQXVoUlVmUDBoSnB1b21mUVM1VHp1JTJGRXg4OUUlMkJ3dGZKcGJBM3ZVVjFZVzIlMkZNdVVBU2llNkZIWjBCbWVCZzVDR2tBU0lwb3V6ZERmS1YyMDhlRTNjUVRib21aJTJGRmRMR2lzSkZpUXYzWHhnSnRLYmNteGJ3cW5UNndxa0dMbTFwQ0RjaUdUNUZ6VnFIMk9IM3Y1UUlaS29WdyUyQlJOelRyVEVDJTJGT28zU3Z5eTd0NjRsT0VIbHhpcFVNd3hsdmQlMkJnNFdqQTVRcEMlMkY3cG5TWVZVNVZWVXkxcU5reE5aVGlVYjNFQSUyRlJ6bzhTJTJCb3ZkM29kb3hHM1FyZGU5NzFTOVhZaiUyRjRuV1NTJTJCa3RteGlqWnhLODN4WDBReTRIQWhZVjhqRjFKWmxsUXJXME1lZDJOekp3JTJGcG41TiUyQjFwbjBvVXpYZm5EZGlzbDlCRnZ1Y1Zld0EwRlpaN0NPUXJwSG5YSFVIaTV2dU05SEJtS1NDQ2xYSXo5eDNKJTJGOWZPY0xTN1k0aE0lMkZ5OGJTeWhncE40VURwbEU4VHFQdjE2NiUyQkN5cm10dzI3dDdyVnU1UDQlMkJ2bDU0dVdGZ3pyU1VBV1dFUlRSWTIxMEFpYiUyQmJWOWtEYlFyV3ElMkJGTjhSNiUyRnBiMXNjcXJOVWhIY2JPT1ZYaDlrTWRiMnIxRzJoejNMeThTakVHTklhem4lMkJPZzI1NEdCN1FUODlUV2FJcTFoN0dkc3A4STduZnZ1Rm05M2prdEFIYmhleEJjQnhadiUyRkdLOERBbXRDMndUSGZmZkEwSkx5RG9GWTl4WmpuekFidWYlMkZCSUJiMzNMUjYzT0hOUiUyQlhOVzgyaHJLa0s1NjlUYUxoV0JQcTF5RlVySiUyRnlxcUlPVHZ2S05NOGdwZHV3WEJGYTlVUGtnTW01R1NSZElrMFhJTVBSM0hpY3Q2OTdTMFNBdnB3RnY5RERIYXZOMWxoODVWNlF5ZDNranFoTiUyRmJuakpYbEFiVjBqOHJ4M1F1WVVVVndzUGt2UjY0bTclMkJzSWpsTDZYR1Q5elQydVk4Q1NVZFBIR3NoMHlteFBXbmNGdEh6NkclMkIlMkJEdkwlMkY3RkNPYTd1NlhXSlA3M2hQSDhkYk9GdnhpcjRxcDg5bkI0TWFNajhLbVVKdUd1QVpGVERlRWIweDZDQjdlU2tSbiUyQkEydHpLekhlMUpxY2l6dFpWWDJWRmZmOVM3bXpGMUFLQjJoZGV4c3gwTUJ3cVJvbHZOaEZocWh3a2VVRXZxcUVwZk9NNWZuOVVSR3diQ2Y2SnVUYzBmQjZGeE1pMSUyQjhtSnd6SDdvME5pcWFBaXI4M1ltVlo2MUw4enVMNk1QS25SS1dkem9JT1dibDNxNkdBY250JTJGZkV0WCUyRk9FY3NOUjJQVTU4TTBnQ2VSZWJqUEZwbnZyVHdOeXFyMTE3S216bmFlWDFlMGZsOGE5OWdFNWJTY3NRT3VzZXZsMGZ0Qzc4WXhESG5JRjlIMWZ6OUZiMjlubFBNV1ZEJTJCVDhyclR0aXdjeTRHdmtQbzNKT0I5MlRYRWhpJTJGRG9CcjdaRW41T1VRTUN2RUlLV2EzaG5mVHpIYlJ1elYwRzJtaG04NWNBR3BxTE1SY0ExUDAzQU04QWdPMjJSbzh1V3lYJTJCckJnJTJGQ3hHZnUxc2pFTSUyQnpMNUtBeDB0NHNYUFRYSHRFbkx6RFNuZG92U0dYS1o0MmNkNFM4ZXJGRkZPZ3IzVk5tRGZ3Q3JIZUYlMkJsTlNjZ0clMkZsc0glMkZucFhqa0EzaFB5WWFibno1aGY0ZFQ5WDZwRFoxNUhZbW9ab3Y3Zm9LaFdmektDOFJKbllCNm4xdVJNQmx0clE0dHBsJTJGb2Y0WDZOVkw1N0c3UDdNWHIySFRxNE10bDlzdkVVaDJCczJoODZiZXhITlUzJTJGUmJXRjFNNzJHdXclMkZUSG1zbHN2TVQzc1hWcFBDYk5CQnNsbU5DU0k2OEo1YUMwQldROW1pTFgwOTBQNEVqMU5KWjIlMkZuekNHMnF4RHJab3F5ck9mJTJGaVNpZ0VrbCUyQkpjTUNYOGRTeDlQZEdWd3pmSkVRRHRHbHFHMGtmS0x1UzhUJTJGbGhNMG43bVhaWVdWaDdQSkVsdW5OJTJCOWNXWlFCRFVGa2c3dFFYSDV1MW5CJTJGaDhNR1NXUXpzUSUyRmRaWk9ha3ljUzd3Q2IyaDEzckd3OVZBZWclMkJRelJiSDJ5Yk1ZMDZtS3lmajRLU3lXanh0YWcwYzdQWEhRQVNKJTJCTmVPOEVXJTJCb3hpNm94NXQlMkZsZkprVW8xTktGdzZuZlVlQmswVGtMS21ic3pYNktkZmxsMmV0THJ2UXh5QnU3enRRanJtTFRnT0M4UGNlNVYydSUyQjRkJTJCN1VZeWFjZHNGYnNxaThLSmVlTUw5emFwWFJZcmxYNkhmMCUyQnVOckFsb1BqdWNxQXRNUyUyQm5vanBLMjlvTlk4d0dzdnZkdlZRVHRWZW50T3VOSGRENlBveEdpMUZVTERqbGJMTDBta3hMa3dEeVhzU1hscU1vNHRqWDlya2pVSVolMkZnWlUlMkY2WmhHaHJjb2JzRmNESG50NnpyemhoV281U2VwZ0hwMGhTb3V5TXlSS1Y1UmRnN3FkSnZWd0s5U1FRT1JJNkFhVDZISmcyWHYlMkZRVEMlMkZQaVYlMkZqZEx6WmYyRiUyQjVkOTA0NVklMkZjOVJVM0Z2b3ZDJTJGSGNGbjYlMkJkdXIzSlVIJTJCZHd6YWpQclVPVTVmaUNYJTJGQmJMb0ZxOGhvcUlldGFwaDRUczYzcWVaYVdNdHRHa00lMkZiR21QaGQ2bCUyRjY1SUtyQ1lRZmFtTUtXNjJaNFZ3eVBXMno4MjV3dnR3MXNtQ0ZrSThRZWJrT3psRm96aEs2cnlsdnFWNmwzN0pWU1ZETHp0ZVJxJTJCWEJkTkYlMkJqOTJ1bEZpR0NqdSUyRnIxTmYxc2VuV2ZEMzU0ZTQ0ZndCeENtY0xMU2pUWDh6bDY1VFZNYnlldUlZNXFCTHVhQ3haZVVSQnBkTHhwWlpxbTZoeXQ5Rng1Z25hWlJlWUxVSmRNbWFnUWI0a0lsTDF6eXlSOSUyRjRiMWlDbSUyQjl4ckNBNiUyRnJ2d1RtUUpzUVpaSmVRYVUlMkYybHE2c29KJTJGWU83Wmx4T2RBTWclMkJmQXk0dSUyRmtJWWh5JTJGem5EY0NlaDRlc2hrSUZuV3J2cHZPcGRreWt0aFRaJTJGQWpUOU4lMkZ5UlNmejE5YzhiTE1KRUMxbUlKMkxDdklmeWwxNWUxV21zN2E5S1dvbHE4ViUyQjlKbnRaSExtNVFlYXUlMkZ4SXZUSFpnMnhCRFR1OXg3JTJGaWFkYU9TJTJCaFdOVGI3NFdjQnRKSmVoZ1lmTHB6aE5OdUI4enF1ZXFlT2N2JTJGTW12MkduZiUyQlpla3E1MExQbFBCcGw1akprSjc2OGVPMFRmTmU4JTJGY3lrYlJHaXM0SnJzM2J1JTJCRzNKclRRaW5IUDg2Zmc3Sjd0dmJySjlhSE1NTGFpSDU5QndIa09PR0pvaG9kZEZTUFElMkY2JTJGbEslMkJreHFCMCUyRnZXRVlmcFlJZ29UbndrR05HYU13WmREcnVEdzVHYjJtbXUyTmYxdFhGaVBvcWl4U3htcCUyQlN2JTJCWXNlaGUlMkY2djFwYThsWGNkVU4zVmJlcm5oJTJGbXBSOFREaFg5TkhjbXZuTE9sOWNMQWpCZnFXdE0xVVJlSEIwcmxOVHJ6VnFVbUZETzZDQkhSTyUyQkpDenh0U2RQbXhqd3FTOXVCOUk0Zkt5a0Q2U0F3eU5PRTNOOEl4d3VrUHhqTkFJSkVDSFN5alZ4RGp2V0dZRHM2bU5yYnZHYTFoR3JJc0hPcW80MTZ0aTlGdHo1T1M3RHBuYjJLRTByRzBDMyUyRkdoZExxQ3JnRTVrcHBxZWJxRHEwWEZMJTJGREFxeGNXZGN2aSUyRjZvSFg3V2J5QzFDVTJwd0V1U2ptcnhoOENON0Y3JTJCcWlnNE5JYjdYOG9lazhNZFMlMkJ6eExFZDJTcmJsRmRtdUdiVTQlMkI3U1VNQkRXczl5U0tYJTJGbyUyRmtQMmZhZ2RNRDlPeWdWSTR3TCUyQlZBeXclMkJwTkxtSEp6JTJGNXpMY04lMkJQRiUyQlVIaUhYVXh4SER4eFdZUkdvZSUyQjhjSCUyQnAlMkZRUzJCZHhVRWdJc0J5aWxBVzBWR0JqY21YUSUyQlNtb2JxNjcxN0pMRUcxMWUlMkJneFBlZzdhU0lKNEolMkZad0RxSnJISm1GZ0FsendlU0lTckJYeThCMFlqd053RDdWSDk3amRtNkllSVpQd1BuZm5odyUyQldoRUxyeGgzQng5YkJsd015WlJwWEEwUUJHZlRiTXNDUDZ2b3JZc0NWNHU3Y0VTUjElMkZjU3FUbktSNGVRJTJGT3dOMm1zM05TanhnOUI5ekh5alhZeW0xeSUyQm4xQ21JTEpSRkczemJYd2lqME9zbjVrZFFDN29RbmdnR21XcnM5Mnp6WU15b0FDdEV4MEwzeW9ITHRxU1RLJTJCYnEyOVUyTVZmODJ1bHNncEZKTDN6MTJpejBmelhCN0tCMVRUbmVXaGU4eWs3Zk9hczQlMkJSWE5YOU42MU9xRHNyYjNZelVueDFLZGhkQnlKUG9TNHk5ZThYekN0MG1GRVhIbHkxUDFrSWF3UzZxbXIyOHIxVEM4amclMkY1cTZEM2k5YVNGSkF4T2t0Z0JDRDRFUjRqMkd2ZHFCckJaVWtrcTV3U1h1MjdVanVGN1NuJTJGVGljeCUyRndDTzlsSEVUQmZaSnlDc1NwU24zVnZYeDQlMkZ3TE9jdkg0MkM5dklCZVpjdEQ3alZSSzM5NjV0U0FPM0VlNkJCYkdrcGU4JTJGNmxxVDRQYk5JVW5nJTJGaW9nUEpVREZWc1c2Um1BTDlOeG9UNUdpNFclMkJQenoxSnhSS2NPd3N3aDhBaGJvTmNqODVXRnI3WXFEMmslMkZRWFF0bVl4MHNzQktqMG42V3lQOGVrJTJGZ2t4aHpjRWV0a3BZT1I1ZE9qTEJRajhqY2RoNHdlR09pREpHOW5SSndRbWVGbjFLUlhPQlphc2xrUWtkUSUyRnhUYXA0VUUxb3clMkY5ZjBhYktUZU5yRExUJTJGWjlhT2FWZTRkVjUlMkJmTWFPaGFSWUF6RU1pS0gzOW9QQUZsaTAlMkZjS1p1RiUyRlNVZWFBMGtLUW9GYkFwYVYzZ3N5U1NLTVJEZ29YR3M5WEh3RDZtOUlWeSUyQlh6MDNxSDFPdnBpenZXb2tyeEViSzRlUGdhNUhrbTd5JTJGUnduY2V3SDdqdzFJUk4xJTJCVGZqSzU4VFE5N051cXl4VjZIJTJGdnVqOWJSMU1RQWJ2a2J1RVB2MktDTEElMkZPMEZUYzElMkJDNEJiYXczZG9MVk8xYUk4NGVUZWtGMHRjMSUyQlROcTVnQUNURlhybjhQeXUyenc5OWZEekpXWCUyQnE3TVZGOWk2N3o2b0RDenNLVExiTXc2UVNQN01CN3lvdFV1cFhCb3ElMkZxJTJCJTJGZWdVVFhtNE1WY0daQmFVVHVaSldUZWFyUm93dHBiM3AwdWtVS3IwbG1MQ200TzdOblNraFBsWiUyQiUyQmFTZGhqa2RVeHFDNHhlMVo2cGclMkJJck5XcEhXZTdqeExOU2JCMXF1eThSYVJMRE1nb08zb0VCV0dBWUJCeWlmJTJCNzFZbWRKJTJGNjlENngwS2QxR0RFWFFQemRWSyUyRlMxUzJQN2w3U2tpMCUyRnduYTZZUkU2RyUyQkN2SzBva3dXRkklMkZidHV6dlF0aiUyRm5iMXg3ZGd5WUdGVnFlZnZ2eERuODlIJTJGakRLbHhjOEclMkZxaHJVckdEODVCWTVjVDIlMkI2RCUyRlRKNFhEaWNBRzlLWUxGM29ZWDBnR2gyJTJGakcycXZNJTJCMEMlMkYyTmdsN0pqJTJCR2QyNHd6aVFLWThlUkF5QUdjZ1R4T0wzN1JZMVBabVhjMXdabDVBWEpQYk5ybnN4TGkydXNSc2FpaUlxS3hDYWtFMUVHTnhJaDVoJTJCVTNSRlZiUG1rWnk3aVFNaCUyQjluek1uT0xnWjNhNkkxbGttWDV1M0Z2djNoazZuVWgzRjd4dWt0YjZOY1FUdWpZNjNJQWRmMm8yU0YlMkZaN1dLVkV4SXdFOUxaTWpGcGp2N0Jmam9ieDZlQSUyQnZnZE0zOHBsd21Wazh5RmxhVGpyQkZFeSUyQnZCeWI0dFQ3aHFGJTJGNWV5cHNsNUF5eWNTVnVxaWZJeWwwJTJCSlBHNnlhUTJCZWpsTk1jbURjRTFPb2w3ZjQ1elBuVGhFR1QydDdoSmlPc040c1FtOTdjWW1QZWU1RHlJaG01RWRPYTdaeldmaU41TXhDbml3RHNEcTR1Nm5uZHl1T2VnbDFhSzZVak5CelNVWm9ZbFpTOXJGQm9TZTYxSFVGJTJGJTJGUUkxeEk0MnNocXJoVkJFMktUNTVlQUI1VnZSYUFxYzh3dnRCZHh4RFZqTjlCVkFlWjFXN0Q5Smo5akNwelhIeW1pQWJjcTRYa3gwWWxlMjRBNENQbkxyVVNjakJKcTdaR2V3bnh5V3hlaDNOaEYzRGpYQkRwcnpLS3R3R0F2RHJDOVk3TCUyQnY5MUhJY3lKSXlPQWZMJTJGaEpvNUVNOTYzR0VLdkJSTVRjUThnMk8zNTZtTzV0UHJxMkVsSHlRdFdQdWxwZGFyUU9QQ3RSbmdZeGlsa1lHTWxwaEVzdWxkSU5qWiUyRmtCUGFDQkI4TW9oU21ubnBRWGFWeDRhSThjYlpZS2V6MWRyWFpZVFpxZkVYVXIlMkJWRFlrOVNmVEJHSlE3RmpjSnVhdGdjaXZhOXZta1dEMiUyRktFSyUyQllYTVd6a0Y1bEp3M0NmOWFUM21XUTJXbXp0b3U2aFZLSG9mVDhPbWhUd1hIejlEVXFheSUyQlgzamZTRXF4NVJ2JTJCJTJGT2ZSaHJHSzZwYnNHVlNjUVR5VEgxVkZqUXgxMTlhQyUyQm4lMkJKeVNCRU5EVGNkZnB6d0xmNnl0M0hPb1NlUFN5eWNNZ2ZienhDR1dsMks0aHhWVWNqbVpxZ1B2cm1DS3NBMmh3bjhoMzExZGpoSkJVUk1aT05jaGFETkEyJTJCWnhNODFDUVFZcklVcWpFNnZFSWdoZTdqOWNCTldybEZFTmxFREtSdlBWaG5ScWZXV0VCMFBReW14N0QlMkYlMkJhQ0tyWVg3Z2JNZHloRDRqJTJCc2lDUnZXZXJucVF0MWJHYUdMWnloMDRTWEk1bWJDZ2s0ckF3WXJ4UENsSWdnakdjTUg5VnZIUWo0ZnZ0QVlXMVN6ZXNWQUM5U3B6MFhPcW1zbXZNRXdtM3lQbXduOWNJUnE5WGptVmFyeSUyQnolMkJZNjNkRU9mazdweDVid245dXhDTFZDMDdjTmRkUG56Q1FvQURvelRESTRpZSUyQnhsM2klMkZTRSUyRjQ3cFdYeExOYmQlMkZQRk5IUk40UjM5bDJxS1JiQVdkR242aUZnd0RLcGJ5allha3NLQm00eiUyRkxLJTJGSmUzYzJQZFY3MG84T0xxdkVWbGhocUU2OHdPc2Y4ajZuM1dKSlVhYm9BbiUyQmJmanFGSmxtaXROVHUwMXBxbkg2aSUyQjM5aVVkWFZsQWtrQ0VlRiUyQmpzczlsSEhDaVRDdmlWcW9nekZuMmtWd3RmSUE0QjI1T2EzYWZxODNmTWdQR1hlUmVqbVpjeklnWEltN1FacjdQeTBSTTVKeFhFQmhDeXJLeUtNOVVleU5jc1JwMzNqT2hzd3k0dkt2clBkRmU0aDN2dm1EWWRVU3FTMlpRbjhuaU14WEhIZll6JTJGbHJKUVM5TE9aanRXaFJFWGttS2ZzOCUyQklHbnc5NnFRY2g1QSUyQk1HcUpCcFlCZDYxeSUyRnJGdGwyMXZUJTJCMVlrOFFqNlNUWXE2QUJ1S1dqdTZyeFpLUyUyRm0lMkIzaHEybU9Rd1Y0JTJGMGJkVjM2YWJadWpISmU0OTlEJTJGMlRJN2cxNVJEQ3c0Z2gzRXp5cCUyQmRiWmZQOUxPS0JHcjlSOGgwRXJHbEg0cVRSajQyblNuWVRVTktCM3hzSVViVzElMkZLS1BXdU1JWDhhdllWMDZOU2thZkVwMVdENmxaaSUyQnc5QkVOOUdMaiUyQiUyRmxDSEZGcVBCU0ZtRjd1bXE3R2VBJTJCazlpeUtqVzd5eXJnZ0NBUW5BcU1TY3dJRlJxUzh0eEpKYmUzcUpkR3NwRWxqbFZtRXZjWmFXS004QUpTcFdub2hLWDF3dTdkJTJCczRsSmxHSFVBJTJGSExSdmpENHJ5UTJ3alFNaGZqOTZ1MFUyQnZpM2FLbkZqMGZMdHJBalJDcyUyQnJ2MHhWVmVpZVUlMkZ1YmhvUTgyYW9ORU85SFhQZU5KZnloZXV2aVJqS3lYV1VidncwYW5icDAxZGtsSGFPbEpsSG5ZUGFBb3YxQmV2WCUyQnZzMXpWTSUyRiUyRkhOMUplRUdDTTN1RDZWNW5zWGp3NFZRVHdOZ1FnMm0yJTJCTGtFekt2alp4MiUyRlhpV0R1akR2JTJCNnNzJTJGeFRsJTJCY1ZxRThVelVLQnpEcTFobzlnRzNjM3o0dGlKMjJSUlRWU0lSY1ZIYkp5S2xCalhJTUoxWHlGZGRKT282NHM4THBzck85dnpLUjlJSkxSQmZ2Y1Z2ZjFwT0tLUWZGJTJCYXVrbVRvU0VIOXpRV1FEODBmJTJCSGtwdDJBJTJCRTY5NDRmeUtpU1k0R2ZtUWRkaEhMdE5XdEZ4Wk02MTZtZTZncnBVWW02ZDFZJTJCVXB5V0xJVDM0a0luVUY0Mkx5Y1dNMHZkNnZaY2hhTE9TMkVGdkJFdkJEd2l0OE1jeFRDWkpHYTQlMkZIdzJBdDRGdGxkT2FvJTJGSm0lMkZsODRyTHRrJTJGRndOMWdqNkxwRFJBN1RNcGxxSng3JTJCbk5vY3lvem9icSUyRkxSajdWMGtaVFpPeHpzVzZ1NUVuNmthSFdRS05RRTRDR01BdjNzYVdDRXp5alN0dXptaTY5ejRpVVlkemZzdmx4ZTZjUDJkTWRWb3hpJTJGU21sVE1XYldVcWl6bVJTUGlrM3lFYXV5RldtTHlWcFhPdGIwbjhhellhOXJXUk0lMkZFQWhRaG1IN1hpVWFBeE54cWFLYVE0UGdNUzR4OW9FNHYlMkY3UWtyWkN4Z0JGYWI3T1VHQnZ2MmlXdGV5JTJGUENiZGRrY25OOEVQcjg1cVRqY2hyRW1VbnEzWXMzYyUyQktzMlNXM1V5RHllT0xTQ1c0WklFbnRYZU9vSXYlMkJIRXpEcTZxamxMYWVuZUhUeWp6alZXelYlMkI2Z0J2YmRCVlhjTmpQUURyUW9VQ0xXaWZIczRMZnlWTklpOXVuNElVeHglMkZLdCUyQkxkaDJjTkNOTUFLODN5RVA2QnEzYnZQaXZVTWFaWktPS3VSVEpjcEFoZnFZU1Brc2JONkVOZFozeDBNbGRiR0x5TUl5bW0lMkJIYmtCdCUyRkZWZklMSjlKcjM1azhwZ2hUQmZLUk1yV09mRXYzdW5ydWdBdDd4Uk11ZTRNWndGRGNLOTlwJTJGU2kxWmFJbk5VTDl2WiUyRjFtVzJsTVhudlZKZ1k5V0lyUmJ6bm9jbm92bUt4RzVJOFlZWWpieVVoUkl4NlloRERHNmVFMXYzaDkxU1Y2clZVVXdnTmRpdFVWSmFCJTJGRWFXYm5iaSUyQk9KTFVRQjhJdEphVXglMkJLVHglMkJHM1h2WUhoeVpjcjZkTUQ4NVl3RHR3Q3FMTFE2OThuOGpLZVhKbDJ5ZTNxWGRZdmQ3QzFSQk9yVE1Ld2lFYSUyQmwyeXl5T2NvczdlZEVYNUdYVXhiVzlINHoxQWZYSzdYWiUyQkNlcWQ1eXZ6ZVRIamIwZ0JnaHR1RnRZWjMwdlppVFo4VGNyeUtqYU5lcGxQaUhyRUkzemluVmVrJTJGMyUyQlJoa0liSHFta2hRUmM4UzI3TldLVWJmZTk3cmxiSVRXY3lCZm9CSHFlaFNRNXlUVDAlMkZkdiUyRnRKdEFjWFh4S2dVbkxzSmxuZE9EJTJGbWJQNjY0YW1URzM1VG55SE41R0g0eVg2RllneXhlZk1sd1M5eFFhWXppSmlMWlh4VGtYMjk2RlkwYjR6JTJCd0tVTkU0TXFGM29wVXM3aUoyRXZvajJIYmtXZjdkWHBYR01oWUtQQnElMkI1JTJGNHpmb2tERm1xSnkyZHZkUlg4VXIxeWxqUnRMNEh6SGIyeWlJSFhrZXJSdm9YZjNqek5ld2Z0JTJGNDRGT1RzMWw4MUNURXE1Yk4lMkJjUWFyVm9KSmVTOXR3S0FqU2QxWFhVWGVHSkdBZUljS3ElMkZjWVNuJTJCbzkzVHNpZGZFMkNwSlBxMU5zdm9pOVJLMTVYTHdzczRkUkY3U0w4MU5qWDAxJTJCMXRGczRUdjZ5NE84bDZzWSUyQnE4ODhwV09qMUUxWGFvdXJkUkJ1MDBIeDVWOGwwJTJGOUR1N1I1STNFUk5mN09xVlBUQTJOQmQlMkJBNEkwSFdlSlN5djI1RHFMcWtNdE5DNmZ6N2ZFcUgwZTRzMkxNdGZTc0x4TlNoMzI5c1hpWlIzaXpVTlFNWCUyRldySlNtbUxwaiUyRk9qV21LaDlCNW5TQ0RqVHFNVnFiekpXbUVSbkUlMkZLWDdEUmM1bDgxY0hNVmxlS2o4VFk4OTgzaE0lMkZUMEFHcEhNdzQwdkd1bGp5THlDbUVJTVNOMSUyRjlSc01zYnU0RzRJblhjQTc4bUhDVXNQYllkOSUyQmI3SFdlMEhLVVJXWFJZSnRJN3JjYm9oV2kzampZdmlnUDNuVnFYd1V4cUJhWHhITlVmQWdKcWtjNWpCQ0t5SEExUTdiYjBwcHQlMkZtYjczcmZ2WlpTenBTMjVIeW1weGNSMnFCODYlMkJKcE85bWJVMyUyRnBTY282Qm84JTJCV3RFS3FEN0luNnZuSzEwdVNJJTJGbTd6em91WmQ0aVpReFN5SktxZ1BmJTJCWEVSdmNwUkE4aGpQMFYyWTglMkIwOU9sMGdpWElkSGdXOE9DckVCczMxTmphOFFuRUhmJTJGVTV2N282eXV1UCUyQnlRMSUyRkxHYlBUM0h4NVF3djgyYW56c2JKV3NIT2NycFNwbGNYRjhLd0pyYU1CaFAlMkJ1ZGgwQ1NkZ1dMbkVXVmtMQTFDWEZSUm9qTTRGZVVZTjJ1QSUyRjBUTEhGTTNHUTdpZElPclRpRWg1RkZnYXFhNVdUWDlGdVFGRWslMkZRcGVwVlQ1ek1iUzZmWkhJRVNMWnVJRHJ6T3NtU1NhZVBTMiUyQlBUUEtYemRzb3Z3VVNhSWxocUZ3d1hkT1hkVW9xdXVLbEZKekFJWSUyRmxrWmFoa21HU3I2ZXRzMFd6dnRidHU4c3JKJTJCeDRtUWNpa0EzSEtYSGY0Z3VzMUxscXBOMkpuV3FuNDIwR2NMczFZJTJGZXhKSVRIVFZjclIlMkJWZHNJQSUyRlNMRXRPc1U2cXNvRFJ5eFhLREdGT3V5WGFHTGJXdHo4VFo2MlpnWHgwVFZVNTRpRlNjMG0zS2VlR0wwcWZXandHUCUyQk1tRHhhZjRIQ015MXNrcjFicndEYjhta1ZURDRTVVZLM1ZkTDl2M3plR0IyMUpXU21ybUJZOGcyOUpuTzZZTndVcGJsZlRtZTgycWVuUHpURWc1UWw0RnA3OGNISldDeVBZZzZYU0M0anhVYzZvMmh0QVFlRXE1MXA3JTJCJTJGZGlWSU0lMkJ3VVozS1Z4bzN0WDNoVTVLS0ZkR20lMkZ2RXQ4NG93cmx2TUwyMnhHR0NsOFAyVFlzSDljZ0RUOHF5OWpqdHFteG1RYVFjYWFrRlNQakdDY1VaSTl0JTJCWlRzZkdHcERwMGhyNTB4eGhPWmNNM3hWUSUyQmZ1Q20wSUtVREZUb21JOHhON240bEowM0xETlpQc2syc3hqVmgyMUJ3MlZGNSUyQkFFRmFEVGVrNkY3eklCSEZmUWtpdkFSUUNVd3hPTE04WkROSUNPZCUyRnE5a3BLb2ZjQ2N6WDBJVlg5d0JsaTJtRk4zcks4em1iZlJYUmh5M1BmJTJCeSUyRndpWUZHaWVrekpqJTJGYURNYlpOSUlZYWNTNlRjcDk1bVFNT2ljeXl3b0hucXE0SFNzYzNKbW5YOXQlMkZOVGprbWNGaWxjS1lGJTJGV0d3cnJHWlhzdzdvUmRMNjBSMXlPU2tFTzNOWWdrdEREdXBVd1E3TnFBeXBadVNKRm5QekNwa1dHN3Z2YzdIVE8zMzlOcTBPd1gyOURiOUJIbnFpQWd3V2JtSDRXZ1FsMUVxTldRMVVlUlpQNHN4eElLdTBnYzBnQTZ1cEVWQjZkVzRPcXVYdmFvdFQlMkZ6a0hrUDYxc2t1MjQlMkJvTHAxRUV6UHd5Z3pRV1ZsM3p0aWFVRGMwaWFGdkNReDNVSVJNUzVhUFRreDZCeWE3OHY5UlQ4bHlhNWZLSjNucEdHcHklMkZibTByamNVcDl4dUR4dDRzOUNJUkI3YVg5RXV0R0I5Y002TEJ2OHFGYVhiSXJaV2RDYVdkcXpOamxmYzJJQzgzU2ZadCUyQjBRbHBjc3A3ejRmT25tYUxjbSUyRlBMWllsSjVScEQxbHE5RDBBU2dqa3pGdUt3Y1MlMkZFckRSTmpoMDRGS0dYTzFlZ0ppVDNMcUhIWmJ0RjkzVjBkY3BaemRDeXhIUm1YSVdUc1dydEpjUlJ4STBZTDc3NmZPY3E2a1diRlklMkZkazQ4NFFoclgxUXZaZnBhSU1XT2VnJTJGUTR6JTJCbHNtJTJGc2lZVVJ0UExvaTBiWER0bVU1bjIyc3diQ1FKJTJCSXNyTDElMkJTNVpOemhubmdRNUEyblJKaGtWTkxGUmF2RVJEMkJHREclMkJzYnNXWXFkclhiQ2wlMkZwYjN5VEtseFp4NSUyQjl5eFpCZEJDallTJTJCJTJGM0FlU25RVkRwbmNrYjNYVExNT3g1SnpObXRkRXNYckRLVXBTMHR1WE1KS1BGVlNicFBJVk1wZFhaNU5oS0olMkY1OHE3M2czMUJ4aXdpYXlENTZMMVhpRUtTdGIlMkJSZGdsemtwcHB2UzVJVWRteUo0NlMlMkJqWDIxaGc1TzV1NDB2TnFUSiUyQll0YUE5THBib2FjSEt4SGZlekNvUyUyRnZTQUNJdmp0JTJGMDAyTnFMZ2ZtaVlJa3YwUGF6MlNibFVpVFRBJTJCbG9uVEkzUnBXbVFEaFpGUXp1akFTZEN6QUpYd2RwZEtleUpDbUklMkY2a1E4d01mamdMUUZPbzNlMHlIOU9ZOGF6cEMyUWFGQXlvNUFzZERoWmtyQkxqMzVxTiUyQiUyRnVLTHdycmlvc0lYZnRXQWw2YjJMU2tabWRFdVZib25LSTFmbExsJTJCa1pUa0FuWnJ5MHM5NWFyN0h2QVolMkZUbkFkR3hSSWVYeW9TS1JRNDlmM2NiQk5mQXU5MVdqJTJCOXhPYnBkd050aGRxQW54ckpCRG1QSWlvT1V6OXhPTDVSYnNEOVZ3VXlERnpOOFprOXdBWHhjY01WblV0QmRUdjVkRGpxbkF1eCUyQiUyQjVBV0xnUUJUU2JsMFRIOFpXbjBMb1NDcWVQJTJGaGpSVHE2NSUyRkQlMkZIamdxUGQ5RUViU2xEZ1Y5U3gyUkFtcEJFVnd2Ym1PR2hFZ2xxdHFUUFZwMXVWMkRDWDNJYlZBMFA2c0NjbTNGS01XZW9Fb2RiOFlyU0J3NVo3NnhOcTJyNmRwN3dEViUyQmdGSzF5b25XUDA4OW9uUGVncFB6VXlvdTMydzVFQ2RrViUyRncwMTlMUm9oeCUyRkNTTVRqYjRTaFJRRGVDcUMwQ29ZT0NQZGQ0SnQ1N09KdENkYk05d2NsWVFDN2xUaXRuS3Z5S1dLb2I2N3BpRWxEeVloZllwSWg4emJ1Z2loS3JUUEJpa3lDcUhDREd2RThRTDQ1U2ZkTENYUEFleGglMkJaVjg1TktlUmYxU08wWXFMM2M1eHR1MVViSkx3R0FXNkZHc1NPc0huTUlWSzc1aEpyV21pRE5oYkpNQkZBbDMyclB6eEpNcVglMkJKVnJwRm9UalUwOWRERW8xcHdVa3hDbzJZMEolMkJNMzJJemk4clN1WUYzb2VBalI2NWk1VE1YWTNlQnFRVHVOWlFpUTlPbmJYT2YwVEwxUlJOMHpVYkw2ZnM1R2Vya2tTQiUyRkI5cXRIcUNtdmxhWXhzblcwWmhubWhFSlVIY01sS1pHSiUyQiUyQlJ1JTJCSjhxMkZiR1BFS3J2aTZRRGlHSVF1MmM2S3B2eXB5THpzdkFtTmNtOXRFVllCJTJCV1IlMkJDTVRXMUlWQ3Q3amxlamI4aVdYMXpjbjliOThBQ2dpVHpWeTBuRnF6OTB0SGhLOVJLdmRoZUMzdjA1Q3d4NXU2JTJCa1JFV285VlVmRGpmRUJ1QnRDUHF6NjJGNHVVZHYzeU16RTZRTVdQSTUwU2YlMkY3Q3E0Vk9qQ1ZOclZmRDRIVEZXdTAwQWV5WDU5T29SZ2I1JTJGM0NaMiUyQmEzJTJCZHBKR1NqOFZYdDZZTlphR2s3RFprMUQ3MkpLUVZIcVZ3bmVCQ1VwZWlFVXVSYXNYa3MzRUJNVExEeFQzWFo4WFJhS0oyQXBZT1pnJTJCMjdBOEdlcFFvUWFaJTJGYXRmYmZCZGg5cUt5MG84Z1lMTjhCY05EN1B6T1FuSTZTTnAlMkZEWEJrYnh3Zjlra1lLcTltZFlhY29PdGt5WmRjVkRqVFRvVWNvM3hOM2R2cDlWSkZZUGZNUXI1aUN5TWw5TjlSUCUyQnJ1VVZhbEp2N2tHMUVPYzJ3QWpFNG8ycEt6TWFuZThUSTZRYThNa0VqVG5lbTlCaiUyQkFhSGFrSUxtQmtubWklMkZLb2Q1VmttczglMkJCSHJVOVZFRCUyQm5hR1IlMkZWd1g3UTRVcjhqS2s2WWxIOHZycEVFbmxrcEt4UmMlMkJ5RVljcWo2WWduZmdkUjBHa0lndHVldlo2QjdjczdQazY3NFJLSjF5ciUyRmxIdUhud2U1VU53VEhWY1lMaDVCR2l0SlNuY0pUVVFIM1gyJTJCQ0k2MFhyVUkzVjBLenpCN0l4YkgzWkpTTDdCcWwxVGhHOTIlMkZHdjRUQnlEb2hLRll5ZmlOejNwS3lMNmZNdkZIcVlwYUtyVGVidCUyQmRsMXpxWmdQJTJGNmg2dyUyQjJDNlE5OHFTZXFHU0VOJTJCY0Nsd1ZVZmNmNkNxVEhGWiUyRlR2JTJCOHFFVmpLTVZyYW5XZ3JYU2RMbDV6RmRyMlNGeGZXUW1hbXNkblRPY2ZiU1hVV1B1QSUyQnZETUtvTnV6a2QxajBaSXBBTCUyRlMzWXlpaU9BNk8wQjglMkZ4bzFPbkVLSlhyT1J0clIlMkZyMzEyUUZ3WVJWQXhBVTBzUjVuZThDZUxLYVZENE5zeFZoWFZ5dzhSVCUyQm5PUHg4bmxsdHIyRSUyRnN0bXhNanUwRTZIM01zNDVsMVFHb3JLQVd6M3A4MiUyRjVCQmJEVVdna1JFbXRCaURLQUgwOWNEajRPUG40JTJCOUJWYVJIdFdEQVdnMzd5VHZoR01vQVhNTFBOZkhXaWZLZnZLRFN5dmprUnNHS2Vjb2YzNjY2UndHSlJ2NUNFemxpTnA0Wkl0TUoyQzhsYjljWno2YURoVk1ZNVR6MGk5TEVnOEdCV3p5SkU1dmZNTmVsUHZwTmZYMEElMkJyMWRzJTJCS09WaEM5czRMUE0wUWJOam80TXZTM3hFVVE0NUZTd1NCa0FxbFF4TFd6dnl6aCUyRjc2OFVEYm05RXpHbEFwTm5GOXlQZDQ0eW10Vk1XT05GWU5aWkFETlZVTUFMTXR0Y0R3NE9aJTJCZTNyVGVMZ2I5aFBEUU1hJTJGJTJCZ1Q0JTJGZEZqRDFzZk1TMFMxRiUyQmpuekFwbE4wWjhnbjBWTWklMkJITFlmUUI3ZXdxZEY4JTJCVXhLQnA0QTdkQkxXRkhXJTJCc1BYQ1U1aVQyWkRGWUx2ejJmTG5nTmtnWXdUcnlMUnBQeld2ZjhpQ3pleVhmVWxLUlpBN2VFQnVaVHNleTVReTVJcDhYbWRzQVI1SWpWJTJGSHR3bkJvaEphZmhlcFZpQjh0akcweGVtcWpXUXlwb2ZCZXFCYSUyQnNZUXl6JTJGTXV3dmJNVk1PbDduJTJCbXhrcHZTdkp2Y1g5T2trd3JGYm5iYjlOYlk2TEZOUHZySnZGTHVEdUtROEglMkJxYWVrWUVsOUMzRDUlMkJINWJaYmx5aHQwcnd6NnQ5V3lyU2VBc0RpUFBZenIlMkZyelFka0ZBVDklMkYlMkJKSUFzQnJOZVZFTSUyRjJiOGZBUiUyRm9UQlVWdFNxa3lxeCUyRk1QV0lDZzk2ekkxOFNlR05jVXN1elFyejZ4N0FZVm5jYkxwbjN2NkdjcSUyRmN0a1JHQTNMWW50bEk5R1lFaWJIWTM4MnVkcHFJQVJ3cWY1OTRyRjhLTWdsSXFXSDU5TE5rRzFVRXhsc1JESlhHbkR1SDNrMUxsJTJCYzhUYmgzMHFodGl0REV1VEZ4cmclMkZuNDBCJTJCUjNOdENMZ1BjWWpzZkVvdUl2NWtoJTJGN002JTJCWGpOWiUyQjR0TnBkcENOVkdNUjNUd0llS0pZQVJ3UWxiJTJCNm91WiUyRlpkNlNnaTd2ciUyRkhTSUR5d2tzWWpnNzBnMXo0a25SNnRneSUyQjNubldxNHZrVlZNUmJFM2xHd0FHVnZqVjJwQzVzYSUyQktvNkRNRlJNdmdDTW9iOHBSaVBqYWRodnFDejJhM3k0TU4xWm8wcVUlMkJPZ2VaOGQ4dVN1YmJBWnFjM0dLMG1ycG0lMkZmZnZzYlVMd1BuJTJGMWVPTVVLRFFNeFFiRnNONWZuM3dCZFowNWZ0UndYVWxqS1FpRHFFT2labnEwQnpPQXY5eDZLdjBlV0hPenpBUVdBb0s0REQ0YkJoZEZHRTJEUmlwOVZwWFB1QWhhd3ZVa2JDNFpqdzBKWjhrNnBFS0NiRzBmR1U1TE1yNlJBZGx2dGpGR2FlJTJGQjQ3M09QbzJ3T2lvQXhiY0k4bDl3czdZWHVNNFJwT21Cd2xWcSUyQm5aUDJDblo0TUgwWW9qJTJCN25VdzBsc01xTWFvcHlscmNLVkRDTmE4NCUyRmFSWHhHVllNeVpIMVlBVTlQQTJSWmRGVnRzQ285ZHpGUFlBVkFUdDg3T1JWbVFVckhkSXUxWkslMkJWeGU3SHllSmRhZ3IzQyUyRjRORGtmMlg0WnQ5eGlXV1oxTUo1dERCMDRXV1FiN0JCeGF0S3F2QzVhZnJGZm1ZNEp3Tmp1VSUyRkpGJTJGWDR1NFA3JTJGcEQ5c3lkUE9BWCUyRmlCZFhzWXpQSTE2dkg3JTJGeGU4UUJRM3RYSXdBbTJxMmY5MUNZSDVvMWg0bGxLWUpEJTJGRU1RcVY4bFU1U1QxdHhic1g4NGtjY0V4REREMHRUQXFXZmpwZUdBJTJGOFY3a3JNVW9FZHVhJTJGeUdOUlo4bEJFT2M0aXBablBzMUlDd20lMkZGaWF3QkVleSUyQjJUQUI1UWVKeWlYemVnJTJGelBmUXVpcWU1S0tEREthQnVtbm5MT3oyRVB4VDhGekI1MjRVZk53UnVEV256T014WkxoWnpYNmV6VzNmRm1Db1lCaEdIOVRGTXVVRWRzNTVZJTJCRHA1MjFVcjJnVnZpVHRET1FMdVNRdFE2M2dQVUVMWGV2Q2NtSUp4ck9MJTJCMXdhUXM1JTJCa0NQcmJ1NlRsRWdmMWkwQ2hZcVMxMTgzYiUyQkh1dUUwNklWRjhXd3IlMkJPc0dNRTlGSWdpaEF4RUtLVlAzJTJGZDVFaWpGNSUyQmQlMkJWT1RsJTJGa0RPMk0ybWdvbDhGJTJGT3kwT1kyZk1YVG1LTW5UdjIyaEN5a3FSUDZDdm1jWGVZZEYlMkJaUTkyeTVaOXJNQyUyQkR4YWlSRUglMkZYWjhpamdpMjB1TGJJbGRvV2RlVUxFckNNbWNyU1loTFoyekhDWnFPbDVJUGl5SiUyRlpJa3g3bkwyZ2dCaDgwSXRGaHNuczhLdTQyYU8yYzE5SVhrcVVORG55T285YXNvbzlXZmc3U1pMMCUyRkVUVUxWZ3lSbG5ZUEFtJTJGN3E5RTIyaUxoSEx5VUhVelNmdFh5WGxyMTlRQncxUCUyRmNudDZmOURtUFA2dGFQU3JBU2MzVTYlMkZvSVgyY3JqbUk1dU5sSzBlMnlsS2t0ZERNaVZNOVdiZ29PbWE0SXp1bkN6RFdPaTVuV1NPMjNtcmtPbGRRcGt5WWwlMkJ6MFh0TmZIRnFIR29kUXo5MzNCblBVaWI4eHpzcjN6NGhpT0x1RHJkak1XeDdqaXpsWDlGenBzTmFrNiUyQjBxZ3BsTWIwclNRMlFjT2RnQnE3YzFEVW5mVVg3dzZFMERSWiUyRlV1QXhuSk9GNXpBVmkxNyUyQlFzMHZCSW1hanR0MUVPOVUlMkY4NDVRaWZCQmlJUTFvVlRzY3l4TTFKMyUyQlFnWjdWc1IlMkZ1bTJmQmc2WW85T0tLRk5MJTJGVHNsdVhqJTJGWkJBV0dLSGsxQkMlMkZZMll4YnFJdnRHbm90ZVhoc0NVaFZuZGc2NnVpQmZ4OHMzbmxuNCUyRkVRUyUyRlA4ZjVTWTBaeHFSeE9wMDZsODZVNzBVd1FNVjdvbVIlMkZqc2o1Rkc3TTk5R1ZlY2xIaTZWejhvajhqWFVvJTJGcnRNY1p5dTBrYyUyQjg5OHF5VkpKRzFOQVVGRExQc05US2x6d0ZCblVMbDJtbEtwVm5VZUwlMkI1a2FUczNmRFk5cjFYWWU5TUFwajNVbTNEQmx3eFVzSUxjJTJCYk5XU2QxTzd6MERKU2QlMkZhbVc3UGM5VU5mVWlFQSUyQnJ5c0UxZmYweFJHWGhqWk5VcG5saU5Yb3BTQjVxSjdwMkRqWFE1MTNZZFcxeiUyRk5udkRaSkFFbTNiVjQ5aXY2NDlUS0lBSXdhNUEwa2FPNzVvY3ZtSUJPUHpDNXY4NXlITlhBbnpiRkNKeGJvS2N2TEtnWkZtUFBYbnp4NmUxSWFCc3RjdXo3SGxKS2FleG5CJTJGdDMxc2Ezdk1vTld6WmNtMllESTRXdWxCTHUzdiUyQjFkYlE5TEFLMVFNV0pvb0w5OHRHWFdCR2Mwb0clMkZOWHhZRGtBQWRWV2g1YU8xd2xQNVV4WHVqeDJQT3k4dm1EMmhhcHhHWEVuejB0UnBwcnFIcDFkSzQ3QUpJVHhYOHh3MmhCUk16QVZLNWx4eG0zbVBNajkxRWxTNkRlNmkwY01DejdxZFA2V1ZtVTFkbVNXTkZIWERUQlpneEVWc0tUeXVROXJ4dGEyR1ZDV1B1bnhLaTQlMkZ0WFZwU3pEM0ZLSGR6ZDJ0c1ZvSGh5NTBYQ2NldHJ3WjlSbTZRcFFSYm1QQ0wlMkJ1anhsYm51dU94M0tKNk51cUtCQ3JwMSUyRmM1QXQlMkZnenJzaWlGYnU4NGdTSlpzZVVDSzd3SXdxUjFGMEhxMFElMkJSd1dMMXp1U3hjOE42VHBLNkZXUTR0ZlA0akw4WU5PY2R2RThKNW9CMXhIWExjOGdvcFZiOE1Tc2RHZ3NGSXQ2MEVTdHh2S2lTJTJGMmdFandKV1A4TiUyRnZ3QU9yOGQwJTJCUEZTJTJCQU9RbVZ6dG1vMjZMNlNrJTJGRG5yT2hZSDZkNFNzRjhvZHdtTXV0TVUwZlpNUWRuJTJGWWdEMG45dXFJOGltVW81NDR0M3BtRDN4QXVvU1BuZXpSOE9aRUc3dyUyQkJXZEElMkZTR2tIQTYyMVBCeXZudU0wdThaRmJUcTZ0Q3N2WmpXa3VRRmxLTTJiSWk5b0s4b1NYNjFSYUczUk9DQ01JaXBVWVY1dzJGS0FVV2R4ZG43R1cwY1dXS1ozJTJCTWRPWENkRFo5JTJCMHNrbm1KS25hUkYlMkZRRkZiUHY4JTJCSEd2M3lKMER5eW1PQU1RTWNZJTJGM2puTk80JTJCN1FZRmZMUFduWXBzQ2RVRm9uUG5FVGhiYTE3dU0zdTYlMkZGOFJVYWJ5azE1JTJCRmdreEVzOUNKenElMkZKbjJJY1paYUpNZFlPRXJlY1pGdUlNd2FFWVVPbzhqZFFneW5pS2ppZmFwTlZsJTJGWDkzciUyRnlSMUdybTB4Tk1zOXBlaUJVbFdKNVg4bHJpJTJCMXFtdDJPUTFXbHc5bzByeiUyQmZtZGszeUZpcDkzZ0hJa25QbTloM0k0clpNN1BhY3dtV2pMSSUyQkpWTm9OSVcwTVdmT2RXaXpxQkNoS2ElMkZ6MVJNN1g2aWdocVpjYlFUQkp3MmY0ampWNEoxY1BkWlQ1ZmZkRXYyS3J4cDd2S3U2eEs5NUhkVFlmQ2JMcFMwcE1WbVBKQktJY3NUUnF6SGYlMkZUMG94JTJGM1l3RXRxRXNEeEIlMkJFeFRwVzJlbFAzSm1RNTAwQm9ONkJ2enBxSnUlMkJNWU4lMkY3R1JsR3owTERuS0xxUllDaGdQR2V1TFkzWmlHMVR0bUJxRzIlMkJxdSUyQiUyRmolMkZ4VWFSTDNpZGIwVUEwR2t3N3BiVE1XOERIaDduWUFXTHIyWXJId2UyTFppd3oyUUlOY2ZWWkQxUG5udiUyQnhyT3MwV3BXdEpMVyUyRjdLbWxHeTYlMkJRb0lqTGV5cmpTb3piSzRiVHlZU2p4ZEVuQmtHa3RONFJZYSUyRkR5OWpZaFl1UUJCNyUyRjZyYURLN3FWOElzZ2x1dGxsRk5LbDFSRldGWU05MSUyQjFCWXJmZ0o3bkY2MDFhaCUyQloyV2hNN0p1YVZzbm5RWE5JRldkMzhWZXlrSE4yVmtJc3lLNzBRYnUxT3JJczlJTDVYVmVyY2RIaXM1N3NCMkg4RmQ2aUNDanhRMjBtMWRsZ05Mdm1zN3d0QkJSJTJGVldndVBjek5COVl3YlBCYlRYeHFXSlRXWHBJWG4lMkJraDclMkZ3emd2MzhUUjJ1Q0dxM3FlZUJHcE1STXpaWHc2cjVxV3RhaVg3elh5eDhBb3NFTHZHR3RzbE4yTkRxN2Z4a0tVMnB1M1hCMW1SM3pKRXFLdllqek8wRyUyQlk3amd1dVpLZiUyQnBNUWxHTE5xWGFHUmt5MGxZZ0hhT2dXTlpISSUyRk9nemFrTDF0czB5OG5RalBRenlURXJhMTBNT1FMTm0wekdRaDVZJTJCdExkZUMxalRMODNtdGNDNzM3czd4dE10N0NrbjJtYzkweDNpUmt0V0k3VktGeEdxekRpRmVPUkZSayUyRnFpYUNaejg1aHhsNzRuVVpKWXQyOFpla3VFdzdsM3BmN1hzZXZPbzlwNHRPcHlyUiUyRll4ZkVIWXRFR25uZzZwNkxLbDlOUUI0blFuYSUyQlNPdCUyQk5WbVNUNEVweUZVRXJTTDBMb2FzOSUyQjExTUtXMko0NyUyRlk2VExPT0lSdnVJbGtNODZBQnhzNmVEZ2YyQWdBTnolMkJPdzRpUVpaRjJnakwlMkZubnRWdDlkZXozZVBXZzA5RSUyQlhGWXpPQWxFSkkyMGw5ZGZveW5zVmludUg0MXFLS2dNNEdpTiUyRlpLSnlLZVBsWFglMkJ5NUFUUkhmRCUyRnJZUzE2Q2t0Q3V2bkRQM2QyT05UMnJYcUhZaUc2RXQyRWhUREczWjQxQWFIbTJNUVpSN01vV0I2bnFtcEw5Y2tiRVYxeUI4cWRpYk9uOHRtNkxXV3RvdW11UngzbUhPdUpMaU0xTHlucjBtSDVhVUJXNldqVkxuUk1OdHdHUnFlTHVTaU1DallGaFNhd2pMUGc4QmtlMExwOU9OVTlJOVNRQmhzckxPMzF4MWJFOEVIQmZPdTVVclZMajdGRHNZTW5kdGpXMUw1VzRxNm9jZ1E2S3hOb3VaR0g2cTBySUhwJTJCUTRsQWZmSFNSZFJ3MUxpayUyRmc5VGhZY2FycnBMUElTNldvNnIyeTMybWo4VDNDRGNOSnpDOGJGd2sza2YxZ200QnlKclZYb1FTNDZWTmRrVkg5YlhBQ3klMkZpWXlDSE1aMllwTyUyRmhYRk05YldnQUkzJTJGRGNxMEhpR0laJTJCRnRMNlNuUmVEdmhSbXhVVHJmZlVmZXQlMkJGMTgxOXBqM0tvMzBLYm1aMSUyQjV0ZjFabldMdzRUMlhzSjhpWHRLZnpMbGFyMWhkNWo4VE5YY0JKVmw2WEFqRUxUQTRjWiUyQlFLUXNWOXR2YlAxTUZSd25DWDNBUmxLQWd4Mmh4JTJGM2hkZnhCWEpFRTNPUFNuZlN5M204aiUyQkRzM3E1Wjd0WjY0eWtjaFRCMTl3T3V1ZGZ3dGRXSHVGV3ZYdGpKRkxFVzBQSU52WkRlTlR1SUNJNyUyRlZuMURsb2UxaklIUEtDRjVIJTJCa0wlMkJ1cnIyRlRUNE5DSFdtUWVzdE9IRGpFSzB0ckxtNnRyYnZtcVp4OGpqTG81WnYwY2RQR3UlMkJKNVY1R2lJbkx6UnZUaGlkVm00ZjNxbjFObEFxazVrSkQlMkZydzI2SlUyYzFLc21zYVdQeXdqRlZIS04xYmVGcVdFalFlbG1hcEQ0MGR0QXlSclo3c2MzUFlPaFJqbFpJQ0ZyZjVSJTJCZWxNRzclMkJXemhQNVRYb2o2dU92YW55OTI2aG9maFRHUGkyTHRTV2dScjZnNlhzS1p4QjM0ZllMbU9iWU1lU1pvSmVSanpzVnpTQmpjVkt6Nk9xNkt3ZXI2N2oxSlVlcWp6VW9BMEdZclFiTWNUV1ZmVjdJM3ZhRHc4a0NydzZtN1poZyUyRjNwNUdwTDBTdFV2JTJGVldxRXI1RVhSYnZOUHl2RkFrc2JaQ1RHUFFLcW9EMVB0QnZHOUo3Tnl2cVJSVkpubE5IWDFNV24ySnN4RHdsb2VqWlBWclIwSm1sMFl1VDFKbXZXdkt6TlJ6UG5BZWkzMDVrVmhicDhxTjhzZWpoTHU5MEVNdkNZUTBmbzVWSFRQJTJCbWlYTG5jbGhWNjBEeHBtMkJraWVJbiUyQm5Wakt0MThnYkNsUWY3TkNXa2Q5VGV6R1RTQlNhUTlkbXd5VlF1NlZhVnFQbEwlMkY5MSUyRmxvU1dsJTJGczI2ZWhvSFZ6eTVYWlk3WFhad1lnUEJOdkpaRXVrb0tkTzVPaUtFRFc5NHRXelRBNWZRSUVLTXlSbzYlMkZXMVBVcnNpSmtPaWxvNmk5Tm51REtoTEFCSnhoYUxndU1wU2Y1WFpWWG15cTBhVHZYenBFSUU4NHFBQTljS3B1dyUyRjF3aFEwSmNQc3ZWWFdtbld6YnVNbHN6WEwzRk1jSE5NM0FDa29DQUtOZEQ5UWh4Mm90ZDV3WXBwWkx3azBhd1liR1FPSk5NWlZWUTdvVGNHczVxWmtadXJSTDRIbXNYV0l5cmx3ZTA0N0tKM05yT2l1cWh0MjhrWXVxM1R6JTJCRTMzeEV1dVhXYThYN2NYNURYQlZFbmdZR2YyalRQb3NoRW16eUdkVFdQcjlqVFd0Zm5HOXVieFVicXBXZTdnRmJmOVdRdSUyQkElMkJEaVFLd1Vha0VyYXdsNklkbzVTckpXRDZSeWFvenNKNGJRMGNlTU9UblBkUVlxUk1tUkZwYXMlMkJuUjFrQzklMkJPMkFqWUxtWVdqYjhhMmVQa0kyaEsyeUZ6NVBPR1BZSHNjZ2J0UU9WWEt2VU13N1NvelJIc0o0bVpiZkVLeWJxNzglMkZHbFo1c045VVVSQ1FNNUh1RUl0WDdSbXgzYnpPMnJSM0NpM1piUk9hVTVtbmdadEI5Z2d0M0kycE4lMkJmYWExQThuOTBaU2I5eUMxeFA5NTdKdnFBTjVxQW5mcFpKcXBCdklEWGhBVzhQdmVtJTJGS2hkJTJGblRzV3hqN004VHJWajNFJTJGVE9laXNicGp5eXl0dnkyV2Z1c3NTV3psWjlSY3owUjcyVE1nMTVZZFJzUExOY3RwNDlTUWRtbTFaZ3pjRyUyRkw2QTl4SXo4ZE9hRGN6WFE5MEU3SFNoZndyNnlKUnNjNnJZMUpkN012Y0ViWWpwM25zcHBVREJ1aWNNOTFmeEVWU2lpZWlXMlhqUTdtZnVkempMdGglMkZkV3NDJTJCVlIzUFZ6RnhYWnNNWnpFenIwc3ZkTG9XMUolMkZMZmt1dnhURzNwJTJGTWElMkJQc1RMeWtaVHA5TiUyQlZ2VjBhYlJ2VzZXZHlNeFV4eE9HNGJmaCUyQlAyS0dIcGN1cURpa1hQSjdpencyWVlmYWQ2ZUlLZEd2VVpHM1U2JTJGTlk4OWdXekFGSjlLNzZlUXVpVWZhZGM3VDF4M3k2Rk9lRmhNTm0zRWJrZ2VYVjN1cENDZU9XVktLcmUwRWMlMkZOWjBQanlrTExsWDU3MG9EdEZqbWlZNE9vc1ZlNW0zbmZlY2NyMElJSG9FUyUyQjVYOGxVR0hoMVJBYUNCTmNwNmFnY2h5OUd1bkpzQiUyQkMzT2ZTU0dWM1RaZldhbmdEdlluNW1hNVZwQWdrZmR5MUpiV3lTdDJzbmEwdE83dyUyQlFlbXVmNXQ5QiUyQlk5VzBXazJnV3RHMlpwZUhOMW5Qd3lLdmNuTXpXRVFsV2xnRWpZMENocyUyQlVlRUNmOEFCM0hrV1JjZUp2RXF2RzFNVURPNFRpT25QYnFsSDNmZzNGeXZ2WmxCeTFPR3U3SERjMEdaMXBRZlN6anlndUJSbXJUMGxkRUU5WGlybVZ1Q1dLN1RtZm43YXYlMkJhTlRkSUJoMng3ckowREdxMHB2RVJKeEhJZDF0WDMwM2NUYjlkMnl3QVZ0Q3lXR1o4QUtzYlQlMkI4V1ppRVVxZ1FIZTgyUWJodTU3c3J6RnNaZGF3dHJSYkw1MFAwb0dVbWFyY3VndCUyRldaaVNSdTRYSmlNb0tnanlQeG1uMUU3dWZheVRZYzd1SkhFdFhsWUFVcEZpbmhqaGQ4SDg5QUVkWTZGZUhUSWR0M1JZNzFSVk9GNk53YlQ1U2ElMkI3QTJVSW5sREJiOSUyRm5rSU1aMnRCczVRbHQ3ZE9iQXFvbmVObngzTm1Bc2k2T3l3RnR4VnhrZkk5VCUyRkJuOEE5dWxiQTZPOUhEOHpicm42c1hNazVQeHM2cEI2R29YcHN3NVglMkJWUzY2SnViJTJCbUozRk5XemtlaTBWcGdNYlJiWTdEd2lLSzZiTDRtOHA1V3F4T1lJWXMlMkZweG9ub2JhbmZhMDRTZGxlYll6cHRDWUMlMkZEcEZRUUJybHFTMDIySlolMkZFWGh6MVRocWZiUHhCcW5xbjJ2ZjFXa3NQS090Sms2SSUyQnNnbjE3MnREdW9QU2t5c1VGVXExJTJCTm91WllTRlRHcnhMSW1mVlQ2MSUyQnlLSmVna2Y4anBFek5YeTRVUU5LWmglMkZibE5sZFBuZUpYJTJGJTJGZ0tSJTJCeWxTV3N5T3l4VU5mM0l4d0R2S05yYXY4S0RoeHRiT0JNSVdFWjVhZnZyZDZZWVExSWZkWkdua3VvY0VkdzgwVzdoSG9rSjJnSUppeVNzMHozeTJpWGljMSUyQm1HWFBuMUlWZFQyazlpWUloT0tpJTJCdERsS0hMd2ZEJTJCWmc3eUdJN0x0YWp0cnNTek94UUpnaG5oUFolMkZRWUowb3d3VUlIUVJKNDduVXNLMTVweTh6bnJnVlAlMkZlazlOayUyRmRLNFhaMHFER3RmYU55WDNybTlwdFc4ZHBQYzFsUVo1OXJubjdWNVVPZGdMelBubyUyRmw4TlZaRSUyQk9pZkV2TFFKTjQ5QXliUVh4YSUyQmRiJTJGMW9XYjVURmclMkI3S2NLaW5JTk1IdVpDMHc1UVp3dGJodHUwbVdXQmhSV2FmZURBMmFFRUFjUUxLWTZ5Z1FvVmtYMjFHMnolMkJ2MmQ2VmVVUnMyclJXZ3h2em1mUzlBSkgyV1dmQ1ZFU1pPaklveWxFUG9pZGpqa25JalR6Zzl1MkZuOVh6U0pTenl5czROVnU2dHUyWGJsbmRzJTJGTXpaTDhYeEs0N1olMkJsUDRhM0NzZXF1ZnFqN3JOendYTUY1OFNDVyUyRk95VHNWYk9BWWUlMkZ3JTJGOERLRFNPYlNncHpmSkYlMkZrTDNMMjdZdHFITGpNSzVzME04N1h4bk00NVhGTjBydmlJWkpQM0pXWEVmanhSVzJMVTQ4NHAlMkIxNkg0NG5aMnM4NDFXZSUyRjYwTktsZFklMkZ2ZWdyY1ElMkZlRnA3aWZQQzdXbW96eDUlMkZEczElMkZhJTJCdU5PZSUyRnZNYktyejhuR2plNTROY2U1QVBIT2NpWHA0bk5IMFJKRWhzM08wNFZQVGxHMWZzR0kzJTJCdXZlQ0VPSzNMNWJFc1JDUGhiclBQSDBYS2xXaWVwZUJ6aGFuWUhMV3o2dWhaNTM3bmpBJTJGMFdxTFJiTktyayUyQjRnOFRueUFkWnA3ZjNTbHdHc0VqJTJGN3BzRHZMTTVsZ2hkR1BpMWYlMkZlY3c5MTJKMDA1VFYwZjlodHpTVjI5MVZPVGZOUE9lY2NkN0l4JTJCektWbHhEREJhT0pRVHFUaXlVODc2WHJiNWZOOW4xNFdoME9yOGhKaWZjM3lyRDkzOWVTRThpSnpaaEpWWTBHWTh1bllIdmR4Vm9JRWxXdGdZNm1pdVhIaFBKUGJMeFZMQXB2Qm85bUprYjlYMlNBYjZyanFGYlcxYlltNjczTE5BdDVGVmhEdGsxV0l6OSUyQjc4RVY3SW1jJTJCTGhiNmdjRDNyZFdUQmw4YjJucmF4TnRxMWRPR3JYSCUyRlA0NGpHaHNVU2hLclhEJTJCd1I5YnNNek1lRlBaMUtkQlpHJTJCVHM4S01vS3ZmYSUyRktOYUZOQzViQVY3dXNUM1pJOVBhZEQlMkJTRzQyRDJVbm4yTTBZdFhMczdSOWtJJTJGVmpsJTJGRDBKNUN3SzZDMWNabGNocnllNktRN3NaS0FER2dEVzNLNnhFOHZVM2I4cnA5OUZaU0JWSXdIYVBaQkN4ak5NTGdXeXZaQlNqNSUyQkdmWXNoazFnbzhXYWhJdDQ4NFRQbWZqTm9YdDA3S0hsUjFDZjlmYmxTVjRXY2ZRQmptZmNCVjdyJTJGTlYyQVJNanJzcmxsWHhUdlhVTXFLSDRWJTJCVUhPV1JzeDRvWmtWU1lTMnVJR3FuUXYzd0VaN2hZQ2lzNG9hVCUyQm55ZGJESWVadG4wYm1PbXRjcnR1bVdmTllEUjJPb0YyNE9LSDFvbGFBc3AxR2lXT1VvTHdzMk9FWkhTcDhDTVVzNjdwRWltN3E5eWRzU0ZITkZwTmJmQU91RGVTQzdtbFhWbXZYUFhjNGJ5ME5yd3pNWjY1ZGxiMFlXVTNaOTlGdHBZTE5yRFozYU92bFl0cnAyNDFhbjE1YkJQRmM0OUxrNjFBaGVpMUE4TmNKVXROZjczRnhPU09QWThmdGJJbCUyQllrb0xDVUZXVGVvT3l2QlJQTlZyMU1rN1YwbmRCbGZoNFFJdE5RbVhMU1BjME8xMGFCeTlGVUJRcTVEM1U1SHUwdlBoWkRhb1FnVDR0ek1TTzNNaHkwMyUyRnk0TjRJOUk5ejJGQ3NjcUxTZnBia2FUOUQ1NG1qbmtPd3pyWUlPMEhPaE9WcGRzTllad3lLMk5XeEtWMERuNW9aNWQlMkIwUk1PSVdnN0xCVEZsJTJGTzRuRzF5ZldVcGpTN0ZSYU10NFEyaVh1VmpCWE1mdmNOWjhDc3pkdTN3ODlTVERTdWpZWnNLRWlodmMlMkJqcEg0OFV5WTl6d2JyaWFYeVh1bloyaXNUQ3ZYSlVFRjk2UjNvc2VXc3g3JTJGNWslMkZobXRXaUpQOUFpSno3eEo1dUZlY0xsZFVtSGZYUmx2TldUekxzSSUyQjhWcUEwNWpGM2kzSXo5JTJCTVM1bGczcmxlZ3R3RThxTTgzbXZsaGlqZ2ZaQW05ZkhVOVY1azhBaG40TDBNTUh1RnNodW5uMENYRXB6bjh3aFJuN3VJVCUyRkxSdWsxUiUyRmdsU0g1NXZDM21PajdleVNPdlhydXh4TlBOQ1BldGFwSU1QQ2JwcG9RN2hjQ2NSdGRTR2ZJVHVYd3lkRlBjdWZzVTBpQ2JzUTBza3ZMUmpXWWlMV0ZFOEhhRnZnc3RuWHhQYnBMclpNZGxxZmpCSiUyQmY5blJRJTJCVnJxTDlvbyUyRm1ibUh1cCUyRmV4eGRQMFh5MTc0JTJGTmZhRyUyQmlUeEdqTjZKZHVvcGtUTkdiUW1sQ214elNSdjZyZUx6VFo0JTJCUE1VRWk3Q005RzNMZUY2RHJlekJjV0s2WkdaMzlHS3laeWp4b01Db0ptd3ZWVzdOc2JuZUxESElTV2VSbCUyQnV0OGJFSW45OUtiSTJGSnprZFlsJTJCQzRtbTYzR0t6cE5BUHBRRGV5OXJXJTJCNFEwMCUyRkxzaVdNVnhuM25pYnJvNTE5a3Ztdk52Y1QxOW5WUFlqb1B5elRLSjBwciUyRkZaSjRKYzhhSjVSVnJ1ZDVpR0RXcWFjZjV1dWZSOFVKdEQyWHEybjA3QzlqMmZ3MkNubFBJUHk1Rjg3JTJGMG94c2F3SCUyQlhxSnJ1aXBOWXllSnV0eTV5elpvR3VtZkJ0SmRlSmV2U0R0VE4lMkZKaTBOMXQ0SWtxZ2xFJTJCNHRpVnNDWkRWcGlOYk8xRjlJRUljdkdzRmIlMkJlY1pjOFpMRmV3S2dQQVMyMUowWGFWNDglMkIxV3hyeFFwY3RBNmRMS05yUncwVmk5JTJGNTdLaW5OczlWZiUyRlNUZnBrdWloQVZLTzBhZTJEZFludXRJYjRpYkM2R214eGVERXNMM1RCeGZoMVpWcEIlMkJRcjMyejFEZjVSb3pOcEd4OVRwJTJCdG5lbWRGdG9VYnlLN3JBYnFmTWJDbUFxbGglMkZnV1oxb01TMEowJTJGeDhsUVNxY2IyNVBMVlgzRTVYcVM4VVpWcjVKR3FxNVklMkZRMVh1SEVyYlVwZUplMDk2ekoza1BWSHZqYTBQdHJ2aGwya1hGbTdRMTJ2cHZTTEo4Wmo0cm5aQlhtS25Idld4MkdvSiUyRlR3NGtBSTRaNEhXbmU5MjNPUDFFUWxwaVgzYkVncXViVlpKNzJxSWdOalNaSGVwSkt3VzclMkJheUhDT2haYTFmWXg4WTl2ViUyQnBaa2hVYWhxM21vcXV4ankwdGIzOG5aYk5BaDc3dmFhMmM5ZUVDdWlmQ0tiUHZqeUZRcUs0SG9pOEpKMTZoNTdFVks2VERqSGpCYnAycDgwWnBURTM1RmVoNjk5R1M5eTR0UXJQZE9jWkF3U0NUTGdJTGh4UmZPbldlZDliY0hXUEglMkZpaTh2Y2FweDlaYlB6JTJCZWZVc3VsR21ueTZDckQzR0JsM2dzRFBWNTFHZ2ZxNEUlMkJzVHNJeUlsbmVMNHN1eEY0bWVXajNMVTR4TnhEbmlhN0olMkJGeXIlMkYyMlF1c3J6UHFnRXJaNThWVnpuakJQZ2NrR3hxV1MwUFNTJTJCbHRMZ3BGREJtJTJGZG5aTGU4aU54QzhoQkhLTmE3a1NMZmVJdDF5WGMzU3hSbDVVY3olMkJNN2RCVjlrYXNQVXNucTNxb0NkVkdxSXRuR2Y1bkZ6WnF2b0lSZVRRY3pEZVU3cXBqenVMcmhadXpkRWlqT1gyaWgyZjlueVlzSkU1ZnJSSnc2JTJCT3Y4aXE2cWQlMkJtZDEzcVNUc2xlNzJuQVNWeXNSMUtzdnk3Y2o4TE01NXklMkJKb2FDWTdERXhzJTJCWnMyc1NSTWNISlhZJTJCZXhsZWFjc1NibkglMkZxTWg4dTNYN2FmQ0hJJTJCTjBPY2pXUnpqUlhUcSUyQkxrWjV3NzV3N3NWNFlzN0ZxRlIyJTJGRiUyQmd6ellMY1ZvRm4zck1YNldxZU9jUTk3NkNYWkJuYWVCSjV4NEEyJTJGN0ZSJTJCc2lZTUg2VWRHOHNOZ2pycVJGcjF5TnlUWFhvTzdja3JQWHdaYXJCbVZBcjFlUEFLTmVGODBFcE9KcDhlOWtPUFdKOGh1V1A0emE3SGVUbkJlblVpMzBmQzIlMkJXaXA2NUN1TnQ1UlYlMkZHJTJGTnlMd21DSG5yTElrdGhpSmlCckN1SkZGSkFQdlBJeDBuVktxQXdUTFR4dWhtcmtmMnJTeUhZJTJGRTQlMkYzMG5abzdldDVzJTJCZ1ZrMnlZajNTMDh5aiUyQlhSMEVvN1VoUjVXVWcwQWNpMnNmMnQlMkZIMnk0RVp2SkxoMFBQaTNQOHVXUEVNOUE5UlNWa3c1a1FwJTJCcXFLS2tjWnB4NThXV2xNMWtmVloxMzhyVEZhamhrcDFqNUdqb0hrZGRXa1ZzJTJGRnRiR3N1NDBZaVdQcmRCcWd6VkU5JTJGRWVzOFZmZWFhVlh1Q1ZWaHl4a0NEcFh1JTJGRjZzcnBTOTc1SVkwWE9uc1FSYTQ5SDdLa051M1ZKdHR3OXUlMkJVUENvWmRCUzM4QWVoWmRQaGVia1BEUDhVUXpYRDN5NGN4ZGQwZ3dLaFAlMkZPNzhqZ2ZYOEJVTkclMkJNQXFYZ0pkTU9LdXR1TzYlMkJVOVBacDUydU1EbkFPZTdrUDBpaEFDY3JjV1ozZU5zZmViV21hb29tdkZrJTJCcXVaNlRzaXN5eG1uTXJOMWU1ZXJvb0J5UGZZa0tXcHhMbUJPeCUyQmdmdEVLeHQ0cnYzVlclMkJvWnBqaFFOdmslMkJEMm5KaG1wdTMyUGl5VE9pUHRMdjhZdWJsUDZjODNjZ0g3T3B2dkFyU2V5SXFiVG9iJTJCczFqMTNnY0MlMkZhSCUyQjJWM2llb0JkaXpNaG5hVWpRSVpaOGd0b21nZTIlMkZ2RURCVmRUdGZRNE5IeldraXJ5Q2ZxVlY4MGVhOEpPUnVFcFolMkZ1UFpnc2NCcUcyMnpLJTJGVkpHVzBEdTFvSTZDUm1YRW9NRXdpYWxObXVYV2lGSWNiQjF6bmYxb2R4QlREY0tnbXh6YUMxc280Zk1icmNvZ0E4d0olMkZVSFJEVno5SHRadndmSFBSMUdjM3BIS0JHOThIeSUyRjFzZXMwNFZLSTJQJTJCUG05N1lPdG1Hb1pyM3NYVWVUSUdtVFh4cm5MOWJZcjhSJTJCR2JNQ3ZaeW5QN1B3QlFqSlgwRTlTVThHd0dKTWxJNFhXVnlLcVZ1YSUyQk9XNFVEWEhIeUQwcnA3ZktRa20zU00yRnpla0NhRm50RW11bTEyJTJGZnpObWo2allKVTVVYXFGYmQwVlU4b1BGWnBtZW9tNzI0a0UzNW5raGRzRThmc0c3bjREcCUyRkFWSjJiT0xTWGVFc25hRElxR2ZaaVloZGU4WVZGOEZvbnJoTG5raXk4SHpYeEg5NUpJcGRFc0prSkt6cVhSbEFyaUZqWGxZWFdFWWl4eFRYZGZZa3diJTJCdDElMkJrb0w5S1N0dyUyRiUyRkQlMkZBJTJGMTdtcDhEOWQ0YVZnV0dZbUlWSTdMOTJONXd5bXE2dUJLVHY3OHlvaUNRU01qVnd2a00wa2E0dUZOSXBVVmVNa0RkNHlod3p6SHlXaHRUOTk3M1BtWkpJNnV1c3VxVjZiVVpRTlFneW1YNDVOSDhKek1KM2xxY2pGWUZaV2o0YkklMkJxdUJhNnBnRnRWcjVTdktaWDRZOUFua1RYeSUyRmRXM29VTFlOOG4zaDVJc0YyV1hWaXJMOHY5ZzV1JTJGZnU4eWclMkY0T0FJMSUyQjIlMkZQcTJRQkQ0YjlPciUyRmZOaCUyQiUyRjl0Z3RuJTJGZyUyQm4lMkI0dk94ejdmbEZXN0FmeDlBY2VEZlIlMkI1JTJGN3lINHYlMkZkbm5XM1YlMkY3WmglMkZ3JTJGNmIydVYxMlgxdnhOaiUyRngzNlRvcSUyRkRlWCUyRmQlMkZhJTJGSWlCJTJGMyUyRm1adlM4Njc3ciUyRlhjTGZhd2lvczMlMkJmZ2YlMkI3aDdqYjgzOWIlMkZtMVl0N3Y3YjhOYXhkUDNzdTdqOHYxTGZmZGJwM0dueEVuZUdlTmFiJTJGVTR2UHVUY2R2RyUyRmoyZyUyQjNaUWNkcVd5N2dQR1QxMjQlMkZMdXolMkZJaTNydnQlMkYzY0dzcXZMNzVQYk9MMWI0M1hLMCUyQiUyRmVpdnJLMzh1aiUyRnI2USUyRk45VzRIOWJ2bFBGVyUyRnglMkZNUG52N1ZkUTc2V0pkTzFSdW5VQ01sJTJCTzM0aHB0bHV4YnZtJTJCUXIlMkIzMGtTVG4wJTJCWldYTWFmTDR0ZEVDSmZ2QnVvM0Q2Zlp1ZkpiY0R3cGZQOG8wM3pWR3F4N3JmU3lEaHJTYm9VcElsR2VJOXd4ZEYlMkJiVk1MaGtjejRiJTJGVFczdTk1Y0lRWDclMkZuNHg1S2FieUhqVkp0ZkYlMkJHJTJGWEwlMkJQR3ZzQnhKJTJGazZTRkUwcTl6dWwlMkJxNERPc21mM3BUJTJCZSUyQmJqY3pmOFNPczlDMWJTVWgyMjVFU1MzejJjMzVFVnlTenZVZDk1U2Iya2hTOGc4bnU5dmtjYzcxJTJGMSUyRmV1ZjFIZSUyRnBWQ2RwRXZTVEQ3UXd4U3I1UFZkc3NsZzMlMkZIMWU3YnMlMkZaVkw2cnY5OE4zanZMJTJGJTJGN290OFdSSVpsWlR2dG8lMkYzYiUyQnNXNSUyQkM3QSUyRiUyRjI4dTh2OG41UCUyQjM0ZU9Ka0ZUcmY2dXh5bVBJZjNYTyUyRjEwVllkdkVTRElWOU9UaWZmWjVMMyUyQkw2azJPODFhbEx2cGRERiUyQiUyRnZlUFMyVlFjSnNXTHNyZEtYYnJaWDVITjJWdjY4bjlYdmY3SiUyRjdIeUZ6aUtCZEk5dVhGblNrclVYdnVOTmRzaW9vbWxkWll5b2RocG5DdXdQWW5Xd05CaUwlMkJTOWRvb2Q5MlIlMkJONlJ6RnhoQVdRU05wcTR6SiUyRiUyRmJXdDNCZ0x4NFFmRHdJa1E1NHcwSmp4WDEyMHoyUE9neWZEbUNjQUNLdDJNMVAxSHZkJTJCaENtaHZUUkRudDlybEJvUXI2Wk9jdDNUU0xXb2Q4NXhjMGd5b3VtMEw4aWpqZSUyRiUyQkk0cm9ycFoydjglMkJVMU8yV0lmdjVVOXFTSVFFR2N2M3clMkZlRFhFamxvWWVLbkx4T1JrekJCendYJTJCZkprdVAlMkYlMkZBJTJGJTJGSlZPTUxpejYlMkZuRnZWWHhPRjNVbTE4TXVKd0V3bUZNTiUyQkZGZUwlMkZTOU5WWkV1T0pNSFR6RjRNU3pFemF5ZG1UdUhwUiUyRm8xODJyVjFaV2s4REEzYzl5Y1FxS3RPNW1YZ2lCWWk0MXpjSFRhMTJEckYzVmFFakppaTVYU1lTZSUyQllQQklIbVJxQVglMkZyczNvOXFvWWlBalNLN2pKODNSYTBFdHo4OHdqM2hIYVJsVkF1biUyRnJQbjl1aHRLWlVQaFAxZnBST0tSMU05Q1FnVnVhSmFyQkYzVEx4d01EU3h6VjVhd2I0Y3ZydlhZalglMkJtT2Q3QjFYJTJCMHdwNktoYmlEeW84Q09tWWxFV21UNm1aNDV6enRBcUolMkI5Yk10dGdKNDNraUhRS2VRYVV6eU1DaVpJcWRYNFZiZW41VWdpWldFRmNXdjRPa2VwWU1qMmFVUGxYZEVBSUNkaTVldTZYY0pWUSUyQk5jaVJ5VGNuVkNlQnMzYnN0dXRSY1Y4NmNNcHpORVR1NDBMbHJyQyUyQjUyWUJJUzglMkZQcWVuSTMydGhWYmtXU0VraVVGRXQzanVITWF0TG5iWlFHTHBBRW1YMiUyRkhtb1ZBdyUyQm9tRWo1WVdoYU5vY25jYVhRb0xGYUVxTThueTNUMjVINTFBNyUyRjE5JTJCZjJGbFEwTVl3TTdPbjY1bEx6WDFBZDVoM1Q5WURWRHBxSm5rM3JVNG9tJTJCTW9lOWlZeEtDSjc5dW5rZm5XUk9hTzd1cXhjUUpnWndDSnp2YldNUlZOT29TT1hUUHRzRm45R1F2ekI1YTJEUXYybHdFZ2FkREk3JTJGWGNYQnRlYiUyRks5b2piVDFNZmwybSUyRkw3cWtRTTNONGF5RGE3R25JQVpkeXhVbktYRzd5SDZuQmQlMkZSWFBxUnF6SFhzc1ZHWVg0dFBSZE8wVmduRVFiRiUyQmRLc2xabVlNd21aWXpJaTROWEhYSDY1VFB6MTRtRUxFcDFQbXJjT0tnUnFxd0RqRWMwYTFyUGk2Z1c4YjhWWVROWXpZMUdORSUyQmxFT3BZREk5OUpuU3IyUkpWeW40V3oxcDE2ejg0UzNtMVFzYTh5OXhrYWhiWlJkNm9qVHM5aCUyRlpZWEo3QkdpSiUyRmpvaW1HM2d0N0I4cmZhMmptQzV5MTVaTXVPOURseDhtMkNqVGVyZnhrclBVRnI2QVNUSFJORm5GQ20ycnh5bFZWYmIzdjRBNEtNS3Jxb0ltOXRSTXJWaFl4SXdvVVpiMEZjS01MdUolMkY0b0JPY2VJVEpLOUtvVVB2dnZpaEFHN2VtZjYzbU9CbFRPVlg0SjJxZEMwQUVoSjlhWmVPS2pPRUFrQ0JIcUpobnN2NG8lMkJDdUMyN29XZXM0YXJab2dWSkVZcWpQcnc3RzBQb2ZZN2gzSmFtUXglMkZ0JTJGbFd5bTJISyUyRlRLelR2am1sWFUlMkJzcFBSOWtYMVJkS3hrMVdpQjZmYnA1MDFqS01rSnc0YlJwMUFyUnNqQ3dmYUFjalBmb1I1TTNPbVVVQnRmNUN4bDJKcmRIMmpQYVJNMVFRbWZXZjkxMmx0R3M3NnlySEwlMkY4cHJvQkNDeEFhQVBkMVhQbGRiQXpxZ2M2S3lFd0JVbmtOM2ZFa3pQT0hJaFdiand4N25vUHNkOE5jM1IzOEhmQUJmR1lkaWNsJTJGakJuMmdKM2YlMkZwclVlTDV0YVc3aXNqRmYyTSUyRkdzd1YyWSUyQjFIVUJEUHM0dDFkck4lMkJFWHR2WnQyaGliSXRGdllmdTNMa0ElMkZQSEJCekdqSFpIcWhGSVY4WkJlQzZGR2RqcUVSSjZwdXU4NTYwd1kzR1AwR3hFNlZscFpPZDQlMkZIcnZYMFU4SFdkOGFsd1llVDhYS3EzaEU5eHFYVzNENldRd0I0V082ZnpPZTdxdWN2NkliNmdsUGR6ajIwb3dkJTJGeXBNbXVQSjRmWkklMkY4VSUyQm1GMVlKQUw4NE90dmUySCUyRmk1OHZDd2plU0FWJTJCZDFoazVlN2JMRVRidDNFJTJCc2ZTN2hoZ3FTbmZreWVkWXJKQ2tUejV2Q1kxRiUyRmdIWDZMZEpvUTFDJTJGQ0wyNUN5NUtVS3NDUUFzTm5EYmVUalE5ODlzZnVaRGlwR01aUnN4NGpHNkNVM3Y0cVg0YmppWUNWUFNSZGhFbFh4eWtwYWIxME9Pck5JZklmSFhNbzFTNmN0TlVJelRHY3lkbjVHNyUyQndRbnpsbTQ3MkpYJTJGV0FabnNNcGpzdktNJTJCM1NYUEF4djEwd2ZCNSUyQnpTM21ReUJIYyUyQmJ6eTVFYUV5NlUlMkIyTG1iVnZqSXZTdjdPQ21DYVg4bDg4WUtZWWU2MmMyN3hlRWs4T0hpa29iTGJQakRMY2p0TXFSczFwM1B1aGpPR3BQOHd0ZElXc0Z1SE1sNGFud0JxZnZZNEpNVlRYeGxucjdXY0pzbDc0SG5aRG8wYVRqOEFNck9hUTFqaDBwR2taU05zWFIlMkJYeTJZOHI1Zm5EWmYlMkY3TVViJTJCZW9RS1lKdHBKciUyRkxIalRQeGFNJTJGM1c3SjF1ZmZWUXhOTDNveDclMkJ3R2tSUzBDelgyTXIzSm9remdmRk4zTWtmZ2lBWllpa0t3YktVaXFQWUElMkJWSWN2aXFUYXBlOHppMlVUbHh3Q3F0eURLSjd2RHByZlJEeDZyampaTWZDJTJGdXUlMkJCMHFWVjRTdkNwMFo1U2lId1VDMXg0MzhTRVVFZkJvbFR4WWw5MWxTeTFjaVdkaXR3WGhoYloyekUzJTJCUCUyQnZsR24xbERCemV4bU9HZ0VQa3FCejklMkYlMkZIRE1TQldPVzdlU28lMkJCdHglMkJ4V21zY2tSR3lGdjhpc0djRW5zd1FET1l2MENqcFAyaXRSeUhteUpJbG9ic0w5aFYwUmZNYkV6VHVzb1dUNTd0VklOJTJCTUNOY29zbjVScTE1TTY0dldxVmVGM0RIZ1V0SFZCZkZsQnBzYjhyJTJGSkdOQThRR0V5QWpDS1NZRHpIWWNQdGJIRGoyVXM1TnUxSHAwdUNINURuVGVydCUyQmpwbXFXNVIlMkZ2NnFwZE9ZdElRJTJCcVU4YUEzYURnU3VabFJxJTJGZFlrQ211WmRvaWdmMTdPWjlNdTAlMkJJN0lOWEY5bFZsOTA5bjZBUVolMkYwblBpMyUyRlMwdUNNRHdHT1MwZElwVTVRMk9hTVZ1WHVLc21PVE5xV0Noa1FkZHBRSjRaSEFNbDhQY1JMdmd3RVRJUHdUMGl1NDhzYnVZaHdXRFFjZU5MWmxxSlo5RyUyQnJ2OExOYzZQMmRzTDhwc3FDWHNkNG1zRjluZHJvcjNKTnhJazBiJTJCZEN4TTh5ciUyRjE5VHFEdGdWTTh5JTJCTjF2OGFJMWJmOHNEZWJ1JTJCYlhacGZxM0RYUjdha0dXVnFoV1pURiUyQkV4aHZYcCUyQlU5ZXZoTHIwTHV5aWZJRVB3Q1FZcGhiRHFrYVRtcDd1cTFzZnFlT0gxaU8zazRmUzB4alF3WW80MFZZJTJCdG5xSkYyNk85WCUyRmM4RUV1ajMzWFBjZFF2ZDEwUUtyJTJGaUhKSGNvRmFRTzI2SUY4WkI0d2h5cGFnSEg5WDJsb3d6bnRocHpGenhOTUhSSm5NVmJIdkhUaEN4QnlGa0Zmc0pSNHZ0QzFpdFJ1dXJLY2NCWFNXaXQzZmtDZmFLdlFYc0Fld3VEZkJ2R0QzU0ExJTJGWFNkSmhZUkdHZHFQczhqYmNZS0E1R3NQNWtLVlU0Mk9XdW1vWVZYN3Q4QjFYZ2wxOVNzY0haJTJCVXhpOGpOczhmekpZQTBRdzc5V3V2SnZMcm81UG9LZUZUMjF6NHo0U05tVSUyQkpTRFlQRGwlMkZNQmdJN055b3RjaCUyRjhuam5ocm9pUnZQaEJ2OWdPN3NnRjV1bExqT0tKT1o3N1JxNW1rdjNLeWglMkZjM1olMkJ0YWhxYzZ0d1QlMkJkOUs3dkRIbkdraVB4Y081d0Y1SU9ETFRVRUpuSTElMkZValIxeXJSOFVMTlFCZXlnb2l6QTNXSDVHcWx1VHY3TDM1dyUyQjRmZVZmd0NwbExadVBFNSUyRm5TaFdTRGZTRkFFQW9zJTJGJTJGRmp1c3ROTiUyQlhuM1VxaHRDQzVLQTg3eHh0R201am96WFZGdXNsMjJjc1NNOEVzdXA3UFBkSnFyZHVSVTZaRWRHUmdnJTJGTU5rekxjendzbzkwOVgwSkgxVWFieTUwTnBEeFZ0QkRRd1U3QyUyRnlaS3YwQXNQOUI2dXdmVCUyQnVxTEw4WXNHREpXR1FwVGM2NSUyQk5XNzFTcGU4cFlFZFI4c2RYZ2M5JTJGS2J6Q29YdjNieTdoajF2MzclMkJJJTJCWmRTc2NzTSUyQlUxWGxRVVI3RDJJdWZZUSUyRnFxaUdJRkhDQ0VOZlQlMkY3SzIzTU13dzklMkZ5ZXVjMnpRVnFIeVhiWjBJTEJKcmFiWExKbUJ2SjduOFFmNGZ3TFdYN1lTWUVmWm96ajEzT01BZDRWRENvSEFkaUZvVWRwR1BNMGhJVVd0UnNzR1E3UnZCSUl1JTJGJTJGQVZmTTFEOVpJR3Jyb3RiTXFMJTJGZkJ0TlZGeTl1cDN5MHNQSk01blg4V2RHZnpMUzE3dHQ0R1Q5a2NQeUpXeWhZZHdMUjF2VUhOZVMwdlhWUkVPeFFzbE1mWm5KWFRJY1kweWlyWnVFWEk4RnpBcnRMcSUyQnlod1d0dHp6M1JTMUR4aHVPUmIxUVMlMkJBV2Y1JTJCdnFGc0dwcE5wRE1ROGZZaWFpbFlldFRPOTl1SldocnFYNkJCUHI4b3hkRHZVTTkzVDV3WGYxM1lpMDJFTnBKRE1YdEViS28ySUZtWXRlTG5sUWtvZUhJN2xWbjB4RWh2RkgyYjhUYzRMJTJCRGtKcW9pQWhKVWs2QkVvTkhkbXA4d0JuRmE1WWpyT2JaTUZha3gzdiUyQlk4UjdpbnNTcVhyJTJCWXhlb1g1NXhEeEhkOWI5VklzaER3JTJGc21KN3IzQzlCT0lueHRBM3MwYmFSb3Y2aHNMbms4cHVUaTJiY0docUt2VW9sOGd4a0UlMkYlMkZnQmRPWHppREtmcHhJRkx6WkdRODZFNWlPMzYlMkJEMGlybWZzNzJvZHJmbyUyRkdybktQOHd1Qk1vcmtaMFM5MkY4JTJGYWlaWUZPTjQ5U3FrZ05WTmhNa3ZNeWdJcklheTBLdkY2SmRQUVFtb3YyU0xNMUdDWnpmeVNIY0Vnb1NJRTc5QjZpODJmJTJGNUhic1VicXVpdmd6SFlxRnhnMDQ2MXpEdkd2JTJCb1VLdzFFNXp4VSUyQlY3b1lvTmtiM3NHWE5nNEFqc3RRV3l4OHNrUzEwMGJPNGxDM05wRlNYdFJESUFzZG5SdkJZbWNVT1JCdzc5Ym5ZMmllRUV1T0FBM3lJQmphJTJCcmVMNGhLYlJQYzFmTTkyUkdVTzl3cnJDZE5ZanI0WVVKSE82d2RSJTJCY3Q4JTJGdnZJMEVBR0NTJTJGVG9nRlRUTUNlZHhRZmZ5SnpzWSUyQmcxciUyQkR2ZkpvTmNQV3ZOTGpHZ3hZJTJCeUt2WCUyRlVWbVVXTkEwWjNUaVYlMkZMRDN1dldVVjVWTkZ2UFVMS3YxTURIYTVOV214N3glMkJsdFdDeTZyd05OSHFkR0ltdENpaGhiTzZQMyUyQnd1aVo3aVlkMEdaeERhanJpUmkxZGR1ZlpLa3BmcU5kZEI3OCUyQkQ3YWlWTWxnUyUyRlNWVXZySDNaWUlIcHozV2gzdElJTmklMkJMM1RoTlFZa2dtUkdQV0slMkZYNUc1QzZzOWdscnFxOUhCSkhEanYlMkJyMGxoVnZ5Z2s2YlJXMVpXaE5tYng3aSUyRllBZlhaS1AlMkZpTzNjbUF0amlhR0k5NktjSmdxTVNYU1dHd2NWMEQwbzVuZXhGUUY5VXZlYkVOV3pGd01hYSUyQmlsWUklMkZscmJtMmhMcDR4bnBoR2RVM1pLYUVQUjB2dzZmbnk5R09Gclk3ZWo0ek03YWJSUGdmRG1udU9tcHlhaEFCSkR2eVNCVE9JOEJkVDMzZnRWV3JRWVpLV0ladGIwWjBYVzhPWVhYejc4QzglMkZNJTJCUkZpNElpbkxEcTdpRDV4MHhGOFklMkYxOElIN3BMVFhsYkxIYkEwa3hHcmppdENuclklMkY0UjVvd25wTkdMS3dlMWpPYzRKZ2VVWjclMkZYaGRybE05cTNZQVJnR0RBOUxhTnFrSGg1Zldnalp4S1d2M3pQajdNZThiZVNVTFRuM0xYbmMlMkY4ZEpyaXkzVUdOWk4zb2RyekJYOGNnQkxzWCUyRjJ6VVBrRWZJcnFDTGZycWZiMWVSRXhUOHNhNWxDZDV1SFVMbFF0JTJCJTJGVmNhYnRMeGkzVVBHckRwbkhYTENXeXZEZTZ2V05LdXhkYzdkYkFvRW9qVlRMdjVMWXRROXJmS0lMbkZpWGprRmlBRnB5dlBONnVtOWVzb0xzRnk4UDBCR1E3N2NVdHFYJTJGYTNaaVFYQXJhcCUyRmNzcHdTOE9SRE5IcVdzemIwR252V3ZtQXlpRzRYMzU0Q3ZxQmZGRWVxYVh6VGs0V3c5YURXU0NCbSUyRlhpc0klMkZkTHQlMkYlMkZhc09RVSUyQmZiUEhqOGhzdTIlMkZGSEMlMkZiZGZXVkgyanhyV2NHRkRpZXdHeFRRJTJGS3UxR21sVDRuajYxb054Zk1qR0wxZncycTFqcWNDQVJhdEtLdGRqTGV6YU9zRFg0ckFDZzVrU1dJbDAyRXlGempwR1pVd3olMkZ1UmFPUDQ1VVR4WSUyRkF0UmFBSjB3VmU0SUd2azl4RDZxekVUTXk5UE1KejZrc0FaV283M05BZVk1UVMzckhpMTFhTCUyRkFXbHRGcENSMzFTNzV2OHNnTDhmTW55cjNzUCUyRlJJa3RQZlloams4VGtKa3FRNzZDV1R1NXlLcmZWTkUxMEt2U2o4d212eGxFVllEYTRUTEF0SnBUb1FJQUwxOEhjbUM4cEpGdDRRdVdacXVwdWlJbjdNWWFpWXdaUnRoNFYlMkJsTjVLR00xWjd5cUZGY3BkYVVHd3V0dFhUdFBESGJONnZrcldoeUh4WGR1ZWNiS1RCVWFBbGwxYWdqZW1jdTk2JTJCc0VhVSUyRkFJOVd3VTZTR3VReHB1R0R6Y2xxbFhCRHNiVkRyYmpYT25BYWxsMUZKWWRpQllJZzdjcEZ2cDFRV2RMJTJGSUtLMVAlMkZ0blFVN3dTOG1TNFIxSUl2eUR1aHNwOEFFaHNycjlXRkRMaEljV1J5R1c5dHZYSXVJN1JnMzNPeEZodjJ5RGZSRUViTnUweDJ3UkxkQlBYSkpQQ3pDV0FxcVdzeFVWaG9WTEFYTEZPWG1OUGV1cHE1QmdRb21KZkFQeDRaMXhMcEpnNUtHSyUyQnp5WjA1ZjI3RVVnSGQxTmNhV0dJcTFKdFVGZkJFTDhhdnl6OVBsM21hajhkc0F1YzdxNTFJemZRZHdzQkFJNiUyQmpDOVZJcFFsN0ZmQ0xtNjR1bkxkRnc2aFFOa0dqTmd1SXZjd0xyenhQVTFmcjVKY3JhR2JWZmJNZHFTNnV1YiUyQlJEZ0JXWGdnVFZETVV3SDk3aU8lMkJiQ0t6ZlRqQnZ1cWJ3TExHeHVWTXdKWG94NkQ2MTNmbnhCMzVaSDN4cHl6VW9wSlJOTjZBUjBGWmxoS2ElMkZnVDVVOUk1V05kN2pQb1ZwcmVwcU05ZXFuQUVUd290cDhzS2tXWUw2S0t1bm9Nd0JacXZmWDRwd3d3cmFrOUlybWJGajFNaDIlMkYwRnJPdzdvZ1VOeFhPQVFuRjhGU0xLb0ZEWjNWUCUyQjB2YWltVUtuSU1QYVZ2aWxVZEhDJTJCcEpreHFzeTA1TEpsS0NOTzFvU2N6V2ZpbEpBJTJGQ1BtTUpidG1jS0YlMkZTSHVOZWxEVmRrVlc0SyUyQmlFNkdVbHNacWJMQ0lJY3EzQnA4YWpGSXA5JTJGclB3U0VJQllGZVJSeTJkbUZtVWNHeVRzcjRMVVNZbXVlWlJwZWElMkZGYyUyRmkyWEcwdDV5eXhvRUJQJTJGQlpkQVE5U2olMkZvRGZ2UmdPOFJvcVJnMSUyQk9XMVA0R0NEcHROWk03VDRyN0tkZ3R5ZUd6bWhIUTA2cmFpcDZ4ektPS0ZiU2J2ekZuZlZ1cjZJU1olMkZzUjVKNnFnMWEyY3NsbENyblpiQmdvTmxwU015bVNvMVcyYTNXWEtWNzdieUZJa1M4QjlwY0hVYmZ4eVhLamtBclBsTHRsWmZPU1dFQ2ZVZ0VJNU9TdGg5cjFVWXp1WTJ5OFcyUWU5YUElMkZiVllsVkZCJTJCNXp4TCUyRmJzM3V5cGJhaWYzaGltUEN6eGFMTnBYMkdCSTFZNERtSk9zTHRxRWhSRGNMd0NpbTlDNTBoNlhpSUV2c2UzcE5KRTFRTyUyQnMzalo4OUpjZk5JNWZrOTZOSmZ6YW5mTUFxZlNZUDBteVpSNkJZTWtVJTJCNTc4aHY5SkFwdFAyQ2VDVHp4T0FlTnBuVDV2amN2N0olMkZPRU81dlVMMlglMkZ4REZVa1JhMXg5cGQ2QzAlMkZ1cjdjcVFYa2pyZ3BvN1N2WXJGR2lzcTFreiUyRlZ4UVdVS0pHNURPJTJCTWs2JTJCQUFmY3VEZWM4ajJrd0RCckNCJTJGUUNoY0VoSURkTXBaZWo2eUdmN2FhbGhiV29LZWkyMVdPN2VpMmMwZkFhU251NEFNVjlEaHgwcUFacEtRRnZadjEzTGRIQlBmMGtSJTJCdVhIT283cEhRU2tkaFZEZjglMkZKTzJUcmhDekMlMkZyRVhCaUFWeWR5amlYN3BQakRvWDJxRjltTEtYRnJtR1g5NHBWdXhjRnFkczJtN2cwY1RZakgxaTFaZDQzQWx3aXNRTTR2eEN0Y1d0QUklMkYlMkJBRVZlTUE2Z1hJeTZIMnN1anBSdDlubmxEVVltSDJ5SlRaMU5LcDhHWTJabWRJbE1ibVRtMzA3NG1SYXYlMkZ5VktTZGRUbGdtSVJuJTJGTSUyQmxNUWowekFPb0lKT1l2cFdEdiUyRk0zWDZuRGZURTFnRGVMOSUyQkZhakM0ZEJ2UkxEV0dud0Z3Q2dnRG50emZlYnBxcVUxSnU5emVkUEdqVWdvM1ozMWV2UkFmZm9HTnlhcnhpcnU2eUE3V2FNNHFsN1prYnclMkI3WE5YaHBXa09NOUpmWFFjb0FtRlVwS3d6czlYb2ZtUnZPcGlkb0ViVGhzdCUyQkpFV3RjdnprYTZZdDFvYWxlT0FlbkNyZ3JMJTJCNE9HWGc3UzN0Ym9sejdJNnhEMjJETlFwVXA3Q0k1OWM0VmxObXRJdSUyQkxuSGdiVlBrRzhxNHVhWWxPTTh4UDRUWUVrSlhBR0dTY3hQRnlvJTJCRmNhVFZjTHUzNnI1RDRYS28wMVBoWFolMkIlMkZPUjdvQWhKamxVd0hyZHNoQ01mZzk0NlN5dnlZZXdsdzNPcHdUamhhWFdJOWlZZGNrYVI5OGFaS0F2WjB0M1ZZRDg3cWRDV0k1MXBnUXAxS2RsbWJoQmZBV0VnOXQzbG45JTJCSzlMajFwdlc5dUp4TktLTVFFTXRaSDVlRnlpRmhoeSUyQlRrWWZialpPaTVmaUU4MVNVYWMlMkZmejdFN2tISjdFUWdzQ0JsRFVaTlU2emd2cnk1dmFrTUVIczduelBIeTAlMkZGV1pEMjI3MU1nNiUyRiUyQmNYcm5TOG1vUzZlZzN0cEFSblJ0N2JTaDlrdTRpUFgzVFNxU1hZTmVvaklCUUhoUjY3WjZHYzE3am5iNVNCcmhaRHR2ZVdYVDZqWDdDTWhNTWJJRWZ3TTR4UDdUZXQ0RTlLJTJGeWV2SDF3ZTl2QnFEdWdydndnM1A1OWlCbGpGQ3RNY1JqNUlBMVZOTXNOMkZaWnFjZ0FDbGg3TU8lMkZGUyUyQnl1WldYNE50dHczYk16VXZBTHpya3ZnMzZ4aW1RYUw1d0h4Z2JRT2h5UVh3NE83T1ZVUGt5cmx3RVZOOFViamJ0eVF3WE9USEo0MDYwNGRvVXdFc0JXVmhLdmhSQzZNWEx6NnRmaEZQdlpqYnlaUndwZXhrRWtkdXpjUFVKcW1OZlolMkZsczZmTEo5NFl6ZkNQZFgwb1N6VkpDMkJlRTl3UHJCRG80NWhLSGJrWjJaRiUyQllneXp6c0F0RmRHS2xtY0RwN0l2TG83d0UlMkJ4VXZpQU0lMkZqaEl0NFZUeU1YWnByY1VxbEZRSW9JR2ZvcWdDRjFzUWVzVGpJSGJ0NzRPck1RbzFlN0pVN3FBUTdOQ1JHMUE3NlVXZmh1a3BaYWxpVkk0cjNybWRNYWc0MVF1OFZBMTNFJTJGJTJGbXdLV21MMmQzJTJCa1ZxOVRCVWt4ODJ6Yk5mM3lyb3h2dnZKNGloa2UyNjVZbmo3NzZRYm9peWgxcHpKdzc4b1dROHBWSnVjWUFrMm1xJTJCYWY0JTJGcFhQdElnRGJTampGQkIxUTB0cmp3b093YlBkdDhPNWNQVDZnOFpBbVV4WmdkT05uelhpRkUzRFBmSmhwaXlsMjVreHFNQXA4enlmNUhtYmNvJTJGWHZORkYwTUpUQU9lMSUyQkFQbEJsQ2gyNkFSVHpYJTJCU0w1NHh2V29lT3dpcWRFc0NjQ1YwcERBYnljWmZvQnV3TzBZSU54U0UwS2NzSE9jOXlBU2dHUXVGU3olMkJNVFMzeXVqNTlYa2llRFhudzh2cVY0dnpYYWpaVDdrdTFkS3BYOXIxVEJWeWdFMSUyQkRaeFlXSTNTd1JPYUdJNFRQVnpWelg0RGlVQTElMkJ2YjVGeDRHV2VIcnZRSHE2a2ttRjZUNEhVbFhEWFJBc3NtQ3lHZjdsOTElMkJDbkk2Qjl1NDRCS05ranNPR2htYWZWd1NaJTJGSlZBWThuTSUyRml0NGFUQ3h0OER5d0FZMVh2cFVhdEMzUG1uRU9WT2dzcjVma1VVd3FBbnIlMkZMVlJMZjYlMkJtcG9sR3RDbG9vNDh4WmV4dVpUZzdBYWVJbEpkbExNJTJGTE90MFE3RzBnTEZ1aEtGQkJYYXBHVFpPZVMxSVZHNVgxSmdsZ1d6MEF6M1owUWJMYlhrdnlqMnloN2ElMkI5Rjd6YzVXcGFXb0FKa3BoJTJGcDdzWW44aSUyQnBXMzR5MnpVb2VxN3dGaGpveGtLTnQzMlNTWXNpalBRRlNrMDQlMkI5M052TEZBdm5HMkEzUXoybHI2MlB3OUl1STlCM0lPTDYyMURTUzc1VmpTTzYyRkJDQmE3UThZTzZwcGsyNUhTSHV4Y2IxYkdBMzVLczNTN2t3NURMc3QwTEZkS3B3bCUyQlJnZUlacnB2SFpQTEExSEFHVUVDSjBtOGhJc25ReVZ0Z25QY1Vja3VQTXZLQU9sOHFjRDh3eVdqSk1MdGpFM1lLWHo4djRRTjV2U2l5cVloNGJnOW5yVnEzQlpGck1YdEVlZTl0enBZajhVQ1BSWkRmRnFDZjJnT0FkV3d2b2xodEZCJTJCbFlJNmZUOHluaW1BTWhheUIlMkZLVHVTTjlHb1hQM3ZUdlREZXk5R0I3RERhSklNWHhmZG5FRCUyQiUyRmZseWVPSE42eXNIWk5mRk44QTUlMkJyNWk3dlRxJTJCajBVWHZZMlNneDNmeGJoUjBiT2Y4bDlNM3gxMFFGV0pVNXkwJTJGMTliMDN0Vzd2d293Mmt6bUx4bVYlMkZwemNpbk96NmlYczl6dHh3TWl5ZzlOMnJyd3lrWDFiODF3WnJOU29IaDdld1VxT2tUVW5IVUd3WHRGUDFaUVBpd1QlMkIlMkJ0T0lYYjBYYkw3RWg3b2o3TiUyRjBRdEdDcUFDcHIlMkZ5UWI0SnBqVmdGVG5YazN4Z1VFRjAzUDhDdFBJdmRzdlg1b1M0eDVNWFdpQnpKYWc1S0JWdiUyQjJVUEtDdDVpNCUyRml2M1I3MXdGWTMlMkJxanVHaWpJNmIxN3o0M05XaGlYb1EzS0FDb21KUDI4TVZ3TTlGZGJJOWNYallWJTJGOFdHS0JQWDZZS2VwZHlBOHQzWnU0VXZCbGozVlg5c09kUzFOOVphVUNCZmJYRFFSSzN3eVFiJTJGd2w3eWpTRiUyRjI3cTY3Qm41ajVKZFVwVzR4U3FyaTJ3Wmtzc3REMnpMS294MTViaDIyM3JxTTFjMGdLRng0dHpMdWI1WUpkYjhpY3lic3VndWh2SmM0WHlZMjRDU1dDRnRCNGVLJTJCZkZhaUVtVXdUQ3l5MU1paVM1bmpzbm5DdnI5QVhhbFRyd0F6SjBDT1g0WDNrTGFWMkFZVlp2WE8lMkJGTjd5bHdTZ2FHajRka0xTaUJqWGw3MyUyQkZhS1dzQTVuS0pVJTJGYUV1SjJZWWElMkZWb1o2WUJEeFl2aThGd1lIbDAzbFhtYjJBMTJKNEJPUkVTc3RJVWgwSGpTNSUyRkMzM2NTdHY0MXdodyUyQkMlMkZyajRsbFlSYzZrUEslMkI4WlRCQWcyVVklMkJuVzdRQU5oY3llbXhsbnFZWmdxQjhlbktiZjhhWndFOHhwZGpHWk1IJTJCNHczVU8wek9EQXdEczBvOWFZNk1iWXFvb0xJcTZtc3N0T2xuc1daS3F4N3pXdURVRHBaJTJCZ3R1UncwckREZGVnSWMwbEdtNGJVdjBHODI2JTJCd2hTWm53WlJ6YkxNT0wycHklMkJjY3NENHRic0JHMzIlMkJic0lGbHdpR1ZwQ0sxdUx1eWhFUDVwVXRNZGg0RzhHNk1zNmgyR21HN0EzJTJGdnRjcTNRMXM2VyUyRkpnQ0RUNmdyRHQxQzVucGhEJTJGZk16TWVKbXZpSVRyNTRudjhPSGxnTW9iU3VzTDNvN25TJTJGZ0s5aFVSNTNEbTEzZHZVb2wwYVJFZkEwODlYNmUyWVY4T2dTTTl1S2xjTE5SRE9SQ0hsZVR0MlNQUTExaU1sb2Q1YXlheVg2elZNejJCazk3ckdqSmdVMWwwQmhMbUdsS3BrUlpLeWhUbzVHZldRQiUyRnBTYWg1TklZZnZ0TE41djNRekZVTDFiR2J3MmZDT0JFV2ZrcURUUlRzUjNvYjR6bGJ1eU1aMlZaNVhKZzdRQWw4Wk5TTVZXWEVEcWI4MkpGNWxjOFljMDFoN1NaVWxwZFFScGpaVmFwek1aJTJCJTJGUWYlMkZ0TlBHem9Yd0ZHRnpMUGslMkJBWmVOV0tPNDRZJTJCTkhwckQlMkJOdEltd2t1NzJ0eTczZ3p6V3JMZUJNMjIxUEM0dyUyQkxGeEVDaSUyRnlSJTJCRTBWRXFHJTJCY20yZ3RURE8lMkZwV0ElMkZSQ3hUV05kZDV4em9HVkpnaTU1T1VZdE5FNkoyRlBXb2RUWGs0djJsbnhWV1p3c29Udk1WRUpzJTJCcWpEUUNHMVc3Y1FTNkp1R0ZSN2tsbWtiNGFGWTV5ZmE0TEVrRGVyRFFPJTJGelU0Y3BWckg1SWR1djBSMlBNN05qeDhQUGJ2NzZqMGNzMHklMkIzWXNKRnVVbTExRkxMbUVhVDdCZmhqNTFINUgxZWxFWEF1RkgzelEyVkNEcFglMkZBRURUJTJCaUhWSiUyRlBkTEZVViUyQmxtaWJwcFVoVW5ZYnFZbWpqRU5OYVpaY2pPWnJkZnVlZU1ER09QZnp0UnNUT0F2R1BpcHJOam1ZV2FjNUh1aDI0UEd2MUwlMkJhZm82TGYwMXJUWWRWWk5WZDlPN25VUnN5UXJ0eVdYcG1SYjA2ODhsRkVHUE5yVzY0ZjgwOU1kYUlYUXVKZm5WczhYYkhnVTlEY3BmRENiajJJcDdTOTZTQkpxNWU1WkglMkJqTnlNbFFPT2wwQWhHelFScVpsTEJZYTVCdzR6cjRWUFdHSmxKcGN3aTRud3RQYks2NlNpbEVDVXdSQTJRRmxRNmEwR0dvWExYNjFXdVlVc3hJUktMd3RGS2YlMkZoJTJGa1RQZUNIa29wM2lybDdqQkhncXBtUEw0azAwdlExRGNtYnF0dFZaYjRNSkhRY0hqMjF5c0wlMkZCNHc0aDNmeG40RXVvdCUyQlBxYkYwbThoTEJEQ1N6bzZlRFd6djN6TTh6VE9pTHhGVGZSZ29lbCUyQjklMkZTNERsUkRlNFFLSG84TEZ5T00lMkIzUVJiZXFMUDRrTm9wdDNiUzV1Yjhod1pZSm92a29PN2pXWWRlVDJFaTcwbmZZc1E2WHdiazlPT2VPa0pNa0RKalFnbHlRYjVuazM0ZE1wVVhNbEcxdERTVzBuVEtkY3hNc0lBYWlDMHZhZXFCUUI5UHU2SEVBSlBERDlqbEFuOXFtZ2hlanJSMjFqd1BkMFdQc1lVTm9POGgzeG9GTWtKTkF5eSUyRnQlMkZ6bndMaGVSOURnTThGTFc1aklFdTRvcFR4c29YMUc0MCUyQnN3ckFaSFJldVA1bVpJSEI1cWNCJTJGSmhkbk1pTlluNzZVWks0Y21zSkVMNjhPRXBrU1NtcE1iMEpUajRrRjN4djNsMENRR0lGWks2dVFJSG5PaFlGcEYlMkZNdW1wUldoYVoyU0lXM3ZmNWVkY2pNdWIyZU1QYUFxdk1LdkVPeDR6Y0NVNkpQenhscVFuOHpGNjNoJTJGV0liT2kwTFNHczklMkZWcmNudnZNenE0MWdxb3lNQWx1elhtOGp1OVJhdU4xVGJBTGJPc1gxdzlVdk8lMkYzYXhTdFpvZ1NyQkl2aGlhdjUlMkZlMWxtSUxCN01iJTJCeSUyQmJoRU5kYzdaSG52NXF0RngwdFFUeiUyRlJrMEJwU3NHS2UyOWJpYkpRSDZyTFFTVDVlRUl3UDdJYlNlNzUzQmx2aDQlMkIwOEhQJTJGUlZYdVRSUXZTRno2QzNNUFFSYTQlMkZNa05HaW95T2k0TkhHQncwYTZYZ1ZiMTR3N3lpJTJCamNFUFFwQVhEaGw3TVhWYnRLRERiSVlWb09QeHZ2UFRKekljJTJGWWo0M0RwOVgyR0Q2eVZWdXBnJTJCVm81ZzJjenJhZUtWUDBTMGZRdEMwejBmVUxNZWlZR0lPRDRncEVmU3YzNVlmOFl0UVA4S200R0xBYlVsa1VLZyUyQmZJRWp0TW9XaTVWNE1OUGlZRk03dWdwOGljcU1BdTlOdzhMQVBkbjY3ZjRwTmgxUUw4SjNJM0RFWE1Wa1hlNTd5WUVVR3J2WEdWNUluVTVhWEJSMzIwQnVGWXU2elZtcFZOcjlLcW5qVDdDdlpFdmIzWEpJJTJCeDNTSFZWelhnWnJOdDRTa1V0MFh1cU5RczhNczdWWmFxYVR1cmI5OGl4R3d5TE94ZVJMYWJxUGlSblVaMUVYU3JJamVvS0VmWGh5R29FbkxFVjFXSERxYTltWG1ZcHh5eHV1NFpDTE9sNmlSV1B0SSUyRmJTODNxMGJRdTFMUEh3WjVpTWM1dSUyQjZuUEs4ZXozaWJYQ1FFQlFUSVBWelFKRnVnVEp5cCUyQllEM2UlMkZCZlQ3dUolMkZ2WVhuNElpaklNYkFsT2gyYkF0JTJCcDFuRGdUb2kwSTYlMkZSUEgyRms5N0o5V2lEZnRWS3VBOUpFNENWN2tsMzh6VTYxcVQ0Ull4ZlRPUyUyQklJS1E5MGQwaFNDUGElMkJsV0owS0toaXlkUk5WQSUyQnF1RjdjZTBHcDVDdmh5N2Y3MCUyQiUyRnZSbjJKVVJEbWRzUHRxUURGNHZ1bmFZenJaTjZISCUyRmN0TW1DNDZVMEw2M3k4Rm5vMmV6STJKZnp0UkVIaDJVJTJGSUZSbFVtanRPTWVua0NFUDFYdHVZbGVaR3lvVWtsS3kweEFRRCUyRnlLaUFJRDZNelZsdzFDUGY5UFhROTNiTVdZN0VGYU16Nzl4ejFYcU1lSlNUblpXNTR4M0I5OVlMUG5IJTJGUk9xVFhqWXFubXY3V3YzWDJTc2RnaXlWWTlNSlZpU2tDckxPb1dmd3dlZGx3bWY3cXZsd3lCc2RtbFFPYUN2Qkgza3kzZVhxa1g3bTNsdVowZXUzN09YTjdxOTZWREMlMkYzZnR5N0kzZURKMnM0JTJCRDEwUGJjVHlZd2pabTVDUFg2TjhjR3BmSTZBR0JBeXI1ZFV6NWFGdGdVaCUyQmJpRVE2cXFGT1dYJTJCcWppMmFxbklwSndvOCUyQiUyRlY1eVNSdlI2WE9iV1czY2s5V1BBT2xQeEhwbkZVZTMlMkJ2TzNydEJLUTBiSzlINjRndnNZcXhSbkFjeDlNeENaNTMzQjJjdlh0SXhYeVgxR3VNZVlHNW56UnAlMkZ0dXZvOFc2eWw4dlFvM3ZKWWpvblE4b0h3Qm1oNWZZZWkzZlB5ciUyRnJTV3Baa0xvZ3Q3VGJBRWJlUCUyRjZpYSUyRmNLNzg4SjFLUyUyRlZpNDN3VUUwQTZQNndBM0RQbFM3aFNUOWhWUzBzaUp5dzdBRThsMkFMeiUyQmtWWXl5dDZkWFNJNlJJM0E5Q3NhTHA0RiUyRldYR3NZdnglMkJyMVhzZlhYUlNWbDdZOFQxZmZzZDVsZXh4TVc5U1NjQiUyRjhKYUUwbFVyUzBmUmYlMkZoJTJCRG1uQ3luNzFHd2FnZGNWTWNHbldmNDFjQ1RZTzVRNEVTM2ZlOUNDdm1vdDB2QyUyRmFsWllsank1QlQlMkZVVFolMkZUaTkyS3NOMEN0aUpWR3A3Z2NVVGZPbEtCVjRrSXhUZFNUWmZxVFg0Wm1vRndPOTB1bHF4RHZpSlJUZSUyRm9zRFNKUWNhRFlxVnNxSk1ucng3T25lODRpU0R1Q0JsUGg0dThwN2VRVmExelE1emRDeTRrQno4OGJLSEJLNnclMkZhb2d0ejU5dDlSRmdDQyUyQjFlZyUyQmg1bW9VdjRkQ2VIbXE5MjNXT21ZaTN0ejFHdmx5RkZVdEJSaSUyQmRaeGRDOTZ1RDVvWTlnRGJYSlU5bW8xSDA0VHNlTzFCTHBtYUVCb0NnN2hqQyUyRmNBS0xuTCUyQlhiRHVueHhnYlFXOXRZMzVsMERUYmJBaktGRjlSRSUyQjF0cFFQWEhZRkc3dkVqcVVoU3FyQnBXWnBFOW1OMHprek11U0hXUDBzYXdEdWxUazh3M09xWkp5aWtZNGg2Qm1nVmFuWXJGdEVuU2h4VGZkN0IwOHFTajVlZWp6aGxWU3VjTFJya2dyMjclMkZNaTZjamlFJTJCMUpMYnB0RmJKRFZ5Y3R2SGFiQ3VvbHFQTDdVazViRlpGRFZFc3U4YVgzdFpaOW02dDZKQnlxV1RPTzk1QXVmQ1ZKczlqU0JrQ0NQV0ZrTEI2eVFSY2hLYjA2alR4aktmTndmTWFPNWthaFVRbHFzRTRTUXY0Mk9wb1F2czBNTyUyQllpaSUyQlBIWCUyRldjcU5KMHNsZUlIU1VDMUYxaHgxa2NNalpXbHVUdG96YXdUM21mTSUyRmg1T1BlQkZ1Nm9udFFxMXV6a28wNzg3YnZxYUI2aSUyRjYxRlVaNHRyMUlraHZuWk50WllYTzk0TE56T0olMkZTTzdsRzJTYXdlUDNWbURueUNwYkNiTGVNUm92JTJCcWNXcyUyQmRkblFCYklEWThIWTQ2Wm1aSU5WZTM1VlZKeDY2S1o4ZGg5MEZuV1o4VFpYYlJneUp4TjdFYW5BZGNSYUdQa0w0MkxlODU1aXAyVlF0bXZTZCUyQkpPU09QR0hVZmgxREtSUmh0RzYzJTJCMjNNQmJmbTVkdWNiZ2RMeERoU3gzb0xSaGJYdE1lNEtidjdOZGgzU0ZWUzE1SjBUZWxaYU16VVVXbXg0d0ZTcE1xWVJLMXhWbFhVZHolMkJWNmJJdnpoOHlDSmxxdWpmMjFPOEJ2b0NTcFdVMXZUV28zeGptc3FPSEw0MUU3U3Q5cXNiSnFta1dJSnk1UlZtM1psdnJoNFFESWwlMkJLN1J4aUowQmVWSmRFckhXdnZ4a3BsSFV1cEJOUFZwYTJMek1aOWo5cWwlMkZsS3Y3R250Y3klMkZEMHZvTkF2d3pKaXJpTVRjMHNVN2lyZ2xiaWZyMGlkUGlrT21Mam9pc3NFdVZwVk54R1lWY1BwM08weHRwcUt1WHBIOWM1VEU1cE51cXJ1enpTYldKZmllbXhwVXlPTnhsMVFTciUyRmtIeURYc0hlTElGb3JneW1WRW0xdG84V0tUV2FPSnBhNHNhSDh0U01PaE96cHI5aGxLJTJCNFZMZnF1U1pZdkkwTlcwdmVrMXNlcE5SbWo2UTJMVHFGMnhreFhzWjQlMkJvTjVUSkpYbTlLTDclMkJ3YXJXYThQYU1va3FRejNpeiUyQjM1MGNObzBFWEt4Sktkd1B3SndEU21lMG9WTnp0clZkNkx4R1JaWmxoalRwWXp3OXo3WkxXMGRwS0s2NjRIeDBRbW52eHNqSThTWmwlMkI1WXpzUzNCN1ZPTjdFRm81aE1pOE1DaU5PbkJra2FRemxHd2FKYUY0OTVEZW0lMkZMeWhiWEgzQmQxNGUyJTJCbjhRSWM4aHVPc0glMkJKNHBQeXg4dXF2allVREFzQUF3alNKUDJNb3glMkY2Q0xndGYlMkJCOTNXd0hUTWdmdHA0Q2IxMkxQOWk0dCUyRkFZZkljMWpIcThkZDl1c09yWlZhb253NnM3S21vNGlXcGZ0OG9wTnZEY0E5Q3FaSE44NVF0cDJBMU90bFpVVFZEZWxWeFlUM3hYQnltZ3p6eUU1ek5ENVhRMHhicExiVm9Sek1zdjRyTUtBcmdlZjc5JTJCR1lRcFZlTWxld0l2cnpWSHE5ZnI3dXFJS2dTV0tmQlJjMXNoRHRldERWUmRhQWVEVFM5V2IlMkZ2ajNRZldFdGZCc05ZMmhXNjg3YmdBQzdlYWhqNXNWS25hVXZnelpUaTJtTVEzQjF1NnM3ZmhNSXZkZHM0cGllcGZWMzklMkIyOW5vVElKMzJFRDNOYkJFSXlVMEhORzU3JTJCYVlZbEZEOUUwWExtejcxV2paOXBoR1IlMkZUUENOSlZMYXBOTkJzRFVYWjRhb3k1eHNtelhCbHcxMDlaT1FFRHJQMXFlWnJMd3ZZajFYamhueXgwa3NTYXAyZDV0WksyaVZLbm52ajZtTGY3UlN6RXQzZjhLNVZlOVFScHAlMkZBbVVabmdZNjRHRVNUa0pYcDFrMmtQRHhJYXgzUWZ2TXdyTGo3JTJGYWk1QlNsJTJGS0tYUXRTaTc0TVVPRG5qRzgzSDJHZVBJOXA5QnNPck5zSUxyTWZSYm01JTJCanZYcTIlMkJNWlM0dTN3VFR0N1NFakJKS0l3ZnU4aGJiT2RkcUlHMnRnUFBJZk96TmRtM0NES3BjeG8lMkY3d29zS3g0bnlsUEp3Q0hsaiUyRmdlNTdVcnB4SEpYRElMVkl6SFFWdUlabzJVTUF5OW5nUFdaUkE3R3JXSSUyQmxIekM3dGMlMkJKdFR6M1ZUSlZiUHBxM2xUZ0ZhbHNxYWluWUhHajBzcHdvUmFIU1JHSjVwa21LdjVGMjZ3Q2c4OGMzRmxpemQydmFMaDIxTktSU1haOSUyRmIlMkYzazdCdGowN3BKUFZyJTJGbWIlMkJPSzlGSVQzYWslMkJkZVI1dEQzS3olMkJJJTJGcyUyRmZualYyMWg3VzZpZ2pLOFdpdEtMUE14R1JiWVQ0NldHOEprJTJCVFhjMFFBZCUyQnc5NXUyc08lMkJaZ1JLQ2daWmlMNmh0V0xseGNaZnN1VlUwUXVLSnRhNnMxOUZSSFUxVEJ2dHE2Zm5yamRrRTUlMkJVcHRlaHl6MzA3MkxZV2pCU1U5QWNZTmY2WGZWVlFNVWNDanBPbUpxc29jb3d4MVJaRm1mMldBQiUyRlJQbndiM1BUUnlycEw2UlNIUDdJVzUlMkJFZlJjbDFkUk81SkxnMDhDRjk1eTdVWmJqZlJGOEI4eVFyWFB4V1lGQnFVaTBlUlN2RHlmeDFwMzZxRjZWbGxFVnJBRkZBYTVmNEU4TXd5YUF3WTFnUCUyRnMwaHAlMkYxQkdBVnZOOXZ6ZlJGN2ZiR092MFpNWENSblpjU2ppb0VUc2l3U1FSRXpaemIzY25UeGZKTU5rM25aWVNZOGJSbzQ4RjRndW9GV1VWbllQSmtkVHEybUpDSnVXejclMkY2dTVYeXBzZlh6OHclMkIwNGlWa2V0blFqT1VoWnc1ViUyQmVINFlRRUkzeU50U1VlMlM2aGFseXNEblc4MksxZGVKb3hUJTJCc0RXSVlCZFRhc2V1YURodGZuczIlMkIzWDVHU1FtbGxsOVAzNmlSZWQ5bDI3bjhITVZYbXhnZiUyRkM4M1FIbXM1VzFSRmF0ciUyRmxDS25DRVFJU3pTZk95VzNqQm9vNWtIR1RrOW55QWJCS0kyZjRiRnc1a0s4VVZheTJxc0YlMkZObFE3T3VkJTJCQUx5NXdRU3FMemJMeiUyRkt3MER0RGVMUU9zbXJlZmh2dGM2QW9NUXluazNqdEZTMGwlMkJYY1clMkJqckFuN2dEUVRCZyUyQndaNWtXTFRGQ2pVOUE0cjJJV1NTcFBKU3pIVTVFJTJGY29Nc0preGdyMiUyRmdwQXM0ajNoRmpYWmtTNDRxblhQOXI3S0xkbUpsYkRNQXVyRk1FcnNzUFl3TDJyYnhkZUYzcWtBQjVIWEhIYzJnWDRZUTMyS0tmMFdxa3B5eU11TSUyQlZlZnh1Y1pCb3hhdjFneGs4NFh0dkowV3h2dm1jNEVsdWM5UlVSY1dFTUxtS0swJTJCMXpSUWRoWHBJdEw0WG5wMWslMkIxWHpsSyUyQjYlMkI2VThWcjhBNHNYczRyaTBDMXlCWEQ5RWVlZTVSUkU3RzJud24xV3g3M0NDZDFqN2tBMiUyRmw3MjZ6VHNiRDF0ZVg3MVJWUlZsaW8xSENuSU9mRFZ6YW5MVjkxUnEwaDU4eXNrOVgxSUdWMFlCQ0xQV2lQem4yYmRVVHhUcEd5ejNMMWoxNFZ4NWZTUyUyRkQ4UXFybDBUcmIzOXhLYzV0c3g3R1hpSVg1bnQ3RDlkaWpYaXRVNHZnVjRxMTVaSDNoTyUyRlg1MjFnd2liJTJCUTk3JTJCb2lDZDloYzdIQnMxRUpUVTBLbjM1MUtSY0xXb0lYeHh3U3d5dXF3cUtDaTlDM2Nrc0ZOTm45WUxhNnVlS21kSzhyMHBqayUyRnAySmtEUnc4amdqUHN1cGhPbEpmem9FVEE1bWJuM2FsYjBxSyUyQiUyQkpPajRHc041cDlxTDI4VWdhJTJCdnMxME55ZTBoSjNiRWxYVjM2NGtKTUw1MllZJTJGOVFKVnFtNDkwJTJGMVE1MSUyRkpCZHdXTmd1TG1uM0c3NUkzejU2YjZFYjFRRVlmMUZYc2RRUlYlMkY1RDkyWm5aR2JpdGF6TVRQSVclMkY4Nmt5c3oydUsxR1FONU5kbTQ3eSUyQlZyckF0Nkh5eFNOM2RBZHYlMkI4WkRSSkE0V0hjUW94eHpaUzlYMEJLWUJpcG9MaE9FODVzSSUyQm0lMkJLbkdqRENXTHphVXVXSDVwakVza0VOSVVCa0FwZk1CODFRZWRBY2xYckg3dm1pSEJZUlpzOE9qalclMkZLMjZiNVVjbkZxQlNpc2lWTXVWYzZIUWxqY2J2OEw4MGM2OGlZRjhuUkxIbnRPMUIlMkJzQUp2emklMkZRWW9kdmF4amw1UjVkSkFmdDdsUTAwdzc4eUI5eEo4aCUyRkJSM3I4SGpSbzRNUzJ2U0hnYnglMkZlSGk1S1RVNHN5OFlnR1BhM1l5SFVrSlJ6dHFnNHhFeGFMUmJOY0ROdDN5eDVmNDlQMFhEQ1hPaWlmUVhaalJOOU5sNU1nM2p1NEVFcXVQeFQ0UkZpUkhYNExyVEklMkZKbWE5cHZPakZhRDNzbW5BOXN5cnRHVVV2Zkhudm9ReVB6eElONjlrYzQ1d0ZIb05Hd0dkWE9SQW9yJTJCejNiQnE1MTFPMGZQemFyJTJGdnZieU9mYmlONGhXaVpUM3YxRmVrZDYlMkJMaDM3Q1MwNjJaT3lac1JSM1V0aUVkN3ZBQVRQR2xDRGhMeVliR2lDSzJpSkVaT21hNVd1dmo2MzUyUXVzUTFzdzd4ekJlM0hKYnVtTm5XZ3hFZUtSd2FhNDdtOFFlcW5ONkxwWlkwVlV0ZWlZRjdhYWJaSVRFck9pVTJnRmZUT1BFSDZ1M3ZGVUs0dnN3a3RzNFZONEd4NWg3ZWVuRjRhelAwWHRFeWhPRDc2S2RFdTc4Vk1lSDl6TjclMkJ4U3FxdGpCZjVIZXpmJTJGYjNlUFA0aVl3RiUyRnp6ayUyRnU2cVplMSUyQmRVeU1RJTJCc2dia1h0eWE3UlNWMDZOQ0xJajI3enFzckx5UDhnWU1hZzZOYm1zaXZtdEl4Mlc2WkxrJTJGNXU4cmpZZGhKdUkzJTJCdms0M0RiS2ZOVm4wQXNiTVNsN1JzTUM4b2txcE9GS1ZIVDh5RTVzTVV0VnAyMXQ5ZUhlVyUyQmJtJTJGODdZQ01kOCUyRkFtMlkyeEF4ZHQwNWFJamdIJTJCOXdjV1hTTUc2bUlrYVRacG05Z2pLYmdoT3RmRW5VMElHaUpWWm1BbXBWd0VielZiZlolMkZxVWFhUGM3VkJwTkRTOHlFbCUyQmhRRE5EM2xjdzhTM2VYRDVEejRLM0dxaUprT0Z3SFVLaTBOJTJCbSUyQmNFTks1WENab040VVczWHNvJTJCaVRncjNqVGVmTG5oZ3ZZVUtETmNrNUg5ayUyRjByZGpiNjljTURxbTVhZ1U5dmpnMGNxWmx3TzczVUV5OEkwZEl4cTdxczJQM0d0SHkybjlDemdibllva1pTank1OGxQMVJzZUQwcXpsJTJGTkh0aWd3RmVYU3Q5ZkxuJTJCbyUyQmJBWkJmam8wWnMlMkJLN1RBWmRlNTBOeGlYJTJCZkIlMkIzdTBHaFl1RTRLVWRwT0VmWGxPcUlJNGxNMW1TcklZZ01adWFPNXY3cFhoRmZXaWw4d0doelVtUEhYSFROUUpwdlJDUFMxWDVxVTclMkZGZUVJSUpxbiUyRk5XWHVpczBwUFElMkJkV3RWc214dmZjRjhqVVRXWjlMSnElMkI2bWMyUyUyQnBXY0ZVQSUyRlh4cEhYZTF3NDZ2TWdzeVdiaHNJVWklMkZ5RWhJMGlnTVhMMEhoNjA3M0tXNThhUVliYiUyRlpVREViNklXZGxHUGVRWVdUYkdwRGlmNUxDJTJCRHhzRyUyQnIzMUJlTSUyRjFOdzVlWmRlZElXVkk0bHRUUCUyQnhzbkZxUzZ3QnBYS2NqMmN2JTJCMnJZdWdNZVlwYno3SWhWU3I1JTJGdnN0N1JnNlp2JTJGS2FzdUw0dXowJTJCMEFHbFZaJTJGR2UyVG5vZ0ZMbkZ5RGFvMks1UXFMb1ZwR3hSS2N3VTFNNUUwZHNJQ1JtSXNaUkUzMVI2ckRmT2dCS2dUTTElMkZZeDQ4Q2kxeVc5YjNFZmJpQnJodnE2cDNXRFdhdnF4QzhiSDAwNk5mYldkbjZFZ2hXc1FqbGNTcGRCJTJGZE5MdTZDQmVSVjdMaVZVamh0aiUyQllSVWplaXNkUzdsZFRkTVJkNzhrSkt4MjRWbXFHVHZzQzJxaERkUWdSMkhGb09yMWpyeiUyQkl2U2ZCOWNSdkZUciUyQkx5RVZPaWtuTWRFTzZEc25iMiUyRkkwUkRZdDhDUWpLamNKaW1KbkN5VHh5NnVVUiUyQm04JTJCQ3klMkJwRWxMS2N3UEt2M3EwOVAlMkJrcVVQTGw1cktUJTJCTFVMdmdETGNMaCUyRjFWMVBxVXVVbzZ0JTJCYUVaMEo1VWd4VTBIc3hUdklVRkwwZUZrJTJCZ1lJSm55SDFWNUg1ZDdka2R5UHNvM0Y3RlBFMnhGTFQlMkJpazZRNFNCTGZnTUNSSUNtdjI1cmxmMGo0eFF3MkxYUVVDZzBRbzd2c2RoVmlUZm0lMkI0QXkxbjIzN3lJT3hGOTZmOHNJayUyQnhDWHN1a1R1NHhPbXMwS2ZNU3FtVyUyRm5yNXhBRWFJbWdIWiUyQmprNmtYOUpkejNEckYlMkJicks4ODVwTlZZcm80U0ZidSUyRlVwcCUyQk4zMHNKZW9XdjF1VERMNXJZdDBmalYyVk5LYnJNamU3YlNvMm1MeHQzVFdFJTJGUkt5UGh4SyUyRklnbXJXalpjcU5ud2o0Q2E0ZG9kbG9IT2RDa1hUeVZMVXROaWVERHFjdExRblBXY0lNRG5XbURqcTdqdG1yaiUyQnElMkZUbk5SU3dzR3klMkJxMlYlMkZmTSUyQnYwVHdRcnI4JTJGOEFCV0Z1N0l0RlFCRkpmaU9wZmhZT1NMbllvZHVqUCUyRkc0V2ZpWFVVT0RIc3lndWpiT3NMa3lWSWd3aExvNWNNOEIlMkYwT3FBY0FXSUJzTDJYWFJHdnYzeGhPOTlpREk1NmtZencxNnZicmd1bjUwcUNXQ2olMkZzVWpCVzVMZ2I3eTVZRDVBSyUyQm1TbThhcTMwWSUyRlBtbGFsZnN6dHZkJTJCaDZRYSUyQiUyRmxZZjN4VmglMkI5b2EyZzlEZUl0Sk9IczhXT2Y1QVRBTEtXQTZPa0pUcmZtRTljJTJCcSUyRmNvJTJGMmNxZXJRb1FqTDc5TnJYU0clMkY3WGpyZmVxQndIZFZZcVVZYkZOQXMlMkZrSGtoaGpuWUVJQ1N5emlIMERaJTJCSDFGZEM2S2VIMElKSDMlMkZuUlNoNUZLcUZ4TlN1YU5pWTF3VDVsYnluZzRiVkc1ZEVCUzB4WUElMkJmSndob05SdG5hOHM3MGhPOWxZWHp3RjUxdG84VCUyQlY4ZnpSV1MyeCUyQlN5R3dWYTV4SmhLVHhVZWZGeE04UUVQMU5UQ2EyRlEzaDYwWHolMkJycjVweXE3RTY0dzZPTzNkNkpkJTJGeGVpOGZRJTJGSGQzRDFxJTJGcEVkZXpYdzE2T0g3c3J4TEd2cXZuVFhWdW1mZDYzeDltSENycnVZMXBHeCUyQmxUTFNTczBjcVk5MTBJa1BFVGNaMjhMdHdMYVZBYVpGdTBzTjBCamZSTUJQVTZ5VSUyQnh3UGZLejFMMDhpSmliaDJqY2dDbzRzY0pZa1dEJTJCM2doS1JQUTdyVzdIUmU4WHJYZmswempsNlVSdURKRGVSaFNSN3Z3S0FpY2lxNzhoU2xMR2VidldpeERDeW9Ob2JrSCUyQkkxeUhSZHpwZCUyQnhkUGdtRUIzaTRLYUExcVVYS1YzMTVzZ0dRaFFTbCUyQjNyYlZBWGUzVTdjNUUlMkJ1NW93d290ZlJvZWllbTdjS2ZjZWVDcHF4TGtmNWxYVHFPc2hXdFdHMXMxc21GZUFTQzlSZGolMkJ3MSUyRk1MbXoxNmZtMzFPNUdBT2xqNjQlMkY1M05ENVFJMU81JTJCakJVdG9DZkZuU3lUbHp4Q1ZuJTJGWml2VTdjZExtcDhncm9NYkc2OThOY1hwWGdSYUxnOThSb0hQJTJGSjU2cXBqbFpWbVNrJTJCMlhEanNkNWlxY2s3dlJFcFNIU2NkM3ZMJTJCM0d5dm9NJTJGcXJGdmpxTm5BU1UwanklMkZWZyUyQlRKVFdqbk53R0NHOGg0c3ZXSDclMkZDZWlqTENUMWIlMkZSNGM3QWx2cmhWMUVTYWYlMkZBcGJ6STJjVzlTZkRmcGpCNVdQY2MwJTJCdVRKRTV6bmZBemM4cHIyZjFtNmlpMjdsUmo0UzRacldwcVoyVHN6TSUyRnZybnoxNWk1eE1jbVk4MTkwdHFVcXFsbWlnOTBRNVdrMkptQVdFJTJGSFhOelE0cXVTWWhVT0ZnVnUxUW5UNlRPcUtpWjNLbW9wUUxyN2RTeFpzaDRLRmJaZGFOMlpPWmJ1NTE5YngyZnJEYlhrN08xQ1clMkYlMkZoT1NVRjVyT3VPZFJnOEFGSDdTOHZldjdjV0xodzFFSHF3b09NWlZ3ZHZyVGc5ZVA3dzdEV2w2TiUyQkdId2NlckFJejluMHZTTmRXR2ZhQVFFb0gwNG1NWklLdGVQNlkybGZxZGhZSFhaSVolMkYzZXk2TGpiVlM1MEFxQm9lMFprVHFQVG5WUk1lUFNpSGg1dWVyOGd5TnpHRXdSJTJCJTJGdSUyRjJsclZHaUk2eldidkkzc0l4SjZnJTJGZjVVQXEyU3FiSDUlMkI2V3ZHYnBLVlB0NFN3YkNVYXJMU1RkN2lBZVBDR1hLcXFiUExpbzJhc1Y5WVduNHhtJTJGMiUyRllJQXQ4Q2xNM1RneTNla2JmZHViJTJCRDJ6dTFrdnVPVkZ3V3BTenpTUWpNMmEycXlVVkpTVXFmJTJGTGYwRkZNNkVTJTJGSUIlMkY5Q1RiREM2SVliZnI3MVJ5cWhORDlxbVh3MDNsc05xdyUyRllJRjl1TDhPWXJhdWxiU2ZNeVYlMkJ1Rk16VkQlMkZDeTBVeDlOc1hiYlJzY3BTTUtzcHZyUHp4RnB5cHNnMlVOWiUyRkklMkJQTkElMkZoNmdldGRXSlNDc2tWdThUUE82ZCUyRnVnaFZCT1loUDNmRXEwMXNKSSUyQkJmUWZscEx2eW8xMzBLaDZQYnhOS2MlMkJRWjlLbyUyQmVOQW1TYnNwMGMyVW1ja04wYmE1MXI2T0dCZGNsMnpVOTdERmx4VjFYZmdzeHlialRuNTFsZUk3ZVMybzdtJTJGWE5hcHBIYzByaHA4T3NyTml3cTVHQ2w4TEQ0Z2M4bGJYNnFqeSUyQnpSV1FXYmZicTlDUE1TUkQzZHpPT0t3bTBQZmtkdExhJTJGaCUyRjNRUUZDVEJ5OHVrJTJGZTAlMkJlQnBxdVpyUENNYXdrT3RTMVpoUTR2U0JROUZTMVR0R1puT25VZzZub2VjRzk5SWUlMkJyWElTbHZZblExMTVibiUyQm9hS2xqTmQ4UzV6TXlLcjBJWXNZaGFPYWQlMkJaZWtZMlE4WWlESCUyQjNJbzZVSXdNbUd5Z2h3S2lMSVBrVlpPYlpKMThaQ2ZRRDJ4d210MnhLZlZpQWU0JTJCeU9jYkEzMUNveWNuaTZpN1BDWThhU2wxdGM2dnBhTmpuTFlwOHNoUG9GT3FlRkVLT0dtaFA2WmFnSlNpaWk3SEpoJTJGJTJGdXFLZm5JN3N5VFhvbVBOYmN1NTYzcG1XVVcwT295N3Npa1ZFdkk4OSUyRmI3eWdxVEYlMkZ3UThDMmUzT3F1OWVWV0JHQ2dySWhpb1d1c3hPVGhUV2ZmSUJ6UnhGaFIxeWslMkZzOFlLNm5CM1pRdjNCY24lMkZkQWVmUVBRZkJBOHgwQ2NDOSUyRkNVZE1kSlJpdlZzZjVEbXVGRnV0UG90bVU3NmYzY1FITzlJd04zajZPVyUyQkVueUc4JTJCZmhWekhYT2VOejhuSjIlMkZibiUyQjBzOEFHVVFHUVlKSEE4N0dIRWhQQnlmdnVnJTJGMXlMRk00ZzFpMFZUM1RwcUpsNXJsJTJGdkpUaVkxVEVJR0g1cjVXQSUyRlg2JTJGZkN5d1dlbEdkQ1gzeDE5VnVtWFI4aHRHaDZUYmJ2UG5yYWxHU3d1UFZHOEx2T1JrSVZKakx5RkwzTWdoVXZhJTJGJTJGTDc1NSUyRkZGdk9yYzB1dlJhNFZBJTJGQ3NMT0JmVFZQWHJOMzVKNUpiZEhHJTJGU0ZEOW1VU2c0R25ZQ0tWeTk2aFhNdEdSaE5XaW5UZVZxdjlNSzJTY283dmhsNnRiaFhobGZlUzJxbXBLOW1sc2JudTlJNkVQayUyRnNaMm1ZTU8lMkJUUng4OEpsUlAlMkJkaWxLdXZPaEtkaiUyRkpvRExkUlpmOWs0SGFiT1VmbSUyQmFWZ29peWphNTZXNHUlMkJhMFlMT3Vza3RIQ3c0YnJqWlVyWTJ1M3czb3NzYVJvZEdMeFNUajhOelNRN0NzbTRIOG1TTkJEJTJCTkRzdEMlMkZqbXklMkJPMVRWVmowM1kzM09FcDhRTiUyRjNNemg3WlppN0pXUGo0cVJseTBxMDhSS1hMTGo1b0NrZiUyQm5XM3lCeVR6RWg5b05UNDEwWmRydkYlMkZhNFlnT25YcDFzdTJsbWxhOURPUEpHNFY5cUlXdkl6cFFQYnhoTFpPSW1pJTJGd1l6bkNYZnhIUzJIOCUyRlFzaWZUVU4zSjM1bTVRYkQ2OVhha2d1QnZ1Snd5OHlEamlSbUYlMkZoVGRiQ3FaNTJuR1FXcHVLTjFpeDU4UnRmSUMlMkJ1dGt5Z05kS3NZMHlsajR0cEZ1TGl2c1I1eXB0R3ZISHBRam9JVDh3cTJGRUMlMkIlMkZwbEV1bVVlNGJuTnIydDFjcDIxVDNFUU50MWI1RGxCaEVCM3JFTDBnWElHM0oxTzhwZTA0WDlqM2xCb2Jsb0x5ZyUyQmE5dW11VWxxaVY4M3A0SnlaNHk1SUlyOUVSa3pVJTJCJTJCQnNKdHRlS2pBRUglMkZqcjNaTEFMcENsNWN5NDNZZ3RqQmZka2xkcnlVSkFGbU1vTiUyQkxEeTZpYkFxRXpCNiUyRk1OYSUyRkNaYVY3WGZ1WTFrVWNrUm11JTJGN2FGYVk4SDAlMkJlOHJjaGNxZnNQdlViT3ZrNG9HJTJCOU5LMEdJSTNLSTZVV29UVE9DRE5BTmslMkZqelo3MyUyQlphMFAlMkJURXFTNWkxYnFxT3ZyZWdPR244ejNreGglMkJqTGR5ZWZvWlZQak5KSlROcm95TUkyQ0k0UlAxeVpUUjEzJTJGMSUyRmFQa2l4JTJCdVBrSDZBcTlGZGRCZ3NSc05PVVdvJTJGc1lsTDNwbTRuNU1yQWR0aTlVTGRUVm5YN3pvamxXYW5IWUlnTHZQd0lwTDdsZ2RTTEE1YjFUN3JpWUw5MldXVkw2amM4WEtmWVRVYXFBN3dqZkd4OEE5djZQSVJWYSUyQmtLWTclMkY1c3oxTEJvd2d1ZjBybTd6MkY4aXE3bnhtWnowU05hJTJGWGJXbHBBcUxRNW1KcG11aktRJTJCVEpvNjBzaER5JTJCbEpHWVc0VWNpbTVsR29nSzFtaVVLYjRWakNhR2tYOHU0NkNWd25ONnYwbCUyRjZnbmpQWElkSkE1c1A3QUl2VEwxZmdHYVZqTmhiRXBtJTJCZEZibXhjazhmJTJCOWV2TiUyRlElMkZsaUpQRDc5UzZlaHpSWEh6RndLRGp1dnZHbSUyQjZKYlhndGtDcUJ1N0ZKZ2RNTDZOR0FWZE5SdXZyNU4wWlVRSWVpb0doJTJCRmI0ZmNaQzVsVTdrbDM5VkI3OW5qN095Z05qZm12QmRxdk1yTDVoZkJxYmxNd1B3cEJWJTJCJTJCZjUzMzN3VnFPenhnUVVyV2w1OW1yZ3RkaFBaMiUyRnkzQmhZUUE1WDRZQzV2WGdjZWhDR2FIUkpxWFpKWjlKOGxjUU5ybm9tbUdJTlZ0aUslMkI5TlRrS0p1aSUyRlY5Ykw1M2xJdjN6YWNvJTJCTTNTaFNpbzA3MzFkcG5mZDhnUG5tZm15cEdKNVludlZIb3FIRmlabyUyRmpYeTdkdGdLR3IxdmtiOHFjSE5nbDY1RE5nSENwMmQ2NnRFYU9sJTJCR29zc21UNENlSWZEdFJPYXV5UGJyYU14blpoRnRmQlVhMlN0Mm1OZTRLZVg3MlNaMnAlMkJ2T0ZMNVlhM0FORUUxSkl2ekhUOSUyRnpyc3hLN3BCekMlMkJrR3d1Mm84NEMlMkZsYUlaazdRT1dqSWlWQlNRN21KJTJGMXA0JTJCODRac056UXJmRVRXUlZBUDhaQkx4bUtFdiUyQnVCR1pBcG9JS2R5RDNYMklZbDhaVHR0UU5DNU1tdWYza2ozTmVUbE54N1hnaXZDT2xZdnpHdXBlWENPU3lDM1lPaiUyRnBmaFRMNE5UMWplajBpSDE5c3hCJTJGcnZzQzkweTN6STFDUm8lMkY5OTVCelh4aVVqYWJ3aHNMdGk4NHVJRWJDRDJuUjY1WW1LNVEyOHpOcmg5bmpXdUxxRTkyRmZqUUNnJTJGd1NjZlFiaXhiVlJ1cVZFbXIzJTJGYmxNJTJGMHlkOXZxQjVhdHJVV01QbTR3Q1lqUGNRS0VVY0dwS3duS09Cb3FOT3JzRENKWlc2MkJRSnBlVk5KbFVwOFI5UlJra2JZMXF5WW4lMkJsZ0ZrVkUlMkZmVVFvSnglMkZkJTJCVG5KalJSb3JEJTJCdDd5SFprSGwlMkY0MUJ3WndicDJZQXBQcm5hUVRxTTJsRExRT0FWSFVEVjh6bUc2cnlFWUJVJTJGWVU2N3lyNzFwVmdud0ZJRzBTOGlYWTBGQkVQejBZRTE2dkNHUWpSWiUyQkF6cWlQNmlzNjdRN0dFUE9neCUyRmhuRlpEMllEdjZaUFhGcWtVdVNBVWM0STRaJTJCN21ySTMyd2g0VlpSTWw3YWk5NjN3JTJGbVFVYzB0JTJCM3RMTDRLJTJGN1pOdW9waFJlazZMWjE0TVBIVkFVZUxiWVIlMkJlakttbVdKbUtheFZSdTl1WU1NMnVsOXZWSTBOOHdVaHRhMWIxSGw3dmhqMHF2dnBvV2Yxa0JTN1YlMkZhaVg3bUwlMkY4bFdYVlg1eTNLVGJKQUxod1E4Ym9KWU1DSnpscFhIMW44YTlabVZRODNQN2JFRGUwUHpKdmg4dUlzRTJLN1JpRCUyQmlGOXpwVWdRWGhxUmdhdE5uOFQzJTJGaEZyVTJ5VDJ0VHElMkJLdll3NWhHNzVOVFhUMmRhNnFFb0NaNUQwS1d4b3Q2aDUxTzJVOGVwd0JISlpwZGJrZyUyQlJkemlsekNTQzhsZGN0SXVjSlYlMkZ1dnQ4OG11WG55MEFocmw1alN3YmFWbms2clF2S0NYaDNZTk55enlaWWxxdlNXJTJGTGpaRTAzM0N4NXFTRUdwa3IlMkZneEwwZHhWQnNzRG9Wc2Z0JTJGdGozbUVYVUh0ZiUyQkZmejdaZEg2YVhEV3doTXpGclV4RDFBbnJDJTJCdUxvbjY3aHNmYkpGcGZ4SlZyWU1kdVhFYXpETHRqVU5qa21obm1zJTJCR2xFaG5HV0JWOG12ODR0YWZ5JTJCTUZ0YyUyRnJtYndSajVzYUxjUmRxYjhXbTF0Mk5CcTQlMkZ5c0hvRFNSd2Fzd2NxTDBiSkU4d2d3eXM1MjRoME9PakwlMkZ6bFBIYmJ2bjluJTJGdXdtRzBCRzZpWkhidXAweFpBN3p3ZlpOODFHeGsxTVVSNWpMa3VRQ1ZTJTJCcjZtWGVVekQxc090T2JDbTU2VjlhNEFBc3lFZmlHNmcwZnI1eHozVGFHdVQ1SkI1bG0lMkY2Um1DMHg3Z0J5ZWZUMUN0aFdrRWt4U3Y5YzJRM3lQeG5MQ0pPUFlodktSTVg2RWklMkZ3eG82aUROSkVvRjRUeGFYeW5aVSUyRmclMkZNRTVxanlPRXdxb2xFSjBvQkpIQ0Z1SGY1dGtKSnV2b0prWURIekxHc2pOOCUyRjEwdXZ3clhoNUdJRnVZVEFYaXZWM2ZWazBrNW9QRXVsNjUyc1JMWDROMWxuT25jRk8zJTJCMTRhZWV5RmxWM01KbiUyQkNsTXZQYUhxbTBHSUJKUG0xVGNacHN1dlllU1c5QzB3Ymt6anhZdURRYm1sckxSNyUyRml5OEdGbzlhVWhLSSUyRlZra1dsRkwxYW9NeFNBUTZKbSUyQjNuekNmQmF1bHdIRmgwRG1oS0FGdkdWYm1GblVmSFV4T3BkVXA2JTJCcTZkdUZKbmVwcXVIc1pQWFQ5aktpeXk4Y0lOekg3d1RmODFoWEo1ODBRMEJLYjd4QW4ydVgwWDNyOFhCdHV4STBLQ1IwZkFocGtIbVBwa0tINXJjdDUlMkZ5cjZZZU9jQktMSDVkRzF0NmJ1anV1VWpORmpUN21Qamkzemw2TFNoU0d2YiUyRmFvckFjV2RvV3UxZ0NmdzAxOXMyckpNbkY3dHBtdDFDMEVMVzgwZDhHR0k3NVNzaUtqck5rVm5yaXZzWCUyQlMyU3Nobm9JVmtqWWZSMlVpVWRWOEo0N2FOV0pXYXhSS1g0Z2IlMkZMeko5eU5uUmVLQlpCem0lMkZLdXFSaHJ6ZWtkeGFWVmllbCUyQmp0UjEwczMyamJZQyUyQkp2JTJGbWFGSmZKSGhISEc3ZTFqQkdTMFUyQ0FYYjVrVUxHNjYwbW9yNTloOGxEeEVOQkdtdzZnMkRKeFE3YmVCdWp1VW5QNXZIc0kxSlpiWEplNnJxVDdjVm5ZbzlmMGxtaUZEUlBWcTJ0Z2ZEN3MyakclMkJNMEY0Nks2TFl3Tk16QmQ1Y2xPZlA1NUNUTCUyQmlzeHJrZ1NmTUlkRiUyQlU3QlhIeWlKNHpBVTlmV011Rm0lMkJFZ3psRUVyM1pwZFpGMVhsWDIlMkI2UmlOR2RMWW5YU3NIY09kQiUyQnQwUUtJOFVIJTJCdU5OQ0l4JTJCRHJ6bCUyRjNDQk4lMkJ2ekU5NTBwR0RNWmNBNmIxNG53R3k2Q3Z1QUMzWk82ZnhVcmdQOFhBY3VHcERMTFA2UEZEbktRbmUza3AlMkIxSmRoZTJ3Tkd1Q29xY0J0ZGxlS2Y4Q2dxUGc0bTFFM1ZEdlhmR3JEYmtLVEZCdE5hT29XT0hZdlpySTZGekdaNXNGdHM5dnFya2lycHhodWNMQVdEOEQ0enVSMjglMkJ2a0s1N0xzdzA0S2J1ZWtpeWluZ2FaTkQ1REg4bmN2QTZqakQ3NlIlMkJmSUxrREwydFBYQlZKc0VIOURUTWtKM3hVU1UlMkJrVjdhc25SRkVPbGN3NVpLczlpc0g5SzclMkJWbFBmSGlTYmJ4Q0ROMWVKNVg1bXFzaU5Rc0ozTiUyRnNhdjNCQ3ZLVGFNa29ic21aTVdTZSUyQlpSdEIyVDZzWXNxVE9wbVhSZ3F0WWJ6dnpHZVVXJTJCU0lZS1Z1bWE5Nm0yVTVDTWclMkJXUEF4V28lMkJHaEtId2RsUW1ielpYQTdaaWpSMjlmcFJqOVpieFY0aGpJUnhyNGpqQ1hLU0tqdnliUiUyQk9ZY3BmSSUyQndOZiUyRlV1dklSdjNLY1ZXejl5SHB0QmxtM3I1UXF6Q2x2eGYxT2pFQ3M2JTJCQ1o5dWlCM3EwdHhydnppOVR4amtJc2ZCQlE5TThUV0dmZm9KY1d1a0xwejl5VW9la0tPVzJkWmZSNlJZc2pCenZYc2VjM0dSNzNBYWlaVmw4eTRHUmxBYkZYVjY2VCUyRjNzZVFodVlPSEQzSzIzJTJGZE43NGNCb0xNa3pJUloydyUyRjgzNWdhM0N4SEwyMnp3NDE0MFFtazdTYVJ0ZyUyRjFBVlEzSFYxZDB0REZyb29mamdqbCUyRiUyRnFPblJCSGNTVEdGbWRMdFpwTXFGcmplWG5nbEpxRFlTVDdWR1ltWm5xVmszakxlWVk5RXlRempJd1pKdFl0YXFyQzhqU005bGp1QXJRVkJkcCUyRm9PSXNQT01vNHFTJTJCMUc1JTJCUllzSVUzRUZLb01TV202dXdaJTJCY2Zkbzl1SE5rJTJGdXJPcGdnR2puc2xFeXolMkYlMkZWQVpRVktyVUtENnNIbGVuNXNEUVVtckVpeXElMkZPaG5FayUyRlQ1Umo2TnUlMkIlMkZDRDJDaFZiQnZKM0xaOWtEUVFPS0ZiaG5BeXlUcXA1bkFyOVR2UXR4U296MXNodUdpZnQ5RmxlUmZ6TGNvRnVnSDNZOEZoRlgxTGxMckFITTBmdm9jVTMlMkY5bkZ1JTJCa1E0RmF2cGhrSDVZJTJCUEpIQ0FaV0ZmJTJGRU9LYUVkSFk0a29CZXFYMTRnaXpvJTJCbCUyRldhcktiQUxWdkZaZUdXMEJkWUlwJTJGSDFUNGZCZ0F0R3Fyd0tZejhGJTJCZ2puTXNITG9QcG11bUVJazR5eDZETHlCZElMR0J0ZUxJcUV4UCUyRlp5RyUyQlBSTmQ5eUNINmlJSEo5c1dZVmZXWkd6U20ySEpjZ3hhYWVsJTJCRzdVdHVZUWl0RGlPNVo5R1I5b3haJTJGR0JPZXpYWmtXMU42cSUyRlRobWFmRiUyRjdIcmlrMjRJYTFUOSUyRm5xQ1VZQkxWdVl2ZWo1cHlCUFBWakVuJTJGU2dsRnh2SXhxOFZzN2slMkZMUE9NTk8xamJkN3oxNUowYUhmWXdScjdsT2lTaTBRcEY4ZnFNeGJLVVU3WHlWaVZiMzluV1EwTHdWV0dVREFJMmJRJTJCR1pPWlpsdlI5RzFEREpzWUtudjdCTWhxQjQxJTJGJTJGSE5KQW95dUFQR3ZEOUx4S1plSXdpU3glMkIwV2xHUHBJcmZzTmN1Sk9xQ0E4dkhtUEQwTjhFVTA0VnFsMGowVlJSOHNpNlFrbm5vMThlUmtPUWxaR2Mzdzg1OW5uZFJpJTJCYXpFRiUyQmd3UUZnRXFvZ2tRWEh2enMlMkZseGdYQzI5ak50T0NrN2JwZkpKdUFtSElxemp2eGY1SmtTTlQzdWlHTDU3OGVzM05rMTZQNDNoYzJYN295NE1ua08wMWFUaXVsVEZEYkx2ZG5ZNE56WmNWMXU4YnElMkJwZGI3SEJJN3M3cSUyQnJtTkVCJTJGT2hzZ0ZuOXVJNjVUaURFV2haejlOVDZuZDdwQml6c1RaTEwlMkZxZWdjUWxQJTJCRUMwY1VBMiUyRkpDZGM2JTJGREdEaHdrSUd4MUt4V0N0WUJvM0NKOUx5RmtWM2RwTm40SXgyQkoxN1hBckp0V1clMkZnT0xLSHo0elR6SDAlMkZCakZGTlAwUmxIYXN6U0wlMkZUc3kyekVtWCUyRkRURlFnOGY4eGFUSllyOGVOaHFjcHppZ3Y3OVVCaFdKalp5NVpLZHlnUzJybUo3dEdVJTJGUVVWNzB1cDlrZTlydzZlZmJHWGRnR2ppTyUyQkVBTG5OTWQ1TFVyOTZnTXZuS0N3WUUxMkQ5eng2VElOVzklMkJlczVNWHpnSUVkRERvbDE4cVFtRjFaTnpLV3lieHpFeXp1JTJGTUN3N1NkT0lvRmI5WkJ2RWVSdTFpb1JYaXQzcGxxclg4d3NMcEFISG5SYm1jJTJGJTJCZXhSajcydVJ6cnBVbERFJTJGdjZuWDBURUw5N29xTVZSQTVzVGpYWnJDUFFmcXRlVTJ0RkhocndoTXR1SGFFUzg2czNhSkVYc2NNNFZwSk1Pb1F1JTJCJTJGcW5FcjBWM0xsSFhPTHZFJTJCb0U3Yk5vJTJGWHBUUmIzNWJESXVTOTlaJTJGJTJGQjhPTHRqamxrZGU1U2ZuT3IzWWxWQ2klMkJ0bXFOU0wxTE0xNWdGelo4eTlwRHYzdkhaOFgybjc0empRako4elJUV3c3ZzRwN2c3aWVFQkFrJTJGZ0lLMnJ1TmNQJTJGdTRaTyUyQmF1QWk0dFdVMEVGcGdSYUp2M2JVSjRMJTJCYzVhM1RlRlBaSXlYUk9paGlYSjBmOEljMXVpblRTUVklMkZjc2RPZ21JamlmdmwlMkJTWVVINkQ5WW84Z3pmNHVhSDBWMWU1SEkzbmFqSDlka0FDJTJCa3pRTXMyMXFyYzFISWJDTlFEcCUyRlhHYjZLbSUyQlclMkJBYUZhYmNRUHNHOCUyRjZheCUyRjlMUVJLUkpMTEFPbnRxYjNFdWNJUXV3SEowWjUwRDN1VHNibSUyRnVLQlI0ZWo1b2tIR1VaSXclMkI2TmhrZ1c5YSUyRmZsR3ZpM3QlMkJETXNhM0lNaFRsYjFVMHBuMm9SalQ4elZiUzBqcEx2S3UxcEsxYXp5N3VheDh3V3Y0ZnJ1aGxXcFdiJTJCMW5QSE8xSUF2b055SERCaTMlMkZIY05kRE4lMkJ6QlVISTIlMkI4MGJPUEV4SWs3YjUlMkZlQTFJaEtYVnBRMTRUVDF5ZjJ5VUJiJTJGWXpzVmJSR1pJOVltcUxaVFolMkJ0U1NidTVNTDE1dDd0WnBnZVMzc3loeDIyYXUwdE4zS2FZTnJ1eUV5OVpPRmphd0x3VGo0SG1lZHlBQ0ZabWxDWFNhdkNJNHBCd1Vma3IzWTdxMlRaSWpZYk9xdnhxaXRMJTJCNHZBUmlqcCUyQkg0N3lBWnhzUm5RJTJGSEY1aFdMZFhFdlNQTyUyRlMlMkZ5bDh6eDlBNHVZbUtDeENmOWl2cm1FVG1pUTJ4dzdCMGQ1aGFtajNJdGV2RGc4bW1FT1BpbWVhRmZ5SmVqTlBVOW9laUlDbDBqakdEQXJ6OEhMNHhQdkdOTXI3VXU5VGZadEpPSjloYXIzcFdzcmxCYktYSFBqUEh4N3pxQmdPZUZaVFpzeWNMaVFzeVRCMXU2VFElMkY5MUxmblg1ZmVQeWpvdFBKWEZQRUdId1lBZ0o4b2FQeWlTN0k2dm5yQWJJJTJCUWZjc1B6RGlaanJYYko5SXNKQ1d5YUhVTmk5MXdoNFF4QXczQzlOM2cyTFR0VUdwZHVuU3hrJTJCN1BXaEdXa0FYRHFOR3hwZDhpZG5vc0Fkb3lUbHRzMmRXTkFtT3U4cDUxMGM5NGJ0NVo3ZTd6ellqWHBObWZ2RlhFa0MlMkZOTU9mTms3VU9DRTNzT1NWZWhINEc4cEpXMmtjdlZKcHU3VlpFbmpldXIlMkY3MFYlMkJpMDRuNWV0a0NhWlpMZ2ZYRUhiVSUyRmN2eDhhV3NYNkNqNnZZJTJGeiUyQld2Wmx0U2ZSMDNISXE1MGpMSENvcnRjeUE0UHYlMkJPeVBPVjZxRVROVU5WS1QwRnMlMkJqd1NaazE1V0MxMnlBcUFXN3BvMHlIaEI2Z2M3bnRkc2h0UU1zdDBQM3RJeDNEdGFGRU1DV0RoS044aUhwWllFSyUyRkY4JTJGelhOcGZMOTlHR2RiQlNROTRJQ09SdkpWZmhMbGYlMkJHOVRuSklBQXJoSDB4WUlpaWJTdiUyQnV2djdraFJicjk5bmdDcnNveFhoNWdYMFV1UXJSSVlRQjB6S3IlMkJKbWU5NHY0a252bjNhREwzNlUlMkJOak81SEJzSGhlV0RpaDJiNSUyQnA2VTl4Uk8zenJYclM1MFozODBwUVRCNnFRcUlTeTNxQ2J4MTczTGJ6YkJ2dkRVVEhQZUFZQ2Z0a2t2WHk0cHc3JTJGM08lMkJHbVhWTjZWNGw0VUZtWWh6S2FaOXVUNUM1QW00eDU5eDVYQU55RGc4dDUxN094NzQwQ0hrNjl1ZkdYbm80R1lyVE80NXNlTlpqSGpMQ3B3ZVJoWU9LT2VMZlklMkZkdHElMkZTS0xKdzV1NWF5cUpRNFlMTVJtVEwlMkJVUHNGM3hGczlrM1VZaCUyRk1DMyUyRjYyVDVmYWJNdk1XT2ZFUjMxenNWYmNQWVNZJTJGMHkyMngwV0YxVGszelRzUjUxcXlzOHFRJTJGYzdDSCUyQjRCbiUyQkV3d2VXQjZmNURDbzZETUpRQUhQZ0xUZjdDS1BWZyUyRjViMFBxYnFCNiUyRmVZUHVjcGptTXpudm1Od2ZZeEtKVXlrQ2c4dWNWZ0JzbnIlMkJveFdEaUFSd3dLSSUyRjd1bEw3Y2FPeTJiZEo4c3d4Z2pSRjM5eWxXM2xvQiUyRmhXcmt2NiUyRnRFQ1ZpUkZTWVE4VDJ2YzFpTFhicm16JTJGNzhRZkw0MUUyNXBIbWM5JTJCNEpBcDNwM3FiNmx0QkhTZHRNZ2FVSXJWciUyQklNYkZXSDNpYTBuRGxxaW80Y1pyTTRMUGdZdkx6c213d2tRR0hCQUFIVkNJSUE2N2RpOHBPcThZMzJ3bzh1WEluand6TVJhblhPRjFETzN3VE55TkNkUUR1JTJCWDlRMTRxdGhlZ2EwMlU2NzMxTlhDMGlhNDljNkNIR1R2Vlhxb1B2a0lnMzVBeFF1NkgxamJyOGp1VVhxWklRM2lWeUd6dlROeEZMdmxqZzJSUjdZaCUyQmR1dVBqSlVCQWlZOVRXRCUyRjlEb0E5aDE5Y00welg1dUxteVp2bjJDaSUyQmVNJTJGUGs4QTNHRVJBNWJDcWVPWXhyTHF6SGJrcE1oJTJGbkRQMk00V3hUZmpoQXEwQSUyRnNrS0ozOUpWcyUyQmNSVSUyRjclMkZOMkRra1RkSURQTWV6dWJaJTJCYnVIVnhPaThlUXZ5eWk2JTJGNmttSFUzelptdEhhU1lBbnF4UWgwY3VsNlBNekxZYzUlMkZCZGtNNkp5aXZ0STZPUWlJZWFIMm9yUW1pRERKV0E2JTJGJTJCVW5ESDA3UThYaE5WSTZia216Q29BeEhTcWVFMEdLYUlDNUhHV1p3WDdJa290WGF6ZDNLQzlzTGY2TFpOVlNZOTJzZUdzcXR2UE5GJTJGbTIxT3dVZzZlOURaRTAlMkIlMkJiN0IzMXZlQyUyRkM5TWIlMkZQSzlCeGNIWVRENGRqMThERlRmMmV6Z2FyNTk4NmIxaWZiS2l3YkJoRERZOHVNRWNtZ2E2ZGFwYXBoNHExTEc3UWxwV3JOZDUwekdGREtQRDdjZEUzeHJsTzBqWjdWN2xnYVFRMXRENFk2SGh3YUs1YSUyQlRydXp6a2g5M2RMc29NdjR0cGZWRnJUeHpOJTJCWUg0bTJXR1hqNzgyT3NJWFlEbWpZekR5czNlcFFwWGNFSnJydzB5Y2lDb1ZlWHViVm1RcUtRU0tlOVNOJTJGSW4ydnVEbmZ0Z28lMkIlMkZiNnFSUzNZVExsWll2ZWclMkY4cmg5TmlTNjdyOGozOGFBTHNVaHRTc1hHb1RRbDJEU1dvbDQzMUphbmF0bEQ0eFlHY2llJTJGdmdqejBVcXk3bFNWaG9oNnlhRUUlMkZRMm1mdnl5dlZqSiUyQjFQelZmSThCJTJCVkFIYUZXWDBWTVNtVzhMSUU2a1BoZSUyRiUyQm5RTlBabnMzWEYwTjNYaTN1VXRiUyUyRjZ5YWFLQW82dGtGQ2ZuOTJJM2xjOGNQSE41S01yZ3h6eHlBd3hjcHUlMkJzZ2Z2eCUyQkF0OEFFMEJXUU5NdTlGdDVURUJhbEVaSFBUWlNoaml6M2Y5eXlzbCUyQjBZazU3OVZrRG5YUXlmNDZvTnhIVjBaZHElMkJySTlUdEc1blMzZFZ0ZUhyZWwwZzk2RVhzVldTJTJCYkdqd1ZzZFhRcWN1NkIlMkZYdXB0RnlWYzlaeFdYT1UySThhcG5uVkZvSzNheFYlMkZLZ1pMc1V3bUtWV2xKRnNuUFJ6eFlrNkJrczdFN2o4dyUyQlN1OXZKdmUxcGVNd0FSdlgyajN1Z0UzbFhZQ3hxWFlxbWV0V0d3b0JqYUE1VEp5NTFxYjlJYk53c2oyNlNuYWQzciUyRkcwZ2tRMkFDTjUxOXFrWk1EYkJ4TFYzR1pHTnJQVGVSQmFWSzZnaGNrRWJFS2JsMEJ4bFRFWDI3MUFkV1d6bUtzNDAxcTl1MklaRVIlMkZrdWd1aU5qMkRXWU1NUkFIcTh2R3VYM3VmVmtIcUhvUHh3Q0JuNnlodkxHclFxJTJCSmhGQ3d0QW5iWSUyRnhESmpCejV6VjFXWWdIYlZ2UFhGeW5Cb1BYUlYwVjFkd0c0VFp4JTJCYlhFJTJCY3B0STh2aGhHSHE3Vk1STDYlMkJWdjE3YVVOeVdGQmlMa2lxYjdrempaQnc1UVFnd3MwTlAyOXJQaFh0RTg1N3hMSGFWNVYlMkZYZnkxMVolMkJXZWdxdSUyQk84c3ZBNlpESWVqOURJWWt1aiUyQmNtaFA5WUNXS2olMkY5d0h4MVdYZzJvcEZwVDBuSTFOM0s5ek5XVUZ6UUJLdmprJTJCVTcwcGxOaTNKV01mYlV5YVhmb2I2d0VPJTJGZm5qWTg2eWh6aUUlMkJGYXZsRDJvR1UxbUl2SThEejNYMXhVZFpOc1JhOFQ3RUI4a3I4NXRaQzkzdG1FVllNSlFNRkc3cFhQb2E0MkU3aHNBM0V2a0FEcHgxMDBOSTJDUzh2JTJGbHhRJTJGd1MxZUhZaWcxWiUyQk1icW13ZWI4N3RFbzRHSkZadzZZNzdmdmtMUkhMbnljdUo1SDlURG1XQnJTeUJhdkM3TzRyUmtWVWkzZ25VUFFkUSUyQnI4d3MxZkhZQzFpRnRsOFlpbXZsOW1Ubm5Jc3lkekRhYVhrZE80MHZEaEt5SUh2a2lyeUhOMWZyRnhiVmRGTDRoRmo5NzhNdjJEZmxnQ2olMkJobDl5WEhVUDFPa1d0ZVZMU0FMU1V4ZFREaWk1VCUyRm9uMzh1MVgyTlNyNG5BcXFxeW1vRSUyQlo4eThGUEJKUHpCY0tsUiUyRkZtak94SkQ2ckJad2hXWVNpQ0xYMTVSTnlsVUxQZEJIWnlZdTFtVG9UMlhRV3Nid2xuV3laaW9tNWFESDZ6eGM1NVdoYWRSdFN0UlFGZkZmcE9qNkhkbGk4MENmWXYlMkI1STJ1TUdjZDBzakJ2QkR6TzZPMUhtaTFzYkh0c1FIMTZYdWwlMkJlMlBmdk1vVmtpaVplZlZ1YzhhYjRKWWlmZGVTY2glMkJJSEhoU3BFc0xyam1Tc2paRDM1Y0k3dnZtNm52aE16a3J6YUs1MzIlMkZlM1RlVFRMQjJvVmxHSE8zR2dCaU1RclZjOWNVbmNzdzVUUlRjbWxJNG1CJTJGSDNTU3pidHJhRlhZZExkYUl5UHpxcSUyQnk5Y3IlMkZKN0I4eEtCSm1MR01mUTh3SzBDVEV3ZkRackpRZnJtSzh3NFlPWG9RJTJCbExERyUyQk41N3FXJTJCZGg1T0Z2bDRWNmk3Sk9kJTJCWWFEeVFseVNWWDczR3NzbDR5cjBxeWhNMjY5S0pGSnYwT3FoYnRidjhWTU5RM1RJNEd5SWJROTNPZWRSOVBBTGpmVVJ6ZXVmd2U3STN3UWpId1BMSVlNWGpINXp2VG90JTJCbzNac3ltcnptYVM5RjJ2TWlwM0VwamhkNWs5V2slMkIzcyUyRjZuY3JMVmZ1UFRmeHFLd3E4WFA2VGNEQmdqOXlLbVoyY1ZucnZla1N2emQlMkZhSUVIaGFCZWRpR1lNWjdZMlZ6d21iZEVUSGNnQTB6NnRpcFdjUmFOJTJGdW4xVnRoOEJsRUNJcmpyTnBhRnV6UXdIJTJGeEJ6WVhSM0puNGRHYm0wNTNvd1duWTJJdlRkOWlTMHY5T1d5aE1XMFo3dlcxZU11ViUyQkdQb2ZDQk9Yd05xTFQwSEpMQkhOUDhZZDFFeTVZUUdUejdrM1h2d1BxamxGWlpkSGdWTiUyQkE4SnpGUG90SmlyU2kzYVpNRSUyQmx6NGxrb1ZkMXBNY1pnVXUyZlVrZXpZSEtSQ3FUbVFEUVQ3Tk1qdFpiVmhoJTJGMmljd0tvJTJGcWs1R0FxRGYyTzhoVzRQeHVtZyUyQjNseXYzZGFmcHNTTWpOd0glMkZqUTFIcTg3b2J2TFBKcnp6M2N6QVFhRjE2NUJpMjc3MHhBazRENFpxbjhiZTFNYnRidFRrY0h3ZVBGeEY2TGNocE1kVWppNkdXZiUyQjdPV2NQOXExaDN0cHFidiUyRnI3ZURkUWlzdzFMbUM2WlNGdEhjV3ZCJTJCSWhmQW8xRGhycDFJcThtREVGa0E4VWxacnRsMUVrSDd2cjQ0R00lMkJzV0F2N2dzWVFReEZMd2Q2dlpzYldvZGlYOGxaZ0VVaEx6UUFhMUU5R2FMZzIwS29hMyUyQm5hSGpReGx2eFFhZUdneEFQSWwzV3FtS01pOU43ZVI1cHRVcWlJMUhYYUFrdXZ0M2klMkZiZ3dsTk9rbFhmOW43ejFYOFJSdnk2JTJGOG9XQzJGMWV0NzF6RGw4SmdLTyUyQkRIejV2bm5yeXdUMlFidSUyQlJCTmdaUEV0eWE4cER4dFhvakdpUDVlOTRIayUyQjh2U091SkYzSzd4SWRyRkE3UEFKNG9BckVpeHdWRDlQU2FmeDJ3bG1GcjFvNGEwSnJRY1YlMkJ2TDJ3bVhoM0JDOCUyRkR4YlM1cmQ1QXdPOFNVZ3h6UmdIVDRjSEpjbnk4bzhmRjVJaSUyRkFiOWRmeG9QbFkzWHRmeXJ1NE11TCUyRmw1dTBmeDBwb3lURWd5bzNIU25leTYxaXhzZ2czdTIlMkJmODdHa2RHTmo0cklMMTZSOGZyNzFsUWxHQzF4SnlwVko3c2d2MVBqYzZ0diUyQktaQmdiVlBjS0NoTjNGU3AzWXY4T0Q1ZkRzZzhIOFBKRmVyMndrYnNTRiUyQkJhYnZWU2tVTGYwV2lQQjZxSUF2RktZZk1tSmVudGlVOGZvUzl3M3J5UDFSbElNTDJwdjhqYVRCeTFvbDl5RlpDWWxWdSUyRkZUR3JlMlBRNk8wWiUyRnZ1c2UlMkJBYkxFUGxpRUg4Zk1aTWR3ZjZ6S2Jta3p1SGZyS2poS1h1NFZ3TGhGNm90M3lqR3dqNTk5REhQbHoxclZ6bFN1d2VRTjMwcVpaWlklMkZWZFBHUG1KckdrT1olMkZoMnhaRjdtWE9tUjlaRW94UGREWXFzUHBJRkVlajYzazdnRVJEeEE3UzBtNmYxVjMwb2dBR0lGJTJCcTVnS2dkcjE5RnNTaWp2UXpzWmd2VlhjNWZxaHZuR3lzWW0lMkI5Nm1HR0pYWlpIJTJGN1hXb0wyJTJCUFZ2RFBhMFVoRyUyQnhrbkU2ZXh3YzdWd1pjZ0JmaCUyRnVpWUhKJTJCMlN2SkNuM3FpWDg5eUVZNGkxcjdUeFplanl6Q2tiYW9yJTJGZWslMkZoNiUyQkVrblVMUXM1OXBpeERneXdlZVMlMkZRcyUyRjdyV0FGJTJGTTFFZnEwelBTWTBmeiUyRmQlMkZFTkN3WU93NGRaQjclMkZCdWRMQVViVkQ0UkFaeFkzaklaZGJJQVJFbzNabk9UYUI3SWl1T2pjSHpZeENhWHhCODNLQ0JrZmRwS29vaWtzYXE1alBMWHljMzhQWDZ4TmFJbno4dERvazZIMHNKR1p0ZHQ3dHYwRlpkdWklMkZlZzB4ZEo0c3V5S3JqSlpCaW5DOFhoM2hRR2FldjglMkJtTEtUMFFBZjBkQ045UXlHVGtiRnI4SElXOTQ1TVdEVUxsWDAyWEZMdmJhQkV5cVI5dGRPUEVpSWlGbGtpYiUyRnJRc0pQVmgxbmNGMDJwbzlMUXglMkY1YXZ0RjdPUmJnaHFTNXZmSWx0eG9yNUVoMFpGbEo2dEpjQmpjUVVUZG9QTGglMkJpaTgzWSUyRkVOM2h2ZEJTNHZ6emZhYzZNdTB6U2ZxVndOODVFRllzRHIlMkYwd2txMmR5dHFRdnJZdzglMkJ1ZUZVJTJCQzgxSXJXSk5oWVJKbEUyMU5yNXRzQjZHZnFOV3Y3MWdYNzFPeHJZRm5nJTJGZlBIeFFyaHdNZm91U3Y1OVdiamF0bCUyRmZZTiUyRm5LTWZaRjhVQ3RMdXpFUXVQYWZrd0hpVlgyeFVhenNZUnZPaGZuQkswTzJkaE82bzhtN3NOMlZwZ0Vwa01YSiUyQjc1ZEFJYll2JTJCU2dPYjFuUiUyQmJ6UTBpekY0Q1NHMUlLamlXQzBibkZVRXlIR0xmRzN2aGdEcmN4dDVSUW54b2lvZEo1WFUwJTJCY3FFbVQxZWU3RDhrZ2ElMkZib2dGdGVmWGFpUFdFMnlJYUNReTNHcWJXd2llOUJwNXNLcXd1SEhQdnI1M01ybyUyQnY0M1ZmUnZvSE1CeSUyQmdiN0UlMkZyTXdHZ3lxQkFRZVdGeXc1UmNpbTJNZFRyV1clMkYlMkJmc0syQk9RSjA4JTJCSVd0ZHUlMkZPc3FlOXZSUXJmYVBYRlA3VHJ2U2p4UnBKNWZnYkFZWUY3cU44anolMkYlMkZvSmJ2UDRnQXlneVhVMnRiZkNaJTJGUFhmSUxxenpSWEFXWUhUYTZkSDRnWDlQQXNTYjcxY2lsbUowYTVUOW44T3BTN1BZTFVWQXVLWEtEZnhiMHlhM21SaHRRaU1NYjl6WEphOHBGMjhjYmslMkZpWWZoZUl1YzNLNW1pZmo2YVlzOWVkdkhnQlFVNUVCSTVocFJzMEFoSmZHOTdSNVh6aXo0U0Jud1p2ZmI0aDYyc21DJTJCd3NqTUpzbCUyRm9rbkdMazl4VnBGM0pBYmYybVdpRWd0STRXTkNYazliSGZGJTJCY3NJbyUyQnpTQkVLaW90SWpreDNic1JYNTB6bTRNdE5NZmZncndVVFpOSzliNFZaOVpoTWRCNlNDWmNlM1JOSWR5aGVTeVZ2d3d4aTViVmNtOENsV3JmbkhZJTJCQVc4VjhldVQzYURnT1JpMzdxWFlybEJka29hWXRaZGpQRU93JTJCc0ZnY0FMck4wSGhZNFB5OVkwWCUyQlVmQUtySHMlMkJEaGRxVE52UDdzSDgySTFhTnVGUXNzSCUyQnAxJTJGQjF5dUZPbWRTV0p4UFNiMkFrb24ybWpEY1k2ZWJ4aThMUmJkSGhRSEk0T2MlMkJicWhBYzlRWlQwR0FrYnBWRDloUWtZVzEzRDUlMkIwbjdDd3lwWjZDeWY2TmNiTkw4cVdIVnZUTDAlMkZ3azhJMHJ6RHdlbDJKVGJnWTZTOUs3Y2pBYmNGNFpRME43TTRPWDI0d1BZN2NtSEVpeGZnY2FPYUpOTTNSbEJWVTMlMkJtU2hNJTJGa1ZGRGFJbG1OZEYlMkJpd2NPeDQlMkZUbnhTVXM5YmJ3M2J3a1FaZEQzY1JldjliTFJFclZ2SnRLWU1tb1Nvd2NUV3NZS2FpdVF2enhyOU9KRjBiRFNmYnNzOXJhR3JONkdJbmFsV3BMRUJvU0ttNmhZOUZjTERTaWVvdkFUU2xlWEhveGJabkt4ZEN0cm9kb2lJN0lMVmdqZjcyMTlDS05leUdaWG9makxhVFF4dVpZUXBBbUlvdjRtcnI3YnNDTWJ2YmlGOThJWGs0VzglMkI4aW54OXBnQ2Y1SUVCUTQ2THVCRnlxYk9RTXR2SFhqZVNEcnYzJTJCR3VMOXM5QlRNM2x4em4lMkJZY25QZGZRelZNb2ZobFh6bFpoJTJGRXlsUmdTbFFmYjglMkZKaW95S3pkMjdmdVdGTlFqUEdBUUtOUVltVDdrNHpPb24lMkZ0YXdsMUFzTDFXZnFLMzExV1RsQTRMZ1ZmWUhOMzR3Q0lhSXlOJTJCdHdiaFdGVTNUcXJyVXpIeEw3ZnZJUDZ0JTJCWTVKSGN1STlrVXhibWklMkZQWFNlbnVGQzdmTWFDb1VuS1lzU1daaFN4NVhPSk1mcmF5OVZRYnRpMm0zJTJGUWhkQTZyMDMyUyUyQjZlaExFYWhTalpXemk2Yjg1VXZNYnJseXdQbTl5Q0dpSFpIRFppOXUlMkZ1TmlWM2lEODIySmhlNUVtM2NxcUVreUZ3RUtIWDVTV1o0amdUbCUyRjFiVEU5Y2JOeG9oU010bkJQVWdvQm14cEJIJTJGbzBxdHJFQU1EdFNYbG5KZVQlMkYlMkJUUGJNRXF1TmVqYU83Rkp6SFh2VSUyRmFtWkc1N1VPNURxM2lXSndPS21zVUlmR2VOelZCYWxnYzNxUlMlMkZHNjhvT0c0Z3pmc0xLZVNhS2lTQkZZTVlJU3lQd1FpZGN2WjBWYmFzMUdiaU9iMkVQNmw4M0VpdmlQdERvUXZ5V0FvSWswWTBHUGNWeCUyRllWTlFhZzJJTzh4TnRkbzExJTJCJTJGVzhYUHZQMUNER2k0S3RDVzlNbjRFM2RQaHRKU1JQcmQ1eUVuMG14RE12dGNYNWRtSmpYem1pSks1UXRQZE9jVURFbXNqdE1rZTRkdlNkJTJCMEpkczlsQXB2UklVJTJGNUpJUXYlMkJiZXZnU201bWI0WkczSDNDZFBydklOalM0S0NhRiUyRk1QUU9jWkJiRkxsS2hOQmtobzdjNk1nbHRQT2lYdXJrY0ZxSzFVJTJCJTJGUmphYzZoMlNCdzlBSmVFMG5Ld2YlMkI4UmczTlJmR1ZYa0E3MDcwcXJDYWM1R3I1ZXhKTU16SW1ndiUyQkhFNmxWM2tTYTRqdGVneG1RSUIlMkZTaWdSbXI1amVqeERsWG85blRrQkU1Tmg5WmhZWkRZbXZSeTk4S09MOGVwQm9LQ3ZDQW5DUlJPUTFCdGtQbEE5S0R5U0JDazFFWUkyTzdBSWk0c0c4VldldVF6cXlGa0V5WTVZQVZ0V2J6NjQzR2FDbGxkWiUyQlU0V3AlMkYwWGREdGkyUFRpQjJtMkdjMnR2dlk0SFVId25mdTlhUzY4cWxDMVBES09qckZFTlNaRFRGaUo3aTc2R09nbk9QblI5Y2tURzBtWkJqSjVoYXczR3FLYnpKSTFhZzh2TVFseTlOM093UXZPOWtuUHRaQjJRaXhqMiUyQlU1V3lYMHNOZE9nSWV3TDg4TG12YzM4Wm15NWV0cFVpT0hobVJ4SFdLWmlMeG9uY0QlMkIyTExsNE1BQXhsU0FwVFU3SHB5eUphdnM2U2l6WTlxNkdUR0tlU3VJbXYwSXF1WHFtbDF5TER0b1dSZlBzbmNTYkpkZnd6eFpQMm9kT0tRSHJ4cEpBMDdpWmdLMzdxdlI3WmJmbDFXVzQxRDBKdTlBTGdzS2duOWdEc2NWblhBSzBEZXlad2JZZkF2V3pjNyUyRkhKOWRlajdFZmlvSVVOUUslMkZGNXNZV1pWcHpzU0xCT1klMkY5bGhFaDJFT3Z1dU96diUyRmJTNzBnTjJBS3pDU0NqWndWeFZPQVF6OSUyRnVzV0R6SXBWJTJGQ1k5bnBEWWZhUXJGdWgxb1MwbjZScDJtMCUyRkUlMkIyQXI2OEpsSnNjWHBMWDFnWk0zVGJPUkFqUXVBcTJmeXpwOVJndiUyQmhrOSUyQlFmZVZPWlVlRXYyNDNvdjM3OUtmbENWYlVWeVU4ZldYT0RENm9ocExXMHFPQld5JTJCbDZTcnUlMkIlMkJOdFhicnlKamhiZ1d6d1gwJTJGMGFwWCUyRkhwc2I0TEI2cE5iNFFtelRFazJyTEt5VUpzdmp5M3FnUVVpbzh3dzM1dFd6OG14ekNWZDNOZ29CRUFxVTZPZWlhb0lpUk9rMU0zbzdraVRCM3pMaG9NOGRSbDMlMkYza1VFVlNTelgzT2dxYnA5S014M251c09yJTJCSnRkdlVNdnEyejZYZklodzRSN0duN1JFNCUyQnBOZUpmOTVZWGpJcFZjN0xueHJocmtPdmRXbHc2M2NWV3BjMmxJY0lOVFlyeHVyWTQ2U2hxS0l2Rk5QTTE0elFKQlJHOXBQWjNQRkM5U2ZObTE0VXJsYXpOOGdEWU8lMkZWZmVsb0VabXBrWFFTJTJGOVBrWnhXckRhNU9jYUk5ZXpxMzBWNmh3NiUyQjFUbGViY3BYMk16SE1aUDJMTHJheUR4Z0x6dEJXaHI0UFpDVmlyT3FsaGlLOHJqWE8xSVZyUWhJOWtTdFpNZ0pjbjRvUlJ5cER5JTJGZFVZSnJxbEo5a2xrdlNaRnRQbndUZVNDdzlkWmIlMkZhbXolMkZ0S0FtYnZCQ0RFOHhmcTRES3dxVEZWem81dUxBM28ydEQ1Z2thZWxyZlJBbUdyMjFMb2M1ZVpiSjl3bHllSk1tU1pEdlc5Q3o3Q2hQZkVXcjRxdzNQN2k5bk80JTJGYVJpNEFrU2YyYmJsdGNMZ0t5dzFQVDdHbUlUT1F2c2FpTHNpTFRLbllybmhJRXFTdTRWcCUyRm5hSmRpZzdSMWVONUFJTHN6QW9QVU5XdUZrNFo4ZkQ3R3huMkVDJTJCdTU2V0lPMGhnTlY1VHpIZUo3aklHTVElMkJUVktxWlJaMXRTJTJCeUZ1RVRRVGZqd0hvRXk1RXRkdnR2U3hPS1NrbEUwSVdaOEZhUWElMkZGWmhKTnNJN0VuaCUyQnglMkY5c3ZMVHF1eHdsU0xQQXNLZEFjVUxhZ1ptTVc5ek9UdHhRdkw2ZkhhZUtCbUpkVWFhZlVFcFFLZ1BQWkdheGczYkd3NFJ1bTEwdnpEdzI0Sk9JZHZMcVlYOEJCWFFiSWlucktJZ2dPREVNZlhWZFJrNTJlZXE4NjhFZEpvQVpuVDNUM1dRZ2JBRnpqYmF5aHY1aXl0aVZ1MjhMdmlHSnJXRzBIN3ZaTkxza2plUVBOaFgyYlJtaEglMkJPcFQlMkJQQTB3SVpPNmtDYU9xckxrNlZaaUE4dWJNZlAlMkJoS0w1MXZ2RWhndWlMa2dKYkRTM1QzNSUyRnlJbjQ5TVVDWUpONDhkR3ZRJTJCVDFSZmhNejNaeks1bW9oJTJGRFZSdzNzYXFadTAlMkJVSmpiVjJNaW5RYUpSOUxxWnRoSlp0JTJCZnJGWVZvTFR1NUlaaVVxRFJVanZ3Q0l3dnpHWG55VTZTSmVIdERmcmMzTTdTaThEWE5oVDhrajZrRDM0NzFyelFvOUlXTlZGYmZpRUZ1TVNhVXpDblFOa2pYUkRxNiUyRlhXanhBM1hpZmVVQngwYWtNZVV3aDBKcjVxeXQ4NUZ6Mk52aVNXMFFpWndyV0kyV3IxRUdaRWttQkllMjFTSmdmYURZWTA5d01UbzhwYTRWamRDTzdGc09KZFBkc3RhMWxMRGxvZ1lvdEl6ZFV0N2dVWFVPVzZrMEFtOXglMkJWbWx2YiUyRjZSJTJCdDBVc3pXTE1nJTJCcDhoTjhyVFlEQVhuN1ppOWtlTG1iMXVzQWtQcWNUVXFzYmg2VVg2ZjZzcXl5dlB3WFBEbFpkZDBRbWZieGdZJTJCZTdPTDVKOEw1MFo2JTJCbUNMSzUlMkZrWDhFMSUyQmhNa3BOdlFZUVdLd1Bzc2tDNkxvTU9iRk9vQWppaTZWd3o5eTJEOTIlMkI2VWR1YWElMkJQOGtTRmRPR0xiM2h5cWZ1eG5XTEJ5UERVNzVLYVNhb3d4SE95d1ZzU3JaVCUyRnBKbEY2dXE3TWlGa3owTHc0cCUyQlJhWHQ0TzU5UndQS3glMkZPRkolMkZJTFElMkZzUFpBZnlmUUNMSmVOOFpzZjc2eVVBOTBzZmFGM3FReSUyQnRhU25ybWhjd0RmbXZyd21UOUtMOWNsQVNQeVU2TVZUT0NiVlc2emV2ekZ0dHgwZ3BZRnFXSSUyQlAyUkV2ZHV0N0htbER5aHVueWpHWUU3TTdxYW15VmFKQ2VydmxoNlJ2NUJrV2QwZVEwY202a3lCMUJDVDN6bVVOJTJCRXdXWDZ2bjJES1BFWnhtcnZHeDRFTlJjTVBnUHJTS1BrRWs5WlhFYTFURlFmN0Z3YkRMQVlWWVNGa05lNlpicmc3V1UlMkJnTjNjRlJxYWdMcjNuY3dWbzl6dDZrYzNMbnJjUzhRbU9VbnE0cUJobTdLN0VCV0JvMiUyRkJPTXUyZUQxJTJGdGFFQlh4cTZGS3k2WDRlTW4lMkJxbXlXaFdxcjhrM1dTSlBwVUQlMkZyU3BEZHNDQSUyQm1IQyUyRng2V1djR0dTYnVKYiUyQnJzdVBmUXpCRzRQJTJCNW0yYUJQQUZmSXg1akolMkZzZ2x2QWZBREZkSWRtYzdyWjBPMzRYc243aFVlREFWQU9MeHNiOXZBQjE5SXglMkJDa20yQWVabDh4Q3JzWG1tTFFKcHFzeFpDODFlRUZpVk45OGVWUE1yYXhZcURQT3hVNU9YanlMQmNwdFVtc2RxR1JwazJGN0NSa3ZGR3BHQ1NZc0p0clNyUDJYR3VDMjF2T1NCJTJCS1ZGV1RSdWRTTFRQQ1M2anh1MFZTRHhObkxMMzJjUWRlaDRsUEFUbjZTcHlWT05TaWY0dDVkWkV1bzZ6YjVzNFpyZ2hjWWxwbEgwcUlxJTJGTXUwSXM0UThYeDNJVlozeDVSR1JFbGZzMzlYJTJGNEVZanh3aEcwMUIlMkJXSzZaMTBmb3p1SmFDbmN6SVRlNCUyRmVkZVJBelRkMjIzTDhrJTJCJTJCQjhZYkZxN3N5ZUdISXBOcFczJTJGTHVhV2x1d25GMzZqVXhFYlVRdGJLNFhhcllLbWRCWFNuSTlGZkh3U0VkdktRUDNiaUpzd3dzcFNTWVpLdTVFbXI0YyUyRmF6SnRzcjB5UVhHWUUyWUY0NHBDMnZrWVN6OXFyTlVDMWslMkJPamtwT1F0bDNWRWxCZVZMVFRlZXpCVzVlU1ZmUjh6NWpWJTJGN0diZE56VGpkeThOUUxIMFpLWHIyWmthbXpjWCUyQkJERmxyNFZqMUlIaTNOJTJCbTJHQWp2aTVuRTAyaURXWmZuZXpDQkY1TCUyQnVYTmFTUll5MTNoOWRJUEg2bXZRN2dxNDc3STUyWHlwaUJtTDBsTVN1THUxcCUyQnJWWEJZNE5YczRhdVdLd3RJejNnbmpkYzExWWRiRVFTenlMNGp2cWI5aHNjS3V5TXllQnk2WlQwJTJGTnY1MFdmeGZ0MyUyRlJIRVFSU1NzSnlvdVN6VmV5WDROV1JGaWhwZ2RWYUR5dVZkOEhmR2N3ZGxQZUgwY3lMcFNRVXRUM1lJbnI5VFNBNVA0U3FWWWl0dENoeFhzTlQ2MGJxMnVSYjBkYmo0WW80NnNEMDE5UzBMU1FNZlRjS0JLc0hxQkNLdjB0VTV6eFFXZWF4ZGVFS3AlMkJVSEhPMXhPUnZ3cHl6ciUyQkFlNlY1VENycG9lTDU3T1VvRUpwR2JONm5rcE54bzllM0RJZTU1bTRiUGlVRWtBNkx2dTU2anRyU2hSQWttMDJLazNvcWNQN1l3M0tUU1M2ZTlDcTBCY3kwN084Z094dDhrVnQ2U251M0NBWkhYbktKV1NsV015aXN4QlRKNHQ1YnNXTERVV2hhR1JTbXhzS00lMkJ6ZVpudFh6N0hTNmY5WXJZOVBRNEFNWGtxN2JGeGVHVGZxNTZ5NmJuWiUyRjdMeWJSZUdXeVFzNWNyUExxanRMVTNOQ1I5YjI1Skg1ejlncHhpUVNFRDRsYWZjMEE3cHVHQSUyQjVWQiUyQjFPY0Y0eDBTWlh6NGZQdzhBUTN4Qmd6YnVKWUVRSmg3S2JtaVh0aVEzZ09HVkRKUWNTTDdHOGlPd1pzNFdkdmpTcXpTclFVQkk5bDlsJTJCMCUyQlNqUmZmN29ySVc5QzVIZkdPYUVoWHdKaFNlJTJGcXYxWFFRWVZ6RXR3WUdnJTJCYVhxenBpWnVRVXlmM3BoWFY1T2RMNjRvJTJGQTJhMGloWGZkMjM2TzFtMUw0WTFLQklNNWNnbk9NWnR3UmVCOCUyQjVvZ2kyWm9FM1ZHT1RnNlN4Rk1yUVhXZWZmaHV5SmVrMlVyc041bzRDJTJCcDV1UFRsNGtMJTJCdEJoOElTQnd2Q3RxcXMxUHpza01UamNmUEJQeFZvaHA1OU9zYUg0c2ZjQlVmJTJGZWFIUjRiREZvc0YzMDFzUkFJd0FMOGxocFFvJTJCeDRKYmt6TloxQXp0UHJGZ1UlMkZWcE5JZ1BvVlMyQ1V0VCUyRlBHd09mNkZJYVh0YnolMkI0TEJ6N2pUbWtNWk5EbEhITEd6Uk50WWJXRWhGZ0pIMGNkdDglMkY2ZmtVbVZRdGFQJTJGZEElMkZPM1hqd1FKZVlJTXZPWSUyQk93WFBBRlN1MDlpOVg1bkl4VVdMREU4SDI4YURYbHZxYTNna3BBczNwOWViTW5tbTY1UkhMeFJtcXY1UmtSdGdhdTViU294WUtIWWQlMkJZbGlHYk9MRE1UUTVxOTN1cHFSOEFBTDNuYzZRS0NPJTJGR29nRTQ1Q056blNuZm5jbmFnYzN0N2gzV1FQUkRmaE9mJTJGellLSVNRJTJCSm1OSGRMN2t0WlNYN04wRGtoR2NzbjJoUksyMkpkbnVBSWR0OWtTeU5kMzN4UVdTZWZ0ZEJwRmJZUVp4dGx1MUZ5TmxxZnVCTCUyRlFtUHVHbnNKQjdYZ2x0M1NVSVVYV25HWFFIajFaR3doYnNjZnhIMDFYdFRJcHUwVmZDNVJKM2QlMkI1d2R5ams2USUyRjhQU2ZwWkpLZXFtcjRaTzIxdGthMW5sJTJCVXJHRGhRVGZqV0pFOE50clFRYkxSd0gxU2ZhYkwzJTJCOFQ3VHo5MDZEVkZkcVhISGt0UDFlMEsyN0dmUTJIb3hIMUpBRExhREZQd3VBZlMlMkZ6NVQzZiUyRmhtSSUyQmRDZmlDNXhxUFRnQTJMdExWT1ZvcXFsOGtzWWZzbExza0hKVGlWV0h5S2ZNaEhNdmJwJTJCVkslMkJrJTJCMyUyRk1BOVRMRmJuQVRhRlE3SGlFMXVVVWdOUGpMeWdYTUVIWmU1JTJGdXl4REgwd0R0OEJsQ1ZEenppTUh5NSUyRiUyQmRENElVdkM4MjUlMkJIbGlvNDF0MTM0VXNMVUJqWWZEdHhHN0xXMmM0WXFrNjhIeDBEUXlvdDVkc1ZpOGZQem5vTTFveGNLTFFhTXJmVkhPQnpYdWpNTlZMeWFhZ0dLY1pObjJ6RXRyN1JlSTAxOU1JTWFyN1VTR0xBcGhPRVV4M2tuJTJGQ3N5aTVIclo3czBlUG40dFlWMEpHWmpYYXYlMkJ3VTZrcFJPeFA0a0R2ZGVlUWtSWDVYMDFnVm9pbzhkV2FjOWEwQWhFWFhLN1VzUDRYSCUyQnA1NWlNazhqVXVheDk3TSUyQnFFUFZ2MVFKY3FmZU1RUjNuNzQyS1dFTGhUYXJDVkM3bUdkNjVHdjAwbDYxZTBzRXRQZXRScmFWZmRxU1AlMkJGRE9HQ1dwRjlES1ZBNUpSQUtMJTJCRlhmdmhUdWwlMkJSV1d1QUlESXU0WjNqdyUyRmhTbjUzMWdrJTJGTmE0UmZFZ3NpeSUyQko2bTNnRUw2bzEwNEh4YkNRakklMkZBcHFSVVFVVVFxZ0NneXpHZm9BRmlaZEV5a1IzQ3VNTnI0cTNBbTRwcUZBbVNxb1Q4NmolMkJIVEQlMkZyMHNtVlJsV3JMJTJCZkNNWEdWbndyWXJUMk1xRFRhQUtTODZtbHc3ajAlMkJKZ2U4SHpUJTJCOVpQWGRUYWlXNTdqcTJYWGhVV0ViVWprSXRiaFZUYiUyRmVnd0pKTTNuNjhuODllTUwwZ29WMjVoVkpVOFNsUWF0cUYwaTF4aUd1ciUyQjVTNk52bU94M1dSVGJBZVYwbjFNVCUyQlQlMkJuSGhDMnR2YWZ3TkZIaGNsdUlwY0ElMkZrT1FvV3FlRDVGSyUyQkpxTTFZU1o4amZsT2ZXUE5sdXMwQ0pTTVUlMkJ1czVvQyUyRmF3aWlNTzZDdSUyQm8lMkI0YWhvNHBMOW4yTzV4bCUyRiUyRndKWFlScW16WUN5a1VMRWF6TXg2MzBsY3BtZE91MUdOVE1OaHA2bkMyejc4dDZSRm8lMkZObHlXMmttRE5hZjY4bnAzSkxXcFo3Y3NrQTN5JTJGcGN6RUxqRlFqekFuUnQxOXJWc0VaQWw1ZFV0b3NFODlkaUJhYmNYckw4RFN0ZWptd3FERUhKT2VwU2FsdmpwZ3VwJTJCQUE5TyUyRjJPcVFIWGZ0WENWMkhzdGMlMkZOVkVKMk85TDZxbkZCR2RjY2RsbEdkVWIweWd1NWFSaXdqNyUyRnk2Y0wlMkIlMkZKMVBZJTJCbmhVMG5TNElPazY4czFuNWFrQ2txaVhjcmxnUFgxSFJXUnZZQzhkbjVtOEFBeGlDWHZNeHI2T3B3R2VqT0FMUkJMZVg5R3Q5TXVydmlzMDJaOHg0TVlLbE9TZTVQZ2pZJTJCUE9HNEo4VHJRWTZIM0RRQ2d2SnZrOGNwemZDJTJCQ2YlMkJUbnFvS2FFcm16aWdxSWUlMkJZY0RJQlRZJTJCYWlxSzRaVUN3VGxrZyUyRm16TlRaRkRQRlhHQk50MHpsWHdCeEltb2J5TDVwVzZNelQlMkY0V0NUTDYzMXN4RzloRExpc2NKeXVCbjAwTEdLNmd1RTdEQklkRmElMkZ0bkkzeFpOU1J3dWRTUnRKYnFmQjFrZ0twbURSaVVIcEhOUlpxeFpnT1g0UmNYQXA3TXROalpTNDh1UkdTYnolMkJSbHVreEExWXNNTkJUdlZOU2VXOU1WWjdMY3V4U3k4bDhCM1FPcEVyamhTOThUekVEZUhGbnZWdjlxTml0bDl3b2tTcDMxY3FodDZWamYwS2FtVkpZSVJmdlh6NEpmWTVaczZ5dm9WYndJaFpKcDQ4WTk5cUZLeDJVZUR3N3k1SkNGUnJ1b25SZjhIYzFzRXI3JTJGMUE1T1MwZWhacXBhbnJkT1pCMyUyQlZZYlFhb3dNNjE5U0NEUm5zNDRQVG1kMzdKeXB4M3U3VGZuTUtGNnlhRms4YXNNU29CZE1INXVheGhmbVp3b1RjSzlLUzYlMkZXUGFPUmZjRCUyRjhxOXFwMDYlMkI0WHVRVGJOeHZTaWt3ejlDWEI5d09wUkkwV2RJdmM1MzA2emRBRG1td3hRWXpuQ0FTeEl3cUhtRFJtUVNreUhFeTd6V0x3QzQ0OW0yQkladFdNYWNhWVEzTTExVGFGSDUxREJiUjNlQjhUZnNkNVRac1YyOFhiSWptY0g1ME96MWV3Nk5pbDB1YmtLS09VbmpONHZ2OXd5QSUyQmFMZG9DTEYzTFMlMkZiTTRFRFhJbG00amE0dTd6bnRxenVhUXlVViUyRlFkczNVU3dZU2M2ZHdlYTkzNXdab3VyMjU2bExEc2hqU1BxdzBTOTdXMnJnM2s2cXYwY3N5V3czY1pVVHFieVNwNDZ5ZDhlbmxxWE9QdElOYTZkaDQ1dXVaOTR4Y3V2N2tzalJMdGJ1b2pZN1olMkJFNk8yNnVVRzJmTkdXTXF1dkdHVG1RbyUyQkpObjhRT0k0RWpscXU5Y2pqWFgwaVZYcnY0TFRsV0VDUUw1eWZkbVQ1NTNFVVJXY3BhQ2ZqbHpvdklKdlRKWnhFdXAwRkMlMkJxVkNpQnkwdFZPYWphTVYyR0NyRUZRTlNWcmdBSDVZalJFMnVnNUFzc1VhcVpiTThPSTZqU25qM09hVGxJJTJCcTg1V1FyMm5sTWpYUzMyazk1YlpsaUJ4JTJGUUFXb2pTbnM4WTdqb0ZRRGd2N3B6bFNjWUtlMGVla3lXcHJvVk1kcTglMkIwVERlN3RuM3ZFOG5IRXBjZkF2bVJyeEtxS2ZtRkp1OVJLcGNrZzlFZnVTeVdKS2c4SVpnJTJGNUdCVjZWNXNrcTFsYWhmaE5hbDRSR1U1ZGxyZFg1R0hMNWxJZlAlMkJCV29idGNsRXMlMkZUMUh5QzJKNGRhV3QzJTJGdGRQTnQ3OXFhM1NWV1Bzd2RqUU9DQnFGbjlGOExPMzkwdncxTEM0OWNPR0JGaUxtQXNYNGhzNE1vbW5TWEZYSkVTdXMlMkZkUUp3U0k5ZDFrOHAlMkY0NEw5cUQ1Um1TYjljRW1ORW5NUk41ZHh0bmF0bFlqSW1UMGRveHNjUVNrWXpuQmxuRjdGdnJtRHhaYXc3ZzJSVnFLenpYUXVwN0lTd1FLWDRpVEVxdEklMkY1N2xLY1hWd2ZFWEIzczliTHAydSUyQmQ1Z2NiajElMkZkZXdGNGJ1TTRlcTE0U2cwRkdLMFNZcldWdUlDZ1Rwam81a1l5YzFSWkczcUN6MVk5dE1hRWRBanJpVjBuR1VNeG1zdGhLJTJCZ2pkOXU4eFpXVXhQd01YUlNYcWxuTjlsT2U2Rmt5blZmYml1OUdLZEhWdDAwTkpRUm1NZGlEamNzRjFQWXBOdkxrSzlOejVZcDZpTFNIdm5SM0pkMXRSNzZrakdhQk5wWSUyRiUyQnNoajRBTEpqMDJ5VlhwWkhLczRwcDJnQTB2RjFCd3Jza2I4a2xHM1VGJTJGdmRvTGVuUzBReXpESzJaV3NQM1Z2UkU1WHlVSGU1Zm80Mm9SZTRuckowWTc4bHFnZmZISzViVGxDRFlvbm5wUXUxSWNsdXNwYm5LSDRtY09EQTJxMm1IUndUb0ZGaW10Z2hqSkp4VHdyTkZMOEJtelpaMnQ5VDR4TTZNcGFnR3cyU0xwOWVxdU5OUkRncTlJMk9XNGQ1Mmtrc3VkV2lsJTJGanUzM2h1M3VHUlJZWUtoQnlFaCUyQmlkNzglMkZtQ3Z5SFMlMkZ0cEZpaURDZThWazR4VUVQeXRZMjI5a0t3N0FhOWhXYlRNM0d2Ynp0TjNhJTJCbUN1Z1d0VXZqVkg1eGtNZ3pLTG1kaUVWNVpjQk5xM0ZJVWJSYU9ZVXclMkZUNno1Z3I1cnRNRyUyQkV0RXVYblV6WW9ENG81VWpIZVYwTGJnMHJkaGRtdGhGTEZ4SFNyekU1MzhtZFptanVjeiUyQjdFcGFLNjBQdE8xNXpzT3pPQiUyRmRnTXNZNlhMdHd5YjJKVmkxM0slMkZZY0JXd0poT1VYWkcwZmpYRnF3Zzg5am5xdDE0STBXT0RkRE5HMXBFaGg4YmxyY3k5bml2V0xpdGhzWXIyRzhpSGFvejdqVHI4V2h2UVZCOWtwRThWcGw4SkxQJTJGQmV0UCUyQmZETDZBcmJoWk1LZDBuY0h2dCUyQk9kdDMycE50cDU1eVdXYlklMkI1dHFkeVVpajJpTmFybk5Ec2VvZVdvRiUyRmZlcGtOaHhJd25leUlpVU1GTHZDSWhlTTBKOTVmc2tnRXVreUolMkZnSEVZN0ZqbUpPZ0VFaWcwb0glMkJzVzBSd1RHMGI2OGU4ZVpsQUxFWCUyRjhxb1k2eWFGR284aHFCSTcyaSUyRlNGY0cwVXRCS2JqdElFUzYzZWFHallzS0hycTBteGM5a0FYd3AwZkphTkU5dFV6b2ZJd0lrUE96TG9ZZzAzUiUyRjRsWDd2TkRPWjZUNXNWRU9iUlZyNWExTzZrS1N1eHZ2UDE5U3Q5MHJHV2VLa05YbWZ1VWZjZEZGQUlCUGJLUnFuWkV6MXFiTUlmQiUyRmlSOXpEJTJGVEM5SkV1bSUyQlZGcVBwMnZkU2t6QUtBcXFuN0olMkJ2NEEwbFYwUDRNbjQ1TE1vWTNQRU9BejhNMktYNGNUcXBDQ0hwVXVTVGdIZ1lFb1ZZVEp4dGtxZnBtd1VEYiUyQk5ZcktKRiUyRmF4azVUd3RBVEclMkJwcWtiT2xUJTJCOUYlMkJIWmZ0UkMybFBTVXNoN2NRZ0lSWW1aZ3NRN3NmemIySjBvbkNYRDVLZWp1MFJGSXIzTkVvYnBsTUVKTjVyNG1WZVNmVnk3MGZRVnBHcWladndzdjRnbTNkNUJweVg2YW5UWGlMRVp4UlNrbCUyQnZHamFkVDk4NWtRczRiMWIyd09TaDJTN0ZrSG05RU9jJTJGR2xqWXVDYVNFYms5RzNwNWpMTFZVWDhENnBxRkZRWVN1amRWdVkwVGRCS091V2lOMjdrTVhRS1hYYk9ObVZmQldJSmVBeDE2V2RZRkFTOXRVNXBld2QwZHZROFBGRmpaTHVlbkh3SlI0bHczcTNkMmZ2V1VkdTlXd3A2ODZvZVhZaFJrTWdzV2hzJTJGbHJTa0h5alZESlFBJTJGV3dZbmRBWkhXQm43M0ZlUHV1NjdOU3NZMzFxazJSODNVSU9RV0k5ciUyQml2N0p6ekU0RXJNZm9yRG9KR1BLJTJCN3Z1dlBYQTFDYWVUMW5VNkJUOFBFVHNGWWR3WUQlMkY0bW5LYWtxN2ZjbFlHJTJGdWJ0WTdISiUyRlZDZW9TWFJsSWg2eXFXZ1h3TE8wWHNHcm5Mb3ZaOEtzQmk2eW1TUVc1d3p5SXZ3eVlyMmR6S1pza29pcjgwR1lMbFQlMkZQR2YwNG1LMVJGZU9oRHVNeHd6YVlCZGhqb3Q1JTJGNW9KYyUyQmNyaG5jZiUyQkYyNkQ5UGFZZnNaVGl5JTJCZUVoMzlXQ2JiUlZGWGFQQWgyWW9FR1J2OWZnODBaZmtlWmdLNkVOJTJGWVdLaiUyRmJLaWtiNWFpVlRTY2hwQlhEJTJCcWR3SlZvJTJGSlFQVCUyQjRtWmo0UnN2U1JaU1ZQVE84VHdCJTJGZ2JPQ25YeldPQjlaNXlma2hFSllUSkhsbWJuOGlwbWR0eDlPYTdoQktDblVCekJ1VGNlN0ZvSTZ3YklvVzduRHdoY1VQSWUlMkZLTXhkdlZRanIlMkZGdko1T3ElMkJldUFTTzV1eTJNMzZSVDFzQ0duUG4zSVFWOG1GN3hZcG4wb3NsQ2dOcmwxUDJtSnBUdHlRUVdFTDZmMFZUdXoxUFBQcXlvUE9HTTZ1Zk5XMjklMkJXOVdkUTdLdiUyQll6dE1ib1FQbUZYbXc5YXR5Uzgxd3ZsZWIzJTJCZjJHV1ZvViUyQnk0QXRJQWVSN21pdjg3SmdxckFxcWV2QVBJV3ZnbmhWVnI5cEMzdFcySVZCcVVXY2tLclNUOUxXN2QzQzd6N2k2JTJCbkladiUyRmgxaXlIRTJmNzY3Z2RpYXZsQXVodnIwUXdVczFWM0VUMEV4JTJCMFByRFI1SFZDY0lMdE4wRXJRc2gycG9PM1hoNlhiM1UxQnp0JTJGd2dReGtYN2dKV2FsN2RuZW5PVTFKJTJGRkJlUnVnSElQbVd6WTVmWmxXYkNwJTJCNjNraVNvbEs1RUVHMWxoUlJ2dDlEblRtWVFUR1dpWUsxJTJGUGtVcHlJbnE0MmpmNzdlMGNPT2dxdWglMkJFbnhZNldBMmw4VkZLJTJCeEg4VmJiMkd6N2U5U3RuYlRYOFRWcG9MWFZrMXElMkJxVzBTb3czJTJGU3pldWlaOXolMkZkTzlPYm1sdUV2NjNIRSUyRkxaY0RycVlzdzRNTkY2U3VxSVZGYWFqbXdMbDJCZGx5YTF3aWFmNFhjMzdnZFdaNjQ4STAzZlN2WGJLMXRNTFBHVDIlMkZmaVJ5V2FzN1NxbHk0M0RBTkwzNHh4Y0pXdVNnQjBvdDhKTGhTZGlSRGY4Qk5VZ2kzaUJ5VnJPUEZ4TGszRGEyUDdtNkJhUjluSWNpSmRFdEpLZGZvV2lkN0c2UjZ4JTJCNXY2OFd3ckJCT1ptczRZJTJGMDNrUUVWU1ZIV2dvckxqYmRUNEpwc1olMkZCOExZWnB1WXZnWVZMMHFJJTJCd2plRXp3N05KT0xHeUFnVDBXOWFtcHZSRnloOSUyQlRFWGs2M2N3RXZwZHF3ZjEzbUNaeGlKR3BZR2Z0MCUyRlRtclJXaDdaamtOQk5XJTJCcVpkQ2ZUNjBoeFpjM1A5ckhIMzMlMkJpM0dmSFdpY21heTJ2N2dGSzRPeXdrS29TTCUyRlZYeGVTTm9Icm5zdkswakV2VmQlMkYlMkI0SFEyY2pPdEFFMEdmTHdMRjRxV3BZbnk0VFcxcmpSY1BNVXFOTXFheUNIRUFGS1VZR1dXS2NFU2lvcSUyRmROWWd5SEg5aWxpMDJmMHkzeVlTbTdpVUhTZEpzWHNDOTRLNEw0eTNBQWZkRUFLQXplM0ElMkZ5QVRUU3lOa0ZEaTdNZCUyQmJPa01UYkRWN25ZeUwwamo1dVNKJTJCMEdidjdGOHZwQ1BiTEg1UGF4YURSNWlZbXBUZGlsMHVQTDFkams1ZEgyY3c5QkszU2psQU56UWxmZkRIdmg2JTJCdGppWDIwUjBlbWUlMkI4TFRCTGlyU0ZJdVExVVREVEVmU1Z3NURCaFp5eHh1WkJaYXAwSFBwVlBVYjJHbzBSQ3paMVc0ZExJcDRHbndTYUV2VnFIc0ZZNE45eXN3UWFMN1NweXcwYzdUaTJrZjJYcEM5QWRWNjJVSGdHRjhrOHpCeGZRJTJGRklNZlclMkZEbHlXM3U2YWV1Vjk2YkpPZXBqS1E5elM1UzE0RUtTd3E5VDhOc3prYmx4b0JsVXVhdDJUQUdYT0p6WmZMbCUyQm15c0ZmZk8lMkZRUmZHJTJGSFFBQjltJTJCaDdPMlB0dmQ3ZEU3TWdwUSUyRnhWNGU4S1Y5WW00b3BSRktWU0g1UG96R0VQT1NRUzZnZFdHNWtOUGZiVnlQJTJGQlhCVHI3NUhOJTJCcktlUjFucEIlMkJlMTY5QUVENTRSMGhQWExIZlQxektQWXl6blA1bVl1SmZ0S0oweUlsSGNjcGdZVEowMzZ1ZlAlMkZKZnZ2WHhpaFlVWjY1ZlZEJTJCWHc1d213c1I0RDBGWGR4QWZCTXA4dWhBbXM4ZnhtUVhvVm9mM3RRZGFBZ20yWElyOE9YN0pTcXJYeHo2dmFtdjhjbXhodTNEeEs0bTVia3diQjdSUlllTGxBNXZqVTg5UVJkNlJkNEdwJTJCYUVSQVBhWlNyYVVhSyUyQlNaODY1RHlsVDJrNlpSWVRKcjI0TyUyQjRFaUYyZUxra2xSdnRXOUN3b1VJMU53eGJMTSUyQkN6ZWEwJTJCTUM4SXV4aCUyRjBlaiUyQmt2MnFCVXklMkYxajNQNUUxOGRoR3BlejU2WDRiZmpHUnkxdnVmeWxEJTJGNGtqcDh5SGtaTW9nM3BIMWphQVBVRHJyY3lhb2VtY01FQU1zTlRTcDkwMG40eUw3RWxQb1NxZXJaeTBPZU92Szk1MEdodGdiNkw2T214VHFpZHdHJTJGRzAyUm8lMkI4ZWx2eDZPNzlpQlZhOG5TcHc3TmVrZzJuZTNQcnZkaGl6WDFMdGolMkJ3QlFFRDVMJTJCd3hkNUtJdDcwVEx1VU13QjBUVEc0THBqQyUyRkNvZWhYU0pxZU1xaU9xaUdoN01lcHpwaU5tZ3F2VXB6RlFpT1BzVHp4dlZwT1lEcVh1Tkt2V1NrYzhLQmF3NktxMFdCcjdRUHo2YXhEb2VRNktsTUx4V0t0THBuTmRBNGhkZ2RNRiUyRjh1ckhHRWxtODQ1Zmdiblp3RUVFVDBuZTR4dnUlMkZrcHg5SUdGJTJCdWV6QmRMVzBGSlBrUVpHNW5PMmVhbm94QUxoRCUyRldYM0Q1SnJla0lkMFl0SmJkRThEdEE3dUR6bXFmOVNENmVIbnZDJTJCVTdvdnI4WG9uJTJCcyUyQkVkbVMxdjEzTHBhVWNHd1hVZlh6JTJCVFU4VExtb09MYnFEVnNndGRPWTl0cjZ3azZzNTZubWVyZVdMb29GU3VnaWJtb3l1dE1PdVdVNjI0VUN6U29sWUZ1OW44RElqVThNaHMwJTJGblY4ajUwWUdvU0IwcjVnSHk1SlJkTEJ6SFNBS2xDZGRYUnV0ZUJPaFpKSkZZWWZKbkRDSGFrRiUyQkdvNlV4ZG9XUllyJTJGcm9FMzVmRU5yTmVPQnRmN2l4OGxDUzUxN016cG84cUl4MEY3Vkk1cnIwRUdNaHRFZ3RnclAySDhSU3hnMTcwc0NjdTJyOTUxJTJGQVFCbiUyRnhTVldZMEdOWTYlMkJyZSUyRjhLVDYzQWZvJTJCOFZUcjh4QzE0S0l2TUdDcUhRbzhxaWlicVAlMkYlMkJlaUQlMkJwVzNoUmNtRElNc3RacmtkanhoeEszd0dxSGdybVg0UnhsT2FzRlBBdG9EUzRhUHdIYWdtcWdTRTRxTFI5b3RlUm9VTFElMkZLWUozeTNYN1dRNVdFWEVkRXF1aThPWXJpYktoa2hqaDVBQUhZd1NzZUp5dkRyUTJaTDBXSEJkTHFLNTNEeTlQTDBLYVJkd3NNRmhSYnRNWUt4UlRjcFp1ODg4djR4ZCUyQm5rb1VKcHhObGhYbE02TWI2d0UyJTJCYjZoJTJGJTJCUDNscmZUMlRadnVXM01wTnFiMWZtMXNoTzBVUGgwSG1kdTBlclNuTFpZZzlWRHAlMkJzS0VNdTJwM0VxVWR2Yjc0eW1Qd3dJV1pFd0xmMUp6TE8lMkZ6ZTZkWiUyQjRKN0J3Qk9qTVFLMDJzUVclMkZVTXFmWEg4V2U0bjVJUSUyRmNJVmFtamQwbkZJd28zc3Rma0hrdzl3R1B6bHA2cUJMS3dTa1JINzZKbENNa2Z2M2p4Qk45RkRyR2NvTjlMUEdRMTFoa2VrNW42RnFxVHA2cTg4MWwxRmZNa2NyeGRBYmM3RCUyQnk5NE9oNlZIZnNLUFZUdng3R2VrZFA0QzliR2dQZmo0S216ZEduNkRnRkVPWjM3TFl4Z0pYZ2tHZFo1YnBOa2c3Tmg4ZktRJTJGczJTajJJSjc0ZmxJa0thTm1zdk15eE1zZG9NQ2VrNm9YSnF0dU5mSHduTWcyUjBLZ2FqWHA5aXVTYlJQZ0liVkZESlNnUTZvQXZyZCUyRjlMY0MxQUslMkI2eFJuTnRIVmZlc3lWU1lCblR0SjlTZk5VU29aMnNDcFBBdDdjRWpuRzlwNVA1TElUODVJb2N1OWR4ZU41Rlp1Yjd3WXAxdWltJTJGRFQ2YkpSb3UwYmhmS0Z1UnZyNUVqNWtwRnExcWJFUHgxak9oU2hjc3Q4dzhNSmZvV1g0TGxOSEpJRzFWOVlWZEszanVXTjJnOUUyVnElMkZtb0FteWdFZGpoekZhNVhqWmJNcTNSeTJ1NmR6OXVlOUZ6UmtoOTBWJTJCU0t2a212OHg5T1E2USUyQldSZjRTZHMlMkI5T1V2JTJGTEd1ZEFGdnBTSjhqS2R3NmlBblhyWGolMkZKMUZCRVJQM1NIVmpjTnVoM0ZQVkNIS2U3JTJGakNiM3hWN1NrRGdXWUszWnI4c0J0UzczWHFSaXpJU3BTY2tPM3RlWmRhaUhkOVlZR0VMc29pRTNmVzFmQW9lTSUyRk5YQllnZWtITnV3eEhFaEpJdXhxUTZHSCUyRjBvN0ZUeDhxeWlmRDRFTk1aWFhSazZJWDVhNFZlbE5Ka0ZuZFFINW1kYnVsbHJOenppSFdWaEoxaEZlTTJOV0hwNG9TRXFmcVVpUmRrRFRuVHJhRiUyRlhDSGRUSEIwUzFhJTJCJTJGTEF4S1A0RllHYTJXT2NFSkticCUyRjdReEFXVGdTc25Jbnh1TDJYaVltJTJGWXFRQk9aY3VKSXQ5WlQ5MGxCUXc3YWdNQnExNzRCYUZ1alhZSU9TSGo4U3pDc1VkVFc5N1JQN0lYMFM5V1l2MnBkRmYzV0xjZWo4MG9tN1VVVWZSNXk4MU9zemtsT1RtNzluTm96UDRVdFNIUnlrMWNtazhsMCUyQkQ2dERoaGZ4R3FSd2VYcjB4YlVySW9IbTN3d2N1Z3lEYUY5Z2ZLaVZhODllMW9SRzlMWlVwRnhFeGxjOXFzak5acXJMNDhWOHFId1hQdmZxelRuOE1CcUtWM0RTWENBTTJPUDhXWE9mMWZ1WDU0Y3ElMkJGSjNsUGxDdnJDWmIxQUdpTlJQUnVGVDZkNDdWRkZ5JTJGc1h1NyUyRm1zWnFJWHUyWDB0b24xNXVDWFZPY2NnZHBJdDB5dHZzcUpsQXdxWnJTSndwdzZpd09zJTJGTXNDVnl4JTJGcWUlMkZQT3klMkZ2SDlVUTZmZmc3NkE5SHZoR09haW1IR3R2RENLM3c0TDg4cmoxMSUyQlVnYVE3ZXdiTHN3SDJPbXN6NGRvUG1NeXd6amFnMG5sWTNxQkxYckpORVY0R1hyVHN2eDFiWmUwdHo3bHNOQ0hVNHQlMkZEcnBZUHd0d1lhYkslMkZsQzNac1NyMWZobllSVUd1VzBFVjRGa1V4Rk9WNE51Mkxta21yU0JqQjBzdVVpNmdIOHdreVpZbUFRNDFqd0NMUTVwdVk3dUNiY2V3S1IwUURuOHAlMkJuajlCMlNYM2hiSXNQa2tVa3pXR1pqaldsMTJISUZMTlFoZlZEOTRBRXdnNzYlMkZZY0lKU21IZmJoZTh5eWRKTUU4ancxTHUlMkZ5RFlKJTJCZzZCUmdkcHpKcnRjenclMkJyRlNHaHlvQiUyQkt4eHJNUGM1dzlwTGRTTU40MW1HWVZnVnZibFlZQ0VWYktTUkpFS0MlMkZpbzIxR3RuZGNhY0FnNXdCcE5WeEtQbk91YlFKUFl1WDF0SGRJRXd5dWRna3ZPTFNzOTdpdnd3b1NXTGw0WmIzT1hqT01IQTZpMEFNWnJYUnNzS3ZQMzV5WVlFUGV1JTJCWkhmdGNOZmh0YmhUWlolMkY0eTglMkI5VnUwWUYydFVlZGx3SUZzMXhjbGZtaVNKbHQlMkJCVSUyQkdGaEppb2NSTllWaDhReTRCM1A3NE5YUEVlQ1lKdTRaN2FXNnNxM0xvclBTYlFicmVQQXIzcXZvMmolMkJsYzkyN0JDQUFpclljMDQ2dXVDMGtQRVN5WU1WOUhQcWhjbzRUbHE0VXVwYU5sd3ZHVWc3YTRqcXdGN2RFUFU0MUR0aSUyQnVxZFVuJTJCZk1tdHdnYnBMQTY2ZmkzTnFOZVhKN0U5R0w2bGFxJTJCSFhUd2xUNXpnOEpxUjZDOHkwN0h1dHI5YTVmTlk0Ym9nckpiTG5nWXlKM1ltNExSM0ZpTGRCNGN2c21HaXR5eFpGMXI3U1RsVUhqaVl5d2F6SWQ5VnhSUUNMRlJFQTZzMVdPVjRLUFRQaFlZYjVvczZnaTNoJTJCVjh1clREb2tuTHVMbVVOZ3MzaGVpaktqa1dnSm1ROXM4WHppYjJrbHNzQXdpeUR6eFV4QUswaFRCQUZOYVRWcnpRUEFKbHhEWUk5RExCNHFmJTJCWGl5V3hHS05hOXJPVmxTMGp0dHlxSmt0YmVYemhIRmdVcTlMcUV3T2dDUWFMc0hyblkyUWt6M2hjZXNlbHZLRmklMkJFdFd6SUhCRDh3ZVElMkZXclU5UTYzeUpmaXpWSzZRWFdleGpxVzgwT21pbmlZbiUyRk1SWlNXV3RjZGZicVQlMkZieDJtcExmV3lZQTBhY3ZPZXRhSjYxUHJwZVc0NEElMkIxTWxrV01nMEpCaGlCckZXdTdrUXpMMU5kUTFQZHpiS1J0VXU0ciUyQmZ3azNrdHMzUkZjc3hiczB2SDlKWFZOVmcyUWc2YXJCOTR0VTlvdUJ6TEhlNEtCV3hSUHJOY3FjWnF3WUdhbVZGeFppc21taDdvbXM4TnBaaWtmRUtTMWNXa2Vib3BSNGQzN3NPYXdHUEZVVGJmcEFUcXZ4OHNtbHNOOHY4UHRHZE1rRDdQbkxJWkolMkJ6d1ZDWVg3Q05lSXFzdyUyRlhlQSUyQiUyRkNnN3pGbU5sYzdHaHhOSVZWWm4xZFY0WnNUNFB4MEdPajhWSEhYMDFFbHc2ZUxJU2dSY1ZTc083TDRLYXJyYkFLTXZZbkM3R2VMNE5xOWt4NzRxMnlUaHRza256JTJGTDdFQmQwNFhiUkRLMjFXcmg3JTJGNmhKZVglMkY4MUZXSmZjYmh5ZTdReFRuVFRGVDVmQm5MVWZJT1JtNHd3aXExUFNBSFQ5dzNVRGNTTkF4YUpUNDU4czY5RjNwQlhucTVjRlRhSmpkdE9VTEZ4a2hnM2RWV1Z1TGV4aERJOEpGR2FZNENHSWs1WE53YXJJZSUyRjJSQXdWakZEV2lPTXVwQWhYZ0VWUFdoZ09leVluJTJGNlhrZSUyRndnc2tDWmY2NW1YcFdXOWk5SWdCUTBhSWl4SmRLeWZ0a1ZXNVVYeDdGcHVJZE02V3pVYXBuOFJRdmNnU2RFSyUyQjJZOGczR0lMZllPJTJGd2pPQngzeVlFbFg2WFpnb2FkeXVYWVlYOFAlMkZoaVpOMllBZEw0cjFYQUZLN001M3VMZFkxNFQ2RkpZVzNydnBhOVJkbDFReVNLb2xFOEZaN0tTOURDJTJGT2tzQ0FESmgyVFltNkxjeWlYbU91SUZ3VlljYUdXUmU2RlFqdExhYWxUWGNUUjclMkI0M1NJcUtYWWpoc1pra25BcFNQNTZEN293UVVTNmF1UGslMkZRS0FtSk4xSzlJNUpWSFVqZjN1dSUyQmI4WFpSS25vUTVjNnpwRGR6NWRlYVNOVThpZzBiMmFFSHhNckpmQlZpTWpLSFlDSVdQbHIlMkJGWUFTTjg1RmVIdE94R05UMTdjM25EZWdLWXFyYklPTTB6UnFadkpHYjdwZEljbU05VzBVVGYycXZPVlFFZTlvM2R4VTYlMkZRcDB5VyUyRnRQaG9BUSUyQjglMkYwbjA0RlhKSGNmeHJVQWtXT3dxcDBwQmJ5WWJ2dTh0YjE1VGRpYWdTbjVzZ3BVdSUyRkVFY0xZUHl6MENkQ3R4TCUyRnVWam9XTHg3Q3BjJTJCU0s3Y2RRTmJiTkMlMkJXTFR6MXdOUWExbnE1ZXlhS3E5QVJJOFNpR1ZxVnBqYldDMmJhNGJnOUJLRXglMkJoZVdTUDZRejFBSDVFYmNyQyUyRnBzTVFLTWE2Ym1uYlBSTDhUWVQyWEolMkJmdVR6VVNld2Y4aEI5c1lLZVNWc2NwNGlkalBQQXRYVmxqRElORHo1MnBsTDNiajVOeXJDdW1aUXJ4VmxRODdwOGtNU1R1ViUyQmVaOGlwJTJGV1Z5OTA5eGVCZHZVdktuOUcybDJCRkUxdWZ1MUpDU0hBSzV1UmZwM2dMNWclMkJXOXJTcVBwTnY4NWVLa1pObiUyQmlPcTRVNlY4YmNjN0pCS3kzRFdvMCUyQjlpcjR5WkpIN1JYVWROOU5LWUlqdUgxd1VwaUREckk3WGEwUzV4c0NybDVGWFZsNjUwWHowU1VNeXJNYjFYczA5Vko3R2VZUHVIN1dqTm9ZT3RLSjBjNnAlMkJCbGI5c3pqa3FyZVBLbHFmRWJ5TmVGdlR0d1NuY2NrNEFjUW5sSEhXTVA4cUZqS1drcyUyRm9YUEpXeldNYkwySWNSWG5IeU9JVXVXY3hhJTJGaWdxbXNZM2lHTzg5Z0RtQVBxSkw5bGVoV2FvNklYZjdtME11ak15TzdQTTZPV0lVQk9jQjZSSGdwSlVRdkZ5UnNWc3VLT3I3OUJkNCUyRmZQbkdXamxQVjFMUWlvb3dJblloaWY0aElUaTl3eThIcVB0N01INW5OeUdMb3BoNlJLZk1PMkxYZVR0VUhRM0syajFleDJ0eUE2RVFLdmpCZHNqWkUlMkZONlhNRFZQcUozclhnSTVNVlNXT3Q4Q0h3a0I5M1F2eGtVMUNFTnh3cEslMkI1JTJCSVJJcERXY0VzZjlnYXQ3NVdmd255NjB0ckNYNUJwZ1VOYWpYcDFycFVkTXc5Sk1PbEpXMCUyRlpqZzdWQ2tjWkV6ZkZ6am9VM2MwUVNMSzVVblRFOWJSJTJCcWZpaGZUTXcyVDBqNHJxRzgyRWU2RnVKOHV2WW1GR0kzdFcwNFV6aGVCQyUyRjdDdm9EZWVqOEVhaGNDbjUzek1BcnBYSTI2ZndGJTJGRlhYJTJGTzJNb1Z0Q1VMM1hQV3NyQ2xTbDNrb2xNMFJPRzM1Q1kzbXRtTzBEOW9JZ25ZZ3hqd3R0bHlmUDlnNUZPdzl0clFxcXJwJTJCQ3d2QlNCSTVYb3U1bDNWbzVPYWRYQ2h5amJLZVFSRVN0bGJGSEgyNEcyVkVDVUZrSFlVRCUyRnlkYWxhUlgyOXdxUjFkbGNySk5PNWJ6NUwlMkZ6bEFFeHl1UlRUS2JRMVhxJTJGRXAzaUtlZEZKTzZ3OVNUdmJFaUowU0RxQWkxa0tPVkhoODFNNiUyRnF0VGNkUms0TUE2TEg5R2sydERtRzdhd25WbiUyQktqT25YU2FDTVB2TkdjblBSR1ZTQTIlMkJiU3dhMUYlMkJHOW5hRTJUUjR3MkN5bkdTWHglMkZhN2xMQWhyYmlFZDl4OGhTQ1YlMkZQVHlVY3VNZGRXdHF6ZlViNEc5Mmw4STNzVkdFRXlXMG9SWVF1YU5UaWJza2hBNUtibUdxb3hpVHV1YkwwMGFVT3JIcHRiSmUlMkIyR2NzbWRQaEJyQyUyRjZwOTVjJTJCS2U3ZWolMkI5ZlVNY2p6ZndqRFQlMkYxUm9RR2slMkJNYm5jTUFDQVJXam5RdFJKeXlsa0hXZDA5ZUFSb0xTTnJQa0ZYVmlnYTN1YmpYUjZwOCUyQkNxdEcwZWw4bzZwdEloaUxvNUxxbE1TSVBIMXZON3MlMkJuZ0dBWlVpekoxaVlDSzZKUExhT0I0Z1J3ck5wJTJCUHQ5SmVlYXAydmd6QlJPVVcwczJmUjklMkJmeXRKNGR0ZzBVeFB2WDRwNUV1eGptVFZScTRjV2hIdHVVbmNvRXVIM1hWZDVCdVkwa3FzSjdYczFRb2ExZCUyQnNzc1U3NUk2R2NhUmU0JTJCeXljejRzMjFnRTFpZmowbWJDRDVTdzhicFklMkJtblZVNGxURk5nMUJYNkJSU1VvczklMkJoRTh1cG8lMkZmMDFnWHdsd2cwdCUyQlJpYkl6bEE1Z1FwdURFZyUyQjlhc29MY3hsT3ljZ0lxQ1glMkJlVW5qblRpcEdmYzdXVHFmYkxSRnRRcmJEUzk3MTRGNEI0WmVXMG1UVFptSnE5YjJyZCUyRk02N2oxbmd2bjB6JTJCUmFoeXl5YnpKSW5vTGxZQXNQeXJDdURnUFY0dk5QUVBST1hCdFA4bUlhWGI2WjVPT3dPOExCemZ4a2FEN0R5dlJmdVljeHBtWlVUUyUyQmJaMWl3RHl5eXV4QWVRJTJCRnJMb0laRmhxUFhwek1CMWdaS3Q1cXVQVGFmR2JSWmx6TzNIUXMzSnZBaEI2ZElxJTJGQTNFMHMlMkZaUUtVMGgzT1FNdnVHZDFaMWVPM2VsSUs2U0RQU2M5Z0clMkZYNkZJQ2JLYXpkdzVJdlVCJTJGTk1lS2c3dk9wZnlUT2p4cmVqZUt6M2JIcE50b3BpNlZVdnJaOWU5cSUyQkdMMG50JTJGT0licWhwRkdwOFBqdlNNY0F2JTJCUlBHOWp1MUI5ZFE5ODBuVExSVXRqWHFETG5aOXp5JTJGVWtvcU5LamklMkJBekglMkZCT0ZYemNURlZCWW5nSHBMMDg2aDRERWdzVlFVMHlYeDN1Vjhpc2U3UFFWWUlpQTVIWUEzanVBNDIlMkJjdjdBblRWSVFEUCUyRlhIZWl2JTJCb3cyTEFTdVolMkJEMERZaFJsbFJJQmc3aHh2RHZTU3NtcXVtRUZJdjh0RFRZS0klMkJmUml4STl0S2JZMElLSnhOUlFzWVZHRkRCZU5KUU5MMUhjeUtaeFpsSTdGeGVVSG0lMkZCU2xxbVVObU5VRzh1RnZ1elkzUmdDZXNYciUyRmh0QXBQQnJGbDNEU0hWMGxQbG15aFQ2ZXFCJTJGeUpFajB3SUExUk9iRU92V21KdFBndnZGcnoyeHdleHc0dWtibEJnUDlEayUyQm42RnphMVd2MTRhcGhaSnpIYWllc2FyR2xyRGo0RCUyRnVwb0NaeE84bkI5JTJCTGh4amtiR3FYOVdpU0QlMkZwZUJWNVZoRzRVRFk4b2d4VnJDM1hrZ2Fic21JUWpvbzNLV3ZuTEwlMkI4dkRqYXJMS2RLbnkxWndiN3drdThmWVk4Nkh0TnFjQmlXYjZQYVpLajhoOGVLZSUyRnh2cjhNcjdTRmYlMkZZeGlSbmhDZmsxVDdKWEFOd1glMkJyQiUyQkV1ZTlmT09QVGxVdk1uejFlJTJCODFhY2FwSzdPdlo0V0Ewa3h0UzVMNXNqa2V5WkpxQmwlMkJUU0YlMkZQOHh2OFk0N09FZGMlMkJkd3prd0piUWRVVTV2aGFpemRFQ2lKdXdWWGdtM0lHbFhjMHRBc1JSMFBxWlhIelRXd1BIYWQyb1VrTWVvN3RhcGZkOW9oNVYlMkZSMTJtT3BxJTJCeENLOVlzUGNSZU4lMkZTV3hoZGJmYmhGN2dYJTJGZTFoZWJTNnp1WTJmamZQSEozd0FPNjVGY0xlVjQ2QW1HMCUyQm1XWFBCJTJGT3c3Um5yNGs0WVF2Z3ZGTnUlMkZzYlNySyUyQnZGUE51cFZrdUZRTkZFZHlRRHBXVWtpNEtmejRZb0ozMWYyMTJwMVNRb2lYYmRyWjN1a3hmaFZhYVRsMXljTjBQMGhHdDJPcyUyQnd0RDBRRXJKOWVPc042UldIbyUyQmElMkJBcm0yM2RzY1c1Q2U2ZUM5dUtFZVp6dnB6eWlJWUVJbUtnMTd2MGZQOTNic2E0WDhxSjhhU2VuNnpEekFvanduMjN4biUyQk9LNmZSM3lOMkRsRHQlMkJ5Zmc2WlZuQUF2RTJXMm53UGZjJTJGbnNmN3dmJTJGdjhFbzRLellrTG9PbW9UNDJiSzhDYVFLUUpLWXRIMkIyYUllSWxncU5aS2VGajRHUWU1Y2dRd1BsV0NQQmJxOFpMdnZ2d1MxbDM2MnhyOXpiOTJVZVh4cDgxd0h5YVVUVzNKM1BpOExBQkkxMyUyQld1NkZGSCUyQlhxVUpFWHJLOEptZE55eSUyRkFib1ppckI3emtVRmZrVzh5anRzclQ0JTJGdXNhaWolMkZIZVRrdVRtN0YwVFRVQjlYcmFOcGQ4WVJoMXo3aURhZ3NQMldSVlFGQkFKc1VySldKeDQ2aTRDaFVxcGp2M1phODVhU041JTJGaXF6M21FaSUyRms2JTJCTmhFU1Brdk9sWVd3czdKOTZ1OXJVVDFlUk0yV254TkFYc3lEMzJaSyUyRjZLaDVvUXkzN2swWnFHUzlrZ3VVNHNFVzhMNllOMXlmQ3UlMkZHRVQ2SDlueXNudHIzN0liaXpwaFVGS1Z3QVBHMFI2Y011OWRxMmJxOEpzeE56UURvOGElMkZ5eGlSMTFCWmUlMkJZcUZOSjIlMkJPRFFBYkFEMmpkUnFxSyUyQjJWdmx3Rm5CelJxUnpwSzVVc3BMRjFlSmJuclpTSG1LbjYyYzA2MGYxcFFPclEySXBNVlpjMnhZUGpLNUtuWjRvaEZKaGtkOCUyQlRVb1AzT0JGS01YUVhMVGw0bnFicUxQdWRWZXhwY0h0ZVhIQ0g1ZXFjYm9UeERndG8lMkY3dkFUMDNoWW90MmIyVTV4dE4wTWo0UTJFNFlPajI3dEI0eVRMTU5oMFFkekxQMWxYbE05MmhlczZyTjZuUmQlMkYyeDZKb1RXbEhFb2h6NGpjeFNXOUYlMkJWTG10WjczSzhjQ0tmbTNQNDBHTnRWRkVRMHBIN29JOEFtVkpJZmpxQTZETlZXQmoyVTRvdSUyQlN6NEZYZkJwVjIyeHZwNHY0SmZUOHdYcnUweiUyRk84YTh2MUNrWXM3ZGRJejJ2d1I3Uk9hNm8ydXpjdnFtJTJGcEJrN3FEdEklMkJsRlUyZExycGZUTHdKQVpISndIRXFvV3VFb2VqejluMTNnWDFCT2xldWFCRVFzTWVGT00wJTJCZVpWdmw1OWJqdWp4b045cmxWTkxOR0xGRDhDelo3c0dVeTZlUlVvazJtd08lMkZJV0puVWdMV2twR1YlMkJjNGFOd0xhNTVRYlp2bEJ2MDl1ZkNGckVzNDZwM29QUDJhTlFSN2pURzZ2SkVKMHpxSldZOFI0QkVyVlZlS24zNVFjbXEzcVVYU2I4bmQ5TVcySG5YOEVKNE1QNDIwblJMM0k0QjNvUnB3TDdwQWpXQ0M1M2pudTQlMkZKZFRYaFRxMk0lMkI4T0lFUFR4WTlIRVBsJTJCeVJYdTRPVFFxJTJCZnNKJTJGOHl5THFnSVFXdGNVbzAwc1NDTUNPJTJCNCUyQmVGNEFNM0RTMzROOTJZcnUxcVVVWmh2cXkyS3hmSjNERyUyQlF1Z2U0dzNPcnkzMmxIcFdya00yME1vam5manFORkYzSE50TENVd0IwY3pkViUyQmpoYlh0V2FMMmNic2xwZGtDJTJCJTJGUUxxR0NaSUllVTFCWiUyQjFrRUJma2JtTE5KYVZ3eERwJTJCbWMlMkZlaENySDhSN2owbklPcGdpSkZlOVoySmpMJTJGQWdPNVVibGFjWVg0dUZQUUppZm9qQzFBaDJkNiUyRjd2elhIdk5IVSUyRmV3ZzNiZDZ3Q0wzMUFpMmZjeGFVZE9XYkJJa3FqRFhSa3c5emRWOVJ1SFY4YUdJSCUyRlVHeFlzdyUyRiUyQklMWU5SZHpJWlJSeHp3aE01dnN1bGY2ajFqamlyblcwQkFuMzFleXdRJTJCblhWJTJGNmR4Y1JXOUFRJTJGUUFYVnR6MUZrZXRWSUF1bG1tcGdGSnlnNWFWakdYd0NObzlRU3VtQXA1NnoxdEMwWTJQSXI3JTJCVWNrQXFiMyUyRnpyNE4lMkJJekliRnAzV3ZzWllVZ29tZ0ZlM0tJeXFRbnNVdVF5NWx6VEh3Z29CMG9rR3AzSHR3eGpJTW5OODJBVk5CZEFyYWV5Y3p1OHV2NDN1QktpWjJLMko4YVlwRTVZJTJCVEljc2JmbTVobHY5RXB1VmNjeVQ0cG5KdEtpQnBodSUyRiUyRkNQeXB2Z1AlMkZDTDNYUHp0WjFQU1Y1WWhqNVYyTDJWTG1pMklEb0dIOWhUckVIdlh3bm5uUUVaVU5Jbmg0RFpwbDl6QU9TZG0lMkZiNXZnWElGbDhnNTh1TmpiVmJMWVBBU0ZTJTJGaG5BUks3MHJpVjhLa3hQTTV1TnN3VVFCelZIa1lhMzl4cFpjMGthQWhhMlBtYTFKUDJIWDhxRnY4VVk1c3M0byUyQjR6NFRqUXNjbjlHb0xqMDZhTUJzS2xMQ2lIZEI0dFlmUzltUUd5RmQ5NEh6dG8lMkJ6UGNuUUcwMWJsMExyM2I2RnY3RkZkMWpZTGpwZkxPRTVXVzg2Q0kzaXNPemdqdk5hRnJJQmpmU3ZqZmJFaDY1VmRMd0hoQzByOURranprWTNJc2h2T3ZKQzJwbmV5S3pxdG15WGc4amxpbll0JTJCV29UUHNxcktkUmdVU1dnZlBIVm9UQmY5aGRLSlladklhYiUyRlJHZkVyWGdubmc3JTJCNHJhODclMkJvJTJCJTJCY0diZTFPdWFlWjBvTU53JTJCN3Q3ZUl6cFVaTW9ZQW1Yb2x5enowT1NISzdNTVVsSmF6JTJGdjkxNENMJTJGZGhEMUlLWDl2a1ElMkZ1aGk3NlRZTVVweDNDajVEZHZOdmR6MUY5UUdvdHE1S1VhMHZEeXc1d3Exc1JOQXdSZkUwdHllbTBqWThaelpXVXNlVlVKYThGeWdnNUV1MDRPYkx0dXZoRHFTM3MlMkZkWU9oUG5XRWxtNDlYN3A3M1hyTlElMkZXaGhDUlFWVnZEbDRyYW5HdGd1ZFN6TWZ3M095RHVQdEFoNk5SJTJGdkdwckdzOTlQRXFJRmtFRlRmJTJCWFRFejBlMWU1clJMUEQxNVFlRCUyRjdKZlVLQzdaQTdDTE8xaEk3NEtvSkxDeXFJUUh0RExsJTJGcExUb3FZbFdYQzhHb0R6RlY1RlM0a1dyQTFneHZVRG9aUEtZdnpKV3FXUEVtaDZ3RzVuUTN2dWN1dUIzeHV3YnZRVSUyRkFSd01Nb3dGbjhXSlFjOVdlWSUyQkNRUlJ4T2luSFkzJTJCTWdWeVk1Ymo4JTJGcWtyZHY0MmtzRWhwV2xhY0wxM3FQTjEyS1B0am5Ic1pud0VLNFA1NHBYOGlEcEQwaSUyRm0ycDRwODhqa3czd0NPVjQ2Sjh4WiUyQnFaRjUzc25FWVJmZ2w0Y0RLMUk4Q1VkSkUxcENlRTE2VnUlMkZzQmNTNzAwVXExbUZJdGxGM0EyYzJIU0FlWVNSYk1IV2dMQ0tzJTJCT2thN09XV1l3JTJGQnFac2EzVE1QRmpBdWlBc0diUEM1V0NPYWNhQmY5dDJET0s2MjA5Zk1VOVFOWjlCU2RPWTlVTXZqQUtEZnZuUUslMkI5TlcwNSUyRm9pbE1jVW45WTNVTjZPRVl6REtjU1NlWWdRbnMzalVWZFgwbU9ZVVF5RnVncDRRJTJGdzlZcFF6UFJIbjZMeDMwb2hLZFR0cmtzbUlFS3czY05GQWFNcVZvZTlXeFVMRXZ6U0I4dTljWXdPSlpBNmI5R21BTE9JU0g0MHkwU1ljUlgwUVJSSWwyOHQza2sydVo4emJmUmNxQnhNUE5WUCUyQnE4cFNTRWhmNTFXWCUyQkYxQzZqYkllTlpDNXlmcUpwWnl3Skk3eWk1JTJCOXNCcFJuWVpmWjBCZWJzYnNMd285OHJCRnpjakdEMlN4N25pUVFNYW1LSVRwVmRlTFhxZDNXaXpUQnRCQUdQR0UxbGtJdXlpd2FPV1Q4S1VHZ01BdiUyQjZ4RUk5MFAzUyUyRktCY2J4QyUyQnJYNEcydVhTclUzWXpQWXZNJTJCSzgwM21OeDNHTnk1MllEeHhXTVcxcUthUjZlRGNtc3VCMFExN2ZhdTg5UERGZU1PTExvMGVTZDVsSnE1NXo5M05vdWdPTWtLZDM5aHIzeVFESEtoa1JkTGl6R0Z4Vnd5d2E0b2h1bGxTU1Rkc1FSamxmVCUyQlVoSHc0eGZDeWgxTGNJWjc0UXIlMkI5bVcwZjluMHE5a3Y0aFZNaXh5Rk1yOFBzdVBmZiUyQjAxQ1VRJTJGc1phQXJDb1N3TUxHYSUyRkxINGhuM1d4dkhOdyUyRjRiUUJUalBORE1PSCUyRiUyQjJ1UFhiY09mNDRRbTI2Zm5wJTJCMDl6SWlEa3NmZjNBRGRHREFjT3h5OTJkSDBGJTJCZlJ4R3pTYTQ1WFpMaHBTTk9NSmhZZ3FZRVhpclZwdk8lMkIlMkJFUzF3WCUyRlhCamJVdVUlMkJtT3FRQiUyQkFvWDZwVzNDYUdXbHdxUXZsMVAybTZZVmxjb1ZZckJEU1p2WGNlOTYxQk5MME5WSCUyRjVkZHVhbVIlMkJDdkhuZnpZRHhvWGRWSlIzNVhCR0NYOVZ1YktZN0lZZmtMOE1TdU1PNkVUUVglMkZnbEwwSGcxakdJejZ1Z21abU5qOFJWaXVDTlJqcWdvVHlkcjNxMTdKJTJGRlVqWlR1YjYlMkJvSyUyQjRuR294R2d6YVNJOHE4VVpWJTJCNjAxbWNOUkVZN1ZyeExMbUI5OG9IaUdCT0lMQ1d2ckw5SnJiNHVwd05nMEwlMkJYb2VWYW90JTJGQ0dCNHdjOXRyVlhiZ0J6QTU3cTdjNmN1JTJGS2lCM1VBZG9CQ1UlMkJFYkFQcnZJd0g4TlhYeDE0JTJCYm5MOEQwbSUyQmdIMHVyJTJCZFglMkYlMkZiUDRUSHpXbWglMkJMJTJGQjlqNGllcUhsTyUyRm5vaCUyRmd5d2xCMXgwUDNOeVRtTlRJa2drbmdaNSUyQlpUUnVYMGdRV3FXOEhVZXlTMUc0JTJGWEpWeUlOaUNIJTJCOGltV284QzBVa3lzS0NyYm9KbVolMkZsT1pERFVEb2ZYeHglMkZIOUhZdmttYWZBMjJCSlF3N0RkOVVIeXpsRzV6Y1BpYmR6RWdQZVFiYlpweEJVUyUyRnJ1dmR1SXZ2dmtTWlVlZFBBU3N4b0FIUExNN2phNlBtYjclMkYlMkIzdWR6MFN2cVp3VlVJb0JTWXNXOE1JZjg4UDN6OFdtdiUyQkZWN3VmWmxpRmJPU25BMTZvZVo3NUxZOGNvUm5GREZ2Q1lldENYbEF1NEpFZWY0MlBqNmkwTGVCc1kzSHFlQmx2Wk92dThlRSUyRlBzYTM5TTdtdnZQeFdzenpSTlgyRXMlMkZRcnVRejdFZW5mRGFVVTY5RExmbTdPdkRWJTJCb1I1TDVYbFJFV2wzJTJCSlF4JTJGZnR2Zm1NS0lUY09KOVl5cEpkNG9XN0VOY2R0VjVNck5jZlRralNmRFIxJTJGamFKZzBWa2E1SFBNZnpoWlVOQ0tDZzJvJTJCUU9HZGxxZTVDdDY1c1NPWnRybWdYNVhveVBhWEljWmZnbmdFTGNqRDR1enBYVmRpRFpod0dtaFJjU2tQMVlUOGkxZEM4eUV2eW95cFNGanEzJTJGMGRFZHBTNUJBcTRYVjhlUHZiSE96VDc0UjBYcCUyRnU0VCUyQlM4MlUlMkJGNDJoODEweUs1WmczY3RtUUpmUG1yM2Y2bmNrSVgwMURVWnB6YXR0bE4lMkY4UlBKemdDamJEWDE1Vk81JTJCd00ybEczWkYlMkJWSVB6VnJEbjNubkV1NGNBTXlTU2dUdERVRkRMT1dodCUyRnNJSzhBRUJ6MGQ2NDhXbkQ2ODEzYVhxMzlweW9MeGVjU1ltUWNTaUg0VWhvbFBWYnJNVWN1eWRyT0xsMW12elYlMkJIUG5zS2ZsanBUZjUzM3ElMkZQU2ZjdFVqdjVaJTJGSVhUa2lpNjkzeGhTcjlLOXVaV09VU0lYSVNsUWMlMkI5OGRNa2RaTFB4enV1RjBqTVUyYkc2bVhzcm9MOGNWeE8xWm1YNktGRjZVRENwVEpRaVlKaEhiaUlxJTJGWmNaS1NURjNDa205QURwdVhQOHpvaTg2R0U1YkY2SGdBdGZSeGkxTXlNMSUyQk82YUFIRVVzZkQlMkI4MWtwdVQ3NEIxdiUyQjYlMkZ1TTFxYlI4UHpvMm9obkYwZjBXNGR4dm44RnJOMDZ5RXJsdUdzT0t0JTJCeFVyNmhmRG5DT3oxZHVrUlFEMEpvUmJKWUFlY1BxQng0aXRJVDcyJTJGN0sxRW83NXRYU1NOaHlnZTZaYTl5Qm96R3VEYjM1M2NYdzdMQzhiNzNBRlc3NTZzZmRWa0owWnd4JTJGZGd1eTcwdHVPbmswQmNhWVR2aDRKMks5MHY5a2F1NHFUa25rJTJGUjZwZzQ3em5nJTJGUkV5d0FvTE5pRm9IeSUyRkFuVmdvTnFuWG42eG9vR1d2T1E3R1dISFFrM0Q2eUlwemE4eFpXJTJGbFBqYUttWk9KVk40YjdJNVZRYW02MDNsSVBEbVcwTjA5JTJCNTdZbTVUTzkxbklJTERpR2I5aTZ1ZExuNzRpTGFCNjEzQ3ptTGsyVUlsOE5aQUNxOHFqWHZwNHlyUTNabE5hUmdTakxUUkRONnYlMkZKZXhweGNwaVhUOHZBdVdabjllbDZIc1FvMHpCR2VTdVZMMXZjcENYM20wbFdzb1A1emw0VlY2eng2TTNqMXdvVTdTc3VPY2VTNXl6QkN2ZiUyRkp1JTJCZ1R5UGh3ZUFQNFQ5cFlub1RGZ2RweDVzSSUyRjI0TWVqc2VqdGwwJTJCJTJGOUV6dUdpTjIlMkJLUzdDNmJDV0RjYXczTTl0anlxQ3JsNDNRZDNEOENsajQ4dG5NVHJmd2xHTExYR2ZQYSUyRlpRZjNXZVFubmdBJTJGRDV6RmJmJTJCJTJGMzNYRmN0WTVmTE83clFldTh0MmJuYyUyQjFwcU4xdzU5d21uUVZrRmZDcnM2RkUwNnFsMndPOEdjVllyczQza1N3c00xR2tjdjlCalI3cVdVTThMdGVKanRidm1tbTh3cWI2eTNQRk8xNld3WU0wYWE5VzV3czJmaDRMTEYzeTFEU040YXpQWXR2dFZUOVB0ZTRKRkx6M3BKTUl0YTElMkZmbEoyJTJCbkFFU3ZwbklCaHElMkZlZGpaTDk4NmI4NVR3SUlmbVZlJTJCaUFqY3lWWGhuSDNsTkR6SFZOczc3c2prUlZjN3UlMkJrblR1d3FNVyUyRnNKZUhiVDBmaGt5JTJGSkpTbk9RWSUyRjVYZE5yOXR2VW5xR3cxb1ZwVWdXcVdiSEprJTJCVWRrWVJZZVBQYmtIU3E3RkJmNmVDU2Yyb3UzR3c4bkpZaWhWTHlqZnVtUmNBV3F0UklCbW1mSE5MSlhrUyUyRk5VMjgwOUc4dlRFSUlYNExzbmRlMHBvVmt6bkVTM1dNdnclMkJleDFhbkxzVVhHaU5WenJGdE1NM1AyZmNYRHRVVlVBdnZwV1hwSUF3JTJCeE1MJTJCNiUyQkRpWEY5eCUyRmIlMkI2OVhkaTFqNTN0djkyZkg4T2NtQTI2a2NWRTBZb0J3bXQ3cVlJdmVPbUJtOU5NOW5hWXhISlg3c1NDbk9hdTNMd3VmSVVOS0c1VzlpS1BYaDlqSGtGaW5WR0NnYnFXRkNLbXZaSXo5UUNKayUyQkVIUyUyQmpyZnVvam9VbVpYcmNFbE1HZnZNR0hxTTRRYmc0WGJnN2paZ2dlaXpvZjdxSW5MbTEwckQyTzV3Nkp4RkJGUmRjNDhmU3BrMW1tVDV0RGklMkZMMGFOa2x3d2x4aWxYbklNNnJ1aWpqMDA4MmRIRHN2cDNJJTJCVEVFZnlQNWF1WTBsT0pRaiUyQkVuN2dpUGZlYzhNTjNnJTJGMjZ4JTJCOWVpZEZLS1JkYU1wa1psVlhyZkp1NmFCZ1JoWkhjUjclMkJCTzYzSmNrTHEzaWg0Mks5NjQzbFVYWERGUTd5ZnZHSUVReFd4YiUyQk9WZnltclY5dlNvbHJ6ZWZ2aWRvbFFSampVRW44cHFvVjVBeGtrQlRtTHdNMXltMGc4SkVGSmcxckt5OG5hbG1JTW41VjFSc0xPa25yTHl6eXlPSlB6aEg2QTNUdm05SURlayUyRk1FWlRjclM5OU00SlFKeDJRT3VJMU1vNE11Y0xzYyUyRnJYMHBYRW5jeFNrcERNdHFzSzdTdDFvQ3pFUEdTZEFWSzhUQkNxNENKRFJZcDQ2JTJCSzFKeHF5R1REMXg3bHdvdEt2JTJCOWNvbkRXaUlsVkdNQUd3ZDdzREp4QyUyQkJ6b1lLZkhROE8zQk9WQXh0eUtvbnhkUjNkTEZQSkR6NWhFWkU5Wm5QSjl3NzBqVU9QNHVmbElPUHR2TUhMaUIlMkJhVXpwWkZRR1dqallGdFJqUlQ0ajN6Qm4lMkI1RXF0NU9oUFdkVjRvYVFCMHFEJTJGdExoJTJCWnYyR3lyJTJCS2JRZ0xsZUtqRXJibHE0SHZOeW5HbWFkTlhtY05uV3U4OHVFOGlaJTJGODJqbjlPeXBBdE5sVmxmRzlNVmxoMXkxJTJCNTg5NmdZaGElMkZWTUxzczdERVdFcEpvWHUlMkJYTk43WW9ZRkFCa1prQ2YlMkI4WGtvSlp5MmlBQTVma1BTbTJQZkpIaDgyUE1FVzU2M2xWZGYwWDFJRDFKZE5GSFNLUnA4V3MxOVVwWDJYblBSbHkxQlhuYlY3TTh3NXZWJTJCeHJ3Y1Z6R1BBTnZrQ3JCUGJTUjV1MG5JJTJCTiUyQjQ3NDVlb0QwQiUyQnB3MDE5RGl6ZzRLRCUyQlJzTFlVNHlUV1VxaG5JazZGUkViV3Blb0NYJTJCbmpnOWJKYyUyRjk4MlFieHJGYkFFbDZRSG9BZ3VTSXJCZnpDVEd3cTJkcXZwd25XYmFPRTFmRnBBUU12VEJpZ3RzaEJJSzFJQnIlMkY5QWdHbVB3U2kyZjdRM0F4ZU5iaSUyQmU2ZHhHJTJCTm4xJTJGUVFZOFF4dWVWUUlkdkNCdkdpYWNzazZXdHl4T2RQVjRjTCUyQkhiMjA1UUx4SjRhJTJCdCUyQmhvNGxLY1ludkt3TUNCOVNpM1pTTzNJQldqNDR1QWFiTkdHR1phVExpREFTQWI1c1hDOGxWRmRNcDVjdXBHaW04MlhFZVdvOFFqdnQ4V2FYZEJhU0p4WlBFNjcydE1KMnpHSXRweGNIdUsxQkoxUm52cnM5OWIlMkJmYk42JTJCJTJGN1NuekYzYVZ0JTJGT0tOVGV1YjlZRVNKdlY0WFFkOHhrJTJGaWZDQTMxcElTSFM0ZGZicXRDSHlYek9sZk5IMFpZQ3RWJTJCcmhIY0Z2dkJLUnhCU3BpWHhmaDEwRiUyQlZxM2hrNGp3JTJCcE11cXFCV3JmT3NPSXc4OTJkeXFHZ0pINmVtcThFTTI0RSUyQjlRcFdydW1Wa1ZNNVJGbSUyRldyVVRIb25OQ1BXMmFsbWxHY1hqQkp5c0dkczZBVnFkTVh6Sk5lajFQOFFuZnVyOHFEUGpUZXk2NXgyTjNOdnhna3BuJTJCZGpheCUyQkVLa0xLaUREZnZNU1pQSk9LJTJCM2dJSnQ4aFJSQ04lMkZJd2dYUTZMQWVZZlpxQWRraTl1QkpnSDRvM0FJRyUyRnRrJTJGSTdmenNmNlo0UDJjWHNoJTJCRVBLd1VpSm9iWjRlZEpucFdUam1CdSUyQlZKcWFocXJPJTJGZkk1TjIyT2hMeTVyR2w1USUyQmFOdiUyRkdEY3ZRME0xSjBoJTJCUXVKamtvTDlEblR3YURJRWVuYWF2emc2dkVMcUUzc2hQcklWNmxBUmtMSWMzVm03eTBqNnFtWFZaajA0VWk2a09tUyUyRmdiMEFBcU44WlgyZkdtWFE2OGpsWnNhWTdxbDR6cVRFcXRjd3E5NzQxeUJ3NXFDNnhpMXZnbDBiY29WaGFURVJ5Z09ZanBqT2pibjB1d1hGd0NoMHYxNmdINTl4JTJCZHJ1NThkWDd5R0Z1TW5wMVBYbWUyWiUyQnd6V2xFekdpZyUyRkVqZTdhWnFhRU1hZCUyQkMyYkolMkZlMUElMkJWazRKSFVuOW5kZjRLWjdxWktQN1dQelRIJTJCUGgwJTJCSWtpd3lXbzlYUWJ5SkRDallNV3dXWSUyRldieTdTVzVYdUoyVGpGQ0x6UmxiNmwlMkZTaks0WU02STY1MHR4RXFqcHpwOSUyQmlFQ2V5emlxeUklMkJoaE5RZzFkZW1NZmVoR2lsUVZxc2tZOVB0RW1GUXhoJTJCMVlNMWVIMWNLWjNma3dZOElUTGpLcHNCeTJEZ3NJMUVsZ2d4ZHdkbU16aU5HOGNZUG1tTjQ4blNJaEg3dnJCYU1MeFJZaGZMbVo1WFR4R3JCZUNwcEFCRFhYa3NOeUxIOU05Ym5WYlJhbDdINUprbzI2aE9jM3N6RzNjdG82b25IU2xWVWN4QnZWbVp4MjZLdnJGZWVObzJiaHNDSHdnZzJzaEZlSUJwYzd0a29WVGFDYiUyRllsN3BSNUZZN0d2ZGI0N05FWVdFS3JqTkJ6UlZ3eHd6dXR6WDFNbkVtSUNKTXpnM05WJTJCb0RYVVBlSFF6V0tuQ3IlMkJrblIwbnExTVRJZ01QM1JjalFYaGRXcUl5clBEJTJCUmFkWExMdm9FMldJS1pvSUxqa3dXYWcwNVQ5NzNjMk9tbWZKWmthdm44JTJGdlJxR3FnZWRLMlpwOW5BJTJGS3plc2FtMWp5U2JWODZqOHklMkJqRHJIQ2d0SHYlMkYxOVJaM2NuSkpMNGhWJTJCY1Vvck5Icjc4b2NpNlY3QTFzemNWWEhKMFA3ZDlkOU1tNSUyQlpIWXNWJTJGcHVMZHQ5N0NtZjBqZTdJMU45JTJCYmhlUkZNbzRCVHNhZ2RvblVublBlNkxnSndTeEZnVXhFVSUyQkdjTjMxODdZbkI4ampaWXl3eHFtMGZPYnVZZERUUiUyRnhhbWVBS2pwU3c0ZEk1dUR0a01yekw0VHBzMjJ5Qzc4MFRKMW93eWdHRnZPaWU2UUJUdVNlMGVMOXhLaHJ3TkRqYTBLUVolMkZnWGY0aUolMkJCTllMJTJGT1Y3ejJ4MWN3dzMxUWNla2FLdHRNYlBmMlBTc1M3MlJOeE5aJTJCMzBZYXhKUnB1MjBCa0ROZ1RDa3RBMUZFZkZxRXlTJTJGSkx5MTlwUHhSYVZxemhobWgxNFAlMkZYVGZ2aWJtMWUzTGVqekYwYVdIT1dLNGY5YXFBbUIyNiUyQlNiZEFTMm5nZ0NMaTJNTGszUVJQeiUyRk5UWllNellXWHdTRGNUQW1JbDB3aEdJcm1qJTJCbHYwNFlqSk5rTndoJTJCdSUyRnNkTk43OHJLZlU2U3ZpS3RvRHo0cGZ5TklwQVlhcnN6ckNsTjJVTlclMkZmRTdyNlRYeTB5JTJCRjlwaU1QT3pMZ2cySTljTWJCbmNvZjBMJTJGM20zb2lSbzNpQm5xb1ZtV0RMZnNGWHBWMyUyQm9OSjB3NnBvaVlNQW1wSFB6V1VPajNpUXF4YnIzd0VneDlGNEFwYW93aHhVRVV0b1hpJTJGRkxER2R5ZlZtcVR2JTJCaXRjRms4aG5rWDlaWGdTJTJGZEM0Q3VxYThkRUxrUHBtMVE5RU1hUGFjOWdWUmlxeVo4JTJGTHAxMUlsVmMzd3AlMkZHVU1hejZRcWVuVktUbVplVkRNazNiaTllR0pZOTFIMWNwODRHZWRmbzJsJTJGWVpRc0RmbXNRV21NVUJhRHhqbHlSJTJGUkRxJTJCUGxLcUFKWXVRTWF1eFQlMkJhTTRkSjQ4ejhvQzdtUkZMRWpLb3dmMEhVSXIlMkZhYlFEdkdpSlVrYUglMkZIR25vJTJGN0hWVXhIRkhaalMlMkY4Y1hzNkg4YWExQWh5JTJGSzF1RzFGaXR4R3Y5ZjBTOEJ4WiUyQkVKYzJ1Rk1MY1YlMkJLd0gyam9GM3pKM3NqY2FyQmtzTGRMU0N1SzA5WG56UzYlMkJIOUYlMkJXR1FUamUlMkZQTjN2VFNzblNjOUklMkJWbmQxUk8zcm1sZ09iOEI0UTdjMDE1eW9KRVB4MzklMkZzSSUyRkRtazYwUXp6Rnc2dFN5TTdpQjNEbkhWZVNiZVRkdk42JTJCVjAlMkJ4d05LVGthT2NJdWg3U0hpdGNHUWkyNkZlOSUyQiUyRmxocll3JTJCR0x6aGNud0VhcG5udEV6RVN6NTM0TVlrUE1yWGtkYzN1a1ZJU3lYa3lTUzRQaWl3SmFpSWlBQmdpZFVidzlvaVVaM0JZWWt5MWdJNUp6NlIzZ1pOaldkcVUyd3pyRlRKdGNsb2RHRjRUOWhMWk1leCUyRnFZM1gwSnNpaThFWGdsMk9IYWlKZ0RJbHFQUXRSWVUlMkZmcFRueCUyRmpVTGUlMkJKdnpGSko0QkMlMkJxSGJMdElDUEwlMkZaZE1oMnRkQ2ZQYk5ZbUpOc0RQVTNDUW51cUlJZHd6NnklMkJlVjJIYmpsNEUxT21wSDFhTUlIRXY0ZGRRUTNhNUh4biUyQlFtdUdPbHhMd295RW9iVUtObXVEVVpaTUtjVTdpJTJCOVN2MFptZXJ2S09aQ1c1VDVzQ2thcXJWMG1EQkFlUk9nUktjTzFVOSUyRnpjdUFUUFRFSzhVcDQ3R1RpaGQlMkZvciUyQlZMeCUyQjZVeFVsZncwdnB5RWpCWHU5JTJGczRQNU1OcWJnS1pUR095ZXVPSkRHQ0V6TzgxdEolMkZuM0tEZlF3dzBsWCUyRnBQZWViM3Z0eHJlbDc1UDhHWmw0VUFBMFBGNk5ITkJpJTJGRkQ3YU9UbTlPQ1dWendvbWFiaksxNjY3WE1hN082OHVwRCUyRng1bSUyRkJlN2ZUSDZPQTZ2Rnk3cGxwVkM3QU9iWkw1N0tjV3R1V2Q4JTJCUk5jUGhTaGd2OCUyQjRZZXNKOE5rSTN0QTBhVlNXUTd5anZHNkRBSHpUVVdISWhkS0JqSWZEcHBkNU9QakFXOXVmTTNMSCUyRnBMbzYwZEF6eGRYb3U5YTIzQWN4Q1lIeEE5cFNKY1NvJTJCYnAzSTFyMDBwenZzQXlBYmR0ZlZkNXE3T1pTT1JQNnlWak5qS01jRkdZSiUyQjcwRGdXcVdkeFlka0tZRWwyWmtEY21ETDdQVlclMkY1eGZJZ3BTOVdRaHhkYnVWa3IlMkY3NE80WmElMkZ0dFc3QzdvZERGJTJCeVV2b3JaQ0dobFlmRzg0MWVlJTJGWkc0VU4lMkZFJTJCekp5b2lTbHlET3dmVlFzMU5PVERSJTJCRkNLaGRHWmdmVW1RcTQ2UmolMkY0NXFpeEt0aTJ4NVJzUlQzT2dTNEpLTWRXcHFUc2VsTiUyRlhRMnEzdG81dGRiM3F4aXBwUXN3dkIzTnViJTJCRmF4SWdGTkZ1N2Y4SmFybzI2VkMyczB1bk9FQTdPSk00NlltJTJCYiUyRmROb1h2bDRacmVTUXFtSERtd3p1R3l4aTloTHMzTThWNUxJdzNoeEdkY0pzd1U3OWMyTzFybFQxb0cyMU8zUDZnOGFZSlljbEU4SmNxbk15JTJGbzBxRzQlMkJoUWIlMkJRaTR0bXJCNiUyRmNyUnk4ckx0aEhkeSUyQlVPQmx5cFNRaU8ybGZsZHBNbVc5RTA2JTJCSHFNOExMbTBzdiUyRnR3RVo0bHhLYXRFbnMxVllSa3JDRiUyQjIlMkJHQWoyR2FQSm5xZU1XSUtrRHJPMzBCcDM1eDFWSFF5ZjJJNElBS3JHa1RNJTJCbUVKUzJsdzNuWHZHRUUlMkZkb2xpd2hFbmUxNyUyQjZmNzZCZ25IQ1RhN0V4TzFQVnI0ZmJFY0NaMkEwNmE2dFMlMkZieFROSWVTMG1TWnRSRW4lMkYydGJhamdQd1NBYUtodjVrS0dOMDF3VWM4blpsV2V1T3FURHUlMkJmRWIwcHV1Rkp6NnR0NzhBMlZlVjJOZFJDcDFLUDIzVnBoVmNnRSUyRnZYTlBtNnFxcU9BUnpvcUZoNGtYb1pQSDYlMkZpRHI1TFF2dXZUeUhodGUlMkZHRktiRWdYMW5tRHc0WHpSJTJGa2RpUGZ3YzVtM2ZwbVZJMHQ4JTJGN3BCWHg2ZlQlMkJRc2lmZ0V1M2pETyUyRkJsWEtxMTJDOERCTk1nU1ByMVJLR2VjZXhOSHlzSlJKYmlTOWJZV05yeGhyYlFoNjNqOENWJTJGdWZScFdwSGM5UGFoRzJxOHp3JTJCRzBlQ2NkUTNwb1NRVHRsODl1M1Jpb3JOJTJCMjRBbHBkaUZXM0VxTGhPUDA2NlYxJTJGb2p6Vk5OWjNEVWdIdHVBdHpmVERMVVZLTFMlMkJzQVZHMnQlMkZLSjFKd3lLN2ozajlXOWg0aHFKckFyR3ZrWE83dnM0Ymh6R29qRnZUUHVNZWNRdW1kYnNtdlA2R0NRWXB1VHgyQXNtNDBRQzRaJTJGRUJERnNPa1BmN2NmSFJNQ1I0b1JYMzJYZHVIUzhhdGZwMmo3TUlWJTJGbVlab3p2RWNDRVJFbWhmclBZaVNZOWZmdUxldm1GTjd0MnpDQmdDZDFBWEZYRXF1MFQ5eGZkZEVHVHNFbnB4NElnVWVvSzM4TFdzOExYamhQaE45dlglMkJCSCUyRmR2TiUyQkdtMk00U1lqNTFsWCUyRkNzRWRRcHEyOFlPYTNORDBwWjhNbkNzRXpSRkx2MjV2Ymx3MjUyU1R2UVZOQUZteGZDUGxPMDhleFY4ZDBWMyUyRjBiRyUyQnhPQ2ZzR0VNUWUxYmNpU0RoYzBJRzFSWTU1TyUyRmxUaTFVcTFsbENrWTBpMWFrOTZOZGszSGpHcEUxM2NMWTVhWkN0bm1XdDJVWDlsbmZOSUVHeHlKdlZ4MHRrdGc0Ulp2NG5rT01TWXFFTnJKMXMlMkZoOFJmM0lrNkZKQm9HY0hhWTZuVHdmJTJCQ1A4VWlRZGdUNmpRSnQ4elhWM29ZNVBldHNwSmtlTjJEQ2hKQm9VZFJUZk00TTg2eGhVSHNvN1IlMkJPSkY5eTBQWWl6JTJCalolMkJqcmd4S2ZhM0hrcyUyRm9vRXNzR3ZqalFoNjRzMVBCVVpYakEwYlo3eDVjZWxncloyQ2JPZFFmaCUyQkxTWmd0VVZKWWx6JTJGSiUyRlNDVjhpZURSdkdtaFpmQko2U3F5MWFXYTAxSGN2ZWxxeGclMkJVSG8yd3h3U0h6eHc4bzFvcmdxeSUyRnNvWFRtNjc4YjF1eDJXRlQ4azMzeU44dlN0MUtFNjZOWkZBS3ZpVTBBZkdGc243dHREZmhCWW5wUHhPTU5WViUyRjZ0QzVPYlJnaExvRFM5bWQlMkJRdWhHbjBxSEJ1cCUyQlhQSVBBRThPV0lMSlhKVlkySFpGSkg0RWt4TG9UbmtKa3RiMnREcDJMaTMzS1NhdSUyRlRqdEVTNSUyRnBPRjdacVRTT09yTmE5OGxjY29NQlR0SHlVcUw4U1JkWGx1bVlRMzNHVTE3NFh5TFIlMkZSYWR0ckhJNmxjVWhuNmR2d1BpNXB3WFRNOSUyRk5LTyUyQmZEckxFNTQ2WkdKVHlicVlySjlQYWg3S2lCMDdmYnNHYlRyaFBUNVdHbnhRb0RQRHFqQ3JwQ2ZiOHVlbFR4ayUyQldDbm1vclFVTlclMkZKT0hEc1dPJTJCcG84cVBwSnFydVJLbDkzZnRrWnVUdld0N3BFSEMyQm1aaWFIeE52UnFhd1c2NTdFaXFoT0hlaUF2aE01WkR2eFJWbVhvWlh4bmNsYVh1TzJsdVEyaXlNdEt4aGFuVDg0dzlRMW9WY1BMSmt2bHhxdmdLVUJZeW5rc1NpTGhqenB0RnlrUiUyRkZLa0k0dk5YUlN6TkpUcndxaDcycTREdnglMkIlMkZ0VjB0R1dnTE1LM2dMam9zd2NrV2pKTmduSmZjaFVaRHgxQW1HRzFVczBMZUZIZGclMkJBTlZ3S0JSYURyWW1nTFVSOERIdjZyOW1IYiUyQiUyRlN5SDNwd2FNblNleUprRVJYQmJMMmtET0FvRHVycFolMkJYcyUyRmtWYmYlMkYlMkIlMkJ0T0hrMkpxbDV2ZTQ3cnpPQlAxWnN3RzNjZXh2NHpXUTdZJTJGNmxmZlN2ZUdkVzJpRkJVS0hKREhVWUNrdVdXQzYlMkZjRnEyZm8lMkJGZU9zWVU2ZWtEelV6eUZkMHJWbHdINGo5aHVvQ3dDS0pHSiUyRjB2SkJYJTJGZnA0M1lvTnFaUGpLS0RiMldUU1NzWTViSHAyNVJpZkIzY1pGcnJSdzJtcmc1aFklMkZyc3lKYlZnSGVVUEJxeXlHTWltMDFhQ3I2am1TbmNNNkkyMThXRnRzUEZrJTJCVSUyRjNCa0pZVnMlMkI3R0dEY2R0SVRRQWNhWldnNHd6YXlCOVIyMUtveFIlMkZmRWhsVzN4MWxYMUZ3RmFVUFMlMkJuQ3loc0pRa2RXJTJGSExZTEhsNGlYZFoyb0dHM0ZRU1JnOW56RjRRV2VETkdtWVBpbWZNYlZRY1RQSzJoJTJCOTFNWGszUmh6NW83bCUyQlozZHRoYWtXaGVtcXlseCUyRjBBbHZzNkdZYmpJSXRITFJ1Q1M3c3FESGZzVzVNVmxUcjVHQk1waUM4dGl2SDJtJTJGR3J5VSUyQmx1VzhrSDJsSVU5M1RMejAlMkY4WGNtRk02WGhyUlFXVkp3dmJVOUJNcXBwb3BLZ1dVSDBKMDlBYXgwJTJCNkZQVVhHZm1lbnRNeDFNelh6RVVWbDhGWDB3MWxQS2xVZXB1WHltdHU2VmZPZnF1VmZNOGY1YllGb2lwZGpuWjU3dDYzJTJCN2l5a1laY2VZeU1rRWVTeHJrUk1ITThNQSUyQmFuTDlHSmZNMFhzOU9IVkV6UnRiRHFYZjh2S0FIMWZBVGtubCUyRkd6UU52YkZSNW9mMHUzVGw3MU1jdjVvVk5jdm9lRDBicFBPWGJIV3lEckVrczdsaFU4bHZxNmNrTnVRRTdudWE3RzdpU1l3MzdsZmxyNDY1UkFzRGM0RTBCeXhrdDlVVWxSJTJCd2Frd0l1JTJGYmtGRHBqWUhxVzJ3TUlocTRZS0tQWXFpZWpNR2J5TTVyVVZhWkVQamFxM2Zja0lXWkF4REE4eGZEYU5uMVRZM3VFb1RQdWN1RFdLNUt1Yms4U2wxZlVKd0doWkNpOXpGaGpsMEFqRnhPVlNlVml1dDc0ZlBvNU5vTEhEQ0dnODBQUDNCNVpubmkwT3I0bU8lMkJQV0loMHd4JTJGVnNzNHRiTzhzeWlLTWROdDR6UVd2SWxtbVJCUnZTem5qQ0x4ZzB1WHkwYm5yJTJGZERCNllyc0c5N3RTaSUyRlV1eXpSbHNmbEYxQlFtdHBESjZvVGxWdXJLTFkzVHE5Ukg2bmcxbkN0QnNYemtqZ3owdE00a0Rnb0xacWx5JTJGMVB6VGJURGx5Q05FMlFtUUJLUUZybHlqSHhoRURaJTJCa3RoNVp2eGtvQmkyNlJsV3Z3JTJGMHZGejNHc0NzV0g1V0xkQ0Y2dU9uaTdtM0FXVzQlMkJOUHJwJTJGbEt5NXZoN1lpZDcxdkdoMEg4TmklMkZTYnMxUWhtclRBZFdFM0R1SmNydjdkQmNkM2oweEFTdUViU0k3ZUtKTVQ3aVBmTFpDMFNoZTJCTWlMNEFSdzR0R1dMNHAyUlhUJTJCSkpJN3ZZeVMlMkJ3UjFRdTNtOGVuaWJ4eGlCVEtPNkZ4SVdWbUhGY1FRekFCSWNuJTJGT0RWMEV5WURiNHo1Qm9KUGQxVmF1RiUyQlVkS2YzeDhkc2FycEJQTWh1aFc3bFRObzV6bSUyRjM3ek9SJTJCRlczYnUyRzR4bkglMkZBYXowT3VzTktQS0ZrJTJCUG53MU9jaFNXYVV2JTJCME40Z09jdTRtMlgyZUNBOGQ5OHFrem5paSUyRkU5MjNjT2RGaWZnRE44V3ZVenVrcE9yYyUyRkV3R0tST05PMmVDOVdIVjdmTkZMblBudVJUcE5uNGtPbmd6RGR2T3l5OFNSVUtYa2lQQk15amMxc3JkWTRDMmRPMkpsUjVOQnFDdnRyOWpkWHZheG00Q0hpQnJvTTN2Nm1CS3Q3R3ZJNnh5TG0xYjklMkJUOWM1RE9HTmVtVFp1dVBIQ3NzcG5RJTJCOUNkUHRJcmVYJTJGVUw3ZENDQ1hMOTB1JTJCdmR0Z1BtOWNmSkNVaHMlMkYlMkZFdnYySlVaNW4xT09JdXZLcHNPaVk3ZEJRRmRYcnclMkJFeFlqSHN4N082SUdqS2NtU3dlcGgycEkyZEkyWXNPeG1mTXRudmh6YUI1MFduUSUyRmJKUVU1NCUyQiUyRmRYaGM2Q21FbXJka1NleUhuUjB0ellmTUlOTEdBbGVGRUdQVGdWYnZBekxic0trSlZTJTJCJTJCVGYyekI5SDlqams5enRjbGc1b2V4R1lOM1olMkZqQ2RIM2RQckF1STVpbGsydzE1NXJhQzhSdWgxbVJoYXZuYlVBblFMQUlJQ28zWmlvWUQ5bkNKMW9jJTJCQSUyQmxHUENVV1lxeGpDM1dPajJWSjNGZlZlZEZoM1g3Y1oyQWFYTHo5ZmR5eU1Vd1lsSkZYYnBWbWFlN2tZWENsQjdTYXJCU3JFdFNtU1Rmd2V1cSUyRmZTUzdKQUZxJTJCZWZCWmpNNXdjeFhSOU1xVXdoczQzMTlEOXhOSDBtald3YjZzMWNyaSUyRmVkVDhYZE1WRjFkTzlldmpMSHFCVTVvOFF4WW5ZWDB6UWJNQjBBZjAzZSUyRjRCYXF4eFJmblZZOUMwenNSUjFsJTJCUDE4c3FvYVlhVmJ4V3UwV1NuVENRN3RZV1BQNjVBR0EzQ3NiVDZVajFibFoxYzB0M3BBTHBUWFdVMThRZXdSMHZ4b2tNM01hdGhwZ0ZaZHJqWFI4OGU0cnR5NEJRd2ExNk9qclRqd0x4V2xsR0ZMWmVQMUFBMkZQd2RFQloyMzRWT1luUVhldGJWb2VUcDclMkJ2M2xKS0VZR0lkTDhaaE95QTMwdDZGTkZDSzhLcVMlMkZPeTdYMmh2Z0J2SFM5TnRRWGpQZGtHMlhaMGZFZndlb2xnV3FGZFc0cnBzTG43OVBTNSUyQlNiTUExQjZTUGpmWWZTVWlLVTEwUHk5RmhtOE9aZHRUZFJIUjJEQWM2TzZxNkhSMWZHV0glMkI4dUNsTFplWW4xY3IlMkZJcGN5NHB3Tnh6JTJGS3ZublpVWUxwekRjM3lCclpmVlVjRzhPSzE4YUVSaWs5akVVZTJnSyUyRnpqR2hKRGV4MXROSmJyRnk4d2klMkJORmhKNTIyc3c1JTJCUXZMR0FyS0NrY1JWcXJsSHpKQmdGQllKTGg1WmxNYVd6OWFOOHdxMXdrUTJ1bHQ3R1R2dGlad3NBQUMxc1ZpT2lJYXliSU1HZXQyOXdGNzhtMWpVeW5SRzRTNUVCMGxQeXN0WjEzeU1QdWI0d2NKZ0dOZVBlSzdHM2pMTkFSJTJCa1IlMkI4aWlYS1RrZXpvJTJGYXVlUUo1OWNKVm03dTJ6MDJBbUVwaFg3V1BiJTJGaTFOTDM2WiUyRkllandoZDV5T2ZmZURBbDVpQVdGR0xhajZ0cUJGJTJGdFh3JTJGbDVmZ0F0SFNTQiUyRlZEWVc5b0JKUHhKaTFPb0ZrZDZZNjd3bSUyQk1qelpQcEtrYUtsa21qS3djUHBVT2pPUnZGeTJPdHdoZSUyRlBxWHdPWkhRYTBjRndnVGhyTld2TFhXcU8yJTJGUWxPaDZaS2tzOFglMkZhbkNFcFFjZTFseDZDMXBJdm5SOHVyYlFtV1pFNjdJVWV2SVFuM1VCMUx5UXBYNlVGWUtKYkQ0d1RWV3ZPJTJGZDhvTVNHQUxONWZrWXNSdHpkS2dsdm0lMkJzdFlCdUdHJTJGZ1NlMDRmMnFJcm9qN29zY3dRVXJDVUZET3kwQklkNXBET0R2NG9EVFNJVEFNZ0pvcW1JJTJCc2F0VFZXaEdERGlQaTNIT3JDcm5QJTJCYldXTyUyRmlBS3BXQVJxaUNpUnNJNmZVTHhvQm5PNnlFT2pIVThVSU16aEE2MkVUV3o2djYyMGxGSjY5cHQxRlh6b3llZFBtRm1CT01YeGZOSG9RbVNHQmJ2Rjl1MSUyQlNWVVlkaCUyQjlYYmZlcFhJTG1vbUh0cHB3clJQMDNuSU1PZ3hzamJWM2RTR3ptSzltYjRzZ2l4NGVYbFJ4VFRJOVBKQnp4eWVjSXJ2ZmJaOG11MmN1cmRzdnh5cGpaMFZTYVlGOWVNVSUyRm1sd25qaDdYNENKUlE1VTVZdEdpTjVJRFdSYVljdjNEdSUyRlg1WGJkbU9pUzRrMWRIdzVnVGx0N3piNXNQS0lUTm41eEw2cU5vMjh3NlIlMkY0M2liOUJ6bmZGJTJGS1hPUWRaU3kyUnZOMWhBa0pHTUFjRjVQMHBrbWhVRlU1bFp4dGRQc0tuZmhHbEg5V1hFN05maU4yJTJCME9SWXB0VEdaclMwWWYxeEllalhLbmlXYk5mMHZBeHRlMU5tdlhLdkRRcGlvNHp0UndXWFFvYkw0ZWlabmRTZkhKTmIlMkJDYUVsJTJGdVI5ZmpNcGl2SGtvUzJYRGN3clU3YWlaNEhNajdYSHdRZnUxQXhpaU41Z1hXYVlsOUV2MVlkYVE1cnFWcmhqQ3ZWZWN3OGYweGlKa3hSaDltWGo5YXQlMkJjTXZQcURKZFphZXIxQ1ZmMDBKblEwT2tFdVlXSkVhbG5RdkV1Ymk4ZjdSZ3NGV0RPdTdDSjFYc1EzUHd1TkpoQWEzSGRZYTNldXVxemVKaExjY2RvM2RLaXVudVVRbjNIakkwN3JadzBLRzltJTJGSTBwZVhGYUJpNUN2MWNuS3VDamtoZnFza3pyTHlueVlHWGNpJTJCdEFyOUdaU05ESmFnVEpJUGJRSHliVFZzZndxaSUyQktOcDNMZG4yUWFPV1NuOElyZ25mVk9YQm00dG1KNyUyRnl4WlhaeHJQT3JTUkZYZmNOSm04UVVSTnpqc1I0RnVjSXZaTG5nJTJCclFZcW03UkFvMFhrYngzNHhmamhvQWVldXo2MjVUZGo3ejRiUGhMRXNmcEZVR01MUmh5bVRnSkVhZ3RjTG0xVUNlMzlxbktzYVBaY3FWclJ5c2ZuYjAlMkJCYXo3QlFkNWNuMlRITzlCZU5vM09aTDNWTWE4VHpJT3RuMHRaaVdyUUNrJTJCZWl4Uzd1RUZzSVVHaklxUlJsV2lhTjdMTTRUTnVQJTJGbGt3SUxmZ3B6WXZ5JTJGZDQzT010RkY3RzNMaVVLdVhINzJSckxOcDJSZzVrcTdEUEU3N253Rk1rSm1oUVphNHp2JTJCSWtQN3lNc2x5MExMaCUyQjZoVWFLOUJKZnZTQWJ0WHFKclg0aVBQSkdYUFNxbGo0JTJCTFJ4d2dSUkRZS3VIc29NaElzREslMkZ1Q2dZcE5UUzEyTGhsdVhKTTN2MVNXZkJkSHR0dVJ6eWdVcmpPUVg2aEpOWCUyRkllV0lRbnk2MzdGUGhiQ1U1VUVYJTJCYlNKMklQVFpFdUIwSnYwM1Q2QkpSa3M3TW1SJTJCTXoyZ05WVkdRV0tEeVZzcEZVWG80VThsb3V5Z0pJTjJmYVF3MVpRRzJBNnZxJTJCRGJqN2MlMkZZSFJVUFpiWVNkVWRZNDJOZmVaOHRGVkUzdnBGVFJqZmhRRyUyQjB4WlVJZFk3amFtdTNLJTJCMnJRcnRpeWpUaSUyRjNnd0J5eEglMkZ0WTBwM05yQzB2Rzh5UnpmaSUyQmY5WHlrbXVJRWk2SVBlanEyWlRLMHclMkJoWVdHVnM2SFhMbEFKcSUyRiUyRjY3THNVUTdhWDc1SzUlMkJqSWtyZUdTeXFRZCUyQlhQS3JqWTIzdEpMR3ZTTSUyQnd2OXliMHpyNXZJRGlOVGtETjFmTyUyRmxnT3VnR3BJOWlwS0lnNVppV2hrYmtMUkRjcElVVUVoVUJqcVR3WlRmN1FVN0ZlODVURU9QMnFvUDZwdCUyRlglMkJhSVJZTnA4QnIxSGdBVk43dlBVZXB3ZUVkZVI2JTJGbEdUTTJUclNZSUZKTmw0QSUyRmY5V0tjV3N0NkZXdWJCa0dxZHh2UXRIbEw3NWdxY2xwdWpqZVBGODBqcm5kbGVWZDVHVWJTZTNTbnlhWCUyQkhWdUl1R1h1Nkg2MnpWNlptSnolMkZvS0xzRER6aTk2dmJXJTJGQjRkUll0cEc2elMybUw0T3dDOGVWUjZvdG5NdXFqTDVHZTRTaWc1SXhhclE0a1N6cjQ1dnFhdEcyR0JZeUpnblVnOVZPcCUyQmloJTJGZmxyaUhocHpyTllHOGZyaXlNdUlzT0xySnhIMzJXZmlIMWlkY2U5JTJCMVp4UjdBMzhLVDB0Qk5ISThqWE5xbm5iYlREN202YSUyRmJOblJKWjUyakZlREtqc2lxQU1xMTM2VmlvJTJCb3I4MFgyOFRzenB4TTlvJTJCRnhRTk45TDBaTHYyVzZteUk4cXBzTG4zSkdwZTlhZU8xeXFXaWdTa3ElMkIlMkZmUmlWaE5SWUl6S050WW1NM0ZOVGVERSUyQjhoZDZLM3ZmT0Y2TTJCZFRWNEkyeXhsd3Nrcnp0WG1pN1M3MnFDWDlYSXlSR3BSWGRIY2lDTjJ2JTJGYTNTTTBibDBJSE8lMkIwN3RpSW9kZUh3ZSUyQjk2bkVPNVBqNElZcVptYUUlMkZGeWlPS3IwTSUyRnhGZUgwZ2J5RHU5NkE1d1JVWHR4SGNpUWhJQ1ZhRUNZVHo3Y2YzZERmbnRHdHBNSGpVN0lWTVJFYUwzdXolMkI1SUglMkJqcFQ1a05FTWdvV1lUN1BsMUtxOTg3WlhSTXB0Q1hwcXgwUGF5aHl2VGpFbTg3QVkyRzRYek1KUDVvM21EU3F3TUM5U3hLTlVaUTV3NGVMU1hxYlZsYlElMkZJMXoydjAzdWVQMzBqdlE3eVM5S2tWOFclMkZYUk10ZTA1T01WUnYzdmN1TTZyM05POGhlOXdwSVVSQVRwZFBiRCUyRlZEJTJCV05TRTJCekx1MTV3YiUyQmlmc3NjaVBXOVFiWTFCQmZSc0k5TEJjOE5uWkFlZ05yaXZwM0QlMkJYalkxaGlpM3YwJTJGTXpTNWxhbk9rdmFqQ0glMkJuVk02NWVRaXRHNERISDc4T1VjWEV0QVNreDRSVHpTaG56aXlvYk9UNVhtdTVGbkJxcnlYRzhxWWh5SmpzYWRjVWpENzAlMkJybmxrdHNDQVJwRlcyR2tCR05NOXYyZDhZSXd4NGVoaEZGTDlJMnUlMkZLN1lIYnlEZ1N3MkVhbk82OFdHUCUyRjFTbzNsdzVwcGNkYXpWbnU5M3RpWnZDVXhzQ2d5MXM3eER6TFQxTUpMV0xpZWdJMTFhSiUyRmZPMW1SNTl6cks4RzZIRkJ3c2VacGl1Z0R1aDhPN1QwdCUyQkxNTnZ1ZnZJaldic2w1cUVqVFZQdWxCbWFZTEpIQkdCbEFUa2w3OFVKTzFhdkxiNXo2dTBaYzFpQkw2YXkyYW0yJTJCYTJHSWx3MnlBS29tVm9OdDNlRCUyQmdsSXA3bmVkUmNQdnVVcTNwZHhLRUQ4d0pwdEpva1hod3dDZ2NzWUZpb3FWJTJGYXdlZ21HcEhMakVsTkNYZ1pvJTJCOFVDZ3l3UllROHdDUWF2ZFVCc2Z3aDdPZlUwdVpYcllqVzM1dTJXUk84d2MlMkI0S2twTXluMGNXQnpQRENodkd4NUZjTVVlTExYd3NEeFYwbFh5ajRqRHZybEE4SU1MNW5TQ2pReTZXTFIlMkZkbDBoT2d2MkFuYk5BUVpSMG5idWRESGxKNjQlMkJuM3dONUJ0a0ZRbUl2MnpqaGNOcVQwcXFzMjdKUDZwRSUyQkJqZUExa0JHY2llJTJCTTlJMkN6eU9sU3E4V1lBWjAwazBjYiUyRnJHWERoaHI5ZERQUGdUUFlEdTZlRmNCTUQyUDBHaGY0bzA1ZGttMHlBOG5vYXM0R0FoVTJKV0NzZWwxZFNHJTJCJTJGU1cxdGd4aVBkTW9XbFE0THcwSEJ6UiUyRmlWZE5rZXdxb1BXWWJZYVR6U01IdFNjd0hHRTh1SjN1TXlPSW11aGY1TXV6b3JSd1pkJTJGSHB0Mk9Ya0VkUTU2c2YwUFhMbVdtSXlJS3FMQzcyT0xKcUYxbHVyJTJGJTJGRnpXeE9rWG1mUWpGaSUyRlpZTUJGSE9HRFF4OGpPR203NG9Fb3UyYTA1SFpsZk5RWnJWaW53N0VkMTRob3glMkJORzNycDNRcEozcklCR084WVVORTdtVkZUdGxLdGxhZGFsS1VHQUwyU1VTVlREUSUyRjhDU3dIM0YxanBxR0p3Tk1aZlhWZXNSZHlJVSUyRlpjVDQwVXBzOHklMkJIeUlENjk2UmpUS1VkQ1ZjQldjZ0ttdGIlMkZ6ZGtrbko3eEpiNXdlNVV5eWczdmY2QmExJTJGSTdqJTJCQjNPTzdxVzNOVyUyRnJoS1VvY0QzbEV5SGtrNUZVJTJCZzJkUFNlTyUyQkY4MjNRMDVyT3JuNG80NjFsTW4zR0syblFlNjdIcldnbGx5M2klMkZSa1kwRGJUaWdib0k1bUl3QWtlZk1hQ3loNW91c0NYdEFNNjZCZ3dWMHVkd1UzWE1tM0REN0QxRUdwbEg2bFplRk1iR0REZ3Q4cTZIb1RqaXNOZGVlclQ2a3l2V2FnMFhlTjIlMkZJdThVV2llSSUyRkI1Z21CRiUyRkxYSFZJNFhtRDI1b1BvSUo2OWdzJTJGcDElMkJKQ1VHJTJGMUJlNTU4V2JUMFRNNUs2T0V4cHpNR2FjRnRMN2lzdzQ0UFRFSkN4QzZFSjhYeWYxa3JHWGliOHNtTHU3VXQ0UTBWSU1acXRUenVqdER2YWZtVSUyRnFHS1BCSFlQQzUyMU1UYjg4ZDIlMkZYdHA2aW5zb0dqTHZhMWlrMmMlMkJjUmI0ekV4c3RpWGlhWFhlbWdkNTVaY1JRZ2todXJpc1NFZDY0ZHdKQ0JTdWNIaU9Fbms0ejRnTGM1TEp4eG9JWTBQJTJCYzNSODRhNSUyRlFHYjFFZUt6TURZVUw4eE5ERDE0YXNMeHF0SHFBc3d6eWc0JTJGbE9jbWVVeGhxdjIxeGp6UFJMNE52SDRxQmVkZEdhdUs2Ulp2em4yVUlxTzRSN1ZIMjIwZEJHR3Z6UXVIQ3V2aTlsOEx3U2pQa1clMkJqOVIlMkIxVGhEVTVGbkVuNlBkZHNvcU1ZdlNyZXIwWXlQSldlMFBrU3NCanU3cGpwR0ZHTUJMU3V6JTJCenNUcjJEZDlnZjR4eUslMkJUYjdPSUl2T3ZYOUhjdiUyQkc4ZSUyQjJNeVZ5NW9iUEVSWHVUWFZEd3lsQXUxc2ZJSlFJazNURmMzM3ZCMDRoODZZQjIwTllnT0JiZ0dNYXJ1RTU0aXpkeXBYd2RZZjZRaWZYZjhlelhYdXBBMHd1JTJGMENsbXgwelBQVDVmU2g4ajRPbXRoaUl2aHV2cFlCeTRLTSUyQms2V05rbDB6MzR5cVZ4dHRBWU5ZV1ZvenRRUEZJWVIlMkZIN0M3dGNnNVVrbkhkV2FoWVhUd05URWNRQUZla2FvOWpiUXZ6T1p4OCUyRkJ2UUVId0tIRGJlVktad2kzVnQxZ01tJTJGaEc4T2I0SDJjekhHaUgzYng0Z1JDeTJ2SzcyJTJGOHpRJTJCJTJCa2I5aUZCWHFySjFRTTYlMkJtdldyejdIZGNuWEdzRWJ6VGVBR01aQlFvVXdxWTFHeWpCJTJCNkhWS3NodkQ2VTVnUU91aEs3UDJZamRDZnQ3eCUyQjhPd01MalBya2NKdHEzT1prdXpkdlNSODhaeWVteVp2TEZWbUZzJTJGcU50a2FDUkl1WDFqQmJ3NHAlMkZJd2FrY2VzJTJGYmYlMkY4VEk0Q2daJTJCZWFGMDRTa0FhYjBITFQlMkZGVEJaSnBscU9CT2tUMDRjS2ZZdHRwQjJRMUpKZG5PMms2YVFhYXF0QmVVaDJ0SEVhYmNTenJhekFiVmp4cjU0UGJMcUs1bDBVblVjQ1lka3doU2hFdVd4VHNOM1J2U0ptc0UzOXJZNGFjNW5yYmkzaEoxMnZwR2NTbzVNbFIzZjJpdGRpdm5aWHhHMDNPTnNWbG1YdTZYUHRCcnVvbGRmZm41dFJPd0p3VWslMkJ2UFYzdXRURTl6UjdSQmFtc1pQTE1EV0VUMDdiY3FvTTRKUGdTVVljdUxWdzR2cEZZYWxRMGMlMkJUWXNIaVdKMmN1ZXo0RXNhMjZtTElsZ3JXQUo0dGt3UFRuRkNXYTcyJTJGZjJsNnVONXp1T1F1YXBGWlMlMkJqJTJCWDd2Vm1uWDlNV2hVSzRMJTJCOEhpZUhReG1FRnlvdXRpZjQlMkJ0M2pyVW9GJTJCdFZGeFNUTXZ5MlpFU2EwVW5pQTRrWTMzVkZ3Zld6dnEwcmlya0FHWUhZYkVka1EwYXpIcTMlMkJ0bDVHeHhWZUZtZkpla3BoNm5ac0JNVmxXOFBuWVZxTUtRJTJCSlJKUzdSY2NmNUxnM0dlaXZYV0ZwTWdsUjdvJTJGZno1NEoybXM3ellDMzVSNkxOVnR5MU9la0pud0RobjMlMkZoZDUzRm1nVzZMV1RSQVZxTUV6eG5ZY2loejVqeTc5ZnpONjJkdCUyRkMlMkJPOWFobmFSTkI1VVpRQ2RaWkIlMkJ2dkt4M25LZkFEN3l5YVp6YzQ1bFNqZ2tvT1RqMVBidWdUVSUyRjdNTjdFSmdnbURiOUJVdWhZSyUyRlJrZTd1TmFLMHc4aDglMkJZYWlvJTJGT3l0dTVEeEFKM2tnMHQ5TXFhYXV3eTN2eE9yJTJCN0lLOEw5MXYwJTJGbnc2RTVDR0VneW1TZlhRQ2xidWN3cm5zMEFhSjl0djlXSTIlMkZjVjh6QmJPV2VBalBQY3klMkJodWhYYVhPNkZ6SmlNd0ZGNkxzeElxR1Q5Yk5CTUU5VVdjUU9wU1JiMmpGJTJCRFpjemwybm40UkNQMm50R0hmbUJMNGU2c05KMzdQY3h4TU8lMkIlMkJzWThnNWFENERlV3Z0YXJjcG10MVBwalB6UkswbUd1d2YzaXBacG5FMUE0c0ZiRTNtWEo4VHRka0hMVUl6SHZ5Tm5FUGwzSCUyRjJMVyUyRmNLUDVpYUZjQ2RsJTJCZGF5WXpZbHlLVWloNmxlYTFWSTZvY2pRU3FCVUN1UE5pQURKZnZFRG9EWnZUd0lJMnZkWGt2c1dsYjElMkZBMDdueGtybWJQdVo5NjA4Qklqdm1SWkQxMGNBUGI3ZVNtSzVBV28yUGJzVCUyQkJjZHk5d1lOcE1CSW5IbE9WMDU5bU9KcmtaRjFTdktBSTBrbiUyQm93NFdFbWN5TUttZGlCNXRXJTJCJTJCbjJkYzBzUFpyNWRNSHpVbWEzQlA2WnlacGhXVGpmWFEwVTlFU0FoeGl2Mmg2RFZGOFNrODVoVWwwMWRhdFpxWWhlQ1Bzcmh1VTJuaVBxZnVGMXRPWEE1eERLR05VWDB6QUpCdmZBWnBiMUZDWW9wbk15djZTV2twaCUyRm0xV0F2UCUyQm5FNXlpYTRpbWV6RW04S1ZSUEMlMkZHcE80Yk0lMkYwdklEQUs4SERFaEVNb3V2UHl0TDRxSXJsd2laV09MeUszNnRhTGk2aFowN1ZtcVRodTV2UUtxSUczUnk1Wm9lQlBCNzRxNE5halVjeThFWTVyQlJlZlhjeTZyV1NhJTJCZGUzdlpqTDhuNWNuWjRtcWNCaTNLRENLT0xCWEdYMkxXWXZtY1Q4WmRFcG9oYXlIYyUyRmxaWVdmTDBjWUlTbXglMkIyeTBRdDFyY21jczBmNTYzcXBsMnNMbkVuamt0MnV3NmglMkZwbXZ4ajlzell3MHM1eUtaR1UlMkJXTzdQOUxjbkNZNEhxcyUyQmh0MyUyQlQ5VFl0aVBONmpWMmhjMHVkMFA1Mm96JTJGTVpIeTB6b3NoamglMkZSMGtndzVvZk8xRGU0NmpOTXZJbDRHQmpOYzhFM1RjemcxTWFlaldBWjhHSGl1cG40U3NHUFFTJTJCaFBOQU5ycHFBSkdPJTJGaUpOUWFwYTZVY3B6cWo3WkZmSkYlMkJNRlBLU25qbUg4ajVpMmU1SXlKM0FEaiUyQjBmRU1WRDltdlIyYlFWYTYzNjAxQkFDWGZTeFJDblI1RlhEYjZyc3cyVkNlS20zSndFVUFNaEM0RThrZXZuRVZSTGRTNDVQb09KaHdJYXBCVGpPelglMkZ2YiUyRm81OHRyNXNBZ291V29NbjZ1SXc2N1kwUHpndVNhcWRnRFR2QVRRc2dtRWw1d3lGJTJCclo4SnJabkpqVGs0eCUyRnJRem5PMEE1ZFkxeU9wY05zYzRkQ2pra0JmdFNIVnIxdE02OFJMQ2xpTDZjNUZIWWdoRWRvMjRkbFE4bkY5N2l4SzkwMSUyRkZBRWJHZVIlMkJzYjViJTJCRm5BVzBQY0tFaDlpWlJSVGRLTHBiSGFRYWhsaXlocGc5N3VhT3gxaW9EbEo4bkV1c2lEWkFScW9XSzJzaGQ1Y3oxUURtVHVMOUhoaVQ5RE5CWHkyeVV5SlVNJTJGUEFtN25XdFBKQkNyVlFiVE5LU3hEd1dOZVgxMWtTSTZBQWNsMnBBZ1dUbDk0QkVhQmtBSEcwdE5paXkzNTRrVFRNelg4dFFtRHZiWG9pc3NxYkk4c2I5WHlLOW8wTkkyaFB4TSUyRlJ1UVN1bkQlMkYxSkpwTGZMRmZmY1djQnBwN2tISTBUWXlMV0ttT3RxbjR1M1Y1NjgzdGhsOWIyZ1dVMkFQWHY5cmwwSll3NEIlMkY5WExuTEpGMVE2QVM0RmpqcVJmQ0t0eW9wR1lhQkRrTnhycyUyRjlkeHFVcjRCd20zVHoweHVIV25mM1piaVJTNExEUnpmaFF2WHVUcGpPdXI5JTJCM2xINFNIJTJCTWo3aHd4aVVsbFJLc3ZwSmdtZFg3RFdJNnJneGVkbEx4ZEdyREMzMHBmUHhRYlhpbUMxMjlaMVglMkJpSlBid3lpdVROczc2WTYzWG9wR1BnR2lIbzlidzY4bGxJVWklMkY2SjF0WUhlaFhNemloVzZLMTRYJTJCRktUNm5IY1hmRldEZGNhbWNBRUFaWXVqazc1OVZTSWRrRWVIanFZamtaMkRKbDg5a3ptS3FsVk5BVzhUekZ3QyUyRiUyRm40Yk45RmNsV1U0emZHcDFTcnlxU3lKWk56VDFiOGg3eUJ2RXFrSCUyQkNqeHE4MGZ5SzFYVW11dDU5SVFFOWdTeHZJWWJMR20xJTJGaTRMNUNacHVOWXZ6OTZuMFFNR1NMUTdWeTMxanU1eTJsN08zYiUyRkFuYlZ0bnlyJTJCZEtqRG5qc0pDajhvb0tDUnVxOXJ4Nno2aFJ5eVRFcGpIdWZCbDJrRGNyTUltcyUyQkc5RDRYcnl3NDBLdG0zJTJGbldRd3plMzhlZiUyRmJUUG9OYWFUOWNZTGYxM0RXeWVFaGtlVmRrOVByWEhNSUF0ZUoyWkp2Z1Q0U3g3RnZqbVYwYm1DS0pOTk9OTVltNk00U285YnMxVEslMkI3dUJCOHBRd0lOJTJCOUoyOSUyRm1MMTlzWk9YVngwS0UlMkZ1aWdaVzVmUzlzdGdPUldtTmZPampqODZqcXdtNG8lMkYxdyUyQk01bThaUzhSQXg4cmVkRXpKRE5DTHcxUDJ2M3hiUjljN1g5d1IzN2pSazZucTNGd2xiYWQzd1BrdFBXUWlySlN6dUNhdEdqWmY1NUhPMElhelJybW5NZ3UyaHdXdWo1cm1paVkzeUVwVmM0Nkdjek5OTmxzbnB2YzZ1TWhhSWpmdHM2YUg5elVDS1RYaWY1QUpTbXN3eGZtTGs2SFdNdVJ5UDB0T29wNlRjR29UeDVaN3JHYTBQTU5qJTJGYWklMkJWd3poSnpnJTJGaDclMkZKTVF4UUolMkZYOXRESmRkbXRMS0IxbUNDRHBLNW1qendvd2p6SmhOTCUyRlRDSnJJM05SamtybUdZOVMlMkYlMkJ2ckwlMkJtN0dPQUtTSTcwRE0lMkZzT3gybm9IQWU3ZDlyeWtoZnJySUs3YVp3dFEzJTJCVDNSS0t0M0pxciUyRlZvQTJPT3RjWWJiNmJpU1QxMkd2JTJGMm9pY1ZSdmFUaldlajNDN1UlMkYzdkZGdmJEcDg4WGo3WlpTWGs5YzUlMkZqRW1MM0Z6dzVMb3RyYmxWdTVpV2ZzM3J4cDhPTTMlMkJjY05SVk80dVpzWkxUUERpMERLSE5mZnhEVG5wcWRveVlRR1NOOHFnVVZmWERIMnhlWVhMYUF4VVdDZTV4WlhnTkcxbEtxcW1GVHNNNmxPR1Nka1AzdTRzNzVYTlp2eDJoVFkyelBnYjhGRjdkRU1iM0JwVWFRVExwTnRaMzhsd0lyQkdMYmtaRCUyRiUyQm1aTkdqTDJPQXAlMkJwaVE5dnBmTUxja0ZNMXJUT3ZkQ3p1WkFYZW9Ddm1YTFd3NGZDJTJCMVNudXBHcVVHNmptUXJmODZrUkZncEp3cmxhTVUlMkJlSEZHeXkzTlh3RDF6dEVKaG81RmJoV0Z3cTBGTUxZbXVidmFHWGp0MEtub3ElMkZPRW9LMkkyYzElMkI3M01rT3d1R216aWdWZG04eCUyQmxSS3V6UzNDVzBXUmRKODM2dkVUVk4zdThEZEVISnJla0IwMDdJWEhLUndWbFYlMkZoQ0thNE9pN0JyM2VKblhuRDUweUhmeVBQa3h3U2JzN281Z1hjSWFJUnQlMkYzNjdmN3J0RjdEbDQxOGFpdHRsckU3bGIlMkJsOW9zdGlubXlCM2hTMmslMkIwSjd4NlZEZzM5WFBFY1QyeFp3djhpM01ETmpaaHJ5clNQM3o4czhwVmtaQjFIYmlxN0pPZloyYjQlMkJXJTJCTExsMkVuYm43ekpDJTJCSE9KazV1WDNSNkJ4aGpVU0lMZ0tNcU9iMEN5bGxBRXVhNGt0Tld2Vm1aYjZSMUYlMkIlMkZYSng1UFh3UTJWM2FqTXdMSWQ3MzJnZEV4Um43N0pXNUUxd3hSNDB1eWdKN1NqdUV1VWRrc25teFByMDRqJTJCS21HU3JSdDFZUEZNVTUxZmU5NU9DcVFTYjFWRDNqTG1kOFRjaklHQkVUJTJCd0VtZCUyRnZHbVptT2s5S253WVQwWlQzYUY2Mmt4QXQxeFhEOFdsRzB6N1V6MWlnS2NId2pYZm5FT09QVnl4cEJ2VkF2NWhva0UlMkJSOGV3VWZKZGR0N2pscFg5VE1ETE9BaHFrbDJjUEp6TDRRTiUyRmxiT0RTd20lMkIlMkI4R09YTmhyNmFiNyUyQkNuUHFwQXdiYkV2SjlUdSUyRlglMkZld2E3UDBraUNPJTJGY284eTc5TmxxekxtNzlEUGtXNVh5SFd4d0FqRXJyVWl3ZnZtbG5ITlg2blVEMHpUUUJLT2l5WGFoWkVZdTdvaUthN3RubHhERlhONERId2kwRWVKZHhjTzZxeWNJaUxsJTJGMiUyRnVDMVZqR0RMbzE1cEUlMkJSdUZTTzJSRnJwa0x1Zkh1ak43eFlpZWtadTBLZSUyRjlaYXVSZXVObno5Z1gyQ20xUVVOMktNQWZteGVYbGIlMkZNblJpJTJCTmZaakdEZUJsbkhMMmxyZ0VYOG9vdjZZdTZENWpSc0w4SXZzTXIlMkJJeWhjV2lQYjFHcFF5OTM2bTFFdmVCMEhZS3dWOFRlOGFQYVN2bTI5SWsySCUyRlNGVTg1ZmVHYWlzNzglMkJWTE12R3VUSmJqdFNaWXRuZ2tWZlJDa2o3Q3pJNEwzUUo2VTdPaGVhTHFBUHN3dVhOUFRVVXRSTWQ5bkdWcE95dTJrbkVsdlZLclRUMzNoaXN1czVXTHVEdDViViUyQlNZaW4xZ25qTHRqNlRTS2VFbVclMkJtQnIlMkZ4SllvdGVhJTJCMVR0aTZMNyUyQjhPTlRvckpvZFJDbVJWTEt2RWIwVlR6RVFuTkREcWIzNmtrY3hZSU5RdHBlQiUyQnJYeEh5MGhqWFk4UkRBeUd4R1o3VjBSbDhQRVFYTkY3JTJGM0RFQWJncHhuVGFlMXlGJTJCRDNjT3p3SXcyd0x1ZlR4JTJGalhZMHZsU0YlMkZYOHozcmJRSW82SUE5c2Q2S1dlbks4OUcwUiUyQkJFOGRaTzdla3pmcjNYR1gxdzVnaVRLJTJCJTJGQ0dJVzN0dER4JTJGZWRBcyUyRnI1dmFVOFJoN043QnRVSTBucVMzYVFueVdTcU5JVlRRTUoyUUd1WmV2MlBtUTU4OVpiJTJCaEhENktKY2FmZ2ZIQnNmeVBobWRLY1dYNE9XQjU5dUpkRVR0NyUyQnV1dFNGV2lrTHdMcVdKdDdnWVdtVGVTaTYzZk0lMkJwJTJGNExBMXBoJTJCaFdWMlN0TzZtc3N0TFVkWmI5UkcwT0tDVnpSJTJGVDVFSlA0MWJxTGlvd1Y1elFxeSUyRmt2bFY3ekRLWnNibVglMkZyOWdJaWtzUjhqZnVzMFZrJTJCJTJCbjkzQnoxUTZBYWRqUnJwWFFKVnRjblpic0dMTDFra3E3T0V2VDFjekRxaHpuclRHNmV6Rjg4UXhKVGxRNFklMkZLYWV6RjNyM3RjUnglMkJ4cTdKa1ZRVXlHelJJTDFWY1JBMmhDM1YwTXZ5R0tvYWI5emRnOU13STNoQk83TFVsWWNHeGdtZGhOb3ZpJTJGYVVNdlc1THNSWkplSSUyQk9uV2h3eWlYckRNMkNKWjIzZiUyQk1IJTJCTWxIa2dsMFlaVjkxaTZMOGZlclpUUW1Ib3QzeGIyQTdFS0ZzMXZjdk1Nbm81U0EzaW1KdTNkcHc5V1J3MDBpZkh5QWVTWkMwUlBMbVJmJTJGWGYwdFVvc0kwM0lOT1gxa2dZYUFDSlZlUGMxWjlCTWJkVVg2Y3p6Wk1lYWZZZGZQQThNdFMxU3ZhQ1dyVmYzQXdGMkJXM0FsOXo1NWVuRERNU2Iyb3htVndRSFJ4U0htdUJPZEZCajdTYWlsUGlDcjlZVjRFMXRUd2J1V01MTUNVWUZRaWFibCUyQkREc3VrN0Q2ZkJ4OFZqSHpyTVlkZUJ4d3AxdGxOY0dEVGhMa1NwejQ1MW1KNTVHMnMxV0FYZENIYWR2UnY3JTJGM29STFZyTkRoUjh5VG1DQyUyQnFRWGxqOUloajFudGhWdkVrMlhiMDRKU3ZqMHQ4aHNYT0tQTHR0UEpWaWgwcUkzRTVCV3pkZVN0M3RjNm5tMGtJQnJaeXR3MndNOVBoSVRYRzJUV1RVZUN4eGtWc29pJTJGJTJCWEdYUiUyQkc0UER0bVZBOEtqYmNQNnBnTU02MCUyQnhDdU50REtSSlFzTWFnUzBmcHNmVmUlMkJhbjIxRE9takFUcUw0V2lxSFR1ZDFtJTJGb0duNjJacnBXTCUyRjRZa1hKbjNSWGJlYlRyOURTJTJGWXN1SEZySHl1NmNVcVhyd1BZZiUyRmdVYkolMkIlMkZVNVJUVHIxYmkzeEl1OWFOd1VlcGxuOGFEeDBIMnpEY256YWduN1hIdjJXUkp6MEdVc004RGtOVSUyQjRmNjNIRnBGdmZUdUdhVWNJSlRRSEhieTVWd3lkeUVra2hmdVFrV3laTDFCd1Y3ODJ2MG1ERGVybmJWMDUwMW5EUDF3QWlmN09POENHSmllZ21XVFNkZ2FBdkNVSUlCdFkxczAyVGRBcXhUT2kzSFBNbmN6ZGg2YVNUcndhdTRrS3o2bTVBSXVlWDZwSkpld2I5RXNPSGZPRkJnR3BiWFc4blNJcWYlMkZ4aDdyMjFYbFZoYjlHdjI2MjFnRXglMkZKMmVUNFJzNDU4JTJGV0hHblB0MDg3alhXM01aUnVNb1VBbDlTNnBwSXlTMkFkQUQxcVA3YkF3RCUyQm1sMENCRWQlMkJkUjRJMzhDN3UwUEl1cnZHZEhDZWFVTDQ2SmFUT0JDdjUwV0puazBNNWFmVlhpOWYyNFg4azlMMG4xZUNQS3lRbnBScjd3VjJsJTJCNGQydE1zVXhmTG92enBkMXlCS2l2d0wxdWs5a2tFNlpxVVhGdVAlMkJNU1U3dWZWMFMlMkZVYUY0M1pObDJBTnVtMEd3M29ad2FjbkFCV1JPSW9aWGoxTEtoaktyWkJ2Tnl3JTJCQjczM0tpU3pxTE9XdUhoUHN2TzklMkZhSXZrTDlsdW9WbSUyQkRrdWNhdGNSNTdxRkplUk5Vc2hhbmZ1SkFGem9ORE1TanJ3VUdhWHhTb1NzeWFjdnc3WmVRMjdDMmtZdlBBdlNTZFozZkJsZldpV21OVXJTVG9HRXVkSnZZdEV5NnYlMkI5WSUyQmZKdUZSa0dGd2laUERmRVBXSmxYUkwlMkJHWUVFYXJxOFI0MXZnb3ZQaXVxSCUyQjFEUGpVa0xPMHNGRVUlMkJMZSUyQjRFUjNabHdrUkNKaGhOQWtVQUE2QTB0TGFUcFRUSGlKNWpGRkxRdGdSVDNZUjB2UGFUc1RFdWQwTjRZS1F6JTJGOVFCbWVQZ0JlM0dwaGhmNXRwU2xJbyUyQlMlMkJuQ1FoS00wWW0wS09sdFM5dzF3T28wNm9qUVdLdWltelk1TlQlMkIlMkYyb2pVMXFTcVhoN25ubkVSdGY1RXh4T2Y3RGhoRjczeXZnVWw5ZzhjciUyRjhnWGxhM21lJTJGY2xIY09ycDM5b3BzSnZrdEZlaWI3cFZuNWI2VVZuVE92b2twcUhBVXpJT0dnJTJGVFRpbSUyQk01Qm9odDAlMkJTOFkwUEI3JTJGSUsxTyUyRmZvUGM2eFBXWExBZEQyJTJGNHA3Tm1GSmx6UVJWWEtZVU54TGt6c2dTUk1lbnolMkZ2YllPdmYlMkJrYUpxZ29DMDBTVHZWSGklMkZycnpTeEdZeTNNOUVvdHAzWHhtcEgxNTA3eGg4UXNYbHJ5WmMxMFJrNWVnd3hOVW5sUWZ4ME54JTJGRHhHYSUyQnB5ZTFFZkx0cDhtendjdWRWcSUyQjROTVpVcXZueFIzYTNiaklWVks2NlFKN1FxaU1sQno0dnFRQUZ5QXZWZUlIODdTaE9Yb0dPZDZ1OUE0Y0dDbG1lUUclMkZWOHg4JTJGNjlobUUlMkZIcDQySkp0U2I5UjA5NDFWQyUyRkxIRXNrRUhJZ3B0cWNzM05sODZrNVg2UnFpU21TWllUdVBERHgxaGptRUR3alJvd20wajVOa2tFbjNxMWx3WVBWVDVReUdjVW4zOWdXU0dEMTJPb2xOdTVlRHp0OVhUNzFjSjYzMXFabWxROUZpaVFwRkFxVXlMeEJGcVNlS1Z0TGlYZEFqQ01LNXZoYVRPZ2Q2UVAlMkJBbUhybm5SVklnTnFwVHU3OUk3cUpyVENXWm5NOHhSSkpDUm1iMnNLdEZINGN6M2NYZWR4cm16MHlHbk5JcGZWdE5rSzglMkJBWGFyUnJ2M0RteGJpaDVRJTJCazVST3RmQno4SXpONjU1R2Z1MnF2ZGdLVmRMTmk4ckY0MVJWdnZjUTRuUDU0bkVrb2JxYzElMkJaNXcyR3hCUzlSZEx2eURmbyUyQlJ1VXhSM2xkJTJGeDF2JTJCYWYlMkZDV0g2NUgyakFrV3k3S0RESXhhQVhrS08wb3luYXp5SDZCWW94WkdEZEVaQXAydU5PTTFnanMlMkJUcVViczN0dVVjaE5yUmZnRDZXZTR2eXJxbEtzYXJXcERNUG1NOXFuME1NT2pRUGdBSVZEOVRMeFBkZWdKdVZlM3olMkJOWE1ZRHdqTUpJQUg0aDZFRVdSWEVSMzhiOGFQOFdMdEV3JTJCcjNtMVFlVTlPQk9hQUwxNiUyRjZQZFZnUTNHJTJCaUZ2VVB4ZW5pJTJGN3dNZzhWQVExREZST21mREFjbVpaaVcwMVRqJTJCNmJKayUyRmZOZHVQUDVOQ0xVdzJNRWg0OGNoRmxoRm8lMkJienR5SVhDQW93OHQwQm5LOUZQJTJCSm5qZkloaUJhNEdKaUp3QU91bEFBaHRwbktBNFZvV25DcXNVbXdxcXBMYklKSXpOQnpUaUUwN2xVS01XdXoxeDdWeUhZTGs4WlpnQ3kwc3VpenY3VktHOGp1b0l1Tjd5d1VCUnR3QUpySVdEMDhaQ1MlMkJzZWNVMzVjWWg3d2k5NFNxUDlQbk1BTUU1eW9JSTZONElRcFRXWmhwUnplRmxhNGFNSnRScGI5WThjd1hpTEVHUGllMXNsdGdwZFFFMyUyQlpaZUZreWNHc0Z6WEE3QyUyQkoxSGpTaHFyJTJCZjRzVklUVmZNU1hIOGRYbmtFTHF2S1MlMkZvYkZGdEhFTGZlcnN2aWpydlgzN3BGTmk5Uk5xd2g1RTJIdUVCN3N6aTZsaE8xR05jZ1BzeEZQcTZidTVjOVk5TTdDWGVqWE9qd0NsbCUyRmVXWmpmSXZ5QzdsbHlvYm5JMFc2MloyclJrRmR2cWdkaVpnJTJCNHJZJTJCVXl5cmhybTQ5bjNVVTFCemtiTUFOazg0UEUwT0NndHpQJTJGOTFNSzhLdmRnYkhIYm9PSDhsdHlCdmloZHE1TlpyN01LTiUyQkFhWDRZeXpkZCUyRmg4N1FPUjZ5NiUyQkc3b0tIZmNyV3RCaSUyRnZWWCUyRmk4UzhVUHRmdDlSelJSVUVsTjZEM2g1UFQzdm1HbWV2Nkp1MEVUVERHTTE1M1VTQ2xMJTJGMVRuRVgzUEJTcllnTnh6OGlFVjBhYXVzcHpyN080VyUyRjhlbHRpYjU5bG9QY0l1JTJCSGJvbkJzTmFkZFlJWkpIUEJSU0szbmE3VjclMkJ3RmpBWkF0d0lkVjY3Y0EycENCQXltQVZBSDlydyUyRlIlMkZWMiUyQjJCbG53TWRIMElQdG1JZUNhaiUyRk5zdDY3eSUyRkVEb2NiJTJCb2wlMkZCUDJVczVFbTE4MVhHd2olMkJIM2o3Q2wwYk9NUVVHY2JoTCUyQlRTbGdyJTJGMlV0ZnY5YzZRM1dSVnB4WHZySEElMkZLckc5WVI2anV5eWg5NWNReXBSWHg1SGVWbDVuT0J4YlUlMkZOVEZzZGJvODVKR3RQY3lzWkgwcE1OUHI3YmMwaXdYUkFkVzRzMUpmUGhsSWRtcndnbllQTU8lMkZ3c0JOeUJBZk1DNzM2NHFOOXdHdFNxeGM4ck1OZzJFdWtEaEp0SXNzdWt3VDV0c1pzZnJEd2xacHFxWHNtWmElMkZ0YUhNNW0yZXRsUktST1E3VVEzOCUyRnVFY1lFU0pFNGtDTmJQZkliUEgwR2IlMkJ4cG84N1MxJTJGRkNSSERnRkFEN044SnZxakx5R3FaSXVlaiUyQk1WZ2N1U0JZOWx2aCUyQkxqWVJhYWp3cldzN3poemxjcmhMVXYlMkZ0ZWdhVXRmejAwOEhkczRIeFlXN3V2ZWYlMkI5MXRFVnBZQndVMXN4dSUyRmdQazR3czNPTHJHckxZSUdSOEYxR3VaMTJYdldNRzRUN3FJJTJGZVVMTEtzMEpNMm1qOFhONkxKWTlzWHhVUXNMTXJKTEg5TDFFZXVDOE5idjBrRyUyRkxmJTJCamFINDdtTSUyQlJTbXV2JTJGbzFnOXNTJTJGWVFlclVQRkhaZjlYS0dUSyUyQmg0T2ZTUVNFRWh6anNiSnFqN1glMkZBdzljM0FZdVVPJTJGJTJGYjlZaTlFSzZZd1VHM2ZBU2Q3M1JlOHhTaGVvZVdkSlpMcjc2VlhwU3B1d0FKREl0Q3VRQmtQMWw1ajhyanNkTCUyQkU4S3ZSUWdIMllCYSUyRldvbWFmeWZJcE1qbnpZVm9XOE9za0cyYiUyRmFLb2V2SyUyRnljJTJGeWN1eVVmYW9jREIlMkJDNlZMSGx4THJOa25xVFBEOGwydmhQQUduOCUyRnZCc3UxcVFwdW55TDBzbU5GakMxaU9OemVEaUVtWnJSNVNYdXd2ZzdUZzVMSWYzRFpCdVN1YTMzMFVMQzdHNUZLMjZrcHk1ZjJ6ZVBOVFdDUUtIaFJ1aTk0byUyRnVRNXVVWkxTYVdXanRoN3NidSUyRmFoVyUyRkZZZ0ZiQk9COU1JQURrcjQ0bnhpSE9veTlPVm9xbTU0YkpMUSUyRkM4a1BhSVRoRWY2U3pJOW9IQjlkUXV3RSUyRkVTWmJiVldjQ3V3ZG9NJTJGVE9tWXRHblhhJTJGUiUyQnZEN2dVckFQYTlVMElHNnNpemUwZldoUHlydiUyRmxpOFF5QVFQWmFWcmlkcFZTdlhJajJ5VWpYMEpacGZ0cmVlZktDZVMlMkJWU2xxVDFUdWx1JTJGSHpvSTdjNFhwRTFWZUp0RzVBTXljcWU0b2RRQ1VzeTd6ZzlDVHk1aUVXNTc0elpzejd2NXpGOGZlNkI1b0M2WVZtaFZQNXUlMkZ2cXo1SjFkOUVualJTZlJUeFdQZ3FRcGUwWXJRVGRRdUFLck1TNFZSME1XUWc0M1ZSYlk3JTJGUGMlMkZtaGVnN29GakNWcXVpYXE0R0F1MVFxaiUyQjdNeTZRUXU1UVlPQ3BuTzJ5Wkd6YW9LTDZYODlyU0NMJTJCTEJIbGI4bDhzb2NaN0pXa3lVSTd5Mkd2ZEFaYkxzZFd0TXZWT29EakclMkZtcE4wY1J1VjhqdjBxYm9HMlcwQ1Rud2ZKRnJ4UG9aVW12ZkttVFpUc3RvQ2tLc1laUiUyQmh6VWtkdEVNbWF2OW9ETjRISnNzJTJCJTJCc0FEMmxWQlRGRGhXc0dYMUFXcVAzJTJCTkdHazJmanpIMHFDTkE3SWZCNCUyRkdoQzRHbTZ3JTJGV1JCJTJCcFlNc3QlMkJpNThHSkp4bUpzbDlac1JYRTZtQ0kycmVOV0x4cm5STW5ZODZBWjRZJTJCQlBiVEJRQkZrOXJ6NmRjeVN6OWppNkFKeXFHWHFIVmlDMWJ6SGVDZ2h6UzklMkIlMkZVMVhIbkZmVEtHRTUyVmNBck1jc09LRzd1RlolMkY3eE9YVnZ6TDlaMzYlMkJOQzFIQWdxdFBkdlU3dlk4NGVKQ3JCVzN0N2FRRzE2c3M1VTV2eWd5SHNtcHEyTW8lMkJ3YXFFb2tseHAzYWNhVjc5QkQ1MVRLUVRmQTJwOUE0Tm90NHBDSnZaVnExUGJ4VlJ4UkJ4M3ZtSGg3ZDQlMkJBWDBqbDZtRk51c210VTdFekVXJTJCTm8wUUhvelJ5YURQeHluTm1NUiUyRmNZRVBHOHZXY2xKaElYeGhWbzJmZnR1SjJwQXV4YXdMOFJYYmQxJTJGZCUyRkFpWmdwQkUxQjRvY2lsWVZUb1JmeVg4UlhYcHBDS1A4QlY4OFJ4OG5TSiUyRmtsSWQwWXZacFVyUnhFSVdZZ1hlRzdkck8zNHAydk9VdXdqbHhNamVPSmZkZzclMkIyUjlyd1pUU0R0YWxHN00xN0RRc2VYNFJCV25HbUluQ1FwWmpyeFZPa1lEciUyQkFBN3ZtZUJHWm1jRG1UUzNpUVZvSGNZN3lDcTBPOGhIS2xxUzluUW9zYkhIWnFqMlFKZUNIUFZ4NWpnQ2xvM29jajd0TWY4Rmw0N3lqNnZxOGRPRWlNZlpkVmltOVFYREpNV1JjJTJGakNwekpkdFR4bkJZN3RxVDE4VlJENmhZdnhxQ1BmRDZZbnJETkxGVmhsYkRDNmtSSEg4V09rZGo2aTVPbmRud3VoaDhEc3hUczNtTWM2ZGJBSiUyQjFlU2wlMkIlMkJNUDJ6NkJVUFJSS25BcHg4MkpxU2k4ME5XJTJGNDF2ZXcwdUJ1d1V2aldLbDR1UzY0eEdINUxHSiUyRk95S3dZOVJ2QjB4ZFJXejQlMkJCY28xOHZmNGN0dkZ4ZU4lMkZKb3dvcyUyQk5qVzQ5TDd6UnJmc1NMZ2RGMnBycFJPb1FOaFBaZCUyQmRjbkI3UGklMkJaVENJeEhLdTFtblVuUDZRYWptaFlTeG1kdmVzdmNGdVB2Nmc2dXdzRlN5TjJicFI2SFpCUFdsNkh2SUpzSXRIWVFmWmhmcTUzRyUyRllKRm51Q3dDM3VsSzJlcU9nSXViUXZrcHNqVjVSZFdSU2Q2JTJGMEwzc05zelVtazJaVG1pN2dzcyUyRmt5eXVtJTJCa2h0MDRnQVBOcHNWNFBZMlhQbUslMkJWOFN1WTA0WndIOGs1bVVEOUdEajFxR1JJVDhVd1NBbEdjeXNMRUw2UFN5dE1sUkJHeUVnTmZLVEF1bGxFTXdmU3pSM2lnMXVxbkVPYk13ODN6YTdzNW5qMlFrS0N4S3Q4aXI5SnB6YUV3b3glMkZ4ViUyQk1kbDh5alZZZlNmY1VYT09FMldOSmFaajd5WWpjJTJGZ2tOWUthVXR0R1JSam1zNmt1RDVHcVk3b1M3JTJGT25RNlh4TlFGQ1R3UVJUZHpTdG1iSVVTSDBVY2FYS000NFZmazViWnZSVWd4eEhiJTJCcHZJckJ3alhTTWVNM0Y4dURIVWJFdXpqZDNzVDJ3Y3NCbXRuOSUyRnU1ZVNMY3JWMkRnZkJodlQ2VnJxc2F3eXpBallkZ1FFTW93OGglMkJ4VmFmNmkxVSUyRjBRVFNxZjl0ZTVSUTRHeE1KRlZlaG1raWFibklkb1RuVUlXOHZTcEExemZxT2wlMkZldGFNdnlOZmF4UU1JdlUxU2doMHMyJTJGNkhHVkw1TUh1QU9iTFplNWtJZjk1ajVOVlpGS1V3aDFnMWo1ZlNaUjhYVU96QWlHRDB4OTJuNDBkdjNNc1VpUGJXMXlQZmpqMXY0UlhFb2pvTVk1Q1B0TkQxZVl4aSUyQmJuSkVjOUJleHYwVXdIWSUyQiUyRnA1dHV4aHZSR2E0QnBROU9BbTlLelJRR3p1S1ZDOHlORzhRaGpEWEFVRWdIenR3enZpcHolMkZzekJIdSUyRkRkbngxaHQ2Q2h4MDRvaVdUelk3YktibmJrN2ZKVzBoNHpqejBUME1TJTJGeFhwOWVwYzlNdVZEJTJGdE8lMkZrUTJkMDNGQVUwb2lIVHh3Z1NMQ3VWcCUyQkc1WVlBVmRpc05kRmFKcDlnOFU3TnRaWXglMkZkNndRSEx1SURhV3NjRUNmOEp6UkpwRm9vb0xSYzd3ZnJ4YXBGSm8yQ1JURlRXN2V4TTAyZ2RpT3R0ZlJuJTJCbk9yVjRPZnFyTEg2clB3eGFJTGk3VExRNXFjeWpnZDNUWTZrZFd1Z3hJekFtSExpY1ZhTGdNd0Y4YmFhRFB0a0lLJTJCJTJCd2M2TWV6VElJa1d0S3BINloyTXBNN2dzJTJGY2hoYm5OeWFMWUREOVI0SlZoNUNQVWx4cE5uNXc2UyUyRjRhZTVTUXhOMXgyNXIyZDZOS2hrQXBPUTBLcnI2aUZ4V2F3UGxCVERwdkVxeDc5SE1PRWN6TDB5VUJUT0pBc1ZPJTJCbFpIY1JYeVpMcHkwQ2JWTHRtT2JjcVR2M1kyb01MM29RSWdlNjVFb2ltbzAyN1ZvVHpaakxtUEFRbWhRTElDYUlOa0NFNnclMkJYbnZUeGFzVWNpZ1NWUyUyQmNWR0F6MXR0YU9kMzJJNGZzQlFETiUyQmlIOUJaYXlYYm9KVTJtUlprQlB1WlVIUXBhbzFZMW9KR3BoaFIlMkJ2SyUyQk5BTGpHJTJGVEFXMmxiYzVyMzNzM0w5alFSOXolMkJSTCUyQk56cTFsMzBScnNXSG5MY0paR0tUT3pLWjMlMkZ6Z3JQWXYlMkJBM2hsSzlJRjNtZklPcVBpMGM0dG8zNFNVVE1ja0JYZyUyRmMyUlVTNXolMkJkUVA4YzZIVkJYWk1zNHhFVXNyeDhCSkFNeHNHemo5Ukp5clRhQjFvMGtjSkdTNFE0MFdCOSUyQlZDZmNzUDhrc1JmRSUyQjElMkJUaHRmd29RM21SRXRuekU3aGlVUWZyWm5MOGVDR013M1Rjb2l3eTR0YmlaZ1BaRDg1YURFWjBPcGJFQ0RqV2NjVEFhcjBDeXk0a0FHbXpTWTVEblQlMkJSWklCTUJ6JTJGdDdjVSUyRmhmelJIUHU4MWVsMTloa1FsREphWnRuR2gwNmklMkJyJTJCMXNSZEU1aG42QURUZW1jOWY0QlNPVGhlbjJFVWg2VGlHNUF2M0E0d2hqbE9wSU5tSGxnZ0szJTJCNDF6aTVhMjVzOGF5cVpzQ2klMkIlMkYyQkhUbTVobHVhU3htQ0ZzS0ZydmlqcXg3MEY3NEZkOFNnZVZZZmNIWk40N1NGRkNEdDVzU3IyeFhmbE1MQmd3YkxIaGVPdmZpTk5VMG9rbmVDcEx1aWVBclhCWVMzMllLSUIlMkI4OCUyRkt1ZTVBNVhYR1lmaW0lMkZobFlhdGh6ODNaemVTWHBBdTRWdFVHelJTSEc0THM3JTJGS1pwS00lMkZlMnlXalclMkZJTTlnU0pJek5sY1FpZk4lMkJIOWF4NXY0alNGSEZRYW9Ra1Q4bE5PeVklMkJ1dEJIZ1o2bTVycWJEUUtWQjZHMlBhZlVTRHUzeDI0YyUyRjFZaUpqQUVTY25mR2hNJTJCazhNeTdzZndsdGl0SXZYSnNBc1djb093JTJCbEprOGNDNmxXbThUcnQ5cVFzNUhzUXVaSjBDTTJSdkoyWkVKbmJ6SVF5NG1BN2tiVWxJVDdhVXZGS3VrNHlPcUJCRGhCQmxBZ01iVHVDR21rWm1ZY2g4V2htJTJGbXlmamwxVmtLM1VGNThBT010QVA3SG9WZFRiQm1DR3cxTjhRbUJsaU1SU0pmTURwOWVkMkc3NWhTa2F3VzIxTFpLNW90b1d5SGFVZE9rdkNYbnRvSFAxJTJGZE1KUmFpZVVKRXFyVFNjZUY3a2dtZmlCJTJGODFzQ1YzU2lxbVhPM0NhRzJRbjBOOG9GdjdFeDUyWXBLd1FsaDJDSmp3aDhSbHFOb1pVd29uSDhVWGRqdFdyJTJGM0dSMjVnaVFEYVZDMyUyRmVvRGFOcG56ZCUyQjJGakZhSDdDQmZsWHJ6QjElMkZ0WGNPNWU5TiUyRnJVcVolMkJiYVA4ZHNxNkFoYkxUUzI2dW5uUHhHRFRvJTJGbnpwJTJGWFVQWlVDOFdGUlJQOFNOSUhOelJKRjNUOU9sWFMzakNwY091b0lrRFBMN1ZNJTJGNHNGNTlkWkhvZ1dhb2theEFGWHh2MzZLJTJCellXdlREMDFmckVWNktJMGFjeWxLTFIwdDV6aE8lMkJjZUxkYWdPJTJCODl6NUwzU1hOVTZ2cmtwRUVtd3glMkJrNTJwTDlzWU1JdHpEZ1NQYjlSJTJCRUw0NW51UTMwMWpnJTJGNnZnQWtDNnJEUTJYVXpVQkVBN1JBTnppdnE4bGkxZCUyRkM3VUNxRmpPeTliVXYxQWNVeDZoMnJtT0dCNVB5ZFAzZHl1TXhrRzVVUmhSWXJmbDM2aVIlMkZUVW1qRmxFZmRHRWVObDUlMkZYQ25LQURyNW52bDVNbFNJNUtTYnNPcTk0M1ZyWkZobVpTJTJGdHRteE94N0glMkJYWEEzYTJhcTFING1qTzFEYUs2MU9wY1BOemkyVlcxcnBsb1IwUVYlMkJNcDZhWXdJMzExOXhXVldBUkZGU1p2bCUyRlBNR3NMUUNFdEV2a2lNYWxibkR0Sk53Z2tlUXFiRGpldWZXMXJyQlphMTl6JTJCNnFyejZ0RW9pWiUyQlpiZHJwWEVxMyUyQldJOTN1OGhVWm9aUUdQbGxKZDVESjJ6UmM2Qjk5WFRoUGlnTXVkMldzenIlMkJTc1ExWnBuUDQlMkJHVnVUTlk5cTZKeHdlemJETGtGbUxxV1FsVDlFTnozc1RVUVptUlJpMG83Z2VjRmczdmJNYnJJekt6UnRqMWozM3lYUEtGNCUyQkdHMThRSHEwM1Z6TDZ6VjhNMlVwZnNtajZBODk0ek1sR1N1WnNIUGE4JTJGWTMxcXphTFY0JTJGMXd1WmZWWWhOa3ZGZlJCbFhQOXRmTXdUU0pvYkdsUm1wUndhNVolMkJROEpaQ1B4QjFzREdKaWNhdjVQYTdQdVgxYmcxTVl2S2tEbDNKbVNYQzUxMmZvYXNpMU1FdXQ4VGpLZGkyVEJ4bkFmdkZwYUFvc3lIU2xtWE90SVNTalZlVTNmS1NLbWlNdSUyQlNZUzFvcUtpVHI0U0ZrWGNIdHdRN2RibGtOeW90ZXpSVXdaZG82T1pwVFJLdlJPNVdSQ1czbiUyRmxieWdvN210b0d2a1lLejBrdUI1UFliQmxuZXJrRHR6ZFdqZjBRTlljSWVBNkNzV1BBMzd4MXclMkZocUVJZk5ZbkgyOGIxN1NkN0ZhJTJCanFUZ3BKSVBTSzVkZjhvSFdOa3o2SVFra2UxJTJGOXlpUG85OW5ZcjhBdUo4V2UlMkZFREg0OE5lWmVsanNyeER3QUdIZzQlMkJRU3pRRHAzbUpFMzFxeTU5NndtTzN0dUZXTlpMZnVoNnI3UHhBVDUlMkY5aFllY1J0bG5OT3JYMURIb2Q0dCUyRkl0akM1UUpnNk9MMWpibE1FNlFGUDJ5Z0sza09CQkhjNDlNbjdJSHBQNEo2emh5YTh0R0RMQjlJOEFpcENrTFlYMk5mbW1tWHcwc1YlMkZMVmVWY0Rtb1FOV3JaeWRZMURCaHJ0RjdUQUw1NlNQQ0hVTEw1MHg5eUtGUUI0TEo5cnZ5ODhxJTJCdFZXSkZxUGlYUG5HMXNhZFAyNVhmRUVIWXBjWkk1akNPNndabzYzVHYyUEM3WmswV2ZUNXgwZGxrQk9PS3ZmMjJYWGZNWEtUNSUyQmFmZ0lwZlBuWjltNW1hWncwdjFSbjczQzFWJTJCVkNyWmhKUUxTM0NzJTJGQjVBYUlDeGwlMkZuU25HM1hMZFhvcFBScU1lZ3hac1pNY3pZV0ZpYXJ3TTdaeDVXZjd2RW1kOTgyNjRDakZRN1ZkbHhJaXZRd21sTnJkM2w5NFBNcG4zbzclMkJHaFVJa3JVcHVyVDNZMVlTZWI2aUc4UCUyRlh1MXVYM0RBYjdIeU5CSWZLaVYzaTdKM1BzNVg4YU1QNWtOJTJGcTU3TlV1SyUyRndsMlNVJTJGJTJGbjJrOGttSE4xZHIxME9RWXBHdXpIbUczUW1wTCUyQjZjYmMyanRWZFpReTVRcUp6YklBa0lyQWM5Qjl1cXYlMkZPJTJGUklITmxxUHJyUEZKJTJGSjc5WHZ5SDczY2RZeHBmRnM0Qm1JU3dOb0V3RWE0REowcW9FVzNMejdBRXAlMkJsTUV1OVFEODBJMElsUk9BSjEzcWklMkZiUjNPelFMNFpWeXI2ZkNPRiUyQjNWak5DVVFnQlhBSTlOdkVTJTJGZHFVaG1STVM4Wk9rVk95ekdyRCUyQlFFMWlFVFBTYiUyQkwyajkwTDRwJTJCeVglMkZWNWFlJTJCWWY0NGpJcE9ReElyQ1QydUklMkY5aGIzUFFhUzhvdjVPbHZtSGU1djFvNTloSTVLTnRwSVBabzVIMXpWQ1NHS3U5VW9Xd0g3a0xzOU45NEh3MEhIWVc2cm5OZDlQWWQxVWMzSVh6Tlc1bTB5Rkp1VUxYV2xtYkw2cjdSTTRYSEVGZzU2c1JVSjdlZ211Z2FiTE1NWmtWM2l4d3RybG9ZaVpDTVQ5V0lxU0tYb3k3NVN1bHJFelE2SFVidzh0aTRsdFFMNEpSYldVMExIeUtRaDc3ZEdWJTJCViUyQkVINSUyRmdQJTJGNFpZd3hkbXRGTCUyQkVDTURld0J0JTJCQ1FrcjlGeXZRd1NnSk5IeE5EblQlMkJLYk4wUEs1bGh6TkZ6U0wzdjNEYU1GY1lBRnhIUlpIZTFqRnVDSzZCdnR3dzUwT3VyTFZrS1lqJTJCdSUyRjA0M2tBc0FqckwlMkIlMkZ4ejFQJTJGOFdkd0J5a2ExWTMlMkJUYVNJSCUyRjNzbnNvdHpTeW1WWiUyRnMlMkJYJTJGZnVqaTNIWSUyQkxpdnUlMkZ2OTdPVkxGZyUyRnh1JTJGbCUyRlBwJTJGJTJGJTJCVUJIdnJ6SUNYeng4NEglMkZiWnJpSlIlMkIyJTJGMmZUbCUyRnVmTDlOZlFqNzIlMkJiYThLQjM2NzRBdjlPJTJCSSUyQjklMkZIJTJGJTJGdjVyTE90JTJCdDl0MlAlMkJIJTJGdHRhNVhWWiUyRmUlMkZ2WXY5OU5WNyUyRmJTaiUyRjc0OERCJTJCdSUyRlV3SUVlREg1Q3hmJTJGdTRLJTJGOXglMkJvenY0ZGclMkZ3M2hMamI4MzliJTJGbTFZdDd2N2I4TmF4Uk40VyUyRmR4JTJCYjdTWUxoMUduZHFuT1NkTWE3MVZvJTJGRHV6OFp0MjNzM3k5MFlBZjlndmx5R2ZjaFk4WnVYTjc5V1Y3RWU3ZjlQNzlBZFhVSmp0ekc2ZDBhcjFPZWdyRVY5WlclMkZsMGYlMkZuWkQ2MzYzUSUyRjI0QlB4VnY3JTJCMm4lMkZuMzg4TlB3eWh6ekNwOXVuWkFpbEg4UDhtZTdGZWVXN3pzVWZKUmZDWHRsbFdMWG5JRWZzSVVKYU1rUDNtMDB6cndmODdQa2QwZ0V1Z1pJZ2VqUm1zZTU0QWdjZkJ2TVRDQXo3eTRvRVV3UVVUSXBBZXpGcyUyQkYlMkYlMkZtdTVCZjdJZjklMkZtVDhwNXY5c0VYWElSQlVmeEZQdDVkeEIlMkZJbVpTdVVoUHluc043ejlLWjZqbXI0SXVZQU4xMk0zdjl2UDk1ZmQ0Tm4zM0M4enBmbjhPbGJ5ZmgwOWFnbjN2TmRMQnU2OTZYelVMNGthWlVadjNPRFlmbUF5Y2d5elpFN3dtSlNNVWdVUmhKa1dwSlRXOXg0SiUyRjhudmNGJTJCdzMzMiUyQko3eXZ5N3ElMkZmOTd2SmVHRDdSZEVWZUIzZjdRRkYlMkY0MmNvcUMlMkZHMFZSSDVNMVRseDdOUU96am1pb3N5VVlMb3RpWkVka3dwVDVIdmNPbFEwZjlmYWFrS2NNRnFoY3ZZbiUyRm5rVlpNaTE0N2Q2bk03JTJCJTJGcHIlMkZuQUU4cU01a0piSTlLaWxsTzhmZU9ad0NmNzNmJTJGcjZUUEdDeFVwemNsUnJ4ejhQJTJCZlJkb0lsWDlJeGpXYSUyQiUyQmZDaWJvNGN1T2dKNDNnZVFyWm42blVITmNJZjdnV0NrSlloblpVeGQ4RXUlMkY5VFIwSkdvSElVJTJCQXJjMjNLNlFUTmFUYmJ0UmZzeWVST0VaejZlS1pOREJITkdVMk1vMEpETFRzJTJGMzcxamFSem1GdGN6NDVnbDI3cmklMkYlMkJuRGRNZG12JTJCYTlMcUZVSjRPTnVJNDUlMkY2ZGZ4MGxuR0hIOHVwZkdTdEdHdVVKWGxlekFsS2hRemElMkJiMCUyRmo2VjFqVmJtYkhBVUQ5ZW90SlJrMGFMYVNHRVVsdUFVRE0lMkJQJTJGNXk4SmxzQmczcWVTRCUyQiUyRlVHazREeG4lMkI1bCUyRnhudVVVZiUyQjd1dTh5Q3BUV3pBTXkzWVdveFJ3bG91ZmpxWCUyQkpUS3FDQVBQRmwyaUJzeUR5SjF1Q21hQlF4aHk5JTJCSnk3VU1nN2JGSk9tNUVJd1A4V2MzZ1AyMzd1cSUyQmNpJTJCbGhmSlRlSjBUd0t0RFE5U2Y0c2lIU3JSYlJWbHEwJTJGbnFrcGxpTjh5Q0U0bmZiejZReW03ZGtmVDUyUlRQZDBzeFcwN2lTeFN5aDJxTDAzbEc1YklSUjBtT0JRZ2ZsS29RZTVqbVZxZ3VtcTVqZiUyRjJWSko0JTJCdFBpVzExSEFWR2luOWpYREE3WjNKb3JzT0M1UEVwQzc3dVpVMzVGVjRNM05EeGt5cDFMciUyRlh4MG83UFNhTnZyMFRGaFY4bUplZ2NCV1lNVm10NEFBSkNGNEl6Z3J4b2VzZWZsMkdXcFZ2Y2xFa2tFZWhHN0R3UEVmcTlxbnhKaFozZDRIbjFtbTF4UTZ6Ukc1RGYyUGk1eGpYU2RMaTJNOEdRZU13eFR4ZHpxQWF4dkxKU0ljV1FEcnBCM0hxMk90R2NFV3FhZ1BRNGNDOEtTUUZXMW5HJTJGWW9JN2Y3NkplZGZRa1l3MnJGOVJyRmk0QW9jSUMlMkJzbnozRkpaZ2ZIJTJCajBYZmpyRWVTOTV3Wm9HaDR5N0huUHJudzBja2dUM0ZnYmRoaXc1R25qRENpMHp2Y3g0RzBuVUtPTE9pVXJiWjdiS0pTWXFFNFY4Z2Z4OEVWYkg2TWZnRmdybG5YeWRsdTNYOU5BMm1WJTJCJTJCdWd2d1EySXlBVlExTEVPYnJKVXhKY1lnSlFWaHc2UmhnT244cjhKUmVNS1J4eEliYnFYMFVRNFFHcVViQ2R0Y2xNdVZRSlFFemJzaEdNS01ybTIydEphOWJjT2NMcU5ZOER4bDBYTUlQd0JPQzRjbTB5WHJ2anZUTCUyRjJpMVQlMkYwSm5xaWc0WXk3RGZnSmNINSUyRndaNWVMalV0QUVjNlk0V2ZGMWlVWFJqSTNjQ1dCQ0lqV3V6b3lVNnRENUJYQnBkQjFTenFQdU5rNUtjZHR2WVdCRk1oTmVydUlFak8xc2llaGR1bFA4YUFOQ1RzUzdvaGVRT0RGaWg4UDRWa3VnQm5GWVZBazJBSGVRTTJMVGMzVTQ5VXZKVTlraERxUmtCTHNlZU5FeFl5SFVNUm5MbERLYXBBMUJNYTFFSTI1SGYlMkJ6cEVEV3FPdTVsUjFIT3NNYXdha3d1JTJCaU9Dbld0Ujl3aExGNSUyRnVEWWJGaXUxUjl5YTV2OW5jRzRqdmIyYkdGaDdSTlcxOEpKczJmaWluaDV5UlkyUnU3QVZVQ1piRTNRVnBVN085VUFtbkU0ZDk1OVVJdjlMTFNMWTMwciUyRmRoTGFYUGlKWTBMcVltYjJTaWl2SWhpQnpKRyUyRlNYOUJEb2lmdU5sR1VzUG1YeUczbkh4WmZ0Ynh0YjVRcUZERnNTNmZsMGtFbk9KNFhsNTclMkJPTkpUT3paNGFnRk1wc244NVM4R0lnOGY4M09WSXlGOCUyQk1nSWxvZXp6WDJKN2JNVWdNd01PU1ZPRVowQ3l1WDBvSVN4eW9SYU0wVk1nQ0JrYjA3RkVuJTJCMmhVQlRwN1BvenllaVpxclRtOUg0aFFqS2pQUWRtYllrRWxSVm40OGNIJTJGY0RuR0pkMG1wenRkeklibFMzcTN5RldKYTNQa0NwOXc1VGpPdjRvVnQ4QXFXaVdYb1FiM1Q4U0pUSDZLQ0dqN0drdTVLNkZUM1NTTFEySjZadjAlMkZNeE9hSFBhaFAlMkZZNWJ2bjBHQjVvUW1QbUslMkJzVTdIUiUyRnM1MmQ3aG9Nbkowc0c1MHp0eXElMkYwdUlaSDklMkJhOVdXMW56MjF3MmhRS2NuZlZmNVJWYmVXZGRtRlpmOFVHSm5ZRkNyUjFwUXA1JTJGZm16TEZaTExXUTgwOGdGSm9rTmFoYjJsdk9aYkhKaDhtN1o0M3ZKenhnVGdXUEN3dkdyWmltNzhSTndFSDVOcG1WakkzTXdaRWoxWW15UnBmZXlBUk1EU2lGdldBM0hUSTJMQXpJR0FVOEk1VVNyUDlNQ1VZMWg5S0Z1R3gyQkxTQzRLY2dIZDFJOHNOSWMzaVVZd1JhOTRxYUlDWlUwOEM2MiUyQjNrU2lNcCUyQlJJYzFvNW81V3lyJTJCNzUlMkI3bGlXNkxENmVuTUR4SElwTnI5OU9hMnk5YUtsbVhtSWFwc0JaUiUyQjV2bEFnWmhnV3UyaEtqMU5vUXVmQ2Y5Uk9KM2UxUyUyQkxHUlZRVFljUnBvWU1hQWxTdmZscTlCWHlzVzY2aFRDZ013aHQyUHRkejZROE9abW4wTWtpT0ozYXNVSEx6OGclMkZHNUw1aUJWUmNoUVdZdWJLUDVGQlJjV0hEa3pwYkZHelpkbjB1MXR1VXglMkZ2NGdETmJpY01CcEFTQTMyMGxpdW5QSUZLdEw4Ym1zTzcydHU0R2NreVBmOWprSGlXeGhkeDE5cmxLbDV0YllTWk8lMkI4TWpYNUZ0ZmFDME5pcTE3RzdiTVNFdWpITXVxNkYxdTZhSjVvYVlveTlwM0pveEhFN21jQksxNCUyRnR1dWJ4JTJGeFJKU2tLcUxiU0pFbGZhS2xSYzUxeWszV0w1czJmSVdpdUk0bUZoY2NtUW0yZSUyQiUyRndIR1RGbjNGdTZ3U1RmSGwlMkY4YiUyRms5eVU0Wkc4Y2FGOXZYeHZHZmliV2t4blZOY3FZc0F6YW9pQ0sxMjlKd0l4UW9jJTJGajl4TlZUT0hDV2U4cElocEc2THlNNUdkN2tmOHNkaDJWQ1lvZHZmJTJCNDFxVHElMkJ3OXZIRDNLOUFWSjglMkJCcGtUMnI1UVg4WlBuRjZPNG5mTnFVSFhQdHQ3SXNmZXhQRzlFYTlpbzllUWNKNEpmWjVTWHY2UHFJbzhaNklNdExuTjFHODFPSk8lMkJZbVVJbCUyQjZuV0tCRiUyRkhDJTJCOHlKdE1hNklzNENOczY5ZiUyRnRTMk5ic2ZYUGZYTnJtVjQwMm0lMkZuSDh0b2NxQm9aNGtoNk5JJTJGTW5MJTJCNVEyMjFQYkdOTFJ3V09RM3Q1QUt3dWdVNVFscWxlQjVLZmI0Ym1Ua3VMTHBKWmhMWkJ1b1l4WnlnOWUlMkZzd2padlRXTkxmcWVKbmUlMkYlMkJsSiUyRjVWWE1YeXk0aVpLd1lqZnhtVyUyRm42eFhoNGdSMHNUUEUlMkJxNG8zSnEwMmFjV1diVzQ3VE9qbmRCeEtaRWFoYlhSMUolMkZycTV2eUpIcTVnUG1NT21odE14TXRWSzBTdzlLbGxvYzNJN3BxMkxkUzNnOFlyNGNNNmpwNW8lMkZwYWNYTmxSaXduM0V4cmYzVGFrWWRQTTN0TXdKclRIeDJGTVBaVkxUVm85QUtHNlAlMkY0ZyUyQnp1Mmtxb3hvdHpwTG5Wa21HTlo0N1RVMjBLMlZ2MG8wallvdnJoek1sWGtwOUolMkJkVU5oVVhaSVJwNHp0ZmxuaXlWTVJqZE1nVFdjSWJFcUhBdHFYZWpoV2dPdGNGMEZ6OFB6Q2xGNU44SFVCVnQ1ZlhkSG10QTE4NnQ3bk5pUjJEd21CSENsVkR2Ulg2eThlRFo5MFV2Y0VUZGJmdjVTWlJPcHNxJTJCd2RNSXd0VjlnSVgyM1NIM2ZhMGQ1T1VpNE52NHNaTlNyMmxVcUlvN2hKZzZsS3NsNnNaZDhsaVBVZ0pDUU1ESUkxemRnTmRKRDdKRDlTZTUxQnJFUTlkbjdoRiUyRnYzNUY5SSUyQjJ3aFhSUjduMGdtTGxyQ0dBY1hoUHlrZFJmZlV2MXRTaGZPb0txWHlzSGUlMkJBbFY5MmVVZHVLOGglMkZMWXpmaHlEQjhncXhvJTJGS0RhJTJCNiUyQkRyaUlFYnVnSXVqQiUyRnRBbWR4MzFzd3olMkZrb1lqJTJGQllkZmh1c2IlMkZEMEF0ZSUyRiUyRmNxaDVsYWhoWnRNNG9GdlJ3djNBT1FJQ0N5R0lCMW8xdjU5SDZ0OWR2WWVaSTEwNkR0dyUyRiUyQjRNZVV4OXBYZEh1emlkaW9ZYTc2Nm9kMUE4cmN1WmpMbk92JTJGeWpFZnpITDM3b3FHTHZ5QzR5ZGdmbDRVVjY5WHdxS21naCUyRkQ2SHJzOW5FVHNKaGlmSGk4U0psVmIlMkI3SXpxWlhyV2YxQzV2WGJkQU03U2hqcXpJWHZPTCUyRjhzcWM3eWRPdzhLJTJGYjJXOSUyQmU0c29FRFhkVSUyQlhCSlJQeUVRJTJCWkx0WENTQXRDTnc1aHFMdiUyQllSYTVac0M5MGRQT3UlMkI4RnAyckV1TCUyRlpYOWVIZ3g4b1Jxc3BZVXM1cDA5bzVLcVRUZ2VUa0ZGTVN6aTM0Q1ZpeGltVmNqTDg0ckFiM1Y2eXhlazBFJTJGZ0FEWk1EdzlLWCUyRkpTTVo5TUtmUSUyRks4RjhEY0RvMDZNdFBydFltcFRzUHhwdFVvZmNqMGJQT0I1ZWpVNllONDZlMEppcExwWVlIMFdwd0klMkJReXU2RmZCc1lOb25qaERuWk5Pa2JDb0FWJTJGT2l1eHdUdEJHcmNuaVMyRlo0c2YybnNkb2VjelRmbENUV1pYNHZpUHVrZkVkQWJDdmJVYjFUVE1TMXV2OURUSXNsMXlyZ2k4ZllsVnByZk1IUUxJa210V1ZRem9KSkFZcjZ5RFR5bEdnQU94T2pyJTJGUmYwRWZZU3BiMzc0TUhjZm1YZk9SSDhLdEFjM2p1cjJIeHIyaHZRN0olMkZtRjk4S0pHOUJWQmQ0cHpZaDlLZGRCQU9RQ28weGZ6WkRKeUxYQ3J2RzY0JTJGQzdXaWtiejZvT3dFeWphdHZOMGJrcFZTVjljNjY3V005SmNJcncxcEwybGE1SmFDaldBZWglMkZJSnNua0xKZEsxWFhKMjhRdjZ3UE5WNmVaUm4xcmtMdVA3Mzl3RU5xeE8wcjUlMkJMQ25MZTRxcmQzUCUyRnJ1dWNjSzN5UWdEJTJCWldKQmRrZkNxT1YlMkJhcUlhWkJGeWFaZ2t1aGZqOThYQTUyaFdVMWp0MXYycnYzRXI3R3FOYlBCSFZIR1NmN2hZQ05ra3JjWTF2anBqQm5xdFVaemZpSGpZa2YwazVvOEhxMVA3czBNbE9xJTJCR2tXa0lZOU53aDVMSkMwMGxycGdraU1odGJ4RDRsY25nRTJWZEY0VHBCU1BSYzJ1ZVUlMkZnJTJGeVlxb3F4NXh4WjZnaWdMc2h0WnUyOThkTGRLV1I5JTJCdDNrNUlnaVJmOWFCa1Vpb0U2bCUyQnp4dU5pVFZ1WjNWWiUyRnlqJTJGelRUSms3dlclMkZIb0NCMDhoWVQ2eCUyRnklMkZybGZuNlV2NmEydGdGN0pBUElydGNuTkxwNGF2b3o5bHNQOWRVTHRiWDNtYXdZMUhZQ3lzQjI5UW1lUU9ta21tamRucFdFWk5xbTVmeTh6ODZkcHYwYSUyRlVMemhXamU3V0JOSnpNMnZ4aXU0eWR1dkNpNmFPVyUyRm5DTXU5TXBkZ21zRHFxOUN2T0VKTTFRY2tMSkMzZDlwd3c3UkoxT2U2b09LJTJCSTNraG8yUWJnN3R4NEw5TmNMVHYlMkZLWmlodWUzM1NCWGY2SVAyRkxlMlduT1BRcXBQWXBYcnU1ZHNWcUtFcmcyWDE1SlBWblYwSjZkJTJGQkV1eG5IMG1lRWlvZVRFWDZuNlc1ZE1tcVVINE5TSGViQ2t3VkVOUkpmVGVkelBrVUtqVmN6Y2cwJTJCalVYJTJGU1RmcWM5RCUyQlhMRE45c3JkY0RISURsR2hndDliUm9yelhsJTJCaFNBT3M1ejBBNWVKZlZxRlI5UlczVmd6a3Z4S0JXODlsQnFjRU5KWWhBRVhFcmRHZXN2UnIwVTRNQlFDVWx3Um5CcjQzSXRBQ2dCJTJGYUVvbyUyQjZUN2JzJTJGY3dGcldyMVVRdjBROGc0aHVYS3N2ZGZsZkhhbk5OVkFPQ0MxaUJDZ3VGWEFRT1hlJTJGdFdzbFFoeEpXZiUyRmElMkZic0JJN0ZiRmQlMkZPS2U0ZFdScUtGTm1mRkxpS2hPYUphVGFCSXViWnAzUVNzbHFONmEyS1hrakdqazIzVnFMblZ1c1hkNGVVTHFveHRscGExMUd5SU55bTE0T3NJclBHTHg5RVlObVQlMkJHaiUyQjYzJTJCanNibjRERUZ2OHNRZ0c2akI5MU5kNjhxOWxzNDc0VXByTzB4ZTVZbkl3Y1lTZDRSbGtVMVd0UlM5UWlBTWExcHRMZWtwQktTbUt4a3VlJTJGOHB2bWxKdE9kdHM5WGNiZkVUcldDRmFjVFdjZ2o0NGhhdCUyRjFGJTJCRmtPcnNoVHJVYXZwVkZ3NG91MFVmSmJqOXBseSUyRnR1bVU3VmQzUzBFWE9pM3Q4amZyMTJYMkRlNFBWakxQWGpRTVI5JTJCU3k3T3F0Smc4dURHekh2UmZMeDVMSk1GTDJpNFFsNUo2T0ZsJTJCcmEzR3JxUHJISWpPckglMkZwSiUyRmN0RFAxWnVHS3J2b2dBTEczWnF2a1FNaHRacXhrazgxMk5oamRYbUwlMkJZY0pPUUJQRmVLZG9idHRUOFlDWUNjdTc4cTNEc2IlMkZRaGRMWlU1ejNXSldoUm42cGxYREI1OFFYaHE4NjZGdnVwZUxxOHclMkZwZWxQSHllb0ZpejRnSnE1cDRVTldMOE10ams1V3ROQ0tqalpOa2xlYnFJJTJCZzNZOXJIYWUlMkY5ek8yVmsyWFZTaXdvOHIyTjU1OVpNQkFNYzRnZHJNZzNPT2JRazBCYkVFTGhkNEpoZFRac2s2JTJCc2JUZnVRTm0zR3MzeiUyRmt6TVY2VEpEaGxDSTN4TW5GaThmQSUyQlVTekklMkZnc2d0VXN2S3RQV2lFWlBwS0FxV0xwbXFiZzI0VllTTGxoU3ExV3NWaDR6Nk1LJTJGcFIzZVJaJTJCM0dWUUdNT01nZnNsSlZHRUhpa3JPZ294eTgxOTVwOWMycmNnMWMxbiUyRnRXMG54QmF1MkNvTFBkOHclMkZ6bCUyQkYyTEJxRGNFM2xWYzZmMWRERk5rNVNERnI1eldhd3c2dDclMkJ5ZThsOTclMkZxdWc0MUNUZDFnJTJCY3BSZnFyJTJCbCUyRklmNFRodjYwMGJ0NEZIS2FyM3lPUk4xcnk0Wms0U3BSU1olMkZKSHMlMkY2V3NORzlnalQzbnUlMkIwN1FpNVRlSU9qbEN0YTNrY3pySHFMTFJQTG9MRldYUHYxMDNSJTJCV1g4R01sUzRpc0gzbG80RGtoaG82bFBOakVoSlBqMkc2cE93YXJEJTJGSUJRWHhPSU84cVROQ2ZxajNTcWtvYVlLcG44YXZWV1lmaGhsVW9LOXlmMjhZa0FmNmFSeUdSd3dkaFJXSEozQmxCc1R2UTQxcW83Qm5hRmhqMG5PeGFDS0pMU3NVQ3dXVG9WWFZ6UWRlZVFFMEUwcm8xMGVHN3NWZzFQNDNXM2VIWkolMkZLT0U5SzZiZ1hRb0FIb3I3UW1MM1ptTFRXelVQSUhDNiUyRjJvTEolMkJVaVhqWk9xSkV4RmJQY0p6VnZrWklOOHVUSXAyTSUyQnN5VTJtQ1BFcW5FNElWZUlqUUNSdUVXbkFQOVZtSHVwbm14SnhXWmxGSXoxJTJCRWtESWY5ZyUyRmVQWUNjenhRVUpqcFY0alVlVGNpVlV5RGd5elhYUlN6SWVNbUN2cThnbjQ2WkVJMTc4bnlkd0NXYUZPY2t2eEx1bVY2UmN4NUtGZG5VNWJzcThJYVVFbG5sZWpRRmpEJTJCSFFSVHcweW41ZFI3NVdxbWdQOUNpdFl1V1M0WmpoTTZDQUZlQ3M1RmxmbkhrV2pzJTJGTm5zRlpmOE0wREQ3NEUwYlJ5V211TGlIMXlQZ240NVp3JTJCeDd2eTFndlpncEEyNHQ5VVQwWk15TkRZQW80SXlkdThKbzJhVFdHcjQ2enNjVDR6TEkwV2RWS1lGQnlJeUJueHNvcmhKMElNJTJGOUslMkI5eUUlMkJ4cXVlamh6UTdKUlU5eUJsMjNORUIwc1pVTm03ekFORmtsTXBiMVJ2TVYwMmFXdkpaeGQlMkIzOGZkUk1EOHExaEtDR1VYOFZkWU84RENFYmJqdUp4Y3hOR3RzJTJCb3VkcSUyRjhhWmZQbEZaR2xadWY2ZzREMjU2YW14b3Rmd3RadjdxJTJCSkVYOFExUnBiaERWbnA2JTJCMUc5Z1JpaUFvMDYzeDV4Sk5YYWRyOWNBSmZEJTJCYXNaZ29CbVU1NEhZNjNWR1FrVkxNalZscGYxbFl2cHFPTXRhTG5ZZWM0ciUyQiUyQjk2TG5iaW84aWpHeHlzUUVYS0lxN3BSdU83WjFEbVVyVnV2JTJCVWk4OVZJVzBuYVVzRnQlMkJMbHk0ZktrblFqWHBhTDVEMXVwZUh0SXIzZ3VDalEzam9CZFozTCUyRndvR2pYRENBcjJoVnVUVDhGQVd1eFVYdzBOM3dHcTZvOE1oUVBMUHZTWTJZSUI2VjJvazdxd0NDdnh3MXc0ZUxocWF4JTJCZjBJQ2xrQWF5MkdZVHRVSnRYY0pJbGV3UWZxOUEzeTlKSVVEcWdwMEN4RDkxb1dtOHFoJTJGaTRQUVMzRXhkN1BKZ09obzVvdEdFY1FkUEwxMkp0SGhTQWpFeEk3amFQWCUyRjY3QyUyRnFlM1Y0Y3BrakVkRnVyOVZlYVJlJTJCOUtKUVV4ZU9GMk1pbmEzZ1I1QXVoNWdmWHRPMGxQblhhWXkzVks2anh2WEFubUFRJTJCZkRXeFI1dmZnckp3TDhuUDcyMDFYYVR2MXFwenJ6dUNia21sbWJvUTMyY2N0c0pVOHdoNiUyRnhjcUJSNEZaJTJGN0pudWZyeldtcjBPNWZXWnpNYlNrd0lveSUyRmgwVmFXMFgzQVZaMXNsUnEzMGd3ZjhMJTJCZEhUJTJGT1Z2akZycDJlNHgyMkpObWppNUJmMVZrQUxSclBrcHp6MExNM2NJeHhPT0FMT1QwQmF4Smk1R3N3M0xmOCUyRk8lMkZmNTgwYWEwMmxHdUp6UTlXalcwS3pncWN0a2tST3k2N2dxa3RxREFLa00wN0NHZ3FFcnBZY08xeGVDTHYwZ3AlMkJCZTQzT3E2WllJQ0ZsZlFmUllFWjVaY2ZmbHlHTnRueG94VFJOR0cxaDNYNSUyQldiYllUd2duRlp2eVFPcFFVNFdHbUJFc1dVWXolMkY0U3huMHVzYXBzSVBEdzQlMkYwMzhiOWVudGFNMHFRSkY3eSUyQmhMcmdhbyUyQjYyVmtXNlB1cDlLVGdsWG1wSjNRYnhxSm9ESnBiM0dsMWwwVEkwMDNvNzVPSll4enRXWXMzWmQwVUlha1h1VDFUWHFjNktVcUx4WXU1SHE4M3NuUEVKaHB1ZDBXaFF5N1IlMkJ0T3MxM3VXaUV2ckRKQTBFQ29LYkZpT0ZpaTNWZ2g0SDdrWWxaZ0ZiMGpmU3hQMXpKYVAzOXRjJTJCN3djckZKczAxc1hDWmU0bjRnb2k3UjNJV1B1Q1J1cnh5ZFJOUlFiVjRpdGNSNmJETnBWUmZScU1iWXhNS0VKZkZlcFJBeHNXUkNSRCUyRlNxVCUyQm9JRE9jdHJDdUpObXdIa3AlMkZnSyUyRkVRaCUyQlFtN24lMkJ5TTBKQ3hoS0JGQndoYmROWFFZeDM5Qm01ZkNpcDV6dlBYV3J2S1htb2Z6aXQ5T1doRSUyQmZmdE9lbGo1akpiVlhrJTJGbFh4WGN3eUdOSzBWOHh1RkgydzF0Sk9FM1pVVDdPUXRaeVN4M09vME9GJTJCWmtvZEJWTWhzbThsJTJCTUZtVXRGeEs1WHI5NTBTciUyQlF1RTMwS2NwT01NV0Y2NVRENkZrcmF1cnNDMmtmM0xKUDJ5dmdXbEVnRm1YcHpiTDJocVBURmtQN3VIJTJCVjN0aWlyNGtnQW5HMVFybkhsSmN3eDJLeGNqMDlFODZITW5rbCUyQjZtJTJCMHk4WGFPbWIzRlNMcEslMkJCYlNyUkVDYjlMRUxyRjV0c2tnVTFjSkw4M08lMkZwaUszQkRBYjdLUGRIJTJCN0R2dFBDYnBQMmQ5aTRsRFFFQUFQN1hyQnUybmRjUTZMcHU1dkxuRUVINkhEOUdxZkRiVFc5aXElMkJmWXRUREg2c01YUXhIRnlaTTV0R3h3a2ElMkJnSHNRNkhIakdON00lMkJjUmlQaEdzYXFEdGRnbFhEMTUxNkwxYjRyJTJGQlNRakxhVnlCNFI1YTFrOWhuVDZubjM0RkdFUUxYWUkySTVBUzUycEc1MkowT2NBSzlxdGFrUjF6VU1tREJFQlJENk9NNGMwRVZqS2pzVjcyUVp5YU5PRzg3djVGVjEwV3dzTFlrTWgxWUNXTmd6ZjZxdDNlemM0JTJCTjlMaEolMkI5Y003VU1vOVdWbUw1Yk55UDZ2VDFwRzF2WG5hd2RlTGYxVkxjeEtrSEtFSU1HTHBoWUJTVktUTHlodTN2bUI1UyUyRnoya09ldXR1ZFVXWkl3dFElMkZZb3RlJTJCc3ZNRnMlMkJVYzQlMkZ6Tlc3U2VyTnF4VyUyRndCNjVyeFI5UlJUQk1LZFlBTFRrJTJCMUZjNUJWVnQ4RVdhcTgybVdNNXIyZk9LMElweXpZdGJxOFBFc3VpbWZzdzdQN1N2V24wdWltNzdrSDlodnlrcExic2k1MjhqMVpsRFV4VmJHWmtQVHhDa0VNRjRMb1F3QkpSU04lMkJqNEFsWGtjTFJiSnMlMkJIUXRsNVF3b1g0cEdrVFNRaVdLcUVhZ1ZVcDE4eUpYdmh6TUVYS3dSRTJuRmFpT0tqN05EMVN4a2picXZGNWtvVVlHQklRVUFGdHQxbjlOZGRXJTJCcXdJQmJ1NzR2OGF1djFFeG54VFN4Uk5VeVppRHlmelIlMkJ6b1hOZmM2R1piU1hyWVk2M1ZPJTJGMWFiMXdHMG93WUNhVnRXaEZBUWl5U3ZaWURnbDU0YTh5YlhuZmtWeVkwaWZFeko2ODc4aUFiNEVBZlRUS3FlbmFPWGZPTkdiQ05zVXo5eVF2amR3SG8lMkJrWXBVcnNvMHdwOTJIZHZzMU55MmxmS3YlMkZTVTFGQUklMkZtRVc3cTh5cnVOY3JqUkVvcXE3U0JKRmlCYUQ5ZEg5eEsxM3k0S3NGZHdKZmVUOSUyRmZ1dUw2V2x0VmRMZyUyQmIyNVUycjI2cEtkcVd5SlJnb25NYWZhdG95T2RqeEVRejhNS094NzVIV2ZPdzdNWUIlMkZ4SUk2bENuODVWaGxsbGpVd29oeXV5NGNCS1dlc0NDOXRYU2ZUVmtCUWRUeEhxbzZ1aElMTjBlWFFjNlE3M1lpSlVaY2NvbmdLZCUyRmdwMjhEUENpNTk1bVVicHJocElRSkxidEVGSERYS1M5MTlWVThzczVIS3FkV3JrSXI1YTRJa29hb3VWMlhQSHpRa3NhVFBMZ1lJUmNEVEpGdnpUR2kwV2hmVUZwNTNnT2tXTlBuODAzdGhqSmF4c3c5VWJaRXQzN2VhMDVlc2VDM3dwOTRRR3dwTiUyRmFFeTZHdE9vemklMkJwZEVyaElHbXFLNjc4YWdEZVIwV2NheWRZT1lvaEFJT1hlRmxpN2FhYnBlQ0hkWGN3anhmVkNwclo0V1pYM051V2VsYWMwS2dLJTJGc09DcFBIeXMxR3cxZCUyQmxCbkFnWnVqVlAlMkIlMkZGaXFxbm41aGNIU3RjN0s0eVFoS2hMWnZUajFRd2NNbDBucW5FYWNUU3BEaVZhaVZ0N1V4NTNnR1Fham1HJTJCNTczVU5CeUU3NDB3Z3ElMkZ0JTJCWXp5UiUyQnpGV2tqczdFUU9rVUdod0xLTjlKWVdpY3BnS0FWbGJRQjM0c3NZTSUyRmR1cWIlMkZZSDVTc0h2UFFXUCUyQkZlRlRvSDczNThkY0VKVm1kQ0JCdWhQMDdsT1lNNm8lMkJxUFFLTDZ2RVBGTDZTemk2Y0d1eVMyVDdjT0tXbGJ6R1JkJTJCVzFoJTJCJTJGJTJGa2piZ2hmMHpMOWhyQ2RSNGIlMkJlRVNidXF1cUslMkZ0SUNFMUpVTHBsOHFob3lWRnBYckhaV3hTYUxYODE5MlNuZThWRiUyQmZ0JTJCdzFLNjNKVEtDbmVMZTdPNlcxaG40Umk4a292dHBJNVVOJTJCck5qNGhMakRzNSUyQmZBRlk3MGolMkI3VVdRSEw1ZSUyRlJYczhvZ0dIeWMwNmY1bkN4clN1MzVZUldmJTJGQ3BxeXFVRFg5cTdNd3pFYjAyZzB4dUxlY2lTTTI2eWwydnE2WHpyQ3d2UFJQdXY0cTRWeE1YWFdTcjQ4ZHY3a1ZCeXpGT085OU9QbERJMkZLUiUyQlV1eEJhTmxEJTJGYXdmb3h6Vk9XMzJ0MjgwdUwxUVolMkJxY05CN05DY1ZOUWwlMkZsSWNiUEdHSjRJczJlYzNHZlg5SXZHaGwxNlVxJTJGYU1TcWolMkJZTG5ZakFSZEFzdE5qdSUyRmZBJTJGSDMwN0t1MGREem53VWlIUE5EemNPJTJCTSUyRmlOcjhHdGxSbWZySFhWbk0yekUwTDBlRDhDZmJ6MHc1OWhPa1l2SldqdjM4MyUyQnpIcDc3bWF4R1RqUnJWR21kWlBBMDVtdmh5bmFic3JLdlFNWU5ObFViaE42dGRHQ3p4biUyRlhiVHJGVFAlMkZrZWolMkJGOEdGZmtZY3B4ck9xeWFSencyV0tMRE1EeVhKWEslMkZPVklsMTZsUnQ0dGc1aGd4Wms4REtsemx6TGoxZE1ZN3llaDJXOWIzOWlFNzNHZVdjWWVJT2hGRDE5diUyRkQzbmRzUzhyMFFMNFNIbXFKOTk2enczdnZlZnFCMjklMkZNRTh6eTMlMkZYcFU3ZFVKTXBRaEZJcHdhWG00R3diJTJCYU42Ujd0eFJPaXF3SHpxd0pNTkt2ZDN3b2JoUDlscmt4STY5Y043dGtEJTJGOXBaMmhjVFVYa2JUdkpKRmFJVUlEY3U3RExUdVZ5UXdqVXJrZmw5SEczN1JoWkl0c2FhJTJCakVhUnNBemRkSSUyQjU5YUZwM3NtOWRsSWltOVR0aTh2WEdQY05UQXlBYUlma2NDMkxrdDNTeWtUanZnQ0MlMkJnR1hreFBMb3RXTCUyQnlEQnd5bjZUS1hOU3JhY0dseElwbHNjczdPYnFmS1hCWnRKVkRFVXRleTkzMlBKY3BoZzFmemQ2WElxZGgwS0oyeWJQQzMlMkIzUGEwU1Z6WFpiSVlmeHJOV09KMEUlMkJUNGhvYWFqSlFnWlhCc0ZHOEplaHlUdkRjREZwdGJvTnFjSnpOR2IzOW10NzFnNU9SZTd4WDlMam9jbFlFajE5OUdZWkZSJTJCOGFpJTJCUllCSFpzSVN0V1ZsNjZYYiUyRlNwZXZ2bEVGOTIyM1pLbmxzWUxZenByUUZlWUpmYjZ2NEN4NUklMkZxVEF6U2x6YnpCdUglMkJPN20wWW1idFU4cyUyRnNDY3EzNUtxVHBpZmdPYU5UcVpVVlFKTk52eWZ3Vm1qZ2p3cW0lMkZXeW4wY3RlOEd2eWQzRjMlMkZ5VDJEajVZQkJpME9YWUNlUlhsenpxJTJCMVo2bGNwTUN2QWV3anhESThxczlqSDMweXY5ZmtSOEMlMkJxMlFCNWJuQW50T3k0JTJGSmVWeHhkNUZjVkFncjVCVDVxWGlzdjd4UnJWMzNGRHZmR0RaYkdmY0R1MW1MaU4lMkJzYjFKNkZhUFc1U09Ud2pLamVsS1FNcmFEMFpiV1MwYjFnNGxWUmtqUUVOaHVTdk1GT285Vzg0UzFzU3h2cVhaNHl6MzlCM3FQOXpxTmV6ZzFOMm5lU1BUaG0yaXhoYTV2eHE4WVd6VnI4Rm43S1BpdUd2dk8xT3RwZ0FZZTdyemg0WVFBUldyZlNPNVNZVkdDajVyMndEZHhuZ2xDUUFvNUV5enRlRXNlb3clMkZzY3ByMzMzVnEyYlVvJTJGdWpyWXUyVzFUR0RMRU85Y05nVTg0OXUlMkJXWDdBVE1WYVJWZnlzTDZIcEdMQTMzalR6JTJCbVFGa3RVVmtTUU13bkdZNjBzRVU2JTJCV0NlRlZHTjNUUVFKeG9DZTJYbVNFdkNtJTJCeG9aJTJGUFVRRjlFaEZlSjJZdkxYNXYwcjhob0ZMR1c5ZTZBTUxwSmhmYXZxSlFKVFJOcnA4JTJCZHBYTjVKS3lNdUhTYlpHTVpXUlRUSFNvSUFoaFdPSnFCc3B2ZnF2NDY5cHNuYVRvTUlvTWt4RSUyRnZXTHNTJTJCMUx0ZGZxWnlzcUFqcXVDT3VKakpDRnFKZk4zSjNhdFl5aHhpUjZLZW5DemZnNFVRamNSN3RSNHhaYTR6WVF5akpWb2JqMHMyQnB2VzhhZ2dycUFkZGVWZVRYMTVvdEV4ZSUyRkRsa0ZxNW5CaXM2JTJCdkttTjZ6ektSS1lNZjBrV0VTbEQlMkZ0cTNVOVJtSnB3MWtndXZySSUyQnRMWGpkMVF1eXJyUUVwTVBtJTJGSmYxdE5naFMlMkZEOUR1YnB0WFdpUEtZcVhONjV1c01HcGxrUHZDQUhnUExTTVd2TU5tRFBiMmFaajVmUkNoZHczd2loa1FBWFV1em00b0hQYk5mQ1JtWnNFaUtNVHZCVXJvRDNscE5qOVRUYkJSdyUyQkpEdUpkdVpqcmlXdThubFRFRDJOdmRVWDVwVWRobjlIaW0xc1RuTnBQQ0NGQW1od3NoVzNFUWFwYUlhTkt6d2lQZXZSSUxNSlY3NjYwNFhUb2pBYTVmZ004WkxtY09HdEZ2N1lDa3NIZ3JqJTJGcHJhZnRtT2FKZzAlMkJUc1Z3a0s5bURTaVZpdm5lMDElMkZSVUpmbGtaS1htaTE1MnpKOGh4TEJBUEhxR0NUTVVNdk9QSERVV2pkWk1PM1dXRzl4ZXNKVlRacTNGQmNya0lka3pZRTh3dHV1SUNVUzlTNkNqbW1xVGp6a28xcWRzWTJLaDR1Rmt4TDdoZGx2Z09LOGI3U3FieWtGYzR6JTJGeENmaVVUV3NaWUhwUyUyRndhNkpoc2xYcnFMR1c2cml4SHpSU2J1JTJCSWxjZ0F1MzV4Smp1bm1uUzY3YnVIUldXRGZaOFR1JTJGWWdkTzdnJTJGSUt6ZjdoU283dmZzeWkxcktZd3habXBab0ViJTJGWFlTUDcyYiUyQkFtd2ZhUlhWemx6JTJCJTJGSklHRWxINlFQcEdTZjM1Y0VCbEtOMEhBNVo5ZTZFViUyQkNZUnhEbzloQml4QmcydDVLNklBZ1FZdXBQZHpGQUolMkZhcW9YU1FGanBkMFg1alhoNnFaWGFOdnF3WlFtJTJGTGx3QWYlMkJrRnZpTjdDNE14cFJDMkVnWjgwR0N2dzZ0aDJsJTJCVDJsd1ZIJTJGUnpjZEVpUzlLdSUyRnZ5dlRhdWt4QnZINFl6RWhKMVRRVSUyQlF6ajQ4diUyQnFJS2ZaUTYzdSUyQjZjM3NNdk1pSUxKR1NQYjNGY0tnV0ZxZWtYd2hSdCUyRkdnMlRMd1QlMkZlS2VQSHI4RVY1STlERGJEYjlwdWFsam9GS3FOempSbCUyRmVYczYlMkZweTh6JTJCdVc0WXJPMTM1blZsJTJGVllGcjFManRYNG1CWlNLQyUyQk1NWElmSnp2ejdSSU44YWJINEZkUGlmeDhieDZYcXFyS2JPSk4yZGZSWGtmWkJCOWhwcElSJTJGZ0tLdFBHWCUyQk0xbXVoRHNXSTd3bEVFaFNoTW5BcWx2Ynhxdnk4RUpwR25WYlVOaTNJWkg2R0lHYmJBbHp5T1Y5UER1NTZleGhLQmhxbEhScFFlM2NkQmp4SnpWckJ4YWJ5cGxOTDRzbFEzYiUyQmlkRW1La0lmSWY3UldzcWdMNWZmJTJGaWVWek40a3lmS3Z0cExhUm5veGIlMkJKa1Z4UnFxMWZ6SiUyQmJLU3RndmhFOVh3R3FIMDBYZWNqbzRURndsY0dWTXJlWFJHckFOYUl3dzVKMnZlSkFHMUpFM1VlczFFY2kxcHJnaCUyQmlMaE9PMFdwS0RxTHg3bjduelZGUjZCbEFHc1dRdSUyQkFla3podVBjUzk2UDRRc0p2RUpKSGtYT1dwcm9xVFFvWmJ3bnRySTdkTFM5SWhQYTlOJTJCdDl3aHZLNFBnNm5FdVpmRXlqOEpQT1lodVR5ZzdzVXRJMTRHcG5UUGQ3T0cyTFViamNJa0JYJTJGZEFESnhTJTJGUklCY3pZcjAzOEFBekdZYXdFU2U5ZU90M1Nla3Z6VVVEZTZMSkFLM2J1NGllJTJGbFVKTTRNTkV4MWRzNkhRbEpueDF2V1NOTVp1MXpubFV5NUtsc0hjYSUyQk9JSVR2MFlvSVN0ak1yempMRmxNZDdBUzQ1MHdHMGZDUVFzOFJQR2tCZ2M1M3JJVVlvMUclMkI1dXBVbno5c3lmRHJmUTQwdCUyQnJNczkyaWp5anNvV1F1THZxR29MOEFqS0s0Q25CUm1ENGhoVExUMTg2Yzl3djZ3cEppZ05vQ0RtaVZxRWhSWEhzZFclMkZJenpGSHZlRWtkdFlLWURFUTNPUDBYdHF4Y2RkQzVNMjdUaFBBclQ5UkN0MiUyQlZFaTlaanppQWRtYlhWMzMzSTNhV0JMVVpmVTdPRDhsZmtPRHBGZzBmTHd6JTJGeDJqUyUyRmFWWWlWTm9VdHIyTVBsVDBEQVB6RVh1cldJTVk2WEFiOWlxb1UyM3VRWmZucVlpVSUyQmp2V2dWVkdNVkp3UE91Umt4SmZtaUdISGRxOEc4TTI1cmN6YUcyempwN0ZSMXBDVkVvbzNGYzFRT3B1NjJTYmFPWjJLaVl2Q2Zva0ZZUGZPZ0xkaSUyQnBHJTJGOXdHJTJGSWR0Z2tENHc1Z2NYWm9vZ3M1UmZXeEwza0EzREFuTjlHUkxJc0VXSFpaMlRnSzR4OGNDNzVzVUtwTzJ5a1BoRVZOJTJCQWFIQkpZVGRoUjlLVU1UUWxrUzhqcUEweGo1JTJGMVdUaXZ4UEwxaGxTSFNFdzVLS0pIYUJJVTkzdm5BWnpsVVZ6eU4lMkYwJTJCSEFOc3NBQzJlVkVNNnA4YmY5VVpETE96SWR2ZzdSTnRTJTJGJTJCU01FQ2w4d3ZqQmVHcng2YXlDUmdCQ252dU0xd1gyN0R3bGFMRENEQUN3TFZ4SlhRcUVQQVRyYk9aSzFGalU3ckZuJTJCQVJxVzRLaWtTSU5RbFNnWGo4VFolMkYlMkJCSHRHSURiTUl5ZjlCc2xKRXRsWEFjZ1lLYWJ2ZjljdHczNWx0SXclMkZDUG10JTJCQVg3c3JiN1hxYXZ5dDFmMjlJVGQwUERTZG41cGlGUnNIUkQ0elVwaGFMWHBlcjczYzJXTWJaUTI4NHBwWmRRViUyRmR1JTJGa1JvR2JvWXpGOWhXUyUyQmZoRVVCWVN1Y3dBaUdiMG0zNFZxVlpxNGlHVSUyRlRDQkZtN2NCUGFMemJOM0Ztbzh5UjFwNTNkeEN6WHFTSTVZclFWTXA3REYlMkZNOHI2SlJmWXNYMlclMkZoSnQweEg4S2dqUDJBN3VKUSUyRmtGU0FzOGxjWmZFbyUyRlNkYk5aYVZqOGZoTnY0ajNkR2h1WjFwYnRXQVFjckg3djBDMHh2dHVoM2h2dkdEeTF5eWplQTMwVnRuUW0yQkduV2dVczY4MzlyeFhmZDVsZ21qdjByd3pnV0dqbjhyN1dENzdvbWNQTXlVcDA4TTdIcTYlMkZoaDFiWVpmNWdYdTM2VyUyQkczSjRFend3SGxPZjNIWTAlMkZNZVpXdmk5OVU0a2NPQ0x1d2x5cllnZWxOamFrb2dJSGJWTkxqUUFMbWlUSm5nUmFseFhQbURIamtxMXdzN2E4QjdJSldqTUxPWU1NazVMVUxhYXF1WmtXaCUyRlJDM0ZEMHlEQjhMSVNZdjdVJTJCZFZiN2labVdWZmtrdW50cmlhaHlNV1U1eW9JUmtFZ2haM0xERE9KRHhNNEh2UUZhTlVmbmhWbE1tWWxXeHlZd2dEMiUyRjRJdHI3Wm5neDh4dlZjdTM3OW8zUSUyRms3YjBNRDhXTHV1UWUyNTRiaUM1RUlPVnMlMkJtaHphZ1hLTENhbmFTMmlEUlJKTzI5VDFHTmklMkZDNzNwRVlOV0lmTjFkT0tHTUNBakdQdld4ZXhGZFRTdHNSS3Q1eWFabXBlV1ZaWDRmZndsRlg2OTdoRlNQVjVNd2VtRzhjUSUyQkljQUI3TWVNRGM0Z1V4RkcyT2I0MUFLbjRRbWpWOHNYWDRSbEVCTzdMOW5zaXViN2kxVWpveFE4emsyNHU1SldNWDZDblhIMW1IbXlodlFpNFc0NDg3VmI0R1ByS1B2UElDaSUyRnp3a2JzSkQxRDlGNDFOZWtyYVAyZExEblJrQ1N3dllRcWxPQzJ6bUtBU1UzU2J2NDREdVpSNmFqb092MjdnNFMlMkZ1NDI3OVdCRDQ2enAzODBwdjBSb1AyZk5Gemk5d3A4ZHJqVlo1TzUlMkI2R0VSSWs4SjYzaHlNV2lPbmMyeTJoJTJGcFpVbHRTOVgzWXBXM1FQSmZwYmM1TzElMkJCOGVhZ1YlMkJ5STdiakJYdkk5TnZFeVhPcHklMkZKZSUyQnIlMkZvWFZKenpPenpqSWlKWkd4YTVrSENWZyUyQndIVmw4ZHd6SjdWR2ZEcUVWS1RJMEd5VUJYUEx1YnBVY3VveFVVUiUyRkQxc0pjR1NMJTJCOXdsMElwVnBJUzBJMHFycWpQRWlVUGtKU2toUDdtdzlZMmpXVlRwY1pzU1BqNG9JRkp6SFkyR3ZWM21vT2JIRFpnaWhleW83N0RFZCUyQmRJZ2Fib1BsTXE3SHM0dHpObHVJSXNlbFpFZHJNN2klMkZ3N1dPdUpld2tHVCUyRjBqUDZCWXdVdE9jV0Fjb2p5ZDVmekYwZ1lyZnc3b3BNS3JwYTNhZmY0YlJLTyUyRlR2YXh0RyUyQmVZcEVSSmJjVGJWVmIyWkJrNUc1c3VVcEQ0b2J1UFdGWHozQ2hQZlk2U3VaNjF0a1BoZjFVbFQycCUyQjNUZ2Jub3V2Y29IRWtiYjQ2cFVVenI3TWZyR3cwUjk5c2x2c1NQSVdiQWFRNTJDZ0ZQVTdVMXRpMWolMkIwSWJld0NpNXpEZUU1OFRqNXFXJTJCdkgzUkdLeTRhdzVTMVJMb3NLZ1JsNTg2ZTlPVkZYUFplbXNnOWYlMkZwcmZoRFI2dFNzOSUyRkp4OVVubEo4dmtjdUtISktYaW1rTGxjZ0p3MXh4YVJuJTJCOUZIYXJkYWp5Y0NSRUNkdVRyaTd4NTRjdVMxeDE2ZmhWWElHJTJGZW1ZVUZnbEtGJTJCTmYxek5WdiUyRlFkb0xRTmlmZ3pLZnElMkJmM0pXa1BXZnZRdkhLMlVTNnFhRHBMTGdBNmRueHYlMkJoa3JKRXVkYUFwcTg0aThaZ2Y1dkxONjlWRiUyRjRPeGpwOFpzajNrVTVBNjZ5ZEFYVU9YQkh2T3hHV2RGenBpeVJXRG5oSkNYJTJGUnFUTUh0N3lqR1JnTDU3ZDRRTGolMkJUMXc2cTMlMkIyQUYlMkJlZmJhRWNoJTJGcEFzZ2VNcThVQjNES3k4cnVqMGcxQzVkbk0lMkIxeEtMcSUyQiUyRklCV3NyWFlQVHVwNjVpMTQ3bm56UlJ1SWdDYnY2ZDRUJTJGMzVhSTZNMktYMEJnbkhYRTV6Z2JDUWxLbmxmcU5YT1lYSmNoYW5iUWh1JTJCb1dWUTdxTzRTRTNNcVlDTGUlMkYlMkIzYVpQMGVoajFZWjVaYkphNWdEJTJCekVxa2Vtc3FYS3BPNDZMeiUyQnp6U3cyZlhvbzhZNSUyQkVWWURoS1Vxb1pONjl0ckQwbjZVVXIxJTJGdHcyeXhvJTJCdE1xMFhMa2dONTNXaDIza2tDVEN0V20ydEwlMkJleTN6ZjlyZWlzRW53WWtjWXBWcTlXdmRXeEdoVktSVEslMkJxODVUVXRySSUyQnVHdyUyQkZ2JTJGYzlyR2xsR1FKZlhGT2NDY08wR2xxN2JEcVJYNmZXS21IRzB3VEpTZ2o3OTRZOWY3OTA5a1dHdDIlMkJHc1F1WktkalBQQmJOREszUm1KZVRHZVRPZE05RWRoaFhpJTJCQnNmN3ZxclZDdjlXcjBIMUpUMFpTTVBnOXJYdlZQNmpqd21FakxFZHNnZThYZ1p3N1VlOEZkemwlMkZJJTJGZWVQWXklMkI3MWZDUWNsYW9UMHY3YmlSZFJlNzFya0llS0pDdWM3bk9BJTJCVjlHVENnNkpUYTEyWk9jNFpMaCUyQmNVd3Z2VExaZmRxbTc1ZGd6R0JHdiUyRk82cWVybHNXbHZGVW9Fak5ScEhsTEtsakhqTllPJTJCakx2Vnh4QkVxT0RGM0ZXclIxa2VYZjlNSjBSVTEzYW9mU1lRVFdZUERGTDRsSzdSVWpyR2xRejVka2xhd1owSlE2SFFIV2RyOGhKYkpBTUI2R09YODJzOFRNZmlvTWdab0JhMjhoQzNTNWw4NW9MMVA2Y1M2UmslMkZtUlVFMVh2cFY3NE1sWmZHZndyT2REbk82cHZTbnRpeGh5VHVHZldhak9ndmo0ZW5LamNPdmpYdnlES1R0Mll2WXZZeHVDTkVVM3JscUx0VlJFUXNXaGMlMkIlMkYlMkY1NzRHNzZCT2VSMzVwRTZFYUN2YnE4RWFzSzRUb01zczUxd3d5SkhkeVh1MlN2blJLWEV2MXF3UCUyQkZxdndFSFglMkI3aVZja2VxQjhMQ2JsR3Y0Zk53eHI3VTgwYjNiTTUlMkJwU0N4a0lEdUdhSHRSOUVmJTJCOTN0RjF0bVVBb1VPNGhVaTBPTnQxZmJVMndQdnElMkZBSUhxN1hDWXlUJTJCQmMwJTJCRm1DV0JmbCUyRkNBbElJa0M4SiUyRiUyQjB4dHcwRDM1bk5QeHV6SGxQbGZUOTRjRlJ6TnBhS0hCWk9pNyUyQnhrd29ScmJDUjhNTXg0blFMNnJIMGhFbyUyQlRmYUhQYiUyRkMxeSUyRkQ0T0R3emVyM2ZQc0cySjdjUlpCJTJCYmQzdnlKYiUyRnpGRHJKMGFYWHdlWW4lMkJUbzFmaGglMkZuMjlCeThubW9KajNGS0lYek4lMkZpaVNjcFozMDAxc3FyRDBvRHBTRUElMkJWU3V6dEs3R0k4VFZsQk9sWlh3OHZxeUo2JTJCMSUyQk40U25vVFVPYWdmbkZSYWxzbnNFZW4lMkY1THVnQSUyQmUxRTFJZ3kxWkJ5WVhQUDY0aUFXZllDakZwa1BaVzgwYkpiQ1dDYlZNejVDdGdxRDU5OWtQaEdEbE5NamdZUTFMelJJNmV6Q3FpTzFnS1plUzElMkZkaGhHZzl5Vm5UUW9xNmhXWkZxVzhaTTZWZlB3aElaVXFESHpMMUxCQVU2OUF2dzRVNHJlM3BCUHRrSno4NklVZjI3Rm9vTVBZJTJCN0RlbXk3MXVIREVyVmc4TVIzZTZrMjN4WHFtZzQxNzVmakFkNjlxJTJGUlNBa0xrUDdUZ2NQdWNwM3M1S3V3cTJDd0I3T1RnZnF3eUc1eXlLalglMkZuSzlGaTUwdlZYaXJSRE51b2hzS0tzV3E3QzlDWDg2RkJWdjhIVExhUGFWWHZyQ3A1TXZaMFNvYTlaZTA3JTJCM3p2dVdmNVF4bTBCYm1oUWNKcTV0WExhd2NGU2FzczhPOXc3VGJyWnBJeWJMNUFaR1I4NzYlMkI2NjdRJTJGa0JaakFqSGo1V1YyMXdRTnZraDQ2VHFrd0NXWDU0MTFZV29zS0lPNzJ5MSUyRkFyY09mZ21haTFtbkszblVWNTBsSmE2ZzVHZVBVVjd0NG1veHN1U2l3UkhzYXplbzVDM0JiMWFYWHhhaUtTS3glMkZlcnZVa21tbDNpZE5lVWx2Z0ZrZmhzJTJGa1lmUnVVWkJkeUNzMEpQeGF4ZE0wNlBJa1hIZjA0Vk9BN1YzTEV4MEJSdHpnVjRrZmR1eEVReXI1T24xeSUyRlRTVktjSjA4ZkRsdDFrVXlBdklQUU5neG81S1lVTXVjTEpkdWFxZjRtVTZhaXA0dFI1czZ5Q2RBbUxPWUF2QXFpMnB1dXlVYzBvR0prMWgzVmNjMHFabFhHRVdkaXZPbFZaeXB1UGZQUlgyJTJCbmtqYmFlMG9yeURQcnh4JTJCSmFldTZMdG9CdU5jaXpaWVRrYTJmRzNxamN2UTYzaEVIWXRHMU5Fb0ROOVhXJTJCa3FxJTJGbmN6UGd5OFdJU2clMkZKVUtoWWNwb3MlMkZJM2JJbUE0Y3NvJTJGcnJKQ2R1JTJCT1UzS1RsdGZORnVIRktFa2pxNiUyQndVYmsybUNIRXpSeDhBcVVYejFmUCUyRlRQZXVwZ1gxSDBiOFQ3ak95OXRzaUE1ODNjcUFaWTlMSVU5QVFFRkg5R2lqUFo0aDBQanF0M2dRJTJCOEJmaTAlMkJxQmxVbjhHNzBwMmptJTJCeUxzYk9MciUyRk5mYWgyaCUyQkdMY2JTUjJpdEg2cHV1QlJRWGNJYVpaSnRNRzQlMkZNSnZhQkV3NkU5a2g1eDElMkI2JTJGQlpmdHZTSERra2NWJTJCNVNDbXVFdHkwV3dkVkF4QURLbzRPNjVucXlQWXVzczZvWE5EWENuQjI5Y1lKQmRZWjVWNiUyQlczN2FCc0RSeTdyc1ZuZGxoQ3NFQ2FjUmIzOFFuVXc3Q0djMDhubFZRc1NQeGhPMmsyZExEcEk1RjZTSWVXZ2FHaWtMMWslMkJHeENnJTJGdGZMemFWRjhINlNFbjlYbVFOWiUyRjdwUllpQjBHQjJ2UFQ0aVl1bkRJRzdKNWlTSVhidTIyJTJCQngxQnR4bUIwTGVEdzZsbTMxbFo5QmolMkZ4WnlOcDNVeWNINklwRVNwa2xoMkRuMm16dnhGMlJ0V3p3WjcxQjcwdWtsMWtvNXU0aUtWd1ByWEZHNiUyQmZKdEtVaWkzWFpRSDg1aVh6RWw1dGk5Z24zSjJ0T09kb1Z6WVVZd2dhOXFLNnJOUG5ab3FOTThmOFdGTTFHQ0hnNllTQlBkRllpOGtmaVlyWGY5NnRtbkRHRmJxckJ2akt4aEp2ekNvOUE0Z2U5MmVxbUNYOHBlV1ZjNGYzVThGJTJGb1IyUllWSkx1UkMlMkZrNWNTMlR3eU1DJTJGZTJUaVczQTFtJTJCOUtuJTJGanVnRnFJRUJab05qOU82T3I2cSUyRmtIWFYwcjVOQ292bnVud1JuN29pT0c2OHBWSWhlSjBhRkcwa2UwM0ZHMVFrOGk3OUNmbkh1VEJzZVlLdDUxQUJlSGVCRjhsYlclMkZPZTZETXhDZmp5dSUyRkpDMHdoTWVIWSUyQnFZVU5lb2s1RmJCM3ZDUDJaYm1rMjIyZFN6TGlPeWFpdlZVZjVTRkljJTJCMnRodnR6bUxLRGdEWVlzVzhZZm5ncGlDZ2RMZ0hOc2hYN2x4ZCUyRkJBZmtWMXdqVGNXdjZROXllR0hrRUR6WjJpU1F0UjFXdjRGWkFJTHBKZHQ5akliVk5MT2lpenF4Q0dhbWFXZkZmbWNnQW1iRnE4cE5kSFJvWUNWNlNzdyUyQkxwcSUyQkllbmoxU0xFOUJGcU1QcHk1Z1dSSXJiaW5tR1dFdTRzblZXJTJGOUhTSXVQVllUQjRtY01LbXFUWWhERFpYM1k5a2lnOXNXR1gzOFhEYXolMkYyNjMlMkYlMkJ3QnR2aUp4T21FJTJGQVd5OUxWbGxZUTdJWnE2eFY1V2F0TUoxMHBWTzFRU0U3Y1RrSUFHc001ejBOTnJDSFIlMkJlSlFxelc3TzMlMkJTWmZybVZNRkw3b2s3OTZpSyUyRjFTeVpVbXF4OGtTZDczSlglMkYxMXA4Yk1IbFVqWXNPbk12bUNFJTJGODYwZzVJY2FqWDBnem92RlNzc0ZkZndaQjFVaE83Y0VCNlN2dlJNOFQ0JTJGTDBPMURUem95VHRpek1HJTJCZ0xvVmR3cDBoS0EzSzVHWTRzdUlmTDJRSFJPaHFnSUhoRjkxNnFRdlBaYmFSNWhOcGRYaXptMHBkdEszWXczNVNVN3Yya1V5N1JxbXIzTGtxRWhuZTNtV0w2M09ESHFoV1h4bG9oOGwlMkZ2aEwlMkZVckZmJTJCZWgwNnhUcnQ5Mml5RjF6Nm53ajBqUEI2cmkwUHBYVnNPc3NJMXFDOHVpN0UxT0d4d1ZCWDkzOVJieTZNRXJEbnJzRDY3TXBKJTJCaFZUdkxMc1pYVXYwcCUyRmklMkZnUHRudmI2Q0xSeEVGZk4yTUNGT0RyWkQ3UGlZblhhN1clMkZFUzhYJTJGMDhycWM5QnV2emRqJTJCaFc4eDBKUW9DNlNzUkMlMkJrOEY4TU45aUxzJTJGYTJ3MEh2REpIeiUyQjc3Z2dZY2owbFNmUWVhMmNmVFd2JTJCdnQxayUyRlViaEhzSFklMkZMR3RrblQlMkZlREI0Mnczc1pJM0xpS1ZhekZsMFRmWVNYcEFpMTFST0FNUTAxJTJGekNCRVlRN1dBRlh0OUpHYXFCZ1diVEZWJTJCQ1hWbTZxMnRqVDg5Y0YxUjZVZHhQekQ2eksyeFZPJTJGOEclMkJYSzdVR0hHeCUyRjV5RGRhUEZHYWpGck14Qlk2JTJGOGJ0Y3NhY2JEUEpUSXJ5ZFZ1aG9IZSUyRkdtR3o1a3U2blRiNTQ4eCUyQlRkZjVYWiUyRnd1NG1BRDBGRk1kTiUyQmRlQVhUVndOMEc2d1RDazlJRlhhcGZLNW9tdGZwRlE0akZ6VSUyQmVxeWVtZlZpTktFUmdkY1FBMGlYbU9lcUcxNTI2VFJ6akZPSjN4djhjS0tBTEpQbFB6TGZsWGV1eGVQUVZUQTZoWmc2Wno1akYlMkJ5JTJGMEM0VVNGSG5kYjZPR1VUOWJQNmxSJTJGazU5YVBPYWdKajVlWkFHTU03QnRiYllLYU5jOTQ5QkwlMkIlMkZSTVRDZ2p1cyUyQkIlMkZiOTRQYXhPV3dnU2REQzdhd2wlMkZNTk1xOWVxSjRrWlE4JTJGMnRMb3hHMU9UUVlaRWIybElQZHJ5OFglMkZSYkY2b2x6cndmUDMzcXl4RlYlMkJjZWVTTUljY3BqMiUyQnhiZ1lxMUh4OUd0ZlolMkZQRlpKNDFYaG03diUyRnJ2Qm5iOXgxMWU3czAlMkZIcWwlMkJ3em5SUFU5R1RGckNQUGFLZmh6eU8wVkFTYndwVHJ0NEpWaldtUnhZOWd6VlpWJTJGSVJCWVZzdjV5VWpxR3d1ZkJTTDNYNFQ1T3RWdUlzbU9MZjlVQ1g0MlRraVNhR1RIWHk3dUs2OVhXNVRNSkFwTEtWWDM3cjk3USUyRjZiSXE3ekZCMXNvMnV0dGkzN2h6Z2NRUm1iZENjbGZOMnBmRU4zNEdyWXlkbHo3cFdFNUJ6UDdGS1lPUTdqQXlKbjRxTnp3QVZEVllPTWU3Rm5nNiUyRjRKSUxNSTlrUnZwTm1wcWtTTjRCZTZKUkVMVHVhTDhKOFVvNHFXRXRNMW1iZGhGUHNWUWxCS3RzJTJGdjFvVHNQMklsTHQ4TXIlMkZTYlBSbGtCb3pCVUNLNWF0JTJGR1gyNk9JUWVKVkN0SlVYTGw3MzJYTzdJbEttOGp6WWFVSmpMeUVGek1rUWtjaDBnJTJGaFclMkJKakRwJTJGeVpxekVaM3BMMmVmOGlZWiUyQnYyS0V3WCUyRnlJM1B4ZHNzTVZ0RDdHYkElMkYzV3B0RnlWOTc1RkRpY1lleG5OJTJGZEU1JTJGNzVleVBqRXBQd1hMNyUyRnVrUkhmOXNzTW1MdU1lY1FGeU5WZ3plYnY0N1BGJTJCMWYlMkZkS2Q5MDIzZ2ElMkZGMmFMMGolMkZmYSUyRlNmUWZUOTNUdVZYb2FyMEQ5bmUwTkE4R1AlMkYlMkJ2OVNyMlczU093NWdqMFpBRU1KSU90MWJlREFMSHBZRXYzUDMlMkJmZkgzJTJCRiUyQnlsVE9ROE9kaWl6YmI5ZmRXSjEzUU9PZmZKeWlxWnpmNTBPMUs2TDRid0J3ZnYycjZlJTJGV3NVOVJmSzV2dmF3YWxjWnZ4cDNJdjdDNWJsZk0lMkZ5VU9OUiUyQlpiOTNPRCUyQktTJTJCSUhXenpsQUxFaWhuUXYyOWl1cyUyQk5kTDdObVRiTW1waFVxSEtoN2d3TEZpSiUyRjVVZkg3dUolMkJFTm5CUG1qSUJjSVFrZExOSGRKWkJLJTJCb3pncXRuckttY1JrJTJGaVYlMkJhejkwTWVnN1lsMUhiY2tHTEg5aG0yUFo3MGV2NWgwbGlrSVExUHRpU0UwJTJCNUQzNWVEcDFaNFZPc0h2eXFnSkQlMkZpdVBIcmtFUm40YzlWd1VTeThqOEZVTTM3U1lxJTJCVG9VJTJCS1lMJTJGeER6alBnY01kWGJaRjBiVnROOFBmQzlDTVZRMkJXZE9MNXIwTFFQVENHYkZmUnVtS2VRS1ZoJTJCUDNhMlVRZmtFMmI1N3pBemJwbVRYdGE3REVvempFV2lraXk0VWROQ3FPc3p4azBOc3VWeGU1SlFVbGg4dXFzeDlxMU9ZSG03dEFVR0NMYXl2QjNrUktyZzZ1JTJGZGFYM3A5dXlqNTlURlNiaUlWRWh4SUFtZjBjazMxWDJMa0QwcVFpS24lMkZkNGx2QjVRTkQwZDZkMUN2SnRxcDFtcXh6OWlzNmolMkJpb0FTaUZXdVM1R1lzQmIlMkJsWkFCQmRzRlJPdmlNTTM3bloxVzJRSGNzWU5sZG5ZdTd5THVMRTh2TXpGVXJKbHd5MjJPMWRhV1dKY2RFOEg1c3VWcmQ2UEpkWFlLblJhRTFybVhkajVlZWh3WG9kYnlMNFRyViUyRktSalR4VjNIVCUyRmhVMURjJTJGbiUyRjhxOUQ4WkhrUHlCcGhXSmclMkJMaXk3czdQJTJGU3k5ZmFyTDhMJTJCUXZESDMzRHFCVkQyRklJSUxUT3g5M25uY1dPeVNKbXZ0MVNYTnIlMkZ6UWZMdmw3MExaZHNDN05iNlpubWlWYndvOGkyUk0wclB6TkVsOGVzejRwT3hBY1diaDNvWGE2VXZFQXIlMkYxcmxPayUyQjlkSzhSeWx1NFVHelMlMkJ3V2RFMFMlMkJrcWVPQVA3YUJEJTJGWDR5cGlITU1yTWZhSkF1TiUyRllPMUZzM3g2RzVhaXk1ZktIY1B0Q2NoV1RvSVBGdjglMkZQWUlpSnRZM1c5MlVvMWVJaTVWcGpudkdEcSUyRkQ5RTR0JTJGNk52M0xmeHJ6dzZlVml2STN0MXE1YyUyQjlva0U0JTJGeG1vMjclMkZtQUc4OGp6eiUyQllHNCUyRiUyQk0lMkZFJTJCNzk0VzR1NlAzZmg2elU1VXQlMkJvcmxCTGdKUnRnM2xmcklUZmZTRlhGdnZVMEd2bUl3cWZpRDN0TnBDRCUyQiUyRlhDMTFENEdvSmklMkJ6VjB0MSUyRlp6aHUxNWNsaWp3WnlBJTJGdzFGUHdaV3NwSzNGOURJaURiJTJCN3RYVVZtaWxPUXpoSHM0QkZNdEsxV1hldFY5JTJGbWRHSlR6UzdvTFpPVm52SzJCenI3UVRTaXkwdnlGRndPZU5WTXRKMDZWdURaUUd1T1ZNWDNZUkpSYXlrM2QlMkY3MThFTVBmMU5STno5RXM5NnZiTHdIaTQlMkY5Q3RTNVhYQ2xaOWJuJTJGJTJGbHdJZWVYZnV6Q0NDcDBKZnBvOHZaOU9TSnVVQjNQZlRIclZjUkZkQlJJQlp6JTJGWm5CSmxmSTV1djEySzdoJTJCdnEzRWdNNnFJaTd0MzNoUm5pTTY4UmF6M1g3dDJWZCUyRkg1TDNWMzZjU0VocWY2MFVWb2Z5YkU2UXN6Z012UXZXY3RKOUYxJTJGeG41WkI0cTFrdzNUNzVPczhNdXJydFRuJTJGR1hJQmYlMkZCb0wlMkZUV1BaVG8lMkJoUjlkU2tlMDFWTndHbUxJVGQzZEVSZjBaa2w1RCUyRlFzcmxqb3B4dnRaZTNZdEJmbDFId0s5aHJ6WDBCOVd2R1pzalc2WFF5UjJ1elp0NlAlMkJaQWZQZjluOTlPSnlHMThoamNDWTdlVmhMVUZ4aWRHb2dXWWplczdWdEVadGt2SiUyQjE0OVJSd3Q5ZnBUVGdZaDl4Mmd2NWxPeWxXNlphWWxwbGVjMzRsVmwlMkZlMWljUHF3RmMzQTlKNDRNMVVRTG82SDkzaTZzY2FZNmVmSVgwU2pRMGJxMFVLeFJibGs3dG9rZnA3NmZOMk9pa01McyUyQkV5MUtmNW5LcGRmbkZtOFpxd2xwVmNVbHRqNXphbGZuSnElMkJ6QWFjYyUyQlg3aDJ5aUtsSVVDZXZ3MzA1T3AwQTJJUVowdUQ0dG1IOW01TDlReHBIbjFMa3hZVWpSJTJGelAwaGZIZEZrczBucjJtckNTOFZwUVdXZm10cUduYm1Od0RmMlBQT1hPczglMkZMNDE4eXJkd05LdHRLcGVHWGZaNlIlMkJqVVRUYThTUjdZZmdUR0R1MkMlMkZrRzVGViUyRkJucCUyRm94OHlGdSUyRlJ0ekduR1Q4TnBSMmJObnQlMkJJeHN3Wkc4WE1Qc0JmYmg1UWg5alR5WFM4bGVQaG1rNVQlMkJvODBKeCUyRnZKNHNXMEx4WFlJM25UN2diMGhJN3FLamRPN0YxenU5UVVYTk1aZXBvclMyUHYxM2VpSzYxSExsdkVMWHQ0ald5YlA4SSUyRlBSNmhrJTJGQjdMWldUUEh2JTJCSFhmJTJGRHJ2OWgxJTJGJTJCdzYlMkY4dmRxRXV5WHYweDUlMkJtdnlFNmpWOWl4UUxnNE1PZTdwYzZyRkRsJTJCVEp4RFZIQWcwUmNtVFY4SlIlMkIlMkJ2WDFybyUyQmUyR3hrZjViT0tFUjBIaGclMkJ2eDU0JTJCeFpMbVF0cnl1dkVUSiUyQmlyUFByVWtVZXElMkZmZHZ2a2Y4R2ZNJTJGRW5vUUJSNEFrRTFJS2ZJVkREaFJnU2tubWhmNFFtQTRFJTJGWUhRdVdCYkdGODRVc2VRWXQlMkZCVmpwYnVMWkFBZlJORkIxdFp0a1M3Q0I5S3V5c1haNUIlMkZkaTBra1psQ1RsOEhzR2dmYlRUJTJGT002OVk5QTRwJTJCVFBzRzJQQ1hWa1ZVd0VRYnBRUXAyJTJCV0lpQlhxeTNaNiUyRnZVaWNXOW9mJTJGSFVIcG5XS3hCZyUyQkNhd2NmJTJGRTdoMVlla0FNRlZGR1lYZCUyRkIxaVdRYm1HTEZsaTAlMkJHTTh1RmQlMkYzb3daeWVWeWRjMUwzejNkM2RIMEppSXNYaERlUUpZJTJCdXR1T242RW1UcUlCdnhxTGFsMEs0eTElMkI5SmtYMjRvdlF4QkZMOXZxVWUlMkJvYm5xazFXeFcweGVBdnhPa2clMkJCNnFYZUhtTHlaSmNkUVlGOVo0cEZKRzc0OFQyM09YMG5xVFNpRHQyQWZPMEJ1ZUw4ZVAyNk5SenJnMTh1OGRlNUxZM3k1QldKNURkQWg4Tmk5a0lWTXNBTG1DU2p4djVUcEsyV2dZbElUNXpOJTJGdE5SUFd4d1FWTW1QNFJYZXhmUCUyRjd4JTJCelFqNCUyQndFNU01cXNyYkNhYmhGQTFaZ2VMUHk0byUyRlA1YiUyRndlMVJPcVJWYjdpWXNxSFB4QjJlc0VLak94YVU2JTJGU0ZBaWNZRyUyQk81ZzNZdk5TQiUyQiUyQnZLJTJCSUdrdWY3SEtFcmx6VyUyQktES1dsbnFyc04lMkZUbFliakptZHZKdSUyQjdJJTJGNlUlMkJWOW4wZyUyRjFVTktSWjhZZVNxblkxN0lXbUt3NCUyQmNTdERCcFRVSFhOTGx2cHRjZUVUQks2bUs2dm1Ic2ZaNTRUcTUlMkJ6ZiUyRmtlcGtmeDlhbENLQ0tmN01XQWFKbFAlMkJKYm9tTjNnaENBVVpXNDNyS1g2TjU1em9HeDY2M0hydktMY2ExUzFnZFpiWkdUbGI2QjlmOG5DJTJCbVhBbXlRcGZzM2dXYzZkcFdUc294JTJGM0xzbTVibU9zVUkwTnRnRnNtQ2ElMkZHMXQ4ZFVJVjVkQ3U3N3I4NElyeHc1OXo3Y2N2V1ZTUnFtOWp4Z3VyMTlYN3ZXdEg2MlltZFVMdWZDT3JHJTJCTE5rUmdiM0xvb2E5JTJCV2o4TENvUFNjWmRaRmNldk1zN2QlMkJDYlA5SUFUanRCanZDSk4lMkZpWWVOREFXMWlIT2hRJTJCTWdqeGRmQkpSNkN0VWE0UTh0RlFqYmRIMHBzcUNzbHlkWXQlMkJiODF2dThsZWpnZGVDY0JaVE92V3BCbWJIdG1hRlclMkZMSHFHY21CRldzMVdRZ1ZNdXZFOEtKWm1Pc0Ztbm50ajJocml2VEVndjkwJTJCUU9tM3lKRndxbTNkQ3gzcUUlMkZDZWdYb2k0dUMzNkJaYXZLJTJCTm44QU1uV1VUUVh1Sll3WHhJaFUzZGhoVzVMckJzZktWZCUyRksyWHozSTUlMkJTaEZFNWxBUFFsOFQyWm9hb09XRUh0a09oMVh1U0tJdktaclBBQyUyQjlJRSUyRmNaeWExSG56eVZ0MXhGTGFHSU1ZJTJGNUZNVFhXNkk3cmJxdHpTY1FGa1F2S01sM2pUYndyMGRSY01Ya0dRTGNaQVRTNTRORmhDSEJzc2UxZHd0Y2xsRDclMkZmNDB2bU9COGo3U2g5a0c3Z2FYeUdTbG83T1Q5bG9WOWxJdGtIMmNhTkc4JTJCSTZ1cDZrbHJiUzVaT2JGTGV2ZTNhcDFGOHdzRm40VWFrdERiamlzV2h4eFV6WkVFRHZ0NEJqelZtdnpiVlJLdjZ2d2IyJTJGT2wlMkZBWE9GM2tlOEJwZnBOaU9NelJybm9xZjlHM3olMkZmVnZ2SFpPSHNORSUyQjI0Y0lOcWNiTGF6QXdCZ0hsUSUyQjI2YkZIQ1BMcFZ1cG8lMkY2R09ueEdObiUyQndjMDVtelhoZkNsaW82bHgyaVhyQUM3YmtFeHQweEY3WFRkSHRaaDVra2RCQzh6b0dFZUtxcjVyTkFNRkpCQU9hTE5SNXZYaDAybXRhaVk1RlRJTlNIU3BrMEdCdGxWYTdkdXo1ajJSZm5talcxJTJCJTJCT0NSYnQycXF1eFZRc2ZXQzNsUUhSOHZ6blY1YU5kbkE2eFF0RyUyQlBtUWgzdkVGQTVNWXNFUDNBSHZWSVZuSWkwTGRSMU9EbXVMZzBKMHhMbDNVVFZrYnoyWXMyYk4zY2gzT0dqVzBpejlmZTdnZGRTTm1MayUyQko2TzFZMEtoUDJ5NWozcEQzTVdxenp0QTQ4UzZFcVp1MXRJcEFRQW9FeU9tT1RoR0pXYXBNZ1R0Y0UlMkI4TE5oYUs4RUJXSVU3ZnRqSkhLb3E3ZEE1cHpnNmhwd1lyelFJYjhyOG0lMkIlMkJyc0ZjdkZHRG11TkNHd1B4ZVpBbFVyNWc2aThHdmZxdWJFR01WM2pEVSUyRnpyakVvSkRYaDRXTUxubnMxJTJCM2xlR2tidVk1UTVQa2ZVTSUyQiUyRjdSUVd2WEloTGtWbkpkREF2UmhyYWtMSzdlcW02eGRIWTJ4VnRIa2xXSXlGJTJGZXRWbzFPenl0TmwlMkJaUnpHZ2xvV2NSbWJBZFU5VGZkY1k1V00wT2RjdnklMkI5SUhMQVFQREJyenFKWWlQU0JRMENEaUVTSkdibTNaaVhiZU1sRDFkcGFYR3olMkI2bDEwSkdvSFFONEZoJTJCZEFpTFRrdjZrRjFYeVJkb3VSdWlVS3BtNTY3M2Z5R2R1Y3h2MVRlU2Z3dGZDc09pc0RSN0V5Znh1a1YlMkZtZ0ZoUCUyRkw4NG9zcEJKTXFaN2xBUnYxYTF4OHBRcFBLR3JiTXA4cmEzcHB3ZzElMkJ6a2lQQnBzVm5Qd1lINkQlMkYlMkJIMkxXOHdhMjNjRTVVVUJ1JTJCZ3VmJTJGclVTanBvWk5ZSk9kTFhrR0NxTHhRbzlsNjZjZkRxdHhYcXVCbnVmWmFEY3VPJTJCRXNQNVQ3YlVEUmNmOFIzQzltbWQ1MW1rNU4xJTJGZW5pd1VsU0o5SzZNV0VjU01hcWhDNE84NnZYQU9mZVl2N2VjZ04lMkZZNnhGQ2hBOGc5d2hCdzVieWxlRm1hQnk2QWlkdmFIZE9rUll0MmZXYVNBMXElMkZDcTVzYk41VyUyRmF1OEcxR1Z6QXNydVJlZFhnTzRKMmx1OVZLcmhQNTNjS2hUSlFqZzV1a2glMkZYYiUyQjVQRGt3RHdleSUyRjNsazFDNm04WDExQk95cW1lVHk4a2Vab3VGbUtwcUFtZ2d4aHE4Mm1nT2JDTjM5ZExmTlhuREUzTlZqSEVFaFZIYkdsZWlOTjJaSkoxNUNEQ05zQWhRbVVhSXQ2TUFKSjBVV1o3M21uZnFObDJMb1pDNUVyMmxCNDNaOFNxWnM4UXJhRyUyQmlNZENhJTJCR1M2amFwNCUyRnkyeEx6Z1ZHdHdTWmw1NjRMbDdWSm9xRlMlMkZ4NDZlSXZGaUlCV3NHY2tDZng5ZHhPU0RmTWR0NHd1aXlscnNUUHBXVzRZdUJFYmlGOUU4cSUyQnlZV2FlaWY1bFhQdEVhb05XM0xkcldWQ2JwTTBnTmtJSUFvNThjVWFzeVElMkJjU1dGdzJyTWh4cXk2V1NoMHNKOWczZW1Bb3hURTg3VEdTZXJJUGxMTGcxOTJVUXBPOUV1RDA2Yzh4dXY1cXVLQW9PcGI4RU14TUFCU05kY2pMaUdVcWpOOTI0cGplWEpSRGRhMUhnJTJCZnNiakNKbFFvQzVSNTQ0JTJCQldSUG1KSTZ1MDRYRDh6MTBLNElJZkpYVldRZHo2b3N5QzZ1dDd0NEYweDR0cWpzdUFQQmRvR3JsTmEyMlVBN1RaR3AlMkJPQnloTGkxUkh6SHdBSDQwYyUyRjJvZWh2RTBMVzl0Sjl0RGR2cWJtS0JMT2gxUFZKdDRTcEZWcE5GU29RY0slMkIlMkZSTHNPa3ZpNSUyQlJIQUU3WTJtUDMzSTZ1YjlzJTJGZHJtbHVmNXJLNDFrMHV2SGxhSHd3JTJGWGxlanM3YVNxU3h3MDJqMndockZTSEJMdjg1blNaR3dIMXdiWDJiMzhJR0huQ3hUdU5zV3lVaEcybiUyQjUlMkJQZEx2NVp1TGoyUnlkeTZLbHdPb3ZzU3daZWhEODVOVGx3Z0EwRmNxN1Z6V0MyejQ5Mno0YXJKb1F4TDhpNW9KU2hac1lSJTJGNyUyQlUlMkJrTSUyQjNUVEdJTmNvTDk0NWJXJTJGaUoxbmpPV05oR2ElMkJQRXlqMkVJc0NXV0dLQWpCbHl5RE55elRHN1NxckZwS0N2cEp2dlNGN0NSeXdSUTBIYXFWbDVVT3JFMyUyRlZCNmJqJTJCY0Z0cWJNMUhKM2h0OVRWajU3N29MWmVGSXNuMFB1aXlIbjgzQzRiNTVVYWozbERFdjRpaVElMkZ6b2xIblh6QnI4ZDd6bEc0WFdkTXo4RWxQd2ZZM29YMU9RQXY3NnJZQkJ4VXR3Nkp2UTN4RVBHc2p6JTJGcHRnUjdNam9OSnZENDVnN1NJaUlmdXpwdlFRTWN0cnpUZVFHOFc0YUg0cUlId1FNT0FBRCUyQiUyQmpjMmcyYzNUaFNNbzlQYnk2NDEwbXZnTCUyRm5mYkV0Z2hocGVxJTJGTVNkUUhZczR4UDJRZ2V1bG8zS3lPdFd6NGpyb3hTMmhGUUNmaUlUNnFQQzNRRndjZnBlRjVqcTRwZXhuVE9tb0xuTGdPWUJ3ZFRQNmZJMHVPV285UVE1VWFSbzFIbzJ6ZWolMkJmUktWNFNKOVBUMWRWV29icjh6N2FsaEVBd2dTVFpqbTFqWEFUZ1FrQ2RYRW9yc2t6Q2dhJTJCWmtMc0FUNlFZOUhrOEV5M3c1d3pJVlFOcE52ZXI3JTJGbXdvYnRnNkpUZDhWckhHMHdseHEyNDVXSmFxcjlZb3ZVJTJGTXFLTGdrWUxpNUtLNjJsTG15b2F2WFNDQiUyQjZLNlZSNkN3aE5sRUNNdkVUOVFVY0VQcXVxVmlYQUNyZGNwVmNEenpubTV5ZExScFUlMkJ4NzR4a1h4amJrTVJaTkRlVEpSMEZXamdwdlNUY21ZMVklMkJXcTJYYUlocnY4M1UwQkhqa1M0ZEpkdUliRlRZMEc2eVI5bnk4UXE3Z1hVUWRXSHBsQklKWXJVekVqVFJXQ29CQndhMzU5cDRxRXY5R2t6MWdKWk5FeDVLYU1OUFJmRlZsVWJJazlhZ1ZXMG9RUkNlVnJKR3l4WmRTaVVwdWtPcncxeFo5UDJDWFQybjNmUUlEN0clMkIxeXIzT1JZYzNxUHhWZ3JwM3hCeksyUW1TR3M0UjBadjBSSlUyWTFvY0Q1aXZXYkxWVnQ2OFo4NDZrRUYzdGJ3dVQzU3VXcnVVSHJXdCUyQllXa0hiS21iRWY4TWdtJTJCZ3NZd0U0YmZIcCUyRlpjRzNYZGNZT3VhVVpUZG1QdGxOM01ST3lpRXo3OWVWSW9POEFxcDU4YWJiNTZtOGtFbkdvRlQ1dTRHZkslMkJET3pKWWMzNUtjekZUTFdvUFJWeWVxOEEzc2I4YkxZbTA4RXBmNGRpd3NxdHh3Q1hvaUU1bXhzaUN0RXFjNzk2b3B4T2klMkY1WlZQZEhRQVdYUE10b0M0UEs4dlhkekM0aUVLQTg3d2xjZU5aVWY4TmZQUEElMkYxMDEzRU9PYlNoMGZXWHhCaGZHMFVaSThtUyUyRmFqcXJCWGhNWmx1RWpHTnZxWmlheUhIekFiTmVtbm9zd1JaMW00JTJGelpmdEpyQWgzcEpGSGRXYUxkU0clMkJ3MGlwVUxUc3RNbjRDcG5nbVdXRUg4a0hwJTJGVCUyQk5PVkgyJTJGSWpQVzk0WjdYNk5QUTRLRHZlc2xZd1RremloQVJURyUyRlJobUloZ29YUWY1MEFZV01MS0pxZzA4MU1ES3dMd1owNGdNVFV2QXpvMjNEYjhCZWQzb2lrUFc2WE5lQWFFU2oydHpGNjBkYmYlMkZobzA0ek12azliWGpLVExGQlN1cmpialZhUG9YekhBdVZFemtuUFJ3ZkdzeDQ4a1RidzJoNTE4dXNaZ1p1aTBKV0Z0ZkZodmF2T1liNDR0MEtzUmFZQzJlcTBpQ08zSXMyQTdFUyUyQm84bE9jRVQ2TzlhaFljWVlWWGx2b2V4UzBHVGp1ZGp0TGp2QktUaHUxJTJCdE5xaVVZdSUyQmx2Q1Z4S1I0TnVRTSUyQmtBVFBla3JBeHEyQmN4OHgzcDZhMUlQVU1nTnNqeWpQZnFsJTJCYXFBN2JsdWNqekhWN2tWYzZNNncxZGprOHNFcmhmcDlmS0RrbUIlMkZLTDdCMiUyQjV6MzhJZm9PSlU3b1hyQTZ5bUdyZTNYS3F4YWR5WTRZMWZlN0MlMkZNbmdaWFVDSVdUUCUyQldQQmRVYTRvUGFaaGpVdnRXZW0yTmh4NzVUcGcyNTNpVXBlcTAzSmFkeXQ0YXNkRXAwQkhEVjVtYlBFNWVmMTZheWRNN3V1VXR2RSUyRmxiRmNNdjVGUkxtUzNJUlFNcGxJT3psb1BDblFzS0hhaWslMkZ0TmpySlgzcGtrVk1XajE0Y1liJTJCOHRpandQOHF0bCUyRnlLQUJiJTJCTWslMkZVSzNLeWx5bU9vVnFwWnRyR1NOTEdiYk5SVmxxV3FSNTlOJTJGdjM0NVRwRkpKdWxNUlAzVmpBZ0dYZjlYeVoxSmNWWW9UQkZ5STZ2MnVUY29kb29kWUhvbmxHNUk1N3p5UUdHRVF4VkVjc0FDMkpKbkdwRzFtQ2phUlYxWjBQR1dzdjRqNzBjRWFHYkd6U3JWNCUyRlNNYjVXVXY3aFJObWFFTjJsNkpTdGtFZTElMkZXdndoQiUyRmlYNEMwS2Fqdmc2R0ZEQXU0RmJWbmFpUlFUSkZsQzJqZHQyZE1NRGNqeXZnWWZvanlKdERqZnJHVEd2Vm1seDRVblhBZzF6VTFxNTlCQ3MlMkIwQUl6amRsRGpyJTJCTm55eUF4SWFGN3NpeSUyRnFKbW9mSW5ZSExxODJndmxjUzNkUmpuOXJURXVRQ01GJTJGazVMYkpQNEpYeTRsJTJGaXpQOHhoVUNWdCUyQkl5TWpKWVVRZDZQbDhLRXBGV1p0WFklMkJLS1VicWxjT3dEUzBiY3BySXE2eVY4VGpWVW9aS1E4Um13SHV4Y2owV3VJSzZlWmROUGYydlNLSmVNemZweHFnREtaTjMxbjBhN0klMkJhJTJCUHFsQWhBdTNOZmJlS1JhcVRNbGI4aWJINWZMZmx2bDgwZ29Gckk3cldxRG9LbkpkdHUxODNqRWhNV0NPZVBnOHRqZzdONFM0cW1EeDVJMW5qYVVwTjVraHBHU3Rpc0F0UTRUVFJ0R09lJTJCTUFtbUY4eG1uWUE3eWM5aU51VzlxcmtWOGtJQldKS0JDU2YlMkJ2dVFpakNOanVLSFducDJoVVV4MThIZmU0JTJCR0pwUmo0d2tLQVpwNk1icGRqb2UxYzlGT1AxY2loT1Q1bTFEeko0WDZKcGVUVnF0TjNsdkhNVDdud29NcEdPTU85MVcyOSUyQndTZWNBQXRhTk0zVXVHcFA1bU5KWFlxcCUyRmxaaHVIZlBmc1hiRFNwM00yaEFkcFNkNmtxODVleE1BJTJGVFRWbjM3ZmFpN1hLSWxMcnhSVkdIa3o0ZUNZTGZSbFp0eDZRaDJtVmtubjFCbUVtRTk1JTJCR2NUaFZyNzJpTHJ6amFMQ04lMkJwY1hKcURrRyUyQkdNOWRTRUJPRTRwU2puZko1d0Q3R3FBNnMyJTJGd3pqcUk1NzB1dkRSbFpYbXJCMVExcWtkaXZSJTJCYWdTd1NYMk5LciUyQjJuUUw5c04lMkJjQ3hyYnhJJTJGcUM5SHVTdGF1d2RTY0o5OSUyQmpZV1F2V09IemZCRHFGUWc3JTJGWG1MOUxKOW5lcXZPaEtNMVRpdGtmaUtzM3NKdDhiQWYlMkIwMDNuYnczVUJQOUM5cDBjcTdWVEY0eXhYc0FBMFY1Y09mSVZTcXZRQkp1a1FSV3AzbGlscGVVQlJQd2xTSzJKeU5SRzFzVWJyV1pyMDB5UjVzQm5lY0lHMm01SG1qdE9ncGV1RlJsUTZyR0dxUU1uOGdMY0IlMkJsYXpLdDFXV2t2bEFUN2tScWpybENOVnF2YUVMNm5DTVBwWkM3cm1PZmJ6Qkt4bTBOeVloTlZacGxFc1pmeVJNcEJVVXBrViUyQmZnV3JuRjZoeTVGYjYwb0ViaGVFSnBONlIycFh2WmlsdkdmR2E4cDdrcnolMkJuY20lMkJCeVVkN0NVM2slMkIlMkJaTWZqRjZ0VGRyWWFVWmxGVHpYaE9kYzZzM3VyakNyc3V1ektadHRpWHBNSEVETDdJbUhZRW1TbTZzTVc2OWZ0Uk5LZjc2b0hsaTFuVkc5Z2xuS0VOJTJCQjNPMElQZGtVUUtHNTh5MDBxd1AlMkJ5JTJGU2xvSnRYNmJmRkdhNEdJcnpoclM4cFdOT0Zma25BQ1NvMER4JTJCQmpkN3VwZ2NzZUYzUFJXY0UxRUElMkIzSUo5WTJ2N1A1TlR3RFlERWJJR3I5QTU2JTJCQlQ3WGpLT21FZngwa2tqdFUlMkY0YVhEbXl2akp6b0NIdzhMcU03QmRRUXowSm1iVUVDQ2FuJTJCc2Z5OXJwZFJDOWVLRGJxUEdXeWtlNElxbU0lMkZxbDJNTmdzZ2RmTEpvbjJKc2JDN3hTZGZBNTU5JTJCJTJGYnhqWnElMkJ5YnU0YTF2SXpEeWd3T2dhV2tFekp2TEZHYWRpNyUyQmpGT0tkekNMY0ZiJTJCYWg2Tm1ZS3lwUkpjVDBmVXh0NHpYRWdHeCUyRjZ5bnBacmRjWW1nbnlvQmRjMUozOHJXR0xwbFZ6NGlhYkFYeklwTVpTJTJGa290NjFHUVd4dW5VVW9BeThxVEtJSGJsOG5SeXMyd1AlMkY5aUN4Njc2azFVUm02JTJCbW5qOHZRUnozcXAwN3FldHlFTHBlc0FCYSUyQjB2akh5bklQT0RLUmxsSHQzbWdjQVZoZjZqdGR4MXhkT2RTeU1kcWFZYzVZbGEzYjA5WVFVeXhLRHdBdVpITDZtWVoya0FnVVdxRVJvVWxHRXglMkJrRURIRkwzWCUyRmx3STlsTVI2RWtNQVZIeTRDQWRuS01mNlFPeVhlJTJCRENIa0xhV3hvTWNYYVhXWFl3SGJWUTIlMkZxWnpORFNGN01oNk1YWW04bEJhcDNEekd2V1NpTFQzbU9TTiUyQkVFcUhmTWZtc0oweFdqZSUyQjFKTyUyQnJDWSUyRmJTbFp5WWklMkI3ZWdJYjNSbVJidFlDQUgxSDBvWlMlMkZ1bFdITlBhVFJSMmxkdDhJNUw2azRMb3Jta2V6aXZVRERZMnhUVCUyQm9uVHlYYTE3ZVV6VWJ0RnFyTVp0QUk0MkpwNXFiVnB5czUzTzdlczFhWXRZUmNaaTh6cE1EQ3JIOGU5MlJjQzgwZHVOJTJGJTJCYWslMkZPZXVDcjJTUG1Od3dQYzNLRllOYUVZTTJKTXBCOVUydlZKQTJxZWswSlVJRkZyQVppMjlTcDdtRjBTR1BDMm9QM1BXekhEUXVjNG9RMWc0QVczcyUyQmpWbG5NNlVVdEVQcFB3QndRTjRVclhvaUpqTmtjakZSUWl5aCUyQjlUaFpHczZ0SlRDOTVhYjVpOXJvVGNPUUV3eUZiU3U2cVNvWiUyQnpPaWxLN1JYY0dqTjFCNnlPUCUyRkRWQ3NlWmN1UHczTE4xeW10emElMkJFbURzRDdhb2lIc1p0bGFlbWhoRjYlMkZZM0pwbkE1SzVLM3VJc05lczElMkZYZFpKaldsbUdYcm9zUUdxdXNreXVnRjg3eGV6TXVXUFJnMGdnYm96U1FzdXVaZmNXNXI5aGZGZnRpazE5WmV6MlU2WHE3WDFwalpXYTBBcUluSmlFcjZ6eGF3STJEd1M1QmNUS2JsQTFoMzJJamN6cnNzUTAya25iNkFmOUUyMVBkM3RIZUtBT3JVTzRYQXNqcHVKV3JjbWxvazdCNWlyRVppV05jTXdIUFN4QkxtZXlwc29NJTJGNyUyRjBQWWVhNUpDTzlib0slMkJFaGhuanZQVE5NNENIdzd1a3ZPJTJCdDBuNTc5b3p1cXJ6SXpEQ0F0TFdsTFN6OFpqUlBwcFViTTkzeHVIVWZLVFRNSlJrN0ZodDFsVjV6TVgxenJPYXF4TTAlMkZSdU5tSkU2YTE5SFg2cVloZDAwYVJhaDJPd3pUVW5OeG5LN2tUNHZLclB2VyUyQlZuTTJ3UVdhb001QUNjcmdGcEVjcVZpTEpzMkpLbDlpUEw0MTZ5TyUyRjhBaDAwUFJBR0s2U2duT1V1eDNGejJKZEJoRTNGRU5BJTJCZGlBZ25HMEJ5Q1ZKSGhRT3N1OUthMGVGM09qUUxrQ1l0Y0tsSW05YjIxemR5U1ZSQ3VpaHpFN25pR25BeDlqejR4TUJjZHZKS0wxJTJGdGE0SURNc2hIbUlvZ2pva09CRElDZ0JOZDVObFd6UW93SU0xTGtYcDBKQiUyQkZEdFI4OVg4QmlhUiUyRkt6VGlIcUg5VmNpM1JQN0U5WHk1aDdHVnNIR1dEOU9GT09EajJ0ZyUyRjI2VnMxOTg1azB3RlR4N0ZGZUp4WWpBeVJvbFk0dHN3dk9GTWRZcHNvVkFPRUgydmRPQ25ETDhoZmloRGRid2NUU21ITlFoTnJMSHZaUDAlMkJXUkJIbFpMaGI2T2swa2drVFBkcllleXc5bDVPT3JGNFA3ZWpZWFozeUJCTkFGU0IybTdOanpOJTJGM0N4S1B2bmt1a3k2ejlVUVdpTXJ2cTVobjNmZnQlMkJPY2U2dVZwM2lHa2JqMGVCZVpLaUU2dzM3MDZVenJpUGpUYVYlMkI2cSUyQk8lMkZyVm1uc1FFazhVMVZpUU5vS1ZIU25ZTTRUYjZ3aVFvUGZ4anR3eWQ2NmtTZEVEdVhPZzFDTlZFeVg3JTJGbXpnRFhXd1ludXp1SHhUTDRXeEd2NFFxbzBmUHpESGk3bHlsb3o0Z2QzZm5UMjAlMkJJM1pHbG56Z0tpYXhma1FEUXBvbEQ3Wmp4JTJCVzg3V294T3VUNyUyQmZvM1o5aXlXVTlpZE01eWRadmZqRlB1ZnNuYWZpbUpacXVrRE90OEclMkJLbWkwJTJGOVJJNTU1Tm5vJTJGeFZETDJoNnA2NW5UYWN3JTJCJTJCS3JZcUF1Y2xxUzc5MEM2b1g2RVp3cmtEQVk5R1BTVSUyQnd2VXZERGFvYSUyQlRnenZLdmt1aFJYJTJGNEtIbHdqdiUyRmF1bnpKdk01NiUyQk1vRCUyRjZCZWNQSTlJNmhGcEg4YVpnampkVlpYSWhmYlRFZGt6cGFZRVFTN1dJVUJRRmVUSVFnWG91UUNUNjRUZFhDa2d3QlZTVHVQU1ExVDdDZjIzRzglMkZLNWU4QjBSYnVFV2pnS3g0WVgxJTJGemtwY0c2eVJ6ZnZXN2RqY3NiQ2pETXZ1bWZmcHM4ZUhhRWIlMkZ1WkE3SlJ2bnZ0SHBrUVI5dDBrV3I0MmZqNUpTVGNZcTM1Wjk1QTZ5bFQ4RHdiN3pIb2JQZ3VndWRYeFVBajlKdmtuWSUyRkNaSTRJVnZzSk5aRXNsY1N2QXppYkV6YURaaUE1N1dsSFVKSHZSYnhVQng0VktCUTFsZiUyRmJCZ0EyTnpFc1g3VVBYUTZoOTJOTVIyTHlWaGFNcnJVJTJGMDRGMG5kRnhraVFudmx5YzhaZk8yeDUzelgzZFh3OWtNdENGM01iVjdLZjJyeHBmY3NyUTMzR20lMkJZYnJLMER4QU8xSldPVHBMJTJGSk5pRDRtYVVIYktDZnBvcFBHcUNuNlBlTkxSWUZRelY1YWpwUDZqUzJTS0xPZFE5dUFvUUxnRGRlblI0ZDRvRWZUWDJXUnZiSEJndERsWHBtR3U4STNEUmprZEtMbG5wbjBOWlJxeXVqWTdzR1ZWdWo0bk9HMG9ZbkZhZElpZSUyRnhpR1FRa2ZwbWZwUVlkcDQ0WUZRa1QyRFVMRGlDVlpVTUkwQkRIJTJCQUJOTnI3MFhzcDJHZlYlMkJHcDB4ellWSWVOJTJCJTJCcTdXNkRNS0Fkbjk1VGpDJTJCazNKQ2o5Q1NzdlVEM1prakpJeFZ3dkVURzN1Nk0lMkZWMW9OVWhBdEVuMSUyQjMlMkZtU0xWT3lQTTRpeFgzNHhLVlM3dVNkanpWJTJCbEpWemxUQVZhY0M4NGY1b1ZDT1VKNm1PVVZmckxkbXpMVGt4U0U5SjBMOVhUUjVzUEtuRHFJVnBRQUlsYWNqVyUyRjlWR2s4eFR1SWNEbXRNaEMlMkJoQU5SV0liTHlMRExBcU81cGNDZXpkZ3BGN3FTREtMWTRoTUR5T0NvbXJHSkZqVSUyRndXaHhCcXhjQ2plRGpBMjN6NFlnbGpjdmhKbmJ6RVBodVQ1TFZ3REpiYk55MmdrdG5QdW5OJTJCNE9Gc014REpaUkpSQllFYXFldURZTzZwVUcyZG1mJTJCVEVKS0N3JTJCRmpSWXRUJTJGRkZjOExnbFhQQjMlMkJzZ1lhc29PT2ZRWlFiRzNzeDFPU28wWk9uM2x6RkNwTnJsbnRqcXh6dWNWdCUyQlRTVWdTU3h4Ylc5RWJvRzNOM3dtbVVFOGFJU3hhRk1HZnlKUFh0YXh3dnRVRUpVVHVrJTJCOUdzMmZxRzk3b2Z0dSUyRllUT1NhRnZEaEVWVnRqWDF6Qkg0ZTVhdzlsY0ZEMjUlMkIzVG54T1hxam1lTnMybHVZbmlUVkVTYSUyRklCYXN0ckF3eklzeHFYaWFhYTRyVE1EQWRpOHlRUHVZNjVyS2dIc2QyazVJeXJaSlAxOCUyQng4ZUpYeTJ4dmRuRWFielMlMkIxVm12akVWUHNLUXFHWVNDejZFNHIlMkJZVWk5JTJCWHlaTng1cW9NUWVjJTJCQ1JmREZ6OTdzYkY1V1V0ciUyQlNsM1FFbU1MZU9oSzZKb25QS2swZGtMR240TldjWGZZRFpoVVkwJTJGSkpzaVljS0IyVzhDdG1xZmVyNEFuVSUyRkpYNExIamxsb3FTNDVrYmR4bzg3M3AyV3NFb0U3TmhwczM4JTJGdlNtekd2V2dIYnZpMjd3OXBVUjNyZG04JTJCN3BmR29SZG1DNiUyRkFmUllzeThxU3ZrZG1ubDBWbW43Wk1EdzJBbkhTQUI5RmFxeG9FUk5lZ0xPc3JQU1hmOVpzV3BWZUV1T0p1SUVkajlMQyUyRm5QdGRvanJ0MnpERUwlMkZTS0dqSkVZbmNqaFBDaWZtNTZVSDhQR2dTeVBSaW96ZW51TGk5RndSeG5UT1NkbmE4VXl4dXM4Y3pjb01xMHBCeWVOVEZuSlNiZU9zNjB4c1VHSmRuMG1MNWUlMkZzRzBjMk1MbHBlN21ONGZZNnp1VlZLSyUyRkJTcnk2ckg4MFBYa1pLTkk1NVI5TW4lMkZuSUJKM09weGZ3UXZmNlh2WDhJbURya0NOb00wR0pMRVRhbXhSS1NQZDclMkI5UmE4JTJGWERRS20xMmpmOHplcWgxbVhGcmVuNUlJMHYyUnJQNXpkNWhRbW5HMUslMkYyYTdrUXVFQ0FYd1pUQnBhOCUyRjRqbXpmUnljUHpkM0R5MyUyQnBtVDIxQWc5cGU2UVVLYkclMkYlMkZ5ZVdxV3M4UmVsJTJGRjZpb3RTYWdJSnhIWCUyRjB0UVljRTM2ZHllUHZORldOd0U1TW9vUFN4WmFHYTF1N1glMkY4QTlucFltdkhMdSUyRkFqaERYUHNsTE4yYWpBTGwlMkJMY3htSHclMkZKVmRkWG1lYzVzcGM2N2JNc3gwTXMlMkZXckdhbjclMkJEOXdBUHJya1FkVkZkeE9FZUZHNjF4S043N21oOTdYejd3eVlpZ1dxcE9sUEFUN0gyT2M3WFBJSE8lMkJmdE0ySktPWUJ0NTkzQjIyS0J0RVl1NnlzSG5XYlNqc1JkUDhHV3FkM09XTXBvZ29RUjh6UUhFeGxzb1JyNUp5WkhEUHQ5WFlHWWV6Rjc1ZDIlMkJ2VUZ4eEFTU1IxNHpRJTJGQ00lMkJtaW9IS1FpQVVaaXZqQ1oxd1UwdWxOdUZaY0xSaGtqT3hxR3hWWjIwMWlhaWJpJTJCS2k4a1k2Tll2NDRLalJUcUxOaXFJVlpnazhmRTdlZHpGd2hzNGRqYWRtc28lMkI4MUlyTzZYS3NXVTE0REFockRyR2dKWWZGSDdwJTJCdEhrTjJZU2I5TnR0d0pxaGRLTUFmaG51OEpRYXBEeEN1U2o3eWR3aDg0Qkd3MlVma1hVQTkwbGFKSGJRbXFWJTJGR2VwWTJKME4lMkJsNEVIWTFBT2E5V1NGMFF5a3lISXc4ZCUyQkVtSlFlV25FTWZOJTJCN2U1d09xazZtZjlOakNQOXFhZnVHNkxOcWZGNDEwOWlaRUg5R1BiaCUyRjFNdWZrRGN2Mk1pJTJCZFE3Wk5Ra2Z2WXQlMkZkWnVRSW5EcHl3NVZ0U3F6QmE4UzNDNnolMkIxVSUyQkYwUGlVMHhXSHZOJTJCdU0zb1hpcU1YME45UFFmZnklMkJzZXhmUjJuJTJCa1J1b1BZJTJCbjQzcVNjMSUyQnZIRyUyQlJvN25oUnRUNVI2akpYdkV2ZUliV1RKczcxUDZRQXZzZHF2emlZamYxWkNSaEpzS2hnMHAzVXFmTE5KSDJldWhIczZtdjk4aUt5b1VoSVlVdVVWa3pYUEhyZTg1Mk9oOHRrdFRQVGFiVDJTNWR0T3dONk1oVjJZM2R5bURiSGh6REFPOHZNZ2YlMkZmSFplbHFiMGVPUE9laklxZmhDYm9TZnBkR2lnalpTZjliMWdrRGJwMmdER2hYaFpZJTJGNExteVJwWnp2MWRLdUZmeTFRTSUyRmRnUEN5NnYyNXRybHZGWHh3MUhFM2h3N2xrQWZlUUxLQWthalRJUkRpTnZzcmNkeXZibEQlMkJWMktTUEFwYnhDZVg0d3pUUyUyQmJyTUhtejElMkJpZGN6S0gxRmNVOGprdFNibVNpeCUyQnJEYk1JdWZBZ3hDRHByUmVlRjhHaWNLSWVGcXY5VlBJUzFpY2Y0dXU1eUF2SkZWTmhObG1xZ0lpbnJsRHZsZWpad01vZkMxeGN6Nmlvb1BBZ3U0dVluNDBnT2l3T2l2emV1THRCdnBEV0llQ21BNEVGd2FhTlBVbCUyQjRyMGZPaFgzSDVLJTJCUk91Qnk4N0hMQ28wNkNVS0tPTWx4aDVXV0Z0czlYY2FQSW1QbUhqUjhxNE9yVlNUeXpmaSUyRlZkUG9MR0JmVXglMkJMU29TMiUyQnMwODlxRng0TFJlMkhzQ0haR2JYck5GZzJQUFh2NVNiWlFJcXlLbjclMkZMN2R2aVZyQnl5U3pMS1lXOG95em5PMkNXQ3JpQldoR1hJbFA1MHJId2ZqNUc5QUFrYnRKa2tWUUklMkYlMkJkaHZrSUxJJTJGM1FSd28lMkZvNWhuRE5vdFpVTnJVTzFNNWpqamNFVDI3ZkIwMHhyMVZGRGZsVjJoQVJIeEVnUkU0OTlXc29HTTI1WE1NZXBtRGszJTJGWFdHdDFrNE1pOUs5YTlIS1pMeG1yU0p4a3FRSGFEeHFmN25pN2hZWTRFb3BoaEpCTU5vTlpwV01tQndYeiUyRmZ5WmlPU2k2MGQzbldPbVJUeWpsZnUyQSUyRm05RiUyRlBRZDIlMkJBSVp4dEgzdldOcnVMU0lOOWZsSjFWSTZIaUtJM0NrczJzNFM0RnM1MURCTE5heGxkJTJGYUI0VzFzRVhjYnlxTSUyRk1pSk5wMXJzUmJjc2htUlpCb0IlMkJKdGdWdFNadFZtM1k2eUdmeTh1TmNkMTBhbk54WkJId2RFNk1kU2pUcnNBVE9paDZoQ2poNVpXUW9abERFc0N2cEF2ZTBLTjdVJTJGVWNUSmNIa2l2Mm1aUXZwY2xiM2wlMkZObTE4RElORWFWREs3WGdzcyUyQmlkJTJGOG9sTzZHVUVhb2wlMkI2VXQyT0ttQmVqekhqQkFJWlo5dnF1MjZ6OXE4dlY5UlNRYk5sWGQ5UzVlQ3o5d1oyb0hHdnFTaHkwS3dPSk9mRVRXUjYlMkJsSmxtTDRuVHZ4cEhaTm1JY2loYmxMMXhFNEZ1cWdKejlmamJSOUJ4T0J4RFlXUk1icEMwT1AlMkJ5NUpqeUNUTzZCZWc0Umw2c3VQNEV5Y3dZc0kzVXYlMkJKbEU1RjB2RVdtTTdsOWNiRGx5dXdtUDFDa2F5elVvJTJCa21kU3pPdmZCZERkN3piRlFSJTJGZHhYYWtWOXlQVXRDWVBrdGczYmpxa0Fnc3JoYkVGck5iTUtaRk1pODBtSnV1SHVsak02T1NiQ2NWbU10TnFnSThFRnVJbVpFWVNlNm9Sc3RmR3Rib2swVU1icjR4UEclMkJjbWFYdjlWZlBWVjclMkZHMW9kJTJGNFNPem1KMlZiYzhoeXFPSll6bXBQWlh4TTI1OSUyRnVhd0FiWCUyRmViN0ZJUmJLTVdKRnd2a1lNZ25lMkh6emtyVFdwUmFWazhva2xrSHB3djNyNkNvNXdjeUklMkZKTmU0ZnlaaFdhZlFWa21XWG9FMFRmUnRMRkMlMkZwbWE1ZTIlMkZqTjd0bjlpTjhGYnhHN2p2UDkzUCUyQkhhWmpnZmlWcDNHakNzWmdHUFElMkZaa1AlMkZyQ2JFNUxqJTJCcVZNOUlmNEt1UEtnTFZOREdVaTNUVnA1Q0dYREhaSmpCNVFwYyUyRjRyVWlGejB4QUprb1VzOTI4b2VLcnZBN0NSbUVldEhnS0p3OU4weFhOOTd3ZGVOVnVkQ3V0RDVEWEZBQmloSjhHJTJCa0tKTVVyTUM4MlNYJTJGNmZtV1R6SWFGZFl0bDhsRldZcjN2SEtlWWk3QmZYYSUyRkV5VUxEeGJtYlZKSE16MHZNTzFwdXN6cEtSTFRaRWFrZjZVRCUyRjBoNDd1NjRpY3VMNnpKdEN5aTBOTlhlOE5ZMzZtcnVBTFZDSVM2OU8lMkZDUUZMZm0zdmM0NmV1QllSZ1h1bmRmQlYzYzhmUmZUUXZWUlI5TWMlMkZid3E0dk01ZXJ0JTJGOFFHSUhjNlRwZkxrZk05eVIlMkJtUGE2Mlc2MDJla1IwMW1XR2V1U3g5QkolMkJRckpqSWFtRmtqZ29NbjlKUmNJY094M080JTJGZ0luclRhS1glMkIlMkJ0cHFBbTFYWElTZWhNSjRTUFI4MW1pcXpOek5nT0JiSjE2Vm9GNU04JTJCUVZ1MjF6ZnFHTmxxayUyQmpacTE5NnZXOTNxOUI5dEV2Ymw0UWVpZGI5SEJsVEQ5anFURmtDT203OFpqOExhWSUyRmJ5MmdySExHRmltY2lBM2VvcnlMRWtjV3RlaDElMkJsdXVWY2FBZHpLZHpoSlIyaEhlOENIRWFScWRiaEozNzBEdXhMVzk2b25FWklHR0pySGVBM2Zod1FFJTJGdXRTWWRDaEo0UnFMY0hneU43ME5ZWWg1YTJTaWR3cDZueVpuaXY3QnVYYThrenFaNDFsTXFWTCUyRjAzSDZXRlpUbk9sY0d6NFAlMkJHMW9ISU1CZ0JZTXJpNEhPNVVuWGglMkJ5Z0JTaEhwOWt1NlRBVTlJVnNJUVlXU0IyVjVJdVl4bnBKU0JiRWdiODR2clFtVEV6eFlxVzlHQ3dtbCUyQnA1M2tRVFprcHhlRG1LdXRjekIxVllxeWU2QnQyVG0wN3VDdWJpJTJGN2dPSThHdkRmVnNCN1p2SThCSU95SE5MbUYwVDJmc0NPZXlmaFB0MUx3M0ZpOSUyRjd4TDY4UmhKbkI2V2pON2JhOHFkd3YxUiUyRkUlMkJyZW5Cc3FjTlQxRjZjUVF2dnZMZmVTNnN0Tjh0Q0hjVll0Q2c2WGF5ODNpTEpDSkYlMkZER2VJbldHbEtZYUNVUjB2TFdwJTJCSlU2aURwQVJoSVV2M2lHYktYJTJCdGtPJTJGRm5zZ0t1bGk0Y2FUbE5KMmZhZk53MWlNaUkxRGkxTzZraiUyRlFrbW43NXh2YnVOcGVIRnJ6OVI3Q3BwUnZuRFljc2E3M1ZqZUNwUFlmbk4xdGRPbGpBT2Z3amJYbjhoJTJCNjhEckg0a3J4YVJYSWIlMkZoV0kyR0hYYlZZY3NOdExBeWJHQW9xbE81cWhoaFd3MU16c1dwOG5SWkprejJHYU00biUyQklrMXd2WmxyVnl2d3RCTHdXaG5oRCUyQktwNTlJUldCcmJObGhxS3F2UEF0dFlOTDBkNThMME9GcTJPU1dXWnBOSEFienJ6MzF3d1JCMVloJTJCRFkxMDB6bks0V0tyV0tVdXlvNDJ6TUZERVl3JTJGWEJZZzFEUXc2c28zN3lyRzNpbjBxNEh0bzNFOCUyRnZkJTJCTUZWbzFqcUc2UjA1SUs3UDRqMnAwSGMxVFhkNjRRRVVyRGtTZE9wNTJ4OG5LaU1aMVI0OVY1VHplMEhmYkpUVVduJTJGVk9pV1FCeDg4TW5BcVFCUDYwdUFZc3JKenBNY1VxRVVYc2J4VyUyQm1kd1pCN2hnbDhjRlQyVncwNTcydFIwWjl5M1VCZDJlU2R6VWNwdDJwUUtiNTA4dlVlVDBBa0hZczhkZHYlMkJSJTJGbTAxZjJHeTBkRkNLUXFicWpsMnk2NGNXMWxBeGZJQWFNdTBPWVFWUEdPTlkxZ1dTcTdqNUVXZ2lJSzUzcllmQVJRT2M0ZUpDRGo5dCUyRjhZSjhRZXJKV1hDTTVjQTBaMkM3NWh3Y04zem9meDhwYUpWaFp5M28wV3NVYXlpY0VldFQ1JTJGR0hRaGJiTnEzN2k1N1RYZmxpallhS24lMkJxbmRHTWdqaGs0TklkbnU3UVJ4OWx1VHFCMiUyRk44SFM0TW02VUJxWkxIdnlQcWJkdktKNHlaJTJGaENtZ1RKNUJIUlZ0UDZPeGZ5UFRUTVBGbVlPU1VPbHhxMiUyRjRxQUl0aWFpeSUyQmRQRUIyU25aZzBOUzJyVFU3WEIwYTFzJTJCZkZBciUyQjJvaVZ1VmRYSkZLUjBKN1VyYjFEVmVxOHBCamhKMEt5cnVzTGNUalNHWVZIN3QlMkJjZ0VKZ1BLUiUyQmRUS1ZJQWFsOE5LbHJtNXJGJTJCYzBmR004RnQ5NTBmeU1oMzdORHp4RFg3VDNEWmtJdmF0VlZDM0xPZHF3ZHVrWWw0SFJLWmtnY0k5Z0ZDYjlVd21LMFJ4MUxtc2xXOVFic1pvdjZCWURyZVlKUEdXWmwyRldiNDBYbFhpVGphdiUyQllXZzhReGVqSEl3cEpWYmF2WjF0VTMyVkk2OUdyY1NydThpcmElMkZ2NU8lMkZQNDdQN1JMemN6eTlreGQlMkJJQTVYWFpOV1RSVWRsOXFCcnowN1dvaVE5ZEZ1MG1lZ1lGSlhXVGRXZ2l4ZnY3Z1NicFhoVzJ0WEsxOVV1YzJ3U2lUaSUyRmozMEE5NFQyWUclMkZ3VyUyRmh6cmxlNnVrWlB4ZFdJaUp2QUN4MnNwaXZ1TTUlMkJVWWYxelZWQUhMcTJLejQxSnluY21jQ1ZzN3B6cUVuNGdZcTdMYndoTUU3OE9VQUFmZ3NKNkgxeHRGaGpUS2E2cDc3bnhiODlEdHdTYmJFdzlpaEhyUnZKUVYlMkJweTg0dkN2MlZwdnBVOTVubVZnbDZvcUVFUnZIdXlYZEdMZ1FqcEpCUzlqY3clMkJXZXhjWTdiU2FKaFVjTnJZNU85Y2Z6MWQlMkJMODY0MFpHYnFPSmFka1p1bkJsOEdzVkZWMktTZGp4U2l6WTkyMXV6anZDTzFiVHhjJTJCbHJWOElSVEtUd2NrSnhCRkFtZFpsM3NsdHNnUG5qNEVIbCUyQkVPRExIeCUyQjlsZkxsemM4UFZLRExWOWl4aTVxeXE3NVQxZDI2R01qZ2o2OUtxNTI3QnRKNDNoTEQ3UyUyQk13OTIyM3lsb0ZrbmpTUmlrT2I2VHZubkZoUjNkJTJGclFGdlFOWVVuaGxlQkdKbE04OHJHaFRzNjRxJTJCajBpUUhXeVl2aDVZWXRwdXBORFFsbEVFeUZhclVaRGdPQUJySXhReWFVYTlqTXZrV2tiSlQxcmhDSjVoTHklMkZRcmw4aGQ3VFdpNE9VbHMzZlNtY1lIREdJa3pUd1cycG40bCUyQkwwVDVveWlQSDhxMlJDVUJ5b1hxbXVVWlNPZiUyQmVvNDZqejhSJTJCaGRYNU9aJTJGckcwekZqTkxGRTZuTzBJN2RJRTJISlU3MDlzSERQJTJCblBaVXlLa003VWgwT2Rud3gwa0Y0MiUyRnpJMHAlMkZtYTN0YzdZMFFSWjdPNnhldllmT3RPNkYxRDFuUFFHJTJGdmFsdDk3Z1pmZXZ5Q3BjYUs5NGpxR2JwYUwwbjAwTXNWZW0lMkJ1QWZZN3d0SVZqbXRFN1Y4Vk14JTJCJTJCSTZaR0UlMkZoYk1GbG5ZeTV0eTJOUlMlMkJENkZBaVRWbzNLdHdBNXJ6UkdkMEdqY2lLUm9zcnI3cUdRb2lvT2xTU1R0Mm9rejZDc01iN2I5WXJNRE5MekFuYzIlMkY4V0hCWExsVW9EcWlIbzVlWG9PTHZoZTVnTloxeG52d0NnVU1LMDYlMkJVR25EOGdZYXU5eFRYeFh3NiUyQjg0Rlp1Rk8xNmlwQTVRb3E3ajVCUnhOeWUlMkJUcWF3TkI5ZmxQcFE1dFRNNWRMVmJHcmQ2eURKcDE3QW9EOUlMTDU5ejBYMU5nemEzbWVHeGdHODZvYnh1TzB2V1Z1ZlFoUnhxVUtoOVNUU045VFp5Ulc4TWUxMSUyRk1tdVJJY216akFFUVFCUnRkc1ZtYlBvem9tTlJNTjZJNnRHV1NwRm01NnJUYTdBMG1hWGZCNjlVRFloMU1ZaXFqblhSQnVHMnlmNHp4M2FMWW1SVm16bHZoOTBRMUxiJTJCVHVEQzN3enZlbSUyQndRa2NIU3VCVzJUaE1Da2clMkJJYVI3SndGWncwUTJ4SlNuVDl2dEtPWkJPZ0xlTENtRWg5Skk2YUNwUk1WbEtISlJvSFFUJTJCM3plQ2tHc3FMNzg0dUNUbmRjbmF2ZWY3dWwwNzgwVmhqS28xYnRYOXpxZFNOTVN2MzdCTjAzJTJCa1Q5SERWUVgxbE5VYjFjV3dYUERwVHNBSFd5RExjak5sejhMQkxHdkQlMkZ1R2V2dXZ1YlRtcW9kSHVzdGpGWWJFTTVRWUo5SGZPbnhWbSUyRjNUJTJGSGQ0b01OVVBTZ1BVUEs1cDVqS1h4NVo5bG96aGRndmY4eWU2RW1lbVpHZFdPbjVWaXhDdDBnNXpRZ21WOXRVbm9lRGllVFVoVThDclVOejdPcnhkNmwwTTZSUG9wVUx3U1ElMkJjT2VSSzJ3cWF6MFBZOGNna0JtRE5rZXpIOXprUTBGaWFnOFQ4RzlMaWdjeHh0bGRaWjVaQjYlMkIxb3Y5aFhScEVkUVVuOVdLejR0dzF5eERtNjVzTVhmY2RONnNtQnpVVnNvJTJCckdoSiUyQmYwandla3NrWDElMkZKUDRnUlltZ2NtNlZNb2R6NDZoQjRZemNVdlJtdGQ4blZtcGFzVVgxOGt1NlZRT1ZjUGR2JTJGMHdjY2MxaHlqUXV1enh6ZElxR2I0dXhPNGVtSWtRbE9LQVZ6UHZhQWh2UTk1ZlBkNmJKJTJGNG1WdnN5WFRFVEhhRm5oQjJITmdHOXE1NW84ZzAlMkZkaVRNWEczejZYbkclMkZleDRnaXhzUk1QeG05Nzl3MVZNYlZNSFJoYWxjcmM4QjclMkZxS0MlMkZXbjZPJTJGRlUzUzB5R3NsVSUyRldnb1M5OTlFYWY1WnR2YTFWNVVVTVpTcGk0V2dIVXI0VGQ4ajlzSUFOaHc3U3dNZjlZNkpxR2ZZJTJGMFhadHp1cXR2bzlSR2xPSlRnaGs0YU1lNHBqQmR4aHVCbTAwT2lQSTFEM1gwWkVGOWtuUE8yb2drbmNseGpWMEJna0pSZVNYYnpSRFVvbUhqcGFOWVZEYzNndFZDSTVCM2tsZnVkSGhyUWRKN1F5RTJWJTJCJTJCM2VOT2ElMkJLaGR6TDVoWTlEa2M5M24zaW5MbXdCcmMyJTJCUDh5Y0pEVyUyQlplNjJiVjF6Njh6S2k1MDBEd0R5U0FGJTJGJTJCZ1BST2QlMkJmTnRheSUyRlAxR3YlMkZVZkdmbTM2ZHYxNWNOYWo2QUpJSlRINEVIZm0lMkIzUmNpbTIzOTlib3BlRHZjeEY1ZGdiS0t3RkVoeDdUdWJvZ21rekVkeW9XMExlV0t5U3NIYldlNmUxNzJRWTA4VGUxU0hBalRxaGZ2R1I5MzM3a0NmU3c0dG1mYzAybWQ1Rmo5NFBjYyUyRlhGYjFUSyUyRlJMRTNnc0I5dUNaQ1dYYWYxUHZLeTMzQiUyRmY4Nk4ySmxZNE91RSUyRiUyRnVvVjlKMyUyQlRoc0h3UEo5STBNZGh0aFdncGYyZk5jYnczTHc4dWNMME5ISVNNV1U4WFhTUHhJbTJEeWtiWVZRbkdXaWtwTyUyRkphckdyaW4xZk9RWWc5Y2t3ZHltZEJZRTRUJTJCOWhGMllVYnZEd1pwcDJEZUpqdG4wbDMyQkdMUW5Yd1pjdDB5VlUwJTJGV2FHYkxzcm9ldFB6VHZGbWY2UUFseUslMkI1RklCYkM1JTJGZmZxJTJGcmRlWU1WUngzSWxTeXk0bkkxcERTSDRjcjBtdkVOU1RSZXdpTCUyRm51a1d2SmFvaSUyRmJla0ZBY2JGaVlyZE1lY25MNCUyRnEyb0tWa05ObmdMRXRYWWRWSUdnWEZiTjA3cnZOSnk2ZFlFd3A0Y0J2eVUlMkI0bXNnZXdiWXRxa0tWdDAlMkYyYTdGJTJCQUZLSjlBVzhKOE5KUzlyNmtBREVVTiUyRmk1T0dPQjl4UE42czlORzB6ZXdPWHo3T1VzU3glMkJ1aDNzaGN6ZUltUlU2WmZxcmJ4QWgyZ1pxRzVUV04yTUtRZW5QJTJCMm5DMmpYSUEybUl0T0tQMmxwV3I2dnlvUFczVHpsOUplWCUyQlVsUVB3R3ZjUUtOV2M4UHdwWFNBJTJCd0djeUFFZXpCRkduN1pYTUo5JTJGOEVHN0RTd0Vwc1hPZVUzblp5JTJGUzU2c2s0djVvSFlSYmElMkJjUjROblRDOXRiVCUyQnoxTlZTdmVXWVBUQUZONE13V3dpQmJmc2xJV3Y4R0olMkZRYTAzSE9hYWQ3b2NBdDhFUGk2YXVzSUp0ZGxDS01MZlRWa2gzU1M3dnZGMSUyQm4lMkZ1dnclMkZKdmhTa0VDQW9xbVdlSlgxcjhUazFzOCUyRkdUZmh5RWZiU2JveXh5WHgxN2F6QjRxdWM1UjFHZm9SMVFQRlFZdkRVaGw0anlWcTZpNFFkQWprcEpKSWtxQk4lMkY1OFA2MTRrQTdZUFRVSHBjODhjZ3p2UzYlMkZtWjI4bEUxWXJFZ3gyeThMZjlmaWR1dGFZeDRaTUlWWHJWakg4JTJGZ21xVnpqcTBhMDRWVEFVTXJheHFINWJERExFOW9zd2JoNllhNERBaklTUWxTaURtQzhqSnNkdUdpZTBwZXhzSWxPSTFVYldWS3p5OElnWjExeE9jMTBkN3F4V0h6cnZYdkFwc3dxaDFCckl6YXVpNThIMjl2UHUlMkZhVzVhRURYbjM1dGpEOG9BTWJEN1dOJTJGZ3ZTWHZyNlhiN2tIeFQ5M2glMkZKQ09kS09lb1d3SFZmM092MWh2cXJ4T05IWCUyQnpIaXprdSUyRjczelRPV0NLa2JzZHY4SDk0ODc3MUc4dlZIeEozNzF2N0F1cHlVaG1nc1N1YkljajIzaWRNN2MxaiUyRkhidnYlMkI4cE5tb1lDR1R3Q0FVQ09reVloaTJObjVPV2pqNDVqeUJJZjlxTHpuWXBzcXRqMXQ2SkFHZ0FrVGc3VzU4TTk4RTMxc0FCWGJEejhYMDZIOGM1RTRaNTQ5WTZaJTJGYzhnTmk2S2p5bmN2N2Z3azI3JTJCVnNpbnJqZ3dzem0lMkZSSnptemFzM3lkVlZzaDVRRVdmWHFrVU4lMkJ2SEolMkJXMW05UjBvc0slMkY4ZiUyRmV1JTJGUlh4JTJGN1lCem80dlV6Y21QVkZqS0dRbXBrJTJGJTJGbiUyQnNNYkRQUkdCaDN0JTJGVWg3NEpHMXZ3WTFFOW11MVFyUndnT2FyUk1MbVltNElkNzcxRW9HSUlPJTJCdzFXZXFjTE1UZG5mVHJNdHg3WVpKUFRKdjhHczFFekhYMVpuYXpqS2YlMkJ3UFJRZlhsUEdmb2pIU2g3ZUNoQU5GSGc5clY5YiUyQkcwJTJGMTBhQ3k2dEVScjNHd1JQZFBVSzBBS3B1RHYxelIlMkZzZloyOGRqejQlMkZmQ09sd0tuT1NmZHMzNWNNJTJGNVZmeWVUbnIxOE16MnYzcmtpaVhoUjg0cSUyRlN4UGVTd3Q0aSUyQiUyRldlUm1PVWZVcXRVTnByb093eEo0UnFzSGZUSGglMkJQMXlPemJrTmFqY2VNbHNLTlAzTkdBb0dxTXZrdzhqbEl2a254cU9IQSUyRkhuRWVwN2NiT2N5a29TUkVacCUyRnJOSFc1S25FNm1EN2NQZnZmQiUyQmVQNzM0U0lwZHBkJTJCVnliaHh1VDl6eEloWExGRVRFRjd5azgyNDh3dng0RURqdWw4R25MY2V6N1NQVjdNYzdEenlyUzlhMk9YalBpUE0ySiUyRkgyME1pdElrUFZOQTFZWEMlMkZ5eHhhc0xTcUx4RCUyQjYxUHFuV3ZmWSUyRnphOSUyRnlqJTJGazFmZTk4NGZ5S2lscXVjRHV1Z0ttRUklMkJTcFB2aG82UDNvM3M5eTElMkJWYmt5TW5wam5tJTJGN0ZDejElMkYlMkZyTEQ5dnNGdWhDVGx0WEhydlRUUnRyMkExOVNCbXFmaElGbUh2aXVZZmptMXZKWCUyRjQxNiUyRmYlMkI3MXEzb2xqRVhVbFlSUyUyQkI4YkhQRFh3V3FXMnUlMkZIJTJGSm5SYXdEcEQyalNWTWdzaDRLQ0I1NVJJdjVBOTh4T3Z3NW1wMGRnVEF4d3NCZFZSY2ZlSEVGejExUGZPeE00V1BZZks3elIxd29GcklDSDNNNFB3JTJGZmZ5enZmeTl0ZTkwcTlmJTJCN0ZySk5PcTE3JTJCejcxcTJKd3psJTJGdXZoNmVOR2I0ZWxiek81Vk1vJTJGTThPJTJCJTJCVzFRJTJCc2tGeG1yOERUN1AlMkJoUng5M3JYRGxNSlc3TyUyQkFOR0ElMkZ5YURSJTJGdEl2bCUyRnZSdjI0MVgxJTJCSWVnc1FrJTJGam45V3FIbXZGV1lWR2dZWDAwRG8lMkYwRVA3bEplNE1JWCUyRk80dzluVXRHamRNMFglMkJCNiUyRiUyQkppZUdvVENmSzdOVFJjTUolMkZIT3NQTXhxeFlVUGdXRSUyQmZRTHdMVmlLb3VmRUxzaiUyRmclMkJoJTJCdkRwejllaDFMJTJGM0Y3TjVqVVB4dmNUTmU3ZHJiQVMwSE12UHklMkZpRUVidlAlMkI2RmJJTTZyTXFuU1J6SFU5bHIxdjlIOEQ2OCUyQm8yYVY3SFN1eW9rUFhpODg4SzAlMkZnRnJKZ25iMExUa1FiJTJGTDJiUXFUd2xmclJGJTJCajhzOWtWWmVVMUZtSUJiJTJGZGVuUlpUdmF2a0IlMkJSMWh4d3Z4endZN1dBTTJpUFpRbUh5WSUyQjdYdSUyRjJBRyUyRndMV1BSMEFzUDZEeFQlMkZEQnBDbFJmJTJGald2JTJCODJwaGZBaFVNelBaQ1ZuVDlzOEVYc29BTkh0cTZQalBYdnclMkYlMkZQNWhodklCVnY0RGxQJTJGOVEyTTRxQ2N5N0xMQ1gydiUyRjFhczEzWEdqUk9XM2ltbVA0cndWdSUyRnl6d0t4WEp5RXZhRSUyRndQYW1TdmE0bmRuMnY5QjRmJTJGWEt2dzVnR3ElMkZ1dlpMeUhvSnpjV285ZTFMT1B6endMeEZGZ2daUkszQ1FJWjhWJTJGVWtFUGxkYXdYc01SJTJGT0V4Ykx3NFRiV0Rnd3YyJTJGWGwwRW9zYSUyQmdQViUyQnRjcFV2eG41endaZnlIcHRFRFBnUHJmanpZRCUyQml4czI1YjZRdFFQSHl2JTJGaE1BMXd1SDlCNjliNiUyRiUyRlZyWWpaZXYyNlNqUzc4OVUxZCUyRjZ5d24lMkI0ME44N3MwTERxJTJCbzliJTJGVU9OJTJCa1dOUDdmNkR3N1RlVE55cVJDaUFMVCUyQng2dGYwSHE5bW45VW1weVU5dmhuaGE5YmRWUldqZCUyRm9jZ1lJJTJGVCUyQll3YjJZOGY4N0h4ZzQ3Q0NIenNLeSUyRnRvUHowSmxod0ZWaDg5SU5DVVclMkZDTjFIVzZBbGpnazZhazVaT1VJbWwlMkJuNDVPNFN6bHIlMkJzeEh2eHJCbTVHRG1LYmNFZFcxZ05tZXQ0MXZ4dkdOV3c0VUQ3RFpJTUY3NFd4a2VYZHRIN2lzMW9lM1hIcmw5SHhnTXdMeXpUTHlPQjI1aXVJRzVuN1pteUd6WlhVMTklMkZDaktFWjElMkZYclVPVndINTd4JTJGZSUyRnY2bXdPVkV4d0g0bFZzZ0Z6dWNiakx2NjlNSkRmR2xvbkttOTYlMkZIOGpYWjdLQUtzQWRETlI3MzlXc2U3UEM0RSUyQmZOaXNLJTJGRXhZNEpoaUI1WTlMS0M4dHhOTFNTSjN6RFliUklENkYlMkZrOThnMVFYb1lLVnRrN1FjVXclMkIlMkZydWFRQiUyQldrRHRSWGE4SzN5V3FWWHVHS3FURWZCdXJ4QkdMVWVOUjRlYXRxZ2tBVVgzakhzcHFzeiUyRmFZSjV5anlxRkNBWWwxRlM2VWhoZGFsaUVKQ21CaFglMkZhS0JBUTh2OWtrYiUyRnRZSXZwUHptRnB4c1FJWkVYbUg1TjBIQXluMjh0bjh6ZHc5eDZSWm5rMUIwRjhUQUpWMG1DNkZCNFVXWiUyRmRqdVRkZXVONTN1d3F0U09vRzB4YlhBOWRSZW4zSVlDZHV0NWhYWnAxT1EzZFJodGx0NDZaV3JLJTJGTGclMkZsOVZMJTJGdm5uQ3JJb1FpVm9iTDFjSkslMkZScUNQQmU0Q0JOOHVIWUdjb0ZSOEElMkJHUEJ6UjlkQ1BUS2ZHODd5UmQ5M2J3OURCbXZZaldpUmRvc2ZxJTJCOEE0MWxzN0lMeVJwdEl0NlFkbkVVYW1rdlZsU1BuUldoJTJGSEVpRmdkVDRwc25zdVluMFpZNG80VG9lRFQyRmklMkJFR2l4dlRrc04yWGdNQ251cm9MNzlqJTJGYkp0dm5mV3doNUI5eEMxdEhtOW5mWlk3TzlmWEdlaiUyQmtiNVpVcnBvanRRUmhSWEdkSUNibkg1WFdReWZYJTJGcXE1JTJCUGNoTW41JTJCNDdyakl3ckRGdTdoM01TVTVvRDZxJTJGbSUyQjMlMkZldWRUQVNmUEluN2FZemNraSUyRnp6Vmt4Mk8lMkJjdlNpSDRUcVNSelFrMTF5a0NWJTJCTWpubHVCR1kxd0hKJTJGdTlUQlAzbmFiR2JsZk53RGg2Mm01bDRXUkVLd2ZVWnpUNGZjOGxMWkZYZUdFOEdlVWFXV2YlMkZRTllIQjR1OFRjclpJUVgwYmx1RHdnMkhsN1BBM0RidHc3WHRFVDJIRno4UTZSZTlScElJUWh3JTJGMjhFeGI4SU9wZ0hmMGN6MVN5QkdvVEgwJTJGMlF4ZXhpb3lERGpNVzM2ZXh1JTJGb21ueEVzVFUlMkZ2VmFVUzJaSE1tZXo5JTJGdnpoT3BEb1llanNNR2RURmZkVG5nVE5tQzc5YzJmeiUyRlNWdUFaMTJmZVZXNiUyQjV1MWpUc0FLVXlvZXNvdUtET0dPOVB1c3cwam9qbXVla0R5WGdBc2UlMkZGaVVHYVRrNzFIb0xDNU91cmhIVHNQSFVjV1pxVk9yM0dZYkJsaUhYaW9zQlolMkYlMkJJUk9ubGhJVFByJTJGTlZUYlJSbDhjMHNUUm54dSUyRmRvTWVSR3NqbnlPc1clMkZYV1pKVURTZ1hQUmh0Vldyd2pBU3YlMkZrJTJGUzdRMTM1WUphd0xSMWZyVnpoN0p1Rko3bE9oN2hYbmxvUVJZU3U5NFFQNzRjdjVhVzZ1JTJGc0l2VTZiRWFxOXFKRkZhJTJGcnQlMkZHY3Jlbm84MFhIS3NVbGZsdVZ5Q2tXRzRSS0VrTUxFTnk3YmlhUzM3czVZZmhVaVAlMkZwMzgxMllqJTJCd2VGV0x3dUR2dTZRMDZhWjF4SmVPbEhnSXpRc0lqRTMydGQlMkJqR0FrZ0VmaWtLaSUyRnpabEtvNzVRQ3pmb0txZU9WOHd5VjU1M1VtNUhPMjMxUnJHOU5vSHl3cVdTTFR5V00lMkIzMmNZdyUyRkNsJTJCZ1RFNGtXMmxqT3dQRHRQcEwyazhqWTVUeWd1YmVzTnBYWHAzSzEzMGpET2hWd05ObWgxM3k0TDdqWFY5a3p0NVpOM3VYUGpyQWhPJTJGRnNkZlpVbG5QUXdLRlJrT3Z4OEs2OENTQk1aOHB0TUh4NHh1RTNMJTJCSUdiaUxKWU1odFhEWGQlMkZRYkJ3c0lyYTBKNzk4a0F5c1VEJTJGU1I2Mlg2MU5HQU82WVNzN3lzVWNCMU9hcVlvMllrUFprZk0lMkJhTkRoJTJGazlQOUxubER3U01KVDdZZFp5NnV4WTJad1lxWkVNeEN5YlJMa2YwS2JpR1hFdjJVdnIwWXlwVGZyMDlQdzNlTTM2aU1yMjlrSm91bWtoUUNLV0Q2VHJ0MjhGdHNLRzkyNmElMkZlSmtITkx6N2xxdlBJJTJGWlF2bjBpUDVkd3JTZThza1NMVjBOY0R5WXZqZlBiQnVkSTJYdjA2U3JJeVFxakxvR21uQVAyOWduRUV2RFhyNWdxMUQ1OE9Jc09uS1RpZUxOSTgweHRXJTJGa2J6bno0b0tyMGV3NWl1TkxwUmp3aDk2bGE0REJ3aU9NazF1dFpla1g3SVBuREJvRXpQbjhoQ1BsQUxFMWRnc2dHallsRU1aT1pGWm5wVWtNOUklMkJ4VUkxMzNwOGR4d0syeEdXRE5mbzVSdnpFQlNpRzc3WjEzcyUyQmlKVHhCaSUyRmRsczczSEJRT1FTT1I5U0NTOEl4SmUlMkJTeURHJTJCekx4R0RQc05jVXdFNXd5V3BvekR0UjBiajVlR0x5cjBjbkpUa2hLbnJrdFFZQVJ5TllLZG40bzBiMk1jSkp3ZVEycVhwRzFSSEUlMkZDMTBpNUtrWFJBUnY4QmU0Y3JHQzV1ZnNSODExejBPM3pLVlViQkphb0RSMWZPd2lYenZTVlVOOHc0UCUyQkwzQk4yVkRldWN4UnNrUlQydEM0NG1XOERwTVpPeVBxcDI5UGpwUzNJNGhUa0NUYUtKbWQwUWFmNzJDVFVYVEdrSWFlTktCYWJob1ZzVnBqSXBZV1N1Uk5hS3ZWYkxWY2Z1dGFKdExDSnF6ZlVWeFFwMXlCNHBZNW5OYmh4WGpHbW5HZmNxMzlMazQlMkJGd012aEVWc2VkcWNuMXFzWjJyTDZUd29PaE90UyUyQnZ4Z0JBemRTNnNBQXZvRVklMkJ1aTlLeEkwb3h5TW53cE1vVG41WXVIWW1pZGhBaXFIJTJGbGN5S0hPVU9OY2lhVzhYQTFkZnRBTWRMOEFKMk5tVE1HNVI0WGxORSUyQkdpJTJGT1lmVWx1ZUJuT2hWRGklMkJ2N3N1M0MxMWFQSXRFUUp3UE5zJTJGczlTdGQyODJyc01MTmZGYjhwNWFZRHFndTBrVE1lOTk1TDE2VjMxMnIydTlQdEZvWnklMkIxUkc3N0dMWkl4WFBOYnM0bkJnJTJCNU54WkFEOWlOb1JOOGx1V2c1ZTlNTVQzc0F3b0JQWnJ0cjhYd2xKVHRIbk1iQ1VIM2dlMWF6UzlweEZVRnRuQXNaOExyODhUdDlBTjM1M3BHUUJOSHglMkJqJTJGZ1piT0hqenVTME5PbnR6JTJGJTJCZDN6VGJYaGtkQVZaYlRYRkNDdnczMVVUM2xmTEJkd3BFbTVrejRUU0RockhvRVdLbGNGbHp3ZU11VmZnM1N3UlUwZmtvZDYydSUyRnJlcmwzc1BCVENUTmVGU2pOODg2QkxHJTJCJTJGUnAlMkI0VzdvWiUyQkVQOFZrMEt5YUNJY3FkNGJaMDY3cmdEbVd0ME1tOHJuSGd3VTFXMHNkTnI5dml2MUZBR0RjNXE1TjFrMnkweTdVSFQlMkJtTVFNWVd2eWdKaiUyQmtXRENHVXdCRTVxYk1RVGtpVyUyQm9heFUlMkJ6eWZIQ2F2SlF1cE43Q20lMkJHdWhTNkd5dTVFVCUyQjV0WXc0WERZcldPVFZmQk5jU0FreVR4QlBOeVY0OWUxbFNUdzhSMnB1S3haVHB6ZmdaMWVFczZHT1ZsSCUyQmpJNFpNcm9TdUdGOUhzRUc5Tkd1eEx6U0F3Y0tDTDVRZ2slMkJmdnR6YWY5d21BdkpNTW5TcGlJbUtnOVViUEtEQ1MlMkZiZEx5NFFJMGFBRkpTQTJsWGQ2bDdCWUQlMkYlMkJhcXpubHh4dUJYZDEwTUdCalJBZEtjcmVUMExKcnNzYnVmYTQ4RXVTYnpzVGFUQ3BXUEpDJTJGVnVIdlp3cEFnSlh6RHhFdU03RnptV3NWN1lBa0pjRmpHMDhpajhJWXBsbUdZbjNPMjA0bUo0VlZMNWNZWEcxWUQ1ZUtMb1c1RHZVbG1YVHc4amclMkZLNHl6NlA2cXl4eUF4JTJGMDRXVHVFSVhxSWFiQWZHZVc4WjJjcTZUYTVyaUpjQ3dKblFMVTVmck5VckRqbFolMkJxZ0NKMUh5b1BMOVZpaWdYWiUyQjh5bSUyRm9Gd3l6aFNIR0RmQXFvOTFCMXE0MFJuVmtzWXc4dXNZbjZjTjZ0dXhnM2RTbTNadmJNekdhQjZ0THprdmgxSWxva09WVVlJVTNWenFPaXAzRHM5VFlPcDdkbmI3VkczbGpiUm85ZUNNak9uMDdnVlVMaUZOcXNraGhOaFFzYURvZ3F5Q3d3RTAwOVBYRTExU2RMSjF0WXllS0xrenAwZHZOVW5IZkZPUWZWRm5HSnMwQ1ltOGk5NUVxY3F2UkxsaWVOaiUyRk5rUlpzNDU2RTIxbzVTbW4lMkIwM014Q0o1SzZtdGRCb3FCJTJCJTJGOHc5RnZVS08wbzcwcCUyRmFkT3FtYlFvRUtlaTkxTyUyQkpad01aVmNOREFDeEpUY3NzR2haelMzYUQ3M09lblhUdHlmbGJCcDBWV2pJTFlpSXI4UndpREkwJTJCcXNyNHoxSFd1ekV4MEdQT3hSaUluSkZCT3doWFd1YzJ5NFl0OFpvaWU4d0VHM1ZjN2tyZCUyRklodEc1ak5ZcXpRMnBKejZodXZvYVVObFJCWlBJb25ITzB2VXgydzVIJTJGQUFrUUtTZnlZcVZka3lwM2RSR1hsU0pyaiUyQlJLR1IlMkZuNiUyRmh0d005S1dEeEJNdVpydXJMSklxdFJoamlWUVk2TnlVbmRiUld2RkRvellXVGdlSDB3YzZaZzd6ZklLWVZDQ2VMZUR3MyUyQjIzTGNMWkc3a2slMkJDMEZpUGRNWnRDJTJCJTJCZjh2ajMxM2RkUHIwJTJGWHZjeGJQOUx0dG81UEczdnU3TllMZnpZeEtvVWJmYXZQUTJGMmxWQ0Q5V1lidVI5N3gybVVCMVZnZHc5dU4wbFRDY1NPb252WEVpeGRzb3U1OUVCSDF2bllHMzI4cCUyQjRNWkVKeDE1Wkp3c1VPUEtDVHVUSiUyRnBSJTJCbU9Wbzk3RkpXZ2VueG5DeiUyRmdUbFVzZGxSTXMlMkI3YjglMkJRWmp0ckxFVjR4VXNOS08xQjQwVXRUcUtaMzlicHBJUFQ2b3AlMkJUeG5XaXFYU0NnaExPaGlJNFpON3d1d3VNb3g0Y3lyJTJGVUtScDlhOE5XbXNwY2xKSUhFamtSVkZJNjZWR3VOUTFueFQ5M0NqTVNiT3JRJTJGVmUxWU1VN1p4RUt5OFpOMnZFaUlhZlVkUVNEQmswS3c1VW9waEozczBUbmxtSzc0RzdDeGZ0VWJhWHp6Rm11NERsaWQ3dlFpdXNqdnZFd2dNUmRsTGtkJTJCaSUyQjFhSmR0Sm9BaUJCREZOc3pUdCUyQmc0VHFENGlNWklGa245VHcwRGlIOFBLejVVNXBHVUZvSXluT25UJTJGTFh5TXllV1Y1bTFtZ3J1WXBqNFY3VFpuenpOdmx1dlY1ekdmc0N6Q21mSkI1R2VTRnY4Nkl1MkdiRXhtUzZBQmMwb3h1em8wMHpYVmxlRGxFbjZnNyUyQkVRbjV3SyUyQll5VW12UGZvYW9EblFaVzhmQ25heFVrZzdGUDVkSW52JTJCeHRib3VvMkNMdnk3cUtyTHpQQ1VFbHM0bWZJeHMlMkJoNTRFekdRZjBOaDlka2dUJTJGa3ByNlZObDZvOSUyRm9DdWhRUiUyQjNnQmVkU3lmZUU2U3A1MkljRkZnJTJGY0NyNTAwUFBwOFdjdGVzQnU5ODhDbHZUYTN1a081VnVoYXZySXR3MEk5Rk4wR1NTSndEd2JEZzc5QUtjZEUwTXo4Q3NBRGlLeGhrYzZWZElDU1hlZmlWTSUyRjJ6akcyMlY4RzhSUkZsWks2T2c1RlR1a2IlMkZlVU5GQnhhQVVWWjl5eTNnaFA3Q3VsWkZtSzFJbmMxWUM2dUNFWWM4OEd5SG0lMkJNa0dsOSUyQnF0R1cwQ1kzbkc4eVBvSm43ZDJvJTJCdSUyQmo5JTJCVDJ2ZGIxWkRWN1V3eVhxMHVpOXlLTlBwTXlraFhQRFVzZTE1MHZQZDgwM3h1ZEh6UTIlMkZmakw0VkxqaldCNXlYUXdJbkpUYURHNDU4U2QzZnVya1VFekRHYTFvVmF1OUNaJTJGVnRiWDBScUF0cFNYJTJCSjNXZzRUaGRJZk9wN1ZKcWVHR0Zib0VaaDY4WWhoNFRoVmVUNlk4QmFXVTBnd0pWJTJGRWdudWUlMkJhZTRzdmE0cVVubXdnY0paVjRBNkJMWjlZaWdpbTViZ3NTWlQxQVM5d1lid1E3dDhTYjZLdjM2YktYd2JBSzlqcHgxZ05RSmZUODhrVzBGekE1VDE3bk1nV29ldTNDakJxQ1RhOWd5Y3BvT29NRWszeXZSSW5SZFZpdG1POUpWV1o2Y3BIZU9xZnMxQUtFc2tGMzNLaEpITndUcW4zb3R6N0xjaiUyQnVpbVFibTcyMTVWVENOVyUyQjhIZVE4YkEzMGtncUdxTmY5dHdxdDRPUXpwOTNINWxoYXc4TEplS3hnWVllYmxzV0tOJTJGbVIzUG9EQTRORzgycUpJc29zMzh4N2R0TWNUa3FHZDhnbkxwN1BtdzlEak9URHpIWFpZVGphV1RxMzhSRldDUE5Va285MEI0SHpvc0hFMmxMR2trd0RycmtLZThQdXdSMnpOJTJCa2FiVjA5MEoxNTZyRjYlMkZTT3djSnclMkJBWVgwZ05iVEg2OXk2TkVseWslMkYzeSUyQjg5MUptck5JNjVrRW9tcnZSQ2dYdDk4TFhxbkdlVmdPaFM4RGM0RElzRUJMYjRzMzZ6TndrT3VkUDNvOUQ0VEtQZDBIVjkzYUdkVjZ4c0x5U1RscldCSGtLcG8zckM0d3VSJTJGdk1meVFZUTJsdXdRSjFCOHlNSHFFbGYyM1JveHYwVGRMemlKbiUyQlpicGt0WGNobWt1ekdKYU81QXB5VXBmbUdJNVg1Q0FKR3o2eUEySDFHUnJCakVKcyUyRm1VOHV5dDhGSGNCYjc4eCUyRm5kdWwwaUFySGd0Tnhqd3I4MkVRRzB4JTJCMEVDN3JhR0Izb3N5QXhvUEczemU4MSUyRiUyQnEwRWZGWiUyRldDa2pVVDFNNXpiMUVYNHI0RDBmZVhDN0RMSlNEdlZCR3hsM0xFREZ6cEx2VXhBZzI0WXlhRjElMkJjVDdJdHFqSVBsRGZzejIlMkI2JTJGUTJ3TiUyQkFhVDhZcUVMUmpZakxzJTJGT21aZG1kSzklMkZ3NVNlZjc0VGg1OXBZU3B5Z2Y5MCUyQnVEM2JaN1JZUUR1YUdaSVlzSFB0Rm9kSTZoRHVkQUNmRU1Zcnl5dFNuZ2dhRmdlTDFuVDY5bk5LeWtBQkEzb2pTMjAyd2tDZzNsTG9DMmxleFdHdHJJJTJCMFpLMlFDYyUyQnp0MUdHSTdtSTY4S1BpM3hiQiUyRjdwQiUyRjFIekh1WXBOMFZCUmZVaFFnJTJCRSUyRjBuY0JnZXA0OWFwNG5YQ3pRU09oUFJQbVlzcjNYZk9ZelElMkJ5UnI2JTJCUVZ3JTJGMTJwS0FkUEhveGNmdjgxT1hGSE5CNE5KJTJCenJhcHBpTkdVNDZKUkRQZjg5YWdUcUoxQWZ0ZlpiRklCcFJIMEVtMnlsa1NGZVBqTTFsM3JkN1VHQTVQQ3Y3WjlSZ01ObnB4NWdjNGRnVmJrYXBQS0slMkI3MFclMkJGaGV0UktPbTlSejdGQiUyRnpscVZIS3A3RTUxS1E5RnhvdW41elVHa01DQWlBdXl4aExwcmZxMGF1a2JmYXY5T2dpU1RPeEhGejdqRFdwblU1OEVuNFklMkJWJTJCSFJsQWZ5MDglMkJDY0FIRnhNMEJTbnNnOWozcEdnakdTSWVlQXQxJTJGNWNUWFk5dHZlbm1MemNWcFRqODZSSXpjWnVFMyUyQmsxYjFZMUlTNHpTN2dEbGNKaXBkNnZDYnRLajVhRE8lMkJJZEhEQm5VRUk5aXdlemlSYW02c2VQaGpUV0JuVmRSYWdlS3lNOGMzUTlGaTY2c2JmWkN3MWpiQ1pUMHJWMFZuVWI4S04zTmlKcnNidWI3Mmk3JTJCMkxVTFE0bnV1bUZpb09uZWdkcUVuVzJPV0pRUVZTU0VKbjFucFloUTVyenFJV2tqTk1UM2NVQkVFS1I2OHFOcDklMkYzMXhiUHpSNU5KOFVyRXBmYUxtOWZ2WXg3anZ4aU02QjZobXIlMkJqYmxYdkd3eUJiTkZUSG8wZE10bkNMYTVjeDkwTDN5UDdQYk41SU5PRk9DOXRQaDNCSjR6TXpQa2hpclNERUN3MDdvJTJGV09tRGolMkI1ZFkxRzk4M0FXOHl3Z1VONkNObWthMTVicEtXVzN5NUgxWVVWclE3UUh6bk5TVHVjYThlSTFlZiUyRnJQQjhjSHAxOWdHMFBibFVYOERkR09DMjBjS0laWktWM3lMR3NaSTQlMkI3SXUzcHQ1MXF5Y2c3UmtVejJwYTh6T0FYRXpvJTJGVjNBQWg3d3RuMFNpZGolMkY1emMweDhzVG9UVFVIVEVjTzN4OW9TMTNObXdPM0tVSUNIaW1Lb01vcm5MeHBobmVUdUtrZTB6ZEQ5ayUyRlo4R1c2dzIwZXk3NGZ6c2RDZ0RQTXE2Z3czUzBwOVBSeWhiZ25GZEZTRnFVem0zbVpPVFV3ZSUyRjM4cTVCNm9rc2hVR0t2aCUyQnFmMkpXaml6OEh4Y3ZUNWZobGdQREhQamhpRzdXTTBydUlsS252dW80Mk9JeVg5SG5oOTZXdDM1eEIlMkZ3TGNXOWlYMFlMdjJ5UjNydjlzJTJGT3MyME44d3BWYTNZZ0RLTFh4S0x2NlBINUVsT2RVbDR1RnpUbTJVQzN6NHk3ZEdmeVhjenlDcnBGdVJUTURaM3ZLU3ltTVN6cW5FN3FBTWEzNGVzYW1rbnBtVEwyZGZIQTJHQURmdDlUNE9vOFNFZ1d5dUtqcjIlMkZiOHR5bUp0bzNKd3F2ZGswcGl4USUyRlFrRGdHazhSNmIwSW9vYU1KV1UweG9URlNVWnBLem9xUE9idVRIVHAyaGlpczlwOFdvdnhFNkh0VWl2dXV1Wm1nRm1ONXlHM3U2UjRWN3p1SE9PN1RYTWYxOWNOMlZlMzhLMG5tVWdWdUFUYkIyUkRqelRlWjltZEFpJTJCcHFQdEN6bCUyRmp2TVdsJTJCeGhINGFyZjVkNENqbjk2ZVVSYTlMeW0wNWpBeU9BYmdjWTd4SlNZYXNSQnNiUzN2cmN2eWRVcGoydiUyRk85TVVja3YlMkZrZDJmNzE3JTJCbXVrOVBPbFdWenJVaHZQS0IlMkI3aTJYVWFZT3h0MHp4QmRVQmQzelclMkZTc1JjWDFkZG01NXV3dVJ3OHYyd0RseXVSUTlhQkE2SVROZjNwRGU0OFZZd3d2M04lMkJwREh3U08ybXpiMjQxSCUyQnROdjE5SWZraVJrV0VWJTJCSkhlMlBMYUZmUVhISFF3Zk1NVm5SRklPSnoyRkJobTBHbnc5cjh0JTJCUk9GYlM3V2MyYVBmT0tQQU9lb0hnamd5RHZlWUtuZFVGJTJCTEFvQ2JRdjVVb3hwVzNJdXNPRGRQa2JhSEdxMFo4OFZVTEY3OXFSTXV6eDhvJTJCUHdDYmNIeG9jZjY3dW8lMkZ3UUFaVVd2WCUyRmV4U2d6T0FvaWxOTVFMQllFRnNXSjgyczVMUlVQSTBadyUyRjM2ZUlydm5ieVhSN1hhR3c2NXZoTyUyRjNhaXdpJTJCcmpUanZzM2Jrc0tXJTJCOWU2eHRCaGZPek82ZE43MDRUVTdvU2QzODhScXNyUG0lMkYxOTc3N1gwT3JOa2lUM05pWkF1WmdMZVhNSURoQWNJZTZPQXQ0UW4zTk1MeFgzJTJCTTlOOU9ucWtVV3RHb1poOXNja1BKSUVDS2l0enJhdzBQWHN5RDM1N080ZDR1YmF3OFMlMkJ1ZnhlODA1clY2VDFQbzN1JTJCSmVVUDglMkJnMDRmcjZjQTUyRlZtbmFieEt1blVNV2xPajRjbllNMXp4dVlXVEF4JTJGdkNpZm9lS1JmTGkwN2xkR3BiZFJSY3NPdklVVTRFeG1sdmxnSU5zbUJaWjVmMTF3eHBpZ1hRWUJPMmpJU1pzdTUyaGxrMlBhMVZiaGVzSjMzTDV4RkJ6bFpaTlJVR3IlMkJUcDVXV3oxUzlrSVFxS2o0NkY3VERnUTV5eXZLRGx3TmVQS1JPTXdJbW9xJTJGVmRyYUxXOGxIM0RPSUxzNXY0blJ0dXBpMzR1cFhiSFo2SmRzbnJtVGY1JTJGVTkzb1Fxclp1NkYzd3FmMXVrT3ltNWRZV2g4cDQxTGZGSzBZRWFZNEhkc0FCOUFNMDFvQVZGNG85TzYlMkZMZzBnVHRWJTJCUUg1MTUzMFo1bzN1ZTF5cGdqNHpLOWdnQjMxJTJGRU9BajdpdnpDV1FqVndHNTlYZVBJVlBGbkxlYkcyY2VGblJhJTJGUUdXNmNlQUlRMXJHTmE4RDRoeUZ2N2xsQk5vaXB6S1R2ZzlwOEpxelVualBaJTJCRHpQMXdrS2dCJTJCb2ZtZXR4ZzBtVHk3dHBLTXBta1JYaWx2bEYzVkZDbEx6RlUxQnB0QzdGUmRRZzZJSVlHU3clMkJDa1lnRWlPSWs4cU5mRDQ0alNIJTJCWGxqNnFHWGNFTGNxblJlNTRmNUFwd1dvbEtlbHlGMGdEakN1THBoQXljS1RVM0p5amoxd1haakVmSnBwMTNDc0tWT21UbWZMWUlLZ21hU3ZaaFpORThnU2RiUThlY0QzamdUV0IlMkYlMkZrenQzVm9FdFVmSzdWMG40YTBYUHZTMGg5bmJFSHNhNDd3NTlEOFElMkJDJTJCdnBaeThZMGJVMEhORVMxamtZTjl4U1JwM0M2Q0pHeG54VG9xMiUyQk1OcjhBandreXl3ZDVCS3NJdnQ2dmVCeEF3WkhMRmZkcjY0alhaRThQU3ZUJTJGSEwyWXY4eVdXOVRrTGFYQXVzQmVub2UzZlklMkIxc3V5QTdZYzJaaElwN1ppb1BpaXZxbzY0bXE2eDdaREw4ZTh2TEdXbllhQVkwN29NZHl0NExzZ2Q5Z3NRTUJYdldhU3lJbmdUamY3NVFyT3N1UmgxWW9UVEZCZ2E0JTJCWWhCcHhqa1NIMDFvcGhmdGxUSzRJbWJQZGJwSVNSZUhuNmd0aFRJWDMzVFBiZ2hvT0lYeCUyRlhlS1dCNThCRUwlMkJ2WlA1cHZXRW85bFlLYzZ6ZDFnampQTlUxcFlFVll1RUw5bVFFWWhsbyUyQjVJY2R2VHE5dTNURWNSdkVzNUVNMyUyRk9oOW8xV2JWaGxudzBJJTJCJTJCQnlxVG5tc3dIa1Fma3h0NWlKM1BVa2lJeGJhY2YwJTJGUWd0V05EQ0NpbWZtRHFGeEl2NzBCekVRblhRYUlXeGdrS2t6aU5hN1hlY2NpeEFwa1BtJTJCUyUyQmhPcXplSm5EUVFRM2lwVHVTWXpWczF1JTJGOGR4NVhoWFJwRm9jSTJ5OWNiR0ZQYXQ0dGpoVDFmWmhQTTZwbFNuMGlqZEZyYyUyQmlkR0xzVjBIbDAxNXRmamlWQ2Q4R0duN1ElMkZZVzhGS2Z2cmxEU2M3NndiNm5MbDc2WkNiMkw1eU1velUwaHElMkZ0bmRNRzBpTERTaTVMeHk1MExNYUZtdkFvRiUyRnREOWJYUzRXQWVhMzc5JTJCbFp3QiUyRmVxWXg3aCUyRkdTVmJ0MVlob2VxU2xoemxkblRtQyUyRjN3bUs2Yk1xVWxiSkZqY2RKZDYlMkJQalRvRlJ2JTJCNFhFMG1ZeGRPS3klMkZqS1o4R1hwcUNOa01Gb2NxRjM3YUslMkZxQW5iUlB1ck5YbWs3NXk1bW5ncnVHdGhHdERqakw4NnIlMkJPeU5YMFdkTENPVk5MRFJvem5yV2YlMkIlMkZFbUclMkJzdCUyQnV3UFpTaDg2RG56cndLTlNidlQ2WHQ5cWExRDVFS051eFAlMkZhS2FPM251dTJnVWY4YTdEcjZ0dyUyRkljcTF5WHolMkJPcjB4Wm1oSTcyZUtPMHpkSHV2TWdjaUNPVWdLbnR6ZVp1dGdENnhoQTNMOEd0WEZCMElaMnBsVmFYSUV1MkZ2UzN1c1Y4Rlk2YzgwSDdUJTJCdFhOWlBjaDQ4U3ptZXMlMkJXM2JvOFZUR3Y5bFclMkIzSjRYUm9xWjdLJTJCOURBMjJZRzJKNWRSUzMwakJDTmxqcERKVk1idUFWaCUyQmRMTHFaZUV6S01ZZ2phdktXNzhXdHVZNVh6T2R0M1o3dHFCcEw5eEswVk1LQjhnaGZHNnV6R3NObU1VN0EwMFJoYjNxbWpwUlRXazN4STM4T0xDOXZDJTJCTFgyeEdmOHN5ME9lM0lkT29FeVRMa3hza0RWdEJVVzAwS1dVeHcybGxCSEFhJTJCZyUyQjJsJTJCcWpFRlBXYzgxMEk1WXBSWjVzUlJwSEJjNHRJSElrZ2RzRnduYURQJTJCb05YMUZTMDBIZzFBdHVydENpRkhoYU9CSSUyRjFpRXNjalFJdGpJU1dBSTNUWVJBQ0pIOWZrcSUyQnhVUGF4RmFFZE9tS3FYT0h0UFdER1R0NTlXM2MyTk9JaUo3MDBRN1B3VU00bkglMkJPRlMlMkJHTiUyRnVLJTJGUVQlMkZwRlZ5U0lwbGJHNW5JY2JjcHhTZU0yS3d1RVZ4QXElMkY3VlBZWmFOWHZjODltSWd5RFM0eVlSR0tuZ1laVktmeFVkTExCRVFGYzJuSDF6T254TFh6Z29oZ1AwYUlBdTV1eGhjMW1jdGs4JTJGbnJSTEI5TkM1NiUyRnFHQ29aJTJCOFQzNlZhRTJiYTJOcVZLdTVndCUyQjhOb1VTbkdLdjFiSjI5MDVCdnBjNUZNTGdhbE16OThZSGFuZUFCYmxKYzJoRiUyRlExMElsMHlqUmM2Q3JqVXQlMkJqUEczRkZjOFRFV21zQ2V0czBtU29YNzhScCUyRjclMkJDVUJoWW1wJTJGQ1NzRTRJemgyam10dklxRkhwYXFwbzl5M2N3Y0FjMk8lMkJUV1A2QUw0RE52d1Vvb0NFb2Q5SWgzTk1kVDhvOGVTc3lranN3Y1E3JTJGZVc0eEFSallEclQxZmdHMktUMyUyQkdsdFpUQ1lQTE00cXN5Q002RXpleDBTU0QxZWFDMURBY09CZjJLOXNUc3Blczh4WEFpOEEyMGZXdGRIdG02Tkw4eHFjNWgxTjdHYSUyQlE1NVh2NktEMGwwRTVydmlWWVBuQVg3VHY4b3ZaamlsTFZTNWRxYmRBeExTTWMzVUxmS05hcEJDUml3MDEyWTdGWmRBUVduN28yajAlMkZQenc0UVYzc1Nwa0U0WUl3TkFCMmlSenhPaSUyQkIlMkZjYVpLUEZSJTJCcTQlMkJhJTJCbVJwbDZqclFKWnQlMkZ3cWRIJTJGNlhOS1lRYXk5Y3JoaGJSMkYyTklyaEo3MXV1RURKSmZ5OFp4TWJBdTQyeVVucmlwWnkyJTJCY1lYRFJLU1gyNWRqSWc1eURBSlkwcmVqcnp4VzVaRm0xb1FFbUx5NVg2OElEdmFKQjR6VnRTOW52Y3ROUyUyRmJHdkNZTUlsQnFWbDBFRWJ4SHZXMVA1VnElMkZya1VmUHE2b0tTVkxTSXl2aktwYjhGSmFRJTJCJTJCMEN5aFd1NEU1anNWNWpHVHcxcWRxSHYyN1AlMkJLYmw4UG8lMkJxbThidyUyRjE1YjNCdiUyRjlQY1JIYVM2OXIlMkZ3dndYczVVY1JkT0x1eGk3c2xtM3p6YWhOMEpyRXV6eFloUGtBWDNWYTBUMU80TWZObGFLMzJHQnh6VG0lMkZTUjVIWnY1OXQlMkZpJTJGaFg2NXBiZUhhVUh3ZHlZSk1na1lkNERaTmZ3SWhuMDdMOXV5OU53S0ZCdW8wd3N1V21pTUxFRmR1bW5EcWVESHEyYjUxbEQ5WHdTVW0yS0g2WVJ2STZISHFxSGU0JTJCaXNQMUlyNk5EZFNQRDlTOERiSFpXT0dCVEZTWTJESDEwejM5YVZoS05uMjIlMkZqQVhKTkNCMUR3WW9BNWtwRmI1WVNlVjNVY1FyaEhBZEIlMkZDMTlNSFF2dlJWelhFcEdLcXp1dDdOdGJ5Q1Mxd2RPNnBSZSUyRk1XU3pYTE0lMkZQbDYwckt2RjElMkJBWTg1dXR6cnZKQlolMkZJTE9pMWZHQjBSJTJGZkduNTQwMENRQTIwbmx4U3BaWWlwNGpSaSUyQiUyRkZWM3VaZGU0JTJGV05peiUyRiUyQlI0UmlDaFJVSVZQU1JlVHVKWHRyaTRYMGJDUmNaUnZxZCUyRmsydndpdE9nQ0RpbHFyeXM5YnA4ZU9pd3ZPTElaNncwVjQzYTMlMkJLYmVoOEFZNWhoRlRPOEVEMGhWcmJKcU9sOVNuYjJuVmtGNUlTa0E3dkp1NUFCTUppdGF0WWdQVHdUT2RTMzFucW0zMGdiRCUyRk1ieVZueHEySGtJbGNoV1lkWHlncDdWNExrdmo4MDdvMHh1STViQzlHdGN2Z3I1U1Zrb1dUc05mdVk0N0loUW4zZ1kzaVRKcHcwRnBia0x4a1YlMkJNQnZsTFRyQlBCZ3hOTnRJQ0ozOGNyMFB4eFFpdllKTFcxejdrbzAzVUxDeFJoakRnMVpYdm9TMVlzZFVRMkdLTHBrdjd5UFZ0bEhQTXBzNm1SS3dmQmRyUmVPeEdCYzlsbFZaaGtQSFNaZ044cGEybCUyRnp5STh5WFNxZGlNJTJCYTJoTjQ1SGl4TGNQR1A3OE5kanAweHpOTTM1SzliZm1aV3E0a1ROZTlNQ3ZNUXhLQWMzJTJGRlhXNHlpbUZFWlJIM3poY1JwQ0hIayUyQksxSHN2aTdmaWJWNmRZZG1INnRPZGglMkZGd3AzZnpxSlJhWW1rRmk5NzUlMkZrYlZYN2NZWW9lVW11OHMlMkJFcmdQc3VOUHpBdkhvQWNRNmNXNktwR3FVQ0sxS04zZEwzYnVaT2tGMmVKcTdENFU1MzZQRjV5RVJ3MlBsaSUyRnNraExqRmZUUW93MmNvTmlqclVSWEl0RFRsbjJEek1pdk84YlUlMkJWbEVuc0psVlltejZPWGdteHlveGFMWGZTUmdJRzJCMUpSdjJGTjdxTzBFSk15WVVUa0olMkZpQyUyRlZTJTJCVFlGdW45ZGMlMkZpMzFZNFlFdHQyZHpMODVXaUklMkJlQ1RnYnNYT3V2SzZUWXdSR2ludE9ObWppU3pCVzc3RGNHOCUyRndUVEVNazhTSVVKNEtsa0V1V3clMkJLZEdXNVJMZlBtUmxrUGFoa3MlMkZ4NTdUbDZ3SUtoNzZ5OE55Q1dJSWFDTUdiYkpaMks1a2klMkZXVXF5Nm0ybCUyQjNhTzNHJTJGN2g1ZEUweiUyQmYxYzg5dEpRTkhBcFRJbjBtUWolMkZxUWMwJTJGcFRTUTNxWm5tNHlMbjYxWkZWcXBTbnBON0dROGNhaGZhT214TXpUWHc3M0FtajJ0M0ZWU3NWZm1pVXdseFIwbnh5cmlXdlVtYkUwajdrS0JOV0luJTJCQmhLVjQ5SUE1QlpTTUx0SW0wSGw3Wk9IJTJGMkxIYUtta2h3T2k2c3kwMmVMSHo4dUNidnRjZUd1a3gzQXA1RnZydFdabFhiamRVOGclMkZhVHVOaTklMkJyeGRGU0lsNDM5VUxJZDM4b2FsJTJCWDclMkJOciUyRmdsdjRHZHltenlXdXRtSFVOY3J4JTJGJTJCdHhoeGtPWWNSemd5VThUNlpSTzlJT0FQTWFkQVhFRm5OZmE3NDRxNkVpJTJCaXBVeWJDb3k0RHlvQmY0M01wY0NTaEdSQnRZY2ZXQkozWHVpWnUwTmRQOU9GVSUyRkFZbkduNXZ0TFpUOHpVc0RlempUUjBOYXA3aWlTaUptQ1NiM1NtdVBaYXlZWUptZDZoZCUyRm1GdWtSRkhnM1ROVjkwVDluNkx0Q3cxZHNhMUdNWmpqNFZNQkU5RUVZYmpGZkhvQ1RyOU56QjA4Vm5wY0lkSmRNOVl5cGJRdWdYUWIlMkJZYTBwN2taZDFySWRINVVRNjBncHpST2JmclFQVXEzaWlYWGU5TkRMeVBsJTJGaXZyOG1VQWR3JTJGSXpQNkw5ZnAzSm5FayUyQmI1cGpxM2liOEJjcjFqM1Awc1A5TDkwaVJkUjlqb1hKeDIyT0xDJTJGNGxzWXFMbDFhWXprV3ZMTFI0ZFl5bzkyTlF5czZhSlhSRzJlNVZRNGU5N0t6MGZqR2N3eUhXOHdDMGkzbWJVNTlIeTlINzBhSWVheXlNSEVUZ2d1NHFyaWclMkYlMkJLZHJFOHRDdlpJZUs5N2xsWTdoJTJCNTU1Z1BNNjRveDF0bGNYUXdrclZJVjhWa0x5WnJvZjAlMkZoMU92MU1qa2Zpd1h0aVgyeWlTV0pPSTBxTVlya1RjeiUyRjNRTEJJS3pGRyUyRmkyMnQxN1lPamJkajlIWDRCdExNa2Y2TGpzQ204QzlPbnRTWFlzbjFYNjVGU0txb1l2TXRpZ3R6MGpNdlZ4ektWU3NETEZsTkxvaXI3cHJNVHBmd05XSUNObHN5NGVzN096Z1B1cWVlZXlSUDBnbFZNYWQ1eG91UGFJRzNSJTJCU29sYU1Ta1hUajVSUGRYRzhsc09hOCUyQlFxbFhvOFVxQjVnczdzN3FWQkwySXhOWTRKcnl0RnIyeWJ6ZENYYmtXVzh2ZWloQmMlMkJ3RWx4aUtLNEs4SzAyUFhISVF6JTJGSWo0N1B6bXBiSmZQa2s3bmxlMU00dnZ3VGh1ZWl0NzVKdm16cXFHUHFEaGE4QjVjJTJGWkdKNXp2USUyQmxYaWczczkxcldJaklpWGh5blc2OFRGTWYlMkZRZHFTNjVFY0Zwck1VdXlNakRJcWVES3pXNFNCaWhGcHVyV011RzdxZTAlMkZkTDgwTHYzQiUyRjg2QmdVeWswbFF2RHE4UFdoa1ppZkglMkIzTHRPM3g3Z0NRaU9xUEtncnk3NzNENkJlTHI1ZEtIJTJCV2o5eiUyQmJ2c2p2MThVZjRkVXVnaEhuJTJCNHZxMzZWeEZ3dkF5ZDVTb29XMEl4YlNURnhnQ2xyRENIcGlmTiUyQkJRa1dQM2VVakpQTUZwWE5kRFRoVXk5TGZZc1N6UyUyRmVDdkFwNFY0NlVmdGh4OGhZWTVNQ3o0REVsZ1daV2tJM2c3TjFjJTJGcFpoakNTOWJUdk5wbENETHVCTkpFYlA4YjhYa3ZmTjZzcEVPck1VTWxraElYYjZiTEF6dzBzdmhZREtZbkE1NlcwNlBldVpFc09ESUlHNlBoM2UlMkJqQiUyQkttM1JvUFBVUjI3R1F0amZuaEZhYjc2VEhCYXBXbGtKQURrT3dpeFBjOVZaTVZqNTlBYzhRWVdBSWcycjFLaDdtcHpPRjd0N2VTTTJ4ejIlMkJvWmtnZkhzbDREOU9kSHhhUSUyRmNpRWxGY1BiTSUyRkZrUFdOTTAlMkJVN0F1bFFndFFpZEFtTjlta2Fod2hmdmdoTHJiaGtCaTBWeDdtN3I3JTJGYmhvZTMyc0IwUG1tRjZFN3RVZkUlMkY5NnJzVzNNYiUyQjNWYnpXSm43UkkyJTJGalJFMnZFVDlwdTVac0lidWNEWUpOMUlOY0duOElXdDhzOSUyRkNzaVdCMzFwUTY1S3NFVzBZaHk3bkJwRFc5UXNlZHkyaFhhRmVWN3pRajBFd3NDRlBnNzJ6TzlBZGhCMGYycVNqN2xmclZKODBaczNOd3Q0U2hMMWxsQzJXNUN5TDRzSmkzajkyQTBFJTJGZlFNZjk0ckpsdUxrTG0lMkJUeVRjbHVScWNOODR4OEhqMVJOcUFjUklMakxQOGFsSXRldXNjJTJCSGQ4SFZHUlQlMkZSbEV3ZFEwdUxMVzBCc01kNkkwJTJCSk9DJTJCcXpwU2h4M2QyVDdyZk5YNjNpJTJGekpLdldVeXpVeUFqY2xucVJka1VQS1NkMkQ0SW9DJTJGeUNuJTJCYjgzc1dJT09pRE9vbUpVSERBbDY1Y3p2dVJqMEZjUmZWNiUyQmcxZllONExPM3MxJTJGRHFKQnpJJTJGa0kzVWZSSldrNDZyNmw5MUR6bFNQRnJZYUdJbSUyRlJ5VUElMkZaNVpUMFYzaHIlMkIlMkI2JTJCQlEzQkFuTVRYU09TOFhwa0pOdUhEYTFrVCUyQlJ4MU1uZkozeVFMR1JkSEozVnJYeUJUdWV3SE5LM3JLNWc2YXpXYlR1NTM1UUhLRDRING1ZdGUxaDhVVG9mRTc1VkElMkZtbVV3VTRtaVFVU3NzWFlWYUxqNTM5ZVo2SzNSbGIwZkMyd2dxMzBzVjNrV2N4OXpEUUVoWiUyRm9USyUyRk5xVk53SkROc2ZuOWxyc3RFMzdrUjdPYXRoclhOcHo0NFVtJTJCbFZLdXRVQjNoVGNKRjFSdDJUTlJYZVFoZ2hxaTZkcFhEJTJGb1ZUS0xmUUlLTEdQYXhDN25CcmVqVHJqendleGd2eVlnRTY3S0w2OG9YRzdpTDQ5Zjduc1QxUlM1QktOS2J5NU5RTXBTb2EzdDlBdnRPYXM5MmU4Ym4zQnlHcXo1YyUyQmh6YzR0ZEY2VXlaN2RLNGw1MlYwajQ4YSUyQnVoYjd6SG0wNVZkTERpQVg3YldPc1I0JTJGZkQxclBIOW5TWFhoSHNhMCUyRmI0OUhVekltMzU0QUg1azAySU9DSUx5MkZjR0QwU21mUk9xZ0JBN3ZNWEwlMkJZVmMzU0Q5VXhMMUREUWpROHJvMjZSV2lwazZ4aFRyMkQxNXhwQU9peW5rd1NIQlhWbG9lQ3dOZldsTms1SnVvdER3V3Rncm1QJTJGRE5paXBJTHVKcFJ0VmZsJTJCU3kxZFolMkJsN1djNll4cmolMkZBREpIa1NlQUxVajdRZTltWkl2YUp0bHdpcXZmJTJGb2NoZlFyN0ZOcmVuVEQ3cDd2U05XcG56NmRiMWFOSGk3d2dCZzN5WXFFak9TSGxvJTJGNk0zVVN5NEVLZFN6VWN1b0h0eGxORFNNV042cXY5VHFFbDFIQVdUaXRxZTQzTFduTnBScUVqNzJaQVNIaVFZek5zekJXdW50VzNNNVFKWmZwZTl5V2lJS2lXS3NuWFg5NnQ0JTJCbUtGTng4YWtrSGFlYUVlbElHMGQ0QXQ3U2hyYldOTyUyQlZ0c2FwbXk3M05iRjZ4QlJtVXNOSm90YXNEQkV5aXNRckR3NjJHNDNZR2xmajNnJTJCeFhWYlNjdWdYd3ZIT3dIVk1zemFQTFFMejllYmJwcno1em5zWDBtazl1dEJsYUJWQ3A2QU9ybmZrMVhlNWhVZlJTUW9EJTJCcER5cDJ4bm94JTJGT011WjhlZVV0cjBmU2QwekVkb0FmVHhibVlUblhjbSUyRm14VzN3Z3ZHZHBUY0tqN3F4dFBGOU53MlklMkZtcFhpSjVUMkJIR3dYQ3F3TWMybSUyQjRsJTJCTlZkaWxQQXRVckl6T0U5TUolMkZweDYzcjFUVUlBejMlMkJXaDkxbFdkbko5V0haSmp1cjBBZWZBVHh1NFJ3JTJCZk93Q0JEVWd5TE1YSEIzc1RIeVJENFF2STd5dFFnUURuN2I5WHFabmZGbElpcjRSZFVyRUlmbyUyQkZYQjlkZWg1elBtUDNVbEtBcU1XVFdaOHN4SHRRUUZDdyUyQm02bTZqMDF5Y1h4cEZXT1RBT0ROSWJqZ3gxS1ZyOFZFSWRhYllpTjlUWmpIOGJGVEpHNUdzbkxZQ3BhcXJCNVdWdmdkRFdYMk5CNk5oMEU0JTJGNmtwdm8zY2sxSENFam9RS0xUTHhsUlNDbCUyRnRTJTJGanpRN1VCWG5OZW03QVJBcUJMMHQlMkI3cVh6djJlekglMkI5U09XQSUyQkRzZ0lhSFdoUXlMZTB5JTJGb1pPd21yM0NGbVR4a3NGVTkwUiUyRnpOWDEydUI4VHp5UWtjNnRWckVaeWMxWDNCWkxsemhWJTJGcjd3MFFQZUVkSkZNazExJTJCJTJCVSUyQnV2dW1Jc3JCRDFORWdhOG42M09oekFqbnQybmZUdkFoVEY0THlJTXpVV2pZMVJDdFlkdlJKRE5xc0JkYWhsJTJCQXp0dDRBWHduSjFSWmQ1eDNic1dibkJhTU8xWmxVS016YWpXRWJFUHRHS09xenJSeTI5c0czJTJCNXk0ZW5XVEl3WVJhZlRRZUFKdzdIcDFra1ZlclhKSWhYaDJEODZGcFZrSGQ5YkZIVHgyaWY4RlMybUhkMmxCWVZKenUzOUozN2ZmVjVEbFdISiUyQkZIUFh3Q0plJTJGbGFPdTJNbDhrSSUyRlFpQktlM1MzNTFjcWFBR0NyMkN2bUdjRktkWEkyMkxmWHEzZ0JlTFV0ajFHT2hMWHU3ZzZZUUE5VzB4TzdjZ28xS3BPTktwdEpQc1pjUTRxWGZMYW91RmExaFRwUEJucVR2dzJDR1dGdTBSZFQ0SEJPYkNETFhKaGlBTUhkYklDcFNkYnpITmtuSUFQcWJaWHlzam5yaUhtWlRoS09wWlIzellxT3JlZFJkNDN4Y0xTJTJCd0dwcWU1JTJCWVFONnNnRllRcTFyYmF1RkpMWVlMbDZzb20lMkJySG5HSzVpMWZHZyUyQnJKeFViQWIlMkZ2TUpqSiUyQkgySmVDJTJCa3dXWGU3WVFxME83SXh0eTh1VDAlMkZZZUxTVHRkM09pVXlhV1FlTjc3bHVIY2R6ZGFqZmIxeG8lMkJCakI4ZUt2JTJGSUJ3dXE5aElhQ3NxZzBCUGdnJTJGSHFzeWtyRldKSlRvbk5MU25PckdYbllZZzZCOXFVWWtVZ1lvNSUyQnVWMkpQSjhLYXhldSUyRklnc3N0QzI1NmxHQjMxRWZvN0ppS2RjeDl5d0l6TU4lMkZtUWkxNk9tciUyQlU0bU9IaVl6V0lkQTExY3lnT0dEYUVxTyUyQktnUkxaZGNENUhxTU1XODVISFJxMlpkdE9UeFQ3VGl0RFBqcyUyQm9vSkYlMkZpY0Q5JTJCOXJHaCUyRjdKN3N4TXFoWEh5bmYlMkJoVUk1SURoTjRXQWxhNjJ4OFBHbyUyRjBMJTJCYlJzaFQ5bGp0Mk5oQnhabTdkZmRTdCUyRlVKQzRYaXhSeThVRHd1RXlRbVpPT3pyVCUyQnduNFNqbVluZXM0UEN6VER2aGpmcU5RWHlzQnk5TXNSUDdyZjZCNzR1UUgxWnlKJTJGUnNEaHFxUGV5MDdtN3J3c2ZCUHRRM1EzRzJYTnM0RDBNNjUzR0V1SjVuV0Q4NiUyQlpwNDdMRmVtS2RUJTJCY0V3MG9DJTJGSEVIMHVrbGRQaU9WTTJPSnZTcHdGdzQ2WGF6QU5LelhWbGRHaUFyWXUzbXMwcU1CNXRwVm9vZTZCZzhCRHlPTU51VUF0b2d2SXFkbFclMkZuM05lc0hjZE9raW53bHpYaWwlMkJBdE9rd3hLbE01c1RUSHoyaUtaM05MeGtwbHlUUXBJNyUyRlhZcU84WEYzS2pOZUtnekJVcUU3aVBjMjd1amolMkJTJTJCT0IxT01QT0hXeUs1OFhvRmwlMkZpYlhSbWM4MzE4bWpyWnVYREIzc3BxczZ0VFlmaFhHRmdHMzF4WHVkbmM2aTRDQTZnUWh2YnZxWnh6c1Q4a3F6NDVseDI2d3NpVlJvJTJGMGlrRHolMkJROHc5clFSVUprbjAzQnQlMkZ2T2liV2k1eGV5dlpabFpOeXdhcFlvUUVSbWtKVVk0VmlnbWNvRFlCYWVUZ0JsSjZWWHg5TWdyc1ZueVp3alQ1YW83cGRyNmVmeTRpZ3VUQ2dIaGJuRGtObkQ2VzRHOFk4aGF3elFjSnpGZk9iN0pzZ3RLdFBzd1dwZXh4THZFWUp1NTBKR2RtMSUyQm9TTjlwMEx2YjZ0dVhQWnlQT1d5ZXR1VzBsZW5ldnc1a3U1YjIxbWUlMkJUNW9ldm02eGhlU2NnMzFoQnUxdWIzbXJ2SU1nN3pIcFpqZmxwcUptZHlzc0xIbjBKUklPeVBoSWNwMDljOVFoRVF4VEk4R2Y5MXE3VXAlMkJ0bzdyeklURWFldnNOMG5rem9VM2M4aXUyS3NlQXBwSmN2V1FhMHFaV2dURWVhWlJES0h4SVRPY0s4bWRSWjB2dkFTcDIxcVpDTVFFJTJGJTJCcWVxUjRoYiUyRlhGdXZRQ3VnSzUlMkJ0NXo1NVZ2NSUyQmxxUm5SOUEwNkpJZGd3d1VaUlg2U3RoTWdLS1BZak1rWlQ2WE9TeE9NTWdyJTJGYTZCdUFtSkRQZTlNZlRVMGFWTUhpRGp2YVZEU3kxdkRKanRiJTJCWkk5eUElMkZZTTIlMkZZYXEwQnFVQVNxbElrM1BrY1BhViUyRjJDJTJGRWg2VmVzMXYlMkJWNWh4TVJyN0JKczEwaCUyRjNINTliUThJcEV0aHRwRiUyRlhNakJqOUc0WUVhRk5EYUlySmRkWk1MTGw0VTFoNzRXblpTQzV4UW9kVlpBMiUyRlU0TjVrYk42dmFVM29lTFhtSU9NT1hHa2wwUVJhUVIzcVVVanliNWVkNWpPc3M4akNvcldnYkc2Sjl1RWRzeXR0WjVtMWMzT2JIdXVaSzBFNldkQVZMJTJCS2dEQUE1RzQ5akQycjliZUN0ejdURHJmQzY2aDZQMWlmSGZwUW1GRFEyV3g0QlNXZmRQQ0xNUzczVjFvNnRmVlk3eUxnWUpTSVdaOFUzbHRlYzBSQWtjTHMzc0VYdCUyQlNlRlZOckd2VGdJdURheGxDaFdMd3JkeVg4b2ZXQkdZSkswVmNIOGYwbiUyRjlwck0lMkZNWmF0NktoemlXQkM5bG0zVCUyQkpDYTk4UERGTm5KT0hSaWp0M2VNSElPREVVd25vbkViV1JOZiUyRlVVVmpWSHRWR2FwbFM2NVV6MTZ3dlhpS2Y4YlVpYW9lb0R3Yk1ic0U0RUthY1ZONG11ZXFtdGlsRzYzSU95MkcwRGxGR3RuT20xcW5QeiUyQm5OTWo1MVozVHk5dGtjanZDa1UzQXpvTmtsS3dSV2FNUHJDQVZaTWp1ZWV4bEhFbTFsM0hxdzQlMkJ6R3RJdXlzSlJEQkxGSnZhcGF5MnlEYlAlMkJhZ2p2dkxaU1lyNzdMQ29iMkhoWUJtZXN0VWxNeHNPQWg1c3R3V0NBOSUyRllwWEJCR1ZjZzJZV2d2NHBrWXR6OVlkeE1tRGpIV1hnZlpxcnQ5YmFmaDFoaiUyRm12Ym9vQmpzbFk3SFhCUHJ3aTIydThjc3A0eHMzYThzbGhYajJRbkE1U1h1ZGR5eE54RHVwOFo3bjc1NnBQT09UYml4anc3dWQwUVAxYWNuaTAzalIlMkZVNkdKb2xieVVSSWUlMkZuNTRNc2xYdGRMNjFlYlg3VEtZSWRsa2VtbzJORm5jY3BvM0Y0VXh4TjZoVE02OFJwcnVNYmticHlIMHpOUERVanYlMkI4bFVSdUVSVHZOU0hQQXZNdEdJMzM1VUpDdHhCc3hOaDFTS2MzWkJLT2U5NHYwRkh5QUdzSDZhV2djcUZITXclMkZzc20lMkIlMkY1OVBUV1lVcXpEMTE3ckxxc3Jlckp4NmtnelptanVCazZQdnJpSmVLMU1vMW82SkNISEoyT0U2UW5PcENZQ0FhZU9TQTV3Y1F5QzVVMVBwN05WTWwxaGVIdTJaSFBWekVhYlJyMjVkS3dJSFhJRGNuOGh2anRUMTZXQXhaYnR2WW1XUUxUdEElMkZEQWIwTmtkcWFldnI3ODFlMURUNFRGSW90bSUyQndrdWZ6N0tlVFhPQSUyQmtaVEMyMWlsMWJDcUZ0QWZDeXlPRCUyQmtCQkpsNkl2MSUyQm01S2hIWkI3cnRmVXRETTFLaldHTkZ1a1Z4aFNxOTkzRHVyaFgxeENhQTBGdmJGd0JlTDFBQmJLZU9aVHUlMkJQdXczQkZkdmg4VWR0dTBWZEhyRDVyWXFLZTVkbDBEMkVNbFFkeGVMTGM1encxU3BwS3R2T3hOTU5qaU0lMkJ3ZVglMkZGUGs4JTJCJTJGY0JjTnVLRjgxZDF0VUpramxod3hQbVo4M3E4V3EyUUUwVkoxeXY3emdPaE0wanV5RUthaVRZVWV5SkhMUkwlMkZ5b0tjMHV4ZndQOEQ1OVNqcmFucCUyRlJuYTYlMkZYbG52c2E1OWNSdkFVVDhmd1FwU2dzRmg3cDdqYk5zeCUyQmM3OURWOUN5JTJGNlU0SWtzMTBLU0VxZzJyc2JoV1FVZ2JlREFKc3lOWXB2dVJJcE5RZzh1YUxQRSUyRm4lMkIzM002OE1nUVl1NE1JOCUyQkE0ZEZIdXklMkJCckU2SmdJS2g3MlRwTGU4aTR5S3FLZng1WlRFWEI0VXIyakxRcFRQZWhBZjB5RWk2R3VpcVk4TlRiZHBmVzNqUHRjUGU5S0YlMkJCWWNtQ3RzYnl5U0VIZjdBdDlMS0FKa3AlMkJ4MzRzdWhqTG9aeTRUSE9Jb0ZJTCUyRjFHVVdENVpSckg5SDR2c2hhazE4d0RPTGpHM1Fkdm03MEFkRldielVOMGMlMkJ2b2wlMkYlMkZWMFclMkZ2c3Z3aDdaYllnOUFNY0FtRG1LZHI4dWs3MmM2VmRRczVicTlUS0dDWGFxcldzWlhGaWhGWWNYbzZiVnQxWVh1ZTdhMEZobmpnT25zT2dKTEtlZzZ6NXhPRGNwWlR0SWpYdGhPdFVneVVvZlFjTEN3d1NtUGElMkZrTCUyQndMZGFMZ3RaVlpMcFNoMTZZNm5Yalg3NzY1ZGg3bWFCNTFRdyUyQlc4Nlc4NmtJYldmQ09BU2lqUlBpWjFqR2VBV2N4RjFhaXNiY0VlYllrZXlpJTJGVmk5Qkw0JTJGWW9taHNCSXp1ZFRDZDg4OVVsSWl1RkhFSFNuNkpwZEZDVGE3T0VKdmpsYlozZ1lSbEx2ZzN6biUyQkFxdjlCVnR4TFYlMkJYWDlxcjBBVnFsUVlGZTdaOWZYY3dkOWc1SVphdDBlaUFMbFJ3JTJCOTJZNG5mT2toJTJCZjJEU0olMkZmNE9RMnZ5dFhNY1BnR0EyUXpKQW93Qk1OWEU5VHNlMGdpR1YlMkZ0SmJxemxMeXNlNzVhbjVYd1E3QjFUNE94WUZpaWExOFo3QmwyYk5sclY3SW0lMkYyN2lhWGFkdnJqQzhZVUQlMkJQWWdwcE4ydnRVZXZCN0lndXRPMEhUbU5oM09HeTVibVRkdWNrJTJCJTJCN0tmbDZXJTJGUU9tWGxBTUJHaTRlQVlDUEdqY0ZQN1NURUEzcSUyQkxVOTZKMHFsaWFiQmR0dDM0Sk1PZ3hVTUlKWGxZJTJCdFQlMkZNTUNOMlJKRnhTdkNTZmxjOTFjJTJGMlpXb3BZeCUyQmNLcGc2cU1uVWxVd1VwT1o0VWFyeHZTRzF2VWhTUGUlMkZLd2olMkZPWXh6alRsaDJKQ1ZVQk5kUWFpSEpmRWZEbERnOElRVFlFS1JhUzlyRm5saGx2SG9mWFFwRFBITiUyQmNxM0FnVWNIcXZvRldibmYybCUyRnpVNzFLWGJHdnlJc21aUFNZcHllR0xUQ2FyVkhpZWNoWHl4c2Y2R2NnejhROGRCMkZXJTJGNTRVeGZMJTJGSFNsaXNuMnluSEFocGIzYVh3MFFhYkJIQ1VKbFQwQ0swViUyQkp4OGwzejh4RnF5N1lDZ0I4UkttdVBJV2ZnMCUyRkZQRU9lbSUyRlByUzAyVkg1QlhtV2RlVTE3JTJCREx1YlJvSHIxTSUyQjBMclV6Um5KaCUyQkF5ekRsa3VEdGxvdkxpRTdTOUcwNjRWeFl3RmdFdnUybGZZRlo4aklPSExwMVUxeGlKclREelR0RUZkSmZiaE1wWWsxYnlrJTJGSkxtVG9ic1JNcXhMZjZEcyUyQk9wREw5SWRRMGw0dmJSd1E4cXp6aldSVWxNbDlqR2NhZUQlMkZzMFJSTnVWMDFrZm1LZHlHM3JHZE1TdkpTRiUyRmpjNmNYMkJCaUtBJTJGNE5NaHJBZm94UjBxSVpNRGUwVFBMT2IxVWFJJTJCNEVPbDRFOEJVODZ2VGRPZHVwOHUlMkY4d1ZzTGZHUnBwNzFTcXA4V1d3TDBsREFzOTJGbXBuJTJCMklKakl6bno3cXlONlhpSGZ2UjJHNThaRmpJeEdDQmtlTkswdjNLczU2RmdISkkxbVFKbElCTEhLcEVQdVM5d1E5c1Q3UFJ3bkNWQ3FLZTFSSm4xSjk3SUJOZ28lMkZGVmlEa2VSakJQV0VNZmZEMTdlNGhrRkZvbFhDYXdDR3BZRWMlMkY0RzBHZTNIZHp1UUtYdFh6U2RLb2F0ciUyRno3OGk2Nzl1bFBDWlVJcjd5YUlQejR4d1h1RU1ST3dMJTJGOEY1TXB5Zjk1MGdaSVZMc3BReGhSSzJ1cjBoWlB6NW9wbURBdGNXd0lmOHIwdiUyQiUyQkxacDRUQnJnRHdBWURNVzgzdnV2RnRadWw3Y1pmSDhaZnZDdlpEWnU1QjVwWXI2ViUyRmJvalNhR0dkUmZSbGloJTJCRVpaR2lUaEtXMUQlMkJWZHZmRnNvWFhNQzNwbElkY2kyNEZ5QVhqJTJCNnRtOWZ6QURBQWVoRWFSQTREMiUyRk4lMkIlMkZ5clRvcDZZbHZCYmMzYThoT28zVk1LSGZVRGozeVZmeEtMWGclMkJKS282aW95ejclMkJITVhiSXBEJTJCdkFXUUg4UzhnTyUyRjdFYXFEZ1pQZlc0QkJxUzJ4SG43YUZNUzhsQzB0blpKZGdYbzFwcWY5cXJKVUxXNWdtTjZCbFclMkIxWlRiMUlVbE9sdUVXcjhHS1MyamNFdlFuaHRTRWJyZHZ6VkcxeU1CM1hsM0MlMkZSMCUyQlZsU0JvaUhqN29ON3p3S1hzZzVyS0x0ZGxBanJqUUx6ViUyQjUxaU5FcEVBWG94czh2S1UlMkJ5MGpHdGFqak5RQmJvbGNCTmlBRkY2eDNVJTJCRkpMbUlmVkJHelNFJTJGRU8lMkJEUFlJT2h5YzElMkZUMXJKNWZxQmVCUHA4cSUyQmxwNFlOT0FOajFTJTJGeXV3WU1PUURyOVBXQ2hzenFkMmlGdjRnTkolMkJFdkFUOUM3V1B4Z1NLayUyRno0RVBTRXIwTkhGZW9KR3U4WU9jY05nUEZVUlYlMkZCWkZsbjNIdUdzR0JpZnhPRkZtQ1FRU2klMkJ2V0ZpdFl3VU9mZWglMkZDMFJiTUpVclNHRXglMkZTdEwzUGwwUzV2blA0QXZjQiUyRldjUGdPUHBzNmclMkZhMEpab2U5YWYlMkYyTlZvRzN2RnZQNVM1NWI1djJpSmdjQSUyRmlHd0VoYzBUV1RkOE1xU2pFenBzZFhhOFBDZ005dzVveEd5Q0pBUGFlaWczNiUyQnZQRkxjJTJCUzUlMkI0VXpLJTJGMXl4JTJGcHNmRlY4SzhEQ08yeXk1ZElQd0NxS0d0a2NYJTJCR3AwU0IzaFYlMkZLeUFEZGRpS2g5VWdxMUYzcDh3R1ExazdyWmN1Mm1UaGRuT1RmalBUM0tXdUVFUkxleHZXZm5yRTdJZkFTNFA5JTJCa0NkaCUyQkZvMWIlMkJkckhGJTJGeEYyVWF5cEkzSDBtT1U5R3FsRUVZNHJ1cTJMb0Y5ODZ2UDNyYmtqbzZmMGlRZ1ZGZlo2JTJCcUNEb3BwN0ZVdTk5aTklMkZ3TWZqSUIxa29sT2hPalVxYUNXZ0gwckhNUU1QSUE3aWIlMkJkJTJGdTI4TDZIczFvcjBiZGxza0g5b0Mzd1BIY3FwaFNLSmpuNlhsR2NVenIyNDNSZU1nTmklMkJqUU1hWWk2T0huZGVXS2FWSTJRVEIlMkZjYXR2bTRvR3RqdTlZRjZJbXAwRUtwbnBkSm84WkNKaWp6M01pcnlzUzd3Q0VhZm1yN3A5VUZ6NGNSQTIlMkZwRDdDVWpvMk5MdGxnU1NBVEh6TDhlQWF5Z1NXWWNOZXIyNUxwZDJhMEVWV254QkJKZlB6V2JNa0dUbFRyRUJGUyUyRkhWWUhNeXRwUjN4RmtETlBMYU56YW93NmU5MkJIQ0xieVgxZjZCcEZ1SWdGUHBDTHpUcUs0aDY4NiUyQlpkV0xiWmg5NUh0WFNMTE1wbTdmRWh6cGdaWENqbSUyQlBQZXJsWXdkeUNRNEhlbkN5ekF3MWphUUxzdlZtNnQ5YlZibHFHMWxhaVg1aElkQTdZYjllUllvWlNXSVlzam5WUU1EZG0zQXBuc1U3eEZnS0g1Y2dqZzUzUHd0cVRlbTRhZTcwY0hQZlQlMkZ2Y3VKbVZaQXJFaGIwSVlyQiUyRmZzMDJvT0Ezb0pQUWlBbSUyQnhObVlXJTJCQmJuQzRDUnBzc0ZnaDRFd05IMmt4S2RUd1dMeDVZMGdFYmlvMERVOXZ5MSUyRjFlNTQ0VHdpWHdqWiUyRldTcjIlMkJ2Nll2ODBDd3NXUkNVNjVadlJWNWZaNDJFRUpZJTJGVlpjakl3MHdZbCUyQmlxOUtIRU5kcTglMkYlMkJwS2RFZnIzZ3RkZmxXNG01M2txUUM0Rk1IclIyMHowWUlZN25aeEM3TzdVJTJGJTJCalp2dVhub1kzVnZ2QXNnNkZvJTJGVXlwNFRIUDQlMkZQWkZzdlliTGpCQXA3WUtrNDBtdmpnTWdQV21RMlBHR1NQbSUyQjRsc3M0ckh1bkxxWVU2ciUyQkNWcCUyQlkxNnFkczRtWFVra1FXSlR6WCUyQjZBazR3NjRrVlVXWm1jbHhnVEhGdWhuaVRhOWkzWUtzY1Z0c2Fnc1JETHF1M1UlMkZldm51ZlpvTnNrcmlRJTJGRlRXR1JkVW9WWmVMclY2UVQyTVBYMzFyJTJCaU43aXl2NW5WOVNyUWJiZkVWbFlheHRqdG05akpKU09GS2hHYmpkYUpTWWZ6OU9Zbm1yTyUyQnl2UiUyQmkwY3J2OFkzTGVOZk8lMkJyTTBEakF4Z2h6dng2eTNmZVBHb3VkYyUyRkZjbWZFJTJCZXdMMmlPaWQ0cm5qWmtOWGYwVEh2aWF5cHE4OEo1NFpaVXglMkZXcDJYQW4yTkglMkJ1UEVpbm92VndXazlQNW1saXBIRlNNQVlVN0s4WVpVbmNVMk9ncnFYb3h6NDE4V3MxYzNLc0hadWslMkZDWU5YTkgzNWo3YnhLM1diY3NtY2hVQldzWkFGZTFRc3VwQWNseHYyQzZTTUR6aEV6dkxhS05ad1ZUZ04yWDdEWDAzdEhZVFI5a0REaTJMWnZERlFlVUkwMGZONzJtTEU4NSUyQlZ3UG40c0FGeENRbDhRQ3VXdDRHTEpBaVBIUGdKazh1WmxjV2E3cU5ST1VZY2VqN0VpTnZvZEFIbDBocmRTVU8wR1h3R1psNEtydXRBVFJpVFclMkZjYSUyQlBtaGp1Zm5FY2N5dGllYzdrc3FqRVRkMkNsVjJ0alFKM0NpRlhuSGMlMkJjWXhQcUJmWHh4Tm8lMkZNUWcwTjR5Z01Sd2ZxemVzZDNwa1N3Tnc3RmI1SjhwblBpZjR5dzB6JTJGWlB6dXQ2T0tDcmxJWnIzMUVWU2xhanp2ZjJtVlZITWV0Nm5pZ2dYYmdmbWVieHFpNUxRNFZ6M0p5c1hUWlZ1RHhlZXB6aTJJSUREMUY2MWp2dWsxdzBmNDNKOGwlMkJ2Vmt0SU0wdXRwQnVUJTJCMVo0cnp5SG1NdjRncW5qTGNZTzBycVBvdmp2NkV3a3VlNTQxSnpVcDRXZVE1bWdqTk9xdHJMaVlubzNUaiUyRmZSRkx5cjlHY1ZqOE1paTlxdDFCTnRPSnlTZmxuN3R2blVkUCUyQldNQnFkUTMlMkJwSXpVRmlIS2o5RCUyQlNUbHRXa1JEVDZLdTVxdiUyQldYQWFJZjVxU3M1UnRoSTNHa3o5TGtqM2RQS2NlSkx6V3k3WkFjRWdCS1U4Y0pBeHM0UEJ3aCUyQlRBbVRWazVFMyUyRjloOSUyQmhiN3pma0NMOHdDUiUyRjZjaERhMHl5RlV6QVdqVUFjeG8ycGJRcnF2WlVXaUZrJTJCaXdoT28wbVpCaUNsVnlMd2x4QlY3eHEzbThINUxXRzRDTHBiVkx2ZW9mYkdUcnA3MDNYYjFJSzdhOEVxJTJGQzc3MWRrSkZLWDl0bE94QklJaHhYYzdTMEE0ejV6TWtDRWs0VXJQVkJ1Um9iQUk3MDhTMXppVFclMkZ3TXd1MmNQVEUyYnA3enlzM0NxJTJGWDhkYzU5M3hVTklsJTJCSFZaJTJGUUpQRmZ6MGxDaVFwOG9GWFliYXBOSHVidjJiUDdvSHp3WXE5QjBLSmZuM1dIcUxiMVd6bUJkMzd3NzMxaXhtdzF1b0tyMW9UNklNOFZzWFhIWEZrdTNzV2JVTU5HUWpacE5EMElYaHZYY2t3eFVoMDFHJTJCZkI5MlJhb2xNUU5vT3ZHejJiZ1pzdEVXNGslMkJRJTJGcnZSSEdZT2NXcmFrVW1XSE43dHY0R1hyZ0ZTTTR1NEhYYnJJMTJRaUlZb3JMRUxyVVFMR3ZxYkJIWUIlMkJ1S3hLV3ozb0JpMXlEVUZnVm1EY3V3eXpCTkRtSHlIRWtIY0xvVGtBSVYlMkZqdlBZeGY4aTZWVGQ4MHNtejE5M0xCY0lVZVcwR3A5TDE5U2JwcWYxT3dFS0p5eFVMSTFnd0doanBZY2twYWhXbHMlMkJwbWNSNUVVOFBTR0ZZcVZ0dXh2VWc3Q1d3eWZDM0JvRzROOXY3azhJdWdUQVVBMDNlS3A0RDlvSW9XclgyUGNSS3JqTEZ3NXdGYUhqVCUyQkJqc2ZKeCUyQkJqVFZyRlVwMU5ScEc2eVA0d3VHRHR2QXFVekQ1MHdqYkhnamVsVHVUY2RFTEl5UlRwdHJNSG40YkdBM1BOUkNLaHprJTJCckMlMkJoNVpJeFhPcmxKY2d6RlRMMUVPaiUyQmZGSFV5MTd4NGhhSThFWCUyQlVrVXdlTFU1OUVTNVI2R1JwZFNKcUt5RWZseVFzYTJObSUyRm5lTURqUER2ZFVwcEhCTiUyQkclMkY2TExtQ21vUFZHTjJpOGJJJTJGU0k3dmlEcDVZRGpQbVBmVVg1Y2YyJTJGZU9UNnNNM2pJV1FsMDZOdHJ0OERtb1RwJTJCaUpYSTZjJTJGNmVlTlpWVXNtUG0lMkZkOWVMbkRpanJtU1ZhRUtoaklpVTN4NzllZWh5NkFTN0xLUW5ZMmVCamdCYjAyVjNrQ2cwcXUxWmVpQ0xuWU1uJTJGZWoweTVSJTJCNVdwN1ZtY0R6R2gyWnA0NlpYdElBTXl6RkVIVFo3RnVGUUtmVzMzdCUyQm9PSnB2Z0VnUmx1ZlJ0dGZwQkZBa2glMkZIYnFzaVVRTG9BZW9BQVdrNDBJZ2pMcjlaWjY3OFU4QjIxbnlYWGhuJTJCTmxTQXJ5OG9LUUQ0TVpTeTFBcWdRdnNNRDFEeGtUVkh2cWMlMkJyaVFDN1pHczRBZFRCdkpkeGR0VG15cEg5SVlJNzc1R3dZNmVLQTJnYmozNVo0QlJjeXlSTlFxN21lY1doWktZQUIyVEtsWTllNmpzOG9QVXNEeWtDNUxPbXl2aU5ESFBWTGo4UEpWZTZvZSUyQko5QTFqTVZ4QmVaUm04ckM1eXhTVnR2YWFpMWZjJTJGRFlkTEg2WFdwSERFJTJCeTJyJTJGUjJrS3M2SkslMkYyZTZXYW9zNm1IRmFCcVBQOXF3Vnl3c1p4UjQlMkJwZGNSYjJ2NGtjSUJTWnJTRE1qMnl4T1liTDRLR2IwR0d0aGhwYkpjaWxhWVl0eE9uZllOeG4lMkJyVXJuWmpyNmZ6JTJGb0FrM1lPd25CQ0F5bHZTYkdQSVc3cHBWaVJJVm5YclF1MWlMY1k5dHhQTU43N1lJYk81NElwcG5uZ3dUeUtvMnZ0c3h5R0J1T2huZElyZk9HaFZyS0QxaklWUUhaT3ppVElZJTJCaGRZT1JmSFhHU2MxelkyJTJGZTI1bFk3UHl2cGJMV3IlMkJxRk5aSUZYYVJDeGNkVHVmWHY5OEphSG0lMkZ4MkEzbU1XbTZyVVhndnFuNzhxdkhyV2FFdlpCeWhkdUxyM3c1RTg3c1JlWENPZ1F5MzZFZUhBdlJCQjVsMVVEZU5jSXFIYnNFVnloRU1FMFg4YTRhb1BmaEFtazhXM1JZTWt0Z0RuYXc4Y3hwSzJaNUZEYndZVVp3a05PYlFnTGR2SCUyRmhqZVJnSjZJZjB1dHVxa1ExbE1kVDJFN1FBa1hGZUIyZXVFTE1KcWNzRk1qWHRZQ1dhNUdVbW1YJTJGM0VVUkVYQmx4MEJqdXdJbDVZQzdOVUQlMkI1WVpleTB1OEx2Y3ptaUtCbVBKeFRIcUhCNlBpOCUyQlhKQ3RkRWZkayUyRmZ4NWlKamVwN0dSSk0wQjc1WkhzYkdrbUF2aUlzOUtqamgxdFhTMWI4MHJkWDdWSFVxYTQ2Y1N5bXJ6VTZJV201TDZoV1g5WEQ5bjhsaElLenZudVlvenVWQlR3VjJXWTBoSXd5TnZPb29LS0pvUGxvTGVpYUxQbzBBeTd3NSUyQm1ZMnM1Z1d0M2FjSXA3V1l1R0UlMkI1N014cFRDOU9ybXBYWHFYUlFxbXdrNU56b1FRWFRGSFglMkJ2SGVwNWUxY3VSWndoYTRuMGY0cU50Tkh3V1RpY1U2QXhtZk5rUTUyTWd6YTVmNlhlWWJYS01OVHA3ejUlMkJrb0FvbUQ1RUhHR3dOcWtwRmh0eEpLRFNQdW9JYVNscXo4VUV1SEFpUHdZNzRRMW80bnBjYXhOS0JaeFBIWmU3WndFc01GZ3E4eHdGMzYlMkJvR0tpdUZKUFlqd3RmS0tFcXVjTHBwWXg2UTFMbG8lMkJYcHR5bSUyRjlVTWQ3OFo5bnM4VmxqVkRncVNRNDF2TkU3b1kzd0FlTUZnT3ZualhuY1dRbWwzR1drTk00aEtxUjBnS3dFaVUxYjlma3JQNVhEWmozVFZuWXBrN3dyMTFEU0Nkb2tTeiUyRjRoT1VOdFl4cGYlMkJBNXlTdGJkZk9nU2UlMkJOTmo3ZHdqVklQVXl1OWlBNGZDdjQ4c3U5angxYXFXYnVQRlJPOGplR0Y2enVNMjRuRDk5anU5S09xSlIydHlhJTJCRDFJNkFFVFlheVo3TGZkZTk3SXprWFN2UXozSHpyJTJCZDRzZFZiRklUdmd4a0MyMFRVNCUyQlUyTnhvRjUycHg5UlZFVyUyQkMwZmpxNGVCdWFtZ1h0N2R3RHFYb0g1OUtKaGZuTThpNFR4eXFuWXZXUEdXNmZHZDUzelJHVWhvTVJ4YVMzdHlpYnBmZXNrVVhleVJRaDg5SVc2UTZISiUyRnk4MmhIS3Z5Y1p5RHNoYnBjSzdBU2ZLY0JieDFlTmFOeiUyQjJwJTJCSWtSZkR4M2YzY2dDNThYOUpUY0l6WXdrdjMxazVDeVpFeVZzcUtXVTFDZ29namVlVWQzTExzNTZUNWMxZzUlMkJIem44NyUyRjlNNnRPWFBKcW02eHZ6aWwydktHMzFQdmRPNWhFMiUyRlRsSTElMkZYaDlabkZTRnQlMkZ5cm1YWDhTTUVhU0EySGk1MlAwSG1VNGZkJTJCVk15RWVTWjNpT3JYVERXYkNDbVZPcWRQVDlQZVZDYVBOSzNZZG9BZWViOG10T0pCRnR2bnI3blZZJTJCTHRZSGppOVVJbFRhZERya3NFNGRmY2w1ZkklMkZBcXVCM0Q2QzFEZTE5WmN5MFQzNktSZUElMkJ2Qk5zc2I3SVE4TmxGSkIzSG9jakFmeVliTnBrTXUwUHBkZk8zbnJ6eiUyQmtwbW9FUGxteldGZjFwamtEelkwSXhDWlVSdWZPZXdmbzFRYzZTZWluUllGbUJHejJNZHdQVnJydUFEQ2NDS2pIWTEzMjRPd0pYYk92SUpVRWNQTXBtbkg2eE0zdUg5N2pTeTZjUHZXdnpIUDBzWU11bGU1R08zUUNVUGFNN05ZUk85S2RNbWcxTjRud0k2WCUyQjNlbk1wTSUyQmdLdlNEN2J6ejZodnd5VXlZTjB5QUptd3NMN296SWE5aXFTNEgzb3RTWGtOOTRQdmFKamt6dkpMcXpia1ZmTDVQbjBQNUVPUThESXklMkY5Nm5CJTJGUHUlMkZtQ0Q4dThOSTNid25NNUhPd3RjWGZrSFRKbSUyRjhuUzQ3ajhHcVJHZGNUUjQ1cENhaWpIZnJPalp0ZnRudTBBeHhRZW9xUXprOFh4dm43NmdVSVR4dTNvTFkzeXpaeVg0ckdLdkVXc2FRRDF5QWZZWSUyRm5JSWlqM0wxZXFaNk1HaGowcDIzdURlekNwUkdSNGY2WUxCSHBybCUyRmpxc1dhY3NPOVJWbkRSaE1lQ2YwSXZ2enYzYUg0NzdHJTJGcE15Z09Vb0wxWXR1TDhHM0FYQXk4b09EUWxEMTdiJTJGcXREcVBBM2xQdWNVakYlMkJpZzAwdVlEJTJCJTJGZ01VZ3Y3ODVQcnpONEwlMkIlMkZlJTJCanliZjZyMlBFZjhiJTJGZnVhNmFLcjZyek1UZiUyRiUyRnVReTElMkJCNnAlMkZuUDduZVA1ZEZNREJreXY2JTJGcTh4JTJGTjRqVUpQJTJGJTJCUTMlMkI5NXRJJTJCbSUyRng1OGpmRUtKJTJGcnNDVzQzTUxDTFJ1ViUyRiUyRm5FMkwlMkJqbjk5OEolMkZXNW42T01zOFhZSG82Znc4RCUyQnE3RjhwJTJGV29pJTJCeTdjOUh5WGNiZnglMkY5OWRQblhmWDMxMyUyRiUyRkd2OVhUZ1dWU2ZZdmYlMkZWTzZoRzBidnJYbCUyQk9mbzgyd2d0dCUyQkVCWDAxOVdCRlA0RzhQJTJGcW9MUnYxdVRQb0NCdUhOYnhPZVUlMkZEVTgzRlBmTkpkODE2WlVoWjdadGFkTHZWdnczaHZsUGg1RiUyRk1XamtFVXB3dk40JTJCejl6ejhQTTI2WnRxZU41bmozQVd5M01BaUc2VEpUM3o5dzglMkJUWjZEbno5MDQ1bmZKUDJkQ25yJTJCbnNabTJINVNock4lMkZ3M2x3cnVmVyUyRjhqQTc5VHJ0b3hkd1kzOSUyQkp5WEg4WUJuS1ZzJTJCdjVmSGZvUFdETFBldmpQQ1BFdlZnMzJiNndhbEliJTJCZWNsZyUyRndFcmh2aCUyRnZtS3clMkY1NFY4N0EwYkZzZU9mNGIlMkZpZ2ZKdiUyRko5SiUyRjM4SiUyQlglMkYwMzRQJTJCRCUyRiUyRmM4WCUyRnhwU3V2eDNTJTJGTCUyRkQ2UU5qRmhNUGswUDVNUXZsandaa3Y4WUljVCUyQnBkckdvSDlMYmY4YkFvaiUyQkJ3Z2clMkJmODlBVVQlMkJJWURJJTJGeExBJTJGeUVDaU1MJTJGRXlXUSUyQnA4cGdjVzYlMkZiTUFQcEtIJTJGaSUyRkolMkJ4OGllVGlOJTJGN2NsRCUyRnFQa1R4QTAwWXdLZiUyRjRURnFTcWRiSHZBRGYlMkJEOEIlM0MlMkZkaWFncmFtJTNFJTNDJTJGbXhmaWxlJTNF+X1mawAAIABJREFUeF7snQm4XFWV71fdzCBkAiEJsy2YCaS1FbFR4oAIaPtMaFEbx2c3qB0UJAlKEugWWsBnt9qvHQIICSTIKCQyEwZNCJnTrYAMr1sBCTKoQObcW+/77bPXqV279pnq1r1VFWrz8d2bW3XO2WcPa/3/a9qlG66/vvzob34jndYZgc4ItPYIfPWrX5Vhw4a1RCeXLVsm999/f0v0pdOJzgh0RiB5BE499VTZf//9W2KIOnKjJaah04nOCGSOwKc+9SnZb7/9zPdK06ZOLV9/ww2ZF3W+0BmBzgg0dwReeOF5GT16r+Z2wj79ggsukHPPPbcl+tLpRGcEOiOQPAIPPPCAHHPMMS0xRBdeeKF84xvfaIm+dDrRGYHOCCSPAKT+6KOPrhCFDevXyMMr7+iM2S42Al8//9syeNAgOe/rZ+xib/baep0vfe08+fFPFkmrEYW5c+fI5o2PvLYm4zXwtqvXbZAvfW2uPHTPz14Db7vrvuKS25fK1FNPl1YjCrNnz5EnNr5iBr5cLkupVMr3e7ksZaybpVK/XEffjDW14PNeq9etW7tK5px9hixZ+qD09PQkzmtXV5eZP3dsdR1krQf/uiLz435Xd715XvRBzZrSfrp9y/s8t5/Roo2emPZ+NePSw3qP9kdDx6Us8X11HMwzesqmn3f+/BY57bOnSIco7Lq6oerNZs69WIYNHSLnndMhCu085R2i0M6z13597xCF9puzUI/bgSgYkuAAlwi4lQxgyQJHNde1KOGQfiY4zXre+rWrZfbZ02XxPctrlqOC3RiYSsmAVQXRLlkMAfoQmVTg7T8sBtYAc4PRnWcp8TOfRK2mbxaYu5/7IF/HOPRsl4Qk9S3x2YZMVIhF34xLNB6VcbGjxHtLWe689RY57TM5iUIVI7JvFf2tBPmKXiYiYnFLviaajHhdFLnGzFBl4NzFoYOY/LdK/zL7Fr+jJX9WUOlC8ie/9tnuuFSsJHn6W9dY2gWVNv6z5l4kQ4cMNR6Faibez3NnBjF6Zt/OQ+W9ojHNOw/1z11frmldcx2i4Ivbzr/7cgQ6RKEvR7f/7t26RGG2PPncplgXoBOwphorNP91lXJZpBtyXQnZX8fzOteZOWMOdB7WrVkls2dMl8V31xKF0KpXLKAYyyWGSd/XdeLjMsUWvhfAvY/2U/vsYrjEXWlJHp8XvbdLgos8O8ZIAcLc1+PC/Tsehf6T0S3xpJlzLpJhQyOi0GntOwIdotC+c9eOPe8QhXactdo+ty5RmCNPPvdqFVF49dUoFKmr1CU9ZQhDZFzs8UKTuqy1FzuQfrd310XP2H3318VA0LXidnfvlK1bthhbZfXzbN8ixFobtuL205KgqJ/FrgOcDh06rOrZ9G/nzp2ydesWM2YGhDboeZC1PP0cOHCgDBu6m7FAK3gn9GjuzK/IknsejOdWV2XIMs5n8fO6uuJ3cUmAb1gMfV89UDoWLvEIEQrtU5l3tYTPvzbUt4jIipRy9tXfkUlhU/psDYFq3LhULPlpz477aT17qUThVytul64ux9TvvOWOHTutWb//BGip1CUDBw6oeSCW4p07d/RfR6qeVJJBgwYGn92MMTIbuqtLBgyoHSc+c4mCsvUTpn1eVq/9z34fv8MnvknuXrwg+NxDj3yv/OnPL/d7n3jgXbcskCMmvanm2eP/6jh58cU/9nufIHa/ffgXsdBEwHaIQr9Pw2v6gR2isGtMfysTBXIU1MoK4B1/0KgocqAZrSyyfP3jMmZsVOHFbdctmi8zvvIPzeiVeeaU9x0vl199U83z16x8UKZ9+D1NG7PD3jRRbr9/dVW/jEdh5nRZfOdyE0GA7jr98x+XX/3n+iiiwI0MCf2e93N3neh96YnCV3ufyxbcKIceNr6GjNx8wzXy7YvOj/quz+zNs13YnNU3EVl8x3IZPmJEHPLEPti+fZu8/11/GR6j3vQtMC5V72w///ndK2TPPYebIbnj5zfnDz1yV8ABE4+R5/7wQr9ulpOOnyI3LPiPmmdu/MMLcuDE5lRwIDH4ld+HQfbBk98tv9/4h34dIx72D5/9uHzv4jnB54Y8Cscc/zFZsWp9v/fzyMMnyMp7awUeHXn9G94mf/zTn/u9Tzxw5dIb5cgjJtY8e+xh75DnX3ip3/s0bNhQefnpDfFzESJfPvv8TjJzv8/Ea/eBHaKwa8x9qxMFg1FKJWMZH3/gKJHImN3/rSTyy7WPydhxtUTh+msWyIzpTSQKx0VEwbWkoxPWrnpIpp00pXlEYcJEufXelWauMFbSlCjccueyOJF56olTZN2qh5oyp0vuWSETJk42HgAdPzqyaMFlcu7Xpvd/n8yCF1n18O/i6oWaH4HXasLBo5u2B9Y9/nsZMWKkGae7blucjyh0d3dXWan3G//OJhCF98hNV/8gnkxegAW58bnnZf8Jf92UScabsHnjr6tAnLrUDpr0Lnnm2ef6vV+nfe4T8v1L5iYThd2GyXmzKpuiWUThzZPHy0NLbwp6rVqBKOj60oFsFaJAf9rRo7D3IW+Rl195NXM/DBIR/qdtzvx29heOmDxeVi6trdaDTHvduEnS090cNHLyR06Qq+b9a80LXL7gWjn9zNnZL9YH34gMH7+qubNPFH65fJV85O9O64MeZN9y0vhD5f5bF8U5ce4Vh731vfLiS3/KvkngGwPfKrLjb3pk4Owu6a7jDnf9bL4ceXitgWH8X71fnn+x/w0M5Ko9suou2WvUqPhtWp0oaAz31m1bZPwBTfQolPAoPCH77DumJvzo2kXzZWaTicKlC26IwLgNDWGC165aIdNOaqJHYcJEue3elXEYDvpzw7o1kUfhruWxtbzZRGHi5CMM+FUyw9hdfeW85hEFEVn9yO9k1Oi9qpKoIcsTDhzdNOK37rFnZMTIaA8aj0JS1aNfP3R7zAJ9udkoovB3IvIvIoLd/XYR+ab1/GwNCOoTjjtWbl70o5pPWoko0DlCoRDSrUAU/GSgVvIoQBRW3Rcut9hrojBCRP5SRDg38JliWt94FFD4XtTdvm98e90gpFgPqr/tehR0Pk8/c45ceuVP26o8al6iwNTta73GjzYgwDGNKOwxbrJAGJrR2pko3PeLFfKBj366GcMmh096k6y+7+YgURh72FHy4kt1hAdeKCLHRYqodGIUX160LbvzOnnrkYfXXLbfhKPl+edfLHq7hnz/qUeWy+v3Gt0GRGG2PLGxYkTYtnVr5FEoPg0NGTdkP6FH+44ZZ+7nJp62ikchXqM2DGXNyhVy8oeaSBTGT5TbH1gdg/AkojDtxCnG+9HvrSRiPAqToj2q4wdhuPqKeXLu2U3yKIjImkefMkRBS8jSN/bAhIOaRxTW/OZpGQlRyJvMHMo87w1R2F1EdhORz4jIx0TkSBEhwwA70O9E5PcicouIXO6tpJOOr/Yo6MfNJQqDZPPGWgscfWs2UQjN28w5F8uwYdXlUZvlUdDQI90cbsWBuojCEGuKZnFNEJFPicjdIrJKRDZaxLkpG3m6oUfuGGZ6FAbbhdwb5cYhyyBl2rPRDz/0iL/tyh4F4Od5uK1FhOPbHu6lRkkiCiT/jTjgCIlyibLbGy2BIcWyEYF6bUcUzporD1nPTD1E4XUikmP7ZU5EnxCFH4vINBEhmna8SD0uhQ5RyJy64Bc4cM2co/DcK5GFnPjsbdvyEQVkLg5BuH5v5G6gZ8s3RDkKJFJXEqpLkkgUMCzhCqU/IZFC2iC6aWAp+s6f6+vwlOOOl8uuujEuZxkVwikJRKHZoUe33bfKJpxHYxbnKNwVVT1Cx3/0hGObG3rkEAX1KjTbo6BEQckLP/EoTDxor4av67y7dN1jv5cRI0ea/VioPCoP0BCk3hCFqSKC0n23iLxZRCAOj9l9zn6/HjeaiCz13ujE446Vny36UVxNQEFcc4lCFHoUAuWFiQJAFwHSy7xsQo/IUQiV+jJEgXMUqHpk3Zbv+uApTclRaLhH4a9E5BAReYeIfEBEDnXcU6eLCG6qxfZnym6BKLz58Ak147fvGzOslUQdsJB7M39TSDKxigZ322uQKBwlIh8mT0VEDragnIwbQGY9rRGhR3da/Ejk7f8TEYJucFbloxjhXjeCKIBLWG4HiAjBLdmBXekjmBp61AuiQLkH7HXABfr4pIhE9VmKt4YTBYwKOKrJ2/upiOBdqAPDdYhC8bnkCkMU5syWJ5/dFNXRF5Ft27Zmhx7tLyIn2E34uIg8Ya2M9XWj5qpl6x+rTWYui1z/06tkxvS/r33KJBF5i7V44gpFQCAw3iMikIS9rAFrT6uDADgQU8BOAaemEoXqDmiOQvM9CvTLAN6SyPo1q+PQI+1v0zwKIrJkqfUo0D1K7pajsNNF85uYo2A9CiNHVTx/YLfNmzc1mSg8I3uSYC2l9PKofuiRG9dVL1GAUP9QRMgoQHnwbxQHBh0IOYr3u3bf+HjL9SgoMOcnSdVpOQo8Q41EOIDxXOxjn8E+3SYi2+sULoMG1elR4GUjr2bUALGgIiwkxGGh9fGgIzcLKq3UHIXAgWupHgWEGxPFIDE5CD4QHL9j8ebvCME6LDp4FB5aemPNKYMMR2GPwhEiQj77P9rJBYkQyzbLjh+TTp9RKICB+0Qk7AiKk5n9Mmw1HgWIydmW9cJ8mdOviMhllvDVs6Y+LiKErdNXm0fnehR0D37xrLky74prdsnQI/ToQSLyZRH5W4vhFokIujcUkpg1zGlEYc/9JsvOndlamqWidbA4dxoi8z/Ft2ZVVxtBFBAXeGapNQLwpk/1AnA612iiAG461oqNv7HeIebw3yzhypq70OcNJQooB/YuNTKwWKGIautl5OpmYaIALviolff3NIDlBXrZPqFHlfKoJpl5i01mTtN9p1ghgdxdISK/FJH7LXOOKqv2qi3f8ITsO2Zs1T3o27UL58vMM7xkZjbiJ+z/gAoYMUQA3YlO4nPiKQmfGGmTrwibgExAUgEmOVOlqoiCrfBDv0zVo2bmKIyfKLfdj/ve6sKyiDlwzeYotAxRmHy4Ab/qKaJf9XoUGH7skkw9U4n8xaBFVAz/zgvdNEfBGTrZsmVzIlEgIGiyiLzNLpv/a5c9SyjvM7M2x9rHnpYRI0YZfJar6pFrLS/qUQBX7i0iuJ15uXdayxJ7BvWMde5mEXFr9LC3Qpa6Ez8wRX628Ic11nvfo0D0BkQAjHugJfGEMjCB7F8I/Il2r95rlRd1i36LNzBr9LzPQ0RBx6vKowCY5sXwGmD+Y0CIq9AyV+Q8v1dEkEusMEJmbrVKpFLwJlfvXI+CAkvt06y5F8vQIZFHQf+WShSoyoll/ud29dNvBB+CDYs3coE+6w7J1cPoSw2tesREEnqIiRUk9192EWBx0gYBQzkDBK4TkWXhziZ6FA49qrY8KqhWQ6KZS9AtYCOn0K/pARIHFMXmGBN9+loLPeKd0avoVPYxvIm9i1OIfQoQLmLJTw496pbh++cjCoRBYXgmufopEbmNUsMF1nroq70hCogTbJqftOIEbILSusCKD3ASWzKpsQ0wjvh4qtFE4SwRuchTXjzzsxZH1YPnGkoUUEQQ8ktsyN98u+DqmNvCRAEl9GOR0s0iZZLzQBcNbu1EFCiPqiUztxKfnZWjgDwHdGtDh5LwiCJHd9Yrg+39NPTINUoCmkx5VD+Z+Q0iMsMiRgAI8luFFDo/XGU+Mlb9HwuCcoKPsEehBZKZlSg45UkJPZoz6wyTzKwn/zbbozB+0uToED976jDzW69HAQj0HSs69LUxcKMvKCcD/8/TVj/6Oxk1aq94/TNW27aEcxSwbXzJLnVdViy3n0B4ROSBPA/M8R2IwsiRo3OEHj10e3yMsR9ak9ejwB75XxawY9V/v2U+7HEUPvjuigylpu/kexT4O5PtE4W/sN6+o21+GoQBoAHGjYp2Rc8GtyNLsFQyoVeJSEFMLrk9CoA+UA+sibAYNDUx9G4DcDLze1iCAKN50ALHAmbCNI/CrLmXyNChg+W8c6ID15jXOPQIyxpWGbfhjgeVsPpV8CII+R1XEJYc3ondgjWnQOIwoUeUR9VTMN0qBLk9CkwoBIvjGLAI0o877OTyk/FEAQPiP28RKF4GPCEQHtzVXvNzFHSdBXMUGk0U2DC421iMtANFhg3YNcqj5k1mNuNtp5Pf11gnFmnvLK9rLWHIIevMVxoRegSOgy8T7QAmgIey7HrT6iUKcGGW+smW84KPECfIM3g9HJ4IOHK82LbkfbkNMfQha5Fi+7JNtCZPI4kChiEw0KkeUUCsIepwvN1Qh9O0oUTh7dY9hGxA9n3VMsE6JrYQUWCB8/LHiwhJOQDGrMx9ZAOyGFkGy8t2hEnbEAVCjzZWTmbOVfGFhY13W48MYlwYE/TSPxNPYhdaEauCM+/LNpDMPNbE2rvJ7cEcBQxmhL1GZeerm0sSNH9BQxjQX/yOgwK3ILl0/DuF5ChR0DKa5mElkbUmR8ELPeLZPIP/ERC48xplcvZe8zDXo2Cx2drVK2OioP2cdkLfJzMDoZCRLIfnnX7GoUclkXJPdDAcc7tw/qV1VT3Cpve/ReTfrREaHYH6xvvMULMEMZRkNXIURo4aJSXpisPvkvYAOgiDt1+4F0xNPh9GrIuzHpjjc3IUONuBlitHQUmCWyI1jSiwHvUIMhTYfzt7mYkDooLrisbThjwKodCjr4nIR6wiZLFgbUMmE8YAG2PvkHOPwtQwdixbfA5xySF/42FWouCOTY1HgQfiPcAkykO1AVbdTctqU2bF9xCAIBKALR3LGfsOUfjuRbPjGHs3V2HWeZfI0MGDaz0Kr6wXwZXrV2Vk8BBarBfMvAwOpkreCQbGv/k7riFYFowLq1yO5nsU3DHMQxRKe4iUcd1Drw+zY8muxKWLwIXS807QexALk84OxnqIJYpA84BXQYmCT477hSgwbiAs4lrJefgnMSdpu+co8JVdOZnZXzp4GwmvYZmxxPAwgOnyhiGlexQONyeaZrW32nBoCuOALeGXWPSjY/Dqa/USBcYBuUqUG0t4od2K9Ms6oUwOwLcsd0f+0jRCEJc117LEcKyxZbQmT6OIAkuYHDRIndsQF4rr2JJEXXCsYpGaQA0lCgwESgCrErL26/1EFBgUkqeJw4XVIfsJifSVDwAP8Mn/Gi+L7GUB5iju1DZEYXZ16NEWashneRRAQyQ84qH3G5ZBjGwQBioPYGgrCJAfJPRo7Lg43t48ghyFawI5CixiwEZWI1yXaup3WfCO1QGDIZsa9Pc9DvKxXpGEewWJQlJ5VPQcwgLrKR73/yiJbDLJiQ1vLlFQ459bHlW9RVkeBZY3qu9dVp4BNzBkqJSm6wyX5o0ztVyDx5cp4HdwHlCK63AuYRChxUTBe3uXKCi3whAE/IGPauSyexnPIBqGiGc8C0Qzs00xiBNuCZ5km2KPrBTQDw97RBRGVx0Ql0QUWO4sFTdyXe+KUwqH2rctxu7NJFeFHi1JKY/68Mo74vAUv+5sElFgXX7O4mKtha4uGbDbEmv09a1ceV4olKPAdb5HAdyI0ZsB/aANNaK6En9jkfF3FgHMDKschgD+jqWLs/lYWHn3ke9RcMFlHHoExcRiBcpQiwOrG03tYhQGBQHHINJhkjXUCrBQpPT3UenVrJbqUYAoDKl4FLjXMR/8mKw4eL3I9+2zia3QRv/YDVBj9YqoxR7XOTFdmoPDbuR7KL0cjYThVfbANf+cjlxEgYIAX7Rxa5Aunk3sBWZnrDJY+yE4fIZZAcIDmoIoHCFSOl2kDMrymht65OYpjDn0HfKCXw+dxc0i4tk0JMsXLLHz4yqQYiRTM6+EcjG2WMTY3fTRbcz70IhNV4Ue2VC1XTlHIbR0SCYmHhNhDCflJ3wwT0stj7rfZOnOkaPAc7AcEeoDaaBhRGQL19vqJQpEyxAXSwQdywTxgkLEKEloJ45BQi7hwGApyAxihb+DiRlHKgdjdbvROiz1HRpFFBAlbE01jGqNBpY69gXwL5gJnYARJ4/VTfvYUKJA5jwTC9CjikZUnKWuVtijgCsdhIEMhfUCEn05gC7A4EEmOLKGRYdcwBrGpshobUMU5syWJ56NTIem6tH2bdnJzCh62Hrt0RXRwmNM2RjzRAR3ZBZa88Zy2YbHZMyYyG6rep2QleuuCYQeAXoIGwU1qpU0NDd4jtCf6CkYM6AEyymxeMwruhb2zs+EBlHQcxRi/UTVo1WBHAWEAkiVcAqeBbjB1bihJPJqudfhWW4XlSiop4OfftUj1nCaRwF1Dach9Q+iwL+1SIPLodUYjQpVCAU8YQi1IWNQwfBFPKg0iAKhR66XiH66OQrAGUA+Q0eoKYYW1DX2Rn5XAxXGEPQQNkq8pnA/uDt9A1PyN2QgqZLcK63iG6FHo0fvXSmPSuiRCb+rLY8KhIT/Mq1mbdpoa0gJYwdhwqtwpnVSZsmIpM85R2H4iCi2L5dHIXSjNI8ChVuIAIHAgtcgBwwuXgSiU3xZmPdFTvrAFLnJy1FISmbmHANAtaYFIDfc6B3wGQNNDC3Mk/4hr8GQRVrIo6CkKiYKWLIJuIbqaWgPg4CiQPD7ViQYFqSC6htUUqDhTVBfWkYHQzkKesk5510iQ0IehZfWizBxoAmbjxQ/BvMkcf9a1xYigcsGJUc/+TsanwkGeIcPqq7pda+qHiERNNuVjEnGB2sgWT1pC0yJArubXQxK8Vrhk5m5J6gVBERDiTMWkAZMHygy5hFTLoSR8SGehjHFosTh1FgzE0zkr8UcBX9OCP9F8CIINWqD7YG71XUth7ZG+jkKk6Q754FrLBnCKMGVGBvYzljNCf0p4oXUPtZLFFhOOMowRGJVwkmGAmProqDgrXB4lhO4BEKBskXxkrJDShE2CdzUiKNrnEFrBFFAuSNHwT4oMc0NY/6IoYVogVvASeBfcBFbJW9rKFEg/gm5B6LQikd5O+J9rxBR4Fp0AuEmNJQPShNkosYgBghFBVFg0aHEGEwIAsYG9ErGwmsbouB7FDZvzq4hz5igA5CtoCTcjchRxgwPEX9Hl2Kh5zM8N+iyFBDuTinnKIwZt19NlcVgMjObj7hqGDhsXMs56g2ZV+YKoA5pUVcbwoy+kvDExoBJg/LABgl6tCZHwYbRrFvNycxe6BEbDH3EpudZCAoMestLIg+Xo/XE/+j0PMlfgCVM5nyfvjqGTj/0CGxW5VEwY1GWaSe+J3iOAlOFAZdhBKhXp5HXbkonFSL+0P0bcplusjQw8NCMR8EmM2tlJkKQ3JOZmUrEAmqdaYNfIi9R13A87omRA9iDIQY5hwyF52vZbD5jCcBR4WcYlJ5OCQohmXn0XntX5d8mVT1inCA+37DLDXGBl51ljihDR7HM8dgG4I2RvRiJmO60oyn1wLXc5VGx9hLL5YawpBEFDKpYlHghiAJGVMh2toM/XULn9SjkkfNgOnAlZB75i+sbS13RlsujwE0p48ImI1GZFlrl7sMx+51jVwTAF+ECMsiBSNI8Cuec920ZMnhQVB7Vz1GgfyANdkRWgxCohwTqjnBjV0C9GVAGGBSX0jT0KFRaNtWjgALFfMCYoGxpmE1JZGMnpzWAOSFgjC9eAO5hzyvQy/zQI+1f0KPARRAAghVBjcwrCp+dS24Epl3iQ3geVB8lRsOCCYrjPdggSBjWBmPP+DneCCUK7ji9lkKPGC74Fu5U+KnmCWJ/vNSGr/CdJPnSqHMU2Booj5MsUGeKwGuA4qJhlPS3XqKAkIcc4DFguYEvCG9H1qI0UBQQCXASoB1jCdYvPud/LFzgUwzokB23NYIosIRRtjSWMkQOI+Y/WSCAIRhshLghAY9+sH3z5p42lCgQEvo+kdIPS1K+qVy/FYv5KHrgGvEJgH6NdQDN8G9NGIERElqjtYHxlGImZMEhK1BggN+U1jZEwclRQM7lqiHPRsSCgFcITxCM8wdW17IB8Dag1DXWDSUPmmIMQZFpmfQlkV+ue0zGjrWR4I6+TjxHAWRLvh5uPeYNtx+LnKZn+bDY2bgaD8hn9A8dQZ8RMJAdctQAUQGhFhMFp0+JVY+wDGA5oEIfpmhFiWxSjFWMAesNNIy+5n/GCSGLedzHG2phxZTO9Q7SVKJg9FRXSUplkXVe1SP6OfWEY4NEQfPBkfHIK2QZ0CIpD5yh07QUfue7WiSSYSNaGys/RAFYQoMocDIzfeT/0DkKqHNqydAHlgkwga2I7MSmB3/Du0BjWIFCTDtwjahr5foMNeGfOJswkjBkSSGWfjIzhu4o/C584JpGw0AGkJssN+6PMYgwXfqDzEW+uo134l0wekFc0ANJRprcOQr1nszMmif6BHxLmABuF9w/btQMewKL1mMlkf3LkcEEbwOEO6n19sA1wtlZTFTSROlD/mF7GBvIFGfQizYlCsQ5DxxY7XOsqnrEg2h5kpIRLiQ8s8qwTsD48TWxOvlfFUlCZ5Uo+OE8fP2c878tQwZViAJ/yzxwjZ2KBR/mp6+IoFELOjdh8FByqrjoNxOMpyEBAdR4FLQCVFZ5VOg6O5R8CO7NbsY1hLJww6aSJpMFQBlSlAi73HPhFz5wDRKIUsJfygLDvIASB4UR+3alE55FnxDAzDFjqtn1CGzeB48IyAoSYTdMyKOwK5/MHJo2ypGSPoMRC689nAuQjq5i6nEogQFCkXmNSGZ2+wQ3VWMuwhnBjH7NERVY9Wr1EgWIATKWJazpQSgilgxKCr5Kfiz/q+Jkm+DZRZFRtTEJIzWCKIB3keeERDFO4DfmShtAAG8QZAJFxVZAgWmoEkQmrTWUKLBPAZzIVKy8OfOrQv0rTBQOEin9m0gZGYFcRYOjMN1a3biMYFEgA4wNADcGlL/BvDJieNuGKHgeBYjC+AMyTmZG7hKzhnkVNIgcx3pOI2ICUy8mauQ8BhkWGRuBBYasBfWB3BI80Jqj4B4ICrhA+08PAAAgAElEQVS8btGC2vKo7oKAyYPEMEtrRAC5KAgL0CvP9IUFBiW8I2wcGmgUweISCvuRhh4Bxk2z5yGtW7UyfOAawfQgSPrEOoKMAIC4HAECNuF3+oVeR5DgjqSvGNEwavE/wpcS7rwHQgRzunMapht6pMOhoUe33LXMVBqiJeUocDu2ItCBR7El4TZ0n24q3+MeGsqIvFOgy/c0FYW/MfyAfNdRr0RBoz7MOQplqfIosFxwxGBK5blwUUI02WohGU9kCv/TZ8QJMg19RH/Zsshd8iyw8wbPoy6JrHmkNkchrTyqOhu1TgsOSd4XWwLTzJbAsEZDtCh0QyeAveGyTB3QBNkc2gKao8A98oUemcMzqnldkkeBvQgxoEoIeIf1hOdUMSUTS6fZE2BKLGFMDHsCsE6OQJISq5coaPIfAINBY+9iJyCEnX0AwWchsP4JbaBfTHoexe96FPw8jsIHrunuosPEOkJR8SVhrmRAlShkZP5pMrNbRUhv/fXzv21qpatHgb9nEgUECQKXWDJoP43V5gYE8jdWGyAY4YaVnDwLdhsILrASXaLA6biDBg2MD/RL9SgQ98ECAiGyWFj9SBWsbBleDCOrkEb0DamC5QaG6jQ/9Egt+YknM6OESEzEfYzFBUHL7zBQfI7sRiVYSDikEO5bCAs/6RNoCQLENXhmcD1bUqlEwSV+rzWPAnIF/gUuQCEA1jEk4qZmygHN4Aamk+F060mn5yhMlO6deXZ6tECYJixFbAWsTliu0J30oajHtF6iAEfGKQY5oIF3UFTwZXgnhmqUEpGBWiAHpQGXZ2ywOyT1tbdEAQ8GDjXEFjKWrQ8Xh/9qQ6GxBZlTxAV9Zlz5LjiYraNxtyEF1lCigLUIcEYnIAr+KZ/VoiH1X4WJAmAW9w+AHxMgngIAJaY+GgPIZGJZ1vgCXGgsPHQEpkqUV4orpt2IAsANQJnrwDWs5aA4LPcsHjYCi89tgGIIGId44MbSmDf0BZ4FCJduXtd6XhJZtu4xk8xMPDvNWKGlLDeYZGbvHAV/ZTC3IDCs+cwVQBuwg7XjFyWRbQGZw4YFAEEaQLoYnwKJWBCFy6/mZSuNvpGjcPJJ700FLqWhXVJ+f0+k/9BJADP0F4RUEaVWSWJtQVjoNwQC3UTf8MKDmqkQxrjbBlG4/YEolk51phKFJXdjKo5a0snM2NVY8ux5DM0ECKAegR4YF/gd7IgMwx7Ik1CtAHH+xrUA5TTPpFv1CLxGG9A1oCpHAf1C2BA2A4YGjAiGxTuhtV386YaTsiK4lr4phgUOAMoZUrYrEck1rSSy2hAFqh6V4jKyhB5NOnjv4HzC84hihpiwbNGDjEcIO/MZ8pitQJQ2PJY3Z8zAuSzJ0NExmqOQ+xwF98W2b98hgwcPkiSiwMAC+omfQiGBbWE94CWNV2U98j0GlZfUamDgYTyH3yqJdAf2UYgoJOUouH0+oCRydjkKf4qKPUUNpskE6h7GmIMVjLWPqwrvRloMF/fIXR41tEDS/gYSwcRGHJfW2WIlFgg96jHlv6oJnksUOGhq4MAB2USBfkJaQGOhxDE+15Wnj3PLm0BxCZrz5rTuHAWQGdVKIAtIFV3pebw1LDrMmVBudj4oxis8rETB98gkEgXen0WNNCGuAjMIpIWAbBYYdJ/G+9NflBW/I6SRiDBW3glTCoIZCeWYMDo5CtHyASOhn9Dt/GTIcLrBY5lOpp/hRxhCKjQdKD306M2yY0fOcmJ2GjWFCH3JsldygjEzw9lXtePrJQp4RnErs7R4d8YC+YqixZqPAkGOYZTR77DsSNLLyg/rLVFArhMXi1KH92q4tQtrmD84sjpZGRSNokBssF3g2niLID++4m8oUUA2MWg8GOMGFS3qbIWJAoYWFjGDA8tlUgG6KCJeGh3AZCP3mUgs4chhlBP/xggD/kqZ1HYjCuhzyMKO7duzPQqao0CsCI1FA8IL8X6sg2wSgDvIje+AkiBdIE3kLkDYuVY9CkZ0E07DKi2JXLdwvszwD1zz1wwLenJJ5LxyxIrVHE5kwHyR0rNdUt7irWzWAPoAZEw/bhApfaFLylurv6ehR+6BYVHoEeVRp+SzcNr+lgaXpIxg+MtytCnpK2SAClsudMCyAApl3YE0+YknzPHGK1FwQ2RdomCAeUnk5IQcBeSYVvJBBWIHVHlKF4nmxUjCd5AtAHLyAnxumLZ9Fy99UCZOOiIOo9f1ds2Cy2vKo2J8QZYiy8CwvCr6JuDkMY/E601uGOpcG1scWwRRaegJCEWorXn0aRk1enR8CBxzm3SOAtczHgB/RAGiC0gRSnFkCuk3S19Ds/BuoCsgMXjmCcRAX/jbhtCjPYbvacKz7vz5LXLaZ06RZcuWydFHw7hFStOmTi1vWL9GqHqU1JKIAp3BwEvINp3nJTgMF3lMp/k3HYKkatI/2A2DCoQBOf2+hJeu16OAERwCwqRp7CzvhSEBpQ9eA9sBNFBeYD5AANZLyA7RMxCGEEZvOFFgkFgFbEJCWQCPeBCUYudQYmk5CnV5FHgm7hi8CAizUCMREAHDrsCC4jYEOqDZO/5aiQLJRLEb1V6X6FFASLFAsLLB+gD5p4qUXspXEcogSpTtG0VKXxYpY0X0dghEAXDJBiHRdcCAyKKUShRYzCAcJQpc4JInUBvIh3hjl5hwa4gOY6u1ejENwGpZC0++Ng9c85cYxjl0FXIFg6vGr7Jf2S4sB4YSDzs/GWJyFjn7LjX0aOxEgVAXbehRODzkQMv0oVDUGJznfvUSBdzZKDGWDA2jNJEUyC9iVTFEgp0wToMjUSYQLf6W5fXoLVFg/BlzIhyQoyhxnKHIUW1waJyCfM4SR76C05D9eogdodzMH3LYV4ANJQogD0xuaH88i7049KwwUdABwViBe4U8JSy3xCegIJGnuM2YVEyCDBxWOMJSkYPIEwYONpXQ2okocOCa5kHmCj1C5mIKxRKPzmGR4RHCdOtb+FhofB+dgQVQc9sUkGA0wtxLWBeNHIW1vzHJzP45Chy4NvMMgv8yGl4FIgMARMQD8m82IAgXNu2f2EgfMX5hkAMFQhjRF1pRzz5OPQoaZ8+Y9epkZs2R0TEC1aKzWWcAM9x9/FQxyfcgDaxL50SxtNAjPApKIJJyFBgWHs1+B+6gTtX2xyORs+RmwZvxqPIZMoPhyit3IQqTJsMmRbp7uo03gRY6mRn5hCMKBz/gH3MS8ggoA7f0G3fycyrQE+gsPCIsL7ZvqEEU8ChoYz63pCT0Mx58W8OuGAtkr9ZG4D70H0gPUdBUGTwwEBaMNfBRxhjDGmIPme22tVQ9Gj5curoGyO1Lfianf/bjyUQhlGjKzdLKozKR4Eq3sR+1aAN4ETxFaBKdo6Ncg9LHOAKYD53bVW8yM5OFmx7mBE7UeuIsMvrEmTdMJMSG9Q9bReZgwKEvWDKhTAymb91yiYKGz6ilvq7QI2YUQYe2h+Hjm8fqwc7Iaa7sC6JQ2lOkjPCiH+wedqyGQGnVCVxCsD58gMhRgqn5DJCMEkT4OMl3dXkUmBAUA0KWCYXyQ5HzYj2kDcoFywnkBiHsBQ4mnaMw5rB3yAsvJEwCEoIQAsbHdVvpJkBxYebGCuijNcgVrmAWJ4yaXQ8AQBA/2yEK4Dg4M1OFHvcTh5EbTCeCEVcsQhRlw/4224iD/ZYi4j2Z1N0te+w3Sbp35k2jrVyPkKYyB1YujSpDx4M3WI557lgvUcAKxNJA0GMcZVuBOxgXotogEMg4IgNYdsgu/s11WSGVvSUKkBbCnLTkPxY4wo7w2moDk2MV1BKp5HmC3VC+mlzNFkGHQAB9d3pDiQIsk73HQ4jJZoCQwZjbmEgWUp7JrCeZWQcE5UcsAd4DrNqYU5H5yFLIAnFkgE0GDNmKp4GFjQmWidfMygACaSei8ORzr8ZgMhdR4H2PK4n8sBwBCXQACwcdg9JmUYE+XYchY8iYwrYZQ/QTa4B5ZrPAWmG314g8uO4J2WfMWBMKomQhd+gRfbPAu7RHl5RP64mQHMQPgIFO1OIfkELAECAJYISRiO+x/ghF8vJm/HMUzJkF5R5ZtypQ9SgBmKb+WVE5go01CCllzbFG6R+omfElhhALhG1pRIGTmbVsalKOgnoUmEYALUYY/6gQusR2Ra7QLXgKcjhHoIXp5RI8CpPfXFVdiL9ffcU8OfdspGd1Q7cAtgkW4KfmRKCewZTI3DToQVg9Mgx8ydbW0tr+c7TqEX/XkrdZhw6CV9E3cF5EFmOBDOVv5KbzOTYIvByaCon9GTEHzkYGozcxKCH2wMHYHnS74FEYMTIqj3rHz2/O51Fw0xT4ff8J75Tn/lCbL020DHINQ4029i6GW0LeUArgS0C3GphZl4QbEm1BQ0YS8u1HkihR0FwA7ZN/jkKevaGubr7LusfCBeknJJDPcBHhpcSAjrGCNyUckoF39YYShRChqosoAMI1iZnOsbroDLPnJA6lvaNPFNxE67qSmfVhoA0Gg93q7hD+7gaFq0LDpQ7Z0aBxiIbj2tdzFApVPQJMIzwJgWJnsAvykgTegyQUsqZQEN8QKd0kUvaAe+HyqNyXMWCB4C+N9lZ1o4+gJcwfjqOuNECkzO5m8UFiGDtc6GwkiFC5QxQIY0Q/IdTSIkMYOmxFGPFQMBjkCD9qVNUjd0LVugX/VXkA34QrIr/yYMt6iQK6G8sb+JICKTyXd0VE4Gzj78g0foJJ0OnkesLdXct+SIb0ligQesT2xJWNzGTOXJKA+EAsgINQZGwX5D76gD6DOxhb3pEQUAiRbzBvKFHA0AD45n86ipLCBQ1mwCABiANAZsVs9YYoMBHkJCnDA0BiVGBQAIu4qtDgyFEIDMoU+YfMQVfAVhNa2xAF5xwFAOXWLVtkfNaBa7wzlm6C2clT0JgUNgJzhhzFW4sOdQ83ZdyGipT27ZLyp3siwILMJuSAjcvCe7/I8lWPyz77jo1P74Uk0K5fdFV6MrM/F8wbxiP6hzDDUs9iB52hR38pUjq3JOXflStVCnifFKJAjoKW96wkMzeIKLj9V28DSJTNyzghaBlf9otDwvxzFFiv69esltkzp4smMxO+NfXEcNUjN/QIMEtYUeisHLAh+VbIGrYIhcuwkudp7jkKfF/ndOEVlwaJAt/h1YFlGEGAG/yb5UQfOTASi32Sp9YlCugiDcH3+wpRGDW6Uh2GPZBUHlWvZRrQA9g96Rd4GvmOrGcJQwpYcm6IJ30lwpLPmUL4MWNJ2BQwBBs1y5K25jdPyahRexkSeteti7OJQgjIJXkUsGLhXtFQdvYdHUIOe5En8VihzOggRJ5OguNCR1D7oUfKvCAs+09ANTW2QRQIKcQAAWlhj0PuXWXrehTUk6C9qIsoMGPEPBGGQgMZQZmTAuMCrxydozDXzz8333TPUdBLM5OZ6xlWdohmh2usI1YcFJ5tIY+CrrXE0CMELGgI9IOQYtVrpYg8/VSUskOk9I8i5UBNbffANTf0aN83HiUvvpRwHCrvyD5nsaC4IEXuSZ0sIPyCSDnl15qRC1oiVpnGzmXcQEg25mJXy1EABDKNGO+yGt8DB2H0w8KEEMxq6Fi2i94/verRROkJJURlPcT2idAesBtTjV7HCk5cPn6nrMyHeokCXUNnY1lDuLsrkuWF0YWG7EKZMG6EVoIpsTSlWd96SxR02DQf0lWgYCW8CxjOIQO45AlTQj8QBso7gdnI6WU8CT9Cn/gVpRpKFDB6YOkFlQDIGRzMhpgracgXJtU/WyawPuoOPeJegFQmB0VJn5AXmPrIp4JRAXrR+ChJvoO6w61F3/hbgnJtG6JQz8nMjBtjhRzFXYZ10s0xBkyAiCALGNl8yyP5e4eWI/RGaBd6hbHmurERUeDANcCkGhYBcIQeZSYzh+QHCJPkdOYOVyDGIRpMmHgWCA16g76wFhFgAA5CZZ3mhh7pCcj8XLuKHAXvHIUccqxRX4nLo1oPDAbddWtXydxZXzFEgYZnJin0yCUK8HNkmx+dxT2QcXgtldsDlCELeZp7jgLfV7wRCj1y74f+YYnhhMLIgQEZnsSyQo7pWQuIDm3wUaYYmYd9ElKB1znU1KOgFbb4TlrVI/ceBCRwNIYecMwyRyfoORQETmDQguzgeMN5ijghYgY/u5ZwxYDPOyo8gSiMHDnaeILuyDqZWTukGeJaSSctRwFjLcoK1kWsMAOKgk8y/IKRyOMiBImXwvIVUmYnHjdFfrbohzVuo3o8CnkWFYsBaxd7FiWG4gKf4trRpkTBr3jE54WJAsoCizlmSYgCg8DDoItucxKNSlgk9hApswpYiRglPvcJ+f4lOMsqG0E3hE8U+Pu7PniKrFilR4XkGZmM7yBV2RmQAgXLCEOs6Zg/LQCu52Rmc5AewhQFDuLkfnmPxwXRaQ1NQDtDFPDSJJ2jkJqjYIekNEikDMMkMBvSoI3dSbC4noWBpIM4MZdIPJAl5lTMDpikIQu2uURB57Fdqx5tfuVVMywYEpANmlMYWlEsIzzeYDRct3y/nvMK0ojCnvtNFgh+PQ1MQQgxuh/HGeAYvU7EgxZTSbtvb4hC6L7gEEQA/WL5IEsxaqCs4MMo06RSeHq/RhGFUP9wuDFW4CDwHQoWMqMeBxQdRnX6yDsQZoZh3y/y1lCiQEcBbiSxEU6JhmWfkuDMAkSuooVzhH32iigwKMTYIR8wlW4RKS0TKSMLiLnDlU3sg6tEQSUEPoOUErzNbUMUrEdBcxSyrKk16wvZDmFA7mKwAaUhW0E9hG0xh0nCA/kLYIHBoneZ98MgCk/ImDHjIqJQKsUW/OsWZpRHTdv0WOQBPOgsvFl4r+gruSnkoNB3dCeGNhg0ORfoE6dp6JGGRJk1QYnNlYGTmesRbHVeo8nMbpJ1VXlUO4ZJyczIKPQCoBzey5DAk1HVLHXwJDKOaYIfEwXCduX3FKda1dtUnaOghKbcI4vmX1aTzOwPA0YrnEHIU+y5WOuBMoBt+op3F4ID1GGpsSSxAWIER54RdRyqLsRz3NAjfS5EIekcBbdvLFdwKTiV6DCWPZWaIC/0gS1B33CQYhNhiWPAR7QAowheQC7TuFYLR1L1aM8RIwy5yyQKoco53DDtwDUNa0P9MmB0KmTwQDHAzpCNAAG+Q4wx8Wmhph4F18ORp+pRneveLEQ8wIAVPf0c4k/kiFrxQgeuERJFtaFCRAEmgmWcXYBCgBZC+/ALQa0reS7R7KrFC9MpAoWZRpl9WOS090ZEIeQJmnXeJTJ0yGCZO2t6nDjWcI8CK5fVSL8Rivyb2AhWKvG0djHUlaPASoZMIRlAZnmIAguNHQOaY6eyu6HT7KDAaSNu6JG7/vMQBfOu7FhQDsAj3vUW8WIipcE6NeOTf4OGWVh4PFAYjvUr5FH44llzZd4V18gLLzwvox2XZb1rvRHXXXDBBTJ37hzZvDF8Yt/eh7xFxr3yqrFaMBUsDV4TI2DIiMBQkoCLosAqDlYqcoKvvlNy6FG3DN+/fqLA/ekj7wK/Q4nxb0KksYSxxNK8Co0mCvSHJUc+B25ntggueSxahAAxluQAJBwAboarL4kChhfGBBxEKg/iwT3rkLEDI9NHfsfVj3jjf9eL23CiwMNgLgh5TYoFqGE+1IFEEWSEH/WKKFBQZ6RIGW8kB1m+3hIFZD0TCAgGSaFMCbehv1jXCFkCBCdY4dqGKMyeIyQza8uKzw7KK+YO4M1CB5mhE7EuQPzIAUjL5MegBUjHCEW7QWT52sdNjkK0z9UtLpJ44FoRIYpOgixQvY/+8mzN7tf7gI7xMnjBEuYchauiM3e7pMsQGf5bu+ohObnJHgVTHtWUkY0aRGHOrDPEJDNDuMoiU0+cEjxwDU6OTRT7GYSB7Qb/JeGWZGU8tswGsgzLPECdv2NcCJ5PEJiPuDyq/UwJ4ML5l2YSBS6B0wFtZtnlBalB7iNTyVtAhbMd8Y4QBksaB58zlZALx/5X1Ts/9IgP83oU9EbYYYGPLGH8N4gNciLIkQCL43ngf+AT+W0YaHBaIVq0ICOYl61i5s4lCmk5Cnrgmht+oZ1KIwpsKZiNxvfjig81Jh03EvHHgAZwLosCj2uoVSUzO5VyGu1RMFbrcmQMR1lRbhkwAENDX+DpVWXbsKpHkASyY/Q4VVYamhSzGztGT4NjcJlprSzEbDOzaFx20kyR0074hHz3otk1VXsY01lzL5GhQwfLeedEJzPTGkYUWHG4hdg9AHrYHyuPiaX/HBPonHOARwFQTmPDuussMfQIJcn7Y3ZkDFjp0GnGSkmTu3iId6A8DTsGyQJK1e+TAxBQHm7oEbdSwjXm0KPkhRcTQo/cZ8KUeXfoO7sWpslORFpo3Wk+05J59IHdjFWTd/AC3H2iQH++fPb58uOfLGo7orDplVcNv2Pvw6XAZkwDggzVR9ynFotC1xOjyrARzohlOU/svy87+ir0SJ/D+yAbkGHoengwyx1rWFrF3kYTBfqB4YWKSDYFx0QuYqhGaUC2WPpJIaC8T18SBUgM2BaRAIEh1N7NOUGhYt3CAocyhtODm/3a5Q0nCu6CYRBZZMgwZAoTiNuDhZnRekUUuDfPRrYxUKAMzJeYKDEcsVGweoOgYFkAS1xXyIyUE+ralSjkOkchbT6Qrch79EPagk+5x7L1j8uYsePisqga6twQomCUniWFCDf0EzoT3aGchBAo1h1hqE5zPQpKYPi5xoQeFSuPmrWmi3zuehTMci51GaJAjsJiDlyzHoVpCeVRuQZViXMNTy3hkjoUQBw8pGwLZBuygoI3cGc8lXmbEgXtiyaC5/Eo+M9gqohqg+8BS7DlutHGqHwM5cgvHH94e5OOeVrzqD1wzeZNdJkchc0y8aC9iuVg5hgIyAKkhbow8GrGGSgFTifKTek6RGHEyFH5cxRCz04jCgwWygqGyFoH/MP4XKMHhJpBJsEfxcG+BuvCiJwzPKoefeIHpsjPFlZCj+rNUXhdVA7ZnHkCe0XmMpmr7KqcXY48BvyTsEUGFqyOkvVLbStRcEGuAstCHgWoKAqB1UcDHeHDgjCwK0AAbgOZaCk/zJZ8F4X2e5HTTq2EHvlzN2vuxTJ0yBBz4JqGS+UiCjYBzASXM5EIXwaGxspiBzB5uEmx3qPY+EnoFOCXvAvovwPMj4Qo3Ft9cIz2N5EoqLeCxYMA5d9IEczNlDdkIjVYj5vRB8ZFQ7noJ8TBO43ZHSf1KPjetFweBb0R/UL400fGhDHTPAS+g9cAdAzRYx4hUQnHklcOXKuUam3X0KOXX6n2/+NIAfvood4AWq3xrDnf/Buvo5sUm0Mexl9J9SgcMFl27qgv9Mjvgx4YxHZgGRLMhyEkqTWaKPAc8jNwWoE1Efosd4yqYElyGrIwU18SBaIu2Apgb0QC4ahungqRl0Rd4nhk+6BDkLe+Ib9PiAJKAE1PJzAN8jsdRK5ivnTKQCbNZ6+JAjfGkzFSpESoOUail0RKs23pZ0QlcfgwKMJlcL+DnlJOZ24bojBntjy5cVNslCkcelREIOT87rL1j8nYseQoRE3BZcOIgtsPNi4sGYGhRkAMf4An75Quv+pR1DlCj1Y03aNw2/2rokgF61VQjwJVj7QlVT3Sz1naGFrAXsAhPWSNz5kL/sfRAo8nnymrQIM7zHGOQjmqFKWhbnk9CqGlQ7oJ9kAcgRAZLUUKLMOyD9djCsHCSYYuJQoGgtrDjdPKo+ZcwplfQ9xBzIBH2F7dplWPciUzh2LvuVkaUYBdETeGGwkgAOPjLAU6A1YCc+JtgEiDNRlQPsd9j0tHs679t6z3HAW9D9Y+jAyUgcLQDDFB7tInnolewKhD7jCghN+JIkFXECqI0deLCqk5cM0N9ylEFAhqxhqvq8x9eXAMoTtQP/5Hy2KIB4nozmEFYn51chTMgWFdXWbh6TzOnHuxDBs6xHgUtK+5iAKhTgSXQ/lZzZh32aU8n8kDjTCw7Bbd0Wh4yA+KjPAozz2uRKFQ1SMdFxQ5ORD0izFhEYGGGAclM3yXnYBrlzEEvdFPFC2SJqEZj8LkCfHZDtq/QkSBYeoSKUPwQGj0DwQEgWIcyFHQSlawaMYzAa8qUdB+tLNHwScKGElxg+K5Y2goGMBywrjG7+xRACM6s1443xflUUNLB0MC062HSSJTWG7Ik1DrC6KAUxKLPFuAJDWMNqTisE1QwGlhR/SRwzRfeYaYx+q2et0G+dJZc+UhW2b2vl+skA98lODvYg0vEYrdj5Shf3BqdAWWLkQcdgdCD/r0wDXtPvFQhKpgHsQVhHGBfxMaAmPNkXnfEKJglKuVs3gOcAehgFCkGFn0pCU8Cbi2U85Q4FZtQxTiZGbAW1f+qkfFll+hby/f8ITxKLhyt1fJzGlPBxQx7wg+rWWJ/qS8qxdvqUTBvR1hPetWrWwJj4IacE2C9eqVJvTIlEe1pw5nEQXeC/mFHCDQAimjsAhDB1uRympE3RVtldCjKLFDiUJWMnOe54B7iSbWKkNMn3uuQdo9Yo+C4VjR4X5FQ4/y9LHId+KTmU151FvktM828MA1OgLeBUMy0WBXTdpHCcC4IMyQBqxd4CMsTFQPAYw7RXFq3sn3KOgX8oYeoTQBJbi1mFD2IPtSs8O5n1Y3cB+OsRdGCJZPOkfBLUGqJ/oWIgr40MiSQWPizsAsaZOUjHanAyAQLOT466HTeBUCLap6NCfeBO5XZs65SIYNHWo8CtpyEQV8VKA5GCCMCyblHkHIzfib0ns9iIJsnoTYUEMUlt5UdQKkjmOiR0E7rVlNUHmUKcxTq4VggoAg0FCkxFywyEBtfmZkYPySzlFIrXqUtAPp00CRUo9IWZPY+K5Wbcqxc3e1qkfuK7P/ODoCGcHvkHWcaUwfS50lBYCslyTwrL4OPdL3gQdiyUf+YQ1j2bP8mPZQCHlfEAU8pIwXUYCQLMQJ5AXoT1RLVk0YiYwAACAASURBVJXPviYK7tzr4USMDbwfI5FKJYzn/BvC4I9dn3gUtP4h5AA3BiAdWcFA5jyErWFEAcTB/+gDPT8BmUbMBQqKRUX+BLH3Ga2diAI5Chrdvn3btuyTmbNevjefl0RM6NEYPSu4crPrFy3IPpm5N8/OuNbkKCy4IS7bGuGWkqxe9WDTPQq33r/SbFiK3gB416+NyqPmOUfBlw2oTryQyC2WP/CHSDyMtRiT6vEwazKz8SY4eScLr0wuj5p3KuF6vvcjyzCj9179qC2PWo5KthK21R8ehbR3w6MwfMQIM4933ZZWHvWhO6qRcyWfJ9WjQHIi1newIl5clWcYaTS5GdZF7D8KQU8Hzzo1tLceBZ6J7KXYAMocIAJZoE9ubBlkxVVOuGSSCEwomVlZaiGiQAwW6AJUAQFQo57NBC8tECnbcwxKm9JPIdaqR74lhEXhEoVCHgVQHN4CWBa/w/Z0F6Dx6RuZOmTy0EB7xJPwHgmJdm7VI3/BZhIFvQBpAkJjMdEv8mghMBrmgylaT+HMueP9cxSU+BX1KOR8XObXXKKgROqLZ86ReVf+tO1yFHyPgr48qSPwPf7Xoh/kKsGfU3I1M8cujSgwliMOOEI4KLERDVAOX4X4gDuJZuGE0SQ81xdEARFN6BHVgtgONEQIFjjc+VmEqz+JAtsUjy3iAxEBSdBQLXQFYxcqNtQnRAGlxcM1mZUAaOQwf89hXGCcG0YUdDGykEhkRcZxlg4NuYtVjbqyKV5RvUX7EIXZ8uRzUegR/23fui3fOQqN2LgJ99Achbi6EN8ri1x3zQKZMR2m1pwWPHCNUqSr++AchQKvqDkK7iVu1SPALwA9qepRgUfV/VU/mZkbgdca4VGou1NO1aNyOcrQY81t27JVJhw0uuE5Cnn7GXsUKI+a98A1s0dwidj4qbTQIyItqLihJYk1KxxXPEwQtYwSxTWeEipe8z5+1SPtT16Pgt6Q0HGwJQnKlIjCYKRFB+gbCjVv0qQShR07dpgwJLcVIgpciGsDkyQx99SzqrOlncw8Y85FspvjUUgtjxpyr0AIcAuRgc7/NNxDhB0RkEf8WM4WylHQccxNFJxnlTBD7xApZ6GhjP65RMFd83V5FHKORdrXdmWPgvveYCIi27AcsRUoO4fjrMh5eqFxTPcoTJKe7ry7PXsy6TfWcQAwhghyLpJaXxAFnsW2JNwZroyrnkJjyGPGMutN+4soIFrwdGCEIZoGPk9+vzYcl3wWKh3QF0ShtFtJyu8tRyWjUFJ4bdMmLzCpDScK+gyYFO55FCq/oxtymirbhyjYqkcmfzD7sKnsndj7bxB6tK9WPbK4h7vWfY5C77tk7uCGHrlJua1QHpUcBQz1hDoP6BoQJzObqkdUiIGPJ1Q9atDwpN6mqjwqeJazNEhxTDlwrT/6peVRozM7oj5t2vyqTDpo794rwDpfoBBRCMWP89w0osDnWLUI86FKEEoT4wx5V73BcL31KPjjhXGG/vSmT65Hwc/nKEwU6pxQ/zKIglY98j+bMedbstuw3eS8c6an5yiwVkkgwdcXyBYyJwoPFiltjbwbWimqyCtAFFbcE7lQ/VYPUSjy7LTv+h4FnddmexTcvbirJDM3as7S7pOeozBRunf2lorUPh3OinxJc4/3FVGAbFHVk3wFjDSE2eeIUjEv0V9EASM5ZQ3xHBCaTdMzNfA0QB6Syt73BVEwHcCVRU4VicsoLw7FKND6jChoHwg9wmNaQGG1D1GIPApmHZTLTY/Pph+/XPeYjNtv/xjgaljUDddc3XSPAiczd/d0mxAVJQuUR22Fqke6XP3QI/17M4nC4qUPyqTJb67JO7n6inmJJzMXEAF1f3XNo0/LiFEjzXyqV23b1q25zlGo+6EZF0ZVj0ZKd0+P3H3bkuyTmUP3yyIKhBIQXxadx9eYpkTBB+RFPQqN6U10l1B51LqqHjWwU6kehdkQhWH5chTQ1iQhJyimesiB+5p+6JELhFuFKLh9ajZRcMeuQxTyb5jkqkeNDT3K36Pom31FFLQfRAqSDEjUSt7KIP1FFOgjtRFI1dEDaukj508Q3UjUTVKf+4woFJ1A7/t9ThTq6F/7EIU58uRzrxrwiyWacxTGHzCqadZUhvrB/3xC9tl3bFWuH6EzNyy6quk5ChAFmmIhxm39apKZm3syM+co6By6ROGWO6PyqLRmEoXYo2At90q2elP1qI5tWXMJycyjRo2WHtsvxqr5OQpReVRaauiRnqMQqnyURRQaMXj+PRrtUWhEHxt2jkIjOmPvoUTBTbDW28+AKOy+m5w3iyjmqOVKZm5g//RWDclR6IN+4VE4YtJ46RoQeTpaxaPgvmqHKOSf+DSPwu5jJ0q5p/EehTy962uiQB9C0YNpfetPokA/CPPEeE8kDRVCSDWiGFjajHSIQp7VFX2nnYgCycxqHa/rwLX8w5L9TZKZ1z0m+44dF1t5TUhIidCj5ucoqEcBUqX6ifKozfYoEHoEmaJfNJOjMGO63HLXMhM5QF85FA7vRzOae46CPh+ycM2Cy3MduNYnfS6JrHr4tzJ69N7x+mcfbNr0qkw6uHmhR2sfe0ZGjhxlPBwQhdM/+3FZtmyZHH00bleR0rSpU8sb1q+Rh1dqJfMKWNKBaiZRaHWPgo5RM0OPOJk51AxRsB4FHcfrbrpVnvtDPWfe9m7b7DV6pJwyjZoGte2y+dfKli05A3F7142aqz829STZe6+ISbsehXlXXiPbtmZVom9wZ8hlHDhATvv8J82Ndc46RCH/OPdXedT8PYq+2R9EoWif+psoUCedMCQajss8no8OUcg/q+1EFPAoRPLWwBDZvr3/Za07shgBXflv0qyJsy2LdHeTzehUd8k/Jb3+JodxDfTyIekXuqF5/YryV+kXhhd+5/81qx6SubO+IkvuIQM/alNPOFbWNYso3PuQvGnipDgPwC2POvtrFeNpryepwA3KnIHxCAeujTKlgdUjY4jCIc0jClr1CIJcKJlZ351F+dQzz5qTdPuz7TZsqOzzetKOndKhKJjubvnd0xQD7P+GUDvoAI2yrX7+U08/Kzu7CwSUNqj7e+6xu4weReBXNdDl326OQoMet8vfJilPp5kvfvqZc+TSNqt69MKLL5l8loq9WO3drt27yO/5vjtg4AAZNYLDNmrb8y9oXZ34WKVA/9zr8j2z1iZee92QIUNk+J6vs2NSecbWrdvklVeJ0C/6rAaMZ0lk79ERSXZbo85RaMSe6RCF/KPYTkRBy6Oq5V4TYk3ByLxOP2fLFAqNDVxXXe3IJuI5Q6/Jufyp0LOce9RzXcViX5YeW3OfMQJkMmYKfuPHJImRrGVU4Do94ZgYe8Ud/NTyqCQza0lS8FpSK+oB1fvkvW7AgAHx+DBWjFtX1wDZ2b1TShEHzNXyPs+/WdJ19EtzYLQ6FD+JCjHrq86iHr25rmtA5BVibu9YcnPyOQoaeqQTX7MAcw1pY7+kpSq1T2zWUDJsY5+afTfDom0GfSuNl9/zUHnUVgHCrdiPVumTzqOGk7WjRyF7F732vlEPUGjGKPlE4dVXN8mT/5NyJHAfdpJzYA79C/eY88rDHn70Mdmxs/+NM/TgjW84yHhr/dbMPk0a/0YZMEB9NiJLbl8qU089XR544AE55phj+nCW8t/6wgsvlNn2wDWuUq9p/LPcY8J/DLCzVZGqLP22eo1a+zXEhe8mXxeRD3R20nWxHjflKqOylTEwdQ4xjQBndHZAah9LUdlLPWeg3uu0D0pUYlxma/Ab0G4JQyOel6ufFmKbQ9VsdUz3wDVDFHqYRybQjrkBv854UBrUnsEQjyPftR4KF1fVc52e7VBFAJ1l2h9zlzSW+m4ullVipfPZ32tMk6qZszuW5DhwzQdLO3fslIGDKsInv0jo3Te1H35/enogC81xA6a9UbNAZtJp2vSV8qi7Dxsmc8+ZLu644R0aYGPyEwWRWc0ZHldHWMVj4xprnQHT8TH9MALEG80czzJXONeZe9pY0sS58e7rClyjPHhJpxxeVT+tYqkizTn6acqe+feso596jw5R6J0saZWr25UotMr4dfpRbARalyjMlic2VupcqZyrAcPFXrfub+tzXR2u+RNNNU5a/ap98PsZ99eQp9pqgnUPSJ4Lbd9cQO96FPTvLtnKc9tGfUcJIWrXJTM+MW3U8/LeJ17jAa9Bs4zzPoYsHHrkg1/DOiyuiphPhAoj4hghOJdFRQTSXsP3LPDSe0TXRoyYFjMsB4xV9cH5u/9y7j1cN2HiM7y+hq/hgZGbz8F98XsqazVuU+u6cccoOjbcjgns2YJzE/poD9uA9LiLumh/fUCq1/PTP5k5RBDyLvC++F6zyFXSu9SS0p6me7DoE0Rh3hXXtNWBa32xXtr9nh2i0O4z2F79b12iEJ2jEKUnRPrPBeT67zTg5MrqpN9DsxX6rhqczPOsvvYNUNo/30rskpyk32Pvg+OJSPyu8URE4FavU1zkE6lqo1clkCZxPFzgFRic5OtqDYYhArV29UqZM/MMWXz38pq7670bOW8uSfEf6BO92OviRKX4mM/cIwF7JuHQ+Lk5rwMDuyFj8TgGiFfN+4WMlM6cFpm/GMg7a03H485bb0kvjxoCbjt3dpsEy2aAOn1mu3gRmjFGOjahZ8+cc7GQ64FHoTKWEfhVopVn48bfybEZgvcrSxSzaJOeXHnF+lLvRo3w7MXz1POQ9H4u0awSwPaZIU9NlkIqNJaOZMu6b8ej0F4ALam3HaKwa8xju7xF6xKFysnMaigsBqYr9ecLXdfVZUJbTDiKhg4B3om1x/KM4dN6rJUoxAC3h2KWTshRHtBvnlffda7lW0FviDjpZwoqi4yHq2/zXqdr3zUM67Ub1q0xVY9comC+54BgE65kE8XzPtOAac4cyHmdGnBD+zSJQPigPI0Y8g6KZ4pep7kJ7jhWrTvPcO6TikaQ0Ur4WtQbHw9mEgV/YH3wWVm8EXvFGq4vwsPdnAL9nXtUBj3fdXTcHxD3Pjw76VkAZ4CnLxAUpNZ7nT8WSeRc8xew3jM++calx4TQwDaLXufnbLj9nDn3Yhk2ZEh8jkIziIy7ppr9/KIKvtn91ed3iELRmWvN73eIQmvOy67aq9YlChWPQiTjIhAYA2LraYgsvE5oaOh3zXzWzaWu/wLXAeY1ZMCX+VUgXYszFH6WNVUXuM7Yq+z3TRKzH9IQWLQKyg0yL/Cs6Lv5+qjejlAkA6FHc2adIeQoMG9usrCOq3qR8j4vDuWIT3rN7meVUdTmSagnKFpalg3ae+o41zzLXX9mndmQ6ay1lXCdC/rj6fM8AiYsW9d/oedkj4u/l9y5dAl7ajIz5VGTgFHYSlzZwEUsqVnW0zzAshHPy7pHnn72QAbicCJH0AVi3kPMs7JgozWY1Sf/HpEgja4NWcBnzb1Yhg4dak5mDo1rFSnBsj8wynx3vUhKvFyXa9HrlKSlKeSoqhYkj1rRUciXMt0QyavuYxT6VfS6WGgExjC0F9i3PRwSRNWCKgLsEr1u02/6XOljynXWyxJVhyjV5I7Qxy+eOUfmtVnVo10VfPXmvTpEoTej17m26Ai0OlHQ93Gtt5oA64aK+Bb12MpvSYJrVa+QjQjUhXQqf6PFlucYA9oE5ISB1uuM3rCGPdWLSX1UwKzVbVTn6L3c6/y/+ZZx18vhdtH3Kuhnqjez+xiFiIfG0e2v3s8dB/dZ5hyFmdMNUfBBsXtv9TDkeV4V5nFyIkPjF+GhiKBUYY6YsUS/6D8Vf/lrzX1mvFaw9Hvrzfc6ha5z3zEUQua+h17vv5s/B1Vj4lS7LHpdaA3R30I5Cs2KZ9f4fddS3mzLrivQfE+H66HQ7zVv7KotD7PmXiJDhwyuOpk5JhRWOEaLK8ql8Mc5/ncoNs5ZZYnzE7guNF55FKCrHHxrhi+kk+6Xto6yQo38e4aJRNjyk7V+0+7FZ18++3z58U8WdXIU8iyUFv5Ohyi08OTsgl1rXaJQCT0KyVUFRnmmxLe0h0BsnvskyWglMe49YkBmw2jUkm/AdtT5+OtJoNr5QhwWHwNYtf85FYMUHPpj41qC/fj6JIBp/u4U18jsowPA9VrXX6D61yQzz4AoLDfGaw3xcgFwKALCnx8NT/KBbGgO9G/mfbSykv2jmTvPWxWcR2e+Qs8OrUd/zLKu0++bdWbydSvh337+hBsWnmftFt0DLoZ114jO0123Lk4uj/qrFbdnVhPSsBoftKmHxI+ri9mUU5os7qRXYlQ76b+03lPBt7uhE59nGWX1d8PVdvxYff/dNAcgdPKxTmISiHU3rg9Ea8cwApk8z08Oj2PINJbSYbb0wQ3zchf1rPMsUTjnjLjqURZo1XdKet8QoK6MQ5WMjNc4c8c7qZCINkZtgjgXZFVn4js7duyUQYFKXEkkJA2I+/kvPsP3BYu/PqM+F096LnJNJ/Qoj7hs/e90iELrz9Gu1MPWJQpzRA9ci4GeB4pdAFNlebbgXD93QZar+6os2BYYK7hNAkox2LNVhFwd7fZHdYTr8fb1BJ4RDRnyDVtBHeMZ1ZKe4b+Xr9v083x97KrKHYzfweYUunPjA8rQPtGqR7fcGZ3MrLqRn/6/XeOe71XRd6qcFBPlBPhroqpPjuGTsVevQhVAj3MkKwa9CLhXqjC6BMCdtySvTGhcIuJYoVIRYYn6lNTn2ANSZcD1vq/Xe16RPHvAX8t+v/Vdc3sUKIdKCIqCVsBXCJT1pUDlpQCXJFG3UsubVF0EBPbV++3YsUPmXvhdGTxoUOxR8IF0IsFxOhWHBnpxkknXpt0zNC4h0O8Lv8RnWbKZ9P3M8q72PZPIU1XfAonVtc/1iFLAo5LYV38hOALji2fNbbsD1/pqXbfzfTtEoZ1nr/363i5EwQfSjLQBVyQDO0Yx36in36sB6fY6N7TEvZfeW4Gs+/z4ADiMW1qR0AG66FC+r/cL9dNN3nXBtpvP6QLU+O8OCQqB4iqCEQCUeg0/QzmiPkFxQbFe6/bR1dehUCJ9N71P1YFr9myHGIRb8qHj5h8Opp4RBcyqJ2vHzEZbW2LpWvLduXAJU8U4Tah4hCld4G/WUcBwndSneBwtwQiNoz82ulZdkmrGwhqGIZVuaBLfc+cwtJZ90ug+Mx5nHSd7RoUfjqfv6K6dO3+ecY5CmtW8YlmvrYLkL0C3w9oRt+MK4ABitIEDB9aUH+XvGuNdg6EsaGWy+c7gwYPMV9zFVfU8ewMFfj5g1b+7II7fXYHi9sG/Xi3kUSnVarej+2/Ae81R8V7cu7uQ3Gv9MU0Ct24/Z8y+yFQ9Ou/rZ+TKf0gaZ//vSfOi71dUpSa9Sz0hXEnX5OmbrwRC45/0bkljsn37jnh9utemhWD513Q8CkVXVGt+v0MUWnNedtVetTJRMCczO7kCWGAVLPl62J0fVz/6OtoFTj6IqtKr1iJr9LsXe666PZb91lLmAtaQLlZMYwCwjaCoAt3d3QakukYvBWdJf9PnqIUakORiD5KwNYRF76UVeXzQqWNlgLit2hPMAQS8Ogm/LkGI+qEl76vLt3L/kEcBPadYzH9eiJS4INz1APmE0bWA+9jPnXufbMZrxklyNmMVmOeqsdakeQdnutjWJbEx6NZ8CSdErfJ8e6q1ixm96pDue/nzp4Dfx94h8uCTJv23/qTvAxgDi11vX3KznP7ZU2TZsmVy9NFHm2VYmjZ1annD+jVCMrN2LH5REXFBSxqRcF+qRvBmxLhnCeokIpJ4XS+f5963qrqRd9+kEJis9ynyeRohyIr3P3v2t8yBaxAFmpYiTQPASd6QPMQkiWjkvtaxkhQB6WlrL+vZ+nloLNOuDVm33PcvMo7u+g49s0MUiuyY1v1uhyi07tzsij1rZaLw+LMvxwDSB/gKLl2g7MpT/dzVEa6Rh+tqAG5CiG6SjA9Z012AFfQCuOEvTugNz9Bzluib9tUFmvq7ftf0y8tVCD3fxWo+mTE6zfSjkqTtkhI3HKjquQlhYDomru5TAEs//nP9WpPMrKFHfn9UzxmCZsfHjIVNDHfJnAGvztlUquNDBChojLaLxyeTPsatEBNKuFfIjz9nOm/aJ/VImNK6gdyImMg4Xgf+pjjDJVCGvLmWf0tK8DZQLCdpLbvVQbV/bg6IPk/v7+4ndx34z04tj/qrFbelHi6VBHz04du3b5fBgwfXyNskS27W/ZIEt7uxQ5u8nuclCYs8eQk+SNVrQv1Iek63U3HIfe/t27bL4CG1Y5pFEPQeZ5/7Ldl9twpRaAdlWO+6aMS7Za2tRjyjnnt0iEI9o9Z613SIQuvNya7co9YlCpWTmRXM6Dy4FeUUwLjx/i7IDBkQfR3rglusxljgXWDkgvY0y6v2TwGeAtkkI4/fjzSDlOtNUTyh9/eBZUwMTMB7dRy8Cwh5XqiP/kFbLtHQa1wA6gJKd6+4QF7/rlWPIAq+oc/11LhEI0QI4mvd8xestTs05zUhOk6YU4isuADarXxl5j8QFeKPUfDfbmJ7QKi468HHOG4f/XXiYj2fPOpjQrjFJ0j6DJ8guPOk19x12+L0A9fc99MbugSgr0FcFlBLAtr9Key3bdsmQ4YMSX1kf/bTJTM+OfGJQtF+6Xz7G1Gfo2vDXyv6ufYtz3N9oqnP9IlX/CxLovw+6PeT8jF8Ypenb6EN6f+tx7qWQ3tEn+F+liY4/MV1+ldny6Xzr+1UPerPjd4Hz+oQhT4Y1M4tE0egdYmCPUfBsaL62MMcBdBVqXfv13x3AZVaUdWy7IIf83uCpzoqSS22tHYlWTY6hqBihdf7ud93vR76jLI9VJTHucCYZ9QAPjeMyAl74buutR3iFFndo2pKrvXcLTNq+mvj7BPJhQ2bccdayZFLTEJjEoVUVUriu2Os4+J7FCJgWz2O/ryFgKqfT+ICc3cudGxccmnGz7Hy6/Nccujqbj53802q1g7zxhkbzpzofRRHVHIuKgnRLhHyyaf7PPe9zDU2HMrf0C5+8KtqufcIrTEF/+47KNnyyZ4+J7M8qg80k7wEIclU1JIPoFM3js8+88r+UP+SyEySdyCp36FEEu1X2rjoc/x+JF2T1K80ABt/lhBipZ+7oUf6t29//1L5798+bcPRSBar3gi8o7rVfGBT2WyV63QBVllu7EC587r/uDEy8yt/XzW1bj+3bNkaH12vlhFd0Lr5qXygcY56o0rcY3WfKn2Nwg9dl5y+IwJl5lf/XvYbu6+5nbvRZs69SF7dtKWqv/6z3U3vChB3TKqFsoZCVvJY3IoLjCEk9DsXnGMu0/HpeBTySoTW/l6HKLT2/OxqvWtdolB9MvO2rVvl5A+/L3ioWNLBppF8DFfZc+fRCS0PlgA395GyXH71jbLXXq+P5a7e/O47fi7//q8XJT6rUuwj/Ywzt79+v9Mipd/ytqNkzj9fUrM0H334v2TmV78Y/72mHzZ/Ie25ecdQH+L288CDDpHv/ejKqpwJ36Og+tEFwUnGMtV1/DQA1sbMK9B19aviAxej5cFLLjD2+8E7+kTTJSWhXIc4DMqe9q3fd8mXf52Opf8+rr6Px1tDkPxzP7zVYPrtnOml3gPzTs4p0i5pcfMyDB5ycmgyk5mrN1glMfc9J31SXnzpT1Un7UXxc7a0lHNhTS1gp5RT1PGY4Fe9buVMjOi57/7rt8v3Lp5TU9v/hRdfkvd+6NTg4XC6kON7ec/r7d8hNmt/cYuzOSuHlBz3kU/Lc8+/WPV+7lhEAqnS3HHI8/eksfvbj54o3/jaF4MlOt1kZn3yMcd/TFasWt/vOvHIwyfIyntvCs7b69/wNvnjn/7c733igSuX3ihHHjGxpl9jD3uHPP/CS/3ep2HDhsrLT2+oWmPteI7CMcefLJs2b47fo0KNdCVHHyUdOOoqeHfnuHulakPZ2x76hkPkmp98v2beEJ5vnfJhiSJKa1voeanPqn4NW8U7+d3ef+wx8i/nz6x58I2Lb5dvXvz9KpCUOCZVQqTSu6wx0Yf6c0A1uxX3/KymT6vXbZAvnTVXHlpa+1m/b4jOA+segdYlClF5VLOcy2XZsnmzTDxkL5HIZlVpfmmcukci+8JfrvuNjB23v5VJpfhAzRt+erXMmG4NXD54qdp4KX3XjZfn++47l0SmvO94uezqG2teYO2qFTLtQ++pHTMVQSEwlD0MFYCW8d3DJkyUW+9dWVWZyk1m5nLA6Oc/NVV+tW5dhH6MYCMuyAKzGC35c26/546be5pyLG1NbBIuhOi+HpCa/9Mlctj4ibUy99qr5aJvzq4gsrKehkwfNdPd/qRIEv3lGWZ92oHtsu8Rj7crXbFM6vftNbFnqyy3379GRowYWYU5tm7dIsceNanyDBRVdAZr1CoJK5X3MeyQLlF7vlz9fb2wxLjg7bDjo9Yq12pVFrnjF2tkxMhRpk+5cxRcdsbv+0/4a3nuDy/kWWYN+85Jx79Hbrr6BzX32/jc86Y/zWhULNq88VcS8kIcNOld8syzz/V7t0773Cfk+5fMjZ/rzp0hCrtxMjPnKETW+GYRhTcfPkFW3XtT1fjoOLYCUfAnbsyh7xBIaX83lyi0s0dh70PeIi+/EoGB/mxHTB4vKwMAF+vTHvtNku6dPhrpn96d/JET5Kp5/1rzsMsXXCunn4nS6v8GUXj197+uebBPFNZt+LWc9Y1vGhNuFJKB3omUoUkAZEhJusPjZxV3VPavZHSU6O91XPeGQw6US/89suj67SOnfEFeeeUVKZtKLjY0oqsU6UQTjx4p655SdPCSdPdIT5cNTejpqeM67leWnpLID75zgbzp0DfU9Omjn/wH+fPLLwfGBWVuyzAmjosdw6rxzH/djVf9SEaOGB73qXWJwmx5cuOrBjiil7Zv2ybjDxoVBr39tC2WrX9cxo7bL9aTUdiMyHXXXFUhCv3UF/cxU95/vFy+8KaoX3o2+sPsVgAAIABJREFUg5RkzaoVcjJEIY189GF/IQq33786JlRgi7VrVsqcGWfIknseNE9mDKedNEXWrnyoD3uScOuSyJJ7VsiESYfXYKOF8y+Vc782vf/7ZJ+4+tGnZPTovWKXGOO0dcsWmXDw6KbtgXWPPSPDR4w0Mjsz9Ij38OOr+Nt+49/ZNKLgu5RagSgwJn7IULOJQsj1NmPORbLb0Kg8qm7cd33wlOZ5FJbeFDNkt7+tSBRaxaPAvJ1+5py2O0eh1YgC+3X4/oebyl/NaO1MFO77xQr5wEc/3Yxhk8MnvUlW33dzkCiMPewoefGlPzalX8vuvE7eemQFhGgn9ptwtDz//ItN6dNTjyyX1+81ug2IwhzRqkfgjW3btsqEg5oHkmAED254QvbZd2zFxm0Nx9e3AFG49KobakJn16x8UKad1FyicNt9q8xa05AW9SgsvouTmSNDwbQTp8jaVU0gCiKyZGmFKLhh0FdfMU/OPbt5RGHNo0/JyFGVfcoY4lGYcODophG/tRCF4SPMnGWHHiUEyzWTKPgSt1WIgt+vZhOFkGaaOediGTZsiPEoqMesWR6FRoYeHSUie4oIKeXY/DeKyJN1qmZCj7BC+zkMYw49Sl54sQAIOVhEdhORp6yrsk5juh96xGu1Y45CqxEFPAq7j5so5e7mmOA6RKG+DdohCvnHrZ2IAucoqPFq+/ZtMv6AUU0DSfTjl+sek3H77V91wBtgtxWIAh4Fk6/n4DPjUWgmURg/UW67PyIKmougOQoQBSUQU084tqlEYfzEyVVleCGmi+Zf1jyiUBJZ/chTMmrU6KrE9M2bN8nEg/Ay5N/vjfwmHgVCjxifQlWP3E7sakSB8K9RsDgRKYLnNPQoqk88QLp7uuOE7MJEYYqIvEVECIt8WUT+H4WIRWRNsenX0CNl8FUJuRCFoUNijwJ3bjZR0LeLk5YGDJCiHgVO/PhLERlBjoGIkDXyPTufRfeZ5ij4o57LozBQRDjrjzjGL4vIQSJytYgQmk+aAZ9v85JTMqZXiYI7jx2ikH9PJIUe4UkYfsBk2bkj3aOwu11bfxCR522YaCOyZxpJFJBdLDHkV29a3tCjXcWj8FYR+ZPdlm8UkbEi8gsRebiOQex4FOoYNBG58MILZfZsW/XI1pgnR8F4FIoK7/q6ELwKj8Lr9x1TczrutYvmy8zp/9DAJxW7lQk9uvqmuGynFvgwOQotQBQ0AZjQPhN6NOsMWXznchP2B+iEzPgehf1EhBiHPURkk4jcLSIrRKSAWS7XIOJRmDj5CBPiYziWrbLV9NCjR34nowg9ctqWLZvrIgpAj0NE5EgReZe9H3rhlyLyqIg8KyJ59JcJPRpO6JHIHWknM//6odvNQIZq9OclCsT0gZu255rG9C81MkfhOBGhngEOYTA5IaBYpVG2d4nIby0gyOq2EgX3exqqVZgoULDgEyLCwXdoL7TVVSLyw6xeVH/u5yi4n86ae7EMJfTonOmxpaSZoUcPLb0xWNkiL1Fgfb3ObgJOl7jV/hvo9w2L09kk/23XYJ5odCUKfsjdvm/MCGsAZXxSRP6XiEwWkWE24Qhi8LSI/EREjhWRL1gvQ04l6HoUdC9+8ay5Mu+Ka9qqPGqreRQgCnvun56jgEwAhJP+tsOuo/+y8uJ/im3Lmm83iigAdpFbZEOhWPOs8aSuvxaIAvqIeX2HiEwSkd+LCLLjTSKyj4iQrv3PdcxtYaKA8MJwMNK6QXfW8dCMS9rJo0Ays1bXo+pRXxEFxSQMHXsladiXb3hc9h0zzoyw5idw5sJ1ixb0LkeBDoDmWIiamEon+D/H5oUoXHbVDTWlUZseemQ9Clr+FOzoehQ093bqiVNknRd6hD3t2yLydhEho4Y9uMBisUbuigpRqJw4wdw21aOALdgJPYqWREkgCvXsASIrCAoFSmI8pQFBsDdfJyJLLWHIGtd1j/3ehB5Bqe66dbGclnUyc+iGSUSBKCuiLRQDwRA/bIUxoSCkybE/wMFF5aISBb/MaN7QIwYPJgWIvNAa7lESeBAoegnOQ1FQT+DHIkIdoEqNlvCwukTBLUfFtwsRBQTHeSIyVUQwbbFaoNb/bhFv1qw6n8dEwbol3dwJQxSG9NKjwAQa6Wknsc5KFKFkZvWC5CUKKPgTReRHInKZiHzTdo0QJIjgNBH53yLyIRF5zG6YrKF0PQruWkv1KDB/F9sdiuKnscBZbHyGy4r/YS10GLNlztD4TuhR1oylf56WzJwVenSWiBBBhmzAQkNYG9YuhDFrDpkWBUwUb70lCmNE5KMiMt7KU5TBFZaDFu9NdEWfEwX2QA4wlNb/3oYeoZ+wXlJvCtHFnCLSIIS0a0Tkn0SkKBEsRBR48HsiS1XpTyUpP10urhBzTHK7EQW1jpOj0FehR9htcfbScNjfnqDnSWYeM2acsYRrv/hZVfUoxxxUfYV5Z6HB7s+0Vi32Ay7x2/A6l0R2pluQDFG4+sa4T1qie93qh5ruUbj1vpVVhj+IAh6FJXc9aAobJOUoDLVDArhlW2BYpsxJpSRL0YEOf19zFKrKtHZ1ydVXzut1MnOeQlZJb1FNFCgI0WNyFIqGHmE0JUvqImsE+bld22BwsPm9IvIflohljeja3zxt8iYyqx49vPKOYOlKHpBEFD5gLTOKgQBrZ1tr1322gwhqimNhtQeIozsAfFluc9ej4Ca+5iEKuE8eL4tQp4lFCJDEAofRFxCAolhiXTUTLD7HOk2uPgo4qYU8CvrdQkQBc9b/FZHDLMBUsNkbouAkomufZs29xOQozJ0VeRTYuIVCj2B+7F5kGQMI7WeX83vWBHqDSI6CehT8xOs8RIGQkOMtOPq6FfiP22dgqPsISb/WiP82EXkip8stKfQoKUfBVBVjXFhckAJtoA200AetlwgJSHtERAgzw52Vw6tQCT3iCPuomGcn9ChLzFU+Tw492inDDzhCdu5INlkQBfhmEeEUC2QZghiBSwgSzj5IA3KNhschx3TGHestUcDx+IDdjjix9rayltA7DCL1uO37lCgweOwPBO5fiAibFV94wdYbosDj3ysinxWRd1o9xLY9RkSoVwTpo0I9bvr7C/YrN1FgHHgYjAT3NWyUxdMHrZ2IguYooJM2Ux61D0KPIPgMOfqAhncQEBUihZqjUFWDvlSS6xbNlxn1hB4BMtio/M9D0ZuIc4QG/2NFJaqAxZfSqqoedXUZLwxjliv0CFca3m0spL0k7H4XKTuqOQp8FnsUZkyXxXcvj8N80pKZGRb+R+YSsVtbE653m0SJQhweZcdv0YLL6iYKTCOhU9GJG5FaR6ZgfM4yNOvbrLahR0pgyj09smXLFpl4cLEcBeAjRtE5Nk8T4sV0Qw6IomHKgZkYyrMMXHgU9hw+3ITe3bHk5mSPwq9W3FYVn7djx04ZPDgyJ/tEAeDNmseq+zFvLlV58vN3No6cRXClja6BOKDksnTGSR94j9y08Ac15wPkIQp0Cb2Ei4vBwruAIuBtsMhhLfw/1suw2E402I89DaFJao0gCqUhImVmDz8RuUBoeGYVUyauDSolFojdSg09Ou8SGTp4cHqOAtRY6TGWDjQq7hVM8sRpnWYHUQUcg7NQROZb5JJzL2syM1/3w3yyiAJrjbABonmI8gHQuYVoIQqnisgsq5OpsUC3UQxZzSUK3d0VcF7jUcBHCuDBDXWENUPSsesdlxQWQ0ogglBYUJTfJq4BCfJdkdJlIuW0BQaZ9c5RoP+d0KOsWax83huPgt4F+UAQwvsgac6joRgoNQwjyLYC21R6SxTYlliIIMOsawwc4AyU1A1WFucfpeibfUoUAMfsF6xJeE4xgvybHbwCoKVeooBCZ5si5wlxwNp2vvXAEN2JrEe5I4pRtGkGotC45iYKoIq/sR5klBDavMjCganiBs/RwXYiCoQeAZRMacg6rKlpa529gRGfPQMQjY7TjAz5n7O42b+eHIV9x46LDWrqVUhMZkZn7m49AqE8NIDGVywrJYgcw5omI2OxRO+jRwEgeJwT7BcxUejuiU+qpm+pHgXWzF+JyN85VgQqfhCHkpSQwyChp9D5OZoSBReEuweu6UFjaUQBAgf0Ya6IDlid47lFvrJk6YMycTISu7rV61FArTOtbGdCGcEda62BGTyCxyqP/ZRk5tF77RWTPnpXT9UjDgn4W2sIgSDQL3Ava/xbFnsz5XhTs07OwstBMrOpenTrLXLaZ06RZcuWydFHY6ISKU2bOrW8Yf0awaOQ1FyigPH7O9YNznrXCAv2CvIPxQVGwk3PurvZ6ov3iwhuQAaUcB9eJK31xqPAfRESKAZAI2EDKPZQA6+D9egnRuE0y5wShe3bd8QkCkKFws3jUTDW6H+0JktMHb8SkTtt3OoFdqDIzCV+JmdziYJvqT8HojBUPQrEXvbIu0/4eKU8KswJuolQwcyWt6G4SApA+edskUfhpuABW1lEgUfs3SUyqyeSvURrUVxIDXMoBlYvGxdPEV6tPzPWOUy+/oFrSmKCRIHkc7wG6klgsc+wgh5B7JoU2ByXisgpdoDOFiktFCnzvZSmREEVFRt3V/IooF/xKGqkVs7lk/traUThdeMmSk/OqkesM1JQwLt4s9yD2nD3Il/g+XiuWIfIvrSovN4QBQwzJOuzTVFQyFoi2ljnWIrusc6+okU5+4woMBBsSoQqBhD+jX8cVE4caoEKEvUSBZy2iFfmjfmhK3iKnrFjhQeSRD/UIHNH8nqRlpso6IKnuAHB2OcWiMOF7WBsgCHyMhnyrH2Iwmx5YmNEFACU1JAff2Bjqh4x71+1YajgED26EvJ/rcXuoblevv5x2WcM7q/ooDDVA9cuvFJmnoHZyWnoTTzH/BlgAVNHn7vtM1a3ogcA58S/sRAxOEGcAUSQSD5jcX6nJLKtdoIhCm55VNXxqTkK6CnwBO40BC2bAFYMEkd/MRAKiBAuYBL6pBYQXKd8jmBJWHNKFGKreLksWh51yd0PxiA4jSgAbCFyFCXBnuaDWf6OQw69TjfoHvgxr0MuMfSozvKorC2GDvyBDGZIGT70GbgRssMyABYQSZ4Ubcw5Clr1SPMQN216VSYdvLfsYc9Q4x35Py1kH24H0cLwwfe+Zvt3grXJwBeBJBiUCAZJa4XPUeBmTL6CYYCKSxSQe3QMCy6DhKJicCiIhRGaEB68qzAZLF+8LGF6J1v2g6JbJCKfz+g4ROHGq/6jJvk1r0dBbz8I0m+Pe3cfyVk8g8tRnC+LFRcO8jjN2NUIj4JBHpADBgXFQWgPypSYd35iYcgaHOdF0g5cO+f8b8uQQYNqPQpr1ke+M9xBsCN2Iqs96dhaf64YJPqOmwhknqO5HgUVdnlzFNCXsGRkHxvy3dbVxtrCOwVooyoyHi7WX5EwjNxVjzB7gDRAGNpAkgh5pAZjws7le0gItNTlNt6N7yN88XljVWSzJARF78o5CiwvVDHVGVDg6MgcXC7H6qp8JYkoQOhHHniE8DNPQ7axRfHek3iHF0sdb7+xnJ5QRSw4kFRibAl1AAeHCENviAL9Ramy3XA3EwpF3+D4RLhhxUKMgFeKtD4hCgwc7hhMbYAkOocbGUslFk6ASoFJr4coIM4Qs7jgsbqhh/AqsC2RJYg8cBFrcZ7FcAW6ZIY4N1EADCInkK+AxH/JmYWOMYKFRseIOSNGisWWkuvUPkTBVj2yVWhCOQq6jELrGfAD1wzxTewyeN3AGRBn9gT5Peh4bHCAptCxqCZHYew4sUf3mQPzaMEchQPsWib5gRAiDEI/9XrKZ6BfWCjA4tOWCCAk2MwsQIA8+2KZXYQwV69pjoKeA5Ar9AihRZ8wf6u3g/UHS8a6QeQCwosBZBOwUQiPpYFy0VWEK11WEnmuHETmEIXbH4gOXKNPVIFct3aVzJ45XSAKaujKSxTYk0rqNO8fna98CqlNl7BPYpzJs1+rQo+6u801AwYMqCtHgT5hmef56DHWEgnD2EOoWUKgAX/HQI5NBIiXdPzumkfJBxgV6RN74rRJZj5wtHykHMkoPAQYQ/nfDeZwlwciAq8pwR2H2ihnjPiIGzAR92HcWPNAkbQxU6LA/Tlw7fTPfrx3HgXkPOsQHETDYg+TiSrnJjeAOPgXkIchOuss0lDVI5Q8B+z09mRmBhiLHEXPWIgYaxhcwsnTmksUfOt9Ho+CubdLFDBtoOEREBAFlArMhdWYs6WFHn0dojBksMlR0GZyFB5dH2XBwOgQWm6jT+xIbSg3BJ0mAiDoaKAjQDJB0iShZDSXKPhfzfIogDewEpH/zSORwWxCrA24L8HuDB0hBMxlkQZRmDzxMBk4kBestH3+4u3y0h+BfbbhesIqBNihsQNJmmBsaJidseSwkGgI5k85N0S6wWBg0/zOYoMsIKydhRfyKJz+1dly6fxr277qETIDwQduRI6gt1AOLCUsI4C2AlEpwWlO8yjsNnZC4QdgPEQJIIjV5+rn6AJEmVK2FIAEJxPrla2OZQl81xuigLxCTCBnsbJp5Ar9wGtKyB2ACHmWpxSeDlyfEAVd+2xUOos8Q87AbEjoJIkTppOz1UMU2IaMFcYplDEGhQPt9mMssa4RegSQRCfB3TF45QEf2u3cRIELWETEsCE7MLD8wD48bQxgptSMxBLCROOJYJIp6ZbAdduHKMyWJ5/bFIPMHdu3V3kUkBNgXLA08wc3cm1YyBA4FGqHuWWPMSToCXAIwSaoVPAuS4/9iAbkmqQcBXPgmvUoaBlNdPx11yyQWb5Hgc0NI0EnYHUnZAFLgdtIpGNzMl9YsECP2tChCDzmlBclwx7vBOjTW4QQhXkLrjcAV6tE4fFY/dByOflD7w0vWvQSsXY0rDEMIHHY6CgGChT7Xbvw0VfE9NIQvgZN238zcOxXdJa35tzQI30tE3o0Y7o5mbkoUcDooZXh2avY5HgNtoE2usAcYjEHSGc1iIKeo6DfpV8Lr7y08DkKiC+4FbZcQDfcilAjCC32QQA7QwoOYbhJScKpFGqE+VAeVatrMeQaerSxHG15oALGdqLT0JEso1BDxCL/8S6HGn1hGbIE04IZTNWjEdGBa5knM/sVhvTBrkeBzYlVC7cLGxPFREcXRKfbJ74MQhtFitLF3UvpJvZbUmtkeVT/GeyDj1tiTS4R+wPwibJPaw3xKIBuoc8nRZaE0jdEyviyIApgVQSQZulm7QTky+c+Id+/JFwvAKIwOORReHZ9JIUJ4FVJrAiNCWaF0rRILyU+6TeSl3hL+snAYTXEIpKjXAFE4cG7b4gTdLdt225IDC2LKAAseQTCHvkLg8aqxIYiURGAhCcBL31RoJnbo0C8NegCiyAN7WTn0IwhVlQWOfOYpyHx6DSLDguP7fiu7FFAT/Ha8FAEHE4t5IjKEvQmw4DHMU+sZ2iYU0OP9pskPTuLrpAoZBLOp7reJwrqJgYPgoHh1gh6tgmuavTtRz9yglw1rzZd7/IF18rpZ2aZTSKZy1pHZxMqqaHEKFSUBJECYHLIFkszT+sTooCWBzhp2Q3YPV5SQhuJ50Lw9zFRAGiS0wQuB3+hlghrQF5A9hBhGFD5OyINQFK0FSIK3BwkoSW0SLRiItMaY6iJgLwIuQ1qDU5wHbUPUah4FBgCQJJb9Qgip/aYtCECOOJswdNMmD+kD2MRHgR4KgYkCD4yh5aWzOyWR1WiwM9geVR0AJZPQD6oke3rozAUFR4FjEDk5mDhd1ppcEnK3yxHpfroMPHYXyiJbK4GUepRcPP6IAprVnKOwpRsokAQO8Y83KKYvhFmeA2w8MKiELbEZdEQKgA6F52DjgkfAZk7MT/qUeAyJQXuycw6hkU9CshMeDT8BSIPeQBqgDXZAqhhOBDBEFlpO1oeVcN7+MnYhc5RAMMiN1R9u3VKeEe4HZFbyHas9fTLde6pdR9bCH3DmANeIdrGD3sn9GjkyFFVB8HpWSILyxGMUGKMAYOITcKa0JN+47lEwdF/vGihxl7AG83PpMbJzHvuGSUzp+YoVCczxx4Rc1+XKLAhYex42iCpeK5Y46y3R7oi3PnXPRH+4SXAT6xVrDzoDhgYaxWZx6AmJZz2NVFgMgCcTAK4GKtSVh5PI4iCSXZlMHBfYvaALrJCsTAg6bAqAOBzNojC9y6eE4do4TG13ixJ9ChsWx/F8hAmAwrHfcrE0jDJJ7mHmDhoM4PH5LJyUXhawDelzyGPQt7QIwAYnh/kGnyFTQvDB3DyGd1SvI2SKWIZjIlCWaoO0KvxKKgvnAXDwkdKgHyZK8wMSDL+TvKm3/gunho6iysEaQhq4XoQDErFltPxiUJPT1n+ccb58uOfLGp7jwKu0P/P3p2AW3ZWdcJfpypkICFDBTQTYVDBTAQaGwEb2qDNbLct0K1+n1+LPRFoAzJkUFIJ+oEEtG1bv7YbIjKGeY4MQRCRhCSVEZkFB0BECbOZU3W+57f3Xue+d9+9z9nn3FN1q8J989RTlXv38O53WGv91/qv9VIC9KJlx16y9DPHj87lABNt4EGZpQi6ltuyqEfls02V7UJYz1Px0/okY4DcJ60TKPAlAFLGRb4CJ6RGtjKGk7U4T1Gd3QoU8MtoMHKFtgQUyDuWuonmLR/QFokoZOl6YDTpDOYwowtEly1IfLHHUWnn8M1UvZ4bKABQDEcTJqrAemU8co+XTSe5KClH+gB3yjU81uSIDlO4HW3fAQp1RMHGV7e9TT3iUHSWCbDXbmwPdA+iVjMkAAP2DnvXnHOYq1LmTwkU2POgeheQRj065tjjJkZvvteBa2siCui2XsbTwRufZWfKzvIsEBgWG8uWF6vd3M8i5jhkiGfUqLiujCiUlF05CoMiCoC5dUTX2INqLLNkGf10EgOOUEPzgLq4yf2eUwy4JZzpJt9izzZCOYFCaYR//LprKurRuy4xG3XrOnAtf5c5CuaZ84NNabiIB8b47zU+BstfF3XHEMmTBe6ZHtOqjpc5CsZOVEZ/3/DaV6ypekREGQrPh6esm2yGwFAxe7g2OZnpKS4nYIbfI2m1HOEcSkw8AIEzPP2u+TzUo23btlWGWkaJUI/kKDxwXNs6/LhAEf3IPONjoEuIjXaOBlMSy8ee4bsk13yPJWrMgBZ7Ydp5MRlRME6DTmYuE3Xzw0qgwPAnqwyEzhOwDhU2WDYxMOojU8kn17ckdhh4H4t5QZd00bWf+JjT4u0X1aePzVsetUuI5s8MqkUoLGRf/MEo4pvN6ejT7vO7Eii0x2kw9ajrJQaHJUBxGEwIfmDriiikt743R+Fz19UTZSLyFKIECtPea9XKQufaNLELAIXbb7+9ovkk59LrZkUUXEN/HjKK+D/jejNg7BAYbG+fgSQkwCEgs1tyFHJcbACCVKw0yZTlmKXVmz+jxYQ/KHfWCiOBW8LCZwTYQOC+WOYt3VWP7kzJzBSBpFzK2tKjKyl3ulbjqTGElAS7kpCckf+9asUum3pkijkOTVFZP4PsoywoF07BSk413wXM5uHcIgp08HqoR56NxsTQIfjZmmQmGQqfWkpC1MAydhy5NiThb+lAgZbk1uJBtQkZt7SmkB+ZRuboKI/swKjCIkAhFwSxykai/IFU2670FBJfOL28iHPkV1ePnxsokJ00NyoHZwEXIG3eFlYWnEUlPATl6JwEC9EY1gqLigzpsJD2HaCwPVQ9Sg9vu+JLFuLLnKByg+fv2L2YCSJq9Lm9l15YBpyh4ki3/ziP3Id2JLjV1S697nNx9DE1UNjC09YYcZ05CtYvJZTZrXQBQVU2JdPklRBwkAud2dUkGUH3NjbQ0QouZtWjSr40eRN11aMr+yMK+Lj0CYFFGPhwC9zi12dRPoAhPWq8Do8a1Zb37eMKRY8O2hLjS3bV/WId29OszYZm3EU9umrHFXH+Oc+a5Cjo5zSgAA+bQ7KeHENFJTfJW/MLs7AA2ZsaPQE/2zrmFfd+mnNX1aMTTz61cKLWJeK7qh4ZLnLckNiqzJuykafkiXVEdPl/68y/rTU/Z4/YzqK/nGGGzTPbrLRMZi6ff9NNN1ZAQQfIKlFPS8w0mQLvYxpaanleQk6fda9vbCFOVD/PgpVMFWKH2O2jQumHHIVDDzu8Lo/6x++cXvUoK/i0+fft8qhQEuEL3VDiBsoA0ROUPwdzucktgFRs9o0/kj6sWYvER2R4MAcvIwrtvsybzJzPy3J5AA7EaFIZJ0DyUA90O6JQ9m1dQMGq5CYBFFgf6wQK+c1l1aP82VznKHQJNhUSCBq7FhpU53bBiEI+fghQcK0NAWUTFl7LScOTCjHzUBMyBAgHPdk35IwzEQWHwVUJWVtXzIjeA9fsfga/zKbMFMpD6ZLjmRrLwiL47VqLPFMeLHweJ0TqfKV7D4k4aMuB8Z0vpx+0HqHTn709LnzVG/f5iEJ7OYkKWfqGM52kPIVkh60AKPi5qOPQPToNKNztuOknM3ctd32hb0U5RK5ME6HNO0N2WI/WG0Hte6xLsoUtKMLPLqSE1gsUvFvQkd1N+WA00P3e6+fGiXcLw4BHbghYXjZQGB08itHJEbt+f1xvTFmlOsY7abOKMtgHBmwg+lsPUDCfxoenTRMMTC8dfWA74u9SwANrMkyWyNxAIe/Ek9Ep2p8Rh35YlnvJwx9YS4+OGD1nFOObxzVgIG8IuA4uu8fvS0DBOQpZWvPWW2+NE+esekTEMi7JDrJfEBwQzEb2Z7XP+rzlmi3UN88l9ai6uHH4OEdhTdWjEiig8KBTWFxlk6hsM1pwwoCMjgwDltfhwAEKkjdF81v8kaQe5S0JFqaWR7V25MQINZZAwUPoLxaod3JD43gywvSVwMpmgH9oFPE/xnXFBJQk3+jPDRHlOQppB5XUo3zMNOqRPcg/SpXCxoxc4IGsy4N6edGzGqXr4S94RZVxQzpNziX1KBPAkw7VRT3ibBE1lrvq3QI80g/JcTpAlMvtHVrgAAAgAElEQVRU2bqGzB/rL/2ArvH/SaNFGcIQRAVtY0jlUbcdiV9TN+MnqiaZORWdNc1MsBz0jbHPASUgSwQI8sw6mgn1ieOUn4HYnWZWZkRBfy7543fNdzJzes3bQIHi5NigGK073kFrDGDwOx9ZNh/FA2fwyEcfzZkC4ZCL9IawU5ZMpIyXTT2y+OwbYR0DJyI3hz1efU4ChTKXI09CXhdQAJ8Z26Azl6ryqCXvwiosD2sprCYRhd+94NzJGRgJ9vT37CHnKLTmaub/gvEQlnWe+Q2kdFZN6HkA6tHlH3zrpJ/lZUOBQt5jzZGDusEJI1HHWRnChYaJzCXPCJhZXPf+A9ceFjd8vUuyN70gMexiOzfdD4wiwEBHLDiNpNPKZLCMRBDYroeuRRYeGHHQfjVQKEHonSmi0LU8yABOBs0wENTm0TARdpS77ZBCe1qWwdQD1+75gLjjjiHwcXUvKTHg1B/TyLCkO1GLKDc2sSlndFqHmm1c9nO9QMEYMYTIzXTIg5OMXQqT8rQkOfyyLPQscLVsoFBl8gEDNibPj1AKbWt9S6Tg3iLouYH76lW3Fsh6gUL5OIENnj7GANobwAfI0V98H0kxmCUzPHNhoMD9bWETXJDwyyJG7xjFmCfXIs9DYxiYFBbKB08bgjbLITnvHSGQfQko5DkK9YFrN3aeSkvOE49kguTQruMnDKXlJSBuf5AVDDYRuKQnmS/OBjqjL3B+mXMUjjqm9j5XpbXr3dN5jgIFw2gBBoAA3BKLq2wJFETRhDaA5TaX0oa1GDOpmQu9Lls/aavKoxbR6qtQj574qG4PCmEklEZg+eCMKORTrS1hSd4Olq39yrtQ1O6oLrUWgQnKlEXPkGP5vnMFKGR5VJfnOQrv/sCwA9fcY844OogOgB5zl6Olzl5cKXfOsaw7QB/sw/wwx9MCkwkUci4TmPYduAYoCAKJKHACEVkpx00vbz5jPZ1AQCr5n05xf6fMBQ7Qo4DV9rSLKBzZJDP7RpW2qj3QceCatYydJk2U3ewdRKhIKKcUWdWnD6139w4BCuhQhx9xRGwZNRGFp/aco/CJy98XW9QMbVF9/H/XycyQnbVPSVpDQiXVvc1HWHOZQM+Q66j8VYEEScTArEXAaSJMY1+1IwppOC0aUQAQrHcgBkVFtTKUo3naMnIUOt9ntQl12LgGwuqXiaV/Zhn6spNYJoTJCgVwajLz2ee9pDrAa+GTmfsGB9rjkbAK2V2yfFoCrn1rO0ehNIS//wcfEt/45jz1Wmqh4iA/jklReorEMPK2SqOoDtcZRXxnxhwDCqeecuJk7We/eyMK5YcRopREAgUvhT6HFLRnGcty5XWlMLhIToo4aOvaiMKdHSiUQ5reP+FVxlw2zjZOabYSGlDv0jzlhLjyQ7gZqxuaw8HHnBTjvooLPQ+E4elUEQ5LncICZJJyP1R+rBco5Hu8W9CRIhWWpsgoWl9sqfOwUWAU2iz60dKBAuAsUkarIvJSAoACC4/By3XIHbZBQCHHUO0AmMUYaQJ+uqtx7g8Jdlz2gTfHgx/Iz7i6HXfiw+NrXxsgACxoypM3939HbPn2KHaVCgmBmyNBVZ3MjEeSPiJi9PRRjP9urWDbl4BCdTJzk1BX5SiIKLQsHh5k0ThURXYBn0u74Ah1yYiSKsaAw9DitBTw5p1WQJBtQtRybvZUpQ5A4fuPOnpCU0lqbGcyM/4yS42OZmgDC6xKiZfUmO8gNCB6lrCf/8zaROVKYdGhokw8EKJLKErF1HZRj/RtKlAgRIVPrC0fzGKlX9JZxfPBTW0j6D+Pmn+3gYKlTdDYs2iyNoZ//3oNFJRH1drJzHIU8iyKadSjcuekkc34h3OYE+aWLUitJnjXHcNE1JB7nCV9tMEyopD98c4+oAAgcBiQ7RwKfBlyuUWTAU35dYYt6VBAQJvOmN/EVGPGdZkfqh4dse3IVfTrqjyq08k7rH7fzFzQN0DKv+khfTM2Aoxdp0KXEQVBr2kFAq7+rLyJOspRUY8AhY9OOXCNx22//Wo+RHqnu4CC39svJsseAVI132mS2x61LoVqcfhw3n0GPKAgMxua64ooUPZfu+EbC5VH5egidEwcIwTPbd7WBgrlWK0roqAjoCMhATVxG2bZUisBLcnqsGtIO3zBppU5Ck4WBvZSyAEKBx5wwPSTmecdBNcvASiUr10UKNi4hseQQe3WEAALQPg5p8os+JERhV3GbuvKQTtH3++hccPXZxA4GPiUhLmBhLmzeGdm2QlAnwiMncxNQNKRAr8XcdD+a4HC05+9PV5+J6Qe9S09QpqSMIei6Nk4qTPC34705zX9EYWdcdjxp8Qdt/dHFAh93uXMMWDfAgrAJ4dbynD6vasCxbSttCygwFb0h41JUVAAcKql5N/0ueVHRMwq+blUoFBVsWhi3RBdO8kTeOA5zdJXUE6X96g1iMuMKOSj6Ssh+bVnttbGKFBorZVMjPbcrhcojAir50aMGXS8zqwPHrVMbqaCdebjEaO/GsX4v45rT8iU00r3NaCQeqrPSDIElgm6neVlfQMEpXglgqlLgRoBeV7fXFYMLHY4u9i9gERfRbA8R6GcZwbwW9742jjrjNXhgtHhoxj/xnglV48Vy3Mv2uNvHeTFtw/8TtkeSLQdEiHo6HwhQJvVhhZhKFr7HIU0yq/ecXl/RCETRbnHWZSPHUV8edwPFOgei74LKLBLcGg4LFnOPHIXrACFZFf4+/prr56co5Cf8KTH/3hcswM/aznNfNsq2Ch8lezFPvCX5VGrszGqtJPaEf66KQeuYcnw9cGAxJn1wjHDz5E/p5esM+bYIu2az345Dj8c4q9tNXNa5en0AAXv0HP1EOwFS8TapoMAGKuzTW9yzzxAQZ+AF32pqEe/NAModH14H1Bw7YGjiDPGNb9LZIEazvJRQwoRQpC8OOQmsMozJrRSAoWS6rNoRAFQEMkVTfjvWyJ2DelcazB2W0TBe2xIFgAPMw5XnhMOzjIkRRJ4HkBoQqVp08qjnrW9jiicf07rHIUdsw70nrH820Dh2oiRKOiUuoz1ycxvW3OAnjctAhQ4bNA/UAey7Bc8LH+GAGGzY3ANAQqMSx6Hsg2KKHB9kIFcEZloKO+gK0aeD8cTsRhFIySdibPa8TLunhNx0Ph7O6JQzoFcJh50AtrSzwbsJ4WAPi62Q0yjHh16z1Ni5x27KiFr+PmD4TyKx7aynlQUMi08mPQ+BgFvZKafEMyciX7f5cXp2znLAgr5fM4VRhSvFQOIB0t/6XyEOcaS75t+0vx+8U9fWatirrr2+njGc86LK5rIzIf//PJ4zM8kqarnC20fsotLl0eTYcHazr0gFIS2wIskabJEgFPEze4ACsZFwQ24pd3IC0ERNts0vL9eoFC9l/VqnCwu9E2l94AFDXVRqMP4WaD0gTEm8HqyEvc1oJB5AE5mPvE+3d5URr49yQGENy56YMWyv/PATWvd0PCYzrsvc/7lKGQy86ryqG94TZz5y6tLFo0O3BLjZ+yqLTRKh/FCUPHYM14Y1OY1TwaloFZU8MqSExaHZCBXH8QidV/RMqIwqXi0axyjLaPp5VHtLe8zWJz+TxhFfKOo2FJGFAgx70di78oMZjlTpvhdzIYGSWdEobTNkno09MC1GVZG56/JbkE2EQVd5hBh03W1jCj4XUk/mgYU8jk+lywlrkRoy0bVszPgp/mJrBFlRCHndVpEoXw3wIBSJFUUtY4uZCJieTJFSl2IrWN50WezIgqrgMJ7mhyFaRGFrgGfBhRc/19GEc8Y10BBGOQXRxG3DKD1pBNKdDp5XuRlVe7vsY+Kt7+Oz3ilWZT/+LWvzx1R4HGj7EVACBKhq0XaUg5c63sxSYd+JOxBqdJS4qdCLl+MGH07YtxhmZTlUUU4gOZMyj1r+wVx0IEHTiIKFuUjH/ezcfl6gQJBw0JjrTN6ecGEOXkbWC0duyepR+3kdMMxL1Aglyl5CkJBiUyEZ69z0IkWwSyYDgzAaWChL0fhqPs9NL4+K6JAiki+scBoMbuxK1RlHmm1TFRkc4kocF3rnBCachyv2gQK5fZAPSBGUAcIbvYn/WcoDSnuslDwEKBQUY+OPSnusnNc2au2GOe2SJR/Wy8iCqbROuJNtt3oSZ6c9CtYczxNaOPzKIllAwXjoR/YKQJa6HYUBcDAw2aPiKjxtnIUdvV1qRGFnDihZYaRfYCWl84Dg8zla8+gMMh16itBUyyC3QEU0P1FiYgw4orD1x9lbOF2Dis5H/RSHxhcClDwnRa5RSxSDOERbCaQYEsdCihAy9yYJWJu6ZJ9Dig0/e+jHvm1dayKt+XkbwwajAC2K9COWmTI2A+oivMeuJlDWB64ljqq9xyFSmk11iqeCh3NOC+djzrEwYdaIcpc0IUn02ZOLUYoCLgwtyJxLerRH75utYNN/6ZGFHg87C1rC/hs5yiUQOFbEaPjtsT41l3dZHf7Vt8JXMYZ3ueFa6lH+lRGFCrD3DERTzxtqREFQMEnoaP52561d7ta+8A1ffKnj3pUPoM5xlHFr8eLn5RY15geLHHG+ZQsxl4Tc3LgGm91EVGochQGOLBFVZhdbB2MHiKXr5HzjMVsPOhEgMLysof4IadV3V914NrF71x+RMFo6AzvrUgCdEfudUWx2hNBKOOK+gALAKCmY1D7TmuAQk5uenwXiSjYh5Qmp73n20PTwsp9M5xAAcVn69YtVWJk0rTWTT0i7A6NGFOkwssWDC+Fws+UiCzFDm0/LaJw5vYL4uC7HrT2ZOb1AgUDxHqSpGLy9MvKtHtJ8ilAodpo47pMWbZFgAJnDsOOPs1jHzzxbqOIt47r3HBGJVtlGsMBUGCMlBWP9GtQRAH30+IXC+T5w0XlcfmriNEnI8a3RowOixhDqZQGYEDgmmOKhQdHaFjY1323ry6Pmuvs6c85L17+yjfc6aoeDQXrcFiegMlryHGHjWep8fqUNKCpyczHPyC23L6zwrOcBvSoUnbZTBEZUZbQbNcQsA2FpRmYA2T65NnLBgq5BSkKDAaOTIDZOKlgjJrEHueNNU5dwb7dAhR0rMzqKyeZtuLK55JjJA0oybw7gAIbD1AwNpykFCoageRAXjrzb1sSw30U1aUBBeNDkFnQSPXclCwRnPEmcjC6PmKsY/h2U4qh70tAYXUy801xUk9EoVw+cJK5wsCyhNjUnEEalg0Kqp8v0sqIQt5fAYWOiMKq528d1fN2dnOkroVl/dMHwn4W0dqUqfoR9gCXrwRKC5FBzwNRtDKikDkd+jX1wDV0VnQmjkcg0+C0M/RFrOglFjCh2ufYBRR4GxhMfxAxelUNKsochdTpVdWjM8+Id/9JrZH1c9nUowQK9LtEd8Pbd1BuO0ch+9lV9ahrzdAFWcIVTRFLzFY1vd4LoM46N7Hrue2qR66ZRT1qP4cMMzWcRQK5ppOdw4ZmE+mfXHanWPM7EL2cH31tMFAoD1xrP2xWRMH1vHMQfeZ28szZ2GzcdI67JssLUm68veX6xBsVWaWwl131KCvhGFzv5H2TE8EjIcIgjEU5TFP+u416xGLBWzcAYKINTsi8NmJ0ScR4SlZiAgUHc9mYhf0dZ557QdwV9ehXn1mV/wS21l0eNRdHGyiYNEKnh7zddeBaPmpeoOA+w2SDkMF0qgY587CaV7gFLZQQmVbFZOGqR16Y9dlYHIQzC0NcnBfJorLwGQB+z3VN8gB/yuawSkRibBQAgzB2yUGb1KMp8mzyK15/rYwm+P+pJzMfe1Ls2jmuthf6CZAAMMB5bFa2mOVLRgACmmWejWzADJH7xyDZaKDQN04UGeekvcDQtbzo+bYtsNuAQk/HRk8axfinx7VDgfCH8vClprTdARSIWg4qcsL2y4qtlK61gGhiDEXH+whXSwUKXIScCP6gn+KLoTCyiHnTyBKaH42Ekupp+xpQ8Bn0UlUacgo/u/xcYlYwimNSs6ZVwcHMmkUznbbOyhyFpB65vrPq0fQlW2+8PMhh2rVAMyOdZ5UbGHBoebX6chTw/ntPZp7Vv3l/T8+xNn1TE2Lroh5dfdUVcd5Zz4qLP4i4V8/t0GTmIV2iXu1dQXyyOivitc8szGeV1KPsDxuo6xyFWe+nunnx+QXRUZEHUFAHpFmteXRV9Ug+QPGbafS7vr5xYnEM2ROoWPwK/I90IkoqVhw9yTEOVE8rAQ0oqHqkDUpm7urUEKAAWB8zrp0eSQcQXZD4wXbCkxIiSeezD5TX4P8pXPvknFHEtU3OTQkU8vQ/fVskouA+uRDeRxkYWBEPYUr/FvWTAqDi2bRyWyVQcHCY/8+2cETB6rMRJSCxXvK0R3vtsxGjL9XFIfratIjC8859cRxy8F33TESBZcUjkscl8qgUltQygQK9Kg8BruKIZ7yRX3AKnYpTiIphI89KOi2BQrnOBkUUGBRbI8aquFDwWbAdsOPOtZhYpdmAKR1FLwPv8URatdM2gcIskT3990OAgifIUZBiYopQHTlyORM49Dj44DhTw3NFSbjWEseJhfvKardDerw7Igp972XkYvlwxqBpkMNkcDuqsKeBQsXjYuEJGTOGs4TwlAHcHUCBkSEYyhYXhUlmBh1Bf5l/4osIBhq6PJZLBQrl95s8HfFS7kAIto2Ge8ZrXwIKqh4lb/y2227trHrUtyxEy1DvUJGoRswYlTtnVfmatk+riMLRx06oIK4dFFHoeiiLNpOaprx0tP8oxoTOvxnXCsuct3R9RhTKXACPnBpRGCKQ5r2mDK1ysjRVj/KMggn16Mwz4l0fqHlWSm0+6QnLS2am9zlxAEOefR5zjkIR066WJzOXOSf6O4R61PU88hSri1EuT8KfaQe+9Q3xVZ/+Yhx59zrzIffAvBGF8tnEKpEBLBMZ9kZGxdm56Egc9tMyU/PANc+tTmaeVfWo7MBtt90W+++/f2d51K5BoFChfUYcRUoP6BxFxRZOGh97iRJmMzHkXMtu8jGSC7V2RCGNuEWBgoUFrJho1AE0Pf3V9AMybNIBerdQO6JQgoXBQIHbWwQhmTc0kew5Vq7ZhVqeqNLFdICQnVyJKNQRg7I97/kvrqhHIgrZlhZRsGvwHpAEgRxkPVaW0DjyKJhfGMElUFgv9ci64g1E80cZwCVmCGH18KSKSom45lqaJhPXFVHIByec5440t1xbvIGaeba4KAAbwYbQ4dVnqk26mEDhe+kchWnzM+/v+oDC7XfcEUccf2pVyW1WQ1XkuBUQIicIYXQetEXLfZ7chHzXngQK3smFQXEQLxyB9gtnSNn2NFDYcsCW2PXUXTVQIOtoejHxdqHxopO7CyhwZnk9PA8kstNEvClV0QagkNMBO6PLU71bgQJlRcMLZ3EmDLSA9yWggHqU7aYbb4yT7juMn+0ech71Qu4SsSpAy8aeJ8LXlgEf+7jyqM05CgU9dib1aJYwmfb7PEGUYcT67ag8sOochcKwvNo5Cj/1E8NPolxPPzvunUY9yogCHTbtwLV5u5RFKKhPbTBQaKoepU5dJKJQ9lXgj8gauC3XfGYChUmC+ngcN99001x7oP1QZgaTQ4CKtUeW+RmZLwKe9Oy+MQcUDj8C5BgYUehKNh0SUcgObN0S8fhdNQBgeJN5vHGMNuFvkQR2JTFBX7Avqe52zkAJFMpcgEWBgv4BCrhm2CCoKzxLcnAJGp4le3WaB7ormTnBwmCgYCa5rLKIN4kHsRgA0k68WxhmYJsWUXju838z7nbIwbsnoqB/VqbEFLGt5CUniZsGLiyqBAptz4jHzEs9grWEHVHd8hwPGxeLwdDO08ochTw8z/1DIwqTd0EvXJM6xc3BEtEseETaaZWQig53RRRO/5Vz48JXv+l7NkdhnvmcFlE4+JgTY7yANWF7stks6Vm5V3193dNAIfsh/YWHCdBpU6UXAgr2uTVtww0oWLFmPHC4m5Pdt7x/FLuePa7dcj3P2h1Agf7hyAWiiGGvpw/IFXRuQAHTAoAQZe5quw0ozLPYW9fuS0AhT2bmALz9ttvihHuvPUdhHUMx960iCoBC6Wyjq976xtfFmWesrno098PXcUMVUXjd26pIRzY22jU7Lo8n9x24to73Db21DRTcd83VO2L7WWdEVj1ado4CoJDefHaaiKCIKfpgV2tTj/KaIVWPho7DItcBCtuOvPskT3Ne+t2sd6JGJcUeW3FIGdcECvoyd0Th1ltujQMOPGBwRKH9AZzkwkXCIahFHM2Ag1xOvPFEhl0fnkAhIwk779gZW/fbOjf1KE949Q7Obn3hDLftMq8CaEnAMm0SlpKjoBMyq4E3nQP5hJcNzg0RWy6er3TrVKDwa79ZUY8yomAsf/wJP7/+qkc5SDQrKwT5DSGOFYUop15XC263qUe33npbHHBAff7ivEBh1kaZ5/d9EYW5gcI8L51y7Sb1aH0DOZV6dNxJseuORazb9fXJ3RsFFKbLswXKo2b8nyUtFDsv8OIOFnUHNpLgS/juQaBgTPg4UFdEiXwS3jOWoO7pGqeR3AUVsrraJlBYbE+86EUvinPP3R5lMjPaRdeBa4u9YbG7Mpm5otKwD7ZsqWghzlFol0dd7A2L3TWhHu3cGVu2bq14/wzwPZqj0NF1QOG9f7ZjVVGSqjzqmWfsthwF1oLqscABzr0cWFHfvhhxu+rR3gIUsuqR/phPbZ48ncVW0vS75o4odD1unojCsj5iWcnMohoEP4evEt8UAdtcMgomXd9hHV3fkUChNHLzusERBTfwwstNkLQhgpAHASwweAkUbrvt9th//5WcCY967q/9ZtztbrsxopAaV1hG9QbgB+rqMB6m5Sh83w88JL75rfWkoi0wcM0tCRTakbRNoDB9TF/4whfGeedtj5u+2p2Neo/7Pji+892+MzMXn69Zd/ZSj26/I4641zDq0ax3LPL7Ow1QyI8nauzzRXhYBDLZ13VKUGtwd0dEwSsksaOgSvYW1NVIIMxB3UNrQEfqa5de8ub4kQet42TmRRbRjHv2pYjCqqpHqEdKQ24Mhq9GdVUysx80HvzOk5l3w9z1PbI8mdk1WQXy2quu3HPJzB2dmwCFhtbjkhIocEqKziyTejTvsGeOgjHTl3Q6r5d6NG8/2tdPzlEoKFE33XRjnHyfe2zYHgAUDnMIXEM9Ov2pPxeXXjrlZOauQdhIoFAmmOIXf/0b35zrHAXObhEMHiSeAmWAOfQXkUlLiSjkAGckcZGOFJNURhTGzWEs+WvUo0PuuhJR8POl5SjkS0RFRBPE8KGunu/pAgppnG84UHjASSs5I8137S1AQTWrXz7zBfGyP3r9JvVogISeFlG46zEnzu8BH/DOIZfc6YDCkI9ewjW7CyjoGtGlMpp8JsFRDVjwb2zBTaCwhAlsPSIjCkk9ogOqcxSO37aYUl5SF5N6VK2LLVsmVQLfeNGr4uxnKnewMa0ECpW+zHS3vYB6lBEFNpr28euuqU5mfvcHaka8/i6z6tG8M/DuD34sTn7AAyfl2JeVozBvP9rXV9SjbXevDs6bHLh200115a912oOL9i2BgmhVVfXoF392GFAoPawbCRTywzNPYd4cBRhJyCrtcty2RX3XCRS6vPdzRRQWnc2O+6ZWPXr+i+OQQ+qqRzmfSwcKA79l2oFrGw4UTs1yRSsfc/T9HhY3fH2R41QGDkjPZZvJzOsbv2lA4W7HnVydzLwRbRMoLDbquxMo6BEHkriAanjqpAMHrxiQgrEZUVhsPkug4AmjGMXNt9wcJ95r44HCUaoeNY3BtNfkKFz09lVnD1UHru0Fyczv+fCVq0DVVTuuiPPPeVaVo5BAYW+IKFhj41gBWRe96sJ4/vO6jspebE3Pe5fyqNu2HVnRtvYWoHDN5/4ujjh8W2UozwUUfHwaxBsJFPLQqZyMeYHCvJM47fqlRhSW1LFZ5VEPPmh11aMd13w8vv3tgfX2ltRHj0GB+tEfkUoeVeUZiZS5ST7851eEROKNaA/5kVOrhG+bNsOl/v1nH70yJKrv6cab9ah/KWXLONUleJ/x3PM3IwoDJ2Ja1aPD73nqhq2zTaAwcAJbl+1uoCCqgEWFcpSJzQhzsxx7m0BhsfkscxTSmLz1llvqHIVZg77YKwfdddn1qh4dPUlmTt20t1CPkstenZUUo7iqAgqP2rAxQz0CFCqwV53fNKqpR01EIcdvo4HCCSedsiqPQn83GihkjkKyZcytcxQ2kn7nHIVDD5NB3JRH7YsofPKK900GNA253GEf/+Rn9riCPfyww+K+91afYnW77fY74hOfUqNoz0sVm+FBDzipMuCc5FtWSPiLT30ubr99YGmbQaJr2EV3P3JbHH+cIo51OdX2gWsHH7z6ZOZhT909V5UVj7qqa+2et+6bTy3H6vRnb48LX/XGfYp69I6LLwklSfd0O+KwQ+MnT+MfXt2st7e9+32ByrUR7fjjjo4f/RGntqxuf/O3X4od16pksOcb+fWkf+148dXtqmuvj2c857y44kN1naQP//nl8Zif6Tt6bPf2e3cDhUV7vwkUFhu5VdSjxtPLYProRz5U1d3fNZZMXBue1b8bhVbxzCsPLM3v35KNd1VWwOz76sTkvvvc/6MP/xdxwAEHTpKF8+u++vdfib/8bJ2HpT+a67NlHfzKO9zYJPpf/j7f23VP/TV1y/vKZ6qOc9Ipp64Z7O9+59txzdVXTrzS9XisfGPZ1+ljuth9d7vbofGgBz+ksoGyv9XJzC3q0RUf+2h86xt7PjJvwH7skT8edzv0sFWee3390hf/Jj758Z765Ist67nuetSjH1c5AHPsKiflHXfEJe+7eCPM2qrvP/EYfaqLy1zynnfNph61PfhzjcCSLy6Npa6ymkt+3WKPs8tX5MJiz9hNd525/YK464H1ycxame+xm14512MB0v3227oG8c/1kCVcnGvL2qeX2udRLOEVCz0iy9FMoPUAACAASURBVLXuixGFhT5486a9YgTaQOHb3/lufPwTM45Q3k09P+SQgyvnTFe7fMe1GxL10xd90rd2u+Kqa6tI/Ea0h/7zf1ZFarNd/L4PxZN+4fT4yEc+Eo94xCM2oktr3tmmHrkgwYB/rzK8cfILoFD9uwEK5enJXf+e977saPu+7E8mxLqu9O6XDq+uf+f9mffg/vx3Xx8BIopoMhb1INUR+MbYqH5XXdYYH1PGpbqioLnkOM8az2n35bdWuhNwclhuE1EoqUeTcW1AYc53e57bP1/GfQk02+OclaN631mO/YB/l2tn1nflOpqslQrc1W3Wmi4305Bry2tm9rFYI4OAguvLevKrFtUeEjXluQltgLA3eKJzfDa6L7PG5qztF8RBqEfnrOQo5BRKfE5Bk8lIoiTZ6p+NYuvWxuNQreZaUEnCYVT7/7X3RPWzLgFbvrO9lMrrS2FaKpKkB9lZSuXm2uQlTgO/FOJ575rvG0fs3LXyfdOWtXvJZIAmldR631d7tWuEWeuEteNZgrpNoLCHBM/ma6oRaAOFzWHZN0dgbwYKk6pHsRI5KI2oSnYXxuVMQ2eVYb3i3Z91X74zdWlpRE4M80ZGp44qjbo1xn5xWNs0UDPtvjW6M0FAcu0L8DTr+1YZlB08jEEGZ/u+hrlQabBm3PX5+muvrsuj/slllc5kX8wNnlqHo5X251TQ1bqvhk8N2GrmL+e2ilY1ZWbb9m32d5rBP2TuBoHXQqz0vbcChjPGcei7pn1bFZFq7LsqR8HJzPNWPRosJhfxrrfvKf5/FWptnTpcgQmG7bze/CnvG/ydPRd2Rj0aA3sVJ2jIi5bQz7O2vyQOOvCAVSczD3n1Hrkmv2+RNbMbOriiKJRSq/MVShC0G17Z+cgSJKcQe8Zzzo+Xv+oN+xT1aE+N1+Z7lj8Cm0Bh+WO6EU/cq4HCV79buUpKQzKBwVAvfRpIU4314h1t4899E6Oxtv5XVcgpaTUr1zWJseW97ahH8868vwQjq97ZcV+5Tsp3dtk5bWDV9X19hnCXAT50PEtDO8GUT7kW9ag5R6EdDam+q+RG578TUVTlnAru9Kx/z7hvAuqK57QBQh1RmfHO0n6bdW35jT33jROgFGAl53HVGO2mcembh3LtvP/iKUChzFHwsDJPYU97zsv3pQHXLv25EYJ3zTs32MidRcc6+zxA4cA4T0ShVTrVt5TJu2UUqYyYeAeDOT31WY/Y3zbakPtKKk/XWlr2+3zbkH7OWkOl0X4HmlSTgL2MMenqY19/NiMKs2Zq8/fLHIFNoLDM0dy4Z+3NQEF51NQFSYGZ2+m3pKFte1r7DPuJHmvoIn2ApgkWd3rTpxnu+TldkYuJx7flDc97JhSkJY1J32NqYFCDqvTKZ3+riEKRo1B+TwnmOmlYDVNhmqe/976OfJZyPMrxTL07K6KQ8zSIMtYCvOX6yfetAnG76lySBI35rq779kQ/vbeM1gyuejQvMJjlAZi66Gag6va9E+DQ8Bd7N2vHS5fRz/Zjy2dmfkfljd6yEu2Yp48pZIYIlPZGbM8boHCgiMI5dY5C+5m7WaZMHr835b0M+eZ51/+QZw69pmusNoHC0NHbvG4ZI7AJFJYxihv/jL0ZKKAelfqoNDp7Ofzpqa8Mw5rZPY8hl0nP5X2TdxUHh6XnuR1RSIOqNPCWOculMdk2Hv1/l+1Tvr809krH3ixjc9H7Sg+9f1991RVx3tnPqs5RKOkw02ymVbq2y/HawTBZY8e07lsVGWl+1853nQqsisjBfLbbSnSifV/XWuqjfQ211ZZhz5bgJIHw1IjCp658/yoE3GVUtpH3ZEKaTdaVfLwKSTWbe9p95aKah/5RbiLP6ORtLdrPBsyU39fO4+jy7neNR3vDd/V1nvvaY1QunnPOf2kcsP/+FfVoTf8GbMBOg3me+1obeJoBPktgDNoUHYJm5kZvl4oqFmDJaZwlnNq/733vjD527bt9serRMhXo5rP27AhsAoU9O9676217NVBoqEdpsJWGbdsD3DU+qd8ncnZodJ8tVxx01WU3lD9LQypBQhkdzz63dXi7v/mNZZ/La0rDtn1Nvm+V7dVENPJ3JRBIR+M0Q71zvTXjN+2+NhVqcj5BQ9nKqkdVMnPTx3J+91SRkAmgYwc2lPVcU23DeBYrY1l7swTC7fVQAoPysL9lvXvWc9ogfTpQuOL98/P9Z/Vgyb9vG5pZZ37Jr1n4cWm0t0HEwg+c48auBV8BhQP2XxVRKB/ZHs9SGLVDd6Uw6zP4l+GJ7+rD0Pe17+37nmljUP1uDmO+753l5l/l4eip7NFeM+Xa3owozLERNi9d9whsAoV1D+Fe8YC9Gyj804R+UQ5W2xPelSfQjiJM7m+SfkvjPo34VcZ04Uic5fkuDbc0QPOZ5d+duqblhCqvmfSxoQQnNbjU4xODt8mHWPOOljGev581hiUQq2lftSe89z7sjeK06nIcyvm59podsf2sZ8a7Lrl0VdnUac66NYCv0L+L3lfSnKpxzkTd4hvLa6oIUaP227ZGV/+67JFp9+U6KSMwq8BBUxa4LJe6Zr329K8arq7qYC0J1Nfnqm8t2tfgqkdtI2eW1BtqyM16TvujV10/wGOwDEO179unGZ2MPDz+0rjuAwvL7OOsiMs5L/itOOAud1mdzNwzjsucwxzDadWhhizuIetl6DWDxr1HsA99x6x9M6sP7d/n/28ChXlnYPP69YzAJlBYz+jtPffuzUBBjkI2epMuS+576YXuraDXlApVvS7z5UoDuGsW0mgsq8iUer3sT1sWtw34tFXaRqDSpjubvD6/y/tSV7eN9Pz/NgOij2I9ub/RVWUCeAlc8t9rvi8jB0VFqVnGcRrRpX2WRm8ZJWgfuFYavuV8lGNZHjpWRk+65qLrvrYdkffltV1OumodFJGGEgy0PfrtecgxyPeWz+9j1ORYubek1XXe28NwyGe331FGJ9rfXq6/IfflN73v4nfG6bOqHu0NXvpZBtVGiuJZfZv1+2X3PTeaedtvv/1qsNIIg199wW/F/m2gMKMDt912W+y/f33wRtmWBXymjc+0tdf3u76fd4KoKTSjacOSfe4bm+53TT9foyzZOiv8efqvnBsXvvpNm1WPlr15Np/XOQKbQOHOsTD2XqBwbnzhH26sBrltOO/auTO2NGW61/xuV3PgWVEBscugT4OsyzgtDfO2YeV3bYdgXp/FMcpKeF26rOv57Z/l/0/uL6hE5fvphRIotY3nyf93lNAsDdqSolQZ70Vhkza9tryvPY5935bGagkUsm9toFJGIPL7ygh+BRibCEr5DP+eBiBLJ20JbPK+1NFlAZe28d2moU36VyykNf1rolhd/WuPQb3gV47Ym4Ct5hyKcj2V/W2Ph8dMgFCRW5M2RRsEde2DcrxKEOvnM3MU8oFpEE01eufgn08MoZahlkbetPeURlhpFDvZLu+79dZb44ADDpiMRxcK9Mvbb7s97rL/yn2zvrdtAN52662xf/GePsOxnJi24Mnf9X37NKOx/btZoKQPKCyDGjXr3b6z7z05buYNKCkXrfvaC7ettvvePaRP057VdX/fHC/yriHmR9dzNyMKQ0Zu85pljcAmUFjWSG7sc/ZeoLA9Juco5IFhxVDt2rkrtmxde/rxKr26qz6VOQ3pUjeWHuK8Zyg/vm3Y5v0Z9fC+NDbTOEzKTlI40pDzrHaJ7bzX7/qoJm0veBqYq+rpt84paNsZpUHcNm4Z0pLBs39dACC/od2X0lhve7PLA9eyPyWFpnxPm/pT2dBFDmtpZJdGeLkGyrFs2xD5/122RAUIm3Okcg5yPsr+dkUj1vSlRf8q+93uUwmCy/kpv70EJ+V4rLILWnPfN3b580k/OtZMrl3X5lodVPVob4godInX3WWYbawoX/zt5QbIRd5+WhsotA33eQBDH81pCFjKfvWBoFlzOw089d3b+20DKGzzzkrfuyoAu/8Ba3J/uihZZUSofd8mUJh3RjavX88IbAKF9Yze3nPv3gwUVpVHra2vycBVhlzDuy696m0jtTTE0qhK46grtyH1ZPu+0kjMyEHpla3+PR5XlKL8eQlGJgbzFAO8BBddAAMIaIOdtpG7Cgw0h4lJzJ4AlSKXoTRoJzqypxBJ29DsG0tAKE87LgFU/vuaq3fE9jPPiHd94NLqR20wUIKjtsHdti+6DOlcF23w1dXfciyr72+tsXb0IPtTPqvL657fmuCrXCd5b7mG2qA1/z9P3y7nrs+OK+2nyf1NVK0aN/8u9k+7bwl+yuf3AUs/l6Nw+lN/rvvAtU9c/t7JJhgi6toTWd7Ta/jNMNK6jM5Zp0RPNTKXaBR2RQC82zig/PS1LiS45to58wamzU/5vs4chdb5Cfms9ji2F9KQNdG+ppy7rnnqAxllOHRovxbpb9+zy+/QR9GrtnLp9PosMkite7rGZBMoLGFgNx8xeAQ2gcLgodqrL9x7gcJq6lElW1uHWdXFT+uWv6/UZOtQtC653NZppRFayvzSKCzvkfyaBBHPbxvirm17nv3MYVppsLX7md9RGai+oSj1Wp0g3NTV77Kj0kBO8LMKPLUqOVbX7hpXEZkub3qfAT6JbhRUlhyfNpDKn6/ymI8duLZjco5C+/tz3EvDOQ1qP8s/k2+1JpqxLw1c17VBxbR+lmAl5y2jUNmX/HmZ6zL55p07q7malgfTtiO65n7V/Df0L3OeP8/3TaIkDSgFTvMbasAqGlT/rA1kPKsrclbvn5X7yrlon/mRQOFpv7iOk5mrF9RftlcIyHb0Yypo2EM97vO8t4XXeroz9DtVPXKOwnlnn1F5aIS+tnaAmnxeGwzlz3fe4b6ta7o8tB954zwRjPJlbSpZX39T2cyag/aHDP0O1/mGkvbW+64pILUrvDlRiB0L4xnPOS9e9srNk5nXs2c27x0+AptAYfhY7c1X7r1AYYV6VBpvlbHd0E9WycOkdzSDnSCiNPRK46w0kEsDLY2rNcChsWnoR9V9VjmBCk559q3k/K8ylls8+jKq0QYAbdpRGnklKCmNyAQs5X2lgVz2uTSmS6O+yk2ogNau6juTflOOY1sPtY3w8nltr7yTmbefdUZc/IGP1Z9bRElSx3axA/r0YfWMQo+2DeM8q6rU39Wzmio+uZ6mHdZbfXszLvXr6jFq2x/5s3Z1pHLs+gBDMxQrdnOzpkoQmknOOf7ttV0DzC0rc1ecI9Ze42v6UUWfmjmfQu/K+y7543fF02YlM7cFXx+XfpWBNMUoype3O79q4TTocYgBN8uo66u0016off/fJfjbC7GN2qb1qStfYtaYDFE+fV6S8t7yHIX8+a/9+m/H5z7/12vOzBjyzvVcc5973TNe8htnTR5RCoz//MvnxLe+vVIFYz3vmffeC379rLjvve9Zy6SizNgvPeOc+M53vrMiNNoZUvO+aOD1++9/l3jdhb9TXZ1reTOiMHDwNi9byghsAoWlDOOGP2TvBQrnxue/Wh+4lkZOCRDyZ23vaWm0tu9LKkclw7OUZJO0W54k7L4uA35iaLfKUJb9Kmkm1fuLJOKub3FvGxCkQdimRpW6pwsslTZDubDaHvq2Hiv/f/KOBniVY9gFurL/kwTcJupT/bxIwM3+VOcotKhH+c7Mi5i8ZxyTPJQuR2DZ77Yx3p6HVWCoGPOucZysrSIqlOsDYGh7+fPb+gBaHuK3Zm0UBwnnt5RgoFqHhd1bjkFGhBI0ZoRLXkkf+CyBZBvAVfuhKXHbtYfa62lqjsInr3jfGiSVD3jsz/xifP0b31ol+NoodBGpmPiiGrCG35fPfeTDHxK//aJfXfPYG77xrdCfKnxXeB8akVMFO9oLpH2dxbB1tCV2Qlmtc+P7vAnEwl322xqX/clbqle1veOPf/J/iq/dcEMt+PKZTV/yffVpksuxOLOfT/7px8VZz/qv9fi1qkKcff5L46AmopAD+YjH/vu4fMd1i0zXuu550ANOjCv/9O2TkG05zt/3Aw+Jb37r2+t6/qI3X/mht8UDH3DiZO0nADvm/g+Lr93wjUUfu/B9Bx10YHzny9evWl/74oFrL/uj18dtt9++8DgseuO2Iw6Ln3/Kv1lzu73xf15xUcUz3oj2g/e9dzz2Jx+55tWf/Mzn4k8/cvlGdKnitp7+n/7vNe/eBAobMh1Lf+neCxTqiEKZSHnF5R+t9Galq1NFpmO3OfC21p41TWeVnq3+b4WsVF43ORRsMrordkOlg3iQI+JBD/7RqiBKSXt1yz/+w1fjC1/43Mp7Sw9388yVH9UdTzWfdsAaEIBVU9w7OSStMujS8Vz37dC7HRYnnXLqxKbJZ/3Td78bn/iLWo/nd3hOsrhyDEtWVzVC5TWtFVc+Z5VZVDqCm/sPuuvBceqpD57MhX6hHk1OZm5A1Mte/jvxpS/+TYy34L60ra2I8dZxjHYm9ar+/sD8Kq4djyoXfGlZ1TPe3Ft+RklZe8bpZ8bRRx9TD29jL+rn1Tsuj3f98RubCkTN3aNxjMb9TJmMNFTrU0rArpo+Vvcqqu+rutn8cGXd1ZNd/3xc3Xf2OS+Mgw85ZNWc7tx5R7zg1587sR1XfdvWcYx3Nn0rxrFeu/n8+t/5s3JcJ6utujd7vNJ3//rVX/3NOPDAg6ofDjpHob2oDfBxJ/xY/MM/1kbwnmpPfOyj4u2v+4Pqdbtw7rbUA/XVf/ha3PPEf7GnurHqPSgnN331E6s8z4ne7n3yI+Pv/v4f9ni/nvZLPx//8yXbW2c4yJvYGmef95I48IADqnMUcl43Gih0DdBGA4UHnXrSmm4dfb+Hxg1f/+Yen88ECuWL98WIwj3u++D4zndXvIZ7aiAfeMoJccWH3rHmdYD9Yfd8QNxxx865uoJwdwjHQETUBR0Xa0/56cfHa19eR4rK9orXvClOf/a5iz10nXeJXn337z6x5iltoHDdxz8VZ27/zXW+bbHbf+A+x8cf/M4LO29+8i+cviFrTGd+76UviPv/0H3X9Osp/+Hp8e0Nio6+6ZW/H4cfftikT3svUGhyFBoKxq233BIn3GdbbcS0fWmLsJyn5SauSn5ohmoUcdm1fxlHHX1s9YOajlLnG7zlDa+JM5/5X0sc0hiyRfnr9vvyHXWqw+oDPLv61nV/892nPfqx8YrXvL3ipZfRiKuuuCye8q9/oh6zVahjyj4rAVg5zuX9bZDmcR19vv8JJ8X7PrxjJddiy5Yoy6NOHJlPPC2uufKKxTb/eu4aRVz8wcvjxJMfUD2ljO5c9KoL4/nPO2M9T1/83lHE1Z/+Uhx+xLY6z6BJELcHTrz3kavX2eJvmfvOaz775TjiiCMroDETKOzcqW7vSlmyfNuGAIXHnBZvv+h/r/pgkw2wbDRQyE6VoGojgcLvvfS8NfWf9bECCgceGOefs7IpNgoo8Nrv+NO317kSRX1om2WjgUIZUci53eiIQrnwN4HCcLl36iknxJUdQEGk6JBjT4pdO0tLYfpzt0WEGMALIuILEfEzw7ux1oDch4HCh//88njMz/yHdXz94rc+4OQfjqs+/M6Jp6580jH3f2h8/Rt7Hszrw6WXvDl+5EG1EVK24058eHzta19f/IPXceeXPn1ZfN/dj5w8Ye8FCtvjC1+tT2bWbrn55jjxPkc23s4BA8CYrbzOqwIJA27sv+TS6/4yjj6mBgpaGrpveeNr4sxfriP2G9FO+1ePjT983dtWvMVNiOCqHZfHU574qA0zLO9/4knx3g/vqCMUTeWl66+9epLMnLbRk55wWly7Y36gcHREnBAR91B8JSLE9q+OiHl4Bxd/6PJJNGbC4xhHXPSaC+P5z90goOA7PvOl2Hbk3WubrV5tcfPNN+0VQMGEvn+eHIWSA78hQKGJKLT5a3tDRMHUZohwb4goAAply7k7a/tLKurRZkShX8SjHmVEoQR+R93vofH1vSSi8PTnnBcv38eSmTcqotAHFKqIwvGnxh23iw0Ma4+LCH70UyNCrPCoYbd1XrUvRxQ2gcLaKd0ECotthhe96EVx7rnbI09mJnOdT3TCvbYNN3r/r4h4orBORFy2WD/adyVQKCmx+lZFFM7YC4BCAxCSp371lR+LJ28wUHjPn165EuUYbVmpenTJZRV40J70+B+PaxYACsijz42IhzfR3Osj4tkRgaQJOAxx9wAKIgplrgXL/HWvfPnGRRQaoJARhbQ5brnl5jjxXhscUdhWOxkG5SiUSaYZYdgQoLDEiMIPRMQREXFgs8g4IjgkPhkR35lDziT1KG/ZmyIKXZ9RAYWDDojzz3nm5Nd7Q0SBMEYBEb3aGyIKDzj5hKovKG4amtveElGwxv7b814QOP833PC1OPLIu8+xYnffpS984QvjvPO2x01f/XTnS+YCCneJiB/lph2oAaZ8Vi9Q2LkzDj325CDThrZ/FhG/EhGIjpTU24fe2HHdJlBYbPA2IwrDx23fiSislEf1dTffNKc39ZyI2B4R6l/8WUT8+ya6MHyo1lx52fUiCsdNzjLIXIU3v+E1cdYGA4VXXFRLnrI60DU7Lt9YoHDCSfHeP9sxYTLQ4+0D1+guUY9FgALSOSx4TDNTt0XEZyPiORFx1cDIQhlR8JgEDK971cs3PKJwRGOU50KsomobTT3admQ1RjOpR337bEOAwmMfFW977f+qDMnypMZFIgpnNnYIvCS98paIYJuowfPxOWyTrhyFHLONph51zd2Z2y+Iu6Ie/epAoJAVUOejcQ8Sz5nMXIKrvHGjqUdLyVGAPEvWHlT6ksYd8oqI+FREkHYzWuYolON0p6Ue/duI+OGI+P6I+LmI+LVG4V/UbNJZg9Xx++nUo5MrWTKkYXpzXP5WM4Xvi4h/N+TGnmvWCxSAll+OiP8UEQ+LiM9HBKIEksvfRYRz6efNCBmao7BbIgocjngF/zh9UDeBwvBFt+8AhdXJzLfeest83tTHRoQcfDLjSxHx2xHx8sVlhhEWUTjq6GMmlKOsYvPm1796wyMKgELqgwQLewNQeN9HrloFrJJ69K5LLp1U53nyE05bCCggnT+hAApceCS3PLE/joj/ZwA2BBROOOmUSdSjygkYjfaKiEIChQq8jLbEzbfcNN8eGC4WBl15zef+Lg4/nDt9QDJz6eUtn94HFBwzxr/5fY2S+kpzE0N8va1MZs5nLZKjQIG+NSKkq/5tAxAoWEr11U30Un+HhLLaEQX9ykTrjQQKZTLz7ber818fAHfmuRfEXQ+aAyi8sUFSik19cb0zuPr+BAr502rcGAt7QY4C43JSJ7mpwDV3ROH8iDg9IhDbSbNrGoK7hXVTRDyjkXAzKNVlMnMqh33xHIVBEYWfj4ifjIh/3hBS/zwiFBUDqnYsYPmiCfXkKKAeHX78qWF/DGkcCU+NiP8TEX/VGOnvGXLjkoHC3SLi+Ij4wYj4xYh4WUTsHxE/1Cwzy8uS+khjJ83D2N9QoGCAWQJ/UnxIx9htAoXhi25fAwpZjRBQOOH4OahHDA4RyAsi4v4RofgP4AA9L1ho7dLrPhdHV8nMownnnk54yxteG2ee8V+GT8KSr8wchdQFqac2nHrURBR8bnrqy2TmNICf9IT5qEdknVkQNDLF7fg5effliIAV/7JhiPQNeTuikGO3N1CPjpDM3JyuDPyJKJx0n7sPM0RnrTGOt5+KiI9GxN8MA9DXAgpHbFs8omDCJQ93VT2ScPK0iHhWQxXkEGT7sZGGGN7TvvcJjzkt3tFKZnb9vBEF6WaSEREkeAd5Cn88IqTmoRRYjPhvQ8yHLqCQ37CRQKHKUSgqEyR9rAQKM6sesUhYQiwQHpoPNAabAXJ+ypABmjKhbaBQXjp3RIFigAA/1BgaspwY5wvkEJY5CiUgPfaHHz68PKqNqTALg/fDEXFcRPxYRFwYEdCzhYdWY3M8LyKu7R+oTqDw3PPvfNQjYJTwsO6UFcqWc/jfG28hDjKEP7BNjSgcc9KEXjbrcfITyAisBvLswbNumPH7RSMK/yoiXtw43w0Tp4bA1OHNlqzqkzdggfODtw11e4ajvurthgEFkTeCWHa4xI+X9kfb9ghQyMTYOeZ4GTkKArjpKZ3j1b2X7jtAoT5HIUs73nTjjfMbSeS/TfnaiLhPRHBB/3pE/P1iI5nUI8mHpe3y5te/Js5S9WiDGqCQ1KM0ylVkuvbqKxenHlnvmd07LMC65uurqkcfQQKqgYLa/9dfVyczX/wn9YFr7JAh1CM+A8HF/xYRj2rUJ/YHd+fa0jq1KfI/mumeVokugULdyeYAuJ074/WvfcUGU4++HNu2basrRjlIbevWbvod3Whgakd/xM0NGJ7FTDB4HJdCz+wPulQ+uft7GqBw2OFHVA7TqTkKn7j8vZ3HP3tuX0TBMVVC4c9vFBjlxPsG7S0I7CefkRGFshLTPBEFjuH9x7Uc4WXD8ntv83QLUL//v4bqaBynjOGkT11AIQ3wjQQKv3vBuZ1z97xzXxyHHHzX6mTmbL05CiwPA/TAiPhM80eplxOhs8ZlefFiXl7vzqpH/p1Rjxy7qUDhro3XyKK3YSgFIWeCDufCH5mmJhB6TuN8oADsAgr6OFdEAZEdjYbR87qI4Bm3wSXBkGSnNWgaQKDMjGtP+54oj/rkiPjPjVbIcSA1oXmhP3NrLQIN74qIzzXjSLjMaFOBwnEnx647hi0MMowH//GNLfL0WS+e8ftFgILgFFsaUFgpernyonZVRCAChv7DJlo660t3O1BgCXfRGCEeHi/2F63v3z0CeCGg4L3CLlkVp0uxskDyOv2Zs7L1MoAC/SnYuIB/o3O17TtAYXUy88LeVGCB7H1RQ1z/hUZWLOCl/Oi1n61yFMrI8t6SzAwoKFCS1BmTj/f/5CeeNp9HlsAwZgxJa/72UcSXxgsZa4BC5ihkZOjj111TAYV5qUeipY9p5O29GllXcyK6G5GCCkp8jC24bQAAIABJREFUsDn7mNIXf+hjceLJ9RkUk4TmiLjo1d1Vj+xHflGiQ2NaUDnYbctsOz71t3Hk3RkIddO3NfQ7cwQI82pnooaST69qHI3T1rhBdN3Jjd5E5aVHmwOzu77l2s99JQ4/4oiqL6oenT7vycwe2gcUfqmZXJnp1iB5zPtG+P3bUcSNeU7GAqNcUo/KCkxDIwrGiiOXZ1Ck4y8aJZpHaFkM7E7hK1RyIf1ZXrh5IwoJ2ukjc8aOpbtuGUU8cFyH1STo/PU6IjDOUciqR+3j0Z/3/BfHwXc9aFiOgk5amITtrU0tMgtUEoeQkUnmwRfe/Z/NLppjXkug0L5tKlCg0MUiTaBJI+hYTiSJkBApc2iT2OZ3nBwMD4twQFsKUMBRQZq0eRHJUWfKjSwT9uymjMObpocCEyiUc3n6r5wbF776TXeeZGZzCYCmJWusCA/CzVhyMVW0tAZ4Xdl4Rt42e0L7gMLtd9wRRxz/wHDS/JBmudsKmFFYY5cMuWnKNYsABdtR9Q8JfKm88hWGhoLMYUrPG7vb8D50QBBwtwGFu0SM7jaKMSEHNLeHnJHypEZAG2jAuQfVLAQUnlKjpRE+1rcixmvPlKsFMiToWmeJslQ0CmKWx25J5VHf2QQaOcPnKarRt8z2NaCQjqKZ1KPM/8rwSylbyQrOGH8zSN68mDML9eiYY5mKK40MfusbX7d7qEd950O0DMCMKKQxnkCmOkfhp35iPqDA28Dhxtv8oMYByMHFaOtrZT+LvmVEoezXGurRli2Dqh6JIlCRbLUhTTeYJXLIGPLMla6WVY/Kw4GrHIWOZGZHjT2k8V9JL/QOfj0EAX93iYQ8IqNts09Ksfb0C1C4+93vseoguDVVj9g1ZOR/jAjVeLyEbMLJ4ozMRm6Sr2UnJLQBBig/mqg8Wrk8Htd2yFo5Coceelhs3bp1ekThU1e+vwofZVmr8hv7gIL9S5EC9PeLiIOL/jLSlRacp+5t+c715igwwpXXYreJJryyYam05874OXLIGHKcT2slUGiXbW1HFDiX0Z5EM7B5jJUIBlu23HvsbrRsQMU6GBLZKPsIKPRGFJ7/4jjkkIERBQ9leFOqIgq4w3YPNyVKjQWq45Qqy8lOnaOzbaBQGsK9QMGgWey4zDzxKCg8fx9scigIPgsP1Yf7lece/LfjB2peQOGUk364OqCubN//gw+Jb3xzwOo10ZAoQ0MEgbIqieLCh6+JiJ9ouCzvnh5uu7NHFEYHjmL8kXFtqFHumrlCd2Plyo2hyJBV8/evjwhnkn1tNgCcFlE4+JgTYzzLzd50SW6CKWVrovJImVhPWwQowFKwuqH4/WI4bAteaAE+y5/OpywtNX9L/bBFZh0Qt3SgQAHcL2L0rIixkIxQM1oeAJhuOaA/+VRcgvYMbb9soECwq4rD46yYQLtZf5QwWUbJutYe/UrE6BujGM84b2O9EYU3RMQjmmGBfzmr1tv2NaDgexlujKTeHAVynRJnSVongB2lmY3YVh0cmrbOfmMx+hHq0fcfVbtuq2OwUBJGEW+66FVx1jNxJJfY7t149Mk3kXzeYx5Lzi1/Fw1QePlr3lIZcNkY53IU5gYKB40ifnxcU/0cUmDP0Ud9B8S4flt9EnHVyOlGJZbUo+xXBRTOPCMu/uDHJqduD6Ee8bGRb8THPI3KYNfxy3W1VdSj5gJO5zd0UI/sR7ZssnzyeY4ZVlir6x1+B0RgPpd5ucwRZkqfS+qqz3wp5ChIYs4Tn6tzFMryqOwtZZ94fMqGX2oeknPKyULOssfIME0HeB/Yc5RC5Z1uHDIovx3JdiIKdzv00IqZgnp0+lN/Li699NJ4+MN5ciJGT37Sk8a4ZYBCuyXin1b1iA35LyMCFscxy8b5jOOLMrhAJDC6gIJnD40oAApsC7pIMrMFhXbfbmwQdh1QMyvENE9EATpmYPD6QahanSq10uw5SBWNTCgNep3Xc1lGFNrfdua5L467HnRQXR61efHU8qgsDCj2/REjJ5xblJLFhFoIYadPoQIp/6KzGTNPg04HenYHoHDFB9+6iiI1k3pk4LhVgQRRA8hYQon20YjRX0eMCV2WVNawBGT0c4BR6THrjijw0KA7oUTxZrXPpeINYG1KwCOQkcinGKt3aqBA4fxIk/TNSjJm6ZYBEgg1fytHxoBLj4iNKTHrfzWFtDv2cf5oGlC423GnxM6BJzNzdHA4w6brzU/Qt0WAgvuIaTXLyFjB6iztjF7EJkKJsh2xsyg6DYMLXkVDmpZatHSg8OCI0b+LGOtwenrNubkU5qDUKDOVriCv322E9JQqawtHFGh+awt/QP4QT0w2DpCfbvYtGUOJuv7RtXNk9JhRjK8fTx289QIFQ8K2pdPNHf1EnP7RlLU961f7ElD4wj/UNbp27toZt992Wz9QYERjzvL6mStca+DSxrS4yQU/4+WjIxgkFv6crap6dMwxtfHmkKRq+YzjrVUy8wI5CgAO3QAoa/Tvv24cWnQCqzgjp37vW6B8TjoNKLog4rRjHxt/+Nq3TaoeVY8ajSqgsNA5CpxrhAbjyPv/JGL0b7fE+JYOpYSfDYT5Fo1HuDkkPYGC+du6ZWs1jx+/tqEefeDS2OJ063HEkKpHVCaSQpmqNmT6GOhS/hjyXQ1Q+OGTTp7MaVay6oooWI3EwjyN/cYHiLpeptHxHTI/+sQaoHCkUqS5NEbNgWslUGDr4EHNarxBOgFplZXKCRcGOdsoWzrk/t+1Dx2co1BFFJwU11R9KR81DSgwyA0MBw1jO/sFUbGL7NlFqiCt5Ciot1+jaWjwazd8Y9DJzPrFvqQwTSSwYGGVzade3tCj6BG6YhpjZWiOgjxWi5/DqmzYJxkm4y1k07LDgUb6TBGHt4zqw9yGtmkRhbnLowIKvH2MWXHxxuivznphkAtnCYmRm1yWOk1442jgeglh4pZ3RBoeeMqJsePD3ZXoeyMKBhLtiZUknKbcQVcjYQg/VYcsQOPHo6ifM2pG9gGFo35o9qmv1bhAmgwMYAoX0KLLRjkQtgwioSR9FDac0qeuZObTn709LnzVG+8c1KM8VdVi5ylkSDIeCTqRBPrKWMkiJtA4+cwn3p7x60L7xZAvo+oRTGfKyGnLmWyAT3UddlnE+bEoUMCwY0xyvMDo+mY45MZzlusLWpJlz+/J4er3tu+fNkuSnOlqSwUKBC5DgleewLdvKStRPlqUQkvjxLwzoEQKzeuUthBQAEKtHc4DMsw7GG00u/VlIiWf/E7TDyFlFgcjSI4MOUb58t70JBCsByjwWpobc4cpqUu8qsRp0i/SOThUD7huXwQK+i1HoffANfKB151yhtgZ2JRoImC6xnqjn5gJBpK3bZ6ck1HEpdd+Lo5qTmbeEqOJETc4mRmKxxPEKafcgRZyzAa2IX0HryA9oZ+ZUMwtDSlyhlgIrvE76/Z9Eacd/9h4xevePjlDIWk0C5VH3Tqq+/WkcQ3eUR4Yj4SFv9uW7SnNvklPJ92mMgy7pUlm7qIevesDl9aJ6jEMKJgyTtUSKBAXukXm2ROGst2GAIWTTqlzFLQcu4tedeGaA9e8z2daNqJ7wAeR8bNNXZK+fWj5cTTzgXAszRBnley56lNfjCOPbHIURjVYdujgqoiCfALMCGub8cqDIBJEAZC1hH56nQkL3maUFJTLN22J8XN21XmAmd+QH0BxcKS22tWf/VJs21bXmJqazLxoRMGDTaIcWHoAzYYytZ8ZxuIUwDEDfGDUv+psGVEoAcw8EQVrmg3L4ctubB8kvt8o4p3j+uRVzmD7YFp5waERBeDEAgOgfHfa/RwKaUMzQixO70bLFpHhMBUJFEUa2sqIQhvonbX9gmB4iijMrHrkhQw1wgO8FzEom0VpcTKKWVGsD9YL/gOBQvBYyAznDrTVFVHIx3cCBYJfmMyg8RqlBdQ3MAQyDzXLjtVkQhmWM9hDJVAo6VCDkplxPkQL0hXxjmbBlUAJN0SfbFieVmBiAFDI+fL3ne7ANQLCWJgfY5fUD8ZjGgCkNgEJSYs08ITxsgiz8rplslFrPUynHp1U0StntXSCy7m2lLEeOEJgaLaAvUt+kxV9/Nj2OxYFCp7D8ScKTaaKJOgfeQ8HJ1PB1qR6LDf2rf5xVFh6fUbn0oCCwUFlAIoZ57xDPxkxAhLuGzEGGuxNwo2yYMgDD4zyGW0hoEDDkx0mikeaAUQZkE2cDYwxzgchGREu1gE5Zi9TzjhnqJc4oT0n/64HKFja5o8DkJjCtrPGBFoMnS7SRbNX6urB29eAQsq4mx02NetkZsaNuQQuedb61g5vJRkL/M3RLrv+83F0AxRKw3LmOQqUvE1nTQPJ0B8k6A9jiMEvTwdHBeg02aUhZKPSUXRCm9z+jxGnPawGClrZr96IAmtbX6x5e40DL1lL/p+MFaYU1TCe+gK82BPtBedZvoMA0T8eV+WGOoCCn113zVV1MnMDFBjmXSczY1bAfoaKGO+iHmHzUqc884ZVAJDvqGzULFMFBakLzyf1aFfDN03w0pXMnBEFw8GXB5fan4aKmiJSNIErflTmz30b08dQmmZ7Vwoi0GDK+xw0V32mBgp5JoZxWkM9yoiCeeGg5fiAQgh/jhYdInd5kTJ3RwcpJEpCwReRoLJsFGXGGOZhajVAQXlUz/rAe94dT+tLZv7kFe/rjCZ43pAD1+hy+4LeJ5PJYX0EjIAhIH8Wtafs+xMf86h4+0V/sCrKMU9EgVJFk6Xc2RhsDnK/3TgAcEQ5NNENplHb1wAFSLWJwJQ5CqKfbCEL7SujFdvn1h7JDzE7iZCiBxJ+bIvSYsOkHKBQnqNQ3lUBheLANYLmkY/72bh8R09s1oRBoSwMgyJpuN0IF9EEi1QjkBhujAI7o6ffXcnMatvvt99+0RtRsBlsCpJCdKHkprb6VU3DvSLGFDzhaMfOARQSJOTfR9/vYXHD13us0fLdNL1sLGiYhGkEaXWJPjFOJIwxgIEeYzel3ampR/ndNAXD0abLKkfWDUONkZaeLQCU98EmprQoLInN8mh44jraNKBw6HGnVCeCD2kS5UyXrl7esB04yHWXTBO8UohgaLR0PUDBMrL8DRdnJb1OMYpa5k5OHC/gxzlOT+DVUq4cll1taUCBd14HGShAOnQCCKJ5HxAxprjIh1RakA9Xup/NaAsBBbKJHGNUJr3t8ojRM0cx/otxbRTxbJEPZJ3/54ww6RAZq11UgdLg1uxo6wEKnILEgnXl8VhZ6GOcWnQHcWpuzdtANVD1cN8BCnV51KxEs8ZImrYmyALOKoCUEcsYWikgUw+sA5IgsTnCMlV51KOPC0YlikpVJHUcs89R4Okjs1iyJpYxTc/z3DOCOGlNqCRI+oulOQcCPO3RDfUoxpPciSpHYcflVenRNc/ikGKAsVwZY6zYBAp58BUn3LSWkQ2WOJ1ugbLqLdaGoZ5Vj8rHtIGC3yX1KANDpkvkk2jHICO/dEtwLyMKhoffEXb3WqDCdoX9S5HhOnYTUA1QtFt14NrJp6wat74D10rqEYeyapnEFjuSOZH+QMPKnjUUfB2WI0cvkE+zkM18rJlS2TXMqEfbth05iXDo00033Rgn3bs4R0GOArnK2SFaQOmQqUwS60ynzC2B4ZiPspYsx4d1l/OexWl8FABiPlvt6s8q2cq4WyCikM8aAhRcS1nZH8JIPOr6CSxzJPlZXXV3WFtvRCHfYhyNGTuOYi8b43L7uFb6ToQ3jkOoR2ngetZ6y6MaI7pJNMHYGaOf3CIcNWycpkYUzmuAghyFpk3NUWAF0VYmkgHcmrDR/g0FCdSHCi04UQRKlVabIgAXqnpkF+Kr8/oBDOgAfc3GMZAMdVKJS2AOoNB+7KCIgpt8OylBquCoFJtwtDVinMpKtAaQwC+Y0sqqR1kO7053MvN+EdXYkKaMOeOXa4cmgLApKJKXsATErAHGKHcra7jH3u+nHu2Mw+45HChQTph4GFJ5FAaMwgjXONDJXDbxkIPY1gMUvI9ni5eKWKArbE2OoRIE2LYUGxuZY5Xu8A3C6F1MjKUABRofx5TxZg/i1JAh2YQ6KDTGN/RibzJAeH3PbC6aUhRhIaDgsd7JO5WlIJWW834cstujLtpx73GM9ZciZWCyBkQuoSva3n7lMOmQxYsCBcYQFiVdg0JsbuhIW8DSt+4EZkSr6FAGkMjzkGNs9h2gUJ/MrDHMb7vl1n7qUZesBBAYS/ce1W7dM8c1QLW2yAfeNh58m4SumtUa6lGWR63AwqhGtTMjCjaYKKe1L1oAwJhQE2ZviH7QSz73JaOIT4z7yesd/QQULnztW6v+ZL/8fe0O5yh0lEfl2eBi79LFFheDg2AgwIyhflr3FlzKVN4PeovBRF9BtDwTRf5hST2q82rHcd3VO2L72c+Kiz9wWeVAZRslUNAtBASvtiVF1bwSUBAvIUKo+2xUqXzcTEnMHFipKmVLoNBVsTKrHrm+rHzURT3KFCVOZeIAWABMyHyNz5Q5od9UE1lMpbMrTTOw4I9hN9Wupzu6piEjCglGPX9N1SMODuvGQ72AjPRSdBXC3ED6g5tFN0pQoxC6Dp4AVMlbaMzcdsizsjzqJe95VzztF3+2O5l5vRGFnDyLhqwF6jPHlSxmE1iPQ9sTH3NavN2Ba0Z6VB/eAekPpR7Neg+QcOi4VvZCRWhSfdnz+ax2RKGk+ix6jgIQg+JgfOxNCl50aGibFlE4+7yXxIEHHFCVRx1EPcIBtfCEflJDGf4tEWM71S61aC1MO4ZQFqaBfmdUnXTg2hUfetskaqU/5lT+ycwcBeVOeTLsvIYKUOUHlLsQJwRH2gZjnPA48+6ILEwhDqIeMS4rL1KRo3PU/R4aX//6gHNuKQnfzoOVtYFNHoFsnCx6/TwnYvT6iPGM0lpdEYWnP+e8ePkr33DnyFEoFzbXDAOTwsrGVUMgMtxI/2MjRo+IGNsk5piVjMrCCOyIN089R+HYk2LXjGo2ZfeygidDjUEnHM6BqKEyWmIUTEdu2Jrtu16gwEnO2YLtZmnR3yIMaEYZaPNzHjsKV6Ef8pedxDEuet1mvC0FKPBmcX+nK1yHEgCUo5CVaxh5rGWhWyFfzb6hvDoU2MJAgeEoQ5icyqRAE4rYTNGKIuiLQSXzgND3RYxujBhz7wP8lCsHwBKBAuYHmW+YGCJSbkqRwAnMvhWh57xiiOjKAEm0D0UUaqCQ8naNN3Wo8nMdY4mxYW7LGsLkhLnOiFEaSDyBHU6GLI/KCE+KCh0jR2FqMjMvI2eZd9tgot8MbO/BbTbhjxjX68i+EFkYUH43hwBQqHIUWvSZ3hwFa5n7vav8qj2BfmTd4/ugGeunUCkhkjxK/TN+Fl+P829yjkIR6cjyqBd/4GO1zTbeFU95wqOqMx/gqPYxOF7jZ0ABFcAGyybaIDCSNEpyF80oKUB53VCgkGutrzyqYCggIDBkKn0+B5D9SdaTv8wPQSJLjuznkKGGDDlfluuyUfv8EkBFO+o8AQqNvWGcbr3lltU5CgaE3WP9kl9kkPCKcAzvVDkv0A3nGVABqOpsOf/kGF0LqfW0TGb266lAIXMU0HsYTWVS89CIQvaBZ9ygsTuBWFETdidbjwIbEvwvIwold3xRoGDcKE6LwB97yd41xiaarWvBTispODSZeaicI0PY3pwS/k1fsoWSEug59nZGkKwV/bVuyBtt1TkKO53ytwIpK6Bw4AF11aMm+jGVepTIrm304ztQqIwBu4R3kPtLBMLumebpb/pZRhR2tfrZCxTwhcUg7TibxEAQZrx83HIaY5Kws1Eoey03iRqXrKkpCLCdzJwCZXBEweKxoHlmyoVtGkhHLl9WJqkiVjkjUvQ9QT3KDSJ5FPC0ISnYbDxY3KqfjRjdJWLMO0yCs8hpF95CGxagaCmyqRGF40+JO24fIn3qjljenMvkGGchRZYpL5YgL7Cptbtm6f71AgXGJZqkrWCoRA44FeD0zGmyPdgAaI+MTVuVESpaKa1otwAFESEggFCCpLysq/H8Ev40sr0C5GVFGL8jeEuvZvOMhYFCui45DoB4jQyDsKCmDC8DLfreLjeHVfrBiDFXZ4dSWDSioBscvl5LdLaNJ6ILewTtTVKldafozwzGYvV5+05EoaYepRdw5jkKPUtq1Y+tp6zOU/6CbuAWRi0je62BDtrix67/fHz/0ces8jx7zFveMAMo4A2bIB5dooWQ4OQoQ3g2K2I7ipS1NqPARtn9pB5VKq3x0vNEX1sduNZBPZo1VhKaydLnj2ugwNjwDWTqUB5lk6PgwLUVT/04rrumPpn53R+4bFL288mPP20CFORTlUXITQd5ausxqMvaUhzoyAFsXHtEiorIQ+ZV+0yin7iRatRHPTrx5Aes2LLVqdvjuOjVf7jmZGbq2p4TCeAT9R7PpIpgQWaGZGVqR1oAc4RpwvY27cSMf2ehPt/Dxmf7tqO5V3/mS3H4tm2rKFHod6uoRwQE3ciZglVBnhkUzlCdbAM4A2s+6U1Rh7KiFjQFKLSr+RRrZeGqR+UhZ/MChXw/o5cwxFIhazl3Vduawb6obp9EFMqFP4746j9+bVDVo/Z+Ee6yl/0R0WFvWrjsXQuWY4zyt2/6GDTLjihYA1Azg4QniYPeWQsqkSa4B/Szti/7Xf8sUkBHv9dEFJoIjO8/+/yXxIH71xGFbFOpR31CJrO9xePQQghavHsdIyQzPjdFSLUjCuWlvUDBpEkUAwpMkN1ooEQI6gT9WrgxPAwepMUYBzBAf/SpAUABiEmBlwB5MFDo+2aeGygQqPInF9cMQf49BRTMJ++H+bKezDWNaA4pdBuBxKVgbVx8Db+3EYwpAdgCXlPLox57cjjpfZ6mi2SYbliC2eA+wRBdlUYx60iR9QIFiszS57wEXjhj5FSJaGRFJluffuDgp0DpDkqKbrEN2m1dEQXzYN5o8vR2AQK8+O1G8+Js8YYZSCiGQcUSYNz5vY7/VMToMxFj1zQCcGGgoA/JxfLerOPNAsD5YXVQSq7hzdJvaywtEQRpDpCuxLZ1HLhGFwICbEnzwn7EiiqbLrELAAoMEfNO7tsOutNHQ9p3gEJ9MjPPPcNtDe0iB8Oizzry/rZGGDxdPBNeViCUUZ6ncBk8m8KApfOLzDDoaA5FE1FAPdKq2vbNWQpvuujVcdYzp5RHtX7pR/LdphSNZ/gkZ8YD6QJRULQBfBUeyYHHcU+Awmg0oR5xZi10MrO+HDaKeMa4NsQsMguLIONtWAAo5BAas+sLoJD6NKlHRDiRzva13cszEwQXdQM9qWzAsW1K3Zta9nLZ3GOPkG1dEbekHlUUH6ycpvRt38nM+gQUcNxyApk29hfRhT7PSQssZH4FezZtWaaQ/88cCm8kn+3ZsmqpfgAKKgxlvzpzFFJ+WZLWq6IeeFrokAayz0iFqCAn8i5LRbHPhJYpi7KOazGYgIJkZmvrkj9+V38yc0YUSvpFevIXBQrCRPYEh7TvsncMnBDSrNaOKCRfe9GIAptTBIFdYi/n/kgnOkBNCZMffSc0J1AoIxzryVEAFBghmc9pjLLaW44PHZYxAr+3ViBfvGltWo7COee/NA44YP9JRMH1CwEFYV3uTHE4q54Hws8IFtZTz+Ir5zgjCl0leGeezAwUELAEMYWh0oRNQDpw5dKsBsZGEPOzm4XhxA4HAIUHnXrSJAyehw6uCyjom4UG9ZGKpAflNYBovAYoqGT3vPPjZX/0+jsf9ahakE20wBqyAVm33ODcNNw3eBc2AZI+DiaNYxxRkUjvVptOPTo5RLPmaekIZ3OkA9z99i5sA7QLdMya2vUCBXLK8kc3NRyWPBAAEKAW+SpLzNITtmeMkhuGUuTBUmy3dQEFrjNzJapjrsgGWrariazR8rxdjG8A2t71UZwP3IoUG4tYYijXXDNN6wIK2RcGEWucHCFECU+Dpk8GjAUgauVnOcnkCWuetu9o64ko0IPmUQBmSn2G6q2JX3SZLQy/9B3+t68BhTTIVT066d5HrjV+WG4cQDyhosXmEchk7KMblY37l5IHVv3BJ3aPOS2btSrCxALNwhNVjsJfxjHHNkCh8TwDC2+pzlEoXQQdi8EaIo8YN9aX/jLMhP0BAvsEZxBt1vegtqnKNMAwT+pRVjzKyki9ycyzhBtQQ84KS+qrXJyfHkXcNJ4Z7S4fXVKPMp9j5WTmSxvG+GiSo2Crk6VSzgTpBFZWH3G6tuMAgsCf4eVDalf6ZIoA26a4q/pceTJzmd/x+lf94ZryqPl26oUfAZXI+8h3NiyxZbj0BTbkYyidyqiCcirkFWmzgMIR1TkKNSD19xrqUXbIgzlj6T9OGfpwVllM4WM2UGaHU04Gy/0+pqMcUwIFr527PGr2dVGgACAYPLadRZFJIOhxsw7OfcJjTot3yFFotXmBAnDP8WWhiSAABNggbDdoFMXc4mB8K5gDrFG0Xa2MKLQN3nlzFNAXTx/XpbunnUhozoEHC4/iF4rGqElA2Y4olInWFVDYf/8qopARooWAAg6jwdLQRAg6f3SI0s9DYqYIKRGFK/909TkKM6se5fMoCFIGgiOzLSjuBxuGwZEWkcnlhrDAuHxZSZQGzdpTJQf1KCMKJQAcco5C5+fuHzG6Z8SYFcC1S1FwS886Jrd52PdURKEcQAKCpctQSw3COkpvCAPBZqDgCD6VOxIoFs9ZxjkKZbcw2uj/rIKYxCV7lnTigBZiniXP1gsUsk8YFJxLbNu008k1oEGuGgDBDuAVyzKq7KqSP5vPWjdQYGhxkcsF4lHtqwQBzaAoscMMJnmSLkCoByhQycpHXRMxun4U46/WEm4pQMGgmEihZJaKaCNjkSFnMAlh3F4eLHKGNWCNcTRAgx1tUaDAQDJPRBmc0i6w0X4VXe+PYBqxh7PNbmAYtZ3S+xpQyG/tpR4BjkAjg5qhRPf4N0M7f+XDAAAgAElEQVSC4UP3iCJrST1iCPEIMtbdLxqNwyVfLOVKAoXUaWx4JzOjHhXkbs7JmRGF/AgyXuSKg8p7yTJAkzFB93C08fQCCEAFIDOl+Ec+NpOZ06DMSMdVTmbuox4RTjjuZaiKoAISGER0KK8DIn5ydwb0pVybq89RqH3jGVFQHjW99+0D10whf4K1z+Cm2rvSKaaYEtWvLAMefFuYr6GLUFoCBfckWOg6cK18H7q3ofIH/uR/nFFlvaKvyzni29KMB/2gf2Vwye9EFACFZC+wJddQj8oOHTyKuOu4nq8hzFlrn/e5PAiXPGM8UlYdycKSmQ897LBqMqryqH3JzOs5R6FvUilX3ixg2uATeFAi41w0ZZrttCqiUHDahwIFsp4O4IRE07FPoE/2hsUqmIjKxYFloQIKFgbOMZDNtmzTCYaeozBrkfu9ohv/cVzrTJsFcOTwyvM1XAPAYM+IgDBEKH5/l0AmIwpdnvpzXvBbccBd7jKhHkn+feTj/n1/edQhHSeAGOl4ycjbrBJabEZbqOpRzzNH+0WMCWZwv4/vCSjwBiIJ2uldlIiOk5mz4u3CEQWTCJlCc1wSlJrFvx6g8Jzz42WvvJNGFMo5Vk7zHRFjoXCbNJ3/NiXPNcGRzUYR1RICLHJqpkYUjjm5SqCfp9G5DDSOBt7cLPEMBzL6OJ4lnc5qywIKEu7Y5mxeWIns8ocDE7MBb5atUjZyjxxuOzD3v8td4rtfWVtw/qprr49nPOe8uOJDdcmYD//55fGYn2kdOU5wGRgRMwYXTkyfsQHgSczzOIiFImuHYEQokofLVbcM6lE5CJQmgWrgGJiMOYrX34xOABXfwCDhEdivEBjjzsS32qJAwaNVYYV/jfy0Knv5SnLfnEpS103dlnaF8la2fQkoVMnMjUe1N5nZnLEqLXBAgefRRiQb8hhrRo/5YYyzPMldzqGkwwKiHEf4epnszHIjN9A4mgYoHHV0TX6pDLhGEcysepQPAHJFxfBUbFB7wfrh7KADhCRFTyE9NKS+w0Nbc1pSj/wqjd3ecxQYOkAvoydrePp/uoiBo48El0i8pFHAiod0zgYovOfPrpxUh2J/ZHlUOQrZuk5mhtlhFcQEW66rUM+07pDg8CMVzwToq6NSAYVTHrAil0zrrnG8/jVrcxTm/Pw1lwMKpr7UBfpJRUmJKlsChfpnDjnesvYchfV0CEjllGmHbBiPPOEdCmtwMvOyqh61vw8Dw5q135NuSOfPOsXu8Y8+Ld75+sUjCiYOiGcf0j+cCKKVBC07JA8QJXeyWj6nmL1MjylQ0EaCCRRuv/2OuMtdPHWlzRtRcKe5ZO8CNQA+G9ziesUoYsu4/v88LbCvJPRU6lEBFAZVPRqyOAltyVB5ABq0NeDUqQeeckLs+PA7qjr2+23dWocmG1fCVOrRkD51XZNAgedS3LCtVZt76ojCSVVfSrB19P0fFjfcMOAchfa7KSNeSZ4ai4lhhHIxIMzsUd+zEYUcR5vTfLGoUoPQBFkX3HW8KpT/NyO2/Okodt2xYqFOzVE47uTYecd8QMHr2CuZR59vYn9wQOsmp0NSAfuW67KAAnnAJhElYPOmN46HmhOGU6Z0JMHx5FpXaep1AQW4IT2XshFl/XUNLQCgExQBo4SHaABVMcdxKRGFclIQjPWdUsLntS8NaipV1jtPrGgkY5QR1eKze9yiQMG9hkIQdhZdrew2G5mTkE6DuQAFuBlHmrGk7WtAodrKu3bG7bfeNrs8KqcUzgp9wxlD/ZID1h1ZQGnblBlRaOfNMUASKBgwHv1M7hxFXHbd6ohCevAHUY9yoqwhwoChQ7FzEHknBU9w8OiKqDFEBqqWpB55RVZkqpOZe8qjbhtFnDOu0eiLIkYfGsX4vCZx2V7kkSRLrXFgv11BZ6C+rahHH9lRGd7+27plS1y948o4T3nUD35scjhcF1Ag1skoQUaOWtvR0KXTY1oXMuWEZUilTzsqI89RSGpUZZaPJTNfuCaZeeBn917GfjOt9mY2fRU8alfoverTX4wjjjxyEr0yfuh3J5fnKKynQxQDJylPUqkMzDvUgg3Salke1RqbGVFoe6XTIF6UepR9AWAJNca5PQNvitL1UXzcV0YUqk0yZ3lUOgy9SBSSvAcUKHNRBjLHmAH1UG1Gcyw8EyvM5N8cmWVbZkShax3YQMZnVmJkeW8ChZI2k7//1Rf8VjAG1p3MXL7QwFKeBjPrjA9Y1AkUui7drUCB54RwbhdgbjrSrnqU/Vs4osBjA0TJH7eQhHrFRgfap9/zQIHxxpIyX7zVNkVdqHvFM0QCU/S8dK22bOpR39JmlJNf2AUDAmqxLKCgP8L2bCXLLBMA20o2l5tiCXRDF611XUAhB4aGnxYOJ/RFFCAZFCWG+JRyxe3xXjpQ2Bqx5bBR7LrfuBb+jE/7FLhXl3pHxOiyUYy/PZ5qya8HKAwQl72XmG9bQ/UrjkP6CqYx3/sMUNh+bnz+7+uIAs/9bbfdFiccX58KO7MJj8k9IBt4A8vEITfbjKzIrHQ184H1BdWBa5nMXNTdngsolO8CWoAF+TloTyIenEYLHLiWJzPn4+tk5su7qx7Rz8KKNn7W8SQsyFFGD6celPnWiNHvb4nxLQMVU2scV52j0Hj8JuVR/6QGCn0nM+ejdInTQ7DH0SvEOYO7j4qkp5ziZJnrmSDTet+mHnlvBRRedWFvjsLA5bLmMt9h6WE2lk1EoQ0USurR5HTym26KE7vydBbtECFhHQCr2QwYwYG/2mrXfO7v4rDDDq/mbFB51C4Ky3qBAiYGp6qiEvYNT7113HPoZfUJZUQhE0z9fCj1SKSNHUumeA/6OqEqggA4iBi0ebtZfpAnX4SmXXZ2dwOFRdZEGVFo398GCjNPZh7SASuf4ofAcJSHFPjmwDvlhOocBWKAXLEgM29itwAFkoTLl9sO1LdxOhTRusujtsdMJlSetCLEPM+hGK2IQo7Pne7AtWnrjHeXFwRnHXdR9IortajmVSk8G7njqPfp1KOTYteuIdbI7I3AQ8++tKK7EoXbT1gmUMCwoEw5higmNGz2UuIp705li3WAxt1VvnUpQGHWUAkdQzY8mGg8jJRZhN/imUsHCp6NA3vXiNEREWPEYhY37xGloJ8DIqQbBRR0nx6T54DRgk2SxtI+AxTOraseZbvp5pvm96YmZSBpYaV1KYmDAh/aigPXKq/9aDTh2C8MFIa+e8Z1GVEQedm6ZevEAL/qisviKT/1E2t1mnFg9ADlwovluEiIYQi9JWL03i0xvnUxkKDLGVGoIi8NKKiAwplnVBGFbE96/I9XFZr6GrBgKmEZ3ngUSWzArkZsECECztb+rIjcuz/0sTj5lAdOnMz5zFk5CotMXRdQYJdLCWhXXm7nKHjfXKeTD+kg/aiMKspenlw+BShkRMGj33fxO+L0p/5c94FrXTkKbrII7nniv4h/+MeebNABncbH/5VxjRwVl+DJt4+n2VAZUUhP+bwRBRMnCkhmZPRFpI0znMceMu0yGcp91f79PgkUVD062yjUbaFk5rxZVQmhVNCZJ1HYklE8oHUlM+dtuwUomEguCqBGSEvoueCk5rsTKCibubU4g2LhiIKyncYkcycYuULdA+3T7/mIQk4M8iqDTkSG0KMVgAjz2pTS7Fp2u4N6lO/xaqWqsaOk6IgmcDZkPuW0bbBMoJDvER01NByWnOPUPtsAs0dkUn/x2vsSZvcIUMCxMV+42ewHnekr29MxgLsFKLTfY6B4qPF4hiQLrpN6NEBcTr2ESMEuIIoZTtn2NaBQOYtQj+aJKJQjY12JQEpOZxCR89aX5PTVdTNmDvml14koNDkKTSUaEY+3vP41cdYz8R82pk0OXGtyq5xxZcyuu2pH98nMurk1YnTQlhj/m111Er+cjVsjRreOYoxqd5tIzvq+JyMKCWD8/fFrr6nOUXjXJZdWYGtWRKHsAXNCUI9jYzWpe+Uq+J0IkfoxpIkonHTKqRVQyMRhQPANr3nF0qlHGF0YlpzN0psMLyIDE2lVZbNRxNWf/nIcsa0+9KMibomq3Xrr6gPXhnzgtGvoTqWbeOc5QTSZ2fITOsLLV3/2S3H44UdU+RKqHs0ECnUZrtrrm229EYV8jmicPqP1kMtAQ19rU49cx8P6tRu+MegcBf2v8tccnNO8RCRhHlpPu2/7JFBoqEdVbsB+W9cHFMB/O4IQ5smRODYwSbfMUWCQ7/aIgsljSUm0BHBgpQ4DpR1RSC/+QkDB+ECjkDyppuQB4DDQ+NDlTaDQ2nW8IT8QMToqYpwWuY2NetTRdgf1SDRfqWfGOJtExBHdV4LpENqRbu4OoJCfL1xPJ+D7spXQn+UwYPugYfclzO4RoHD3iNHtoxgfM67pF/7MYaTsEaBgICmJOfq1kRGFkmZW+oT3JaBQnsx8y803z85R6DMUoKU8SdXfHAp00oCoUPlIQOGoY46ZRBLyHICZB66t16CbcX+Zo8A2ywTwXupRPs8iMTZZIMDPre851vi0riVQcM2uXTsrAzOTmS9uqEd+15WjsJuHbPL4BArt9+2OiAKgw7fFxk2gkIddtyMfOz79t3HkkfdYdXbT0iMKPlqnKKs8iFBHROE7FAKgcMQRdSWmmeVR2zz39Z6j0J6gFHBZEmvaXu4CCp43lHpU7pf893r3SAKFrGlcnl69SDLzMjYM6tHvXrA9tgjbtFq76pFfryui4AEmj0uLwh9ycl7Tpz2eo+C9di4rCu/Cru2okFSWRy2jCgslM1NU6AvoDLjYwEl/1LVz+jeBQsewoCDh1rSrC8wBFADAQ45ZjHokNM5hKT9dVyx7XRGsWgmyT9/NuxMoeDMqKhADwKQ3iwNpmszbI0BhnUJujwGFOfu5kUChr6v7KlDorXo055wsfHlDPTrm2HtOqgpldaE3v97JzDPOUVj4xbNvzKpHlc2BstscBNdb9Wj2I5dyRZnMnHZQAgXlUbPM7N4AFCqANR6HaMzuylEwqPyE/syiRO349Bfj7ne/xwT0WWuAwsn3vsfSgNy8kwwo5CFwl1w89MC1ekVO2rIiCvN0vg0Ukgc3L1CY552zrl32ycyz3jfk99NyFMpzFBLcPPJxP7u+8qhDOtVxTXkyc5tOtluoR/qgJA1w0Fc/raM8Kg470LVQRAGIUmf30REjxMT3R4wHRlxyyEqgkGteqcqXvfINd84D1xZcT323TaMeHXrcKVXVrUWawAYqO9okT738AGcXDG27GygM7Ud53SZQWGTU6ns2gcJiY/eiF70ozm1yFNJr33uOwmKvWOiuj173uTj22HtO+PYVUIgt8eY3vDrOPGPKycwLvW34Te3yqBlVuHbHFd3JzMMfva4r88A1D0k9VQIFlYaM4VOe8KipOQrr6sSMm8uqR1kxyi27o+rRPN9Rncx85N0nVazcK6q21GTmeTok8twABbbZB9474xyF3R1RmKfvT3j0afGOpjxqLkR/y5WQM7ERrYt6lF7ojYwo/M+XbK9CRuaPoYtipC3tZOYlDHY7olCutd0GFAb0u4wolIn8CwEF7xPmQ8+aowRk2c12REGf/tvzXnDnPZl5wBzNc0k/9WhnHHbPxYGCPmREVKEcZ3ENqYGffd8ECvPM4sq1mxGF4eO270QUzo0vfPXGuuoRltAttyxOPRo+PFOvvOz6z1c5CiVbgHH51upk5o0HCmXn6/KoewdQKFkVWfUoIwrz5CgsaRpXPSarHlVgpjmzQ59e98qXL73q0Tz9v+ozX6y895VOGdXJ4BsNFJyjcOhhh1dRl5nUIx3vKrO5kRGFEiQY1L0polAujo0ECr/30vM61+nZ5780DmxOZs5x/N3//cr42y/OwRmaZwdMufbYY46KZz/jl+qNIbloy8oxK7/2G78dN9888KCBJfUnH6NPxx2LsFG3HKdzX/g7ceON85iCy+mY8zkueIEyMSvte6rq0TqHcU9UPZIojM02D5VxEygsNrGbQGH4uO07QGF7yFFIeXvLLTcvN5Fz+JBNrnSOwtHHHlcDBYSK5qCfwQeuLfDOIbdkRKF97dU7Lu8/mXnIg9d5TR64RghW5xSMIq656orYfvazwoFraZhvNPXoxJMfMDHGk360O3IU5hlOQEGOQq5/f288UPhKHHr4YdVcvv/id8bTnvqz/VWPukqj+og/fPWb4qab1pMGPM8w1tfe+173jJ96nBS91Qdh6Yf+bESDtp7xX35hFZjKMXvFa98SN/7TnDyTJXzEKSfdP/7lv/jRVYkx+dizz3tpHHjg/nH+OYr6rx7HJbx6XY/ISgSlR2JdD1zHzX3rfh2PXOqtT3/OefHyfYx69EvPOHNDwN+9jz8ufvN8iSKrm8jffzj9ObFzQerReif0YQ/5Z3HG09QKXd0+9JHLqrndiObgw9e8XKHN1a19MvM/3XhT/PXfdtSi3QOdPujAA+IH7+vkoLXt05/9fNyxczEq2Xq7/oP3uVdVeKDdPv25L8Qdd8xiKa/37d33n3C/H4j99lupGXPx+z4UT/qF0+MjH/lIPOIRTn/c+FZSj7I3qh496/T2eeJ7sK+jiBe+9Pfi8CNWKtHUpzNHXH7pR+I1r1C1Y2Payac+ME4/43krh4E2Sfd//YW/jN960fkb0ynntxx3z/i1X79gEoHRkfJk5orhMN4V//1FL4i/+sLQOkX/f3vXASZFsXUvICAiJgSRqGIAAyKggoI554SRp/jjM6BiRiSqGMGMGXPCrGBCQVAEJUhGwYDyRCUpOcf/OzV9e+/UVIfZndmZHW7z8e3uTFc6VV19Tt17qzLbnNt63UP1GuySVEeUMPLrYTTgFezvnoOrHNH9jzxNW28Nt4OiK9fPQN/HnqGtqm4dL5gZ1XZZFHIApynSrku+E7pc4STLlRh16dWHtqxc2Ry4lmvsuPx8Gl/ATeJiW69y1Z82RmXRopAr7LTckiNgC4WS56g55AKBvBUKPT3XI+9gM3a/4J8ctCsDeP252RzS5rlsyN9FsG+66ezy/XpYnSY/d9XV0xbB9ROuJrKNJi9Hu5KK9wKZpa+9+d4TDil5WNjGKS+6fcn15IBvdteC61HPLtcZi4K8gtqX1f4Wh+bZzx6wxsp5uuMkI+PSq4wZKzyOrWDrrOIS9vx4dYs8cC2ITOaC3OUjgeMBZ5Ne6ddY2i+EMHJ7a68+hBU5tigwMbYf4o0bNiaOYa+QiG3AhV1iMGB5pwDzoXfoFcdDoGw7DW7DZ4wJ109aDngLUi6Lx5dMg+9QNr4z86G3Xy9vq+oqJ24axoHzlOPbtnSgrlwWp+N6xqmfbCPXD+nl+GY8kZ/rUqFQ2k/V5l2eCoXC6P+8FQoIZp63LMl3j1egfQInSbXoDieZte51Ef2gdHZPS9FgPNv5necdLMbvIvnO5985L3MImeeYGKe+gek8UsffszuP3MrXkF4qn5nybHER1QdiS2G0c8L3YxNCYWhCKJh3nNgVJw4W6fRdWJ8yEU/qX6u+fh863Ehj1bU46TzBlzRmxGdxyvXHixffEDYm5VgMSifFJ8bTkE8jgpmRkU3igsicLJTJnCQ68rOUB8lrID4PSmOXGzR122XLsuxBZwZtuYSlgkmb/xDixFbvxGBXfeNaN+ThHoHlmZPro8uz62jyxr/yiQAYHiB+G7wTEvnvW3t6QqFrwvUol1eQoMmFCGUc5FjPZT3s/pN1AW5lMZg5l2NNyy4ZAioUSoZfvqTOX6HQg2bOS+w2Lxd++X3NhNgnxiIY1RBQLzA16Peo75PSeeRRppH9Z3OIdPJO515ZJxl4i8/9lXuv3T4/cVgZXGX6osUTMEzeo+oXJ53EynY9YitJVDmpbTfMJrKfg9JxnWzxKUWg5HhBmCU2/ix+PYLaHVQ/e96Iwi1O/8QaDzgcT5ywHRnMHLY6XZqTn8uyIetmE+Rs183GRdaPyWbOiKa34uHCwAiFKgmLggs/1B1CjVfp2TrAbUIatAufS3EVO115HBGZWjNX/+Iuv9yNsHAUWRRQP7uO8AOGP25QHaVlQAoCmY4tCrI+tnWBhWwmynNhmWLBWL+BKni7VjFyalHI9hOu+UsEVCgUxnjIX6FQZFFwEXR/Xo4QBC5yHUdIJKXzFteY1DK3YHJuVlvx/tlUjjaVS6yQRxG4jAgYz/Wa3322m48/QlF/zxJdVK8EhUy/rvHT2Xjh7wnfj6Ne7HqE9/5GV92k0OPtINLFNDqd32+CAG8y9UleZE2nL9O5N3Bs8ngzCnmTOahOLvom1TuN8V/cZ6GoH4uI5BefBJyjMHDQQKpft3bo7Mj7y+fVFBpCkvOqnlmoTBDZlkX9u3Cx+bP6DjgvMP+ujRs2UfkKDiWRg6ryJJKDoiOLxGnkS5ctz7tzFLr36E4Nd8FRX3oVEgJr1qwljLm6dXC8nF5lFYEVK1bR3PkL8i6YuVv3btSgYUNsl5MKLSz7rs+z0QkpZQlC4RFd8lxes1F8aJ42tym/iWhDOSL83OTh5nlB5KyOsgEbN5ltbv/9ZwHVrle/6KTzXLze4/DC0hxnFk4p4xtbbHGflvpA8wpkPDZuopUrVtCCOXNTdz364MMPacfqOJ1Kr0JCYPnyFWY1pGpVnC2rV1lFYNmy5bRy1er8Ewrdu9NONRP7QetVOAisW7feCNN8XWAoHKSz2xIIvsVLluafUOjWjWrUxFHrehUSAuvWraPly5bS9jtUL6RmbXZtWbNmDS1dsjh4e9TNDpECb3DXOx4gnL6KXY/0KrsIqOtR2e27slhzdT0qi72WWue8dj2au8x4pW70ghTkxhD8u8s11MQ1eqf+osXZTOdb7bH5RJplmboXM529WYbfRkfQq8TIhYXByMMrEE+xaUhSHnHSbdroB6VPnjieet7SiQYhmNmzJCS56yS53aTbd9huNeFylFafo22oo7xgtCqfCAA3ngSe1Uh6aXD8rHS59n83W7+mn87lUodqGYw8o4LpKxFDK2Ne7fqVZIyFjc3Ik5kLY3rUVjACKhQKYyyoUCiMfiwrrVChUFZ6KryeeS0UzK5HHgGXxJ/JqbfxiNw5xxAqScDTTOcT8JB09imKKDNJmKRRZsbSCeLIPuUyyJpxidM+F54lSWcOW/Ou8ePGUK/brqdBX4wysQAcIFvUbwlSHAsXJtDenrPh6Tzi7qmToPJ8Eg4PLq/eRiyI3ZlKOsYix7SHlS+gWFDZAliMMz8IO6J9UhTi9+Kmi9z1KGXHI8vXK2cBuzmet5PiAeL4v+WovnbcQrGEQnHb50hXVsdLSr0ziEmsoWGVd9WNPem5l9/KO9ejXr160sq502M1SW8qOwioUCg7fRVW03wWCnwyc74hLXfD4RXgfKijWRX3VsKTzlLwrAz5UEeco9Cjcyd/e1SuUz7hiDq5tlwvDfzssWVbCFgMlUZd7DLk+Io8mTluBZlI4bTTChXKm91qePec9es30BZbYB99dMgGf099BEPj3kQnYX/+cs50cevguq9o15yESiwfo7ykem40+tIzbeFnySJxErhUSIk1SuzUw3gkot+BH/BEmXiwGEe0k39PZztPlNHtzgeN61Gv2zr55rWS4Bs3rWxf3DS5vi9fRQ2w1O1Rcz06Nq/yjVC4uReN+fLDzavhBdbafBYKv85d5u/24nQlSSyJBh6IaVZPHS4z5o1dwnTy7CB7t0D77BynO4g4yNN2IwqrH6ruKo+Hpf1doqkJjMLcpOS2oLKMoPIkkY7TPt4Z0AiFWzulHLiWVGaIO5ZsT4o7UDHT2fgzllHubdJiU9I+53aFTS9y9Z9dpdLqN5cFIsClKqltXjruQ/R9pOsRE/+wwcS+W9meU4sGvrePcPny9PPMWfToky+aLaX63tWFtqxcyVTjtbc/ptHjvqftt92Wene/IWOk+J9/F1G33g/SD9N/pmrVqtENV19Gxx3ZypTpeoC8oxGcW4JmCq8o64b83hy45p3MLEUIH5wW1I7AzxNHCYa0v8hvr2hyS/yWsBgnn/1wa8/7afmKlbTrLvXopms6eFugbqRbetxLCMRr364tHXjAvoHlhY3TIL/Dr0eNp1cGvEPtLzqX2rRqljJWXGdshOHEWEZNqHYe6aRT16PMPD2PP/sS/fL7LKq+3fZmGz/ew73rHffTytWr6bKLz6d9G++VmcKIaPDQr6nfMy/R8pUrqcnejeihe7pTxYoVM5Z/tjIqC0Lhrj6P0b+LFlH9enXoho6X+VDcenti7jj/rNOo5YEHZBSiceMn07Mvv0nnnnkSHXtkm4zmnY3MyoJQYFLOvEJuFx3mn52NdK4V32efeYT+mPU71a5Tjzpec3NiAbJ8eerdqzOtXrOa/nvFdVS/wa6hfu7wgy9agPS2WyW4yyR+l++Gzz8bRK+99CytWrWCmjRrTr1uf5A2wZrgbRGeOIOtSBjwe4e3Qk3C0TtzKRRHbM/p5eeT5zjp4OPvTaCmTBy4NuF76ukJBT5DCm2zy89G36UzVhB03fv2zoSDZjtccS3tsktD0wdjxoykjwe+S9tuuz3d0uV2X4BJUYH+SqcsOZ773t+T5s+dRz3u6EPVqm3jizzuf8aFx4SfNmCsJKVLY4w503njAN9FnszMk5XtgmS7tMiBbZM/Ow+pgplnMtjcAUEn0tqT57r16+mIEy+gsROm0CknHEUfvP6U6bTt6h9Aq1atpqGDXqPDDz3Qf/DMUe/lEx2Lyy7PnD4M64ZneZArCXPn/UMtDj+d5v/zL1WqVJHWrl1nLCJvvfgYnX7ysX7VolaiXdglKugFsWBFQBz2BkKPuSNhYUhYZGySKcsMzJ+IuvTqQ1taJzNzWv5pp98g9vFP7ruiw/FsixLqJ08wDsrb7s8ZP8+kJoecbB7I36Z8TfXq1KI77+9Hvfs8Ts3235dGfv6mT6y4LoyJjY2cIH11DCw9C83sv+fT40+/RC+8+jYtXrqMnut3D/ZvnBwAACAASURBVF1y4dl+lZw4ei5APibe33yvjSUmHowlHudFlqCE5Y37saiuRVarhJDDmRZFFiQuR4VCZmjUpKk/0sFHnWksnn/PGEPbbluNnuj/Ct3Y9W4jVid+8zFV2XLLjBT2/KtvUccbe5qXQeUtK9HqVWuo1k416MexX1DVrfJ7F7KyIBT+mP0n7X3Q8YR3Avpt70Z7+H3ZeK/dadzwDzMmyhYuWkQPP/E8vfTG+zR/wb90/52d6fqrOmRknGQzk7wVCj170My5OHDNrLgl1kwcp8wGLfbIOIVMpnP1xbjRo+i804+lSpUr07gfZlG1atvSU/0eoL539aRdd9udPvtqHFXeckvnwmHSO9469ZiJtHzHPv/MY3R3zy6Gk2xRqSKtW7OWdqpbm74ZO8OcGRR1ubBAmnRWptPBU+aN3+WBa7KuSeVL3hOBSRLvyWA65HvxeafSyOFfUsvDDqc33v3McMRjWu9Ps36dSd3v6kOX/vfqRMBzSh2DLVZBvPjFF5+kcd+OpC8+GkQbNm6gMdNm0Y471vDjbbDwLQ8C5nGfFGPgLbSmWloEjywhnqY/qRyZA9cuPd+969EPYwabioedlyDVETKNIslRA9v1vSxDPkQ8KKf88BM1P+w0Q95/nvAlPfrUS/TwEy/QkYe1os/efcEn2TLvRYuXmheK69pum2omL/t6oN/zdNvtfahli6b01adv0KdffE1ntbvKEO/Ff0zyiR+nSxlQYv9lkEB0p4v0p42R8F8PWsXmPI1QqFyZena51h/00l8wTGQE1au4abj/bJFzctvLaMjwkXTYoQfTx289S7vtfwTBkjPs49epTasW3rukyFKB9P9450NswknV3mEziUFOVGPHHZImbW7HN2Om0lEnneM3i4WC7DeMfT4EJWr77CAcMvV5ouGJRnW8qRf1f+lNjVFI+2FJToC+ueK6rvTygPep0Z670aSRn9DOexxMi5YspeEfv0GHHNw8pQQI0oWLlgSWXAPbSluDZfXqNVS3cStatnwFjfjsTWredD+q26glLVqyjD566zk67qj8Xo0uC0IBHXLhZdfRewMH0yEHHUCff/AK7dXiGPp7zrxQjBf8u9DfpUV2Klw+q+/g3iL8p19nUpNWJ/m3q1Ao/oN4zz33UI8ePUm6Htk+7K73ftC7zvW5+SzxwnET+ACPAFer8I657sr29PGH79IBBx1M7340jBrX34HWrllDHw8bTXvv2yTlHYUVa2wv6Vvui84HM59Vr1EjSSAhg9WrV1GLvevTqhUr6f3BX1Ojvfc1f69Ytpze/XQ4NWtxcCzQJXY2qUzgYoIa0sIlKB1XiAktXI96egeuSXGA9PZ70dmflhgIFYqyD9NMh3ovXbKEDmvRyPx84a0Pafb/ZlGvztfTPk2a0qAho1Lc2lBf9Cn6Vnpk8O9VqmxFVbbaKkmUMQat9m9I8+bMScBVjmjstN9pxxru7YFlmw0FEDtFxRF8Mo0twm08pSiUgyvwwLXJk8bTj2M/dw7EIDHgGpDIAPdD2PAKKlc88RMr5UUPbxjpDFuVvqXHffTIky/S7rs1oF9/+5+p9+zpI6lWzRp+G2Tex5/ZnoaN+M7Zvi8Gvk6HH9Lcr1difilH513aid4f9Dl1ufEq6t3terOSVKfRISaPpX9OpipVEiuPSQLBO/mPcYA5LugwFNk+LhOTkjExeg8zV9jGwsfeKy9IwLksCrK+fj0F2Y4s0xu4QYKSPw8TTrIjVq5aY8jaylWr6NCWLWjU6O/p0ovOoWcfuztlAkY7cYhc7b0S7l/2td1229CCmeOS0sl6Iv3J53SgoV9/S/373UPthUXBmaH40LYepPS92C4t6JkJ7Ecf04RVQV7AUWMUonon/vfYV37nPQ8289QF55xKA979iM45/QR6rf8jTpfF6T/9Sk1bn+wsAGRk0R9FcwHf9PPM32m/lifQNttsTQtmjjcfd7yxBz3/6tv03/bn0+N974hf4RzcWVaEwtq1a6nBvq2NkGvdsgWNHP09nXPGifR6/0ecqKHPt955H9/CLG9CX833+ioI8gs7XEfvDRpsLAo3dOzgu67loItiFZnvFgWbLNqNSlgOEiumfLlERUnT2eQM+UkSNX/eXGrVpKFZlDrt7HPpw7cH0FnnXUQP9OufRIDZ/WfypO/pzBMOcx86Vo5oxqxFVLlyEX9Auh9/mEwnH9WStt++Ok346U+T7w0d/48GvvsmXXrFNdTzrr7JpNFbR/LryoevWWAE4cVCSr7HZNKkdKIPkM7XPcIKhLS2RYE9R/CTsXEN3LA+lcLG2c/iQ+YcBhMhiOx28X2PP3w/PXTvHdRwz71oxfLlNH/uHHr7ky+pWfOEKLOJdbu2J9GoEcOdz95/r76euva81/9O8mMur+ketWnZsiU0ZurvVLNmraRxHfZAB43PWH0nOWqiUaYoiZUsG59HxigggXTTsTOwH1b/QRe+63aDw1ZYTXfyIYNyC7AiV/gk4LnzFi5aTLs2OYJWrlxlvu/d7QbqcuOVvoXDFjFDho+iOXPnO/vimCNbU+1aNVIsKSwuHry7K3W68hL6d9FiqrV7YgAt+mMibR1wiJnrhF+bOMa1xLjuCxJo3DiZpsvtfU0cx+23Jc5RCFP1Ehy73HTKxCxijpvnY+Ud+w3b+T/05Et0a4/EQ1Zt66o0bcxgql2rZoqaxvdYrX37g0+dfQnL0Plnn5LSTlnecWe0p+HffEf9H7uH2l90tueHmCxgo04ht/s4UDR7rkgpz4QQlEHPh6yzuh6FTaPpf/fGOwPp0o6dTUKI+f/9MJJq1nAfHAdh8dFnXwYWcmHb03xLId805YfpdOARZ9CO1Xegv2YkFijueuBx6n1/Pzr5+CPp/deeTr/SpZiirAgFQNL/5Tfpmpt7GXSqblWFJo78hBrUq+NEC8/aG28P9Pdjlzdh04fzzj4lFOULL7ue3hv4GfW5swtd3/FSFQrFGJNsUcCuRy6y4lrldM2RciHKXr3masVNZzeDyTDSs/vJ6y/1px6dE+9RuLNO/mUObV1tGycCCxf+Q8OHuBdekeDMtuebTU7kNX7cd9T25KNp59p16dvJvxhs7u/djZ7p9zCdePqZ9ET/1/3bk9yDPP916SbjqlQYDzNEXlhfZL+40rmIK8qUQkGKhDjDJKh+KX1jiZM46ewxJf8+rnUz+vXnGaaYthdeTPc/8rQfh8I4cBnffDWU5s+b52zOno0a0377NzPfybHJf6PM/XffOSEUYFHYsabZolWK4KC2xMHSbmMULpwn6mePndAYBXY9kijI4GYbHZ8c2avoaaxOhw0gaA8E8dgBHlzu+x99Tue172S+X/bXlBT3IQnUtOk/05Ily5KCjBnY/fbei7aptrWpilzxPbtdR/po8DC6vesN1O2mK2nBPwv9lewlsyfRVltV8e8HsUwYD5IDk5xEPwmvonT24GJs/DyMqk81ULC7jI0T0t92e1+qXKmSOXBNklkz2YmJIWoFXA52WU+ZLkzUyH4OSlN/nzZGzD18bw+65vJ2SUMjEcOREJU4NXbshMmJ+htXoaK2wPf84BZN/bRyDHC5x51xCQ3/ZnRKjAInihZFibgC+76ivxN95Hp2cA+/fCSm9vixrRcqFOK8auLfs379eqq+WzMTN3BXz5uoc6fLA0kfTsSePO3HwMwPbt40xQr00y+IvTmJtt9uW5r7y1iT9pYe99BjT79MF513Or3weJ/4lc3BnWVJKACeJoecSD/98hv16HwNdb/l2kDE8OyNGT/JSVC3qFCBDmy2fyjaLBTuv6Mz3XC1WhSKMzSNUPBiFMKEgk2GzXxpHXKQdI9FIM17wTgg2amSrQWuNriIGVyNmjTciWDFuv2+h+ji/7vST2qTtBUrltP0H6cm3hGIkfPO+9oEY/EmMivWcEP2PQGoHE2dPIFOO+5Q45IyZupvZk7pess1NOCl5+nci9vTvX2fSLF42qv0YYRYvidlm6MwDErH7zFuA+5zWRRcfczlhxHgpLZ4sabJ78yiQPCg9tify7py/sOHDqYOF55l8DYEvkZNk0zeyxjMmDGNli9N7Na1qZznxuUtku+0c22qV7eBU3AxL2zSsBYtW7qExv4wyy/HVXeXWJaYSUxdnNO20PCzwE+CFMCusmIFMwcaBzx7U9RqccqDZ+0J75M36/OiINWiwE87L0ky4XLU+MDjCBP88jlT/W1YOX9JhKNcjw5r1Swl5gC7HfV55Fk66bgjaOCAZ2jKtBnU/PDTTZVWzv3BDCze7lW6i9h+8y7CaH8mCW2IccaHI0hU4AaZtxEKwqIg8YxSnMV5CXCa5PaAHCfIs+wTvpf7fb+DTqAZM3+nd199gk4/6RjfwmPXE8HldfZKuIDZF4jZ/Jljk3YrsNOzUIBF4eILzkwJWDJ5irEZJpZLglFUWmnV0BiFKLTS/75Oo5YmFubdV56g0046xh+jvBMS5xjmeoR7Fjtcj5YuXUo77XGwGfOzpn5DNXasTnsfdCz9Nms2Pfvo3dT+onPyejW6rAmFI04+n74bO5Geerg3/V+7cwMHA7sebfCs1/JGLBYt+C3hJhZ0+UJBXY/Sf+C8FDJGAR9JshhGkOIUWNL0dhl2fs0a1aHFCxfRgA8HU8tDD08WnOLFPXmi53rkqjRcj/5YRJUqVS5a7CpHtGjRQjqwcX3j3jR66kzaYfvq1ObAxvT37Nn05AsD6IRTTnfG3tkLfkwSUwSA59YchWMUhiZ/LwDd7r8UoeCdvI00+GdzR66LLDOQl3j42hyCuVBQOv5eLqRKoj3n77/o0KZ7UMVKleinPxf78Nj4Ye64+NxTAl2PLu94A3XplXCXDsLQWBSMUPidqsOiYAVLu4RMVH+4+K6hMGJHKikcWFwGiUzca4KZ2wcEMyNGwV4VlucjyAFmE0GXgrEHZGCaJFJWdO6CS1FKgsxCoWLFLYxFgQOFXQ/CpCk/0uKlS53PSJN9G9MO221rvvMVPrb6mjyNWh9/nlm1PuPU42jIsJG0ZOkyuubyS+jhe7sm5WWvLst85I02nnKFOYq4B+Fnrz7L8qRFgQeP60EL6j9Xu+ytwoImHt4BKGjc2On2O+h4mjFzFr33aoK4uS6QdpzNMWqM+4WOnSHgq+y6pkz/jbrdfh+NHT+Z4Lq23957Up3aO1O/vj1pl/p1/SR2m1152ZjbaVxiL/WeRIA255UIeE+N7cE9alGIer2l/71LKLhyWbFyJX0/Yaq7gHJEbVodmGJRwM1X39SDnnvlbRNcf8ShB9M7Az8zCwv/+2EU1TQB9+nXubRSlCWhgDXjwz2h8OTDvalDiFDAMzji23GJYDnrwvujtbd5gv3dX3/PpY439aCJU36kefP/oUZ7NqRd6tWhPr270F57JLZWzMcrb2MUevSgmfNW+As6YfzBRZSCxIUkhfy+k/0i0+Fzf/61XEDkO1KSymaN69DifyEUPqeDWrX2V/jZVYnbATI4dfJEY3lOWPyTz3xoeehhqdaBcuXoxqs70Advv0G1atemfQ44gL785BOqsMUWNHXmPBMoy20Kqh+fhMw7PUbhKnmK304vHoHjK23yat5XsN44BrwRCp070cdDv/PtOIF1dViAuE9skhtGlm2ibFfLJ8bC2wNpmKTP/fsvOqTpHlSpUiWaMXux8TqR72W0l/vvx6mTaenSJUXWBmgm401SjnauU5ca7LJbUvFc7xuv7UCLFi6kUcO/JFizWx5+OO3ftDnd2vWuRHli+1OXaDKfOWJQJNm3+WMULnIsJfXRxk2J7VGDdj2CUAhbqUbGcnVVigoXyeUtJYPSIL8glxeJdhCB/uPPv+m08y+nihUq0OhhH/i7CvHA5sEQdwJ31WXI8G/p5u5309Kly6lipYp0ROuDzYpgAmS3i4ld35TTrr0KudoVh6Rye2R66TIlJ4fb7niAKleq6McoyMFh4xJEZIPEQhCuLnEZJJyQB28heuaFV9KsP/6ih+/tRke0aRlrbLjqwP0i+xN1mvTDTOrQ8caUJO+8/Dg13LW+k+zJ+tm4++M6yGLmqFzQwxzkpsR9efVNt9OzLw3QXY/iPswx7jv61Ito0ZIl5myDI9u0jCTu3kJajJwT88LqNWvpiuu60XfjJtC6tetou+22pZef6kv77dPIO1MkVlY5uaksCQUAdGnHW2jytOnUo/O1dOYpx2ccs1mzZ9NZF3WUYZzm95eeeoCa7NMo4+VlKsP8FQqJXY9w+SSF34vez6QVXblTj2/wTezeY94tJm6gvG8Jlgtw/jvPSidXxWU97HlekqhzTj2Kli9eSvc/9jTtf0CLxDtKHFiV4q5ibUrC87/LrQWfwb2p83VXEHYP2rB+HW23/Q70xPOv064N90hxhQniSFIsSZJryrYCfOXfPl8S7zOb4zFOdv0Zy8kTxycduCbPduB3mU1uWXSY72XQdEggu4vvuESPLTCShKRX1j8L5lO7c06iShUr06ChoxKuwd4mNP772ltY2OgJDB97xw6UEhsur905J9PCBQsSvNFzH8cZGfc99FSSa70/bsWY4s98/CyB5fM9ry48JhPbfiUvfqeIQS9MgJ8h3kgndHtU165HUcIhakJzkWF7NdYm0kGnD9vEz2VBCCuPewl+WkFKO45wiWqzPzGVS5w8HWbpkOXFKTtINMky5T1d73jAnMyMGAU7/zChEqeNLAiQb1AbXW2KwsRVNvLhiUz2XdGD7O2NGqfiIfcEid8UrCxxUMJiE0PTC/p24aMWhUwgnJoHk/84IiDOPUG1tNOWJK/sIJGca1kTCgkC4zQUZAwu13bJ+WwVQsPzVij07EG/zlmetHJrk68UMmr1JL8PfDIM66xYKQ56VyaIYGLfer5cZcn0Pon2EkiiynnwPTZnssWELVDsAcrkVpbpKk+mS5DshAe6XVebG3C7XCRanjNlE1S7nUxCZZoJ2B6VD1yzzsWQGPOKPfIwbttiAxSfzAb0t3QLDhoj9qo81zEFM++DJFEqD1Rj64oVd2uLAclJEqv/iJ8oOhvBF7KiAtwP3N6gMSZxD+o77mO+Vx5o5+JL/BkfBMjpjINY+RiuR0ggD86yB7F0J4kk7iFkyiaQxSGPdt1cB3Gl85aIFBkiM1nfqLpHfR9HIKTTDnmvEQqIUejSKa0swuocVV8X4Q1LA1McBm6YoEqr8tZkzmM6rmALGldx6xCEXdDnciWEywAm8oAdFQpx0c/efZkkopnMKxstLotCIRs4JBOL7AqRbNQ/f4VCT5o5N7HrERMl+zDUIMIInCTR9MmmByDn6doFiFetbZEgd+jj/H3y5fnYsDuPf5gnrAnCx5zT2fWWgsYuxydoglTbAsEmfz7Js84mcAkLFlFsObDFgSSh9rbctjgJEhZy3MIS0uPWTjToi1FO1yrca3MBe7E0aBGX+9xVT3s7ebsPnWPMqPyi07E5f/wMW0i2t5V13ZsihBKDyQjZFFERsq2633/WKoUUGRIPvp+fCZeQ4vLlgr0cY/h8yKcfhbseZWOySidPSZCiCLb83iZWsswoYssTggtA1xafnLdLLMQpKx080r3XxkxaFOLmFYalnQcP2KA0LjyCVnqQN9ffZf2QDz9+5zI5jSvfQNLuOH06Lj5B97m2xZUTcdDkE3YPt0mFQkl7p+TpM0nuM5lXyVuWmoMKhVRM8r3PXOMgn4XCr3MSrkc2kQ4jqExWJVlzvettawDfg3RsdZCkiomVTfLlnB30bg+zfLhEQhzyzj7rXC+/jd7KuySzfp3F6rdvZRGuJa7YQpNW7Cgk08l+kCRa4m2TdxYKJkYhxNwWRwzJ9P4qfciOmvI96qqj/XzYIi+FP3gWGhcRT9q8xmonj00XaZd94OJS/JldpusUb38MeGNCpo0SefK5w+9SNKDsyGBmSdZcEw+DaQ/EoJeVi6hxpaKEQHFfgLLD0yXu9mCxA3JtQhy4Shywf35x2iTLDCPZrrylUAhy6fLzdASVc572QIpqRzp9axP+qLzxfTrCJGxMxxnPHGsj6xUmaIIEQcrY8lak+KENS6cHrsUZFdm9J5NEMZN5ZaPVKhRSUc33PnONg3wXCnJxLokkCV91SbaZHNmLMjYpcxF+e65nEm6v2vpixFvl53SSFPu8wguIkyu8QcJH9o9tRZbpXSSO68rf2e+MsBVw2U7XfZK026SRSa/TEmG2fi2fdHghxyi4hIKPn+ciJttUJJ5S93/n+rGbjHQj8om347RpxjtQlFgB7PbzE4QLYy/f54yPHCsSS/k7j80UTMVOUhIbu89TRIpIJ61yUsC6+k8+I9JKhvJKZFEIIqnpknHZIa480yGZcV+SrjpGrZzLNGEEPVZ9HUEvsdL5oz3YB1/WM8qiENRX6QoQrlZabYjRWbZ1IKxtLuJuP5BJE2OG4woixVPM8uyVG/kS4H5Ri0KMwZPlWzJJFDOZVzaarUJBhUI2xhXyTJyjkHA9kvO7nE+ZKAWRKknmk/iE2AKU537kYZP3sLbJ+dgmfjKdfZ/0C7dX7+13hdzBRpYRVFcphGyyn0JSPXcaWVe7PpIkuoQN0vqiTKyY2yLK9sJgi8JHQ75NgtjVXylcRGwVaAs9ro/EJ4mIW5YWKQrNoWbeeRZ2P0StvLssSq66yfqZ3xMfFB1enMbBatwu1zMgRQMDbI+HItGVCPYPswZJDCU2sU5mth+iFDIooqu5MUHEMdCi4EWV+zzY8t2SZMn1cOIzO+8wFxRZDneq3U57ECYNRO9mGb8hyXXSoA8giLK+UcTc9b2cNMMmOpnW7HrkBTMHlWl/zn/bP7nMIILM9we208PFLg/3YyILWhWRbY0SJkG4Ba2kyAnMXiXiMYjPEd8QZ3yF9Yv8zh5rst6uNqhQiIts9u7LJLnPZF7ZaLEKhVRU873PXOMgfy0KPWjm3BVFB5xy8KfHspKInhUHkMQNzM6DiajkqD36nau+luVCviecBF7wFl7hloLEFgs2R5DkL5DAee5R5l6vPLk6zPxFvp8TsCWIoYvH2PdyHklE2XNBssmzXZ5dFyk05K5HNpY2pzPfC05gvx8D4z8MVyzv97ctOCSRlyvpXE8ZDC3rxLjZ/cnE29QH4zQkLkWOMblK78eIELaVTQhXiY90ieNYBu5TPiSN7/ctM149uF1Bz4wsK8qqExnM/P4HH5iThl0X7xEc5zv73rC0xXnByfyyXVZQe83gKk7lc5Bm7Zq1ZvLAHsGldaXb5+ncn869mWqvv0d2Dvt9zZo15kTqf/5ZQNWr75ipppUon7vvvpu6d+9O1bauWqJ8NHH+IYADyVavWUNVq7jfCflXY62RCwFsPb1q9WoaMWIEtWnTJi9AgkWhW7duVLVataSdh0zl7EU2+aIVuxQl3cdpXGk5TRzrLpcVlCbO90F1lG2TdQmrcxJzjrGrXxB2rheYrKcLdy7bhZvEm+/jrTjXbyC8q6pUTZz54BMliWmCARddUf1m32+P4qi+lf0Whr1sM5dp97nrCQrDKG4fugilPQ6j2hE0PoPwCfl8w4b1tHrlKho1ahQdckjicNtyAz98f9PUaT/mxSSilVAEFAE3Ajis6/rrrqMtq3iTcI6B+vbbUTTi669pg3egUI6ro8UrAoqAA4EK5cvRRe3+Q/Xq1csLfL4bPYaGDRuGk6ryoj5aCUVAEXAgUH4LuuTidlS3Tp2EUNgU5HSl6CkCioAioAgoAoqAIqAIKAKKwGaLgAqFzbbrteGKgCKgCCgCioAioAgoAopAMAIqFHR0KAKKgCKgCCgCioAioAgoAopACgIqFHRQKAKKgCKgCCgCioAioAgoAoqACgUdA4qAIqAIKAKKgCKgCCgCioAiEI2AWhSiMdI7FAFFQBFQBBQBRUARUAQUgc0OARUKm12Xa4MVAUVAEVAEFAFFQBFQBBSBaARUKERjpHcoAoqAIqAIKAKKgCKgCCgCmx0CKhQ2uy7XBisCioAioAgoAoqAIqAIKALRCKhQiMZI71AEFAFFQBFQBBQBRUARUAQ2OwRUKGx2Xa4NVgQUAUVAEVAEFAFFQBFQBKIRUKEQjZHeoQgoAoqAIqAIKAKKgCKgCGx2CKhQ2Oy6XBusCCgCioAioAgoAoqAIqAIRCOgQiEaI71DEVAEFAFFQBFQBBQBRUAR2OwQUKGw2XW5NlgRUAQUAUVAEVAEFAFFQBGIRkCFQjRGeocioAgoAoqAIqAIKAKKgCKw2SGgQmGz63JtsCKgCCgCioAioAgoAoqAIhCNgAqFaIz0DkVAEVAEFAFFQBFQBBQBRWCzQ0CFwmbX5dpgRUARUAQUAUVAEVAEFAFFIBoBFQrRGOkdeYLA4MGD6cQTT6RNmzZlpUZHHnkk/ec//6H/+7//y0r+mqkioAiUPgI6b5Q+5lqiIpCPCHzxxRd0/PHHK4dIs3NUKKQJ2MyZM+m1116jXr16pZky+vajjz6ahg0bZm7MFhmOroX7jmeffZZat25Ne++9d3GzSEq3ePFievTRR2PjOHr0aGrVqhXh58EHH5yU15lnnknffPMN7bzzzub/unXr/O/LlSvnY4k+O+KIIwLrv2bNGtpyyy3pww8/pNNPPz0j7dRMFAEgkM1545hjjqEvv/yy4OeNZcuW0ccff0yLFi0yc8EBBxwQObh03oiESG/YjBG444476PbbbzcIDB8+PPT9WNowvfPOO/Tjjz/G5ghR9fv222/pggsuMO3cbbfdkm4/99xz6Z9//nHOoZJD3H///XTQQQdtdhyiIIUCOn2fffbJ2ACTowKrzl999RXNnTuXdtppp6ixmfb3eHD79+9Pf/75Z9pps5kAD0vHjh3piSeeKFExV199NS1dupSmTZtGkyZNii2IunTpQlWqVAns0wkTJlDz5s3pqquuoieffNKv47x582jAgAF0ww030E8//UR77rlnaP3ff/99euSRR2jEiBElaqcmLnsIH0HNdQAAIABJREFU6LyR+T7L1LyBebdly5Z07LHHmgWGoUOHUufOnSPneJ03Mt+nmmPJEMjWPANiDf6Ad2s6F/gMnq9ffvmFdt9993SSZvVe1GnFihU0duzYjJQDbwEsMl5zzTXO/MaNG2dEQBiHiLOAW4gcoiCFAl5OUMnZWPXHCJs1axbtsssuGRm8diZ4OFjdZ6WAYma6YMECWr9+vVmxL8mFyWybbbYxlgH0UZwHb86cOdSiRQuaMmUKVa9e3Vk8rDyYCF555RXz076w+jhx4sRYVe/QoYOxKJx22mmx7tebCgMBnTcy34+ZmjfgLnD55ZfT2WefbSp52WWX0fPPP09wJYB4cF06b2S+PzXHkiOQrXkGAgTPG1bM07luu+02uu+++2K9i9PJNxP3/vzzz5GLe3HKGTNmDB122GH0999/B3KIHj160F133RVoWdl1113p999/j1McFRqHKDih8Oqrr9LFF1+cd+o4zujC6netWrUI5i2slhXyxSbPOEIB90Ltw+0g6ILFAJYAiIn99tvP3Lbvvvv6qytbbLGFETpxrn79+pnx89hjj8W5Xe8pAAR03sjfTuQVT7gNMgnCggOI0cMPP0zXX3+9s/I6b+Rvn26uNcvmPIMFOKyGgz+kcx166KG07bbb0qeffppOsjJ1L+aC77//nj766KPAeh944IE0ffp0Wr58uX+PcogEFAUjFPDSqFq1qlnpB9EDMcRDA6sCCOapp57qWxngpwaf3hkzZtAOO+zgD4o333zT+LDxCwkvo7Zt25rvBw0aZFyCYJqC8uQLyvG3334zabDi1b17d3rhhRfMywur0mE+8ciDX2YNGzY0K2OoJwY03Gj4gpn9gw8+MP56+PyBBx4wX8HvGabCk046iT755BMTOwFffahwlIu2d+rUyXzHZvv27dsnPShLliwhtPuZZ54xqwr4G25bHIvAhB6k+dprr/XTwvJRt25dwmrE2rVrTZvh43fKKafQhRdeGDmJpCMUUBb8sLt16xaYLywG6HMIClwgFwh8XrVqlf93VF9w5jAdIiYDQZB6FTYCOm/k/7wB94Onn36aateubeZnfr4xLwRZEHGPzhuF/eyWpdaFzTPcjpEjR5p3MN7XuOxFNLz/33rrLUN2TzjhBLrnnnvMew6utnCDfvzxx/14gzBvCvAa8ImVK1dS165dCbGRyEu+3+Gm+9RTT5n3epMmTejmm2+mM844I0Eay5Wjpk2bmmcSrjy33nqrcQ9izvH111/73gLgRC4+gHbgP9px3HHHGS8Nfj9ng2vFmQsqV65MzZo1o++++86fY9AXffv23ew5RMEIBTwwGMAQB3vttZchyPgbg++ss84yJBPuKyDBL7/8svm+UqVK/grVpZdeanzm4Z7CJiiQ6hdffJEWLlxoCDoCjREEg8/w3Q8//GBWrd977z1jEkeAHR48kGWo+/POO8+Ii6ALZPnzzz8nBNmA4ILwY7DK+ARMHCD/8MuFKJAEGw8pVtJB8jGpoEzcP3/+fPOSbNeunXGfAQaIDYCggQjhCw/BTTfdZNJgEuC8n3vuOWM6Q1AvhA8mIEx0yBcXHiYIKJj9q1WrZrDBZIA2XHTRRc6AYxuDdIQC+hGT0hVXXOGEEpNao0aN/IkG5le09ZJLLjGTXboXzJToz3yLE0m3HXp/NAI6b5SteYN7FPNHnz59COQqKKhZ543o8a93lA4CPM8gzg9xcpKfoAbgHCDvb7/9tlmku+WWW2jHHXc0JBwXRALewXg3Y9EP4x/X4YcfbngO8gfXgMUCF7sw261jSxy4C8oBf8HviPHj5wiWBfAKcA4QeU4Da97kyZMNDwIHwEJanTp1qGfPnkZMsEsVysS7999//zX1wCo97sMFSz24CNpw5513+nmDT6Dt2eJaUXMB6gg8WKzARemPP/4wi6jF2dik0DhEwQgFDMK//vrLrHJDcYOs48IDBIXbpk0bo5wx2GF1gDBgBQzzNfxd8dLZbrvtTDoMLJDMK6+80tyLtFj1P+SQQwgrzthpBw8uBjh24QCJ5YcP6aG4MSFg8LsutnLINPvvv795ACFEcOEntuq0LQyoGwJ0sboGgQDfOQgXXkVgUz3KZosILBwQNLNnzzZ5I6AYbUE9YYnAxRMC7gGOEFKwFgCfd999l0aNGuVjg3LteAqsCqAuCHjGhBh2pSsUUD77J9v5woKEiRf1RHtwQSBhtZFXQWQa1BPlM852fjyO4ApWs2bN0Hbol2UfAZ03Eju4lYV5A/W0LcRBIxDzZCbnDVhVQZLOP/9881/njbL/7JdmC8aPH28WKyU/QfmwXoM/QCjw+2qPPfYwxJ+JK6wGCMwfMmSIWcUHOQfZxuIiLvAR7CQYFp/AMTt333234Qu48N787LPPDIHHBa4B7gQSD2sAX3jXQ1Rg0fDXX381vvx4j8ry7NgL3Id2yE1EbrzxRuPNAcHBF9KBm2BBNxtcC+VEzQWwnGBxVLYH3h1Y/MQipLwwT0JM4Tv2HCn0uaCghAJUHB4eqEE76BYWBZjXbHMeFCwIOuIa8ADhgnqH5QEPEBQ1X4gbAJmGa0/FihX9z11BRCDaeLCDIuxBZOGqA2sCLiYrmBwQqIcLKh1WClgM+OLJhsk4zGQg/LBMwISH68EHHzSmQtlWWBfKly9vyDMuJuoDBw70g3bRDqyic53kJAFc77333qTnASseEFMsRiCgQOZdW5jaD1K6QiFs6zZMlphA0e81atQwRUEYYkKG8ENZW221lVmlgZjC9qloS1h8BCaWfNsuzsZQ/84MAjpvlJ15A3MmLJjYCAGkIuyKeoZd8wa2gIYQsecNlNW7d2/jmgmfblhbXa6MUWVmZsRqLmURAV7QsvkJ3lVYpJQXVrExxviCiACRxgXiCiLP7118tv322xvyj8WyoIvfubJ8kH8IDyyU4oJgAceQngf4HBYBjHte+KxXr57hKbw4ybxEvjPRXrgms88/uBZW7UHImWux6LcX5TLJtVD/qOcSwh+uUHDHwu6KuGCtAa/ClunADhwImypcd911ZpEYeITlG1VmWRrDBSUUpCuP3Qkg3HjA7FXk119/3bjoQOUeddRRJhl80jBQ4WrDxBN+snio8HDC5C0vPDR4IHgVHWrz5JNPNqYrfGdf2FoVQgZuPxxvgAcVDx5bGHh1Hgoepjq+uG58HyYG3IN4Bb7gBoTAXX6o4YqDtmPlglfl8RPEHg8xYjtwQTDBcvLQQw/5efEqoy2a4KIF6wdbH5AA+OM+CIWoKx2hAMGECSnIooAHEoIOZfPF8SVYzUDfT5061Td/cpuChAKLNhUKUb1YGN/rvJHox7Iwb2CugvsFrKkgHiAjQf7YmZw35Gop5mPMr3IBB/jpvFEY80G2WhG0EAerNcZU1C6N4CpY2ANXqVChgtmBB88DL2zalgq7HbBmwEuAFwJ5xZ9djXE/LAAQ4nC54Yvfl3wfL05KbwhwC7gps3sy0kKIQ9jz9qYuroW4CCzUyC1QM8m1uA0NGjQwvMbFIWCJgUs5+I+0KMATBWIBHAIu7XzGFRYN4JWC/sIujPDIQDykvAptLigooQAij1V1DAgoVahcfvgw0XNsgexQmPTwkErSCDPb6tWrDdGGpQGDh91yoMaxYg/zHFaU+KGBKQ0vJlwYSBgo8OGXh3VwuexTL4koRAIINvZARhr492EQQnTwIITaxYoXYjDgeoQLkw92SpI79MCaAd9GDk5CG2EiwwOIekOg4KGXW6nBLAnXKsRvQBzhezwAME1CFCBOArjwg4QXNMpkkyXqwkIK25PaZjx70kpHKCDuAVubuWIUGEsILrTLviDeNm7caGIc7IkvSChg0sIqiwqFbL0y8ytfnTcS/ZHv84Y9l+LFjfmRF1vsUZXJeUNu04h5EKSLgxy5XJ038uu5zrfaYNEQC402P8FKNd7xUijgXQ1BAM8DvMuxOMnvK7yPwQPAORC3yIuMbClwcQ5gYbsoczqQe3AWeCHACwLkWO6chHc53I5B6LE7EhYn4XoEjsIXFluxcQhciHCxeAGvgfs2+Bh4hc21wNcw/yImIhtci+sHqw3q6OIQsNzAdStot0nUGeIJbuDywqIq5kxwJvsqtLmgoIQCm3oaN25s1CEGLcxD2DUIOwpBgdvnH/DKOKtjFgQwbWPFCrsi4QEGsYWyBnnES4JNc3ho4HcnTVa8+oRBhC07sZptX1DuIOEg81wmhAdcYvBwQTBgYMPkhwGMiQMBT3gA4T7EvvOYfEDa2bcR24Oi7VJgyFgCnkTgkwfBg3YjT7QPwgiTEe7BqgNW4vE7Podwgk8eCxIEamOnBf6bVx2QH9qB1QW0L+hioYAXMLAIuzApwW0KgeL2xf6dNqnHpAlXAQgE4I/JKK5QYDMv3AwQIK5XYSOg8waZbYXzed7gjSnkSMTmE5ibg7ZHzfS8gbJBKuDCCFdH+12i80ZhzxMlbV3QPIP3NzgKrAW4ePGRz4KyD2iTXAT3y7+xig8C61o0A5/AhQVADo7G+xqLmhARa9asMVwCC6oIbsb1xhtvGJdtcBB4SeBCfeAtIT0HsKgJ3sICg/kAXKRBtFEm3I7ghcBcizkAVvnBc7LFtVBnWEvhTu7aOZHrEcQhwH+wICp3yERfQvABA7gs2VehzQUFJRSgukEcQaqxow8GJT9IGJxBgcUgzfD5A9nnrUjxkOKBkIMAg4MDb6BA+aGBDxv7/nN5UOtwywlatUb9UF9+2dWvX98QYQxIGYQMpY28EBOB+6HubcIrBzgeUDwMyJ8Ds/EZTybynAE81BAmwAmxEXgBYoDD6sD7DfNDhMAmaWoHDlKMLFu2zJj18ALF6kjQnszID2ZNuFZBfOBC0DfyDtq+FH0A1S7zRD7oaz5pGa4ACLyW/2GehVBk/8u4QgGuXLAGYZs2vQofgdKYN4CiDKzF3FKW5g0ZA1Xa8wbPX66RCKFQWvMGrxJiJVWSBq6XzhuFP1eUpIVB8wzyxMKb5CfyIMERI0aYVX6+4CcP4o0YQVx4l2JlHkQfq95BLkxwTwbPAVcBqQe3wP2wFMCiAB6EC+MY4gBeBv/973+NxU4umLksIOAD0vWJ+QAW7FAfjqdgroXv8VzD/dnmWmg72pipORP5wFqC7fDlWUw2hwB3QPyi/AkOAT4TFPsBPoa5wLYuFtpcUFBCoSQPsaZNDwG4dcmzHtJLnd7dWL2AqRWrHDw5ppdD8t1RMQogHohR4R0lSlKWplUEFIEiBMrqvAHCg1VPXvjB6ihWZeWl84aOdEUgPxGAQEAwOMQPrB/FvZAe57nw4ix4Aly47djXQpsLVCgUd8RoulJFAMFEMB0G7SIVtzIQCbjgjgWzKPwm5e4RMLUipsPehSJu/nqfIqAI5A8CmZo3sGIKCxCCR/Efrp7YZ54vzBvYAQ6xXHopAopA/iEAqwtiPkrKIbDLJDwxECANTwt4Nkg3xEKcC1Qo5N941hoFIICXtX2mRLpgcZA7p8PKIAsFuEIh5gMm4qitF9MtV+9XBBSB3CCQiXmDFxhkC9jlSeeN3PSrlqoIpIMAb+GKWEvEmRb3wi5J2EgB1gW4HmEBga9CnQtUKBR3tGi6nCAQtKNDJiqDnZWwg1RQLEsmytA8FAFFoPQRgI81DpnMxqXzRjZQ1TwVgcwjgI1jsAuT3LEpk6UU6lygQiGTo0TzUgQUAUVAEVAEFAFFQBFQBAoEARUKBdKR2gxFQBFQBBQBRUARUAQUAUUgkwioUMgkmpqXIqAIKAKKgCKgCCgCioAiUCAIqFAokI7UZigCioAioAgoAoqAIqAIKAKZRECFQibR1LwUAUVAEVAEFAFFQBFQBBSBAkFAhUKBdKQ2QxFQBBQBRUARUAQUAUVAEcgkAioUMomm5qUIKAKKgCKgCCgCioAioAgUCAIqFAqkI7UZioAioAgoAoqAIqAIKAKKQCYRUKGQSTQ1L0VAEVAEFAFFQBFQBBQBRaBAEFChUCAdqc1QBBQBRUARUAQUAUVAEVAEMomACoVMoql5KQKKgCKgCCgCioAioAgoAgWCgAqFAulIbYYioAgoAoqAIqAIKAKKgCKQSQRUKGQSTc1LEVAEFAFFQBFQBBQBRUARKBAEVCgUSEdqMxQBRUARUAQUAUVAEVAEFIFMIqBCIZNoal6KgCKgCCgCioAioAgoAopAgSCgQqFAOlKboQgoAoqAIqAIKAKKgCKgCGQSARUKmURT81IEFAFFQBFQBBQBRUARUAQKBAEVCgXSkdoMRUARUAQKBYFRo0ZR69atU5ozcuRIOvTQQ/Oima+//jq1a9eO8qlO2Qbm33//pYsuuoh22WUXevjhh6lKlSrZLlLzVwQUgRwjoEIhxx2gxZdtBFyEpkGDBrT//vvTaaedRm3btqVtttkmrxvJhOe1114zJKA0LiYcKAvlV69evTSK1TLKCAIqFHLXUTwfXHHFFSliQIVC7vpFS1YEcoWACoVcIa/lFgQCTGiwsrjvvvuaNuFlOnToUJo4cSLttdde9PTTT9Phhx9O5cqVy8s2Dxw4kJ588knq2LEjnX766aVSx0WLFlHnzp1NWX369KHtt9++VMrVQsomAvm4ep+PdcpE76pQyASKmociUDgIqFAonL7UluQAARYK9mr8xo0b6csvv6RbbrmFFi9ebFbN88VlIgcwlUqRYQSH+wmWnrfeeot23HFH33piWzRWrVpFN9xwA9WpU4d69Ohh6s55BzXkzjvv9O/le1yr4scff3ySBSUqX1mey+LTu3dv6tmzZ1K1XHUplQ7IYiFhpDwKw2zhESUU+Puo8tGH7733nhmXWFgIu3766Sc677zz6Oyzz04Zb1mE38+6NCwKpVFGaWClZSgChYKACoVC6UltR04QCBIKXJkpU6bQ+eefTwceeCD169cv792QcgJihgplYsZiQJIuJtS2UPj888/JJnKZEAph5FW6dESR3CChEOSaw/cXmt98WRMKTHYxvmxxaA93FQrJiKhQyNCEqNkoAhlCQIVChoDUbDZPBKKEwoYNG+iOO+6g5557jj766CNq3ry5AWrq1KnG/xduPwsXLqSjjz7auP4grmGLLbYw93DeX331Fc2ZM4fuuece+uuvv+jSSy+lW2+9lbbeemvq37+/cRtasGABXXnllXTTTTfRDjvsYNL/8ssvJs13331HWInEBRcoCJdLLrnED0TkcphcMlHG/Z06dTJ1f//99016uFjdeOONfhnr16+nQYMGmTrAgoKy0ZYOHTqYn9wWe3TIMjgoEoQJdWFcXnrpJdMuXj2tV69e6CADmXzllVdo3rx5SSuuTDxg0eGVW7YogMjhksTaJRRkwVF9zulnzZqVEn+BugCrm2++2RkIGpaW68CrypMnT04RObgHOCDYVFqwXJYHW0wErVZH1cmVt8u/neuGMRQkgII6OGr1ntNF9Y1NQjGuZX2CVv8l5igL1h1+HlyiDPW4+uqrzTMNLGxrkJ2fq92yLi6MZZogcczjHffCEoExg0v2jxQ1Uf1i4/fAAw/4Fi1Xnwf1m2usxRHNLqxd9S80oRw68emXikCWEVChkGWANfvCRiCKmKD1INLw/Wey8PXXXxuivuWWWxphUK1aNRoyZAh98803dN999xmyD4LNeR9yyCG0bNkycy9IMFwUQPZB0n/99Vc69thjafr06fTxxx/T9ddfT7169aIKFSoYgQBB0bJlSz9YGEQB9912220mRgD3BQkF1LtSpUp0wAEHGIvItGnTTNrLL7+c7r77bvPdY489Zlwg0B7Ub8mSJfTyyy/TunXrQoOUg4TCI488QlWrVqU999zTiBoIo3feeceIjmeeeSY0lgFEAxgieByWHHYpQvtAtK666iqDjXQ9atKkCX3xxRcGIxYsJRUKJQnUjiLlGE9MGuMEn3N+wM6+bHJZHKEQRmBtspbOvXZdsyEUME769u2bgouNa3GsN2grxm737t3psssuS9klqDSFAosV2dCSCgWOKXrzzTeT8LPFQraFQhiOKhYK+92rrSs9BFQolB7WWlIBIhBHKPA9WCG85pprzGo7YhieeOIJ4wePa82aNSao96mnnqIPP/yQDjroIJ/Ad+nShbp27WoEBSwUIOaPP/64IfoQFdiiMA7BRDmbNm0yFg7U6Y033qAaNWoECoVJkyYZd6kWLVqYQGwIk27dupnVd6QF0frPf/5DO++8Mz366KO01VZbmbbgvsGDB5sV7aAg5SCh8PzzzxtcTjzxRCpfvrypL4LBQbykRcY1lFgowLLSvn17k0+zZs38eIOjjjrKrPJKoYD6//bbb2ZVlAliSYWCJPOuld6wxyCqH9N1y0B+WPUFWZQ7SzFpl2QqXaEQVheUu3LlyqQyIcROOumkJD/8uDtuZVoouCxJ/JwGkWiJlRQPQZYZxCdhF7Eo16Ko7+V4SSdGQQoz2zoBERO0vWnYnBbkUiUFqcQjHaHA7Yw7xmVdXONYiv8CfPVokxSBUkNAhUKpQa0FFSIC6QoFrP4fd9xxhuhffPHFSZBgFfycc84xrkVY8bdX+vlmuNc89NBDKcGPLsKxdOlSAin69NNP6X//+59xPYCrkySwYa5HNpmQZdeqVcuslsLagfbsvvvusbs4zPXIDi4eMWKEsS5ErRCyUIC1A4IGIuzcc881LhcQDbhcQuGEE04whG7u3LkG0/r166cEM8uGxelzKRY4bRzRECUU0iGKYZ3hInDpCgVJDqMCdoPqEhfLTAsF1MceZy6CKkU+B7ZzW4LqhM9fffXVJIsWzoQIwijbQiHqubH7Jo5QcJ2j4MIqm0IhrJ7sxmj3cewJSm9UBBQBHwEVCjoYFIESIBCH6MCtCOIgyq+ZiUrDhg3pwQcfpPHjx5tDp+wXPV5+cJmwd0mxCQcINtyE4OcP152mTZtS48aN6bPPPqOxY8f66dMRCnbZcAvCCj7EB1bw2rRpQ8ccc4z5GXYYUzpCIUgw2d3GQgHiBr7nwAi4QyzhswkTJjiFAkSCJDmIH7B3PSqOUOA0tutKWHBrtoRCkP93SSwKaJ/L9SPMJSrIjSfKjSrTQsFFdF1CIU4QdVR8S5QrWjaFQtzdlOKO77DV/rC4g7jxMKhHXItCVExDHGFegqlfkyoCmw0CKhQ2m67WhmYDgSihYAczz5gxI/A010wKBV7tnz17tglG5jMeeKVbEoiSCAXkBzcGxC4MGzaMxowZYywXZ555pokpgGuT68q2UPjjjz/84E0moRxgarseQSjI1XHELAAfuT1qXCIVNsbirMBHCYUo0mmXH3R/JiwKsixXLISLQI8ePTpJ4EY9P1Gr9+mshkeR0EwIheLGM8Ql9OlYlNIRIHHHtwqFbLxFNE9FIL8RUKGQ3/2jtctzBKKIDoKML7jgAj9YFq4/WOXGajd8oeUV1/UojkUB+Qbtt24TiJIKBdkGxFogpgBB1djRCQHOuRAKsGa42ulyPeLTqJmEob477bSTsebY7ib4LqrPw4Zs1GpplFBgoYeYijjuPkFjJR2hkK44cfn6B1mF4mKZK4tCWP1cdYranci1M1A6hD6fhYILq6B+C3PpinpG+PmKO3by/BWi1VME8h4BFQp530VawXxGIOhlhWDlcePGmWBjuOVgdxDssIMTiRHMvHbtWkOo69ata5oXFsxcHNcjBD6DAMPfXp7fgMBdbHn6559/ltj1qGbNmmbHoDPOOIMqV67sdxPHFOA7xGTkSijY5YZZFPhe6c4QRMSjCAoTHQRKswix8w/KO45QkO4+Ljcm5AHssdOWa4ckmV66/PDnsEaxb3dYWcABvvh2HAunkcGkjKtstwxGjRI9uRIKLkzQl3Kc8PMZ1u8s8FyWg3R2sQqqj+sZS0eAyPRxYhTs80ekyJYuka5+j3rGGEeOGQo6hC4omFkKCfyuB13m8xtU61YWEFChUBZ6SeuYtwjwSxV7sbN7D15gQ4cOpYkTJ5qgYexk1KpVK78NvD0qPmjbtq3ZPShse9TiCAUEFiPgGTsjnXLKKaZ8uOMgqBkXzjvgF3pxLQp8FgGEEAKwEf8AC8qLL75oBFAuXY9c8RFxhEIQeY3yhw4iwK6BG+Y7HUcoIM8oFxeuT9R9yItXufE7YjPsrVSxew/iPOxzIaLydsU/8D7+LlzCYjfixAoETRKuvokbo8AE3z79Gn3IlkFuZ5D1RhJXWKnibr/qsj6EbXfr2o42rktTcYSCC++450VgjOE8kaATpoMsM3Gx47qlG8idty8arZgikEMEVCjkEHwtuuwj4CJLDRo0MAIBbjcsBGRLseUn3IywpWicA9eKIxSwCgdS8eyzz5oVX5B5xA0gUBfbr2YiRmGPPfYwMQkDBgwwZ0UgNgHlYmtSWE2C4hOARbZjFIorFCQJlwQzHaHAfe0iO1Er53GFQjpl2GOUyRbXTxJ0OzgZ9eXgbtcBcq5g5iAh5Mobrl2MbZiAyqVQsMUC44UtgLFAgOeTt+HFvUHbjoa5cLnGV9ChdUEHpJWmUIC1DGILwoevoIB0u22oJxYZglwj5fxgi1ZXGXHxKPtvG22BIpAbBFQo5AZ3LVURUAQUAUVAEVAEFAFFQBHIawRUKOR192jlFAFFQBFQBBQBRUARUAQUgdwgoEIhN7hrqYqAIqAIKAKKgCKgCCgCikBeI6BCIa+7RyunCCgCioAioAgoAoqAIqAI5AYBFQq5wV1LVQQUAUVAEVAEFAFFQBFQBPIaARUKed09WjlFQBFQBBQBRUARUAQUAUUgNwioUMgN7lqqIqAIKAKKgCKgCCgCioAikNcIqFDI6+7RyikCioAioAgoAoqAIqAIKAK5QUCFQm5w11IVAUVAEVAEFAFFQBFQBBSBvEZAhUJed49WThFQBBQBRUARUAQUAUVAEcgNAioUcoO7lqoIKAKKgCKgCCgCioAioAjkNQIqFPK6e7RyioAioAgoAoqAIqAIKAKKQG7iB4nDAAAAbElEQVQQUKGQG9y1VEVAEVAEFAFFQBFQBBQBRSCvEVChkNfdo5VTBBQBRUARUAQUAUVAEVAEcoOACoXc4K6lKgKKgCKgCCgCioAioAgoAnmNgAqFvO4erZwioAgoAoqAIqAIKAKKgCKQGwT+H70NuwC+pAveAAAAAElFTkSuQmCC" } }, "cell_type": "markdown", "id": "9ee1890a", "metadata": {}, "source": [ "" ] }, { "cell_type": "code", "execution_count": null, "id": "8c441b37", "metadata": { "scrolled": true }, "outputs": [], "source": [ "import torch\n", "import pytorch_lightning as pl" ] }, { "cell_type": "markdown", "id": "1bca038c", "metadata": {}, "source": [ "## Initialize dataset" ] }, { "cell_type": "code", "execution_count": null, "id": "23c5c320", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.datasets.mnist import MNISTCausalAttribute\n", "\n", "# dataset class initialization requires mandatory param `data_dir`\n", "# `download` is passed to torchvision.datasets.MNIST and downloads data if not present \n", "data_dir = 'data'\n", "dataset = MNISTCausalAttribute(data_dir, download=True)" ] }, { "cell_type": "markdown", "id": "eb49277b", "metadata": {}, "source": [ "## Initialize data loaders\n", "\n", "`get_loaders` returns data loaders for training, validation, and test. `loaders` returned is a dictionary of `train_loaders`, `val_loaders`, `test_loaders`. There are two scenarios supported currently to initialize validation domains:\n", "\n", "**Method 1**: When a domain(s) from the dataset is explicitly specified as the validation domain <br>\n", "**Method 2**: When no specific validation domain is present, a subset of the training domain(s) is used to create the validation set\n", "\n", "Run either cell below Method 1 or Method 2 as required." ] }, { "cell_type": "code", "execution_count": null, "id": "64dd7f3c", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.dataloaders.get_data_loader import get_loaders" ] }, { "cell_type": "markdown", "id": "51cbb17d", "metadata": {}, "source": [ "### Method 1: Provide validation domain explicitly\n", "Provide index of validation domains as `val_envs`. `test_envs` is an optional parameter." ] }, { "cell_type": "code", "execution_count": null, "id": "a75b74fa", "metadata": {}, "outputs": [], "source": [ "loaders = get_loaders(dataset, train_envs=[0, 1], batch_size=64,\n", " val_envs=[2], test_envs=[3])" ] }, { "cell_type": "markdown", "id": "9da42119", "metadata": {}, "source": [ "### Method 2: Validation set using subset of training data\n", "\n", "`val_envs`, `test_envs` are optional parameters. If `val_envs` is not provided, a subset of training data is used for creating the validation set. The fraction of training data used is determined by `holdout_fraction`." ] }, { "cell_type": "code", "execution_count": null, "id": "5201cd08", "metadata": {}, "outputs": [], "source": [ "loaders = get_loaders(dataset, train_envs=[0, 1], batch_size=64,\n", " holdout_fraction=0.2, test_envs=[3])" ] }, { "cell_type": "markdown", "id": "2b599691", "metadata": {}, "source": [ "The code below handles more than one validation or test domains, if present. Run the cell below irrespective of Method 1 or 2 used above." ] }, { "cell_type": "code", "execution_count": null, "id": "afbd74ef", "metadata": {}, "outputs": [], "source": [ "# handle multiple validation and test domains if present \n", "from pytorch_lightning.trainer.supporters import CombinedLoader\n", "\n", "if len(loaders['val_loaders']) > 1:\n", " val_loaders = loaders['val_loaders']\n", " loaders['val_loaders'] = CombinedLoader(val_loaders)\n", " \n", "if len(loaders['test_loaders']) > 1:\n", " test_loaders = loaders['test_loaders']\n", " loaders['test_loaders'] = CombinedLoader(test_loaders)" ] }, { "cell_type": "markdown", "id": "106cea0d", "metadata": {}, "source": [ "## Initialize model and algorithm " ] }, { "cell_type": "code", "execution_count": null, "id": "5b977c7f", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.models.networks import MNIST_MLP, Classifier" ] }, { "cell_type": "markdown", "id": "cbe00718", "metadata": {}, "source": [ "`model` below is expected to be of type `torch.nn.Sequential` with two `torch.nn.Module` elements (feature extractor and classifier). We provide sample networks (MLP, ResNet) in `dowhy.causal_prediction.models.networks` but the user can flexibly use any model.\n" ] }, { "cell_type": "code", "execution_count": null, "id": "9a663fe7", "metadata": {}, "outputs": [], "source": [ "featurizer = MNIST_MLP(dataset.input_shape)\n", "classifier = Classifier(\n", " featurizer.n_outputs,\n", " dataset.num_classes)\n", "\n", "model = torch.nn.Sequential(featurizer, classifier)" ] }, { "cell_type": "markdown", "id": "adf11498", "metadata": {}, "source": [ "### Initialize algorithm class: ERM" ] }, { "cell_type": "code", "execution_count": null, "id": "51fadf20", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.algorithms.erm import ERM" ] }, { "cell_type": "code", "execution_count": null, "id": "633da7eb", "metadata": {}, "outputs": [], "source": [ "algorithm = ERM(model, lr=1e-3)" ] }, { "cell_type": "markdown", "id": "94828541", "metadata": {}, "source": [ "## Fit predictor and start training\n", "\n", "Note: The optimal accuracy for `MNISTCausalAttribute` (and other MNIST variants introduced) is **75%** as we introduce 25% noise following previous work." ] }, { "cell_type": "code", "execution_count": null, "id": "27f43e54", "metadata": {}, "outputs": [], "source": [ "trainer = pl.Trainer(devices=1, max_epochs=5) \n", "\n", "# val_loaders is optional param\n", "trainer.fit(algorithm, loaders['train_loaders'], loaders['val_loaders'])" ] }, { "cell_type": "markdown", "id": "8635befc", "metadata": {}, "source": [ "## Evaluate on test domain\n", "\n", "Perform an evaluation epoch over the test set using `trainer.test`. `ckpt_path` determines the model to be used for evaluation -- 'best', 'last', or path to a specific checkpoint. If `ckpt_path` is not passed, best model checkpoint from the previous `trainer.fit` is loaded (https://pytorch-lightning.readthedocs.io/en/stable/_modules/pytorch_lightning/trainer/trainer.html#Trainer.test).\n", "\n", "We report accuracy (`test_acc`) and cross-entropy loss (`test_loss`) on the test domains/test set." ] }, { "cell_type": "code", "execution_count": null, "id": "4f382191", "metadata": { "scrolled": true }, "outputs": [], "source": [ "if 'test_loaders' in loaders:\n", " trainer.test(dataloaders=loaders['test_loaders'], ckpt_path='best')" ] }, { "cell_type": "markdown", "id": "bb318eea", "metadata": {}, "source": [ "## Prediction with CACM\n", "\n", "We now train and evaluate the above dataset with CACM. We specify the type of shifts present using list `attr_types` provided as input to CACM. Further instructions regarding using CACM with multi-attribute shifts is provided in the next section." ] }, { "cell_type": "code", "execution_count": null, "id": "08881e8d", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.algorithms.cacm import CACM" ] }, { "cell_type": "code", "execution_count": null, "id": "48c87949", "metadata": {}, "outputs": [], "source": [ "# `attr_types` list contains type of attributes present (supports 'causal' and 'ind' currently)\n", "algorithm = CACM(model, lr=1e-3, gamma=1e-2, attr_types=['causal'], lambda_causal=100.)" ] }, { "cell_type": "code", "execution_count": null, "id": "2a9b1e8a", "metadata": {}, "outputs": [], "source": [ "trainer = pl.Trainer(devices=1, max_epochs=5) \n", "\n", "trainer.fit(algorithm, loaders['train_loaders'], loaders['val_loaders'])" ] }, { "cell_type": "code", "execution_count": null, "id": "cf1640b9", "metadata": {}, "outputs": [], "source": [ "if 'test_loaders' in loaders:\n", " trainer.test(dataloaders=loaders['test_loaders'], ckpt_path='best')" ] }, { "cell_type": "markdown", "id": "72066ad0", "metadata": {}, "source": [ "## Extending to different datasets and algorithms" ] }, { "cell_type": "markdown", "id": "a249be81", "metadata": {}, "source": [ "### MNIST Independent and Causal+Independent datasets\n", "\n", "We show how to perform the above evaluation for `MNISTIndAttribute` and`MNISTCausalIndAttribute` datasets. Additional `attr_types` should be provided to CACM algorithm for handling multiple shifts. We currently support `Causal` and `Independent` distribution shifts in the data." ] }, { "cell_type": "markdown", "id": "9a025cb3", "metadata": {}, "source": [ "#### `MNISTIndAttribute`: Single-attribute *Independent* shift" ] }, { "cell_type": "code", "execution_count": null, "id": "8c5cd482", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.datasets.mnist import MNISTIndAttribute\n", "\n", "data_dir = 'data'\n", "dataset = MNISTIndAttribute(data_dir)" ] }, { "cell_type": "code", "execution_count": null, "id": "4b28707a", "metadata": {}, "outputs": [], "source": [ "algorithm = CACM(model, lr=1e-3, gamma=1e-2, attr_types=['ind'], lambda_ind=10.)" ] }, { "cell_type": "markdown", "id": "aa460184", "metadata": {}, "source": [ "#### `MNISTCausalIndAttribute`: Multi-attribute *Causal*+*Independent* shift" ] }, { "cell_type": "code", "execution_count": null, "id": "17b446be", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.datasets.mnist import MNISTCausalIndAttribute\n", "\n", "data_dir = 'data'\n", "dataset = MNISTCausalIndAttribute(data_dir)" ] }, { "cell_type": "code", "execution_count": null, "id": "9448cc87", "metadata": {}, "outputs": [], "source": [ "# `attr_types` can be provided in any order\n", "algorithm = CACM(model, lr=1e-3, gamma=1e-2, attr_types=['ind', 'causal'], lambda_causal=100., lambda_ind=10.)" ] }, { "cell_type": "markdown", "id": "829479bc", "metadata": {}, "source": [ "### Additional datasets and algorithms\n", "\n", "We provide our demo on MNIST using ERM and *CACM* algorithms. It is possible to extend the evaluation to new datasets and algorithms for evaluation.\n", "\n", "\n", "New datasets can be added to `dowhy.causal_prediction.datasets` and imported here, as we did for MNIST. We provide description of the MNIST dataset (and variants) in `dowhy.causal_prediction.datasets.mnist` that will be helpful in creating new dataset classes. We currently support `Causal` and `Independent` distribution shifts in the data.\n", "\n", "We have implemented ERM in `dowhy.causal_prediction.algorithms` as a baseline. Additional algorithms can be added by overriding the `training_step` function in base class `PredictionAlgorithm`." ] }, { "cell_type": "code", "execution_count": null, "id": "6206a050", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.11" } }, "nbformat": 4, "nbformat_minor": 5 }

{ "cells": [ { "cell_type": "markdown", "id": "7a84e574", "metadata": {}, "source": [ "# Demo for DoWhy Causal Prediction on MNIST\n", "\n", "The goal of this notebook is to demonstrate an example of causal prediction using *Causally Adaptive Constraint Minimization (CACM)* (https://arxiv.org/abs/2206.07837) [1]. " ] }, { "cell_type": "markdown", "id": "900de7ec", "metadata": {}, "source": [ "## Multi-attribute distribution shift datasets \n", "\n", "Domain generalization literature has largely focused on datasets with a single kind of distribution shift over one attribute. Using MNIST as an example, domains are created either by adding new values of a spurious attribute like rotation (e.g., Rotated-MNIST dataset [2]) or domains exhibit different values of correlation between the class label and a spurious attribute like color (e.g., Colored-MNIST [3]). However, real-world data often has multiple distribution shifts over different attributes. For example, satellite imagery data demonstrates distribution shifts over time as well as the region captured.\n", "\n", "\n", "### Multi-attribute MNIST\n", "\n", "We create a *multi-attribute* shift variant of MNIST, where both the color and rotation angle of digits can shift across data distributions. Hence, we create three variants of MNIST -- `MNISTCausalAttribute` (single-attribute shift), `MNISTIndAttribute` (single-attribute shift), `MNISTCausalIndAttribute` (multi-attribute shift). To describe, `Causal`, `Ind`, and `CausalInd` datasets better, consider the causal graph for the data generating process below:\n", "\n" ] }, { "attachments": { "main_fig_mnist.drawio.png": { "image/png": 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" } }, "cell_type": "markdown", "id": "4d22f37d", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "id": "f2667794", "metadata": {}, "source": [ "Distribution shifts are characterized based on the relationship between spurious attributes ***A*** and the classification label *Y*.\n", "1. `Causal`: Attribute has a direct-*Causal* relationship with the class label i.e., *Y* causing attribute (e.g., *Color* here)\n", "2. `Ind`: Attribute is *Independent* of the class label (e.g., *Rotation* here)\n", "3. `CausalInd`: Different attributes having *Causal* and *Independent* relationships with *Y* co-exist in the data" ] }, { "cell_type": "markdown", "id": "56115bbd", "metadata": {}, "source": [ "### Domains in multi-attribute MNIST" ] }, { "cell_type": "markdown", "id": "403765c4", "metadata": {}, "source": [ "We describe the domains for our *multi-attribute* shift dataset `MNISTCausalIndAttribute`.<br> \n", "Each domain E_i has a specific *Rotation* angle r_i and a specific correlation corr_i between *Color C* and label *Y* . Our setup consists of 3 domains:\n", "E₁, E₂ are training domains, E₃ is the test domain. We define corr_i = P(*Y* = 1|*C* = 1) = P(*Y* = 0|*C* = 0) in E_i. In our setup, r₁ = 15◦, r₂ = 60◦, r₃ = 90◦ and corr₁ = 0.9, corr₂ = 0.8, corr₃ = 0.1. All environments have 25% label noise, as in [3]\n", "\n", "\n", "Other dataset-related details can be found in `dowhy.causal_prediction.datasets`." ] }, { "attachments": { "MNIST_visualize.drawio%20%283%29.png": { "image/png": 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" } }, "cell_type": "markdown", "id": "9ee1890a", "metadata": {}, "source": [ "" ] }, { "cell_type": "code", "execution_count": null, "id": "8c441b37", "metadata": { "scrolled": true }, "outputs": [], "source": [ "import torch\n", "import pytorch_lightning as pl" ] }, { "cell_type": "markdown", "id": "1bca038c", "metadata": {}, "source": [ "## Initialize dataset" ] }, { "cell_type": "code", "execution_count": null, "id": "23c5c320", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.datasets.mnist import MNISTCausalAttribute\n", "\n", "# dataset class initialization requires mandatory param `data_dir`\n", "# `download` is passed to torchvision.datasets.MNIST and downloads data if not present \n", "data_dir = 'data'\n", "dataset = MNISTCausalAttribute(data_dir, download=True)" ] }, { "cell_type": "markdown", "id": "eb49277b", "metadata": {}, "source": [ "## Initialize data loaders\n", "\n", "`get_loaders` returns data loaders for training, validation, and test. `loaders` returned is a dictionary of `train_loaders`, `val_loaders`, `test_loaders`. There are two scenarios supported currently to initialize validation domains:\n", "\n", "**Method 1**: When a domain(s) from the dataset is explicitly specified as the validation domain <br>\n", "**Method 2**: When no specific validation domain is present, a subset of the training domain(s) is used to create the validation set\n", "\n", "Run either cell below Method 1 or Method 2 as required." ] }, { "cell_type": "code", "execution_count": null, "id": "64dd7f3c", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.dataloaders.get_data_loader import get_loaders" ] }, { "cell_type": "markdown", "id": "51cbb17d", "metadata": {}, "source": [ "### Method 1: Provide validation domain explicitly\n", "Provide index of validation domains as `val_envs`. `test_envs` is an optional parameter." ] }, { "cell_type": "code", "execution_count": null, "id": "a75b74fa", "metadata": {}, "outputs": [], "source": [ "loaders = get_loaders(dataset, train_envs=[0, 1], batch_size=64,\n", " val_envs=[2], test_envs=[3])" ] }, { "cell_type": "markdown", "id": "9da42119", "metadata": {}, "source": [ "### Method 2: Validation set using subset of training data\n", "\n", "`val_envs`, `test_envs` are optional parameters. If `val_envs` is not provided, a subset of training data is used for creating the validation set. The fraction of training data used is determined by `holdout_fraction`." ] }, { "cell_type": "code", "execution_count": null, "id": "5201cd08", "metadata": {}, "outputs": [], "source": [ "loaders = get_loaders(dataset, train_envs=[0, 1], batch_size=64,\n", " holdout_fraction=0.2, test_envs=[3])" ] }, { "cell_type": "markdown", "id": "2b599691", "metadata": {}, "source": [ "The code below handles more than one validation or test domains, if present. Run the cell below irrespective of Method 1 or 2 used above." ] }, { "cell_type": "code", "execution_count": null, "id": "afbd74ef", "metadata": {}, "outputs": [], "source": [ "# handle multiple validation and test domains if present \n", "from pytorch_lightning.trainer.supporters import CombinedLoader\n", "\n", "if len(loaders['val_loaders']) > 1:\n", " val_loaders = loaders['val_loaders']\n", " loaders['val_loaders'] = CombinedLoader(val_loaders)\n", " \n", "if len(loaders['test_loaders']) > 1:\n", " test_loaders = loaders['test_loaders']\n", " loaders['test_loaders'] = CombinedLoader(test_loaders)" ] }, { "cell_type": "markdown", "id": "106cea0d", "metadata": {}, "source": [ "## Initialize model and algorithm " ] }, { "cell_type": "code", "execution_count": null, "id": "5b977c7f", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.models.networks import MNIST_MLP, Classifier" ] }, { "cell_type": "markdown", "id": "cbe00718", "metadata": {}, "source": [ "`model` below is expected to be of type `torch.nn.Sequential` with two `torch.nn.Module` elements (feature extractor and classifier). We provide sample networks (MLP, ResNet) in `dowhy.causal_prediction.models.networks` but the user can flexibly use any model.\n" ] }, { "cell_type": "code", "execution_count": null, "id": "9a663fe7", "metadata": {}, "outputs": [], "source": [ "featurizer = MNIST_MLP(dataset.input_shape)\n", "classifier = Classifier(\n", " featurizer.n_outputs,\n", " dataset.num_classes)\n", "\n", "model = torch.nn.Sequential(featurizer, classifier)" ] }, { "cell_type": "markdown", "id": "adf11498", "metadata": {}, "source": [ "### Initialize algorithm class: ERM\n", "\n", "We have implemented Empirical Risk Minimization (ERM) in `dowhy.causal_prediction.algorithms` as a baseline." ] }, { "cell_type": "code", "execution_count": null, "id": "51fadf20", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.algorithms.erm import ERM" ] }, { "cell_type": "code", "execution_count": null, "id": "633da7eb", "metadata": {}, "outputs": [], "source": [ "algorithm = ERM(model, lr=1e-3)" ] }, { "cell_type": "markdown", "id": "94828541", "metadata": {}, "source": [ "## Fit predictor and start training\n", "\n", "Note: The optimal accuracy for `MNISTCausalAttribute` (and other MNIST variants introduced) is **75%** as we introduce 25% noise following previous work." ] }, { "cell_type": "code", "execution_count": null, "id": "27f43e54", "metadata": {}, "outputs": [], "source": [ "trainer = pl.Trainer(devices=1, max_epochs=5) \n", "\n", "# val_loaders is optional param\n", "trainer.fit(algorithm, loaders['train_loaders'], loaders['val_loaders'])" ] }, { "cell_type": "markdown", "id": "8635befc", "metadata": {}, "source": [ "## Evaluate on test domain\n", "\n", "Perform an evaluation epoch over the test set using `trainer.test`. `ckpt_path` determines the model to be used for evaluation -- 'best', 'last', or path to a specific checkpoint. If `ckpt_path` is not passed, best model checkpoint from the previous `trainer.fit` is loaded (https://pytorch-lightning.readthedocs.io/en/stable/_modules/pytorch_lightning/trainer/trainer.html#Trainer.test).\n", "\n", "We report accuracy (`test_acc`) and cross-entropy loss (`test_loss`) on the test domains/test set." ] }, { "cell_type": "code", "execution_count": null, "id": "4f382191", "metadata": { "scrolled": true }, "outputs": [], "source": [ "if 'test_loaders' in loaders:\n", " trainer.test(dataloaders=loaders['test_loaders'], ckpt_path='best')" ] }, { "cell_type": "markdown", "id": "bb318eea", "metadata": {}, "source": [ "## Prediction with *CACM*\n", "\n", "We now train and evaluate the above dataset with *CACM*. We specify the type of shifts present using list `attr_types` provided as input to *CACM*. Further instructions regarding using *CACM* with multi-attribute shifts is provided in the next section." ] }, { "cell_type": "code", "execution_count": null, "id": "08881e8d", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.algorithms.cacm import CACM" ] }, { "cell_type": "code", "execution_count": null, "id": "48c87949", "metadata": {}, "outputs": [], "source": [ "# `attr_types` list contains type of attributes present (supports 'causal', 'conf', ind', and 'sel' currently)\n", "algorithm = CACM(model, lr=1e-3, gamma=1e-2, attr_types=['causal'], lambda_causal=100.)" ] }, { "cell_type": "code", "execution_count": null, "id": "2a9b1e8a", "metadata": {}, "outputs": [], "source": [ "trainer = pl.Trainer(devices=1, max_epochs=5) \n", "\n", "trainer.fit(algorithm, loaders['train_loaders'], loaders['val_loaders'])" ] }, { "cell_type": "code", "execution_count": null, "id": "cf1640b9", "metadata": {}, "outputs": [], "source": [ "if 'test_loaders' in loaders:\n", " trainer.test(dataloaders=loaders['test_loaders'], ckpt_path='best')" ] }, { "cell_type": "markdown", "id": "72066ad0", "metadata": {}, "source": [ "## Extending to different datasets and algorithms" ] }, { "cell_type": "markdown", "id": "a249be81", "metadata": {}, "source": [ "### MNIST Independent and Causal+Independent datasets\n", "\n", "We show how to perform the above evaluation for `MNISTIndAttribute` and`MNISTCausalIndAttribute` datasets. Additional `attr_types` should be provided to *CACM* algorithm for handling multiple shifts. We currently support *Causal*, *Confounded*, *Independent*, and *Selected* distribution shifts in the data." ] }, { "cell_type": "markdown", "id": "9a025cb3", "metadata": {}, "source": [ "#### `MNISTIndAttribute`: Single-attribute *Independent* shift" ] }, { "cell_type": "code", "execution_count": null, "id": "8c5cd482", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.datasets.mnist import MNISTIndAttribute\n", "\n", "data_dir = 'data'\n", "dataset = MNISTIndAttribute(data_dir)" ] }, { "cell_type": "code", "execution_count": null, "id": "4b28707a", "metadata": {}, "outputs": [], "source": [ "algorithm = CACM(model, lr=1e-3, gamma=1e-2, attr_types=['ind'], lambda_ind=10., E_eq_A=[0])" ] }, { "cell_type": "markdown", "id": "aa460184", "metadata": {}, "source": [ "#### `MNISTCausalIndAttribute`: Multi-attribute *Causal*+*Independent* shift" ] }, { "cell_type": "code", "execution_count": null, "id": "17b446be", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.datasets.mnist import MNISTCausalIndAttribute\n", "\n", "data_dir = 'data'\n", "dataset = MNISTCausalIndAttribute(data_dir)" ] }, { "cell_type": "code", "execution_count": null, "id": "9448cc87", "metadata": {}, "outputs": [], "source": [ "# `attr_types` should be ordered consistent with the attribute order in dataset class\n", "algorithm = CACM(model, lr=1e-3, gamma=1e-2, attr_types=['causal', 'ind'], lambda_causal=100., lambda_ind=10., E_eq_A=[1])" ] }, { "cell_type": "markdown", "id": "829479bc", "metadata": {}, "source": [ "### Additional datasets and algorithms\n", "\n", "We provide our demo on MNIST using ERM and *CACM* algorithms. It is possible to extend the evaluation to new datasets and algorithms for evaluation.\n", "\n", "\n", "New datasets can be added to `dowhy.causal_prediction.datasets` and imported here, as we did for MNIST. We provide description of the MNIST dataset (and variants) in `dowhy.causal_prediction.datasets.mnist` that will be helpful in creating new dataset classes. We currently support *Causal*, *Confounded*, *Independent*, and *Selected* distribution shifts in the data. \n", "\n", "We have implemented ERM in `dowhy.causal_prediction.algorithms` as a baseline. Additional algorithms can be added by overriding the `training_step` function in base class `PredictionAlgorithm`." ] }, { "cell_type": "markdown", "id": "3b9e1b05", "metadata": {}, "source": [ "## References\n", "\n", "[1] Kaur, J.N., Kıcıman, E., & Sharma, A. (2022). Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization. ArXiv, abs/2206.07837.\n", "\n", "[2] Ghifary, M., Kleijn, W., Zhang, M., & Balduzzi, D. (2015). Domain Generalization for Object Recognition with Multi-task Autoencoders. 2015 IEEE International Conference on Computer Vision (ICCV), 2551-2559.<br>\n", "\n", "[3] Arjovsky, M., Bottou, L., Gulrajani, I., & Lopez-Paz, D. (2019). Invariant Risk Minimization. ArXiv, abs/1907.02893.\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.16" } }, "nbformat": 4, "nbformat_minor": 5 }

jivatneet

c87b511bfd95caec2bc50e4686ed21ca448e9c37

0fb1314c5ddaeb6805109453433ac6248063f3c5

add a link to CACM. And then move all references to the bottom of the notebook. This is to avoid interrupting the flow

amit-sharma

py-why/dowhy

Adding general version of CACM

This PR is the first step toward a general causal prediction API. The API supports _Causal, Independent, Confounded,_ and _Selected_ shifts (individual and multi-attribute settings) currently. The regularization has been implemented using `unconditional_reg` and `conditional_reg` functions, which can be used for the general _CACM_ API. Follow up: implement Phase I of _CACM_ for deriving conditional independence constraints given arbitrary graphs.

2023-04-18 10:36:38+00:00

2023-06-15 11:59:01+00:00

docs/source/example_notebooks/prediction/dowhy_causal_prediction_demo.ipynb

{ "cells": [ { "cell_type": "markdown", "id": "7a84e574", "metadata": {}, "source": [ "# Demo for DoWhy Causal Prediction on MNIST\n", "\n", "We're adding prediction functionality to DoWhy. The goal of this notebook is to demonstrate an example of causal prediction using *Causally Adaptive Constraint Minimization (CACM)* [1]. \n", "\n", "[1] Kaur, J.N., Kıcıman, E., & Sharma, A. (2022). Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization. ArXiv, abs/2206.07837." ] }, { "cell_type": "markdown", "id": "900de7ec", "metadata": {}, "source": [ "## Multi-attribute distribution shift datasets \n", "\n", "Domain generalization literature has largely focused on datasets with a single kind of distribution shift over one attribute. Using MNIST as an example, domains are created either by adding new values of a spurious attribute like rotation (e.g., Rotated-MNIST dataset [2]) or domains exhibit different values of correlation between the class label and a spurious attribute like color (e.g., Colored-MNIST [3]). However, real-world data often has multiple distribution shifts over different attributes. For example, satellite imagery data demonstrates distribution shifts over time as well as the region captured.\n", "\n", "[2] Ghifary, M., Kleijn, W., Zhang, M., & Balduzzi, D. (2015). Domain Generalization for Object Recognition with Multi-task Autoencoders. 2015 IEEE International Conference on Computer Vision (ICCV), 2551-2559.<br>\n", "[3] Arjovsky, M., Bottou, L., Gulrajani, I., & Lopez-Paz, D. (2019). Invariant Risk Minimization. ArXiv, abs/1907.02893.\n", "\n", "\n", "### Multi-attribute MNIST\n", "\n", "We create a *multi-attribute* shift variant of MNIST, where both the color and rotation angle of digits can shift across data distributions. Hence, we create three variants of MNIST -- `MNISTCausalAttribute` (single-attribute shift), `MNISTIndAttribute` (single-attribute shift), `MNISTCausalIndAttribute` (multi-attribute shift). To describe, `Causal`, `Ind`, and `CausalInd` datasets better, consider the causal graph for the data generating process below:\n", "\n" ] }, { "attachments": { "main_fig_mnist.drawio.png": { "image/png": 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" } }, "cell_type": "markdown", "id": "4d22f37d", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "id": "f2667794", "metadata": {}, "source": [ "Distribution shifts are characterized based on the relationship between spurious attributes ***A*** and the classification label *Y*.\n", "1. `Causal`: Attribute has a direct-*Causal* relationship with the class label i.e., *Y* causing attribute (e.g., *Color* here)\n", "2. `Ind`: Attribute is *Independent* of the class label (e.g., *Rotation* here)\n", "3. `CausalInd`: Different attributes having *Causal* and *Independent* relationships with *Y* co-exist in the data" ] }, { "cell_type": "markdown", "id": "56115bbd", "metadata": {}, "source": [ "### Domains in multi-attribute MNIST" ] }, { "cell_type": "markdown", "id": "403765c4", "metadata": {}, "source": [ "We describe the domains for our *multi-attribute* shift dataset `MNISTCausalIndAttribute`.<br> \n", "Each domain E_i has a specific *Rotation* angle r_i and a specific correlation corr_i between *Color C* and label *Y* . Our setup consists of 3 domains:\n", "E₁, E₂ are training domains, E₃ is the test domain. We define corr_i = P(*Y* = 1|*C* = 1) = P(*Y* = 0|*C* = 0) in E_i. In our setup, r₁ = 15◦, r₂ = 60◦, r₃ = 90◦ and corr₁ = 0.9, corr₂ = 0.8, corr₃ = 0.1. All environments have 25% label noise, as in [3]\n", "\n", "\n", "Other dataset-related details can be found in `dowhy.causal_prediction.datasets`." ] }, { "attachments": { "MNIST_visualize.drawio%20%283%29.png": { "image/png": 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" } }, "cell_type": "markdown", "id": "9ee1890a", "metadata": {}, "source": [ "" ] }, { "cell_type": "code", "execution_count": null, "id": "8c441b37", "metadata": { "scrolled": true }, "outputs": [], "source": [ "import torch\n", "import pytorch_lightning as pl" ] }, { "cell_type": "markdown", "id": "1bca038c", "metadata": {}, "source": [ "## Initialize dataset" ] }, { "cell_type": "code", "execution_count": null, "id": "23c5c320", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.datasets.mnist import MNISTCausalAttribute\n", "\n", "# dataset class initialization requires mandatory param `data_dir`\n", "# `download` is passed to torchvision.datasets.MNIST and downloads data if not present \n", "data_dir = 'data'\n", "dataset = MNISTCausalAttribute(data_dir, download=True)" ] }, { "cell_type": "markdown", "id": "eb49277b", "metadata": {}, "source": [ "## Initialize data loaders\n", "\n", "`get_loaders` returns data loaders for training, validation, and test. `loaders` returned is a dictionary of `train_loaders`, `val_loaders`, `test_loaders`. There are two scenarios supported currently to initialize validation domains:\n", "\n", "**Method 1**: When a domain(s) from the dataset is explicitly specified as the validation domain <br>\n", "**Method 2**: When no specific validation domain is present, a subset of the training domain(s) is used to create the validation set\n", "\n", "Run either cell below Method 1 or Method 2 as required." ] }, { "cell_type": "code", "execution_count": null, "id": "64dd7f3c", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.dataloaders.get_data_loader import get_loaders" ] }, { "cell_type": "markdown", "id": "51cbb17d", "metadata": {}, "source": [ "### Method 1: Provide validation domain explicitly\n", "Provide index of validation domains as `val_envs`. `test_envs` is an optional parameter." ] }, { "cell_type": "code", "execution_count": null, "id": "a75b74fa", "metadata": {}, "outputs": [], "source": [ "loaders = get_loaders(dataset, train_envs=[0, 1], batch_size=64,\n", " val_envs=[2], test_envs=[3])" ] }, { "cell_type": "markdown", "id": "9da42119", "metadata": {}, "source": [ "### Method 2: Validation set using subset of training data\n", "\n", "`val_envs`, `test_envs` are optional parameters. If `val_envs` is not provided, a subset of training data is used for creating the validation set. The fraction of training data used is determined by `holdout_fraction`." ] }, { "cell_type": "code", "execution_count": null, "id": "5201cd08", "metadata": {}, "outputs": [], "source": [ "loaders = get_loaders(dataset, train_envs=[0, 1], batch_size=64,\n", " holdout_fraction=0.2, test_envs=[3])" ] }, { "cell_type": "markdown", "id": "2b599691", "metadata": {}, "source": [ "The code below handles more than one validation or test domains, if present. Run the cell below irrespective of Method 1 or 2 used above." ] }, { "cell_type": "code", "execution_count": null, "id": "afbd74ef", "metadata": {}, "outputs": [], "source": [ "# handle multiple validation and test domains if present \n", "from pytorch_lightning.trainer.supporters import CombinedLoader\n", "\n", "if len(loaders['val_loaders']) > 1:\n", " val_loaders = loaders['val_loaders']\n", " loaders['val_loaders'] = CombinedLoader(val_loaders)\n", " \n", "if len(loaders['test_loaders']) > 1:\n", " test_loaders = loaders['test_loaders']\n", " loaders['test_loaders'] = CombinedLoader(test_loaders)" ] }, { "cell_type": "markdown", "id": "106cea0d", "metadata": {}, "source": [ "## Initialize model and algorithm " ] }, { "cell_type": "code", "execution_count": null, "id": "5b977c7f", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.models.networks import MNIST_MLP, Classifier" ] }, { "cell_type": "markdown", "id": "cbe00718", "metadata": {}, "source": [ "`model` below is expected to be of type `torch.nn.Sequential` with two `torch.nn.Module` elements (feature extractor and classifier). We provide sample networks (MLP, ResNet) in `dowhy.causal_prediction.models.networks` but the user can flexibly use any model.\n" ] }, { "cell_type": "code", "execution_count": null, "id": "9a663fe7", "metadata": {}, "outputs": [], "source": [ "featurizer = MNIST_MLP(dataset.input_shape)\n", "classifier = Classifier(\n", " featurizer.n_outputs,\n", " dataset.num_classes)\n", "\n", "model = torch.nn.Sequential(featurizer, classifier)" ] }, { "cell_type": "markdown", "id": "adf11498", "metadata": {}, "source": [ "### Initialize algorithm class: ERM" ] }, { "cell_type": "code", "execution_count": null, "id": "51fadf20", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.algorithms.erm import ERM" ] }, { "cell_type": "code", "execution_count": null, "id": "633da7eb", "metadata": {}, "outputs": [], "source": [ "algorithm = ERM(model, lr=1e-3)" ] }, { "cell_type": "markdown", "id": "94828541", "metadata": {}, "source": [ "## Fit predictor and start training\n", "\n", "Note: The optimal accuracy for `MNISTCausalAttribute` (and other MNIST variants introduced) is **75%** as we introduce 25% noise following previous work." ] }, { "cell_type": "code", "execution_count": null, "id": "27f43e54", "metadata": {}, "outputs": [], "source": [ "trainer = pl.Trainer(devices=1, max_epochs=5) \n", "\n", "# val_loaders is optional param\n", "trainer.fit(algorithm, loaders['train_loaders'], loaders['val_loaders'])" ] }, { "cell_type": "markdown", "id": "8635befc", "metadata": {}, "source": [ "## Evaluate on test domain\n", "\n", "Perform an evaluation epoch over the test set using `trainer.test`. `ckpt_path` determines the model to be used for evaluation -- 'best', 'last', or path to a specific checkpoint. If `ckpt_path` is not passed, best model checkpoint from the previous `trainer.fit` is loaded (https://pytorch-lightning.readthedocs.io/en/stable/_modules/pytorch_lightning/trainer/trainer.html#Trainer.test).\n", "\n", "We report accuracy (`test_acc`) and cross-entropy loss (`test_loss`) on the test domains/test set." ] }, { "cell_type": "code", "execution_count": null, "id": "4f382191", "metadata": { "scrolled": true }, "outputs": [], "source": [ "if 'test_loaders' in loaders:\n", " trainer.test(dataloaders=loaders['test_loaders'], ckpt_path='best')" ] }, { "cell_type": "markdown", "id": "bb318eea", "metadata": {}, "source": [ "## Prediction with CACM\n", "\n", "We now train and evaluate the above dataset with CACM. We specify the type of shifts present using list `attr_types` provided as input to CACM. Further instructions regarding using CACM with multi-attribute shifts is provided in the next section." ] }, { "cell_type": "code", "execution_count": null, "id": "08881e8d", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.algorithms.cacm import CACM" ] }, { "cell_type": "code", "execution_count": null, "id": "48c87949", "metadata": {}, "outputs": [], "source": [ "# `attr_types` list contains type of attributes present (supports 'causal' and 'ind' currently)\n", "algorithm = CACM(model, lr=1e-3, gamma=1e-2, attr_types=['causal'], lambda_causal=100.)" ] }, { "cell_type": "code", "execution_count": null, "id": "2a9b1e8a", "metadata": {}, "outputs": [], "source": [ "trainer = pl.Trainer(devices=1, max_epochs=5) \n", "\n", "trainer.fit(algorithm, loaders['train_loaders'], loaders['val_loaders'])" ] }, { "cell_type": "code", "execution_count": null, "id": "cf1640b9", "metadata": {}, "outputs": [], "source": [ "if 'test_loaders' in loaders:\n", " trainer.test(dataloaders=loaders['test_loaders'], ckpt_path='best')" ] }, { "cell_type": "markdown", "id": "72066ad0", "metadata": {}, "source": [ "## Extending to different datasets and algorithms" ] }, { "cell_type": "markdown", "id": "a249be81", "metadata": {}, "source": [ "### MNIST Independent and Causal+Independent datasets\n", "\n", "We show how to perform the above evaluation for `MNISTIndAttribute` and`MNISTCausalIndAttribute` datasets. Additional `attr_types` should be provided to CACM algorithm for handling multiple shifts. We currently support `Causal` and `Independent` distribution shifts in the data." ] }, { "cell_type": "markdown", "id": "9a025cb3", "metadata": {}, "source": [ "#### `MNISTIndAttribute`: Single-attribute *Independent* shift" ] }, { "cell_type": "code", "execution_count": null, "id": "8c5cd482", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.datasets.mnist import MNISTIndAttribute\n", "\n", "data_dir = 'data'\n", "dataset = MNISTIndAttribute(data_dir)" ] }, { "cell_type": "code", "execution_count": null, "id": "4b28707a", "metadata": {}, "outputs": [], "source": [ "algorithm = CACM(model, lr=1e-3, gamma=1e-2, attr_types=['ind'], lambda_ind=10.)" ] }, { "cell_type": "markdown", "id": "aa460184", "metadata": {}, "source": [ "#### `MNISTCausalIndAttribute`: Multi-attribute *Causal*+*Independent* shift" ] }, { "cell_type": "code", "execution_count": null, "id": "17b446be", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.datasets.mnist import MNISTCausalIndAttribute\n", "\n", "data_dir = 'data'\n", "dataset = MNISTCausalIndAttribute(data_dir)" ] }, { "cell_type": "code", "execution_count": null, "id": "9448cc87", "metadata": {}, "outputs": [], "source": [ "# `attr_types` can be provided in any order\n", "algorithm = CACM(model, lr=1e-3, gamma=1e-2, attr_types=['ind', 'causal'], lambda_causal=100., lambda_ind=10.)" ] }, { "cell_type": "markdown", "id": "829479bc", "metadata": {}, "source": [ "### Additional datasets and algorithms\n", "\n", "We provide our demo on MNIST using ERM and *CACM* algorithms. It is possible to extend the evaluation to new datasets and algorithms for evaluation.\n", "\n", "\n", "New datasets can be added to `dowhy.causal_prediction.datasets` and imported here, as we did for MNIST. We provide description of the MNIST dataset (and variants) in `dowhy.causal_prediction.datasets.mnist` that will be helpful in creating new dataset classes. We currently support `Causal` and `Independent` distribution shifts in the data.\n", "\n", "We have implemented ERM in `dowhy.causal_prediction.algorithms` as a baseline. Additional algorithms can be added by overriding the `training_step` function in base class `PredictionAlgorithm`." ] }, { "cell_type": "code", "execution_count": null, "id": "6206a050", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.11" } }, "nbformat": 4, "nbformat_minor": 5 }

{ "cells": [ { "cell_type": "markdown", "id": "7a84e574", "metadata": {}, "source": [ "# Demo for DoWhy Causal Prediction on MNIST\n", "\n", "The goal of this notebook is to demonstrate an example of causal prediction using *Causally Adaptive Constraint Minimization (CACM)* (https://arxiv.org/abs/2206.07837) [1]. " ] }, { "cell_type": "markdown", "id": "900de7ec", "metadata": {}, "source": [ "## Multi-attribute distribution shift datasets \n", "\n", "Domain generalization literature has largely focused on datasets with a single kind of distribution shift over one attribute. Using MNIST as an example, domains are created either by adding new values of a spurious attribute like rotation (e.g., Rotated-MNIST dataset [2]) or domains exhibit different values of correlation between the class label and a spurious attribute like color (e.g., Colored-MNIST [3]). However, real-world data often has multiple distribution shifts over different attributes. For example, satellite imagery data demonstrates distribution shifts over time as well as the region captured.\n", "\n", "\n", "### Multi-attribute MNIST\n", "\n", "We create a *multi-attribute* shift variant of MNIST, where both the color and rotation angle of digits can shift across data distributions. Hence, we create three variants of MNIST -- `MNISTCausalAttribute` (single-attribute shift), `MNISTIndAttribute` (single-attribute shift), `MNISTCausalIndAttribute` (multi-attribute shift). To describe, `Causal`, `Ind`, and `CausalInd` datasets better, consider the causal graph for the data generating process below:\n", "\n" ] }, { "attachments": { "main_fig_mnist.drawio.png": { "image/png": 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" } }, "cell_type": "markdown", "id": "4d22f37d", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "id": "f2667794", "metadata": {}, "source": [ "Distribution shifts are characterized based on the relationship between spurious attributes ***A*** and the classification label *Y*.\n", "1. `Causal`: Attribute has a direct-*Causal* relationship with the class label i.e., *Y* causing attribute (e.g., *Color* here)\n", "2. `Ind`: Attribute is *Independent* of the class label (e.g., *Rotation* here)\n", "3. `CausalInd`: Different attributes having *Causal* and *Independent* relationships with *Y* co-exist in the data" ] }, { "cell_type": "markdown", "id": "56115bbd", "metadata": {}, "source": [ "### Domains in multi-attribute MNIST" ] }, { "cell_type": "markdown", "id": "403765c4", "metadata": {}, "source": [ "We describe the domains for our *multi-attribute* shift dataset `MNISTCausalIndAttribute`.<br> \n", "Each domain E_i has a specific *Rotation* angle r_i and a specific correlation corr_i between *Color C* and label *Y* . Our setup consists of 3 domains:\n", "E₁, E₂ are training domains, E₃ is the test domain. We define corr_i = P(*Y* = 1|*C* = 1) = P(*Y* = 0|*C* = 0) in E_i. In our setup, r₁ = 15◦, r₂ = 60◦, r₃ = 90◦ and corr₁ = 0.9, corr₂ = 0.8, corr₃ = 0.1. All environments have 25% label noise, as in [3]\n", "\n", "\n", "Other dataset-related details can be found in `dowhy.causal_prediction.datasets`." ] }, { "attachments": { "MNIST_visualize.drawio%20%283%29.png": { "image/png": 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" } }, "cell_type": "markdown", "id": "9ee1890a", "metadata": {}, "source": [ "" ] }, { "cell_type": "code", "execution_count": null, "id": "8c441b37", "metadata": { "scrolled": true }, "outputs": [], "source": [ "import torch\n", "import pytorch_lightning as pl" ] }, { "cell_type": "markdown", "id": "1bca038c", "metadata": {}, "source": [ "## Initialize dataset" ] }, { "cell_type": "code", "execution_count": null, "id": "23c5c320", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.datasets.mnist import MNISTCausalAttribute\n", "\n", "# dataset class initialization requires mandatory param `data_dir`\n", "# `download` is passed to torchvision.datasets.MNIST and downloads data if not present \n", "data_dir = 'data'\n", "dataset = MNISTCausalAttribute(data_dir, download=True)" ] }, { "cell_type": "markdown", "id": "eb49277b", "metadata": {}, "source": [ "## Initialize data loaders\n", "\n", "`get_loaders` returns data loaders for training, validation, and test. `loaders` returned is a dictionary of `train_loaders`, `val_loaders`, `test_loaders`. There are two scenarios supported currently to initialize validation domains:\n", "\n", "**Method 1**: When a domain(s) from the dataset is explicitly specified as the validation domain <br>\n", "**Method 2**: When no specific validation domain is present, a subset of the training domain(s) is used to create the validation set\n", "\n", "Run either cell below Method 1 or Method 2 as required." ] }, { "cell_type": "code", "execution_count": null, "id": "64dd7f3c", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.dataloaders.get_data_loader import get_loaders" ] }, { "cell_type": "markdown", "id": "51cbb17d", "metadata": {}, "source": [ "### Method 1: Provide validation domain explicitly\n", "Provide index of validation domains as `val_envs`. `test_envs` is an optional parameter." ] }, { "cell_type": "code", "execution_count": null, "id": "a75b74fa", "metadata": {}, "outputs": [], "source": [ "loaders = get_loaders(dataset, train_envs=[0, 1], batch_size=64,\n", " val_envs=[2], test_envs=[3])" ] }, { "cell_type": "markdown", "id": "9da42119", "metadata": {}, "source": [ "### Method 2: Validation set using subset of training data\n", "\n", "`val_envs`, `test_envs` are optional parameters. If `val_envs` is not provided, a subset of training data is used for creating the validation set. The fraction of training data used is determined by `holdout_fraction`." ] }, { "cell_type": "code", "execution_count": null, "id": "5201cd08", "metadata": {}, "outputs": [], "source": [ "loaders = get_loaders(dataset, train_envs=[0, 1], batch_size=64,\n", " holdout_fraction=0.2, test_envs=[3])" ] }, { "cell_type": "markdown", "id": "2b599691", "metadata": {}, "source": [ "The code below handles more than one validation or test domains, if present. Run the cell below irrespective of Method 1 or 2 used above." ] }, { "cell_type": "code", "execution_count": null, "id": "afbd74ef", "metadata": {}, "outputs": [], "source": [ "# handle multiple validation and test domains if present \n", "from pytorch_lightning.trainer.supporters import CombinedLoader\n", "\n", "if len(loaders['val_loaders']) > 1:\n", " val_loaders = loaders['val_loaders']\n", " loaders['val_loaders'] = CombinedLoader(val_loaders)\n", " \n", "if len(loaders['test_loaders']) > 1:\n", " test_loaders = loaders['test_loaders']\n", " loaders['test_loaders'] = CombinedLoader(test_loaders)" ] }, { "cell_type": "markdown", "id": "106cea0d", "metadata": {}, "source": [ "## Initialize model and algorithm " ] }, { "cell_type": "code", "execution_count": null, "id": "5b977c7f", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.models.networks import MNIST_MLP, Classifier" ] }, { "cell_type": "markdown", "id": "cbe00718", "metadata": {}, "source": [ "`model` below is expected to be of type `torch.nn.Sequential` with two `torch.nn.Module` elements (feature extractor and classifier). We provide sample networks (MLP, ResNet) in `dowhy.causal_prediction.models.networks` but the user can flexibly use any model.\n" ] }, { "cell_type": "code", "execution_count": null, "id": "9a663fe7", "metadata": {}, "outputs": [], "source": [ "featurizer = MNIST_MLP(dataset.input_shape)\n", "classifier = Classifier(\n", " featurizer.n_outputs,\n", " dataset.num_classes)\n", "\n", "model = torch.nn.Sequential(featurizer, classifier)" ] }, { "cell_type": "markdown", "id": "adf11498", "metadata": {}, "source": [ "### Initialize algorithm class: ERM\n", "\n", "We have implemented Empirical Risk Minimization (ERM) in `dowhy.causal_prediction.algorithms` as a baseline." ] }, { "cell_type": "code", "execution_count": null, "id": "51fadf20", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.algorithms.erm import ERM" ] }, { "cell_type": "code", "execution_count": null, "id": "633da7eb", "metadata": {}, "outputs": [], "source": [ "algorithm = ERM(model, lr=1e-3)" ] }, { "cell_type": "markdown", "id": "94828541", "metadata": {}, "source": [ "## Fit predictor and start training\n", "\n", "Note: The optimal accuracy for `MNISTCausalAttribute` (and other MNIST variants introduced) is **75%** as we introduce 25% noise following previous work." ] }, { "cell_type": "code", "execution_count": null, "id": "27f43e54", "metadata": {}, "outputs": [], "source": [ "trainer = pl.Trainer(devices=1, max_epochs=5) \n", "\n", "# val_loaders is optional param\n", "trainer.fit(algorithm, loaders['train_loaders'], loaders['val_loaders'])" ] }, { "cell_type": "markdown", "id": "8635befc", "metadata": {}, "source": [ "## Evaluate on test domain\n", "\n", "Perform an evaluation epoch over the test set using `trainer.test`. `ckpt_path` determines the model to be used for evaluation -- 'best', 'last', or path to a specific checkpoint. If `ckpt_path` is not passed, best model checkpoint from the previous `trainer.fit` is loaded (https://pytorch-lightning.readthedocs.io/en/stable/_modules/pytorch_lightning/trainer/trainer.html#Trainer.test).\n", "\n", "We report accuracy (`test_acc`) and cross-entropy loss (`test_loss`) on the test domains/test set." ] }, { "cell_type": "code", "execution_count": null, "id": "4f382191", "metadata": { "scrolled": true }, "outputs": [], "source": [ "if 'test_loaders' in loaders:\n", " trainer.test(dataloaders=loaders['test_loaders'], ckpt_path='best')" ] }, { "cell_type": "markdown", "id": "bb318eea", "metadata": {}, "source": [ "## Prediction with *CACM*\n", "\n", "We now train and evaluate the above dataset with *CACM*. We specify the type of shifts present using list `attr_types` provided as input to *CACM*. Further instructions regarding using *CACM* with multi-attribute shifts is provided in the next section." ] }, { "cell_type": "code", "execution_count": null, "id": "08881e8d", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.algorithms.cacm import CACM" ] }, { "cell_type": "code", "execution_count": null, "id": "48c87949", "metadata": {}, "outputs": [], "source": [ "# `attr_types` list contains type of attributes present (supports 'causal', 'conf', ind', and 'sel' currently)\n", "algorithm = CACM(model, lr=1e-3, gamma=1e-2, attr_types=['causal'], lambda_causal=100.)" ] }, { "cell_type": "code", "execution_count": null, "id": "2a9b1e8a", "metadata": {}, "outputs": [], "source": [ "trainer = pl.Trainer(devices=1, max_epochs=5) \n", "\n", "trainer.fit(algorithm, loaders['train_loaders'], loaders['val_loaders'])" ] }, { "cell_type": "code", "execution_count": null, "id": "cf1640b9", "metadata": {}, "outputs": [], "source": [ "if 'test_loaders' in loaders:\n", " trainer.test(dataloaders=loaders['test_loaders'], ckpt_path='best')" ] }, { "cell_type": "markdown", "id": "72066ad0", "metadata": {}, "source": [ "## Extending to different datasets and algorithms" ] }, { "cell_type": "markdown", "id": "a249be81", "metadata": {}, "source": [ "### MNIST Independent and Causal+Independent datasets\n", "\n", "We show how to perform the above evaluation for `MNISTIndAttribute` and`MNISTCausalIndAttribute` datasets. Additional `attr_types` should be provided to *CACM* algorithm for handling multiple shifts. We currently support *Causal*, *Confounded*, *Independent*, and *Selected* distribution shifts in the data." ] }, { "cell_type": "markdown", "id": "9a025cb3", "metadata": {}, "source": [ "#### `MNISTIndAttribute`: Single-attribute *Independent* shift" ] }, { "cell_type": "code", "execution_count": null, "id": "8c5cd482", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.datasets.mnist import MNISTIndAttribute\n", "\n", "data_dir = 'data'\n", "dataset = MNISTIndAttribute(data_dir)" ] }, { "cell_type": "code", "execution_count": null, "id": "4b28707a", "metadata": {}, "outputs": [], "source": [ "algorithm = CACM(model, lr=1e-3, gamma=1e-2, attr_types=['ind'], lambda_ind=10., E_eq_A=[0])" ] }, { "cell_type": "markdown", "id": "aa460184", "metadata": {}, "source": [ "#### `MNISTCausalIndAttribute`: Multi-attribute *Causal*+*Independent* shift" ] }, { "cell_type": "code", "execution_count": null, "id": "17b446be", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.datasets.mnist import MNISTCausalIndAttribute\n", "\n", "data_dir = 'data'\n", "dataset = MNISTCausalIndAttribute(data_dir)" ] }, { "cell_type": "code", "execution_count": null, "id": "9448cc87", "metadata": {}, "outputs": [], "source": [ "# `attr_types` should be ordered consistent with the attribute order in dataset class\n", "algorithm = CACM(model, lr=1e-3, gamma=1e-2, attr_types=['causal', 'ind'], lambda_causal=100., lambda_ind=10., E_eq_A=[1])" ] }, { "cell_type": "markdown", "id": "829479bc", "metadata": {}, "source": [ "### Additional datasets and algorithms\n", "\n", "We provide our demo on MNIST using ERM and *CACM* algorithms. It is possible to extend the evaluation to new datasets and algorithms for evaluation.\n", "\n", "\n", "New datasets can be added to `dowhy.causal_prediction.datasets` and imported here, as we did for MNIST. We provide description of the MNIST dataset (and variants) in `dowhy.causal_prediction.datasets.mnist` that will be helpful in creating new dataset classes. We currently support *Causal*, *Confounded*, *Independent*, and *Selected* distribution shifts in the data. \n", "\n", "We have implemented ERM in `dowhy.causal_prediction.algorithms` as a baseline. Additional algorithms can be added by overriding the `training_step` function in base class `PredictionAlgorithm`." ] }, { "cell_type": "markdown", "id": "3b9e1b05", "metadata": {}, "source": [ "## References\n", "\n", "[1] Kaur, J.N., Kıcıman, E., & Sharma, A. (2022). Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization. ArXiv, abs/2206.07837.\n", "\n", "[2] Ghifary, M., Kleijn, W., Zhang, M., & Balduzzi, D. (2015). Domain Generalization for Object Recognition with Multi-task Autoencoders. 2015 IEEE International Conference on Computer Vision (ICCV), 2551-2559.<br>\n", "\n", "[3] Arjovsky, M., Bottou, L., Gulrajani, I., & Lopez-Paz, D. (2019). Invariant Risk Minimization. ArXiv, abs/1907.02893.\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.16" } }, "nbformat": 4, "nbformat_minor": 5 }

jivatneet

c87b511bfd95caec2bc50e4686ed21ca448e9c37

0fb1314c5ddaeb6805109453433ac6248063f3c5

what is ERM? expand the acronym and say that you are using it as a baseline

amit-sharma

py-why/dowhy

Adding general version of CACM

This PR is the first step toward a general causal prediction API. The API supports _Causal, Independent, Confounded,_ and _Selected_ shifts (individual and multi-attribute settings) currently. The regularization has been implemented using `unconditional_reg` and `conditional_reg` functions, which can be used for the general _CACM_ API. Follow up: implement Phase I of _CACM_ for deriving conditional independence constraints given arbitrary graphs.

2023-04-18 10:36:38+00:00

2023-06-15 11:59:01+00:00

docs/source/example_notebooks/prediction/dowhy_causal_prediction_demo.ipynb

{ "cells": [ { "cell_type": "markdown", "id": "7a84e574", "metadata": {}, "source": [ "# Demo for DoWhy Causal Prediction on MNIST\n", "\n", "We're adding prediction functionality to DoWhy. The goal of this notebook is to demonstrate an example of causal prediction using *Causally Adaptive Constraint Minimization (CACM)* [1]. \n", "\n", "[1] Kaur, J.N., Kıcıman, E., & Sharma, A. (2022). Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization. ArXiv, abs/2206.07837." ] }, { "cell_type": "markdown", "id": "900de7ec", "metadata": {}, "source": [ "## Multi-attribute distribution shift datasets \n", "\n", "Domain generalization literature has largely focused on datasets with a single kind of distribution shift over one attribute. Using MNIST as an example, domains are created either by adding new values of a spurious attribute like rotation (e.g., Rotated-MNIST dataset [2]) or domains exhibit different values of correlation between the class label and a spurious attribute like color (e.g., Colored-MNIST [3]). However, real-world data often has multiple distribution shifts over different attributes. For example, satellite imagery data demonstrates distribution shifts over time as well as the region captured.\n", "\n", "[2] Ghifary, M., Kleijn, W., Zhang, M., & Balduzzi, D. (2015). Domain Generalization for Object Recognition with Multi-task Autoencoders. 2015 IEEE International Conference on Computer Vision (ICCV), 2551-2559.<br>\n", "[3] Arjovsky, M., Bottou, L., Gulrajani, I., & Lopez-Paz, D. (2019). Invariant Risk Minimization. ArXiv, abs/1907.02893.\n", "\n", "\n", "### Multi-attribute MNIST\n", "\n", "We create a *multi-attribute* shift variant of MNIST, where both the color and rotation angle of digits can shift across data distributions. Hence, we create three variants of MNIST -- `MNISTCausalAttribute` (single-attribute shift), `MNISTIndAttribute` (single-attribute shift), `MNISTCausalIndAttribute` (multi-attribute shift). To describe, `Causal`, `Ind`, and `CausalInd` datasets better, consider the causal graph for the data generating process below:\n", "\n" ] }, { "attachments": { "main_fig_mnist.drawio.png": { "image/png": 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" } }, "cell_type": "markdown", "id": "4d22f37d", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "id": "f2667794", "metadata": {}, "source": [ "Distribution shifts are characterized based on the relationship between spurious attributes ***A*** and the classification label *Y*.\n", "1. `Causal`: Attribute has a direct-*Causal* relationship with the class label i.e., *Y* causing attribute (e.g., *Color* here)\n", "2. `Ind`: Attribute is *Independent* of the class label (e.g., *Rotation* here)\n", "3. `CausalInd`: Different attributes having *Causal* and *Independent* relationships with *Y* co-exist in the data" ] }, { "cell_type": "markdown", "id": "56115bbd", "metadata": {}, "source": [ "### Domains in multi-attribute MNIST" ] }, { "cell_type": "markdown", "id": "403765c4", "metadata": {}, "source": [ "We describe the domains for our *multi-attribute* shift dataset `MNISTCausalIndAttribute`.<br> \n", "Each domain E_i has a specific *Rotation* angle r_i and a specific correlation corr_i between *Color C* and label *Y* . Our setup consists of 3 domains:\n", "E₁, E₂ are training domains, E₃ is the test domain. We define corr_i = P(*Y* = 1|*C* = 1) = P(*Y* = 0|*C* = 0) in E_i. In our setup, r₁ = 15◦, r₂ = 60◦, r₃ = 90◦ and corr₁ = 0.9, corr₂ = 0.8, corr₃ = 0.1. All environments have 25% label noise, as in [3]\n", "\n", "\n", "Other dataset-related details can be found in `dowhy.causal_prediction.datasets`." ] }, { "attachments": { "MNIST_visualize.drawio%20%283%29.png": { "image/png": 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" } }, "cell_type": "markdown", "id": "9ee1890a", "metadata": {}, "source": [ "" ] }, { "cell_type": "code", "execution_count": null, "id": "8c441b37", "metadata": { "scrolled": true }, "outputs": [], "source": [ "import torch\n", "import pytorch_lightning as pl" ] }, { "cell_type": "markdown", "id": "1bca038c", "metadata": {}, "source": [ "## Initialize dataset" ] }, { "cell_type": "code", "execution_count": null, "id": "23c5c320", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.datasets.mnist import MNISTCausalAttribute\n", "\n", "# dataset class initialization requires mandatory param `data_dir`\n", "# `download` is passed to torchvision.datasets.MNIST and downloads data if not present \n", "data_dir = 'data'\n", "dataset = MNISTCausalAttribute(data_dir, download=True)" ] }, { "cell_type": "markdown", "id": "eb49277b", "metadata": {}, "source": [ "## Initialize data loaders\n", "\n", "`get_loaders` returns data loaders for training, validation, and test. `loaders` returned is a dictionary of `train_loaders`, `val_loaders`, `test_loaders`. There are two scenarios supported currently to initialize validation domains:\n", "\n", "**Method 1**: When a domain(s) from the dataset is explicitly specified as the validation domain <br>\n", "**Method 2**: When no specific validation domain is present, a subset of the training domain(s) is used to create the validation set\n", "\n", "Run either cell below Method 1 or Method 2 as required." ] }, { "cell_type": "code", "execution_count": null, "id": "64dd7f3c", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.dataloaders.get_data_loader import get_loaders" ] }, { "cell_type": "markdown", "id": "51cbb17d", "metadata": {}, "source": [ "### Method 1: Provide validation domain explicitly\n", "Provide index of validation domains as `val_envs`. `test_envs` is an optional parameter." ] }, { "cell_type": "code", "execution_count": null, "id": "a75b74fa", "metadata": {}, "outputs": [], "source": [ "loaders = get_loaders(dataset, train_envs=[0, 1], batch_size=64,\n", " val_envs=[2], test_envs=[3])" ] }, { "cell_type": "markdown", "id": "9da42119", "metadata": {}, "source": [ "### Method 2: Validation set using subset of training data\n", "\n", "`val_envs`, `test_envs` are optional parameters. If `val_envs` is not provided, a subset of training data is used for creating the validation set. The fraction of training data used is determined by `holdout_fraction`." ] }, { "cell_type": "code", "execution_count": null, "id": "5201cd08", "metadata": {}, "outputs": [], "source": [ "loaders = get_loaders(dataset, train_envs=[0, 1], batch_size=64,\n", " holdout_fraction=0.2, test_envs=[3])" ] }, { "cell_type": "markdown", "id": "2b599691", "metadata": {}, "source": [ "The code below handles more than one validation or test domains, if present. Run the cell below irrespective of Method 1 or 2 used above." ] }, { "cell_type": "code", "execution_count": null, "id": "afbd74ef", "metadata": {}, "outputs": [], "source": [ "# handle multiple validation and test domains if present \n", "from pytorch_lightning.trainer.supporters import CombinedLoader\n", "\n", "if len(loaders['val_loaders']) > 1:\n", " val_loaders = loaders['val_loaders']\n", " loaders['val_loaders'] = CombinedLoader(val_loaders)\n", " \n", "if len(loaders['test_loaders']) > 1:\n", " test_loaders = loaders['test_loaders']\n", " loaders['test_loaders'] = CombinedLoader(test_loaders)" ] }, { "cell_type": "markdown", "id": "106cea0d", "metadata": {}, "source": [ "## Initialize model and algorithm " ] }, { "cell_type": "code", "execution_count": null, "id": "5b977c7f", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.models.networks import MNIST_MLP, Classifier" ] }, { "cell_type": "markdown", "id": "cbe00718", "metadata": {}, "source": [ "`model` below is expected to be of type `torch.nn.Sequential` with two `torch.nn.Module` elements (feature extractor and classifier). We provide sample networks (MLP, ResNet) in `dowhy.causal_prediction.models.networks` but the user can flexibly use any model.\n" ] }, { "cell_type": "code", "execution_count": null, "id": "9a663fe7", "metadata": {}, "outputs": [], "source": [ "featurizer = MNIST_MLP(dataset.input_shape)\n", "classifier = Classifier(\n", " featurizer.n_outputs,\n", " dataset.num_classes)\n", "\n", "model = torch.nn.Sequential(featurizer, classifier)" ] }, { "cell_type": "markdown", "id": "adf11498", "metadata": {}, "source": [ "### Initialize algorithm class: ERM" ] }, { "cell_type": "code", "execution_count": null, "id": "51fadf20", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.algorithms.erm import ERM" ] }, { "cell_type": "code", "execution_count": null, "id": "633da7eb", "metadata": {}, "outputs": [], "source": [ "algorithm = ERM(model, lr=1e-3)" ] }, { "cell_type": "markdown", "id": "94828541", "metadata": {}, "source": [ "## Fit predictor and start training\n", "\n", "Note: The optimal accuracy for `MNISTCausalAttribute` (and other MNIST variants introduced) is **75%** as we introduce 25% noise following previous work." ] }, { "cell_type": "code", "execution_count": null, "id": "27f43e54", "metadata": {}, "outputs": [], "source": [ "trainer = pl.Trainer(devices=1, max_epochs=5) \n", "\n", "# val_loaders is optional param\n", "trainer.fit(algorithm, loaders['train_loaders'], loaders['val_loaders'])" ] }, { "cell_type": "markdown", "id": "8635befc", "metadata": {}, "source": [ "## Evaluate on test domain\n", "\n", "Perform an evaluation epoch over the test set using `trainer.test`. `ckpt_path` determines the model to be used for evaluation -- 'best', 'last', or path to a specific checkpoint. If `ckpt_path` is not passed, best model checkpoint from the previous `trainer.fit` is loaded (https://pytorch-lightning.readthedocs.io/en/stable/_modules/pytorch_lightning/trainer/trainer.html#Trainer.test).\n", "\n", "We report accuracy (`test_acc`) and cross-entropy loss (`test_loss`) on the test domains/test set." ] }, { "cell_type": "code", "execution_count": null, "id": "4f382191", "metadata": { "scrolled": true }, "outputs": [], "source": [ "if 'test_loaders' in loaders:\n", " trainer.test(dataloaders=loaders['test_loaders'], ckpt_path='best')" ] }, { "cell_type": "markdown", "id": "bb318eea", "metadata": {}, "source": [ "## Prediction with CACM\n", "\n", "We now train and evaluate the above dataset with CACM. We specify the type of shifts present using list `attr_types` provided as input to CACM. Further instructions regarding using CACM with multi-attribute shifts is provided in the next section." ] }, { "cell_type": "code", "execution_count": null, "id": "08881e8d", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.algorithms.cacm import CACM" ] }, { "cell_type": "code", "execution_count": null, "id": "48c87949", "metadata": {}, "outputs": [], "source": [ "# `attr_types` list contains type of attributes present (supports 'causal' and 'ind' currently)\n", "algorithm = CACM(model, lr=1e-3, gamma=1e-2, attr_types=['causal'], lambda_causal=100.)" ] }, { "cell_type": "code", "execution_count": null, "id": "2a9b1e8a", "metadata": {}, "outputs": [], "source": [ "trainer = pl.Trainer(devices=1, max_epochs=5) \n", "\n", "trainer.fit(algorithm, loaders['train_loaders'], loaders['val_loaders'])" ] }, { "cell_type": "code", "execution_count": null, "id": "cf1640b9", "metadata": {}, "outputs": [], "source": [ "if 'test_loaders' in loaders:\n", " trainer.test(dataloaders=loaders['test_loaders'], ckpt_path='best')" ] }, { "cell_type": "markdown", "id": "72066ad0", "metadata": {}, "source": [ "## Extending to different datasets and algorithms" ] }, { "cell_type": "markdown", "id": "a249be81", "metadata": {}, "source": [ "### MNIST Independent and Causal+Independent datasets\n", "\n", "We show how to perform the above evaluation for `MNISTIndAttribute` and`MNISTCausalIndAttribute` datasets. Additional `attr_types` should be provided to CACM algorithm for handling multiple shifts. We currently support `Causal` and `Independent` distribution shifts in the data." ] }, { "cell_type": "markdown", "id": "9a025cb3", "metadata": {}, "source": [ "#### `MNISTIndAttribute`: Single-attribute *Independent* shift" ] }, { "cell_type": "code", "execution_count": null, "id": "8c5cd482", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.datasets.mnist import MNISTIndAttribute\n", "\n", "data_dir = 'data'\n", "dataset = MNISTIndAttribute(data_dir)" ] }, { "cell_type": "code", "execution_count": null, "id": "4b28707a", "metadata": {}, "outputs": [], "source": [ "algorithm = CACM(model, lr=1e-3, gamma=1e-2, attr_types=['ind'], lambda_ind=10.)" ] }, { "cell_type": "markdown", "id": "aa460184", "metadata": {}, "source": [ "#### `MNISTCausalIndAttribute`: Multi-attribute *Causal*+*Independent* shift" ] }, { "cell_type": "code", "execution_count": null, "id": "17b446be", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.datasets.mnist import MNISTCausalIndAttribute\n", "\n", "data_dir = 'data'\n", "dataset = MNISTCausalIndAttribute(data_dir)" ] }, { "cell_type": "code", "execution_count": null, "id": "9448cc87", "metadata": {}, "outputs": [], "source": [ "# `attr_types` can be provided in any order\n", "algorithm = CACM(model, lr=1e-3, gamma=1e-2, attr_types=['ind', 'causal'], lambda_causal=100., lambda_ind=10.)" ] }, { "cell_type": "markdown", "id": "829479bc", "metadata": {}, "source": [ "### Additional datasets and algorithms\n", "\n", "We provide our demo on MNIST using ERM and *CACM* algorithms. It is possible to extend the evaluation to new datasets and algorithms for evaluation.\n", "\n", "\n", "New datasets can be added to `dowhy.causal_prediction.datasets` and imported here, as we did for MNIST. We provide description of the MNIST dataset (and variants) in `dowhy.causal_prediction.datasets.mnist` that will be helpful in creating new dataset classes. We currently support `Causal` and `Independent` distribution shifts in the data.\n", "\n", "We have implemented ERM in `dowhy.causal_prediction.algorithms` as a baseline. Additional algorithms can be added by overriding the `training_step` function in base class `PredictionAlgorithm`." ] }, { "cell_type": "code", "execution_count": null, "id": "6206a050", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.11" } }, "nbformat": 4, "nbformat_minor": 5 }

{ "cells": [ { "cell_type": "markdown", "id": "7a84e574", "metadata": {}, "source": [ "# Demo for DoWhy Causal Prediction on MNIST\n", "\n", "The goal of this notebook is to demonstrate an example of causal prediction using *Causally Adaptive Constraint Minimization (CACM)* (https://arxiv.org/abs/2206.07837) [1]. " ] }, { "cell_type": "markdown", "id": "900de7ec", "metadata": {}, "source": [ "## Multi-attribute distribution shift datasets \n", "\n", "Domain generalization literature has largely focused on datasets with a single kind of distribution shift over one attribute. Using MNIST as an example, domains are created either by adding new values of a spurious attribute like rotation (e.g., Rotated-MNIST dataset [2]) or domains exhibit different values of correlation between the class label and a spurious attribute like color (e.g., Colored-MNIST [3]). However, real-world data often has multiple distribution shifts over different attributes. For example, satellite imagery data demonstrates distribution shifts over time as well as the region captured.\n", "\n", "\n", "### Multi-attribute MNIST\n", "\n", "We create a *multi-attribute* shift variant of MNIST, where both the color and rotation angle of digits can shift across data distributions. Hence, we create three variants of MNIST -- `MNISTCausalAttribute` (single-attribute shift), `MNISTIndAttribute` (single-attribute shift), `MNISTCausalIndAttribute` (multi-attribute shift). To describe, `Causal`, `Ind`, and `CausalInd` datasets better, consider the causal graph for the data generating process below:\n", "\n" ] }, { "attachments": { "main_fig_mnist.drawio.png": { "image/png": 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Pnjw0ftw4m87AQwbo8BEjGIfbCJg6EpTsmdOnI7DA5d7LemgPmEfz5s/nyDDYzAWpy4GvBDkSaABoBEhTWydA4AIoCglORiesG4o+ADLB1gkQDDly5jTEusEujptt506dbH1ZCFmqadKmZShcQepyIIogB6Qp4lqxIPYANA+gLYDq375929BRBNK6nTp5UlMALLW2LoC2SpYqpft1e/LkCYWFhlLGjBm58pWt0+MnT2jq1Kl8m0f2pyD1OBBFkAMJD0kKSI+2F4IDDZVVgMBnVEI424ePH2nE8OFGnYLZ4x43fjwlTJCAfvn1V7PfVeuFAwEBHB2FyC97IXxLUAQryFBIxl54Jsc8IwQ5MIiRpIBFUBNPXI5JWNMGbHvQypGoYUSoTmndTp44oSqeuDU8l+NdmP9KlS6t23XDugDOolbNmqwc2QvhO1q/YQONGDFC0UIY9sJPU+cZIchRmOHKlSt2YRuPzhzYylFX0oixsFi3c2fP2oVtPPq6wVbu7OKiy3VDuCFgnuvUrm3qt2gzz6EgBiLDbCEZzSiLEiHIEY4H25aR0/AtZTrCwxDBAqeU0ahIkSI0buxYQ6fhW8pzpO+PGDmSgMqnN8IBCyFuj+F4KOyMFP7GjRvrbVlsdjwsyBHj2r59e8aBtlcqWLAgLV68mEobAE5UWiOsW9s2bVjzs1dCAREUPtDTun348IHW+/nZdBhoXPsNyU8VK1USCIlxMUqmv7MgR+ga4l2BZmavNGzYMI5/NVIoItbt1cuXNHToUHtdNpo4cSKlSJlSV+sGbKIkiRPrqtKP2hvkyNGjXHwGJesEKc8BFuTAVEHYkD2aVSQWw7wyYMAAQ2GwAFMFmZyurq7K7xSd9gDzCjI99YTBcvr0afrv0ydDh7Rau9x37t4lJG+hpq8g5Tng8PTp08+IHUe1EnsnAPwjptwI0SuIisC6XQ0Ls/dlo9y5c9PtO3d0sW5Yl3HjxtHgQYPsfl2OHTtGDRo2FJmeKuwEhx07dnyePn06AcXM3qlatWrUr18/Q0ATAGLA19eXgDdu7wS8cmh+eoCUePfuHf11/DhHQdk7rVy1irHKgVkuSFkOOEyYMOHz8+fPFU0Cqly5MmOahIeHR8zm8+fPHGfbtGnTiH9D5Mz58+eVnfE3WkdyEKIMjGBzhm0YGY5qJQEh9X/tunWELFIQ6kzujXT4t+/QgXbu3BnB3fuR1lrpBUVyUJYsWXSxbij2/f7dOy7GYg2tWr2akBtw9tw5/nZga27h4UEpU6akmbNm8d/iooioEQcH2rB+fVyPy/73y5cvM165EW64sk9e5QYdWrdu/dnNzY3rcSpNtWrV4oSjw4cPU7p06bg7oNnh1G7bti1fkbUk1PWEXQ945XonFMgoUbw4NWvWTNWhulWpQjAfHDp4kIWKRDt27KDNW7YwpkjJkiVVHdPatWsp8PRpLoCiNSHB7NHDh1YlZ7Vr35527dpFHs2bU8+ePSlHjhy0PyCAULoPxVGaNW1Kv5qQ0VrD3Z1Danv17KnJIXcqMJC/d8xBkLIccChfvvxnHx8fVRyds2bNol69enFBXQhuaHmomgKboh4IDk9vb28+aLQm8KZLly6xYlaUK1eOBg4YoLqjE2s4YeJEWrxoEbm7uzObkGXZt18/zcw8cHjCWY9ICaUprnUZOWIEVXZzowIWmlag0XsPH05IdhrQv3+U6UCIoyD1nzt2kLOzc5xTzV+gAMdzL1u6lGA2VJseP35MMDUBF0eQshxwyJkz52eU0lIjTAinM7QLYIAjixLhc+hbL3Tt2jXORsNvrSlJkiRc2AMfNEIjo4MQ5cyZk1atWkXZf/hB1aFKgFU4iCdNnMh9w6yCQwWx+FrQzVu3qEWLFnT9+nXFu49rXc6dO0fJkyUjOM7NJeDIt27ThpyLFmV875jI0cmJrl29SkmTJo3yZzgWcROS4ACQWZn1S2HnS8HBMSYm4Z3o9uvg4GBKnz59RFFlCOLEiRObOxV+/tGjR2yOg6IoSFkOOKRKleozBGzq1KmV7elL602aNKHt27cTNEpsXD0RbgfAXcFvrWnGjBk0ZMgQju93cHAgAJpFFuhYL9hJI5s31BoznIuo14j1Q+gqkskmaPixvnz5knFX1Fi3uNYFZoTevXpZJMi9vLxo46ZNjCkP2IiYKLrwRWYrmyrTpqVcuXNzXgEKpoAn5StUYLyTWzdvRjQF0w/DcYSEULKkSdlvhRsE1m/hokWsXIGgOOAwwm1ryeLFVKNGDbO3FxKjwq5epSpVqpj9rnjBPA44xI8f//M///yjWkWZdevWsV0XZgNTysjBYQLHHrQtFE1G9qlSmh+0GGhcQHDTA8GPAHs0CB9kZIEOBxLwx+PHj6/6UFGJqGevXhx3f+zoUdqwYYPqY4jcIdYN+ORqrdu31gV4RVmzZDGbH8///jtiX69cscKkrFCYWQ4ePEjbtm7lYjBwRONA69qlC1WsWJFatGzJChOyTCWCxg/lAH2Advz5Jx8a8+fNo4GDBtGsmTOpTdu2/P70adOoeIkSMZp5TJkgAhom+foSDj9BynLAgYiQE6RsL5Far1evHkE4A1QIm/5bIPQQXCBAzAKREREuiJZAfUClSOpTqfatbRdXZ/AQwlPNyJDo48YVPyY7rrXzs/R9jEdLktYFB+zYMWPMHsqZs2ep9heALRyOMEFGJ2DKAFsHJNnL4aeAvwIkCfKyZcpQ3bp1aeiwYdSnd++IpJw5c+dyBizG17lzZ34Hpo8+ffrwWuJrQ8BBt+7dCbVfYe9H5AtuCgiIsIR8J082NES0JXPW4h1VNXKYA2BDxCaE0xNx0Aj5i4ngnClcuHCUTYBYdzibIoe5yck0vWnksFU+ffqUp6gnjRyRPS1btSJ/f38q5uIi5xJY1JbaGvm31gU+hDQWmCmh2EgY3jEd0Cio7VKsGP2xYAEHJkDwIkQRcLGe3btHEe7A30mSNCmXXYus3UMoHzt+PIqzFMIewQdzZs+mRo0aMXCez4QJUZzZFi0KEWv+MBUBkE6QshxQzUaOzQ8BjlBDEOpLAv8c5pLoBM89KtxHT7se80XTGTVqlCJc0ZuNHPHscHjqzUaOKKNly5dz9Rs9kNo28m+tS6dOnWjokCFmVwR6//49lStfnmDD3rd371fmQ2jJH96/J3wbICluf8Xy5RE26F69e3NuBhylM2fO5N+hISHUf8AANp1Igjyys7R6jRrcLsIdEyVKxAc0Dmq8Z22VH5hsEZIK/44gZTmgeNQKwp/gpENSUOSK9dDIEcoG4V6nTp0os4Q2Dqco7OhqkohaMY3bHi1a8MevtW1cGq2eolbgy4n/3XcWFVVYtHgxh7+yxt2vH5sRzwcFscaMm+yqlSsjci1wO4UtG/HkVatUoX379lHvLyYShC3CXAKzCQ4V2NGxVtDQx4wdS2vXrOFEHf/t21nIAz0zX968zE6YqGBGkWzopu2ImJ969eoV3wAQ4SRIWQ4oFkcO7TkkJIS2bt1KuBaOHj2aJE0atm44LPEMymAhlA6LjYgRVEeH4wbv4ndkwoYDbKlSZKQ48vLlyrGzUU3ALGhrCEdD5IRE0PRgj9WSEEc+ZepUOqqDOHLvYcPYwVisWDGLWALhu27tWhaAiEjCzRWC1cvTkzXmyLRixQqOHkKYH8KH0a8ULx4SGkrDhg6lJ0+f0sFIMMeodI9vDOab3HnyUI3q1aOEIEJrh5IFZ6e1BHMRDjYcOIKU5YBimZ2ILsEGi+w8lDYHNF/8RN6Y0t+kQsIoXCtlf4IFAQEBvKGVdMyKzE5lN5tSresps5NhKD5/FkBRRBzQ8PTZM6pfv75SSy/a/cIBVbBWzOU2tALY1KXwRMSyOjk5KSrEMUaBtWLuSunjeT1hrSCNHkpKxw4d9MEcDUexe88evnkDblmQshzQLfohFh+4zhJBI1fSrIJ+BPqhsptNqdb1hH6ImyRAs7777julpmuYdmHyAkyAQIJUfskEHnkkHhsOjzxrVr6+2jvB1qsXHHlEPnl6enIyjb3T/AULOIY9um3f3vmixPxFhaAvXBUVgpTYXsq3qccKQfAN/f38uSbwCcpz3LQe3r59yxC8ogCzafyy9ilRs/MLB0XNTmu3kjbv67FmJ4p4v371ikNo7ZUA41CocGEOOxakPAdYkKMaO4CPEGlirwSnDD5APVVjj2stsG7I4kNYpr0S/CZLly3T1bohQekeMjzTpLHXZSGEPyKc+AeV0TntleEsyDF5VOdBeq49FmCGWQVpxADhNxoBe2Pc2LGqxpPrhUcwqwD9DxgkeiPgk6Cijxrw0HqbO4p84FvCLVeQOhyIEOQMbXnlCi1cuFCdnnXUC9Df4FkHRrrRCOt27uxZxqCxNwLeiLOLiy7XDSn3gPktbmFikJHXEhgrSESCcihIHQ5ECHLApQL7BNjkQBq0FwK2Ba6ADx8+NGRtQWndgE3u6OhoL8vGONmAbNXruiF6BZnLPby8zMZdMfIiPn7yhCZNmsQKoaUFKYw8f63GHiHIMQCA2wCOc/LkyVqNR/V+kQQEHGtA5RqV+vbpQx8+flStELMe+IQkoIQJEtAvJtSu1GK8AIPLkT07a+WWFGXQYsxy9ImCFd87OVGeaPAacrQt2oidA1EEuZQeD63cHpwUt27dYm0cMchZs2Y17D6R1u3UyZNcTd7WSSo3p7d1w7iQjTx//nzGPUFBhU0bN1LDhg0jSrDZ8tog5BBl3VDTFbd7QepxIIogR7co9YRrqz3YymEbhzkCYPtGJ6wbKgbZg60ctnFUBNLLugFEDMIb6flA7OzatSsjF4LOnz9PWzZvVh3JU4v9jOzrps2aiaxWDZj/lSAH/jVQBxHBUrNmTQ2GpE6XwGpGpEpoaKhNbDysG5DyUO/R0mou6nDeul6AlT18xAhG8NM6DR5gXRDguBFBgOMneg1VBIUhPDRjxoxcV9NWCc7d1WvWMDomNHNUSgLonfQ78n8L27n8u+ArQY4uEMw/cuRIDiHSoiak/NOM2iIqysCjjiK1kTHSle5X6faxbsO9vdkua6vrVrVqVRrv46PZuiFGHMIbJpRs2bKx8EYN2m8RtHK8g7XReylBS/YoolRmz5lD/fr1Y+UP2On4xlDVKlWqVOyDkn48PDwYX12QvByIUZCjC+BFQMszpUCyvENSvjVcfaHNoQqRrRFqLb55/domHdaDBg/mCJDffvtN9WVDnVgIY/w0b96cBXjZsmVNHgcKrOzw97fJTEdEDiFaBYcsQMPgd0INgsiEdUMdAhTCiH5rMZmJ4sFYORCrIMcbpUqV4sIPtlRzDyajZcuW0cmTJ212WwA5EvUXO7RvbzNzRPWcjRs3flX+T+kJwgQH4Q07OBQACHBLHcpTp06lBvXrE8DZbIVQSB11OVHtSyJkSKNCEW4vIETCoT6vkUq+vX79mg8lhCc/fvyY3v3zDz14+JBvGPAhogzehw8f+OYLpTB9unSUKHFi3huYL8zTSZMmVW2ZvynIoYWUL1+eli9frnkVGDk4grJyrVu35jqhKOxsqySt28wZM6h69eqGn+bu3bsJ9SjVXDdJ+4YpBMJbqjpvDTNhXkCVLMAK/FikiDVN6eJdZESTgwPvMRRWj0zu7u5cBxQEcwsUJ3x7MGdaWwtUqclDaKMwCDKG4c9AVBQyp1F7FBm6ENoQ5BDc+IHFAr4B+AVQJB17BRWRYFK6ceMGZc+Rg2EaUAEterUzuefwTUGOzqCRIHwKH5OR0/ex6bDhNm3aZNNOXGmDSOu2ZvVqQ6fv46MC3rga6wanpWT/hgIDAQ6BJCfBuQ4H6N07dyIiW+RsX622IORevHzJAi0m5zrglfPly0ewnyNHA6UcR4wYwf43FO+W42CUY64QwpANcMBu3LCBxwmN29JSfZHHBK1+x44dVKBAATp95gznE4AnSmjqcQpyDAxhVd26deP6m0YU5lioevXqcfFZ2DfthbBuMAcsXbLEkMIcQrxtu3bsp1Fy3VDrEwJ83bp1EdEnUvigEnsFUSwXgoK4kEl0TVaJ/uRuE/bvSb6+1Lt372/ebCHAERaL7w7aOAi8hkAHQaAj3l4LQmTNnDlz2HSMeH+Az0HbVorOnDnDQnzsuHE0auRISp4iBUcyyUUmCXJJmLdr1478/PwMZWaBOQVwovCUKykM5FoQuduBMMe6oUiykcwsuAF26dpV0XUDbyDAkcgj2b/VuvY/ePCAEA8PW3JWAyVxQbNEwWeAgpmC7ujs7MxFwlu1ahVlay9YsIAFOqLGINBNaUuObwM3BBwsxVxc6HxQEFX86SdVD1NAN7x69YpmzpxJy5YvZ/OMHBFmJgtyycyC8CEkYhjBAQrHJjSC1atX24U5JbaNDjML1g1wBEZwgMKxCZgIJdYN0SOS/RvZyzCfIJpCC4J5AuUMnz55QnXq1NFiCGb1uWfvXh4nzCYlS5Y0692YHobpAcIcPjjYzhEppyTBx4Ks9XgODlxLNHPmzEp29822MXfcTgD3C7A+aw8yswQ5RgZHWocOHcjFxYWvJnKcJnJzEzGs0BigOSxatMimHZum8g7rBs0cG3iCj49u1w3Qp5cuX2ZNXE6HtBQ+CDNNixYtWICXKVPGVPYp9lxISAgB+AxZuRUrVtQ8ySmmieJ7wg0i4MABvtXKDWcBRygEOpyKEOhK1OaFbR6mDESilNXBukt8fvjoEdvmizo7c6SZpWS2IJc6wukJWx9SwvWUAQrtE1dWbAZbjBO3dKGl9xBnHrB/Pyd86SkDFBmb+Igru7nJGicOZxM0cNjbpexLS8MHreX9t96HnbZ+vXqMrS6HtivXWBHJgYIz796/V9xBicMbAr1WrVpsbpHDhgw8JbQDUwbs4HokmHtwUC5esoS/ywwZMpg9TIsFOXrCKYfiqhCaGICWQFtYMAgCHC6IWbWljE2zVzWOF7BuMLOUcXXl2F4tBRvs03CKHf/rLzanyLFuiAqRzCfx4sVj+3enTp3kZqPs7SGiBWn/bpUrcxSFlnDS0FzhEFzwxx+srKmVxINwPghz3JzwPcOhaikhIgV4/YgaKa+RU9WcsePWiAMTUS3mKllWCXIMEsZ6xMYC9Qy2HjBezQ0IjQHaDBbM29ubMaC1xuAwZ/G0ejbyuuF21ekLgJha40GI1x8LF/KtSa51g81ZEuAVKlRgDdxoELLQzrZv3063bt7k2GXEIKsZ2QLNEFmYiErp1asXx1HjMFSbYBaFQEd8NgQ6InzMoeDgYL7ZDejf31C46AcPHeKEMcA/gPemktWCXOoIMbjTp09nsC2E9CAeU8lQRYQULl26lLM04XgFzoPctjtTmWjk53jdpk1jrIymTZpwhI+rq6tiU4KJA5FP6/z8uOhCv/79rV43KXwQ7UrmE/gCjEwSPAZC1kJDQtier6RzDhmasFGfOHmSIzlQtEMPGagrV65kgY4izhDopiiJiFRDQg8UOi2tBJbuPyinyJT1nTzZZDhg2QS5NGg4bgCBC080nCQNGjTgpAo5hDqE986dO2nz5s3srENsKqBogbAmyDoOSOuGg/Hjhw+syeLjkUOoQ3gD4hSZfgkSJuSDXo51Q1QLNHBk40kCXK3wQeu4bfrb+IYwTxywUFa8PD3p1u3bHD5nLeEQv3b9OqM3QvuH5o2oFL0FMOCWAmEOExxs5/CBxUaPHj3iJJwM6dNT8eLFrWWRZu8D3sBv/XrG88F3GBfJLsgjd4gq7zgdERMMhwm0CkS7QFvClRGnK+AtcfJj82DTIiwH1ymcSkh3xXu4Zh0/fpzfQyx03bp1dVU1PS4mG+3v0rrh0EQdV2C3IEEmb548rOFkdnSktGnSsC1PWjfYU589f04P7t8n+CtCw8II19vAU6cof4ECfJjLsW6Iw5XMJwBngv0bQs4eCN8CklaA2YKqUPgN4Y5DEqYHHGiSvwNCGQIQP8AKAf7HtWvX+FsDbgzQPy8GB3NYKhJ8zLnGa8Vr2JAh0HHwQKBHD7J4/vw5H+i9e/Wi3LlzazVM2fpFaKpLsWIsF2Fq+RYpKsgjdwyNDwICEJe4xgGLABsPQhsbCYOFUIB9DsLdycmJcuTIwY4KJBWULl1aaN6ybRHTG4q8bhAkOFxh38a/R1833IycHB256AMOXTnXDdEcUvo8kkvwwcpxWzCdE/p6EoBN+JYQkQH4AoBxoewdDjZorkiBB3gVeIW/t2/XjhAHjvBBhDwioxLC3YiEDFwIdMgEmFtwoIOQ4JUvb16bql174OBBlpM4sHUhyOPaMEmSJCGcqAJ0Pi5O2dffcU1GBANijSXziSl2Uvvikn3OdvTo0RxkAe0ceFCAEGkdLYPUFjgDJ37wpUvU/htopqpp5HExFJ55XMmVxDuIawzi7/rgAMwBkvkEtzQIcCOED+qDe/Y1Cpj+gAMFU9HQIUNstnAH8mMaNGzI1oqYSDeCHB55VFIRRVvt60OMPFsc5JIkaqRdAAAgAElEQVQAR5YjBLiR8GHsd+W0mzkOfcSenw4MtAm7eGychF9g4aJFjBOja0EOOxeiUuIy6mu3ZUTPSnEAGBgQ4Bs2bIgwn8A3IkhwIC4OoNgInOqdDZDwFddc4vo7opVguUBIanTSjUaOwSHCRWkA9riYJf6uHgcQVgf7N5JQJPu3HmKX1eOA6MkaDiD7NOTKFb7F28O+QYDB5ClTOFcn+nx1I8iLFi3KseewdQmyXQ7AoS2ZTxCCCgFuL+GDtruq2szsQEAAY+gADM5eCFWmEEZaKVpsuW4EOeqDAk1RT4BB9rI51JhnUFBQhABHIhcEOMLHBAkOWMIBwBHjRle7Vi3DhlFaMm/UDUU4KcIvIydu6UaQI/MToUTAyBBkOxzw9/dnAX7q1KkI8wlyBAQJDljDAWCiHzp4kJES7Y0Arvby1asoGPa6EeTITAOSYtWqVe1tXWxuvlL4IOzfKEQL7Rsp+YIEB+TiwKRJk8i9Rg1F8WfkGqvc7SDk8umzZ9S4ceOIpnUjyJG+jaw0I1RKkXthbKU9VF+R7N+ANhbhg7aysvqaB+BpAcVctUoVfQ1MxdEAb6rCTz9FFPDWjSCHw6tZs2ay4FGryE/RFREdPnyYBThCwaTal/nz5xe8ERxQhAMIikicKJFdm2EBdwsIDCkUUTeCHJgQAMFp2bKlIosvGpWfA6tWrWIB/vDhwwj7N7ByBAkOKMkBgEl9/u8/TQuiKDk/U9oG/sq+/fu5BgRIN4IcKdhARxS2VFOWUbtnED4I2zcEOBDmYD6Ro6qPdjMSPRuJA4ilRvH3QV8EmJHGLvdYjx0/TvXr12fcdd0IchRLBlSq0pW05WamvbQH+ATJ/g08cRE+aC8rr695vnr1ik6eOBFhG9bX6NQdzfIVKzhcG/4o3QhygMUD1S4uuEZ1WSV6Q9kxCPDAwMAI+7ejo6NgjOCAJhxA4ZO3b95wwRq1ydHJiZyLFqVz589T2TJluPvPRASMfEBzg+6Hh6s2rKALFziBEjhVuhHkw4YN47RT/BakLQdQZkzSvhMlSsTad4cOHbQdlOhdcICI/TF379wxqeSbEgzr7unJFcqiC+zTZ85wveIjhw8r0W2MbZ4KDGTEWJS61I0gB0A8BAiKJwvShgMIH5Ts36jiDQFubtFbbUYuerUXDowcOZJzTVCtSgsqUbIkFStWjObPm/dV941//pk2rF+v2rBQ+Qkp+8WKF9ePIJ88eTJXC/L19VWNEaKj/3EAqJPQwKFpQHgjhDAmhDXBL8EBrTkAX03yZMm4kpjahNuAs4sLF7Lo9CXBrU+fPvTrr7/yULp07RqjgFdqnI+fPGGYggkTJuhHkM+YMYPLv0lMUWryot3/5wAqlEOAo2CtFP+NOpyCBAf0ygEERcCPlkyDfbpnzx5q07Ytg/uVcXVl+NwePXuy81ULQnnMKyEhfEPRjWkFV3oUWY4NOF0LRtlinwjfkmpfAjIYGnjkVF9bnLOYk+1wAE73752cNKkENHXaNJo2bRoXIMd3BKUHmjls1FoQTNGTfH1p1qxZ+hHkS5YsoYMHD3LBWEHycwBXUsn+DdhPCHAgTgoSHDASB1BUetzYsZoMuYa7O+XJnZvxwEEQ7Ph/xHIHHDhAPxYpEmspNqUGDFM0im7rRiNHBewtW7awzUeQfBxAsQ5o4GfOnImwfyNcSZDggBE5cOnSJUqbJo3qQ5fs4zD9NmvalPvftn07uZYuTRkyZKCfKlZkNEY1CeB0Gzdt0lfUCjB2ESOK34Ks4wBsZ1L4YJIkSViAf6sCt3W9ibcFB9TjADLAhwwerHpFoB07dlDHTp3owIEDlC9v3igT3r9/P8eW9+vbVz1GEBHAwyDI4TPQjUaOKtGw9YBhgizjAJzFkv0bDhAIcAELbBkvxVv65MDt27fpu3jxOC1dTYJtHKaU6PHjMAcPHDSI1q5ZQzly5FBzSPTi5Us6fvw4V0jSjSAPCAjgsB6cboLM4wA2EwT41q1bI8CrRPigeTwUTxuDA97e3lS2bFkqXqyYKgOGAAemybFjx7i/8uXLc0Ui/ABrH5nP7u7utHjRIlXGE7kTlHy7fv06tW7TRj+CHIwCktfRo0dVZ4hRO1yxYgULcBShleK/YUoRJDhgqxxAJmOC+PEpXrx4tjpFk+cFXqD0G5ytutHI4YyDRxoQlYJi5wCSpiT7N7RuCPBGjRoJlgkO2AUH1q5dSyjz1lFARhDM0YUKF+boM90IcgTXN23alIPsBX3NgXPnzkXYv4F7AgEuClWLnWJvHMDt8/27d6rbyPXI56PHjlGBAgWoYMGC+hHksPfUqFGDT1tB/88B2L2hgUOQQ3jjR4QPih1irxx48eIFeXbvzok59k6QCwMGDuTEJN1o5Hfv3iVXV1fCb3snAOFI5hMsEtLn4ZkWJDggOEAMKfHs6VNKnTq13bIDuOzng4IiirroRpDjyoQ6j/htrwQPtCTAgToI7buKHReYtdd9IOb9bQ4sWLCA3v3zD6HOr73Shg0bqEDBggSUUpBuBDlOGCcnJ8JveyMpfBBZmJL5BDgoggQHBAe+5sDbt2/p1s2blEaDDE+9rEdoWBjHrWfNmlVfghzmBJgR8BsEzTx9+vR64Zsi4wCKGjRwAPBIAlyEDyrCatGojXGgW7du1KZVK8qZK5eNzSzu6SDCD5mkkYvwaKqRI5ge9R+nTp3KFWjix49P//zzD02ZMoUAIN+rVy+aPn163DMz0BM4oCTzCUxJsH83bNjQQDMQQxUc0J4D79+/p7179lDx4sW1H4zKIwDGysNHj8jFxSWiZ00FOWKiEYEBAZ4iRQpChXb8xiIBLwSaqhYA8kqsCyB6JQHesWNH1sBLlCihRFeiTcEBm+cA6mSimlivnj3Jnm6xkJHjxo9nuO+UKVPqQ5BjFKhugZ83b95EDAp1Irt3706//PKL4TekFD4IGFnJfJIpUybDz0tMQHBAaw7cuX2bdu/eTbVr19Z6KKr1f/XaNcqYIQPlzZcvSp+aauQYyefPn9kWDu1bIqTfIv00S5YsqjFIzo5g55ewv1FQGgJchA/KyWHRluAA0b1792i9nx+H4KkNoqUF/1+/fk0+Pj40cdKkr3JJNBfkYMhvv/1G/fv3Z/s4CJCrizQAobF2cZDUJJlPqlevzvZvKTzI2rbF+4IDggNROYAIt9atW1Pu3LlpQP/+Ns+evXv3kkeLFjEeWroQ5FgBLAYEYeLEienkyZNUpEgRwywMMIohwP39/SPMJ3k0qvJtGKaJgQoOWMEBmCx79+7NgFH16tWjDOnTc4EHWyWEXK7z86MBAwawTzE66UaQAwwH5gckwCCaxQgkhQ/CASHZv3EQCRIcEBxQjgN9+/blamIo2F63bl26cOEC3+qHe3vbJCoiolRmzJzJVouMGTPGyFjdCHKMDplacHLq2RyB8EHJ/g2wGghwET6o3EcrWhYckDiAmy+0cER7QYjD/yTR48ePad/evVSpUiWbY9iDBw/o7xcvvjk3VQX5p0//0cWLFykkNIyuXb/BuCqPHj3mQeLqgJBDODoTJUxIyVOkoHRp05KjY2bKnv0Hyp0rFxUsmJ+cHB0VWSiM5VvOVQThS/ZvlJuC/dseY1gVYb5o1GwOvHz5kgMEwsPDCUIMDnbs4VSpUtGdO3c4gAClwPA94ZaYInlySpI0Ke9xRIXB9IcEPKPQqFGjaM6cOYSama1atYpx2JMmTaL69erZVMZnUFAQbd6yhebOnfvNpVJckN+7F06HDh+ho8eO05mz5yh3rpyUK2dOypo1Czk5Zqb06dJRylQpKWmSJGz7wTXiw4cP9Pr1G3r2/DkD5Ny9F043b90mpKViU5YuVZLKlytL5cqWkWUfDh06lCZPnszx69HtT7jCQYDj+iaZT2K73sgyGNGI4EAsHICAhuAGZj8KEOO/gUmP0N2ChQrRp3//ZSGGPSwVXkAAAfb102fP6OOHD3QLpdK++46uXb1K+QsU4OiHH374gbJnz65LvkOB6tOnDyFkF0L8+++/j3WcOMyQ7eheowYVKlRIl/MxZ1CHDh0icnDgCkSRY8ZjakMxQf7nzl203f9PCgu7Sq6upai4izMV/bEIC2JrCBvx7LnzdCrwDN0Lv081a1SjunVqU86cltXLQ8FnLy8v3uw4QED4LdW+BAMhwNu2bWvNsMW7ggMWcQBa9ZEjR1iL3rJ5M2dA4xtwcXa2qL3IL0Gr37NnDwHXJ/D0aapcuTL9+OOPutHUAVULJQsC3NPT06T5hoSE8HMInCgWKfPRpJd19BBCKx8/ecLl5EwxNcsuyFeuXktr1/qxxu1WqSKVK+uqGHug7QccPEx79u3jjd3Coxn9WKSwyf3hxMPmxS0A9rbI2Zc4BSHATWGiyR2KBwUHTOQABPjyZcuoXv36NHv2bOrUsSMLc6UImi+KFIweM4ZGDB9OqVKn1iwKBEIYtnBo2BDiGJc5hGCJq2FhVLNmTTY1GY0QLz55yhSCU9fUucsmyLds3U4LFy2h/PnyUp3aNSlvntyq8g/a/5Zt/uTi4kxdOnWgLFliv4JhYKjGDawCKREJHwl+YPuGAEc4pCDBAbU5gAQ5RGA4Fy3K5rxq1auz2VEtQgQW0t/nzJ1LiMqC3wqFhtUiQNRCiANraciQIRZ3CwchBOGggQPJUSG/msWD+8aLMJudOHmSeZAuXTqTu7BakIeGhtGMWXP49GzWpDEVKmje6WnySE18cK3fBlq1Zh15eQIdrUWsb8FRCS1EIjh+SpcuzaGPRnICmcgW8ZgBOAA4YwggXKfz5M6tKfontMLAwEAKu3qVweuU1mzhsIXwunHjBmvh+BatJShrEIxou17dutY2p/j7e/bupTp16nA1MFgKzCGrBPnqNeto9tzfqUO71lS7prs5/Sr67L3wcFq6fCUhSmbwoP6U7Qtmr9Qp7ICXL19mbSMy4QOCVx8x7QLQStElEo1H48CmTZvY8Q+hU7ZsWd3wB47UxYsX008VKxKKnTg4OMg+NnxvcGgioxu4S3ISvnOA8wHyo1LFirpM5YccQsTRsePHqXHjxux8NpcsEuSwKY8e60P379+nriaYMcwdlFzPb966jdb6baQR3kPIrfL/x5dCw4BzE7Y4hD1G/kGcODYUrreCBAeU5sDNmzc5ImPWrFnUulUrRQSltXP49OkTw6bC1AKTh1wFHfANQgs/fPgwa+E4KJQiwGE3atiQFbjI8K9K9Wdqu3fu3qWLFy7Qp//+I4Q1W0pmC/IHDx7SsOGjKPsP2ahLp/aW9qvae+eDLtCM2XOpZQsPaunRTLV+RUeCA3FxAOYLCDDAUZQuVSquxzX/O3JA3v7zD0NNm3r1Bz4IypJFV4x27tzJQhzCGzyIKe1c7glfuXKF1q1bx+GJCM/UEpRPiv2HL8LX19dqM5pZgvz69Rs0YPBQqlK5EjVuWF9uPivWHg6fKb/MoArly1G3LpafeooNUDRsdxwIDg7mJI9+ffsaCnMf2nPSZMk4GAAx7HERTAUwG0H7lpymgwYNotWrV7MAx9/VJDiT4QdDqTjMAfHzahZxfvjwIR+EY8eOpcFDhjAPpZh/a/hgsiC/cfMm9e4zgBrUr0u13Ktb06cm70L7meA7lUqUKE6e3bpoMgbRqeAAOAAMbRRDSJgggUX2UK25CJMqTEHTpk//ppkFQh8hgLCrnzp1iiPEoIUjpA5CPG3atJpNBeai+fPmUe48eTjbHPZzJaNbcBuACfdUYCD7QEqVKiWrA9kkQf7k6VPq7tWL3KtX1ZVT09xdAMfNmPETqYpbZWrbJuY0X3PbFM8LDpjDAWRj7tu3j9H6nGVI6jGnbzmfxbeE1PEKFSqwUIqJypUrR8eOHWOBXatWLQJiIQQ4fFB6ITgaYfv38PDgakOoBQrnqBzoq3BgIhsXSrDkwATIF4Iq5CaTBHk3z55UIH8+Di80OsGZ6T1qLJtYarrXMPp0xPgNxAHEZ/fo0YP69+unqPanFksQvuvs4sL27eiwFbCLQ2ADMxwmFQgvZFE3atRIreGZ3Q+0ZkDhTpw4kXr26MGIg926dqVDhw+TW+XKXBBegggAzAG0egR+YF0Rfg3HdcZMmejo0aNUIH9+LpCMjHD8vWjRomaPx5wX4hTkE32n0pvXr6mHZ1dz2tX1s1euhNDQEaNp8cJ5lN8EO5+uJyMGZxgO+Pn5sV3WMXNmw4w5roEGBATQ3Xv3aODAgVEeBSjX1atXo/wb8jNQlg1RY+XLl4+raU3/jsxaAFYhomjz5s1Uxc2N/li4kCESgLyIurt//PEH/z98AE2bNGGB37lzZzbVIA5ezUSqbwryrdv9ae269TTNV97YTk1X6Evnu/bspf0HDtHSRQv0MBwxBhvnQGhoKG3dsiVW5D4jTx+a6pmzZ6lNmzY8DThxgZ0NYSgR0Bjh8IRJBsIPGZyC5ONArIIckR7NW7amMSOHU768eeTrUUctISzRycmJenp119GoxFBsjQPA0Q48dcomhTjWCiaGrdu2cT0BCGxo3QD6ypUrF4NwAbcfNxHpR2ROy7/DYxXk3iNGU4b06ah505/l71UnLb589Yp69O5HUydPMgtsSyfDF8MwAAdgQ4UmeuLECZs24yEdHqYHxIsDPRRROYLU40CMgvzI0WM0c9ZcmvnLFPVGolFPMLGcPHWa5sz6VaMRiG5tmQPAs78UHKyrSA2l+I00+NRp0pgUX67UGOy13RgFeYdOXTlWHMUb7IEGe49kgC2EJQoSHJCLA8jeCwsNZfOdtTj8co1JyXYw3ylTptDc336LUoZNyT5F2//jwFeCfN/+AFq5ag1NGDfabnj014mTDIG7eOF8u5mzmKjyHIBt/MRff9lVURKE4QHBECBbgtTjwFeCvGv3HlS9ahVFC0KoNz3Texo0dDh16dzRbm4hpnNGPGkJB4DrDVS/WjVrKpIAYsmY1HgHMLzrN2ygESNGKFoIQ425GKmPKII86MJFGjPOh+bMmG6kOcgy1r37Auhc0AWaOnmiLO2JRuybA0DWRFX3evXq2R0jEH8NND+k5wtShwNRBLnvlGmULGlS+rlRA3V611EviC5o1a4jrVq+lDJnzqSjkYmhGJEDQLSrUb06Fze2N7p0+TLjqvz8s+1GvOltTaMI8mrudWjqJB/KlCmj3sapynjmzlvAHncBd6sKu222E1SuR4p61SpVbHaOcU1s46ZNjMMiB2ZJXH2Jv0dydsLhN3/BQrtyckbfAKfPnKWt23fQvN9mi70hOGAxBwDChDjq8uXKWdyG0V9EQlDxEiUoR44cRp+KIcYfoZH/OnM2xXNwoKY/6xfURg2ONm3RmrZv2UgpU6ZUozvRhw1yAGBSn/79l7JGKzFog1ONdUr37t2jnbt2kbe3tz1NW7O5RgjyNu06Uoe2rSl//rjB4jUbrQodj5vgS02bNKaKP1VQoTfRha1xALZhHx8fGjhggK1Nzez5AMK2br16qoJHmT1IG3mBBTnAbapUr0Ub1q60kWlZPo21fhsofvwE5GVDaI+Wc0O8aS4HYB8/dfIk5c2b19xXbe75xUuWUJkyZahSpf+vl2tzk9TJhFiQI+xw6rRfaPLE8aoOq32HDnThwgXCNUyi++HhXE3ay9OTHjx8yGWwgOW7Yf16VcYWePoM7d67n2b+Ok2V/kQntsUBYG4D9rlhw4a2NTELZnPu3DkqVLiwXZuYLGCbRa+wIN+6zZ9OnDhJPb26WdSItS+1bNWKK2ls2bw5onRUnbp1+SQH7m+a1Kmt7cLk9x8+fEQjRo+jrZvVOThMHph40BAcQELMvbt3IwoQSIOGmaFxDOF4+fLmpfIVKtD4ceNMnh/aAqFkmDkEBSlrlixRBOu2bduoS9eudO7sWcbelpNQYCH8/n3q2bOnnM2KtmLgAAvy3+f/Qe/fvdOsAtDChQtp+IgRNOPXX6lp06Y0Z84cQpFUVFPRgho19aBDAXtjrewNBw7soIIEB6JzAHsDYYeoSxmdzp8/T+41a1Kzpk255BlIEvBly5ThkEVTCAWLQ0JD6eKFC6Y8/r9+jh/nQsdjx4zh4gcSIXln+YoVNGXyZJPbMvVBFBr+99MnKl68uKmviOcs5AAL8nHjJ1KO7D9Q1SragEahjqFrmTJUo0YN6tK5M1fmgCauFXXu1oMWzJv7VWLQokWLOPX40aNHtHLlSj50BAkOROYAzAkpkidnk2B0WvDHHzRy5EguujCgf3/+88aNG8mrRw/G7d61c2eczMTNtVTp0tS4USOaPdv0MNlp06bR1GnT2ERpriYf56BieQAFJ1avWSOUHksZaMZ7LMgHDBpCFcqVpdKlSprxqryP9uvXj5BE0KlTJxqucchSv4FDadRI74iCGrgx4AMEfgacWXXq1CFcSQUJDkTnAG6RfXr3jhH9DyYM7Bto4TmyZ+dXYW5BSTSYVlCYF4TyaYePHGFs72zZslEZV1fW4PHsixcvKDg4mAseo1am5DtCOTKAdK1dt46g3YOg4V+6dImqVK1KhQsVoovBwVSyZEn6uXFjrubj6OTEz+FQweEi0f79+wnmTlCJEiXIxcWFNXmM/eChQ3T50iUOK8T/B54+TR8/fOACxtHDLYHDfvXaNapix4lRan0hLMi9evahenVqUdEfi6jV71f9SFdMaOLm2AuVGPCwEaOpd08vCgw8FUWAoy9UN8GHFlvlcCXGI9o0DgdOnTrFdujohFtcDXd3gg3d0dGR63aiPFqnjh3J09MzohizpDlPnTqVWrZowc1A4E6cMIHatWtHUHig5UJ7hxYPQo3IatWr0/x58/gwmDBhAs2aPZtNL+nSpeNnfvrpJyrt6hphQmnYqBEVK1aM9uzZQxnSp48w60j9w9TSqlUrQom6ipUq8SHx8sULQtX5Ro0bU86cOWnSxImcvYnxRb5lSHMH7MUkX1+aOXOmcRbQoCNlQd7NsyfjqxQuVFCTaUBr6P8l7hZa75HDh2O1T6sxQDg7C+bPS4MHD+Lq2JFJqp6txjhEH8bjgLOzM/25Y8dXA9+xYwd17NSJqlWrRsuWLuW/w0QCUwmex3sgOPg/fPxIx44ejWgDghJa7Yrly1lwJk2WLIoZprunJ1cgOnP6NL/j5eVFf504QacDA/n/JSVp2tSp1OLL4XDw4EGqWLEiC2Fo+7Db40CoV78+F0aWxgjtv2q1amxSdC5alFBVq3379hFjlrT3zZs2ccHh6DRu/Hj2eQlSlgOaa+QQlIWLFKGQK1cYkH7cuHFcmLVO7drKzvwbrQuNXDPWG77jK1euUOpUqb6ax5ixY+n333+PornC+QgnpGS3hm8IWntkZyi0dtTAlDReCF6YH8eNHUu7d+/m6BgIWmji0Mhfv37NqfGupUtT127d2MwC2zg0bWCj++/YwQWQYU4ZMmQILV22jP8d406ePDmbb/BsCw8PngO+xZGjRtHqVav4kBk1ahTNX7CArl29yrfTPn360Lbt2yn44kU6dOgQVa9ePcrcN8AH4OVl+HXV+wQ0tZHfv3+fqteoQReCgphPyIpDKBa0j1kaXseEjVzv21a/44NpcPCgQV9BPCDDMTAwkFatXEmVK/8vqECyUe/ZvZvt3idPnaJ9+/ax2Q5C+dz582xvT5Q4MWvgkma9aOFCun3nDo0ePZqQdwHNHo53CGcI1S5dutBkX18aNHgwm1fw/3+/eMGwuhDyEM7FXFz4gECB5LRp03L2JcYFQS6Zcc6ePUutWremypUqRThWcaNImDAh+fv7R8wBUSlw7qLI8qiRIyMWB/6kTZs3szlIkLIc0CxqBd763+fNo9mzZkXJgpNsdJFtgMqy4OvWY4takZyeImpF7RUxTn+ojpMwQQKCCQ4EbXjFihWUM0cOFqb4e7++fTm2GnsdwvnsuXPsRIdQhlY7ecoUNrnAyYhQRtiqJRozZgx/NzB/+K1bx/8MO7ff+vV8q23StCkhUxttwwQywceH/z5x0iS6fPky27Xbtm0bMTY8BxPLmtWr+d/gwPx1xgy+VSRJmpT7h21eIhw+v82dSw0a/A/qWtL28d8Yf2R6+fIlHbezCkla7VTV48ixcRYtXszaNwihUJLnHZmesJejIneKFCk4/hRXOrVJxJGrzXHb6W/w4MHsWCxerJjtTMrCmVy+coXu3L5Nbb4cHBY2I14zgQO6yOw0YZyqPSIyO1VjtU12hCiP5MmSUbx48WxyfuZM6vqNG/T06dOIsEpz3hXPmscBTbFWzBuqOk8LrBV1+Gyrvaxfv54dhwgrtHfaunUrObu4iFBdFTaCQD+MxmSBfqjCrrPhLmAy/Oft2wgbuQ1PNc6pwd6fN1++iNDKOF8QD1jMAYFHHo11Ao/c4r0kXiQiOPiAZQLcIHsnOGWHDRvGUS6ClOVAhCCfMXM2OYgKQdTUozVt3yoqBCm77Wy7dUQ1PXv6lFKriNqpN44CSgAx8B5f4tH1Nj5bG0+EIBc1O4lEzU5b297azAdhqm/fvKEmTZpoMwAd9Orn58dY5G5ubjoYje0PIUKQY6rV3OvQ1Ek+lClTRtufeQwznDtvAeXLl5daejS3y/mLScvDgbdv39LVsDDKmNE+vyNwMSQkhHLlzk1ZYsCdkYfLopXIHIgiyH2nTKNkSZMy7oq9EQB+WrXtSKtWLP0KvtbeeCHmaz0HYCdv364dg0vZGyGDFUiLgAAQpA4HoghylHwbM86H5syYrk7vOupl774AOhd0gaZOnqijUYmhGJUDgHDduXMnlS5VyqhTsHjcUIqePH0agc5ocUPiRZM5EEWQ462u3XtQ9apVqFxZV5MbsYUHBw0dTl06d6Ty5cwrn2ULcxdzkJ8Df//9NwPA9ezRgxInTix/BzptEQlAgAMAnkuSJEl0OkrbG9ZXgnzf/gBauWoNTRg32vZmG8uM4Ojdss2fFi+cbzdzFhNVngM3b9yIERFQ+Z616yEsLNnxugIAAAajSURBVIxy584dAQim3Ujsq+evBDmm36FTN6rlXs1utNPB3iOpTasWVMVNm1J39rXl7Ge2d+/eJWR6oiwb0AVtnQChO97Hh6ZMmUIZMmSw9enqan4xCvIjR4/RzFlzaeYvU3Q1WCUGs2vPXjp56jTNmSUSOJTgr723CXTD/fv2MYa4rROKTDRr3lxktWqw0DEKcozDe8RoypA+HTVv+rMGw1KnS1Q76dG7H02dPIl+LFJYnU5FL3bFgU+fPrF5BbCwKPFmqwTo3HV+flwAQ4LwtdW56nFesQryBw8eUvOWrWnMyOERRYj1OAFrxjRj9lxycnKinl7drWlGvCs48E0OnD9/nub9/juXS0P2tK3R58+facaMGdSvf3+7jp3Xcl1jFeQY1Nbt/rR23Xqa5jtByzEq0jdMKvsPHKKlixYo0r5oVHAgMgceP35Me/fsiagOZEvcQaTK02fPGIddkDYc+KYgx5Am+k6lN69fUw/PrtqMUIFer4SE0NDho2nxwnmUP18+BXoQTQoOfM0BHx8fatSwIaVJk8Zm2IPiEZs3b2aNXJB2HIhTkGNo3Tx7UoH8+ahZk8bajVSmnp88eULeo8ZSty6dqKZ7DZlaFc0IDsTNAaTuo3gxamAWs4EKQgcPHqTPRFz7ExW9BGnHAZMEObK0unv1IvfqVal2TXftRmtlz6gePmb8RA4zbNvm/+sgWtmseF1wwGQOAIMEdO/uXS5WbFS6efMmvX7zhm3+KJYuSFsOmCTIMcQbN29S7z4DqEH9ulTLvbq2o7agd8S4TvCdSiVKFCfPbl0saEG8IjggDwf27dtHoSEhbC83ItQtFKJJvr7Uq1cvKlKkiDxMEa1YxQGTBTl6uX79Bg0YPJSqVK5EjRvWt6pjNV9GBM6UX2ZQhfLl2KQiSHBAaw7cv3+fBgwYQH379DEUQuCZM2fofFAQdevWzaZs/VrvB2v7N0uQozMIxWHDR1H27NmoS8f21vav+Pvngy4QwgxbtvCglh7NFO9PdCA4YCoHkM5+8eJFevzoEdWrV8/U1zR7bueuXVS/fn26evUqlSxZUrNxiI6/5oDZghxNfPrvPxoz1oegVXTt1IGyZPlel7zdvHUbrfXbSCO8h5Bb5Uq6HKMYlH1z4PLly1xp/s7t21SxYkWKFy+e7hiCpKbw8HAKOHCAWrRoYagbhO6YqdCALBLk0lhWr1lHs+f+Th3atdaVE/TevXBaumIlffr0Hw0e1J+yZc2qEPtEs4ID8nBg5syZ1KB+fdbQ9RTRcuvWLQoJDSVkbnbpInxL8qy2/K1YJcgxnJDQMJo5aw59/PiRwxMLFSwg/yjNaHGt3wZatWYdeXl2YyAsQYIDRuEAIlrWrl1LdWrXJod48cgxc2bNhv7o8WN69fIlzZs/n6ZPn07p06fXbCyi47g5YLUgl7rYsnU7LVy0hPLny0t1atekvHlyx927jE9s9/+ToWhdXJypi47NPTJOWTRlgxxAujsSbO7euUM5cuakHNmzU7p06VSb6YMHDyhZsmQ0bvx4Gj58OEPS6tHcoxpDDNKRbIJcmu/K1Wto7dr1lDVrFnKrVFHRAhUwoQQcPEx79u0jF2dnauHRTIBfGWTjiWF+mwOwS6OIc65cuejK5ctUvnx5ypQpk2JsCw4Opg8fPxLQGtFXuXLlKHny5Ir1JxqWlwOyC3JpeH/u3EXQksPCrpKraykq7uJMRX8sYnW1lFu3b9PZc+fpVOAZuhd+n2rWqEZ169SmnDlzyMsZ0ZrggA448O+//9Ly5cvJw8OD47Y7duhAz58/J2dnZ6tHdy88nK5fu0b4nS1bNm4P0TPx48e3um3RgLocUEyQS9OA1nzo8BE6euw4nTl7jnLnykm5cuZkjd3JMTOlT5eOUqZKSUmTJOENhHp/Hz58oNev39Cz58/p0aNHdPdeON28dZtCw8L4IChdqiQXvShXtoy63BK9CQ5oyIFLly5RwoQJCY7RAf370y+//kqe3bsTUuXd3Nzo2bNnjOYJ8wygZPEt4Qf/jv9HuGP6DBno+LFjlC9/fgoKCqJ27drxwSASezRcWBm6VlyQRx4jrosXLwazg/Ta9et09+49evToMf394gUBhwLaB+xxiRImpOQpUlC6dGnZ4ZM9+w+UJ3cuKlAgPznZMKazDOspmrATDkDZQYRLqlSpaMuWLVTT3Z1++/13LmABQY/fS5cu5XBBf39/atWqFReD7tChA124cIHNJwI33HY2i6qC3HbYJmYiOCA4IDigHw78HxXcrdS3tzNjAAAAAElFTkSuQmCC" } }, "cell_type": "markdown", "id": "4d22f37d", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "id": "f2667794", "metadata": {}, "source": [ "Distribution shifts are characterized based on the relationship between spurious attributes ***A*** and the classification label *Y*.\n", "1. `Causal`: Attribute has a direct-*Causal* relationship with the class label i.e., *Y* causing attribute (e.g., *Color* here)\n", "2. `Ind`: Attribute is *Independent* of the class label (e.g., *Rotation* here)\n", "3. `CausalInd`: Different attributes having *Causal* and *Independent* relationships with *Y* co-exist in the data" ] }, { "cell_type": "markdown", "id": "56115bbd", "metadata": {}, "source": [ "### Domains in multi-attribute MNIST" ] }, { "cell_type": "markdown", "id": "403765c4", "metadata": {}, "source": [ "We describe the domains for our *multi-attribute* shift dataset `MNISTCausalIndAttribute`.<br> \n", "Each domain E_i has a specific *Rotation* angle r_i and a specific correlation corr_i between *Color C* and label *Y* . Our setup consists of 3 domains:\n", "E₁, E₂ are training domains, E₃ is the test domain. We define corr_i = P(*Y* = 1|*C* = 1) = P(*Y* = 0|*C* = 0) in E_i. In our setup, r₁ = 15◦, r₂ = 60◦, r₃ = 90◦ and corr₁ = 0.9, corr₂ = 0.8, corr₃ = 0.1. All environments have 25% label noise, as in [3]\n", "\n", "\n", "Other dataset-related details can be found in `dowhy.causal_prediction.datasets`." ] }, { "attachments": { "MNIST_visualize.drawio%20%283%29.png": { "image/png": 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" } }, "cell_type": "markdown", "id": "9ee1890a", "metadata": {}, "source": [ "" ] }, { "cell_type": "code", "execution_count": null, "id": "8c441b37", "metadata": { "scrolled": true }, "outputs": [], "source": [ "import torch\n", "import pytorch_lightning as pl" ] }, { "cell_type": "markdown", "id": "1bca038c", "metadata": {}, "source": [ "## Initialize dataset" ] }, { "cell_type": "code", "execution_count": null, "id": "23c5c320", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.datasets.mnist import MNISTCausalAttribute\n", "\n", "# dataset class initialization requires mandatory param `data_dir`\n", "# `download` is passed to torchvision.datasets.MNIST and downloads data if not present \n", "data_dir = 'data'\n", "dataset = MNISTCausalAttribute(data_dir, download=True)" ] }, { "cell_type": "markdown", "id": "eb49277b", "metadata": {}, "source": [ "## Initialize data loaders\n", "\n", "`get_loaders` returns data loaders for training, validation, and test. `loaders` returned is a dictionary of `train_loaders`, `val_loaders`, `test_loaders`. There are two scenarios supported currently to initialize validation domains:\n", "\n", "**Method 1**: When a domain(s) from the dataset is explicitly specified as the validation domain <br>\n", "**Method 2**: When no specific validation domain is present, a subset of the training domain(s) is used to create the validation set\n", "\n", "Run either cell below Method 1 or Method 2 as required." ] }, { "cell_type": "code", "execution_count": null, "id": "64dd7f3c", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.dataloaders.get_data_loader import get_loaders" ] }, { "cell_type": "markdown", "id": "51cbb17d", "metadata": {}, "source": [ "### Method 1: Provide validation domain explicitly\n", "Provide index of validation domains as `val_envs`. `test_envs` is an optional parameter." ] }, { "cell_type": "code", "execution_count": null, "id": "a75b74fa", "metadata": {}, "outputs": [], "source": [ "loaders = get_loaders(dataset, train_envs=[0, 1], batch_size=64,\n", " val_envs=[2], test_envs=[3])" ] }, { "cell_type": "markdown", "id": "9da42119", "metadata": {}, "source": [ "### Method 2: Validation set using subset of training data\n", "\n", "`val_envs`, `test_envs` are optional parameters. If `val_envs` is not provided, a subset of training data is used for creating the validation set. The fraction of training data used is determined by `holdout_fraction`." ] }, { "cell_type": "code", "execution_count": null, "id": "5201cd08", "metadata": {}, "outputs": [], "source": [ "loaders = get_loaders(dataset, train_envs=[0, 1], batch_size=64,\n", " holdout_fraction=0.2, test_envs=[3])" ] }, { "cell_type": "markdown", "id": "2b599691", "metadata": {}, "source": [ "The code below handles more than one validation or test domains, if present. Run the cell below irrespective of Method 1 or 2 used above." ] }, { "cell_type": "code", "execution_count": null, "id": "afbd74ef", "metadata": {}, "outputs": [], "source": [ "# handle multiple validation and test domains if present \n", "from pytorch_lightning.trainer.supporters import CombinedLoader\n", "\n", "if len(loaders['val_loaders']) > 1:\n", " val_loaders = loaders['val_loaders']\n", " loaders['val_loaders'] = CombinedLoader(val_loaders)\n", " \n", "if len(loaders['test_loaders']) > 1:\n", " test_loaders = loaders['test_loaders']\n", " loaders['test_loaders'] = CombinedLoader(test_loaders)" ] }, { "cell_type": "markdown", "id": "106cea0d", "metadata": {}, "source": [ "## Initialize model and algorithm " ] }, { "cell_type": "code", "execution_count": null, "id": "5b977c7f", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.models.networks import MNIST_MLP, Classifier" ] }, { "cell_type": "markdown", "id": "cbe00718", "metadata": {}, "source": [ "`model` below is expected to be of type `torch.nn.Sequential` with two `torch.nn.Module` elements (feature extractor and classifier). We provide sample networks (MLP, ResNet) in `dowhy.causal_prediction.models.networks` but the user can flexibly use any model.\n" ] }, { "cell_type": "code", "execution_count": null, "id": "9a663fe7", "metadata": {}, "outputs": [], "source": [ "featurizer = MNIST_MLP(dataset.input_shape)\n", "classifier = Classifier(\n", " featurizer.n_outputs,\n", " dataset.num_classes)\n", "\n", "model = torch.nn.Sequential(featurizer, classifier)" ] }, { "cell_type": "markdown", "id": "adf11498", "metadata": {}, "source": [ "### Initialize algorithm class: ERM\n", "\n", "We have implemented Empirical Risk Minimization (ERM) in `dowhy.causal_prediction.algorithms` as a baseline." ] }, { "cell_type": "code", "execution_count": null, "id": "51fadf20", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.algorithms.erm import ERM" ] }, { "cell_type": "code", "execution_count": null, "id": "633da7eb", "metadata": {}, "outputs": [], "source": [ "algorithm = ERM(model, lr=1e-3)" ] }, { "cell_type": "markdown", "id": "94828541", "metadata": {}, "source": [ "## Fit predictor and start training\n", "\n", "Note: The optimal accuracy for `MNISTCausalAttribute` (and other MNIST variants introduced) is **75%** as we introduce 25% noise following previous work." ] }, { "cell_type": "code", "execution_count": null, "id": "27f43e54", "metadata": {}, "outputs": [], "source": [ "trainer = pl.Trainer(devices=1, max_epochs=5) \n", "\n", "# val_loaders is optional param\n", "trainer.fit(algorithm, loaders['train_loaders'], loaders['val_loaders'])" ] }, { "cell_type": "markdown", "id": "8635befc", "metadata": {}, "source": [ "## Evaluate on test domain\n", "\n", "Perform an evaluation epoch over the test set using `trainer.test`. `ckpt_path` determines the model to be used for evaluation -- 'best', 'last', or path to a specific checkpoint. If `ckpt_path` is not passed, best model checkpoint from the previous `trainer.fit` is loaded (https://pytorch-lightning.readthedocs.io/en/stable/_modules/pytorch_lightning/trainer/trainer.html#Trainer.test).\n", "\n", "We report accuracy (`test_acc`) and cross-entropy loss (`test_loss`) on the test domains/test set." ] }, { "cell_type": "code", "execution_count": null, "id": "4f382191", "metadata": { "scrolled": true }, "outputs": [], "source": [ "if 'test_loaders' in loaders:\n", " trainer.test(dataloaders=loaders['test_loaders'], ckpt_path='best')" ] }, { "cell_type": "markdown", "id": "bb318eea", "metadata": {}, "source": [ "## Prediction with *CACM*\n", "\n", "We now train and evaluate the above dataset with *CACM*. We specify the type of shifts present using list `attr_types` provided as input to *CACM*. Further instructions regarding using *CACM* with multi-attribute shifts is provided in the next section." ] }, { "cell_type": "code", "execution_count": null, "id": "08881e8d", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.algorithms.cacm import CACM" ] }, { "cell_type": "code", "execution_count": null, "id": "48c87949", "metadata": {}, "outputs": [], "source": [ "# `attr_types` list contains type of attributes present (supports 'causal', 'conf', ind', and 'sel' currently)\n", "algorithm = CACM(model, lr=1e-3, gamma=1e-2, attr_types=['causal'], lambda_causal=100.)" ] }, { "cell_type": "code", "execution_count": null, "id": "2a9b1e8a", "metadata": {}, "outputs": [], "source": [ "trainer = pl.Trainer(devices=1, max_epochs=5) \n", "\n", "trainer.fit(algorithm, loaders['train_loaders'], loaders['val_loaders'])" ] }, { "cell_type": "code", "execution_count": null, "id": "cf1640b9", "metadata": {}, "outputs": [], "source": [ "if 'test_loaders' in loaders:\n", " trainer.test(dataloaders=loaders['test_loaders'], ckpt_path='best')" ] }, { "cell_type": "markdown", "id": "72066ad0", "metadata": {}, "source": [ "## Extending to different datasets and algorithms" ] }, { "cell_type": "markdown", "id": "a249be81", "metadata": {}, "source": [ "### MNIST Independent and Causal+Independent datasets\n", "\n", "We show how to perform the above evaluation for `MNISTIndAttribute` and`MNISTCausalIndAttribute` datasets. Additional `attr_types` should be provided to *CACM* algorithm for handling multiple shifts. We currently support *Causal*, *Confounded*, *Independent*, and *Selected* distribution shifts in the data." ] }, { "cell_type": "markdown", "id": "9a025cb3", "metadata": {}, "source": [ "#### `MNISTIndAttribute`: Single-attribute *Independent* shift" ] }, { "cell_type": "code", "execution_count": null, "id": "8c5cd482", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.datasets.mnist import MNISTIndAttribute\n", "\n", "data_dir = 'data'\n", "dataset = MNISTIndAttribute(data_dir)" ] }, { "cell_type": "code", "execution_count": null, "id": "4b28707a", "metadata": {}, "outputs": [], "source": [ "algorithm = CACM(model, lr=1e-3, gamma=1e-2, attr_types=['ind'], lambda_ind=10., E_eq_A=[0])" ] }, { "cell_type": "markdown", "id": "aa460184", "metadata": {}, "source": [ "#### `MNISTCausalIndAttribute`: Multi-attribute *Causal*+*Independent* shift" ] }, { "cell_type": "code", "execution_count": null, "id": "17b446be", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.datasets.mnist import MNISTCausalIndAttribute\n", "\n", "data_dir = 'data'\n", "dataset = MNISTCausalIndAttribute(data_dir)" ] }, { "cell_type": "code", "execution_count": null, "id": "9448cc87", "metadata": {}, "outputs": [], "source": [ "# `attr_types` should be ordered consistent with the attribute order in dataset class\n", "algorithm = CACM(model, lr=1e-3, gamma=1e-2, attr_types=['causal', 'ind'], lambda_causal=100., lambda_ind=10., E_eq_A=[1])" ] }, { "cell_type": "markdown", "id": "829479bc", "metadata": {}, "source": [ "### Additional datasets and algorithms\n", "\n", "We provide our demo on MNIST using ERM and *CACM* algorithms. It is possible to extend the evaluation to new datasets and algorithms for evaluation.\n", "\n", "\n", "New datasets can be added to `dowhy.causal_prediction.datasets` and imported here, as we did for MNIST. We provide description of the MNIST dataset (and variants) in `dowhy.causal_prediction.datasets.mnist` that will be helpful in creating new dataset classes. We currently support *Causal*, *Confounded*, *Independent*, and *Selected* distribution shifts in the data. \n", "\n", "We have implemented ERM in `dowhy.causal_prediction.algorithms` as a baseline. Additional algorithms can be added by overriding the `training_step` function in base class `PredictionAlgorithm`." ] }, { "cell_type": "markdown", "id": "3b9e1b05", "metadata": {}, "source": [ "## References\n", "\n", "[1] Kaur, J.N., Kıcıman, E., & Sharma, A. (2022). Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization. ArXiv, abs/2206.07837.\n", "\n", "[2] Ghifary, M., Kleijn, W., Zhang, M., & Balduzzi, D. (2015). Domain Generalization for Object Recognition with Multi-task Autoencoders. 2015 IEEE International Conference on Computer Vision (ICCV), 2551-2559.<br>\n", "\n", "[3] Arjovsky, M., Bottou, L., Gulrajani, I., & Lopez-Paz, D. (2019). Invariant Risk Minimization. ArXiv, abs/1907.02893.\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.16" } }, "nbformat": 4, "nbformat_minor": 5 }

jivatneet

c87b511bfd95caec2bc50e4686ed21ca448e9c37

0fb1314c5ddaeb6805109453433ac6248063f3c5

is there a way to avoid this verbose output? can you output a progress bar using tqdm?

amit-sharma

py-why/dowhy

Adding general version of CACM

This PR is the first step toward a general causal prediction API. The API supports _Causal, Independent, Confounded,_ and _Selected_ shifts (individual and multi-attribute settings) currently. The regularization has been implemented using `unconditional_reg` and `conditional_reg` functions, which can be used for the general _CACM_ API. Follow up: implement Phase I of _CACM_ for deriving conditional independence constraints given arbitrary graphs.

2023-04-18 10:36:38+00:00

2023-06-15 11:59:01+00:00

docs/source/example_notebooks/prediction/dowhy_causal_prediction_demo.ipynb

{ "cells": [ { "cell_type": "markdown", "id": "7a84e574", "metadata": {}, "source": [ "# Demo for DoWhy Causal Prediction on MNIST\n", "\n", "We're adding prediction functionality to DoWhy. The goal of this notebook is to demonstrate an example of causal prediction using *Causally Adaptive Constraint Minimization (CACM)* [1]. \n", "\n", "[1] Kaur, J.N., Kıcıman, E., & Sharma, A. (2022). Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization. ArXiv, abs/2206.07837." ] }, { "cell_type": "markdown", "id": "900de7ec", "metadata": {}, "source": [ "## Multi-attribute distribution shift datasets \n", "\n", "Domain generalization literature has largely focused on datasets with a single kind of distribution shift over one attribute. Using MNIST as an example, domains are created either by adding new values of a spurious attribute like rotation (e.g., Rotated-MNIST dataset [2]) or domains exhibit different values of correlation between the class label and a spurious attribute like color (e.g., Colored-MNIST [3]). However, real-world data often has multiple distribution shifts over different attributes. For example, satellite imagery data demonstrates distribution shifts over time as well as the region captured.\n", "\n", "[2] Ghifary, M., Kleijn, W., Zhang, M., & Balduzzi, D. (2015). Domain Generalization for Object Recognition with Multi-task Autoencoders. 2015 IEEE International Conference on Computer Vision (ICCV), 2551-2559.<br>\n", "[3] Arjovsky, M., Bottou, L., Gulrajani, I., & Lopez-Paz, D. (2019). Invariant Risk Minimization. ArXiv, abs/1907.02893.\n", "\n", "\n", "### Multi-attribute MNIST\n", "\n", "We create a *multi-attribute* shift variant of MNIST, where both the color and rotation angle of digits can shift across data distributions. Hence, we create three variants of MNIST -- `MNISTCausalAttribute` (single-attribute shift), `MNISTIndAttribute` (single-attribute shift), `MNISTCausalIndAttribute` (multi-attribute shift). To describe, `Causal`, `Ind`, and `CausalInd` datasets better, consider the causal graph for the data generating process below:\n", "\n" ] }, { "attachments": { "main_fig_mnist.drawio.png": { "image/png": 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" } }, "cell_type": "markdown", "id": "4d22f37d", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "id": "f2667794", "metadata": {}, "source": [ "Distribution shifts are characterized based on the relationship between spurious attributes ***A*** and the classification label *Y*.\n", "1. `Causal`: Attribute has a direct-*Causal* relationship with the class label i.e., *Y* causing attribute (e.g., *Color* here)\n", "2. `Ind`: Attribute is *Independent* of the class label (e.g., *Rotation* here)\n", "3. `CausalInd`: Different attributes having *Causal* and *Independent* relationships with *Y* co-exist in the data" ] }, { "cell_type": "markdown", "id": "56115bbd", "metadata": {}, "source": [ "### Domains in multi-attribute MNIST" ] }, { "cell_type": "markdown", "id": "403765c4", "metadata": {}, "source": [ "We describe the domains for our *multi-attribute* shift dataset `MNISTCausalIndAttribute`.<br> \n", "Each domain E_i has a specific *Rotation* angle r_i and a specific correlation corr_i between *Color C* and label *Y* . Our setup consists of 3 domains:\n", "E₁, E₂ are training domains, E₃ is the test domain. We define corr_i = P(*Y* = 1|*C* = 1) = P(*Y* = 0|*C* = 0) in E_i. In our setup, r₁ = 15◦, r₂ = 60◦, r₃ = 90◦ and corr₁ = 0.9, corr₂ = 0.8, corr₃ = 0.1. All environments have 25% label noise, as in [3]\n", "\n", "\n", "Other dataset-related details can be found in `dowhy.causal_prediction.datasets`." ] }, { "attachments": { "MNIST_visualize.drawio%20%283%29.png": { "image/png": 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" } }, "cell_type": "markdown", "id": "9ee1890a", "metadata": {}, "source": [ "" ] }, { "cell_type": "code", "execution_count": null, "id": "8c441b37", "metadata": { "scrolled": true }, "outputs": [], "source": [ "import torch\n", "import pytorch_lightning as pl" ] }, { "cell_type": "markdown", "id": "1bca038c", "metadata": {}, "source": [ "## Initialize dataset" ] }, { "cell_type": "code", "execution_count": null, "id": "23c5c320", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.datasets.mnist import MNISTCausalAttribute\n", "\n", "# dataset class initialization requires mandatory param `data_dir`\n", "# `download` is passed to torchvision.datasets.MNIST and downloads data if not present \n", "data_dir = 'data'\n", "dataset = MNISTCausalAttribute(data_dir, download=True)" ] }, { "cell_type": "markdown", "id": "eb49277b", "metadata": {}, "source": [ "## Initialize data loaders\n", "\n", "`get_loaders` returns data loaders for training, validation, and test. `loaders` returned is a dictionary of `train_loaders`, `val_loaders`, `test_loaders`. There are two scenarios supported currently to initialize validation domains:\n", "\n", "**Method 1**: When a domain(s) from the dataset is explicitly specified as the validation domain <br>\n", "**Method 2**: When no specific validation domain is present, a subset of the training domain(s) is used to create the validation set\n", "\n", "Run either cell below Method 1 or Method 2 as required." ] }, { "cell_type": "code", "execution_count": null, "id": "64dd7f3c", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.dataloaders.get_data_loader import get_loaders" ] }, { "cell_type": "markdown", "id": "51cbb17d", "metadata": {}, "source": [ "### Method 1: Provide validation domain explicitly\n", "Provide index of validation domains as `val_envs`. `test_envs` is an optional parameter." ] }, { "cell_type": "code", "execution_count": null, "id": "a75b74fa", "metadata": {}, "outputs": [], "source": [ "loaders = get_loaders(dataset, train_envs=[0, 1], batch_size=64,\n", " val_envs=[2], test_envs=[3])" ] }, { "cell_type": "markdown", "id": "9da42119", "metadata": {}, "source": [ "### Method 2: Validation set using subset of training data\n", "\n", "`val_envs`, `test_envs` are optional parameters. If `val_envs` is not provided, a subset of training data is used for creating the validation set. The fraction of training data used is determined by `holdout_fraction`." ] }, { "cell_type": "code", "execution_count": null, "id": "5201cd08", "metadata": {}, "outputs": [], "source": [ "loaders = get_loaders(dataset, train_envs=[0, 1], batch_size=64,\n", " holdout_fraction=0.2, test_envs=[3])" ] }, { "cell_type": "markdown", "id": "2b599691", "metadata": {}, "source": [ "The code below handles more than one validation or test domains, if present. Run the cell below irrespective of Method 1 or 2 used above." ] }, { "cell_type": "code", "execution_count": null, "id": "afbd74ef", "metadata": {}, "outputs": [], "source": [ "# handle multiple validation and test domains if present \n", "from pytorch_lightning.trainer.supporters import CombinedLoader\n", "\n", "if len(loaders['val_loaders']) > 1:\n", " val_loaders = loaders['val_loaders']\n", " loaders['val_loaders'] = CombinedLoader(val_loaders)\n", " \n", "if len(loaders['test_loaders']) > 1:\n", " test_loaders = loaders['test_loaders']\n", " loaders['test_loaders'] = CombinedLoader(test_loaders)" ] }, { "cell_type": "markdown", "id": "106cea0d", "metadata": {}, "source": [ "## Initialize model and algorithm " ] }, { "cell_type": "code", "execution_count": null, "id": "5b977c7f", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.models.networks import MNIST_MLP, Classifier" ] }, { "cell_type": "markdown", "id": "cbe00718", "metadata": {}, "source": [ "`model` below is expected to be of type `torch.nn.Sequential` with two `torch.nn.Module` elements (feature extractor and classifier). We provide sample networks (MLP, ResNet) in `dowhy.causal_prediction.models.networks` but the user can flexibly use any model.\n" ] }, { "cell_type": "code", "execution_count": null, "id": "9a663fe7", "metadata": {}, "outputs": [], "source": [ "featurizer = MNIST_MLP(dataset.input_shape)\n", "classifier = Classifier(\n", " featurizer.n_outputs,\n", " dataset.num_classes)\n", "\n", "model = torch.nn.Sequential(featurizer, classifier)" ] }, { "cell_type": "markdown", "id": "adf11498", "metadata": {}, "source": [ "### Initialize algorithm class: ERM" ] }, { "cell_type": "code", "execution_count": null, "id": "51fadf20", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.algorithms.erm import ERM" ] }, { "cell_type": "code", "execution_count": null, "id": "633da7eb", "metadata": {}, "outputs": [], "source": [ "algorithm = ERM(model, lr=1e-3)" ] }, { "cell_type": "markdown", "id": "94828541", "metadata": {}, "source": [ "## Fit predictor and start training\n", "\n", "Note: The optimal accuracy for `MNISTCausalAttribute` (and other MNIST variants introduced) is **75%** as we introduce 25% noise following previous work." ] }, { "cell_type": "code", "execution_count": null, "id": "27f43e54", "metadata": {}, "outputs": [], "source": [ "trainer = pl.Trainer(devices=1, max_epochs=5) \n", "\n", "# val_loaders is optional param\n", "trainer.fit(algorithm, loaders['train_loaders'], loaders['val_loaders'])" ] }, { "cell_type": "markdown", "id": "8635befc", "metadata": {}, "source": [ "## Evaluate on test domain\n", "\n", "Perform an evaluation epoch over the test set using `trainer.test`. `ckpt_path` determines the model to be used for evaluation -- 'best', 'last', or path to a specific checkpoint. If `ckpt_path` is not passed, best model checkpoint from the previous `trainer.fit` is loaded (https://pytorch-lightning.readthedocs.io/en/stable/_modules/pytorch_lightning/trainer/trainer.html#Trainer.test).\n", "\n", "We report accuracy (`test_acc`) and cross-entropy loss (`test_loss`) on the test domains/test set." ] }, { "cell_type": "code", "execution_count": null, "id": "4f382191", "metadata": { "scrolled": true }, "outputs": [], "source": [ "if 'test_loaders' in loaders:\n", " trainer.test(dataloaders=loaders['test_loaders'], ckpt_path='best')" ] }, { "cell_type": "markdown", "id": "bb318eea", "metadata": {}, "source": [ "## Prediction with CACM\n", "\n", "We now train and evaluate the above dataset with CACM. We specify the type of shifts present using list `attr_types` provided as input to CACM. Further instructions regarding using CACM with multi-attribute shifts is provided in the next section." ] }, { "cell_type": "code", "execution_count": null, "id": "08881e8d", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.algorithms.cacm import CACM" ] }, { "cell_type": "code", "execution_count": null, "id": "48c87949", "metadata": {}, "outputs": [], "source": [ "# `attr_types` list contains type of attributes present (supports 'causal' and 'ind' currently)\n", "algorithm = CACM(model, lr=1e-3, gamma=1e-2, attr_types=['causal'], lambda_causal=100.)" ] }, { "cell_type": "code", "execution_count": null, "id": "2a9b1e8a", "metadata": {}, "outputs": [], "source": [ "trainer = pl.Trainer(devices=1, max_epochs=5) \n", "\n", "trainer.fit(algorithm, loaders['train_loaders'], loaders['val_loaders'])" ] }, { "cell_type": "code", "execution_count": null, "id": "cf1640b9", "metadata": {}, "outputs": [], "source": [ "if 'test_loaders' in loaders:\n", " trainer.test(dataloaders=loaders['test_loaders'], ckpt_path='best')" ] }, { "cell_type": "markdown", "id": "72066ad0", "metadata": {}, "source": [ "## Extending to different datasets and algorithms" ] }, { "cell_type": "markdown", "id": "a249be81", "metadata": {}, "source": [ "### MNIST Independent and Causal+Independent datasets\n", "\n", "We show how to perform the above evaluation for `MNISTIndAttribute` and`MNISTCausalIndAttribute` datasets. Additional `attr_types` should be provided to CACM algorithm for handling multiple shifts. We currently support `Causal` and `Independent` distribution shifts in the data." ] }, { "cell_type": "markdown", "id": "9a025cb3", "metadata": {}, "source": [ "#### `MNISTIndAttribute`: Single-attribute *Independent* shift" ] }, { "cell_type": "code", "execution_count": null, "id": "8c5cd482", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.datasets.mnist import MNISTIndAttribute\n", "\n", "data_dir = 'data'\n", "dataset = MNISTIndAttribute(data_dir)" ] }, { "cell_type": "code", "execution_count": null, "id": "4b28707a", "metadata": {}, "outputs": [], "source": [ "algorithm = CACM(model, lr=1e-3, gamma=1e-2, attr_types=['ind'], lambda_ind=10.)" ] }, { "cell_type": "markdown", "id": "aa460184", "metadata": {}, "source": [ "#### `MNISTCausalIndAttribute`: Multi-attribute *Causal*+*Independent* shift" ] }, { "cell_type": "code", "execution_count": null, "id": "17b446be", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.datasets.mnist import MNISTCausalIndAttribute\n", "\n", "data_dir = 'data'\n", "dataset = MNISTCausalIndAttribute(data_dir)" ] }, { "cell_type": "code", "execution_count": null, "id": "9448cc87", "metadata": {}, "outputs": [], "source": [ "# `attr_types` can be provided in any order\n", "algorithm = CACM(model, lr=1e-3, gamma=1e-2, attr_types=['ind', 'causal'], lambda_causal=100., lambda_ind=10.)" ] }, { "cell_type": "markdown", "id": "829479bc", "metadata": {}, "source": [ "### Additional datasets and algorithms\n", "\n", "We provide our demo on MNIST using ERM and *CACM* algorithms. It is possible to extend the evaluation to new datasets and algorithms for evaluation.\n", "\n", "\n", "New datasets can be added to `dowhy.causal_prediction.datasets` and imported here, as we did for MNIST. We provide description of the MNIST dataset (and variants) in `dowhy.causal_prediction.datasets.mnist` that will be helpful in creating new dataset classes. We currently support `Causal` and `Independent` distribution shifts in the data.\n", "\n", "We have implemented ERM in `dowhy.causal_prediction.algorithms` as a baseline. Additional algorithms can be added by overriding the `training_step` function in base class `PredictionAlgorithm`." ] }, { "cell_type": "code", "execution_count": null, "id": "6206a050", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.11" } }, "nbformat": 4, "nbformat_minor": 5 }

{ "cells": [ { "cell_type": "markdown", "id": "7a84e574", "metadata": {}, "source": [ "# Demo for DoWhy Causal Prediction on MNIST\n", "\n", "The goal of this notebook is to demonstrate an example of causal prediction using *Causally Adaptive Constraint Minimization (CACM)* (https://arxiv.org/abs/2206.07837) [1]. " ] }, { "cell_type": "markdown", "id": "900de7ec", "metadata": {}, "source": [ "## Multi-attribute distribution shift datasets \n", "\n", "Domain generalization literature has largely focused on datasets with a single kind of distribution shift over one attribute. Using MNIST as an example, domains are created either by adding new values of a spurious attribute like rotation (e.g., Rotated-MNIST dataset [2]) or domains exhibit different values of correlation between the class label and a spurious attribute like color (e.g., Colored-MNIST [3]). However, real-world data often has multiple distribution shifts over different attributes. For example, satellite imagery data demonstrates distribution shifts over time as well as the region captured.\n", "\n", "\n", "### Multi-attribute MNIST\n", "\n", "We create a *multi-attribute* shift variant of MNIST, where both the color and rotation angle of digits can shift across data distributions. Hence, we create three variants of MNIST -- `MNISTCausalAttribute` (single-attribute shift), `MNISTIndAttribute` (single-attribute shift), `MNISTCausalIndAttribute` (multi-attribute shift). To describe, `Causal`, `Ind`, and `CausalInd` datasets better, consider the causal graph for the data generating process below:\n", "\n" ] }, { "attachments": { "main_fig_mnist.drawio.png": { "image/png": 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" } }, "cell_type": "markdown", "id": "4d22f37d", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "id": "f2667794", "metadata": {}, "source": [ "Distribution shifts are characterized based on the relationship between spurious attributes ***A*** and the classification label *Y*.\n", "1. `Causal`: Attribute has a direct-*Causal* relationship with the class label i.e., *Y* causing attribute (e.g., *Color* here)\n", "2. `Ind`: Attribute is *Independent* of the class label (e.g., *Rotation* here)\n", "3. `CausalInd`: Different attributes having *Causal* and *Independent* relationships with *Y* co-exist in the data" ] }, { "cell_type": "markdown", "id": "56115bbd", "metadata": {}, "source": [ "### Domains in multi-attribute MNIST" ] }, { "cell_type": "markdown", "id": "403765c4", "metadata": {}, "source": [ "We describe the domains for our *multi-attribute* shift dataset `MNISTCausalIndAttribute`.<br> \n", "Each domain E_i has a specific *Rotation* angle r_i and a specific correlation corr_i between *Color C* and label *Y* . Our setup consists of 3 domains:\n", "E₁, E₂ are training domains, E₃ is the test domain. We define corr_i = P(*Y* = 1|*C* = 1) = P(*Y* = 0|*C* = 0) in E_i. In our setup, r₁ = 15◦, r₂ = 60◦, r₃ = 90◦ and corr₁ = 0.9, corr₂ = 0.8, corr₃ = 0.1. All environments have 25% label noise, as in [3]\n", "\n", "\n", "Other dataset-related details can be found in `dowhy.causal_prediction.datasets`." ] }, { "attachments": { "MNIST_visualize.drawio%20%283%29.png": { "image/png": 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4TWU5eHM3MDhVVHd6Qk9nOUJ3cDdoZGhYcmM5V1VkOVk5R2VmSmVGdVNaNmxoYkFZMU5wJTJCRVM5YUNndmN3bXoxNk1oMmpBdWZEcHZsNXZaOHgwWEVzYkM2NVRSM3BSWTFBUFJqOEtmNEowbnhjWk9vWHc5M2F3Nk4lMkZzT1FtNXE3STk1dlpYaVNvNm1zNGRYQjIzZTZiUHpRZXclMkZzZjZOTlZ1NURGJTJGenRSeDZpbGNicHppbFlWbjRvd012RU01MDVleVJSdGxyUUo1MmdHS1dOOUNIRHJ0eFN4NDdTQ3BQJTJCNG1heXYlMkJPU3R3bVdweTlqJTJGaFZFTTJjYnFIWEVVQ2EwcSUyRmxIeWVqcVY2WmZHOVlNRFdhN2NicE5lVU9yeE0zUzJUSVUzTVd2dms4N0RUdVNCJTJCTzFsUnFZZVd1VDRyWHRTOTJtdUVIR2g5WWQlMkIyYW5wJTJCRzhFd1JZMDFQdVpreUtoTmJaJTJCUFA1WG1zJTJGY1BacW5Vb3ZHcWp1WlNBMiUyQkZPV3lreU1yNHdVekRvalNDeVJ0NmxiWnVtVnZPeXlFbVlaWWZOTzlJVld0YzJlZktGVE1PTHJLbmlQbnFMYWY4RmlycDhJRHpYNFNLNTFwTGVqSk1SUzZwbSUyQkhJZjdrNXdrUlR4WUhkUEdiTFdjcTI4WU5Kd0hxWlp4STc5NVpFVnRHbzclMkZqUjFZOUVSTm8yM1hSWWZGTlRiczdpbk40WGhmJTJCd3Fld2JXQ0pwZTJHeEcyTUttOGRBSHFxVndPUEtzdHNZUG5sZThjdmUxajRvM3JNVE1pNjd6eFViSDJyRlZUNDNDcjRZb3ZYQjlXczFDNGNmQ3ZhciUyQm9EcUladFRib041VUNMenM2bnY5Y2JXa1klMkI5NFp0VGRiNHROYjVOdDFENzglMkJuVmZYOXpoUHQyU21DOVNEV0hnY05QaVJnemdYUyUyQm42dU9helBTYU9SUlVxRjhsVXNkTXJlJTJCVjhQWWZZdklWOHBVNTd1Rk9nZUNBeGNaSyUyQmZiMXB6QzclMkJSMCUyQjlBYzhjWUNZbTFibVdSaENwOUFHdkg4WjBLZGRaVFN4RVJ6UGl5T2NYaUxJRkZhd1VZQW9aMjRYN0lEaDNPTm5ldUJiUjVkVFdINHJwaUlMJTJGQ2UlMkY2bkhUZ0NlMmlqalloYU5WdWFKJTJCOW1YRXlCYUVhendJbU9Mc2tUblZjcE44UzRTTjZPVmFDU3Z1V1RSUlQ3M0dqYlpFU1dmbzR3WGE5S2xuMXFGUUhnOHNhRDNMMkw2bmJCUmtHJTJCN2xrYkxyWWZ2aGlkWjdESFMxbXVuMkFVNFZVVVM5VVhBUlRhSkJHdTNPRmlMU0RNN1QxMkhTJTJGQ0Y5UlNHeFhGOEU5RFlHbEJycFg4N05sTllBWkFGWHhROFJBZXJ4NHdCbE41JTJGa3dvdlNBb3lVeUFBZFdUOSUyQmppd2JEdmgxM2ZIcVRwbEhXcHZxbHRYc2ZjNjN5VzhKRXdkWEhwNGQzNjd2SlM5Qkd6YjJmR0hHbGhlMTFsUlhsMThqRyUyQmtzckd1WU0zJTJCUG1mc05IU1Q0aFdPTWZ6T3djNnZGRTZ6dGFha3U3YVlOTVI0SlNwWEhjZUFiU3l4UFhvUWQ4bG44S05EckVkSnJaQVI5VGV5M1YyOGZtZXhSMWMxVFZkZVFIVDFNcWFxR2IlMkJucklSc3RSM3pYaW5mdVFyUzBZb3hpajVnMXU0bSUyQmYwZnlISGFVUWphaXdBNEVwa0puNVI1bVFWJTJCVXdqbyUyQlJvV2RKUWFkM2I3bHJHMTM4bE5UUUVENU1ONjc5SjBVZVM5VHBPT3A4WHVMTU9Hcm5ub3NnUHYxZWZENnFadTZJckMyT09TcjclMkZYOFdSUWt0RnJOaGhwdVo2bWRqMW53S3FFcFQ4VGNFUHhWZVhXdmN0YjdwNVU2dmZiYWUlMkJOVFJFMHhGWmpZMjczJTJGU01VSHVVbU1TZmk3cVltMzVsdmlidEd5WEdENmRxS3lRUlFEJTJGUnhGVVd1MktwZlIlMkJxM0V0T0x6bFlkM1JqZ3pOSVZlJTJCNnk5NHFOTWU5MGhZRjc3Z3FDRVpLJTJCVHp0eGFkVzE0MyUyRnFLUXpvZDhLdnpJQjFOZEY0aTNJOWhVcGFSTkQ3WSUyRmszNU9ycVA4c3IzbCUyQm95d1dLUWRwRCUyQmdPakMzOUQlMkZ4WHRudmpnRTBPd2JxJTJCd2dXM0duTWFoZVRxVGYxeHRGdXlXSVIlMkJHVGVnZ3QxSXZCUGNvN2ZKOHIxcWIlMkJNN25VeTRVdXpxeHJZWXZZdU9EdXMxRWoyeU95cXh3Zjh0JTJCZ1hCVDVPUVk0dzJZMkpHWVJ2MDZ5akFnWXB4eEo2b0FBMmkxcGdvVGNQbHpwWTJwSHNZUXdvUmNHWlhGODVXWURpSzFuY3k2cjJ4UlhYYm1VSlpTRWE3JTJGQyUyQnIyR1JvM1FaSWQxWXNabnVqRE9oY21EQ0tGTmN6OE5naTdJMiUyRjZxOUxLWm00ZnhHWmVPWjRQS2s3TFlGUnhDZVlVTlJSMUk2eUhGMkFPJTJGc1pYOG9KWVhOSjB4ZEdBTjhkWDVwcDlyRU1aUVExVEpRbWIlMkJRNTI3OXRSUk5ZNEdXeEEzdDlVQWZ4cFdBR2IxYkM4Q1k4OE9ac3RleHgxNG1XcFVwSTc2RGd5VFRHNEQlMkJhR1ZpR3ZhNVRpZzYxZTZFcnZjb2NvYlVJemswYiUyRk1RSVFUWE5oVlVxSW9hU01hc3BjY2xZckUwQVY3SFhlbk82dHc5ZXllM2ZYREpDQkZzaU5hTGhRRk1HYnNDUDBLY1NZZkpJQWVzVXdnSUhwWE92VGs5cFdVOW8yMUpqRWI4OGJXZ1R5cFlPWlJ5bWIyTW9qVDFQOE1GT3dGOW04bWV4cGhkT2ZyQ29IcTJ5dyUyQmQ5d2hCVkR6eG53Z1E5NnJRcmM4YWV6N0ElMkJnMEV2WG11R3FUblJKREhid3BMTnp4JTJCOTF6NTUwczNBeVFCM1ZvM3VNV2NjS2J2TlVyRVBHcGE5dU5DSWhwRUpqVnJiU3VibFlRNCUyRlc1dWR1JTJCWG0xJTJCVER3Wmw2T1ZhOVhTNzRuJTJGTlJzVUVkUyUyQnNSS2ZGZVd6dnpFJTJCSUd0ME5wMHlQOXpjNGlaTWg3cTFZT0tyZGVYU284cDRxY00lMkJRJTJCJTJCekk0NWliZW5IOEZpSjA5UlZlV1ltZWhuYlU4ZmVUZ1dSWEZOZ0tGOVc0SWFzWVdpcXpmNWl4M2VYeWt4dndiQ05aSEJkQWFTRTZDeTJ6M2p0alJWYThWdXdmNyUyRlAzQ25GWVdXaWxKZXcwMjJ3WThMWXpTZ21EQk5oRXA4bGdVVnpZYUtaWUduS3czanV5JTJCREU2Zk1EcHVNU25qTVJzWDNnREZXVzN5JTJGRlc1aEpoTndzUWZKMmVGS2x0b0pCMWVvJTJGMVVuZTBIcG9CRnoxUkFqU2pDM1c5Q0R2YzhQM1ZXN1o4b3FUS2w0N3QxMVRmcHRFdzdYUXM3WmRkaU92UFU5bEpYWjVzNkl1YzY4NUt4QWwlMkJRblhwdXZNQnZVR3V0YmpCR0JWNUtydVI4bGpxTGs2ZzhmbEVZakFxVnFTQkMlMkZNS29nVUx6enI1YU1FVmFDVER3VUlNaEwxeEtLOERQNE9hOFYzVmNEc0R0MmJKam1EMmhTM1B5RUJyQkklMkZ4Qnd0UE44aE1YcEJRZ0xqZzFlTHZhR25mUW5rV2hNcDVtcHlCZTM0UGI3Y0VWdGN6Wk5lZ1ZNaTNNdFZNMEdYSHklMkZVRzZwNmc2ZWtUMXNNSzJNYUJuMFNRbCUyQmlrc1RPeWxEc3EwckVpMVpWQXNldUlrU1ZrZlJ5bmglMkZXT040JTJGVmJTeEN6JTJGeEh0R3BoWmo4QUtyYllPWlZ1cFpremQ4dzFTYnlmR3BkZyUyQmM5c1FJcEFoQ0hQOVNPdEtyb3hra1VPYlB6SWhGJTJCSG1ZUzRVZGd3WHR0bmVncCUyRmo0V0VmbTNzUFdibndvS3ptZk1tTnJMTE1NOEdlb3YzVVhzJTJGWmtVaEtGWU00S0VmN2JjanRuMFlqWWIwMVVDc25rS2RpdnJvc0xVeTREaCUyQnJJcThSNnBYdmdRRzNKOFA1YkRXUWk4SlNIY0FlV0pSUzUwWWElMkJITyUyRjc0aWZJdk83ZlhIJTJCS2kwQVBpaTdtSUtObWh0QW9FNVkzJTJGR3RqYzBUeXJqc1VUdTlaa0tpWDUxNHZhand2bkZyNFRpU2ElMkY3YmZpNUM2SkwwWmJPeEh1Sk80NyUyQkd6M2QzcDFkQyUyRjFlMmpwYU94bkhLSE50Y2dVdWZvc2Ftb0RHOWlQaG9nZURyOUplV0duYTJMM2pzdXcxOFFaWGQyJTJGRGdYeWdFZGhobTEzZk45SlM3VjliN1Z1OUdaNmNxaG0lMkJldG0zaXpkSG5RVGtOaTJkNzBrdUFPYWhXbDBoc0pqdlhBa3J3JTJCYUtBWGQxdlVjWUxZM1ppRU9JdXVoVExjdWpzZExLMDR1YUExNzBEQ3lQQUp2Q1Y2TjZlQ05HYWhaREpKQ25XWmVJJTJGeVhvQzklMkJpOVdkV1BTaHBCJTJCYXdDNVR1Q0NOQjJrM2FNT2dqNEclMkYzZURUOEpRTWNNJTJCV09haWEySG5KRXdmQmxqQzFaM3JHMmg0ZXVEY3VVNWlSU2F6SmVHMG81bkxZMVlFcldFYWJSekhJM2tPZFd1Q2ZyRTRKODhXT0R5JTJCTXlzRFclMkZRZnY2RFczOThTSlczdmdwTzdyblJqNXZiZk80Q3lUZXJpeUhQTWVLWDg4RDZBMk16VUNBa20xajJyUkoxUmozTEdxZDl4Zm84ZE1NZk55VyUyQnpjaGpHenFSUFRTJTJCJTJCeU9Dd215eEVSNWxSJTJGWG0weGk2MlZoWHdoMmpQMWpWQlVQemVDNGVKZkVVU0Z6TDJVRHlVJTJCbTRDYTNvMUxEVWdEbjVQaVZsdXhwRTRMR1JmYlY4c0VET2RuMjVIbnZhcG5aTHRPQTBQelhVaEs2YWM0SUduWjkxbWlBOVpZSDdFbWJqelFCVFFrak9ya0lFRkxmNFVYTm9ISjVPJTJCbzNkOUd0c2lFYlh0R3pxdjBuWXNrcmVQbm9Ib2E0dmI3cEZ5WVVIYzclMkY5aDdoZiUyRnFjUzFZOXVVbCUyRnpMdDlJTVg0Z2pWVlpLNnNzQ1k4ZnlFeG1zMnU2Q3BwdDM3VUIzZmwlMkJIZTBpUDMlMkZxek1GclppaERLNDNkWnh0alltVm53cTNJcFdQNnYlMkI1RWlQTEY0algxdHJIQVZhdzQ1bldzQk1BQ2hlVUlpNE1kRzN5U1hlMGtJcE5XZU9kellQRThBSUZnazVjcm5JQmZUcEtlTWVuWE5kUDdheEtjZUd4RmRjYks5cmxhaVhLeSUyQjNwcExjUkJLUFRSYVE4U0Z5bFB5R1VIN3dGcE1FVTZEdll4TGZWdllqY2R3U1QzQjN1VWs5YWI3V2I0aUl4Q01JdTBtdUtyd0czJTJGMTV5M1clMkZHM2MwY01VUkp3VDR5UGQyYmNWcWdvRE4yVFVGMDFmc3R0QzNJYyUyRlRjczRVUkJHcnlvUkRjMiUyQktWejhlZXRtcTQ0dVFtZGJmc3hNT3VhY3NrUmMwN2Rya3NsSmRxUHNnWWxZWGRuNmdQZ1VXRUJtTUJJbkFSU2dPZVVQM1pMbG5LWVVtUUR3RXFiQktqY3ZzUTd1JTJCTzRrWVQlMkY1QkVQQVFlV3hWaXBGQmV0OXclMkZOdUVwbXRZamd2JTJCckVVaDB1TTBOT09sS0cxbmJHcWJIQkkzZ0pTb28zNVhLVWZ0VFpmeGhwcEdidjZCQ0FYRFZOTEs5a0l3c0k0SWRpMSUyQnUyUiUyRkE4M1dOb0lidEw5aCUyQkRZU2c4N3kxMFRqYkVvNGFadllTWWxNJTJCbVJpRTcwS053TXpKV2pzMDYzNDM1andKcjZvQ0N2ZmtyVldxbExXVHZiNGFuTWppN0xqbDhmTlRvczRjTDFoWEpqc0tsWTd6bXJ2b3p6cXRYMmI0ZHFKMjZKJTJCVnFQOFh0aW12Yk16UnJ4RjViYkkzcXM1ME0lMkZ3YWJxVXRUN3JobVFmQ241MjdOVzJOcHp0aTZqcXJ1ZVR0aHlRYThmazgxSXM5OW9aanF6dUJmbCUyQkh0bjhCZ1JUenFobSUyRnhUQTZwRHc0JTJGUnE5TWtUS1NiS1VJRGJtMEhWMEluJTJGbFBDendyUUlLSDVkdTVDekRIcG95YU9USGNINEQlMkIlMkJIdW53NXJHMXVFJTJGREtjJTJGcnU5ZmtuTk1jUGZzJTJGbHg0SSUyQk10V0JkVGp6Rk5XeUE5R2Y4S1J1Q2ZPemY0U2Y1MXRzdXRPbWdnRElqbiUyQlk4SHA5UWZhak9mMDF3QlRIc1luWW1uMjVQN0pyYkklMkJqQ2xzZUxJZjBHU3c1OEslMkZaOHFNcklnakdTa3dHVTJGdHhKc1l4WjhncGQwdFlIck9IViUyQjZoZnRYSXMlMkJXMUZtdSUyRlFiMWpVREZnbUFNbVNoZU9zUkJkcVlMRFZpV1FuJTJCSTFWaWw3VW9ZVTBwbEVaNER6QUNCRzJjWW5YMkdURGwxVTQ2RVhpNnk1VmI4QVFRVWdLVm44U0JHVm4xN1VYaWlWVUF5YlFjc3BSQnFHUjlleTJEY0cwSE1zZ1ZMVUhrbGc4V0NFNHd4VzBSa3VZQjRCb2glMkJmJTJGa25WR1V4JTJGckZrd05KQW54aVMlMkY4V3pYdXlWOUJlVFg5R3Z2Qkl6S0wwQmtoa2xMejZkN3VNSzd5TkpkV1BadFZFSEpjeW5qR1Q2eE45VGE0Rm02RG5Ja2RjWHFIakYySXlNRiUyQjBCOFl5Z0lDTEFoUmNnSDR4JTJCVDFFbnVOQ1hiR0Y5SGlIWXl3WG1JQVVPRjNWelRiMWFVSTglMkZYMlVEV1pMQlglMkJFRkpYc052YVpYTTFERWRraUF2ek5ZUHN3WXM3JTJCcVBQdlJYZGkxR2dkU0RkRlh0WGk5JTJGcmdFcmhMRmxTWUdZak10ZGJOZ3ZlMkZnczRBSmlVS1hNM3pIZk1nNE13Q0hob1o2V2ElMkJIRmNKNlI4RU56d3lGOUxhOHRyMzJhbkgwT3NMcndQSW1Zem1qTENSUlRwUDdZJTJCViUyRmpiMHVDJTJCRTVuaTBkZGhoR0ZnaWR1U0ZveFNCUEF3QUQ1cHJ5ZkRTYkVGc1JwOWdxVCUyRlMlMkJIVTBseVhzT25wdHEwaGVJODBTcWc4Q0JtYlBZUlZMR1gyUExibHI4MENQSnU3WTl3SVpJdW9JeVFkU0N2UXhTZ2tIdGc2U2o4WGlhZEFzMWQ0TkVoZ0JRRHFTbElXT1F5TmRPV082a1ZBJTJGeWpmMjFTM0hOQ05ib0ZjSERHcFIlMkI4JTJCOU1DMUsxTUclMkI2c1VWM1k4aE4lMkJoZjBCZUxDcVNUaXJyak45c1ZNalR3Q21zZVNIeVFtJTJGbmdQN002Mjh0RkYwRzd5akxaJTJGTmFKYmJxY1Qyc2ZxNWJxUlNBQyUyRnMyVEROV1hEeEQ5NTBpd09EYkxES21GaVpPcW02ZHgyTXd2ZmE0NkFHbEtPNGxvblVjbWhqczQyWFR5YThOS2N2bnVsVEZsOEd4ZWJjNHVFNFNBbHRYZnglMkZ3a2tpTk1pbVZCY0N1WGlHOFdqQ3g5a09La1l1U29Fb29RJTJGWnAwTHZpRTFOMGJ6dHJ6Y1BWS3FYJTJGMlpBNW5tOGROcHJIUko4dVNEQTdKU3RkZTk2R3B5ZzNkaEdlZlFiRzNiMEJ4SiUyRkV5SWlFUXFrUmklMkJDQW02JTJCNGxTTjBQMkRueHdSN3A1UW5KTURzUlp4JTJGSmh3MUh0dXBJOUlZN2slMkJQcDVxVyUyQk5nT0YlMkZQeEZvJTJCMHJEWURBZFdxYWM3OU5ZU1cyU2o5U0hUeDl1MlBPdDUwRnpOQ0x1cmNjbGFYdDl1UFhnRnNUdzRZcmNCams1Y0daTUI2U05ZOXolMkZyUkFtMktBVGlRRFdWZTQxMG10cnFtRjdKMXpuUVJMWVJPYW1xWllSMEh2WEJsOSUyQk1ZSVRKRlF0VFBVJTJCblNyUTJHREclMkJtRTJ1cFlZcUdZNUU2VU1rQm1McXZrUUJuRHVhVTAxdFlWNW5EcXVqdW83aSUyQjc1JTJGSnQ3aGVBQUUybTVJVHBLdU5QeDZuaXBnNVolMkZ0UTlmdVRSd0JsWWslMkJiJTJGSUJ2aUR0S0FFZGNiQ1pjbXdRWVU2N3VCR0F3eWZ5ZW40SVA1dFZGS0w5aXZ6M01UQkl3NVBqSiUyQjFKVVAxV0ZhYVRaYmlJeGNNNWJTQTZ5OVVYUDdGZVI5UkxleWIlMkJ2RUlDWEF2MTVSJTJCSTFFNE1iZWZoS1NiZWFVSHI5eWJHSFVxNWVkMGxNYWtPSU8xcUVJaSUyQmNBNlkwSGhJZlN0R2I4Wm9sMk1jMkVicWZHN055cGVtekFVOEdTQXlhJTJGU3RNN0pLMnd2Mzk1RXJBUkl2eDB5a2tYRVhKaXJ4cGhzcmhYY1h2QmZvQ2hXMk1Pc3BxTU5rNGpwSXdmUDRqWTM4MEVMNXN6UGtjbTViVTBVTlY1eElzeXZaSlFCTGNrSzYlMkI5TlZ1VTZ5UGlpUFRURHpVUEU5eGFlS2I1SVluelVGZnVLeTY5ZCUyQmlCZDR3OHpLdFZDdGolMkIwcEF6RCUyQlo2RUxFVU9XVlVlcVRDMUdibGNrQldqbHNRdHMlMkZJTDBiaVd4WUtuejF0V1JtNzl0OSUyQjhQWWFuJTJGZTVLSzJXMnZReVJMWG5DaVdQd2FqMURRciUyRmFwSDlHNXFCSlZ2MFJ0VCUyRjFvNlp1ZE1MSWx2WG1VaWFiN2V6YWIlMkZKYWxGNTJ4cVM1JTJGRHNuMzlSMURvZWN0ODNBYkd0MWhRRDRLOW9qd2NPSWJtZVJqUlpDbjJwaHVzTkJGZUlWdWR6SlFrYzFVVDYlMkZscE1rUE5oeU1aMjNDWjVYUU43eGlOTFpwd2hHaHppUnJ6ZVBEZFlXRG41a2h3TzdJc0tOekRxektFd1NldnFSaWUweXBJcWhZQXhUMTNtR0pqMHk1cWpLYUpVSkpOSjhUWW9sTGpzaTNGWkpTYnlHS3VLMTZXQVBkUkJLSXYzTiUyQlJORVBoT2F1QSUyRnV3b1QxN3AlMkZkJTJGU3BjbklVR1N3VGJyayUyRktxNXNzWTVRZVJ0SnhOYWNYaGNZdHd2ZmtjU0EyWFd2S004VTF2QmJ3c1I0UUw0WVBDaUxEVGtXMCUyQlM5allXQUlxdmZra1pjZ0pndDdNZlVUczRVeWlkZWYxM2RYaGFSM3FjVnY0d1hHZml6VWdBSWdCeFMzeWs2U2JoU1NmUXpZdmR5dWRxY055ZE0zTGxuQUsxUlFDbW1RR21yZHRZS3RybFBoJTJGREZtcSUyRlZCN2w0dFlrRGxWRFElMkZPV0tuV2ZRbSUyQjlYNjllTEE2ajVkTEtqMEhYcyUyQiUyQjVoYXFGdyUyQm1TTiUyQnVTcyUyRkM2T1A3UTQ4c01nZGpyaDRrUDZCRmt2RnFKJTJGbmVMS256OHh0YUhrWjFVdTJpejNPTzg4WUFBVlh4Y2VGblNlUE1VRmJoJTJGSzEyZWxRMFBMcHZuUXNZMzhQSFp1OERyMFFyOFJqRE1qb0xjM0pwQmlVc2llck9FYXpVUVRQUlIyVGRma3JmaFM0MHM2MmkwQTJUZE5iSjV1Y3ZpUUhQdSUyRnpuVDJuVVp5UVIlMkZZYmx3VFVTZDMlMkZDbjR1cndFb3F4UkZoOW9qOFBOcGYySU5hODBZTFVmT2dKN1YlMkJTUGhCWFpJUjNqOUwyTjZBc1Q2NnE4d3RxN1ZKdjRSRWJOdlcxc21CSkNOY1B2dEI5RGRYUmJadE54ZVMxSHdMJTJCdUIxNGRtV0dQdVU5dUZoY0NlbFhhNE5vUkt3UDZ2aUtEN3FNcGpUU2xOdyUyRk5JbDNLcGVPNFd1SDg2c3g3OVk5VWYlMkJyMER1V3NJejZWdHIlMkJTaGFNZVFGOVc3RUZNdG9qWDVBNiUyQjgwc0lHUllRJTJGZnMwMVN4JTJCSmFFVkRFcnRmSjlGT2trelFDaWRRY1VIUUJaT1dMNG9Mc2JhTGxDZG1TNFVHdVIyaFRxbXIlMkZteUFocUlhU2klMkZRb0I4bGdaSlk2WFYzJTJGJTJCODl1U05OaGJKYk1laHZ4QVp4ckZCTkR3UWtJcXZFZDh6ZFNXbldicWFqZWY5JTJCNExGVHdLcGZhSVJ4Q254bU9DZGNMb0Z2V2RjRHlhdjdDZ24zYUtncmRvdnJDaE5scnZUNmFNdG1sWlU2eksxUiUyRmYxWndPc1dLdyUyQmUzWjhQQjFkYUVLOHVxJTJGa2htY3ZYTWFCd1pENUZOZUlLd2owcnJUJTJCeSUyRk8lMkJnbWNJaTZ6am85YTB4RkpSWDVuZ29FZ244JTJGVG1YUEhoWmElMkJTb0Exam9xMDZVcVFUS1ZIOXVHV2UxV3cxV09JYiUyRlA1U0FLbFVJUjN4aGNIQ1NYYU1tdkk1dHBSNEZHdFhwcGJjN0FaMzdhejFjdnM1YSUyRm5JUWNJeSUyRlNLZlQ4aE9EMFNDN0VMN0hDSkNYZXpoM05tT2tuNiUyQkNxSlNnZ2dWUEo2MTJ0UGM2dCUyQndwdjB3VlcxV3lwdmslMkJyY2MlMkZiVUhqYmJhd2hxU05nQVV0dmxMTUtaV3JWJTJGaXk2MThtU2IwSEJLbUVWRmJzTjBzN1hRZ1Y3Z2h3ZnpCTGxDdGpQR3NSNFpGNkpkejcxQVAyY21Md21PRjBBU21oY1h2MGppN0E1cXk2Um5KU2VQQ3V1aHowMjhRMyUyRjJUSlMwaWtrUzAlMkJTTDVHM3BNakFTckZJM0d1VklhdWZUOE9UdnB5T2pJZHZaVDJYJTJGcXViMGhWbHIwbnZqV3p1Z0g0NlB6eDgwY1NDd1VqN0pIcXdPa0VsQVBhdm50RG8yNHNmWXpwa2YlMkZ0NnQwZm1PdHFrUyUyRnZMR080bGJhc3lDVXFVOGNZbiUyQmk0M2c5YXQlMkJ4Z3FONzRnRXJxSXJTM1dXZ3BrNVBESDlaODJVMkRwd3F0NnRPdG4zNmpwMXVoeUtrcFJrRkZvRkUlMkZQVWEwd1dibTVDb0FweVZYWTdWaHZXUXMzMXkzWWxYcHRlaUtRTXFZclpPRE4wTklYRmxhbG5TMFRIZjQlMkZXUzdWNVB5VW5JSmE0bVROU3htTThDY292ZzREYk5NeklGVEYxcURVOXRRQ3glMkJMOGRKN1o4UENlRkxJdm10Yko0ViUyQjM1OGVmeVBlUHgxS2pKZW9nMjIwOVV3UkRXbWFabkNuWFpRcmZ2OVIwcUdoZDM3MW1YcDI1ZmFoSnNYc053Qm5sdlZoYzNsVnpXSnNvZGRYaDQweTRwJTJGU3dodzZUWXhaWTlQVkJBRmZNZDBhb3pteDFBcUx1V0pGdTVmTWtsZ0x0R01hamxFTjgzcFpnWG5jQU5WakVQVTR3RG5LVE5NZmZ4WHY5UGczeFZZWFJIYVlKZjMzSWwyaUVBSEFhd2tjcWRrZkRGZzJkY1VDOG9iTHlTaHZDdDl0eE1lY2Y2dE5rcjdBR25GQ1RicjBSMEVIUVQ1cU5BbUZ4MTg3RWFhNDBjJTJGRDElMkYlMkZHNGJQcGVyME5ybmNJblhDQU9xJTJGMFFpRlVBRHhCWmFITjElMkZHdmZMVUtJbTNnU2ZuNWk4N3pVWHloU2lVNCUyQjBJQ2RPRzlDWk0lMkZDRUpXUEZGa3V5eE5hejlGdCUyRkhwZmRrZUVSSlhTbHVIZDlRQUhhYmxTMHN5Uk55TGoxOHlFYlB3aDBEcVhjd2hvdFVxWGVZSkVxbWFOakxNbmVxV281QnczUFVuQm5IVkQ0RVhBdUk0V3VzQzFKQWUxODlSSE4xNXc2Z0RjZmYlMkJJcWdzY0d5RSUyQjBzc1N1JTJCZ1Q2bnBGS1dzY2FtTkVWTmI5cGFURm02YmklMkIxWFAxdiUyQmVxdXVWWFdvd2dvemNOQzJVUktQM1pHNEJFY3ZrTU5JNGlvS0VFTEszSjlmY0c2VGZXYllxaGw3RnFBdzZ5cURyQSUyRmFkbWg2RUY0d0pFYnFQTE9EbFdOcjFCM1hiUVdRQ0QzbXowaTJ2Z1I5Y1JCcFVOZHB1TWglMkJrUFFRTGMzNnBjYjFqYkFVU0RiVVJ3ZnlFcG84dTZhZUhWS1g1V3pEazNDbnBzcXZpaHVUQmRZNkJZVzVNRkl4RFFiUCUyRjdUbEE3Z3hwJTJGUFJJWHMzZjA4dmoxRmN6NDdoYmFFSVR3STR1blZxdndNSUFBUEtGaFphbVRPU25BM1czcm1hY3ZkVkNIREhCJTJGc2J6VnglMkJQNXNuV0VzVmpISG8lMkJkQWNPbU0zM2dWMm5iUWJQc3FFJTJGSEpFNVpQJTJCRHdMTzVqNWJKVEQlMkJudFlkVmJiOHBuWENvSU0wQ0N6Z3dMZlp1MlZxaEdTYVIzcXE5WHZsV0w4RmJLS0hmTEhMOVpNWjRoZVdQMjFHRmQ3VDF6JTJGeEs3T0wlMkY1NHZPOFpvYUUlMkJuWEVzVjM1QUZJSWlKJTJGdjcyMlpQRGxxaUlXQTVEUjhVY0tjczgxcHZyUXlZYXdNOHdnJTJCaEswRSUyQjdFWTE0eUFKcjEyd1JVVUtKciUyRmxWRTJkY0h5UEpjSWNjVmg4amxJeXVXOWJITWt6ZUdDN09rMjd2c01FVmpaZEVjbGVsUnNVSE9McTJaNm1nMWNwa2YzT2lmSXclMkYySFklMkJZVXFwelowODZ3WlQ0NklBbjBHSFJqbGpoQXNTTm8zTXJiUFNUNGM3V3VmQ1YwSWNNQ3hMM1doS2wlMkJIakExQVlmRkwlMkZyUGdwZ3AlMkJ3eTN3VG9OTlFOTEwlMkI3M3Z3TjV0SVdJWHZIWWJwdXVIeU9pUUVNaHJvclhKcXhhaFYyZWRxbGJpMjh0dVQ2VmUyM0tTck14VHplMGJvb1UzT2ZYQ2Zaa0Zka1ExNDElMkZ6TVVTM2pTbkt0bUZHdEJZUlptMSUyRk1oRzZaUFYzRUVMaHFlT0FQQjg4RDhsbWk5eXJFMzRXcCUyQm9KM0pyQ0IlMkY0MDdocFI5SGdzbFM1WUdkaFc4TWlKSzh4eVljWDY5YVZIYjRJOCUyRjhtRkNTeTI2MzFPY3RMNG9DS041R2xyU2Fad0VZS042b29SYk5WTHVWQjFKZTV2blB4dEdQQnJaZUhZS2pzcG81ZmpqeEtKJTJCanF4WFpmVWpDR0hZV2dFTWVxOHMlMkY2eUM4Q3NDdDFLZENmUHIwQ3JUa3BTY2NmQ0sxWW82NGVJQTVDZHNFclpKYkQ5NnpVdkNzQ2VuQUxVeDBYZUVBT3dBJTJGRExYVVJHUERqN3g0bDdlU0xUR2dxNjFoOXlDRkVHSzZMNXdwOG5rS2tDJTJCMG1GUSUyQnV6WHE4YnVBOVR1RUU2cHElMkZmcThUVk1vUUg3TWNXR2hiSmNhS2VvdEhxdjBwdVdhYkd6SU8yUEM4azE2VkowdWF6d0NBOVl5YjM1ZVZYayUyRiUyQm9IRm9WbG9Za3RoT1l4TVczVyUyQnhHQUNLTXpWbEdReWpxNDZaMU5KS2NRUW01WTVMVnIzNFY1amVFQjd1NmdmSjJGUGh6WEpaOEpUcmRzZHE2S1RSZVduTDFwZEg1NzJXQ08zZ2daaThJciUyRnlJRGNRd3Eza212MWY4dWJ0NGJ4WjNmRWtXQWl6eiUyQldxcUYlMkJBdkRqdFIzUjZtUGN2RWR1Z3hXSSUyQll2UkNxVmpqUTZ3RmtKeU9uVzdVdzVLNlFJMEY4Y0slMkZZbFdMcmdsdDUlMkI2RDE3QXc1JTJCeHRHSzFaT2lXWGslMkJMNDdWWDBEa3ZyNyUyRnFIbk8lMkZhJTJCOHdzcCUyQmR2MzhwQTdHQ3NoSkxSUExxd0I1aXlNbUd6N0RSWkVWSUh0YVlqUURXNlBHb0pOUGtXeHdtR3R0S3dIa3p3dTFrZHc5SDM0OUxYUDJQY0daeHZGODR6OVJtTVo4MWg3bnRnZlNKb3U3RGdlU0swWTBmYXBWa1ZESkw1cFFQRDQwQ0ExaFdVZ1VvcW9VcU1WU3NBVzBNNWpRVGx6WVRvOWw5YjZMUDhpYjAyS3dISHNMMDNKZUtJZUo0dXJ3R2pWUHE2d0p6TzFTUjNiWFVxSWRHWiUyRjNUV3BzaU94JTJCalpacXJFbDNLOTk4TSUyRlNOTVFiTWNHSUphbFdRSkRHeGhIdkxTWGdxa1ZRd2o2ZVExY01yMm1QNE96WmxpJTJGTm1ZakNUakNxWlZJRlo3RmQ0RGdaS2hramVRTmJ0cUZSRm5OYXlRTkM4dWtNVHBwJTJGQVVTNnpmN0N4TXdHaUdYZk9IYjNDQSUyQlJLZlJPZ2IwcWpDUzhPcW8lMkIzOE95eEhQQzVTOVgzVU85YmVwYVJ5ZjVuU3FCazYzNmUxdU1jNmVoS2xBS2hNJTJCMnYlMkJtNnd0cHJoMnVwcTlFZzQwJTJGSFNaYmxVYzdXanEyeDlxbiUyQlByOWF5ZnFxYldFUGUyYkx4dUpFS2gwZVpMUXRlZWpJNUlmRzZIVnI0MDNkNG5IZ0p1S3UzNCUyQmVVMzM5cWdHS0Jza2FWT2I2bkNwS05GQjZKV1lMcFF6SyUyRnQ3MFFvTFhoSXRXOVhtMWxOa2pGT0RBVjQwekpnSVdxQzNrUnVWYzd5VEZoJTJCMVFzNXU0aSUyQmdzbE13bk8lMkZmZ2IlMkJOUElyU3VrdG1sWW5raHZiNXhVcG9idmJSbjN4cHRhYmtQUkZmSVZwZDZnOFFFdWZCUUwlMkZxY2tReGk5NTRqenlNWko1WWZaRmZQSHUzOHozJTJGZjFjSG54b0ZDdmFFZFJ4cEMyQmJDbzVVMDEzNEpYZEpUNm15YlZWZ05PU1Y0NkYxZkk3bjd1dXFlUDVzc0xGTmtUQnVuQ1cyMHNNMERBSUl4MzhlWDFma2RUYjhweGZqZXRKR05tcDc5RU9XMDJSU3lUQkZ0a0RacXlQMzlpdWhXUXklMkYlMkZXYmowR3ZMdUNUNTFkYU9OMm9GZFlDTCUyQmNKSlZ4ZXRJMEN6WWczJTJCVGslMkZrMVlEQldhRk93NGlVaE40alFZN0E3bG53VWY1UU1taFhUZERqYVBzM203b3hid1lCUFN5R2V1OWVPWlFCMnl4VElsTFNnJTJGT1B6UllSTjkzck8yeCUyRmZSNmZ5cUhUaWVjWCUyQlVadlo4NVc5Q2RaaTFxVnZVa1Z6djVrNFl2dUNkM0FFYm1MZjZEcCUyQmtac0tvdEJnaWFzUXNuJTJGSlI1eWY5MGN1NjclMkZsSXBBNk9OOW5mMm5UdEtiMmxkeU0lMkJMdjZXdTRoNmRPRWNyR3ZZMzVxUldrZklBZVZ3YVpsWDJGcG5URHlNWTRoZ3FaWjNkMHI2UWZ2TlgwWDJWOW8xQkxVbEd1eWc2amlWanZzcyUyQmhVVHFEb2VJT1huNFlVZjBsSjVmU2VBNGpjQWIzTXhldVExUVNCN2hMOTdvdVklMkIzTnZ5YmtPQ200cVJ0YWNlVmxXRlRkbFpzJTJCTGdzVHNSTkVPZG5hNzU3ak1vM0ZwWkZpc0tOTEx5YXAlMkZlOURJT3ZoS0VmR29ZTmlXaHF0RCUyQkwzZ1NzSkFYSkRtaWp3b1R6JTJGSE5yN1BIeGE0bnc0SmYlMkZNWlNaWU5FeU1LbCUyRmYlMkJ3UkZvUXZZTWdqQTZkJTJGemJwQ0xqNFFpJTJGaiUyQlNOeGhNQ2dUUXBCeDBPWTB1MDglMkJPd0RINm9mZ1lidjROb3BPTGNHWGt1WnlDalMlMkZTS2JDR0JHVDUySlQ2bSUyQkZVWnJtY2JsTmJuY2pPT0k5VzNMOXNOenRBcERZbEJZRk9DZVM4S1VJR0xQWndvRkNid0VIeldxNnY0V2NCRWkyYmRBY3BneTV3bmlSbWpZTnNlQnYlMkZnejNkSjdJdHBVUiUyRnVqOTdKdTBEbzVSdWxIN1c2OWZVWDF5eDUlMkJIUUlzM3pMeHVGOXNNblFxZnZ1YTBYRmE3dXhHRCUyRmtGUWhweHl1YTVIeGRqWEFSSnJxSlVhVjhnZnV5VGY5NGZWYyUyRnNCJTJGd2d5WExxbXklMkJIRGhUbDBmUldHOGYlMkZCenNpeVdhWDdpcXVMdmEwTktxaFVOYXhlMjdIVjdWNVoyWFg3aVN3WndYZU5qVkV2SUdmcUdXdjlDQlN0Tmpna2dTJTJGMEtYM1laUHglMkZQMWlrYmtsQm1zMGpqckRsNiUyQmJNRUtYMW1LZnV0clN0Y0lqSGRmUHdTdWYwR0c5T3VpRlI2R3UxQjM1dlQwayUyQlRjcjduTE4lMkJwT2pnWTEwbDVlSzFHQkhCU3UzUkE1b2FVMk96c1g0V0Fjc21aRkZsZ3lTVUZxa2FaYlIwTmFHSlJ5dVpnclhkeHhHQ1J2NUUyU1BVbHJvc0pmUWdhMjJFbkEzN3VaSHJoZkJQeTZ2Ylk4ZUwyR1U4VlRrRkVrOFNJcGJ4ZHRXSmpZdjdVaU9TJTJCRnZjZnYlMkZnbVlhMzQwdU5odkMyOVVYa0JDbkdpMzNLbWxPbkgzYnlRaCUyQmlwZmsxWkQ4MjNTRDJtSFhjVjZka3NJeiUyQlVoblBTTXFwRUlhclp4ZFJ6NnZia1pIamVMbXNRbXRYJTJGR0pwWFZGYVEycjJWelF3aTVXbUc3aHFVV1h5YVRoUVdzTjRWVXpVOGxiekhkJTJGb0poVmV0Zlg1QnpxeUJXYWVmaiUyQjhyNzl6SFdQTDBOJTJCTk5qRVJjSm93M0dDMUdITnV1R1dPUWdocVY2VGM1S1kxMFhUJTJGcXBFVUFSa08lMkJ3c21ibFZ5Y0hMM1VKaW9HVllKUksyZXd1QUwxQlNHRkR1VWdjeU52QSUyQkVPRExmcDdkV2NRclpoQ2VmMHBZdWZDMXMyaXlhbVNsRVhxVXhybW04TTNsZSUyQnlnTmNrOVplYnlxZjREOGpLNzVjQU1rMW9XbnVCZ1VEREFFYWx6MGNOcEpjbUZ0ZE04ZEMxR0tldnBqRW5aNndQa2ZYa3BLOUo5amVETUtZbFZkRHM1OFRmNjUySWQxdXlvMzM4TGh5eVR1QWp3Sk5lbCUyRktranNWJTJGQ1NzcVhPYVp4dDBROW9RVGpXYXNwanA0TzY3SGN3Q2pyVjklMkJDczlERjVDeU1XTzZwT0g3ODB2MzlpV3kzbUV3TUdjMmk4Y3dZdFZPYWNZOXFJVXNqWldqMFdNNXlIMk95aDRBM2R2R1FjR29kbWxsYkN4REQwbzNJTEJpJTJCSE1sd1JiVDVvMTBtR2F1a1lranJjMmpqcjElMkI1UkFiWG1hYlh2ZmpGWjhyUGF1M3Y2JTJCdDNvbSUyQlBkM2lFSkgwWEhyd1lEZmhBbktJcDNzYyUyQkdJTXBUYjFNd3p5MVpnYkR6JTJCTUFJaG5PZXRwc2x1N2hzeEJyNElFcUwlMkZIRlZsaVhtZks3c0d5SVBXSmZMZ3M1T3JFSWR5RWxtUGVwNzROdGFpZG9ldkx2TTJEQzk2ZllkQnJla0t0YVIlMkZPS0RCOXg2OE1KQm1wRTAzRmJTTjcxaGtaMnRSRWdlSVg0JTJCMXV0Wmw3M0VqRVZRZm1Mb0psSnNickFUWGdGTGxnRE1WM2pjJTJGVTNZSHdLJTJGeHJrdWYxQTJrTlUydnRQemtkdW55T2Nvdm9LMXZXRlM1MURTTm51Qk83elo0SlhvUUZJWldYUDRRb1hpJTJCelM4cDZ4OEI2RjRONUtyakJTJTJCd1RlTDU4JTJGJTJCSU1za2NsOGhvZEUzJTJCanJKU1ZYYmtVUWtLRHhIN3VZJTJCWVJYTk1kVjgxbk5sREU3SWdvaVk1SE9reENyMzBWa3kxMlpXVmVwbFZtSzJkUlVGendscGxJSHdvNlU5VG5yRlEyakFoeWxLQXdMaW5CVU10WGF6bXpUOWsxZnNQMVk3NUYyNWclMkZZV0hFd3BKZWkxcjk3Znp4N0FrVDNzOU9obUhTOTRKSnk0b2MzY09LY1VJUFZlcnRCcW82dU9sMURWTm5DR3l4cmFIUUs3YlFScSUyQnRhenZ2THQzdTQ0TEZ2dXpJUldyRVQ0VklGNjZNZ3dLJTJGJTJCWFROUFdKdEZNa01uJTJGbmR5UDJtbnBQMzFMWGRwN0R5M3J4MGx5YnkxTHNLUzZCaGN1ekhWcDhVTVR6UjNrTWUlMkZHb0hZU0QzQWkyMm0wRDU0dGNLWCUyQkQ4bXJySkhJeUVmUEkxYVIzc3ZTcXRIOFRaQjFiTmd4UzloTUpVJTJCWWxBN2pTbkh1VkFnNXdHZVgwTVNXNWt6T3NRR1FVVTNhVldCNjlCQ0lmJTJCUXZUd3ZWJTJCbTR3d2JOZGRHTUhMNnFPNldSZkZWaW0yZzB2VkVCdWtyRGRnVndoMVNlcFBuZ1hRYWptb0NXTUdhREJZZGpZa2VIdVpEMTF4TUtyZjJ0R0hYJTJCQzFPTElyOUJ0TCUyRk1rJTJGbTgyJTJGcFRvaGZsUHY0Mlc1ZDZtYVpwZzZsSWVtbUZJeDBKRlkzYm9FZUg3dFppbFlNb0hyUGZqJTJGeE5QNTJiR0JQb0xkSTZFcUhWZENUMzM2SXhWbyUyQjZ0bXM2dGY2aUlyTVdqUEdOciUyQklkQWRrMzE1am5aNFBSRWRiNmdxUzNQOU9sQSUyRldIMFpiTzIlMkJ4VVN0ZnZxdUo5cGRUR2Y4YjJtb1Fva1hmQXVOd0lCSktCT0xIdEIlMkJFRjBzZ3QzTnNNZkRkSWdsbXNaVzQxOEI2eGtsMHVoJTJGNnN5SVZlRFFIMTlzcE1DVSUyRlp6cGIwcG0lMkJZVmVNTVpXWUZwdzdUZlUzRUIxa25ya3Q4RFVOOUxuekl5bSUyRmJFJTJCd3pHTlBOalRzciUyRng3ZzNsNExwTXd5cTlKc0N2QkFxbkxmN08xWVhFYmZ1NmtCcUxqSnozZ0VKeFp3QkY5UFplRDhjNU5Kek9zWVYzSEczV0k5bVhNYVhDJTJCV3RMcjdnNTVzVjFxdEFVT2lkcUpoOGpXMVRKUG1TYXJ3bzhodzhzQkpuT0ZpY09VMVM5a2h5d3l0a1Q5JTJGY0FtY1V0QnFXbEJIVlJ2S0d0NXlVSWhYJTJGYndYNWJabHlKTE9KY0RKbnl2VTB1SUxGTmNpaENpVTRKZTlXZjQyNTNIaDhaSnlPenp4Y1hsV0p5ZFZwRnphaHJLc1g0amRwcXZIemllMG5CaVY5RkJKSjJiVW1kZW91ZkRaQSUyQmFkckolMkJRUThtdWk2RkxNM1E2bzZ5b2JCVmw0ekZDVXd5cmcxTXZqdjRrQlhPalNLWUJDbUIlMkIlMkJsc3YlMkJ2MHAlMkIxakFXdkxlTHlRSDBwdUZ2emclMkZYbUtQNURQRlRoUjB6aTdWc1NMZFp2aEp3b1ByYkFOdlNoZ3Y0Tjdna1ZkN3ZNTmllNzBCQVlCMFNRUU9OV1FuVENLTklNMUYwbXpOeldEVktsdGZoQ1RPZEI5cmlKZ1pGc21FY2EyeDA4TkNZSWwxaHpGMjE0WFF1WTd1YjlJTWVwenR1NGN3VGs2SXNlUUtKa1VtMkVWZmpzTW9lYjJtVCUyRkZ0SmVZSSUyRk0lMkYzTXduOXRqams1UUY2TFB3WEkwSnVhU0ttZGY5OHlVVzdKMFYydVpVZHljMWNPNGxTd1M5Mkw4Q0ElMkI5S2FBRU5keiUyRlc4TGZKM0VHdW1JTTJ6RERYMW00VXU1UEwlMkZZMSUyRmxsWnVrVmhJR3R0MUVrbmczSWhraGZ5RGVTMjA2ZTBMTmlpTFZFTGtVQkc1RUYlMkZNZ3gwM0lrTXlXMUhHUWFTc0JtczMxcWhGVzhsJTJCQnBsdjZROXBQMktuaDVIS2dCZGJHM05MMkRvRmlVZDloUEhkdmljJTJCJTJCaDFzUmpwajZQc01xVmtsNUFSNWRNd2dRR0s4Mm56aG1yJTJGclR1eU1RWmFuR3czSmw0djNyekx5c09ZNVpmQXZBb1Jka294SyUyRkVGck1FJTJCMHMzNG50bTROdUZwdTNHc0lKTnVLcWZvUVhmQjZRZnRwZUh1Y0FNMVp0ODZ6RjZYRHlSMFRuZGhWUklPZFc4OGVmbmRSYnNtdElIYmloUUN2NkpxU3hLNWVnZUxjb1REczY4emglMkJGZGtBbnp4aWJuMU8lMkJaNjh0ZGl1cjkzcXR6SzUxJTJCT0M5SVI3NEFVZFp6QzNXdFBaMWJCSWQlMkIlMkJVUkJqJTJGY1lnd3F3NVhLaThNRUR3eW5DTGJJWWlZTFlSY2hIczJPemtPdmxrSUowVFhPRlBZUGhtWktIYWNRVkt5dFJSY1FWR3VmSGE5VmhNbXQ3ZVB3Q2FSaVdZT25nTmczM0Fudnk4dDZOUG5zWlAlMkYxYm5xQUhuRmtNRTJsU0tqZVNNc3FvamRzVDYyNkw2MVROdnpWdDBxUUJYbkJEcktzeDZNYzNEaHFZTkVRbWFLZXVvWkJNdjZmTGZvJTJGRlclMkJkT1hIdFJOMk85bFNpM3pzc0F0Z2ZkZnJMZDV5OWZ5aTFlcGJaalhIVFA4T0xYMHdLc2xJZFlUOHVwamptbnJYVTRnS0s4dUJVZUtLSUVPREJhWnBBa3ZUcmJKRCUyRkJuOUtnS3hNQXh0WmJQV0F1ZFBDb0ExQzQ5U2wlMkZ3b1N4SHYxV052UkdaVlAzQUtnWFRIT3JMeSUyQkdxOEhINDYwcnVCaE1wZWVrYW9vZHUyTEp5WnNVcFNXdkhCMnUlMkI0Z05mazZKeTFMZEE1MHZrMjFKWENDZkhVdldCM1oxNnVHcGZtQUpGWWJSc2YlMkZlaE1aJTJGZkwzYTlBYyUyRmJub1BHUUJaZ0xiZTczNnk5aEc1VjZJejQwQkszb3UlMkZjSmolMkJySHBYTW1TbEFjMVUyZUEwNSUyRnVZazA4ZzFNcnBScGl5NmdYalNzYTJtNEQwZUxVYTFEUTlRSjlGbVVGbThrWWlDSndRTldpSzlNJTJCb1g2djdHQWk0VSUyQmxxU0R5U3JFNk5GeTZzb3F3M1NOeDdLazM3ZyUyQmxjbnVRbkVyOUdhZCUyRjAwRUd2Q1lQaFV2bjdqMmdWeHplVUhSOXN2Z3pmWFl1ODV1cHZQMzclMkI5Z1dMa0dabE4lMkJDUlhIOCUyRktkNjlNWHRjV3FEUTlKYkNsenRnWEhJWkZTN2s1ZG5UUlYlMkZpOFdnbzNObUdqNGk3M3Y4JTJGaklHM2NOUHczVW42ZmN6bGtiVWVaR3QxT25MeXBOS1BPMjA2aVgwSURLaWdVeUswbW0lMkJ4bVpBOUgybVhabk1Db1lBbEpmSHZpTHB1SEtKVEFhS09KZkFaN1RuUUU5bDhxakxnRHhkVE5TVnRidmlqTGI5ODdQUUNXQUlkRkpQVEJyWWpQWXFTbXpXWjBsZ3h3WFFDSHdtSlZDTGdiWmFwOTJoaXN2UzNEdm8wUFpWQ2g2cmppdE1HdXlUck8lMkY4WFNkU3hKaXNYQVg4SkRIZkhlZTI1NDd6MWZ2OUN6RVJNVGMlMkJpZW91QkptU21saEgxTkZHbnllVzY3MTJZbTdvMXJTOFNKUWRxQjRMa1lNbjh3THVzQXVlRzFWVmFkRHI3bmtMY1Zrd2tTdVNsUG9ab2tOaWVweUtPRVBrR0dBZkJ5UTklMkJxNTFHMkhhR0d3MzIlMkI5eGFYcyUyQlRQVEpad205aDlqaTRJcUdDRHhLRzJnZ21KN0VkUHJVZVBGQkJQNVRwbE1IQSUyQmVPcW5EUzdETHNYc0pmbGZWJTJCZzU3V3B2WmJuS1B3ZVNsdWltNFdlUllBbFZ6YXdnJTJGZnNhQk5jUzZyek5jMnJzVk5oRGlhRW5DNiUyRnVCNVU3U216JTJCQkFlWGZQbXBHTmZDYyUyRndTMmp5V0Y4a0VNYVolMkZ2YyUyRlJnNWE1cGVNRGhOSTdpZUNhM0NHODU3NnliNkNOMkl0ckpPamdYTUFTdjVyb05nYnNIaiUyRjVxaTh3ejJweEIzcyUyQmQ4aXI3MzYxelN6RzJmTFgwS0VIdSUyQk0zazF6NHAzV2pCTkNDM1l1YUZnTEtuJTJCaXNTRiUyRmRZUGglMkJyJTJCaCUyQjBmbW1Wb0pLJTJGaXl6TnF5dG5ZaExIU1d1RWYxbVZ0cSUyQmxBbkNobElKZHNZNjJ5S1VWU3hEZCUyQlZ2OVBjUmxDJTJGWW5qSDRIQXhBVzlEYyUyQkEzU2N1T29vbmdXZVB2b3UydHUwbTV2UTBLcDZFa3VOWW5FdlRyOE9OdlJaM3NDOTRDY1VQJTJCTWVxZ0s0czJTZ0F5RmhqZmxqZGpSOWUlMkJKRlZvU2E0d3hhdU5XU1M5QXVxSFNaQzV2eFEwR0t5SFprYSUyRmlKVTA2ZDNabXVqMVRjYSUyQiUyRmtsQVhGS3QlMkZmY2FFS0hrbFo4MSUyQkF6alklMkJzSmlTWHdqeVJtdmlYM0xSMVl1JTJCa1FKV3RaOXlldFUlMkZQaHd0d3ZTV1pVNHJnakRCcVFOcHE1WkdEWnclMkY3UFZkTldMMjZVc0NkNUtSYSUyQllhUDNnUFFSMEk2T1pCR0FlV0luRzhNVWNZQ3k4SkZCMjlKMCUyRmZDRiUyRlNOMU52eHRPRiUyQkpNQTdlU2s3UHF2dDA0eUZpc1lGRHJaMVYyUHVURGVoR1k4R3I4Zmsxc0tESjZ6ckhuZzhXJTJGVlVFVUYyR1I5R2lVak85NGZ3TzZXSlJoMnl2MlZ1bEhoUVJ0TVNSRlc0OVElMkIzVUhmclkxWVd5YzU2RkVLWSUyRndvbW5CMVc0JTJGRnZweVF6Yzhma3R6Vm1tN05lRXIlMkJLeHhSVm1nSzg1Mk9adnIlMkZ2VkYlMkJjTFJwdlNjdHpWNGhTQmR3TFR6MVVFVTZBdUVjWUY5THRKUVkyQm5RaUJ6RzRRaHY3T3FOQjJqSHhJeFJVb3ZRajZqajRLdEJQQW01TnhLanRyWXZneVNHek0ySGw4RzB1RlBXTnZTZWZ3dWxNTEJMTW1pMm9GWDE2MXM4Z1FuY2NmZFVZYUZUZW1QcHdacndtVWJsY2R3NzRmbnJXcmIzdnR6dDdGRjYlMkZ1akw4T1NiYkMzenh3TlFTN2hWZERaMHpya2t4NUJQT1JFQzJRMDdhZzdqdnZhRHJZbUdGJTJGNEtPWDRPVXJrTWdDNzdHOUlyR1V3M21NZUZIcDUxVGdpeXZ2VlplJTJCWU9nRTlPcTFwb3k5T2pIOEl2QkJ3aXU3JTJCSyUyQklDUSUyRkZUUXZpNkduTUtDVEdlY3c3YjRENWJCJTJGYWJUUzM3cFl5N2t3NXZSRDM2ZyUyQmlVS1B0SmJpYWFNaWx2T2d5SFlsRThUb1FSb3JtUDlHTTgxeGRSciUyRlQyaVBtY0klMkY0WU94dHM5NlZqdlc4YkFGMjV0NFolMkJiSWNpdjc0eHVCVVd0Vzl6UkNuaVpkbkN1VWMyTFRnODlSS2E1V1NCdFlxOFZlQVolMkIlMkYwZDFaTVYyJTJGVFlKaEk0UTVTMVElMkZPNFFwVEJMMEpheVdZUkY4dmZoQWRjcjZiY0tzUmw1JTJGS3JLJTJCZ3BCWDclMkJuNzF1cGN0bFh1V3UxRk9yMThNQ0szMzU2dEhnelVTQXoxYWJlWG1sYzY2VHVMeFIlMkJzckJWdjJkSFFzTm4lMkZjMDRSWXgwJTJCZDB3eHpjQmZDWjFHVkNVSng4bTBqaTBlRUkyNkclMkJDajYlMkYxayUyRjBpQVZtckNPJTJCJTJCWXI2eHZRbEoxNnJ4TWJ0VmZoT1RaOEJxbEk1RW42Y2dpJTJCJTJGTjRuT3VFMkNzSTU4V1Vwc2lqWE5qTHNnMXlacmRhRTl0WXlyN0dhcSUyQko0Z3FwQlk0ZllqSmVVZXZVZ2tlZFVjTm1IUUx2VG9BVWRxV3FKTHZIaHFCeSUyRiUyRkhHRm5mblB3SXpLMlZFVm1Bd1lsME1qM1I5Y2UxNlFFSkFIOWdDTnJHbTliNHRydFVaR0lCNkY3eVA3aGNoJTJCQU9hWHRMaVp4ZTZSeSUyQk9VRnpGRklFQ2NkNHZXcmsxSUl2ajZpVWFnTjRkYnMlMkI4MERiNjJBc0NYOWRrTW5FdHpFVSUyQnhGNUJYbG4zWllDandSdUo4REFsVFMlMkZjMEh2dHI1YTlSWUJRRmtBSVBpNHE2aU1HJTJGZldZajlMSEFBdjkwUjNEMSUyQmY0TTNrb3J5VGFZcUVyV2hsWjR1ODVEb0lXN0MzN3hjbTRmUmlQMDI0eGlVJTJCNlVReDR0cWczM3M2cW94QjVmcVdma05sbiUyQm44aU45NjNqc05WanFVV01iTzVWJTJCdXFGNFNVTTBQc0clMkJxaW56R1V1a3RoMmpZZjlLcFJmTnQzclMlMkYzYkI1NWRTVWNyU2ozUExsT0QyJTJGTTMlMkJoSTZpdFdYZVBueFNTdnZNcDZjSyUyRnk2Y3NINmdzN1FHcXlqRXhOUkM3WmZMbHFNa0xNTVFiMlZMQ2E2WUVhQktQJTJGMHI3YjFIdjJnJTJCWG8yNFFEcGt5ZW5CWWNveUQ2eWdEQXFoQW5WJTJGcGduRTAlMkZhU1FOV3d4aVpjbGJSMkYwOFV1eHlZTm81Y2U5RXlqbW1XWWN2ajQlMkZZM3UwN2FMenRka2U1SGFpMHFYamtJJTJGa3Z4Wms3RUdMVjdUQng5eUh4ajE3VXFmODBhTGoyNEN6Vlc1SzdWQ1dXU3paUnVGTCUyRkxCR1ZvWHVOWVNueEQ0ZzJ2dFhNSWZoYTJEYmRRVFVVdm9IJTJCNkxLM1lKSkxRVXgzQ0tLSGU5S3Q4Z2NsQ21YaDNid0ltaTFBWWRYWnJqcHpuSkQzVzAyU1hjTVJOeXlYZ2g1UnlPYjV1S2FKYWZ6WGh2NFQ5QUdXUG93QnVOSXpQWGtGMVglMkJhb1dpWXp4MFU4YUhaWUklMkJiJTJCJTJGWGxnWUdKMlpBYSUyRmlXRGZmOE5QbU1RZnZUYnIxRURCNmNkZElvdXJMS2Z3bU5qdFJ1Y0tFQUlWZGI4c3FnTXA4VENlb2JYck5wNFpiWm0lMkJWdSUyQnd1ckU4SVZYOFpuQk9reW83YmJ3VUloVXNyZ0VvdUgzUEV1UXROd0tDZ1NHeWVraFZ3NW4xdXVkV0szJTJGSGFBN0RYSCUyQjk4dUd4aUdOWmZzVzJuSzYwRXhlJTJCdnJkVVEyJTJGJTJCSjZVdmRMS2QlMkZaVTdqODJqdVIlMkZKYmhzQzdMQ3lTMnNwYXJib0Nqbzk5a1VMV2pLYlVhQXRhYzZ4QmZqTGxxYlpzOFQlMkJzJTJCYVZyVkM1TktwWnkwVFZiTzlxRkRsSFFYN2xFQVB0VHViJTJCcXZiUzlMUU4zWjJpUmU5SGl3blhoJTJCU0Vjdjc2QlolMkZsM0Z4OXR1a3VHUXJ2akdzZElneDFOVjNubW5XcjFEOTFha1VQRUVia3B6RU9iYjJNRlVCdjFuUHclMkJjZFNzRyUyRnV5MiUyQktLeVFKcktqMW5yUmpCcHFFMVppcnhCaDZ1U0tHdlZrRmx5Q0hQQ2J0d2ppdFJ6SjE1NXZLZXdRZnZ1MHElMkZodFViYVZkTWplMHFwSmI1aHFSUndGJTJGWGQ3MDR3ZCUyQmNwN1R5YzZjUm01byUyQkxLV0IyWTNUaE8zaUZQRWZneGNicjVpbTExVVFSUSUyRnowYnUxb2h4OUMlMkYwaDRXUWZzRk9mZjVWOGxpJTJGTkJGZTNHbXNYSUhUNmElMkJ0YzMyNkU5dE10WmxPeGs3VTQ1aUxESThBS1ZJUW5PdjNuZHcwd0REc040dUklMkJBRyUyQnVDQU5CbzNxcTlwSFNyWExJVzNlcHk5Ykc1aG5BNU1LaWJiT00yZEp2NWRKQmF6c3llZlhPNU53M1d4VyUyQnFxelg4T3dneFc5Nk5MM1pRQ3AlMkJRY01QJTJCM2ZGa1pXdjd1bW45OTB3OFNmTDFWOVFkbEs3QUlieVduYmFITWM1MXRzdCUyQmtMVW5wamRXVmdIWXIlMkIlMkJhbTJoY01FMmV6bjdFTzdLUzlWVUdDM1NUYkkxblR4N2VHejlCb01KZFU5ZXV3WXFxaUIlMkJ0QXg0YjdhUEZhJTJGRDklMkJ1VHFTUVlobDJSME83enYlMkJLJTJGVlgyaGNIZkNkRndtSzRDVk45d3haQkg0bFlNbGtqVmU4QzF6NldaSG9RYzRESnhlWVhyV2tBZUtsSTlWeXJqY2RPTnhzSEJ2M3d2bmVJTHlZT2Z0S1hGMTl6QnM1eWdYcnAwNzl4WHg5UjNPRFdXbDhYN2hLb29EUyUyQlJZd0hMNjhFUHcwZlV5ekklMkZtc21rVXQxWWtCMlpCWnlndVFOVVE1cnJMbVdXanI5cFpiaCUyQm1vcm1jMzBwZGklMkZtbjBPOUJYMG5pbFElMkZsUXRYcCUyQjhXbHp3NURIJTJCT0FKRWJYZzAlMkIlMkJtTVZXRDdZMmpuVWlxankwMGVuZSUyRlpVbmZ2aWh5dmZIRnllNUF4OXRtdE91N2YlMkJOeFNrSCUyRmNKVjlEWFdOcTZ3T2JBcmd0MiUyQllmNlRWbGw0UEdOSU9tVElZZldWelQ1bXYlMkJKdWQ4TGxackhSRExiNEh2RFo2RUw4bnU3V2pyMmFUMkxsb0FxbUslMkIlMkJORlBYbmE3cHpybjNCenhaYyUyRkZ1R0tCbFJLcFZxRlpUJTJGREl6M3I5eGVLcGlhTmlHdlc0YUlibElRRkZ6Q2NyMWFQZmJXZzMzSjdYREtBYlJPajVic25YSlI1RjhrTEslMkY1MG45VUk2bFl4Tms5JTJGblJsa0NIbVA0VkNLJTJCNHBCeTF5JTJCbFQwYjlhbSUyRlFWN3NhMmtsVHVyNEQ5RXVZbkh3enlWZlg3V01LYjFmU0FWMFdQYURhakdZTVJheGR0cGNnVzFmZGsxaTVLQlhneVBybktEYSUyRjg1R0d4Nlg2emVHTCUyRnBuZUFYaUl5TUY3NVEwM2RsT3Jjd2VXSUpiMlNVb1J1U3JZYzIlMkZPRVgxQk9FbjBOcGlSMjJ2dWtoWk8lMkJQeFpCNFhIWFN2b1B0NFJTdUZJYUhoWmdUbW5VTkMzQmZjbTFONXk4dnB0V0RyNUljWXhyZ09VUUs4MzlHYlhseTBlT2hDZHQ1dHNJQiUyQmU3cHd2a3hHOHd0NFcyV3owZDk5azl2a2RjRFV2Qm90QWRRRHZGQ0doN0V6WjElMkZmNjJiJTJGWERTeTdsUXF6Z2dxMFZ5NDNVZU54czRXbklXVFlkUEliQXJScUtrZW4lMkJ0UzFBbTdmNFpqNFVDJTJGbE8weGglMkZwQjFndjc5ajFQcUF1RnU4TEpyQW0wckFOWk5qJTJCTkMlMkJ1SE5jTTFCZ3ZLdTk3S3FiVWFwNTBOdFFjdFk4UTU1dG9EVjljZjc4M09rSzdydyUyQmhhRkRQdEo0QmlHaTlVUlJ0WDRPMTN4U3d4RmlHTnh3N001Mzc3RGp4SiUyQlRJWHpvRWFtTzZVZGFsRXRYMkNhR2JHT1NCQ2VpJTJCbUxqY2VuWTZUbG80RCUyQkFJWGQ0VGJjaHNtejQyQk44V0VMcnVlcGRrV3dEclhmZnElMkJVVFVQd2dKYTZJbmtSeXElMkJkVmZZUlBxSnZjY3pXZU5KUXFZdmliJTJGWlV3VGhKWEpOTzdCZ2NXJTJCUlU3dTFsaXIzTW4lMkJmcFJpYjN5bk9YTWpINkFwOXduR3NJNSUyQkpmU1Y1T3UzbHZvcG5SaUJXSjl0JTJCUHFlZVF2UnRScllxU0tMbkx0OFZ6OENVZVFFYkZXa09JNDA2M1pHYWtLWmJvbCUyQjcyeTIyYlZWUHZNbTF6NUN4TmNyZGNhWGJoN0R6UDRieXdnUmNGeXAzMlVRRVFWR0pJb1R5UFlOSWRKbXlmS1NCb0UzZGRpMDBHMm1TTVN0dXlYRWJMMVlLNkxoSVp6MjJyOXl5WktRVnZSWWlNQ2FXcWwlMkI3MmpTRFZYbXhqNGcxTlNoRUt1S0xNSFlzWkFxTUZxMHNxbjRJMU84M2ZnbkFaRjBmb0VzYXBWQ2xUYUJqWTglMkJTUnlQSmZQN2pNa2F2VkViSGZIbmh4Y2dRTVliMWpoTFVWSzhrdG82bCUyRmwyU0t6T0Vhd2hiMDVEZzFyVVd2RDg4T1A1TENTWTR2MVZkYiUyQlEyQmNKc0ZZUlV0MHV4JTJGU1ElMkZNTlI1QW8lMkZxOEwwS2JmVlN0UVlTR2ZOQWhHTmFiNWhUJTJGVjhkYTZ4S2FuTmprM2lWWTdOT0pMWlU1eTczeGtqbEh0aTV1a1A2ZkZNRlolMkYwNVQzdmJSd0dMbWdrVCUyRmxOJTJGZGVPZEhFYSUyQktCZDRKUDFBZlVCOXlibVRKNjZJbnBBZEpydDc0MkZGcDRVZGRwQ2c4b3RhVXlzSEROOFlrT1lnNW85OUhCalFoS0dyeCUyQmM3Q3NPVVpONFRCYlhxYjh1bW0wWEdJb1BZdEZKTVhlaG84SUQyZ1l4NGhwZGg1WG1wUGJ3JTJCUnJHQm5rVVVCajI3eWk2WUJmQnVMTGVHQmxmenVsZ25paFhuM3BkaDZpSXFCVjJ0WGZQaEhaSkVtUnBDU0w1Vnd0WUQ1M1JERjZScG1ycUp2dlJoJTJGQkZRTVBCV3NWJTJCZVpuZE4wMXQ5aXdrZURRM0NEdGZGSktPb0VQTGk4RHJVZzE0ZDZtSmpoaXZVQ1VOTnFSVHpiUjl4QTllUDNpbVlwOVpock5JcDdrT3hEQ1QlMkJKc3RwUmpxRlRpSXZjMEgxJTJGWGV2a1c5dHJhbVVQb24zNDdNOVcxY093Yk5lWnNlMFhUWU04aXZXdUhyRUlZNWF1bjJjTVcwV2pSWCUyQmlGbDZ1T0lpWVU5N3p1SGE5MjJ6WnI2Zk1mVmQ0aVhUT21QV1VyOXREalVrUnFGbFkzTkElMkIxM21LVHJyM1M0U3ljVG9naCUyQmdtVXRjZHpENjByZkNPVW5mVklBbjRpZzJkJTJCZjN1TlRwb1NxUSUyRndSTjlMeXZRbFlWJTJCUEFpMVAlMkJ2JTJGRzdSZ0pZNDhLOU4wbGV5SlFtVFZ6SGRmNWI1aGFQdzFYOUZJRUI0Q1Y4SFZHUkhEZlpQR3NLSmNncnp6QmY3RzVSVm9LMjg0R1V0ekwlMkY1dkpKOHdjYUtXcTQ4dzBaZHFoJTJCWG9IWW4zdDhwdmRRN1g3MnlzTUVqZE9rYUszdjRRSEVUOGc2MnFpRzN5TyUyQmhxV056Qkd1eGlrNTZKeHZ1MSUyRjZYaW8wbThYTFNlQzJZUmUzVlBrb00lMkJXQkxiYnhWQ08wWXR3aXZWN3RUTzVSNjNtQ0szUjh4OWQwc2xYVDZoWWJiZW41REFWM2tWUmxXUFhvQ092UmlwUm93bFI0ZjdiMTJBSG5OJTJGWTFTa21FS3YwV3ZVcUp4dGI3aGxHTFglMkI2a3Y3aVB1andSZ3pFVGVzWkpDeVhycGVaazFMTDB2MFFxcjNQSzFWb1l0Tm5mT294JTJCbjZ1am9tJTJGd3RUOGNsTEk1RGFaQXVSZllpdndpOUtRSUxENlhEbG1pdXpjUnltZVB3N3dYdU1xMGJ4WWpyYnkyeVlva3h3U1M0WjFuRW1MT1VudVRTcHBFVk1Wa2hyUGZUVmZ4OHpCaEMzMUVJVmElMkJPJTJGV0RTbWdMbGUlMkZLQUI4Vjh4dnF0JTJGTHZ4Q0VxRWdyV1pxY1A3NmJLWSUyRjRabGttUFQ1c1JwUGNKSTY0aE0zJTJCV3BQczlEQyUyQlcyWFZaVTFkUlBYRyUyQnFKQkJQbUs3SlRBdUg4VmtvdGRJeXAyakJ5Z3lrM2hIMXppJTJCZWolMkIwb1ZMcVhTT1ZzaWhsUFF3ZjhFUVg0YkNHa3g5ZmVzcUtjaERTc09IdFVPZlhDcSUyQmR1ZWtTUWVUbVBleVFHa1RGeER3Z1phMHNaQnFiallPTlBNcmlMVU8xejYlMkZ0V3BzdGhJTVlGeDY5bHRJcVpYZnpnRHRPZ3Y3YXRtViUyQnprYnclMkJBdnVnN1Mwc2k4aFF1UlZNcEsxcnolMkZsUU9STGZOdjV2bG1yOVlFVE1aZkclMkZkbFpzOGlSall2NWs5cmNCTURiVnljSHRFQ2FEb2dtOUI5amQxa2lDbklpbmJUVW5lZ2d0JTJCS3piJTJGTlROTlhzMHo4VFZxZWlyZUdCYWEyR0wwMlpRbzkxeUZkZ3RWY3Y2SEJFaTJ2UzhFMnBiNEJaQlJkbGpGQW9MbDVDZEJJUGFZJTJGN3R2eEhBdWszenFGaVVEYUpVSldTUldzM3Y1VURrJTJCdTA2aGQ5TDVkdkZFU0tMNGZzSiUyRkJpN3pyekNtd29lN0lhVldZJTJCd3lwMzkzJTJCOWljcjd0JTJGMVBhN3VxbElDa0lxZkFaaDF1Z2olMkJ4eHBhckJCVTJ6Wjc2VHJIRTV4RkV4TEhrQSUyQlUyR1lHdVZIMFJrWGZ4TmhiY0NtJTJGS21kcE9hMyUyQktkQmF0MHZGYWo3NWlrQXBWbXhPVTZKS2VjUDZvZFlMTGpEQmZSSmVVUlRLTEdXaldNUzJRdEhPY1N2WDZ3eDlYVkhhR0lIYjZNM01vY3FJVjFINmZwJTJGbkpkdW14THpmSUxOdyUyQiUyQk1BbnUlMkJGM3VpSWFtd0xqTUp2dG1VT2hLdm9xekxubEdJN0J4RlFlQiUyQjZBRHVwOGZ1Z1pUZDVGVnlybW5idklLcHRsYXpZZmRCeDVWZExyc3g3TXZzeUg3THBWVGZoRkh1dWNvS3NkU0dNM0ltTSUyQnFOWGlkVFRxTnpqUjFEb3VwUVFnZlRYWVpsaGtiaHVjRXhNSklFJTJGRmJ4b244WWt3NyUyQkY4bjR4dDU0Um9Jajl4Zm5SJTJCZCUyRjhkZ2QzSzJlckp2Q3JtbGZhN2U0bDBTVzlHVkxtakMlMkJweDdwYm5sMFFwcThseG5LZHRCN1hiTVd2UXJ4M2NRV2M2cmRSJTJCQk5Va04lMkJqZWlVUUVZMXR3QTVVUyUyRmlLazElMkJDZjRmTjRiT293WFRFWlV1dlBjbzRvbFQ5RmRoZXZkaHhiZTZ1bWVabVhUWkIwM0N0M1dhbFMyNlN5SCUyRjVwMnBXNHFuQiUyQjFEdVYzVndKazBpJTJCSHVkNVcydjY4UTJSODc5Y0dpN3JxNEt5blU1ZVI0d01PRENPY1gwMGhKY1JFcE9sRmROTUtXQmtDOGEyYzNTb2h3U3d5M1ZxemEwaktydjBLUEp3NjBzTXVibjFRMSUyRkUlMkJpajdGSCUyQkxQcHlrNlY3SUo2dnpCbjQ0VFZidWszcEN5JTJGcnlxM1NzYmNYYnBteCUyRmU1c1NIVW11VVUlMkJrcnFFQmVrN3FlbTYzRjlNM1g0cmtybXJaUGkxY2hFdDJoJTJCdjkwM2hxZlRBZG9NYmFqWHBwUFBFZkxtZUtQRzZkWDl6RGUlMkZuT1c1b1EyM2RBdTAxN2hBMlAzcVVRMGp0eHQlMkZNR1RxUUVVdXFqSVB4aWtCMUw3YTBOdyUyQlVmJTJGaE0lMkZmeXM0eGpyYjY4eXRLbDV0UTRsNHd4S1AzRUF6dnNhdzJxeExhT01YRVhwenplMjNLbmlDRUR1em9wdjJzMiUyQjlGSjVMdElMSmEyJTJCJTJCUE9tazc3S3BGUjdxWVhYJTJGYlhLZU9oVSUyQjYlMkJuNWJwJTJCaTY0dHUwa3NLN1l5Y2ttVW1HSGlleEszcU9POFc0Q0RncXpqaktYdjlsYTdWa3RxMXFzZVAlMkZIZGhybTY3YkRmSExFOW1CVTVKJTJGTmxscHMxTzBTTTA4aHhTV1FKaEphNXdHVWdtYWJtS2pIbmE1aXhDVldtNk9zOUoyS2NWWXB2czJiTmNUVFJBM2VFQ1lNR0hYeDl0ZEFsbENHVjJGRTNiTXU2NU4lMkJPYkRqM01xNlRXVnBoRUlZdlh6UVNvdnpBcnFzQ0VjRlRxcHNtdUhJWGVaZzRacE84ZERXTTNVMW4xMGtjRklsM3llSWtsZGlieXJiYTltN3prMmJBaENvOGEwR2o2SFU5NWxnQlVQNDc3TmFMNTklMkJqb0YlMkJzNFdma2l6OTE2JTJCRWFwaEMzQTElMkJlOUdwN1M0NXA3aUJnbldGJTJGcmpzczhYbDZqVmd0cmxtSHElMkZnZWklMkIwRkdLNlJxODA3bHZkWEpnUnJ2anQlMkJDWHpMZGlTc3JoajNwVjBveFo1QnRITU1zUTVWZU9SZUVCVzFXdlR2M1ElMkJnTnU3aTklMkI5NmxNTnJ2VyUyQjNQJTJGJTJGeXltMjM0WFY4U2hMaUc5QkZudG1wWUFGMTBQSjc5bFJZTjJWRWJCTTdyUzY1U2dhSmFMRm1hdG9ZQSUyQnlwdWk1TFFvUG9xJTJCdTNHMXNVJTJGcjAlMkJoY09QTSUyRnVvU0poOXUxdHBZVE1Ja0pmJTJGZnh0TFFoaTBTNmNsSWh2UGQ5ViUyRjFvJTJCdjdTJTJGOExYWWlVcEFtNTN4Z25KeCUyRmRNbTI0JTJCR2t1bDI0eEpiQ3V2UE5QeEwxUG1rYWVDV0l4RE9OSFQlMkJRRXFDWVhQTGZzQ24zTlZ4YjdoVHBSRjhKN00ySG16ajlhb24yb3FwTDNGWHM1S0VvcWtBZWFtUU1pUkthVmdGZU8zSHNOZHY2SEl6ZVp3azdXZml2T1BmJTJCa1NiJTJCeERtZU1sTHJGejJrdTNxWThBWXc5dFRjU3dCbkp5Qk4wYm54Z1dka1JMVEJWclJ1aldGbWZ1SXExOW5hQW1CRm5zak1UYUpLdWtGVW93bDIyVFExRzZrNGhNbkVLVnpuelNZZkpMdWJmYmhqZjF3UjklMkZZMXNSVnRiUDc0MUczazd2SWtKbmE5WWE0Y3o5WkpISklWJTJCdlJuT3hIQnIlMkZlcElDMjlsNHdnbTUwRXlhdnIycXo0MjF5YTJxb1cxU3o3R3VldjQ4QTRWVm1PJTJCenBkMUI2eWxlV3pWZkxyR1B1YyUyRjMxWDQ5dFJEWDY4cWx4RFV2cGJCVld0d2dQJTJGYTYlMkYlMkJSaFd5b1l1N3pHcVVHOURaTlFCUHpHMmVVa1M1WGdYd3pTRDduVW5KbFMxUVZFWEh0RHBZeUxwWTJUY1lscjljYkdFT1VCTUFnTkNjSlc2cHNlSzNxT21vc090JTJGRjA3anY5SlpCSzRERnFYYXhaUzFlbDlDSXJWJTJGbk9zJTJCc1JFNVJxU3h1WkFuVmRQRTE0a2dkMiUyQmFYcGFaNHdjaW1YcmxTZm9TTkxCb01HdyUyQlolMkJleiUyRiUyRlV2OHhYeVprJTJCRG9GWm9EeGxzd0k5clptazlvYjBvJTJGRkNRbXRPTFJSeGJIaFdSSFVkTDkwNUZ1a1BuRmtoNTBSUlRzMmk3TWVFUnlYVFNpJTJCZHplZE50Vzh4QWFrSDl5TjlIdVBRR0M0VCUyQmxNbVM1aVViU3U1JTJGSzRVcEJuODVZVVd3SUVLUVglMkJNRzhFMm1xY0JEZ1UwZ2lWV0IlMkJoT0hMV25PM2hOQSUyQnBCaHNHZURzMVpVOHhndW9FZURMY005ZWthMTlTYiUyQiUyRkpoczVjQm5XMHh6THc0T205RUNEJTJCZkVVbjdxWW90VUhYOTNYdXJISXZ5YmNCJTJCJTJCY1dxNmxPeGV6aXdpTExFJTJGRDc3cFV4UFk0TEpaYiUyQkVwUVIyUHRiMUFnM3k5N21TYmJRcTRFYXRCTTU5VWRMVWRkU2JiVG1YU1Q4JTJCYVUzMnJEdlZaTXZzMkdFb3VtZzBuNWwzc1pVbFY3SmdzUDJtNno4eWIyJTJCRW9CTHFpclBKcEJ0TyUyRnppTWlYWWp1TjNaVENkWEhaWkl1aEs4RkFjRnhZMjBtYzBJSnZ1dEtBV3RCTHllb1VSamg5M1RLdThlZDFXQ083bXhiUHR3RGxZZ1d2M1E2YzBzR0pKWVNaYXA3bVJtTEpDNWdWZUhhbjQzQXpEMTFmd1dyUWRhNFRUdyUyRmdvJTJCcEUlMkJLZUlKUlhEbE51dEF4aWFtbXpQODJvZGNlanFWN3pzcTRTYlFpb25lT1YxWW5EdWZ0WEdCY3NXdk1idVNmYnRiZXgyc0ppdXduNmpBeGZmU3hTRyUyQmxjazZwb3hNdEFhcm5tTyUyRllSREJzeHg3NE1IUHZuSmNwbmFvaWtPbkNWViUyQk4lMkYxVGFOenMwVkdCbHRNMWs1eUtQZEZ6RWdLN1ZPZlg2VGFzUVpoSFJWY3FQMHNTODBKNG1ubCUyRm8weWZwOVNra29lYnFLOXp2YUlUSDNsOGRkOFVtaFZPNzY3Z1BzaFRMWXRrSEhPWGtWczFwTWt2YyUyQlE0ampaampZMU5CZGV1JTJGTHJrMU1SYWRySlh5WmtWQ2FIeENJMzU0cFRpZzVHM3loeUdDVVp5QXRsVmNKSWolMkZMUFU2WExEdVdscWs0QVU4bUt5Y3lyRDd0U28xMlE3dSUyRktKemo5NGFtckhCWW9DM0Q4WnpQaFNaZDdrc2lsVndYNUZ6JTJGNmh1RFMlMkZGdnd2MXVhUERlQm42NkRIYk1zempXVTJRcHZyNDgxUTREOHdJamVtb0pKSHNBaiUyQmpxRlpRa1dIOE1YJTJCcWYyaXh6dFRQeW02a0JQc3JodktSOUh4cjEwbUhnejJXQjFWSSUyRmNEVmxZNEUzMncwZW1YS1pXbkFGdkJvamdrakxVR0hzRnJWMXE0NVJNbjAlMkIxYVlHNm1DWHhaY1VkVWFBcG8xWjBuenhKWDZuMFVjMVQwcFA2ZWNlRTZvTlhmdVZyUnZvSFdtaHclMkJBdSUyQiUyQjFxdlk3c0pjbVBRZ25rVkpRajFRSCUyQmZyZ3NTcG5QSm5lbTJNek9KSCUyRnQlMkJRbVB6c0VpMnZIZkM3Y2lubFhsa0tpanRvNmNxTk9aanB6OWVIUmRXYmp0NXVwMDBTZEZhS0JGQTNEbiUyRnVjaWZyMyUyQjlsZWhYZE1wWlhONUpaS3V6VmZ4NXZGR1pLaDQ4UzUwRFg2STJjbWk2Rlcxc1hQaFV1VVYwQ3E5UkdxJTJGczZNJTJGWHowN2c5WW9EOE5QcGRoJTJCbkZTNiUyQjRtMDFhRmh2TVExbXhXbGowc294TU5vN0xobnlJT0gwS25ZbUdJOW5VbGV6eUdkajhGbzFWbWhUejFvMTlPeDNpanlKaCUyRjJlJTJCeGR4TEpiTzJ4OWs5UXRFM1VlSjEwRWhlQXBFZFU0SFpOMzExM3FVRnF5cXJJTVBpWEhpT2M4R1NmVk9hanBKcnhmeWNNaU0wODNxbjc2bno2YkRxZmRLJTJGWVUzUSUyRkhmWXdTZjRJODRHcjZreXF2JTJGeUh4UXVuN01ZWEhycjc1aEZwOTRFdXp4dTJnTUQxQVVKT2R6VGJnbGclMkZZcVR0YWZRQnpzTzIlMkZEQ28zelhaVzVLanZkc3gwYkxNdDd5VXIyTUlsWjBIZSUyRktFbiUyQk5seHJOc0YlMkZTV0lUS0tzcFo5NGolMkJlbWFZNWhHdW1LU2F5RGsyTUlrQVZDaVdSJTJGazNKcGFubHZnNnRucGlwUkl0TTNFa1hWVndyRlBkTVpkbVRtN3E5JTJCU0FKYiUyRlFWTkNtQ1kzYXk5d1NDYU9kVlVPdzZWU3JCSmdiM0x1Ym9MbHV5RjBVazZROFFrWmx1OWwybjZhc1pRRXolMkJmbWpRZ2R0UnNwNm9ZSlNQWUxyaklxU25QdmVxVm5UMUhlQjRQQzElMkJnOVJvOHV1WGw2MiUyRldmVlFOeVZ6azR1UkRqd2JHbURxN1pidm02UlZhZzJHSUxRcEUzWnYzTUhLVHclMkZzbWs1JTJCbkNtTkhnSjQ0bThVUDF1eFB5SGd0bWp2ZzMyU0Z5YjlaU3U5aWZIMzZNTnFlNmVOWiUyRm84N0RLJTJCOTE4OEFNM1ZiRllCJTJGbFVOWFpsZTlRaHdjallqQ1dTcmRkY3BYb1BXYkI5ZFljem5TYzFZVXVDMFklMkJ5TFRnZGloZyUyQm1ENHpDNzlXcWx5d0JqTzl2bjloTE56YlglMkZCbTFERzNTbWVPdWNlUGtkNWplMzVEdW1Mbzd6ME9uODl6b3FDdkg4d3pQTHBCeHFvYWpjcWNydERtQkh3YTNtYmtzbmo2ZEhtMFZZaTR3RGFFMUdhNUVtQXRESDNNWmFnSXFqRnFuS2N4cjJqVVZGQ3dQR3ZHSzZ6TENkemxBMm1wcUFiODVNTjYwNDdRRElGdFVrdERsbE0lMkIwRG5haFYlMkJneHoxNzFjJTJCZyUyRjM2b3RkYURyWEpnOExSV1R4U1VCRlRVa2wyck9aYk1QQ3lJWDRyUDBmTyUyQlpVd2M0RXNJMFFxYXQ0YnBkRFNqeXJkSnBhVFZ0RXM0JTJCdFc1aUJjSVl0clBOdHI2ZGJQM1A4OFh6bGIxRm0zQ1lubTllUDZHbiUyQktHbExWd1JxeGhuREdnbHpVcWptZWRreWc0TXVqQ1c0ZVdSUXlUN0wyVWhtUXhDODU2U25UZlV3ekhPeVhKaldGUFZONE1EVmc5JTJGQ0Z3Nk96d2pnaCUyQnJ1YXVEcXpzTzhGR1ZTVHdKQW1RQlpFQmRmQWwlMkJKN1lHYTJyNWRHWWdYWU9JdjVUV1UyNE5DaTkyMTVKdTNNanNYVWxyWDM5SUtGOGtTVm5ib1pqeUNaRzE5TCUyRndGRHA2ZWglMkY2M29jJTJCajhvU2VDbXhQc1VuNXZlVFVDMWJnJTJCdHJOdnlwbGdocE41OTdqYnFDV0VNZUVOSGtJRFFiemhVRExNbnFZJTJCVzdseHNJdXVmYzRLZlpYNkRCUk9sdUJHa1JKbktJMjclMkZreUptYnBVUkprc3FPeVUlMkJHWDgyRk85T2VyNUFFSnRMOU40VzI1S2FKcHJvTHhhdCUyRmF2NlclMkZKVmslMkY5ekV1ZDdsdzJNZWZVM3dnVjd6U1lveSUyRk1tSGk5cDN5dlNaUTBYUzF1WiUyRkpEJTJCZEZzcTlYVmk0dUxPdTdSTnFkUWc5VlRxRHdYdFFhT3MlMkIxSjNuSSUyQm9FZkd6S0lOOEJyNDU3V2MlMkYlMkZNTHBhOThEeU1hdUlrQ2xWc1oySmJKSEJnJTJGazljZjUzRG1mZDhCOE9tblJiTjRVak9jdUp6WHpxaCUyQkVjc3NaWjBQejc2QllYWnd6JTJGcm1qSEtheTd4MWtPM2JYbkpBY0ZEaWtXJTJGcHl2RyUyRktEVHpONHo3QkNZYXk4TTBBbmwlMkZINUpZJTJGaTVseGhHbkw4TnBlODAzcFFHQ3loM1VxMkM5TDNHalNTJTJGZlBvcnpucGdteFV6dHA0WWI4VSUyQmJoQiUyQklpVXFxb29jamZZdFglMkZXRVlaUyUyQndVUWNQVSUyQiUyQlZQbFB3YThzN1liQU01SnNDZkY1WkZZNWJTUVY0RXlSc2JTTHM5WSUyQjE3Vm0xRmk0NkpjU0M4ckFSaUtpSXNsN2ZqNlQ4T0xHMHM2JTJCZkNPWkkzWjQyOUtnSXU5TjN5Y3ZhNTdxbGJ3ZGozc0d4JTJGYWdndDAzbVFOZlBBJTJCdVFWZUdHdVplOUlCOU9xNWk5cXRPVHpkbFN6bllQc01WNjdrNm5tYmdPeXUzb0g2bkJZV0xHb0ZrdUNyakhUTUpVeGp0eEtNYUFIeVlMbHJFQWtBcjkxMjFjNkE1NmtGWjE5U2FZSzVSJTJGY2hQa0RYRFRIaGNxenpsZXIya1ltYnoxakRwQ24xSkVVM3VCVGY5clZIeHdiWnglMkJSSHVkTDJNdDlpMGNYdm40aDdJMWxXd2FtVWh6WVd0WWpHQUpyTXUxTGc0ZW9wJTJCUlF4NjJTTkd1VHdyT21QRFE0cjdFblBkWGhUTDdFQXlqbGpUdVV3Rk1EZXFia0daRmE1Q2tSRFd6VG9LcExGJTJCdXFybmpoa3F0MkNqM3l0ZDlmcmV1a0Nhc2g0Q0hWcDFjdDNkbnM5WXV4NmdxWmFYeDNCNEhpbTRMMlU1S1VycFpVU1JtZGJqN3Rrc0ZhekxJSG82aHA0bFMxOVlnclJld0RkbUo0WVVablp1dFpEOSUyQnpScjNRcGI4QnMlMkI4WFRhOFYlMkZWUDVNb3IzbUNPQnJtaDJ6cCUyRll2anpjWiUyQllBMjFVOXZ0TWdFUUZqWllGdnVBRGdXU2JoSFlEVHNmVDB5NmV6RFdCZm5UR1RkODhvYk5KRWVLMmxOcXhvVVZwM1FGVjZFMkJVcEZ2bHowV1VUWGRqUzVPNDhXRU05blR6ZVpRbVM1SWlzVGt6eGptZTZaYzd1VWVqT0IlMkI2NWFQMXclMkZ3UiUyQjNFSnRFdTRkU0hhY3g1OEl1U1pXV3YlMkJEbFFMSFRDcjVidGVlOUNWOFBZSmRKZ2UzbDU1V0hBJTJGOE1Jbmo4VXBPZFRnRDQ2ajBFVjVuR3A2bUJrd0FyT1JxOGZYRjB6MjBvMFolMkZGRWlGTmhVb0p4MHprTWxlYXNjSXVzTzdJbjVhd1Z5dmRqMHp6bWhnZDdaJTJGTjFlTkhEZG4xV2dnRVlEcjNnamo5TklJY0h6R2tNYW83cHRoTGFPJTJCeVJPenlyYVMlMkZFbFBraXJIU0cxTG5sYTEzMGUyNE85WEw1cjRhMFVqdlYxS3pQNVBZWUpVdHhUTVhFejlDb1NCWHA5clVLWTVFS21vM0JOZXU0S2ZuSXJKJTJGdzFaa1FZNmk2YXNpSzZrVFJWdkVVaVhNZXl2S1pJQ2tqOXAzd0dwNEh5RVBRZGFDbVRNSTZnWXlCbU9DQ3dwUDBBQ0xjRmRVZ3AydVRsJTJGUFRBNWJXVm05WlJrYWp6VSUyQm53dHhvZmNiQWowbzJ5Tmp5NTI4ck9sU2dqYllJcTFrMGVyR0ZUU0VzZENjVE9lUG8yOEFxVnhiZGdYQmFzRHNPR29iT3FuYU15aFJKUUxNdFdwb28ydnoxMFVqWGRzJTJGazJIdFVoUFZDaDRIJTJGVUI2VHpxQ2dRWm0wMXpNdHNIY2E1S2tySlJlcnBhY0p1SnclMkZJc1BmYWtCeno3OVpKTDk4SHFTN0VtS1BiVUZLNm9mRVhzdTcwNk42a1ozTFZaMCUyQlpxdmY0akVpTmZrdWFaZGx6S1FUNmZncTlVTjNDb1ZqcndLY1BUT1h1b2RkUGY3YkZPUm9tN3BQRzJhYlBrRjJldE1IZGk2MUJLNzdTWHNsWWVCRkI1VE1RYXhvR3VuTDBZcERIa3VIYUZhTGpjM1BpSWNpSWQlMkI3ekpxcG5ZYUJYV3VvemQ3ZHRaQVhuR2E3VE5YaUZGV2VSZ2s4N1RGM0ZCMlBpV01oRHVWNTlaVXdORjJ4OGVjaEl4eVJDM2YlMkZ0b2N6d1VreGRMJTJCelZLbXd0bzNva2k2Q1hRb3k1NlhPTTh2SDN6b3Z6Y2pvJTJCdllpWDZ4MGVuNGdPd0NhR1F3Y003aFlmM21RbGc0VHlrTHNSTjhvdVU5bGV4MXA3NG5yeE9kb3d6enZWWUxSS1Y2aHR6SWxNajJ3JTJGZDE3a0U0ZW9tVFpmNVlveGNyNlk3eGlGejVpWGZ2bVR6ayUyQlRaRldOTHNzQlJrTFlZQzIzVTVHUyUyRjFCRjdlNjdaaUhRVnd6JTJCOTEycTFvQ2RUSFNkRXBuek4xbzFIMGNIaktKQnNCSDdQVXlEUjBiVHp1c3QybXRRTWt2YnhHT0pZWHZhUmd0RWs0amY3U2QwSyUyRmhKbWIlMkZkd0NtN2tsOXQ3YlE5QXd5RzBaTTgxU1o4cWx5OFYySHUwTlNBQWJZaWhURzRHcmdjOEI2THJLdDdBYnBjTEhnRElveU1ST2RSUyUyQnJ1RXJVbXJ3aGdBbDdsOGE2NnhESWJPcExaNSUyRnZxZHhLQkpBa2hXYThJQUxNTE5QdHpXZTViWWZ3ekJYaGU3UFBPV2xqcFJqZGxaRjdYTlU1TkV2ZDFsNTVVeXdYViUyQmlXMmFkSElhU2w5aXlwRThkZ2NpT0Z0cTAwOVBUbnNvWkl0aVJQeWdjT01lZEhjUVdPcXVLYVpWJTJGUTZaZjBVNTRIRGJ3clE3SVBRdlRkQmRZWkVqbnhVdTB3SlVRVXhXeCUyQlJoUUNjUnN1cHklMkI5dEZWRSUyRkx1MmNuSmF5WU9KYVNFNkZaeFpXWndaUkdlJTJCcjVPJTJCWmFJUlBqb2E4MlNkM1VlTjl2WnBaSG8lMkJlYzlLQjVIOHl2TVBxWGttT1Y1QnJkJTJGckpRRnZJazByJTJGMVc3ZDElMkJpdlJNZnlGSzJOUktEbXhkVHZZUVplckhoJTJCWHoxOTNwMVBnbFBXM2RQZGZ1QzhJQ3lEeFVvOEw2JTJCM0U2JTJCMzRrcnZEbSUyRkVaTnRzSjlacSUyQmFaNXdkeUNCVCUyRjZTQmNGQ1JQc1VXR1U0eSUyQlFwejFIRGFBWldvOXd5b0JBZzJ4WXFQQjI5YnRrc3ROS0E0WkJ1cXFNZXlBVHRvcGxraElJeXBmJTJCJTJGN0xpdjElMkJ0Rzl0ekgydXJoMHBlQm1CUkJ0ZEhidG9ZbjVydHFoVFJtdWFkWnFqRnR0WkxzOUtYTXlIWXN4NzBvbG0yJTJCVUNQTjJON3VkVk5KUE5uWm1vWGY1eWpTb1BIT1A2S0czY0NIOElHUGR6eXhVY3hYM3FMJTJCY1JLampEdGRsakUyVHlHRXBGbWE5ajlCTk94VjJyS2VRdVhSYzBtR2FGTFNMcWtOTWdOOVY1anFvOW5pdnBRcyUyQk5pUXpXRFdrRXZhOVFJJTJCOHF2RWklMkZEZHV4YXY3QkhYc0FNWms5YW8zMThHYlc1ZzQ0dWZXenJqaFpJMDBwRFZ0N2pkJTJCbklpJTJGdldXZlgxd0ZmcTYlMkJLcXhYUnhabXFQYlFPVXRQVEt3OXBtdmdpdE9wNExoc3VsWkNENG5XeXFZWHU4RnQxTHlyU1RZOUNrU2JsVE1MUkhubW1TanFwbE0yUFpzeGdiOUJFYXZEOUxtWUVhMjB1TnVWa0d6NHZVMXBheEhqNXlCMHBjanA4V3VLQU95cDJHN2FiaTlzcEU5YWdlUSUyQmxTODdBc1ZUOXdZMHZaVFJTaDMwT25yYk50ZkZad3lwaGo5ZlMyaThNRnVsb1FpM1AlMkJlbkI0N01sZzZZMlhaRlFwUFVGWmE4JTJCdXJrTkdZeDU2N2Z4djlyM1VIWXVWV3dLMmM5VmE4eEc1MEdQQkhoRGI2b2RPaG50Wlh4OVQ4b0Y1Slp6OUl0R1ZVYnIxSWkwYm9EVnVOc1MzbWNXeklpU3BSaG5ZczdsUnZHejV5WlB2dm5hS0ZlJTJCVlJkakY1dUJBTXNFOXR4OWFpa292ZWl1V3gzYWt5YnMyUkhiZFdXSGk5RWtrQVBKTVU2UUFzMlNTYzFMZUxkWXVYSUZjbHRkSExiSGhBa1hCSk9nUURyZkQlMkZ4cXhybjMwSndycHVsTXhrVmZ1c2ZXcEFtamVYVmxDc3poOU5lZkxGWmRzS1Uzbkx0bDJTOGVMb3NETU9MVTg1dlBxTkZxTE5LaVd6ZDBqVTZicU1qT1Bab1J4OFRvZnhYTmVCSElCZzA3ek02U3Y1QXE3WlRXa2hWMyUyQiUyRlJYNXIxbklvVFRQenJ0Z3hibkRHbXQ4M1RaVnRPbDZrM1JtYkdCVXI3MWF4dXZaeWNuSnFGSnZabHhKU3RkcmU5T3ZQcDRJMmxSZDhURUIwNXpRdFBHVzE4azVudFZLZWVBT1dHVzNkMDdGVSUyQjNSQkQ0dWI2MElWMFNaNFd0WEFnMGZ2MWhVVEJqQmV6JTJCMFlZQkU2MDBoOFNYaGF6c1ZwOFFvcjd6TldrbSUyRmpaWWNrZlkzSnFvV1lxdmtTcjNzYUlsSEVZYUgzODJuaWolMkJIT3BXTGxGbTclMkZZakNXWHElMkZmb0pBZTVDOG1aSGRlUVZkeEQ1Vk5kZzNuZnZrcFJNejlhaXNKM1ZYcWFmViUyRlNLV0pJZ2FhbHhPaDI4a3JkJTJCbDQ3VFloUEFuV2ZUdjhxMkQ4cXpDS2tpaTFPJTJCMmgwdEVidGRVVWV0MVF6b0E1eU81anhlNE92eW1MMGp0c2tXN0hkUnlONTRWNzVWdmlKZ0lVZ2dmYWxlT2dSaHd6cCUyRmIzaWRPV2hYWGluQWJTRCUyQnZSMFJiNnJHRFZtcVJXbzFyYng0dkRReHZwMjE2Vk9VZmxBWXp1bSUyQjFLM0dNJTJCNXNaSjFpY2lkNmRWY1c5U0Z6R1gxN2R3VDhoME1XQmV5VnRUdFJybmpxRHVMMklyWXpPMlZFV05FU1hObFJTSXR4cUhYT2FZbWl3VGxwOVphekxpUkM1REN1aTE1TmhJdHpHcmt4RzlPaCUyQjJiY2xFYmpJdzQ0RkE2ZnpYY1oyQWI3QnJQZ0s5aXNGeG91WXByZDVNWlNQeUNCcXc0OHJTTjhmS3JpZkdYMjd2TWwlMkJqczFxRFZMdHRVRVFlVU1vTnU5T292bktOMnFpNjdCYUUxbmZZZFVnJTJGaEVmQ09lZnptdjd0c1RmbnBib3ZEb3JJYiUyRmUwbldZRDNZbWR2a2ljbW5QaUtrQ2E3JTJCaHhSbkdnJTJGWG40a050Y3g0c0NlRWxvNzYlMkJaSlQlMkJUNmNoSnZSRmZNN2ZTbEwlMkJLdWVvZXRBOWZzNXRtdGh0dXpWS3k5dEdhJTJGSkMzZUtDYmo3JTJCamFLUUlaRGhiYnZORzB6NXFPRUVIdlpLNWgxMFZQNzF0ZFlYdEZNMmF4eHZYT08lMkZBZ0ZsZHF3ckVtWSUyRmthTDFMQmlzNlZYd0ljdHBjU2hzbVZtMjNWUFAzUmtJMTQ4RFJFbHozcVB6YnYlMkY5cWZJYiUyQkp4RTNFV3QwaWFZaGUxQ3RjeFpKZSUyQmkwOVFia2QwS3Nnc3RWVm5ONjdSb3J2R0klMkZiaW9lVndEdnFnODcyeTVIbEZ6VXg3QXl2JTJGMTVwaTl5WmxUYklZUXJpbEFEWFdpa1FaTE5ZdE5EbFJKczMxZUZDYWJBcCUyQmtvb2RRNHBkcmFjTHJMTlhMenNTTEhsZkxLJTJCY2t3TEtsQlV1WjMlMkJLOXNLbDRXUWZoRmpveDVKekY5JTJCaVhDJTJCclNOMlRmWlZRdktOeFl5clY1dTlKN2RvVyUyQnFYODdUTGJJNXZaa2t0SXA2cnhBRk9LWjVaYmtMbHBMVFdwR29CdiUyQjU5UlhUZ2olMkZ4d3pRbXpBc09uTVFHJTJGU0dVMmk2eXJjUXlWZWpiODJzMmY4SDZGRGp5ZUVuZmV4YkFRTzZlMTdPbU5BWHBobiUyQnUyaCUyQms5Z2NkQTg1SmlEVko1MnlwVll6czNFNUhkaUhRUG9yeFFBVzVMZmZVbU1TRnNFOEtWYUhCNGphNkN3TENhNEwwNlFZM2ZwJTJCQTk0cDdhNnR5dVR5OXg2WGlKUEZYOE9xRjlBdURjJTJCZ2dXaWdMaSUyQmVGdmI1NVNqWWU2MGZxU1VYYkdxM1RNa1oxUHklMkY3TmNJMFRzRXQydWRUMFRVa2slMkJVaFZ3cVJHWjZWSEFPRUFOY042ck56RHQ4em1pWUolMkJtMFRrN3ZiQnV4NU9pbkh1N3ViVUYwTyUyQkp2NVFEMVJmQ1FZckU5Vkw1cm4lMkI3RjFCckcyQVN6c3dqMldGRlZURWdCRmRVazRYa09JUWh2ZzNyVTB1clhvQjNVY1VnN0FKaTVRJTJCcXREcEU1dW9BbHJuWiUyRnBIeWpxY2tzOENFaE5xalhBZWg5d2NEYlppSjluZEdRSktMNUt4UTJUbEc3djE3V0VJSGJaRGJZJTJGa3FQaHB1Vzh4MnI4eEJNWnlaMFkxN0JqNlY2ZFRtc3MwWHlxbUFoN045d0JmV1JuciUyQk81YVFTS3F4TXYxQ29ZcHg3dGMySFR2ZXNxZkNTV0F5JTJCS1M2UGJQeXgzZU9JcEZ0blBqWllSWjFqZkV5eGlxQk5IJTJGaHBYSEVsbXlENkFONml1UVBtMDRHNyUyRkdsNWE4UUVBNlU2U3Z5ZmFWRHlpM3JCYzlUQURMS0pJTlVWTCUyQmJpT3lnTml2bjlWTFB3WldVWXJpVlRtTExYWXpwMUZ5VWM3ZUElMkI3Y3JVNEU4dnYlMkJ1VDJ3bjRCWWRhZWZBYjIwc2FObDdVSDdkUkdkTTd4WjFMNGFoWCUyQjVaMGU1WTN4THlUJTJGbjBUVGNZc0hBZWNBclgzMXVGQjVBWCUyRlZoUmtpU0xFbTJZMDNQNGlVczJGNHVkOXNGTGo2dzRyMHFoOSUyQkxjQ2ltb1JVb2h6alJ2M1gxaFpMNjAlMkIyMVZxOHhMMnkyc2g2OXladkExcUFyUiUyQnZHSTBKeXFRR1JpVVU3T095N011TnpGJTJGUjdWUmdOaWNsWnBwUU1MMHh2M2piR3JpdmVBQ3BRVDRTVWhUS25KVVVVaiUyQjVxNG1YVEclMkYweUpwbjFmWWRqMTE0eWJjJTJCTXNUWWU1NkVIZjd5VXgyMHJnWlJrNWRMTDNrSXowNHlYSUtGZHFqN3pKNDRPUGN6TUlXTmxwa01lRmhYbmgyYW1DYWkwaG01TnVnJTJGTm40eGNFeDA0JTJGOHpCYTkzMEJYVVQ4SGglMkZSUWkyZTVGZFpYN2xxaEtwUUV0NlJOazZ4OTNzJTJGV2hpeDlwJTJCM0haMlAxRmlUJTJCbmNqJTJGJTJGeGlvSCUyRm1LbmpCdDhKM09xRmxkbmNaZEJUcU5aMXVERGliM29GSWNSVFpSZnhiOHdDWSUyRiUyRmFXM09lZW5WY09FNjVhZnIyeiUyRk5jblhLU0xkJTJCUEN6VlFwZm1jJTJCRk96aCUyRmNVb0s5Z3A4R2tQRXRLOTRpU2xYWFlaYU4lMkJBbzAzeWo3enpEa2hXRlg4U0xUS09KTDhlMWxveEtUNTQlMkZZeW9iQ2g2UEFOcDV1U1AxN2ZlZnhHNnptQmVxbDFkeUJaemg3JTJCU0hrdHVYeWJndXRyQm1yVE5xYWhDeGJIdG0zRXBUNFI4Y2IlMkIlMkJ6ekU4N056RWhyR2taN0J5NWhBUE4lMkJvbHVQUXdFRU5JRURNckR2SDJCVGs5c21rJTJCUFlJOUMwZzBkbEVSU2RnQXl0TlNzeGR6Tmk2bDNtYXhTOUx5djZmd2VTYjBIQSUyQk04aTVZSzhVTTIwZ1pjWkk0aktKc2o1R2YweDNIJTJGdGFYc3U5dGF1eDlFS2ZiTFVvckpXcHQ5UyUyRnJHNTBudVltOVhrbE5xdVFMM2xXWU9qMHpWWVhxTXNnUCUyQnNPSDA5N056WDFIRjJzYk1wYlhudGdKa2VmeDVGUUs1NVJQbjNReWE3RDhpUXJqdkpweG9PWExkU29XN0hZdCUyQmFnTnF3YVFuZXJoc2pBS3ZhTENhYkNYRGJWT2VJZSUyRkh5VkVySUFLZkEycERsRnQxbWJ1aGg3RzJsR3FVT2Y0USUyQjJTOGo0JTJCN29jem85WXgwQlB1JTJCMWg1UWxJdXNnOTlyRUpaa1VGWGwxc2x6bkJ0WldjdXJSaDgyOU1oQm9QR1ZOSWpzWHNUclNqdWElMkY4U1olMkZUY0NGS1JSd1pjeHQlMkJYVGpJeVhlaXI5JTJCcU9LWFhlOGhubmdyWG0lMkYlMkJLRUQ0N0xqTjlCMDJvUW55MXViZVlVcVhQZ2JUSlVSNU50T3oxN0cwVjNZRXlyMldha2YzRkdyaFNhZ2pRZTZBajBvNnhxcSUyRjZubSUyRjlaWmpCSXB2QUZCTzVzUmZMZXZyR1diNkpPZHNNc1dYbURNVW1aT2MzJTJCMnQlMkZ2WWYyVGRVOHZaMDBad2pBdWdoQ1R0bnlpTDAwcWRXdWt4c1VpJTJCTkV5d0pwZ1R6NzkxSGRBNTB3RXhOanB1dVNTbTVCdGk2MVFLTldxbWhDREdieVA4UnlYajVyY3V6dk11bW1FNzYzQyUyQmsxMUtZcGclMkJ2JTJGUXBvMm8xc1hMY05uaEtOS0taSnc1ODdBUnJzSGRhb01nVG9DS1haQzNaaXB2TnN0WDVIdjdpOGJJYWRwWjYlMkJXN2RiUW9ZWGgyd05UQnFXcER0JTJCTjVQTTQ5N3NMSUhVbFVHSkxma1pTajI0TzdCYmY3TWN2TUVqN2JyZmVScGclMkJyVjR5NzN1dTJ5MW1xdWhGY1VBakNqbSUyQjdyczRmTUdzRWolMkZSSDZuNTBzMGFVUk5EN2YzSk96eHZjSUUwcVNQZGJHT296aWhUVjdiZ0FyVXVlaG9qYmFVTDl3S216V2xlaXQ0YTNqS0NXT2JkMEx6VGFSY3RqN3hZNFVNMlNoUVZNUHk0N1l6N3Q3bE81eXBKSDBtVE9FNjBGZlBVWjBLd3hwTFlyQWF2T25uZjBEMzRaV1RWRFp6b0x2cWFZaCUyQnJ2a1FrNmFubDlPQWlsaGM0Z01Pd08xT3h6Wk8zVUMya3JtZDE2N3ljbXE2bGxOTTZNeVFhem16bzclMkIzUyUyQkZjeEl6WDlKVFl1SWNma3VDeXBSQ2YyUFlPJTJCRjIzV1Nza0RKQWl0RnVXMmkyOSUyQkZsV1dIbVhlTGpxcGlKN3E5NWJaendReU92ek5hblBOWmRRRERzNnNld2RjN2ZONXo3bFdjb3owYlhUNFc4dG5UbkhvU0EyVGVTJTJCRllwUVBhMWRSVng5SHlrQjhWS2V4YmFjOFZDZFVPdGNjdHl6R0R5c3FIT0Vnek44SWlGeUFWT2Z2emNUSTJQaGl4SjRFVmRrcTNiWU1JUlpPelNZNjdMSHUydEh1a05UVUVwdDFxJTJCJTJGa1FGVXpJN1RJbDF6S0k3YjBscXVBbEZ5blVGbUVWa1RSbSUyQkZiR1hMb2NpVEZiNEk3dGt1M2NZbzVKU2VKdnJzNkxRVHlnVFRSVWswdHZJV0hwOFJTb0oyTDRjUHFQWHVhVEkzWjNTcXlYcHRWbmU5bks2RnFRWkJYeFBDVGVUJTJGJTJCJTJCTldGV2RmQzcyJTJGaERuVUtmRXR5OXZxdG54aTlnc0oxazR4OGl2V0prNGE2REJ4TW1EWGg5RCUyQmVybUxaYmFiYlBzMmRpMkVvWnJad0pyVFlZbnI2WDMzeTFhM0tJTWxKYktsN3cxb2JmVGE1NVVOM2VWajI5ZEJ0ZnQ0dEUlMkJUTGRkTXpNUVRCdG4lMkJKTHloUUtCY0paelh1Z2ZLM2xMQ0h4NGRHYmNWcHA4R3owJTJCV3YxT3FDTllSaUxVZGFQQkxYcWhTOVo1T2VDR3Jwb1ZsUFolMkIwbmdEa01OZlRLaDZZaHhNNkUwTXc0TU1qRFBqMGF1UHQ4Q2NrTDgxallWRWJrWmI0S1oyQ3BzRTdheGUlMkZqbDlBbFYxTTNoMzIxRFFwVCUyQnl1YjdhdTdyNlVwbGZRRmhRaGsxMlhRUk8yJTJCc2IyUzAwSnhLZ2oyczlEUTFaSTRVTE9jVWVDMldlJTJGRk53bmhucjh4UENxYTRUell5VURRSG9UVUw1bEQ2RXZ2d1ozNmpHQURlZTJ3M1RnNmZtQ0IxNUhia29hYVh0cWQ4JTJCY2Z0RUFaeXl1TEFzTXQ5S3YyZWZzN0ozJTJCaDZieEVUVkFST2FYSXRsUlJqNWUlMkJMdWJaNUgzbXlVRyUyQiUyQnl0MHRybHU4eXRGZWZnZHc0RE43dlBRNzk2eHNDazY1bTN2TFBySUVIT3I2djQ0RkQzb0ZhZFE4SHIwcUMlMkJHMVVXZ2phbXdzR2JwOFBlUlFpd1J5aFVDVzd1REw0cUxtRXczUDR2ZUplbVZBTVhMNTh5a1YlMkZTWmQzSWpVNlFBOGdrVGRFbVRNNmklMkJLdkRscm1TMlEwWkZoU1MyQ0Y0ck0wM0pZM0dza1hqeUJaQWVnRUhxZHRNZ3BQWGJ5ZFV1YWlYYjBiaGFWR2lSRlpuTW5IM0psbFAlMkYlMkZOak15OEJ3VHJUS0lEWiUyQmVVN0pjOENUY3JxMk03NG5KN3EyU2JtR1dtc0QlMkJwUDNTaDFBNyUyRllIb0dMRiUyQlBoNXZiNjI3U1BOR1p2MCUyRmFlSEk0R1p1dW1uZGV2RXRSMlVJNjNUU0pWc1BUdmRWNHI5YWphMzZTZmZOaURLSWdKN2olMkZyd2pIOGhxdDZjMEpTWlVnS1MlMkIzMzVqZjlhSW5oT1hBREJXZVk4d0FMT01EJTJCNlY2YzhORDVpTnVlJTJGJTJCdkJhVUF5UmY5WjdLNFJKbUNrbm90T3JPNWxoemJWM3BqUldwRFh4JTJCU0pxQTR4OHRobkFWV2ZRa2Q2Q0F0Q21TSFBXNnR3eDlYMlZ6NEhWZGZQSWhZMjhKV2g1ckl6NDl2ckJEMCUyQkN0R2IxdDV3Y3olMkJlQVRucXRmYyUyQnJXZjlsQ0lUOGZpVTZnUWVXJTJGMklqMnozUFVWUUhTdE5aR0d6SiUyQnlrbyUyQlFYJTJCTkVyZDdBeGJpdnY4cWdBbVVlMmptWW9UeUpWelRsNzBTd28xaGtPJTJGWllXZFFMOGUzSFVDWmZIRFdoc25RWiUyRkZ4WG0lMkZPNnFyRVVFalFiSlRqT2JseHZxcERHcW9LWlZYbiUyQjVPYk5NTDg3OXR4SUYwJTJGMHJqVVFNWFk2NVJLV3lKTVVvd0VhVjN3bnlnVWRCMUdlVnRaWSUyQkh1djhWYXU5a2JwZXR3aTlnVHZmdkVOMTVlaTFVVjN2RGFIYlh4Qjlmc2FsQWcyNWhSeW5hSTl1WnVQQkxqYjklMkJSbTl0dUxaNGo3S0glMkZmeTNBOXglMkJKbERPamxMSFlTTmtqcjJ1WGcyJTJGc096Ujhxc2hVTUJJOVYlMkYxTWdzR2NOcVpNeVdWZzZpajNseVZ4a1kzUGxuWmNFVVM0ZmNYcnc2Q0d1b2trRDFQbEp0dFJJMWpYdnlDOFYlMkJyOFBIcXpsOFZaemZGU2trc3NXZmlQTDFGTzVzU21EcUJSbHdRZlNMJTJCM3pqJTJCckpzeDhJQXo2dUtIbWZyNUE2bGxlWWJnZXZaSTc2bTlza0ZodFBUdkpTTDdBViUyRktPNTZ3QkxNamtpcTkyaSUyQjB5d2UlMkI0UkZRU2ElMkIzekdkR095YjJ0TVN2Qm1JV1VXbWpNY1J3N2FYcFpQWEJnUjExQ2NQMDR1Y1RVczIzNGJvZnQ0eWxoMnBNSjhJd3IlMkZhN2FRbWs1dXk0RnlFZEFnVm9TWGFTN2RLM1dyNG9OUSUyQndVTEtQVDNZZ3hQdmFCREU3MDZRNnR4bVdmenUzVktzRzVUZyUyRjk1b1k3azdkVG14MUh1UmY4ZjZLZnNnMGltVkVDUUpCJTJGJTJCaEM3RURGNXpadWVnNEt5bG9HMG9rb0wyV1kzenNVOG5tS3MxJTJGT29rRVUyak9TNXVWZDh3blh1bDlhYjg5Q3dFMHduYjBhYkhkREdjOUxoMFZGWk1hUDh4UE5wTWx5JTJCRkNEaFRaSktKTGo1cXdFdWdVWExMd2Y4UHFFM1dHSWhkV1JCZjFCdmxyV0k5SzklMkJLMjRCQlZVb3JuQ3AzWWNkRkVDbjFmSUhrelJaaWNGY2NSUWx4eWxRWE40UFp5VFFBNlRJcXZUJTJGZ2ozY0FiUVVkN0M5MCUyQk1iTXBJJTJCZ0czaUNRcW56SyUyQiUyQnpGa3BkcjcyOGVVS2dTMERNYU5sekl3QWxpTGsyVlYxaiUyQiUyQnhHMk0wY3FvJTJCdVBIZnhOVktGeksybzh5SVZCZHBMbUZoOVd1Z3AzVDFibVNDTDdKJTJCVmc5T2RpZm5wcyUyRmYlMkZRJTJGJTJGeGJjUWNQQmpTUmhsSjltQTBWbGxkM0R4TjhxbkkwTVglMkIzJTJCdDVJUUxFaG1vUXdGOGxkVjRFdkI5bEdXS0Y0aiUyQjdzOTBBJTJGTjNCR0tKRTN4Y255S2pydDhlcUc3YzZxYTg3TkNENmxtalpVamFsaDl3M3V0YTVGRTRkb0VpOXN0azdnb2JOdVc0Y2M2TzdZYyUyRks0cGIzaGhRWWVpRUZOSFJHdXdxM3kwTzNWNmJCSmRKVVg4dFlhdTJudCUyRnNDdVo3OWFsMkNYOSUyQklKTThoZ2ZNSTNvczEyaWEyS1JpZFZ5ekFtQnQ1d2VQTHF0cDk4JTJGZkp2MWYlMkYwRElnWWF4YnBpRjhwWDR6RndiOVI2SEglMkJiOTc1RFpXZkxSZEtrTENzMm1INVF5UDBTZkZzbHBHam45M2pWRjk2dHBNckFXaWZXa2lNckR3VUQlMkJWendzaHdjMUwzc1BZckIyaUdnaW81YWZ1S0wlMkYlMkZjSHlkWmpjVlAzSEYyd2tWWTU5aGRRUjRVUm9WQ3ZTREV1cWolMkZqNCUyQm5vJTJCZ0RwUTVUTmloaGluNVhseGw0U0F4bWlZR0p3bmpTbkE5WlFpT1hQNSUyRmw1ajRtUUJzTWtSZVY1QlRib2lzWHAwbkRIekluTWhpJTJGJTJCQlFhaGdYMWRTelNYZzBRMVZtN3hzQjFQJTJCdmEwelVnaXIxV1c1cm01RWFmNTlVaU85aUZmbUU2JTJCR0VpcXRraUZ2b09wWElseWZqNjdOTEY0JTJCUk8zdjdTd24xbHNyUlhaNExxJTJGU0h1eG5KVWVhZ0lmUGhvUUJPVWtGYXlQbGU0bGVZQm0yQWdFVG9KdTZEZjdDc0VwdzNDQ2lhOWQ0V3dUczVtbXhRQmpUWXVpYW8lMkJCOWFYaTJXUktJNWh1VGJtcWZvSGhmNU94V2N0TVhqN1FPaTE1JTJCQ0YlMkI4ZHFIRXlZb1QyNEpoMGYxZmxrSEk5UXhFcjZ2b0MlMkJkJTJCeWZtSFB0Rk1yQUpxTEpRdWdFR0pJOFoyVGVIbENXQWNJJTJCOThuelBJME1WS3duaEY5Y2pmSVFCQnZ5SzFiYWJ2SzRZSWIwb2hDUk1oJTJCOHNtRnh6ZVpUUk4zYkxRMDRUZjBOJTJGaTZEQ0tyMjExOVNKeklaZVk5WEVwNGhjQmJIQjh6NEElMkZXQ05QeFR1Z1NXdXZLV2VjZCUyRmE1R0ppMmNnJTJCSnZ6dGx3SUx4VFFNOUlJS2pQQ0JUN3BXNGw2bnpPJTJCYVVnenVETzhIYiUyRlJ4ckFXQmJHaWlFWjcxc2xQZjgzJTJCT2Zsd2Jxekd1NXhibCUyQmFpZWtsQnJGYUNOYWpxYUU5a1UlMkZMZE10MWs2NkhWUVBRUGZINUwyWEM4ZjlMJTJGSnRxb3o0SjRTdjA3TGFCOE4zVW5zOGdIRFkzJTJGVXhqVmZSOFR2YXd3JTJGd3JWRklBUXBrRGIlMkZlWmlvS0VSaXNTcEVLUndVOXRKemVwQWJaNSUyQlFlJTJGa0kyQm9KN2NrZVYlMkJORlpCdEtQdmhIZmdicnNUQVhOY3hEZ3YlMkZZdzZIUDNYSktjNEFKTTY5SmdTbDBzZEpZZ3hUU3hYZmNTV2ZGRmhsY0VScUxyVm1VNUVYc0o4NmFjQlpCeGhDd2RYenk1UmFEWHQzZmlCVlJxRlFRT2Y4aUFhakdBaUlmUVglMkJNeVlIUHJ6M0F3RGI2JTJGWWc4YUt0V1NBVkpIQjBrd1k1eCUyRlVWMERlWXJ6MXJEYXdtcTZKekxSQTlPSHhyM1FmYjJVc1JhSTZQJTJCa2RBUFZtdHMlMkJwMWZ5OVV4UkNnRms2SzNhSGxZJTJCOGRLQ1llSHVDMXJkYSUyRkl2MThUSyUyQmxjJTJGTnZYUGo2UEljJTJGaVNKQkJVQzdFaXVUVlFtaXl0OE5rWWZzdk83Y3BLSUI2dG5KSTN5elFtZjVNenhjdmN6d0xqQmpHa04zb2kwamlrWkVLeUVOR0wlMkZwdkFEbE83bG9CWjRRSmw4U1dSZmhVSENPJTJGeUR0eFZKOGFPcHFYZFFGaDMwRGs2TnVrN2duV1lZdTAxWGh5Q3h6dE5hY2UxSW5YVGRqakYweXUyNHFYWlRVJTJCUW56bUx2VHJ3TE9HMjVudXM1UDM2VWFuM2ZnQ3B2bmJWRjdTaHIlMkJxVWFqJTJCZUdZaFU1OWZhQUFIa1p5bU5hR2gwVjdjMFJ1QlFRWCUyQjRydmNydnQlMkZkdWlyY1hUODBRcXVQZ28lMkJuVzNpZ1N0VUVoV2ZtdFpKbFNid0VYS3pPajJSdXR6N2tKRmxYUTNTWGM2ZnZGeUhHUlV0RDE2aTB3QzBNeExBZ2NqaFIlMkZ3SGZQaE5iWFN0cmRwQXQ5YkJnQlAzTEJZUyUyQlpvV3ViZzNwZjVGYWxpTnJvQ25ZQ2lzdGl3MCUyRlFaMm5McjdmcnVkeHdxbGFxWTN0d3JNZDhORllpMWZURURuRnd5JTJGdUpobm4lMkZkSXRSY2ZQeSUyQk5ocFZPbWVYOVNLJTJCTyUyQmU1NE11VDFBOTIlMkZUcnhsVVQzWnRWY3M0c2wxVSUyQnBjOXRCRGpkblU1RFZLNHFHUkw3U1FXNVRBdmZVSUhraVJZbTVleUZiczVvVDNiMVBROWdnc0MzWkMlMkYlMkJkMnM2bVc4SSUyQjc5VmFyOWNIUUNRZzExZzBIbFlYaU5yT1czWG51eFNjMWc1c21SS2p5dmdDM1hTSkZWaiUyRnl1ZzBFZTVmNFpZZlUxSXJjbkE3WFdHVFU5YjElMkZpbnlxUyUyQmdzVHclMkJoWHM3c1hQV0xVVDdlS3ZLdm1HSkZBNkVoY3JRR0l2VHFXSjE1aGJXcFZCU2FpbUxBM3dReDZ2NDltVXA4Mk5nVjdHeGx6bWgxJTJGNElqcnltOFNqZDNFJTJCJTJGSGZVc0hEck1rZk1tc0YwbXBSZTBSU1ZJMHYlMkJSVGtSViUyRmE1anV4RG5mTDElMkJ6bktXdWxqWFJxcXdJdWdqSEhEVGR1elNMN2dhNnh3dkM5T0hwSzklMkZRVmxRVjhpVSUyQmxGQk5Ta0dSWVhvd1FycXZ1M3YzbFU5ZDUySm9YUnBkdnE2bzBTMGlJRTE3QWRjWVAlMkJZQ1pyMERHQk9IQTQxWHpCRExZbWY5WXFwaDNEQkpFdWRSaXkwVkJQZVAyVG92SGZ4QldobEdJVGYxQ05GJTJCd2VSUlcwdGU3clRyUUY1TERNYmlCNmo1N3dQS09DTHBjVCUyQkIwVnowUDVuTXg4ODhscXhuM3YlMkJQN0k1NCUyQkhicWNhQUV1MnU3U3daVSUyQndCUWtsZVpRc2tYbDJoZ043YXRTWVVGdWlIeHlteVFJbEI3d3BVekprbER0NW02N0dVeGVOVEdjTzBESmZpYkh2MyUyQnN0dkxRa2l5SnU4bGVnSmNISUNscU96YUVWVUFmVmdFJTJGZXhDWVAzMGkyQ1hnbTF6d2FGdUNjNkpRbGNxSTE0M3FVUGQ3TlklMkZPVjhsVmQ5biUyRkZwV2NWWTlNJTJGYmFRRVpmU0E1c05HbXh6ckx6QyUyQkpKc3VpcWluNEdYZVRxVFJHcjd5QVUwRTRpaHBMNHZjaHh5WXc1NHZrUzY5OVlBUDFiYTZ5cTJURU16RiUyRjNROUZEMkpJWDJPazglMkZqMGhrQ3JnNEFIRVByN2ZLS2M0ckdkM3NYNDlyJTJGVzN0eTZxWDdidmxLend5dUJrVEtOWkp4NXYzeTlQUTZjNVlmUDY4czU4RTc2c1VzZUpjeVZhNHlBWDZmdjVRTUpZbnZFV3VDJTJGR0dYVXk3UXhRWDRTMlNIMGZYUXhIU25yQm9mZmVQNFVodlJMeXM5elplNks4OTRoanl2eFhxRjMydlVIT05ZZTl0OGtkSTJaZ3o2OTglMkYwVU1OVUYzODlwckRLWGh3NGZPJTJCT0tGRXJuek5kcSUyQjNGOUR5SSUyQnd0aSUyQkdRUSUyQndtdmtOckslMkJveUxDSzUlMkI1bjhHdFFhRk1nRzhuNEZTN1o0Q1lOUEpid24yVXVrZ1c2QUZtT3I1QThPS05rZjZKc1Y3NDElMkY3b2NsQVpBZVJkUiUyRjV2d0ZRakVQc2FDMm1nMGNKOVpGd1NQc3RxVWFwVyUyRlRuOHJ3cndOd1VGQTVGdHJMMTllelclMkJzZFpZQ3BBZGVjVWIxdG8wak9xSzRlRzR4UW5kU1hwMEY4WTZ2UG9EU0tzUXdJcWpvSVN3ejFsbXpuS053Vm54N0QwUThGSkxXcGFuaklMMU1rZmElMkJmNkd3S3k3biUyQkZlYjFEM3ZwdmYwZ3J3JTJGMlRkZUtaS2tlc0dYcE9DMm9YanA4NWs0V1NMMlRRUDliUTdHJTJGTFBTWmJEV09JeHRDMFpRUzRhNUM0N0pjMzlVQXNXMDRXVndZUkFybjglMkJvNzBmVjNmRnYzWDJiMWdmbEx4V1gzTWVvOW5KN3RGVGN3bTFsTzNlMExZTTRKaEhJczFkTExaMUclMkJ1SkdyOEdXa3RQcjZINXVMMG8lMkIwYWN3Y2lkZCUyRlAyT3dycCUyRlNZZDlBeU1nVWc3ZkxrJTJGUUhTOHczNWFQTkg2V0p0ZEl1VlI4ZzhBWnhsOUtoOXNTZ0xMSVZJUlZXRjlIWk1vVFlmc3dFSnZLSDlwMmIzTFJOZm5WaGVFdU9hYXF6dGdBNFNKTjNUaXElMkYyJTJGYjRGRDhUVVN0VXJ5RVQwamxsRUpJRG5uQnFhblNzMFllSiUyQlBRNHRJY0g4VzZ3WFBOc1VNUzZPanRMTyUyQnNxcTFNWiUyRnZLSXdHbTBJRXgzYXdkTEVweHRTczZMVElsbHQxZnhwaklueFA5eENHWVlFdmxscDFjU0xrQWRDMzI5aTZqMDNhQVczZ1JnQ01LMEJRSG5ZOG00MGh0N2EzTkltb0ZQS1BObGk4MEVQSjF0UFRWUkRZUGwzcm5GajF0UzExRmhjUExaWTI4MlZMaUpkZTlqbHptenBMa3liNHM0bWFhUE9QaHVjaXlKZEQ3SEZuT1dEZGZ6ZnJHeHEyJTJCTjhFdmZ3QjlnSXZUWEl1NGpwMXlrYTlDJTJCTDZFTmhGSmo2b3gxaFpXMVRrUzc1Wkhub0clMkJXR0gyaXRON2xqYyUyRmk3cjZZUGgzYUNla25vTkVHM0hLTmpjWk9UbWlBcGRWTGZKeWolMkZldmdXM25iJTJCc0JVZXltU2Zmc2IxeiUyQjFKNVBuQVRWMzYxUHF0cFpKJTJGUXJ5U1BmdUdQbEJHNURlY0FmMlJQNGtnRDdVc3VNbDRndDQlMkZUSEowR05WUDVpTm5ZZ0E3dFp0NGtQeDlKTGw2ZVZRUTJEaEV4QjRnT05EYmk5dlBXYm8lMkZoOGp5MFdxNm5ZSmNsT2tRV1hnczRxJTJGUFN1eCUyQkxTTGlkNEVRVmdSamtMZ3lDZWtQbEswbk5zJTJCNWpUQXpGN1pVaGpzbVlHRnElMkI2OTlsM3B2TzdPcjJKT1NHUXhveHZmWTFpNzBWTjdQb2kyMWliRSUyQkgwOE9mJTJCJTJCcHExdlZMYmwwN1BaSmJ6bXdSYUw3MGtlWCUyQlBrd3pIbkR2NHlncFRvTzVGekMyVnI2VGtDT0hWVHljcFFMdllpYkFGZzE0NFF4aWFhbWVYSCUyQlU5eFZWUXJ4VEFvaUpvUDIwb0FEenBrc2wzOWNMeHFUclk1ZUlXVWRPYmtGOWRrUlVRT0U3ZXNPcnZGTU5CcjJ5N0JjWUpJNWF6c0tydDAzJTJCU1I0TkNxUDQ5V3puOGpOS1p1VHBDd1NxWmNXJTJGYiUyQlVseGl0cGJFa3k0RkN2MEZGM1hFMSUyQkk1SSUyRm5aM3olMkJZajZWSmhRRjgzSU1qWE9ZdUJGaVQlMkY1QUY1RUdLeldqMEh0REdzN1hyeEVBZEhhaktudUVoWk0wRW5IT1FZdTZjZmJlbWljTExPVlQ4dGQlMkZWZlBnQ1hNJTJGdDVzRUZyU2JSb096MEYyRW4yYTBoYkI1WEVjNTJBU2FsT3B2Z3UzYXJhSWxxV043bmolMkZrZE5pVURlb1RwcGRVYXF0Zk16cm4yS1ltOG10eUhEMEI1QXA0Vm5xJTJGWkx0T3BhQUhZbXJScHZGa3F5Z3NuWUVqVUJnR0MzaGRXUGhkczJYVjhJdlNONmNNWCUyRlJkVHk5N0hJalN4aWVWSTZCVDdxS09KZ1h4QlJGRlM5UWc1TXM1WTVMYyUyQnA4ejh4JTJGJTJGRzB3MDNZNGhLQ1paaGx2ZlZ5bEdKS3RCdTBoTWxHZDZQTmRVdVdiY0lMbzZnZ1MlMkJhTTlaSmkyTiUyRnFKUjJkbnFGc2lyVklUM2VvdiUyRiUyQjF0Um02NU5lSDlzNWtvazAyUEpyYUxiV2wxaldvSTZ5WTNBb1JZSGRmd3RndU9mZHFVQVByb1Brd3hTN3FmcVBzQ2pSb2VueUYlMkI1UUxGam1Va25YNEVocVJxbjU1VzZST2Z6QWc2MVVrZmJUQ3h5d2dWRjZuTGI5cTNjR2w3JTJGTzR2UThmM211VDJyd1dHb0xkVDBpbXRWJTJGTjkydVgxVDJKJTJGd0pkd1lVcXhyUU03bzQ4WHphUDlpdiUyRjJydFRPbjF1TyUyQjNWWnJpQTFSd1BraWVScHFCeExYU2hHNVlxOGo2NktvJTJGbVBPcTY1WVhCeFNWWGolMkJ0eUg1MXZOWFdiTTlpWHdNTyUyRmElMkJQRzljNjB3cFZldmtObko3aFRSRWw3eXBZWEh5RzR2ZEJPbXpoRHRGenlmbE5uSGQzNnFEdFM1YyUyQk5iWkQlMkZ0WTVtOWliTyUyRmxzcmhDJTJGVlE3UGVrUDU4cFlNMTVjNm5zZTJrbiUyQk5VZjk5NjZEaDlWRjElMkZFNDl3cEthR0FuMDJkcVVrRTBCSmpDYjlZend2RnQ0a2g0SWQ0dmdIbVFraUZzNU5iandKaCUyRjQxZGslMkZvS1J6JTJGUjdHaSUyRkZxRHB6b1J2UVl4NU1GSzVLaHNQcEUyQVVNUzlMMzFibGJDNUlNY0ljS0J6JTJCUzM3akhSVUJiUE1ZQkYwdExvTFZKS3FOcjY3OTl2dHFKa0ViWG92bFJVSUJTN3dJWUtDWnplJTJGUDFwY3RFU2xDR1RjYmU0ZEdOMEp4cmlaUUJacmVvUWg5WHFlRnljTiUyQmxjdE1QNmRBZHQxbzVyVnclMkY4d1hjVGo5SXhhWEVBWjhqT2t2dHdMaDVMc2pFVDFuTUhaQldmMnJFVHVPdG1DMHk2dFh0eTAlMkJYTGVkM0ZTbmZWdyUyRnJsOWhDWGp4SSUyQmZpY3M4U1FZa2RLalVaRndxRlk1OVVVblBRb2QwUjdDJTJGeWp1cDA1U3NtaHc3UDY0c3ZscWNDWVFJaVBicXhCJTJCYTVjZDZuRTl5ZUh6cjFidiUyQnU4MGk3TmRFa0I2ZzlpRGVlV29aQ0lPUlFFOXk5JTJGSzY2VjFJZmwlMkI1YzJrVWlLWmVRMmsxZU9ZbERpRGlTRnpuc0Q2NFJvdVhDNU1JMyUyRlVydDh3Tlo2YzNlbDJ2dXo2S1gyQWFDd3dtNVVTWVN5dVhyeUNvU2pDZnlNZVlJejk2NXpSMExQRHNqOVI5bHQ5bEdiWDQzcG1pUXphdHJBVGhRYkFlNkp1eXh4cEtDeUNTNUhlSUdQSmtaV1AwSEpvdkRldkVRR3ZXZ3F2TDlXWnglMkJkTDNaaUEwY1hGdWRueDRmN1d2amIzZmptWjRoU3V0WkdFZGxBVXZxTDdoajVKZXRRUyUyQlhDbkNLZWJUQWQ3QzFtbVNDSk4ydzlCVzhWd3F4SnpQT0tWRGZLJTJCWmJmR1RWTmZYWUdqeW9LN2JoNTlTazdCelp0QjYlMkZkY2JtSDZodmxSUDFqS0xXZFNZc21YRFd2UjN5WlllQTA5RyUyQnlpdFoxRiUyRk4zaTJDbGVLczU2U2R2T0cycW9xaUZtcmUlMkJuSTVRQWUxM1pGRVhic2xwdE1IVDFSamZPN0FSanNMcEtFT0lQZ3FmMDhnTVB4OWdEczBzSUJ5bFlnNlpIc1NYNkFTd2lxTHBNTW5ualVuMU83cCUyRlR5TUkzb01RUHU1ZVRHMFJNdUI4dW5IVlM2diUyRmNVRnYzYll3YWtvek5LcEolMkIzNFRqR1NXSUVpZmxDSHA5WWFhQ3gzWnFQaU01YkVha01TRTRRcTkxbllVak1ZWElieSUyQnR6bkx3cHB5c3JpRG5aUGM1blU3UklFTE1POGFIN1RSeEkyQktaSlpiWmYzUUM1diUyQmFPQiUyQlBBRG1hbUxUWnNONzRQa2RnY0Z4U0M0ZzdEJTJCTXNiMWxKbGExQXB6MGNWdWhmM1N4N2lPVWFGUXBnT1hhMWtjS2hmY3lmYmYyVDI4OWNENyUyRiUyQkxvOXdGaDBxZGF2eHdEak53ZHAxeFhwTUdTdVNRTWM4V25KOVNJT3N2Yjc2ZDl3MG5sZmZ1bnNXYnVKRzNudnFiWjlRMGExUm5kNURESE5ROEI2cFA4RXRFJTJCOVpYNnRYVmsxVmw0YnA0aVp6ME83bXNFUiUyRnpid1pyNjRGSGtYRU94dGpla3RhMWtiVDhzMlh4a3hyYSUyRm5LU1NyQndBcFVMVEZ4WFpiclVsUEZXUlFZJTJCaWszVENHU1N5WHZ3WmlPaGs4U0VTdVA2YXgyaEVmd1Rsanp5MDQ2dEdYc0lxU1VvVmFEWHB3czllcVI4OWNsMFl2em1JM2JXTFoyRjI5QzNSWWhKRkVYZTNLek9XYVhBWGFiTFNXeEJ0UGo5dmZnOXNzSWlpeUR6dEZ6eEJWd1czSEY5c0RMNFQ2bDhGbmQ0JTJCWXVCcEglMkJTQU5QMWpTTnFDc3FvaU0wQ1htYnhzZFhIWmolMkYlMkI2dDFkQVhkNkhRS0tDa0R6VGx6VGk4V0FtaHFWMUxGeklJTFc5U2I5RmhTdjlpZ3hzQ0FnZGw3aDRmdjFXalIlMkZacHhhQXNFUGhGcE9Sdk1sQ3ltcWF6OUZpeG5XejN5QlVYQWJoZVQzRHRsbmk4diUyQng1QW05N2V2UkprN01icTEwT1gyNFZsazlXR2hsNmxKNWZrM1VmOXg1aVRHaWNZaGNGYmNuM1FTYmp0N0NrRnIlMkZxZ1l5S2NzTFkxbFZRTDNnWmF0NjhTOEZNV05RMyUyRklpbjU4SXZmd2NNQUVHTmFrQ1FWNzdseVdXNjQ2dm1YNSUyRkRvVXNpU3ptSXg5Y0dxWllGeGtvZHZFSGo2Y0FmWFRKYkx3OHJJMGdNalh5emFQN1VzbVJwemtkZDdWRiUyRlZIam5tSmVWMmpaVHhIWlBFUk5CcTVpbW15V0FQRGVtcENla0hCSjM1OEpReTducVV6eWRFUjRvajJpcUg1ZVdqRmwzUW1IM3N3QiUyRnh1SVZ4JTJCNGU4ZlZneGRDWlM5Rlk0eCUyRlRmNkdpT3pxaG9Id1VzVmclMkJSSyUyRklzbFdzYnhaRU9Cc21aa205QVhXVGxpRU5SS21wZVgyT283Q09RSTMwTzlxaGdNOHo0cUt6MVRUd0tPbyUyQiUyQlZPY0F6SFlZV2dEc25JMjZrV2dtaElJJTJCRHZFT0ZUdTAlMkZnck5rMkM1YVk4Rm81NnJ5RkxMcnZqUEdNOGo4U1VxMEpyZGdnczZmZGxUeVRwaE5rWHRtNVBrejVQb3hMbnBXU2ZLMzJjSXpsODhNc3pVNU1pRWNMcDJVTEtmT3FvNWYzdlpPNjYlMkZkUm02eGglMkZ5MW1LVG5GUkVxJTJGVERWVDlNV0FDUSUyRlRVaXlYJTJCNElWN0xHRDlFUnVKSElLRnJOeUpoaVVURThMdEpEWjNNOTV0QjhYdEltVkVkMCUyRkFNTkZwUlBYWGVCZTdJbnBGJTJGWDhVTSUyQll2SnZFSGYwWFlETmVURUpLVDZVUHVzJTJCaDljV3BEN1BDRmFQc0thcGdwbFZWS3NPUllBJTJGQ1ZmeGN1dEo4T1piVXJJd2REMWNtY2RoVUNPb1Z1TEpXMTJidHB0bHpqQ0VMMmJRT1dQeCUyRmN6QnREMGloTDA2WEhGVHg5QVJXdk1pbU9CRlZ5QnI5bHBKRUo4TWtNNVRLdTk4clhNSHNTN1ptcjhZMGtVb05MNE5PUGxGWmlzazZMNCUyRnhZM3VHVEI0ZWhtWUV3bDBKZiUyQkh0dVpWTUl3MWRibUViWU1xZkMlMkZuaDdOM1dSTXVaNzkyd2g3cEZJMkxsaHZIUUdUMUdlSmY0S0F5Yk8lMkJzbWs0Zmt5bUl1RnpwQlVVVXglMkYySjdTcTdrUDM5NUF1dE9XaUpIczJCNEFreGp6aXY4b3Z3SEYlMkZDT2V0d21KZkxNWjd2c29HWnlpVW5xUzhVdWJaZktOYU1OZE5DRiUyRnBOYnhBdktYSXlBSEY0ZEJSVWIwVDRVZzZqdjMzUUlyT3NGMnNiSHZuVWE0akhaZkFNc0xFTzVuNnRLR1Z6RElpbldLR0NvOEZ6VUw4WU52TXNRWWoxdTR2SXpWc1VkeU5sbkw3VWg3c3JBVGhSMHFWekpvOEpRdDJENVMlMkJrZHZZNHltRHFyWnk3RktmdTJpNmZiJTJCJTJGR04zZFdhOEw4T2tETFdXUVBkWFI1dlc1TzhaMVo2VlEwbGtSZkJsRXhZWCUyRjdpZGxQaUxmS2tJSUlUQ2tMNHgzc09ZbnEwZTZ5SEdSMERkJTJCc2pMZVJZZWdYUmx1dDYzdVhyYVRWbmJoJTJGd3J3OU55Wm45dEElMkJEUm1ydWFwd3ZaZlpCTUtYb3N6dnpIeGZZVEJaSmVNOW1vOERDZzQlMkZPb0h6WmQ5a2pqeUExUnpNYnczam1zUENyczZkbW5qNWJmNkNXeDJYR2JCWHZsM0EwZzl4OGVRYkE1aUp3blpoVjZqNmIyZDVSelVsQmFoWk1QUmtsNkdxUyUyRm9HR1QwckFBbzZPTjNyTW1Fa3E2bSUyRk5lRUFiSXBXJTJGcXNVckFiVXklMkJ3ZEM3cHdVJTJCRmxPelhWSjluanpzNlg3aDc4bzNEc0I2TENNeHBocmxwblNiQ2J0ajN5VmVUMGRmdURPSHN1YjJIOXN2ek1QRmVzc2I0cm1CNzg2RW9qZnlpY0p1aHZjWXlsRFlia285anNPYVFiaEVHRnpQZnJKZ2NLUmNsbklCbDBKN1kxNVgwJTJCeFMlMkJuYjdOaTFHbkNVNVU4RTUwbUJtSjE5eXFhV1d2TXZhb2luQkRkak1Gb0V6TTFsZnBGJTJGUXJNTEVEaEpjaEh4WGFHWSUyRkNOZE9UZiUyQmdvSGpVRGM0Sk0xNXg3QllnY3VsMmJqRjJOOHhQcUo0ZGluNnJnaWJjaWdvQkk5UTd1MDElMkJEUVM2RWdXWExFRDFzMlh2ekFZZU9PVzNyek56bjVPTWFMJTJGU3Rrc0F6c1dkQWwwYndRWkxmdGJ4RWN5dlkwWDRmUFJsdGZLSExkdGpYd1ZwZkc1NjhaZllBbHZZZGZ1MHpMJTJCJTJCalVra1B0ZUVxMXZLMUd0UkZFb1pEaVYwSXN1THJvVTglMkZrR1plJTJGVCUyRmtZbmhOUVAlMkJZMSUyRmlpN1QxJTJGRjk3JTJGUXNtNnM2Q29JcDQwa1pGUkVWQzUlMkZWUzJqOERWZFFsMTRSTGRXVHg2TyUyRkc4clJWRjQzMDRkVjcxT3daU0I0WFoyWUhjVEkwTDNNaExFU3ZrNXBPU1FpQzhLUnNIbGtsS2ZtVzQ0UzM2dXlSbFFPeGl4RmxoMWRqRjVLTXY4bkdpelFtR2tVdHFoWG5OeGw2MW5iSXgwY1dTVSUyQnFxRXNRc3JjVyUyRjZsYXZwQzJ2UXJqejZ4aXlHdkxpV3lLeG9VdDJaNlAyUjNhWHYyZGJVME5rQ2NGSk4xRFpHeHVwaEdDVHVCWUV3UldiemVGTWwwR01IWTRWZWZsM2VkeWZxdm5kTmpxM0JGSDVibFRzOG9jckc5M2FIWFpxaWFlWUI2ZU5zZzFIenh3azhyUkMzdzclMkZGUWk3bEdqM09YZEY5bFFyNXBMS2pjVFJHSnVveGVQbmxKQ3Y0eGclMkJxeFNRRHVDaXpkJTJGamxJem1KYkJEalclMkZ6THgya2pxMHJ0UEladGw0MzMlMkZYQnZ3JTJGR2FOQThKJTJCJTJCZkRyeExmTTFCcEI4YmZGekFrYUJwSGYzJTJCRyUyRjh5V2NwQiUyRmE2dGs1NWFqckNZTFZOYUQlMkZEeFVEUFFSb0lWTFdhRVlZNEdqeWNtRG5BWjJFMDVUMlo5Y0hsVk9JeGZyUGJGaFBPaVdqZWRxOUliUDNNUzJRcGxUa2U5UkdGb1VPSyUyQjFzVW0yWG16NWR1VVA4SkFISXJpVjNFNGdpWE5DQUxPTTN5OXBjWXhkZGdBbkNHaThmNnYyNmFKc2VFVGV3SzhoZEFLdHg4REFtRDYlMkJwV1Rhc3NZd2xwOVBUZGVxYnpSWEliWktodW5vOU1rWWxxQSUyQklnTWU2M1olMkZBVVd6M0F2Tm8lMkJGdjdIZ005bjBIUm4yNmVGVTVMJTJCTVI3NlZieVFSb2ElMkZ2cnBOcmxqTFZ3Tml6eWhkTTRQOUFzOHV4UyUyQnJjMGpUNTFkZWJ6Vlc3VnVRRldmNzNBWHZDQ1gyd3ZkamF1bWRFSzRBNnVxbFhHejRUTmtvM1RLWTVBSzZtV2dVaU5obUtxVXBKUTRlVWV6RmI1VnhobVlNYW9lUCUyRlYlMkJKaEVqUDMydnlWNHhrJTJGUERZYVBoOVZvJTJGQnh2dCUyRlQ3TmZFUCUyRkpjTkxoaUUzTWFCNm4xWHhHTW1rJTJGMEhNU0w1OERCV3c0UUQxbU4lMkZmVGJIMVFUdm82Y3VnMk9melFKNW9Sak5VMDc5eWlwM1dnakVpNzJJeXBDSnh4eTglMkJ5dHpncFJKbndmdXF6eVFMTFNCTSUyRjRhMzlJZkd2b2xDdFE5Szg3bFNlQWJiSmdJZkw4eTNDJTJGS1M2VnZFU2hKUGVHWk4yVTZuY1Z6SGhvalFBTDhVNk5mNDZra1o1MElkOTI0YXA5ZW1EZzRvelRCM0RtSWwyWlVXSlVEaHkzJTJGTlpiWWhBeUNKRHZsQno4Z0Iyd0RWcHRLMEJNT25Ud3pFbzdEQ0VOcEpZaHExWjM2OEJqWHc3JTJCSkM1aldtTUs4RVI3dkUyNFRJNWZNRFZ4TnV6U1RhdUFDNjNndHhNMzJvTmRaUXV2VHJNV2VKMlp3TTFTemJ3QXdCT3owc3lna3lTdGwlMkI1b0pEQm0lMkYyWUMlMkJvR1BHdjhXOG84NUdDcGluQUdVeEVxV09UN0xJSm9zWFdpNSUyQmJRU0hEVEtJWUFMaFJ1bVh1Y2JxRHhlJTJGT0VrUVdnVVo1V3hZeUV6UUZ2bDZ6YjJWbVV0TFMyN2s4cnAwJTJCRTlXY1hxNmM4WDN3S01ZRTc0eUROQkJBajVPNHo1eVoxRFhpOG8yNk9JJTJCWUc3NXBDVWRvd3Z1eXhoMTQ5VVJyN2s2djF4aFNkJTJGSEY3RVJyTFdobzMwOEZEJTJGNWhpNnIxd0d5MGZGZlhYVnVZZEdVWFBUemlnN2ZTc1ZGbFE3NzYwcTRrZnZTJTJGN0l1eHJWSlA3UmdUNklzVWZYZmdyOFlIbCUyQk9LcGtLQSUyQlUlMkZZYjZ5VzdzZUJYckJBOGdMZiUyRlhka0d5a2J1eHpWOE1pWHNWY2RYSEVWTktJdTBCMk0lMkJXRWlnUWd1ODRCSFh6ZnU0Y0swJTJGQ3A0MUxxektNWGhSVUNGZ3FoWGhHSjNBbTdqZnJSbWZWQmxwRW9vN3o5SyUyRlF0NTRTdCUyQnkwVThJMm9QUTR1dWZlMHBMOGtuMDMlMkZodmVJbllKc0JVcmU4YjZ1dEgxdzIxc284aTZpVXFIeDRnVHVpdFllQyUyRkZRQW9pJTJGMnlyY2paY3AlMkZqYjB5JTJCRXF2SFhIMFhQUU02VGJqdVVVNWFpNEpyYWUzVjkwUzViclBvUnJrWGlNa3BlaHpvVE1qbGNnRExHQ1BXRzJXZkRLQzVFd0d1N09lZG1TQ09FNlNEMXBFVHJFNjRWYzJpZjExQm9Pb0J2dCUyQk5JS3YlMkJpdmUzNk5tQnQ4eTVPNmQyQVc5JTJGUXU0RnYlMkJpOThkdVBqJTJCOUZjanZsUHlNbE56UEY3RWxqajM2OXU2Y2x0Zkp1Z0xMMWVIM3h2U0w0M2h5JTJCZWpQbFE3Qjh6Mk1DTFdkcFVES2JYOEY5dUo1cmhXM3lOR1lQRjZOZmR2RFdLbnhUUUFZUEtxWVpzSGRSNElCYkgyTW5IakFxM2MxbkhYUUN5YTVtWiUyQkh5Z1l0YldlYW9SOWk2Nm9peWR5TVBWJTJGUXlra01JWWFRa1BsZFpJTUpIMzZMJTJGNGpaVmI0T2NTb2M5aUJJVWt6UkhZZWl4OU5YTDhyU0lIN1JHMFE0RDElMkYxZXVxbSUyRm1EU0xoNThqaGlZQkF4OHc3RjE0c0NZYmE2Mnk0UndKemp5TVQ2VjNqaEVZMzlhbGhUalhKRG9wYWtteCUyQmIlMkZ6R0FTUEY4NUkyMXdzVWE1MHZUSW1yZlZlVmVRTU5ZeEo2JTJCaVA5QXYxSndBMlRJN09xaWw2WlVJT0VZR1lUN3ZoamZXaSUyQmFjNHZ5OGRjZ0RsWWZ1dXJmODN2cVMxWXFJJTJCc1p4T1VBMVklMkZPVjBGcVp2JTJGYkNVJTJGb2dSZ29RJTJCalQ2VGtsWlV5b3JvZUF6TlBmdGkzQiUyQjhZRTFQRjF0MDZsYU51ciUyQk1CanBITWZZalY2N2pkN2olMkJsc1UycDg5SlY0U3FSQlhOTnBqdFVsN2ZCZktJcVowVGJiYldyZk8lMkIzMktXc1VlNXpreCUyQlRxY2ZxY2J3OXpOM3BMblROeHVRak1NJTJGREF1Q2RSOGhiJTJGZUprbzFteGptUURwZk94RnhtRnRXRWRDR3ZlVmNxT1JHeGFDeTY4bk04V0t0Q2JCZCUyRlc3c1YlMkJOdUlrdTE5UHNqZzlMZEZsZXB4dWFHc1J4ajFhaUpIVzVnaDRHODZraE8xN2NKJTJGekE4T0xkQ0IwSHBkNURCTUhXQWF0cFdTbGZLbmdQbEFFNmV4OVhOVWpOMFhQYmV4bWZ4dHZLaTRDa0tvOUppT1hnTHVWWnR1ZVF6bFZsQUJFYjlTWE5Ra1FKMzAlMkJHenVWUE44UzZjQXRySTFqVXlJVVlacFhIcGo5S0xrQnVJJTJCRDElMkJzVCUyQnJ5N2dldjQ2alJSUnh1d21mcEZvalRka0QzQjBEQllhVXRNUklFZ3hxemdGRnV5d2VxMTR0M2pGSGNkMHllOEF2U3gxJTJCVHZpQjJMd2MlMkJoZHYlMkZ1cEprbWdYVjRqWVZqQWdvcTBMODBXaU85THZvQ0ZrY1p6WGF5cGlpTjZvRHJwckNtWk9BeVgycDlDSjFNbGdrUDY5NFMyWUg4MnVBJTJCN1pHaWh2SzQ3TkxBJTJCT05vU3pMSmJHTzVkV3VaTzNhdFgzSDFueEJWZXIxZlZva0VGMFhUOGpERW0zMDE1WHR0TTdwZ3g5cGdWVDc4WFBucG1XWHElMkZ0SiUyRnBsamVHWkFRRjlIRldFYUo3d2lkQzRlaXg2UmJmJTJCYjlyWWxjUWU3aXpPbjlNUzVySDl0dk9wa08lMkJRUGZ3MzdlNllmZyUyQk82dXl6UmUyYXB4cjllNjhkTkt2NXZJbGNBd05XT213ZkVyM2U0aXVSaUpKYVJMUVBYWHFLYml2Y1AwamJGYnpEYSUyRjQxaVp1bWlDQ1hkMHJlTmtuTVNmYzJZRkVDTzRZZXlGWFYwcEhIMmNpRFYwNjJvdGhQanpHTnI5RUlMR0U2RVlLOFhGR1RocVp0SyUyRmZwQ05HUnJCS05lZ1Iyb2NKdnolMkJ2RzR1UUl5YWRLRzRjWWJjayUyQk5OayUyRm9iYlowOUtMZjY5Tnpqblg5TWJxZmszeTRGNWl3TElYRmNDWkJ3SEZlJTJGYnpSb3pzWklQNnFaN1dVSnJod29ORGp2RjJKTjQ1eSUyRlE4aFNFc2VIS2U4UiUyRkc0NmEwc3VVTlklMkI3SGcyNVZ2VmJZRWlkSU9RJTJCdnpQZGY4QWFnQWI3djdLR20yM3UybnhmbTJFVG5RYSUyQktBRExoJTJCRVUlMkJZV2phS0FZWEpmWG9yeW03QTd6ck9nWDVVdTdmUlpzeXJKOFB1JTJCNTZMZiUyRm5qS3lwUzh0YWw2dXduTlBoSzYxRWdwczBMNGdVNDJLR04wSG14VXo5ZWl2S0pQUXhWcm5MT05pbWRDMTdSN3NleFlQMWJDeGdNb3AlMkYlMkZXcHA5Y251YllYYnFXQWRsJTJCUjVmZ0cxWXZmNjBMd1JvWCUyQlFwJTJGRUNUam56ekxIcTR3Nm1LTmo5a1hGM0t1NEdaTlpLZnhTWlA4a0RFZ2VCRHNOMXRvN2olMkJtME9IdFR1ZmFhT0RTMmRzOFprOWl6TzIyWjJCeGF6byUyQm9LaUgwazdCYW5kNG5sNTdYRWhPUFpKMzZtUzBiZWxZT2xhRWw0M3BQVjBsMERWRVY2SEt4M3dmZCUyQndKNXFSeWY3V2lKM2lhRiUyRjZRWjlKdGElMkJ5JTJGcXpMckRwNWlkTjlBY3ZXTmZ1ZHpJZHByVkpEcGU4eXEzSjFKcnVmRXR2eHBkcyUyRldLc3k5UDFxN09JczRpekFxSkpJSGZDVHZiNCUyQmFYY1g1bU1HUXVmQzNGZ0FQVWpmd3FLWDklMkJxbjQlMkZjYmlOTFRrOUZubEJLTHRlczF6b1gzZ3RuY04xUEFSZzBVOGRlZUpvdXR5JTJCOGElMkZvODJYc2lZMDdmT3ZzZ1o2WHc0cUhkbWR1WTdZbnBBMUo5akJmR0o2cDUzQXVoQkRibCUyRm95YSUyQjE4UUhoVnlzUmEwY2ZEWDRMZXY1VlpGRVZJa0k2JTJCNDRqQnA5ekVlM0VsZVU0cHJybGRnWEFHNkRVVGk5SGZVUTNLdSUyRkxUY1AyTFNoMGwzenZGWkxDNmRkVW1LSEI1MHZCbjVsWVFPV3BxV3htTjRyTGI4djFOQ1N0ODhtUlhDJTJGcUY0dWRHZmdUWVdzaUtmTnREdXh3eTZienJnMUx6OFRaUFBqaWwxNHZ5MVU2NzBaclE0VWNZbUFuM29iaml2cjNzbWR5MjZxS1hJQmVCODE0JTJCVGxOSXRRMFpGRUs0aEU1T0hUbE91Ulh2WWswUGtaMzI4TUFjdkRUb1YlMkZuNzU4Y3o1TlVqdW1XZ3VBTWNTUW4yeTUyUjRidzRnRHlGYktjJTJCbWE2MHgyalpaOWlKMnllMHBldDhyZnoxJTJCcUhGdkhyMkZjTUpsZ3NzZUgwUXVjdlV1alpvcWFaT0MxTUJpSUhFUDIlMkZYcEpjZ0gwbHlTN0FyVUxvZFFzbHIwS2NUJTJGM0tzS3FxNXVITm9wJTJCclBYNmpmUXp0WkJia2ZoZmpnSWJmNlZCJTJCZW5Sa2t3UzdMT2xVRGFyYTVaM3ZLSlZoUE1YM0U3cXQlMkJwRTRZaERXZkw2eW84c2dueEVneEpXd2R4aTFMT1BKSVBCQnRxNXVUaWV3NjQyTm5WcVh6MEQydjZsbE83RFZzcEcyQnElMkY0dDZMUktoSkhRZ3pmZHFKZ3FNcjVvNTNHdjNDV1NsSzlTcUNhRGl1bTkzY0hqWk0lMkZra3hzcG9NYTJFUGwlMkJVMktmaEZNJTJCMGdpaGV3aEh1RkQ2eDNUckM3NHh1U2RzbWpWJTJCbG1sa3dURkhaQUR5TE9WeWxINnF2WlklMkZObiUyRkhjVnNGU3MlMkZEMzhqbk9rU3olMkZSTjRnbTdLNEhhQ1BIVktnVyUyQjZ1JTJCc2lDSVRZQ1ZOQWJXMnRWV2k2ZmwxaVBBTU1PUyUyQm5BJTJCdWJnVkVNTVglMkZUZWVaU2ZuUkY1QUFsYnYxcmcwTmdpNFlRdjlqJTJCc241b05tUGFLSTB1N0hyUzlRSUh2R3BuZlVEcjE5M1pRMGJ0UGxtdkV6cURaaVFSY2F1dlpHcXN4TTlvSjJDQUZuM3VUblFOaXV4ZHZIVzNIUkE0YXplRmF2T1IlMkIwejByOENnUGRybUtWUiUyQlRhVjBLYU5qakU2eGglMkJOWnRMdSUyQm00TXVmV3VYdHppY1hSeGR0VXRBdDZoaFhoS2s3RFBXamVqM3RPUGozRENEZWNieFVQR0d5TnExdEdzT0M3NHZyZndpVm8yR1VJc01abkhGcSUyRnFpZEY1MDBqWCUyRmtpTUhyZ1oxJTJCRiUyRlo1Q2pqJTJGVzJyMjF6VzNUaWlQdzVFUzV1S1AlMkZZa2RyeW1VbjFQJTJGcXp1emNZaGJyVk5YRTA0andYbDV0OTIlMkZLSlBsSGhKc2l0NDlsTkt6S1I1SHpoJTJGVVIlMkIxM21qZ1A5VW1USk9HZkV6bk41M3JqMW10RiUyRmlxdjBobzRHbmh0djdobVFHYkp3N3E1S3Z1dU9GbDJWJTJCWCUyRjlkUW5vSDhQcG5Lc1NxQWUlMkJ2Z3BTcjV1MFozbXFjUVVQZ3hQanZXUCUyRlZNNEU4cW5PdEVlJTJCVWtwZG1zYzBkOXk3WlVtbFFJN3ZhT3F0U0pvQUNsbXJFeURoQjI0Um5pRXBSSzJpa05hZmNtQlZERTElMkJzUGk3SnFxWERUOFl1aDMlMkZGNSUyRkJ1RDZWN3ZNWFJaNjVSQXFxamM2UUtVWll2RGVrMk1MNXNzMG5JeXJlcmR5M1YxJTJGcG85eDgwMmlhT0hyWk9Gd2xqQzZlWGlMd2J4blB4Yno2OEpydTNFRWpNVGFiUFlOM2lraUJHSzVjcjYwSll5ZE1sJTJCREFFTSUyRkZMQVN2Q1FyQk1nb2xaMVk3aldjVTZaM045Y3lKdnBXUDgyVlk2YkVncnMlMkI3V0psYjV3OWglMkI1JTJGeU1EcnhUa0pQV2N6dXYxVnFvWHNSNWRPdTZXNU9JVCUyRmQ0VGQzc2VieTZRblhSZThkWjFDOTRhUmR0amIzcUdMb0M2RnpBaWpCOW5wYWh5MzV3JTJCVU1KMDZWZXFKcHh5SklzWTZRSTF4MjVLRHNFNWZxYnJ1NFg0dWxWJTJCYVhsbkJCWUxWSWE3TUtpZkxTcDlSdXFLNXFRNkdBajFxUktqRE1NTFNpTXhjZVJSSEVjRUhVUGMzWHg5Nzh6N2k0dHFYQzF4dkdXQk5ZaXBwaGJISUNmayUyQjUlMkZMM2NOaWRFeEZHQTNiZ3UyWFlGSkd5ZnZzODJYYkQzNWtOMm12NzgyS0NXZSUyQmlCNGJvSkpZcExMdVdIcDVVUXBXejglMkJlVk5FaFhZNVhGWG5rdmZSbVlRZnJsYnJ4dFBNbTJZTmolMkZ2bVVTWU9PNG1TelglMkJQa3EzU3ZmbHFMZ3BXNnhWTnNWc1ROcWZNclBHQUNKS3Fvb2ZqdWVNR2N1aFFtaVhOYWx0UVpYdiUyQlMlMkJJVWdIRXRXZ3BaeW9YQnQ0ZnUxVTFxOTVUTmlLNm9ncVBBcjlwY2NBNnNuemtBc1h6S0R2RkFmJTJGUnZVWnRyNVVGelgzWlVHTzQweUVJb1U4VEM2S1hLNGpEbkRkT3hFeXBnSTRkZFZZdFZiSmpKbnhhOHJFT3h2YjdTdUxmOEhnbkVKRGZtTHJHbzEzZzZVSlBrOFVvMnVNYWlLUVk2WnRYVDFwbzRsJTJGQ3ZFJTJCMEZzbnNSd2s4akszcUNkZCUyRkpZRmx1ZmYlMkJ1b0t4Um5mNmJJUEsyJTJGSmdhZHdEOEtTeW5lTE9BWk5JTWE4Zm9Mb1U0SUN6ZXJ5bVZTWWs4Z2QlMkJJaFhodnZVcCUyRiUyRnluRGFYJTJGZUxHN0Uya2swUGZhREdVVWdtbm9UdW9HZzJnd0xxOTVES3JESUdVc2RSamw1MHIzaEZVajhmdHJDdW5nOVZmbjBCSElnOW9YQWp0dFlUc3E4UnA2WTZBbzZTZFV5akdzUDNqYzJxJTJGUlZKTU5BbWRWSlBoMFdka29ibyUyQklEa0dyN2pWMUU0akt4c1Z4OFhRTlVNQW1YUjAlMkYwMlklMkZlWlYwV3cwcm9GVnVqampLQ1FndlFGdjIyJTJCNG43RFhiVXRMVURobFc3RyUyRm1zRiUyQk9LVnVWakJRNU1KYnJrMTFqSjl6OUFNVHNvJTJGQzRZTkQwRHNMMEpXSjROWSUyRnpaZkRWSjJXRWI4dWZJMU5uWlI4JTJCZVZKa3Z4dSUyQnY0OVFYRzVjeEM5Y1B6TDRPJTJCOUhwOGtFdDFiM25ONlYwWENBQ0tzc2tua3dSWEN2QWM1UFpSTzdacHZrSnFLNVhBY0FKYlN4T0lhWXZTbXJ2WXhIJTJCQWlNbFF5WWd2SVM0SSUyQkpHRExWT1R2ZyUyRk0zdGRucCUyQm5GU0FZYmxINW9qcTkxcDNMQnNycnR6ME4lMkYydExJTTF0WlA1aTdXcUc3a3FUb3NVallxUm5FUW50bHhqbmpoJTJCdVdEZjRZZkc3QkI4JTJCZkNKdktSbmRaYmdzZTlDRUhWbEdJelB4a3pwNk9kc2pVVnRKQjNMa201amRUN2pJcUl5N3RwbCUyQlk1Mm9LMHltYXNUb1FtQUFqVCUyRklRWGdHNTQ1djYyeWdJQ3QxaVJnZzdhOTNTUUxjczRabVJOZXpCJTJCaXYyVmh3TWRUd1clMkY2MEtFbG11VCUyRmlXVXdpWm1BUnJVS2NjQjBOdTV0M1B3YU54a0ZIdHI2cGs0S3EzZ2RCWENNVnk3enNXSDBrYTdhQUVPVktSdnVCTnNieHlURHg1aElaVm5wYlVRWVlJSHVIZzhENmY0WmV2SVBxYlFHSDNYWkp0WWdyMWpGZDZzWndLUENuZWNDQlhDSFY2JTJCcGtnaTRPSDFVSGZXOG9rWnZDdUxpY0tpM0Y4ZDBRaTA0UlBEdzVQRHBURFpPZkFaZ0ZHaWNvTE5rU1clMkJkQ3FjZ3J3QjVFSyUyQlJyaFVYUnVQa2J1UXZqMVh5d0xYMlNEdXJ0d0tMZUc0bGNQRjl1Q3NpOTFoelZ4a3FPdXZRUUFiSXFGUzZFTXk3YmlKbGglMkZMS0xGWFB1SkR6emt0ZUpkVTRpYjBuSWFzaTElMkZtNHBLVmRUVnBieEUlMkZTNlB1Y1kwMTMlMkZmN2lSV1VEJTJGUWVobElBTUFPcEFTSHk3SFFDNjRuR3loejN0TG9LWTN3ZnFiZWZWRGY0Q3hWSkpOQnBhTGtsRjdobnRSclpMSE1wJTJCQW5KSVlqMlFqV3pnNXFVU0l3WmFmTjQ2Y1FENTNJciUyQnU1YUxSdEg3dzZsbWVlQ1czQ0pNVzRTOGpJUVBvMldDWEpwYzE1Rk5HbjFVZzZJWFU5M1IlMkZvVmhTUHF6OHVzZVduRUI4NkklMkZlNCUyQlR4ZEZCcE5zSlFZbmJCMGJoOE9mJTJGRlk2TmZlJTJGVXI5MEZQbDNSbEJDZiUyQnVYZmdlaVc4MGN4MTZVZSUyRiUyRmRGQjclMkJWTTN6d204dVZqNkxlaGNQTms1cWhzSmt6d3ZXZDFyOUdEc2xxd1cxQkNCczRhZWlyRUtJSSUyQmd0RlBjeUc3eElCeE1hakFSeFl2V2dETld1ekh6NjBzT1BKSk1JVEF1dktHa25iJTJGTzRuVmZOMWJLWlBJWjY5JTJCWnpjaE9PMEZFZ2ZUMWhBWWJXUndSVVBvYjJHbkgxNWc0N3RIWHU0Z0lxbmhXSGQ2MmFNdE5ReUpyaXk5MCUyQlAxODVTNEhvR0RJYSUyRml1SnhDOTI2bnVTdzE0cTRIS3llRmwlMkZCenZCUVpoTXNTaU5wTlpGVWtjaWdWWGZITlJkRGlVeW9DNXRRMXJtZVg4aXh1cGk3ayUyRlNDJTJGUHgwcDIweCUyQktsdFRUbGhIazNIeVpOeHMlMkIxN2ozTGRvNDBJOXFUJTJGTFRIJTJCc0NwaXl5T3UlMkJvdHlMQzNBJTJCR3d4ZmR6R1F2Tk1HcDBjOFFCQ3UzZ0pYWnVEVFlQT2c4eiUyQm92V2p1ZTZQOSUyQldobmlhVjE5d081U2NUSSUyRnNLY3RrZ0llS2dpdWV1NW0xWHlkWmszZVRQN3hQdW5zU0hLRDhHbWYzdHkzdm9mUiUyRjB3d3AwVHdZUEtGR1h1eXJLVmV6cHlEMllwb1NOdnh3bnBSbEhCQ0lsRExxcGR1dERCajg0QkVYSXlJN1dJM1FPQyUyQnI5allUOGNRZ3dDJTJCMlZIc3owNnVUZ1dWdDNZWUs1S1ZSeVNEJTJCNjVRUXJxYk83SDJPdU5FVVI4VG5YWXYlMkZnWk5xdTklMkZYTDVpVzMxaWw0alRzNHQ2c2kzQWxTUnExT3RHMmc3VFNlUjBPVjEzM090dVBVelpMU21IaHZaeUtMVHhlV0ZoUHN3aXp0M0wlMkJ4WHc2eVclMkZjcEdJbyUyQnQ3SlNvYlRwY0d0YUEyQzNSVkhOckRZckE1ODVWNUdQdEZLSFNLYjkwb3hGWXpsRHlmcHRkNFB5d1NpWXZtaTFLUlVFVCUyRmNFdSUyQnIlMkZzd29jYWg2UnRBTjhWRUhWSDVuakE4S2tsbldUSmthbDFLWFlLNWRDOXllRlh5b1U5bHhWbzBUR2RmSzVJUTVRaXdMN0p3YmhxeEh5ZXBOeHU1N2dGNElNSG5CRnp5MTNMRkdaUjZEJTJGUHVKckxxUWczeVY2eSUyRmZETndZWDc5Q216SVUyM1NqNWs0YXZhQUhxWlQ0JTJCdElGZ2s4ZDY5ZmNiMWYlMkZLMzBYJTJGVXlIZiUyQjd6eWRXZ2ZqdWh0QkhMbEhWODlNVVJHbnZDV2p3RmdaeHpJbFBDczlLZE9UeHVuRE9mRW9ITzc4YXIlMkZxWGFvV2Z6aEM1ZSUyQjRmZlhFJTJCZyUyQlREZFhReXRYdTFpZ05GcDlYTG5VSlVTUm9BOUNLMDNOVFhpamFJVUVFVXZzZnZrUmthZDJDalVtZXEzVFM5SEZwMGZxb2U3eXl6VCUyQjdMNyUyQnF5ejglMkJIeElRVSUyRjIxa0t1SWlHY0xhMDVGWjZvZ2t0OUZQaU1vWCUyRmEzaXI1MmJkSWFGbndNa29wd2JuWTNzaE9HblpPeUMzc3BBQ0d0SlRjUFBUVUwxOGVuSUYzbkVrTHhzMGw2bzBWOWJKZXZzMUl1N1J1WjVvQUJSWFF1QWdQd01RVTZkeFBGcnBXNDRYRkZSc3ZXWkh4S00xR1RxVVMlMkJFcUYzdU85MkVYbjZ5R3VNdjJiSFZHVlRiVlJWeUhaN2VTaXBWZFpnWUdJaiUyQkdTdFJEMlRoTHhLc1YweERLbVdHTUNpNjR2b25OMTVFeE1US0laejFCTFNHdmprVUFVJTJGaDMyN3NTZTNvT1czY3ElMkJFUEg1VkE2TWxRNUN3MHlzNXpFZ1pxd2NVU2ZrbXpzdUljNGtONVlDeEJyWmI4dUg2Vmp6MjlQbmFlRkJnTkdrZVU3JTJGb1Rya2p5dzIyNTVEJTJGVTNVbEJnaDhReWRxQ0tPaXB3MmtwVnNSVTdadFNJVVVEMERGMnBXMzdEMmF6WkhSZGJsc0ZjUEh5R25Mbmt2JTJCeGRCVkxsbXBCOEpmZzRrdmMzZG5oN3M3WFAlMkJoNWk0bVlpTzYlMkJGNDVVWlpaazBmajhlcmNsc0FMbHh4YUJ0eDN2MHhKVzF1JTJCeiUyRjhPVTNOUWZXTWhHYWlHMlpZblJVbDFRcFJqTFF0cGFua2k0NXVQVHNqMjJDRng4NTJTdElKMUtiNnlBbk1HWktITlE1b0JucSUyQmdMVGdSZFNqQnBmV2hhaDBaWlpqV0NJMHIySVB4NDFyajduNzRqUmtBb3hhUGloRk5yJTJCWlVBUDdaMWRDOHpSTVVPJTJGSiUyRnROM3VKSXZTRVdVOUFVY2xOSGltTjhhOTdGUjNCdUpFN3RFd2g2VGxJQU9Hc3FyTVdHaWdOYjFCczNvN2glMkJSNjB0VnNMNDhDTjVYSzBjaE45UFRTVmpWOEE2c2hlZm5Jc0dYNXViT29ZN2FYJTJCYlRia3hoZVlnYUhyQkMzUUNFbEM4UnpSd0pyOHkxbCUyRnNMMDd6VGI2UzU2YVRtQyUyRndFQlBLeUY4NEhwaEhIT1hVd3VyaFFibExWMm40V1dpRlU4dCUyRjNJZURLZ0F6aXpXejJTaFdLSlJudjFwRE0zbm1zaHJTQXZPbmZzeHdFRm5SdmRBeTltRzVxRE9ybDNldFlzQ2h3allSUTRCQUVqeDdoZjZiVlJqRDJRSDRMRlZtbVpIMXJsWTJjRkVacUtsOU41NmQ1blRSYUg1RG5nd3Ztd3dnRGpXTWNNRUhERU9QZmVmQm1IM0w2JTJCZldFYnVjJTJCMTIwWFBYekslMkIxaEdTc1JpTndjTFlMMmNUUEJjVTlWTm5QeGxPZnhrbmJCUDN5VXpkSnYxOGJYWTBJcyUyQjQlMkZ1RyUyQnE0S2I2VEkxZXU0MnRubGloUzVDWUhUcVlMJTJCQW94cHhMVk55ZXpZWTFkd2tISzFhV1BjZzNxdGgyUHgyUDdYYWhPSUl5V2dPbThNVUVESyUyRml6ZE9tVlJZZ3FVbDJIbWpReUNBbjl0b08wNTVidGZBJTJCV2xGWTEyTCUyQlNOTjNXOG9TJTJGMXFGaHlpQlZ0TExzN0lYelZZc3ZrZ0swdlJQNkxKejVwbzFhJTJCQ2NkU0szQWJzVmRRZyUyRm9BJTJGMHJiRVZpTUcwaFRFbmpBSlE3cTVvT2hCVzRtbjlzaUFUdEltS1ZRNXBmSmhkMkxkWkF1T0VNdFN0SzJFNXJMc210bzZHYnNjOUw5U2glMkJvdXIlMkJzVEI1dFR6MEwlMkJsQSUyQmJrSXpETUk0ZlJGRDNzSnoyR2tFYUNlWEJrM3BNVDFhMXMxSzhsTWtHQ2VkMyUyQkJ6Tzd6ZjFhZVRscVJxJTJGVFNlYWFxRTZVT3RNQzdyYVR5cEh6eTZBWkMxSEhBWnNCYmVDOTNMJTJCc1lLbG5CMVBaTTBVd3l6RDdSdDRUaSUyQklEaWV2aWElMkJ4eW0lMkZKYiUyQjZXaHZNZngxU1pZcGZjd2E3OGZDZnQ0YWhPUmJWaWZud2NuOFloMkFNbEgzOTFVTlMxeHhiWUVHYmVUNlNPRzNQcGEzQjlzJTJCRWpyYUdxV3M1am1RRnRyczh2TUdnT3FmNDk5Tml1UUxHa2Zjek95b0wlMkZZN0NVRUViaE05RGNqUDJ2OFJUM1IzRWRvdVQ1aXVNOXpMbnNJOUI0JTJCSjB3U1RIM1JIJTJGRE1sUlFCUlBRQlAzeXlIR0VMa1pQdVZZekI5OVFUOXN5ZEZxcUJZZHBnblpQQmxwYjFyTFFNZzZDcElKTGFnZXk3endhMlEwZXlveHQzbjRTSXVBNUoxR1Q3dmw5U0Y0TzZkOWd3NHZ0RWRLUUklMkZSWDVQbGdSN09yZ3BCcmQ3UDM5Vk81N0l3NVJZY0tUd0dpelZlMG9paEY0JTJCa3VPVW1Pd3luRW8lMkIxQVRrMDlKendyWDNOenIlMkZGSGFTTzdzeFhHTnlJd0I0SHZ4THd5WWtOZm9tU0JOY1FPUjZyRkVXYmZ3YlE3aFNyMHElMkJsOFFTNDE0ViUyQlhIRkRBYnUyd1pZT0Y5SHNQeDElMkI0aDVtdzVUc3hyQmhWU2trRDBWYVVFMnlNSW03SkRzTEFueElZYnFQWHo3N1F3b1ZGYnNIYVVZNktKJTJGcG81MFNKQmtBVzdRcURhSlBENlJqbE5zSWMlMkJpJTJCcnc1UDA4JTJCa1VNNDBRNlJnNVYlMkJEeGcyeDMlMkZXcUYyOFVvMkRuNzJLckR6ZDB5M09kYXpwNTlaalJIbjBiOCUyQkRPajN3T3JEMWFmb2ZXJTJGN1BVck1UWHpGOVJaek44TDBBQ21OY3IlMkJCV1lKMmlLUlQ2NUtYWlRTS1BiVUVibEF2bWZjVjlYZkh4enJKJTJGYmtQbUtoODRYZ3JIV29taXZ2ZXlxdlhqalJjSUtkJTJGZFQyeCUyRmVGVHNCTmwydllGTUd2YkQzdEM3TXB0TUViZm5mJTJGVnJKN2d6YjYlMkJZZ29yZGV3b2dHbkJRM1RBJTJCOGYlMkY4QXA1V09ITHNNM1doTU1FcE5Xd3doZSUyQmJqVVE1RXJ6UjlnbTc1UiUyQmNsVThMRm53dzZwTGxLOTE5RHlmNHJ4d0RuViUyQnhKZnlrJTJGSSUyQlgxZDY3bEVWJTJGYW44SmN0NWxwNk5tU1Fwa3BSa3NaeTczVDBaZmtSS1gyMndqYUxJJTJCMzFCYzF1TkhTWjRVbVYlMkZ3YXRZWlolMkJIdkIwcDlWYjBzZzlnbnBLYTg1STE4ZFB6bEFxOXNzTXI2SmtVSHlkaWF4QlJhWVkxTkZiMEptZTBmSzFROVJycHFPdDFaOExQUTB6QnJnajlUNUNEOGtIMUpLem5aRVhmSjAxVG1iTmFMSHdGS2RxMFNWT0h2TXowJTJGZE12TUJReGZqUDh4WGE2YmZmQ01WVUYxVEV3SkMycTZoRE5nVFNsJTJCdnhKRHBIdHMwdlklMkYwNXE4QzBFTTU1M2dRdWYyTlRrRk5CRWpLaUgzNjhMb0J6JTJGR3dSR3ViUndNS0ZHQXJPc3hyVCUyRjJ4MnMlMkZLVlE5dURLaWNLZEtsTXNqJTJGYWElMkJCVVBHUzhudDNrNDY4SDkyRm5sUHFnbnNzQlUzZ0o4UGJhMU1VbmwlMkZBQTF5amxxOUduM2Nhck5RQ3p4N2dLM2FCaU5PSE43WHglMkZxbTcxR1JKZnNVM1g5RVlza3UyRCUyQk9HM0Jua2REeU9DQllxOWxEdkhHek1wVDdzc2hFSzBWWG92diUyQm9PUm1pdDBuUEU2dFVWSTNXOFJTVHRaUks2TVVNNWYzWnBkdDlJZTRnWHlZd1p0SjFGSmZPNTIxMk5rWVh1NiUyQndiUmN6VGNibHRtVVl0eWl6RFNMeGVvZkdsQ0hqYVNWR1ZSeUhUWDNjQlRoVGZKMldPejhMV3gxYmdGd2owNHk4dk4yQzg3RyUyRjlWNDdpMDk4eSUyRlJDMEJxSlZqcG93QWslMkZHOWx4TWVqdkJMc2FVa3Y5N3NXNlBlNVg5OHN4eDczVXpGQ0h2dFdhcHFoNmJMaVNrbHpESWtDalFhbnJsSkZkZWhKemo4JTJGenpYVHglMkJsaW1DeWNXOGN2ZXRFNzRobEZrQVgwUWQ2QkRNcEV2TEZRc3U5a2t1TGpXc3VHenRwWSUyRlNPSE9KOW9LSEVpN1Qxa1U3aktmSGNqNDVRQXhTZW52aGFOS3hxJTJGTFpNZyUyQnpGTUhyYXJ4SkRpaDdEdjFqaGRueXg2ajZGQ0Vxall1WGlJTW82SUhDMWtpVHlubWMxMFVubHRNZnZtVnYxN2IlMkZ5b0ZuNzZvbTBBdDRWVFA2ZDhpemJUUGV5TnlZQ3hIUnpudGJ3N3FpdHdCY2RzS0RXS0hKWkwwTEJUS2JNS0hTNVRkbE1HNVY5SjY3cCUyRmg2cnNlRk1vcFZoYTkyTmdOQ3lWRm1ybzRHJTJGbWxUNzhEUHVrTWFEVk1Ja3RqVVA4MDA3T09uY0VoVTZZQ3BjaGk2ZlRSUmdTNnN2SmVHc1RmUGlkclFuJTJCMG50MmZpRmxlWGNzbHYlMkI4bDJsU3QxUHlZZzk1Tk1NRHR5OFRzdzJlVmFYUDB2MEVZMmZTd0l2blNEWGFUJTJGdHp6WlEwc1R3M1Jud2lyQk9KUHpCQ0Z3ZFRnNDVaJTJGRFIlMkJmT1AlMkZpdVVrSE5nd0dzV1VZR012cUtTb2p2aHI4NmFXOUhYaFAzVDd0TkRWRFdQaUFUeDNNUVh3M3hwQ3ZQUVBLamhpeG1hMXljU2VvWXFGSiUyRmJGR1JodnFJbjFkVXRKYXZwWCUyRk5BTkU0OTZpTlZaY1B1ajJDNnpwWDR0UXowMXZ4a2tZanF0V2xzM0c3Z2klMkZNWVdVTlpibEhJREx2RmhUVml3WEl0SlhyU1NVWGdrMiUyQnpkbjFQQ1dueVcwVHpVczVKRTQ1WVNUdCUyQiUyRmZxRWg2WjRHYWFsJTJCQlhEM3ZxbzJJQ25ISHBmOEQ0Rk11UTJYNHBzViUyRnBQbFc4M3hWZWFMenRBbGl2M3J1ZVg1bWhXRjFCMUdKazRvc2JkYkg1MXB0MlFCeUV6cDZuck81eFJHSXNZMHhmNnBicHEwVzlYZ3ptNFR1bXMxMTl2Q0pWV29yTm9jT1M2WWI5eU9uZHloMWo4ZW1WYTN2ZFolMkZlaUQ5MlpvVjluVG9UJTJCN2FzbWM0QmpsQUduZTNFWWZHSnhSJTJCaXZmSmhZUkdic0J1NzYlMkJEdFRNUndjYVZUMmZsRjcwdm8wT3k2UDdzZHVrNnhkUDRPOUN5RVlCRyUyRmY4MWNWa3RxQXF0WnZhRyUyRmdIZFp6TFFDQ0hnc3Y4MTZIcWwxVHFTZDlkWTdPcUszUjlNWWhPU2tyWFZvakFMMFZsdFlsNXplNCUyQiUyQm1wYUhiYTVqc3hkT2s2aThkV0YwUFIwTWlENVdtVHlDMnZRbFp3UzUyenJMWiUyRnRZc0dycWhKN3dpRm9yVzM0MUolMkJhNlJNNUpCc2V1UERMJTJGYTFnR2RWcjNmZHNTTzZsUktxZlJmNVpvZ1hLeFlPZ0k2QkZ6VDVRSVFacUE2UElaaWw1VDRFWE1penF3VDhEaVNmQWswS2dxZXVNZnZlZTdJWHdSUTlDZTh3JTJGUFIxRDJxNXlKJTJCSm45V1V1aVUxR3p2MGRrSzRGaG5QYXBTcTY2RnV4VGJHV2thODBWRktUNzMzY3Z6SzgxcElhdDglMkJGTCUyRjRSbTElMkI1aUp3Q0FTQTdKUW5nVmZ2WWJsQnJMd01WaDFnUUl3cXdYMjlKS0w3SkNOcDI3d3pYRmhSaWwzU1A5dUZaU2RndnVtVmNpN1o4cmh6bjZwMjhyQXBiSThQd3RlZ2dvS3RBTUFCQXZrWmsxS3F6aFglMkZWTVpUWlIlMkJucnB5VTNuOVpqUWQxMEZEQURpcTZWWm9yc1JnWjljSDJsZjdrcmJWSWlWUElyUXQ0M09wb2NsUjgxU1V5aTVWSmxZRVUyRHpKeE9oWW5JTnFMaGtTVmtJUXY3VTRMVVlwdTRWcm94SEtLYlA0MHdPMmdBUzdZWk83QWZjbFUlMkJXZ1ViUjglMkZqVUJ4VUEyR2J3eTNrTncxUWZjdSUyRk5wQnZoVW5CbmUlMkYyeHFPbjc4JTJCZlFQTHU1VjVuYVB6N29idUZsQ2x5STYyZHVOeXJTRnVLS0U3MVRESlRaSW9Wck1IWUtTdjdxU1ZhZlZVWWk5cGNCYkh6MEtwb2xOZlhUWFQ5VGk4ZkpIWW80aXkwTUZ1ejhIQyUyQjdreUdiNDg1blZ0T3ZwTlRPUjRidkNMdiUyQnF0RmtCeHFyWmJUbjZvY3BYNlNSb1NtOFBpM1JVNnhLZlM1JTJGUHQ5ZWk1T1V1Q3hQdWIlMkJONkhVZUJIc2J0JTJCR3ZTRWNtJTJCZHRWUTNsOTNDWDQ4UXo3N0c4RVZOa2oxS2Y1TUZ1RmEwJTJCZjJ6WDlMMEo1dXVtMVJJMUtQTmRWVGtpT0prbmEwVzJ5UWwycHhVNnVRZWlTUVQwT1ElMkJpZFdGVHdEQzk0REd2S2lCbHpRVU1IQVNuWmc4aEhpczR5V1pyTERZSkMyVTA2YjNZYmd2diUyQk1tN3Z6eXRiVGh3YWFicG5kbjElMkZsaTZvTjBFWmpFc2t6R0RQT1dOTHVycTA4UmdPbyUyRld5QmU5dDJhbm1Rc282NkxwcXAzZjcyNFdNaXlUblIwN3g0N05aTm9hek9iaUx4dXZVeVBmTUwlMkZuT24zOUJBQTAyaTdUYU15dlI3VnJ6YlpKJTJCRXZMOGo5N0x1Y2M1SFJDcHJCM3NXUEtMZGZTdDBJSTVXZEpITDFOR0Vsa1VHTUlhJTJCZFh4cWoyRnpHTVR2ejlMeERWZk83aWw4RlNYZlZmQSUyQnJQNmR5ckV1UWlJRGlEeVJ5T0UzTFg4THlmdU5ZM2NpYXFjbDdSbjFleHA2WlVmanclMkIzNGYlMkI3TlVMeG94YXNDQ3c2OTVpWmxiemJPRktyUGN5dG5MOTc3enBieGdwSDIyMVI4dzdpeiUyQk1NSHRYUkdqSnF2N2VjJTJCc2FYNVBhZnFPblk3ZEM3QkNkdDlOc2FLUlNHNDFMWXpkd1ptbklWNldWbHBteDluVDF4ZDgyV3lUSG9RcUpUJTJGbURIbXFIJTJCYTdtVThHaHhLdjdXeURSb01leHl4UmhOa0hrWjZBOUdGbG5qMzFXeXVWT09VTlVmcXVTUG5CazhFSFNScHZyMG5WUk5FUyUyQlEwaEk2JTJCWWtVOHBucThad1hPd2pReGJ6VkNQZWVla3h4bXl2NTZBNzlTTnZmOXJuJTJGTWE1ZGNTQWxkODNZVDNFYnRlUWt0aUZKTkE2YXZ4Z2EwbE5PdFJQelhaT0swbE51ZW9VSlRoSyUyQnFCcnA1MU05Z1ZUUDFybWVNeTFkOTNUZDFqT0JxJTJCbHFFbVdlWmg5anM4TnBXcWVTcE1mJTJGJTJCWUo5OW5wY1hhdXhqJTJGck1oTFJvNzd5NyUyRnRvdXR5ZWx2RDFlS2JkYmh5ZmFPdXJRU2NNRkFSUGx5clluSXFDQmFYRXhKcjdQclRIJTJCRmxMajV1cHJsSnV6NVV1ZGZybWhCYTY2T0lTc3pmUnlUWmxmJTJGVjl6cVlhaCUyRmJ6cGRncTRWM2hjeFdIQVhTSXBob3BwMGlCYVBVVHBVJTJCRk1iSEtVNmVVU2c3WkNuRmQyaUFieXZQajFBaG8lMkJoczhLS0tPTiUyQjFoQTNhN25yVEZvRkY5TmF4YkgyZyUyQjVzN2tibGNsMzRUTk42ZCUyQnpLV2JHcjNadng1RTMzcXg4cndXTzc3dFVWQzBpJTJGRzklMkZMaW42ak9EaEcwVXZBeVpxSjY3YXk3OUhEVm1IUzRZaExTSWk1REE2MFY1U2tFSXFTJTJGdGxiN0kyT2daVnRVJTJGTGpRQnNmbHYySVNIdmcwbDZnQ3AxVzlZcFdhWmM2Y0laVDgzMDh0WERDZHY0NnBMRHQ3QXk3S1U0aFFPdDlNZElZdCUyRnFWVWJYV1lKViUyQkhaUDVyUmV3cjdMZjVnbGpMTUc0MkVIOU5Yb3pmSE9uOVlBZyUyRnlsS3Ewc0lnQ1pwJTJGeHZLRVJsazZTV3JuJTJCY3AxVUFPT3J1cnNZcEp0UGp6aXU2SHhlVDc0dzNtRUZvZmRZNG5PZzBLNFViRVpCcEFpTmVyenM1Q1k2MWN0MU1PVyUyRmpVU2pHWVFtRk16a3JSOThnVHY0aHQ2JTJCTW1mdHNJODhXd2I1V0k1Nks4WDVwUktUeHRXNVpqU3dpbkwzaDNCVE0wJTJCZnZHV0QyMFI5UnVSNVhvOVRTMHJjR0ZocElkV0F4NXpPenVhbWlranpvWlRIWVRaUENkYjIwSHFaQTFKbUp0VDVEM2dKRWZXMVE1ZkU4a1FaTWI0NG1zZGxkcVF4UXdyb3Z4eXY1S0UlMkJxZnJ6dllpaUFBTHFhVmxDVWN0aDAzazY1Wm9rNFVoUFJaOTNKT2FyMDM4NHU1ZHUzM2VwVyUyQmElMkJoZkZTNHA5QmFFa0pBbEhEWGozVnBYaU9rZUhhekU3YU9yZzh2TCUyRlZuVTlDMXdxbzUlMkZDMDlicE5yNjI1RmolMkZ1ckFBdFhRckdtJTJCcTdrTkRaVFZhNzRleG5lSHBCTGNrVE9iJTJGQXBNZmdROU1MVWxGMnFWVUxrR3I1dDRZNnZzODJlRyUyQkJvV3RoMzBrQ1BPZjBsZVhqZWZYWEI4R1h4Q1NPaUswRDR5dW1tY0dDNGFReExSdHczYWdxeHV3bWclMkZwdTFEVCUyQkZ1eTMwaWJFZGh5ekUzUVFzN01jTVdXYk1lMkRJblN0c2wxdXFpJTJCZ0VBeUZXSGVmT0JUenNIT1plMiUyRmNnUjM2U1pvNzJlQ0xVSHFxbGdpOWZMSkJGcE5JU1UyanJNZiUyQkRINEkxWEVzaHk5MXhIN1BYQyUyQjNwaWpBbnE2OUNQNmdUMUx1cyUyQkl2MlJYcUw3SzBYYWRsWFg0WG1DMU5BTXdScWF1R1BjZUFSSUQlMkZscU42U2NZWExVdnZScHFYY3hMQVZvMjBjVnY4WldBUGNmaUpBOFV6Y3I4NEM3c05KQko2UWN2NyUyRmM0a29FWDNaS1YlMkZNamNIRmJOaFg0Nkdia2NYRVpOT2ZSbjZkMFQ2SjJjVzl1cEtCTmVRUFZrOVZmMkhkcjBWM1JTRHNXJTJGZWg5UkdWMkJsSDNSbzhybyUyRlFMU0hWYXlNaXQyOG1Td0pibVJkYTl1WUJVeWRFT1NYbnByN01yNmQlMkZic2Z1JTJGVFZEZlg2NnpSeE9PQ2pKSDVSSG9VZnRNTSUyQlhlZklLY2hBNWQ0VU9HeXZ0MjNwdDdvbVRWT0pDZERMUm5vWXY3JTJCcHhqc1ZSd3BQaFFINGg0U2N6JTJCczRQRU1PM0F4MkxFSVQwWjFNc1JiR3Vta1o0alJvMVRudVBRdiUyQnpzaG9WT0xkZ3hEVll2V1M0RDBzdGl6WDNsc3hpYmZGYWNJNzBsazVJVmVmVmtHSlpIMWZ2T3RJeDNEd1dHcWpWMlBTb1JZVTBaQW1zMktwT3VPS01GSWkwaCUyRjBTeCUyRktaaWRDOSUyRjlMQVZvY1lHb1lmVlp5NHdwYk92UzdVQnM0VGJsWVBkQXlsRFdUNTBwQ1BtWE1iVHNMdFFzTElFbGJMVjlmTVNITnlwbSUyRmZycDg2OU0ycmwweHFOYU9ZeHNpTk9DSTJyb3hpcmo0VkRUdzZ4RmVJeW1TRnE5bUFWZUhwRzVWOEt0TktERXpoNTVmcXBRZFNYZ0UyY3diQnJKeDd0ZHBVY0dWVGRuNTlxVjR5UWhDSEFMdktmazdxZkZkcW9SQVBRZDFzM1loYTRwNnN2aFdGVjlsdHlBM2NuaHlVNnZmWGN4TVdCY1dVSDZka0RrQXBWVVNqRUwlMkZndnBXRER4UXB4OFA3YTh4aU1scnJybm1Zd3pCSUNwRVduMmNjViUyQm4xNmVuSDFKSFU0VFdaRm1jSEZCRlJMaHZwZTlhNVZXT3d0Z2NKQVhmJTJGTENJeTlRT0JLemlqQSUyRkVoSkVIY2c1bG5UUWRVazdydmxxUWpzYXRMVXZQR2FDSHFwWEFrdUlUcnZwY3lxem9obllMOEliNUhDNUlqUEZ1VHl4NXNoVjE1c0U1ZFp6RFV3ZCUyRndoMlI3SkxaczVLSzV4MVg3UTRHWmVtNmJpVzVLNzM4JTJCNkc1JTJGY2w3V3BKMG5MaDR3WjZSakNCWHclMkZ2TmJwRkd3RGcyTHF4SXA3ZHdYUlRDb0wlMkZoZ1NtRnJPNm84U2Nlb003UUdheFV2RDY3dW5uQjlHdXUlMkZFVUlhTEdLeWZWbmdERks3QkZNcTNaYWQ3dXZYeFRzdDJIcFdWRVB3Y09XTHdMSE1Ia3hVMkR5Smp5bnp6YjRSbUVNRENGQzhDYSUyQjNJeFZuSTI4NUNuMG0wOXlyemxtZEJvZVU1dk5QSmRUNk1rQ25WUVQlMkJ6cDFKdGx5WHZrWGk4ZlJqWm5kJTJCcDQzdnU5Y0JvRHVxaFVtOUp6RnFHN2MzY1NYJTJCc2dtcVRHaWRBYzE0TGkwWG9neHE0aVpuR0FzdkZMSTElMkZnOTVuJTJGbUJUMFNUOHBkYU1pMGoweEF6OFMlMkJsNEV4UjIlMkZBSXAyQVNNSVNYNThTZ1FweXVLSXFDUUxwVWRjRFN1dE03T2lzTDREMjFMYnVzJTJCaThOYVA5c1JJQWFlRnFUcnBUdlJsQVFtSkQ1JTJCendYaVRtdnoxRm1xMGZzbmxuayUyQnNEYiUyQjglMkJxVlFPUnZ2aWhLclZSeDd2JTJCa2JGRjVhTDVtdEp5VUtJM1BCbkFsazE1ZFNDbjloTTF6Z0pVNHJIMEhLWGJGcFFaUDBZMjkzZ0ZNMmVadXFYRTF3bGR1eEpBJTJCTlhldVV5NmU0R3NtTURKU3c1VVA3JTJCMWIybTBPSlpsVWdmeXlEc0hiV3ZtS1BVdmJGUlNRZGpuWmVvQ3RQUHZoUWtUMUxvNXRPZngyUnk3dWtTMGk2NEdhMmlhNEl6TDRjdHp3R1Y2d0VMMyUyQlpVVTZEeGowc2RjaXZvT1NOTXNtR29vaVdoWmlIcFo5JTJGNUpzZ0pReW1oSHpzeVplJTJCbkM4OWNsNHkyWHBMRyUyRmMlMkYxJTJCNkd6b3JvbVRvclExMWpUTDl2clZKN2lFdm4lMkJYMnkyNEdjWEh6ZUpiVUd6dVVWTUVqdWpCTjFsUnV0MmZjMiUyQnRjT3p5eGtqQ2pnbEtqaTJmQ2lRRWxSaURpTDBaZHd0TlJBd1dlSFdITVhpRzNzSkJCN3Z1ZVVOb3hWUVlLYlJpU2g3anBmeEpVbmVpbjkwOEdpdjhxWFRKJTJCTkhyYmpsVGVuMjl4T3hVZlN6MWw5WnQzV3pMWlltbXdrTkllWHpyOWIlMkJFdjY0NngwVnBYcmRoUENvNVZyeFZXV1AzNiUyRiUyRkpvUjhDVnZJNllvWTl1dnRWQjRlQ3hsVWdscjE1aVNaY1N3Rlc3V0FnWEVDR3IlMkJpZUlzRjdEb2xubiUyQlJiQUpOYmx0emZNM0txZ253JTJGYW0lMkZFVXNYc2M1JTJGYzFMQmRjcVI1S1pPN2ZKdEhDOXR6Tks2YkZaUEk3aUxPa0cxbzR4UzZDYmw1aFRoR1JtSmI3U1FkOGNNMVFkeWRvcWJ3UWZ2RVgxOWNiYUVrTHBVVGw5N0FNNWFFd1I3OU4yU3BOM1p5dTZ5S2RlJTJGVk96Z0huJTJGSm41U1UzdXZvSE53dFM4WVdpJTJGSml4WjAzNzBLZVQya3lSczFjQ1Q2eHBqVEVyMTc4JTJGSGxqOSUyRjNVZ1NOYm5Wc0lHdHUxNXVaNjJUYlpVbGFMaTJleFYyYVlCVXpuRFM3JTJCcFB4UkNnYWlPYU9lVyUyQlJGaE1ld0JwZWcya2FZbVZzTHRzYmZVWUslMkJjRVVaVnhVQUNZUCUyRlpJOVpKbTVtNWI5OTZ3QnFsJTJCSDdUU2VyMCUyRkZVWnBkNXpKbHpCbThMbFUwVFNBUlpNQ1d5YXk3WlBudTNBbUlqTlI0V05WYm1PQlA0cWZ1N0NHT2JOWlJUMTN5ejBWYlU5dWxQMWtVSGU2cVJvMzdsbE9ydEk3JTJGQVpZMjh3dUxhWGZuTUsydnBBdEI4dHNXdUptUHJEYW9YeklSVUdCVWd2RWpEdnFROWYyMDJuS3RTdGtzTCUyRmVuRGNFTmV2MzFrTEtzWkVXd0ZVa3Zzb3BqUnhYbFdkeGtjcWJxJTJGRVg1S3BwZnZvVzJlaG5YckdPQ1hEcDQ4JTJCbFF2cFVlYXJjQjg3eFN3amtiTTZ5UGduZEJaTVFHRkpkT1IzM25zRUxCbTFWT1JnYiUyQnJKUzl2JTJGU1QlMkJObFlPcUEzQnQ5eEgxaklibDl0TCUyRmcwSFg2WSUyRlpwa1lMa0cxNUM4Qm5WSTBFZU04RVBTVlM1TUNGSzdnU3k1VWV4aDR5ZnBuWWZnYzA3TWZsMHZINk5FSWdPd0xLcFNrZm5hQ3RvdktFZmZ2ZUhLZHUwRHdCTFlnOEZHZHVoR2hhdGkxZnFNeHl6MUZ1dHZvd3g5V1NFWlA3TWl0ZkxJcFBObUc3c0thMU11R3BodDZFZXAzaEx0QzlmJTJCY2c1cEclMkZtcDdGdlNvOVlpWG52MW9KeVVXSW9haWUzcm5CdnY5ZDBlc0k3dDdpJTJCRWJMRmI1ZmltT1BVM2JYSnJaN3ZLJTJGbU5udTglMkZkNEVvMURnVmJTZnpPc1FRbXRyVThrVmpFamJCeVElMkZzdHlSUHJrMW5qZGdpJTJGeGtCTDhYVCUyQjJXWm1HNm81T1FJd2pqc1hOeDFubkxsc2VlVW55RCUyQlFxOTNzejFqWnFOU00zNm5tendwTE5yZ2Y5VDRneUZZdGRkbUQ2WnJnTkIlMkZHTzNEZWl1TVlLQVZSU2hjNTBodGlpYzhVYTg1MTI1N3NHUFVrejZ4d1pTSGVrSm90d2lSTXpuUzlpWWQxU29pN25wT2psWFZCcnBaTVZqJTJCeEVjRzd0bTE0YkdGWEtuNkZQUU9XN1RzcWFoVzJDdEh2cmIyRlViWG5pU1pSSVdSWk83NE9HTSUyQkFxTmdxYmJZWWl2akxkM055d2NNZHYxajE1aXZVdVBuUlRSTGRXdG95cFhiTkx3U0l2UjM5eHQ0dGIwQnVOQU5vbU55S2FrR205WXR0QlNUTlFjdW5wdGhYRGN5d0JNWGFrYWwyM0FkUjdSWVppVVNZQ1QwJTJCQmhXR2NxTXZpVXR0ZzRyQ1dIVzZBVjkzU1pucGMxR3kxdmVPdXV6SXJpJTJGTGdRbk5qb05PcmJ4d3IwVlRpVTBTVFVvM01oc2prNndlZjlHQnBjd0M3RyUyRm1NZ3F4ckZtT09yUzEzYiUyRk15d1oxT1dsbWljaXNhaGx2YTBaejQ3M093NTVOUHRCRWMyaTBqd1EzN3J6eFA5d1VhVjlRQkxUYTdKMmM3MzY3NCUyQldTa3JEVGVZR3hISFJHcE5zblF5JTJCY0ZNbXpyZ2JTQ2o5dzh0cFJLMGEyRUs2ekp2SlRYN1JsTGs3ckwwQ3p1QnJmN3FscCUyRklvWFJUUmlwQmU3YVhPS1RMc2M2NWFGY21QSVBYa3dDd0xNZDlvYWdLdGFMJTJGTGZuN05LNXNGOFI5SGFhMWJ3aGgxNXo1enAyZGMyaVpsaTFFejV0ZnF1MjdrNFp5TFZDM2xMVjBCVzUxM2VxdDRKOU4zY0VkODRWVGpmaCUyQjlBNDNPbjRHRm1XVm9FYVJHbmtMVFpLalc3MVBqciUyQkxwTGJJcFdwNXJqRzVvY05YbWFuSnBpSEdDUjJVWUY3aTJkWDFUeE96MlpSJTJGaXU5elJFSEVMUlhuODVMTXh6UDNWVGZRcWFLa3BnbU5QajNJVTZxWkwydm8wQjYxU2d3dnpPJTJGUXVLUFlOWnE4bDVUTzh5JTJGdVFWc1BoSzBaMzNTJTJCVktmV3hHNURUZCUyRm5wejJXRnp4RjRkQTd4QlhpelBOUnZiUnBwbjNBMmJVVmt2alJNVXpYMVdzJTJCemZiTDhRQSUyQmJGcU9lJTJCZFZqcTRHZ1M2UjJZcnhlMjFHQ0xCaGJhNzJSZXIyMzZFczUyRkUwYjB1bUM5a0pVaXFlZTNsJTJGdzFvbU9CbEZ5NVU2dWhUcFBveTV0MERMaTBDT3V5OVBvR2l6SVdIQTBOOWRUJTJGdUswWmpFUklkVWpuVk5YVG5tZ0s0NFBoQmRhb2xodDdYRTl1YURWZmVwaUglMkZETDhkNUZIMEJFZWFsVnUyNWp1cUQ0ZFpVaTN5TkxibTJkOXg3ck1waTJ1QVhkUnRlZDBmdWEzZUxBeTQxZ3AwaUZaTXh4NDd3emhybmlVZG9oanR1RnUlMkJRdVhORzlPRUowMURaTW9wRmhVbVJUR0JHSTVmYWlITEZLTEJwaFB0OUVwSW9IMURkeWMzUERtVSUyRldScVpIbXV2UTd2Yk1kaHBTZXluVHhQWDlPcG1pNTlkVWlselh0T21Wa015NURHcnB4Um93clBlRGE5bU5hSE9DOGpHSHg0UGFWdlVJdTZzY1UzdSUyRmxmMHJLMG1iTDJKanptYlkzZTVQMXpia0FtWGNNREpuQ2RueXRDNWhObjVmRXROeFFGNnkwcGZOczJRSCUyQkd2TiUyQngwRGNHYkR2N2YwbyUyQiUyRnVNRGJ5WWY0NmpHMUNUeDU4VDl4MjAlMkJ5UXBHeFExR0Vyd3lFMnklMkJRaTh3RUdyU0MwZlhiTEw2OUJzZTBYdHJQWHlRYWxXdDUycXg5NEcycmxuaTNidHRuWnViVFpNY3JMTkw1aXgzWnJTcUlCcng5Ykw2YTBiN0lNclZoMHBLTFZ5RTEwU2N2V28yQ3lKQXhkejlFdVJWcTBtWEdkRkRXTWNFbjNTS09aYWd5OTR4bXN6M3ZzcGJLJTJGcUFJUWhtT2ZsdnQ5RVZzaCUyRmZrJTJCMzlrdHV1YVhKSVJLUjZHd1h6cTlSOHM3JTJCJTJGS2ozRklaUDg2QXdQU1VsJTJGNkdNYVRsMVdzbXF0dktmQk44RFhWUGllN1hOMWs3dCUyQjVXdGk4MlFoa1dtUU5ZU0ZrZU9MYmtQbU02JTJCQlE1TjFjbHkwS0xHUzJPeFNBZkVKbVFnJTJCT21TeDl5JTJGT3JqeDd4TlZDdzVBemVGcEFOSkthdll6N21SamI1TVhxTHd6cG5SMmxSVXN0ZXFnOXhyS2hIJTJCUksxaWU5b2J0Y0RuYlc4alklMkZiaWlMTWJhS3ZUV3ZNTGZYRXlJT1oyejNqbEZmV0JEZVowQzNMT0RZZ3gxMmRYQUclMkJrd3hCNUVHNGxhdmNKbHhjNDdYYTFMUXBzdUVxaWxhV3dXRjZTUTF5NUkyNm1oWVN5QjJIOWl4ZnJIQnFzRFhBMFlJRjdjRElCSSUyRlZudGJPbndaaW5GSiUyRlR0Yklqc3ZFRHR0VkttamxqRnBCTHVsMHQ4RkxpUXVIYUw5YWU0WlpiTmpzZHoxV251cjlzZFVYTWw3MCUyQnJLNVRWaFVpdDh6SzN5MU5tZzBmNXlwSCUyRk42b2xNYXhDZCUyRmQlMkY4U0cwcFJPSHNLJTJCOHZMTFVVeUxMZEclMkJmNUNHMFJ3Z3A5YmRUQWJ6NjN3ckg1V3dyOVNwJTJGSnJJck1KOVhCcXlDNHlLZXc0dnA1NlVtbnI2eW1KJTJGJTJGazVpT0p1aWkyemVhN2ZjbzBhTyUyRlJLMjY5bWNGYjJTUHZJYVZ3eXd2VFl1S01sVFpvaDZRUlF0aUFNaWd2N1R6RXFydlR6WE9KTzF2JTJGdEUlMkZSd05ZQk14bThnamVjJTJCakx3R1dscHJ3Smp4V2FBczlvNDRLMmxwV3hSeWNxdkgwdTR1Uk5HTGxUM0I3Q1NLUk1ad2xCYXRJNWZRdVZhTEY5SHVMZUJQYkZtRnJsJTJCUG91bkVHcWJBdDVQa3JIRTQ4MUZtazI5TXNnb3ZkbTdWQVY4bk1Qc2s1ZldUUnM5Vm1KbGo5cVJwQyUyQnVaZDBQVmJ4ZkxucUYlMkI2SmVQdnA1MUhXMkxlY3gyYThDaUFOdEU4eFV2VnAwZHczeDJheldhUmlNS3YyV0wzdVp0Wk9pNDNZcTRUQTgxZTFnZFJZbVBJUEJNSlp0V2x6TVRuMkpzcU8yT3h1RWttU29Zd3FleiUyQk1HV290bjFTbUpnT3lGR1l5dTNvTXFHNXluNUo1ZnJMWUNmVWJ0cXV5bWJRWE9BTDh0Tkc3SlBPSWROTGpvQ2VJZ0dCa01pd3AxdFY3dWpuQldMbkZiJTJGNyUyQjlVTjElMkJZcTJmT0xJeHZneTRJN3NwQ2FidjU5WjVUYlpFZEZnNnN6WWI3Z1dGJTJGNHBFcWRGdm53c2pwMkxSY09La0VvOEhFNSUyRkZyUEkxQ3hqNlA1S1pEbXJoeGhNZE5malJ1MGdNU2R0TngxJTJCT1NrNjJ5aWhrY0dENll4UHFhMGhuakJCdTV2MGloenM1NDFkQXJDa1RSMyUyRkxpQ0U5SVJ5VyUyQkYlMkJhVkxTZWxUYkV2SVVVSWlhaHBzdUJYWjFIcXY4SlZPdjJNbmgyQk9QZERmaU5yTm16WXFwMGNqdktxS1MwWkZoMFB2WTNJWldPNDE3ZTlGRGNPeGIzU0NrVEJ6MktkYkVsbiUyQmF6SzB1Zkc2NFJrUXU3YU8lMkJGVlExOEV4RXRXTmNkdDE0RjdsNjc2YldFWUdUOXJDVDFObmJPbTRKNWp1WGJOdmF5MzVncDBLVXU0RXRkaFd3UklBTFVkSXRDbUpwUlZKVzRmZTdIaWZPREhudzhvdlJRWk94WFBlblNvMTlOSm4lMkY5STdiZnllR2lDdXJjSFFCVUhpM1dweHJjbE96WG9aVlV0eU9ndHhqamo1azhGeDFLYWpkWjRuNnZSczV2YkYyQ3glMkZtZUVnMVlUSW9PbmcyNTZWNHRPZjh0SU5LRHhkVzRGYzlpOGd4bWgzakcwSnROTnlISml2bHhjUm1wWjJSQnFiUXVpV0xFdWx0ZGczdmRtVVhlUmRDRlkyNmdoVDN1dmdDMXlxSDJZaXhVSXZCZWY2VzdwTEVTTCUyRnViTHphb3hyJTJCJTJCczV4JTJGd29zR3FJWXB2OGtyVmUlMkJZbGRLb3NvV1pRNTJCSkF1JTJCcWhwZFVjaXJqZWxHdCUyRllYZUZ5cjBaWDZ1cEgxZWdlZGFpeE0yZ283RyUyRiUyQmhDeU5LckE4ZGpKMER6WEZQSSUyRnhiVGU0T3p4cnc1Wm1kSTQxOXUwZG15QUhTMVBJbTJualhOZUpzYVlDaDJkMFMydkhoSEplUG04JTJCMmoxWXNZczh1bUh0OVdsamx4NXV3SHZrN2F2MUxiSlZZdDN5dFRZcVE3YmQycGY2RTFFY1ZhM2lBTXJ5TFluYU1Nc3hiVnJObzNQRyUyRnUlMkZVc0hPMDJXclVqNXZ2ZW1WYThiU2dGVFB2VTViMU5qcW1VJTJGZnFsU1FwbzFVWXdIRUNlMk4ycVlUQzMybEZuSkFTNmZOR0p6YjU5WXlVUkVlY1JSZDR0dXN5TlVrV0o4OEJHZFZnZEs2NHNOWnJjTmZkQmJCYWVvdWxYOTVuZWtJWDA5aXptRXE1N01tMnpuU0pyTkJIYUplSWk3VDZXenNWRzR5aHlaQW9RWkZtM1BrdCUyQkxZS0xZVHVaYlhka3NlZlE1N3VmRGlWWmtTanNnU3Q4NnNzckMydTF2MW9idTJlVTR0TjVFeHlNYnV3b2F5YkkwcXhhZTIlMkJ5JTJCNkZ3RmZNQmlHR2I1d0hKdCUyRjdiVnhhbU9mcjVQWFhlMXF3SnFTNVgwYU9IempMWnZtZFVUd2FPN3MzTFZJcWxLaVA2VThGN2VPU2tEeEFzNTZhNFl1SHclMkJodkFzRFl0dHh0UG50MGFQVEQ3bHp1dXplWkJ2ZmVRelduZlE0a2REM0ZEeDhvcGVkOWlLMzdVJTJGM0UwNzVwdWNKN3VHZEwzWFVad0sxSGZDcUVCTFZucEZrcjIxamNaNFVUWTc4SHB1TFZBaDJOYSUyRjJDRnVQWFo5Wk9rWTdFMlVmbEpQMjVjdG5mbFlSQ0tGYXZoJTJGdDFTSmVpTHRTYndPQVMyT21DNnlMVWNWdTJ1ZE8yTW1pZW5WSkE5YktJc2VtTDRkVmVOQlVnamglMkJMTVZkb1BJdlpxT3ZjRHF0JTJCVjNWeG1HUHlZcXRseUJoYk85SW1rMjNDcFA0alVBbDNtYkNMeWQ0TiUyRjA5YzNPSHQlMkJzdHUzJTJGaGZzZHNlJTJCVUZhJTJGV2c5SE84SW1hb0owWmtXNjAlMkZxUHZEazFPRTNiWVVaZTJhazVjdjVKbyUyRmVsSXMlMkYxaHBGZW95ZmlpVG9mRTVqME5KNThoWkRic0pRYWN2aU9NQXQxMUpJN0JKZkVmWFFRaHk0U1ZHZHNiYXdHdkZwNlM3b2Vxa0c4QjluYzdtaTRUN2dBSHo2ZFpWUG50alVEdk5TaTMyMGtZaWU4aSUyRnpOUFhMWXElMkZwOFZGTnBYUlZkdDhjanFCeGJpTXpzZHQlMkJaOUdzZEV5TUVUUDFic21reVJaa2k4NU5qMHpsSlMyc1ozSzRVdHhVdWhoYTFRUXFMM2klMkZoc1pNeUQ5U0VQJTJCT0txTkZ0SHBQT2JrTEw4c2d4ZjR0dGtlYzVtcDVibHJ5NHJQdHFQNlRoU1R1d1ZHRzB0c2xnaUNuMUc1TWxleG0waTM3QjMzTXZNNmtSTXVaMSUyQnNSWFdNYnBmbks5ODJZdGEyJTJCaFNsSGRad3ElMkJMMTdmWXhGcnppY1lyS2llUncxeE5QdDFWciUyQjZYNkZTT2d3VTh4NjQ5Qng4RFBxJTJCSEo3em85RWZIcUpQYWpVbiUyRlBGSjJ0eElzeVFzdVkyWlBaaFFLcjVCVkhJbmM1OGpjNTh5WWx4WkZWeG9vOTdqS2t6N1lsbnF3dU9zZjJ1UzZCWXRJOTNGYzJxeXJKb3RtaUdnSnJ3JTJCemIyaG1DdlElMkJOYlhkNVo2eElDRno3OUl5Rk8wNXNmYVVBakVMVzhVbjg0M0NPcVNHOUlHNkRxRWpxanR2VGdlN05IVExBUXBzOWlWZHZXckV3ZmRRSHJkd2JjV2RTc3d0MVVGNkQlMkJEZTRST3BVdTUzakg4dXRBbmtGcCUyRjFKVThXS0dHbSUyQng3eGUxcFZlbkpacE5BV3RKeXpOS0VmRm9TJTJGR2xsZk8ycnpOY2l5Rm1PeCUyQlBFS3BxeXUybFhHZVhQYUEyY0R2YVFwd1RDdXlnU3FZN0JwVDd5VDN5WVZwUXN1MXlSUG9nJTJCJTJGMDN4a3dqbDNQWENNd1JYYjVWRkVwb01FVzFoc3RCaEswRjN1WVdtWkgxcDNzS2x2JTJCcXhJYkNtNmU0YTRmM1VlTklrdHloZ210OWRVeFd5VE5QTjRoSWJ0dXUyaTVjUyUyRmVHdVhoRW5BYUJVV0FtZFdKdzZpSXFrZ1ZlWGt6eThrZUNZYkx4QWl5QzlvemZRVUFUJTJCdWEwem5BTCUyRldSUW5YSEg1bWxCMWE0c0NCUEh0YzElMkJtMGxjekFSUCUyRmdVNDdud2VkUnROZlhyam9udXRLRDN6dlhLcGw5S0F0YUgwNGxPZGM4TU42UDhlJTJGTHpuMkJJSzY2bVp1dWVPbUpQc0dJcXhoMEJiZ2g5ODRWYzVuaW80Y1dwN2ZkeGdoR3RvMmtPa1IzejZHYnU2WW91bEpNOVgyaGZqd3pZZlRmcE42NWt1MzNIeWpLcEYwZ1R2dXB4Rk45cFhsdURnUlR4VHZmY28wN3FKM1RMTlpkMnVwVFh3ODZYJTJGZGNVRkNOZFdGNFQ4Mnl3UCUyRnpwUG9DeXo1SDE1TkpkTGx0VDJrdTJQYzY5OGJwcHdHd1pySkRDVXNxbTJYTjc1T0ZMdnZhR3pqbVlkamQlMkZ6bE53YnZkaExvNXVaSTVmTXJQN0k3diUyQmlseVRRN2JUYUYxRzBJQ1JlWTEybUZPaXVPaVc3cVElMkZxNjg2JTJCYVplMEZiMDB2cU5mNUhXNWZObjJXbFlsa05adGVsMWg5eVZBcyUyRm52UGczNlU5RjJJJTJGQm5QQXVSUVlMODlQdDlVeFI2VUlLY3ROM3JIUGclMkJCM1pnckQyeVA0ZUNHJTJGJTJCNmxDJTJCS21HclNuUkJ5NUg0ZElGd2QyOTRXQ2ZqaTRoUkRvRFVzRjJuM2FuUTU5aEVocWc2RWI2NlF4Mkp1azBqa2pPcTkzVjlzbGN3NUw3OGVsM1RGUHIwRWdtS2VOSVBCdDZKWmFDRFJFOHo5ZFUxZlAlMkZRQkp2QVN3aGVGTVJGODJ0YmVxcGx2a3ElMkY1cThjOEx2OHZFbDhiNU4lMkIyUlZRRDElMkZyNVFuWUZzT3VLbG9sekR6cVNEdjlmVHcxTDA2cjR4RGZiTmNEUnFqUkVmeWg5NG1zJTJGeHZ2ZFVqdmMySGFIUTFNVExjTlhMcVRGSlJWNGlwVzhpUVdtZE1IRFJHTiUyQjBJMEt6YlgxdUVHRG92d05FMVkwZTMwTFR3ciUyRmZnZ09jTUdONjBYd2Nla0NCdGhWMHZKWWxPYVp6OGlPQktMVGVEb0Z6Szl2eXlLVEZJYzVJcXklMkJVV1Q4U0pBTHFFaVhnc09UMzBWSXVvS1BCTm9EVnZwREF6bGhsODdnZ3dhaiUyQnV4VWNYbVl5clZrMFAzVXRiMGtkOUdMSDczYmFxUjhsQlhMM2FZR0RxRnJ4aTdqcVJWZk1JTTJQdmZKbFklMkJqbTQzTkV1Mnl6YUdaY0pEUm9Md0FMZklEVTNmWHViZmdCcE9ZRTBlOUw1U1JTb0p0dlRwbm12cDdKT3N4RHNjZEZEJTJGTWZudmElMkZRc2Z5U2pVbmxFQ1VUbUcyV2lWcUhYMERpYUFnMGY5U2gzZ2FQQ29kcUxBSk5QcGdobHhiNkc0QTZwcDF2VlJJbXBYd2NxMlJMc2NoUURIMlNCMVdPNWlhSG1iaE10JTJGS2tFWVVjTjc4cW5yWEV4U0VlYlYxVDNkeXJYQlR2VlV0dmZIQiUyRjdPNGRZUGowaW5BeW5DejlkckRsUHlyaWJlT2dWeksyMmx1MjZzdE5Ta2RYUzVMdnY5MHRqVUhFc1ozaDFIUnNKRmYxdjd5SU5WdXJoYjJLS0Q2MmhITDFZck9kSXU5ZFRvWkxFa1hSTlJHTUJoSnAlMkJYaHl3bzhsMG5uamRDdW5RSElYJTJGS2I4QWo2U2oyQUtMNGJoVVZPRnkwJTJCbSUyRnpPU3ZkbG1NdVltTiUyRndiRVgxVFpyOU1scXViY1JUYjNFeVJXJTJGa3JlanJ3cVZmanpDSTFYYzA3RjY4enAyWjY3JTJCTVU5MEhWRXVyOFBKUXN1b3J0cU9RcHU5TTFqcVpoU04lMkJoNUNNbFc1RG5DOUQzZXc4ZlYlMkJuV0llJTJGdEhEYTRTT2RRenVRNVVZa2VPVmpOc2I2Smo1TnhNRjl0cVBvZU0lMkZVdVpSb1ZzMyUyQmJJVlJMSXIzTVRJczBwdG8zbEYyWmclMkI4enI4clNxWHFyblBybmxBbk12ZUdyRjc1cTIzRHE5OEdnbHdVa2tmWExjMTl5OERtWlpjSFZDM3VkdlN5U2sxYlE4cmMwVXYxSDBqOGFXJTJGUlhTU04lMkJHTU5lS0p2ZGUlMkZlSXI1YnFvUlIwRFZSemU3eGxqZFpCVEwyanRBTXpXclJwam9LWEhmS3hHcE5wc1htRGg2bklsdnJkNDZCWWtzb3B1eHV3bXJKOEwlMkI2S2FlJTJCUFJLV3AlMkJTbEt6R20wS3ZScWJEZ1VyMEptJTJCbHpnTXp2QjFPc3A2MzllWTRHNmFtOXVMRzNxSUU3OXEwSWglMkZlSTViNFVSRG5lbktvSSUyQnhER1QlMkJPUnBwRlNQUFZuM3o1QnklMkJsZE5oN0dySGQydk9Bcm9vMXNzcll1TzZGTVBNJTJGQ1VVU0lNdFYyMzc1cmJycEQ1MmlFV1g5V211OEolMkZGazR2SyUyRnBxT0olMkZOYVJRdjBpWkk3ZGRGUFklMkZ5JTJCOFNwOGFLQm41ZVZQM3p3dWh5STFsTSUyQjMwYjl0ODRnZ2xMQVZRJTJGOHhybVU2c29NaVJOR3gyYSUyQjVtT1E2UldocHQ3M0VkNDlvMFdqZGJ1c2FQNTFqOWV1d0tWSnAlMkJtd095dWtZZWlQcG9RQSUyRmM0NE03NXJJdjhkRFhJWSUyRks4JTJGR2J2OURQNmRwczlpQlo1S0UyZGdpOUw1bTZpbTlmcmxGQ29lZ25qZ0dNUHQxUCUyQno2MzhqOGpSWERwRkUwJTJGZGNPUVVESXN6QU45cXJvQWpnaGhvVUppciUyRkFTbDc3R0Ixc3VTUDVNZHlJaldNTGlCTDkyTkhQNHFNTGlTMFdKcHZnZXpyOXRwd25tU0d0akclMkZmZlZRWUEzR1BLQWw2WkU4d2pPaXI1SUslMkZCcE0lMkZQUzV5aWQ3enZpc2ttV1VVYUZPNmY3dnZHWWh1bHVrNUdNdnNhTHl0OVBIU0dsZERCdXJEeG9pRTV1dHp2VE1Vbkw0ZHFYcHprYXZaUVNBcUE4ZVk0emhyNmpxdktwdllFem1NTVhrVU5UY1UlMkZqUmglMkI1Y0FoSGpKVW8zayUyQkElMkZwemIzMkJTOHFJUkNEOWpsajNrT3IwS2J6OWhsQ0Q4JTJGdE84cjk5M1NjTCUyRlFTVHoyaE9VOG54ZmxsbnJvWXVHb2ZzZWJ2Ulo1JTJCVmtEWlVmeTJyOUo5cXBPSFV2WmpoJTJGWWs3OCUyRnQlMkZJQSUyRmpZOXVBZ3NueFk5VUFQOTRQNFBGYiUyRk10NCUyQlZPRUt1aVBrbXZPN3RPb2tvdEhleW5vJTJGeW43SDd3R3d3ZTN4UWlhRUF3TkpPNyUyQk5MYVJaRiUyRlVkYzYlMkJLcCUyQlRnQnR6UlgyMUpodXhQS3JEdzVYTmxWYkg1ajB2bDB4VEVzcUFzVVVlb2U0ZnljUTV4a0syRk8wNndWM2hBJTJGZ3Z0dTZibDZPJTJGUElKdDliRnpFY2l4dnpoVkdtSFlqMThkMUpWZmx4dFB5MkFmVG1oV2RubFl6JTJCJTJCSWpZOWpFJTJCVTlkMkpSbkhWTElvNTElMkIyd0d2SHZFcGJWRXB2VXFwdVYxJTJGbmxVM3RtOEQzdXVzUTNVWk1MWW55RE9iOEhXZzFpeTFneHlpb3BUUUZSJTJCbXVCbzJuUGU5MVpTcjBtMTFOZURFakdxUlNDVzNkaEU0REZYdzVyaEg3dlF5MHNvWDBDJTJCRlJsZG5BQmdYT2pCN1lQMnY2d1ljQ2YzeFBHaHM0aiUyQlFYVWthVWQyOGdOdnV0OVluNjU2S3R0ZCUyQnlOMFBadyUyRkRJcnFhZXhJWDBBZVY0WjBhUjdpUFM5V0JldDMlMkZmYlZCajlXRVZlTWh2c2lPQ2lON1ZkMGNjWUFUR0hBTkh2OFIwVVBEOGtZYnl5YkJUeEhSYzkza3ZrRzdUc2U5SHllRjEwdjA2VFFlS2QwTkhiS3ZjSXQ0UjY1QTJSTE1mbVhiQ3pDNmtlMSUyRmJEJTJCRk5ndFh0QSUyQldYb3BBSjh0bUslMkJPZkNlTnlqbFMzJTJCRjl0eFp0dWNUbFM3WGFLVExVJTJGenNVZ0g0S2tUdFlnaW9hR2ZkTVRlSTZWVWN3WExCeVhtODFKWTRVdWdPTnVkRlhtSm5pWEZLWjhhbGFtTUVDSlJ0M3Z4MTBuYzc2cTlQcUtjVWI4RkclMkZCRm5tRkplajY4WVU1MyUyRkxNWlBaREZ3dmZXbVpybFlFaE1TTUl6WHEzeGVHWlpHWEFMUVgwJTJGVGxTa25nSDB0JTJCWGxMZUc0aTcxdEI0QmIlMkY4c09LakpOV3clMkZiWVpNbXBGTjZPUVk1cmQzRk1ubGJoZjJnM2k3SFJqaEw1QVVDNnlzNEpKZiUyRlVUam9JV240WXE2dmlaN3VLdnFhZ0Z2TndvWlBicUFXeDN3c3dqNlF4MW5xMFklMkJDV0NURSUyRlczbVkxdEVEUms2WDBOZUFaTHE0eGdpV2Z3UCUyRkFxbyUyRjJwY0pPTHBNdnlUbmJoVlI3M09UdGNpbktTQlNrUjBTVlY2bWx5eFM1TlZMbTNtdm1ZQ1F6OHFLcnB4SDkzdllmMjJ0SWkweTAlMkZ5RzBEcWxNb0NLMVZ6R1dyJTJGaThwJTJCRlZ2alpHdW43UWpVJTJGaTl2MktQejNHS0N5MFF1VWVuR1MlMkZUZkhnMnJlejRFc1VyZE9iaEpOZ1UwOHM1OVJNbm8zJTJGVUJyN2hEJTJGQkR3YkcwZm95bkU4JTJCS2RydWs2eiUyRnZEenR0QmFYTE9mbWp4ZTZmN3JwNVltV2RqcnUxbG8wMGptZFpQSUxhVk5MYXJacTFvdm1vNEIlMkJEMlhyeFZoJTJGWFFSTDBTR2FMTkQlMkJkcTU5Tzd5TUQlMkZzckV0UHYzeTJVYXF6Uk9IQ0pPbWxYNFYlMkZNN2g0UlQ1RU9xaSUyRkI3RXRLdnJUcEhOVHBCT05ZMEElMkJvN2JNeGNLTGFxQTl4UUdsUlFQQmEwTWtBQU53ck9vMEtjJTJGSk9UR1FyenNSZjRoN3JvaHhEcnUyTHpPNllsOG5EdE5iOE1HN0xURFhjN1dMdGhGJTJGbGtmTHBZJTJGWkZCQUVZViUyRklMaTNjQzlMZjIlMkZIc1I3NXo4MnRDaVlWMGMlMkZ1Z3p5ZTQ0azZRZmRCYkhack13VmFMUjdoa2xhNEJ6Mmo3Q29laWV4clpzeHQ1WiUyRk94RDBMdlNZYTFKWmdHNDRYMktwUFlXRzVJMyUyQnZ1aDNVbEtjJTJCcXN2WnlnWkVLNzlzTnFsZ3dlJTJCNTRyVkxTRkFORTRFYzdhYWpzbSUyQm4lMkJRbjFiM2dGQTZtJTJGM3RhejN0T0dYMU9OTHBHSTZjJTJCT3ByM0RZSiUyRkJLRGF5T3Blc1ZDYUtrM2pXekFHcmNpOVVVa3Z2RVJISHJwZmROciUyRmNMbVlSdkJQeXhFSm56VEdsZ2ZkTXYwSWtWRjlKRW9HbEVDNTZtYUx0UTBNeG5lNjRUWGZ4TEdRM1BuWmszTXlDRkpaYWw5aDR2OVFjRkFOaUt6dXFxZXhOS0xKOUZWa0R0aFZPcVVBbmJEdjhnaFJkNXBXJTJGJTJGcWYwSWEwVHU4NHNzaGt3U0dPMDhpNUglMkZNSVFBRzlpZVdFQTZFanNKSzNsdThNWUVjJTJGbmZrTEJJZmZFYk1uUmJRVU50MGVsS0l4anJNclViUFNUdnMlMkJtbkh3UUFBY0wxbldVVyUyRlUyUVNiR0UlMkJGY21mbzdZdm41R1pFR3hmaTJhdzFOOGk2JTJCM0QyQ0g1WHU3VEh3OHNCZDA1R1BtcjhPQWZ2SnV1MU1mMmRtS2FLUTRFNFBwZ2VkUWxGODU0Zm51RktTUGF0WHc3VVElMkZRRjZodGdNSktVU002SzliYUdwcGhVJTJCc2tCdmJXU1V3aVVYbnV6dSUyRjQzdU1LRjg5MmFlVVFiWDY0bGhod00zRVRBTlJaMGlNbiUyRk04dFZsUXRkSFFzSUN6bHh6U3dKbEp1aGZXckZCbjdnYlk4Y1AwNGVXRG53OGt1ZlMxZTM5Mms5TFB2dUdzRHZ6NEMyS0JMOEdNNDF3UEViQVZIUEZ2d28lMkJUMVAlMkJ2aHZ1M0MyRSUyQk5Ma3RHZHhPcmpMS3lwQjRScDFxdk1SNzdQNGtNRyUyQnBKJTJGdmJiemRFbDJ0TlZuRCUyQmE0Q1FnQXN4cWZyRktOWEV5QzIlMkZGJTJCWVVWTTRUNjUzTXZmWWhZSWdicHFGM0R4RWFhTWxKWFc1MCUyQjNxTE9yRVFMWTBiVng2b3hxOGVDMUplbURjSCUyQlBFZlMxZXhJQ2tTUkg4SmwyUGhMb1Z6dzUzQzdldVg3Tm5iVEhlWEFKbFBJaUlqTkJKZEY1eXhYdFg2SjB4b0RnVm5OSTU0bDclMkIlMkIlMkJ6cmpJR01McEZNZm1zY1NjNW1ZY25yOTFsalh0Z2xPVDNzbkxKV1VERHA1QzMzUHJuV0ZnRnBtUnpKdHdoQmxlNTAwT1Q2eCUyQlV0Ylk2cW5tciUyRkolMkYwYmJuSklwb01KTWZNOHRoaWxEOSUyQjMxVmFEdnI3S2FtTEZNem96MEZuTDlGYzZSVWdndkcyUDdYNno1SDFOVmtvZ21ldTNZNWdXcmNLY1F2OSUyRjBtSGIxTXFNeHNhOU1ESzU0cTdVWDc0cjFNTnVGd205ayUyRnZkUzJqTFhMaEVrQTk1UkVLV3BzMEQ5R01aZVR2alVDNUZIMlJQbHZ4aFFnQUYlMkJ5UzNCMWlFdnd5U1djcDdWa3YzdVY2R0FOZE4lMkJmZTk1aEs3TU5ZVlF2U3lNVTJYdkNiMEtpaGRrSVo2eUZvSHFDWTdsY2QzbjU1djFqcUZhdXI3UE4lMkIlMkJvbjUzR2hGRXRTTWlhNFdYOHFxbGppeEhCbzY5U0tTVm94ZGxrekZyMktrQjlQUE5OcXhUNXBvR05sbmhtUk15V3Rxa3pZakxFT0sweHBMbmRrNTl6am5WVkVwd213cFl4b2Y5R3VtNSUyRmlacndVaTlSZDdyaXFLT3d3JTJGVjdBOHVXeEREQXZCYXF3QVo5Q1lzWXRtdTVQcWFPOSUyRjFSUGllV3BhMkpzcGJ4Z09WeVlGNWFob1I0ZCUyQlplV0pjNk5DREIwM2FrJTJCSzZ5cjVrVTl3dmtKM0F0cDNiaEglMkZBcUJQMUJPVXpIZmJDTld0TFNvQXhjOFBRSmtveVZ1Smp1YzVuSGgxVDB4enlpVEt2TWJkZkJ6aEFQOTk1SFJQUmdYdzZFM2t4WEdiJTJGYlBnNThKVGolMkZacVVCZjBHRjBrNGVhVWdtMUY5WEE1NFFZcVc5MVdrSm9OcWtTaTI2NCUyRlhUNWM4dzNOdEJ0dWJmNGFLcnNEVFVoSzNYMGNWM09LTFNZYlhFUW00Q3VwdGx1TUJVaVVZb2ZuMkdxc2FvbGI5SU1NTlNtTE9tUGRWQlE5TVRkbllFa2dmVmUxa3hHaXY1Q2h5eTVRTkFLSXh4Ym83UFZPdkVEWnhiWTA0ZHc3MVJmQmJ6OXpkT3RONmRnMkpEaWg2SXJDemRIeW5sNjIlMkJyTVNUSW11VlcxNkhUbXMwSHpFU3VhbnRMaTdIJTJCSnJidExXNlVpSERHblZaSU5jUlNIWGw1MXhWeFRqaWVJVDdpTE4wSU5PeXRWdGR5NlZHa04zdW54aThBNk9VQ0E2UXlQS2lGVWdGUjVqZ09mMzhOakw0UEZZJTJGTXglMkZia1FoQVlvdXNRYjJhcGdienE4Q1kxVzBYYzZwSSUyRmZGbE9pZThKTnRVckxORzhWS095amloZTI3b0RoMkJKRDFtY1BOTVBOYldMcjJreW1RVmJNZENINGlwTTFCdDhxamtpTVBOSHREbGcycjglMkJRaDB6MjNVMm9qWDdTUGVwSTBHRlh0VWlGUHRXZ1Q1TjFiMjZUcWFKQUdyVzdybTZDcCUyQjVPZ1JtSW90dWdSTXFnbDdrNUt1aDMzalE2NGxlM0huY1lqS0tVQmUwYkJWWWNLcDB2cFVMNTVSWFg3SFhYNSUyQkklMkZ3ZGFDYWdaeDNBbUo4WFZzU3J1ZlNObjMlMkJ2OWJ4cFVvSVBGV2ZOdVVJOWlzTDFyQ0FtY05lOTU1cTklMkZlZWpjV0w1Nmt0SElYZ1ptNXdwZHpFSmE0VjZBVjE2ck5ieGd4Ym5hM0ZKMlVZZFlEWk1TVWU2Z0pXQ0lvJTJGeW1hUE1LdlFvT1ZqOGVoOE94STlMelpHVmYyTSUyRldmSDlHYlIlMkJ3VGptMVBZcnlmTnB6NmliT2pvVlhleGdzdWN6TXY2NUtyMEhsSHNkdFF0dWk3TDlaRnJDQWtJJTJCekJsdDIwbFNDMW5nOVd3a0xveVZvUU1HdUIyUGs1RWJaeTk4eHpxTTBYYUZ0anZEM3pJYlVQZ3c0dmkxSUF1cnUySVJueWZ3WGNjRDVOVVVKUUZVZ0NMM2dwcFY0cmtpUno4dDF1VmRWWjFEeW9pREgzeDMxc0ZUUEYlMkJEREJIcmJEbzM0UFU1YTRoV25XaHo2YW1mMWZaaTRmRTN5cHFjdiUyRk5seEE5ZTUwOXNtSnRXcmtSbEk2dkpxZmF4SE0zNUc2WnFzYmN1cVhhRkF5VUx0SjRHVW5WbHFYa0JHQndKTSUyQnlEc1l1MGZRYk8lMkZzb2RLVDllOWo0RVJuR3dPVEpaNEpLMFlralJwR1pNTGRFSVoxREFUbUdXRHRGblVOUDdic2Y2UjNLUUJsRUVSeW9NQUFzNFZ3MCUyRmtTaVVPT2UxNlJrcVRSanU0N2ZxSHAzQ2Ywc2FuN1BpSVdwQ255MSUyQlptVnczZXh1USUyQjVNVHpHUiUyQkprNkt2dzlHNzcxTEZ1b2VDdCUyRjdUMkNkaVdORHBpSDVZT0JLc1ZJbE9iU1NCem9TVDZia3RYWllacUJkWUNEQ0ZCQU1aaEN1R0tEOGtNUXBFNFhyMTFRVThhT1clMkZvR1o0Q2tZWW9KRU16YSUyRkR1JTJCWkxka3o3Z1ZOVHJEZVJTSVIlMkZkN2xDdkpkYVRQeW9rczhlJTJGV0ZvOXI3WGhBdDRwM3ZnZVpOMm5FNml1Q1hOZnp3azR3aFB4am42aG1yQkVaRjdqS29KdkFRZWZqMUJ5OWxISk9oSEdUZzBTdTZlVDVWcFJ3Q2lHSVAlMkJ5ZXVmbE1FTlNSQVJRajUwMEdyRGVObDdTV0VOd1RDWVUzNWU1czQ0bHF3VTBEWkIwMDh2QTBMaDBXRXJ4b1pxdW5DYyUyQiUyRmZMREUyODZzRXJydWowTUNRZmFvdXBWMUVrbWJzUnZiWFRTTXglMkJsNU5XdmYzQU1VV01OWlJPTlVmWHBMZjhFM0VJTXBFcU9SNlNPJTJGWGxvZnl0MVE5OTluaVd5OFpOemhmbHc0ZVJHZ1JUbDFmM3ExRjBlUnIlMkI2SWRDT0duV0VPMjRPejlvNyUyQjhIRWpTRmp2Q1ZkVSUyQnN2Smt0dzNnbGVtWUhqcUlGbGZ0TjZhaTRiM3JhUm5BVTBvdHRpZFVJN1RucUphbSUyQmtyYkRvMzJkJTJGWW5MTkx6cGJJZXB1RWNYbE5YbWw5eGZLNVREWms5YkIlMkZoelcwaENUOUN4RkFpYjlIVWJuRzFPaWIyU2xuTk9hRWppR2Y0N3F5YklwNklUQ2s1RXFuVE8wcSUyQm1seVRydkp6S3JKVlE0RlhxJTJCN1RpNDhtZzNnYngxSmdRRzd5ZTRGVmc3JTJGRkVqWFNkOFpSMFY1JTJGYTNLcGtmMVZOcE1GVmc3S1gxTm5GY25BbCUyRkZYZkpicWtGVU5aZzdsaTcwRUpzUU9FNWhOMU5wS1QwZzlTJTJGNlU2SktsYm01JTJGdGlBRlhvT1k0S1FHN053NGxieENoSTlmR3hHallXeFRUQTBkcnhJSnNxdFQlMkY4N1hVQ3BOSkl2RHdGeSUyRlZid1hybm9CRG1KMDU1QnZRV2xuN3pZclVHQXFlT3dlaExyQTBIcDA4SjJoWHEyclBYMnJFaDZJT1RDdDZZS0Zaa0Q3dDJaUmtNdDVFUEQlMkJwc005MG1vQTRiMDV3UUFPakV2SThXSjJWWDlhZzA1Wk8zNGtEQTBiMHlxd0k1ME9qclhTWG90JTJGbjNTbzZacVRkbCUyQm5lZU9GZzdoNzNOWjVKVHVxQTEwNXclMkJYS3UyRFNKOWRKJTJCT3V3cHJ6R0dnSHNVaVkyeTFNdlZ4cjUyN042M0dWbHZadVg4M3RmbjF2NlVDSzhlcSUyRlNvSjhUJTJGWkMlMkZEMXBaOSUyRjdFUjZNJTJGQVVrOFhESmprN2tBeDlkcTlrY2JncUN0Tk1iOTNsRDNtMzZEZFJmNEpVeXlFQ29zJTJCJTJGU0pIQnhMOTFHbmx5SVBJVWd1SDdKOUhweFMxQVJYSjMlMkJ2N2lEd3o4ekhlbXhKbUFycXFRWm84bWolMkZiSFJkQnZLdWJkRiUyQmJjRkNodlQ2aE80bCUyRnZuc01WZEs2RnpkJTJGanRtWVZOdGRTc3gzZmxDSU9td0dtWm9rU1JHMmtBemFnJTJGTHdpbjBHZSUyRnc2a2tEMW9UZzA3anRrc0g5eElCbjF3Z1kyQ0RaYTlmd0hBbTElMkY5MU5XUjRVTDBGZktPb3FHbENuR2Y2Q3B1U3oyVFElMkZmeUVFJTJGSzdZTEZocjFqYk1zaFAlMkJNcTU0ZXhFM2tIYjBLN1U2bkQ3V2tZMlVTZiUyQll2RWJKR0NpJTJCWkxqT0JKR0x1bldNS29wZ0dVeW9FVjdtUk90dmV4dTh5dHBIbkZYcUZEVUNTUVFjazB1WiUyQmpwV0szYUNFMVhVdm40TnZsU0xZbDNlVyUyQkx3b1psUmVCUDVuc2l6ckVzVWdzdWY3ZmpMOVRCUHFrNWd4SDFiamZkdiUyQjJSR2diZkpJd0NrakNFSWd6MjhlSEk2TDFjZGxUOEFGZzBzdERmTXlMZGlhVk5rTkJpcG1ZTWF6cDRtY3JkbUZ1OHYlMkZRViUyRnF0R2pEQVloY0I5cWdHSzhwZUVKZmxFSSUyRk5jM1FWOEo5ZjE4ZXhQdXViSVNOJTJCVWo2TFVWYXElMkJSbSUyRnFsbFJ3N1dCY3Y2dHFXM1pPTHNVeVB3RFglMkJGVGd6dWNoSTZxVDdDcnZwS1VJdEJmb2s1Vm1sYnlCRURhYnVSUW84MFZqRTV4aG9mZCUyRk1RcW9UNyUyQnElMkZKU2VkWFlaajRBd2JFOVZLdnAlMkIwc0ZIa0YxREQ1WUslMkJuZ3hXWnllbThxYjF2clhwRkEzVTZSVG1FOHlMY1lDeVlrUUhYYlVFMDRhM2xrZDR3eUZkdmdzaEJzMnhQbzJmWCUyRmhsRSUyRmpWV3dmU1lUbEphcXl6JTJGNUdlektWWGNwencwdnBIeXFxNkF3NlRIeGtzR1ZCdUdUJTJGJTJCeFVlcWdTVnloSE9hSlJianVsSmUlMkJhc0JLUnoxY3ZVc09EenAlMkI3Q1dYcVpOTWRoSnhKRnN0UGxqTyUyQjZBMmt1U2xGRE55cWlwRnVMRFFhN1ptUE5rM056MG80a2JxMjhUNnd5bUdmd0tmZzlIa203MXNvMFNSUkNzbTM2NW01UVhpWUVOZFh1Nmd4b1ZiaWYlMkJUNnJveVI1WW52U3JVaWZSSFB2VFJwV3dhWFhEbjFmek9rNWc1M21pUGwlMkJnUjBxWmR6bURiVThtM0tYMGlBMzVCU0Q5WDhTZ2JYVVQ1eWtUSnpUS1RLZ3RncW9FR2dHdkpVcnVYemhqMlB3SDdxTWdZOGp2JTJCRGZtcEIyMkxNOSUyRnpPZDF6cGNiWnFtRldoWUdZa0k3aGlXQzl1RjdFSnp2Mk5yNG0xOGdGc2hBbW1zRFBvN2FQMkclMkZhMk81T1o4N2FNUTBvaHAlMkJSZVE2ZFNreTNJdm5vTkIySmR1MUpjWndTNExmWDNmNVV5NXdMazJMRnlSRU8xM05pUHh0bFJ3Wm1GaEh6a2Fna0MlMkYxS1lsd1loa1ZLQnRFTWM0TzVxUXdYZWVGNG9NeTVXNDhKb0klMkJNSklhSTNuJTJCQ3JXWXclMkZER1JvN2pQN3lrcHZaanR5QWhuZGk1eGlBQjlGZFJBSHFnTTFWNEVOUnR1RHRDSGlCck1KOExwV0xLcmMzc1ZJNXJQN25OV0VaOTRqTTR6U3g5a2NFcERQcHRDZUxRanVJMzZFN2hoNVdsMUV0dWZYUnNBVHBFczZvRFQwNGVLRUZXaUozYjMxTE9pSW9Ob2h2U2sxVFhLd014OGQ5VFZxQVA5c1hCWWNqQ3gwRlFUN2xGeDRxNVFEd2R4Tk9kck91JTJCb0JWSWNDUm5SUVIwc3J4YTlQRTBWcCUyQnh6RkplJTJGSFlIMFJjJTJCeWxCSkR1UXJ2MVhhNTg0c3NiTmRmN2ppTGI1ZXRhcjRoZDhsdENnakJkRTclMkZhJTJCUEx1cDViVHA3eGtRa0ZQMUIyUjdNV1R2a0tZMU80RlR3WiUyRnVqbzA1OEJkVWl5c0Y5TzFxN0VZMnQ0VGhkR2duU2FlMEw4MHNjWDdERDV6TmM0TEZmODROd2NGY2h4N1QxaTFrNGd6dkJOMm40VmxBZnp0ZmoxeWxxU2hpZW8lMkJ6UDdVNFRXdzMlMkYxdEJQeWQzbjFUS2FXaUZ6SW54dEh0NWt6MnNpR3hia0VMT1JkemQ2ZVhtJTJGNkVUNTY4RFhTeXA1SHpLS1cwZkVTd2phRlBNdlBUb2x6TzNjSFVZOEs4eUpRcyUyRmxkZ29xUm5EdU9rRFUlMkZaVkhRb2ViSk9MVnY1aW50ZVNMQmwlMkZkODdQcjdvRUliUThvJTJCeFFwT3FzQ0o4RkFUWEMzV2p2biUyQjFVdThOU3RFTW9Fcno2SiUyRnJFTnU3QVJkWFFYTW15ME1iV2JSTEJZWGh2SzVONzMxbkhwcWY0clhNbUZ5QkZDRmxwamZxMGxRREZucHpuZndDbmFPbmpvNDZQVk9STTVVdTRrZGZTb3Q4U2tyJTJGWSUyQnFuZFB1Wjc3dGVjRlMwYXRpZVREaFBENVdZUlhLdjFiUFJlcm1uaUZyaGE1dzY3bDc1cThWMXM2JTJGQXRlY1ppUFJZWG0ybFVzRGRKRWFpOGZZYnpGMTlpVTNUVDRhaFdPTERSZSUyRkZXdldQWE5lV2Fzb1lXSU9zUnolMkJhSFNOV0dmbEMzcTJXaVByJTJGeDNvVGMwaUdNdWFsZzVBJTJCdnBaNTZlc3AlMkI4VWlESHdHcmY1emNGYktsT2wzNWVnMFp1S0dnN3g4VDl3WTJVa2hkYVNaVWZNb1pDek1LTzJ6SkJPQ1gyNGduemVQYUFvV2RSaHJZR0V3dzF5dkVudVcya2FkM3NGUnFMSUE4eSUyRiUyRlh6N3RHSmVDUDZVSW5jWVolMkZDJTJGaHB0Ukp5eU1EZU1uMHM1SEh2RG5UcTRYdDliJTJGelZUdFAxMU5kaDliblY3bkxnS1B2ODZPV2xBSFVuQmlzM3Z0cUJJWTh1ek9nZDdJaFB6cHlNNGZNJTJGJTJCUnB1Vk83WWt2emJKeW5MQTk4aFhDYmN1dzQlMkI4ZmoxbWV0U1hPZFVwJTJCMmJYRkZvZjFFNSUyRnUwJTJGR0FhYVFFUU9LOXclMkZEeHlvJTJCSGJZcENkRDNjb3lQOHpjR0N5VVhtckVPbTZaSlR2QlhnZTFPb2g2JTJGREZVU2JFS2VwWDU0MklMSVclMkZFcnVXSzZkUnZOTnlHeDhCYlAlMkZ2cWY0cGFWVGZtVmRFVXlqOUN5c1Z2cW9mSDRJSm9FaG9RS0FsZFJ2NHBiUVclMkJYeWpiUEM2N2x2U2olMkZsaSUyRnJsU2w1aEpFbHJ5QlVkSzBMJTJCdTVEQ2FKZWdsalUzbWZ5ZzhXS2lpMlluJTJGcFVHMGxIdnpFMk5mRmFBZndyU0lVcGMxWmdmWGtnVDJiQ0g0NlVpZEoyeGdKNDVqUHV2dFVrV2JzaEMlMkI1ckQ1MERsUWRadjElMkYlMkZRanBkSFpxJTJGZ04lMkZYNUV4UmxucU9HUE01VkhMZHc0T3dhdmVVUXZFJTJGdXNoUjRValIzZmxGUHlQY3JWZkdiSXo4YWd5dmtSaU53bTlvbmw2bmlYeXluenlqb0w0TXpiVWtnbXE4RjgzNGpsdW1sQ2w2OHFpWiUyRlEzNGEyS05wNXlHTVVxRVI1YlR3OFp4eXU2R2JpUGFZTmZTMmtqeGd2YVBtWmZaVzRkR0Y4SHZlOGcyNmJ1UnZKMHhxSHRoMVBCRmFVc2ZINGRZb21JJTJCTzZPNjVHcFN4T0JkczQxV2dsZzJxd1A3SU82ZGRRc1hzRmN2cE9lNGclMkZhcDJmdTAlMkIlMkZuSHhFJTJCbkppa29pbWYlMkZoJTJGNDRJMHVlZ0pjNEx3ZnAwWWFYaWRKR0FJZHJHYjh4YUNoaXFZNzdTdkdyM0ZaTEQwdUNCN1BFbWV3VkFkN2hQZkdqbDlHMHdCdFolMkZtcmdyOTczdkw4c3Q4R012QzEybnEzRWN4SlA2c0NwWHZwd29EYjFwcU1qNFczVSUyQiUyRk5jeHlVTjhaJTJCZ3U0WktGUyUyQlVKSGh0MzdPbE0xaWI2WTNsNFYlMkJRY3F5dlQ2RDAwRnJyVE5hT2pkTzlhT0hub1pFS3pUSk5weFZYMEllZTNucVVBeWMwcHNQUzZ4VXNTeVJlZkRCaVA1WTUxa05oNUZNZm5yR3dWZmZMdUVDb3hiOVFhZ1pTN3FQOU8lMkZrRFREUGk3UGFXMXd2JTJGN0dXRTJYTU03b05HM25PbkUlMkZybHBWbVNVWVRBeUM0ZFFPZFByaU9KZjlMWGZSSVRtN2xFWFdsRUdMeEF2dXBSNlgwJTJCN09mamVGJTJGRzExdk51JTJCdk9WREslMkZlbGRBZkl2SFdYNHpBd3NwcmZwTnpxODNEdGlWUThCck5IN1RUd3RsdnZwdmJxaXZqblZrRCUyRkc3YkY5SFRUd3RIY3VoeWJYVlZxZVB1Y3AzbEJ6clRXS1d4TG1SdHlxeEE4NzVpVWxHNmlPazNJN1YlMkZWaTBiNXNzSG9adiUyRjVPRHdWZXJXVUYzT1dMcDd2T0ZYRm96blRqM250MWd6NjVldk5kVnMxUEhiNiUyQmZmJTJGMHFIeU41MzhQOUVqQ3N3UmRDclRTRFNzZTNjYzN5alhZOVRVMzN1NlNlZCUyRlVTU2gzN3piOTF4VERPYmIzWDY3RTElMkJDJTJGWXVPbHM2MXFSUXh0dWxaSWhFMUNuVkVubEtVMlJkZXZFdHAzUHBNNlByWUt3R3VhWHVIV2hWJTJGVEpyQm8lMkZWdFVtZ01tNEVaNEt3elFxNERtTGpUVDI2anpvbGVpZTAwUVVkM1pOb1lIZVlrUG10dmlhRXVoT0ZYOTVnVEpLVWNoWVcyQ2Q4VndmTjhIQjclMkZPdmd6WDlOVWVqSnNoVGRGRzklMkI1RERLNTh1VkclMkJPcmZKNjJXWXpSTllkV3p2V1NqS2YlMkJJdHlXbUR0JTJCTk5UbkJQNk1iQUM3WVhjTUpxRmhJQXpURVVScUhDZXIlMkZDVEh2YTdRanlWaWglMkZ0TXRBZEIzOXJ4eEZxeWw4dm9QNGluTkpMbSUyRkRPbTdnRnBXVXZ2TDlpZVpZcERveU9pUnZ5VjJzenJpQ05sSXo1VHB4VFdIS1hhWTBiT0NaR0ZWZ3JGamRGRndSUEhQM3YlMkZGeXRwRWRUNnQlMkZYWDZMQ01LQWdEQUlqMG1FOVVrNUNDNFBTWCUyQlNmOG41dnpEZURxZlJmYjBJdVVwTVJjUlhvczdtMHQlMkJqYTNSekN6dmslMkJDVVUxbzVmYUxaZ0JRTkZ2OHYzck5ORVJJMDhEblc0WklpJTJGcDRwd0MlMkZ4dEdMTnpSZVVyNktvNlolMkJIWUdNSXZIcjBMZ3lnQjltUjNBSXhtdnNPcW1tQUklMkZzUWUlMkJvYiUyQlJCUm9Jcm1qSXI4U3Zic0JKaDAyOHoxOUtzb3Z1aFR4cGlZZTVSemVteiUyRmtDU2dLWUFrYSUyRnVXN0dQNExKeEV4QjFXYmNtSUd1d2hxVDJVYXlwMm53MTdWVDAlMkJaZHZWVGhad2lMdkg5UkhTMnZDM1lkdzcwSUNyc2lWN3BUSFZnVDdUWkhCMVJTbjY2TTI3JTJCWVJvaGZPeW9PM1l3akYyRHcyRXpvTDUwRm43UG5QcXgwM0FES091YjBZZEdnYmVJZUpsZTJoVGphRGF5elRBZ3hqY3h4TGNYTVI4MkxoeHNET3lNQ1MzNFolMkJGZzlFRUV3UnFCemVMUnNMYjQ4WHBZNVE0MDA1MjlIT0JJM0V1NjJxbWJSUGEzcGUlMkJYNEsxTUU2NDdKcWdQV1NHcnB0VnJwalV5bFZYQ1IwWTRyZm96Q3JyUiUyRjFyU1F0OWRTSkVXY2ZCYkp2NXF2VEtLYVVMTWNoVjZoeU91JTJGaE85a2VYbTlsbjYlMkYwa1JRMUVjSlgwMENTcyUyRiUyQmVnTFAxbU52bzdiWW04Sm1hdzlESDBoZUg1ZE1FSDlMR3Y3cndVVHZ5cm1vZkU0NXFuYmlKVm9sUFg2UUhSUkslMkJaaWZZdTRFUm9HYlhaTUVPZWEwN0RjeEVKQUs1emZGOGFScWgxNm9WOXBhbGt2UzFoeWtFa1o0Q0Z3cDNxYWZ6NCUyRmwxM3dJZGFnOXAzczVhUWZ1TjJHM2Z2NE94SzYlMkZTSm5yNk5PampXb1NIVjEzejBHUDdFWnlnOWlMRURrYVBYVkZmZjEwRmo1VHNLQ0NndFY3R204TlZrb2piOXJ2OWEzSElEclNoJTJGRWZ6R1h2TFROUkVFR3VOMnBERk9VZ2tWYzhqN3dQYmtWU1VIZTVTVzVFM1A0WEFaVmltOXo4c05kWkp4eFAwcnVjMEJlMmpMNnI2UGF1SGlCb2ZhWWdRT1BLU0dWZUZiaG9NRmZqMVF3eSUyRkdyVkl0dVhXdm9ZSUVFcGg5U3k0dHp5MXdneXV0V1lkWWUxNXJ6dyUyRnY2b2VvNlpYZmFoJTJCdkVVVnFGZ2R1aTAyWnY5VjBrdWE0WHpBUVRDTDk3dFZaQ2hLMnhVNjczZ0txa2pKZHRjSnplYU1yR2NDdXlRcjFWTmZ3RjF6YWdIZXRBZ0d4WjR3eHd0bnBZMHRpakM0R2NUU05rZEslMkIlMkZMdGZSRWZsWmphaE9qbU05WDZ3bmRMNGEwbWpqeFZXcndKb3NCcXJTWDNWZG1VYlpIZWJ6RkVMSTJEQUYlMkIxdnV1JTJCYm9tcjE2R0c5NEkyenNVRWhxZyUyRmpIN0lJNHdvcnYyTVhOWURPRzhQS2d6SEJCYW9zckNyMUlYTGEzUWVNQ21yZGIwRUJHTnZsNFpOR0MlMkJ3RTNCSXRvanA4RmhxTjRJOEUlMkZpVTRSYTJvVGxpZFVIJTJCVjFmc2ZtaGRqbHpMWnEwODRVVVE0djM4JTJCVkhVekNpNjBkaEdTQ1JFUXFJcXVLM1lNU1dvJTJCck9GekVFbkxneXZ4eEJFb2lka2hSNFd4a0o4NkJTSzlBeWRVZTFqQ0RMdGM5JTJCSEQ2R3dlR1pYTTdQUWJGYSUyQnl5ZjhCTXkwT3YlMkIlMkZJQlVrZHhnbmRrQ2thWWN2JTJGenlZJTJCRWNOOVM5V05NaTFvaUJCMHolMkJzYzZVY05QSU0xNHRzazVMMWNodHhNMEI4cXpybGs4JTJGZEFZSGclMkYlMkJNZHpjeE1RSGNHV1FXMU9sMUJYbzBoUmNLUVJIYlNOU29CMmNQV2o5bDFyWEJPcktsRm5NVTZ5emxkZ01vNVFKeklqRGxvQm1iZm9scGh3VWJOREkxQXElMkZwNzlhbmE5ZElIOTlTd0NFSTlqb2JhRDNkd3FyY1czcjJKNERIUlcwcVpxaHN3bVFIdjVzRXZZYXV4cEYlMkY4NTYlMkJ0bnlaUDQlMkZFSDhYVjg4UW96czZ5WjM2cVZuU2pHVXFPOEpMeHdQRGdVcGxjbWpoMXY4YUdKUnQzbCUyQjRpcSUyRlE1WmUyQ1hXcTRBWkslMkZJTms2T0JJWnhCOG9kTjZMSmRSVkFtNm13d0JWRnF3TnNtSkh0c3pWc0ZJTTVDa0VoZ2pQSG5OcEJMWE1ISngwREE5d2FRN1kzY1JHS2kxamk1bVVvcWk1MzJqREdCWSUyQlRVZXZGYlNTRlAzR1VobEhMRG9vdVRjSXUlMkJtVjJUSmVIVnp5S0RHbHJ6dEVCYUhCbUlJbCUyRnM3MHdtWVVRNG9oSGdIVTZ2Y2dKTXV4b0I4QXNkbkJjeXBnamZNdkgybkdlN1ptQ2hQSHNWaXc1TmVhQ0pOSTY5d3VWUkllJTJGbnJ5b3FHT2E3VnptemRaTVIycWZ0bGxFTlJzYWtBUW9FTGFSWHRsTlFqMnJxJTJGNlNFVmMyR0xobkNERjFPV0Uydk5nbGVUU1lDS1BNcVdYSWh0ZXd3VlhicmdWWFFpYW13ZVdZcjFWdTdPQyUyRnlsTXFRMDB5SFpEa2ElMkJ2NnltZ2hYTER2eFQ5eEVHaUtnSzVzeEp0d0hMdDJscmlOclBRS25XdkpmbkZLOFB3YWZPcGt3UDkyVjl0QiUyQmd3YkJua215dnZEcFZ2eiUyRnpsJTJCZUNEZUhEamhWeElNZEtveE1iZGdRcWRhMFlMJTJCZWNiVUZrM3FvcDJyMUIlMkJ4YzJxZkE1Zks5NlBBWSUyQnhwNmViOHFLOEpYMGZHQldJUVdsTlgzQ21nTWdCMWdYRWtWTCUyRjJLTEFBcXYzdTVKNThRVUo5TWRFJTJCcUo2dHc3SEtVRWVKOFhjQmxsTEVsWmJvMSUyRjhpJTJGeUZSMGU0am03T1RDME9nbGVRS0JCcXViOWNpdWJGMDhpTzVDWHBTR0l5YngyOEdvWWpNSWtORjNFbzBIOVFMZGgzcnlUaVVKY1hwUHExWExqTyUyRmpKZGVNR0hETmxMVEVKbmJrczIya1dodnJnWTl6N2JLNWJaSnJVUE5rcnVVUXF3bjZLbDM2a1FJZFcwVFVPUVBYNjNaT2ZyM2FQYXRmZm5LdUJYYUw1JTJCSVFsSzJLM0R5JTJGZW9WbTclMkY4dWpHQiUyRjhtNzljUDlPcW5wQkVZMWtGNFVKNENHOGNRNmZNS01TOWUxYjhNN3FFWCUyQnVOTHY3JTJCYUtLQm9UMnJJQ0IlMkZib0pTbURmc1NGdlgxU3FxSTZuJTJGbSUyRmtZaTR2amFySiUyQjN5OEIlMkJkdjA4U3BDdWtOeml4Y0d0M2t3NEYlMkJsZURmdHNuNWhRMFM4VHU3Y0E2Z2Y0R0YzSkZVUk5hV1JYOHB0RnFrciUyQkFPZ3ZjS0RjNVlnMG5DSyUyRlVyRG1lRGo3NFBreVI0QjNUWlhCSENlcmJkS0k4Q2ZtZDgwd2tsc1A1anI3Y2xFSmJiRTB5bjN5JTJCYWFmWGFMc0hUNWh2bjRscXNDVnFESjhYM05URldmUlRaWjBwWTZTR0hmV1MlMkJiTks0MXRxRWdIZXBSMFNFZjdmZkRDV1dOb1F4ZUtaY0t1UXp3bWZ3cURyc1lON1o2NU9ZYnZUZTdBJTJGdkw0U1pmeTA2S0RRSkkzRmkyeXZBdG1IYiUyRlBVTVJ3TVB4cU9na1kzQmJhUmJHRmhnNmoxSFBqdVpodlVPNnhBaVVEblJXbXJCSnprMjB0ZWZSbmRhRkVvVGpOazR3V1FWYU5GMUdWbnI3UTJKdUJobDl0czNHQlU2ZjBTMkRRNmhEZWt5JTJCSVJVOEklMkJmT3luUzQ3WWpKd1NhWUt1emVJSDM5YURVemM0UENFVUdmbElpSHk2NCUyRmJINEY3emRUS0tJVlolMkZYRCUyQndOa0FBUWtvZGxJaTRRdCUyRjI3anZqaWc4aE9nbUYyY3FmUGtRdDBheTd0Y1M3ayUyRjdjR3J1UnBZaFhnV2hmandQek9hMjI5JTJCdm1hUHpVYmpSeW44MldsTHJVQWlrcERXYkYwbWZINWdJTDN4MEdxVEM0a1B4SzBPTjM5WGJpaU9XZVpQOFE5NiUyRjdiOXJDamMlMkZXczFmQXcxaWtvenJubk9yOUw3Y3RrWEtoRiUyQkwyRzMzYzM2TzBxbUl6UTVyQXVZV3VCWE45UFYyZkcwN0R2SFZMMGY1SVlsaHBqUkdmRkNZUWowSzFLTDRUNEJJUHklMkZNTmpaNXZjazFnZE5MalBUN3FJTWRSTmhuWjQ4dVlPc2VHZFZOeFRyZjFmNXFmWHklMkIwV1hQMG1BSDZkZ1ZTekw0cDgxaHU4SGNLM1pXWXpzRjVWJTJCaTdndk5kcG5kWmJ0YVlNNFVFTDNLVnV6Vm1wZVVPNUVKUnVyQ2F6dkNaRlNpUERRdWhpNjExQU1nM2dDWW05cGJydVZ6OURURTcweCUyQmE1Q0IlMkJ6dXg5ZzF2c3lnbTRxR3QwUXUlMkY5dTgxVFM0UW4lMkJSR1REcjFLdWwzRVdYJTJCOWpDRjFJQVpUZUwxbDhNQUZRakk4ZnNhNmE3TU9mcWFCVjJCUUlnRzB4V2pGTnVWQjdrVzhXRlNTV1NxOHhKamtoVzM2TEglMkJ1V3ZBbXhuSVFSSzljQ1FyaktvVWJDV1RHNk93N3gzJTJCdDQlMkZqJTJGWm80RDN0JTJCY2RlJTJCemkyM3AlMkZ0OGNFUTBDem1qODFQS3hlOUJqS2Y5S0JIVlppSCUyRk85N1ZnWmsycHplZ0hwVXNmekVGc3RLZXJycWZJUFlxWVQlMkY0cUFNaW83cSUyQjZEMyUyRlpjdE1tcUNKYTFoTmM4RjZJMHlmMzlCSG9GYzB3dzNkQTduMFZTTWdHNCUyQm9YJTJCNDQwbWJIa0s0eW1LQmplWmQlMkZvZHo3VW9sTnBvaGlnMmolMkZLRjA5Y3VJVFRjcU9kaHclMkZUVjJieVZFUm5qVFh2dDc3a0RPNFpiUlVzUjZjQUpvQ1JpeDdUaHEzdSUyQkIxOFF2ZmxPTENWS3JTWHFaSFhSMkpzdU9HUTNWTyUyRllQT25ERmcwWm04eWhqdkhHN3EyelE5Qm1hTjZ1MDczS1lXTG1jTERjem9tSWlOMm9nYTlEYmJUYnMlMkJpMnFSazk1dlJkWE9GVXQ1REptRWhnWlglMkZvRU9PV2Ruam9QNFZjQ0Q0M1Y3VGU1U2p2aG91VUhPYjNhdDd2S2dlOUljUjFaRk9JYnFiVzBmY0hlckpXOWlDSVRyVUlzV0JzSTgweENoam56UHBCSnNSZktCbEowTWZ0R3hBdm1vZnhlOXZLUUczTWtrZDBpREFiSVdXQUdtOExkdGc0ZjJVOE92QkgxVnFXSm9GWU0zeE80bEtIelBGSmozSXRUakx4aVQ1UFc0eEYwZWxnY3NqVXNHV29vdlFwZ2hCeCUyQjBRVDF4WTJPc0tlYmw2VUp5QlpwOCUyRm0yZGNDVGFUMlNqYzhvekhLM2hKR0txS3VDQ1A3a2szdFpQeTJOb1hJUDJiMzdBMVZtVFF2N1ZQNWVzenFSVE9VRGtYJTJCZVlRamRvd3dYU00zVGF0ayUyRmkyQTdsazd1RyUyQnZpNmNFdDJXUzlMV2xBWUcyTHJRVkRLNGElMkZzT0htSWdmZ2lnUDVQWjJQcHp0USUyQmY1bjl1TkNtWjQxN3UlMkJubERFc01OUklPcnN1TEluSUZycU1KeWw4NFpKSXFiVG9KayUyQnFCdGFXR3RDVU9nVWhDbnBybWclMkZkJTJGbFU2d25DYXluUldlRnVsaVZyalZBS0RTazE0TkE0dlFYU0lkaExibTVSZDEyMzklMkIlMkZSVWR5OTFDJTJGJTJCS3BJSTdqZ0tVZ1VVb1B0ZTlOVk9OdkV6WjRjSVFVZjh0cGRyV2d5aEhTd2h0NFBrQlhOSTZmVDR5eG85UXg3dHFZMkclMkJQWU5jdjZJciUyQlhQVUJnVGRrbWhCQ2lmd0hyeG9NeFdEUGZ3NkxzTDZ5cFcxeXhjNnQwWmtCdlFqY0hsTVY3cnFoTFIlMkY1aGw1cnNrdTRucElFaXFEJTJCYVR6JTJCUHRISnBZQlZwekZLWGhDQjExdndYUmR4VElERUVrUk1Fa1lYckdXNlZZRzJWZWdaUm5rZ3htWVNTVEtxWlBpWHhORUdJRDA2JTJGd09FZ1AwVDBXdlc5alBORWh0V0NENVZkYjJJNU5RZWlXR2dMakFOMWRVZHA1YVpGdG0lMkZRdXN3b2VCWEYzS0RSRXdOSUl5SUFBQktBJTJCaDVjY1JyWjRrZmglMkZMeXJBbkRjY1RIa2pDU1E1ZjJ3MXc5NWM1MXgyYW1ENG1EayUyRlN6UGZzVVFMNzdSMyUyQmxWNERxZXQ1MUNYZyUyRmhoVnpqUjQlMkJJMGR6JTJGVUI1VkdhcThhdGxGSlhGdkdYJTJGUzlzanltaFMxWTB1bmZCWGEwd3prS05OV3lTS2tpdVY4MGNQR2oyeGtkQ2xPZWhIT3hTdkhOd3JrJTJGU0I1c0IlMkIlMkIlMkJuWm5aZUJYSFJGRFBLMkxCaTZ3Q2gxVWNKMEhPSSUyQkY4clVoQ2pacnhkRHhRUzl2ZWppT3VFZmJJMDlJQ3hoZ2FIeXdEd253RmUzT0Y4QnowWEM4U3JDZXZtelBSR0lsakZRQWlqaXBpZVk5eTVDdTQ4OXR6TXpMTWhxTDNyWWhidkpwdWR2SEhMQXpWN0VlU2xTTXM2aEl0ZWd1aVJ1Vzl6TDExWmlCUllxUEFrZm9lJTJGS0NRSzhyeTlPcWwlMkZvUHNQZEQ0ZnRyTDQlMkZJeVZoUXN4ZnFBQmhxYXVIMkZSMlh6ZCUyRklJWDZ0NXVvT0ZwZU5nWWt6VFRJU204WSUyRnFxdElKZDFyRGglMkJuWkNiZ2ZGTnVTQ1VWV3dsRlRHT29VZjFaTjl5YVZmdmNxJTJCaVlOUTlxT1dYZ3dIM2ducUxoTFJ4UmVpZldUclBCYzFVWFhIQTVrZ29ER3JQbFVlT2pjTjF3QVJDYm5pbXVSeFZ2dERmWXZON1hMblR4dFd6VHZQNlJ0RTY3TDBZODQwZ010dlRVWEZRS0dNS1VZRWlsNyUyRjZPQ3ZxMVdQVEFNSjF6WGlZbzU0MmJVNjRvb2pjRUNnWUlwVDBBTnVNOWhOcVlNdUdsejA5QUQlMkZnVnpUTHV3cSUyQjhLNDcyTEx0aEozWTRXelBINXBvY0p6RHE4Q1FKYUxRZnNDdjF5RkMwaiUyQkxpSEhnYk01SSUyRklvb1NGamJ4cm9udyUyQjlRakU1M2ZsWjI2cVo3cE9uOGNoeFd5U1FaaWlKZFJQbUZEN1M3U3VrQlNlVkt2T3VlY3NqY05ONHJjejFLWjVVeTU4VHV0WUxCRmYyaXpOR1A5S1Z6bWh4eXVRcDJnYk02S29mYUxsNEpPNDFLM2lxNTBLQnVPM1d4SmxhMjRiWkFKeDdvTHpERnVFdUhURHFoUnhIUEhSMEUzOEx6M3FaUENvbzRBWXJaOEg2R3clMkYyTXNaRU5ydmczcGJvQkp4QVVjMURMaVkwRjZ0SU16MnpzJTJGU3p5RnNTQUt3bXBhUnFIJTJCenpyJTJCSiUyRlolMkJhRXVFZ04zakpHVyUyRmcycFpxa1p4M2FRQUdBYXh6cWhGSTNlaTUlMkJZcWFNJTJGTlhxZGlmMm9hcTMyQklFJTJGNUZHJTJCNUFoZTVkckVka0JzQnlSQmtxUU91ZkN4THk5bTQlMkJSJTJGc3hrJTJGN3czaHBGaSUyQkp4T0RlbDZQdlg0M21wRVFTNFpvWDRyJTJGNVdKc0FScGxMRW43bnVPdGZBZ21MakhsR0ZiMGdKYVBzaHZXOGh0cEQ3NkJrZWVlcTczbVMzUHlJOWx0YWc0dmNYU0lTWURXZzZmU0ZzYzZHNyUyQkxaaXpCaVN1WDJDUDZmREN3VWM4UU1kTm5JeEl6T1dnNVNYYlV0ZlpvSTZRSkhjVmNUcUI0bW44bm1Da0dCSkdWZVhRWE1RMDh2V2doZElianNZMTRXaUNyN3o4NHB6NElCUkRwNU9ndXBrTjF3Rk15ekVWc3dkWGhLOFR6dHRZcWlDQXpHQjJuc0VkQnpCSkp0WDR4TEIlMkY2VDNNdjR2d3VpRjltRnRsNng0WlhFNUhjV2FFb29NaTR2Y2dLaVNXa0J4bHhNd3ZZeSUyRlZ5eXpHTjBnVU9BcFVld2wzSHF4STZ5ZFU1SkpSek5UT0hPVWpsSUVTenhtaGdzSkRzaHpCZHlyT2slMkZNaiUyQm1QN0pyWno2ZnJjYWRrQUVUWUclMkZaNDdsaEw5M3hqMkd4SXlUdjlnN3hoZXM3RHlUVDZWJTJGRFcwM292WUNBZGF0TWozN2MlMkZ4JTJGaDU4Qk41NTdDOCUyRk9HcTBLRXBYb3duM0NVQlllYnFFTmdJZXpaeEwzZSUyRjlqRjV5djJDRHdzdDBHaFBTQmFaQTNQenF0cW9vWUtRaTRvU0I0aTdJNU41UG1MdU0weUJzdTBpYndhZG4xY3B2NmJuWURKUDRHbWV5YWtQQkRweENubVNpdiUyRlNEZVI5V1o1YTlwTHklMkZJbUxQTkEzYzZ0RFluNzVpUnV6Q1J3bW10Sjg5QzlObU5JVFklMkJhJTJCU1JPaTdWa2lweklVYUsyNFpYZUc3cE5HVHJIZU91VWJCWjdPb3lwVyUyQnhyU1BMSDlHQ2RmbGYlMkZRJTJCZDdHUGQ3dDBCRzZPN21mZU0lMkJ2SUlFamMwVVQwUXZ1RTM5Q1BTWXQ5NXk4bUVrOFNsTlNSVE5zUG8zaUVuOXNCZUgyclE2YTlUN0pxTHBSb0xWWmZqdXNQYzNlWEYxSkFRNCUyRlJEOWhWQnlqODBXMVZGVWpNVWRKRlJaYjA1ZHklMkJrT1FZZEthRklLcHhKWUVMR1Nldjc1QlR0bFZsJTJCa0IxZzY0ODVnZzQzQXVoUlVmUDBoSnB1b21mUVM1VHp1JTJGRXg4OUUlMkJ3dGZKcGJBM3ZVVjFZVzIlMkZNdVVBU2llNkZIWjBCbWVCZzVDR2tBU0lwb3V6ZERmS1YyMDhlRTNjUVRib21aJTJGRmRMR2lzSkZpUXYzWHhnSnRLYmNteGJ3cW5UNndxa0dMbTFwQ0RjaUdUNUZ6VnFIMk9IM3Y1UUlaS29WdyUyQlJOelRyVEVDJTJGT28zU3Z5eTd0NjRsT0VIbHhpcFVNd3hsdmQlMkJnNFdqQTVRcEMlMkY3cG5TWVZVNVZWVXkxcU5reE5aVGlVYjNFQSUyRlJ6bzhTJTJCb3ZkM29kb3hHM1FyZGU5NzFTOVhZaiUyRjRuV1NTJTJCa3RteGlqWnhLODN4WDBReTRIQWhZVjhqRjFKWmxsUXJXME1lZDJOekp3JTJGcG41TiUyQjFwbjBvVXpYZm5EZGlzbDlCRnZ1Y1Zld0EwRlpaN0NPUXJwSG5YSFVIaTV2dU05SEJtS1NDQ2xYSXo5eDNKJTJGOWZPY0xTN1k0aE0lMkZ5OGJTeWhncE40VURwbEU4VHFQdjE2NiUyQkN5cm10dzI3dDdyVnU1UDQlMkJ2bDU0dVdGZ3pyU1VBV1dFUlRSWTIxMEFpYiUyQmJWOWtEYlFyV3ElMkJGTjhSNiUyRnBiMXNjcXJOVWhIY2JPT1ZYaDlrTWRiMnIxRzJoejNMeThTakVHTklhem4lMkJPZzI1NEdCN1FUODlUV2FJcTFoN0dkc3A4STduZnZ1Rm05M2prdEFIYmhleEJjQnhadiUyRkdLOERBbXRDMndUSGZmZkEwSkx5RG9GWTl4WmpuekFidWYlMkZCSUJiMzNMUjYzT0hOUiUyQlhOVzgyaHJLa0s1NjlUYUxoV0JQcTF5RlVySiUyRnlxcUlPVHZ2S05NOGdwZHV3WEJGYTlVUGtnTW01R1NSZElrMFhJTVBSM0hpY3Q2OTdTMFNBdnB3RnY5RERIYXZOMWxoODVWNlF5ZDNranFoTiUyRmJuakpYbEFiVjBqOHJ4M1F1WVVVVndzUGt2UjY0bTclMkJzSWpsTDZYR1Q5elQydVk4Q1NVZFBIR3NoMHlteFBXbmNGdEh6NkclMkIlMkJEdkwlMkY3RkNPYTd1NlhXSlA3M2hQSDhkYk9GdnhpcjRxcDg5bkI0TWFNajhLbVVKdUd1QVpGVERlRWIweDZDQjdlU2tSbiUyQkEydHpLekhlMUpxY2l6dFpWWDJWRmZmOVM3bXpGMUFLQjJoZGV4c3gwTUJ3cVJvbHZOaEZocWh3a2VVRXZxcUVwZk9NNWZuOVVSR3diQ2Y2SnVUYzBmQjZGeE1pMSUyQjhtSnd6SDdvME5pcWFBaXI4M1ltVlo2MUw4enVMNk1QS25SS1dkem9JT1dibDNxNkdBY250JTJGZkV0WCUyRk9FY3NOUjJQVTU4TTBnQ2VSZWJqUEZwbnZyVHdOeXFyMTE3S216bmFlWDFlMGZsOGE5OWdFNWJTY3NRT3VzZXZsMGZ0Qzc4WXhESG5JRjlIMWZ6OUZiMjlubFBNV1ZEJTJCVDhyclR0aXdjeTRHdmtQbzNKT0I5MlRYRWhpJTJGRG9CcjdaRW41T1VRTUN2RUlLV2EzaG5mVHpIYlJ1elYwRzJtaG04NWNBR3BxTE1SY0ExUDAzQU04QWdPMjJSbzh1V3lYJTJCckJnJTJGQ3hHZnUxc2pFTSUyQnpMNUtBeDB0NHNYUFRYSHRFbkx6RFNuZG92U0dYS1o0MmNkNFM4ZXJGRkZPZ3IzVk5tRGZ3Q3JIZUYlMkJsTlNjZ0clMkZsc0glMkZucFhqa0EzaFB5WWFibno1aGY0ZFQ5WDZwRFoxNUhZbW9ab3Y3Zm9LaFdmektDOFJKbllCNm4xdVJNQmx0clE0dHBsJTJGb2Y0WDZOVkw1N0c3UDdNWHIySFRxNE10bDlzdkVVaDJCczJoODZiZXhITlUzJTJGUmJXRjFNNzJHdXclMkZUSG1zbHN2TVQzc1hWcFBDYk5CQnNsbU5DU0k2OEo1YUMwQldROW1pTFgwOTBQNEVqMU5KWjIlMkZuekNHMnF4RHJab3F5ck9mJTJGaVNpZ0VrbCUyQkpjTUNYOGRTeDlQZEdWd3pmSkVRRHRHbHFHMGtmS0x1UzhUJTJGbGhNMG43bVhaWVdWaDdQSkVsdW5OJTJCOWNXWlFCRFVGa2c3dFFYSDV1MW5CJTJGaDhNR1NXUXpzUSUyRmRaWk9ha3ljUzd3Q2IyaDEzckd3OVZBZWclMkJRelJiSDJ5Yk1ZMDZtS3lmajRLU3lXanh0YWcwYzdQWEhRQVNKJTJCTmVPOEVXJTJCb3hpNm94NXQlMkZsZkprVW8xTktGdzZuZlVlQmswVGtMS21ic3pYNktkZmxsMmV0THJ2UXh5QnU3enRRanJtTFRnT0M4UGNlNVYydSUyQjRkJTJCN1VZeWFjZHNGYnNxaThLSmVlTUw5emFwWFJZcmxYNkhmMCUyQnVOckFsb1BqdWNxQXRNUyUyQm5vanBLMjlvTlk4d0dzdnZkdlZRVHRWZW50T3VOSGRENlBveEdpMUZVTERqbGJMTDBta3hMa3dEeVhzU1hscU1vNHRqWDlya2pVSVolMkZnWlUlMkY2WmhHaHJjb2JzRmNESG50NnpyemhoV281U2VwZ0hwMGhTb3V5TXlSS1Y1UmRnN3FkSnZWd0s5U1FRT1JJNkFhVDZISmcyWHYlMkZRVEMlMkZQaVYlMkZqZEx6WmYyRiUyQjVkOTA0NVklMkZjOVJVM0Z2b3ZDJTJGSGNGbjYlMkJkdXIzSlVIJTJCZHd6YWpQclVPVTVmaUNYJTJGQmJMb0ZxOGhvcUlldGFwaDRUczYzcWVaYVdNdHRHa00lMkZiR21QaGQ2bCUyRjY1SUtyQ1lRZmFtTUtXNjJaNFZ3eVBXMno4MjV3dnR3MXNtQ0ZrSThRZWJrT3psRm96aEs2cnlsdnFWNmwzN0pWU1ZETHp0ZVJxJTJCWEJkTkYlMkJqOTJ1bEZpR0NqdSUyRnIxTmYxc2VuV2ZEMzU0ZTQ0ZndCeENtY0xMU2pUWDh6bDY1VFZNYnlldUlZNXFCTHVhQ3haZVVSQnBkTHhwWlpxbTZoeXQ5Rng1Z25hWlJlWUxVSmRNbWFnUWI0a0lsTDF6eXlSOSUyRjRiMWlDbSUyQjl4ckNBNiUyRnJ2d1RtUUpzUVpaSmVRYVUlMkYybHE2c29KJTJGWU83Wmx4T2RBTWclMkJmQXk0dSUyRmtJWWh5JTJGem5EY0NlaDRlc2hrSUZuV3J2cHZPcGRreWt0aFRaJTJGQWpUOU4lMkZ5UlNmejE5YzhiTE1KRUMxbUlKMkxDdklmeWwxNWUxV21zN2E5S1dvbHE4ViUyQjlKbnRaSExtNVFlYXUlMkZ4SXZUSFpnMnhCRFR1OXg3JTJGaWFkYU9TJTJCaFdOVGI3NFdjQnRKSmVoZ1lmTHB6aE5OdUI4enF1ZXFlT2N2JTJGTW12MkduZiUyQlpla3E1MExQbFBCcGw1akprSjc2OGVPMFRmTmU4JTJGY3lrYlJHaXM0SnJzM2J1JTJCRzNKclRRaW5IUDg2Zmc3Sjd0dmJySjlhSE1NTGFpSDU5QndIa09PR0pvaG9kZEZTUFElMkY2JTJGbEslMkJreHFCMCUyRnZXRVlmcFlJZ29UbndrR05HYU13WmREcnVEdzVHYjJtbXUyTmYxdFhGaVBvcWl4U3htcCUyQlN2JTJCWXNlaGUlMkY2djFwYThsWGNkVU4zVmJlcm5oJTJGbXBSOFREaFg5TkhjbXZuTE9sOWNMQWpCZnFXdE0xVVJlSEIwcmxOVHJ6VnFVbUZETzZDQkhSTyUyQkpDenh0U2RQbXhqd3FTOXVCOUk0Zkt5a0Q2U0F3eU5PRTNOOEl4d3VrUHhqTkFJSkVDSFN5alZ4RGp2V0dZRHM2bU5yYnZHYTFoR3JJc0hPcW80MTZ0aTlGdHo1T1M3RHBuYjJLRTByRzBDMyUyRkdoZExxQ3JnRTVrcHBxZWJxRHEwWEZMJTJGREFxeGNXZGN2aSUyRjZvSFg3V2J5QzFDVTJwd0V1U2ptcnhoOENON0Y3JTJCcWlnNE5JYjdYOG9lazhNZFMlMkJ6eExFZDJTcmJsRmRtdUdiVTQlMkI3U1VNQkRXczl5U0tYJTJGbyUyRmtQMmZhZ2RNRDlPeWdWSTR3TCUyQlZBeXclMkJwTkxtSEp6JTJGNXpMY04lMkJQRiUyQlVIaUhYVXh4SER4eFdZUkdvZSUyQjhjSCUyQnAlMkZRUzJCZHhVRWdJc0J5aWxBVzBWR0JqY21YUSUyQlNtb2JxNjcxN0pMRUcxMWUlMkJneFBlZzdhU0lKNEolMkZad0RxSnJISm1GZ0FsendlU0lTckJYeThCMFlqd053RDdWSDk3amRtNkllSVpQd1BuZm5odyUyQldoRUxyeGgzQng5YkJsd015WlJwWEEwUUJHZlRiTXNDUDZ2b3JZc0NWNHU3Y0VTUjElMkZjU3FUbktSNGVRJTJGT3dOMm1zM05TanhnOUI5ekh5alhZeW0xeSUyQm4xQ21JTEpSRkczemJYd2lqME9zbjVrZFFDN29RbmdnR21XcnM5Mnp6WU15b0FDdEV4MEwzeW9ITHRxU1RLJTJCYnEyOVUyTVZmODJ1bHNncEZKTDN6MTJpejBmelhCN0tCMVRUbmVXaGU4eWs3Zk9hczQlMkJSWE5YOU42MU9xRHNyYjNZelVueDFLZGhkQnlKUG9TNHk5ZThYekN0MG1GRVhIbHkxUDFrSWF3UzZxbXIyOHIxVEM4amclMkY1cTZEM2k5YVNGSkF4T2t0Z0JDRDRFUjRqMkd2ZHFCckJaVWtrcTV3U1h1MjdVanVGN1NuJTJGVGljeCUyRndDTzlsSEVUQmZaSnlDc1NwU24zVnZYeDQlMkZ3TE9jdkg0MkM5dklCZVpjdEQ3alZSSzM5NjV0U0FPM0VlNkJCYkdrcGU4JTJGNmxxVDRQYk5JVW5nJTJGaW9nUEpVREZWc1c2Um1BTDlOeG9UNUdpNFclMkJQenoxSnhSS2NPd3N3aDhBaGJvTmNqODVXRnI3WXFEMmslMkZRWFF0bVl4MHNzQktqMG42V3lQOGVrJTJGZ2t4aHpjRWV0a3BZT1I1ZE9qTEJRajhqY2RoNHdlR09pREpHOW5SSndRbWVGbjFLUlhPQlphc2xrUWtkUSUyRnhUYXA0VUUxb3clMkY5ZjBhYktUZU5yRExUJTJGWjlhT2FWZTRkVjUlMkJmTWFPaGFSWUF6RU1pS0gzOW9QQUZsaTAlMkZjS1p1RiUyRlNVZWFBMGtLUW9GYkFwYVYzZ3N5U1NLTVJEZ29YR3M5WEh3RDZtOUlWeSUyQlh6MDNxSDFPdnBpenZXb2tyeEViSzRlUGdhNUhrbTd5JTJGUnduY2V3SDdqdzFJUk4xJTJCVGZqSzU4VFE5N051cXl4VjZIJTJGdnVqOWJSMU1RQWJ2a2J1RVB2MktDTEElMkZPMEZUYzElMkJDNEJiYXczZG9MVk8xYUk4NGVUZWtGMHRjMSUyQlROcTVnQUNURlhybjhQeXUyenc5OWZEekpXWCUyQnE3TVZGOWk2N3o2b0RDenNLVExiTXc2UVNQN01CN3lvdFV1cFhCb3ElMkZxJTJCJTJGZWdVVFhtNE1WY0daQmFVVHVaSldUZWFyUm93dHBiM3AwdWtVS3IwbG1MQ200TzdOblNraFBsWiUyQiUyQmFTZGhqa2RVeHFDNHhlMVo2cGclMkJJck5XcEhXZTdqeExOU2JCMXF1eThSYVJMRE1nb08zb0VCV0dBWUJCeWlmJTJCNzFZbWRKJTJGNjlENngwS2QxR0RFWFFQemRWSyUyRlMxUzJQN2w3U2tpMCUyRnduYTZZUkU2RyUyQkN2SzBva3dXRkklMkZidHV6dlF0aiUyRm5iMXg3ZGd5WUdGVnFlZnZ2eERuODlIJTJGakRLbHhjOEclMkZxaHJVckdEODVCWTVjVDIlMkI2RCUyRlRKNFhEaWNBRzlLWUxGM29ZWDBnR2gyJTJGakcycXZNJTJCMEMlMkYyTmdsN0pqJTJCR2QyNHd6aVFLWThlUkF5QUdjZ1R4T0wzN1JZMVBabVhjMXdabDVBWEpQYk5ybnN4TGkydXNSc2FpaUlxS3hDYWtFMUVHTnhJaDVoJTJCVTNSRlZiUG1rWnk3aVFNaCUyQjluek1uT0xnWjNhNkkxbGttWDV1M0Z2djNoazZuVWgzRjd4dWt0YjZOY1FUdWpZNjNJQWRmMm8yU0YlMkZaN1dLVkV4SXdFOUxaTWpGcGp2N0Jmam9ieDZlQSUyQnZnZE0zOHBsd21Wazh5RmxhVGpyQkZFeSUyQnZCeWI0dFQ3aHFGJTJGNWV5cHNsNUF5eWNTVnVxaWZJeWwwJTJCSlBHNnlhUTJCZWpsTk1jbURjRTFPb2w3ZjQ1elBuVGhFR1QydDdoSmlPc040c1FtOTdjWW1QZWU1RHlJaG01RWRPYTdaeldmaU41TXhDbml3RHNEcTR1Nm5uZHl1T2VnbDFhSzZVak5CelNVWm9ZbFpTOXJGQm9TZTYxSFVGJTJGJTJGUUkxeEk0MnNocXJoVkJFMktUNTVlQUI1VnZSYUFxYzh3dnRCZHh4RFZqTjlCVkFlWjFXN0Q5Smo5akNwelhIeW1pQWJjcTRYa3gwWWxlMjRBNENQbkxyVVNjakJKcTdaR2V3bnh5V3hlaDNOaEYzRGpYQkRwcnpLS3R3R0F2RHJDOVk3TCUyQnY5MUhJY3lKSXlPQWZMJTJGaEpvNUVNOTYzR0VLdkJSTVRjUThnMk8zNTZtTzV0UHJxMkVsSHlRdFdQdWxwZGFyUU9QQ3RSbmdZeGlsa1lHTWxwaEVzdWxkSU5qWiUyRmtCUGFDQkI4TW9oU21ubnBRWGFWeDRhSThjYlpZS2V6MWRyWFpZVFpxZkVYVXIlMkJWRFlrOVNmVEJHSlE3RmpjSnVhdGdjaXZhOXZta1dEMiUyRktFSyUyQllYTVd6a0Y1bEp3M0NmOWFUM21XUTJXbXp0b3U2aFZLSG9mVDhPbWhUd1hIejlEVXFheSUyQlgzamZTRXF4NVJ2JTJCJTJGT2ZSaHJHSzZwYnNHVlNjUVR5VEgxVkZqUXgxMTlhQyUyQm4lMkJKeVNCRU5EVGNkZnB6d0xmNnl0M0hPb1NlUFN5eWNNZ2ZienhDR1dsMks0aHhWVWNqbVpxZ1B2cm1DS3NBMmh3bjhoMzExZGpoSkJVUk1aT05jaGFETkEyJTJCWnhNODFDUVFZcklVcWpFNnZFSWdoZTdqOWNCTldybEZFTmxFREtSdlBWaG5ScWZXV0VCMFBReW14N0QlMkYlMkJhQ0tyWVg3Z2JNZHloRDRqJTJCc2lDUnZXZXJucVF0MWJHYUdMWnloMDRTWEk1bWJDZ2s0ckF3WXJ4UENsSWdnakdjTUg5VnZIUWo0ZnZ0QVlXMVN6ZXNWQUM5U3B6MFhPcW1zbXZNRXdtM3lQbXduOWNJUnE5WGptVmFyeSUyQnolMkJZNjNkRU9mazdweDVid245dXhDTFZDMDdjTmRkUG56Q1FvQURvelRESTRpZSUyQnhsM2klMkZTRSUyRjQ3cFdYeExOYmQlMkZQRk5IUk40UjM5bDJxS1JiQVdkR242aUZnd0RLcGJ5allha3NLQm00eiUyRkxLJTJGSmUzYzJQZFY3MG84T0xxdkVWbGhocUU2OHdPc2Y4ajZuM1dKSlVhYm9BbiUyQmJmanFGSmxtaXROVHUwMXBxbkg2aSUyQjM5aVVkWFZsQWtrQ0VlRiUyQmpzczlsSEhDaVRDdmlWcW9nekZuMmtWd3RmSUE0QjI1T2EzYWZxODNmTWdQR1hlUmVqbVpjeklnWEltN1FacjdQeTBSTTVKeFhFQmhDeXJLeUtNOVVleU5jc1JwMzNqT2hzd3k0dkt2clBkRmU0aDN2dm1EWWRVU3FTMlpRbjhuaU14WEhIZll6JTJGbHJKUVM5TE9aanRXaFJFWGttS2ZzOCUyQklHbnc5NnFRY2g1QSUyQk1HcUpCcFlCZDYxeSUyRnJGdGwyMXZUJTJCMVlrOFFqNlNUWXE2QUJ1S1dqdTZyeFpLUyUyRm0lMkIzaHEybU9Rd1Y0JTJGMGJkVjM2YWJadWpISmU0OTlEJTJGMlRJN2cxNVJEQ3c0Z2gzRXp5cCUyQmRiWmZQOUxPS0JHcjlSOGgwRXJHbEg0cVRSajQyblNuWVRVTktCM3hzSVViVzElMkZLS1BXdU1JWDhhdllWMDZOU2thZkVwMVdENmxaaSUyQnc5QkVOOUdMaiUyQiUyRmxDSEZGcVBCU0ZtRjd1bXE3R2VBJTJCazlpeUtqVzd5eXJnZ0NBUW5BcU1TY3dJRlJxUzh0eEpKYmUzcUpkR3NwRWxqbFZtRXZjWmFXS004QUpTcFdub2hLWDF3dTdkJTJCczRsSmxHSFVBJTJGSExSdmpENHJ5UTJ3alFNaGZqOTZ1MFUyQnZpM2FLbkZqMGZMdHJBalJDcyUyQnJ2MHhWVmVpZVUlMkZ1YmhvUTgyYW9ORU85SFhQZU5KZnloZXV2aVJqS3lYV1VidncwYW5icDAxZGtsSGFPbEpsSG5ZUGFBb3YxQmV2WCUyQnZzMXpWTSUyRiUyRkhOMUplRUdDTTN1RDZWNW5zWGp3NFZRVHdOZ1FnMm0yJTJCTGtFekt2alp4MiUyRlhpV0R1akR2JTJCNnNzJTJGeFRsJTJCY1ZxRThVelVLQnpEcTFobzlnRzNjM3o0dGlKMjJSUlRWU0lSY1ZIYkp5S2xCalhJTUoxWHlGZGRKT282NHM4THBzck85dnpLUjlJSkxSQmZ2Y1Z2ZjFwT0tLUWZGJTJCYXVrbVRvU0VIOXpRV1FEODBmJTJCSGtwdDJBJTJCRTY5NDRmeUtpU1k0R2ZtUWRkaEhMdE5XdEZ4Wk02MTZtZTZncnBVWW02ZDFZJTJCVXB5V0xJVDM0a0luVUY0Mkx5Y1dNMHZkNnZaY2hhTE9TMkVGdkJFdkJEd2l0OE1jeFRDWkpHYTQlMkZIdzJBdDRGdGxkT2FvJTJGSm0lMkZsODRyTHRrJTJGRndOMWdqNkxwRFJBN1RNcGxxSng3JTJCbk5vY3lvem9icSUyRkxSajdWMGtaVFpPeHpzVzZ1NUVuNmthSFdRS05RRTRDR01BdjNzYVdDRXp5alN0dXptaTY5ejRpVVlkemZzdmx4ZTZjUDJkTWRWb3hpJTJGU21sVE1XYldVcWl6bVJTUGlrM3lFYXV5RldtTHlWcFhPdGIwbjhhellhOXJXUk0lMkZFQWhRaG1IN1hpVWFBeE54cWFLYVE0UGdNUzR4OW9FNHYlMkY3UWtyWkN4Z0JGYWI3T1VHQnZ2MmlXdGV5JTJGUENiZGRrY25OOEVQcjg1cVRqY2hyRW1VbnEzWXMzYyUyQktzMlNXM1V5RHllT0xTQ1c0WklFbnRYZU9vSXYlMkJIRXpEcTZxamxMYWVuZUhUeWp6alZXelYlMkI2Z0J2YmRCVlhjTmpQUURyUW9VQ0xXaWZIczRMZnlWTklpOXVuNElVeHglMkZLdCUyQkxkaDJjTkNOTUFLODN5RVA2QnEzYnZQaXZVTWFaWktPS3VSVEpjcEFoZnFZU1Brc2JONkVOZFozeDBNbGRiR0x5TUl5bW0lMkJIYmtCdCUyRkZWZklMSjlKcjM1azhwZ2hUQmZLUk1yV09mRXYzdW5ydWdBdDd4Uk11ZTRNWndGRGNLOTlwJTJGU2kxWmFJbk5VTDl2WiUyRjFtVzJsTVhudlZKZ1k5V0lyUmJ6bm9jbm92bUt4RzVJOFlZWWpieVVoUkl4NlloRERHNmVFMXYzaDkxU1Y2clZVVXdnTmRpdFVWSmFCJTJGRWFXYm5iaSUyQk9KTFVRQjhJdEphVXglMkJLVHglMkJHM1h2WUhoeVpjcjZkTUQ4NVl3RHR3Q3FMTFE2OThuOGpLZVhKbDJ5ZTNxWGRZdmQ3QzFSQk9yVE1Ld2lFYSUyQmwyeXl5T2NvczdlZEVYNUdYVXhiVzlINHoxQWZYSzdYWiUyQkNlcWQ1eXZ6ZVRIamIwZ0JnaHR1RnRZWjMwdlppVFo4VGNyeUtqYU5lcGxQaUhyRUkzemluVmVrJTJGMyUyQlJoa0liSHFta2hRUmM4UzI3TldLVWJmZTk3cmxiSVRXY3lCZm9CSHFlaFNRNXlUVDAlMkZkdiUyRnRKdEFjWFh4S2dVbkxzSmxuZE9EJTJGbWJQNjY0YW1URzM1VG55SE41R0g0eVg2RllneXhlZk1sd1M5eFFhWXppSmlMWlh4VGtYMjk2RlkwYjR6JTJCd0tVTkU0TXFGM29wVXM3aUoyRXZvajJIYmtXZjdkWHBYR01oWUtQQnElMkI1JTJGNHpmb2tERm1xSnkyZHZkUlg4VXIxeWxqUnRMNEh6SGIyeWlJSFhrZXJSdm9YZjNqek5ld2Z0JTJGNDRGT1RzMWw4MUNURXE1Yk4lMkJjUWFyVm9KSmVTOXR3S0FqU2QxWFhVWGVHSkdBZUljS3ElMkZjWVNuJTJCbzkzVHNpZGZFMkNwSlBxMU5zdm9pOVJLMTVYTHdzczRkUkY3U0w4MU5qWDAxJTJCMXRGczRUdjZ5NE84bDZzWSUyQnE4ODhwV09qMUUxWGFvdXJkUkJ1MDBIeDVWOGwwJTJGOUR1N1I1STNFUk5mN09xVlBUQTJOQmQlMkJBNEkwSFdlSlN5djI1RHFMcWtNdE5DNmZ6N2ZFcUgwZTRzMkxNdGZTc0x4TlNoMzI5c1hpWlIzaXpVTlFNWCUyRldySlNtbUxwaiUyRk9qV21LaDlCNW5TQ0RqVHFNVnFiekpXbUVSbkUlMkZLWDdEUmM1bDgxY0hNVmxlS2o4VFk4OTgzaE0lMkZUMEFHcEhNdzQwdkd1bGp5THlDbUVJTVNOMSUyRjlSc01zYnU0RzRJblhjQTc4bUhDVXNQYllkOSUyQmI3SFdlMEhLVVJXWFJZSnRJN3JjYm9oV2kzampZdmlnUDNuVnFYd1V4cUJhWHhITlVmQWdKcWtjNWpCQ0t5SEExUTdiYjBwcHQlMkZtYjczcmZ2WlpTenBTMjVIeW1weGNSMnFCODYlMkJKcE85bWJVMyUyRnBTY282Qm84JTJCV3RFS3FEN0luNnZuSzEwdVNJJTJGbTd6em91WmQ0aVpReFN5SktxZ1BmJTJCWEVSdmNwUkE4aGpQMFYyWTglMkIwOU9sMGdpWElkSGdXOE9DckVCczMxTmphOFFuRUhmJTJGVTV2N282eXV1UCUyQnlRMSUyRkxHYlBUM0h4NVF3djgyYW56c2JKV3NIT2NycFNwbGNYRjhLd0pyYU1CaFAlMkJ1ZGgwQ1NkZ1dMbkVXVmtMQTFDWEZSUm9qTTRGZVVZTjJ1QSUyRjBUTEhGTTNHUTdpZElPclRpRWg1RkZnYXFhNVdUWDlGdVFGRWslMkZRcGVwVlQ1ek1iUzZmWkhJRVNMWnVJRHJ6T3NtU1NhZVBTMiUyQlBUUEtYemRzb3Z3VVNhSWxocUZ3d1hkT1hkVW9xdXVLbEZKekFJWSUyRmxrWmFoa21HU3I2ZXRzMFd6dnRidHU4c3JKJTJCeDRtUWNpa0EzSEtYSGY0Z3VzMUxscXBOMkpuV3FuNDIwR2NMczFZJTJGZXhKSVRIVFZjclIlMkJWZHNJQSUyRlNMRXRPc1U2cXNvRFJ5eFhLREdGT3V5WGFHTGJXdHo4VFo2MlpnWHgwVFZVNTRpRlNjMG0zS2VlR0wwcWZXandHUCUyQk1tRHhhZjRIQ015MXNrcjFicndEYjhta1ZURDRTVVZLM1ZkTDl2M3plR0IyMUpXU21ybUJZOGcyOUpuTzZZTndVcGJsZlRtZTgycWVuUHpURWc1UWw0RnA3OGNISldDeVBZZzZYU0M0anhVYzZvMmh0QVFlRXE1MXA3JTJCJTJGZGlWSU0lMkJ3VVozS1Z4bzN0WDNoVTVLS0ZkR20lMkZ2RXQ4NG93cmx2TUwyMnhHR0NsOFAyVFlzSDljZ0RUOHF5OWpqdHFteG1RYVFjYWFrRlNQakdDY1VaSTl0JTJCWlRzZkdHcERwMGhyNTB4eGhPWmNNM3hWUSUyQmZ1Q20wSUtVREZUb21JOHhON240bEowM0xETlpQc2syc3hqVmgyMUJ3MlZGNSUyQkFFRmFEVGVrNkY3eklCSEZmUWtpdkFSUUNVd3hPTE04WkROSUNPZCUyRnE5a3BLb2ZjQ2N6WDBJVlg5d0JsaTJtRk4zcks4em1iZlJYUmh5M1BmJTJCeSUyRndpWUZHaWVrekpqJTJGYURNYlpOSUlZYWNTNlRjcDk1bVFNT2ljeXl3b0hucXE0SFNzYzNKbW5YOXQlMkZOVGprbWNGaWxjS1lGJTJGV0d3cnJHWlhzdzdvUmRMNjBSMXlPU2tFTzNOWWdrdEREdXBVd1E3TnFBeXBadVNKRm5QekNwa1dHN3Z2YzdIVE8zMzlOcTBPd1gyOURiOUJIbnFpQWd3V2JtSDRXZ1FsMUVxTldRMVVlUlpQNHN4eElLdTBnYzBnQTZ1cEVWQjZkVzRPcXVYdmFvdFQlMkZ6a0hrUDYxc2t1MjQlMkJvTHAxRUV6UHd5Z3pRV1ZsM3p0aWFVRGMwaWFGdkNReDNVSVJNUzVhUFRreDZCeWE3OHY5UlQ4bHlhNWZLSjNucEdHcHklMkZibTByamNVcDl4dUR4dDRzOUNJUkI3YVg5RXV0R0I5Y002TEJ2OHFGYVhiSXJaV2RDYVdkcXpOamxmYzJJQzgzU2ZadCUyQjBRbHBjc3A3ejRmT25tYUxjbSUyRlBMWllsSjVScEQxbHE5RDBBU2dqa3pGdUt3Y1MlMkZFckRSTmpoMDRGS0dYTzFlZ0ppVDNMcUhIWmJ0RjkzVjBkY3BaemRDeXhIUm1YSVdUc1dydEpjUlJ4STBZTDc3NmZPY3E2a1diRlklMkZkazQ4NFFoclgxUXZaZnBhSU1XT2VnJTJGUTR6JTJCbHNtJTJGc2lZVVJ0UExvaTBiWER0bVU1bjIyc3diQ1FKJTJCSXNyTDElMkJTNVpOemhubmdRNUEyblJKaGtWTkxGUmF2RVJEMkJHREclMkJzYnNXWXFkclhiQ2wlMkZwYjN5VEtseFp4NSUyQjl5eFpCZEJDallTJTJCJTJGM0FlU25RVkRwbmNrYjNYVExNT3g1SnpObXRkRXNYckRLVXBTMHR1WE1KS1BGVlNicFBJVk1wZFhaNU5oS0olMkY1OHE3M2czMUJ4aXdpYXlENTZMMVhpRUtTdGIlMkJSZGdsemtwcHB2UzVJVWRteUo0NlMlMkJqWDIxaGc1TzV1NDB2TnFUSiUyQll0YUE5THBib2FjSEt4SGZlekNvUyUyRnZTQUNJdmp0JTJGMDAyTnFMZ2ZtaVlJa3YwUGF6MlNibFVpVFRBJTJCbG9uVEkzUnBXbVFEaFpGUXp1akFTZEN6QUpYd2RwZEtleUpDbUklMkY2a1E4d01mamdMUUZPbzNlMHlIOU9ZOGF6cEMyUWFGQXlvNUFzZERoWmtyQkxqMzVxTiUyQiUyRnVLTHdycmlvc0lYZnRXQWw2YjJMU2tabWRFdVZib25LSTFmbExsJTJCa1pUa0FuWnJ5MHM5NWFyN0h2QVolMkZUbkFkR3hSSWVYeW9TS1JRNDlmM2NiQk5mQXU5MVdqJTJCOXhPYnBkd050aGRxQW54ckpCRG1QSWlvT1V6OXhPTDVSYnNEOVZ3VXlERnpOOFprOXdBWHhjY01WblV0QmRUdjVkRGpxbkF1eCUyQiUyQjVBV0xnUUJUU2JsMFRIOFpXbjBMb1NDcWVQJTJGaGpSVHE2NSUyRkQlMkZIamdxUGQ5RUViU2xEZ1Y5U3gyUkFtcEJFVnd2Ym1PR2hFZ2xxdHFUUFZwMXVWMkRDWDNJYlZBMFA2c0NjbTNGS01XZW9Fb2RiOFlyU0J3NVo3NnhOcTJyNmRwN3dEViUyQmdGSzF5b25XUDA4OW9uUGVncFB6VXlvdTMydzVFQ2RrViUyRncwMTlMUm9oeCUyRkNTTVRqYjRTaFJRRGVDcUMwQ29ZT0NQZGQ0SnQ1N09KdENkYk05d2NsWVFDN2xUaXRuS3Z5S1dLb2I2N3BpRWxEeVloZllwSWg4emJ1Z2loS3JUUEJpa3lDcUhDREd2RThRTDQ1U2ZkTENYUEFleGglMkJaVjg1TktlUmYxU08wWXFMM2M1eHR1MVViSkx3R0FXNkZHc1NPc0huTUlWSzc1aEpyV21pRE5oYkpNQkZBbDMyclB6eEpNcVglMkJKVnJwRm9UalUwOWRERW8xcHdVa3hDbzJZMEolMkJNMzJJemk4clN1WUYzb2VBalI2NWk1VE1YWTNlQnFRVHVOWlFpUTlPbmJYT2YwVEwxUlJOMHpVYkw2ZnM1R2Vya2tTQiUyRkI5cXRIcUNtdmxhWXhzblcwWmhubWhFSlVIY01sS1pHSiUyQiUyQlJ1JTJCSjhxMkZiR1BFS3J2aTZRRGlHSVF1MmM2S3B2eXB5THpzdkFtTmNtOXRFVllCJTJCV1IlMkJDTVRXMUlWQ3Q3amxlamI4aVdYMXpjbjliOThBQ2dpVHpWeTBuRnF6OTB0SGhLOVJLdmRoZUMzdjA1Q3d4NXU2JTJCa1JFV285VlVmRGpmRUJ1QnRDUHF6NjJGNHVVZHYzeU16RTZRTVdQSTUwU2YlMkY3Q3E0Vk9qQ1ZOclZmRDRIVEZXdTAwQWV5WDU5T29SZ2I1JTJGM0NaMiUyQmEzJTJCZHBKR1NqOFZYdDZZTlphR2s3RFprMUQ3MkpLUVZIcVZ3bmVCQ1VwZWlFVXVSYXNYa3MzRUJNVExEeFQzWFo4WFJhS0oyQXBZT1pnJTJCMjdBOEdlcFFvUWFaJTJGYXRmYmZCZGg5cUt5MG84Z1lMTjhCY05EN1B6T1FuSTZTTnAlMkZEWEJrYnh3Zjlra1lLcTltZFlhY29PdGt5WmRjVkRqVFRvVWNvM3hOM2R2cDlWSkZZUGZNUXI1aUN5TWw5TjlSUCUyQnJ1VVZhbEp2N2tHMUVPYzJ3QWpFNG8ycEt6TWFuZThUSTZRYThNa0VqVG5lbTlCaiUyQkFhSGFrSUxtQmtubWklMkZLb2Q1VmttczglMkJCSHJVOVZFRCUyQm5hR1IlMkZWd1g3UTRVcjhqS2s2WWxIOHZycEVFbmxrcEt4UmMlMkJ5RVljcWo2WWduZmdkUjBHa0lndHVldlo2QjdjczdQazY3NFJLSjF5ciUyRmxIdUhud2U1VU53VEhWY1lMaDVCR2l0SlNuY0pUVVFIM1gyJTJCQ0k2MFhyVUkzVjBLenpCN0l4YkgzWkpTTDdCcWwxVGhHOTIlMkZHdjRUQnlEb2hLRll5ZmlOejNwS3lMNmZNdkZIcVlwYUtyVGVidCUyQmRsMXpxWmdQJTJGNmg2dyUyQjJDNlE5OHFTZXFHU0VOJTJCY0Nsd1ZVZmNmNkNxVEhGWiUyRlR2JTJCOHFFVmpLTVZyYW5XZ3JYU2RMbDV6RmRyMlNGeGZXUW1hbXNkblRPY2ZiU1hVV1B1QSUyQnZETUtvTnV6a2QxajBaSXBBTCUyRlMzWXlpaU9BNk8wQjglMkZ4bzFPbkVLSlhyT1J0clIlMkZyMzEyUUZ3WVJWQXhBVTBzUjVuZThDZUxLYVZENE5zeFZoWFZ5dzhSVCUyQm5PUHg4bmxsdHIyRSUyRnN0bXhNanUwRTZIM01zNDVsMVFHb3JLQVd6M3A4MiUyRjVCQmJEVVdna1JFbXRCaURLQUgwOWNEajRPUG40JTJCOUJWYVJIdFdEQVdnMzd5VHZoR01vQVhNTFBOZkhXaWZLZnZLRFN5dmprUnNHS2Vjb2YzNjY2UndHSlJ2NUNFemxpTnA0Wkl0TUoyQzhsYjljWno2YURoVk1ZNVR6MGk5TEVnOEdCV3p5SkU1dmZNTmVsUHZwTmZYMEElMkJyMWRzJTJCS09WaEM5czRMUE0wUWJOam80TXZTM3hFVVE0NUZTd1NCa0FxbFF4TFd6dnl6aCUyRjc2OFVEYm05RXpHbEFwTm5GOXlQZDQ0eW10Vk1XT05GWU5aWkFETlZVTUFMTXR0Y0R3NE9aJTJCZTNyVGVMZ2I5aFBEUU1hJTJGJTJCZ1Q0JTJGZEZqRDFzZk1TMFMxRiUyQmpuekFwbE4wWjhnbjBWTWklMkJITFlmUUI3ZXdxZEY4JTJCVXhLQnA0QTdkQkxXRkhXJTJCc1BYQ1U1aVQyWkRGWUx2ejJmTG5nTmtnWXdUcnlMUnBQeld2ZjhpQ3pleVhmVWxLUlpBN2VFQnVaVHNleTVReTVJcDhYbWRzQVI1SWpWJTJGSHR3bkJvaEphZmhlcFZpQjh0akcweGVtcWpXUXlwb2ZCZXFCYSUyQnNZUXl6JTJGTXV3dmJNVk1PbDduJTJCbXhrcHZTdkp2Y1g5T2trd3JGYm5iYjlOYlk2TEZOUHZySnZGTHVEdUtROEglMkJxYWVrWUVsOUMzRDUlMkJINWJaYmx5aHQwcnd6NnQ5V3lyU2VBc0RpUFBZenIlMkZyelFka0ZBVDklMkYlMkJKSUFzQnJOZVZFTSUyRjJiOGZBUiUyRm9UQlVWdFNxa3lxeCUyRk1QV0lDZzk2ekkxOFNlR05jVXN1elFyejZ4N0FZVm5jYkxwbjN2NkdjcSUyRmN0a1JHQTNMWW50bEk5R1lFaWJIWTM4MnVkcHFJQVJ3cWY1OTRyRjhLTWdsSXFXSDU5TE5rRzFVRXhsc1JESlhHbkR1SDNrMUxsJTJCYzhUYmgzMHFodGl0REV1VEZ4cmclMkZuNDBCJTJCUjNOdENMZ1BjWWpzZkVvdUl2NWtoJTJGN002JTJCWGpOWiUyQjR0TnBkcENOVkdNUjNUd0llS0pZQVJ3UWxiJTJCNm91WiUyRlpkNlNnaTd2ciUyRkhTSUR5d2tzWWpnNzBnMXo0a25SNnRneSUyQjNubldxNHZrVlZNUmJFM2xHd0FHVnZqVjJwQzVzYSUyQktvNkRNRlJNdmdDTW9iOHBSaVBqYWRodnFDejJhM3k0TU4xWm8wcVUlMkJPZ2VaOGQ4dVN1YmJBWnFjM0dLMG1ycG0lMkZmZnZzYlVMd1BuJTJGMWVPTVVLRFFNeFFiRnNONWZuM3dCZFowNWZ0UndYVWxqS1FpRHFFT2labnEwQnpPQXY5eDZLdjBlV0hPenpBUVdBb0s0REQ0YkJoZEZHRTJEUmlwOVZwWFB1QWhhd3ZVa2JDNFpqdzBKWjhrNnBFS0NiRzBmR1U1TE1yNlJBZGx2dGpGR2FlJTJGQjQ3M09QbzJ3T2lvQXhiY0k4bDl3czdZWHVNNFJwT21Cd2xWcSUyQm5aUDJDblo0TUgwWW9qJTJCN25VdzBsc01xTWFvcHlscmNLVkRDTmE4NCUyRmFSWHhHVllNeVpIMVlBVTlQQTJSWmRGVnRzQ285ZHpGUFlBVkFUdDg3T1JWbVFVckhkSXUxWkslMkJWeGU3SHllSmRhZ3IzQyUyRjRORGtmMlg0WnQ5eGlXV1oxTUo1dERCMDRXV1FiN0JCeGF0S3F2QzVhZnJGZm1ZNEp3Tmp1VSUyRkpGJTJGWDR1NFA3JTJGcEQ5c3lkUE9BWCUyRmlCZFhzWXpQSTE2dkg3JTJGeGU4UUJRM3RYSXdBbTJxMmY5MUNZSDVvMWg0bGxLWUpEJTJGRU1RcVY4bFU1U1QxdHhic1g4NGtjY0V4REREMHRUQXFXZmpwZUdBJTJGOFY3a3JNVW9FZHVhJTJGeUdOUlo4bEJFT2M0aXBablBzMUlDd20lMkZGaWF3QkVleSUyQjJUQUI1UWVKeWlYemVnJTJGelBmUXVpcWU1S0tEREthQnVtbm5MT3oyRVB4VDhGekI1MjRVZk53UnVEV256T014WkxoWnpYNmV6VzNmRm1Db1lCaEdIOVRGTXVVRWRzNTVZJTJCRHA1MjFVcjJnVnZpVHRET1FMdVNRdFE2M2dQVUVMWGV2Q2NtSUp4ck9MJTJCMXdhUXM1JTJCa0NQcmJ1NlRsRWdmMWkwQ2hZcVMxMTgzYiUyQkh1dUUwNklWRjhXd3IlMkJPc0dNRTlGSWdpaEF4RUtLVlAzJTJGZDVFaWpGNSUyQmQlMkJWT1RsJTJGa0RPMk0ybWdvbDhGJTJGT3kwT1kyZk1YVG1LTW5UdjIyaEN5a3FSUDZDdm1jWGVZZEYlMkJaUTkyeTVaOXJNQyUyQkR4YWlSRUglMkZYWjhpamdpMjB1TGJJbGRvV2RlVUxFckNNbWNyU1loTFoyekhDWnFPbDVJUGl5SiUyRlpJa3g3bkwyZ2dCaDgwSXRGaHNuczhLdTQyYU8yYzE5SVhrcVVORG55T285YXNvbzlXZmc3U1pMMCUyRkVUVUxWZ3lSbG5ZUEFtJTJGN3E5RTIyaUxoSEx5VUhVelNmdFh5WGxyMTlRQncxUCUyRmNudDZmOURtUFA2dGFQU3JBU2MzVTYlMkZvSVgyY3JqbUk1dU5sSzBlMnlsS2t0ZERNaVZNOVdiZ29PbWE0SXp1bkN6RFdPaTVuV1NPMjNtcmtPbGRRcGt5WWwlMkJ6MFh0TmZIRnFIR29kUXo5MzNCblBVaWI4eHpzcjN6NGhpT0x1RHJkak1XeDdqaXpsWDlGenBzTmFrNiUyQjBxZ3BsTWIwclNRMlFjT2RnQnE3YzFEVW5mVVg3dzZFMERSWiUyRlV1QXhuSk9GNXpBVmkxNyUyQlFzMHZCSW1hanR0MUVPOVUlMkY4NDVRaWZCQmlJUTFvVlRzY3l4TTFKMyUyQlFnWjdWc1IlMkZ1bTJmQmc2WW85T0tLRk5MJTJGVHNsdVhqJTJGWkJBV0dLSGsxQkMlMkZZMll4YnFJdnRHbm90ZVhoc0NVaFZuZGc2NnVpQmZ4OHMzbmxuNCUyRkVRUyUyRlA4ZjVTWTBaeHFSeE9wMDZsODZVNzBVd1FNVjdvbVIlMkZqc2o1Rkc3TTk5R1ZlY2xIaTZWejhvajhqWFVvJTJGcnRNY1p5dTBrYyUyQjg5OHF5VkpKRzFOQVVGRExQc05US2x6d0ZCblVMbDJtbEtwVm5VZUwlMkI1a2FUczNmRFk5cjFYWWU5TUFwajNVbTNEQmx3eFVzSUxjJTJCYk5XU2QxTzd6MERKU2QlMkZhbVc3UGM5VU5mVWlFQSUyQnJ5c0UxZmYweFJHWGhqWk5VcG5saU5Yb3BTQjVxSjdwMkRqWFE1MTNZZFcxeiUyRk5udkRaSkFFbTNiVjQ5aXY2NDlUS0lBSXdhNUEwa2FPNzVvY3ZtSUJPUHpDNXY4NXlITlhBbnpiRkNKeGJvS2N2TEtnWkZtUFBYbnp4NmUxSWFCc3RjdXo3SGxKS2FleG5CJTJGdDMxc2Ezdk1vTld6WmNtMllESTRXdWxCTHUzdiUyQjFkYlE5TEFLMVFNV0pvb0w5OHRHWFdCR2Mwb0clMkZOWHhZRGtBQWRWV2g1YU8xd2xQNVV4WHVqeDJQT3k4dm1EMmhhcHhHWEVuejB0UnBwcnFIcDFkSzQ3QUpJVHhYOHh3MmhCUk16QVZLNWx4eG0zbVBNajkxRWxTNkRlNmkwY01DejdxZFA2V1ZtVTFkbVNXTkZIWERUQlpneEVWc0tUeXVROXJ4dGEyR1ZDV1B1bnhLaTQlMkZ0WFZwU3pEM0ZLSGR6ZDJ0c1ZvSGh5NTBYQ2NldHJ3WjlSbTZRcFFSYm1QQ0wlMkJ1anhsYm51dU94M0tKNk51cUtCQ3JwMSUyRmM1QXQlMkZnenJzaWlGYnU4NGdTSlpzZVVDSzd3SXdxUjFGMEhxMFElMkJSd1dMMXp1U3hjOE42VHBLNkZXUTR0ZlA0akw4WU5PY2R2RThKNW9CMXhIWExjOGdvcFZiOE1Tc2RHZ3NGSXQ2MEVTdHh2S2lTJTJGMmdFandKV1A4TiUyRnZ3QU9yOGQwJTJCUEZTJTJCQU9RbVZ6dG1vMjZMNlNrJTJGRG5yT2hZSDZkNFNzRjhvZHdtTXV0TVUwZlpNUWRuJTJGWWdEMG45dXFJOGltVW81NDR0M3BtRDN4QXVvU1BuZXpSOE9aRUc3dyUyQkJXZEElMkZTR2tIQTYyMVBCeXZudU0wdThaRmJUcTZ0Q3N2WmpXa3VRRmxLTTJiSWk5b0s4b1NYNjFSYUczUk9DQ01JaXBVWVY1dzJGS0FVV2R4ZG43R1cwY1dXS1ozJTJCTWRPWENkRFo5JTJCMHNrbm1KS25hUkYlMkZRRkZiUHY4JTJCSEd2M3lKMER5eW1PQU1RTWNZJTJGM2puTk80JTJCN1FZRmZMUFduWXBzQ2RVRm9uUG5FVGhiYTE3dU0zdTYlMkZGOFJVYWJ5azE1JTJCRmdreEVzOUNKenElMkZKbjJJY1paYUpNZFlPRXJlY1pGdUlNd2FFWVVPbzhqZFFneW5pS2ppZmFwTlZsJTJGWDkzciUyRnlSMUdybTB4Tk1zOXBlaUJVbFdKNVg4bHJpJTJCMXFtdDJPUTFXbHc5bzByeiUyQmZtZGszeUZpcDkzZ0hJa25QbTloM0k0clpNN1BhY3dtV2pMSSUyQkpWTm9OSVcwTVdmT2RXaXpxQkNoS2ElMkZ6MVJNN1g2aWdocVpjYlFUQkp3MmY0ampWNEoxY1BkWlQ1ZmZkRXYyS3J4cDd2S3U2eEs5NUhkVFlmQ2JMcFMwcE1WbVBKQktJY3NUUnF6SGYlMkZUMG94JTJGM1l3RXRxRXNEeEIlMkJFeFRwVzJlbFAzSm1RNTAwQm9ONkJ2enBxSnUlMkJNWU4lMkY3R1JsR3owTERuS0xxUllDaGdQR2V1TFkzWmlHMVR0bUJxRzIlMkJxdSUyQiUyRmolMkZ4VWFSTDNpZGIwVUEwR2t3N3BiVE1XOERIaDduWUFXTHIyWXJId2UyTFppd3oyUUlOY2ZWWkQxUG5udiUyQnhyT3MwV3BXdEpMVyUyRjdLbWxHeTYlMkJRb0lqTGV5cmpTb3piSzRiVHlZU2p4ZEVuQmtHa3RONFJZYSUyRkR5OWpZaFl1UUJCNyUyRjZyYURLN3FWOElzZ2x1dGxsRk5LbDFSRldGWU05MSUyQjFCWXJmZ0o3bkY2MDFhaCUyQloyV2hNN0p1YVZzbm5RWE5JRldkMzhWZXlrSE4yVmtJc3lLNzBRYnUxT3JJczlJTDVYVmVyY2RIaXM1N3NCMkg4RmQ2aUNDanhRMjBtMWRsZ05Mdm1zN3d0QkJSJTJGVldndVBjek5COVl3YlBCYlRYeHFXSlRXWHBJWG4lMkJraDclMkZ3emd2MzhUUjJ1Q0dxM3FlZUJHcE1STXpaWHc2cjVxV3RhaVg3elh5eDhBb3NFTHZHR3RzbE4yTkRxN2Z4a0tVMnB1M1hCMW1SM3pKRXFLdllqek8wRyUyQlk3amd1dVpLZiUyQnBNUWxHTE5xWGFHUmt5MGxZZ0hhT2dXTlpISSUyRk9nemFrTDF0czB5OG5RalBRenlURXJhMTBNT1FMTm0wekdRaDVZJTJCdExkZUMxalRMODNtdGNDNzM3czd4dE10N0NrbjJtYzkweDNpUmt0V0k3VktGeEdxekRpRmVPUkZSayUyRnFpYUNaejg1aHhsNzRuVVpKWXQyOFpla3VFdzdsM3BmN1hzZXZPbzlwNHRPcHlyUiUyRll4ZkVIWXRFR25uZzZwNkxLbDlOUUI0blFuYSUyQlNPdCUyQk5WbVNUNEVweUZVRXJTTDBMb2FzOSUyQjExTUtXMko0NyUyRlk2VExPT0lSdnVJbGtNODZBQnhzNmVEZ2YyQWdBTnolMkJPdzRpUVpaRjJnakwlMkZubnRWdDlkZXozZVBXZzA5RSUyQlhGWXpPQWxFSkkyMGw5ZGZveW5zVmludUg0MXFLS2dNNEdpTiUyRlpLSnlLZVBsWFglMkJ5NUFUUkhmRCUyRnJZUzE2Q2t0Q3V2bkRQM2QyT05UMnJYcUhZaUc2RXQyRWhUREczWjQxQWFIbTJNUVpSN01vV0I2bnFtcEw5Y2tiRVYxeUI4cWRpYk9uOHRtNkxXV3RvdW11UngzbUhPdUpMaU0xTHlucjBtSDVhVUJXNldqVkxuUk1OdHdHUnFlTHVTaU1DallGaFNhd2pMUGc4QmtlMExwOU9OVTlJOVNRQmhzckxPMzF4MWJFOEVIQmZPdTVVclZMajdGRHNZTW5kdGpXMUw1VzRxNm9jZ1E2S3hOb3VaR0g2cTBySUhwJTJCUTRsQWZmSFNSZFJ3MUxpayUyRmc5VGhZY2FycnBMUElTNldvNnIyeTMybWo4VDNDRGNOSnpDOGJGd2sza2YxZ200QnlKclZYb1FTNDZWTmRrVkg5YlhBQ3klMkZpWXlDSE1aMllwTyUyRmhYRk05YldnQUkzJTJGRGNxMEhpR0laJTJCRnRMNlNuUmVEdmhSbXhVVHJmZlVmZXQlMkJGMTgxOXBqM0tvMzBLYm1aMSUyQjV0ZjFabldMdzRUMlhzSjhpWHRLZnpMbGFyMWhkNWo4VE5YY0JKVmw2WEFqRUxUQTRjWiUyQlFLUXNWOXR2YlAxTUZSd25DWDNBUmxLQWd4Mmh4JTJGM2hkZnhCWEpFRTNPUFNuZlN5M204aiUyQkRzM3E1Wjd0WjY0eWtjaFRCMTl3T3V1ZGZ3dGRXSHVGV3ZYdGpKRkxFVzBQSU52WkRlTlR1SUNJNyUyRlZuMURsb2UxaklIUEtDRjVIJTJCa0wlMkJ1cnIyRlRUNE5DSFdtUWVzdE9IRGpFSzB0ckxtNnRyYnZtcVp4OGpqTG81WnYwY2RQR3UlMkJKNVY1R2lJbkx6UnZUaGlkVm00ZjNxbjFObEFxazVrSkQlMkZydzI2SlUyYzFLc21zYVdQeXdqRlZIS04xYmVGcVdFalFlbG1hcEQ0MGR0QXlSclo3c2MzUFlPaFJqbFpJQ0ZyZjVSJTJCZWxNRzclMkJXemhQNVRYb2o2dU92YW55OTI2aG9maFRHUGkyTHRTV2dScjZnNlhzS1p4QjM0ZllMbU9iWU1lU1pvSmVSanpzVnpTQmpjVkt6Nk9xNkt3ZXI2N2oxSlVlcWp6VW9BMEdZclFiTWNUV1ZmVjdJM3ZhRHc4a0NydzZtN1poZyUyRjNwNUdwTDBTdFV2JTJGVldxRXI1RVhSYnZOUHl2RkFrc2JaQ1RHUFFLcW9EMVB0QnZHOUo3Tnl2cVJSVkpubE5IWDFNV24ySnN4RHdsb2VqWlBWclIwSm1sMFl1VDFKbXZXdkt6TlJ6UG5BZWkzMDVrVmhicDhxTjhzZWpoTHU5MEVNdkNZUTBmbzVWSFRQJTJCbWlYTG5jbGhWNjBEeHBtMkJraWVJbiUyQm5Wakt0MThnYkNsUWY3TkNXa2Q5VGV6R1RTQlNhUTlkbXd5VlF1NlZhVnFQbEwlMkY5MSUyRmxvU1dsJTJGczI2ZWhvSFZ6eTVYWlk3WFhad1lnUEJOdkpaRXVrb0tkTzVPaUtFRFc5NHRXelRBNWZRSUVLTXlSbzYlMkZXMVBVcnNpSmtPaWxvNmk5Tm51REtoTEFCSnhoYUxndU1wU2Y1WFpWWG15cTBhVHZYenBFSUU4NHFBQTljS3B1dyUyRjF3aFEwSmNQc3ZWWFdtbld6YnVNbHN6WEwzRk1jSE5NM0FDa29DQUtOZEQ5UWh4Mm90ZDV3WXBwWkx3azBhd1liR1FPSk5NWlZWUTdvVGNHczVxWmtadXJSTDRIbXNYV0l5cmx3ZTA0N0tKM05yT2l1cWh0MjhrWXVxM1R6JTJCRTMzeEV1dVhXYThYN2NYNURYQlZFbmdZR2YyalRQb3NoRW16eUdkVFdQcjlqVFd0Zm5HOXVieFVicXBXZTdnRmJmOVdRdSUyQkElMkJEaVFLd1Vha0VyYXdsNklkbzVTckpXRDZSeWFvenNKNGJRMGNlTU9UblBkUVlxUk1tUkZwYXMlMkJuUjFrQzklMkJPMkFqWUxtWVdqYjhhMmVQa0kyaEsyeUZ6NVBPR1BZSHNjZ2J0UU9WWEt2VU13N1NvelJIc0o0bVpiZkVLeWJxNzglMkZHbFo1c045VVVSQ1FNNUh1RUl0WDdSbXgzYnpPMnJSM0NpM1piUk9hVTVtbmdadEI5Z2d0M0kycE4lMkJmYWExQThuOTBaU2I5eUMxeFA5NTdKdnFBTjVxQW5mcFpKcXBCdklEWGhBVzhQdmVtJTJGS2hkJTJGblRzV3hqN004VHJWajNFJTJGVE9laXNicGp5eXl0dnkyV2Z1c3NTV3psWjlSY3owUjcyVE1nMTVZZFJzUExOY3RwNDlTUWRtbTFaZ3pjRyUyRkw2QTl4SXo4ZE9hRGN6WFE5MEU3SFNoZndyNnlKUnNjNnJZMUpkN012Y0ViWWpwM25zcHBVREJ1aWNNOTFmeEVWU2lpZWlXMlhqUTdtZnVkempMdGglMkZkV3NDJTJCVlIzUFZ6RnhYWnNNWnpFenIwc3ZkTG9XMUolMkZMZmt1dnhURzNwJTJGTWElMkJQc1RMeWtaVHA5TiUyQlZ2VjBhYlJ2VzZXZHlNeFV4eE9HNGJmaCUyQlAyS0dIcGN1cURpa1hQSjdpencyWVlmYWQ2ZUlLZEd2VVpHM1U2JTJGTlk4OWdXekFGSjlLNzZlUXVpVWZhZGM3VDF4M3k2Rk9lRmhNTm0zRWJrZ2VYVjN1cENDZU9XVktLcmUwRWMlMkZOWjBQanlrTExsWDU3MG9EdEZqbWlZNE9vc1ZlNW0zbmZlY2NyMElJSG9FUyUyQjVYOGxVR0hoMVJBYUNCTmNwNmFnY2h5OUd1bkpzQiUyQkMzT2ZTU0dWM1RaZldhbmdEdlluNW1hNVZwQWdrZmR5MUpiV3lTdDJzbmEwdE83dyUyQlFlbXVmNXQ5QiUyQlk5VzBXazJnV3RHMlpwZUhOMW5Qd3lLdmNuTXpXRVFsV2xnRWpZMENocyUyQlVlRUNmOEFCM0hrV1JjZUp2RXF2RzFNVURPNFRpT25QYnFsSDNmZzNGeXZ2WmxCeTFPR3U3SERjMEdaMXBRZlN6anlndUJSbXJUMGxkRUU5WGlybVZ1Q1dLN1RtZm43YXYlMkJhTlRkSUJoMng3ckowREdxMHB2RVJKeEhJZDF0WDMwM2NUYjlkMnl3QVZ0Q3lXR1o4QUtzYlQlMkI4V1ppRVVxZ1FIZTgyUWJodTU3c3J6RnNaZGF3dHJSYkw1MFAwb0dVbWFyY3VndCUyRldaaVNSdTRYSmlNb0tnanlQeG1uMUU3dWZheVRZYzd1SkhFdFhsWUFVcEZpbmhqaGQ4SDg5QUVkWTZGZUhUSWR0M1JZNzFSVk9GNk53YlQ1U2ElMkI3QTJVSW5sREJiOSUyRm5rSU1aMnRCczVRbHQ3ZE9iQXFvbmVObngzTm1Bc2k2T3l3RnR4VnhrZkk5VCUyRkJuOEE5dWxiQTZPOUhEOHpicm42c1hNazVQeHM2cEI2R29YcHN3NVglMkJWUzY2SnViJTJCbUozRk5XemtlaTBWcGdNYlJiWTdEd2lLSzZiTDRtOHA1V3F4T1lJWXMlMkZweG9ub2JhbmZhMDRTZGxlYll6cHRDWUMlMkZEcEZRUUJybHFTMDIySlolMkZFWGh6MVRocWZiUHhCcW5xbjJ2ZjFXa3NQS090Sms2SSUyQnNnbjE3MnREdW9QU2t5c1VGVXExJTJCTm91WllTRlRHcnhMSW1mVlQ2MSUyQnlLSmVna2Y4anBFek5YeTRVUU5LWmglMkZibE5sZFBuZUpYJTJGJTJGZ0tSJTJCeWxTV3N5T3l4VU5mM0l4d0R2S05yYXY4S0RoeHRiT0JNSVdFWjVhZnZyZDZZWVExSWZkWkdua3VvY0VkdzgwVzdoSG9rSjJnSUppeVNzMHozeTJpWGljMSUyQm1HWFBuMUlWZFQyazlpWUloT0tpJTJCdERsS0hMd2ZEJTJCWmc3eUdJN0x0YWp0cnNTek94UUpnaG5oUFolMkZRWUowb3d3VUlIUVJKNDduVXNLMTVweTh6bnJnVlAlMkZlazlOayUyRmRLNFhaMHFER3RmYU55WDNybTlwdFc4ZHBQYzFsUVo1OXJubjdWNVVPZGdMelBubyUyRmw4TlZaRSUyQk9pZkV2TFFKTjQ5QXliUVh4YSUyQmRiJTJGMW9XYjVURmclMkI3S2NLaW5JTk1IdVpDMHc1UVp3dGJodHUwbVdXQmhSV2FmZURBMmFFRUFjUUxLWTZ5Z1FvVmtYMjFHMnolMkJ2MmQ2VmVVUnMyclJXZ3h2em1mUzlBSkgyV1dmQ1ZFU1pPaklveWxFUG9pZGpqa25JalR6Zzl1MkZuOVh6U0pTenl5czROVnU2dHUyWGJsbmRzJTJGTXpaTDhYeEs0N1olMkJsUDRhM0NzZXF1ZnFqN3JOendYTUY1OFNDVyUyRk95VHNWYk9BWWUlMkZ3JTJGOERLRFNPYlNncHpmSkYlMkZrTDNMMjdZdHFITGpNSzVzME04N1h4bk00NVhGTjBydmlJWkpQM0pXWEVmanhSVzJMVTQ4NHAlMkIxNkg0NG5aMnM4NDFXZSUyRjYwTktsZFklMkZ2ZWdyY1ElMkZlRnA3aWZQQzdXbW96eDUlMkZEczElMkZhJTJCdU5PZSUyRnZNYktyejhuR2plNTROY2U1QVBIT2NpWHA0bk5IMFJKRWhzM08wNFZQVGxHMWZzR0kzJTJCdXZlQ0VPSzNMNWJFc1JDUGhiclBQSDBYS2xXaWVwZUJ6aGFuWUhMV3o2dWhaNTM3bmpBJTJGMFdxTFJiTktyayUyQjRnOFRueUFkWnA3ZjNTbHdHc0VqJTJGN3BzRHZMTTVsZ2hkR1BpMWYlMkZlY3c5MTJKMDA1VFYwZjlodHpTVjI5MVZPVGZOUE9lY2NkN0l4JTJCektWbHhEREJhT0pRVHFUaXlVODc2WHJiNWZOOW4xNFdoME9yOGhKaWZjM3lyRDkzOWVTRThpSnpaaEpWWTBHWTh1bllIdmR4Vm9JRWxXdGdZNm1pdVhIaFBKUGJMeFZMQXB2Qm85bUprYjlYMlNBYjZyanFGYlcxYlltNjczTE5BdDVGVmhEdGsxV0l6OSUyQjc4RVY3SW1jJTJCTGhiNmdjRDNyZFdUQmw4YjJucmF4TnRxMWRPR3JYSCUyRlA0NGpHaHNVU2hLclhEJTJCd1I5YnNNek1lRlBaMUtkQlpHJTJCVHM4S01vS3ZmYSUyRktOYUZOQzViQVY3dXNUM1pJOVBhZEQlMkJTRzQyRDJVbm4yTTBZdFhMczdSOWtJJTJGVmpsJTJGRDBKNUN3SzZDMWNabGNocnllNktRN3NaS0FER2dEVzNLNnhFOHZVM2I4cnA5OUZaU0JWSXdIYVBaQkN4ak5NTGdXeXZaQlNqNSUyQkdmWXNoazFnbzhXYWhJdDQ4NFRQbWZqTm9YdDA3S0hsUjFDZjlmYmxTVjRXY2ZRQmptZmNCVjdyJTJGTlYyQVJNanJzcmxsWHhUdlhVTXFLSDRWJTJCVUhPV1JzeDRvWmtWU1lTMnVJR3FuUXYzd0VaN2hZQ2lzNG9hVCUyQm55ZGJESWVadG4wYm1PbXRjcnR1bVdmTllEUjJPb0YyNE9LSDFvbGFBc3AxR2lXT1VvTHdzMk9FWkhTcDhDTVVzNjdwRWltN3E5eWRzU0ZITkZwTmJmQU91RGVTQzdtbFhWbXZYUFhjNGJ5ME5yd3pNWjY1ZGxiMFlXVTNaOTlGdHBZTE5yRFozYU92bFl0cnAyNDFhbjE1YkJQRmM0OUxrNjFBaGVpMUE4TmNKVXROZjczRnhPU09QWThmdGJJbCUyQllrb0xDVUZXVGVvT3l2QlJQTlZyMU1rN1YwbmRCbGZoNFFJdE5RbVhMU1BjME8xMGFCeTlGVUJRcTVEM1U1SHUwdlBoWkRhb1FnVDR0ek1TTzNNaHkwMyUyRnk0TjRJOUk5ejJGQ3NjcUxTZnBia2FUOUQ1NG1qbmtPd3pyWUlPMEhPaE9WcGRzTllad3lLMk5XeEtWMERuNW9aNWQlMkIwUk1PSVdnN0xCVEZsJTJGTzRuRzF5ZldVcGpTN0ZSYU10NFEyaVh1VmpCWE1mdmNOWjhDc3pkdTN3ODlTVERTdWpZWnNLRWlodmMlMkJqcEg0OFV5WTl6d2JyaWFYeVh1bloyaXNUQ3ZYSlVFRjk2UjNvc2VXc3g3JTJGNWslMkZobXRXaUpQOUFpSno3eEo1dUZlY0xsZFVtSGZYUmx2TldUekxzSSUyQjhWcUEwNWpGM2kzSXo5JTJCTVM1bGczcmxlZ3R3RThxTTgzbXZsaGlqZ2ZaQW05ZkhVOVY1azhBaG40TDBNTUh1RnNodW5uMENYRXB6bjh3aFJuN3VJVCUyRkxSdWsxUiUyRmdsU0g1NXZDM21PajdleVNPdlhydXh4TlBOQ1BldGFwSU1QQ2JwcG9RN2hjQ2NSdGRTR2ZJVHVYd3lkRlBjdWZzVTBpQ2JzUTBza3ZMUmpXWWlMV0ZFOEhhRnZnc3RuWHhQYnBMclpNZGxxZmpCSiUyQmY5blJRJTJCVnJxTDlvbyUyRm1ibUh1cCUyRmV4eGRQMFh5MTc0JTJGTmZhRyUyQmlUeEdqTjZKZHVvcGtUTkdiUW1sQ214elNSdjZyZUx6VFo0JTJCUE1VRWk3Q005RzNMZUY2RHJlekJjV0s2WkdaMzlHS3laeWp4b01Db0ptd3ZWVzdOc2JuZUxESElTV2VSbCUyQnV0OGJFSW45OUtiSTJGSnprZFlsJTJCQzRtbTYzR0t6cE5BUHBRRGV5OXJXJTJCNFEwMCUyRkxzaVdNVnhuM25pYnJvNTE5a3Ztdk52Y1QxOW5WUFlqb1B5elRLSjBwciUyRkZaSjRKYzhhSjVSVnJ1ZDVpR0RXcWFjZjV1dWZSOFVKdEQyWHEybjA3QzlqMmZ3MkNubFBJUHk1Rjg3JTJGMG94c2F3SCUyQlhxSnJ1aXBOWXllSnV0eTV5elpvR3VtZkJ0SmRlSmV2U0R0VE4lMkZKaTBOMXQ0SWtxZ2xFJTJCNHRpVnNDWkRWcGlOYk8xRjlJRUljdkdzRmIlMkJlY1pjOFpMRmV3S2dQQVMyMUowWGFWNDglMkIxV3hyeFFwY3RBNmRMS05yUncwVmk5JTJGNTdLaW5OczlWZiUyRlNUZnBrdWloQVZLTzBhZTJEZFludXRJYjRpYkM2R214eGVERXNMM1RCeGZoMVpWcEIlMkJRcjMyejFEZjVSb3pOcEd4OVRwJTJCdG5lbWRGdG9VYnlLN3JBYnFmTWJDbUFxbGglMkZnV1oxb01TMEowJTJGeDhsUVNxY2IyNVBMVlgzRTVYcVM4VVpWcjVKR3FxNVklMkZRMVh1SEVyYlVwZUplMDk2ekoza1BWSHZqYTBQdHJ2aGwya1hGbTdRMTJ2cHZTTEo4Wmo0cm5aQlhtS25Idld4MkdvSiUyRlR3NGtBSTRaNEhXbmU5MjNPUDFFUWxwaVgzYkVncXViVlpKNzJxSWdOalNaSGVwSkt3VzclMkJheUhDT2haYTFmWXg4WTl2ViUyQnBaa2hVYWhxM21vcXV4ankwdGIzOG5aYk5BaDc3dmFhMmM5ZUVDdWlmQ0tiUHZqeUZRcUs0SG9pOEpKMTZoNTdFVks2VERqSGpCYnAycDgwWnBURTM1RmVoNjk5R1M5eTR0UXJQZE9jWkF3U0NUTGdJTGh4UmZPbldlZDliY0hXUEglMkZpaTh2Y2FweDlaYlB6JTJCZWZVc3VsR21ueTZDckQzR0JsM2dzRFBWNTFHZ2ZxNEUlMkJzVHNJeUlsbmVMNHN1eEY0bWVXajNMVTR4TnhEbmlhN0olMkJGeXIlMkYyMlF1c3J6UHFnRXJaNThWVnpuakJQZ2NrR3hxV1MwUFNTJTJCbHRMZ3BGREJtJTJGZG5aTGU4aU54QzhoQkhLTmE3a1NMZmVJdDF5WGMzU3hSbDVVY3olMkJNN2RCVjlrYXNQVXNucTNxb0NkVkdxSXRuR2Y1bkZ6WnF2b0lSZVRRY3pEZVU3cXBqenVMcmhadXpkRWlqT1gyaWgyZjlueVlzSkU1ZnJSSnc2JTJCT3Y4aXE2cWQlMkJtZDEzcVNUc2xlNzJuQVNWeXNSMUtzdnk3Y2o4TE01NXklMkJKb2FDWTdERXhzJTJCWnMyc1NSTWNISlhZJTJCZXhsZWFjc1NibkglMkZxTWg4dTNYN2FmQ0hJJTJCTjBPY2pXUnpqUlhUcSUyQkxrWjV3NzV3N3NWNFlzN0ZxRlIyJTJGRiUyQmd6ellMY1ZvRm4zck1YNldxZU9jUTk3NkNYWkJuYWVCSjV4NEEyJTJGN0ZSJTJCc2lZTUg2VWRHOHNOZ2pycVJGcjF5TnlUWFhvTzdja3JQWHdaYXJCbVZBcjFlUEFLTmVGODBFcE9KcDhlOWtPUFdKOGh1V1A0emE3SGVUbkJlblVpMzBmQzIlMkJXaXA2NUN1TnQ1UlYlMkZHJTJGTnlMd21DSG5yTElrdGhpSmlCckN1SkZGSkFQdlBJeDBuVktxQXdUTFR4dWhtcmtmMnJTeUhZJTJGRTQlMkYzMG5abzdldDVzJTJCZ1ZrMnlZajNTMDh5aiUyQlhSMEVvN1VoUjVXVWcwQWNpMnNmMnQlMkZIMnk0RVp2SkxoMFBQaTNQOHVXUEVNOUE5UlNWa3c1a1FwJTJCcXFLS2tjWnB4NThXV2xNMWtmVloxMzhyVEZhamhrcDFqNUdqb0hrZGRXa1ZzJTJGRnRiR3N1NDBZaVdQcmRCcWd6VkU5JTJGRWVzOFZmZWFhVlh1Q1ZWaHl4a0NEcFh1JTJGRjZzcnBTOTc1SVkwWE9uc1FSYTQ5SDdLa051M1ZKdHR3OXUlMkJVUENvWmRCUzM4QWVoWmRQaGVia1BEUDhVUXpYRDN5NGN4ZGQwZ3dLaFAlMkZPNzhqZ2ZYOEJVTkclMkJNQXFYZ0pkTU9LdXR1TzYlMkJVOVBacDUydU1EbkFPZTdrUDBpaEFDY3JjV1ozZU5zZmViV21hb29tdkZrJTJCcXVaNlRzaXN5eG1uTXJOMWU1ZXJvb0J5UGZZa0tXcHhMbUJPeCUyQmdmdEVLeHQ0cnYzVlclMkJvWnBqaFFOdmslMkJEMm5KaG1wdTMyUGl5VE9pUHRMdjhZdWJsUDZjODNjZ0g3T3B2dkFyU2V5SXFiVG9iJTJCczFqMTNnY0MlMkZhSCUyQjJWM2llb0JkaXpNaG5hVWpRSVpaOGd0b21nZTIlMkZ2RURCVmRUdGZRNE5IeldraXJ5Q2ZxVlY4MGVhOEpPUnVFcFolMkZ1UFpnc2NCcUcyMnpLJTJGVkpHVzBEdTFvSTZDUm1YRW9NRXdpYWxObXVYV2lGSWNiQjF6bmYxb2R4QlREY0tnbXh6YUMxc280Zk1icmNvZ0E4d0olMkZVSFJEVno5SHRadndmSFBSMUdjM3BIS0JHOThIeSUyRjFzZXMwNFZLSTJQJTJCUG05N1lPdG1Hb1pyM3NYVWVUSUdtVFh4cm5MOWJZcjhSJTJCR2JNQ3ZaeW5QN1B3QlFqSlgwRTlTVThHd0dKTWxJNFhXVnlLcVZ1YSUyQk9XNFVEWEhIeUQwcnA3ZktRa20zU00yRnpla0NhRm50RW11bTEyJTJGZnpObWo2allKVTVVYXFGYmQwVlU4b1BGWnBtZW9tNzI0a0UzNW5raGRzRThmc0c3bjREcCUyRkFWSjJiT0xTWGVFc25hRElxR2ZaaVloZGU4WVZGOEZvbnJoTG5raXk4SHpYeEg5NUpJcGRFc0prSkt6cVhSbEFyaUZqWGxZWFdFWWl4eFRYZGZZa3diJTJCdDElMkJrb0w5S1N0dyUyRiUyRkQlMkZBJTJGMTdtcDhEOWQ0YVZnV0dZbUlWSTdMOTJONXd5bXE2dUJLVHY3OHlvaUNRU01qVnd2a00wa2E0dUZOSXBVVmVNa0RkNHlod3p6SHlXaHRUOTk3M1BtWkpJNnV1c3VxVjZiVVpRTlFneW1YNDVOSDhKek1KM2xxY2pGWUZaV2o0YkklMkJxdUJhNnBnRnRWcjVTdktaWDRZOUFua1RYeSUyRmRXM29VTFlOOG4zaDVJc0YyV1hWaXJMOHY5ZzV1JTJGZnU4eWclMkY0T0FJMSUyQjIlMkZQcTJRQkQ0YjlPciUyRmZOaCUyQiUyRjl0Z3RuJTJGZyUyQm4lMkI0dk94ejdmbEZXN0FmeDlBY2VEZlIlMkI1JTJGN3lINHYlMkZkbm5XM1YlMkY3WmglMkZ3JTJGNmIydVYxMlgxdnhOaiUyRngzNlRvcSUyRkRlWCUyRmQlMkZhJTJGSWlCJTJGMyUyRm1adlM4Njc3ciUyRlhjTGZhd2lvczMlMkJmZ2YlMkI3aDdqYjgzOWIlMkZtMVl0N3Y3YjhOYXhkUDNzdTdqOHYxTGZmZGJwM0dueEVuZUdlTmFiJTJGVTR2UHVUY2R2RyUyRmoyZyUyQjNaUWNkcVd5N2dQR1QxMjQlMkZMdXolMkZJaTNydnQlMkYzY0dzcXZMNzVQYk9MMWI0M1hLMCUyQiUyRmVpdnJLMzh1aiUyRnI2USUyRk45VzRIOWJ2bFBGVyUyRnglMkZNUG52N1ZkUTc2V0pkTzFSdW5VQ01sJTJCTzM0aHB0bHV4YnZtJTJCUXIlMkIzMGtTVG4wJTJCWldYTWFmTDR0ZEVDSmZ2QnVvM0Q2Zlp1ZkpiY0R3cGZQOG8wM3pWR3F4N3JmU3lEaHJTYm9VcElsR2VJOXd4ZEYlMkJiVk1MaGtjejRiJTJGVFczdTk1Y0lRWDclMkZuNHg1S2FieUhqVkp0ZkYlMkJHJTJGWEwlMkJQR3ZzQnhKJTJGazZTRkUwcTl6dWwlMkJxNERPc21mM3BUJTJCZSUyQmJqY3pmOFNPczlDMWJTVWgyMjVFU1MzejJjMzVFVnlTenZVZDk1U2Iya2hTOGc4bnU5dmtjYzcxJTJGMSUyRmV1ZjFIZSUyRnBWQ2RwRXZTVEQ3UXd4U3I1UFZkc3NsZzMlMkZIMWU3YnMlMkZaVkw2cnY5OE4zanZMJTJGJTJGN290OFdSSVpsWlR2dG8lMkYzYiUyQnNXNSUyQkM3QSUyRiUyRjI4dTh2OG41UCUyQjM0ZU9Ka0ZUcmY2dXh5bVBJZjNYTyUyRjEwVllkdkVTRElWOU9UaWZmWjVMMyUyQkw2azJPODFhbEx2cGRERiUyQiUyRnZlUFMyVlFjSnNXTHNyZEtYYnJaWDVITjJWdjY4bjlYdmY3SiUyRjdIeUZ6aUtCZEk5dVhGblNrclVYdnVOTmRzaW9vbWxkWll5b2RocG5DdXdQWW5Xd05CaUwlMkJTOWRvb2Q5MlIlMkJONlJ6RnhoQVdRU05wcTR6SiUyRiUyRmJXdDNCZ0x4NFFmRHdJa1E1NHcwSmp4WDEyMHoyUE9neWZEbUNjQUNLdDJNMVAxSHZkJTJCaENtaHZUUkRudDlybEJvUXI2Wk9jdDNUU0xXb2Q4NXhjMGd5b3VtMEw4aWpqZSUyRiUyQkk0cm9ycFoydjglMkJVMU8yV0lmdjVVOXFTSVFFR2N2M3clMkZlRFhFamxvWWVLbkx4T1JrekJCendYJTJCZkprdVAlMkYlMkZBJTJGJTJGSlZPTUxpejYlMkZuRnZWWHhPRjNVbTE4TXVKd0V3bUZNTiUyQkZGZUwlMkZTOU5WWkV1T0pNSFR6RjRNU3pFemF5ZG1UdUhwUiUyRm8xODJyVjFaV2s4REEzYzl5Y1FxS3RPNW1YZ2lCWWk0MXpjSFRhMTJEckYzVmFFakppaTVYU1lTZSUyQllQQklIbVJxQVglMkZyczNvOXFvWWlBalNLN2pKODNSYTBFdHo4OHdqM2hIYVJsVkF1biUyRnJQbjl1aHRLWlVQaFAxZnBST0tSMU05Q1FnVnVhSmFyQkYzVEx4d01EU3h6VjVhd2I0Y3ZydlhZalglMkJtT2Q3QjFYJTJCMHdwNktoYmlEeW84Q09tWWxFV21UNm1aNDV6enRBcUolMkI5Yk10dGdKNDNraUhRS2VRYVV6eU1DaVpJcWRYNFZiZW41VWdpWldFRmNXdjRPa2VwWU1qMmFVUGxYZEVBSUNkaTVldTZYY0pWUSUyQk5jaVJ5VGNuVkNlQnMzYnN0dXRSY1Y4NmNNcHpORVR1NDBMbHJyQyUyQjUyWUJJUzglMkZQcWVuSTMydGhWYmtXU0VraVVGRXQzanVITWF0TG5iWlFHTHBBRW1YMiUyRkhtb1ZBdyUyQm9tRWo1WVdoYU5vY25jYVhRb0xGYUVxTThueTNUMjVINTFBNyUyRjE5JTJCZjJGbFEwTVl3TTdPbjY1bEx6WDFBZDVoM1Q5WURWRHBxSm5rM3JVNG9tJTJCTW9lOWlZeEtDSjc5dW5rZm5XUk9hTzd1cXhjUUpnWndDSnp2YldNUlZOT29TT1hUUHRzRm45R1F2ekI1YTJEUXYybHdFZ2FkREk3JTJGWGNYQnRlYiUyRks5b2piVDFNZmwybSUyRkw3cWtRTTNONGF5RGE3R25JQVpkeXhVbktYRzd5SDZuQmQlMkZSWFBxUnF6SFhzc1ZHWVg0dFBSZE8wVmduRVFiRiUyQmRLc2xabVlNd21aWXpJaTROWEhYSDY1VFB6MTRtRUxFcDFQbXJjT0tnUnFxd0RqRWMwYTFyUGk2Z1c4YjhWWVROWXpZMUdORSUyQmxFT3BZREk5OUpuU3IyUkpWeW40V3oxcDE2ejg0UzNtMVFzYTh5OXhrYWhiWlJkNm9qVHM5aCUyRlpZWEo3QkdpSiUyRmpvaW1HM2d0N0I4cmZhMmptQzV5MTVaTXVPOURseDhtMkNqVGVyZnhrclBVRnI2QVNUSFJORm5GQ20ycnh5bFZWYmIzdjRBNEtNS3Jxb0ltOXRSTXJWaFl4SXdvVVpiMEZjS01MdUolMkY0b0JPY2VJVEpLOUtvVVB2dnZpaEFHN2VtZjYzbU9CbFRPVlg0SjJxZEMwQUVoSjlhWmVPS2pPRUFrQ0JIcUpobnN2NG8lMkJDdUMyN29XZXM0YXJab2dWSkVZcWpQcnc3RzBQb2ZZN2gzSmFtUXglMkZ0JTJGbFd5bTJISyUyRlRLelR2am1sWFUlMkJzcFBSOWtYMVJkS3hrMVdpQjZmYnA1MDFqS01rSnc0YlJwMUFyUnNqQ3dmYUFjalBmb1I1TTNPbVVVQnRmNUN4bDJKcmRIMmpQYVJNMVFRbWZXZjkxMmx0R3M3NnlySEwlMkY4cHJvQkNDeEFhQVBkMVhQbGRiQXpxZ2M2S3lFd0JVbmtOM2ZFa3pQT0hJaFdiand4N25vUHNkOE5jM1IzOEhmQUJmR1lkaWNsJTJGakJuMmdKM2YlMkZwclVlTDV0YVc3aXNqRmYyTSUyRkdzd1YyWSUyQjFIVUJEUHM0dDFkck4lMkJFWHR2WnQyaGliSXRGdllmdTNMa0ElMkZQSEJCekdqSFpIcWhGSVY4WkJlQzZGR2RqcUVSSjZwdXU4NTYwd1kzR1AwR3hFNlZscFpPZDQlMkZIcnZYMFU4SFdkOGFsd1llVDhYS3EzaEU5eHFYVzNENldRd0I0V082ZnpPZTdxdWN2NkliNmdsUGR6ajIwb3dkJTJGeXBNbXVQSjRmWkklMkY4VSUyQm1GMVlKQUw4NE90dmUySCUyRmk1OHZDd2plU0FWJTJCZDFoazVlN2JMRVRidDNFJTJCc2ZTN2hoZ3FTbmZreWVkWXJKQ2tUejV2Q1kxRiUyRmdIWDZMZEpvUTFDJTJGQ0wyNUN5NUtVS3NDUUFzTm5EYmVUalE5ODlzZnVaRGlwR01aUnN4NGpHNkNVM3Y0cVg0YmppWUNWUFNSZGhFbFh4eWtwYWIxME9Pck5JZklmSFhNbzFTNmN0TlVJelRHY3lkbjVHNyUyQndRbnpsbTQ3MkpYJTJGV0FabnNNcGpzdktNJTJCM1NYUEF4djEwd2ZCNSUyQnpTM21ReUJIYyUyQmJ6eTVFYUV5NlUlMkIyTG1iVnZqSXZTdjdPQ21DYVg4bDg4WUtZWWU2MmMyN3hlRWs4T0hpa29iTGJQakRMY2p0TXFSczFwM1B1aGpPR3BQOHd0ZElXc0Z1SE1sNGFud0JxZnZZNEpNVlRYeGxucjdXY0pzbDc0SG5aRG8wYVRqOEFNck9hUTFqaDBwR2taU05zWFIlMkJYeTJZOHI1Zm5EWmYlMkY3TVViJTJCZW9RS1lKdHBKciUyRkxIalRQeGFNJTJGM1c3SjF1ZmZWUXhOTDNveDclMkJ3R2tSUzBDelgyTXIzSm9remdmRk4zTWtmZ2lBWllpa0t3YktVaXFQWUElMkJWSWN2aXFUYXBlOHppMlVUbHh3Q3F0eURLSjd2RHByZlJEeDZyampaTWZDJTJGdXUlMkJCMHFWVjRTdkNwMFo1U2lId1VDMXg0MzhTRVVFZkJvbFR4WWw5MWxTeTFjaVdkaXR3WGhoYloyekUzJTJCUCUyQnZsR24xbERCemV4bU9HZ0VQa3FCejklMkYlMkZIRE1TQldPVzdlU28lMkJCdHglMkJ4V21zY2tSR3lGdjhpc0djRW5zd1FET1l2MENqcFAyaXRSeUhteUpJbG9ic0w5aFYwUmZNYkV6VHVzb1dUNTd0VklOJTJCTUNOY29zbjVScTE1TTY0dldxVmVGM0RIZ1V0SFZCZkZsQnBzYjhyJTJGSkdOQThRR0V5QWpDS1NZRHpIWWNQdGJIRGoyVXM1TnUxSHAwdUNINURuVGVydCUyQmpwbXFXNVIlMkZ2NnFwZE9ZdElRJTJCcVU4YUEzYURnU3VabFJxJTJGZFlrQ211WmRvaWdmMTdPWjlNdTAlMkJJN0lOWEY5bFZsOTA5bjZBUVolMkYwblBpMyUyRlMwdUNNRHdHT1MwZElwVTVRMk9hTVZ1WHVLc21PVE5xV0Noa1FkZHBRSjRaSEFNbDhQY1JMdmd3RVRJUHdUMGl1NDhzYnVZaHdXRFFjZU5MWmxxSlo5RyUyQnJ2OExOYzZQMmRzTDhwc3FDWHNkNG1zRjluZHJvcjNKTnhJazBiJTJCZEN4TTh5ciUyRjE5VHFEdGdWTTh5JTJCTjF2OGFJMWJmOHNEZWJ1JTJCYlhacGZxM0RYUjdha0dXVnFoV1pURiUyQkV4aHZYcCUyQlU5ZXZoTHIwTHV5aWZJRVB3Q1FZcGhiRHFrYVRtcDd1cTFzZnFlT0gxaU8zazRmUzB4alF3WW80MFZZJTJCdG5xSkYyNk85WCUyRmM4RUV1ajMzWFBjZFF2ZDEwUUtyJTJGaUhKSGNvRmFRTzI2SUY4WkI0d2h5cGFnSEg5WDJsb3d6bnRocHpGenhOTUhSSm5NVmJIdkhUaEN4QnlGa0Zmc0pSNHZ0QzFpdFJ1dXJLY2NCWFNXaXQzZmtDZmFLdlFYc0Fld3VEZkJ2R0QzU0ExJTJGWFNkSmhZUkdHZHFQczhqYmNZS0E1R3NQNWtLVlU0Mk9XdW1vWVZYN3Q4QjFYZ2wxOVNzY0haJTJCVXhpOGpOczhmekpZQTBRdzc5V3V2SnZMcm81UG9LZUZUMjF6NHo0U05tVSUyQkpTRFlQRGwlMkZNQmdJN055b3RjaCUyRjhuam5ocm9pUnZQaEJ2OWdPN3NnRjV1bExqT0tKT1o3N1JxNW1rdjNLeWglMkZjM1olMkJ0YWhxYzZ0d1QlMkJkOUs3dkRIbkdraVB4Y081d0Y1SU9ETFRVRUpuSTElMkZValIxeXJSOFVMTlFCZXlnb2l6QTNXSDVHcWx1VHY3TDM1dyUyQjRmZVZmd0NwbExadVBFNSUyRm5TaFdTRGZTRkFFQW9zJTJGJTJGRmp1c3ROTiUyQlhuM1VxaHRDQzVLQTg3eHh0R201am96WFZGdXNsMjJjc1NNOEVzdXA3UFBkSnFyZHVSVTZaRWRHUmdnJTJGTU5rekxjendzbzkwOVgwSkgxVWFieTUwTnBEeFZ0QkRRd1U3QyUyRnlaS3YwQXNQOUI2dXdmVCUyQnVxTEw4WXNHREpXR1FwVGM2NSUyQk5XNzFTcGU4cFlFZFI4c2RYZ2M5JTJGS2J6Q29YdjNieTdoajF2MzclMkJJJTJCWmRTc2NzTSUyQlUxWGxRVVI3RDJJdWZZUSUyRnFxaUdJRkhDQ0VOZlQlMkY3SzIzTU13dzklMkZ5ZXVjMnpRVnFIeVhiWjBJTEJKcmFiWExKbUJ2SjduOFFmNGZ3TFdYN1lTWUVmWm96ajEzT01BZDRWRENvSEFkaUZvVWRwR1BNMGhJVVd0UnNzR1E3UnZCSUl1JTJGJTJGQVZmTTFEOVpJR3Jyb3RiTXFMJTJGZkJ0TlZGeTl1cDN5MHNQSk01blg4V2RHZnpMUzE3dHQ0R1Q5a2NQeUpXeWhZZHdMUjF2VUhOZVMwdlhWUkVPeFFzbE1mWm5KWFRJY1kweWlyWnVFWEk4RnpBcnRMcSUyQnlod1d0dHp6M1JTMUR4aHVPUmIxUVMlMkJBV2Y1JTJCdnFGc0dwcE5wRE1ROGZZaWFpbFlldFRPOTl1SldocnFYNkJCUHI4b3hkRHZVTTkzVDV3WGYxM1lpMDJFTnBKRE1YdEViS28ySUZtWXRlTG5sUWtvZUhJN2xWbjB4RWh2RkgyYjhUYzRMJTJCRGtKcW9pQWhKVWs2QkVvTkhkbXA4d0JuRmE1WWpyT2JaTUZha3gzdiUyQlk4UjdpbnNTcVhyJTJCWXhlb1g1NXhEeEhkOWI5VklzaER3JTJGc21KN3IzQzlCT0lueHRBM3MwYmFSb3Y2aHNMbms4cHVUaTJiY0docUt2VW9sOGd4a0UlMkYlMkZnQmRPWHppREtmcHhJRkx6WkdRODZFNWlPMzYlMkJEMGlybWZzNzJvZHJmbyUyRkdybktQOHd1Qk1vcmtaMFM5MkY4JTJGYWlaWUZPTjQ5U3FrZ05WTmhNa3ZNeWdJcklheTBLdkY2SmRQUVFtb3YyU0xNMUdDWnpmeVNIY0Vnb1NJRTc5QjZpODJmJTJGNUhic1VicXVpdmd6SFlxRnhnMDQ2MXpEdkd2JTJCb1VLdzFFNXp4VSUyQlY3b1lvTmtiM3NHWE5nNEFqc3RRV3l4OHNrUzEwMGJPNGxDM05wRlNYdFJESUFzZG5SdkJZbWNVT1JCdzc5Ym5ZMmllRUV1T0FBM3lJQmphJTJCcmVMNGhLYlJQYzFmTTkyUkdVTzl3cnJDZE5ZanI0WVVKSE82d2RSJTJCY3Q4JTJGdnZJMEVBR0NTJTJGVG9nRlRUTUNlZHhRZmZ5SnpzWSUyQmcxciUyQkR2ZkpvTmNQV3ZOTGpHZ3hZJTJCeUt2WCUyRlVWbVVXTkEwWjNUaVYlMkZMRDN1dldVVjVWTkZ2UFVMS3YxTURIYTVOV214N3glMkJsdFdDeTZyd05OSHFkR0ltdENpaGhiTzZQMyUyQnd1aVo3aVlkMEdaeERhanJpUmkxZGR1ZlpLa3BmcU5kZEI3OCUyQkQ3YWlWTWxnUyUyRlNWVXZySDNaWUlIcHozV2gzdElJTmklMkJMM1RoTlFZa2dtUkdQV0slMkZYNUc1QzZzOWdscnFxOUhCSkhEanYlMkJyMGxoVnZ5Z2s2YlJXMVpXaE5tYng3aSUyRllBZlhaS1AlMkZpTzNjbUF0amlhR0k5NktjSmdxTVNYU1dHd2NWMEQwbzVuZXhGUUY5VXZlYkVOV3pGd01hYSUyQmlsWUklMkZscmJtMmhMcDR4bnBoR2RVM1pLYUVQUjB2dzZmbnk5R09Gclk3ZWo0ek03YWJSUGdmRG1udU9tcHlhaEFCSkR2eVNCVE9JOEJkVDMzZnRWV3JRWVpLV0ladGIwWjBYVzhPWVhYejc4QzglMkZNJTJCUkZpNElpbkxEcTdpRDV4MHhGOFklMkYxOElIN3BMVFhsYkxIYkEwa3hHcmppdENuclklMkY0UjVvd25wTkdMS3dlMWpPYzRKZ2VVWjclMkZYaGRybE05cTNZQVJnR0RBOUxhTnFrSGg1Zldnalp4S1d2M3pQajdNZThiZVNVTFRuM0xYbmMlMkY4ZEpyaXkzVUdOWk4zb2RyekJYOGNnQkxzWCUyRjJ6VVBrRWZJcnFDTGZycWZiMWVSRXhUOHNhNWxDZDV1SFVMbFF0JTJCJTJGVmNhYnRMeGkzVVBHckRwbkhYTENXeXZEZTZ2V05LdXhkYzdkYkFvRW9qVlRMdjVMWXRROXJmS0lMbkZpWGprRmlBRnB5dlBONnVtOWVzb0xzRnk4UDBCR1E3N2NVdHFYJTJGYTNaaVFYQXJhcCUyRmNzcHdTOE9SRE5IcVdzemIwR252V3ZtQXlpRzRYMzU0Q3ZxQmZGRWVxYVh6VGs0V3c5YURXU0NCbSUyRlhpc0klMkZkTHQlMkYlMkZhc09RVSUyQmZiUEhqOGhzdTIlMkZGSEMlMkZiZGZXVkgyanhyV2NHRkRpZXdHeFRRJTJGS3UxR21sVDRuajYxb054Zk1qR0wxZncycTFqcWNDQVJhdEtLdGRqTGV6YU9zRFg0ckFDZzVrU1dJbDAyRXlGempwR1pVd3olMkZ1UmFPUDQ1VVR4WSUyRkF0UmFBSjB3VmU0SUd2azl4RDZxekVUTXk5UE1KejZrc0FaV283M05BZVk1UVMzckhpMTFhTCUyRkFXbHRGcENSMzFTNzV2OHNnTDhmTW55cjNzUCUyRlJJa3RQZlloams4VGtKa3FRNzZDV1R1NXlLcmZWTkUxMEt2U2o4d212eGxFVllEYTRUTEF0SnBUb1FJQUwxOEhjbUM4cEpGdDRRdVdacXVwdWlJbjdNWWFpWXdaUnRoNFYlMkJsTjVLR00xWjd5cUZGY3BkYVVHd3V0dFhUdFBESGJONnZrcldoeUh4WGR1ZWNiS1RCVWFBbGwxYWdqZW1jdTk2JTJCc0VhVSUyRkFJOVd3VTZTR3VReHB1R0R6Y2xxbFhCRHNiVkRyYmpYT25BYWxsMUZKWWRpQllJZzdjcEZ2cDFRV2RMJTJGSUtLMVAlMkZ0blFVN3dTOG1TNFIxSUl2eUR1aHNwOEFFaHNycjlXRkRMaEljV1J5R1c5dHZYSXVJN1JnMzNPeEZodjJ5RGZSRUViTnUweDJ3UkxkQlBYSkpQQ3pDV0FxcVdzeFVWaG9WTEFYTEZPWG1OUGV1cHE1QmdRb21KZkFQeDRaMXhMcEpnNUtHSyUyQnp5WjA1ZjI3RVVnSGQxTmNhV0dJcTFKdFVGZkJFTDhhdnl6OVBsM21hajhkc0F1YzdxNTFJemZRZHdzQkFJNiUyQmpDOVZJcFFsN0ZmQ0xtNjR1bkxkRnc2aFFOa0dqTmd1SXZjd0xyenhQVTFmcjVKY3JhR2JWZmJNZHFTNnV1YiUyQlJEZ0JXWGdnVFZETVV3SDk3aU8lMkJiQ0t6ZlRqQnZ1cWJ3TExHeHVWTXdKWG94NkQ2MTNmbnhCMzVaSDN4cHl6VW9wSlJOTjZBUjBGWmxoS2ElMkZnVDVVOUk1V05kN2pQb1ZwcmVwcU05ZXFuQUVUd290cDhzS2tXWUw2S0t1bm9Nd0JacXZmWDRwd3d3cmFrOUlybWJGajFNaDIlMkYwRnJPdzdvZ1VOeFhPQVFuRjhGU0xLb0ZEWjNWUCUyQjB2YWltVUtuSU1QYVZ2aWxVZEhDJTJCcEpreHFzeTA1TEpsS0NOTzFvU2N6V2ZpbEpBJTJGQ1BtTUpidG1jS0YlMkZTSHVOZWxEVmRrVlc0SyUyQmlFNkdVbHNacWJMQ0lJY3EzQnA4YWpGSXA5JTJGclB3U0VJQllGZVJSeTJkbUZtVWNHeVRzcjRMVVNZbXVlWlJwZWElMkZGYyUyRmkyWEcwdDV5eXhvRUJQJTJGQlpkQVE5U2olMkZvRGZ2UmdPOFJvcVJnMSUyQk9XMVA0R0NEcHROWk03VDRyN0tkZ3R5ZUd6bWhIUTA2cmFpcDZ4ektPS0ZiU2J2ekZuZlZ1cjZJU1olMkZzUjVKNnFnMWEyY3NsbENyblpiQmdvTmxwU015bVNvMVcyYTNXWEtWNzdieUZJa1M4QjlwY0hVYmZ4eVhLamtBclBsTHRsWmZPU1dFQ2ZVZ0VJNU9TdGg5cjFVWXp1WTJ5OFcyUWU5YUElMkZiVllsVkZCJTJCNXp4TCUyRmJzM3V5cGJhaWYzaGltUEN6eGFMTnBYMkdCSTFZNERtSk9zTHRxRWhSRGNMd0NpbTlDNTBoNlhpSUV2c2UzcE5KRTFRTyUyQnMzalo4OUpjZk5JNWZrOTZOSmZ6YW5mTUFxZlNZUDBteVpSNkJZTWtVJTJCNTc4aHY5SkFwdFAyQ2VDVHp4T0FlTnBuVDV2amN2N0olMkZPRU81dlVMMlglMkZ4REZVa1JhMXg5cGQ2QzAlMkZ1cjdjcVFYa2pyZ3BvN1N2WXJGR2lzcTFreiUyRlZ4UVdVS0pHNURPJTJCTWs2JTJCQUFmY3VEZWM4ajJrd0RCckNCJTJGUUNoY0VoSURkTXBaZWo2eUdmN2FhbGhiV29LZWkyMVdPN2VpMmMwZkFhU251NEFNVjlEaHgwcUFacEtRRnZadjEzTGRIQlBmMGtSJTJCdVhIT283cEhRU2tkaFZEZjglMkZKTzJUcmhDekMlMkZyRVhCaUFWeWR5amlYN3BQakRvWDJxRjltTEtYRnJtR1g5NHBWdXhjRnFkczJtN2cwY1RZakgxaTFaZDQzQWx3aXNRTTR2eEN0Y1d0QUklMkYlMkJBRVZlTUE2Z1hJeTZIMnN1anBSdDlubmxEVVltSDJ5SlRaMU5LcDhHWTJabWRJbE1ibVRtMzA3NG1SYXYlMkZ5VktTZGRUbGdtSVJuJTJGTSUyQmxNUWowekFPb0lKT1l2cFdEdiUyRk0zWDZuRGZURTFnRGVMOSUyQkZhakM0ZEJ2UkxEV0dud0Z3Q2dnRG50emZlYnBxcVUxSnU5emVkUEdqVWdvM1ozMWV2UkFmZm9HTnlhcnhpcnU2eUE3V2FNNHFsN1prYnclMkI3WE5YaHBXa09NOUpmWFFjb0FtRlVwS3d6czlYb2ZtUnZPcGlkb0ViVGhzdCUyQkpFV3RjdnprYTZZdDFvYWxlT0FlbkNyZ3JMJTJCNE9HWGc3UzN0Ym9sejdJNnhEMjJETlFwVXA3Q0k1OWM0VmxObXRJdSUyQkxuSGdiVlBrRzhxNHVhWWxPTTh4UDRUWUVrSlhBR0dTY3hQRnlvJTJCRmNhVFZjTHUzNnI1RDRYS28wMVBoWFolMkIlMkZPUjdvQWhKamxVd0hyZHNoQ01mZzk0NlN5dnlZZXdsdzNPcHdUamhhWFdJOWlZZGNrYVI5OGFaS0F2WjB0M1ZZRDg3cWRDV0k1MXBnUXAxS2RsbWJoQmZBV0VnOXQzbG45JTJCSzlMajFwdlc5dUp4TktLTVFFTXRaSDVlRnlpRmhoeSUyQlRrWWZialpPaTVmaUU4MVNVYWMlMkZmejdFN2tISjdFUWdzQ0JsRFVaTlU2emd2cnk1dmFrTUVIczduelBIeTAlMkZGV1pEMjI3MU1nNiUyRiUyQmNYcm5TOG1vUzZlZzN0cEFSblJ0N2JTaDlrdTRpUFgzVFNxU1hZTmVvaklCUUhoUjY3WjZHYzE3am5iNVNCcmhaRHR2ZVdYVDZqWDdDTWhNTWJJRWZ3TTR4UDdUZXQ0RTlLJTJGeWV2SDF3ZTl2QnFEdWdydndnM1A1OWlCbGpGQ3RNY1JqNUlBMVZOTXNOMkZaWnFjZ0FDbGg3TU8lMkZGUyUyQnl1WldYNE50dHczYk16VXZBTHpya3ZnMzZ4aW1RYUw1d0h4Z2JRT2h5UVh3NE83T1ZVUGt5cmx3RVZOOFViamJ0eVF3WE9USEo0MDYwNGRvVXdFc0JXVmhLdmhSQzZNWEx6NnRmaEZQdlpqYnlaUndwZXhrRWtkdXpjUFVKcW1OZlolMkZsczZmTEo5NFl6ZkNQZFgwb1N6VkpDMkJlRTl3UHJCRG80NWhLSGJrWjJaRiUyQllneXp6c0F0RmRHS2xtY0RwN0l2TG83d0UlMkJ4VXZpQU0lMkZqaEl0NFZUeU1YWnByY1VxbEZRSW9JR2ZvcWdDRjFzUWVzVGpJSGJ0NzRPck1RbzFlN0pVN3FBUTdOQ1JHMUE3NlVXZmh1a3BaYWxpVkk0cjNybWRNYWc0MVF1OFZBMTNFJTJGJTJGbXdLV21MMmQzJTJCa1ZxOVRCVWt4ODJ6Yk5mM3lyb3h2dnZKNGloa2UyNjVZbmo3NzZRYm9peWgxcHpKdzc4b1dROHBWSnVjWUFrMm1xJTJCYWY0JTJGcFhQdElnRGJTampGQkIxUTB0cmp3b093YlBkdDhPNWNQVDZnOFpBbVV4WmdkT05uelhpRkUzRFBmSmhwaXlsMjVreHFNQXA4enlmNUhtYmNvJTJGWHZORkYwTUpUQU9lMSUyQkFQbEJsQ2gyNkFSVHpYJTJCU0w1NHh2V29lT3dpcWRFc0NjQ1YwcERBYnljWmZvQnV3TzBZSU54U0UwS2NzSE9jOXlBU2dHUXVGU3olMkJNVFMzeXVqNTlYa2llRFhudzh2cVY0dnpYYWpaVDdrdTFkS3BYOXIxVEJWeWdFMSUyQkRaeFlXSTNTd1JPYUdJNFRQVnpWelg0RGlVQTElMkJ2YjVGeDRHV2VIcnZRSHE2a2ttRjZUNEhVbFhEWFJBc3NtQ3lHZjdsOTElMkJDbkk2Qjl1NDRCS05ranNPR2htYWZWd1NaJTJGSlZBWThuTSUyRml0NGFUQ3h0OER5d0FZMVh2cFVhdEMzUG1uRU9WT2dzcjVma1VVd3FBbnIlMkZMVlJMZjYlMkJtcG9sR3RDbG9vNDh4WmV4dVpUZzdBYWVJbEpkbExNJTJGTE90MFE3RzBnTEZ1aEtGQkJYYXBHVFpPZVMxSVZHNVgxSmdsZ1d6MEF6M1owUWJMYlhrdnlqMnloN2ElMkI5Rjd6YzVXcGFXb0FKa3BoJTJGcDdzWW44aSUyQnBXMzR5MnpVb2VxN3dGaGpveGtLTnQzMlNTWXNpalBRRlNrMDQlMkI5M052TEZBdm5HMkEzUXoybHI2MlB3OUl1STlCM0lPTDYyMURTUzc1VmpTTzYyRkJDQmE3UThZTzZwcGsyNUhTSHV4Y2IxYkdBMzVLczNTN2t3NURMc3QwTEZkS3B3bCUyQlJnZUlacnB2SFpQTEExSEFHVUVDSjBtOGhJc25ReVZ0Z25QY1Vja3VQTXZLQU9sOHFjRDh3eVdqSk1MdGpFM1lLWHo4djRRTjV2U2l5cVloNGJnOW5yVnEzQlpGck1YdEVlZTl0enBZajhVQ1BSWkRmRnFDZjJnT0FkV3d2b2xodEZCJTJCbFlJNmZUOHluaW1BTWhheUIlMkZLVHVTTjlHb1hQM3ZUdlREZXk5R0I3RERhSklNWHhmZG5FRCUyQiUyRmZseWVPSE42eXNIWk5mRk44QTUlMkJyNWk3dlRxJTJCajBVWHZZMlNneDNmeGJoUjBiT2Y4bDlNM3gxMFFGV0pVNXkwJTJGMTliMDN0Vzd2d293Mmt6bUx4bVYlMkZwemNpbk96NmlYczl6dHh3TWl5ZzlOMnJyd3lrWDFiODF3WnJOU29IaDdld1VxT2tUVW5IVUd3WHRGUDFaUVBpd1QlMkIlMkJ0T0lYYjBYYkw3RWg3b2o3TiUyRjBRdEdDcUFDcHIlMkZ5UWI0SnBqVmdGVG5YazN4Z1VFRjAzUDhDdFBJdmRzdlg1b1M0eDVNWFdpQnpKYWc1S0JWdiUyQjJVUEtDdDVpNCUyRml2M1I3MXdGWTMlMkJxanVHaWpJNmIxN3o0M05XaGlYb1EzS0FDb21KUDI4TVZ3TTlGZGJJOWNYallWJTJGOFdHS0JQWDZZS2VwZHlBOHQzWnU0VXZCbGozVlg5c09kUzFOOVphVUNCZmJYRFFSSzN3eVFiJTJGd2w3eWpTRiUyRjI3cTY3Qm41ajVKZFVwVzR4U3FyaTJ3Wmtzc3REMnpMS294MTViaDIyM3JxTTFjMGdLRng0dHpMdWI1WUpkYjhpY3lic3VndWh2SmM0WHlZMjRDU1dDRnRCNGVLJTJCZkZhaUVtVXdUQ3l5MU1paVM1bmpzbm5DdnI5QVhhbFRyd0F6SjBDT1g0WDNrTGFWMkFZVlp2WE8lMkJGTjd5bHdTZ2FHajRka0xTaUJqWGw3MyUyQkZhS1dzQTVuS0pVJTJGYUV1SjJZWWElMkZWb1o2WUJEeFl2aThGd1lIbDAzbFhtYjJBMTJKNEJPUkVTc3RJVWgwSGpTNSUyRkMzM2NTdHY0MXdodyUyQkMlMkZyajRsbFlSYzZrUEslMkI4WlRCQWcyVVklMkJuVzdRQU5oY3llbXhsbnFZWmdxQjhlbktiZjhhWndFOHhwZGpHWk1IJTJCNHczVU8wek9EQXdEczBvOWFZNk1iWXFvb0xJcTZtc3N0T2xuc1daS3F4N3pXdURVRHBaJTJCZ3R1UncwckREZGVnSWMwbEdtNGJVdjBHODI2JTJCd2hTWm53WlJ6YkxNT0wycHklMkJjY3NENHRic0JHMzIlMkJic0lGbHdpR1ZwQ0sxdUx1eWhFUDVwVXRNZGg0RzhHNk1zNmgyR21HN0EzJTJGdnRjcTNRMXM2VyUyRkpnQ0RUNmdyRHQxQzVucGhEJTJGZk16TWVKbXZpSVRyNTRudjhPSGxnTW9iU3VzTDNvN25TJTJGZ0s5aFVSNTNEbTEzZHZVb2wwYVJFZkEwODlYNmUyWVY4T2dTTTl1S2xjTE5SRE9SQ0hsZVR0MlNQUTExaU1sb2Q1YXlheVg2elZNejJCazk3ckdqSmdVMWwwQmhMbUdsS3BrUlpLeWhUbzVHZldRQiUyRnBTYWg1TklZZnZ0TE41djNRekZVTDFiR2J3MmZDT0JFV2ZrcURUUlRzUjNvYjR6bGJ1eU1aMlZaNVhKZzdRQWw4Wk5TTVZXWEVEcWI4MkpGNWxjOFljMDFoN1NaVWxwZFFScGpaVmFwek1aJTJCJTJGUWYlMkZ0TlBHem9Yd0ZHRnpMUGslMkJBWmVOV0tPNDRZJTJCTkhwckQlMkJOdEltd2t1NzJ0eTczZ3p6V3JMZUJNMjIxUEM0dyUyQkxGeEVDaSUyRnlSJTJCRTBWRXFHJTJCY20yZ3RURE8lMkZwV0ElMkZSQ3hUV05kZDV4em9HVkpnaTU1T1VZdE5FNkoyRlBXb2RUWGs0djJsbnhWV1p3c29Udk1WRUpzJTJCcWpEUUNHMVc3Y1FTNkp1R0ZSN2tsbWtiNGFGWTV5ZmE0TEVrRGVyRFFPJTJGelU0Y3BWckg1SWR1djBSMlBNN05qeDhQUGJ2NzZqMGNzMHklMkIzWXNKRnVVbTExRkxMbUVhVDdCZmhqNTFINUgxZWxFWEF1RkgzelEyVkNEcFglMkZBRURUJTJCaUhWSiUyRlBkTEZVViUyQmxtaWJwcFVoVW5ZYnFZbWpqRU5OYVpaY2pPWnJkZnVlZU1ER09QZnp0UnNUT0F2R1BpcHJOam1ZV2FjNUh1aDI0UEd2MUwlMkJhZm82TGYwMXJUWWRWWk5WZDlPN25VUnN5UXJ0eVdYcG1SYjA2ODhsRkVHUE5yVzY0ZjgwOU1kYUlYUXVKZm5WczhYYkhnVTlEY3BmRENiajJJcDdTOTZTQkpxNWU1WkglMkJqTnlNbFFPT2wwQWhHelFScVpsTEJZYTVCdzR6cjRWUFdHSmxKcGN3aTRud3RQYks2NlNpbEVDVXdSQTJRRmxRNmEwR0dvWExYNjFXdVlVc3hJUktMd3RGS2YlMkZoJTJGa1RQZUNIa29wM2lybDdqQkhncXBtUEw0azAwdlExRGNtYnF0dFZaYjRNSkhRY0hqMjF5c0wlMkZCNHc0aDNmeG40RXVvdCUyQlBxYkYwbThoTEJEQ1N6bzZlRFd6djN6TTh6VE9pTHhGVGZSZ29lbCUyQjklMkZTNERsUkRlNFFLSG84TEZ5T00lMkIzUVJiZXFMUDRrTm9wdDNiUzV1Yjhod1pZSm92a29PN2pXWWRlVDJFaTcwbmZZc1E2WHdiazlPT2VPa0pNa0RKalFnbHlRYjVuazM0ZE1wVVhNbEcxdERTVzBuVEtkY3hNc0lBYWlDMHZhZXFCUUI5UHU2SEVBSlBERDlqbEFuOXFtZ2hlanJSMjFqd1BkMFdQc1lVTm9POGgzeG9GTWtKTkF5eSUyRnQlMkZ6bndMaGVSOURnTThGTFc1aklFdTRvcFR4c29YMUc0MCUyQnN3ckFaSFJldVA1bVpJSEI1cWNCJTJGSmhkbk1pTlluNzZVWks0Y21zSkVMNjhPRXBrU1NtcE1iMEpUajRrRjN4djNsMENRR0lGWks2dVFJSG5PaFlGcEYlMkZNdW1wUldoYVoyU0lXM3ZmNWVkY2pNdWIyZU1QYUFxdk1LdkVPeDR6Y0NVNkpQenhscVFuOHpGNjNoJTJGV0liT2kwTFNHczklMkZWcmNudnZNenE0MWdxb3lNQWx1elhtOGp1OVJhdU4xVGJBTGJPc1gxdzlVdk8lMkYzYXhTdFpvZ1NyQkl2aGlhdjUlMkZlMWxtSUxCN01iJTJCeSUyQmJoRU5kYzdaSG52NXF0RngwdFFUeiUyRlJrMEJwU3NHS2UyOWJpYkpRSDZyTFFTVDVlRUl3UDdJYlNlNzUzQmx2aDQlMkIwOEhQJTJGUlZYdVRSUXZTRno2QzNNUFFSYTQlMkZNa05HaW95T2k0TkhHQncwYTZYZ1ZiMTR3N3lpJTJCamNFUFFwQVhEaGw3TVhWYnRLRERiSVlWb09QeHZ2UFRKekljJTJGWWo0M0RwOVgyR0Q2eVZWdXBnJTJCVm81ZzJjenJhZUtWUDBTMGZRdEMwejBmVUxNZWlZR0lPRDRncEVmU3YzNVlmOFl0UVA4S200R0xBYlVsa1VLZyUyQmZJRWp0TW9XaTVWNE1OUGlZRk03dWdwOGljcU1BdTlOdzhMQVBkbjY3ZjRwTmgxUUw4SjNJM0RFWE1Wa1hlNTd5WUVVR3J2WEdWNUluVTVhWEJSMzIwQnVGWXU2elZtcFZOcjlLcW5qVDdDdlpFdmIzWEpJJTJCeDNTSFZWelhnWnJOdDRTa1V0MFh1cU5RczhNczdWWmFxYVR1cmI5OGl4R3d5TE94ZVJMYWJxUGlSblVaMUVYU3JJamVvS0VmWGh5R29FbkxFVjFXSERxYTltWG1ZcHh5eHV1NFpDTE9sNmlSV1B0SSUyRmJTODNxMGJRdTFMUEh3WjVpTWM1dSUyQjZuUEs4ZXozaWJYQ1FFQlFUSVBWelFKRnVnVEp5cCUyQllEM2UlMkZCZlQ3dUolMkZ2WVhuNElpaklNYkFsT2gyYkF0JTJCcDFuRGdUb2kwSTYlMkZSUEgyRms5N0o5V2lEZnRWS3VBOUpFNENWN2tsMzh6VTYxcVQ0Ull4ZlRPUyUyQklJS1E5MGQwaFNDUGElMkJsV0owS0toaXlkUk5WQSUyQnF1RjdjZTBHcDVDdmh5N2Y3MCUyQiUyRnZSbjJKVVJEbWRzUHRxUURGNHZ1bmFZenJaTjZISCUyRmN0TW1DNDZVMEw2M3k4Rm5vMmV6STJKZnp0UkVIaDJVJTJGSUZSbFVtanRPTWVua0NFUDFYdHVZbGVaR3lvVWtsS3kweEFRRCUyRnlLaUFJRDZNelZsdzFDUGY5UFhROTNiTVdZN0VGYU16Nzl4ejFYcU1lSlNUblpXNTR4M0I5OVlMUG5IJTJGUk9xVFhqWXFubXY3V3YzWDJTc2RnaXlWWTlNSlZpU2tDckxPb1dmd3dlZGx3bWY3cXZsd3lCc2RtbFFPYUN2Qkgza3kzZVhxa1g3bTNsdVowZXUzN09YTjdxOTZWREMlMkYzZnR5N0kzZURKMnM0JTJCRDEwUGJjVHlZd2pabTVDUFg2TjhjR3BmSTZBR0JBeXI1ZFV6NWFGdGdVaCUyQmJpRVE2cXFGT1dYJTJCcWppMmFxbklwSndvOCUyQiUyRlY1eVNSdlI2WE9iV1czY2s5V1BBT2xQeEhwbkZVZTMlMkJ2TzNydEJLUTBiSzlINjRndnNZcXhSbkFjeDlNeENaNTMzQjJjdlh0SXhYeVgxR3VNZVlHNW56UnAlMkZ0dXZvOFc2eWw4dlFvM3ZKWWpvblE4b0h3Qm1oNWZZZWkzZlB5ciUyRnJTV3Baa0xvZ3Q3VGJBRWJlUCUyRjZpYSUyRmNLNzg4SjFLUyUyRlZpNDN3VUUwQTZQNndBM0RQbFM3aFNUOWhWUzBzaUp5dzdBRThsMkFMeiUyQmtWWXl5dDZkWFNJNlJJM0E5Q3NhTHA0RiUyRldYR3NZdnglMkJyMVhzZlhYUlNWbDdZOFQxZmZzZDVsZXh4TVc5U1NjQiUyRjhKYUUwbFVyUzBmUmYlMkZoJTJCRG1uQ3luNzFHd2FnZGNWTWNHbldmNDFjQ1RZTzVRNEVTM2ZlOUNDdm1vdDB2QyUyRmFsWllsank1QlQlMkZVVFolMkZUaTkyS3NOMEN0aUpWR3A3Z2NVVGZPbEtCVjRrSXhUZFNUWmZxVFg0Wm1vRndPOTB1bHF4RHZpSlJUZSUyRm9zRFNKUWNhRFlxVnNxSk1ucng3T25lODRpU0R1Q0JsUGg0dThwN2VRVmExelE1emRDeTRrQno4OGJLSEJLNnclMkZhb2d0ejU5dDlSRmdDQyUyQjFlZyUyQmg1bW9VdjRkQ2VIbXE5MjNXT21ZaTN0ejFHdmx5RkZVdEJSaSUyQmRaeGRDOTZ1RDVvWTlnRGJYSlU5bW8xSDA0VHNlTzFCTHBtYUVCb0NnN2hqQyUyRmNBS0xuTCUyQlhiRHVueHhnYlFXOXRZMzVsMERUYmJBaktGRjlSRSUyQjF0cFFQWEhZRkc3dkVqcVVoU3FyQnBXWnBFOW1OMHprek11U0hXUDBzYXdEdWxUazh3M09xWkp5aWtZNGg2Qm1nVmFuWXJGdEVuU2h4VGZkN0IwOHFTajVlZWp6aGxWU3VjTFJya2dyMjclMkZNaTZjamlFJTJCMUpMYnB0RmJKRFZ5Y3R2SGFiQ3VvbHFQTDdVazViRlpGRFZFc3U4YVgzdFpaOW02dDZKQnlxV1RPTzk1QXVmQ1ZKczlqU0JrQ0NQV0ZrTEI2eVFSY2hLYjA2alR4aktmTndmTWFPNWthaFVRbHFzRTRTUXY0Mk9wb1F2czBNTyUyQllpaSUyQlBIWCUyRldjcU5KMHNsZUlIU1VDMUYxaHgxa2NNalpXbHVUdG96YXdUM21mTSUyRmg1T1BlQkZ1Nm9udFFxMXV6a28wNzg3YnZxYUI2aSUyRjYxRlVaNHRyMUlraHZuWk50WllYTzk0TE56T0olMkZTTzdsRzJTYXdlUDNWbURueUNwYkNiTGVNUm92JTJCcWNXcyUyQmRkblFCYklEWThIWTQ2Wm1aSU5WZTM1VlZKeDY2S1o4ZGg5MEZuV1o4VFpYYlJneUp4TjdFYW5BZGNSYUdQa0w0MkxlODU1aXAyVlF0bXZTZCUyQkpPU09QR0hVZmgxREtSUmh0RzYzJTJCMjNNQmJmbTVkdWNiZ2RMeERoU3gzb0xSaGJYdE1lNEtidjdOZGgzU0ZWUzE1SjBUZWxaYU16VVVXbXg0d0ZTcE1xWVJLMXhWbFhVZHolMkJWNmJJdnpoOHlDSmxxdWpmMjFPOEJ2b0NTcFdVMXZUV28zeGptc3FPSEw0MUU3U3Q5cXNiSnFta1dJSnk1UlZtM1psdnJoNFFESWwlMkJLN1J4aUowQmVWSmRFckhXdnZ4a3BsSFV1cEJOUFZwYTJMek1aOWo5cWwlMkZsS3Y3R250Y3klMkZEMHZvTkF2d3pKaXJpTVRjMHNVN2lyZ2xiaWZyMGlkUGlrT21Mam9pc3NFdVZwVk54R1lWY1BwM08weHRwcUt1WHBIOWM1VEU1cE51cXJ1enpTYldKZmllbXhwVXlPTnhsMVFTciUyRmtIeURYc0hlTElGb3JneW1WRW0xdG84V0tUV2FPSnBhNHNhSDh0U01PaE96cHI5aGxLJTJCNFZMZnF1U1pZdkkwTlcwdmVrMXNlcE5SbWo2UTJMVHFGMnhreFhzWjQlMkJvTjVUSkpYbTlLTDclMkJ3YXJXYThQYU1va3FRejNpeiUyQjM1MGNObzBFWEt4Sktkd1B3SndEU21lMG9WTnp0clZkNkx4R1JaWmxoalRwWXp3OXo3WkxXMGRwS0s2NjRIeDBRbW52eHNqSThTWmwlMkI1WXpzUzNCN1ZPTjdFRm81aE1pOE1DaU5PbkJra2FRemxHd2FKYUY0OTVEZW0lMkZMeWhiWEgzQmQxNGUyJTJCbjhRSWM4aHVPc0glMkJKNHBQeXg4dXF2allVREFzQUF3alNKUDJNb3glMkY2Q0xndGYlMkJCOTNXd0hUTWdmdHA0Q2IxMkxQOWk0dCUyRkFZZkljMWpIcThkZDl1c09yWlZhb253NnM3S21vNGlXcGZ0OG9wTnZEY0E5Q3FaSE44NVF0cDJBMU90bFpVVFZEZWxWeFlUM3hYQnltZ3p6eUU1ek5ENVhRMHhicExiVm9Sek1zdjRyTUtBcmdlZjc5JTJCR1lRcFZlTWxld0l2cnpWSHE5ZnI3dXFJS2dTV0tmQlJjMXNoRHRldERWUmRhQWVEVFM5V2IlMkZ2ajNRZldFdGZCc05ZMmhXNjg3YmdBQzdlYWhqNXNWS25hVXZnelpUaTJtTVEzQjF1NnM3ZmhNSXZkZHM0cGllcGZWMzklMkIyOW5vVElKMzJFRDNOYkJFSXlVMEhORzU3JTJCYVlZbEZEOUUwWExtejcxV2paOXBoR1IlMkZUUENOSlZMYXBOTkJzRFVYWjRhb3k1eHNtelhCbHcxMDlaT1FFRHJQMXFlWnJMd3ZZajFYamhueXgwa3NTYXAyZDV0WksyaVZLbm52ajZtTGY3UlN6RXQzZjhLNVZlOVFScHAlMkZBbVVabmdZNjRHRVNUa0pYcDFrMmtQRHhJYXgzUWZ2TXdyTGo3JTJGYWk1QlNsJTJGS0tYUXRTaTc0TVVPRG5qRzgzSDJHZVBJOXA5QnNPck5zSUxyTWZSYm01JTJCanZYcTIlMkJNWlM0dTN3VFR0N1NFakJKS0l3ZnU4aGJiT2RkcUlHMnRnUFBJZk96TmRtM0NES3BjeG8lMkY3d29zS3g0bnlsUEp3Q0hsaiUyRmdlNTdVcnB4SEpYRElMVkl6SFFWdUlabzJVTUF5OW5nUFdaUkE3R3JXSSUyQmxIekM3dGMlMkJKdFR6M1ZUSlZiUHBxM2xUZ0ZhbHNxYWluWUhHajBzcHdvUmFIU1JHSjVwa21LdjVGMjZ3Q2c4OGMzRmxpemQydmFMaDIxTktSU1haOSUyRmIlMkYzazdCdGowN3BKUFZyJTJGbWIlMkJPSzlGSVQzYWslMkJkZVI1dEQzS3olMkJJJTJGcyUyRmZualYyMWg3VzZpZ2pLOFdpdEtMUE14R1JiWVQ0NldHOEprJTJCVFhjMFFBZCUyQnc5NXUyc08lMkJaZ1JLQ2daWmlMNmh0V0xseGNaZnN1VlUwUXVLSnRhNnMxOUZSSFUxVEJ2dHE2Zm5yamRrRTUlMkJVcHRlaHl6MzA3MkxZV2pCU1U5QWNZTmY2WGZWVlFNVWNDanBPbUpxc29jb3d4MVJaRm1mMldBQiUyRlJQbndiM1BUUnlycEw2UlNIUDdJVzUlMkJFZlJjbDFkUk81SkxnMDhDRjk1eTdVWmJqZlJGOEI4eVFyWFB4V1lGQnFVaTBlUlN2RHlmeDFwMzZxRjZWbGxFVnJBRkZBYTVmNEU4TXd5YUF3WTFnUCUyRnMwaHAlMkYxQkdBVnZOOXZ6ZlJGN2ZiR092MFpNWENSblpjU2ppb0VUc2l3U1FSRXpaemIzY25UeGZKTU5rM25aWVNZOGJSbzQ4RjRndW9GV1VWbllQSmtkVHEybUpDSnVXejclMkY2dTVYeXBzZlh6OHclMkIwNGlWa2V0blFqT1VoWnc1ViUyQmVINFlRRUkzeU50U1VlMlM2aGFseXNEblc4MksxZGVKb3hUJTJCc0RXSVlCZFRhc2V1YURodGZuczIlMkIzWDVHU1FtbGxsOVAzNmlSZWQ5bDI3bjhITVZYbXhnZiUyRkM4M1FIbXM1VzFSRmF0ciUyRmxDS25DRVFJU3pTZk95VzNqQm9vNWtIR1RrOW55QWJCS0kyZjRiRnc1a0s4VVZheTJxc0YlMkZObFE3T3VkJTJCQUx5NXdRU3FMemJMeiUyRkt3MER0RGVMUU9zbXJlZmh2dGM2QW9NUXluazNqdEZTMGwlMkJYY1clMkJqckFuN2dEUVRCZyUyQndaNWtXTFRGQ2pVOUE0cjJJV1NTcFBKU3pIVTVFJTJGY29Nc0preGdyMiUyRmdwQXM0ajNoRmpYWmtTNDRxblhQOXI3S0xkbUpsYkRNQXVyRk1FcnNzUFl3TDJyYnhkZUYzcWtBQjVIWEhIYzJnWDRZUTMyS0tmMFdxa3B5eU11TSUyQlZlZnh1Y1pCb3hhdjFneGs4NFh0dkowV3h2dm1jNEVsdWM5UlVSY1dFTUxtS0swJTJCMXpSUWRoWHBJdEw0WG5wMWslMkIxWHpsSyUyQjYlMkI2VThWcjhBNHNYczRyaTBDMXlCWEQ5RWVlZTVSUkU3RzJud24xV3g3M0NDZDFqN2tBMiUyRmw3MjZ6VHNiRDF0ZVg3MVJWUlZsaW8xSENuSU9mRFZ6YW5MVjkxUnEwaDU4eXNrOVgxSUdWMFlCQ0xQV2lQem4yYmRVVHhUcEd5ejNMMWoxNFZ4NWZTUyUyRkQ4UXFybDBUcmIzOXhLYzV0c3g3R1hpSVg1bnQ3RDlkaWpYaXRVNHZnVjRxMTVaSDNoTyUyRlg1MjFnd2liJTJCUTk3JTJCb2lDZDloYzdIQnMxRUpUVTBLbjM1MUtSY0xXb0lYeHh3U3d5dXF3cUtDaTlDM2Nrc0ZOTm45WUxhNnVlS21kSzhyMHBqayUyRnAySmtEUnc4amdqUHN1cGhPbEpmem9FVEE1bWJuM2FsYjBxSyUyQiUyQkpPajRHc041cDlxTDI4VWdhJTJCdnMxME55ZTBoSjNiRWxYVjM2NGtKTUw1MllZJTJGOVFKVnFtNDkwJTJGMVE1MSUyRkpCZHdXTmd1TG1uM0c3NUkzejU2YjZFYjFRRVlmMUZYc2RRUlYlMkY1RDkyWm5aR2JpdGF6TVRQSVclMkY4Nmt5c3oydUsxR1FONU5kbTQ3eSUyQlZyckF0Nkh5eFNOM2RBZHYlMkI4WkRSSkE0V0hjUW94eHpaUzlYMEJLWUJpcG9MaE9FODVzSSUyQm0lMkJLbkdqRENXTHphVXVXSDVwakVza0VOSVVCa0FwZk1CODFRZWRBY2xYckg3dm1pSEJZUlpzOE9qalclMkZLMjZiNVVjbkZxQlNpc2lWTXVWYzZIUWxqY2J2OEw4MGM2OGlZRjhuUkxIbnRPMUIlMkJzQUp2emklMkZRWW9kdmF4amw1UjVkSkFmdDdsUTAwdzc4eUI5eEo4aCUyRkJSM3I4SGpSbzRNUzJ2U0hnYnglMkZlSGk1S1RVNHN5OFlnR1BhM1l5SFVrSlJ6dHFnNHhFeGFMUmJOY0ROdDN5eDVmNDlQMFhEQ1hPaWlmUVhaalJOOU5sNU1nM2p1NEVFcXVQeFQ0UkZpUkhYNExyVEklMkZKbWE5cHZPakZhRDNzbW5BOXN5cnRHVVV2Zkhudm9ReVB6eElONjlrYzQ1d0ZIb05Hd0dkWE9SQW9yJTJCejNiQnE1MTFPMGZQemFyJTJGdnZieU9mYmlONGhXaVpUM3YxRmVrZDYlMkJMaDM3Q1MwNjJaT3lac1JSM1V0aUVkN3ZBQVRQR2xDRGhMeVliR2lDSzJpSkVaT21hNVd1dmo2MzUyUXVzUTFzdzd4ekJlM0hKYnVtTm5XZ3hFZUtSd2FhNDdtOFFlcW5ONkxwWlkwVlV0ZWlZRjdhYWJaSVRFck9pVTJnRmZUT1BFSDZ1M3ZGVUs0dnN3a3RzNFZONEd4NWg3ZWVuRjRhelAwWHRFeWhPRDc2S2RFdTc4Vk1lSDl6TjclMkJ4U3FxdGpCZjVIZXpmJTJGYjNlUFA0aVl3RiUyRnp6ayUyRnU2cVplMSUyQmRVeU1RJTJCc2dia1h0eWE3UlNWMDZOQ0xJajI3enFzckx5UDhnWU1hZzZOYm1zaXZtdEl4Mlc2WkxrJTJGNXU4cmpZZGhKdUkzJTJCdms0M0RiS2ZOVm4wQXNiTVNsN1JzTUM4b2txcE9GS1ZIVDh5RTVzTVV0VnAyMXQ5ZUhlVyUyQmJtJTJGODdZQ01kOCUyRkFtMlkyeEF4ZHQwNWFJamdIJTJCOXdjV1hTTUc2bUlrYVRacG05Z2pLYmdoT3RmRW5VMElHaUpWWm1BbXBWd0VielZiZlolMkZxVWFhUGM3VkJwTkRTOHlFbCUyQmhRRE5EM2xjdzhTM2VYRDVEejRLM0dxaUprT0Z3SFVLaTBOJTJCbSUyQmNFTks1WENab040VVczWHNvJTJCaVRncjNqVGVmTG5oZ3ZZVUtETmNrNUg5ayUyRjByZGpiNjljTURxbTVhZ1U5dmpnMGNxWmx3TzczVUV5OEkwZEl4cTdxczJQM0d0SHkybjlDemdibllva1pTank1OGxQMVJzZUQwcXpsJTJGTkh0aWd3RmVYU3Q5ZkxuJTJCbyUyQmJBWkJmam8wWnMlMkJLN1RBWmRlNTBOeGlYJTJCZkIlMkIzdTBHaFl1RTRLVWRwT0VmWGxPcUlJNGxNMW1TcklZZ01adWFPNXY3cFhoRmZXaWw4d0doelVtUEhYSFROUUpwdlJDUFMxWDVxVTclMkZGZUVJSUpxbiUyRk5XWHVpczBwUFElMkJkV3RWc214dmZjRjhqVVRXWjlMSnElMkI2bWMyUyUyQnBXY0ZVQSUyRlh4cEhYZTF3NDZ2TWdzeVdiaHNJVWklMkZ5RWhJMGlnTVhMMEhoNjA3M0tXNThhUVliYiUyRlpVREViNklXZGxHUGVRWVdUYkdwRGlmNUxDJTJCRHhzRyUyQnIzMUJlTSUyRjFOdzVlWmRlZElXVkk0bHRUUCUyQnhzbkZxUzZ3QnBYS2NqMmN2JTJCMnJZdWdNZVlwYno3SWhWU3I1JTJGdnN0N1JnNlp2JTJGS2FzdUw0dXowJTJCMEFHbFZaJTJGR2UyVG5vZ0ZMbkZ5RGFvMks1UXFMb1ZwR3hSS2N3VTFNNUUwZHNJQ1JtSXNaUkUzMVI2ckRmT2dCS2dUTTElMkZZeDQ4Q2kxeVc5YjNFZmJpQnJodnE2cDNXRFdhdnF4QzhiSDAwNk5mYldkbjZFZ2hXc1FqbGNTcGRCJTJGZE5MdTZDQmVSVjdMaVZVamh0aiUyQllSVWplaXNkUzdsZFRkTVJkNzhrSkt4MjRWbXFHVHZzQzJxaERkUWdSMkhGb09yMWpyeiUyQkl2U2ZCOWNSdkZUciUyQkx5RVZPaWtuTWRFTzZEc25iMiUyRkkwUkRZdDhDUWpLamNKaW1KbkN5VHh5NnVVUiUyQm04JTJCQ3klMkJwRWxMS2N3UEt2M3EwOVAlMkJrcVVQTGw1cktUJTJCTFVMdmdETGNMaCUyRjFWMVBxVXVVbzZ0JTJCYUVaMEo1VWd4VTBIc3hUdklVRkwwZUZrJTJCZ1lJSm55SDFWNUg1ZDdka2R5UHNvM0Y3RlBFMnhGTFQlMkJpazZRNFNCTGZnTUNSSUNtdjI1cmxmMGo0eFF3MkxYUVVDZzBRbzd2c2RoVmlUZm0lMkI0QXkxbjIzN3lJT3hGOTZmOHNJayUyQnhDWHN1a1R1NHhPbXMwS2ZNU3FtVyUyRm5yNXhBRWFJbWdIWiUyQmprNmtYOUpkejNEckYlMkJicks4ODVwTlZZcm80U0ZidSUyRlVwcCUyQk4zMHNKZW9XdjF1VERMNXJZdDBmalYyVk5LYnJNamU3YlNvMm1MeHQzVFdFJTJGUkt5UGh4SyUyRklnbXJXalpjcU5ud2o0Q2E0ZG9kbG9IT2RDa1hUeVZMVXROaWVERHFjdExRblBXY0lNRG5XbURqcTdqdG1yaiUyQnElMkZUbk5SU3dzR3klMkJxMlYlMkZmTSUyQnYwVHdRcnI4JTJGOEFCV0Z1N0l0RlFCRkpmaU9wZmhZT1NMbllvZHVqUCUyRkc0V2ZpWFVVT0RIc3lndWpiT3NMa3lWSWd3aExvNWNNOEIlMkYwT3FBY0FXSUJzTDJYWFJHdnYzeGhPOTlpREk1NmtZencxNnZicmd1bjUwcUNXQ2olMkZzVWpCVzVMZ2I3eTVZRDVBSyUyQm1TbThhcTMwWSUyRlBtbGFsZnN6dHZkJTJCaDZRYSUyQiUyRmxZZjN4VmglMkI5b2EyZzlEZUl0Sk9IczhXT2Y1QVRBTEtXQTZPa0pUcmZtRTljJTJCcSUyRmNvJTJGMmNxZXJRb1FqTDc5TnJYU0clMkY3WGpyZmVxQndIZFZZcVVZYkZOQXMlMkZrSGtoaGpuWUVJQ1N5emlIMERaJTJCSDFGZEM2S2VIMElKSDMlMkZuUlNoNUZLcUZ4TlN1YU5pWTF3VDVsYnluZzRiVkc1ZEVCUzB4WUElMkJmSndob05SdG5hOHM3MGhPOWxZWHp3RjUxdG84VCUyQlY4ZnpSV1MyeCUyQlN5R3dWYTV4SmhLVHhVZWZGeE04UUVQMU5UQ2EyRlEzaDYwWHolMkJycjVweXE3RTY0dzZPTzNkNkpkJTJGeGVpOGZRJTJGSGQzRDFxJTJGcEVkZXpYdzE2T0g3c3J4TEd2cXZuVFhWdW1mZDYzeDltSENycnVZMXBHeCUyQmxUTFNTczBjcVk5MTBJa1BFVGNaMjhMdHdMYVZBYVpGdTBzTjBCamZSTUJQVTZ5VSUyQnh3UGZLejFMMDhpSmliaDJqY2dDbzRzY0pZa1dEJTJCM2doS1JQUTdyVzdIUmU4WHJYZmswempsNlVSdURKRGVSaFNSN3Z3S0FpY2lxNzhoU2xMR2VidldpeERDeW9Ob2JrSCUyQkkxeUhSZHpwZCUyQnhkUGdtRUIzaTRLYUExcVVYS1YzMTVzZ0dRaFFTbCUyQjNyYlZBWGUzVTdjNUUlMkJ1NW93d290ZlJvZWllbTdjS2ZjZWVDcHF4TGtmNWxYVHFPc2hXdFdHMXMxc21GZUFTQzlSZGolMkJ3MSUyRk1MbXoxNmZtMzFPNUdBT2xqNjQlMkY1M05ENVFJMU81JTJCakJVdG9DZkZuU3lUbHp4Q1ZuJTJGWml2VTdjZExtcDhncm9NYkc2OThOY1hwWGdSYUxnOThSb0hQJTJGSjU2cXBqbFpWbVNrJTJCMlhEanNkNWlxY2s3dlJFcFNIU2NkM3ZMJTJCM0d5dm9NJTJGcXJGdmpxTm5BU1UwanklMkZWZyUyQlRKVFdqbk53R0NHOGg0c3ZXSDclMkZDZWlqTENUMWIlMkZSNGM3QWx2cmhWMUVTYWYlMkZBcGJ6STJjVzlTZkRmcGpCNVdQY2MwJTJCdVRKRTV6bmZBemM4cHIyZjFtNmlpMjdsUmo0UzRacldwcVoyVHN6TSUyRnZybnoxNWk1eE1jbVk4MTkwdHFVcXFsbWlnOTBRNVdrMkptQVdFJTJGSFhOelE0cXVTWWhVT0ZnVnUxUW5UNlRPcUtpWjNLbW9wUUxyN2RTeFpzaDRLRmJaZGFOMlpPWmJ1NTE5YngyZnJEYlhrN08xQ1clMkYlMkZoT1NVRjVyT3VPZFJnOEFGSDdTOHZldjdjV0xodzFFSHF3b09NWlZ3ZHZyVGc5ZVA3dzdEV2w2TiUyQkdId2NlckFJejluMHZTTmRXR2ZhQVFFb0gwNG1NWklLdGVQNlkybGZxZGhZSFhaSVolMkYzZXk2TGpiVlM1MEFxQm9lMFprVHFQVG5WUk1lUFNpSGg1dWVyOGd5TnpHRXdSJTJCJTJGdSUyRjJsclZHaUk2eldidkkzc0l4SjZnJTJGZjVVQXEyU3FiSDUlMkI2V3ZHYnBLVlB0NFN3YkNVYXJMU1RkN2lBZVBDR1hLcXFiUExpbzJhc1Y5WVduNHhtJTJGMiUyRllJQXQ4Q2xNM1RneTNla2JmZHViJTJCRDJ6dTFrdnVPVkZ3V3BTenpTUWpNMmEycXlVVkpTVXFmJTJGTGYwRkZNNkVTJTJGSUIlMkY5Q1RiREM2SVliZnI3MVJ5cWhORDlxbVh3MDNsc05xdyUyRllJRjl1TDhPWXJhdWxiU2ZNeVYlMkJ1Rk16VkQlMkZDeTBVeDlOc1hiYlJzY3BTTUtzcHZyUHp4RnB5cHNnMlVOWiUyRkklMkJQTkElMkZoNmdldGRXSlNDc2tWdThUUE82ZCUyRnVnaFZCT1loUDNmRXEwMXNKSSUyQkJmUWZscEx2eW8xMzBLaDZQYnhOS2MlMkJRWjlLbyUyQmVOQW1TYnNwMGMyVW1ja04wYmE1MXI2T0dCZGNsMnpVOTdERmx4VjFYZmdzeHlialRuNTFsZUk3ZVMybzdtJTJGWE5hcHBIYzByaHA4T3NyTml3cTVHQ2w4TEQ0Z2M4bGJYNnFqeSUyQnpSV1FXYmZicTlDUE1TUkQzZHpPT0t3bTBQZmtkdExhJTJGaCUyRjNRUUZDVEJ5OHVrJTJGZTAlMkJlQnBxdVpyUENNYXdrT3RTMVpoUTR2U0JROUZTMVR0R1puT25VZzZub2VjRzk5SWUlMkJyWElTbHZZblExMTVibiUyQm9hS2xqTmQ4UzV6TXlLcjBJWXNZaGFPYWQlMkJaZWtZMlE4WWlESCUyQjNJbzZVSXdNbUd5Z2h3S2lMSVBrVlpPYlpKMThaQ2ZRRDJ4d210MnhLZlZpQWU0JTJCeU9jYkEzMUNveWNuaTZpN1BDWThhU2wxdGM2dnBhTmpuTFlwOHNoUG9GT3FlRkVLT0dtaFA2WmFnSlNpaWk3SEpoJTJGJTJGdXFLZm5JN3N5VFhvbVBOYmN1NTYzcG1XVVcwT295N3Npa1ZFdkk4OSUyRmI3eWdxVEYlMkZ3UThDMmUzT3F1OWVWV0JHQ2dySWhpb1d1c3hPVGhUV2ZmSUJ6UnhGaFIxeWslMkZzOFlLNm5CM1pRdjNCY24lMkZkQWVmUVBRZkJBOHgwQ2NDOSUyRkNVZE1kSlJpdlZzZjVEbXVGRnV0UG90bVU3NmYzY1FITzlJd04zajZPVyUyQkVueUc4JTJCZmhWekhYT2VOejhuSjIlMkZibiUyQjBzOEFHVVFHUVlKSEE4N0dIRWhQQnlmdnVnJTJGMXlMRk00ZzFpMFZUM1RwcUpsNXJsJTJGdkpUaVkxVEVJR0g1cjVXQSUyRlg2JTJGZkN5d1dlbEdkQ1gzeDE5VnVtWFI4aHRHaDZUYmJ2UG5yYWxHU3d1UFZHOEx2T1JrSVZKakx5RkwzTWdoVXZhJTJGJTJGTDc1NSUyRkZGdk9yYzB1dlJhNFZBJTJGQ3NMT0JmVFZQWHJOMzVKNUpiZEhHJTJGU0ZEOW1VU2c0R25ZQ0tWeTk2aFhNdEdSaE5XaW5UZVZxdjlNSzJTY283dmhsNnRiaFhobGZlUzJxbXBLOW1sc2JudTlJNkVQayUyRnNaMm1ZTU8lMkJUUng4OEpsUlAlMkJkaWxLdXZPaEtkaiUyRkpvRExkUlpmOWs0SGFiT1VmbSUyQmFWZ29peWphNTZXNHUlMkJhMFlMT3Vza3RIQ3c0YnJqWlVyWTJ1M3czb3NzYVJvZEdMeFNUajhOelNRN0NzbTRIOG1TTkJEJTJCTkRzdEMlMkZqbXklMkJPMVRWVmowM1kzM09FcDhRTiUyRjNNemg3WlppN0pXUGo0cVJseTBxMDhSS1hMTGo1b0NrZiUyQm5XM3lCeVR6RWg5b05UNDEwWmRydkYlMkZhNFlnT25YcDFzdTJsbWxhOURPUEpHNFY5cUlXdkl6cFFQYnhoTFpPSW1pJTJGd1l6bkNYZnhIUzJIOCUyRlFzaWZUVU4zSjM1bTVRYkQ2OVhha2d1QnZ1Snd5OHlEamlSbUYlMkZoVGRiQ3FaNTJuR1FXcHVLTjFpeDU4UnRmSUMlMkJ1dGt5Z05kS3NZMHlsajR0cEZ1TGl2c1I1eXB0R3ZISHBRam9JVDh3cTJGRUMlMkIlMkZwbEV1bVVlNGJuTnIydDFjcDIxVDNFUU50MWI1RGxCaEVCM3JFTDBnWElHM0oxTzhwZTA0WDlqM2xCb2Jsb0x5ZyUyQmE5dW11VWxxaVY4M3A0SnlaNHk1SUlyOUVSa3pVJTJCJTJCQnNKdHRlS2pBRUglMkZqcjNaTEFMcENsNWN5NDNZZ3RqQmZka2xkcnlVSkFGbU1vTiUyQkxEeTZpYkFxRXpCNiUyRk1OYSUyRkNaYVY3WGZ1WTFrVWNrUm11JTJGN2FGYVk4SDAlMkJlOHJjaGNxZnNQdlViT3ZrNG9HJTJCOU5LMEdJSTNLSTZVV29UVE9DRE5BTmslMkZqelo3MyUyQlphMFAlMkJURXFTNWkxYnFxT3ZyZWdPR244ejNreGglMkJqTGR5ZWZvWlZQak5KSlROcm95TUkyQ0k0UlAxeVpUUjEzJTJGMSUyRmFQa2l4JTJCdVBrSDZBcTlGZGRCZ3NSc05PVVdvJTJGc1lsTDNwbTRuNU1yQWR0aTlVTGRUVm5YN3pvamxXYW5IWUlnTHZQd0lwTDdsZ2RTTEE1YjFUN3JpWUw5MldXVkw2amM4WEtmWVRVYXFBN3dqZkd4OEE5djZQSVJWYSUyQmtLWTclMkY1c3oxTEJvd2d1ZjBybTd6MkY4aXE3bnhtWnowU05hJTJGWGJXbHBBcUxRNW1KcG11aktRJTJCVEpvNjBzaER5JTJCbEpHWVc0VWNpbTVsR29nSzFtaVVLYjRWakNhR2tYOHU0NkNWd25ONnYwbCUyRjZnbmpQWElkSkE1c1A3QUl2VEwxZmdHYVZqTmhiRXBtJTJCZEZibXhjazhmJTJCOWV2TiUyRlElMkZsaUpQRDc5UzZlaHpSWEh6RndLRGp1dnZHbSUyQjZKYlhndGtDcUJ1N0ZKZ2RNTDZOR0FWZE5SdXZyNU4wWlVRSWVpb0doJTJCRmI0ZmNaQzVsVTdrbDM5VkI3OW5qN095Z05qZm12QmRxdk1yTDVoZkJxYmxNd1B3cEJWJTJCJTJCZjUzMzN3VnFPenhnUVVyV2w1OW1yZ3RkaFBaMiUyRnkzQmhZUUE1WDRZQzV2WGdjZWhDR2FIUkpxWFpKWjlKOGxjUU5ybm9tbUdJTlZ0aUslMkI5TlRrS0p1aSUyRlY5Ykw1M2xJdjN6YWNvJTJCTTNTaFNpbzA3MzFkcG5mZDhnUG5tZm15cEdKNVludlZIb3FIRmlabyUyRmpYeTdkdGdLR3IxdmtiOHFjSE5nbDY1RE5nSENwMmQ2NnRFYU9sJTJCR29zc21UNENlSWZEdFJPYXV5UGJyYU14blpoRnRmQlVhMlN0Mm1OZTRLZVg3MlNaMnAlMkJ2T0ZMNVlhM0FORUUxSkl2ekhUOSUyRnpyc3hLN3BCekMlMkJrR3d1Mm84NEMlMkZsYUlaazdRT1dqSWlWQlNRN21KJTJGMXA0JTJCODRac056UXJmRVRXUlZBUDhaQkx4bUtFdiUyQnVCR1pBcG9JS2R5RDNYMklZbDhaVHR0UU5DNU1tdWYza2ozTmVUbE54N1hnaXZDT2xZdnpHdXBlWENPU3lDM1lPaiUyRnBmaFRMNE5UMWplajBpSDE5c3hCJTJGcnZzQzkweTN6STFDUm8lMkY5OTVCelh4aVVqYWJ3aHNMdGk4NHVJRWJDRDJuUjY1WW1LNVEyOHpOcmg5bmpXdUxxRTkyRmZqUUNnJTJGd1NjZlFiaXhiVlJ1cVZFbXIzJTJGYmxNJTJGMHlkOXZxQjVhdHJVV01QbTR3Q1lqUGNRS0VVY0dwS3duS09Cb3FOT3JzRENKWlc2MkJRSnBlVk5KbFVwOFI5UlJra2JZMXF5WW4lMkJsZ0ZrVkUlMkZmVVFvSnglMkZkJTJCVG5KalJSb3JEJTJCdDd5SFprSGwlMkY0MUJ3WndicDJZQXBQcm5hUVRxTTJsRExRT0FWSFVEVjh6bUc2cnlFWUJVJTJGWVU2N3lyNzFwVmdud0ZJRzBTOGlYWTBGQkVQejBZRTE2dkNHUWpSWiUyQkF6cWlQNmlzNjdRN0dFUE9neCUyRmhuRlpEMllEdjZaUFhGcWtVdVNBVWM0STRaJTJCN21ySTMyd2g0VlpSTWw3YWk5NjN3JTJGbVFVYzB0JTJCM3RMTDRLJTJGN1pOdW9waFJlazZMWjE0TVBIVkFVZUxiWVIlMkJlakttbVdKbUtheFZSdTl1WU1NMnVsOXZWSTBOOHdVaHRhMWIxSGw3dmhqMHF2dnBvV2Yxa0JTN1YlMkZhaVg3bUwlMkY4bFdYVlg1eTNLVGJKQUxod1E4Ym9KWU1DSnpscFhIMW44YTlabVZRODNQN2JFRGUwUHpKdmg4dUlzRTJLN1JpRCUyQmlGOXpwVWdRWGhxUmdhdE5uOFQzJTJGaEZyVTJ5VDJ0VHElMkJLdll3NWhHNzVOVFhUMmRhNnFFb0NaNUQwS1d4b3Q2aDUxTzJVOGVwd0JISlpwZGJrZyUyQlJkemlsekNTQzhsZGN0SXVjSlYlMkZ1dnQ4OG11WG55MEFocmw1alN3YmFWbms2clF2S0NYaDNZTk55enlaWWxxdlNXJTJGTGpaRTAzM0N4NXFTRUdwa3IlMkZneEwwZHhWQnNzRG9Wc2Z0JTJGdGozbUVYVUh0ZiUyQkZmejdaZEg2YVhEV3doTXpGclV4RDFBbnJDJTJCdUxvbjY3aHNmYkpGcGZ4SlZyWU1kdVhFYXpETHRqVU5qa21obm1zJTJCR2xFaG5HV0JWOG12ODR0YWZ5JTJCTUZ0YyUyRnJtYndSajVzYUxjUmRxYjhXbTF0Mk5CcTQlMkZ5c0hvRFNSd2Fzd2NxTDBiSkU4d2d3eXM1MjRoME9PakwlMkZ6bFBIYmJ2bjluJTJGdXdtRzBCRzZpWkhidXAweFpBN3p3ZlpOODFHeGsxTVVSNWpMa3VRQ1ZTJTJCcjZtWGVVekQxc090T2JDbTU2VjlhNEFBc3lFZmlHNmcwZnI1eHozVGFHdVQ1SkI1bG0lMkY2Um1DMHg3Z0J5ZWZUMUN0aFdrRWt4U3Y5YzJRM3lQeG5MQ0pPUFlodktSTVg2RWklMkZ3eG82aUROSkVvRjRUeGFYeW5aVSUyRmclMkZNRTVxanlPRXdxb2xFSjBvQkpIQ0Z1SGY1dGtKSnV2b0prWURIekxHc2pOOCUyRjEwdXZ3clhoNUdJRnVZVEFYaXZWM2ZWazBrNW9QRXVsNjUyc1JMWDROMWxuT25jRk8zJTJCMTRhZWV5RmxWM01KbiUyQkNsTXZQYUhxbTBHSUJKUG0xVGNacHN1dlllU1c5QzB3Ymt6anhZdURRYm1sckxSNyUyRml5OEdGbzlhVWhLSSUyRlZra1dsRkwxYW9NeFNBUTZKbSUyQjNuekNmQmF1bHdIRmgwRG1oS0FGdkdWYm1GblVmSFV4T3BkVXA2JTJCcTZkdUZKbmVwcXVIc1pQWFQ5aktpeXk4Y0lOekg3d1RmODFoWEo1ODBRMEJLYjd4QW4ydVgwWDNyOFhCdHV4STBLQ1IwZkFocGtIbVBwa0tINXJjdDUlMkZ5cjZZZU9jQktMSDVkRzF0NmJ1anV1VWpORmpUN21Qamkzemw2TFNoU0d2YiUyRmFvckFjV2RvV3UxZ0NmdzAxOXMyckpNbkY3dHBtdDFDMEVMVzgwZDhHR0k3NVNzaUtqck5rVm5yaXZzWCUyQlMyU3Nobm9JVmtqWWZSMlVpVWRWOEo0N2FOV0pXYXhSS1g0Z2IlMkZMeko5eU5uUmVLQlpCem0lMkZLdXFSaHJ6ZWtkeGFWVmllbCUyQmp0UjEwczMyamJZQyUyQkp2JTJGbWFGSmZKSGhISEc3ZTFqQkdTMFUyQ0FYYjVrVUxHNjYwbW9yNTloOGxEeEVOQkdtdzZnMkRKeFE3YmVCdWp1VW5QNXZIc0kxSlpiWEplNnJxVDdjVm5ZbzlmMGxtaUZEUlBWcTJ0Z2ZEN3MyakclMkJNMEY0Nks2TFl3Tk16QmQ1Y2xPZlA1NUNUTCUyQmlzeHJrZ1NmTUlkRiUyQlU3QlhIeWlKNHpBVTlmV011Rm0lMkJFZ3psRUVyM1pwZFpGMVhsWDIlMkI2UmlOR2RMWW5YU3NIY09kQiUyQnQwUUtJOFVIJTJCdU5OQ0l4JTJCRHJ6bCUyRjNDQk4lMkJ2ekU5NTBwR0RNWmNBNmIxNG53R3k2Q3Z1QUMzWk82ZnhVcmdQOFhBY3VHcERMTFA2UEZEbktRbmUza3AlMkIxSmRoZTJ3Tkd1Q29xY0J0ZGxlS2Y4Q2dxUGc0bTFFM1ZEdlhmR3JEYmtLVEZCdE5hT29XT0hZdlpySTZGekdaNXNGdHM5dnFya2lycHhodWNMQVdEOEQ0enVSMjglMkJ2a0s1N0xzdzA0S2J1ZWtpeWluZ2FaTkQ1REg4bmN2QTZqakQ3NlIlMkJmSUxrREwydFBYQlZKc0VIOURUTWtKM3hVU1UlMkJrVjdhc25SRkVPbGN3NVpLczlpc0g5SzclMkJWbFBmSGlTYmJ4Q0ROMWVKNVg1bXFzaU5Rc0ozTiUyRnNhdjNCQ3ZLVGFNa29ic21aTVdTZSUyQlpSdEIyVDZzWXNxVE9wbVhSZ3F0WWJ6dnpHZVVXJTJCU0lZS1Z1bWE5Nm0yVTVDTWclMkJXUEF4V28lMkJHaEtId2RsUW1ielpYQTdaaWpSMjlmcFJqOVpieFY0aGpJUnhyNGpqQ1hLU0tqdnliUiUyQk9ZY3BmSSUyQndOZiUyRlV1dklSdjNLY1ZXejl5SHB0QmxtM3I1UXF6Q2x2eGYxT2pFQ3M2JTJCQ1o5dWlCM3EwdHhydnppOVR4amtJc2ZCQlE5TThUV0dmZm9KY1d1a0xwejl5VW9la0tPVzJkWmZSNlJZc2pCenZYc2VjM0dSNzNBYWlaVmw4eTRHUmxBYkZYVjY2VCUyRjNzZVFodVlPSEQzSzIzJTJGZE43NGNCb0xNa3pJUloydyUyRjgzNWdhM0N4SEwyMnp3NDE0MFFtazdTYVJ0ZyUyRjFBVlEzSFYxZDB0REZyb29mamdqbCUyRiUyRnFPblJCSGNTVEdGbWRMdFpwTXFGcmplWG5nbEpxRFlTVDdWR1ltWm5xVmszakxlWVk5RXlRempJd1pKdFl0YXFyQzhqU005bGp1QXJRVkJkcCUyRm9PSXNQT01vNHFTJTJCMUc1JTJCUllzSVUzRUZLb01TV202dXdaJTJCY2Zkbzl1SE5rJTJGdXJPcGdnR2puc2xFeXolMkYlMkZWQVpRVktyVUtENnNIbGVuNXNEUVVtckVpeXElMkZPaG5FayUyRlQ1Umo2TnUlMkIlMkZDRDJDaFZiQnZKM0xaOWtEUVFPS0ZiaG5BeXlUcXA1bkFyOVR2UXR4U296MXNodUdpZnQ5RmxlUmZ6TGNvRnVnSDNZOEZoRlgxTGxMckFITTBmdm9jVTMlMkY5bkZ1JTJCa1E0RmF2cGhrSDVZJTJCUEpIQ0FaV0ZmJTJGRU9LYUVkSFk0a29CZXFYMTRnaXpvJTJCbCUyRldhcktiQUxWdkZaZUdXMEJkWUlwJTJGSDFUNGZCZ0F0R3Fyd0tZejhGJTJCZ2puTXNITG9QcG11bUVJazR5eDZETHlCZElMR0J0ZUxJcUV4UCUyRlp5RyUyQlBSTmQ5eUNINmlJSEo5c1dZVmZXWkd6U20ySEpjZ3hhYWVsJTJCRzdVdHVZUWl0RGlPNVo5R1I5b3haJTJGR0JPZXpYWmtXMU42cSUyRlRobWFmRiUyRjdIcmlrMjRJYTFUOSUyRm5xQ1VZQkxWdVl2ZWo1cHlCUFBWakVuJTJGU2dsRnh2SXhxOFZzN2slMkZMUE9NTk8xamJkN3oxNUowYUhmWXdScjdsT2lTaTBRcEY4ZnFNeGJLVVU3WHlWaVZiMzluV1EwTHdWV0dVREFJMmJRJTJCR1pPWlpsdlI5RzFEREpzWUtudjdCTWhxQjQxJTJGJTJGSE5KQW95dUFQR3ZEOUx4S1plSXdpU3glMkIwV2xHUHBJcmZzTmN1Sk9xQ0E4dkhtUEQwTjhFVTA0VnFsMGowVlJSOHNpNlFrbm5vMThlUmtPUWxaR2Mzdzg1OW5uZFJpJTJCYXpFRiUyQmd3UUZnRXFvZ2tRWEh2enMlMkZseGdYQzI5ak50T0NrN2JwZkpKdUFtSElxemp2eGY1SmtTTlQzdWlHTDU3OGVzM05rMTZQNDNoYzJYN295NE1ua08wMWFUaXVsVEZEYkx2ZG5ZNE56WmNWMXU4YnElMkJwZGI3SEJJN3M3cSUyQnJtTkVCJTJGT2hzZ0ZuOXVJNjVUaURFV2haejlOVDZuZDdwQml6c1RaTEwlMkZxZWdjUWxQJTJCRUMwY1VBMiUyRkpDZGM2JTJGREdEaHdrSUd4MUt4V0N0WUJvM0NKOUx5RmtWM2RwTm40SXgyQkoxN1hBckp0V1clMkZnT0xLSHo0elR6SDAlMkZCakZGTlAwUmxIYXN6U0wlMkZUc3kyekVtWCUyRkRURlFnOGY4eGFUSllyOGVOaHFjcHppZ3Y3OVVCaFdKalp5NVpLZHlnUzJybUo3dEdVJTJGUVVWNzB1cDlrZTlydzZlZmJHWGRnR2ppTyUyQkVBTG5OTWQ1TFVyOTZnTXZuS0N3WUUxMkQ5eng2VElOVzklMkJlczVNWHpnSUVkRERvbDE4cVFtRjFaTnpLV3lieHpFeXp1JTJGTUN3N1NkT0lvRmI5WkJ2RWVSdTFpb1JYaXQzcGxxclg4d3NMcEFISG5SYm1jJTJGJTJCZXhSajcydVJ6cnBVbERFJTJGdjZuWDBURUw5N29xTVZSQTVzVGpYWnJDUFFmcXRlVTJ0RkhocndoTXR1SGFFUzg2czNhSkVYc2NNNFZwSk1Pb1F1JTJCJTJGcW5FcjBWM0xsSFhPTHZFJTJCb0U3Yk5vJTJGWHBUUmIzNWJESXVTOTlaJTJGJTJGQjhPTHRqamxrZGU1U2ZuT3IzWWxWQ2klMkJ0bXFOU0wxTE0xNWdGelo4eTlwRHYzdkhaOFgybjc0empRako4elJUV3c3ZzRwN2c3aWVFQkFrJTJGZ0lLMnJ1TmNQJTJGdTRaTyUyQmF1QWk0dFdVMEVGcGdSYUp2M2JVSjRMJTJCYzVhM1RlRlBaSXlYUk9paGlYSjBmOEljMXVpblRTUVklMkZjc2RPZ21JamlmdmwlMkJTWVVINkQ5WW84Z3pmNHVhSDBWMWU1SEkzbmFqSDlka0FDJTJCa3pRTXMyMXFyYzFISWJDTlFEcCUyRlhHYjZLbSUyQlclMkJBYUZhYmNRUHNHOCUyRjZheCUyRjlMUVJLUkpMTEFPbnRxYjNFdWNJUXV3SEowWjUwRDN1VHNibSUyRnVLQlI0ZWo1b2tIR1VaSXclMkI2TmhrZ1c5YSUyRmZsR3ZpM3QlMkJETXNhM0lNaFRsYjFVMHBuMm9SalQ4elZiUzBqcEx2S3UxcEsxYXp5N3VheDh3V3Y0ZnJ1aGxXcFdiJTJCMW5QSE8xSUF2b055SERCaTMlMkZIY05kRE4lMkJ6QlVISTIlMkI4MGJPUEV4SWs3YjUlMkZlQTFJaEtYVnBRMTRUVDF5ZjJ5VUJiJTJGWXpzVmJSR1pJOVltcUxaVFolMkJ0U1NidTVNTDE1dDd0WnBnZVMzc3loeDIyYXUwdE4zS2FZTnJ1eUV5OVpPRmphd0x3VGo0SG1lZHlBQ0ZabWxDWFNhdkNJNHBCd1Vma3IzWTdxMlRaSWpZYk9xdnhxaXRMJTJCNHZBUmlqcCUyQkg0N3lBWnhzUm5RJTJGSEY1aFdMZFhFdlNQTyUyRlMlMkZ5bDh6eDlBNHVZbUtDeENmOWl2cm1FVG1pUTJ4dzdCMGQ1aGFtajNJdGV2RGc4bW1FT1BpbWVhRmZ5SmVqTlBVOW9laUlDbDBqakdEQXJ6OEhMNHhQdkdOTXI3VXU5VGZadEpPSjloYXIzcFdzcmxCYktYSFBqUEh4N3pxQmdPZUZaVFpzeWNMaVFzeVRCMXU2VFElMkY5MUxmblg1ZmVQeWpvdFBKWEZQRUdId1lBZ0o4b2FQeWlTN0k2dm5yQWJJJTJCUWZjc1B6RGlaanJYYko5SXNKQ1d5YUhVTmk5MXdoNFF4QXczQzlOM2cyTFR0VUdwZHVuU3hrJTJCN1BXaEdXa0FYRHFOR3hwZDhpZG5vc0Fkb3lUbHRzMmRXTkFtT3U4cDUxMGM5NGJ0NVo3ZTd6ellqWHBObWZ2RlhFa0MlMkZOTU9mTms3VU9DRTNzT1NWZWhINEc4cEpXMmtjdlZKcHU3VlpFbmpldXIlMkY3MFYlMkJpMDRuNWV0a0NhWlpMZ2ZYRUhiVSUyRmN2eDhhV3NYNkNqNnZZJTJGeiUyQld2Wmx0U2ZSMDNISXE1MGpMSENvcnRjeUE0UHYlMkJPeVBPVjZxRVROVU5WS1QwRnMlMkJqd1NaazE1V0MxMnlBcUFXN3BvMHlIaEI2Z2M3bnRkc2h0UU1zdDBQM3RJeDNEdGFGRU1DV0RoS044aUhwWllFSyUyRkY4JTJGelhOcGZMOTlHR2RiQlNROTRJQ09SdkpWZmhMbGYlMkJHOVRuSklBQXJoSDB4WUlpaWJTdiUyQnV2djdraFJicjk5bmdDcnNveFhoNWdYMFV1UXJSSVlRQjB6S3IlMkJKbWU5NHY0a252bjNhREwzNlUlMkJOak81SEJzSGhlV0RpaDJiNSUyQnA2VTl4Uk8zenJYclM1MFozODBwUVRCNnFRcUlTeTNxQ2J4MTczTGJ6YkJ2dkRVVEhQZUFZQ2Z0a2t2WHk0cHc3JTJGM08lMkJHbVhWTjZWNGw0VUZtWWh6S2FaOXVUNUM1QW00eDU5eDVYQU55RGc4dDUxN094NzQwQ0hrNjl1ZkdYbm80R1lyVE80NXNlTlpqSGpMQ3B3ZVJoWU9LT2VMZlklMkZkdHElMkZTS0xKdzV1NWF5cUpRNFlMTVJtVEwlMkJVUHNGM3hGczlrM1VZaCUyRk1DMyUyRjYyVDVmYWJNdk1XT2ZFUjMxenNWYmNQWVNZJTJGMHkyMngwV0YxVGszelRzUjUxcXlzOHFRJTJGYzdDSCUyQjRCbiUyQkV3d2VXQjZmNURDbzZETUpRQUhQZ0xUZjdDS1BWZyUyRjViMFBxYnFCNiUyRmVZUHVjcGptTXpudm1Od2ZZeEtKVXlrQ2c4dWNWZ0JzbnIlMkJveFdEaUFSd3dLSSUyRjd1bEw3Y2FPeTJiZEo4c3d4Z2pSRjM5eWxXM2xvQiUyRmhXcmt2NiUyRnRFQ1ZpUkZTWVE4VDJ2YzFpTFhicm16JTJGNzhRZkw0MUUyNXBIbWM5JTJCNEpBcDNwM3FiNmx0QkhTZHRNZ2FVSXJWciUyQklNYkZXSDNpYTBuRGxxaW80Y1pyTTRMUGdZdkx6c213d2tRR0hCQUFIVkNJSUE2N2RpOHBPcThZMzJ3bzh1WEluand6TVJhblhPRjFETzN3VE55TkNkUUR1JTJCWDlRMTRxdGhlZ2EwMlU2NzMxTlhDMGlhNDljNkNIR1R2Vlhxb1B2a0lnMzVBeFF1NkgxamJyOGp1VVhxWklRM2lWeUd6dlROeEZMdmxqZzJSUjdZaCUyQmR1dVBqSlVCQWlZOVRXRCUyRjlEb0E5aDE5Y00welg1dUxteVp2bjJDaSUyQmVNJTJGUGs4QTNHRVJBNWJDcWVPWXhyTHF6SGJrcE1oJTJGbkRQMk00V3hUZmpoQXEwQSUyRnNrS0ozOUpWcyUyQmNSVSUyRjclMkZOMkRra1RkSURQTWV6dWJaJTJCYnVIVnhPaThlUXZ5eWk2JTJGNmttSFUzelptdEhhU1lBbnF4UWgwY3VsNlBNekxZYzUlMkZCZGtNNkp5aXZ0STZPUWlJZWFIMm9yUW1pRERKV0E2JTJGJTJCVW5ESDA3UThYaE5WSTZia216Q29BeEhTcWVFMEdLYUlDNUhHV1p3WDdJa290WGF6ZDNLQzlzTGY2TFpOVlNZOTJzZUdzcXR2UE5GJTJGbTIxT3dVZzZlOURaRTAlMkIlMkJiN0IzMXZlQyUyRkM5TWIlMkZQSzlCeGNIWVRENGRqMThERlRmMmV6Z2FyNTk4NmIxaWZiS2l3YkJoRERZOHVNRWNtZ2E2ZGFwYXBoNHExTEc3UWxwV3JOZDUwekdGREtQRDdjZEUzeHJsTzBqWjdWN2xnYVFRMXRENFk2SGh3YUs1YSUyQlRydXp6a2g5M2RMc29NdjR0cGZWRnJUeHpOJTJCWUg0bTJXR1hqNzgyT3NJWFlEbWpZekR5czNlcFFwWGNFSnJydzB5Y2lDb1ZlWHViVm1RcUtRU0tlOVNOJTJGSW4ydnVEbmZ0Z28lMkIlMkZiNnFSUzNZVExsWll2ZWclMkY4cmg5TmlTNjdyOGozOGFBTHNVaHRTc1hHb1RRbDJEU1dvbDQzMUphbmF0bEQ0eFlHY2llJTJGdmdqejBVcXk3bFNWaG9oNnlhRUUlMkZRMm1mdnl5dlZqSiUyQjFQelZmSThCJTJCVkFIYUZXWDBWTVNtVzhMSUU2a1BoZSUyRiUyQm5RTlBabnMzWEYwTjNYaTN1VXRiUyUyRjZ5YWFLQW82dGtGQ2ZuOTJJM2xjOGNQSE41S01yZ3h6eHlBd3hjcHUlMkJzZ2Z2eCUyQkF0OEFFMEJXUU5NdTlGdDVURUJhbEVaSFBUWlNoaml6M2Y5eXlzbCUyQjBZazU3OVZrRG5YUXlmNDZvTnhIVjBaZHElMkJySTlUdEc1blMzZFZ0ZUhyZWwwZzk2RVhzVldTJTJCYkdqd1ZzZFhRcWN1NkIlMkZYdXB0RnlWYzlaeFdYT1UySThhcG5uVkZvSzNheFYlMkZLZ1pMc1V3bUtWV2xKRnNuUFJ6eFlrNkJrczdFN2o4dyUyQlN1OXZKdmUxcGVNd0FSdlgyajN1Z0UzbFhZQ3hxWFlxbWV0V0d3b0JqYUE1VEp5NTFxYjlJYk53c2oyNlNuYWQzciUyRkcwZ2tRMkFDTjUxOXFrWk1EYkJ4TFYzR1pHTnJQVGVSQmFWSzZnaGNrRWJFS2JsMEJ4bFRFWDI3MUFkV1d6bUtzNDAxcTl1MklaRVIlMkZrdWd1aU5qMkRXWU1NUkFIcTh2R3VYM3VmVmtIcUhvUHh3Q0JuNnlodkxHclFxJTJCSmhGQ3d0QW5iWSUyRnhESmpCejV6VjFXWWdIYlZ2UFhGeW5Cb1BYUlYwVjFkd0c0VFp4JTJCYlhFJTJCY3B0STh2aGhHSHE3Vk1STDYlMkJWdjE3YVVOeVdGQmlMa2lxYjdrempaQnc1UVFnd3MwTlAyOXJQaFh0RTg1N3hMSGFWNVYlMkZYZnkxMVolMkJXZWdxdSUyQk84c3ZBNlpESWVqOURJWWt1aiUyQmNtaFA5WUNXS2olMkY5d0h4MVdYZzJvcEZwVDBuSTFOM0s5ek5XVUZ6UUJLdmprJTJCVTcwcGxOaTNKV01mYlV5YVhmb2I2d0VPJTJGZm5qWTg2eWh6aUUlMkJGYXZsRDJvR1UxbUl2SThEejNYMXhVZFpOc1JhOFQ3RUI4a3I4NXRaQzkzdG1FVllNSlFNRkc3cFhQb2E0MkU3aHNBM0V2a0FEcHgxMDBOSTJDUzh2JTJGbHhRJTJGd1MxZUhZaWcxWiUyQk1icW13ZWI4N3RFbzRHSkZadzZZNzdmdmtMUkhMbnljdUo1SDlURG1XQnJTeUJhdkM3TzRyUmtWVWkzZ25VUFFkUSUyQnI4d3MxZkhZQzFpRnRsOFlpbXZsOW1Ubm5Jc3lkekRhYVhrZE80MHZEaEt5SUh2a2lyeUhOMWZyRnhiVmRGTDRoRmo5NzhNdjJEZmxnQ2olMkJobDl5WEhVUDFPa1d0ZVZMU0FMU1V4ZFREaWk1VCUyRm9uMzh1MVgyTlNyNG5BcXFxeW1vRSUyQlo4eThGUEJKUHpCY0tsUiUyRkZtak94SkQ2ckJad2hXWVNpQ0xYMTVSTnlsVUxQZEJIWnlZdTFtVG9UMlhRV3Nid2xuV3laaW9tNWFESDZ6eGM1NVdoYWRSdFN0UlFGZkZmcE9qNkhkbGk4MENmWXYlMkI1STJ1TUdjZDBzakJ2QkR6TzZPMUhtaTFzYkh0c1FIMTZYdWwlMkJlMlBmdk1vVmtpaVplZlZ1YzhhYjRKWWlmZGVTY2glMkJJSEhoU3BFc0xyam1Tc2paRDM1Y0k3dnZtNm52aE16a3J6YUs1MzIlMkZlM1RlVFRMQjJvVmxHSE8zR2dCaU1RclZjOWNVbmNzdzVUUlRjbWxJNG1CJTJGSDNTU3pidHJhRlhZZExkYUl5UHpxcSUyQnk5Y3IlMkZKN0I4eEtCSm1MR01mUTh3SzBDVEV3ZkRackpRZnJtSzh3NFlPWG9RJTJCbExERyUyQk41N3FXJTJCZGg1T0Z2bDRWNmk3Sk9kJTJCWWFEeVFseVNWWDczR3NzbDR5cjBxeWhNMjY5S0pGSnYwT3FoYnRidjhWTU5RM1RJNEd5SWJROTNPZWRSOVBBTGpmVVJ6ZXVmd2U3STN3UWpId1BMSVlNWGpINXp2VG90JTJCbzNac3ltcnptYVM5RjJ2TWlwM0VwamhkNWs5V2slMkIzcyUyRjZuY3JMVmZ1UFRmeHFLd3E4WFA2VGNEQmdqOXlLbVoyY1ZucnZla1N2emQlMkZhSUVIaGFCZWRpR1lNWjdZMlZ6d21iZEVUSGNnQTB6NnRpcFdjUmFOJTJGdW4xVnRoOEJsRUNJcmpyTnBhRnV6UXdIJTJGeEJ6WVhSM0puNGRHYm0wNTNvd1duWTJJdlRkOWlTMHY5T1d5aE1XMFo3dlcxZU11ViUyQkdQb2ZDQk9Yd05xTFQwSEpMQkhOUDhZZDFFeTVZUUdUejdrM1h2d1BxamxGWlpkSGdWTiUyQkE4SnpGUG90SmlyU2kzYVpNRSUyQmx6NGxrb1ZkMXBNY1pnVXUyZlVrZXpZSEtSQ3FUbVFEUVQ3Tk1qdFpiVmhoJTJGMmljd0tvJTJGcWs1R0FxRGYyTzhoVzRQeHVtZyUyQjNseXYzZGFmcHNTTWpOd0glMkZqUTFIcTg3b2J2TFBKcnp6M2N6QVFhRjE2NUJpMjc3MHhBazRENFpxbjhiZTFNYnRidFRrY0h3ZVBGeEY2TGNocE1kVWppNkdXZiUyQjdPV2NQOXExaDN0cHFidiUyRnI3ZURkUWlzdzFMbUM2WlNGdEhjV3ZCJTJCSWhmQW8xRGhycDFJcThtREVGa0E4VWxacnRsMUVrSDd2cjQ0R00lMkJzV0F2N2dzWVFReEZMd2Q2dlpzYldvZGlYOGxaZ0VVaEx6UUFhMUU5R2FMZzIwS29hMyUyQm5hSGpReGx2eFFhZUdneEFQSWwzV3FtS01pOU43ZVI1cHRVcWlJMUhYYUFrdXZ0M2klMkZiZ3dsTk9rbFhmOW43ejFYOFJSdnk2JTJGOG9XQzJGMWV0NzF6RGw4SmdLTyUyQkRIejV2bm5yeXdUMlFidSUyQlJCTmdaUEV0eWE4cER4dFhvakdpUDVlOTRIayUyQjh2U091SkYzSzd4SWRyRkE3UEFKNG9BckVpeHdWRDlQU2FmeDJ3bG1GcjFvNGEwSnJRY1YlMkJ2TDJ3bVhoM0JDOCUyRkR4YlM1cmQ1QXdPOFNVZ3h6UmdIVDRjSEpjbnk4bzhmRjVJaSUyRkFiOWRmeG9QbFkzWHRmeXJ1NE11TCUyRmw1dTBmeDBwb3lURWd5bzNIU25leTYxaXhzZ2czdTIlMkJmODdHa2RHTmo0cklMMTZSOGZyNzFsUWxHQzF4SnlwVko3c2d2MVBqYzZ0diUyQktaQmdiVlBjS0NoTjNGU3AzWXY4T0Q1ZkRzZzhIOFBKRmVyMndrYnNTRiUyQkJhYnZWU2tVTGYwV2lQQjZxSUF2RktZZk1tSmVudGlVOGZvUzl3M3J5UDFSbElNTDJwdjhqYVRCeTFvbDl5RlpDWWxWdSUyRkZUR3JlMlBRNk8wWiUyRnZ1c2UlMkJBYkxFUGxpRUg4Zk1aTWR3ZjZ6S2Jta3p1SGZyS2poS1h1NFZ3TGhGNm90M3lqR3dqNTk5REhQbHoxclZ6bFN1d2VRTjMwcVpaWlklMkZWZFBHUG1KckdrT1olMkZoMnhaRjdtWE9tUjlaRW94UGREWXFzUHBJRkVlajYzazdnRVJEeEE3UzBtNmYxVjMwb2dBR0lGJTJCcTVnS2dkcjE5RnNTaWp2UXpzWmd2VlhjNWZxaHZuR3lzWW0lMkI5Nm1HR0pYWlpIJTJGN1hXb0wyJTJCUFZ2RFBhMFVoRyUyQnhrbkU2ZXh3YzdWd1pjZ0JmaCUyRnVpWUhKJTJCMlN2SkNuM3FpWDg5eUVZNGkxcjdUeFplanl6Q2tiYW9yJTJGZWslMkZoNiUyQkVrblVMUXM1OXBpeERneXdlZVMlMkZRcyUyRjdyV0FGJTJGTTFFZnEwelBTWTBmeiUyRmQlMkZFTkN3WU93NGRaQjclMkZCdWRMQVViVkQ0UkFaeFkzaklaZGJJQVJFbzNabk9UYUI3SWl1T2pjSHpZeENhWHhCODNLQ0JrZmRwS29vaWtzYXE1alBMWHljMzhQWDZ4TmFJbno4dERvazZIMHNKR1p0ZHQ3dHYwRlpkdWklMkZlZzB4ZEo0c3V5S3JqSlpCaW5DOFhoM2hRR2FldjglMkJtTEtUMFFBZjBkQ045UXlHVGtiRnI4SElXOTQ1TVdEVUxsWDAyWEZMdmJhQkV5cVI5dGRPUEVpSWlGbGtpYiUyRnJRc0pQVmgxbmNGMDJwbzlMUXglMkY1YXZ0RjdPUmJnaHFTNXZmSWx0eG9yNUVoMFpGbEo2dEpjQmpjUVVUZG9QTGglMkJpaTgzWSUyRkVOM2h2ZEJTNHZ6emZhYzZNdTB6U2ZxVndOODVFRllzRHIlMkYwd2txMmR5dHFRdnJZdzglMkJ1ZUZVJTJCQzgxSXJXSk5oWVJKbEUyMU5yNXRzQjZHZnFOV3Y3MWdYNzFPeHJZRm5nJTJGZlBIeFFyaHdNZm91U3Y1OVdiamF0bCUyRmZZTiUyRm5LTWZaRjhVQ3RMdXpFUXVQYWZrd0hpVlgyeFVhenNZUnZPaGZuQkswTzJkaE82bzhtN3NOMlZwZ0Vwa01YSiUyQjc1ZEFJYll2JTJCU2dPYjFuUiUyQmJ6UTBpekY0Q1NHMUlLamlXQzBibkZVRXlIR0xmRzN2aGdEcmN4dDVSUW54b2lvZEo1WFUwJTJCY3FFbVQxZWU3RDhrZ2ElMkZib2dGdGVmWGFpUFdFMnlJYUNReTNHcWJXd2llOUJwNXNLcXd1SEhQdnI1M01ybyUyQnY0M1ZmUnZvSE1CeSUyQmdiN0UlMkZyTXdHZ3lxQkFRZVdGeXc1UmNpbTJNZFRyV1clMkYlMkJmc0syQk9RSjA4JTJCSVd0ZHUlMkZPc3FlOXZSUXJmYVBYRlA3VHJ2U2p4UnBKNWZnYkFZWUY3cU44anolMkYlMkZvSmJ2UDRnQXlneVhVMnRiZkNaJTJGUFhmSUxxenpSWEFXWUhUYTZkSDRnWDlQQXNTYjcxY2lsbUowYTVUOW44T3BTN1BZTFVWQXVLWEtEZnhiMHlhM21SaHRRaU1NYjl6WEphOHBGMjhjYmslMkZpWWZoZUl1YzNLNW1pZmo2YVlzOWVkdkhnQlFVNUVCSTVocFJzMEFoSmZHOTdSNVh6aXo0U0Jud1p2ZmI0aDYyc21DJTJCd3NqTUpzbCUyRm9rbkdMazl4VnBGM0pBYmYybVdpRWd0STRXTkNYazliSGZGJTJCY3NJbyUyQnpTQkVLaW90SWpreDNic1JYNTB6bTRNdE5NZmZncndVVFpOSzliNFZaOVpoTWRCNlNDWmNlM1JOSWR5aGVTeVZ2d3d4aTViVmNtOENsV3JmbkhZJTJCQVc4VjhldVQzYURnT1JpMzdxWFlybEJka29hWXRaZGpQRU93JTJCc0ZnY0FMck4wSGhZNFB5OVkwWCUyQlVmQUtySHMlMkJEaGRxVE52UDdzSDgySTFhTnVGUXNzSCUyQnAxJTJGQjF5dUZPbWRTV0p4UFNiMkFrb24ybWpEY1k2ZWJ4aThMUmJkSGhRSEk0T2MlMkJicWhBYzlRWlQwR0FrYnBWRDloUWtZVzEzRDUlMkIwbjdDd3lwWjZDeWY2TmNiTkw4cVdIVnZUTDAlMkZ3azhJMHJ6RHdlbDJKVGJnWTZTOUs3Y2pBYmNGNFpRME43TTRPWDI0d1BZN2NtSEVpeGZnY2FPYUpOTTNSbEJWVTMlMkJtU2hNJTJGa1ZGRGFJbG1OZEYlMkJpd2NPeDQlMkZUbnhTVXM5YmJ3M2J3a1FaZEQzY1JldjliTFJFclZ2SnRLWU1tb1Nvd2NUV3NZS2FpdVF2enhyOU9KRjBiRFNmYnNzOXJhR3JONkdJbmFsV3BMRUJvU0ttNmhZOUZjTERTaWVvdkFUU2xlWEhveGJabkt4ZEN0cm9kb2lJN0lMVmdqZjcyMTlDS05leUdaWG9makxhVFF4dVpZUXBBbUlvdjRtcnI3YnNDTWJ2YmlGOThJWGs0VzglMkI4aW54OXBnQ2Y1SUVCUTQ2THVCRnlxYk9RTXR2SFhqZVNEcnYzJTJCR3VMOXM5QlRNM2x4em4lMkJZY25QZGZRelZNb2ZobFh6bFpoJTJGRXlsUmdTbFFmYjglMkZKaW95S3pkMjdmdVdGTlFqUEdBUUtOUVltVDdrNHpPb24lMkZ0YXdsMUFzTDFXZnFLMzExV1RsQTRMZ1ZmWUhOMzR3Q0lhSXlOJTJCdHdiaFdGVTNUcXJyVXpIeEw3ZnZJUDZ0JTJCWTVKSGN1STlrVXhibWklMkZQWFNlbnVGQzdmTWFDb1VuS1lzU1daaFN4NVhPSk1mcmF5OVZRYnRpMm0zJTJGUWhkQTZyMDMyUyUyQjZlaExFYWhTalpXemk2Yjg1VXZNYnJseXdQbTl5Q0dpSFpIRFppOXUlMkZ1TmlWM2lEODIySmhlNUVtM2NxcUVreUZ3RUtIWDVTV1o0amdUbCUyRjFiVEU5Y2JOeG9oU010bkJQVWdvQm14cEJIJTJGbzBxdHJFQU1EdFNYbG5KZVQlMkYlMkJUUGJNRXF1TmVqYU83Rkp6SFh2VSUyRmFtWkc1N1VPNURxM2lXSndPS21zVUlmR2VOelZCYWxnYzNxUlMlMkZHNjhvT0c0Z3pmc0xLZVNhS2lTQkZZTVlJU3lQd1FpZGN2WjBWYmFzMUdiaU9iMkVQNmw4M0VpdmlQdERvUXZ5V0FvSWswWTBHUGNWeCUyRllWTlFhZzJJTzh4TnRkbzExJTJCJTJGVzhYUHZQMUNER2k0S3RDVzlNbjRFM2RQaHRKU1JQcmQ1eUVuMG14RE12dGNYNWRtSmpYem1pSks1UXRQZE9jVURFbXNqdE1rZTRkdlNkJTJCMEpkczlsQXB2UklVJTJGNUpJUXYlMkJiZXZnU201bWI0WkczSDNDZFBydklOalM0S0NhRiUyRk1QUU9jWkJiRkxsS2hOQmtobzdjNk1nbHRQT2lYdXJrY0ZxSzFVJTJCJTJGUmphYzZoMlNCdzlBSmVFMG5Ld2YlMkI4UmczTlJmR1ZYa0E3MDcwcXJDYWM1R3I1ZXhKTU16SW1ndiUyQkhFNmxWM2tTYTRqdGVneG1RSUIlMkZTaWdSbXI1amVqeERsWG85blRrQkU1Tmg5WmhZWkRZbXZSeTk4S09MOGVwQm9LQ3ZDQW5DUlJPUTFCdGtQbEE5S0R5U0JDazFFWUkyTzdBSWk0c0c4VldldVF6cXlGa0V5WTVZQVZ0V2J6NjQzR2FDbGxkWiUyQlU0V3AlMkYwWGREdGkyUFRpQjJtMkdjMnR2dlk0SFVId25mdTlhUzY4cWxDMVBES09qckZFTlNaRFRGaUo3aTc2R09nbk9QblI5Y2tURzBtWkJqSjVoYXczR3FLYnpKSTFhZzh2TVFseTlOM093UXZPOWtuUHRaQjJRaXhqMiUyQlU1V3lYMHNOZE9nSWV3TDg4TG12YzM4Wm15NWV0cFVpT0hobVJ4SFdLWmlMeG9uY0QlMkIyTExsNE1BQXhsU0FwVFU3SHB5eUphdnM2U2l6WTlxNkdUR0tlU3VJbXYwSXF1WHFtbDF5TER0b1dSZlBzbmNTYkpkZnd6eFpQMm9kT0tRSHJ4cEpBMDdpWmdLMzdxdlI3WmJmbDFXVzQxRDBKdTlBTGdzS2duOWdEc2NWblhBSzBEZXlad2JZZkF2V3pjNyUyRkhKOWRlajdFZmlvSVVOUUslMkZGNXNZV1pWcHpzU0xCT1klMkY5bGhFaDJFT3Z1dU96diUyRmJTNzBnTjJBS3pDU0NqWndWeFZPQVF6OSUyRnVzV0R6SXBWJTJGQ1k5bnBEWWZhUXJGdWgxb1MwbjZScDJtMCUyRkUlMkIyQXI2OEpsSnNjWHBMWDFnWk0zVGJPUkFqUXVBcTJmeXpwOVJndiUyQmhrOSUyQlFmZVZPWlVlRXYyNDNvdjM3OUtmbENWYlVWeVU4ZldYT0RENm9ocExXMHFPQld5JTJCbDZTcnUlMkIlMkJOdFhicnlKamhiZ1d6d1gwJTJGMGFwWCUyRkhwc2I0TEI2cE5iNFFtelRFazJyTEt5VUpzdmp5M3FnUVVpbzh3dzM1dFd6OG14ekNWZDNOZ29CRUFxVTZPZWlhb0lpUk9rMU0zbzdraVRCM3pMaG9NOGRSbDMlMkYza1VFVlNTelgzT2dxYnA5S014M251c09yJTJCSnRkdlVNdnEyejZYZklodzRSN0duN1JFNCUyQnBOZUpmOTVZWGpJcFZjN0xueHJocmtPdmRXbHc2M2NWV3BjMmxJY0lOVFlyeHVyWTQ2U2hxS0l2Rk5QTTE0elFKQlJHOXBQWjNQRkM5U2ZObTE0VXJsYXpOOGdEWU8lMkZWZmVsb0VabXBrWFFTJTJGOVBrWnhXckRhNU9jYUk5ZXpxMzBWNmh3NiUyQjFUbGViY3BYMk16SE1aUDJMTHJheUR4Z0x6dEJXaHI0UFpDVmlyT3FsaGlLOHJqWE8xSVZyUWhJOWtTdFpNZ0pjbjRvUlJ5cER5JTJGZFVZSnJxbEo5a2xrdlNaRnRQbndUZVNDdzlkWmIlMkZhbXolMkZ0S0FtYnZCQ0RFOHhmcTRES3dxVEZWem81dUxBM28ydEQ1Z2thZWxyZlJBbUdyMjFMb2M1ZVpiSjl3bHllSk1tU1pEdlc5Q3o3Q2hQZkVXcjRxdzNQN2k5bk80JTJGYVJpNEFrU2YyYmJsdGNMZ0t5dzFQVDdHbUlUT1F2c2FpTHNpTFRLbllybmhJRXFTdTRWcCUyRm5hSmRpZzdSMWVONUFJTHN6QW9QVU5XdUZrNFo4ZkQ3R3huMkVDJTJCdTU2V0lPMGhnTlY1VHpIZUo3aklHTVElMkJUVktxWlJaMXRTJTJCeUZ1RVRRVGZqd0hvRXk1RXRkdnR2U3hPS1NrbEUwSVdaOEZhUWElMkZGWmhKTnNJN0VuaCUyQnglMkY5c3ZMVHF1eHdsU0xQQXNLZEFjVUxhZ1ptTVc5ek9UdHhRdkw2ZkhhZUtCbUpkVWFhZlVFcFFLZ1BQWkdheGczYkd3NFJ1bTEwdnpEdzI0Sk9JZHZMcVlYOEJCWFFiSWlucktJZ2dPREVNZlhWZFJrNTJlZXE4NjhFZEpvQVpuVDNUM1dRZ2JBRnpqYmF5aHY1aXl0aVZ1MjhMdmlHSnJXRzBIN3ZaTkxza2plUVBOaFgyYlJtaEglMkJPcFQlMkJQQTB3SVpPNmtDYU9xckxrNlZaaUE4dWJNZlAlMkJoS0w1MXZ2RWhndWlMa2dKYkRTM1QzNSUyRnlJbjQ5TVVDWUpONDhkR3ZRJTJCVDFSZmhNejNaeks1bW9oJTJGRFZSdzNzYXFadTAlMkJVSmpiVjJNaW5RYUpSOUxxWnRoSlp0JTJCZnJGWVZvTFR1NUlaaVVxRFJVanZ3Q0l3dnpHWG55VTZTSmVIdERmcmMzTTdTaThEWE5oVDhrajZrRDM0NzFyelFvOUlXTlZGYmZpRUZ1TVNhVXpDblFOa2pYUkRxNiUyRlhXanhBM1hpZmVVQngwYWtNZVV3aDBKcjVxeXQ4NUZ6Mk52aVNXMFFpWndyV0kyV3IxRUdaRWttQkllMjFTSmdmYURZWTA5d01UbzhwYTRWamRDTzdGc09KZFBkc3RhMWxMRGxvZ1lvdEl6ZFV0N2dVWFVPVzZrMEFtOXglMkJWbWx2YiUyRjZSJTJCdDBVc3pXTE1nJTJCcDhoTjhyVFlEQVhuN1ppOWtlTG1iMXVzQWtQcWNUVXFzYmg2VVg2ZjZzcXl5dlB3WFBEbFpkZDBRbWZieGdZJTJCZTdPTDVKOEw1MFo2JTJCbUNMSzUlMkZrWDhFMSUyQmhNa3BOdlFZUVdLd1Bzc2tDNkxvTU9iRk9vQWppaTZWd3o5eTJEOTIlMkI2VWR1YWElMkJQOGtTRmRPR0xiM2h5cWZ1eG5XTEJ5UERVNzVLYVNhb3d4SE95d1ZzU3JaVCUyRnBKbEY2dXE3TWlGa3owTHc0cCUyQlJhWHQ0TzU5UndQS3glMkZPRkolMkZJTFElMkZzUFpBZnlmUUNMSmVOOFpzZjc2eVVBOTBzZmFGM3FReSUyQnRhU25ybWhjd0RmbXZyd21UOUtMOWNsQVNQeVU2TVZUT0NiVlc2emV2ekZ0dHgwZ3BZRnFXSSUyQlAyUkV2ZHV0N0htbER5aHVueWpHWUU3TTdxYW15VmFKQ2VydmxoNlJ2NUJrV2QwZVEwY202a3lCMUJDVDN6bVVOJTJCRXdXWDZ2bjJES1BFWnhtcnZHeDRFTlJjTVBnUHJTS1BrRWs5WlhFYTFURlFmN0Z3YkRMQVlWWVNGa05lNlpicmc3V1UlMkJnTjNjRlJxYWdMcjNuY3dWbzl6dDZrYzNMbnJjUzhRbU9VbnE0cUJobTdLN0VCV0JvMiUyRkJPTXUyZUQxJTJGdGFFQlh4cTZGS3k2WDRlTW4lMkJxbXlXaFdxcjhrM1dTSlBwVUQlMkZyU3BEZHNDQSUyQm1IQyUyRng2V1djR0dTYnVKYiUyQnJzdVBmUXpCRzRQJTJCNW0yYUJQQUZmSXg1akolMkZzZ2x2QWZBREZkSWRtYzdyWjBPMzRYc243aFVlREFWQU9MeHNiOXZBQjE5SXglMkJDa20yQWVabDh4Q3JzWG1tTFFKcHFzeFpDODFlRUZpVk45OGVWUE1yYXhZcURQT3hVNU9YanlMQmNwdFVtc2RxR1JwazJGN0NSa3ZGR3BHQ1NZc0p0clNyUDJYR3VDMjF2T1NCJTJCS1ZGV1RSdWRTTFRQQ1M2anh1MFZTRHhObkxMMzJjUWRlaDRsUEFUbjZTcHlWT05TaWY0dDVkWkV1bzZ6YjVzNFpyZ2hjWWxwbEgwcUlxJTJGTXUwSXM0UThYeDNJVlozeDVSR1JFbGZzMzlYJTJGNEVZanh3aEcwMUIlMkJXSzZaMTBmb3p1SmFDbmN6SVRlNCUyRmVkZVJBelRkMjIzTDhrJTJCJTJCQjhZYkZxN3N5ZUdISXBOcFczJTJGTHVhV2x1d25GMzZqVXhFYlVRdGJLNFhhcllLbWRCWFNuSTlGZkh3U0VkdktRUDNiaUpzd3dzcFNTWVpLdTVFbXI0YyUyRmF6SnRzcjB5UVhHWUUyWUY0NHBDMnZrWVN6OXFyTlVDMWslMkJPamtwT1F0bDNWRWxCZVZMVFRlZXpCVzVlU1ZmUjh6NWpWJTJGN0diZE56VGpkeThOUUxIMFpLWHIyWmthbXpjWCUyQkJERmxyNFZqMUlIaTNOJTJCbTJHQWp2aTVuRTAyaURXWmZuZXpDQkY1TCUyQnVYTmFTUll5MTNoOWRJUEg2bXZRN2dxNDc3STUyWHlwaUJtTDBsTVN1THUxcCUyQnJWWEJZNE5YczRhdVdLd3RJejNnbmpkYzExWWRiRVFTenlMNGp2cWI5aHNjS3V5TXllQnk2WlQwJTJGTnY1MFdmeGZ0MyUyRlJIRVFSU1NzSnlvdVN6VmV5WDROV1JGaWhwZ2RWYUR5dVZkOEhmR2N3ZGxQZUgwY3lMcFNRVXRUM1lJbnI5VFNBNVA0U3FWWWl0dENoeFhzTlQ2MGJxMnVSYjBkYmo0WW80NnNEMDE5UzBMU1FNZlRjS0JLc0hxQkNLdjB0VTV6eFFXZWF4ZGVFS3AlMkJVSEhPMXhPUnZ3cHl6ciUyQkFlNlY1VENycG9lTDU3T1VvRUpwR2JONm5rcE54bzllM0RJZTU1bTRiUGlVRWtBNkx2dTU2anRyU2hSQWttMDJLazNvcWNQN1l3M0tUU1M2ZTlDcTBCY3kwN084Z094dDhrVnQ2U251M0NBWkhYbktKV1NsV015aXN4QlRKNHQ1YnNXTERVV2hhR1JTbXhzS00lMkJ6ZVpudFh6N0hTNmY5WXJZOVBRNEFNWGtxN2JGeGVHVGZxNTZ5NmJuWiUyRjdMeWJSZUdXeVFzNWNyUExxanRMVTNOQ1I5YjI1Skg1ejlncHhpUVNFRDRsYWZjMEE3cHVHQSUyQjVWQiUyQjFPY0Y0eDBTWlh6NGZQdzhBUTN4Qmd6YnVKWUVRSmg3S2JtaVh0aVEzZ09HVkRKUWNTTDdHOGlPd1pzNFdkdmpTcXpTclFVQkk5bDlsJTJCMCUyQlNqUmZmN29ySVc5QzVIZkdPYUVoWHdKaFNlJTJGcXYxWFFRWVZ6RXR3WUdnJTJCYVhxenBpWnVRVXlmM3BoWFY1T2RMNjRvJTJGQTJhMGloWGZkMjM2TzFtMUw0WTFLQklNNWNnbk9NWnR3UmVCOCUyQjVvZ2kyWm9FM1ZHT1RnNlN4Rk1yUVhXZWZmaHV5SmVrMlVyc041bzRDJTJCcDV1UFRsNGtMJTJCdEJoOElTQnd2Q3RxcXMxUHpza01UamNmUEJQeFZvaHA1OU9zYUg0c2ZjQlVmJTJGZWFIUjRiREZvc0YzMDFzUkFJd0FMOGxocFFvJTJCeDRKYmt6TloxQXp0UHJGZ1UlMkZWcE5JZ1BvVlMyQ1V0VCUyRlBHd09mNkZJYVh0YnolMkI0TEJ6N2pUbWtNWk5EbEhITEd6Uk50WWJXRWhGZ0pIMGNkdDglMkY2ZmtVbVZRdGFQJTJGZEElMkZPM1hqd1FKZVlJTXZPWSUyQk93WFBBRlN1MDlpOVg1bkl4VVdMREU4SDI4YURYbHZxYTNna3BBczNwOWViTW5tbTY1UkhMeFJtcXY1UmtSdGdhdTViU294WUtIWWQlMkJZbGlHYk9MRE1UUTVxOTN1cHFSOEFBTDNuYzZRS0NPJTJGR29nRTQ1Q056blNuZm5jbmFnYzN0N2gzV1FQUkRmaE9mJTJGellLSVNRJTJCSm1OSGRMN2t0WlNYN04wRGtoR2NzbjJoUksyMkpkbnVBSWR0OWtTeU5kMzN4UVdTZWZ0ZEJwRmJZUVp4dGx1MUZ5TmxxZnVCTCUyRlFtUHVHbnNKQjdYZ2x0M1NVSVVYV25HWFFIajFaR3doYnNjZnhIMDFYdFRJcHUwVmZDNVJKM2QlMkI1d2R5ams2USUyRjhQU2ZwWkpLZXFtcjRaTzIxdGthMW5sJTJCVXJHRGhRVGZqV0pFOE50clFRYkxSd0gxU2ZhYkwzJTJCOFQ3VHo5MDZEVkZkcVhISGt0UDFlMEsyN0dmUTJIb3hIMUpBRExhREZQd3VBZlMlMkZ6NVQzZiUyRmhtSSUyQmRDZmlDNXhxUFRnQTJMdExWT1ZvcXFsOGtzWWZzbExza0hKVGlWV0h5S2ZNaEhNdmJwJTJCVkslMkJrJTJCMyUyRk1BOVRMRmJuQVRhRlE3SGlFMXVVVWdOUGpMeWdYTUVIWmU1JTJGdXl4REgwd0R0OEJsQ1ZEenppTUh5NSUyRiUyQmRENElVdkM4MjUlMkJIbGlvNDF0MTM0VXNMVUJqWWZEdHhHN0xXMmM0WXFrNjhIeDBEUXlvdDVkc1ZpOGZQem5vTTFveGNLTFFhTXJmVkhPQnpYdWpNTlZMeWFhZ0dLY1pObjJ6RXRyN1JlSTAxOU1JTWFyN1VTR0xBcGhPRVV4M2tuJTJGQ3N5aTVIclo3czBlUG40dFlWMEpHWmpYYXYlMkJ3VTZrcFJPeFA0a0R2ZGVlUWtSWDVYMDFnVm9pbzhkV2FjOWEwQWhFWFhLN1VzUDRYSCUyQnA1NWlNazhqVXVheDk3TSUyQnFFUFZ2MVFKY3FmZU1RUjNuNzQyS1dFTGhUYXJDVkM3bUdkNjVHdjAwbDYxZTBzRXRQZXRScmFWZmRxU1AlMkJGRE9HQ1dwRjlES1ZBNUpSQUtMJTJCRlhmdmhUdWwlMkJSV1d1QUlESXU0WjNqdyUyRmhTbjUzMWdrJTJGTmE0UmZFZ3NpeSUyQko2bTNnRUw2bzEwNEh4YkNRakklMkZBcHFSVVFVVVFxZ0NneXpHZm9BRmlaZEV5a1IzQ3VNTnI0cTNBbTRwcUZBbVNxb1Q4NmolMkJIVEQlMkZyMHNtVlJsV3JMJTJCZkNNWEdWbndyWXJUMk1xRFRhQUtTODZtbHc3ajAlMkJKZ2U4SHpUJTJCOVpQWGRUYWlXNTdqcTJYWGhVV0ViVWprSXRiaFZUYiUyRmVnd0pKTTNuNjhuODllTUwwZ29WMjVoVkpVOFNsUWF0cUYwaTF4aUd1ciUyQjVTNk52bU94M1dSVGJBZVYwbjFNVCUyQlQlMkJuSGhDMnR2YWZ3TkZIaGNsdUlwY0ElMkZrT1FvV3FlRDVGSyUyQkpxTTFZU1o4amZsT2ZXUE5sdXMwQ0pTTVUlMkJ1czVvQyUyRmF3aWlNTzZDdSUyQm8lMkI0YWhvNHBMOW4yTzV4bCUyRiUyRndKWFlScW16WUN5a1VMRWF6TXg2MzBsY3BtZE91MUdOVE1OaHA2bkMyejc4dDZSRm8lMkZObHlXMmttRE5hZjY4bnAzSkxXcFo3Y3NrQTN5JTJGcGN6RUxqRlFqekFuUnQxOXJWc0VaQWw1ZFV0b3NFODlkaUJhYmNYckw4RFN0ZWptd3FERUhKT2VwU2FsdmpwZ3VwJTJCQUE5TyUyRjJPcVFIWGZ0WENWMkhzdGMlMkZOVkVKMk85TDZxbkZCR2RjY2RsbEdkVWIweWd1NWFSaXdqNyUyRnk2Y0wlMkIlMkZKMVBZJTJCbmhVMG5TNElPazY4czFuNWFrQ2txaVhjcmxnUFgxSFJXUnZZQzhkbjVtOEFBeGlDWHZNeHI2T3B3R2VqT0FMUkJMZVg5R3Q5TXVydmlzMDJaOHg0TVlLbE9TZTVQZ2pZJTJCUE9HNEo4VHJRWTZIM0RRQ2d2SnZrOGNwemZDJTJCQ2YlMkJUbnFvS2FFcm16aWdxSWUlMkJZY0RJQlRZJTJCYWlxSzRaVUN3VGxrZyUyRm16TlRaRkRQRlhHQk50MHpsWHdCeEltb2J5TDVwVzZNelQlMkY0V0NUTDYzMXN4RzloRExpc2NKeXVCbjAwTEdLNmd1RTdEQklkRmElMkZ0bkkzeFpOU1J3dWRTUnRKYnFmQjFrZ0twbURSaVVIcEhOUlpxeFpnT1g0UmNYQXA3TXROalpTNDh1UkdTYnolMkJSbHVreEExWXNNTkJUdlZOU2VXOU1WWjdMY3V4U3k4bDhCM1FPcEVyamhTOThUekVEZUhGbnZWdjlxTml0bDl3b2tTcDMxY3FodDZWamYwS2FtVkpZSVJmdlh6NEpmWTVaczZ5dm9WYndJaFpKcDQ4WTk5cUZLeDJVZUR3N3k1SkNGUnJ1b25SZjhIYzFzRXI3JTJGMUE1T1MwZWhacXBhbnJkT1pCMyUyQlZZYlFhb3dNNjE5U0NEUm5zNDRQVG1kMzdKeXB4M3U3VGZuTUtGNnlhRms4YXNNU29CZE1INXVheGhmbVp3b1RjSzlLUzYlMkZXUGFPUmZjRCUyRjhxOXFwMDYlMkI0WHVRVGJOeHZTaWt3ejlDWEI5d09wUkkwV2RJdmM1MzA2emRBRG1td3hRWXpuQ0FTeEl3cUhtRFJtUVNreUhFeTd6V0x3QzQ0OW0yQkladFdNYWNhWVEzTTExVGFGSDUxREJiUjNlQjhUZnNkNVRac1YyOFhiSWptY0g1ME96MWV3Nk5pbDB1YmtLS09VbmpONHZ2OXd5QSUyQmFMZG9DTEYzTFMlMkZiTTRFRFhJbG00amE0dTd6bnRxenVhUXlVViUyRlFkczNVU3dZU2M2ZHdlYTkzNXdab3VyMjU2bExEc2hqU1BxdzBTOTdXMnJnM2s2cXYwY3N5V3czY1pVVHFieVNwNDZ5ZDhlbmxxWE9QdElOYTZkaDQ1dXVaOTR4Y3V2N2tzalJMdGJ1b2pZN1olMkJFNk8yNnVVRzJmTkdXTXF1dkdHVG1RbyUyQkpObjhRT0k0RWpscXU5Y2pqWFgwaVZYcnY0TFRsV0VDUUw1eWZkbVQ1NTNFVVJXY3BhQ2ZqbHpvdklKdlRKWnhFdXAwRkMlMkJxVkNpQnkwdFZPYWphTVYyR0NyRUZRTlNWcmdBSDVZalJFMnVnNUFzc1VhcVpiTThPSTZqU25qM09hVGxJJTJCcTg1V1FyMm5sTWpYUzMyazk1YlpsaUJ4JTJGUUFXb2pTbnM4WTdqb0ZRRGd2N3B6bFNjWUtlMGVla3lXcHJvVk1kcTglMkIwVERlN3RuM3ZFOG5IRXBjZkF2bVJyeEtxS2ZtRkp1OVJLcGNrZzlFZnVTeVdKS2c4SVpnJTJGNUdCVjZWNXNrcTFsYWhmaE5hbDRSR1U1ZGxyZFg1R0hMNWxJZlAlMkJCV29idGNsRXMlMkZUMUh5QzJKNGRhV3QzJTJGdGRQTnQ3OXFhM1NWV1Bzd2RqUU9DQnFGbjlGOExPMzkwdncxTEM0OWNPR0JGaUxtQXNYNGhzNE1vbW5TWEZYSkVTdXMlMkZkUUp3U0k5ZDFrOHAlMkY0NEw5cUQ1Um1TYjljRW1ORW5NUk41ZHh0bmF0bFlqSW1UMGRveHNjUVNrWXpuQmxuRjdGdnJtRHhaYXc3ZzJSVnFLenpYUXVwN0lTd1FLWDRpVEVxdEklMkY1N2xLY1hWd2ZFWEIzczliTHAydSUyQmQ1Z2NiajElMkZkZXdGNGJ1TTRlcTE0U2cwRkdLMFNZcldWdUlDZ1Rwam81a1l5YzFSWkczcUN6MVk5dE1hRWRBanJpVjBuR1VNeG1zdGhLJTJCZ2pkOXU4eFpXVXhQd01YUlNYcWxuTjlsT2U2Rmt5blZmYml1OUdLZEhWdDAwTkpRUm1NZGlEamNzRjFQWXBOdkxrSzlOejVZcDZpTFNIdm5SM0pkMXRSNzZrakdhQk5wWSUyRiUyQnNoajRBTEpqMDJ5VlhwWkhLczRwcDJnQTB2RjFCd3Jza2I4a2xHM1VGJTJGdmRvTGVuUzBReXpESzJaV3NQM1Z2UkU1WHlVSGU1Zm80Mm9SZTRuckowWTc4bHFnZmZISzViVGxDRFlvbm5wUXUxSWNsdXNwYm5LSDRtY09EQTJxMm1IUndUb0ZGaW10Z2hqSkp4VHdyTkZMOEJtelpaMnQ5VDR4TTZNcGFnR3cyU0xwOWVxdU5OUkRncTlJMk9XNGQ1Mmtrc3VkV2lsJTJGanUzM2h1M3VHUlJZWUtoQnlFaCUyQmlkNzglMkZtQ3Z5SFMlMkZ0cEZpaURDZThWazR4VUVQeXRZMjI5a0t3N0FhOWhXYlRNM0d2Ynp0TjNhJTJCbUN1Z1d0VXZqVkg1eGtNZ3pLTG1kaUVWNVpjQk5xM0ZJVWJSYU9ZVXclMkZUNno1Z3I1cnRNRyUyQkV0RXVYblV6WW9ENG81VWpIZVYwTGJnMHJkaGRtdGhGTEZ4SFNyekU1MzhtZFptanVjeiUyQjdFcGFLNjBQdE8xNXpzT3pPQiUyRmRnTXNZNlhMdHd5YjJKVmkxM0slMkZZY0JXd0poT1VYWkcwZmpYRnF3Zzg5am5xdDE0STBXT0RkRE5HMXBFaGg4YmxyY3k5bml2V0xpdGhzWXIyRzhpSGFvejdqVHI4V2h2UVZCOWtwRThWcGw4SkxQJTJGQmV0UCUyQmZETDZBcmJoWk1LZDBuY0h2dCUyQk9kdDMycE50cDU1eVdXYlklMkI1dHFkeVVpajJpTmFybk5Ec2VvZVdvRiUyRmZlcGtOaHhJd25leUlpVU1GTHZDSWhlTTBKOTVmc2tnRXVreUolMkZnSEVZN0ZqbUpPZ0VFaWcwb0glMkJzVzBSd1RHMGI2OGU4ZVpsQUxFWCUyRjhxb1k2eWFGR284aHFCSTcyaSUyRlNGY0cwVXRCS2JqdElFUzYzZWFHallzS0hycTBteGM5a0FYd3AwZkphTkU5dFV6b2ZJd0lrUE96TG9ZZzAzUiUyRjRsWDd2TkRPWjZUNXNWRU9iUlZyNWExTzZrS1N1eHZ2UDE5U3Q5MHJHV2VLa05YbWZ1VWZjZEZGQUlCUGJLUnFuWkV6MXFiTUlmQiUyRmlSOXpEJTJGVEM5SkV1bSUyQlZGcVBwMnZkU2t6QUtBcXFuN0olMkJ2NEEwbFYwUDRNbjQ1TE1vWTNQRU9BejhNMktYNGNUcXBDQ0hwVXVTVGdIZ1lFb1ZZVEp4dGtxZnBtd1VEYiUyQk5ZcktKRiUyRmF4azVUd3RBVEclMkJwcWtiT2xUJTJCOUYlMkJIWmZ0UkMybFBTVXNoN2NRZ0lSWW1aZ3NRN3NmemIySjBvbkNYRDVLZWp1MFJGSXIzTkVvYnBsTUVKTjVyNG1WZVNmVnk3MGZRVnBHcWladndzdjRnbTNkNUJweVg2YW5UWGlMRVp4UlNrbCUyQnZHamFkVDk4NWtRczRiMWIyd09TaDJTN0ZrSG05RU9jJTJGR2xqWXVDYVNFYms5RzNwNWpMTFZVWDhENnBxRkZRWVN1amRWdVkwVGRCS091V2lOMjdrTVhRS1hYYk9ObVZmQldJSmVBeDE2V2RZRkFTOXRVNXBld2QwZHZROFBGRmpaTHVlbkh3SlI0bHczcTNkMmZ2V1VkdTlXd3A2ODZvZVhZaFJrTWdzV2hzJTJGbHJTa0h5alZESlFBJTJGV3dZbmRBWkhXQm43M0ZlUHV1NjdOU3NZMzFxazJSODNVSU9RV0k5ciUyQml2N0p6ekU0RXJNZm9yRG9KR1BLJTJCN3Z1dlBYQTFDYWVUMW5VNkJUOFBFVHNGWWR3WUQlMkY0bW5LYWtxN2ZjbFlHJTJGdWJ0WTdISiUyRlZDZW9TWFJsSWg2eXFXZ1h3TE8wWHNHcm5Mb3ZaOEtzQmk2eW1TUVc1d3p5SXZ3eVlyMmR6S1pza29pcjgwR1lMbFQlMkZQR2YwNG1LMVJGZU9oRHVNeHd6YVlCZGhqb3Q1JTJGNW9KYyUyQmNyaG5jZiUyQkYyNkQ5UGFZZnNaVGl5JTJCZUVoMzlXQ2JiUlZGWGFQQWgyWW9FR1J2OWZnODBaZmtlWmdLNkVOJTJGWVdLaiUyRmJLaWtiNWFpVlRTY2hwQlhEJTJCcWR3SlZvJTJGSlFQVCUyQjRtWmo0UnN2U1JaU1ZQVE84VHdCJTJGZ2JPQ25YeldPQjlaNXlma2hFSllUSkhsbWJuOGlwbWR0eDlPYTdoQktDblVCekJ1VGNlN0ZvSTZ3YklvVzduRHdoY1VQSWUlMkZLTXhkdlZRanIlMkZGdko1T3ElMkJldUFTTzV1eTJNMzZSVDFzQ0duUG4zSVFWOG1GN3hZcG4wb3NsQ2dOcmwxUDJtSnBUdHlRUVdFTDZmMFZUdXoxUFBQcXlvUE9HTTZ1Zk5XMjklMkJXOVdkUTdLdiUyQll6dE1ib1FQbUZYbXc5YXR5Uzgxd3ZsZWIzJTJCZjJHV1ZvViUyQnk0QXRJQWVSN21pdjg3SmdxckFxcWV2QVBJV3ZnbmhWVnI5cEMzdFcySVZCcVVXY2tLclNUOUxXN2QzQzd6N2k2JTJCbkladiUyRmgxaXlIRTJmNzY3Z2RpYXZsQXVodnIwUXdVczFWM0VUMEV4JTJCMFByRFI1SFZDY0lMdE4wRXJRc2gycG9PM1hoNlhiM1UxQnp0JTJGd2dReGtYN2dKV2FsN2RuZW5PVTFKJTJGRkJlUnVnSElQbVd6WTVmWmxXYkNwJTJCNjNraVNvbEs1RUVHMWxoUlJ2dDlEblRtWVFUR1dpWUsxJTJGUGtVcHlJbnE0MmpmNzdlMGNPT2dxdWglMkJFbnhZNldBMmw4VkZLJTJCeEg4VmJiMkd6N2U5U3RuYlRYOFRWcG9MWFZrMXElMkJxVzBTb3czJTJGU3pldWlaOXolMkZkTzlPYm1sdUV2NjNIRSUyRkxaY0RycVlzdzRNTkY2U3VxSVZGYWFqbXdMbDJCZGx5YTF3aWFmNFhjMzdnZFdaNjQ4STAzZlN2WGJLMXRNTFBHVDIlMkZmaVJ5V2FzN1NxbHk0M0RBTkwzNHh4Y0pXdVNnQjBvdDhKTGhTZGlSRGY4Qk5VZ2kzaUJ5VnJPUEZ4TGszRGEyUDdtNkJhUjluSWNpSmRFdEpLZGZvV2lkN0c2UjZ4JTJCNXY2OFd3ckJCT1ptczRZJTJGMDNrUUVWU1ZIV2dvckxqYmRUNEpwc1olMkZCOExZWnB1WXZnWVZMMHFJJTJCd2plRXp3N05KT0xHeUFnVDBXOWFtcHZSRnloOSUyQlRFWGs2M2N3RXZwZHF3ZjEzbUNaeGlKR3BZR2Z0MCUyRlRtclJXaDdaamtOQk5XJTJCcVpkQ2ZUNjBoeFpjM1A5ckhIMzMlMkJpM0dmSFdpY21heTJ2N2dGSzRPeXdrS29TTCUyRlZYeGVTTm9Icm5zdkswakV2VmQlMkYlMkI0SFEyY2pPdEFFMEdmTHdMRjRxV3BZbnk0VFcxcmpSY1BNVXFOTXFheUNIRUFGS1VZR1dXS2NFU2lvcSUyRmROWWd5SEg5aWxpMDJmMHkzeVlTbTdpVUhTZEpzWHNDOTRLNEw0eTNBQWZkRUFLQXplM0ElMkZ5QVRUU3lOa0ZEaTdNZCUyQmJPa01UYkRWN25ZeUwwamo1dVNKJTJCMEdidjdGOHZwQ1BiTEg1UGF4YURSNWlZbXBUZGlsMHVQTDFkams1ZEgyY3c5QkszU2psQU56UWxmZkRIdmg2JTJCdGppWDIwUjBlbWUlMkI4TFRCTGlyU0ZJdVExVVREVEVmU1Z3NURCaFp5eHh1WkJaYXAwSFBwVlBVYjJHbzBSQ3paMVc0ZExJcDRHbndTYUV2VnFIc0ZZNE45eXN3UWFMN1NweXcwYzdUaTJrZjJYcEM5QWRWNjJVSGdHRjhrOHpCeGZRJTJGRklNZlclMkZEbHlXM3U2YWV1Vjk2YkpPZXBqS1E5elM1UzE0RUtTd3E5VDhOc3prYmx4b0JsVXVhdDJUQUdYT0p6WmZMbCUyQm15c0ZmZk8lMkZRUmZHJTJGSFFBQjltJTJCaDdPMlB0dmQ3ZEU3TWdwUSUyRnhWNGU4S1Y5WW00b3BSRktWU0g1UG96R0VQT1NRUzZnZFdHNWtOUGZiVnlQJTJGQlhCVHI3NUhOJTJCcktlUjFucEIlMkJlMTY5QUVENTRSMGhQWExIZlQxektQWXl6blA1bVl1SmZ0S0oweUlsSGNjcGdZVEowMzZ1ZlAlMkZKZnZ2WHhpaFlVWjY1ZlZEJTJCWHc1d213c1I0RDBGWGR4QWZCTXA4dWhBbXM4ZnhtUVhvVm9mM3RRZGFBZ20yWElyOE9YN0pTcXJYeHo2dmFtdjhjbXhodTNEeEs0bTVia3diQjdSUlllTGxBNXZqVTg5UVJkNlJkNEdwJTJCYUVSQVBhWlNyYVVhSyUyQlNaODY1RHlsVDJrNlpSWVRKcjI0TyUyQjRFaUYyZUxra2xSdnRXOUN3b1VJMU53eGJMTSUyQkN6ZWEwJTJCTUM4SXV4aCUyRjBlaiUyQmt2MnFCVXklMkYxajNQNUUxOGRoR3BlejU2WDRiZmpHUnkxdnVmeWxEJTJGNGtqcDh5SGtaTW9nM3BIMWphQVBVRHJyY3lhb2VtY01FQU1zTlRTcDkwMG40eUw3RWxQb1NxZXJaeTBPZU92Szk1MEdodGdiNkw2T214VHFpZHdHJTJGRzAyUm8lMkI4ZWx2eDZPNzlpQlZhOG5TcHc3TmVrZzJuZTNQcnZkaGl6WDFMdGolMkJ3QlFFRDVMJTJCd3hkNUtJdDcwVEx1VU13QjBUVEc0THBqQyUyRkNvZWhYU0pxZU1xaU9xaUdoN01lcHpwaU5tZ3F2VXB6RlFpT1BzVHp4dlZwT1lEcVh1Tkt2V1NrYzhLQmF3NktxMFdCcjdRUHo2YXhEb2VRNktsTUx4V0t0THBuTmRBNGhkZ2RNRiUyRjh1ckhHRWxtODQ1Zmdiblp3RUVFVDBuZTR4dnUlMkZrcHg5SUdGJTJCdWV6QmRMVzBGSlBrUVpHNW5PMmVhbm94QUxoRCUyRldYM0Q1SnJla0lkMFl0SmJkRThEdEE3dUR6bXFmOVNENmVIbnZDJTJCVTdvdnI4WG9uJTJCcyUyQkVkbVMxdjEzTHBhVWNHd1hVZlh6JTJCVFU4VExtb09MYnFEVnNndGRPWTl0cjZ3azZzNTZubWVyZVdMb29GU3VnaWJtb3l1dE1PdVdVNjI0VUN6U29sWUZ1OW44RElqVThNaHMwJTJGblY4ajUwWUdvU0IwcjVnSHk1SlJkTEJ6SFNBS2xDZGRYUnV0ZUJPaFpKSkZZWWZKbkRDSGFrRiUyQkdvNlV4ZG9XUllyJTJGcm9FMzVmRU5yTmVPQnRmN2l4OGxDUzUxN016cG84cUl4MEY3Vkk1cnIwRUdNaHRFZ3RnclAySDhSU3hnMTcwc0NjdTJyOTUxJTJGQVFCbiUyRnhTVldZMEdOWTYlMkJyZSUyRjhLVDYzQWZvJTJCOFZUcjh4QzE0S0l2TUdDcUhRbzhxaWlicVAlMkYlMkJlaUQlMkJwVzNoUmNtRElNc3RacmtkanhoeEszd0dxSGdybVg0UnhsT2FzRlBBdG9EUzRhUHdIYWdtcWdTRTRxTFI5b3RlUm9VTFElMkZLWUozeTNYN1dRNVdFWEVkRXF1aThPWXJpYktoa2hqaDVBQUhZd1NzZUp5dkRyUTJaTDBXSEJkTHFLNTNEeTlQTDBLYVJkd3NNRmhSYnRNWUt4UlRjcFp1ODg4djR4ZCUyQm5rb1VKcHhObGhYbE02TWI2d0UyJTJCYjZoJTJGJTJCUDNscmZUMlRadnVXM01wTnFiMWZtMXNoTzBVUGgwSG1kdTBlclNuTFpZZzlWRHAlMkJzS0VNdTJwM0VxVWR2Yjc0eW1Qd3dJV1pFd0xmMUp6TE8lMkZ6ZTZkWiUyQjRKN0J3Qk9qTVFLMDJzUVclMkZVTXFmWEg4V2U0bjVJUSUyRmNJVmFtamQwbkZJd28zc3Rma0hrdzl3R1B6bHA2cUJMS3dTa1JINzZKbENNa2Z2M2p4Qk45RkRyR2NvTjlMUEdRMTFoa2VrNW42RnFxVHA2cTg4MWwxRmZNa2NyeGRBYmM3RCUyQnk5NE9oNlZIZnNLUFZUdng3R2VrZFA0QzliR2dQZmo0S216ZEduNkRnRkVPWjM3TFl4Z0pYZ2tHZFo1YnBOa2c3Tmg4ZktRJTJGczJTajJJSjc0ZmxJa0thTm1zdk15eE1zZG9NQ2VrNm9YSnF0dU5mSHduTWcyUjBLZ2FqWHA5aXVTYlJQZ0liVkZESlNnUTZvQXZyZCUyRjlMY0MxQUslMkI2eFJuTnRIVmZlc3lWU1lCblR0SjlTZk5VU29aMnNDcFBBdDdjRWpuRzlwNVA1TElUODVJb2N1OWR4ZU41Rlp1Yjd3WXAxdWltJTJGRFQ2YkpSb3UwYmhmS0Z1UnZyNUVqNWtwRnExcWJFUHgxak9oU2hjc3Q4dzhNSmZvV1g0TGxOSEpJRzFWOVlWZEszanVXTjJnOUUyVnElMkZtb0FteWdFZGpoekZhNVhqWmJNcTNSeTJ1NmR6OXVlOUZ6UmtoOTBWJTJCU0t2a212OHg5T1E2USUyQldSZjRTZHMlMkI5T1V2JTJGTEd1ZEFGdnBTSjhqS2R3NmlBblhyWGolMkZKMUZCRVJQM1NIVmpjTnVoM0ZQVkNIS2U3JTJGakNiM3hWN1NrRGdXWUszWnI4c0J0UzczWHFSaXpJU3BTY2tPM3RlWmRhaUhkOVlZR0VMc29pRTNmVzFmQW9lTSUyRk5YQllnZWtITnV3eEhFaEpJdXhxUTZHSCUyRjBvN0ZUeDhxeWlmRDRFTk1aWFhSazZJWDVhNFZlbE5Ka0ZuZFFINW1kYnVsbHJOenppSFdWaEoxaEZlTTJOV0hwNG9TRXFmcVVpUmRrRFRuVHJhRiUyRlhDSGRUSEIwUzFhJTJCJTJGTEF4S1A0RllHYTJXT2NFSkticCUyRjdReEFXVGdTc25Jbnh1TDJYaVltJTJGWXFRQk9aY3VKSXQ5WlQ5MGxCUXc3YWdNQnExNzRCYUZ1alhZSU9TSGo4U3pDc1VkVFc5N1JQN0lYMFM5V1l2MnBkRmYzV0xjZWo4MG9tN1VVVWZSNXk4MU9zemtsT1RtNzluTm96UDRVdFNIUnlrMWNtazhsMCUyQkQ2dERoaGZ4R3FSd2VYcjB4YlVySW9IbTN3d2N1Z3lEYUY5Z2ZLaVZhODllMW9SRzlMWlVwRnhFeGxjOXFzak5acXJMNDhWOHFId1hQdmZxelRuOE1CcUtWM0RTWENBTTJPUDhXWE9mMWZ1WDU0Y3ElMkJGSjNsUGxDdnJDWmIxQUdpTlJQUnVGVDZkNDdWRkZ5JTJGc1h1NyUyRm1zWnFJWHUyWDB0b24xNXVDWFZPY2NnZHBJdDB5dHZzcUpsQXdxWnJTSndwdzZpd09zJTJGTXNDVnl4JTJGcWUlMkZQT3klMkZ2SDlVUTZmZmc3NkE5SHZoR09haW1IR3R2RENLM3c0TDg4cmoxMSUyQlVnYVE3ZXdiTHN3SDJPbXN6NGRvUG1NeXd6amFnMG5sWTNxQkxYckpORVY0R1hyVHN2eDFiWmUwdHo3bHNOQ0hVNHQlMkZEcnBZUHd0d1lhYkslMkZsQzNac1NyMWZobllSVUd1VzBFVjRGa1V4Rk9WNE51Mkxta21yU0JqQjBzdVVpNmdIOHdreVpZbUFRNDFqd0NMUTVwdVk3dUNiY2V3S1IwUURuOHAlMkJuajlCMlNYM2hiSXNQa2tVa3pXR1pqaldsMTJISUZMTlFoZlZEOTRBRXdnNzYlMkZZY0lKU21IZmJoZTh5eWRKTUU4ancxTHUlMkZ5RFlKJTJCZzZCUmdkcHpKcnRjenclMkJyRlNHaHlvQiUyQkt4eHJNUGM1dzlwTGRTTU40MW1HWVZnVnZibFlZQ0VWYktTUkpFS0MlMkZpbzIxR3RuZGNhY0FnNXdCcE5WeEtQbk91YlFKUFl1WDF0SGRJRXd5dWRna3ZPTFNzOTdpdnd3b1NXTGw0WmIzT1hqT01IQTZpMEFNWnJYUnNzS3ZQMzV5WVlFUGV1JTJCWkhmdGNOZmh0YmhUWlolMkY0eTglMkI5VnUwWUYydFVlZGx3SUZzMXhjbGZtaVNKbHQlMkJCVSUyQkdGaEppb2NSTllWaDhReTRCM1A3NE5YUEVlQ1lKdTRaN2FXNnNxM0xvclBTYlFicmVQQXIzcXZvMmolMkJsYzkyN0JDQUFpclljMDQ2dXVDMGtQRVN5WU1WOUhQcWhjbzRUbHE0VXVwYU5sd3ZHVWc3YTRqcXdGN2RFUFU0MUR0aSUyQnVxZFVuJTJCZk1tdHdnYnBMQTY2ZmkzTnFOZVhKN0U5R0w2bGFxJTJCSFhUd2xUNXpnOEpxUjZDOHkwN0h1dHI5YTVmTlk0Ym9nckpiTG5nWXlKM1ltNExSM0ZpTGRCNGN2c21HaXR5eFpGMXI3U1RsVUhqaVl5d2F6SWQ5VnhSUUNMRlJFQTZzMVdPVjRLUFRQaFlZYjVvczZnaTNoJTJCVjh1clREb2tuTHVMbVVOZ3MzaGVpaktqa1dnSm1ROXM4WHppYjJrbHNzQXdpeUR6eFV4QUswaFRCQUZOYVRWcnpRUEFKbHhEWUk5RExCNHFmJTJCWGl5V3hHS05hOXJPVmxTMGp0dHlxSmt0YmVYemhIRmdVcTlMcUV3T2dDUWFMc0hyblkyUWt6M2hjZXNlbHZLRmklMkJFdFd6SUhCRDh3ZVElMkZXclU5UTYzeUpmaXpWSzZRWFdleGpxVzgwT21pbmlZbiUyRk1SWlNXV3RjZGZicVQlMkZieDJtcExmV3lZQTBhY3ZPZXRhSjYxUHJwZVc0NEElMkIxTWxrV01nMEpCaGlCckZXdTdrUXpMMU5kUTFQZHpiS1J0VXU0ciUyQmZ3azNrdHMzUkZjc3hiczB2SDlKWFZOVmcyUWc2YXJCOTR0VTlvdUJ6TEhlNEtCV3hSUHJOY3FjWnF3WUdhbVZGeFppc21taDdvbXM4TnBaaWtmRUtTMWNXa2Vib3BSNGQzN3NPYXdHUEZVVGJmcEFUcXZ4OHNtbHNOOHY4UHRHZE1rRDdQbkxJWkolMkJ6d1ZDWVg3Q05lSXFzdyUyRlhlQSUyQiUyRkNnN3pGbU5sYzdHaHhOSVZWWm4xZFY0WnNUNFB4MEdPajhWSEhYMDFFbHc2ZUxJU2dSY1ZTc083TDRLYXJyYkFLTXZZbkM3R2VMNE5xOWt4NzRxMnlUaHRza256JTJGTDdFQmQwNFhiUkRLMjFXcmg3JTJGNmhKZVglMkY4MUZXSmZjYmh5ZTdReFRuVFRGVDVmQm5MVWZJT1JtNHd3aXExUFNBSFQ5dzNVRGNTTkF4YUpUNDU4czY5RjNwQlhucTVjRlRhSmpkdE9VTEZ4a2hnM2RWV1Z1TGV4aERJOEpGR2FZNENHSWs1WE53YXJJZSUyRjJSQXdWakZEV2lPTXVwQWhYZ0VWUFdoZ09leVluJTJGNlhrZSUyRndnc2tDWmY2NW1YcFdXOWk5SWdCUTBhSWl4SmRLeWZ0a1ZXNVVYeDdGcHVJZE02V3pVYXBuOFJRdmNnU2RFSyUyQjJZOGczR0lMZllPJTJGd2pPQngzeVlFbFg2WFpnb2FkeXVYWVlYOFAlMkZoaVpOMllBZEw0cjFYQUZLN001M3VMZFkxNFQ2RkpZVzNydnBhOVJkbDFReVNLb2xFOEZaN0tTOURDJTJGT2tzQ0FESmgyVFltNkxjeWlYbU91SUZ3VlljYUdXUmU2RlFqdExhYWxUWGNUUjclMkI0M1NJcUtYWWpoc1pra25BcFNQNTZEN293UVVTNmF1UGslMkZRS0FtSk4xSzlJNUpWSFVqZjN1dSUyQmI4WFpSS25vUTVjNnpwRGR6NWRlYVNOVThpZzBiMmFFSHhNckpmQlZpTWpLSFlDSVdQbHIlMkJGWUFTTjg1RmVIdE94R05UMTdjM25EZWdLWXFyYklPTTB6UnFadkpHYjdwZEljbU05VzBVVGYycXZPVlFFZTlvM2R4VTYlMkZRcDB5VyUyRnRQaG9BUSUyQjglMkYwbjA0RlhKSGNmeHJVQWtXT3dxcDBwQmJ5WWJ2dTh0YjE1VGRpYWdTbjVzZ3BVdSUyRkVFY0xZUHl6MENkQ3R4TCUyRnVWam9XTHg3Q3BjJTJCU0s3Y2RRTmJiTkMlMkJXTFR6MXdOUWExbnE1ZXlhS3E5QVJJOFNpR1ZxVnBqYldDMmJhNGJnOUJLRXglMkJoZVdTUDZRejFBSDVFYmNyQyUyRnBzTVFLTWE2Ym1uYlBSTDhUWVQyWEolMkJmdVR6VVNld2Y4aEI5c1lLZVNWc2NwNGlkalBQQXRYVmxqRElORHo1MnBsTDNiajVOeXJDdW1aUXJ4VmxRODdwOGtNU1R1ViUyQmVaOGlwJTJGV1Z5OTA5eGVCZHZVdktuOUcybDJCRkUxdWZ1MUpDU0hBSzV1UmZwM2dMNWclMkJXOXJTcVBwTnY4NWVLa1pObiUyQmlPcTRVNlY4YmNjN0pCS3kzRFdvMCUyQjlpcjR5WkpIN1JYVWROOU5LWUlqdUgxd1VwaUREckk3WGEwUzV4c0NybDVGWFZsNjUwWHowU1VNeXJNYjFYczA5Vko3R2VZUHVIN1dqTm9ZT3RLSjBjNnAlMkJCbGI5c3pqa3FyZVBLbHFmRWJ5TmVGdlR0d1NuY2NrNEFjUW5sSEhXTVA4cUZqS1drcyUyRm9YUEpXeldNYkwySWNSWG5IeU9JVXVXY3hhJTJGaWdxbXNZM2lHTzg5Z0RtQVBxSkw5bGVoV2FvNklYZjdtME11ak15TzdQTTZPV0lVQk9jQjZSSGdwSlVRdkZ5UnNWc3VLT3I3OUJkNCUyRmZQbkdXamxQVjFMUWlvb3dJblloaWY0aElUaTl3eThIcVB0N01INW5OeUdMb3BoNlJLZk1PMkxYZVR0VUhRM0syajFleDJ0eUE2RVFLdmpCZHNqWkUlMkZONlhNRFZQcUozclhnSTVNVlNXT3Q4Q0h3a0I5M1F2eGtVMUNFTnh3cEslMkI1JTJCSVJJcERXY0VzZjlnYXQ3NVdmd255NjB0ckNYNUJwZ1VOYWpYcDFycFVkTXc5Sk1PbEpXMCUyRlpqZzdWQ2tjWkV6ZkZ6am9VM2MwUVNMSzVVblRFOWJSJTJCcWZpaGZUTXcyVDBqNHJxRzgyRWU2RnVKOHV2WW1GR0kzdFcwNFV6aGVCQyUyRjdDdm9EZWVqOEVhaGNDbjUzek1BcnBYSTI2ZndGJTJGRlhYJTJGTzJNb1Z0Q1VMM1hQV3NyQ2xTbDNrb2xNMFJPRzM1Q1kzbXRtTzBEOW9JZ25ZZ3hqd3R0bHlmUDlnNUZPdzl0clFxcXJwJTJCQ3d2QlNCSTVYb3U1bDNWbzVPYWRYQ2h5amJLZVFSRVN0bGJGSEgyNEcyVkVDVUZrSFlVRCUyRnlkYWxhUlgyOXdxUjFkbGNySk5PNWJ6NUwlMkZ6bEFFeHl1UlRUS2JRMVhxJTJGRXAzaUtlZEZKTzZ3OVNUdmJFaUowU0RxQWkxa0tPVkhoODFNNiUyRnF0VGNkUms0TUE2TEg5R2sydERtRzdhd25WbiUyQktqT25YU2FDTVB2TkdjblBSR1ZTQTIlMkJiU3dhMUYlMkJHOW5hRTJUUjR3MkN5bkdTWHglMkZhN2xMQWhyYmlFZDl4OGhTQ1YlMkZQVHlVY3VNZGRXdHF6ZlViNEc5Mmw4STNzVkdFRXlXMG9SWVF1YU5UaWJza2hBNUtibUdxb3hpVHV1YkwwMGFVT3JIcHRiSmUlMkIyR2NzbWRQaEJyQyUyRjZwOTVjJTJCS2U3ZWolMkI5ZlVNY2p6ZndqRFQlMkYxUm9RR2slMkJNYm5jTUFDQVJXam5RdFJKeXlsa0hXZDA5ZUFSb0xTTnJQa0ZYVmlnYTN1YmpYUjZwOCUyQkNxdEcwZWw4bzZwdEloaUxvNUxxbE1TSVBIMXZON3MlMkJuZ0dBWlVpekoxaVlDSzZKUExhT0I0Z1J3ck5wJTJCUHQ5SmVlYXAydmd6QlJPVVcwczJmUjklMkJmeXRKNGR0ZzBVeFB2WDRwNUV1eGptVFZScTRjV2hIdHVVbmNvRXVIM1hWZDVCdVkwa3FzSjdYczFRb2ExZCUyQnNzc1U3NUk2R2NhUmU0JTJCeXljejRzMjFnRTFpZmowbWJDRDVTdzhicFklMkJtblZVNGxURk5nMUJYNkJSU1VvczklMkJoRTh1cG8lMkZmMDFnWHdsd2cwdCUyQlJpYkl6bEE1Z1FwdURFZyUyQjlhc29MY3hsT3ljZ0lxQ1glMkJlVW5qblRpcEdmYzdXVHFmYkxSRnRRcmJEUzk3MTRGNEI0WmVXMG1UVFptSnE5YjJyZCUyRk02N2oxbmd2bjB6JTJCUmFoeXl5YnpKSW5vTGxZQXNQeXJDdURnUFY0dk5QUVBST1hCdFA4bUlhWGI2WjVPT3dPOExCemZ4a2FEN0R5dlJmdVljeHBtWlVUUyUyQmJaMWl3RHl5eXV4QWVRJTJCRnJMb0laRmhxUFhwek1CMWdaS3Q1cXVQVGFmR2JSWmx6TzNIUXMzSnZBaEI2ZElxJTJGQTNFMHMlMkZaUUtVMGgzT1FNdnVHZDFaMWVPM2VsSUs2U0RQU2M5Z0clMkZYNkZJQ2JLYXpkdzVJdlVCJTJGTk1lS2c3dk9wZnlUT2p4cmVqZUt6M2JIcE50b3BpNlZVdnJaOWU5cSUyQkdMMG50JTJGT0licWhwRkdwOFBqdlNNY0F2JTJCUlBHOWp1MUI5ZFE5ODBuVExSVXRqWHFETG5aOXp5JTJGVWtvcU5LamklMkJBekglMkZCT0ZYemNURlZCWW5nSHBMMDg2aDRERWdzVlFVMHlYeDN1Vjhpc2U3UFFWWUlpQTVIWUEzanVBNDIlMkJjdjdBblRWSVFEUCUyRlhIZWl2JTJCb3cyTEFTdVolMkJEMERZaFJsbFJJQmc3aHh2RHZTU3NtcXVtRUZJdjh0RFRZS0klMkJmUml4STl0S2JZMElLSnhOUlFzWVZHRkRCZU5KUU5MMUhjeUtaeFpsSTdGeGVVSG0lMkZCU2xxbVVObU5VRzh1RnZ1elkzUmdDZXNYciUyRmh0QXBQQnJGbDNEU0hWMGxQbG15aFQ2ZXFCJTJGeUpFajB3SUExUk9iRU92V21KdFBndnZGcnoyeHdleHc0dWtibEJnUDlEayUyQm42RnphMVd2MTRhcGhaSnpIYWllc2FyR2xyRGo0RCUyRnVwb0NaeE84bkI5JTJCTGh4amtiR3FYOVdpU0QlMkZwZUJWNVZoRzRVRFk4b2d4VnJDM1hrZ2Fic21JUWpvbzNLV3ZuTEwlMkI4dkRqYXJMS2RLbnkxWndiN3drdThmWVk4Nkh0TnFjQmlXYjZQYVpLajhoOGVLZSUyRnh2cjhNcjdTRmYlMkZZeGlSbmhDZmsxVDdKWEFOd1glMkJyQiUyQkV1ZTlmT09QVGxVdk1uejFlJTJCODFhY2FwSzdPdlo0V0Ewa3h0UzVMNXNqa2V5WkpxQmwlMkJUU0YlMkZQOHh2OFk0N09FZGMlMkJkd3prd0piUWRVVTV2aGFpemRFQ2lKdXdWWGdtM0lHbFhjMHRBc1JSMFBxWlhIelRXd1BIYWQyb1VrTWVvN3RhcGZkOW9oNVYlMkZSMTJtT3BxJTJCeENLOVlzUGNSZU4lMkZTV3hoZGJmYmhGN2dYJTJGZTFoZWJTNnp1WTJmamZQSEozd0FPNjVGY0xlVjQ2QW1HMCUyQm1XWFBCJTJGT3c3Um5yNGs0WVF2Z3ZGTnUlMkZzYlNySyUyQnZGUE51cFZrdUZRTkZFZHlRRHBXVWtpNEtmejRZb0ozMWYyMTJwMVNRb2lYYmRyWjN1a3hmaFZhYVRsMXljTjBQMGhHdDJPcyUyQnd0RDBRRXJKOWVPc042UldIbyUyQmElMkJBcm0yM2RzY1c1Q2U2ZUM5dUtFZVp6dnB6eWlJWUVJbUtnMTd2MGZQOTNic2E0WDhxSjhhU2VuNnpEekFvanduMjN4biUyQk9LNmZSM3lOMkRsRHQlMkJ5Zmc2WlZuQUF2RTJXMm53UGZjJTJGbnNmN3dmJTJGdjhFbzRLellrTG9PbW9UNDJiSzhDYVFLUUpLWXRIMkIyYUllSWxncU5aS2VGajRHUWU1Y2dRd1BsV0NQQmJxOFpMdnZ2d1MxbDM2MnhyOXpiOTJVZVh4cDgxd0h5YVVUVzNKM1BpOExBQkkxMyUyQld1NkZGSCUyQlhxVUpFWHJLOEptZE55eSUyRkFib1ppckI3emtVRmZrVzh5anRzclQ0JTJGdXNhaWolMkZIZVRrdVRtN0YwVFRVQjlYcmFOcGQ4WVJoMXo3aURhZ3NQMldSVlFGQkFKc1VySldKeDQ2aTRDaFVxcGp2M1phODVhU041JTJGaXF6M21FaSUyRms2JTJCTmhFU1Brdk9sWVd3czdKOTZ1OXJVVDFlUk0yV254TkFYc3lEMzJaSyUyRjZLaDVvUXkzN2swWnFHUzlrZ3VVNHNFVzhMNllOMXlmQ3UlMkZHRVQ2SDlueXNudHIzN0liaXpwaFVGS1Z3QVBHMFI2Y011OWRxMmJxOEpzeE56UURvOGElMkZ5eGlSMTFCWmUlMkJZcUZOSjIlMkJPRFFBYkFEMmpkUnFxSyUyQjJWdmx3Rm5CelJxUnpwSzVVc3BMRjFlSmJuclpTSG1LbjYyYzA2MGYxcFFPclEySXBNVlpjMnhZUGpLNUtuWjRvaEZKaGtkOCUyQlRVb1AzT0JGS01YUVhMVGw0bnFicUxQdWRWZXhwY0h0ZVhIQ0g1ZXFjYm9UeERndG8lMkY3dkFUMDNoWW90MmIyVTV4dE4wTWo0UTJFNFlPajI3dEI0eVRMTU5oMFFkekxQMWxYbE05MmhlczZyTjZuUmQlMkYyeDZKb1RXbEhFb2h6NGpjeFNXOUYlMkJWTG10WjczSzhjQ0tmbTNQNDBHTnRWRkVRMHBIN29JOEFtVkpJZmpxQTZETlZXQmoyVTRvdSUyQlN6NEZYZkJwVjIyeHZwNHY0SmZUOHdYcnUweiUyRk84YTh2MUNrWXM3ZGRJejJ2d1I3Uk9hNm8ydXpjdnFtJTJGcEJrN3FEdEklMkJsRlUyZExycGZUTHdKQVpISndIRXFvV3VFb2VqejluMTNnWDFCT2xldWFCRVFzTWVGT00wJTJCZVpWdmw1OWJqdWp4b045cmxWTkxOR0xGRDhDelo3c0dVeTZlUlVvazJtd08lMkZJV0puVWdMV2twR1YlMkJjNGFOd0xhNTVRYlp2bEJ2MDl1ZkNGckVzNDZwM29QUDJhTlFSN2pURzZ2SkVKMHpxSldZOFI0QkVyVlZlS24zNVFjbXEzcVVYU2I4bmQ5TVcySG5YOEVKNE1QNDIwblJMM0k0QjNvUnB3TDdwQWpXQ0M1M2pudTQlMkZKZFRYaFRxMk0lMkI4T0lFUFR4WTlIRVBsJTJCeVJYdTRPVFFxJTJCZnNKJTJGOHl5THFnSVFXdGNVbzAwc1NDTUNPJTJCNCUyQmVGNEFNM0RTMzROOTJZcnUxcVVVWmh2cXkyS3hmSjNERyUyQlF1Z2U0dzNPcnkzMmxIcFdya00yME1vam5manFORkYzSE50TENVd0IwY3pkViUyQmpoYlh0V2FMMmNic2xwZGtDJTJCJTJGUUxxR0NaSUllVTFCWiUyQjFrRUJma2JtTE5KYVZ3eERwJTJCbWMlMkZlaENySDhSN2owbklPcGdpSkZlOVoySmpMJTJGQWdPNVVibGFjWVg0dUZQUUppZm9qQzFBaDJkNiUyRjd2elhIdk5IVSUyRmV3ZzNiZDZ3Q0wzMUFpMmZjeGFVZE9XYkJJa3FqRFhSa3c5emRWOVJ1SFY4YUdJSCUyRlVHeFlzdyUyRiUyQklMWU5SZHpJWlJSeHp3aE01dnN1bGY2ajFqamlyblcwQkFuMzFleXdRJTJCblhWJTJGNmR4Y1JXOUFRJTJGUUFYVnR6MUZrZXRWSUF1bG1tcGdGSnlnNWFWakdYd0NObzlRU3VtQXA1NnoxdEMwWTJQSXI3JTJCVWNrQXFiMyUyRnpyNE4lMkJJekliRnAzV3ZzWllVZ29tZ0ZlM0tJeXFRbnNVdVF5NWx6VEh3Z29CMG9rR3AzSHR3eGpJTW5OODJBVk5CZEFyYWV5Y3p1OHV2NDN1QktpWjJLMko4YVlwRTVZJTJCVEljc2JmbTVobHY5RXB1VmNjeVQ0cG5KdEtpQnBodSUyRiUyRkNQeXB2Z1AlMkZDTDNYUHp0WjFQU1Y1WWhqNVYyTDJWTG1pMklEb0dIOWhUckVIdlh3bm5uUUVaVU5Jbmg0RFpwbDl6QU9TZG0lMkZiNXZnWElGbDhnNTh1TmpiVmJMWVBBU0ZTJTJGaG5BUks3MHJpVjhLa3hQTTV1TnN3VVFCelZIa1lhMzl4cFpjMGthQWhhMlBtYTFKUDJIWDhxRnY4VVk1c3M0byUyQjR6NFRqUXNjbjlHb0xqMDZhTUJzS2xMQ2lIZEI0dFlmUzltUUd5RmQ5NEh6dG8lMkJ6UGNuUUcwMWJsMExyM2I2RnY3RkZkMWpZTGpwZkxPRTVXVzg2Q0kzaXNPemdqdk5hRnJJQmpmU3ZqZmJFaDY1VmRMd0hoQzByOURranprWTNJc2h2T3ZKQzJwbmV5S3pxdG15WGc4amxpbll0JTJCV29UUHNxcktkUmdVU1dnZlBIVm9UQmY5aGRLSlladklhYiUyRlJHZkVyWGdubmc3JTJCNHJhODclMkJvJTJCJTJCY0diZTFPdWFlWjBvTU53JTJCN3Q3ZUl6cFVaTW9ZQW1Yb2x5enowT1NISzdNTVVsSmF6JTJGdjkxNENMJTJGZGhEMUlLWDl2a1ElMkZ1aGk3NlRZTVVweDNDajVEZHZOdmR6MUY5UUdvdHE1S1VhMHZEeXc1d3Exc1JOQXdSZkUwdHllbTBqWThaelpXVXNlVlVKYThGeWdnNUV1MDRPYkx0dXZoRHFTM3MlMkZkWU9oUG5XRWxtNDlYN3A3M1hyTlElMkZXaGhDUlFWVnZEbDRyYW5HdGd1ZFN6TWZ3M095RHVQdEFoNk5SJTJGdkdwckdzOTlQRXFJRmtFRlRmJTJCWFRFejBlMWU1clJMUEQxNVFlRCUyRjdKZlVLQzdaQTdDTE8xaEk3NEtvSkxDeXFJUUh0RExsJTJGcExUb3FZbFdYQzhHb0R6RlY1RlM0a1dyQTFneHZVRG9aUEtZdnpKV3FXUEVtaDZ3RzVuUTN2dWN1dUIzeHV3YnZRVSUyRkFSd01Nb3dGbjhXSlFjOVdlWSUyQkNRUlJ4T2luSFkzJTJCTWdWeVk1Ymo4JTJGcWtyZHY0MmtzRWhwV2xhY0wxM3FQTjEyS1B0am5Ic1pud0VLNFA1NHBYOGlEcEQwaSUyRm0ycDRwODhqa3czd0NPVjQ2Sjh4WiUyQnFaRjUzc25FWVJmZ2w0Y0RLMUk4Q1VkSkUxcENlRTE2VnUlMkZzQmNTNzAwVXExbUZJdGxGM0EyYzJIU0FlWVNSYk1IV2dMQ0tzJTJCT2thN09XV1l3JTJGQnFac2EzVE1QRmpBdWlBc0diUEM1V0NPYWNhQmY5dDJET0s2MjA5Zk1VOVFOWjlCU2RPWTlVTXZqQUtEZnZuUUslMkI5TlcwNSUyRm9pbE1jVW45WTNVTjZPRVl6REtjU1NlWWdRbnMzalVWZFgwbU9ZVVF5RnVncDRRJTJGdzlZcFF6UFJIbjZMeDMwb2hLZFR0cmtzbUlFS3czY05GQWFNcVZvZTlXeFVMRXZ6U0I4dTljWXdPSlpBNmI5R21BTE9JU0g0MHkwU1ljUlgwUVJSSWwyOHQza2sydVo4emJmUmNxQnhNUE5WUCUyQnE4cFNTRWhmNTFXWCUyQkYxQzZqYkllTlpDNXlmcUpwWnl3Skk3eWk1JTJCOXNCcFJuWVpmWjBCZWJzYnNMd285OHJCRnpjakdEMlN4N25pUVFNYW1LSVRwVmRlTFhxZDNXaXpUQnRCQUdQR0UxbGtJdXlpd2FPV1Q4S1VHZ01BdiUyQjZ4RUk5MFAzUyUyRktCY2J4QyUyQnJYNEcydVhTclUzWXpQWXZNJTJCSzgwM21OeDNHTnk1MllEeHhXTVcxcUthUjZlRGNtc3VCMFExN2ZhdTg5UERGZU1PTExvMGVTZDVsSnE1NXo5M05vdWdPTWtLZDM5aHIzeVFESEtoa1JkTGl6R0Z4Vnd5d2E0b2h1bGxTU1Rkc1FSamxmVCUyQlVoSHc0eGZDeWgxTGNJWjc0UXIlMkI5bVcwZjluMHE5a3Y0aFZNaXh5Rk1yOFBzdVBmZiUyQjAxQ1VRJTJGc1phQXJDb1N3TUxHYSUyRkxINGhuM1d4dkhOdyUyRjRiUUJUalBORE1PSCUyRiUyQjJ1UFhiY09mNDRRbTI2Zm5wJTJCMDl6SWlEa3NmZjNBRGRHREFjT3h5OTJkSDBGJTJCZlJ4R3pTYTQ1WFpMaHBTTk9NSmhZZ3FZRVhpclZwdk8lMkIlMkJFUzF3WCUyRlhCamJVdVUlMkJtT3FRQiUyQkFvWDZwVzNDYUdXbHdxUXZsMVAybTZZVmxjb1ZZckJEU1p2WGNlOTYxQk5MME5WSCUyRjVkZHVhbVIlMkJDdkhuZnpZRHhvWGRWSlIzNVhCR0NYOVZ1YktZN0lZZmtMOE1TdU1PNkVUUVglMkZnbEwwSGcxakdJejZ1Z21abU5qOFJWaXVDTlJqcWdvVHlkcjNxMTdKJTJGRlVqWlR1YjYlMkJvSyUyQjRuR294R2d6YVNJOHE4VVpWJTJCNjAxbWNOUkVZN1ZyeExMbUI5OG9IaUdCT0lMQ1d2ckw5SnJiNHVwd05nMEwlMkJYb2VWYW90JTJGQ0dCNHdjOXRyVlhiZ0J6QTU3cTdjNmN1JTJGS2lCM1VBZG9CQ1UlMkJFYkFQcnZJd0g4TlhYeDE0JTJCYm5MOEQwbSUyQmdIMHVyJTJCZFglMkYlMkZiUDRUSHpXbWglMkJMJTJGQjlqNGllcUhsTyUyRm5vaCUyRmd5d2xCMXgwUDNOeVRtTlRJa2drbmdaNSUyQlpUUnVYMGdRV3FXOEhVZXlTMUc0JTJGWEpWeUlOaUNIJTJCOGltV284QzBVa3lzS0NyYm9KbVolMkZsT1pERFVEb2ZYeHglMkZIOUhZdmttYWZBMjJCSlF3N0RkOVVIeXpsRzV6Y1BpYmR6RWdQZVFiYlpweEJVUyUyRnJ1dmR1SXZ2dmtTWlVlZFBBU3N4b0FIUExNN2phNlBtYjclMkYlMkIzdWR6MFN2cVp3VlVJb0JTWXNXOE1JZjg4UDN6OFdtdiUyQkZWN3VmWmxpRmJPU25BMTZvZVo3NUxZOGNvUm5GREZ2Q1lldENYbEF1NEpFZWY0MlBqNmkwTGVCc1kzSHFlQmx2Wk92dThlRSUyRlBzYTM5TTdtdnZQeFdzenpSTlgyRXMlMkZRcnVRejdFZW5mRGFVVTY5RExmbTdPdkRWJTJCb1I1TDVYbFJFV2wzJTJCSlF4JTJGZnR2Zm1NS0lUY09KOVl5cEpkNG9XN0VOY2R0VjVNck5jZlRralNmRFIxJTJGamFKZzBWa2E1SFBNZnpoWlVOQ0tDZzJvJTJCUU9HZGxxZTVDdDY1c1NPWnRybWdYNVhveVBhWEljWmZnbmdFTGNqRDR1enBYVmRpRFpod0dtaFJjU2tQMVlUOGkxZEM4eUV2eW95cFNGanEzJTJGMGRFZHBTNUJBcTRYVjhlUHZiSE96VDc0UjBYcCUyRnU0VCUyQlM4MlUlMkJGNDJoODEweUs1WmczY3RtUUpmUG1yM2Y2bmNrSVgwMURVWnB6YXR0bE4lMkY4UlBKemdDamJEWDE1Vk81JTJCd00ybEczWkYlMkJWSVB6VnJEbjNubkV1NGNBTXlTU2dUdERVRkRMT1dodCUyRnNJSzhBRUJ6MGQ2NDhXbkQ2ODEzYVhxMzlweW9MeGVjU1ltUWNTaUg0VWhvbFBWYnJNVWN1eWRyT0xsMW12elYlMkJIUG5zS2ZsanBUZjUzM3ElMkZQU2ZjdFVqdjVaJTJGSVhUa2lpNjkzeGhTcjlLOXVaV09VU0lYSVNsUWMlMkI5OGRNa2RaTFB4enV1RjBqTVUyYkc2bVhzcm9MOGNWeE8xWm1YNktGRjZVRENwVEpRaVlKaEhiaUlxJTJGWmNaS1NURjNDa205QURwdVhQOHpvaTg2R0U1YkY2SGdBdGZSeGkxTXlNMSUyQk82YUFIRVVzZkQlMkI4MWtwdVQ3NEIxdiUyQjYlMkZ1TTFxYlI4UHpvMm9obkYwZjBXNGR4dm44RnJOMDZ5RXJsdUdzT0t0JTJCeFVyNmhmRG5DT3oxZHVrUlFEMEpvUmJKWUFlY1BxQng0aXRJVDcyJTJGN0sxRW83NXRYU1NOaHlnZTZaYTl5Qm96R3VEYjM1M2NYdzdMQzhiNzNBRlc3NTZzZmRWa0owWnd4JTJGZGd1eTcwdHVPbmswQmNhWVR2aDRKMks5MHY5a2F1NHFUa25rJTJGUjZwZzQ3em5nJTJGUkV5d0FvTE5pRm9IeSUyRkFuVmdvTnFuWG42eG9vR1d2T1E3R1dISFFrM0Q2eUlwemE4eFpXJTJGbFBqYUttWk9KVk40YjdJNVZRYW02MDNsSVBEbVcwTjA5JTJCNTdZbTVUTzkxbklJTERpR2I5aTZ1ZExuNzRpTGFCNjEzQ3ptTGsyVUlsOE5aQUNxOHFqWHZwNHlyUTNabE5hUmdTakxUUkRONnYlMkZKZXhweGNwaVhUOHZBdVdabjllbDZIc1FvMHpCR2VTdVZMMXZjcENYM20wbFdzb1A1emw0VlY2eng2TTNqMXdvVTdTc3VPY2VTNXl6QkN2ZiUyRkp1JTJCZ1R5UGh3ZUFQNFQ5cFlub1RGZ2RweDVzSSUyRjI0TWVqc2VqdGwwJTJCJTJGOUV6dUdpTjIlMkJLUzdDNmJDV0RjYXczTTl0anlxQ3JsNDNRZDNEOENsajQ4dG5NVHJmd2xHTExYR2ZQYSUyRlpRZjNXZVFubmdBJTJGRDV6RmJmJTJCJTJGMzNYRmN0WTVmTE83clFldTh0MmJuYyUyQjFwcU4xdzU5d21uUVZrRmZDcnM2RkUwNnFsMndPOEdjVllyczQza1N3c00xR2tjdjlCalI3cVdVTThMdGVKanRidm1tbTh3cWI2eTNQRk8xNld3WU0wYWE5VzV3czJmaDRMTEYzeTFEU040YXpQWXR2dFZUOVB0ZTRKRkx6M3BKTUl0YTElMkZmbEoyJTJCbkFFU3ZwbklCaHElMkZlZGpaTDk4NmI4NVR3SUlmbVZlJTJCaUFqY3lWWGhuSDNsTkR6SFZOczc3c2prUlZjN3UlMkJrblR1d3FNVyUyRnNKZUhiVDBmaGt5JTJGSkpTbk9RWSUyRjVYZE5yOXR2VW5xR3cxb1ZwVWdXcVdiSEprJTJCVWRrWVJZZVBQYmtIU3E3RkJmNmVDU2Yyb3UzR3c4bkpZaWhWTHlqZnVtUmNBV3F0UklCbW1mSE5MSlhrUyUyRk5VMjgwOUc4dlRFSUlYNExzbmRlMHBvVmt6bkVTM1dNdnclMkJleDFhbkxzVVhHaU5WenJGdE1NM1AyZmNYRHRVVlVBdnZwV1hwSUF3JTJCeE1MJTJCNiUyQkRpWEY5eCUyRmIlMkI2OVhkaTFqNTN0djkyZkg4T2NtQTI2a2NWRTBZb0J3bXQ3cVlJdmVPbUJtOU5NOW5hWXhISlg3c1NDbk9hdTNMd3VmSVVOS0c1VzlpS1BYaDlqSGtGaW5WR0NnYnFXRkNLbXZaSXo5UUNKayUyQkVIUyUyQmpyZnVvam9VbVpYcmNFbE1HZnZNR0hxTTRRYmc0WGJnN2paZ2dlaXpvZjdxSW5MbTEwckQyTzV3Nkp4RkJGUmRjNDhmU3BrMW1tVDV0RGklMkZMMGFOa2x3d2x4aWxYbklNNnJ1aWpqMDA4MmRIRHN2cDNJJTJCVEVFZnlQNWF1WTBsT0pRaiUyQkVuN2dpUGZlYzhNTjNnJTJGMjZ4JTJCOWVpZEZLS1JkYU1wa1psVlhyZkp1NmFCZ1JoWkhjUjclMkJCTzYzSmNrTHEzaWg0Mks5NjQzbFVYWERGUTd5ZnZHSUVReFd4YiUyQk9WZnltclY5dlNvbHJ6ZWZ2aWRvbFFSampVRW44cHFvVjVBeGtrQlRtTHdNMXltMGc4SkVGSmcxckt5OG5hbG1JTW41VjFSc0xPa25yTHl6eXlPSlB6aEg2QTNUdm05SURlayUyRk1FWlRjclM5OU00SlFKeDJRT3VJMU1vNE11Y0xzYyUyRnJYMHBYRW5jeFNrcERNdHFzSzdTdDFvQ3pFUEdTZEFWSzhUQkNxNENKRFJZcDQ2JTJCSzFKeHF5R1REMXg3bHdvdEt2JTJCOWNvbkRXaUlsVkdNQUd3ZDdzREp4QyUyQkJ6b1lLZkhROE8zQk9WQXh0eUtvbnhkUjNkTEZQSkR6NWhFWkU5Wm5QSjl3NzBqVU9QNHVmbElPUHR2TUhMaUIlMkJhVXpwWkZRR1dqallGdFJqUlQ0ajN6Qm4lMkI1RXF0NU9oUFdkVjRvYVFCMHFEJTJGdExoJTJCWnYyR3lyJTJCS2JRZ0xsZUtqRXJibHE0SHZOeW5HbWFkTlhtY05uV3U4OHVFOGlaJTJGODJqbjlPeXBBdE5sVmxmRzlNVmxoMXkxJTJCNTg5NmdZaGElMkZWTUxzczdERVdFcEpvWHUlMkJYTk43WW9ZRkFCa1prQ2YlMkI4WGtvSlp5MmlBQTVma1BTbTJQZkpIaDgyUE1FVzU2M2xWZGYwWDFJRDFKZE5GSFNLUnA4V3MxOVVwWDJYblBSbHkxQlhuYlY3TTh3NXZWJTJCeHJ3Y1Z6R1BBTnZrQ3JCUGJTUjV1MG5JJTJCTiUyQjQ3NDVlb0QwQiUyQnB3MDE5RGl6ZzRLRCUyQlJzTFlVNHlUV1VxaG5JazZGUkViV3Blb0NYJTJCbmpnOWJKYyUyRjk4MlFieHJGYkFFbDZRSG9BZ3VTSXJCZnpDVEd3cTJkcXZwd25XYmFPRTFmRnBBUU12VEJpZ3RzaEJJSzFJQnIlMkY5QWdHbVB3U2kyZjdRM0F4ZU5iaSUyQmU2ZHhHJTJCTm4xJTJGUVFZOFF4dWVWUUlkdkNCdkdpYWNzazZXdHl4T2RQVjRjTCUyQkhiMjA1UUx4SjRhJTJCdCUyQmhvNGxLY1ludkt3TUNCOVNpM1pTTzNJQldqNDR1QWFiTkdHR1phVExpREFTQWI1c1hDOGxWRmRNcDVjdXBHaW04MlhFZVdvOFFqdnQ4V2FYZEJhU0p4WlBFNjcydE1KMnpHSXRweGNIdUsxQkoxUm52cnM5OWIlMkJmYk42JTJCJTJGN1NuekYzYVZ0JTJGT0tOVGV1YjlZRVNKdlY0WFFkOHhrJTJGaWZDQTMxcElTSFM0ZGZicXRDSHlYek9sZk5IMFpZQ3RWJTJCcmhIY0Z2dkJLUnhCU3BpWHhmaDEwRiUyQlZxM2hrNGp3JTJCcE11cXFCV3JmT3NPSXc4OTJkeXFHZ0pINmVtcThFTTI0RSUyQjlRcFdydW1Wa1ZNNVJGbSUyRldyVVRIb25OQ1BXMmFsbWxHY1hqQkp5c0dkczZBVnFkTVh6Sk5lajFQOFFuZnVyOHFEUGpUZXk2NXgyTjNOdnhna3BuJTJCZGpheCUyQkVLa0xLaUREZnZNU1pQSk9LJTJCM2dJSnQ4aFJSQ04lMkZJd2dYUTZMQWVZZlpxQWRraTl1QkpnSDRvM0FJRyUyRnRrJTJGSTdmenNmNlo0UDJjWHNoJTJCRVBLd1VpSm9iWjRlZEpucFdUam1CdSUyQlZKcWFocXJPJTJGZkk1TjIyT2hMeTVyR2w1USUyQmFOdiUyRkdEY3ZRME0xSjBoJTJCUXVKamtvTDlEblR3YURJRWVuYWF2emc2dkVMcUUzc2hQcklWNmxBUmtMSWMzVm03eTBqNnFtWFZaajA0VWk2a09tUyUyRmdiMEFBcU44WlgyZkdtWFE2OGpsWnNhWTdxbDR6cVRFcXRjd3E5NzQxeUJ3NXFDNnhpMXZnbDBiY29WaGFURVJ5Z09ZanBqT2pibjB1d1hGd0NoMHYxNmdINTl4JTJCZHJ1NThkWDd5R0Z1TW5wMVBYbWUyWiUyQnd6V2xFekdpZyUyRkVqZTdhWnFhRU1hZCUyQkMyYkolMkZlMUElMkJWazRKSFVuOW5kZjRLWjdxWktQN1dQelRIJTJCUGgwJTJCSWtpd3lXbzlYUWJ5SkRDallNV3dXWSUyRldieTdTVzVYdUoyVGpGQ0x6UmxiNmwlMkZTaks0WU02STY1MHR4RXFqcHpwOSUyQmlFQ2V5emlxeUklMkJoaE5RZzFkZW1NZmVoR2lsUVZxc2tZOVB0RW1GUXhoJTJCMVlNMWVIMWNLWjNma3dZOElUTGpLcHNCeTJEZ3NJMUVsZ2d4ZHdkbU16aU5HOGNZUG1tTjQ4blNJaEg3dnJCYU1MeFJZaGZMbVo1WFR4R3JCZUNwcEFCRFhYa3NOeUxIOU05Ym5WYlJhbDdINUprbzI2aE9jM3N6RzNjdG82b25IU2xWVWN4QnZWbVp4MjZLdnJGZWVObzJiaHNDSHdnZzJzaEZlSUJwYzd0a29WVGFDYiUyRllsN3BSNUZZN0d2ZGI0N05FWVdFS3JqTkJ6UlZ3eHd6dXR6WDFNbkVtSUNKTXpnM05WJTJCb0RYVVBlSFF6V0tuQ3IlMkJrblIwbnExTVRJZ01QM1JjalFYaGRXcUl5clBEJTJCUmFkWExMdm9FMldJS1pvSUxqa3dXYWcwNVQ5NzNjMk9tbWZKWmthdm44JTJGdlJxR3FnZWRLMlpwOW5BJTJGS3plc2FtMWp5U2JWODZqOHklMkJqRHJIQ2d0SHYlMkYxOVJaM2NuSkpMNGhWJTJCY1Vvck5Icjc4b2NpNlY3QTFzemNWWEhKMFA3ZDlkOU1tNSUyQlpIWXNWJTJGcHVMZHQ5N0NtZjBqZTdJMU45JTJCYmhlUkZNbzRCVHNhZ2RvblVublBlNkxnSndTeEZnVXhFVSUyQkdjTjMxODdZbkI4ampaWXl3eHFtMGZPYnVZZERUUiUyRnhhbWVBS2pwU3c0ZEk1dUR0a01yekw0VHBzMjJ5Qzc4MFRKMW93eWdHRnZPaWU2UUJUdVNlMGVMOXhLaHJ3TkRqYTBLUVolMkZnWGY0aUolMkJCTllMJTJGT1Y3ejJ4MWN3dzMxUWNla2FLdHRNYlBmMlBTc1M3MlJOeE5aJTJCMzBZYXhKUnB1MjBCa0ROZ1RDa3RBMUZFZkZxRXlTJTJGSkx5MTlwUHhSYVZxemhobWgxNFAlMkZYVGZ2aWJtMWUzTGVqekYwYVdIT1dLNGY5YXFBbUIyNiUyQlNiZEFTMm5nZ0NMaTJNTGszUVJQeiUyRk5UWllNellXWHdTRGNUQW1JbDB3aEdJcm1qJTJCbHYwNFlqSk5rTndoJTJCdSUyRnNkTk43OHJLZlU2U3ZpS3RvRHo0cGZ5TklwQVlhcnN6ckNsTjJVTlclMkZmRTdyNlRYeTB5JTJCRjlwaU1QT3pMZ2cySTljTWJCbmNvZjBMJTJGM20zb2lSbzNpQm5xb1ZtV0RMZnNGWHBWMyUyQm9OSjB3NnBvaVlNQW1wSFB6V1VPajNpUXF4YnIzd0VneDlGNEFwYW93aHhVRVV0b1hpJTJGRkxER2R5ZlZtcVR2JTJCaXRjRms4aG5rWDlaWGdTJTJGZEM0Q3VxYThkRUxrUHBtMVE5RU1hUGFjOWdWUmlxeVo4JTJGTHAxMUlsVmMzd3AlMkZHVU1hejZRcWVuVktUbVplVkRNazNiaTllR0pZOTFIMWNwODRHZWRmbzJsJTJGWVpRc0RmbXNRV21NVUJhRHhqbHlSJTJGUkRxJTJCUGxLcUFKWXVRTWF1eFQlMkJhTTRkSjQ4ejhvQzdtUkZMRWpLb3dmMEhVSXIlMkZhYlFEdkdpSlVrYUglMkZIR25vJTJGN0hWVXhIRkhaalMlMkY4Y1hzNkg4YWExQWh5JTJGSzF1RzFGaXR4R3Y5ZjBTOEJ4WiUyQkVKYzJ1Rk1MY1YlMkJLd0gyam9GM3pKM3NqY2FyQmtzTGRMU0N1SzA5WG56UzYlMkJIOUYlMkJXR1FUamUlMkZQTjN2VFNzblNjOUklMkJWbmQxUk8zcm1sZ09iOEI0UTdjMDE1eW9KRVB4MzklMkZzSSUyRkRtazYwUXp6Rnc2dFN5TTdpQjNEbkhWZVNiZVRkdk42JTJCVjAlMkJ4d05LVGthT2NJdWg3U0hpdGNHUWkyNkZlOSUyQiUyRmxocll3JTJCR0x6aGNud0VhcG5udEV6RVN6NTM0TVlrUE1yWGtkYzN1a1ZJU3lYa3lTUzRQaWl3SmFpSWlBQmdpZFVidzlvaVVaM0JZWWt5MWdJNUp6NlIzZ1pOaldkcVUyd3pyRlRKdGNsb2RHRjRUOWhMWk1leCUyRnFZM1gwSnNpaThFWGdsMk9IYWlKZ0RJbHFQUXRSWVUlMkZmcFRueCUyRmpVTGUlMkJKdnpGSko0QkMlMkJxSGJMdElDUEwlMkZaZE1oMnRkQ2ZQYk5ZbUpOc0RQVTNDUW51cUlJZHd6NnklMkJlVjJIYmpsNEUxT21wSDFhTUlIRXY0ZGRRUTNhNUh4biUyQlFtdUdPbHhMd295RW9iVUtObXVEVVpaTUtjVTdpJTJCOVN2MFptZXJ2S09aQ1c1VDVzQ2thcXJWMG1EQkFlUk9nUktjTzFVOSUyRnpjdUFUUFRFSzhVcDQ3R1RpaGQlMkZvciUyQlZMeCUyQjZVeFVsZncwdnB5RWpCWHU5JTJGczRQNU1OcWJnS1pUR095ZXVPSkRHQ0V6TzgxdEolMkZuM0tEZlF3dzBsWCUyRnBQZWViM3Z0eHJlbDc1UDhHWmw0VUFBMFBGNk5ITkJpJTJGRkQ3YU9UbTlPQ1dWendvbWFiaksxNjY3WE1hN082OHVwRCUyRng1bSUyRkJlN2ZUSDZPQTZ2Rnk3cGxwVkM3QU9iWkw1N0tjV3R1V2Q4JTJCUk5jUGhTaGd2OCUyQjRZZXNKOE5rSTN0QTBhVlNXUTd5anZHNkRBSHpUVVdISWhkS0JqSWZEcHBkNU9QakFXOXVmTTNMSCUyRnBMbzYwZEF6eGRYb3U5YTIzQWN4Q1lIeEE5cFNKY1NvJTJCYnAzSTFyMDBwenZzQXlBYmR0ZlZkNXE3T1pTT1JQNnlWak5qS01jRkdZSiUyQjcwRGdXcVdkeFlka0tZRWwyWmtEY21ETDdQVlclMkY1eGZJZ3BTOVdRaHhkYnVWa3IlMkY3NE80WmElMkZ0dFc3QzdvZERGJTJCeVV2b3JaQ0dobFlmRzg0MWVlJTJGWkc0VU4lMkZFJTJCekp5b2lTbHlET3dmVlFzMU5PVERSJTJCRkNLaGRHWmdmVW1RcTQ2UmolMkY0NXFpeEt0aTJ4NVJzUlQzT2dTNEpLTWRXcHFUc2VsTiUyRlhRMnEzdG81dGRiM3F4aXBwUXN3dkIzTnViJTJCRmF4SWdGTkZ1N2Y4SmFybzI2VkMyczB1bk9FQTdPSk00NlltJTJCYiUyRmROb1h2bDRacmVTUXFtSERtd3p1R3l4aTloTHMzTThWNUxJdzNoeEdkY0pzd1U3OWMyTzFybFQxb0cyMU8zUDZnOGFZSlljbEU4SmNxbk15JTJGbzBxRzQlMkJoUWIlMkJRaTR0bXJCNiUyRmNyUnk4ckx0aEhkeSUyQlVPQmx5cFNRaU8ybGZsZHBNbVc5RTA2JTJCSHFNOExMbTBzdiUyRnR3RVo0bHhLYXRFbnMxVllSa3JDRiUyQjIlMkJHQWoyR2FQSm5xZU1XSUtrRHJPMzBCcDM1eDFWSFF5ZjJJNElBS3JHa1RNJTJCbUVKUzJsdzNuWHZHRUUlMkZkb2xpd2hFbmUxNyUyQjZmNzZCZ25IQ1RhN0V4TzFQVnI0ZmJFY0NaMkEwNmE2dFMlMkZieFROSWVTMG1TWnRSRW4lMkYydGJhamdQd1NBYUtodjVrS0dOMDF3VWM4blpsV2V1T3FURHUlMkJmRWIwcHV1Rkp6NnR0NzhBMlZlVjJOZFJDcDFLUDIzVnBoVmNnRSUyRnZYTlBtNnFxcU9BUnpvcUZoNGtYb1pQSDYlMkZpRHI1TFF2dXZUeUhodGUlMkZHRktiRWdYMW5tRHc0WHpSJTJGa2RpUGZ3YzVtM2ZwbVZJMHQ4JTJGN3BCWHg2ZlQlMkJRc2lmZ0V1M2pETyUyRkJsWEtxMTJDOERCTk1nU1ByMVJLR2VjZXhOSHlzSlJKYmlTOWJZV05yeGhyYlFoNjNqOENWJTJGdWZScFdwSGM5UGFoRzJxOHp3JTJCRzBlQ2NkUTNwb1NRVHRsODl1M1Jpb3JOJTJCMjRBbHBkaUZXM0VxTGhPUDA2NlYxJTJGb2p6Vk5OWjNEVWdIdHVBdHpmVERMVVZLTFMlMkJzQVZHMnQlMkZLSjFKd3lLN2ozajlXOWg0aHFKckFyR3ZrWE83dnM0Ymh6R29qRnZUUHVNZWNRdW1kYnNtdlA2R0NRWXB1VHgyQXNtNDBRQzRaJTJGRUJERnNPa1BmN2NmSFJNQ1I0b1JYMzJYZHVIUzhhdGZwMmo3TUlWJTJGbVlab3p2RWNDRVJFbWhmclBZaVNZOWZmdUxldm1GTjd0MnpDQmdDZDFBWEZYRXF1MFQ5eGZkZEVHVHNFbnB4NElnVWVvSzM4TFdzOExYamhQaE45dlglMkJCSCUyRmR2TiUyQkdtMk00U1lqNTFsWCUyRkNzRWRRcHEyOFlPYTNORDBwWjhNbkNzRXpSRkx2MjV2Ymx3MjUyU1R2UVZOQUZteGZDUGxPMDhleFY4ZDBWMyUyRjBiRyUyQnhPQ2ZzR0VNUWUxYmNpU0RoYzBJRzFSWTU1TyUyRmxUaTFVcTFsbENrWTBpMWFrOTZOZGszSGpHcEUxM2NMWTVhWkN0bm1XdDJVWDlsbmZOSUVHeHlKdlZ4MHRrdGc0Ulp2NG5rT01TWXFFTnJKMXMlMkZoOFJmM0lrNkZKQm9HY0hhWTZuVHdmJTJCQ1A4VWlRZGdUNmpRSnQ4elhWM29ZNVBldHNwSmtlTjJEQ2hKQm9VZFJUZk00TTg2eGhVSHNvN1IlMkJPSkY5eTBQWWl6JTJCalolMkJqcmd4S2ZhM0hrcyUyRm9vRXNzR3ZqalFoNjRzMVBCVVpYakEwYlo3eDVjZWxncloyQ2JPZFFmaCUyQkxTWmd0VVZKWWx6JTJGSiUyRlNDVjhpZURSdkdtaFpmQko2U3F5MWFXYTAxSGN2ZWxxeGclMkJVSG8yd3h3U0h6eHc4bzFvcmdxeSUyRnNvWFRtNjc4YjF1eDJXRlQ4azMzeU44dlN0MUtFNjZOWkZBS3ZpVTBBZkdGc243dHREZmhCWW5wUHhPTU5WViUyRjZ0QzVPYlJnaExvRFM5bWQlMkJRdWhHbjBxSEJ1cCUyQlhQSVBBRThPV0lMSlhKVlkySFpGSkg0RWt4TG9UbmtKa3RiMnREcDJMaTMzS1NhdSUyRlRqdEVTNSUyRnBPRjdacVRTT09yTmE5OGxjY29NQlR0SHlVcUw4U1JkWGx1bVlRMzNHVTE3NFh5TFIlMkZSYWR0ckhJNmxjVWhuNmR2d1BpNXB3WFRNOSUyRk5LTyUyQmZEckxFNTQ2WkdKVHlicVlySjlQYWg3S2lCMDdmYnNHYlRyaFBUNVdHbnhRb0RQRHFqQ3JwQ2ZiOHVlbFR4ayUyQldDbm1vclFVTlclMkZKT0hEc1dPJTJCcG84cVBwSnFydVJLbDkzZnRrWnVUdld0N3BFSEMyQm1aaWFIeE52UnFhd1c2NTdFaXFoT0hlaUF2aE01WkR2eFJWbVhvWlh4bmNsYVh1TzJsdVEyaXlNdEt4aGFuVDg0dzlRMW9WY1BMSmt2bHhxdmdLVUJZeW5rc1NpTGhqenB0RnlrUiUyRkZLa0k0dk5YUlN6TkpUcndxaDcycTREdnglMkIlMkZ0VjB0R1dnTE1LM2dMam9zd2NrV2pKTmduSmZjaFVaRHgxQW1HRzFVczBMZUZIZGclMkJBTlZ3S0JSYURyWW1nTFVSOERIdjZyOW1IYiUyQiUyRlN5SDNwd2FNblNleUprRVJYQmJMMmtET0FvRHVycFolMkJYcyUyRmtWYmYlMkYlMkIlMkJ0T0hrMkpxbDV2ZTQ3cnpPQlAxWnN3RzNjZXh2NHpXUTdZJTJGNmxmZlN2ZUdkVzJpRkJVS0hKREhVWUNrdVdXQzYlMkZjRnEyZm8lMkJGZU9zWVU2ZWtEelV6eUZkMHJWbHdINGo5aHVvQ3dDS0pHSiUyRjB2SkJYJTJGZnA0M1lvTnFaUGpLS0RiMldUU1NzWTViSHAyNVJpZkIzY1pGcnJSdzJtcmc1aFklMkZyc3lKYlZnSGVVUEJxeXlHTWltMDFhQ3I2am1TbmNNNkkyMThXRnRzUEZrJTJCVSUyRjNCa0pZVnMlMkI3R0dEY2R0SVRRQWNhWldnNHd6YXlCOVIyMUtveFIlMkZmRWhsVzN4MWxYMUZ3RmFVUFMlMkJuQ3loc0pRa2RXJTJGSExZTEhsNGlYZFoyb0dHM0ZRU1JnOW56RjRRV2VETkdtWVBpbWZNYlZRY1RQSzJoJTJCOTFNWGszUmh6NW83bCUyQlozZHRoYWtXaGVtcXlseCUyRjBBbHZzNkdZYmpJSXRITFJ1Q1M3c3FESGZzVzVNVmxUcjVHQk1waUM4dGl2SDJtJTJGR3J5VSUyQmx1VzhrSDJsSVU5M1RMejAlMkY4WGNtRk02WGhyUlFXVkp3dmJVOUJNcXBwb3BLZ1dVSDBKMDlBYXgwJTJCNkZQVVhHZm1lbnRNeDFNelh6RVVWbDhGWDB3MWxQS2xVZXB1WHltdHU2VmZPZnF1VmZNOGY1YllGb2lwZGpuWjU3dDYzJTJCN2l5a1laY2VZeU1rRWVTeHJrUk1ITThNQSUyQmFuTDlHSmZNMFhzOU9IVkV6UnRiRHFYZjh2S0FIMWZBVGtubCUyRkd6UU52YkZSNW9mMHUzVGw3MU1jdjVvVk5jdm9lRDBicFBPWGJIV3lEckVrczdsaFU4bHZxNmNrTnVRRTdudWE3RzdpU1l3MzdsZmxyNDY1UkFzRGM0RTBCeXhrdDlVVWxSJTJCd2Frd0l1JTJGYmtGRHBqWUhxVzJ3TUlocTRZS0tQWXFpZWpNR2J5TTVyVVZhWkVQamFxM2Zja0lXWkF4REE4eGZEYU5uMVRZM3VFb1RQdWN1RFdLNUt1Yms4U2wxZlVKd0doWkNpOXpGaGpsMEFqRnhPVlNlVml1dDc0ZlBvNU5vTEhEQ0dnODBQUDNCNVpubmkwT3I0bU8lMkJQV0loMHd4JTJGVnNzNHRiTzhzeWlLTWROdDR6UVd2SWxtbVJCUnZTem5qQ0x4ZzB1WHkwYm5yJTJGZERCNllyc0c5N3RTaSUyRlV1eXpSbHNmbEYxQlFtdHBESjZvVGxWdXJLTFkzVHE5Ukg2bmcxbkN0QnNYemtqZ3owdE00a0Rnb0xacWx5JTJGMVB6VGJURGx5Q05FMlFtUUJLUUZybHlqSHhoRURaJTJCa3RoNVp2eGtvQmkyNlJsV3Z3JTJGMHZGejNHc0NzV0g1V0xkQ0Y2dU9uaTdtM0FXVzQlMkJOUHJwJTJGbEt5NXZoN1lpZDcxdkdoMEg4TmklMkZTYnMxUWhtclRBZFdFM0R1SmNydjdkQmNkM2oweEFTdUViU0k3ZUtKTVQ3aVBmTFpDMFNoZTJCTWlMNEFSdzR0R1dMNHAyUlhUJTJCSkpJN3ZZeVMlMkJ3UjFRdTNtOGVuaWJ4eGlCVEtPNkZ4SVdWbUhGY1FRekFCSWNuJTJGT0RWMEV5WURiNHo1Qm9KUGQxVmF1RiUyQlVkS2YzeDhkc2FycEJQTWh1aFc3bFRObzV6bSUyRjM3ek9SJTJCRlczYnUyRzR4bkglMkZBYXowT3VzTktQS0ZrJTJCUG53MU9jaFNXYVV2JTJCME40Z09jdTRtMlgyZUNBOGQ5OHFrem5paSUyRkU5MjNjT2RGaWZnRE44V3ZVenVrcE9yYyUyRkV3R0tST05PMmVDOVdIVjdmTkZMblBudVJUcE5uNGtPbmd6RGR2T3l5OFNSVUtYa2lQQk15amMxc3JkWTRDMmRPMkpsUjVOQnFDdnRyOWpkWHZheG00Q0hpQnJvTTN2Nm1CS3Q3R3ZJNnh5TG0xYjklMkJUOWM1RE9HTmVtVFp1dVBIQ3NzcG5RJTJCOUNkUHRJcmVYJTJGVUw3ZENDQ1hMOTB1JTJCdmR0Z1BtOWNmSkNVaHMlMkYlMkZFdnYySlVaNW4xT09JdXZLcHNPaVk3ZEJRRmRYcnclMkJFeFlqSHN4N082SUdqS2NtU3dlcGgycEkyZEkyWXNPeG1mTXRudmh6YUI1MFduUSUyRmJKUVU1NCUyQiUyRmRYaGM2Q21FbXJka1NleUhuUjB0ellmTUlOTEdBbGVGRUdQVGdWYnZBekxic0trSlZTJTJCJTJCVGYyekI5SDlqams5enRjbGc1b2V4R1lOM1olMkZqQ2RIM2RQckF1STVpbGsydzE1NXJhQzhSdWgxbVJoYXZuYlVBblFMQUlJQ28zWmlvWUQ5bkNKMW9jJTJCQSUyQmxHUENVV1lxeGpDM1dPajJWSjNGZlZlZEZoM1g3Y1oyQWFYTHo5ZmR5eU1Vd1lsSkZYYnBWbWFlN2tZWENsQjdTYXJCU3JFdFNtU1Rmd2V1cSUyRmZTUzdKQUZxJTJCZWZCWmpNNXdjeFhSOU1xVXdoczQzMTlEOXhOSDBtald3YjZzMWNyaSUyRmVkVDhYZE1WRjFkTzlldmpMSHFCVTVvOFF4WW5ZWDB6UWJNQjBBZjAzZSUyRjRCYXF4eFJmblZZOUMwenNSUjFsJTJCUDE4c3FvYVlhVmJ4V3UwV1NuVENRN3RZV1BQNjVBR0EzQ3NiVDZVajFibFoxYzB0M3BBTHBUWFdVMThRZXdSMHZ4b2tNM01hdGhwZ0ZaZHJqWFI4OGU0cnR5NEJRd2ExNk9qclRqd0x4V2xsR0ZMWmVQMUFBMkZQd2RFQloyMzRWT1luUVhldGJWb2VUcDclMkJ2M2xKS0VZR0lkTDhaaE95QTMwdDZGTkZDSzhLcVMlMkZPeTdYMmh2Z0J2SFM5TnRRWGpQZGtHMlhaMGZFZndlb2xnV3FGZFc0cnBzTG43OVBTNSUyQlNiTUExQjZTUGpmWWZTVWlLVTEwUHk5RmhtOE9aZHRUZFJIUjJEQWM2TzZxNkhSMWZHV0glMkI4dUNsTFplWW4xY3IlMkZJcGN5NHB3Tnh6JTJGS3ZublpVWUxwekRjM3lCclpmVlVjRzhPSzE4YUVSaWs5akVVZTJnSyUyRnpqR2hKRGV4MXROSmJyRnk4d2klMkJORmhKNTIyc3c1JTJCUXZMR0FyS0NrY1JWcXJsSHpKQmdGQllKTGg1WmxNYVd6OWFOOHdxMXdrUTJ1bHQ3R1R2dGlad3NBQUMxc1ZpT2lJYXliSU1HZXQyOXdGNzhtMWpVeW5SRzRTNUVCMGxQeXN0WjEzeU1QdWI0d2NKZ0dOZVBlSzdHM2pMTkFSJTJCa1IlMkI4aWlYS1RrZXpvJTJGYXVlUUo1OWNKVm03dTJ6MDJBbUVwaFg3V1BiJTJGaTFOTDM2WiUyRkllandoZDV5T2ZmZURBbDVpQVdGR0xhajZ0cUJGJTJGdFh3JTJGbDVmZ0F0SFNTQiUyRlZEWVc5b0JKUHhKaTFPb0ZrZDZZNjd3bSUyQk1qelpQcEtrYUtsa21qS3djUHBVT2pPUnZGeTJPdHdoZSUyRlBxWHdPWkhRYTBjRndnVGhyTld2TFhXcU8yJTJGUWxPaDZaS2tzOFglMkZhbkNFcFFjZTFseDZDMXBJdm5SOHVyYlFtV1pFNjdJVWV2SVFuM1VCMUx5UXBYNlVGWUtKYkQ0d1RWV3ZPJTJGZDhvTVNHQUxONWZrWXNSdHpkS2dsdm0lMkJzdFlCdUdHJTJGZ1NlMDRmMnFJcm9qN29zY3dRVXJDVUZET3kwQklkNXBET0R2NG9EVFNJVEFNZ0pvcW1JJTJCc2F0VFZXaEdERGlQaTNIT3JDcm5QJTJCYldXTyUyRmlBS3BXQVJxaUNpUnNJNmZVTHhvQm5PNnlFT2pIVThVSU16aEE2MkVUV3o2djYyMGxGSjY5cHQxRlh6b3llZFBtRm1CT01YeGZOSG9RbVNHQmJ2Rjl1MSUyQlNWVVlkaCUyQjlYYmZlcFhJTG1vbUh0cHB3clJQMDNuSU1PZ3hzamJWM2RTR3ptSzltYjRzZ2l4NGVYbFJ4VFRJOVBKQnp4eWVjSXJ2ZmJaOG11MmN1cmRzdnh5cGpaMFZTYVlGOWVNVSUyRm1sd25qaDdYNENKUlE1VTVZdEdpTjVJRFdSYVljdjNEdSUyRlg1WGJkbU9pUzRrMWRIdzVnVGx0N3piNXNQS0lUTm41eEw2cU5vMjh3NlIlMkY0M2liOUJ6bmZGJTJGS1hPUWRaU3kyUnZOMWhBa0pHTUFjRjVQMHBrbWhVRlU1bFp4dGRQc0tuZmhHbEg5V1hFN05maU4yJTJCME9SWXB0VEdaclMwWWYxeEllalhLbmlXYk5mMHZBeHRlMU5tdlhLdkRRcGlvNHp0UndXWFFvYkw0ZWlabmRTZkhKTmIlMkJDYUVsJTJGdVI5ZmpNcGl2SGtvUzJYRGN3clU3YWlaNEhNajdYSHdRZnUxQXhpaU41Z1hXYVlsOUV2MVlkYVE1cnFWcmhqQ3ZWZWN3OGYweGlKa3hSaDltWGo5YXQlMkJjTXZQcURKZFphZXIxQ1ZmMDBKblEwT2tFdVlXSkVhbG5RdkV1Ymk4ZjdSZ3NGV0RPdTdDSjFYc1EzUHd1TkpoQWEzSGRZYTNldXVxemVKaExjY2RvM2RLaXVudVVRbjNIakkwN3JadzBLRzltJTJGSTBwZVhGYUJpNUN2MWNuS3VDamtoZnFza3pyTHlueVlHWGNpJTJCdEFyOUdaU05ESmFnVEpJUGJRSHliVFZzZndxaSUyQktOcDNMZG4yUWFPV1NuOElyZ25mVk9YQm00dG1KNyUyRnl4WlhaeHJQT3JTUkZYZmNOSm04UVVSTnpqc1I0RnVjSXZaTG5nJTJCclFZcW03UkFvMFhrYngzNHhmamhvQWVldXo2MjVUZGo3ejRiUGhMRXNmcEZVR01MUmh5bVRnSkVhZ3RjTG0xVUNlMzlxbktzYVBaY3FWclJ5c2ZuYjAlMkJCYXo3QlFkNWNuMlRITzlCZU5vM09aTDNWTWE4VHpJT3RuMHRaaVdyUUNrJTJCZWl4Uzd1RUZzSVVHaklxUlJsV2lhTjdMTTRUTnVQJTJGbGt3SUxmZ3B6WXZ5JTJGZDQzT010RkY3RzNMaVVLdVhINzJSckxOcDJSZzVrcTdEUEU3N253Rk1rSm1oUVphNHp2JTJCSWtQN3lNc2x5MExMaCUyQjZoVWFLOUJKZnZTQWJ0WHFKclg0aVBQSkdYUFNxbGo0JTJCTFJ4d2dSUkRZS3VIc29NaElzREslMkZ1Q2dZcE5UUzEyTGhsdVhKTTN2MVNXZkJkSHR0dVJ6eWdVcmpPUVg2aEpOWCUyRkllV0lRbnk2MzdGUGhiQ1U1VUVYJTJCYlNKMklQVFpFdUIwSnYwM1Q2QkpSa3M3TW1SJTJCTXoyZ05WVkdRV0tEeVZzcEZVWG80VThsb3V5Z0pJTjJmYVF3MVpRRzJBNnZxJTJCRGJqN2MlMkZZSFJVUFpiWVNkVWRZNDJOZmVaOHRGVkUzdnBGVFJqZmhRRyUyQjB4WlVJZFk3amFtdTNLJTJCMnJRcnRpeWpUaSUyRjNnd0J5eEglMkZ0WTBwM05yQzB2Rzh5UnpmaSUyQmY5WHlrbXVJRWk2SVBlanEyWlRLMHclMkJoWVdHVnM2SFhMbEFKcSUyRiUyRjY3THNVUTdhWDc1SzUlMkJqSWtyZUdTeXFRZCUyQlhQS3JqWTIzdEpMR3ZTTSUyQnd2OXliMHpyNXZJRGlOVGtETjFmTyUyRmxnT3VnR3BJOWlwS0lnNVppV2hrYmtMUkRjcElVVUVoVUJqcVR3WlRmN1FVN0ZlODVURU9QMnFvUDZwdCUyRlglMkJhSVJZTnA4QnIxSGdBVk43dlBVZXB3ZUVkZVI2JTJGbEdUTTJUclNZSUZKTmw0QSUyRmY5V0tjV3N0NkZXdWJCa0dxZHh2UXRIbEw3NWdxY2xwdWpqZVBGODBqcm5kbGVWZDVHVWJTZTNTbnlhWCUyQkhWdUl1R1h1Nkg2MnpWNlptSnolMkZvS0xzRER6aTk2dmJXJTJGQjRkUll0cEc2elMybUw0T3dDOGVWUjZvdG5NdXFqTDVHZTRTaWc1SXhhclE0a1N6cjQ1dnFhdEcyR0JZeUpnblVnOVZPcCUyQmloJTJGZmxyaUhocHpyTllHOGZyaXlNdUlzT0xySnhIMzJXZmlIMWlkY2U5JTJCMVp4UjdBMzhLVDB0Qk5ISThqWE5xbm5iYlREN202YSUyRmJOblJKWjUyakZlREtqc2lxQU1xMTM2VmlvJTJCb3I4MFgyOFRzenB4TTlvJTJCRnhRTk45TDBaTHYyVzZteUk4cXBzTG4zSkdwZTlhZU8xeXFXaWdTa3ElMkIlMkZmUmlWaE5SWUl6S050WW1NM0ZOVGVERSUyQjhoZDZLM3ZmT0Y2TTJCZFRWNEkyeXhsd3Nrcnp0WG1pN1M3MnFDWDlYSXlSR3BSWGRIY2lDTjJ2JTJGYTNTTTBibDBJSE8lMkIwN3RpSW9kZUh3ZSUyQjk2bkVPNVBqNElZcVptYUUlMkZGeWlPS3IwTSUyRnhGZUgwZ2J5RHU5NkE1d1JVWHR4SGNpUWhJQ1ZhRUNZVHo3Y2YzZERmbnRHdHBNSGpVN0lWTVJFYUwzdXolMkI1SUglMkJqcFQ1a05FTWdvV1lUN1BsMUtxOTg3WlhSTXB0Q1hwcXgwUGF5aHl2VGpFbTg3QVkyRzRYek1KUDVvM21EU3F3TUM5U3hLTlVaUTV3NGVMU1hxYlZsYlElMkZJMXoydjAzdWVQMzBqdlE3eVM5S2tWOFclMkZYUk10ZTA1T01WUnYzdmN1TTZyM05POGhlOXdwSVVSQVRwZFBiRCUyRlZEJTJCV05TRTJCekx1MTV3YiUyQmlmc3NjaVBXOVFiWTFCQmZSc0k5TEJjOE5uWkFlZ05yaXZwM0QlMkJYalkxaGlpM3YwJTJGTXpTNWxhbk9rdmFqQ0glMkJuVk02NWVRaXRHNERISDc4T1VjWEV0QVNreDRSVHpTaG56aXlvYk9UNVhtdTVGbkJxcnlYRzhxWWh5SmpzYWRjVWpENzAlMkJybmxrdHNDQVJwRlcyR2tCR05NOXYyZDhZSXd4NGVoaEZGTDlJMnUlMkZLN1lIYnlEZ1N3MkVhbk82OFdHUCUyRjFTbzNsdzVwcGNkYXpWbnU5M3RpWnZDVXhzQ2d5MXM3eER6TFQxTUpMV0xpZWdJMTFhSiUyRmZPMW1SNTl6cks4RzZIRkJ3c2VacGl1Z0R1aDhPN1QwdCUyQkxNTnZ1ZnZJaldic2w1cUVqVFZQdWxCbWFZTEpIQkdCbEFUa2w3OFVKTzFhdkxiNXo2dTBaYzFpQkw2YXkyYW0yJTJCYTJHSWx3MnlBS29tVm9OdDNlRCUyQmdsSXA3bmVkUmNQdnVVcTNwZHhLRUQ4d0pwdEpva1hod3dDZ2NzWUZpb3FWJTJGYXdlZ21HcEhMakVsTkNYZ1pvJTJCOFVDZ3l3UllROHdDUWF2ZFVCc2Z3aDdPZlUwdVpYcllqVzM1dTJXUk84d2MlMkI0S2twTXluMGNXQnpQRENodkd4NUZjTVVlTExYd3NEeFYwbFh5ajRqRHZybEE4SU1MNW5TQ2pReTZXTFIlMkZkbDBoT2d2MkFuYk5BUVpSMG5idWRESGxKNjQlMkJuM3dONUJ0a0ZRbUl2MnpqaGNOcVQwcXFzMjdKUDZwRSUyQkJqZUExa0JHY2llJTJCTTlJMkN6eU9sU3E4V1lBWjAwazBjYiUyRnJHWERoaHI5ZERQUGdUUFlEdTZlRmNCTUQyUDBHaGY0bzA1ZGttMHlBOG5vYXM0R0FoVTJKV0NzZWwxZFNHJTJCJTJGU1cxdGd4aVBkTW9XbFE0THcwSEJ6UiUyRmlWZE5rZXdxb1BXWWJZYVR6U01IdFNjd0hHRTh1SjN1TXlPSW11aGY1TXV6b3JSd1pkJTJGSHB0Mk9Ya0VkUTU2c2YwUFhMbVdtSXlJS3FMQzcyT0xKcUYxbHVyJTJGJTJGRnpXeE9rWG1mUWpGaSUyRlpZTUJGSE9HRFF4OGpPR203NG9Fb3UyYTA1SFpsZk5RWnJWaW53N0VkMTRob3glMkJORzNycDNRcEozcklCR084WVVORTdtVkZUdGxLdGxhZGFsS1VHQUwyU1VTVlREUSUyRjhDU3dIM0YxanBxR0p3Tk1aZlhWZXNSZHlJVSUyRlpjVDQwVXBzOHklMkJIeUlENjk2UmpUS1VkQ1ZjQldjZ0ttdGIlMkZ6ZGtrbko3eEpiNXdlNVV5eWczdmY2QmExJTJGSTdqJTJCQjNPTzdxVzNOVyUyRnJoS1VvY0QzbEV5SGtrNUZVJTJCZzJkUFNlTyUyQkY4MjNRMDVyT3JuNG80NjFsTW4zR0syblFlNjdIcldnbGx5M2klMkZSa1kwRGJUaWdib0k1bUl3QWtlZk1hQ3loNW91c0NYdEFNNjZCZ3dWMHVkd1UzWE1tM0REN0QxRUdwbEg2bFplRk1iR0REZ3Q4cTZIb1RqaXNOZGVlclQ2a3l2V2FnMFhlTjIlMkZJdThVV2llSSUyRkI1Z21CRiUyRkxYSFZJNFhtRDI1b1BvSUo2OWdzJTJGcDElMkJKQ1VHJTJGMUJlNTU4V2JUMFRNNUs2T0V4cHpNR2FjRnRMN2lzdzQ0UFRFSkN4QzZFSjhYeWYxa3JHWGliOHNtTHU3VXQ0UTBWSU1acXRUenVqdER2YWZtVSUyRnFHS1BCSFlQQzUyMU1UYjg4ZDIlMkZYdHA2aW5zb0dqTHZhMWlrMmMlMkJjUmI0ekV4c3RpWGlhWFhlbWdkNTVaY1JRZ2todXJpc1NFZDY0ZHdKQ0JTdWNIaU9Fbms0ejRnTGM1TEp4eG9JWTBQJTJCYzNSODRhNSUyRlFHYjFFZUt6TURZVUw4eE5ERDE0YXNMeHF0SHFBc3d6eWc0JTJGbE9jbWVVeGhxdjIxeGp6UFJMNE52SDRxQmVkZEdhdUs2Ulp2em4yVUlxTzRSN1ZIMjIwZEJHR3Z6UXVIQ3V2aTlsOEx3U2pQa1clMkJqOVIlMkIxVGhEVTVGbkVuNlBkZHNvcU1ZdlNyZXIwWXlQSldlMFBrU3NCanU3cGpwR0ZHTUJMU3V6JTJCenNUcjJEZDlnZjR4eUslMkJUYjdPSUl2T3ZYOUhjdiUyQkc4ZSUyQjJNeVZ5NW9iUEVSWHVUWFZEd3lsQXUxc2ZJSlFJazNURmMzM3ZCMDRoODZZQjIwTllnT0JiZ0dNYXJ1RTU0aXpkeXBYd2RZZjZRaWZYZjhlelhYdXBBMHd1JTJGMENsbXgwelBQVDVmU2g4ajRPbXRoaUl2aHV2cFlCeTRLTSUyQms2V05rbDB6MzR5cVZ4dHRBWU5ZV1ZvenRRUEZJWVIlMkZIN0M3dGNnNVVrbkhkV2FoWVhUd05URWNRQUZla2FvOWpiUXZ6T1p4OCUyRkJ2UUVId0tIRGJlVktad2kzVnQxZ01tJTJGaEc4T2I0SDJjekhHaUgzYng0Z1JDeTJ2SzcyJTJGOHpRJTJCJTJCa2I5aUZCWHFySjFRTTYlMkJtdldyejdIZGNuWEdzRWJ6VGVBR01aQlFvVXdxWTFHeWpCJTJCNkhWS3NodkQ2VTVnUU91aEs3UDJZamRDZnQ3eCUyQjhPd01MalBya2NKdHEzT1prdXpkdlNSODhaeWVteVp2TEZWbUZzJTJGcU50a2FDUkl1WDFqQmJ3NHAlMkZJd2FrY2VzJTJGYmYlMkY4VEk0Q2daJTJCZWFGMDRTa0FhYjBITFQlMkZGVEJaSnBscU9CT2tUMDRjS2ZZdHRwQjJRMUpKZG5PMms2YVFhYXF0QmVVaDJ0SEVhYmNTenJhekFiVmp4cjU0UGJMcUs1bDBVblVjQ1lka3doU2hFdVd4VHNOM1J2U0ptc0UzOXJZNGFjNW5yYmkzaEoxMnZwR2NTbzVNbFIzZjJpdGRpdm5aWHhHMDNPTnNWbG1YdTZYUHRCcnVvbGRmZm41dFJPd0p3VWslMkJ2UFYzdXRURTl6UjdSQmFtc1pQTE1EV0VUMDdiY3FvTTRKUGdTVVljdUxWdzR2cEZZYWxRMGMlMkJUWXNIaVdKMmN1ZXo0RXNhMjZtTElsZ3JXQUo0dGt3UFRuRkNXYTcyJTJGZjJsNnVONXp1T1F1YXBGWlMlMkJqJTJCWDd2Vm1uWDlNV2hVSzRMJTJCOEhpZUhReG1FRnlvdXRpZjQlMkJ0M2pyVW9GJTJCdFZGeFNUTXZ5MlpFU2EwVW5pQTRrWTMzVkZ3Zld6dnEwcmlya0FHWUhZYkVka1EwYXpIcTMlMkJ0bDVHeHhWZUZtZkpla3BoNm5ac0JNVmxXOFBuWVZxTUtRJTJCSlJKUzdSY2NmNUxnM0dlaXZYV0ZwTWdsUjdvJTJGZno1NEoybXM3ellDMzVSNkxOVnR5MU9la0pud0RobjMlMkZoZDUzRm1nVzZMV1RSQVZxTUV6eG5ZY2loejVqeTc5ZnpONjJkdCUyRkMlMkJPOWFobmFSTkI1VVpRQ2RaWkIlMkJ2dkt4M25LZkFEN3l5YVp6YzQ1bFNqZ2tvT1RqMVBidWdUVSUyRjdNTjdFSmdnbURiOUJVdWhZSyUyRlJrZTd1TmFLMHc4aDglMkJZYWlvJTJGT3l0dTVEeEFKM2tnMHQ5TXFhYXV3eTN2eE9yJTJCN0lLOEw5MXYwJTJGbnc2RTVDR0VneW1TZlhRQ2xidWN3cm5zMEFhSjl0djlXSTIlMkZjVjh6QmJPV2VBalBQY3klMkJodWhYYVhPNkZ6SmlNd0ZGNkxzeElxR1Q5Yk5CTUU5VVdjUU9wU1JiMmpGJTJCRFpjemwybm40UkNQMm50R0hmbUJMNGU2c05KMzdQY3h4TU8lMkIlMkJzWThnNWFENERlV3Z0YXJjcG10MVBwalB6UkswbUd1d2YzaXBacG5FMUE0c0ZiRTNtWEo4VHRka0hMVUl6SHZ5Tm5FUGwzSCUyRjJMVyUyRmNLUDVpYUZjQ2RsJTJCZGF5WXpZbHlLVWloNmxlYTFWSTZvY2pRU3FCVUN1UE5pQURKZnZFRG9EWnZUd0lJMnZkWGt2c1dsYjElMkZBMDdueGtybWJQdVo5NjA4Qklqdm1SWkQxMGNBUGI3ZVNtSzVBV28yUGJzVCUyQkJjZHk5d1lOcE1CSW5IbE9WMDU5bU9KcmtaRjFTdktBSTBrbiUyQm93NFdFbWN5TUttZGlCNXRXJTJCJTJCbjJkYzBzUFpyNWRNSHpVbWEzQlA2WnlacGhXVGpmWFEwVTlFU0FoeGl2Mmg2RFZGOFNrODVoVWwwMWRhdFpxWWhlQ1Bzcmh1VTJuaVBxZnVGMXRPWEE1eERLR05VWDB6QUpCdmZBWnBiMUZDWW9wbk15djZTV2twaCUyRm0xV0F2UCUyQm5FNXlpYTRpbWV6RW04S1ZSUEMlMkZHcE80Yk0lMkYwdklEQUs4SERFaEVNb3V2UHl0TDRxSXJsd2laV09MeUszNnRhTGk2aFowN1ZtcVRodTV2UUtxSUczUnk1Wm9lQlBCNzRxNE5halVjeThFWTVyQlJlZlhjeTZyV1NhJTJCZGUzdlpqTDhuNWNuWjRtcWNCaTNLRENLT0xCWEdYMkxXWXZtY1Q4WmRFcG9oYXlIYyUyRmxaWVdmTDBjWUlTbXglMkIyeTBRdDFyY21jczBmNTYzcXBsMnNMbkVuamt0MnV3NmglMkZwbXZ4ajlzell3MHM1eUtaR1UlMkJXTzdQOUxjbkNZNEhxcyUyQmh0MyUyQlQ5VFl0aVBONmpWMmhjMHVkMFA1Mm96JTJGTVpIeTB6b3NoamglMkZSMGtndzVvZk8xRGU0NmpOTXZJbDRHQmpOYzhFM1RjemcxTWFlaldBWjhHSGl1cG40U3NHUFFTJTJCaFBOQU5ycHFBSkdPJTJGaUpOUWFwYTZVY3B6cWo3WkZmSkYlMkJNRlBLU25qbUg4ajVpMmU1SXlKM0FEaiUyQjBmRU1WRDltdlIyYlFWYTYzNjAxQkFDWGZTeFJDblI1RlhEYjZyc3cyVkNlS20zSndFVUFNaEM0RThrZXZuRVZSTGRTNDVQb09KaHdJYXBCVGpPelglMkZ2YiUyRm81OHRyNXNBZ291V29NbjZ1SXc2N1kwUHpndVNhcWRnRFR2QVRRc2dtRWw1d3lGJTJCclo4SnJabkpqVGs0eCUyRnJRem5PMEE1ZFkxeU9wY05zYzRkQ2pra0JmdFNIVnIxdE02OFJMQ2xpTDZjNUZIWWdoRWRvMjRkbFE4bkY5N2l4SzkwMSUyRkZBRWJHZVIlMkJzYjViJTJCRm5BVzBQY0tFaDlpWlJSVGRLTHBiSGFRYWhsaXlocGc5N3VhT3gxaW9EbEo4bkV1c2lEWkFScW9XSzJzaGQ1Y3oxUURtVHVMOUhoaVQ5RE5CWHkyeVV5SlVNJTJGUEFtN25XdFBKQkNyVlFiVE5LU3hEd1dOZVgxMWtTSTZBQWNsMnBBZ1dUbDk0QkVhQmtBSEcwdE5paXkzNTRrVFRNelg4dFFtRHZiWG9pc3NxYkk4c2I5WHlLOW8wTkkyaFB4TSUyRlJ1UVN1bkQlMkYxSkpwTGZMRmZmY1djQnBwN2tISTBUWXlMV0ttT3RxbjR1M1Y1NjgzdGhsOWIyZ1dVMkFQWHY5cmwwSll3NEIlMkY5WExuTEpGMVE2QVM0RmpqcVJmQ0t0eW9wR1lhQkRrTnhycyUyRjlkeHFVcjRCd20zVHoweHVIV25mM1piaVJTNExEUnpmaFF2WHVUcGpPdXI5JTJCM2xINFNIJTJCTWo3aHd4aVVsbFJLc3ZwSmdtZFg3RFdJNnJneGVkbEx4ZEdyREMzMHBmUHhRYlhpbUMxMjlaMVglMkJpSlBid3lpdVROczc2WTYzWG9wR1BnR2lIbzlidzY4bGxJVWklMkY2SjF0WUhlaFhNemloVzZLMTRYJTJCRktUNm5IY1hmRldEZGNhbWNBRUFaWXVqazc1OVZTSWRrRWVIanFZamtaMkRKbDg5a3ptS3FsVk5BVzhUekZ3QyUyRiUyRm40Yk45RmNsV1U0emZHcDFTcnlxU3lKWk56VDFiOGg3eUJ2RXFrSCUyQkNqeHE4MGZ5SzFYVW11dDU5SVFFOWdTeHZJWWJMR20xJTJGaTRMNUNacHVOWXZ6OTZuMFFNR1NMUTdWeTMxanU1eTJsN08zYiUyRkFuYlZ0bnlyJTJCZEtqRG5qc0pDajhvb0tDUnVxOXJ4Nno2aFJ5eVRFcGpIdWZCbDJrRGNyTUltcyUyQkc5RDRYcnl3NDBLdG0zJTJGbldRd3plMzhlZiUyRmJUUG9OYWFUOWNZTGYxM0RXeWVFaGtlVmRrOVByWEhNSUF0ZUoyWkp2Z1Q0U3g3RnZqbVYwYm1DS0pOTk9OTVltNk00U285YnMxVEslMkI3dUJCOHBRd0lOJTJCOUoyOSUyRm1MMTlzWk9YVngwS0UlMkZ1aWdaVzVmUzlzdGdPUldtTmZPampqODZqcXdtNG8lMkYxdyUyQk01bThaUzhSQXg4cmVkRXpKRE5DTHcxUDJ2M3hiUjljN1g5d1IzN2pSazZucTNGd2xiYWQzd1BrdFBXUWlySlN6dUNhdEdqWmY1NUhPMElhelJybW5NZ3UyaHdXdWo1cm1paVkzeUVwVmM0Nkdjek5OTmxzbnB2YzZ1TWhhSWpmdHM2YUg5elVDS1RYaWY1QUpTbXN3eGZtTGs2SFdNdVJ5UDB0T29wNlRjR29UeDVaN3JHYTBQTU5qJTJGYWklMkJWd3poSnpnJTJGaDclMkZKTVF4UUolMkZYOXRESmRkbXRMS0IxbUNDRHBLNW1qendvd2p6SmhOTCUyRlRDSnJJM05SamtybUdZOVMlMkYlMkJ2ckwlMkJtN0dPQUtTSTcwRE0lMkZzT3gybm9IQWU3ZDlyeWtoZnJySUs3YVp3dFEzJTJCVDNSS0t0M0pxciUyRlZvQTJPT3RjWWJiNmJpU1QxMkd2JTJGMm9pY1ZSdmFUaldlajNDN1UlMkYzdkZGdmJEcDg4WGo3WlpTWGs5YzUlMkZqRW1MM0Z6dzVMb3RyYmxWdTVpV2ZzM3J4cDhPTTMlMkJjY05SVk80dVpzWkxUUERpMERLSE5mZnhEVG5wcWRveVlRR1NOOHFnVVZmWERIMnhlWVhMYUF4VVdDZTV4WlhnTkcxbEtxcW1GVHNNNmxPR1Nka1AzdTRzNzVYTlp2eDJoVFkyelBnYjhGRjdkRU1iM0JwVWFRVExwTnRaMzhsd0lyQkdMYmtaRCUyRiUyQm1aTkdqTDJPQXAlMkJwaVE5dnBmTUxja0ZNMXJUT3ZkQ3p1WkFYZW9Ddm1YTFd3NGZDJTJCMVNudXBHcVVHNmptUXJmODZrUkZncEp3cmxhTVUlMkJlSEZHeXkzTlh3RDF6dEVKaG81RmJoV0Z3cTBGTUxZbXVidmFHWGp0MEtub3ElMkZPRW9LMkkyYzElMkI3M01rT3d1R216aWdWZG04eCUyQmxSS3V6UzNDVzBXUmRKODM2dkVUVk4zdThEZEVISnJla0IwMDdJWEhLUndWbFYlMkZoQ0thNE9pN0JyM2VKblhuRDUweUhmeVBQa3h3U2JzN281Z1hjSWFJUnQlMkYzNjdmN3J0RjdEbDQxOGFpdHRsckU3bGIlMkJsOW9zdGlubXlCM2hTMmslMkIwSjd4NlZEZzM5WFBFY1QyeFp3djhpM01ETmpaaHJ5clNQM3o4czhwVmtaQjFIYmlxN0pPZloyYjQlMkJXJTJCTExsMkVuYm43ekpDJTJCSE9KazV1WDNSNkJ4aGpVU0lMZ0tNcU9iMEN5bGxBRXVhNGt0Tld2Vm1aYjZSMUYlMkIlMkZYSng1UFh3UTJWM2FqTXdMSWQ3MzJnZEV4Um43N0pXNUUxd3hSNDB1eWdKN1NqdUV1VWRrc25teFByMDRqJTJCS21HU3JSdDFZUEZNVTUxZmU5NU9DcVFTYjFWRDNqTG1kOFRjaklHQkVUJTJCd0VtZCUyRnZHbVptT2s5S253WVQwWlQzYUY2Mmt4QXQxeFhEOFdsRzB6N1V6MWlnS2NId2pYZm5FT09QVnl4cEJ2VkF2NWhva0UlMkJSOGV3VWZKZGR0N2pscFg5VE1ETE9BaHFrbDJjUEp6TDRRTiUyRmxiT0RTd20lMkIlMkI4R09YTmhyNmFiNyUyQkNuUHFwQXdiYkV2SjlUdSUyRlglMkZld2E3UDBraUNPJTJGY284eTc5TmxxekxtNzlEUGtXNVh5SFd4d0FqRXJyVWl3ZnZtbG5ITlg2blVEMHpUUUJLT2l5WGFoWkVZdTdvaUthN3RubHhERlhONERId2kwRWVKZHhjTzZxeWNJaUxsJTJGMiUyRnVDMVZqR0RMbzE1cEUlMkJSdUZTTzJSRnJwa0x1Zkh1ak43eFlpZWtadTBLZSUyRjlaYXVSZXVObno5Z1gyQ20xUVVOMktNQWZteGVYbGIlMkZNblJpJTJCTmZaakdEZUJsbkhMMmxyZ0VYOG9vdjZZdTZENWpSc0w4SXZzTXIlMkJJeWhjV2lQYjFHcFF5OTM2bTFFdmVCMEhZS3dWOFRlOGFQYVN2bTI5SWsySCUyRlNGVTg1ZmVHYWlzNzglMkJWTE12R3VUSmJqdFNaWXRuZ2tWZlJDa2o3Q3pJNEwzUUo2VTdPaGVhTHFBUHN3dVhOUFRVVXRSTWQ5bkdWcE95dTJrbkVsdlZLclRUMzNoaXN1czVXTHVEdDViViUyQlNZaW4xZ25qTHRqNlRTS2VFbVclMkJtQnIlMkZ4SllvdGVhJTJCMVR0aTZMNyUyQjhPTlRvckpvZFJDbVJWTEt2RWIwVlR6RVFuTkREcWIzNmtrY3hZSU5RdHBlQiUyQnJYeEh5MGhqWFk4UkRBeUd4R1o3VjBSbDhQRVFYTkY3JTJGM0RFQWJncHhuVGFlMXlGJTJCRDNjT3p3SXcyd0x1ZlR4JTJGalhZMHZsU0YlMkZYOHozcmJRSW82SUE5c2Q2S1dlbks4OUcwUiUyQkJFOGRaTzdla3pmcjNYR1gxdzVnaVRLJTJCJTJGQ0dJVzN0dER4JTJGZWRBcyUyRnI1dmFVOFJoN043QnRVSTBucVMzYVFueVdTcU5JVlRRTUoyUUd1WmV2MlBtUTU4OVpiJTJCaEhENktKY2FmZ2ZIQnNmeVBobWRLY1dYNE9XQjU5dUpkRVR0NyUyQnV1dFNGV2lrTHdMcVdKdDdnWVdtVGVTaTYzZk0lMkJwJTJGNExBMXBoJTJCaFdWMlN0TzZtc3N0TFVkWmI5UkcwT0tDVnpSJTJGVDVFSlA0MWJxTGlvd1Y1elFxeSUyRmt2bFY3ekRLWnNibVglMkZyOWdJaWtzUjhqZnVzMFZrJTJCJTJCbjkzQnoxUTZBYWRqUnJwWFFKVnRjblpic0dMTDFra3E3T0V2VDFjekRxaHpuclRHNmV6Rjg4UXhKVGxRNFklMkZLYWV6RjNyM3RjUnglMkJ4cTdKa1ZRVXlHelJJTDFWY1JBMmhDM1YwTXZ5R0tvYWI5emRnOU13STNoQk83TFVsWWNHeGdtZGhOb3ZpJTJGYVVNdlc1THNSWkplSSUyQk9uV2h3eWlYckRNMkNKWjIzZiUyQk1IJTJCTWxIa2dsMFlaVjkxaTZMOGZlclpUUW1Ib3QzeGIyQTdFS0ZzMXZjdk1Nbm81U0EzaW1KdTNkcHc5V1J3MDBpZkh5QWVTWkMwUlBMbVJmJTJGWGYwdFVvc0kwM0lOT1gxa2dZYUFDSlZlUGMxWjlCTWJkVVg2Y3p6Wk1lYWZZZGZQQThNdFMxU3ZhQ1dyVmYzQXdGMkJXM0FsOXo1NWVuRERNU2Iyb3htVndRSFJ4U0htdUJPZEZCajdTYWlsUGlDcjlZVjRFMXRUd2J1V01MTUNVWUZRaWFibCUyQkREc3VrN0Q2ZkJ4OFZqSHpyTVlkZUJ4d3AxdGxOY0dEVGhMa1NwejQ1MW1KNTVHMnMxV0FYZENIYWR2UnY3JTJGM29STFZyTkRoUjh5VG1DQyUyQnFRWGxqOUloajFudGhWdkVrMlhiMDRKU3ZqMHQ4aHNYT0tQTHR0UEpWaWgwcUkzRTVCV3pkZVN0M3RjNm5tMGtJQnJaeXR3MndNOVBoSVRYRzJUV1RVZUN4eGtWc29pJTJGJTJCWEdYUiUyQkc0UER0bVZBOEtqYmNQNnBnTU02MCUyQnhDdU50REtSSlFzTWFnUzBmcHNmVmUlMkJhbjIxRE9takFUcUw0V2lxSFR1ZDFtJTJGb0duNjJacnBXTCUyRjRZa1hKbjNSWGJlYlRyOURTJTJGWXN1SEZySHl1NmNVcVhyd1BZZiUyRmdVYkolMkIlMkZVNVJUVHIxYmkzeEl1OWFOd1VlcGxuOGFEeDBIMnpEY256YWduN1hIdjJXUkp6MEdVc004RGtOVSUyQjRmNjNIRnBGdmZUdUdhVWNJSlRRSEhieTVWd3lkeUVra2hmdVFrV3laTDFCd1Y3ODJ2MG1ERGVybmJWMDUwMW5EUDF3QWlmN09POENHSmllZ21XVFNkZ2FBdkNVSUlCdFkxczAyVGRBcXhUT2kzSFBNbmN6ZGg2YVNUcndhdTRrS3o2bTVBSXVlWDZwSkpld2I5RXNPSGZPRkJnR3BiWFc4blNJcWYlMkZ4aDdyMjFYbFZoYjlHdjI2MjFnRXglMkZKMmVUNFJzNDU4JTJGV0hHblB0MDg3alhXM01aUnVNb1VBbDlTNnBwSXlTMkFkQUQxcVA3YkF3RCUyQm1sMENCRWQlMkJkUjRJMzhDN3UwUEl1cnZHZEhDZWFVTDQ2SmFUT0JDdjUwV0puazBNNWFmVlhpOWYyNFg4azlMMG4xZUNQS3lRbnBScjd3VjJsJTJCNGQydE1zVXhmTG92enBkMXlCS2l2d0wxdWs5a2tFNlpxVVhGdVAlMkJNU1U3dWZWMFMlMkZVYUY0M1pObDJBTnVtMEd3M29ad2FjbkFCV1JPSW9aWGoxTEtoaktyWkJ2Tnl3JTJCQjczM0tpU3pxTE9XdUhoUHN2TzklMkZhSXZrTDlsdW9WbSUyQkRrdWNhdGNSNTdxRkplUk5Vc2hhbmZ1SkFGem9ORE1TanJ3VUdhWHhTb1NzeWFjdnc3WmVRMjdDMmtZdlBBdlNTZFozZkJsZldpV21OVXJTVG9HRXVkSnZZdEV5NnYlMkI5WSUyQmZKdUZSa0dGd2laUERmRVBXSmxYUkwlMkJHWUVFYXJxOFI0MXZnb3ZQaXVxSCUyQjFEUGpVa0xPMHNGRVUlMkJMZSUyQjRFUjNabHdrUkNKaGhOQWtVQUE2QTB0TGFUcFRUSGlKNWpGRkxRdGdSVDNZUjB2UGFUc1RFdWQwTjRZS1F6JTJGOVFCbWVQZ0JlM0dwaGhmNXRwU2xJbyUyQlMlMkJuQ1FoS00wWW0wS09sdFM5dzF3T28wNm9qUVdLdWltelk1TlQlMkIlMkYyb2pVMXFTcVhoN25ubkVSdGY1RXh4T2Y3RGhoRjczeXZnVWw5ZzhjciUyRjhnWGxhM21lJTJGY2xIY09ycDM5b3BzSnZrdEZlaWI3cFZuNWI2VVZuVE92b2twcUhBVXpJT0dnJTJGVFRpbSUyQk01Qm9odDAlMkJTOFkwUEI3JTJGSUsxTyUyRmZvUGM2eFBXWExBZEQyJTJGNHA3Tm1GSmx6UVJWWEtZVU54TGt6c2dTUk1lbnolMkZ2YllPdmYlMkJrYUpxZ29DMDBTVHZWSGklMkZycnpTeEdZeTNNOUVvdHAzWHhtcEgxNTA3eGg4UXNYbHJ5WmMxMFJrNWVnd3hOVW5sUWZ4ME54JTJGRHhHYSUyQnB5ZTFFZkx0cDhtendjdWRWcSUyQjROTVpVcXZueFIzYTNiaklWVks2NlFKN1FxaU1sQno0dnFRQUZ5QXZWZUlIODdTaE9Yb0dPZDZ1OUE0Y0dDbG1lUUclMkZWOHg4JTJGNjlobUUlMkZIcDQySkp0U2I5UjA5NDFWQyUyRkxIRXNrRUhJZ3B0cWNzM05sODZrNVg2UnFpU21TWllUdVBERHgxaGptRUR3alJvd20wajVOa2tFbjNxMWx3WVBWVDVReUdjVW4zOWdXU0dEMTJPb2xOdTVlRHp0OVhUNzFjSjYzMXFabWxROUZpaVFwRkFxVXlMeEJGcVNlS1Z0TGlYZEFqQ01LNXZoYVRPZ2Q2UVAlMkJBbUhybm5SVklnTnFwVHU3OUk3cUpyVENXWm5NOHhSSkpDUm1iMnNLdEZINGN6M2NYZWR4cm16MHlHbk5JcGZWdE5rSzglMkJBWGFyUnJ2M0RteGJpaDVRJTJCazVST3RmQno4SXpONjU1R2Z1MnF2ZGdLVmRMTmk4ckY0MVJWdnZjUTRuUDU0bkVrb2JxYzElMkJaNXcyR3hCUzlSZEx2eURmbyUyQlJ1VXhSM2xkJTJGeDF2JTJCYWYlMkZDV0g2NUgyakFrV3k3S0RESXhhQVhrS08wb3luYXp5SDZCWW94WkdEZEVaQXAydU5PTTFnanMlMkJUcVViczN0dVVjaE5yUmZnRDZXZTR2eXJxbEtzYXJXcERNUG1NOXFuME1NT2pRUGdBSVZEOVRMeFBkZWdKdVZlM3olMkJOWE1ZRHdqTUpJQUg0aDZFRVdSWEVSMzhiOGFQOFdMdEV3JTJCcjNtMVFlVTlPQk9hQUwxNiUyRjZQZFZnUTNHJTJCaUZ2VVB4ZW5pJTJGN3dNZzhWQVExREZST21mREFjbVpaaVcwMVRqJTJCNmJKayUyRmZOZHVQUDVOQ0xVdzJNRWg0OGNoRmxoRm8lMkJienR5SVhDQW93OHQwQm5LOUZQJTJCSm5qZkloaUJhNEdKaUp3QU91bEFBaHRwbktBNFZvV25DcXNVbXdxcXBMYklKSXpOQnpUaUUwN2xVS01XdXoxeDdWeUhZTGs4WlpnQ3kwc3VpenY3VktHOGp1b0l1Tjd5d1VCUnR3QUpySVdEMDhaQ1MlMkJzZWNVMzVjWWg3d2k5NFNxUDlQbk1BTUU1eW9JSTZONElRcFRXWmhwUnplRmxhNGFNSnRScGI5WThjd1hpTEVHUGllMXNsdGdwZFFFMyUyQlpaZUZreWNHc0Z6WEE3QyUyQkoxSGpTaHFyJTJCZjRzVklUVmZNU1hIOGRYbmtFTHF2S1MlMkZvYkZGdEhFTGZlcnN2aWpydlgzN3BGTmk5Uk5xd2g1RTJIdUVCN3N6aTZsaE8xR05jZ1BzeEZQcTZidTVjOVk5TTdDWGVqWE9qd0NsbCUyRmVXWmpmSXZ5QzdsbHlvYm5JMFc2MloyclJrRmR2cWdkaVpnJTJCNHJZJTJCVXl5cmhybTQ5bjNVVTFCemtiTUFOazg0UEUwT0NndHpQJTJGOTFNSzhLdmRnYkhIYm9PSDhsdHlCdmloZHE1TlpyN01LTiUyQkFhWDRZeXpkZCUyRmg4N1FPUjZ5NiUyQkc3b0tIZmNyV3RCaSUyRnZWWCUyRmk4UzhVUHRmdDlSelJSVUVsTjZEM2g1UFQzdm1HbWV2Nkp1MEVUVERHTTE1M1VTQ2xMJTJGMVRuRVgzUEJTcllnTnh6OGlFVjBhYXVzcHpyN080VyUyRjhlbHRpYjU5bG9QY0l1JTJCSGJvbkJzTmFkZFlJWkpIUEJSU0szbmE3VjclMkJ3RmpBWkF0d0lkVjY3Y0EycENCQXltQVZBSDlydyUyRlIlMkZWMiUyQjJCbG53TWRIMElQdG1JZUNhaiUyRk5zdDY3eSUyRkVEb2NiJTJCb2wlMkZCUDJVczVFbTE4MVhHd2olMkJIM2o3Q2wwYk9NUVVHY2JoTCUyQlRTbGdyJTJGMlV0ZnY5YzZRM1dSVnB4WHZySEElMkZLckc5WVI2anV5eWg5NWNReXBSWHg1SGVWbDVuT0J4YlUlMkZOVEZzZGJvODVKR3RQY3lzWkgwcE1OUHI3YmMwaXdYUkFkVzRzMUpmUGhsSWRtcndnbllQTU8lMkZ3c0JOeUJBZk1DNzM2NHFOOXdHdFNxeGM4ck1OZzJFdWtEaEp0SXNzdWt3VDV0c1pzZnJEd2xacHFxWHNtWmElMkZ0YUhNNW0yZXRsUktST1E3VVEzOCUyRnVFY1lFU0pFNGtDTmJQZkliUEgwR2IlMkJ4cG84N1MxJTJGRkNSSERnRkFEN044SnZxakx5R3FaSXVlaiUyQk1WZ2N1U0JZOWx2aCUyQkxqWVJhYWp3cldzN3poemxjcmhMVXYlMkZ0ZWdhVXRmejAwOEhkczRIeFlXN3V2ZWYlMkI5MXRFVnBZQndVMXN4dSUyRmdQazR3czNPTHJHckxZSUdSOEYxR3VaMTJYdldNRzRUN3FJJTJGZVVMTEtzMEpNMm1qOFhONkxKWTlzWHhVUXNMTXJKTEg5TDFFZXVDOE5idjBrRyUyRkxmJTJCamFINDdtTSUyQlJTbXV2JTJGbzFnOXNTJTJGWVFlclVQRkhaZjlYS0dUSyUyQmg0T2ZTUVNFRWh6anNiSnFqN1glMkZBdzljM0FZdVVPJTJGJTJGYjlZaTlFSzZZd1VHM2ZBU2Q3M1JlOHhTaGVvZVdkSlpMcjc2VlhwU3B1d0FKREl0Q3VRQmtQMWw1ajhyanNkTCUyQkU4S3ZSUWdIMllCYSUyRldvbWFmeWZJcE1qbnpZVm9XOE9za0cyYiUyRmFLb2V2SyUyRnljJTJGeWN1eVVmYW9jREIlMkJDNlZMSGx4THJOa25xVFBEOGwydmhQQUduOCUyRnZCc3UxcVFwdW55TDBzbU5GakMxaU9OemVEaUVtWnJSNVNYdXd2ZzdUZzVMSWYzRFpCdVN1YTMzMFVMQzdHNUZLMjZrcHk1ZjJ6ZVBOVFdDUUtIaFJ1aTk0byUyRnVRNXVVWkxTYVdXanRoN3NidSUyRmFoVyUyRkZZZ0ZiQk9COU1JQURrcjQ0bnhpSE9veTlPVm9xbTU0YkpMUSUyRkM4a1BhSVRoRWY2U3pJOW9IQjlkUXV3RSUyRkVTWmJiVldjQ3V3ZG9NJTJGVE9tWXRHblhhJTJGUiUyQnZEN2dVckFQYTlVMElHNnNpemUwZldoUHlydiUyRmxpOFF5QVFQWmFWcmlkcFZTdlhJajJ5VWpYMEpacGZ0cmVlZktDZVMlMkJWU2xxVDFUdWx1JTJGSHpvSTdjNFhwRTFWZUp0RzVBTXljcWU0b2RRQ1VzeTd6ZzlDVHk1aUVXNTc0elpzejd2NXpGOGZlNkI1b0M2WVZtaFZQNXUlMkZ2cXo1SjFkOUVualJTZlJUeFdQZ3FRcGUwWXJRVGRRdUFLck1TNFZSME1XUWc0M1ZSYlk3JTJGUGMlMkZtaGVnN29GakNWcXVpYXE0R0F1MVFxaiUyQjdNeTZRUXU1UVlPQ3BuTzJ5Wkd6YW9LTDZYODlyU0NMJTJCTEJIbGI4bDhzb2NaN0pXa3lVSTd5Mkd2ZEFaYkxzZFd0TXZWT29EakclMkZtcE4wY1J1VjhqdjBxYm9HMlcwQ1Rud2ZKRnJ4UG9aVW12ZkttVFpUc3RvQ2tLc1laUiUyQmh6VWtkdEVNbWF2OW9ETjRISnNzJTJCJTJCc0FEMmxWQlRGRGhXc0dYMUFXcVAzJTJCTkdHazJmanpIMHFDTkE3SWZCNCUyRkdoQzRHbTZ3JTJGV1JCJTJCcFlNc3QlMkJpNThHSkp4bUpzbDlac1JYRTZtQ0kycmVOV0x4cm5STW5ZODZBWjRZJTJCQlBiVEJRQkZrOXJ6NmRjeVN6OWppNkFKeXFHWHFIVmlDMWJ6SGVDZ2h6UzklMkIlMkZVMVhIbkZmVEtHRTUyVmNBck1jc09LRzd1RlolMkY3eE9YVnZ6TDlaMzYlMkJOQzFIQWdxdFBkdlU3dlk4NGVKQ3JCVzN0N2FRRzE2c3M1VTV2eWd5SHNtcHEyTW8lMkJ3YXFFb2tseHAzYWNhVjc5QkQ1MVRLUVRmQTJwOUE0Tm90NHBDSnZaVnExUGJ4VlJ4UkJ4M3ZtSGg3ZDQlMkJBWDBqbDZtRk51c210VTdFekVXJTJCTm8wUUhvelJ5YURQeHluTm1NUiUyRmNZRVBHOHZXY2xKaElYeGhWbzJmZnR1SjJwQXV4YXdMOFJYYmQxJTJGZCUyRkFpWmdwQkUxQjRvY2lsWVZUb1JmeVg4UlhYcHBDS1A4QlY4OFJ4OG5TSiUyRmtsSWQwWXZacFVyUnhFSVdZZ1hlRzdkck8zNHAydk9VdXdqbHhNamVPSmZkZzclMkIyUjlyd1pUU0R0YWxHN00xN0RRc2VYNFJCV25HbUluQ1FwWmpyeFZPa1lEciUyQkFBN3ZtZUJHWm1jRG1UUzNpUVZvSGNZN3lDcTBPOGhIS2xxUzluUW9zYkhIWnFqMlFKZUNIUFZ4NWpnQ2xvM29jajd0TWY4Rmw0N3lqNnZxOGRPRWlNZlpkVmltOVFYREpNV1JjJTJGakNwekpkdFR4bkJZN3RxVDE4VlJENmhZdnhxQ1BmRDZZbnJETkxGVmhsYkRDNmtSSEg4V09rZGo2aTVPbmRud3VoaDhEc3hUczNtTWM2ZGJBSiUyQjFlU2wlMkIlMkJNUDJ6NkJVUFJSS25BcHg4MkpxU2k4ME5XJTJGNDF2ZXcwdUJ1d1V2aldLbDR1UzY0eEdINUxHSiUyRk95S3dZOVJ2QjB4ZFJXejQlMkJCY28xOHZmNGN0dkZ4ZU4lMkZKb3dvcyUyQk5qVzQ5TDd6UnJmc1NMZ2RGMnBycFJPb1FOaFBaZCUyQmRjbkI3UGklMkJaVENJeEhLdTFtblVuUDZRYWptaFlTeG1kdmVzdmNGdVB2Nmc2dXdzRlN5TjJicFI2SFpCUFdsNkh2SUpzSXRIWVFmWmhmcTUzRyUyRllKRm51Q3dDM3VsSzJlcU9nSXViUXZrcHNqVjVSZFdSU2Q2JTJGMEwzc05zelVtazJaVG1pN2dzcyUyRmt5eXVtJTJCa2h0MDRnQVBOcHNWNFBZMlhQbUslMkJWOFN1WTA0WndIOGs1bVVEOUdEajFxR1JJVDhVd1NBbEdjeXNMRUw2UFN5dE1sUkJHeUVnTmZLVEF1bGxFTXdmU3pSM2lnMXVxbkVPYk13ODN6YTdzNW5qMlFrS0N4S3Q4aXI5SnB6YUV3b3glMkZ4ViUyQk1kbDh5alZZZlNmY1VYT09FMldOSmFaajd5WWpjJTJGZ2tOWUthVXR0R1JSam1zNmt1RDVHcVk3b1M3JTJGT25RNlh4TlFGQ1R3UVJUZHpTdG1iSVVTSDBVY2FYS000NFZmazViWnZSVWd4eEhiJTJCcHZJckJ3alhTTWVNM0Y4dURIVWJFdXpqZDNzVDJ3Y3NCbXRuOSUyRnU1ZVNMY3JWMkRnZkJodlQ2VnJxc2F3eXpBallkZ1FFTW93OGglMkJ4VmFmNmkxVSUyRjBRVFNxZjl0ZTVSUTRHeE1KRlZlaG1raWFibklkb1RuVUlXOHZTcEExemZxT2wlMkZldGFNdnlOZmF4UU1JdlUxU2doMHMyJTJGNkhHVkw1TUh1QU9iTFplNWtJZjk1ajVOVlpGS1V3aDFnMWo1ZlNaUjhYVU96QWlHRDB4OTJuNDBkdjNNc1VpUGJXMXlQZmpqMXY0UlhFb2pvTVk1Q1B0TkQxZVl4aSUyQmJuSkVjOUJleHYwVXdIWSUyQiUyRnA1dHV4aHZSR2E0QnBROU9BbTlLelJRR3p1S1ZDOHlORzhRaGpEWEFVRWdIenR3enZpcHolMkZzekJIdSUyRkRkbngxaHQ2Q2h4MDRvaVdUelk3YktibmJrN2ZKVzBoNHpqejBUME1TJTJGeFhwOWVwYzlNdVZEJTJGdE8lMkZrUTJkMDNGQVUwb2lIVHh3Z1NMQ3VWcCUyQkc1WVlBVmRpc05kRmFKcDlnOFU3TnRaWXglMkZkNndRSEx1SURhV3NjRUNmOEp6UkpwRm9vb0xSYzd3ZnJ4YXBGSm8yQ1JURlRXN2V4TTAyZ2RpT3R0ZlJuJTJCbk9yVjRPZnFyTEg2clB3eGFJTGk3VExRNXFjeWpnZDNUWTZrZFd1Z3hJekFtSExpY1ZhTGdNd0Y4YmFhRFB0a0lLJTJCJTJCd2M2TWV6VElJa1d0S3BINloyTXBNN2dzJTJGY2hoYm5OeWFMWUREOVI0SlZoNUNQVWx4cE5uNXc2UyUyRjRhZTVTUXhOMXgyNXIyZDZOS2hrQXBPUTBLcnI2aUZ4V2F3UGxCVERwdkVxeDc5SE1PRWN6TDB5VUJUT0pBc1ZPJTJCbFpIY1JYeVpMcHkwQ2JWTHRtT2JjcVR2M1kyb01MM29RSWdlNjVFb2ltbzAyN1ZvVHpaakxtUEFRbWhRTElDYUlOa0NFNnclMkJYbnZUeGFzVWNpZ1NWUyUyQmNWR0F6MXR0YU9kMzJJNGZzQlFETiUyQmlIOUJaYXlYYm9KVTJtUlprQlB1WlVIUXBhbzFZMW9KR3BoaFIlMkJ2SyUyQk5BTGpHJTJGVEFXMmxiYzVyMzNzM0w5alFSOXolMkJSTCUyQk56cTFsMzBScnNXSG5MY0paR0tUT3pLWjMlMkZ6Z3JQWXYlMkJBM2hsSzlJRjNtZklPcVBpMGM0dG8zNFNVVE1ja0JYZyUyRmMyUlVTNXolMkJkUVA4YzZIVkJYWk1zNHhFVXNyeDhCSkFNeHNHemo5Ukp5clRhQjFvMGtjSkdTNFE0MFdCOSUyQlZDZmNzUDhrc1JmRSUyQjElMkJUaHRmd29RM21SRXRuekU3aGlVUWZyWm5MOGVDR013M1Rjb2l3eTR0YmlaZ1BaRDg1YURFWjBPcGJFQ0RqV2NjVEFhcjBDeXk0a0FHbXpTWTVEblQlMkJSWklCTUJ6JTJGdDdjVSUyRmhmelJIUHU4MWVsMTloa1FsREphWnRuR2gwNmklMkJyJTJCMXNSZEU1aG42QURUZW1jOWY0QlNPVGhlbjJFVWg2VGlHNUF2M0E0d2hqbE9wSU5tSGxnZ0szJTJCNDF6aTVhMjVzOGF5cVpzQ2klMkIlMkYyQkhUbTVobHVhU3htQ0ZzS0ZydmlqcXg3MEY3NEZkOFNnZVZZZmNIWk40N1NGRkNEdDVzU3IyeFhmbE1MQmd3YkxIaGVPdmZpTk5VMG9rbmVDcEx1aWVBclhCWVMzMllLSUIlMkI4OCUyRkt1ZTVBNVhYR1lmaW0lMkZobFlhdGh6ODNaemVTWHBBdTRWdFVHelJTSEc0THM3JTJGS1pwS00lMkZlMnlXalclMkZJTTlnU0pJek5sY1FpZk4lMkJIOWF4NXY0alNGSEZRYW9Ra1Q4bE5PeVklMkJ1dEJIZ1o2bTVycWJEUUtWQjZHMlBhZlVTRHUzeDI0YyUyRjFZaUpqQUVTY25mR2hNJTJCazhNeTdzZndsdGl0SXZYSnNBc1djb093JTJCbEprOGNDNmxXbThUcnQ5cVFzNUhzUXVaSjBDTTJSdkoyWkVKbmJ6SVF5NG1BN2tiVWxJVDdhVXZGS3VrNHlPcUJCRGhCQmxBZ01iVHVDR21rWm1ZY2g4V2htJTJGbXlmamwxVmtLM1VGNThBT010QVA3SG9WZFRiQm1DR3cxTjhRbUJsaU1SU0pmTURwOWVkMkc3NWhTa2F3VzIxTFpLNW90b1d5SGFVZE9rdkNYbnRvSFAxJTJGZE1KUmFpZVVKRXFyVFNjZUY3a2dtZmlCJTJGODFzQ1YzU2lxbVhPM0NhRzJRbjBOOG9GdjdFeDUyWXBLd1FsaDJDSmp3aDhSbHFOb1pVd29uSDhVWGRqdFdyJTJGM0dSMjVnaVFEYVZDMyUyRmVvRGFOcG56ZCUyQjJGakZhSDdDQmZsWHJ6QjElMkZ0WGNPNWU5TiUyRnJVcVolMkJiYVA4ZHNxNkFoYkxUUzI2dW5uUHhHRFRvJTJGbnpwJTJGWFVQWlVDOFdGUlJQOFNOSUhOelJKRjNUOU9sWFMzakNwY091b0lrRFBMN1ZNJTJGNHNGNTlkWkhvZ1dhb2theEFGWHh2MzZLJTJCellXdlREMDFmckVWNktJMGFjeWxLTFIwdDV6aE8lMkJjZUxkYWdPJTJCODl6NUwzU1hOVTZ2cmtwRUVtd3glMkJrNTJwTDlzWU1JdHpEZ1NQYjlSJTJCRUw0NW51UTMwMWpnJTJGNnZnQWtDNnJEUTJYVXpVQkVBN1JBTnppdnE4bGkxZCUyRkM3VUNxRmpPeTliVXYxQWNVeDZoMnJtT0dCNVB5ZFAzZHl1TXhrRzVVUmhSWXJmbDM2aVIlMkZUVW1qRmxFZmRHRWVObDUlMkZYQ25LQURyNW52bDVNbFNJNUtTYnNPcTk0M1ZyWkZobVpTJTJGdHRteE94N0glMkJYWEEzYTJhcTFING1qTzFEYUs2MU9wY1BOemkyVlcxcnBsb1IwUVYlMkJNcDZhWXdJMzExOXhXVldBUkZGU1p2bCUyRlBNR3NMUUNFdEV2a2lNYWxibkR0Sk53Z2tlUXFiRGpldWZXMXJyQlphMTl6JTJCNnFyejZ0RW9pWiUyQlpiZHJwWEVxMyUyQldJOTN1OGhVWm9aUUdQbGxKZDVESjJ6UmM2Qjk5WFRoUGlnTXVkMldzenIlMkJTc1ExWnBuUDQlMkJHVnVUTlk5cTZKeHdlemJETGtGbUxxV1FsVDlFTnozc1RVUVptUlJpMG83Z2VjRmczdmJNYnJJekt6UnRqMWozM3lYUEtGNCUyQkdHMThRSHEwM1Z6TDZ6VjhNMlVwZnNtajZBODk0ek1sR1N1WnNIUGE4JTJGWTMxcXphTFY0JTJGMXd1WmZWWWhOa3ZGZlJCbFhQOXRmTXdUU0pvYkdsUm1wUndhNVolMkJROEpaQ1B4QjFzREdKaWNhdjVQYTdQdVgxYmcxTVl2S2tEbDNKbVNYQzUxMmZvYXNpMU1FdXQ4VGpLZGkyVEJ4bkFmdkZwYUFvc3lIU2xtWE90SVNTalZlVTNmS1NLbWlNdSUyQlNZUzFvcUtpVHI0U0ZrWGNIdHdRN2RibGtOeW90ZXpSVXdaZG82T1pwVFJLdlJPNVdSQ1czbiUyRmxieWdvN210b0d2a1lLejBrdUI1UFliQmxuZXJrRHR6ZFdqZjBRTlljSWVBNkNzV1BBMzd4MXclMkZocUVJZk5ZbkgyOGIxN1NkN0ZhJTJCanFUZ3BKSVBTSzVkZjhvSFdOa3o2SVFra2UxJTJGOXlpUG85OW5ZcjhBdUo4V2UlMkZFREg0OE5lWmVsanNyeER3QUdIZzQlMkJRU3pRRHAzbUpFMzFxeTU5NndtTzN0dUZXTlpMZnVoNnI3UHhBVDUlMkY5aFllY1J0bG5OT3JYMURIb2Q0dCUyRkl0akM1UUpnNk9MMWpibE1FNlFGUDJ5Z0sza09CQkhjNDlNbjdJSHBQNEo2emh5YTh0R0RMQjlJOEFpcENrTFlYMk5mbW1tWHcwc1YlMkZMVmVWY0Rtb1FOV3JaeWRZMURCaHJ0RjdUQUw1NlNQQ0hVTEw1MHg5eUtGUUI0TEo5cnZ5ODhxJTJCdFZXSkZxUGlYUG5HMXNhZFAyNVhmRUVIWXBjWkk1akNPNndabzYzVHYyUEM3WmswV2ZUNXgwZGxrQk9PS3ZmMjJYWGZNWEtUNSUyQmFmZ0lwZlBuWjltNW1hWncwdjFSbjczQzFWJTJCVkNyWmhKUUxTM0NzJTJGQjVBYUlDeGwlMkZuU25HM1hMZFhvcFBScU1lZ3hac1pNY3pZV0ZpYXJ3TTdaeDVXZjd2RW1kOTgyNjRDakZRN1ZkbHhJaXZRd21sTnJkM2w5NFBNcG4zbzclMkJHaFVJa3JVcHVyVDNZMVlTZWI2aUc4UCUyRlh1MXVYM0RBYjdIeU5CSWZLaVYzaTdKM1BzNVg4YU1QNWtOJTJGcTU3TlV1SyUyRndsMlNVJTJGJTJGbjJrOGttSE4xZHIxME9RWXBHdXpIbUczUW1wTCUyQjZjYmMyanRWZFpReTVRcUp6YklBa0lyQWM5Qjl1cXYlMkZPJTJGUklITmxxUHJyUEZKJTJGSjc5WHZ5SDczY2RZeHBmRnM0Qm1JU3dOb0V3RWE0REowcW9FVzNMejdBRXAlMkJsTUV1OVFEODBJMElsUk9BSjEzcWklMkZiUjNPelFMNFpWeXI2ZkNPRiUyQjNWak5DVVFnQlhBSTlOdkVTJTJGZHFVaG1STVM4Wk9rVk95ekdyRCUyQlFFMWlFVFBTYiUyQkwyajkwTDRwJTJCeVglMkZWNWFlJTJCWWY0NGpJcE9ReElyQ1QydUklMkY5aGIzUFFhUzhvdjVPbHZtSGU1djFvNTloSTVLTnRwSVBabzVIMXpWQ1NHS3U5VW9Xd0g3a0xzOU45NEh3MEhIWVc2cm5OZDlQWWQxVWMzSVh6Tlc1bTB5Rkp1VUxYV2xtYkw2cjdSTTRYSEVGZzU2c1JVSjdlZ211Z2FiTE1NWmtWM2l4d3RybG9ZaVpDTVQ5V0lxU0tYb3k3NVN1bHJFelE2SFVidzh0aTRsdFFMNEpSYldVMExIeUtRaDc3ZEdWJTJCViUyQkVINSUyRmdQJTJGNFpZd3hkbXRGTCUyQkVDTURld0J0JTJCQ1FrcjlGeXZRd1NnSk5IeE5EblQlMkJLYk4wUEs1bGh6TkZ6U0wzdjNEYU1GY1lBRnhIUlpIZTFqRnVDSzZCdnR3dzUwT3VyTFZrS1lqJTJCdSUyRjA0M2tBc0FqckwlMkIlMkZ4ejFQJTJGOFdkd0J5a2ExWTMlMkJUYVNJSCUyRjNzbnNvdHpTeW1WWiUyRnMlMkJYJTJGZnVqaTNIWSUyQkxpdnUlMkZ2OTdPVkxGZyUyRnh1JTJGbCUyRlBwJTJGJTJGJTJCVUJIdnJ6SUNYeng4NEglMkZiWnJpSlIlMkIyJTJGMmZUbCUyRnVmTDlOZlFqNzIlMkJiYThLQjM2NzRBdjlPJTJCSSUyQjklMkZIJTJGJTJGdjVyTE90JTJCdDl0MlAlMkJIJTJGdHRhNVhWWiUyRmUlMkZ2WXY5OU5WNyUyRmJTaiUyRjc0OERCJTJCdSUyRlV3SUVlREg1Q3hmJTJGdTRLJTJGOXglMkJvenY0ZGclMkZ3M2hMamI4MzliJTJGbTFZdDd2N2I4TmF4Uk40VyUyRmR4JTJCYjdTWUxoMUduZHFuT1NkTWE3MVZvJTJGRHV6OFp0MjNzM3k5MFlBZjlndmx5R2ZjaFk4WnVYTjc5V1Y3RWU3ZjlQNzlBZFhVSmp0ekc2ZDBhcjFPZWdyRVY5WlclMkZsMGYlMkZuWkQ2MzYzUSUyRjI0QlB4VnY3JTJCMm4lMkZuMzg4TlB3eWh6ekNwOXVuWkFpbEg4UDhtZTdGZWVXN3pzVWZKUmZDWHRsbFdMWG5JRWZzSVVKYU1rUDNtMDB6cndmODdQa2QwZ0V1Z1pJZ2VqUm1zZTU0QWdjZkJ2TVRDQXo3eTRvRVV3UVVUSXBBZXpGcyUyQkYlMkYlMkZtdTVCZjdJZjklMkZtVDhwNXY5c0VYWElSQlVmeEZQdDVkeEIlMkZJbVpTdVVoUHluc043ejlLWjZqbXI0SXVZQU4xMk0zdjl2UDk1ZmQ0Tm4zM0M4enBmbjhPbGJ5ZmgwOWFnbjN2TmRMQnU2OTZYelVMNGthWlVadjNPRFlmbUF5Y2d5elpFN3dtSlNNVWdVUmhKa1dwSlRXOXg0SiUyRjhudmNGJTJCdzMzMiUyQko3eXZ5N3ElMkZmOTd2SmVHRDdSZEVWZUIzZjdRRkYlMkY0MmNvcUMlMkZHMFZSSDVNMVRseDdOUU96am1pb3N5VVlMb3RpWkVka3dwVDVIdmNPbFEwZjlmYWFrS2NNRnFoY3ZZbiUyRm5rVlpNaTE0N2Q2bk03JTJCJTJGcHIlMkZuQUU4cU01a0piSTlLaWxsTzhmZU9ad0NmNzNmJTJGcjZUUEdDeFVwemNsUnJ4ejhQJTJCZlJkb0lsWDlJeGpXYSUyQiUyQmZDaWJvNGN1T2dKNDNnZVFyWm42blVITmNJZjdnV0NrSlloblpVeGQ4RXUlMkY5VFIwSkdvSElVJTJCQXJjMjNLNlFUTmFUYmJ0UmZzeWVST0VaejZlS1pOREJITkdVMk1vMEpETFRzJTJGMzcxamFSem1GdGN6NDVnbDI3cmklMkYlMkJuRGRNZG12JTJCYTlMcUZVSjRPTnVJNDUlMkY2ZGZ4MGxuR0hIOHVwZkdTdEdHdVVKWGxlekFsS2hRemElMkJiMCUyRmo2VjFqVmJtYkhBVUQ5ZW90SlJrMGFMYVNHRVVsdUFVRE0lMkJQJTJGNXk4SmxzQmczcWVTRCUyQiUyRlVHazREeG4lMkI1bCUyRnhudVVVZiUyQjd1dTh5Q3BUV3pBTXkzWVdveFJ3bG91ZmpxWCUyQkpUS3FDQVBQRmwyaUJzeUR5SjF1Q21hQlF4aHk5JTJCSnk3VU1nN2JGSk9tNUVJd1A4V2MzZ1AyMzd1cSUyQmNpJTJCbGhmSlRlSjBUd0t0RFE5U2Y0c2lIU3JSYlJWbHEwJTJGbnFrcGxpTjh5Q0U0bmZiejZReW03ZGtmVDUyUlRQZDBzeFcwN2lTeFN5aDJxTDAzbEc1YklSUjBtT0JRZ2ZsS29RZTVqbVZxZ3VtcTVqZiUyRjJWSko0JTJCdFBpVzExSEFWR2luOWpYREE3WjNKb3JzT0M1UEVwQzc3dVpVMzVGVjRNM05EeGt5cDFMciUyRlh4MG83UFNhTnZyMFRGaFY4bUplZ2NCV1lNVm10NEFBSkNGNEl6Z3J4b2VzZWZsMkdXcFZ2Y2xFa2tFZWhHN0R3UEVmcTlxbnhKaFozZDRIbjFtbTF4UTZ6Ukc1RGYyUGk1eGpYU2RMaTJNOEdRZU13eFR4ZHpxQWF4dkxKU0ljV1FEcnBCM0hxMk90R2NFV3FhZ1BRNGNDOEtTUUZXMW5HJTJGWW9JN2Y3NkplZGZRa1l3MnJGOVJyRmk0QW9jSUMlMkJzbnozRkpaZ2ZIJTJCajBYZmpyRWVTOTV3Wm9HaDR5N0huUHJudzBja2dUM0ZnYmRoaXc1R25qRENpMHp2Y3g0RzBuVUtPTE9pVXJiWjdiS0pTWXFFNFY4Z2Z4OEVWYkg2TWZnRmdybG5YeWRsdTNYOU5BMm1WJTJCJTJCdWd2d1EySXlBVlExTEVPYnJKVXhKY1lnSlFWaHc2UmhnT244cjhKUmVNS1J4eEliYnFYMFVRNFFHcVViQ2R0Y2xNdVZRSlFFemJzaEdNS01ybTIydEphOWJjT2NMcU5ZOER4bDBYTUlQd0JPQzRjbTB5WHJ2anZUTCUyRjJpMVQlMkYwSm5xaWc0WXk3RGZnSmNINSUyRndaNWVMalV0QUVjNlk0V2ZGMWlVWFJqSTNjQ1dCQ0lqV3V6b3lVNnRENUJYQnBkQjFTenFQdU5rNUtjZHR2WVdCRk1oTmVydUlFak8xc2llaGR1bFA4YUFOQ1RzUzdvaGVRT0RGaWg4UDRWa3VnQm5GWVZBazJBSGVRTTJMVGMzVTQ5VXZKVTlraERxUmtCTHNlZU5FeFl5SFVNUm5MbERLYXBBMUJNYTFFSTI1SGYlMkJ6cEVEV3FPdTVsUjFIT3NNYXdha3d1JTJCaU9Dbld0Ujl3aExGNSUyRnVEWWJGaXUxUjl5YTV2OW5jRzRqdmIyYkdGaDdSTlcxOEpKczJmaWluaDV5UlkyUnU3QVZVQ1piRTNRVnBVN085VUFtbkU0ZDk1OVVJdjlMTFNMWTMwciUyRmRoTGFYUGlKWTBMcVltYjJTaWl2SWhpQnpKRyUyRlNYOUJEb2lmdU5sR1VzUG1YeUczbkh4WmZ0Ynh0YjVRcUZERnNTNmZsMGtFbk9KNFhsNTclMkJPTkpUT3paNGFnRk1wc244NVM4R0lnOGY4M09WSXlGOCUyQk1nSWxvZXp6WDJKN2JNVWdNd01PU1ZPRVowQ3l1WDBvSVN4eW9SYU0wVk1nQ0JrYjA3RkVuJTJCMmhVQlRwN1BvenllaVpxclRtOUg0aFFqS2pQUWRtYllrRWxSVm40OGNIJTJGY0RuR0pkMG1wenRkeklibFMzcTN5RldKYTNQa0NwOXc1VGpPdjRvVnQ4QXFXaVdYb1FiM1Q4U0pUSDZLQ0dqN0drdTVLNkZUM1NTTFEySjZadjAlMkZNeE9hSFBhaFAlMkZZNWJ2bjBHQjVvUW1QbUslMkJzVTdIUiUyRnM1MmQ3aG9Nbkowc0c1MHp0eXElMkYwdUlaSDklMkJhOVdXMW56MjF3MmhRS2NuZlZmNVJWYmVXZGRtRlpmOFVHSm5ZRkNyUjFwUXA1JTJGZm16TEZaTExXUTgwOGdGSm9rTmFoYjJsdk9aYkhKaDhtN1o0M3ZKenhnVGdXUEN3dkdyWmltNzhSTndFSDVOcG1WakkzTXdaRWoxWW15UnBmZXlBUk1EU2lGdldBM0hUSTJMQXpJR0FVOEk1VVNyUDlNQ1VZMWg5S0Z1R3gyQkxTQzRLY2dIZDFJOHNOSWMzaVVZd1JhOTRxYUlDWlUwOEM2MiUyQjNrU2lNcCUyQlJJYzFvNW81V3lyJTJCNzUlMkI3bGlXNkxENmVuTUR4SElwTnI5OU9hMnk5YUtsbVhtSWFwc0JaUiUyQjV2bEFnWmhnV3UyaEtqMU5vUXVmQ2Y5Uk9KM2UxUyUyQkxHUlZRVFljUnBvWU1hQWxTdmZscTlCWHlzVzY2aFRDZ013aHQyUHRkejZROE9abW4wTWtpT0ozYXNVSEx6OGclMkZHNUw1aUJWUmNoUVdZdWJLUDVGQlJjV0hEa3pwYkZHelpkbjB1MXR1VXglMkZ2NGdETmJpY01CcEFTQTMyMGxpdW5QSUZLdEw4Ym1zTzcydHU0R2NreVBmOWprSGlXeGhkeDE5cmxLbDV0YllTWk8lMkI4TWpYNUZ0ZmFDME5pcTE3RzdiTVNFdWpITXVxNkYxdTZhSjVvYVlveTlwM0pveEhFN21jQksxNCUyRnR1dWJ4JTJGeFJKU2tLcUxiU0pFbGZhS2xSYzUxeWszV0w1czJmSVdpdUk0bUZoY2NtUW0yZSUyQiUyRndIR1RGbjNGdTZ3U1RmSGwlMkY4YiUyRms5eVU0Wkc4Y2FGOXZYeHZHZmliV2t4blZOY3FZc0F6YW9pQ0sxMjlKd0l4UW9jJTJGajl4TlZUT0hDV2U4cElocEc2THlNNUdkN2tmOHNkaDJWQ1lvZHZmJTJCNDFxVHElMkJ3OXZIRDNLOUFWSjglMkJCcGtUMnI1UVg4WlBuRjZPNG5mTnFVSFhQdHQ3SXNmZXhQRzlFYTlpbzllUWNKNEpmWjVTWHY2UHFJbzhaNklNdExuTjFHODFPSk8lMkJZbVVJbCUyQjZuV0tCRiUyRkhDJTJCOHlKdE1hNklzNENOczY5ZiUyRnRTMk5ic2ZYUGZYTnJtVjQwMm0lMkZuSDh0b2NxQm9aNGtoNk5JJTJGTW5MJTJCNVEyMjFQYkdOTFJ3V09RM3Q1QUt3dWdVNVFscWxlQjVLZmI0Ym1Ua3VMTHBKWmhMWkJ1b1l4WnlnOWUlMkZzd2padlRXTkxmcWVKbmUlMkYlMkJsSiUyRjVWWE1YeXk0aVpLd1lqZnhtVyUyRm42eFhoNGdSMHNUUEUlMkJxNG8zSnEwMmFjV1diVzQ3VE9qbmRCeEtaRWFoYlhSMUolMkZycTV2eUpIcTVnUG1NT21odE14TXRWSzBTdzlLbGxvYzNJN3BxMkxkUzNnOFlyNGNNNmpwNW8lMkZwYWNYTmxSaXduM0V4cmYzVGFrWWRQTTN0TXdKclRIeDJGTVBaVkxUVm85QUtHNlAlMkY0ZyUyQnp1Mmtxb3hvdHpwTG5Wa21HTlo0N1RVMjBLMlZ2MG8wallvdnJoek1sWGtwOUolMkJkVU5oVVhaSVJwNHp0ZmxuaXlWTVJqZE1nVFdjSWJFcUhBdHFYZWpoV2dPdGNGMEZ6OFB6Q2xGNU44SFVCVnQ1ZlhkSG10QTE4NnQ3bk5pUjJEd21CSENsVkR2Ulg2eThlRFo5MFV2Y0VUZGJmdjVTWlJPcHNxJTJCd2RNSXd0VjlnSVgyM1NIM2ZhMGQ1T1VpNE52NHNaTlNyMmxVcUlvN2hKZzZsS3NsNnNaZDhsaVBVZ0pDUU1ESUkxemRnTmRKRDdKRDlTZTUxQnJFUTlkbjdoRiUyRnYzNUY5SSUyQjJ3aFhSUjduMGdtTGxyQ0dBY1hoUHlrZFJmZlV2MXRTaGZPb0txWHlzSGUlMkJBbFY5MmVVZHVLOGglMkZMWXpmaHlEQjhncXhvJTJGS0RhJTJCNiUyQkRyaUlFYnVnSXVqQiUyRnRBbWR4MzFzd3olMkZrb1lqJTJGQllkZmh1c2IlMkZEMEF0ZSUyRiUyRmNxaDVsYWhoWnRNNG9GdlJ3djNBT1FJQ0N5R0lCMW8xdjU5SDZ0OWR2WWVaSTEwNkR0dyUyRiUyQjRNZVV4OXBYZEh1emlkaW9ZYTc2Nm9kMUE4cmN1WmpMbk92JTJGeWpFZnpITDM3b3FHTHZ5QzR5ZGdmbDRVVjY5WHdxS21naCUyRkQ2SHJzOW5FVHNKaGlmSGk4U0psVmIlMkI3SXpxWlhyV2YxQzV2WGJkQU03U2hqcXpJWHZPTCUyRjhzcWM3eWRPdzhLJTJGYjJXOSUyQmU0c29FRFhkVSUyQlhCSlJQeUVRJTJCWkx0WENTQXRDTnc1aHFMdiUyQllSYTVac0M5MGRQT3UlMkI4RnAyckV1TCUyRlpYOWVIZ3g4b1Jxc3BZVXM1cDA5bzVLcVRUZ2VUa0ZGTVN6aTM0Q1ZpeGltVmNqTDg0ckFiM1Y2eXhlazBFJTJGZ0FEWk1EdzlLWCUyRkpTTVo5TUtmUSUyRks4RjhEY0RvMDZNdFBydFltcFRzUHhwdFVvZmNqMGJQT0I1ZWpVNllONDZlMEppcExwWVlIMFdwd0klMkJReXU2RmZCc1lOb25qaERuWk5Pa2JDb0FWJTJGT2l1eHdUdEJHcmNuaVMyRlo0c2YybnNkb2VjelRmbENUV1pYNHZpUHVrZkVkQWJDdmJVYjFUVE1TMXV2OURUSXNsMXlyZ2k4ZllsVnByZk1IUUxJa210V1ZRem9KSkFZcjZ5RFR5bEdnQU94T2pyJTJGUmYwRWZZU3BiMzc0TUhjZm1YZk9SSDhLdEFjM2p1cjJIeHIyaHZRN0olMkZtRjk4S0pHOUJWQmQ0cHpZaDlLZGRCQU9RQ28weGZ6WkRKeUxYQ3J2RzY0JTJGQzdXaWtiejZvT3dFeWphdHZOMGJrcFZTVjljNjY3V005SmNJcncxcEwybGE1SmFDaldBZWglMkZJSnNua0xKZEsxWFhKMjhRdjZ3UE5WNmVaUm4xcmtMdVA3Mzl3RU5xeE8wcjUlMkJMQ25MZTRxcmQzUCUyRnJ1dWNjSzN5UWdEJTJCWldKQmRrZkNxT1YlMkJhcUlhWkJGeWFaZ2t1aGZqOThYQTUyaFdVMWp0MXYycnYzRXI3R3FOYlBCSFZIR1NmN2hZQ05ra3JjWTF2anBqQm5xdFVaemZpSGpZa2YwazVvOEhxMVA3czBNbE9xJTJCR2tXa0lZOU53aDVMSkMwMGxycGdraU1odGJ4RDRsY25nRTJWZEY0VHBCU1BSYzJ1ZVUlMkZnJTJGeVlxb3F4NXh4WjZnaWdMc2h0WnUyOThkTGRLV1I5JTJCdDNrNUlnaVJmOWFCa1Vpb0U2bCUyQnp4dU5pVFZ1WjNWWiUyRnlqJTJGelRUSms3dlclMkZIb0NCMDhoWVQ2eCUyRnklMkZybGZuNlV2NmEydGdGN0pBUElydGNuTkxwNGF2b3o5bHNQOWRVTHRiWDNtYXdZMUhZQ3lzQjI5UW1lUU9ta21tamRucFdFWk5xbTVmeTh6ODZkcHYwYSUyRlVMemhXamU3V0JOSnpNMnZ4aXU0eWR1dkNpNmFPVyUyRm5DTXU5TXBkZ21zRHFxOUN2T0VKTTFRY2tMSkMzZDlwd3c3UkoxT2U2b09LJTJCSTNraG8yUWJnN3R4NEw5TmNMVHYlMkZLWmlodWUzM1NCWGY2SVAyRkxlMlduT1BRcXBQWXBYcnU1ZHNWcUtFcmcyWDE1SlBWblYwSjZkJTJGQkV1eG5IMG1lRWlvZVRFWDZuNlc1ZE1tcVVINE5TSGViQ2t3VkVOUkpmVGVkelBrVUtqVmN6Y2cwJTJCalVYJTJGU1RmcWM5RCUyQlhMRE45c3JkY0RISURsR2hndDliUm9yelhsJTJCaFNBT3M1ejBBNWVKZlZxRlI5UlczVmd6a3Z4S0JXODlsQnFjRU5KWWhBRVhFcmRHZXN2UnIwVTRNQlFDVWx3Um5CcjQzSXRBQ2dCJTJGYUVvbyUyQjZUN2JzJTJGY3dGcldyMVVRdjBROGc0aHVYS3N2ZGZsZkhhbk5OVkFPQ0MxaUJDZ3VGWEFRT1hlJTJGdFdzbFFoeEpXZiUyRmElMkZic0JJN0ZiRmQlMkZPS2U0ZFdScUtGTm1mRkxpS2hPYUphVGFCSXViWnAzUVNzbHFONmEyS1hrakdqazIzVnFMblZ1c1hkNGVVTHFveHRscGExMUd5SU55bTE0T3NJclBHTHg5RVlObVQlMkJHaiUyQjYzJTJCanNibjRERUZ2OHNRZ0c2akI5MU5kNjhxOWxzNDc0VXByTzB4ZTVZbkl3Y1lTZDRSbGtVMVd0UlM5UWlBTWExcHRMZWtwQktTbUt4a3VlJTJGOHB2bWxKdE9kdHM5WGNiZkVUcldDRmFjVFdjZ2o0NGhhdCUyRjFGJTJCRmtPcnNoVHJVYXZwVkZ3NG91MFVmSmJqOXBseSUyRnR1bVU3VmQzUzBFWE9pM3Q4amZyMTJYMkRlNFBWakxQWGpRTVI5JTJCU3k3T3F0Smc4dURHekh2UmZMeDVMSk1GTDJpNFFsNUo2T0ZsJTJCcmEzR3JxUHJISWpPckglMkZwSiUyRmN0RFAxWnVHS3J2b2dBTEczWnF2a1FNaHRacXhrazgxMk5oamRYbUwlMkJZY0pPUUJQRmVLZG9idHRUOFlDWUNjdTc4cTNEc2IlMkZRaGRMWlU1ejNXSldoUm42cGxYREI1OFFYaHE4NjZGdnVwZUxxOHclMkZwZWxQSHllb0ZpejRnSnE1cDRVTldMOE10ams1V3ROQ0tqalpOa2xlYnFJJTJCZzNZOXJIYWUlMkY5ek8yVmsyWFZTaXdvOHIyTjU1OVpNQkFNYzRnZHJNZzNPT2JRazBCYkVFTGhkNEpoZFRac2s2JTJCc2JUZnVRTm0zR3MzeiUyRmt6TVY2VEpEaGxDSTN4TW5GaThmQSUyQlVTekklMkZnc2d0VXN2S3RQV2lFWlBwS0FxV0xwbXFiZzI0VllTTGxoU3ExV3NWaDR6Nk1LJTJGcFIzZVJaJTJCM0dWUUdNT01nZnNsSlZHRUhpa3JPZ294eTgxOTVwOWMycmNnMWMxbiUyRnRXMG54QmF1MkNvTFBkOHclMkZ6bCUyQkYyTEJxRGNFM2xWYzZmMWRERk5rNVNERnI1eldhd3c2dDclMkJ5ZThsOTclMkZxdWc0MUNUZDFnJTJCY3BSZnFyJTJCbCUyRklmNFRodjYwMGJ0NEZIS2FyM3lPUk4xcnk0Wms0U3BSU1olMkZKSHMlMkY2V3NORzlnalQzbnUlMkIwN1FpNVRlSU9qbEN0YTNrY3pySHFMTFJQTG9MRldYUHYxMDNSJTJCV1g4R01sUzRpc0gzbG80RGtoaG82bFBOakVoSlBqMkc2cE93YXJEJTJGSUJRWHhPSU84cVROQ2ZxajNTcWtvYVlLcG44YXZWV1lmaGhsVW9LOXlmMjhZa0FmNmFSeUdSd3dkaFJXSEozQmxCc1R2UTQxcW83Qm5hRmhqMG5PeGFDS0pMU3NVQ3dXVG9WWFZ6UWRlZVFFMEUwcm8xMGVHN3NWZzFQNDNXM2VIWkolMkZLT0U5SzZiZ1hRb0FIb3I3UW1MM1ptTFRXelVQSUhDNiUyRjJvTEolMkJVaVhqWk9xSkV4RmJQY0p6VnZrWklOOHVUSXAyTSUyQnN5VTJtQ1BFcW5FNElWZUlqUUNSdUVXbkFQOVZtSHVwbm14SnhXWmxGSXoxJTJCRWtESWY5ZyUyRmVQWUNjenhRVUpqcFY0alVlVGNpVlV5RGd5elhYUlN6SWVNbUN2cThnbjQ2WkVJMTc4bnlkd0NXYUZPY2t2eEx1bVY2UmN4NUtGZG5VNWJzcThJYVVFbG5sZWpRRmpEJTJCSFFSVHcweW41ZFI3NVdxbWdQOUNpdFl1V1M0WmpoTTZDQUZlQ3M1RmxmbkhrV2pzJTJGTm5zRlpmOE0wREQ3NEUwYlJ5V211TGlIMXlQZ240NVp3JTJCeDd2eTFndlpncEEyNHQ5VVQwWk15TkRZQW80SXlkdThKbzJhVFdHcjQ2enNjVDR6TEkwV2RWS1lGQnlJeUJueHNvcmhKMElNJTJGOUslMkI5eUUlMkJ4cXVlamh6UTdKUlU5eUJsMjNORUIwc1pVTm03ekFORmtsTXBiMVJ2TVYwMmFXdkpaeGQlMkIzOGZkUk1EOHExaEtDR1VYOFZkWU84RENFYmJqdUp4Y3hOR3RzJTJCb3VkcSUyRjhhWmZQbEZaR2xadWY2ZzREMjU2YW14b3Rmd3RadjdxJTJCSkVYOFExUnBiaERWbnA2JTJCMUc5Z1JpaUFvMDYzeDV4Sk5YYWRyOWNBSmZEJTJCYXNaZ29CbVU1NEhZNjNWR1FrVkxNalZscGYxbFl2cHFPTXRhTG5ZZWM0ciUyQiUyQjk2TG5iaW84aWpHeHlzUUVYS0lxN3BSdU83WjFEbVVyVnV2JTJCVWk4OVZJVzBuYVVzRnQlMkJMbHk0ZktrblFqWHBhTDVEMXVwZUh0SXIzZ3VDalEzam9CZFozTCUyRndvR2pYRENBcjJoVnVUVDhGQVd1eFVYdzBOM3dHcTZvOE1oUVBMUHZTWTJZSUI2VjJvazdxd0NDdnh3MXc0ZUxocWF4JTJCZjBJQ2xrQWF5MkdZVHRVSnRYY0pJbGV3UWZxOUEzeTlKSVVEcWdwMEN4RDkxb1dtOHFoJTJGaTRQUVMzRXhkN1BKZ09obzVvdEdFY1FkUEwxMkp0SGhTQWpFeEk3amFQWCUyRjY3QyUyRnFlM1Y0Y3BrakVkRnVyOVZlYVJlJTJCOUtKUVV4ZU9GMk1pbmEzZ1I1QXVoNWdmWHRPMGxQblhhWXkzVks2anh2WEFubUFRJTJCZkRXeFI1dmZnckp3TDhuUDcyMDFYYVR2MXFwenJ6dUNia21sbWJvUTMyY2N0c0pVOHdoNiUyRnhjcUJSNEZaJTJGN0pudWZyeldtcjBPNWZXWnpNYlNrd0lveSUyRmgwVmFXMFgzQVZaMXNsUnEzMGd3ZjhMJTJCZEhUJTJGT1Z2akZycDJlNHgyMkpObWppNUJmMVZrQUxSclBrcHp6MExNM2NJeHhPT0FMT1QwQmF4Smk1R3N3M0xmOCUyRk8lMkZmNTgwYWEwMmxHdUp6UTlXalcwS3pncWN0a2tST3k2N2dxa3RxREFLa00wN0NHZ3FFcnBZY08xeGVDTHYwZ3AlMkJCZTQzT3E2WllJQ0ZsZlFmUllFWjVaY2ZmbHlHTnRueG94VFJOR0cxaDNYNSUyQldiYllUd2duRlp2eVFPcFFVNFdHbUJFc1dVWXolMkY0U3huMHVzYXBzSVBEdzQlMkYwMzhiOWVudGFNMHFRSkY3eSUyQmhMcmdhbyUyQjYyVmtXNlB1cDlLVGdsWG1wSjNRYnhxSm9ESnBiM0dsMWwwVEkwMDNvNzVPSll4enRXWXMzWmQwVUlha1h1VDFUWHFjNktVcUx4WXU1SHE4M3NuUEVKaHB1ZDBXaFF5N1IlMkJ0T3MxM3VXaUV2ckRKQTBFQ29LYkZpT0ZpaTNWZ2g0SDdrWWxaZ0ZiMGpmU3hQMXpKYVAzOXRjJTJCN3djckZKczAxc1hDWmU0bjRnb2k3UjNJV1B1Q1J1cnh5ZFJOUlFiVjRpdGNSNmJETnBWUmZScU1iWXhNS0VKZkZlcFJBeHNXUkNSRCUyRlNxVCUyQm9JRE9jdHJDdUpObXdIa3AlMkZnSyUyRkVRaCUyQlFtN24lMkJ5TTBKQ3hoS0JGQndoYmROWFFZeDM5Qm01ZkNpcDV6dlBYV3J2S1htb2Z6aXQ5T1doRSUyQmZmdE9lbGo1akpiVlhrJTJGbFh4WGN3eUdOSzBWOHh1RkgydzF0Sk9FM1pVVDdPUXRaeVN4M09vME9GJTJCWmtvZEJWTWhzbThsJTJCTUZtVXRGeEs1WHI5NTBTciUyQlF1RTMwS2NwT01NV0Y2NVRENkZrcmF1cnNDMmtmM0xKUDJ5dmdXbEVnRm1YcHpiTDJocVBURmtQN3VIJTJCVjN0aWlyNGtnQW5HMVFybkhsSmN3eDJLeGNqMDlFODZITW5rbCUyQjZtJTJCMHk4WGFPbWIzRlNMcEslMkJCYlNyUkVDYjlMRUxyRjV0c2tnVTFjSkw4M08lMkZwaUszQkRBYjdLUGRIJTJCN0R2dFBDYnBQMmQ5aTRsRFFFQUFQN1hyQnUybmRjUTZMcHU1dkxuRUVINkhEOUdxZkRiVFc5aXElMkJmWXRUREg2c01YUXhIRnlaTTV0R3h3a2ElMkJnSHNRNkhIakdON00lMkJjUmlQaEdzYXFEdGRnbFhEMTUxNkwxYjRyJTJGQlNRakxhVnlCNFI1YTFrOWhuVDZubjM0RkdFUUxYWUkySTVBUzUycEc1MkowT2NBSzlxdGFrUjF6VU1tREJFQlJENk9NNGMwRVZqS2pzVjcyUVp5YU5PRzg3djVGVjEwV3dzTFlrTWgxWUNXTmd6ZjZxdDNlemM0JTJCTjlMaEolMkI5Y003VU1vOVdWbUw1Yk55UDZ2VDFwRzF2WG5hd2RlTGYxVkxjeEtrSEtFSU1HTHBoWUJTVktUTHlodTN2bUI1UyUyRnoya09ldXR1ZFVXWkl3dFElMkZZb3RlJTJCc3ZNRnMlMkJVYzQlMkZ6Tlc3U2VyTnF4VyUyRndCNjVyeFI5UlJUQk1LZFlBTFRrJTJCMUZjNUJWVnQ4RVdhcTgybVdNNXIyZk9LMElweXpZdGJxOFBFc3VpbWZzdzdQN1N2V24wdWltNzdrSDlodnlrcExic2k1MjhqMVpsRFV4VmJHWmtQVHhDa0VNRjRMb1F3QkpSU04lMkJqNEFsWGtjTFJiSnMlMkJIUXRsNVF3b1g0cEdrVFNRaVdLcUVhZ1ZVcDE4eUpYdmh6TUVYS3dSRTJuRmFpT0tqN05EMVN4a2picXZGNWtvVVlHQklRVUFGdHQxbjlOZGRXJTJCcXdJQmJ1NzR2OGF1djFFeG54VFN4Uk5VeVppRHlmelIlMkJ6b1hOZmM2R1piU1hyWVk2M1ZPJTJGMWFiMXdHMG93WUNhVnRXaEZBUWl5U3ZaWURnbDU0YTh5YlhuZmtWeVkwaWZFeko2ODc4aUFiNEVBZlRUS3FlbmFPWGZPTkdiQ05zVXo5eVF2amR3SG8lMkJrWXBVcnNvMHdwOTJIZHZzMU55MmxmS3YlMkZTVTFGQUklMkZtRVc3cTh5cnVOY3JqUkVvcXE3U0JKRmlCYUQ5ZEg5eEsxM3k0S3NGZHdKZmVUOSUyRmZ1dUw2V2x0VmRMZyUyQmIyNVUycjI2cEtkcVd5SlJnb25NYWZhdG95T2RqeEVRejhNS094NzVIV2ZPdzdNWUIlMkZ4SUk2bENuODVWaGxsbGpVd29oeXV5NGNCS1dlc0NDOXRYU2ZUVmtCUWRUeEhxbzZ1aElMTjBlWFFjNlE3M1lpSlVaY2NvbmdLZCUyRmdwMjhEUENpNTk1bVVicHJocElRSkxidEVGSERYS1M5MTlWVThzczVIS3FkV3JrSXI1YTRJa29hb3VWMlhQSHpRa3NhVFBMZ1lJUmNEVEpGdnpUR2kwV2hmVUZwNTNnT2tXTlBuODAzdGhqSmF4c3c5VWJaRXQzN2VhMDVlc2VDM3dwOTRRR3dwTiUyRmFFeTZHdE9vemklMkJwZEVyaElHbXFLNjc4YWdEZVIwV2NheWRZT1lvaEFJT1hlRmxpN2FhYnBlQ0hkWGN3anhmVkNwclo0V1pYM051V2VsYWMwS2dLJTJGc09DcFBIeXMxR3cxZCUyQmxCbkFnWnVqVlAlMkIlMkZGaXFxbm41aGNIU3RjN0s0eVFoS2hMWnZUajFRd2NNbDBucW5FYWNUU3BEaVZhaVZ0N1V4NTNnR1Fham1HJTJCNTczVU5CeUU3NDB3Z3ElMkZ0JTJCWXp5UiUyQnpGV2tqczdFUU9rVUdod0xLTjlKWVdpY3BnS0FWbGJRQjM0c3NZTSUyRmR1cWIlMkZZSDVTc0h2UFFXUCUyQkZlRlRvSDczNThkY0VKVm1kQ0JCdWhQMDdsT1lNNm8lMkJxUFFLTDZ2RVBGTDZTemk2Y0d1eVMyVDdjT0tXbGJ6R1JkJTJCVzFoJTJCJTJGJTJGa2piZ2hmMHpMOWhyQ2RSNGIlMkJlRVNidXF1cUslMkZ0SUNFMUpVTHBsOHFob3lWRnBYckhaV3hTYUxYODE5MlNuZThWRiUyQmZ0JTJCdzFLNjNKVEtDbmVMZTdPNlcxaG40Umk4a292dHBJNVVOJTJCck5qNGhMakRzNSUyQmZBRlk3MGolMkI3VVdRSEw1ZSUyRlJYczhvZ0dIeWMwNmY1bkN4clN1MzVZUldmJTJGQ3BxeXFVRFg5cTdNd3pFYjAyZzB4dUxlY2lTTTI2eWwydnE2WHpyQ3d2UFJQdXY0cTRWeE1YWFdTcjQ4ZHY3a1ZCeXpGT085OU9QbERJMkZLUiUyQlV1eEJhTmxEJTJGYXdmb3h6Vk9XMzJ0MjgwdUwxUVolMkJxY05CN05DY1ZOUWwlMkZsSWNiUEdHSjRJczJlYzNHZlg5SXZHaGwxNlVxJTJGYU1TcWolMkJZTG5ZakFSZEFzdE5qdSUyRmZBJTJGSDMwN0t1MGREem53VWlIUE5EemNPJTJCTSUyRmlOcjhHdGxSbWZySFhWbk0yekUwTDBlRDhDZmJ6MHc1OWhPa1l2SldqdjM4MyUyQnpIcDc3bWF4R1RqUnJWR21kWlBBMDVtdmh5bmFic3JLdlFNWU5ObFViaE42dGRHQ3p4biUyRlhiVHJGVFAlMkZrZWolMkJGOEdGZmtZY3B4ck9xeWFSencyV0tMRE1EeVhKWEslMkZPVklsMTZsUnQ0dGc1aGd4Wms4REtsemx6TGoxZE1ZN3llaDJXOWIzOWlFNzNHZVdjWWVJT2hGRDE5diUyRkQzbmRzUzhyMFFMNFNIbXFKOTk2enczdnZlZnFCMjklMkZNRTh6eTMlMkZYcFU3ZFVKTXBRaEZJcHdhWG00R3diJTJCYU42Ujd0eFJPaXF3SHpxd0pNTkt2ZDN3b2JoUDlscmt4STY5Y043dGtEJTJGOXBaMmhjVFVYa2JUdkpKRmFJVUlEY3U3RExUdVZ5UXdqVXJrZmw5SEczN1JoWkl0c2FhJTJCakVhUnNBemRkSSUyQjU5YUZwM3NtOWRsSWltOVR0aTh2WEdQY05UQXlBYUlma2NDMkxrdDNTeWtUanZnQ0MlMkJnR1hreFBMb3RXTCUyQnlEQnd5bjZUS1hOU3JhY0dseElwbHNjczdPYnFmS1hCWnRKVkRFVXRleTkzMlBKY3BoZzFmemQ2WElxZGgwS0oyeWJQQzMlMkIzUGEwU1Z6WFpiSVlmeHJOV09KMEUlMkJUNGhvYWFqSlFnWlhCc0ZHOEplaHlUdkRjREZwdGJvTnFjSnpOR2IzOW10NzFnNU9SZTd4WDlMam9jbFlFajE5OUdZWkZSJTJCOGFpJTJCUllCSFpzSVN0V1ZsNjZYYiUyRlNwZXZ2bEVGOTIyM1pLbmxzWUxZenByUUZlWUpmYjZ2NEN4NUklMkZxVEF6U2x6YnpCdUglMkJPN20wWW1idFU4cyUyRnNDY3EzNUtxVHBpZmdPYU5UcVpVVlFKTk52eWZ3Vm1qZ2p3cW0lMkZXeW4wY3RlOEd2eWQzRjMlMkZ5VDJEajVZQkJpME9YWUNlUlhsenpxJTJCMVo2bGNwTUN2QWV3anhESThxczlqSDMweXY5ZmtSOEMlMkJxMlFCNWJuQW50T3k0JTJGSmVWeHhkNUZjVkFncjVCVDVxWGlzdjd4UnJWMzNGRHZmR0RaYkdmY0R1MW1MaU4lMkJzYjFKNkZhUFc1U09Ud2pLamVsS1FNcmFEMFpiV1MwYjFnNGxWUmtqUUVOaHVTdk1GT285Vzg0UzFzU3h2cVhaNHl6MzlCM3FQOXpxTmV6ZzFOMm5lU1BUaG0yaXhoYTV2eHE4WVd6VnI4Rm43S1BpdUd2dk8xT3RwZ0FZZTdyemg0WVFBUldyZlNPNVNZVkdDajVyMndEZHhuZ2xDUUFvNUV5enRlRXNlb3clMkZzY3ByMzMzVnEyYlVvJTJGdWpyWXUyVzFUR0RMRU85Y05nVTg0OXUlMkJXWDdBVE1WYVJWZnlzTDZIcEdMQTMzalR6JTJCbVFGa3RVVmtTUU13bkdZNjBzRVU2JTJCV0NlRlZHTjNUUVFKeG9DZTJYbVNFdkNtJTJCeG9aJTJGUFVRRjlFaEZlSjJZdkxYNXYwcjhob0ZMR1c5ZTZBTUxwSmhmYXZxSlFKVFJOcnA4JTJCZHBYTjVKS3lNdUhTYlpHTVpXUlRUSFNvSUFoaFdPSnFCc3B2ZnF2NDY5cHNuYVRvTUlvTWt4RSUyRnZXTHNTJTJCMUx0ZGZxWnlzcUFqcXVDT3VKakpDRnFKZk4zSjNhdFl5aHhpUjZLZW5DemZnNFVRamNSN3RSNHhaYTR6WVF5akpWb2JqMHMyQnB2VzhhZ2dycUFkZGVWZVRYMTVvdEV4ZSUyRkRsa0ZxNW5CaXM2JTJCdkttTjZ6ektSS1lNZjBrV0VTbEQlMkZ0cTNVOVJtSnB3MWtndXZySSUyQnRMWGpkMVF1eXJyUUVwTVBtJTJGSmYxdE5naFMlMkZEOUR1YnB0WFdpUEtZcVhONjV1c01HcGxrUHZDQUhnUExTTVd2TU5tRFBiMmFaajVmUkNoZHczd2loa1FBWFV1em00b0hQYk5mQ1JtWnNFaUtNVHZCVXJvRDNscE5qOVRUYkJSdyUyQkpEdUpkdVpqcmlXdThubFRFRDJOdmRVWDVwVWRobjlIaW0xc1RuTnBQQ0NGQW1od3NoVzNFUWFwYUlhTkt6d2lQZXZSSUxNSlY3NjYwNFhUb2pBYTVmZ004WkxtY09HdEZ2N1lDa3NIZ3JqJTJGcHJhZnRtT2FKZzAlMkJUc1Z3a0s5bURTaVZpdm5lMDElMkZSVUpmbGtaS1htaTE1MnpKOGh4TEJBUEhxR0NUTVVNdk9QSERVV2pkWk1PM1dXRzl4ZXNKVlRacTNGQmNya0lka3pZRTh3dHV1SUNVUzlTNkNqbW1xVGp6a28xcWRzWTJLaDR1Rmt4TDdoZGx2Z09LOGI3U3FieWtGYzR6JTJGeENmaVVUV3NaWUhwUyUyRndhNkpoc2xYcnFMR1c2cml4SHpSU2J1JTJCSWxjZ0F1MzV4Smp1bm1uUzY3YnVIUldXRGZaOFR1JTJGWWdkTzdnJTJGSUt6ZjdoU283dmZzeWkxcktZd3habXBab0ViJTJGWFlTUDcyYiUyQkFtd2ZhUlhWemx6JTJCJTJGSklHRWxINlFQcEdTZjM1Y0VCbEtOMEhBNVo5ZTZFViUyQkNZUnhEbzloQml4QmcydDVLNklBZ1FZdXBQZHpGQUolMkZhcW9YU1FGanBkMFg1alhoNnFaWGFOdnF3WlFtJTJGTGx3QWYlMkJrRnZpTjdDNE14cFJDMkVnWjgwR0N2dzZ0aDJsJTJCVDJsd1ZIJTJGUnpjZEVpUzlLdSUyRnZ5dlRhdWt4QnZINFl6RWhKMVRRVSUyQlF6ajQ4diUyQnFJS2ZaUTYzdSUyQjZjM3NNdk1pSUxKR1NQYjNGY0tnV0ZxZWtYd2hSdCUyRkdnMlRMd1QlMkZlS2VQSHI4RVY1STlERGJEYjlwdWFsam9GS3FOempSbCUyRmVYczYlMkZweTh6JTJCdVc0WXJPMTM1blZsJTJGVllGcjFManRYNG1CWlNLQyUyQk1NWElmSnp2ejdSSU44YWJINEZkUGlmeDhieDZYcXFyS2JPSk4yZGZSWGtmWkJCOWhwcElSJTJGZ0tLdFBHWCUyQk0xbXVoRHNXSTd3bEVFaFNoTW5BcWx2Ynhxdnk4RUpwR25WYlVOaTNJWkg2R0lHYmJBbHp5T1Y5UER1NTZleGhLQmhxbEhScFFlM2NkQmp4SnpWckJ4YWJ5cGxOTDRzbFEzYiUyQmlkRW1La0lmSWY3UldzcWdMNWZmJTJGaWVWek40a3lmS3Z0cExhUm5veGIlMkJKa1Z4UnFxMWZ6SiUyQmJLU3RndmhFOVh3R3FIMDBYZWNqbzRURndsY0dWTXJlWFJHckFOYUl3dzVKMnZlSkFHMUpFM1VlczFFY2kxcHJnaCUyQmlMaE9PMFdwS0RxTHg3bjduelZGUjZCbEFHc1dRdSUyQkFla3podVBjUzk2UDRRc0p2RUpKSGtYT1dwcm9xVFFvWmJ3bnRySTdkTFM5SWhQYTlOJTJCdDl3aHZLNFBnNm5FdVpmRXlqOEpQT1lodVR5ZzdzVXRJMTRHcG5UUGQ3T0cyTFViamNJa0JYJTJGZEFESnhTJTJGUklCY3pZcjAzOEFBekdZYXdFU2U5ZU90M1Nla3Z6VVVEZTZMSkFLM2J1NGllJTJGbFVKTTRNTkV4MWRzNkhRbEpueDF2V1NOTVp1MXpubFV5NUtsc0hjYSUyQk9JSVR2MFlvSVN0ak1yempMRmxNZDdBUzQ1MHdHMGZDUVFzOFJQR2tCZ2M1M3JJVVlvMUclMkI1dXBVbno5c3lmRHJmUTQwdCUyQnJNczkyaWp5anNvV1F1THZxR29MOEFqS0s0Q25CUm1ENGhoVExUMTg2Yzl3djZ3cEppZ05vQ0RtaVZxRWhSWEhzZFclMkZJenpGSHZlRWtkdFlLWURFUTNPUDBYdHF4Y2RkQzVNMjdUaFBBclQ5UkN0MiUyQlZFaTlaanppQWRtYlhWMzMzSTNhV0JMVVpmVTdPRDhsZmtPRHBGZzBmTHd6JTJGeDJqUyUyRmFWWWlWTm9VdHIyTVBsVDBEQVB6RVh1cldJTVk2WEFiOWlxb1UyM3VRWmZucVlpVSUyQmp2V2dWVkdNVkp3UE91Umt4SmZtaUdISGRxOEc4TTI1cmN6YUcyempwN0ZSMXBDVkVvbzNGYzFRT3B1NjJTYmFPWjJLaVl2Q2Zva0ZZUGZPZ0xkaSUyQnBHJTJGOXdHJTJGSWR0Z2tENHc1Z2NYWm9vZ3M1UmZXeEwza0EzREFuTjlHUkxJc0VXSFpaMlRnSzR4OGNDNzVzVUtwTzJ5a1BoRVZOJTJCQWFIQkpZVGRoUjlLVU1UUWxrUzhqcUEweGo1JTJGMVdUaXZ4UEwxaGxTSFNFdzVLS0pIYUJJVTkzdm5BWnpsVVZ6eU4lMkYwJTJCSEFOc3NBQzJlVkVNNnA4YmY5VVpETE96SWR2ZzdSTnRTJTJGJTJCU01FQ2w4d3ZqQmVHcng2YXlDUmdCQ252dU0xd1gyN0R3bGFMRENEQUN3TFZ4SlhRcUVQQVRyYk9aSzFGalU3ckZuJTJCQVJxVzRLaWtTSU5RbFNnWGo4VFolMkYlMkJCSHRHSURiTUl5ZjlCc2xKRXRsWEFjZ1lLYWJ2ZjljdHczNWx0SXclMkZDUG10JTJCQVg3c3JiN1hxYXZ5dDFmMjlJVGQwUERTZG41cGlGUnNIUkQ0elVwaGFMWHBlcjczYzJXTWJaUTI4NHBwWmRRViUyRmR1JTJGa1JvR2JvWXpGOWhXUyUyQmZoRVVCWVN1Y3dBaUdiMG0zNFZxVlpxNGlHVSUyRlRDQkZtN2NCUGFMemJOM0Ztbzh5UjFwNTNkeEN6WHFTSTVZclFWTXA3REYlMkZNOHI2SlJmWXNYMlclMkZoSnQweEg4S2dqUDJBN3VKUSUyRmtGU0FzOGxjWmZFbyUyRlNkYk5aYVZqOGZoTnY0ajNkR2h1WjFwYnRXQVFjckg3djBDMHh2dHVoM2h2dkdEeTF5eWplQTMwVnRuUW0yQkduV2dVczY4MzlyeFhmZDVsZ21qdjByd3pnV0dqbjhyN1dENzdvbWNQTXlVcDA4TTdIcTYlMkZoaDFiWVpmNWdYdTM2VyUyQkczSjRFend3SGxPZjNIWTAlMkZNZVpXdmk5OVU0a2NPQ0x1d2x5cllnZWxOamFrb2dJSGJWTkxqUUFMbWlUSm5nUmFseFhQbURIamtxMXdzN2E4QjdJSldqTUxPWU1NazVMVUxhYXF1WmtXaCUyRlJDM0ZEMHlEQjhMSVNZdjdVJTJCZFZiN2labVdWZmtrdW50cmlhaHlNV1U1eW9JUmtFZ2haM0xERE9KRHhNNEh2UUZhTlVmbmhWbE1tWWxXeHlZd2dEMiUyRjRJdHI3Wm5neDh4dlZjdTM3OW8zUSUyRms3YjBNRDhXTHV1UWUyNTRiaUM1RUlPVnMlMkJtaHphZ1hLTENhbmFTMmlEUlJKTzI5VDFHTmklMkZDNzNwRVlOV0lmTjFkT0tHTUNBakdQdld4ZXhGZFRTdHNSS3Q1eWFabXBlV1ZaWDRmZndsRlg2OTdoRlNQVjVNd2VtRzhjUSUyQkljQUI3TWVNRGM0Z1V4RkcyT2I0MUFLbjRRbWpWOHNYWDRSbEVCTzdMOW5zaXViN2kxVWpveFE4emsyNHU1SldNWDZDblhIMW1IbXlodlFpNFc0NDg3VmI0R1ByS1B2UElDaSUyRnp3a2JzSkQxRDlGNDFOZWtyYVAyZExEblJrQ1N3dllRcWxPQzJ6bUtBU1UzU2J2NDREdVpSNmFqb092MjdnNFMlMkZ1NDI3OVdCRDQ2enAzODBwdjBSb1AyZk5Gemk5d3A4ZHJqVlo1TzUlMkI2R0VSSWs4SjYzaHlNV2lPbmMyeTJoJTJGcFpVbHRTOVgzWXBXM1FQSmZwYmM1TzElMkJCOGVhZ1YlMkJ5STdiakJYdkk5TnZFeVhPcHklMkZKZSUyQnIlMkZvWFZKenpPenpqSWlKWkd4YTVrSENWZyUyQndIVmw4ZHd6SjdWR2ZEcUVWS1RJMEd5VUJYUEx1YnBVY3VveFVVUiUyRkQxc0pjR1NMJTJCOXdsMElwVnBJUzBJMHFycWpQRWlVUGtKU2toUDdtdzlZMmpXVlRwY1pzU1BqNG9JRkp6SFkyR3ZWM21vT2JIRFpnaWhleW83N0RFZCUyQmRJZ2Fib1BsTXE3SHM0dHpObHVJSXNlbFpFZHJNN2klMkZ3N1dPdUpld2tHVCUyRjBqUDZCWXdVdE9jV0Fjb2p5ZDVmekYwZ1lyZnc3b3BNS3JwYTNhZmY0YlJLTyUyRlR2YXh0RyUyQmVZcEVSSmJjVGJWVmIyWkJrNUc1c3VVcEQ0b2J1UFdGWHozQ2hQZlk2U3VaNjF0a1BoZjFVbFQycCUyQjNUZ2Jub3V2Y29IRWtiYjQ2cFVVenI3TWZyR3cwUjk5c2x2c1NQSVdiQWFRNTJDZ0ZQVTdVMXRpMWolMkIwSWJld0NpNXpEZUU1OFRqNXFXJTJCdkgzUkdLeTRhdzVTMVJMb3NLZ1JsNTg2ZTlPVkZYUFplbXNnOWYlMkZwcmZoRFI2dFNzOSUyRkp4OVVubEo4dmtjdUtISktYaW1rTGxjZ0p3MXh4YVJuJTJCOUZIYXJkYWp5Y0NSRUNkdVRyaTd4NTRjdVMxeDE2ZmhWWElHJTJGZW1ZVUZnbEtGJTJCTmYxek5WdiUyRlFkb0xRTmlmZ3pLZnElMkJmM0pXa1BXZnZRdkhLMlVTNnFhRHBMTGdBNmRueHYlMkJoa3JKRXVkYUFwcTg0aThaZ2Y1dkxONjlWRiUyRjRPeGpwOFpzajNrVTVBNjZ5ZEFYVU9YQkh2T3hHV2RGenBpeVJXRG5oSkNYJTJGUnFUTUh0N3lqR1JnTDU3ZDRRTGolMkJUMXc2cTMlMkIyQUYlMkJlZmJhRWNoJTJGcEFzZ2VNcThVQjNES3k4cnVqMGcxQzVkbk0lMkIxeEtMcSUyQiUyRklCV3NyWFlQVHVwNjVpMTQ3bm56UlJ1SWdDYnY2ZDRUJTJGMzVhSTZNMktYMEJnbkhYRTV6Z2JDUWxLbmxmcU5YT1lYSmNoYW5iUWh1JTJCb1dWUTdxTzRTRTNNcVlDTGUlMkYlMkIzYVpQMGVoajFZWjVaYkphNWdEJTJCekVxa2Vtc3FYS3BPNDZMeiUyQnp6U3cyZlhvbzhZNSUyQkVWWURoS1Vxb1pONjl0ckQwbjZVVXIxJTJGdHcyeXhvJTJCdE1xMFhMa2dONTNXaDIza2tDVEN0V20ydEwlMkJleTN6ZjlyZWlzRW53WWtjWXBWcTlXdmRXeEdoVktSVEslMkJxODVUVXRySSUyQnVHdyUyQkZ2JTJGYzlyR2xsR1FKZlhGT2NDY08wR2xxN2JEcVJYNmZXS21IRzB3VEpTZ2o3OTRZOWY3OTA5a1dHdDIlMkJHc1F1WktkalBQQmJOREszUm1KZVRHZVRPZE05RWRoaFhpJTJCQnNmN3ZxclZDdjlXcjBIMUpUMFpTTVBnOXJYdlZQNmpqd21FakxFZHNnZThYZ1p3N1VlOEZkemwlMkZJJTJGZWVQWXklMkI3MWZDUWNsYW9UMHY3YmlSZFJlNzFya0llS0pDdWM3bk9BJTJCVjlHVENnNkpUYTEyWk9jNFpMaCUyQmNVd3Z2VExaZmRxbTc1ZGd6R0JHdiUyRk82cWVybHNXbHZGVW9Fak5ScEhsTEtsakhqTllPJTJCakx2Vnh4QkVxT0RGM0ZXclIxa2VYZjlNSjBSVTEzYW9mU1lRVFdZUERGTDRsSzdSVWpyR2xRejVka2xhd1owSlE2SFFIV2RyOGhKYkpBTUI2R09YODJzOFRNZmlvTWdab0JhMjhoQzNTNWw4NW9MMVA2Y1M2UmslMkZtUlVFMVh2cFY3NE1sWmZHZndyT2REbk82cHZTbnRpeGh5VHVHZldhak9ndmo0ZW5LamNPdmpYdnlES1R0Mll2WXZZeHVDTkVVM3JscUx0VlJFUXNXaGMlMkIlMkYlMkY1NzRHNzZCT2VSMzVwRTZFYUN2YnE4RWFzSzRUb01zczUxd3d5SkhkeVh1MlN2blJLWEV2MXF3UCUyQkZxdndFSFglMkI3aVZja2VxQjhMQ2JsR3Y0Zk53eHI3VTgwYjNiTTUlMkJwU0N4a0lEdUdhSHRSOUVmJTJCOTN0RjF0bVVBb1VPNGhVaTBPTnQxZmJVMndQdnElMkZBSUhxN1hDWXlUJTJCQmMwJTJCRm1DV0JmbCUyRkNBbElJa0M4SiUyRiUyQjB4dHcwRDM1bk5QeHV6SGxQbGZUOTRjRlJ6TnBhS0hCWk9pNyUyQnhrd29ScmJDUjhNTXg0blFMNnJIMGhFbyUyQlRmYUhQYiUyRkMxeSUyRkQ0T0R3emVyM2ZQc0cySjdjUlpCJTJCYmQzdnlKYiUyRnpGRHJKMGFYWHdlWW4lMkJUbzFmaGglMkZuMjlCeThubW9KajNGS0lYek4lMkZpaVNjcFozMDAxc3FyRDBvRHBTRUElMkJWU3V6dEs3R0k4VFZsQk9sWlh3OHZxeUo2JTJCMSUyQk40U25vVFVPYWdmbkZSYWxzbnNFZW4lMkY1THVnQSUyQmUxRTFJZ3kxWkJ5WVhQUDY0aUFXZllDakZwa1BaVzgwYkpiQ1dDYlZNejVDdGdxRDU5OWtQaEdEbE5NamdZUTFMelJJNmV6Q3FpTzFnS1plUzElMkZkaGhHZzl5Vm5UUW9xNmhXWkZxVzhaTTZWZlB3aElaVXFESHpMMUxCQVU2OUF2dzRVNHJlM3BCUHRrSno4NklVZjI3Rm9vTVBZJTJCN0RlbXk3MXVIREVyVmc4TVIzZTZrMjN4WHFtZzQxNzVmakFkNjlxJTJGUlNBa0xrUDdUZ2NQdWNwM3M1S3V3cTJDd0I3T1RnZnF3eUc1eXlLalglMkZuSzlGaTUwdlZYaXJSRE51b2hzS0tzV3E3QzlDWDg2RkJWdjhIVExhUGFWWHZyQ3A1TXZaMFNvYTlaZTA3JTJCM3p2dVdmNVF4bTBCYm1oUWNKcTV0WExhd2NGU2FzczhPOXc3VGJyWnBJeWJMNUFaR1I4NzYlMkI2NjdRJTJGa0JaakFqSGo1V1YyMXdRTnZraDQ2VHFrd0NXWDU0MTFZV29zS0lPNzJ5MSUyRkFyY09mZ21haTFtbkszblVWNTBsSmE2ZzVHZVBVVjd0NG1veHN1U2l3UkhzYXplbzVDM0JiMWFYWHhhaUtTS3glMkZlcnZVa21tbDNpZE5lVWx2Z0ZrZmhzJTJGa1lmUnVVWkJkeUNzMEpQeGF4ZE0wNlBJa1hIZjA0Vk9BN1YzTEV4MEJSdHpnVjRrZmR1eEVReXI1T24xeSUyRlRTVktjSjA4ZkRsdDFrVXlBdklQUU5neG81S1lVTXVjTEpkdWFxZjRtVTZhaXA0dFI1czZ5Q2RBbUxPWUF2QXFpMnB1dXlVYzBvR0prMWgzVmNjMHFabFhHRVdkaXZPbFZaeXB1UGZQUlgyJTJCbmtqYmFlMG9yeURQcnh4JTJCSmFldTZMdG9CdU5jaXpaWVRrYTJmRzNxamN2UTYzaEVIWXRHMU5Fb0ROOVhXJTJCa3FxJTJGbmN6UGd5OFdJU2clMkZKVUtoWWNwb3MlMkZJM2JJbUE0Y3NvJTJGcnJKQ2R1JTJCT1UzS1RsdGZORnVIRktFa2pxNiUyQndVYmsybUNIRXpSeDhBcVVYejFmUCUyRlRQZXVwZ1gxSDBiOFQ3ak95OXRzaUE1ODNjcUFaWTlMSVU5QVFFRkg5R2lqUFo0aDBQanF0M2dRJTJCOEJmaTAlMkJxQmxVbjhHNzBwMmptJTJCeUxzYk9MciUyRk5mYWgyaCUyQkdMY2JTUjJpdEg2cHV1QlJRWGNJYVpaSnRNRzQlMkZNSnZhQkV3NkU5a2g1eDElMkI2JTJGQlpmdHZTSERra2NWJTJCNVNDbXVFdHkwV3dkVkF4QURLbzRPNjVucXlQWXVzczZvWE5EWENuQjI5Y1lKQmRZWjVWNiUyQlczN2FCc0RSeTdyc1ZuZGxoQ3NFQ2FjUmIzOFFuVXc3Q0djMDhubFZRc1NQeGhPMmsyZExEcEk1RjZTSWVXZ2FHaWtMMWslMkJHeENnJTJGdGZMemFWRjhINlNFbjlYbVFOWiUyRjdwUllpQjBHQjJ2UFQ0aVl1bkRJRzdKNWlTSVhidTIyJTJCQngxQnR4bUIwTGVEdzZsbTMxbFo5QmolMkZ4WnlOcDNVeWNINklwRVNwa2xoMkRuMm16dnhGMlJ0V3p3WjcxQjcwdWtsMWtvNXU0aUtWd1ByWEZHNiUyQmZKdEtVaWkzWFpRSDg1aVh6RWw1dGk5Z24zSjJ0T09kb1Z6WVVZd2dhOXFLNnJOUG5ab3FOTThmOFdGTTFHQ0hnNllTQlBkRllpOGtmaVlyWGY5NnRtbkRHRmJxckJ2akt4aEp2ekNvOUE0Z2U5MmVxbUNYOHBlV1ZjNGYzVThGJTJGb1IyUllWSkx1UkMlMkZrNWNTMlR3eU1DJTJGZTJUaVczQTFtJTJCOUtuJTJGanVnRnFJRUJab05qOU82T3I2cSUyRmtIWFYwcjVOQ292bnVud1JuN29pT0c2OHBWSWhlSjBhRkcwa2UwM0ZHMVFrOGk3OUNmbkh1VEJzZVlLdDUxQUJlSGVCRjhsYlclMkZPZTZETXhDZmp5dSUyRkpDMHdoTWVIWSUyQnFZVU5lb2s1RmJCM3ZDUDJaYm1rMjIyZFN6TGlPeWFpdlZVZjVTRkljJTJCMnRodnR6bUxLRGdEWVlzVzhZZm5ncGlDZ2RMZ0hOc2hYN2x4ZCUyRkJBZmtWMXdqVGNXdjZROXllR0hrRUR6WjJpU1F0UjFXdjRGWkFJTHBKZHQ5akliVk5MT2lpenF4Q0dhbWFXZkZmbWNnQW1iRnE4cE5kSFJvWUNWNlNzdyUyQkxwcSUyQkllbmoxU0xFOUJGcU1QcHk1Z1dSSXJiaW5tR1dFdTRzblZXJTJGOUhTSXVQVllUQjRtY01LbXFUWWhERFpYM1k5a2lnOXNXR1gzOFhEYXolMkYyNjMlMkYlMkJ3QnR2aUp4T21FJTJGQVd5OUxWbGxZUTdJWnE2eFY1V2F0TUoxMHBWTzFRU0U3Y1RrSUFHc001ejBOTnJDSFIlMkJlSlFxelc3TzMlMkJTWmZybVZNRkw3b2s3OTZpSyUyRjFTeVpVbXF4OGtTZDczSlglMkYxMXA4Yk1IbFVqWXNPbk12bUNFJTJGODYwZzVJY2FqWDBnem92RlNzc0ZkZndaQjFVaE83Y0VCNlN2dlJNOFQ0JTJGTDBPMURUem95VHRpek1HJTJCZ0xvVmR3cDBoS0EzSzVHWTRzdUlmTDJRSFJPaHFnSUhoRjkxNnFRdlBaYmFSNWhOcGRYaXptMHBkdEszWXczNVNVN3Yya1V5N1JxbXIzTGtxRWhuZTNtV0w2M09ESHFoV1h4bG9oOGwlMkZ2aEwlMkZVckZmJTJCZWgwNnhUcnQ5Mml5RjF6Nm53ajBqUEI2cmkwUHBYVnNPc3NJMXFDOHVpN0UxT0d4d1ZCWDkzOVJieTZNRXJEbnJzRDY3TXBKJTJCaFZUdkxMc1pYVXYwcCUyRmklMkZnUHRudmI2Q0xSeEVGZk4yTUNGT0RyWkQ3UGlZblhhN1clMkZFUzhYJTJGMDhycWM5QnV2emRqJTJCaFc4eDBKUW9DNlNzUkMlMkJrOEY4TU45aUxzJTJGYTJ3MEh2REpIeiUyQjc3Z2dZY2owbFNmUWVhMmNmVFd2JTJCdnQxayUyRlViaEhzSFklMkZMR3RrblQlMkZlREI0Mnczc1pJM0xpS1ZhekZsMFRmWVNYcEFpMTFST0FNUTAxJTJGekNCRVlRN1dBRlh0OUpHYXFCZ1diVEZWJTJCQ1hWbTZxMnRqVDg5Y0YxUjZVZHhQekQ2eksyeFZPJTJGOEclMkJYSzdVR0hHeCUyRjV5RGRhUEZHYWpGck14Qlk2JTJGOGJ0Y3NhY2JEUEpUSXJ5ZFZ1aG9IZSUyRkdtR3o1a3U2blRiNTQ4eCUyQlRkZjVYWiUyRnd1NG1BRDBGRk1kTiUyQmRlQVhUVndOMEc2d1RDazlJRlhhcGZLNW9tdGZwRlE0akZ6VSUyQmVxeWVtZlZpTktFUmdkY1FBMGlYbU9lcUcxNTI2VFJ6akZPSjN4djhjS0tBTEpQbFB6TGZsWGV1eGVQUVZUQTZoWmc2Wno1akYlMkJ5JTJGMEM0VVNGSG5kYjZPR1VUOWJQNmxSJTJGazU5YVBPYWdKajVlWkFHTU03QnRiYllLYU5jOTQ5QkwlMkIlMkZSTVRDZ2p1cyUyQkIlMkZiOTRQYXhPV3dnU2REQzdhd2wlMkZNTk1xOWVxSjRrWlE4JTJGMnRMb3hHMU9UUVlaRWIybElQZHJ5OFglMkZSYkY2b2x6cndmUDMzcXl4RlYlMkJjZWVTTUljY3BqMiUyQnhiZ1lxMUh4OUd0ZlolMkZQRlpKNDFYaG03diUyRnJ2Qm5iOXgxMWU3czAlMkZIcWwlMkJ3em5SUFU5R1RGckNQUGFLZmh6eU8wVkFTYndwVHJ0NEpWaldtUnhZOWd6VlpWJTJGSVJCWVZzdjV5VWpxR3d1ZkJTTDNYNFQ1T3RWdUlzbU9MZjlVQ1g0MlRraVNhR1RIWHk3dUs2OVhXNVRNSkFwTEtWWDM3cjk3USUyRjZiSXE3ekZCMXNvMnV0dGkzN2h6Z2NRUm1iZENjbGZOMnBmRU4zNEdyWXlkbHo3cFdFNUJ6UDdGS1lPUTdqQXlKbjRxTnp3QVZEVllPTWU3Rm5nNiUyRjRKSUxNSTlrUnZwTm1wcWtTTjRCZTZKUkVMVHVhTDhKOFVvNHFXRXRNMW1iZGhGUHNWUWxCS3RzJTJGdjFvVHNQMklsTHQ4TXIlMkZTYlBSbGtCb3pCVUNLNWF0JTJGR1gyNk9JUWVKVkN0SlVYTGw3MzJYTzdJbEttOGp6WWFVSmpMeUVGek1rUWtjaDBnJTJGaFclMkJKakRwJTJGeVpxekVaM3BMMmVmOGlZWiUyQnYyS0V3WCUyRnlJM1B4ZHNzTVZ0RDdHYkElMkYzV3B0RnlWOTc1RkRpY1lleG5OJTJGZEU1JTJGNzVleVBqRXBQd1hMNyUyRnVrUkhmOXNzTW1MdU1lY1FGeU5WZ3plYnY0N1BGJTJCMWYlMkZkS2Q5MDIzZ2ElMkZGMmFMMGolMkZmYSUyRlNmUWZUOTNUdVZYb2FyMEQ5bmUwTkE4R1AlMkYlMkJ2OVNyMlczU093NWdqMFpBRU1KSU90MWJlREFMSHBZRXYzUDMlMkJmZkgzJTJCRiUyQnlsVE9ROE9kaWl6YmI5ZmRXSjEzUU9PZmZKeWlxWnpmNTBPMUs2TDRid0J3ZnYycjZlJTJGV3NVOVJmSzV2dmF3YWxjWnZ4cDNJdjdDNWJsZk0lMkZ5VU9OUiUyQlpiOTNPRCUyQktTJTJCSUhXenpsQUxFaWhuUXYyOWl1cyUyQk5kTDdObVRiTW1waFVxSEtoN2d3TEZpSiUyRjVVZkg3dUolMkJFTm5CUG1qSUJjSVFrZExOSGRKWkJLJTJCb3pncXRuckttY1JrJTJGaVYlMkJhejkwTWVnN1lsMUhiY2tHTEg5aG0yUFo3MGV2NWgwbGlrSVExUHRpU0UwJTJCNUQzNWVEcDFaNFZPc0h2eXFnSkQlMkZpdVBIcmtFUm40YzlWd1VTeThqOEZVTTM3U1lxJTJCVG9VJTJCS1lMJTJGeER6alBnY01kWGJaRjBiVnROOFBmQzlDTVZRMkJXZE9MNXIwTFFQVENHYkZmUnVtS2VRS1ZoJTJCUDNhMlVRZmtFMmI1N3pBemJwbVRYdGE3REVvempFV2lraXk0VWROQ3FPc3p4azBOc3VWeGU1SlFVbGg4dXFzeDlxMU9ZSG03dEFVR0NMYXl2QjNrUktyZzZ1JTJGZGFYM3A5dXlqNTlURlNiaUlWRWh4SUFtZjBjazMxWDJMa0QwcVFpS24lMkZkNGx2QjVRTkQwZDZkMUN2SnRxcDFtcXh6OWlzNmolMkJpb0FTaUZXdVM1R1lzQmIlMkJsWkFCQmRzRlJPdmlNTTM3bloxVzJRSGNzWU5sZG5ZdTd5THVMRTh2TXpGVXJKbHd5MjJPMWRhV1dKY2RFOEg1c3VWcmQ2UEpkWFlLblJhRTFybVhkajVlZWh3WG9kYnlMNFRyViUyRktSalR4VjNIVCUyRmhVMURjJTJGbiUyRjhxOUQ4WkhrUHlCcGhXSmclMkJMaXk3czdQJTJGU3k5ZmFyTDhMJTJCUXZESDMzRHFCVkQyRklJSUxUT3g5M25uY1dPeVNKbXZ0MVNYTnIlMkZ6UWZMdmw3MExaZHNDN05iNlpubWlWYndvOGkyUk0wclB6TkVsOGVzejRwT3hBY1diaDNvWGE2VXZFQXIlMkYxcmxPayUyQjlkSzhSeWx1NFVHelMlMkJ3V2RFMFMlMkJrcWVPQVA3YUJEJTJGWDR5cGlITU1yTWZhSkF1TiUyRllPMUZzM3g2RzVhaXk1ZktIY1B0Q2NoV1RvSVBGdjglMkZQWUlpSnRZM1c5MlVvMWVJaTVWcGpudkdEcSUyRkQ5RTR0JTJGNk52M0xmeHJ6dzZlVml2STN0MXE1YyUyQjlva0U0JTJGeG1vMjclMkZtQUc4OGp6eiUyQllHNCUyRiUyQk0lMkZFJTJCNzk0VzR1NlAzZmg2elU1VXQlMkJvcmxCTGdKUnRnM2xmcklUZmZTRlhGdnZVMEd2bUl3cWZpRDN0TnBDRCUyQiUyRlhDMTFENEdvSmklMkJ6VjB0MSUyRlp6aHUxNWNsaWp3WnlBJTJGdzFGUHdaV3NwSzNGOURJaURiJTJCN3RYVVZtaWxPUXpoSHM0QkZNdEsxV1hldFY5JTJGbWRHSlR6UzdvTFpPVm52SzJCenI3UVRTaXkwdnlGRndPZU5WTXRKMDZWdURaUUd1T1ZNWDNZUkpSYXlrM2QlMkY3MThFTVBmMU5STno5RXM5NnZiTHdIaTQlMkY5Q3RTNVhYQ2xaOWJuJTJGJTJGbHdJZWVYZnV6Q0NDcDBKZnBvOHZaOU9TSnVVQjNQZlRIclZjUkZkQlJJQlp6JTJGWm5CSmxmSTV1djEySzdoJTJCdnEzRWdNNnFJaTd0MzNoUm5pTTY4UmF6M1g3dDJWZCUyRkg1TDNWMzZjU0VocWY2MFVWb2Z5YkU2UXN6Z012UXZXY3RKOUYxJTJGeG41WkI0cTFrdzNUNzVPczhNdXJydFRuJTJGR1hJQmYlMkZCb0wlMkZUV1BaVG8lMkJoUjlkU2tlMDFWTndHbUxJVGQzZEVSZjBaa2w1RCUyRlFzcmxqb3B4dnRaZTNZdEJmbDFId0s5aHJ6WDBCOVd2R1pzalc2WFF5UjJ1elp0NlAlMkJaQWZQZjluOTlPSnlHMThoamNDWTdlVmhMVUZ4aWRHb2dXWWplczdWdEVadGt2SiUyQjE0OVJSd3Q5ZnBUVGdZaDl4Mmd2NWxPeWxXNlphWWxwbGVjMzRsVmwlMkZlMWljUHF3RmMzQTlKNDRNMVVRTG82SDkzaTZzY2FZNmVmSVgwU2pRMGJxMFVLeFJibGs3dG9rZnA3NmZOMk9pa01McyUyQkV5MUtmNW5LcGRmbkZtOFpxd2xwVmNVbHRqNXphbGZuSnElMkJ6QWFjYyUyQlg3aDJ5aUtsSVVDZXZ3MzA1T3AwQTJJUVowdUQ0dG1IOW01TDlReHBIbjFMa3hZVWpSJTJGelAwaGZIZEZrczBucjJtckNTOFZwUVdXZm10cUduYm1Od0RmMlBQT1hPczglMkZMNDE4eXJkd05LdHRLcGVHWGZaNlIlMkJqVVRUYThTUjdZZmdUR0R1MkMlMkZrRzVGViUyRkJucCUyRm94OHlGdSUyRlJ0ekduR1Q4TnBSMmJObnQlMkJJeHN3Wkc4WE1Qc0JmYmg1UWg5alR5WFM4bGVQaG1rNVQlMkJvODBKeCUyRnZKNHNXMEx4WFlJM25UN2diMGhJN3FLamRPN0YxenU5UVVYTk1aZXBvclMyUHYxM2VpSzYxSExsdkVMWHQ0ald5YlA4SSUyRlBSNmhrJTJGQjdMWldUUEh2JTJCSFhmJTJGRHJ2OWgxJTJGJTJCdzYlMkY4dmRxRXV5WHYweDUlMkJtdnlFNmpWOWl4UUxnNE1PZTdwYzZyRkRsJTJCVEp4RFZIQWcwUmNtVFY4SlIlMkIlMkJ2WDFybyUyQmUyR3hrZjViT0tFUjBIaGclMkJ2eDU0JTJCeFpMbVF0cnl1dkVUSiUyQmlyUFByVWtVZXElMkZmZHZ2a2Y4R2ZNJTJGRW5vUUJSNEFrRTFJS2ZJVkREaFJnU2tubWhmNFFtQTRFJTJGWUhRdVdCYkdGODRVc2VRWXQlMkZCVmpwYnVMWkFBZlJORkIxdFp0a1M3Q0I5S3V5c1haNUIlMkZkaTBra1psQ1RsOEhzR2dmYlRUJTJGT002OVk5QTRwJTJCVFBzRzJQQ1hWa1ZVd0VRYnBRUXAyJTJCV0lpQlhxeTNaNiUyRnZVaWNXOW9mJTJGSFVIcG5XS3hCZyUyQkNhd2NmJTJGRTdoMVlla0FNRlZGR1lYZCUyRkIxaVdRYm1HTEZsaTAlMkJHTTh1RmQlMkYzb3daeWVWeWRjMUwzejNkM2RIMEppSXNYaERlUUpZJTJCdXR1T242RW1UcUlCdnhxTGFsMEs0eTElMkI5SmtYMjRvdlF4QkZMOXZxVWUlMkJvYm5xazFXeFcweGVBdnhPa2clMkJCNnFYZUhtTHlaSmNkUVlGOVo0cEZKRzc0OFQyM09YMG5xVFNpRHQyQWZPMEJ1ZUw4ZVAyNk5SenJnMTh1OGRlNUxZM3k1QldKNURkQWg4Tmk5a0lWTXNBTG1DU2p4djVUcEsyV2dZbElUNXpOJTJGdE5SUFd4d1FWTW1QNFJYZXhmUCUyRjd4JTJCelFqNCUyQndFNU01cXNyYkNhYmhGQTFaZ2VMUHk0byUyRlA1YiUyRndlMVJPcVJWYjdpWXNxSFB4QjJlc0VLak94YVU2JTJGU0ZBaWNZRyUyQk81ZzNZdk5TQiUyQiUyQnZLJTJCSUdrdWY3SEtFcmx6VyUyQktES1dsbnFyc04lMkZUbFliakptZHZKdSUyQjdJJTJGNlUlMkJWOW4wZyUyRjFVTktSWjhZZVNxblkxN0lXbUt3NCUyQmNTdERCcFRVSFhOTGx2cHRjZUVUQks2bUs2dm1Ic2ZaNTRUcTUlMkJ6ZiUyRmtlcGtmeDlhbENLQ0tmN01XQWFKbFAlMkJKYm9tTjNnaENBVVpXNDNyS1g2TjU1em9HeDY2M0hydktMY2ExUzFnZFpiWkdUbGI2QjlmOG5DJTJCbVhBbXlRcGZzM2dXYzZkcFdUc294JTJGM0xzbTVibU9zVUkwTnRnRnNtQ2ElMkZHMXQ4ZFVJVjVkQ3U3N3I4NElyeHc1OXo3Y2N2V1ZTUnFtOWp4Z3VyMTlYN3ZXdEg2MlltZFVMdWZDT3JHJTJCTE5rUmdiM0xvb2E5JTJCV2o4TENvUFNjWmRaRmNldk1zN2QlMkJDYlA5SUFUanRCanZDSk4lMkZpWWVOREFXMWlIT2hRJTJCTWdqeGRmQkpSNkN0VWE0UTh0RlFqYmRIMHBzcUNzbHlkWXQlMkJiODF2dThsZWpnZGVDY0JaVE92V3BCbWJIdG1hRlclMkZMSHFHY21CRldzMVdRZ1ZNdXZFOEtKWm1Pc0Ztbm50ajJocml2VEVndjkwJTJCUU9tM3lKRndxbTNkQ3gzcUUlMkZDZWdYb2k0dUMzNkJaYXZLJTJCTm44QU1uV1VUUVh1Sll3WHhJaFUzZGhoVzVMckJzZktWZCUyRksyWHozSTUlMkJTaEZFNWxBUFFsOFQyWm9hb09XRUh0a09oMVh1U0tJdktaclBBQyUyQjlJRSUyRmNaeWExSG56eVZ0MXhGTGFHSU1ZJTJGNUZNVFhXNkk3cmJxdHpTY1FGa1F2S01sM2pUYndyMGRSY01Ya0dRTGNaQVRTNTRORmhDSEJzc2UxZHd0Y2xsRDclMkZmNDB2bU9COGo3U2g5a0c3Z2FYeUdTbG83T1Q5bG9WOWxJdGtIMmNhTkc4JTJCSTZ1cDZrbHJiUzVaT2JGTGV2ZTNhcDFGOHdzRm40VWFrdERiamlzV2h4eFV6WkVFRHZ0NEJqelZtdnpiVlJLdjZ2d2IyJTJGT2wlMkZBWE9GM2tlOEJwZnBOaU9NelJybm9xZjlHM3olMkZmVnZ2SFpPSHNORSUyQjI0Y0lOcWNiTGF6QXdCZ0hsUSUyQjI2YkZIQ1BMcFZ1cG8lMkY2R09ueEdObiUyQndjMDVtelhoZkNsaW82bHgyaVhyQUM3YmtFeHQweEY3WFRkSHRaaDVra2RCQzh6b0dFZUtxcjVyTkFNRkpCQU9hTE5SNXZYaDAybXRhaVk1RlRJTlNIU3BrMEdCdGxWYTdkdXo1ajJSZm5talcxJTJCJTJCT0NSYnQycXF1eFZRc2ZXQzNsUUhSOHZ6blY1YU5kbkE2eFF0RyUyQlBtUWgzdkVGQTVNWXNFUDNBSHZWSVZuSWkwTGRSMU9EbXVMZzBKMHhMbDNVVFZrYnoyWXMyYk4zY2gzT0dqVzBpejlmZTdnZGRTTm1MayUyQko2TzFZMEtoUDJ5NWozcEQzTVdxenp0QTQ4UzZFcVp1MXRJcEFRQW9FeU9tT1RoR0pXYXBNZ1R0Y0UlMkI4TE5oYUs4RUJXSVU3ZnRqSkhLb3E3ZEE1cHpnNmhwd1lyelFJYjhyOG0lMkIlMkJyc0ZjdkZHRG11TkNHd1B4ZVpBbFVyNWc2aThHdmZxdWJFR01WM2pEVSUyRnpyakVvSkRYaDRXTUxubnMxJTJCM2xlR2tidVk1UTVQa2ZVTSUyQiUyRjdSUVd2WEloTGtWbkpkREF2UmhyYWtMSzdlcW02eGRIWTJ4VnRIa2xXSXlGJTJGZXRWbzFPenl0TmwlMkJaUnpHZ2xvV2NSbWJBZFU5VGZkY1k1V00wT2RjdnklMkI5SUhMQVFQREJyenFKWWlQU0JRMENEaUVTSkdibTNaaVhiZU1sRDFkcGFYR3olMkI2bDEwSkdvSFFONEZoJTJCZEFpTFRrdjZrRjFYeVJkb3VSdWlVS3BtNTY3M2Z5R2R1Y3h2MVRlU2Z3dGZDc09pc0RSN0V5Znh1a1YlMkZtZ0ZoUCUyRkw4NG9zcEJKTXFaN2xBUnYxYTF4OHBRcFBLR3JiTXA4cmEzcHB3ZzElMkJ6a2lQQnBzVm5Qd1lINkQlMkYlMkJIMkxXOHdhMjNjRTVVVUJ1JTJCZ3VmJTJGclVTanBvWk5ZSk9kTFhrR0NxTHhRbzlsNjZjZkRxdHhYcXVCbnVmWmFEY3VPJTJCRXNQNVQ3YlVEUmNmOFIzQzltbWQ1MW1rNU4xJTJGZW5pd1VsU0o5SzZNV0VjU01hcWhDNE84NnZYQU9mZVl2N2VjZ04lMkZZNnhGQ2hBOGc5d2hCdzVieWxlRm1hQnk2QWlkdmFIZE9rUll0MmZXYVNBMXElMkZDcTVzYk41VyUyRmF1OEcxR1Z6QXNydVJlZFhnTzRKMmx1OVZLcmhQNTNjS2hUSlFqZzV1a2glMkZYYiUyQjVQRGt3RHdleSUyRjNsazFDNm04WDExQk95cW1lVHk4a2Vab3VGbUtwcUFtZ2d4aHE4Mm1nT2JDTjM5ZExmTlhuREUzTlZqSEVFaFZIYkdsZWlOTjJaSkoxNUNEQ05zQWhRbVVhSXQ2TUFKSjBVV1o3M21uZnFObDJMb1pDNUVyMmxCNDNaOFNxWnM4UXJhRyUyQmlNZENhJTJCR1M2amFwNCUyRnkyeEx6Z1ZHdHdTWmw1NjRMbDdWSm9xRlMlMkZ4NDZlSXZGaUlCV3NHY2tDZng5ZHhPU0RmTWR0NHd1aXlscnNUUHBXVzRZdUJFYmlGOUU4cSUyQnlZV2FlaWY1bFhQdEVhb05XM0xkcldWQ2JwTTBnTmtJSUFvNThjVWFzeVElMkJjU1dGdzJyTWh4cXk2V1NoMHNKOWczZW1Bb3hURTg3VEdTZXJJUGxMTGcxOTJVUXBPOUV1RDA2Yzh4dXY1cXVLQW9PcGI4RU14TUFCU05kY2pMaUdVcWpOOTI0cGplWEpSRGRhMUhnJTJCZnNiakNKbFFvQzVSNTQ0JTJCQldSUG1KSTZ1MDRYRDh6MTBLNElJZkpYVldRZHo2b3N5QzZ1dDd0NEYweDR0cWpzdUFQQmRvR3JsTmEyMlVBN1RaR3AlMkJPQnloTGkxUkh6SHdBSDQwYyUyRjJvZWh2RTBMVzl0Sjl0RGR2cWJtS0JMT2gxUFZKdDRTcEZWcE5GU29RY0slMkIlMkZSTHNPa3ZpNSUyQlJIQUU3WTJtUDMzSTZ1YjlzJTJGZHJtbHVmNXJLNDFrMHV2SGxhSHd3JTJGWGxlanM3YVNxU3h3MDJqMndockZTSEJMdjg1blNaR3dIMXdiWDJiMzhJR0huQ3hUdU5zV3lVaEcybiUyQjUlMkJQZEx2NVp1TGoyUnlkeTZLbHdPb3ZzU3daZWhEODVOVGx3Z0EwRmNxN1Z6V0MyejQ5Mno0YXJKb1F4TDhpNW9KU2hac1lSJTJGNyUyQlUlMkJrTSUyQjNUVEdJTmNvTDk0NWJXJTJGaUoxbmpPV05oR2ElMkJQRXlqMkVJc0NXV0dLQWpCbHl5RE55elRHN1NxckZwS0N2cEp2dlNGN0NSeXdSUTBIYXFWbDVVT3JFMyUyRlZCNmJqJTJCY0Z0cWJNMUhKM2h0OVRWajU3N29MWmVGSXNuMFB1aXlIbjgzQzRiNTVVYWozbERFdjRpaVElMkZ6b2xIblh6QnI4ZDd6bEc0WFdkTXo4RWxQd2ZZM29YMU9RQXY3NnJZQkJ4VXR3Nkp2UTN4RVBHc2p6JTJGcHRnUjdNam9OSnZENDVnN1NJaUlmdXpwdlFRTWN0cnpUZVFHOFc0YUg0cUlId1FNT0FBRCUyQiUyQmpjMmcyYzNUaFNNbzlQYnk2NDEwbXZnTCUyRm5mYkV0Z2hocGVxJTJGTVNkUUhZczR4UDJRZ2V1bG8zS3lPdFd6NGpyb3hTMmhGUUNmaUlUNnFQQzNRRndjZnBlRjVqcTRwZXhuVE9tb0xuTGdPWUJ3ZFRQNmZJMHVPV285UVE1VWFSbzFIbzJ6ZWolMkJmUktWNFNKOVBUMWRWV29icjh6N2FsaEVBd2dTVFpqbTFqWEFUZ1FrQ2RYRW9yc2t6Q2dhJTJCWmtMc0FUNlFZOUhrOEV5M3c1d3pJVlFOcE52ZXI3JTJGbXdvYnRnNkpUZDhWckhHMHdseHEyNDVXSmFxcjlZb3ZVJTJGTXFLTGdrWUxpNUtLNjJsTG15b2F2WFNDQiUyQjZLNlZSNkN3aE5sRUNNdkVUOVFVY0VQcXVxVmlYQUNyZGNwVmNEenpubTV5ZExScFUlMkJ4NzR4a1h4amJrTVJaTkRlVEpSMEZXamdwdlNUY21ZMVklMkJXcTJYYUlocnY4M1UwQkhqa1M0ZEpkdUliRlRZMEc2eVI5bnk4UXE3Z1hVUWRXSHBsQklKWXJVekVqVFJXQ29CQndhMzU5cDRxRXY5R2t6MWdKWk5FeDVLYU1OUFJmRlZsVWJJazlhZ1ZXMG9RUkNlVnJKR3l4WmRTaVVwdWtPcncxeFo5UDJDWFQybjNmUUlEN0clMkIxeXIzT1JZYzNxUHhWZ3JwM3hCeksyUW1TR3M0UjBadjBSSlUyWTFvY0Q1aXZXYkxWVnQ2OFo4NDZrRUYzdGJ3dVQzU3VXcnVVSHJXdCUyQllXa0hiS21iRWY4TWdtJTJCZ3NZd0U0YmZIcCUyRlpjRzNYZGNZT3VhVVpUZG1QdGxOM01ST3lpRXo3OWVWSW9POEFxcDU4YWJiNTZtOGtFbkdvRlQ1dTRHZkslMkJET3pKWWMzNUtjekZUTFdvUFJWeWVxOEEzc2I4YkxZbTA4RXBmNGRpd3NxdHh3Q1hvaUU1bXhzaUN0RXFjNzk2b3B4T2klMkY1WlZQZEhRQVdYUE10b0M0UEs4dlhkekM0aUVLQTg3d2xjZU5aVWY4TmZQUEElMkYxMDEzRU9PYlNoMGZXWHhCaGZHMFVaSThtUyUyRmFqcXJCWGhNWmx1RWpHTnZxWmlheUhIekFiTmVtbm9zd1JaMW00JTJGelpmdEpyQWgzcEpGSGRXYUxkU0clMkJ3MGlwVUxUc3RNbjRDcG5nbVdXRUg4a0hwJTJGVCUyQk5PVkgyJTJGSWpQVzk0WjdYNk5QUTRLRHZlc2xZd1RremloQVJURyUyRlJobUloZ29YUWY1MEFZV01MS0pxZzA4MU1ES3dMd1owNGdNVFV2QXpvMjNEYjhCZWQzb2lrUFc2WE5lQWFFU2oydHpGNjBkYmYlMkZobzA0ek12azliWGpLVExGQlN1cmpialZhUG9YekhBdVZFemtuUFJ3ZkdzeDQ4a1RidzJoNTE4dXNaZ1p1aTBKV0Z0ZkZodmF2T1liNDR0MEtzUmFZQzJlcTBpQ08zSXMyQTdFUyUyQm84bE9jRVQ2TzlhaFljWVlWWGx2b2V4UzBHVGp1ZGp0TGp2QktUaHUxJTJCdE5xaVVZdSUyQmx2Q1Z4S1I0TnVRTSUyQmtBVFBla3JBeHEyQmN4OHgzcDZhMUlQVU1nTnNqeWpQZnFsJTJCYXFBN2JsdWNqekhWN2tWYzZNNncxZGprOHNFcmhmcDlmS0RrbUIlMkZLTDdCMiUyQjV6MzhJZm9PSlU3b1hyQTZ5bUdyZTNYS3F4YWR5WTRZMWZlN0MlMkZNbmdaWFVDSVdUUCUyQldQQmRVYTRvUGFaaGpVdnRXZW0yTmh4NzVUcGcyNTNpVXBlcTAzSmFkeXQ0YXNkRXAwQkhEVjVtYlBFNWVmMTZheWRNN3V1VXR2RSUyRmxiRmNNdjVGUkxtUzNJUlFNcGxJT3psb1BDblFzS0hhaWslMkZ0TmpySlgzcGtrVk1XajE0Y1liJTJCOHRpandQOHF0bCUyRnlLQUJiJTJCTWslMkZVSzNLeWx5bU9vVnFwWnRyR1NOTEdiYk5SVmxxV3FSNTlOJTJGdjM0NVRwRkpKdWxNUlAzVmpBZ0dYZjlYeVoxSmNWWW9UQkZ5STZ2MnVUY29kb29kWUhvbmxHNUk1N3p5UUdHRVF4VkVjc0FDMkpKbkdwRzFtQ2phUlYxWjBQR1dzdjRqNzBjRWFHYkd6U3JWNCUyRlNNYjVXVXY3aFJObWFFTjJsNkpTdGtFZTElMkZXdndoQiUyRmlYNEMwS2Fqdmc2R0ZEQXU0RmJWbmFpUlFUSkZsQzJqZHQyZE1NRGNqeXZnWWZvanlKdERqZnJHVEd2Vm1seDRVblhBZzF6VTFxNTlCQ3MlMkIwQUl6amRsRGpyJTJCTm55eUF4SWFGN3NpeSUyRnFKbW9mSW5ZSExxODJndmxjUzNkUmpuOXJURXVRQ01GJTJGazVMYkpQNEpYeTRsJTJGaXpQOHhoVUNWdCUyQkl5TWpKWVVRZDZQbDhLRXBGV1p0WFklMkJLS1VicWxjT3dEUzBiY3BySXE2eVY4VGpWVW9aS1E4Um13SHV4Y2owV3VJSzZlWmROUGYydlNLSmVNemZweHFnREtaTjMxbjBhN0klMkJhJTJCUHFsQWhBdTNOZmJlS1JhcVRNbGI4aWJINWZMZmx2bDgwZ29Gckk3cldxRG9LbkpkdHUxODNqRWhNV0NPZVBnOHRqZzdONFM0cW1EeDVJMW5qYVVwTjVraHBHU3Rpc0F0UTRUVFJ0R09lJTJCTUFtbUY4eG1uWUE3eWM5aU51VzlxcmtWOGtJQldKS0JDU2YlMkJ2dVFpakNOanVLSFducDJoVVV4MThIZmU0JTJCR0pwUmo0d2tLQVpwNk1icGRqb2UxYzlGT1AxY2loT1Q1bTFEeko0WDZKcGVUVnF0TjNsdkhNVDdud29NcEdPTU85MVcyOSUyQndTZWNBQXRhTk0zVXVHcFA1bU5KWFlxcCUyRmxaaHVIZlBmc1hiRFNwM00yaEFkcFNkNmtxODVleE1BJTJGVFRWbjM3ZmFpN1hLSWxMcnhSVkdIa3o0ZUNZTGZSbFp0eDZRaDJtVmtubjFCbUVtRTk1JTJCR2NUaFZyNzJpTHJ6amFMQ04lMkJwY1hKcURrRyUyQkdNOWRTRUJPRTRwU2puZko1d0Q3R3FBNnMyJTJGd3pqcUk1NzB1dkRSbFpYbXJCMVExcWtkaXZSJTJCYWdTd1NYMk5LciUyQjJuUUw5c04lMkJjQ3hyYnhJJTJGcUM5SHVTdGF1d2RTY0o5OSUyQmpZV1F2V09IemZCRHFGUWc3JTJGWG1MOUxKOW5lcXZPaEtNMVRpdGtmaUtzM3NKdDhiQWYlMkIwMDNuYnczVUJQOUM5cDBjcTdWVEY0eXhYc0FBMFY1Y09mSVZTcXZRQkp1a1FSV3AzbGlscGVVQlJQd2xTSzJKeU5SRzFzVWJyV1pyMDB5UjVzQm5lY0lHMm01SG1qdE9ncGV1RlJsUTZyR0dxUU1uOGdMY0IlMkJsYXpLdDFXV2t2bEFUN2tScWpybENOVnF2YUVMNm5DTVBwWkM3cm1PZmJ6Qkt4bTBOeVloTlZacGxFc1pmeVJNcEJVVXBrViUyQmZnV3JuRjZoeTVGYjYwb0ViaGVFSnBONlIycFh2WmlsdkdmR2E4cDdrcnolMkJuY20lMkJCeVVkN0NVM2slMkIlMkJaTWZqRjZ0VGRyWWFVWmxGVHpYaE9kYzZzM3VyakNyc3V1ektadHRpWHBNSEVETDdJbUhZRW1TbTZzTVc2OWZ0Uk5LZjc2b0hsaTFuVkc5Z2xuS0VOJTJCQjNPMElQZGtVUUtHNTh5MDBxd1AlMkJ5JTJGU2xvSnRYNmJmRkdhNEdJcnpoclM4cFdOT0Zma25BQ1NvMER4JTJCQmpkN3VwZ2NzZUYzUFJXY0UxRUElMkIzSUo5WTJ2N1A1TlR3RFlERWJJR3I5QTU2JTJCQlQ3WGpLT21FZngwa2tqdFUlMkY0YVhEbXl2akp6b0NIdzhMcU03QmRRUXowSm1iVUVDQ2FuJTJCc2Z5OXJwZFJDOWVLRGJxUEdXeWtlNElxbU0lMkZxbDJNTmdzZ2RmTEpvbjJKc2JDN3hTZGZBNTU5JTJCJTJGYnhqWnElMkJ5YnU0YTF2SXpEeWd3T2dhV2tFekp2TEZHYWRpNyUyQmpGT0tkekNMY0ZiJTJCYWg2Tm1ZS3lwUkpjVDBmVXh0NHpYRWdHeCUyRjZ5bnBacmRjWW1nbnlvQmRjMUozOHJXR0xwbFZ6NGlhYkFYeklwTVpTJTJGa290NjFHUVd4dW5VVW9BeThxVEtJSGJsOG5SeXMyd1AlMkY5aUN4Njc2azFVUm02JTJCbW5qOHZRUnozcXAwN3FldHlFTHBlc0FCYSUyQjB2akh5bklQT0RLUmxsSHQzbWdjQVZoZjZqdGR4MXhkT2RTeU1kcWFZYzVZbGEzYjA5WVFVeXhLRHdBdVpITDZtWVoya0FnVVdxRVJvVWxHRXglMkJrRURIRkwzWCUyRmx3STlsTVI2RWtNQVZIeTRDQWRuS01mNlFPeVhlJTJCRENIa0xhV3hvTWNYYVhXWFl3SGJWUTIlMkZxWnpORFNGN01oNk1YWW04bEJhcDNEekd2V1NpTFQzbU9TTiUyQkVFcUhmTWZtc0oweFdqZSUyQjFKTyUyQnJDWSUyRmJTbFp5WWklMkI3ZWdJYjNSbVJidFlDQUgxSDBvWlMlMkZ1bFdITlBhVFJSMmxkdDhJNUw2azRMb3Jta2V6aXZVRERZMnhUVCUyQm9uVHlYYTE3ZVV6VWJ0RnFyTVp0QUk0MkpwNXFiVnB5czUzTzdlczFhWXRZUmNaaTh6cE1EQ3JIOGU5MlJjQzgwZHVOJTJGJTJCYWslMkZPZXVDcjJTUG1Od3dQYzNLRllOYUVZTTJKTXBCOVUydlZKQTJxZWswSlVJRkZyQVppMjlTcDdtRjBTR1BDMm9QM1BXekhEUXVjNG9RMWc0QVczcyUyQmpWbG5NNlVVdEVQcFB3QndRTjRVclhvaUpqTmtjakZSUWl5aCUyQjlUaFpHczZ0SlRDOTVhYjVpOXJvVGNPUUV3eUZiU3U2cVNvWiUyQnpPaWxLN1JYY0dqTjFCNnlPUCUyRkRWQ3NlWmN1UHczTE4xeW10emElMkJFbURzRDdhb2lIc1p0bGFlbWhoRjYlMkZZM0pwbkE1SzVLM3VJc05lczElMkZYZFpKaldsbUdYcm9zUUdxdXNreXVnRjg3eGV6TXVXUFJnMGdnYm96U1FzdXVaZmNXNXI5aGZGZnRpazE5WmV6MlU2WHE3WDFwalpXYTBBcUluSmlFcjZ6eGF3STJEd1M1QmNUS2JsQTFoMzJJamN6cnNzUTAya25iNkFmOUUyMVBkM3RIZUtBT3JVTzRYQXNqcHVKV3JjbWxvazdCNWlyRVppV05jTXdIUFN4QkxtZXlwc29NJTJGNyUyRjBQWWVhNUpDTzlib0slMkJFaGhuanZQVE5NNENIdzd1a3ZPJTJCdDBuNTc5b3p1cXJ6SXpEQ0F0TFdsTFN6OFpqUlBwcFViTTkzeHVIVWZLVFRNSlJrN0ZodDFsVjV6TVgxenJPYXF4TTAlMkZSdU5tSkU2YTE5SFg2cVloZDAwYVJhaDJPd3pUVW5OeG5LN2tUNHZLclB2VyUyQlZuTTJ3UVdhb001QUNjcmdGcEVjcVZpTEpzMkpLbDlpUEw0MTZ5TyUyRjhBaDAwUFJBR0s2U2duT1V1eDNGejJKZEJoRTNGRU5BJTJCZGlBZ25HMEJ5Q1ZKSGhRT3N1OUthMGVGM09qUUxrQ1l0Y0tsSW05YjIxemR5U1ZSQ3VpaHpFN25pR25BeDlqejR4TUJjZHZKS0wxJTJGdGE0SURNc2hIbUlvZ2pva09CRElDZ0JOZDVObFd6UW93SU0xTGtYcDBKQiUyQkZEdFI4OVg4QmlhUiUyRkt6VGlIcUg5VmNpM1JQN0U5WHk1aDdHVnNIR1dEOU9GT09EajJ0ZyUyRjI2VnMxOTg1azB3RlR4N0ZGZUp4WWpBeVJvbFk0dHN3dk9GTWRZcHNvVkFPRUgydmRPQ25ETDhoZmloRGRid2NUU21ITlFoTnJMSHZaUDAlMkJXUkJIbFpMaGI2T2swa2drVFBkcllleXc5bDVPT3JGNFA3ZWpZWFozeUJCTkFGU0IybTdOanpOJTJGM0N4S1B2bmt1a3k2ejlVUVdpTXJ2cTVobjNmZnQlMkJPY2U2dVZwM2lHa2JqMGVCZVpLaUU2dzM3MDZVenJpUGpUYVYlMkI2cSUyQk8lMkZyVm1uc1FFazhVMVZpUU5vS1ZIU25ZTTRUYjZ3aVFvUGZ4anR3eWQ2NmtTZEVEdVhPZzFDTlZFeVg3JTJGbXpnRFhXd1ludXp1SHhUTDRXeEd2NFFxbzBmUHpESGk3bHlsb3o0Z2QzZm5UMjAlMkJJM1pHbG56Z0tpYXhma1FEUXBvbEQ3Wmp4JTJCVzg3V294T3VUNyUyQmZvM1o5aXlXVTlpZE01eWRadmZqRlB1ZnNuYWZpbUpacXVrRE90OEclMkJLbWkwJTJGOVJJNTU1Tm5vJTJGeFZETDJoNnA2NW5UYWN3JTJCJTJCS3JZcUF1Y2xxUzc5MEM2b1g2RVp3cmtEQVk5R1BTVSUyQnd2VXZERGFvYSUyQlRnenZLdmt1aFJYJTJGNEtIbHdqdiUyRmF1bnpKdk01NiUyQk1vRCUyRjZCZWNQSTlJNmhGcEg4YVpnampkVlpYSWhmYlRFZGt6cGFZRVFTN1dJVUJRRmVUSVFnWG91UUNUNjRUZFhDa2d3QlZTVHVQU1ExVDdDZjIzRzglMkZLNWU4QjBSYnVFV2pnS3g0WVgxJTJGemtwY0c2eVJ6ZnZXN2RqY3NiQ2pETXZ1bWZmcHM4ZUhhRWIlMkZ1WkE3SlJ2bnZ0SHBrUVI5dDBrV3I0MmZqNUpTVGNZcTM1Wjk1QTZ5bFQ4RHdiN3pIb2JQZ3VndWRYeFVBajlKdmtuWSUyRkNaSTRJVnZzSk5aRXNsY1N2QXppYkV6YURaaUE1N1dsSFVKSHZSYnhVQng0VktCUTFsZiUyRmJCZ0EyTnpFc1g3VVBYUTZoOTJOTVIyTHlWaGFNcnJVJTJGMDRGMG5kRnhraVFudmx5YzhaZk8yeDUzelgzZFh3OWtNdENGM01iVjdLZjJyeHBmY3NyUTMzR20lMkJZYnJLMER4QU8xSldPVHBMJTJGSk5pRDRtYVVIYktDZnBvcFBHcUNuNlBlTkxSWUZRelY1YWpwUDZqUzJTS0xPZFE5dUFvUUxnRGRlblI0ZDRvRWZUWDJXUnZiSEJndERsWHBtR3U4STNEUmprZEtMbG5wbjBOWlJxeXVqWTdzR1ZWdWo0bk9HMG9ZbkZhZElpZSUyRnhpR1FRa2ZwbWZwUVlkcDQ0WUZRa1QyRFVMRGlDVlpVTUkwQkRIJTJCQUJOTnI3MFhzcDJHZlYlMkJHcDB4ellWSWVOJTJCJTJCcTdXNkRNS0Fkbjk1VGpDJTJCazNKQ2o5Q1NzdlVEM1prakpJeFZ3dkVURzN1Nk0lMkZWMW9OVWhBdEVuMSUyQjMlMkZtU0xWT3lQTTRpeFgzNHhLVlM3dVNkanpWJTJCbEpWemxUQVZhY0M4NGY1b1ZDT1VKNm1PVVZmckxkbXpMVGt4U0U5SjBMOVhUUjVzUEtuRHFJVnBRQUlsYWNqVyUyRjlWR2s4eFR1SWNEbXRNaEMlMkJoQU5SV0liTHlMRExBcU81cGNDZXpkZ3BGN3FTREtMWTRoTUR5T0NvbXJHSkZqVSUyRndXaHhCcXhjQ2plRGpBMjN6NFlnbGpjdmhKbmJ6RVBodVQ1TFZ3REpiYk55MmdrdG5QdW5OJTJCNE9Gc014REpaUkpSQllFYXFldURZTzZwVUcyZG1mJTJCVEVKS0N3JTJCRmpSWXRUJTJGRkZjOExnbFhQQjMlMkJzZ1lhc29PT2ZRWlFiRzNzeDFPU28wWk9uM2x6RkNwTnJsbnRqcXh6dWNWdCUyQlRTVWdTU3h4Ylc5RWJvRzNOM3dtbVVFOGFJU3hhRk1HZnlKUFh0YXh3dnRVRUpVVHVrJTJCOUdzMmZxRzk3b2Z0dSUyRllUT1NhRnZEaEVWVnRqWDF6Qkg0ZTVhdzlsY0ZEMjUlMkIzVG54T1hxam1lTnMybHVZbmlUVkVTYSUyRklCYXN0ckF3eklzeHFYaWFhYTRyVE1EQWRpOHlRUHVZNjVyS2dIc2QyazVJeXJaSlAxOCUyQng4ZUpYeTJ4dmRuRWFielMlMkIxVm12akVWUHNLUXFHWVNDejZFNHIlMkJZVWk5JTJCWHlaTng1cW9NUWVjJTJCQ1JmREZ6OTdzYkY1V1V0ciUyQlNsM1FFbU1MZU9oSzZKb25QS2swZGtMR240TldjWGZZRFpoVVkwJTJGSkpzaVljS0IyVzhDdG1xZmVyNEFuVSUyRkpYNExIamxsb3FTNDVrYmR4bzg3M3AyV3NFb0U3TmhwczM4JTJGdlNtekd2V2dIYnZpMjd3OXBVUjNyZG04JTJCN3BmR29SZG1DNiUyRkFmUllzeThxU3ZrZG1ubDBWbW43Wk1EdzJBbkhTQUI5RmFxeG9FUk5lZ0xPc3JQU1hmOVpzV3BWZUV1T0p1SUVkajlMQyUyRm5QdGRvanJ0MnpERUwlMkZTS0dqSkVZbmNqaFBDaWZtNTZVSDhQR2dTeVBSaW96ZW51TGk5RndSeG5UT1NkbmE4VXl4dXM4Y3pjb01xMHBCeWVOVEZuSlNiZU9zNjB4c1VHSmRuMG1MNWUlMkZzRzBjMk1MbHBlN21ONGZZNnp1VlZLSyUyRkJTcnk2ckg4MFBYa1pLTkk1NVI5TW4lMkZuSUJKM09weGZ3UXZmNlh2WDhJbURya0NOb00wR0pMRVRhbXhSS1NQZDclMkI5UmE4JTJGWERRS20xMmpmOHplcWgxbVhGcmVuNUlJMHYyUnJQNXpkNWhRbW5HMUslMkYyYTdrUXVFQ0FYd1pUQnBhOCUyRjRqbXpmUnljUHpkM0R5MyUyQnBtVDIxQWc5cGU2UVVLYkclMkYlMkZ5ZVdxV3M4UmVsJTJGRjZpb3RTYWdJSnhIWCUyRjB0UVljRTM2ZHllUHZORldOd0U1TW9vUFN4WmFHYTF1N1glMkY4QTlucFltdkhMdSUyRkFqaERYUHNsTE4yYWpBTGwlMkJMY3htSHclMkZKVmRkWG1lYzVzcGM2N2JNc3gwTXMlMkZXckdhbjclMkJEOXdBUHJya1FkVkZkeE9FZUZHNjF4S043N21oOTdYejd3eVlpZ1dxcE9sUEFUN0gyT2M3WFBJSE8lMkJmdE0ySktPWUJ0NTkzQjIyS0J0RVl1NnlzSG5XYlNqc1JkUDhHV3FkM09XTXBvZ29RUjh6UUhFeGxzb1JyNUp5WkhEUHQ5WFlHWWV6Rjc1ZDIlMkJ2VUZ4eEFTU1IxNHpRJTJGQ00lMkJtaW9IS1FpQVVaaXZqQ1oxd1UwdWxOdUZaY0xSaGtqT3hxR3hWWjIwMWlhaWJpJTJCS2k4a1k2Tll2NDRLalJUcUxOaXFJVlpnazhmRTdlZHpGd2hzNGRqYWRtc28lMkI4MUlyTzZYS3NXVTE0REFockRyR2dKWWZGSDdwJTJCdEhrTjJZU2I5TnR0d0pxaGRLTUFmaG51OEpRYXBEeEN1U2o3eWR3aDg0Qkd3MlVma1hVQTkwbGFKSGJRbXFWJTJGR2VwWTJKME4lMkJsNEVIWTFBT2E5V1NGMFF5a3lISXc4ZCUyQkVtSlFlV25FTWZOJTJCN2U1d09xazZtZjlOakNQOXFhZnVHNkxOcWZGNDEwOWlaRUg5R1BiaCUyRjFNdWZrRGN2Mk1pJTJCZFE3Wk5Ra2Z2WXQlMkZkWnVRSW5EcHl3NVZ0U3F6QmE4UzNDNnolMkIxVSUyQkYwUGlVMHhXSHZOJTJCdU0zb1hpcU1YME45UFFmZnklMkJzZXhmUjJuJTJCa1J1b1BZJTJCbjQzcVNjMSUyQnZIRyUyQlJvN25oUnRUNVI2akpYdkV2ZUliV1RKczcxUDZRQXZzZHF2emlZamYxWkNSaEpzS2hnMHAzVXFmTE5KSDJldWhIczZtdjk4aUt5b1VoSVlVdVVWa3pYUEhyZTg1Mk9oOHRrdFRQVGFiVDJTNWR0T3dONk1oVjJZM2R5bURiSGh6REFPOHZNZ2YlMkZmSFplbHFiMGVPUE9laklxZmhDYm9TZnBkR2lnalpTZjliMWdrRGJwMmdER2hYaFpZJTJGNExteVJwWnp2MWRLdUZmeTFRTSUyRmRnUEN5NnYyNXRybHZGWHh3MUhFM2h3N2xrQWZlUUxLQWthalRJUkRpTnZzcmNkeXZibEQlMkJWMktTUEFwYnhDZVg0d3pUUyUyQmJyTUhtejElMkJpZGN6S0gxRmNVOGprdFNibVNpeCUyQnJEYk1JdWZBZ3hDRHByUmVlRjhHaWNLSWVGcXY5VlBJUzFpY2Y0dXU1eUF2SkZWTmhObG1xZ0lpbnJsRHZsZWpad01vZkMxeGN6Nmlvb1BBZ3U0dVluNDBnT2l3T2l2emV1THRCdnBEV0llQ21BNEVGd2FhTlBVbCUyQjRyMGZPaFgzSDVLJTJCUk91Qnk4N0hMQ28wNkNVS0tPTWx4aDVXV0Z0czlYY2FQSW1QbUhqUjhxNE9yVlNUeXpmaSUyRlZkUG9MR0JmVXglMkJMU29TMiUyQnMwODlxRng0TFJlMkhzQ0haR2JYck5GZzJQUFh2NVNiWlFJcXlLbjclMkZMN2R2aVZyQnl5U3pMS1lXOG95em5PMkNXQ3JpQldoR1hJbFA1MHJId2ZqNUc5QUFrYnRKa2tWUUklMkYlMkJkaHZrSUxJJTJGM1FSd28lMkZvNWhuRE5vdFpVTnJVTzFNNWpqamNFVDI3ZkIwMHhyMVZGRGZsVjJoQVJIeEVnUkU0OTlXc29HTTI1WE1NZXBtRGszJTJGWFdHdDFrNE1pOUs5YTlIS1pMeG1yU0p4a3FRSGFEeHFmN25pN2hZWTRFb3BoaEpCTU5vTlpwV01tQndYeiUyRmZ5WmlPU2k2MGQzbldPbVJUeWpsZnUyQSUyRm05RiUyRlBRZDIlMkJBSVp4dEgzdldOcnVMU0lOOWZsSjFWSTZIaUtJM0NrczJzNFM0RnM1MURCTE5heGxkJTJGYUI0VzFzRVhjYnlxTSUyRk1pSk5wMXJzUmJjc2htUlpCb0IlMkJKdGdWdFNadFZtM1k2eUdmeTh1TmNkMTBhbk54WkJId2RFNk1kU2pUcnNBVE9paDZoQ2poNVpXUW9abERFc0N2cEF2ZTBLTjdVJTJGVWNUSmNIa2l2Mm1aUXZwY2xiM2wlMkZObTE4RElORWFWREs3WGdzcyUyQmlkJTJGOG9sTzZHVUVhb2wlMkI2VXQyT0ttQmVqekhqQkFJWlo5dnF1MjZ6OXE4dlY5UlNRYk5sWGQ5UzVlQ3o5d1oyb0hHdnFTaHkwS3dPSk9mRVRXUjYlMkJsSmxtTDRuVHZ4cEhaTm1JY2loYmxMMXhFNEZ1cWdKejlmamJSOUJ4T0J4RFlXUk1icEMwT1AlMkJ5NUpqeUNUTzZCZWc0Umw2c3VQNEV5Y3dZc0kzVXYlMkJKbEU1RjB2RVdtTTdsOWNiRGx5dXdtUDFDa2F5elVvJTJCa21kU3pPdmZCZERkN3piRlFSJTJGZHhYYWtWOXlQVXRDWVBrdGczYmpxa0Fnc3JoYkVGck5iTUtaRk1pODBtSnV1SHVsak02T1NiQ2NWbU10TnFnSThFRnVJbVpFWVNlNm9Sc3RmR3Rib2swVU1icjR4UEclMkJjbWFYdjlWZlBWVjclMkZHMW9kJTJGNFNPem1KMlZiYzhoeXFPSll6bXBQWlh4TTI1OSUyRnVhd0FiWCUyRmViN0ZJUmJLTVdKRnd2a1lNZ25lMkh6emtyVFdwUmFWazhva2xrSHB3djNyNkNvNXdjeUklMkZKTmU0ZnlaaFdhZlFWa21XWG9FMFRmUnRMRkMlMkZwbWE1ZTIlMkZqTjd0bjlpTjhGYnhHN2p2UDkzUCUyQkhhWmpnZmlWcDNHakNzWmdHUFElMkZaa1AlMkZyQ2JFNUxqJTJCcVZNOUlmNEt1UEtnTFZOREdVaTNUVnA1Q0dYREhaSmpCNVFwYyUyRjRyVWlGejB4QUprb1VzOTI4b2VLcnZBN0NSbUVldEhnS0p3OU4weFhOOTd3ZGVOVnVkQ3V0RDVEWEZBQmloSjhHJTJCa0tKTVVyTUM4MlNYJTJGNmZtV1R6SWFGZFl0bDhsRldZcjN2SEtlWWk3QmZYYSUyRkV5VUxEeGJtYlZKSE16MHZNTzFwdXN6cEtSTFRaRWFrZjZVRCUyRjBoNDd1NjRpY3VMNnpKdEN5aTBOTlhlOE5ZMzZtcnVBTFZDSVM2OU8lMkZDUUZMZm0zdmM0NmV1QllSZ1h1bmRmQlYzYzhmUmZUUXZWUlI5TWMlMkZid3E0dk01ZXJ0JTJGOFFHSUhjNlRwZkxrZk05eVIlMkJtUGE2Mlc2MDJla1IwMW1XR2V1U3g5QkolMkJRckpqSWFtRmtqZ29NbjlKUmNJY094M080JTJGZ0luclRhS1glMkIlMkJ0cHFBbTFYWElTZWhNSjRTUFI4MW1pcXpOek5nT0JiSjE2Vm9GNU04JTJCUVZ1MjF6ZnFHTmxxayUyQmpacTE5NnZXOTNxOUI5dEV2Ymw0UWVpZGI5SEJsVEQ5anFURmtDT203OFpqOExhWSUyRmJ5MmdySExHRmltY2lBM2VvcnlMRWtjV3RlaDElMkJsdXVWY2FBZHpLZHpoSlIyaEhlOENIRWFScWRiaEozNzBEdXhMVzk2b25FWklHR0pySGVBM2Zod1FFJTJGdXRTWWRDaEo0UnFMY0hneU43ME5ZWWg1YTJTaWR3cDZueVpuaXY3QnVYYThrenFaNDFsTXFWTCUyRjAzSDZXRlpUbk9sY0d6NFAlMkJHMW9ISU1CZ0JZTXJpNEhPNVVuWGglMkJ5Z0JTaEhwOWt1NlRBVTlJVnNJUVlXU0IyVjVJdVl4bnBKU0JiRWdiODR2clFtVEV6eFlxVzlHQ3dtbCUyQnA1M2tRVFprcHhlRG1LdXRjekIxVllxeWU2QnQyVG0wN3VDdWJpJTJGN2dPSThHdkRmVnNCN1p2SThCSU95SE5MbUYwVDJmc0NPZXlmaFB0MUx3M0ZpOSUyRjd4TDY4UmhKbkI2V2pON2JhOHFkd3YxUiUyRkUlMkJyZW5Cc3FjTlQxRjZjUVF2dnZMZmVTNnN0Tjh0Q0hjVll0Q2c2WGF5ODNpTEpDSkYlMkZER2VJbldHbEtZYUNVUjB2TFdwJTJCSlU2aURwQVJoSVV2M2lHYktYJTJCdGtPJTJGRm5zZ0t1bGk0Y2FUbE5KMmZhZk53MWlNaUkxRGkxTzZraiUyRlFrbW43NXh2YnVOcGVIRnJ6OVI3Q3BwUnZuRFljc2E3M1ZqZUNwUFlmbk4xdGRPbGpBT2Z3amJYbjhoJTJCNjhEckg0a3J4YVJYSWIlMkZoV0kyR0hYYlZZY3NOdExBeWJHQW9xbE81cWhoaFd3MU16c1dwOG5SWkprejJHYU00biUyQklrMXd2WmxyVnl2d3RCTHdXaG5oRCUyQktwNTlJUldCcmJObGhxS3F2UEF0dFlOTDBkNThMME9GcTJPU1dXWnBOSEFienJ6MzF3d1JCMVloJTJCRFkxMDB6bks0V0tyV0tVdXlvNDJ6TUZERVl3JTJGWEJZZzFEUXc2c28zN3lyRzNpbjBxNEh0bzNFOCUyRnZkJTJCTUZWbzFqcUc2UjA1SUs3UDRqMnAwSGMxVFhkNjRRRVVyRGtTZE9wNTJ4OG5LaU1aMVI0OVY1VHplMEhmYkpUVVduJTJGVk9pV1FCeDg4TW5BcVFCUDYwdUFZc3JKenBNY1VxRVVYc2J4VyUyQm1kd1pCN2hnbDhjRlQyVncwNTcydFIwWjl5M1VCZDJlU2R6VWNwdDJwUUtiNTA4dlVlVDBBa0hZczhkZHYlMkJSJTJGbTAxZjJHeTBkRkNLUXFicWpsMnk2NGNXMWxBeGZJQWFNdTBPWVFWUEdPTlkxZ1dTcTdqNUVXZ2lJSzUzcllmQVJRT2M0ZUpDRGo5dCUyRjhZSjhRZXJKV1hDTTVjQTBaMkM3NWh3Y04zem9meDhwYUpWaFp5M28wV3NVYXlpY0VldFQ1JTJGR0hRaGJiTnEzN2k1N1RYZmxpallhS24lMkJxbmRHTWdqaGs0TklkbnU3UVJ4OWx1VHFCMiUyRk44SFM0TW02VUJxWkxIdnlQcWJkdktKNHlaJTJGaENtZ1RKNUJIUlZ0UDZPeGZ5UFRUTVBGbVlPU1VPbHhxMiUyRjRxQUl0aWFpeSUyQmRQRUIyU25aZzBOUzJyVFU3WEIwYTFzJTJCZkZBciUyQjJvaVZ1VmRYSkZLUjBKN1VyYjFEVmVxOHBCamhKMEt5cnVzTGNUalNHWVZIN3QlMkJjZ0VKZ1BLUiUyQmRUS1ZJQWFsOE5LbHJtNXJGJTJCYzBmR004RnQ5NTBmeU1oMzdORHp4RFg3VDNEWmtJdmF0VlZDM0xPZHF3ZHVrWWw0SFJLWmtnY0k5Z0ZDYjlVd21LMFJ4MUxtc2xXOVFic1pvdjZCWURyZVlKUEdXWmwyRldiNDBYbFhpVGphdiUyQllXZzhReGVqSEl3cEpWYmF2WjF0VTMyVkk2OUdyY1NydThpcmElMkZ2NU8lMkZQNDdQN1JMemN6eTlreGQlMkJJQTVYWFpOV1RSVWRsOXFCcnowN1dvaVE5ZEZ1MG1lZ1lGSlhXVGRXZ2l4ZnY3Z1NicFhoVzJ0WEsxOVV1YzJ3U2lUaSUyRmozMEE5NFQyWUclMkZ3VyUyRmh6cmxlNnVrWlB4ZFdJaUp2QUN4MnNwaXZ1TTUlMkJVWWYxelZWQUhMcTJLejQxSnluY21jQ1ZzN3B6cUVuNGdZcTdMYndoTUU3OE9VQUFmZ3NKNkgxeHRGaGpUS2E2cDc3bnhiODlEdHdTYmJFdzlpaEhyUnZKUVYlMkJweTg0dkN2MlZwdnBVOTVubVZnbDZvcUVFUnZIdXlYZEdMZ1FqcEpCUzlqY3clMkJXZXhjWTdiU2FKaFVjTnJZNU85Y2Z6MWQlMkJMODY0MFpHYnFPSmFka1p1bkJsOEdzVkZWMktTZGp4U2l6WTkyMXV6anZDTzFiVHhjJTJCbHJWOElSVEtUd2NrSnhCRkFtZFpsM3NsdHNnUG5qNEVIbCUyQkVPRExIeCUyQjlsZkxsemM4UFZLRExWOWl4aTVxeXE3NVQxZDI2R01qZ2o2OUtxNTI3QnRKNDNoTEQ3UyUyQk13OTIyM3lsb0ZrbmpTUmlrT2I2VHZubkZoUjNkJTJGclFGdlFOWVVuaGxlQkdKbE04OHJHaFRzNjRxJTJCajBpUUhXeVl2aDVZWXRwdXBORFFsbEVFeUZhclVaRGdPQUJySXhReWFVYTlqTXZrV2tiSlQxcmhDSjVoTHklMkZRcmw4aGQ3VFdpNE9VbHMzZlNtY1lIREdJa3pUd1cycG40bCUyQkwwVDVveWlQSDhxMlJDVUJ5b1hxbXVVWlNPZiUyQmVvNDZqejhSJTJCaGRYNU9aJTJGckcwekZqTkxGRTZuTzBJN2RJRTJISlU3MDlzSERQJTJCblBaVXlLa003VWgwT2Rud3gwa0Y0MiUyRnpJMHAlMkZtYTN0YzdZMFFSWjdPNnhldllmT3RPNkYxRDFuUFFHJTJGdmFsdDk3Z1pmZXZ5Q3BjYUs5NGpxR2JwYUwwbjAwTXNWZW0lMkJ1QWZZN3d0SVZqbXRFN1Y4Vk14JTJCJTJCSTZaR0UlMkZoYk1GbG5ZeTV0eTJOUlMlMkJENkZBaVRWbzNLdHdBNXJ6UkdkMEdqY2lLUm9zcnI3cUdRb2lvT2xTU1R0Mm9rejZDc01iN2I5WXJNRE5MekFuYzIlMkY4V0hCWExsVW9EcWlIbzVlWG9PTHZoZTVnTloxeG52d0NnVU1LMDYlMkJVR25EOGdZYXU5eFRYeFh3NiUyQjg0Rlp1Rk8xNmlwQTVRb3E3ajVCUnhOeWUlMkJUcWF3TkI5ZmxQcFE1dFRNNWRMVmJHcmQ2eURKcDE3QW9EOUlMTDU5ejBYMU5nemEzbWVHeGdHODZvYnh1TzB2V1Z1ZlFoUnhxVUtoOVNUU045VFp5Ulc4TWUxMSUyRk1tdVJJY216akFFUVFCUnRkc1ZtYlBvem9tTlJNTjZJNnRHV1NwRm01NnJUYTdBMG1hWGZCNjlVRFloMU1ZaXFqblhSQnVHMnlmNHp4M2FMWW1SVm16bHZoOTBRMUxiJTJCVHVEQzN3enZlbSUyQndRa2NIU3VCVzJUaE1Da2clMkJJYVI3SndGWncwUTJ4SlNuVDl2dEtPWkJPZ0xlTENtRWg5Skk2YUNwUk1WbEtISlJvSFFUJTJCM3plQ2tHc3FMNzg0dUNUbmRjbmF2ZWY3dWwwNzgwVmhqS28xYnRYOXpxZFNOTVN2MzdCTjAzJTJCa1Q5SERWUVgxbE5VYjFjV3dYUERwVHNBSFd5RExjak5sejhMQkxHdkQlMkZ1R2V2dXZ1YlRtcW9kSHVzdGpGWWJFTTVRWUo5SGZPbnhWbSUyRjNUJTJGSGQ0b01OVVBTZ1BVUEs1cDVqS1h4NVo5bG96aGRndmY4eWU2RW1lbVpHZFdPbjVWaXhDdDBnNXpRZ21WOXRVbm9lRGllVFVoVThDclVOejdPcnhkNmwwTTZSUG9wVUx3U1ElMkJjT2VSSzJ3cWF6MFBZOGNna0JtRE5rZXpIOXprUTBGaWFnOFQ4RzlMaWdjeHh0bGRaWjVaQjYlMkIxb3Y5aFhScEVkUVVuOVdLejR0dzF5eERtNjVzTVhmY2RONnNtQnpVVnNvJTJCckdoSiUyQmYwandla3NrWDElMkZKUDRnUlltZ2NtNlZNb2R6NDZoQjRZemNVdlJtdGQ4blZtcGFzVVgxOGt1NlZRT1ZjUGR2JTJGMHdjY2MxaHlqUXV1enh6ZElxR2I0dXhPNGVtSWtRbE9LQVZ6UHZhQWh2UTk1ZlBkNmJKJTJGNG1WdnN5WFRFVEhhRm5oQjJITmdHOXE1NW84ZzAlMkZkaVRNWEczejZYbkclMkZleDRnaXhzUk1QeG05Nzl3MVZNYlZNSFJoYWxjcmM4QjclMkZxS0MlMkZXbjZPJTJGRlUzUzB5R3NsVSUyRldnb1M5OTlFYWY1WnR2YTFWNVVVTVpTcGk0V2dIVXI0VGQ4ajlzSUFOaHc3U3dNZjlZNkpxR2ZZJTJGMFhadHp1cXR2bzlSR2xPSlRnaGs0YU1lNHBqQmR4aHVCbTAwT2lQSTFEM1gwWkVGOWtuUE8yb2drbmNseGpWMEJna0pSZVNYYnpSRFVvbUhqcGFOWVZEYzNndFZDSTVCM2tsZnVkSGhyUWRKN1F5RTJWJTJCJTJCM2VOT2ElMkJLaGR6TDVoWTlEa2M5M24zaW5MbXdCcmMyJTJCUDh5Y0pEVyUyQlplNjJiVjF6Njh6S2k1MDBEd0R5U0FGJTJGJTJCZ1BST2QlMkJmTnRheSUyRlAxR3YlMkZVZkdmbTM2ZHYxNWNOYWo2QUpJSlRINEVIZm0lMkIzUmNpbTIzOTlib3BlRHZjeEY1ZGdiS0t3RkVoeDdUdWJvZ21rekVkeW9XMExlV0t5U3NIYldlNmUxNzJRWTA4VGUxU0hBalRxaGZ2R1I5MzM3a0NmU3c0dG1mYzAybWQ1Rmo5NFBjYyUyRlhGYjFUSyUyRlJMRTNnc0I5dUNaQ1dYYWYxUHZLeTMzQiUyRmY4Nk4ySmxZNE91RSUyRiUyRnVvVjlKMyUyQlRoc0h3UEo5STBNZGh0aFdncGYyZk5jYnczTHc4dWNMME5ISVNNV1U4WFhTUHhJbTJEeWtiWVZRbkdXaWtwTyUyRkphckdyaW4xZk9RWWc5Y2t3ZHltZEJZRTRUJTJCOWhGMllVYnZEd1pwcDJEZUpqdG4wbDMyQkdMUW5Yd1pjdDB5VlUwJTJGV2FHYkxzcm9ldFB6VHZGbWY2UUFseUslMkI1RklCYkM1JTJGZmZxJTJGcmRlWU1WUngzSWxTeXk0bkkxcERTSDRjcjBtdkVOU1RSZXdpTCUyRm51a1d2SmFvaSUyRmJla0ZBY2JGaVlyZE1lY25MNCUyRnEyb0tWa05ObmdMRXRYWWRWSUdnWEZiTjA3cnZOSnk2ZFlFd3A0Y0J2eVUlMkI0bXNnZXdiWXRxa0tWdDAlMkYyYTdGJTJCQUZLSjlBVzhKOE5KUzlyNmtBREVVTiUyRmk1T0dPQjl4UE42czlORzB6ZXdPWHo3T1VzU3glMkJ1aDNzaGN6ZUltUlU2WmZxcmJ4QWgyZ1pxRzVUV04yTUtRZW5QJTJCMm5DMmpYSUEybUl0T0tQMmxwV3I2dnlvUFczVHpsOUplWCUyQlVsUVB3R3ZjUUtOV2M4UHdwWFNBJTJCd0djeUFFZXpCRkduN1pYTUo5JTJGOEVHN0RTd0Vwc1hPZVUzblp5JTJGUzU2c2s0djVvSFlSYmElMkJjUjROblRDOXRiVCUyQnoxTlZTdmVXWVBUQUZONE13V3dpQmJmc2xJV3Y4R0olMkZRYTAzSE9hYWQ3b2NBdDhFUGk2YXVzSUp0ZGxDS01MZlRWa2gzU1M3dnZGMSUyQm4lMkZ1dnclMkZKdmhTa0VDQW9xbVdlSlgxcjhUazFzOCUyRkdUZmh5RWZiU2JveXh5WHgxN2F6QjRxdWM1UjFHZm9SMVFQRlFZdkRVaGw0anlWcTZpNFFkQWprcEpKSWtxQk4lMkY1OFA2MTRrQTdZUFRVSHBjODhjZ3p2UzYlMkZtWjI4bEUxWXJFZ3gyeThMZjlmaWR1dGFZeDRaTUlWWHJWakg4JTJGZ21xVnpqcTBhMDRWVEFVTXJheHFINWJERExFOW9zd2JoNllhNERBaklTUWxTaURtQzhqSnNkdUdpZTBwZXhzSWxPSTFVYldWS3p5OElnWjExeE9jMTBkN3F4V0h6cnZYdkFwc3dxaDFCckl6YXVpNThIMjl2UHUlMkZhVzVhRURYbjM1dGpEOG9BTWJEN1dOJTJGZ3ZTWHZyNlhiN2tIeFQ5M2glMkZKQ09kS09lb1d3SFZmM092MWh2cXJ4T05IWCUyQnpIaXprdSUyRjczelRPV0NLa2JzZHY4SDk0ODc3MUc4dlZIeEozNzF2N0F1cHlVaG1nc1N1YkljajIzaWRNN2MxaiUyRkhidnYlMkI4cE5tb1lDR1R3Q0FVQ09reVloaTJObjVPV2pqNDVqeUJJZjlxTHpuWXBzcXRqMXQ2SkFHZ0FrVGc3VzU4TTk4RTMxc0FCWGJEejhYMDZIOGM1RTRaNTQ5WTZaJTJGYzhnTmk2S2p5bmN2N2Z3azI3JTJCVnNpbnJqZ3dzem0lMkZSSnptemFzM3lkVlZzaDVRRVdmWHFrVU4lMkJ2SEolMkJXMW05UjBvc0slMkY4ZiUyRmV1JTJGUlh4JTJGN1lCem80dlV6Y21QVkZqS0dRbXBrJTJGJTJGbiUyQnNNYkRQUkdCaDN0JTJGVWg3NEpHMXZ3WTFFOW11MVFyUndnT2FyUk1MbVltNElkNzcxRW9HSUlPJTJCdzFXZXFjTE1UZG5mVHJNdHg3WVpKUFRKdjhHczFFekhYMVpuYXpqS2YlMkJ3UFJRZlhsUEdmb2pIU2g3ZUNoQU5GSGc5clY5YiUyQkcwJTJGMTBhQ3k2dEVScjNHd1JQZFBVSzBBS3B1RHYxelIlMkZzZloyOGRqejQlMkZmQ09sd0tuT1NmZHMzNWNNJTJGNVZmeWVUbnIxOE16MnYzcmtpaVhoUjg0cSUyRlN4UGVTd3Q0aSUyQiUyRldlUm1PVWZVcXRVTnByb093eEo0UnFzSGZUSGglMkJQMXlPemJrTmFqY2VNbHNLTlAzTkdBb0dxTXZrdzhqbEl2a254cU9IQSUyRkhuRWVwN2NiT2N5a29TUkVacCUyRnJOSFc1S25FNm1EN2NQZnZmQiUyQmVQNzM0U0lwZHBkJTJCVnliaHh1VDl6eEloWExGRVRFRjd5azgyNDh3dng0RURqdWw4R25MY2V6N1NQVjdNYzdEenlyUzlhMk9YalBpUE0ySiUyRkgyME1pdElrUFZOQTFZWEMlMkZ5eHhhc0xTcUx4RCUyQjYxUHFuV3ZmWSUyRnphOSUyRnlqJTJGazFmZTk4NGZ5S2lscXVjRHV1Z0ttRUklMkJTcFB2aG82UDNvM3M5eTElMkJWYmt5TW5wam5tJTJGN0ZDejElMkYlMkZyTEQ5dnNGdWhDVGx0WEhydlRUUnRyMkExOVNCbXFmaElGbUh2aXVZZmptMXZKWCUyRjQxNiUyRmYlMkI3MXEzb2xqRVhVbFlSUyUyQkI4YkhQRFh3V3FXMnUlMkZIJTJGSm5SYXdEcEQyalNWTWdzaDRLQ0I1NVJJdjVBOTh4T3Z3NW1wMGRnVEF4d3NCZFZSY2ZlSEVGejExUGZPeE00V1BZZks3elIxd29GcklDSDNNNFB3JTJGZmZ5enZmeTl0ZTkwcTlmJTJCN0ZySk5PcTE3JTJCejcxcTJKd3psJTJGdXZoNmVOR2I0ZWxiek81Vk1vJTJGTThPJTJCJTJCVzFRJTJCc2tGeG1yOERUN1AlMkJoUng5M3JYRGxNSlc3TyUyQkFOR0ElMkZ5YURSJTJGdEl2bCUyRnZSdjI0MVgxJTJCSWVnc1FrJTJGam45V3FIbXZGV1lWR2dZWDAwRG8lMkYwRVA3bEplNE1JWCUyRk80dzluVXRHamRNMFglMkJCNiUyRiUyQkppZUdvVENmSzdOVFJjTUolMkZIT3NQTXhxeFlVUGdXRSUyQmZRTHdMVmlLb3VmRUxzaiUyRmclMkJoJTJCdkRwejllaDFMJTJGM0Y3TjVqVVB4dmNUTmU3ZHJiQVMwSE12UHklMkZpRUVidlAlMkI2RmJJTTZyTXFuU1J6SFU5bHIxdjlIOEQ2OCUyQm8yYVY3SFN1eW9rUFhpODg4SzAlMkZnRnJKZ25iMExUa1FiJTJGTDJiUXFUd2xmclJGJTJCajhzOWtWWmVVMUZtSUJiJTJGZGVuUlpUdmF2a0IlMkJSMWh4d3Z4endZN1dBTTJpUFpRbUh5WSUyQjdYdSUyRjJBRyUyRndMV1BSMEFzUDZEeFQlMkZEQnBDbFJmJTJGald2JTJCODJwaGZBaFVNelBaQ1ZuVDlzOEVYc29BTkh0cTZQalBYdnclMkYlMkZQNWhodklCVnY0RGxQJTJGOVEyTTRxQ2N5N0xMQ1gydiUyRjFhczEzWEdqUk9XM2ltbVA0cndWdSUyRnl6d0t4WEp5RXZhRSUyRndQYW1TdmE0bmRuMnY5QjRmJTJGWEt2dzVnR3ElMkZ1dlpMeUhvSnpjV285ZTFMT1B6endMeEZGZ2daUkszQ1FJWjhWJTJGVWtFUGxkYXdYc01SJTJGT0V4Ykx3NFRiV0Rnd3YyJTJGWGwwRW9zYSUyQmdQViUyQnRjcFV2eG41endaZnlIcHRFRFBnUHJmanpZRCUyQml4czI1YjZRdFFQSHl2JTJGaE1BMXd1SDlCNjliNiUyRiUyRlZyWWpaZXYyNlNqUzc4OVUxZCUyRjZ5d24lMkI0ME44N3MwTERxJTJCbzliJTJGVU9OJTJCa1dOUDdmNkR3N1RlVE55cVJDaUFMVCUyQng2dGYwSHE5bW45VW1weVU5dmhuaGE5YmRWUldqZCUyRm9jZ1lJJTJGVCUyQll3YjJZOGY4N0h4ZzQ3Q0NIenNLeSUyRnRvUHowSmxod0ZWaDg5SU5DVVclMkZDTjFIVzZBbGpnazZhazVaT1VJbWwlMkJuNDVPNFN6bHIlMkJzeEh2eHJCbTVHRG1LYmNFZFcxZ05tZXQ0MXZ4dkdOV3c0VUQ3RFpJTUY3NFd4a2VYZHRIN2lzMW9lM1hIcmw5SHhnTXdMeXpUTHlPQjI1aXVJRzVuN1pteUd6WlhVMTklMkZDaktFWjElMkZYclVPVndINTd4JTJGZSUyRnY2bXdPVkV4d0g0bFZzZ0Z6dWNiakx2NjlNSkRmR2xvbkttOTYlMkZIOGpYWjdLQUtzQWRETlI3MzlXc2U3UEM0RSUyQmZOaXNLJTJGRXhZNEpoaUI1WTlMS0M4dHhOTFNTSjN6RFliUklENkYlMkZrOThnMVFYb1lLVnRrN1FjVXclMkIlMkZydWFRQiUyQldrRHRSWGE4SzN5V3FWWHVHS3FURWZCdXJ4QkdMVWVOUjRlYXRxZ2tBVVgzakhzcHFzeiUyRmFZSjV5anlxRkNBWWwxRlM2VWhoZGFsaUVKQ21CaFglMkZhS0JBUTh2OWtrYiUyRnRZSXZwUHptRnB4c1FJWkVYbUg1TjBIQXluMjh0bjh6ZHc5eDZSWm5rMUIwRjhUQUpWMG1DNkZCNFVXWiUyRmRqdVRkZXVONTN1d3F0U09vRzB4YlhBOWRSZW4zSVlDZHV0NWhYWnAxT1EzZFJodGx0NDZaV3JLJTJGTGclMkZsOVZMJTJGdm5uQ3JJb1FpVm9iTDFjSkslMkZScUNQQmU0Q0JOOHVIWUdjb0ZSOEElMkJHUEJ6UjlkQ1BUS2ZHODd5UmQ5M2J3OURCbXZZaldpUmRvc2ZxJTJCOEE0MWxzN0lMeVJwdEl0NlFkbkVVYW1rdlZsU1BuUldoJTJGSEVpRmdkVDRwc25zdVluMFpZNG80VG9lRFQyRmklMkJFR2l4dlRrc04yWGdNQ251cm9MNzlqJTJGYkp0dm5mV3doNUI5eEMxdEhtOW5mWlk3TzlmWEdlaiUyQmtiNVpVcnBvanRRUmhSWEdkSUNibkg1WFdReWZYJTJGcXE1JTJCUGNoTW41JTJCNDdyakl3ckRGdTdoM01TVTVvRDZxJTJGbSUyQjMlMkZldWRUQVNmUEluN2FZemNraSUyRnp6Vmt4Mk8lMkJjdlNpSDRUcVNSelFrMTF5a0NWJTJCTWpubHVCR1kxd0hKJTJGdTlUQlAzbmFiR2JsZk53RGg2Mm01bDRXUkVLd2ZVWnpUNGZjOGxMWkZYZUdFOEdlVWFXV2YlMkZRTllIQjR1OFRjclpJUVgwYmx1RHdnMkhsN1BBM0RidHc3WHRFVDJIRno4UTZSZTlScElJUWh3JTJGMjhFeGI4SU9wZ0hmMGN6MVN5QkdvVEgwJTJGMlF4ZXhpb3lERGpNVzM2ZXh1JTJGb21ueEVzVFUlMkZ2VmFVUzJaSE1tZXo5JTJGdnpoT3BEb1llanNNR2RURmZkVG5nVE5tQzc5YzJmeiUyRlNWdUFaMTJmZVZXNiUyQjV1MWpUc0FLVXlvZXNvdUtET0dPOVB1c3cwam9qbXVla0R5WGdBc2UlMkZGaVVHYVRrNzFIb0xDNU91cmhIVHNQSFVjV1pxVk9yM0dZYkJsaUhYaW9zQlolMkYlMkJJUk9ubGhJVFByJTJGTlZUYlJSbDhjMHNUUm54dSUyRmRvTWVSR3NqbnlPc1clMkZYV1pKVURTZ1hQUmh0Vldyd2pBU3YlMkZrJTJGUzdRMTM1WUphd0xSMWZyVnpoN0p1Rko3bE9oN2hYbmxvUVJZU3U5NFFQNzRjdjVhVzZ1JTJGc0l2VTZiRWFxOXFKRkZhJTJGcnQlMkZHY3Jlbm84MFhIS3NVbGZsdVZ5Q2tXRzRSS0VrTUxFTnk3YmlhUzM3czVZZmhVaVAlMkZwMzgxMllqJTJCd2VGV0x3dUR2dTZRMDZhWjF4SmVPbEhnSXpRc0lqRTMydGQlMkJqR0FrZ0VmaWtLaSUyRnpabEtvNzVRQ3pmb0txZU9WOHd5VjU1M1VtNUhPMjMxUnJHOU5vSHl3cVdTTFR5V00lMkIzMmNZdyUyRkNsJTJCZ1RFNGtXMmxqT3dQRHRQcEwyazhqWTVUeWd1YmVzTnBYWHAzSzEzMGpET2hWd05ObWgxM3k0TDdqWFY5a3p0NVpOM3VYUGpyQWhPJTJGRnNkZlpVbG5QUXdLRlJrT3Z4OEs2OENTQk1aOHB0TUh4NHh1RTNMJTJCSUdiaUxKWU1odFhEWGQlMkZRYkJ3c0lyYTBKNzk4a0F5c1VEJTJGU1I2Mlg2MU5HQU82WVNzN3lzVWNCMU9hcVlvMllrUFprZk0lMkJhTkRoJTJGazlQOUxubER3U01KVDdZZFp5NnV4WTJad1lxWkVNeEN5YlJMa2YwS2JpR1hFdjJVdnIwWXlwVGZyMDlQdzNlTTM2aU1yMjlrSm91bWtoUUNLV0Q2VHJ0MjhGdHNLRzkyNmElMkZlSmtITkx6N2xxdlBJJTJGWlF2bjBpUDVkd3JTZThza1NMVjBOY0R5WXZqZlBiQnVkSTJYdjA2U3JJeVFxakxvR21uQVAyOWduRUV2RFhyNWdxMUQ1OE9Jc09uS1RpZUxOSTgweHRXJTJGa2J6bno0b0tyMGV3NWl1TkxwUmp3aDk2bGE0REJ3aU9NazF1dFpla1g3SVBuREJvRXpQbjhoQ1BsQUxFMWRnc2dHallsRU1aT1pGWm5wVWtNOUklMkJ4VUkxMzNwOGR4d0syeEdXRE5mbzVSdnpFQlNpRzc3WjEzcyUyQmlKVHhCaSUyRmRsczczSEJRT1FTT1I5U0NTOEl4SmUlMkJTeURHJTJCekx4R0RQc05jVXdFNXd5V3BvekR0UjBiajVlR0x5cjBjbkpUa2hLbnJrdFFZQVJ5TllLZG40bzBiMk1jSkp3ZVEycVhwRzFSSEUlMkZDMTBpNUtrWFJBUnY4QmU0Y3JHQzV1ZnNSODExejBPM3pLVlViQkphb0RSMWZPd2lYenZTVlVOOHc0UCUyQkwzQk4yVkRldWN4UnNrUlQydEM0NG1XOERwTVpPeVBxcDI5UGpwUzNJNGhUa0NUYUtKbWQwUWFmNzJDVFVYVEdrSWFlTktCYWJob1ZzVnBqSXBZV1N1Uk5hS3ZWYkxWY2Z1dGFKdExDSnF6ZlVWeFFwMXlCNHBZNW5OYmh4WGpHbW5HZmNxMzlMazQlMkJGd012aEVWc2VkcWNuMXFzWjJyTDZUd29PaE90UyUyQnZ4Z0JBemRTNnNBQXZvRVklMkJ1aTlLeEkwb3h5TW53cE1vVG41WXVIWW1pZGhBaXFIJTJGbGN5S0hPVU9OY2lhVzhYQTFkZnRBTWRMOEFKMk5tVE1HNVI0WGxORSUyQkdpJTJGT1lmVWx1ZUJuT2hWRGklMkJ2N3N1M0MxMWFQSXRFUUp3UE5zJTJGczlTdGQyODJyc01MTmZGYjhwNWFZRHFndTBrVE1lOTk1TDE2VjMxMnIydTlQdEZvWnklMkIxUkc3N0dMWkl4WFBOYnM0bkJnJTJCNU54WkFEOWlOb1JOOGx1V2c1ZTlNTVQzc0F3b0JQWnJ0cjhYd2xKVHRIbk1iQ1VIM2dlMWF6UzlweEZVRnRuQXNaOExyODhUdDlBTjM1M3BHUUJOSHglMkJqJTJGZ1piT0hqenVTME5PbnR6JTJGJTJCZDN6VGJYaGtkQVZaYlRYRkNDdnczMVVUM2xmTEJkd3BFbTVrejRUU0RockhvRVdLbGNGbHp3ZU11VmZnM1N3UlUwZmtvZDYydSUyRnJlcmwzc1BCVENUTmVGU2pOODg2QkxHJTJCJTJGUnAlMkI0VzdvWiUyQkVQOFZrMEt5YUNJY3FkNGJaMDY3cmdEbVd0ME1tOHJuSGd3VTFXMHNkTnI5dml2MUZBR0RjNXE1TjFrMnkweTdVSFQlMkJtTVFNWVd2eWdKaiUyQmtXRENHVXdCRTVxYk1RVGtpVyUyQm9heFUlMkJ6eWZIQ2F2SlF1cE43Q20lMkJHdWhTNkd5dTVFVCUyQjV0WXc0WERZcldPVFZmQk5jU0FreVR4QlBOeVY0OWUxbFNUdzhSMnB1S3haVHB6ZmdaMWVFczZHT1ZsSCUyQmpJNFpNcm9TdUdGOUhzRUc5Tkd1eEx6U0F3Y0tDTDVRZ2slMkJmdnR6YWY5d21BdkpNTW5TcGlJbUtnOVViUEtEQ1MlMkZiZEx5NFFJMGFBRkpTQTJsWGQ2bDdCWUQlMkYlMkJhcXpubHh4dUJYZDEwTUdCalJBZEtjcmVUMExKcnNzYnVmYTQ4RXVTYnpzVGFUQ3BXUEpDJTJGVnVIdlp3cEFnSlh6RHhFdU03RnptV3NWN1lBa0pjRmpHMDhpajhJWXBsbUdZbjNPMjA0bUo0VlZMNWNZWEcxWUQ1ZUtMb1c1RHZVbG1YVHc4amclMkZLNHl6NlA2cXl4eUF4JTJGMDRXVHVFSVhxSWFiQWZHZVc4WjJjcTZUYTVyaUpjQ3dKblFMVTVmck5VckRqbFolMkJxZ0NKMUh5b1BMOVZpaWdYWiUyQjh5bSUyRm9Gd3l6aFNIR0RmQXFvOTFCMXE0MFJuVmtzWXc4dXNZbjZjTjZ0dXhnM2RTbTNadmJNekdhQjZ0THprdmgxSWxva09WVVlJVTNWenFPaXAzRHM5VFlPcDdkbmI3VkczbGpiUm85ZUNNak9uMDdnVlVMaUZOcXNraGhOaFFzYURvZ3F5Q3d3RTAwOVBYRTExU2RMSjF0WXllS0xrenAwZHZOVW5IZkZPUWZWRm5HSnMwQ1ltOGk5NUVxY3F2UkxsaWVOaiUyRk5rUlpzNDU2RTIxbzVTbW4lMkIwM014Q0o1SzZtdGRCb3FCJTJCJTJGOHc5RnZVS08wbzcwcCUyRmFkT3FtYlFvRUtlaTkxTyUyQkpad01aVmNOREFDeEpUY3NzR2haelMzYUQ3M09lblhUdHlmbGJCcDBWV2pJTFlpSXI4UndpREkwJTJCcXNyNHoxSFd1ekV4MEdQT3hSaUluSkZCT3doWFd1YzJ5NFl0OFpvaWU4d0VHM1ZjN2tyZCUyRklodEc1ak5ZcXpRMnBKejZodXZvYVVObFJCWlBJb25ITzB2VXgydzVIJTJGQUFrUUtTZnlZcVZka3lwM2RSR1hsU0pyaiUyQlJLR1IlMkZuNiUyRmh0d005S1dEeEJNdVpydXJMSklxdFJoamlWUVk2TnlVbmRiUld2RkRvellXVGdlSDB3YzZaZzd6ZklLWVZDQ2VMZUR3MyUyQjIzTGNMWkc3a2slMkJDMEZpUGRNWnRDJTJCJTJCZjh2ajMxM2RkUHIwJTJGWHZjeGJQOUx0dG81UEczdnU3TllMZnpZeEtvVWJmYXZQUTJGMmxWQ0Q5V1lidVI5N3gybVVCMVZnZHc5dU4wbFRDY1NPb252WEVpeGRzb3U1OUVCSDF2bllHMzI4cCUyQjRNWkVKeDE1Wkp3c1VPUEtDVHVUSiUyRnBSJTJCbU9Wbzk3RkpXZ2VueG5DeiUyRmdUbFVzZGxSTXMlMkI3YjglMkJRWmp0ckxFVjR4VXNOS08xQjQwVXRUcUtaMzlicHBJUFQ2b3AlMkJUeG5XaXFYU0NnaExPaGlJNFpON3d1d3VNb3g0Y3lyJTJGVUtScDlhOE5XbXNwY2xKSUhFamtSVkZJNjZWR3VOUTFueFQ5M0NqTVNiT3JRJTJGVmUxWU1VN1p4RUt5OFpOMnZFaUlhZlVkUVNEQmswS3c1VW9waEozczBUbmxtSzc0RzdDeGZ0VWJhWHp6Rm11NERsaWQ3dlFpdXNqdnZFd2dNUmRsTGtkJTJCaSUyQjFhSmR0Sm9BaUJCREZOc3pUdCUyQmc0VHFENGlNWklGa245VHcwRGlIOFBLejVVNXBHVUZvSXluT25UJTJGTFh5TXllV1Y1bTFtZ3J1WXBqNFY3VFpuenpOdmx1dlY1ekdmc0N6Q21mSkI1R2VTRnY4Nkl1MkdiRXhtUzZBQmMwb3h1em8wMHpYVmxlRGxFbjZnNyUyQkVRbjV3SyUyQll5VW12UGZvYW9EblFaVzhmQ25heFVrZzdGUDVkSW52JTJCeHRib3VvMkNMdnk3cUtyTHpQQ1VFbHM0bWZJeHMlMkJoNTRFekdRZjBOaDlka2dUJTJGa3ByNlZObDZvOSUyRm9DdWhRUiUyQjNnQmVkU3lmZUU2U3A1MkljRkZnJTJGY0NyNTAwUFBwOFdjdGVzQnU5ODhDbHZUYTN1a081VnVoYXZySXR3MEk5Rk4wR1NTSndEd2JEZzc5QUtjZEUwTXo4Q3NBRGlLeGhrYzZWZElDU1hlZmlWTSUyRjJ6akcyMlY4RzhSUkZsWks2T2c1RlR1a2IlMkZlVU5GQnhhQVVWWjl5eTNnaFA3Q3VsWkZtSzFJbmMxWUM2dUNFWWM4OEd5SG0lMkJNa0dsOSUyQnF0R1cwQ1kzbkc4eVBvSm43ZDJvJTJCdSUyQmo5JTJCVDJ2ZGIxWkRWN1V3eVhxMHVpOXlLTlBwTXlraFhQRFVzZTE1MHZQZDgwM3h1ZEh6UTIlMkZmakw0VkxqaldCNXlYUXdJbkpUYURHNDU4U2QzZnVya1VFekRHYTFvVmF1OUNaJTJGVnRiWDBScUF0cFNYJTJCSjNXZzRUaGRJZk9wN1ZKcWVHR0Zib0VaaDY4WWhoNFRoVmVUNlk4QmFXVTBnd0pWJTJGRWdudWUlMkJhZTRzdmE0cVVubXdnY0paVjRBNkJMWjlZaWdpbTViZ3NTWlQxQVM5d1lid1E3dDhTYjZLdjM2YktYd2JBSzlqcHgxZ05RSmZUODhrVzBGekE1VDE3bk1nV29ldTNDakJxQ1RhOWd5Y3BvT29NRWszeXZSSW5SZFZpdG1POUpWV1o2Y3BIZU9xZnMxQUtFc2tGMzNLaEpITndUcW4zb3R6N0xjaiUyQnVpbVFibTcyMTVWVENOVyUyQjhIZVE4YkEzMGtncUdxTmY5dHdxdDRPUXpwOTNINWxoYXc4TEplS3hnWVllYmxzV0tOJTJGbVIzUG9EQTRORzgycUpJc29zMzh4N2R0TWNUa3FHZDhnbkxwN1BtdzlEak9URHpIWFpZVGphV1RxMzhSRldDUE5Va285MEI0SHpvc0hFMmxMR2trd0RycmtLZThQdXdSMnpOJTJCa2FiVjA5MEoxNTZyRjYlMkZTT3djSnclMkJBWVgwZ05iVEg2OXk2TkVseWslMkYzeSUyQjg5MUptck5JNjVrRW9tcnZSQ2dYdDk4TFhxbkdlVmdPaFM4RGM0RElzRUJMYjRzMzZ6TndrT3VkUDNvOUQ0VEtQZDBIVjkzYUdkVjZ4c0x5U1RscldCSGtLcG8zckM0d3VSJTJGdk1meVFZUTJsdXdRSjFCOHlNSHFFbGYyM1JveHYwVGRMemlKbiUyQlpicGt0WGNobWt1ekdKYU81QXB5VXBmbUdJNVg1Q0FKR3o2eUEySDFHUnJCakVKcyUyRm1VOHV5dDhGSGNCYjc4eCUyRm5kdWwwaUFySGd0Tnhqd3I4MkVRRzB4JTJCMEVDN3JhR0Izb3N5QXhvUEczemU4MSUyRiUyQnEwRWZGWiUyRldDa2pVVDFNNXpiMUVYNHI0RDBmZVhDN0RMSlNEdlZCR3hsM0xFREZ6cEx2VXhBZzI0WXlhRjElMkJjVDdJdHFqSVBsRGZzejIlMkI2JTJGUTJ3TiUyQkFhVDhZcUVMUmpZakxzJTJGT21aZG1kSzklMkZ3NVNlZjc0VGg1OXBZU3B5Z2Y5MCUyQnVEM2JaN1JZUUR1YUdaSVlzSFB0Rm9kSTZoRHVkQUNmRU1Zcnl5dFNuZ2dhRmdlTDFuVDY5bk5LeWtBQkEzb2pTMjAyd2tDZzNsTG9DMmxleFdHdHJJJTJCMFpLMlFDYyUyQnp0MUdHSTdtSTY4S1BpM3hiQiUyRjdwQiUyRjFIekh1WXBOMFZCUmZVaFFnJTJCRSUyRjBuY0JnZXA0OWFwNG5YQ3pRU09oUFJQbVlzcjNYZk9ZelElMkJ5UnI2JTJCUVZ3JTJGMTJwS0FkUEhveGNmdjgxT1hGSE5CNE5KJTJCenJhcHBpTkdVNDZKUkRQZjg5YWdUcUoxQWZ0ZlpiRklCcFJIMEVtMnlsa1NGZVBqTTFsM3JkN1VHQTVQQ3Y3WjlSZ01ObnB4NWdjNGRnVmJrYXBQS0slMkI3MFclMkJGaGV0UktPbTlSejdGQiUyRnpscVZIS3A3RTUxS1E5RnhvdW41elVHa01DQWlBdXl4aExwcmZxMGF1a2JmYXY5T2dpU1RPeEhGejdqRFdwblU1OEVuNFklMkJWJTJCSFJsQWZ5MDglMkJDY0FIRnhNMEJTbnNnOWozcEdnakdTSWVlQXQxJTJGNWNUWFk5dHZlbm1MemNWcFRqODZSSXpjWnVFMyUyQmsxYjFZMUlTNHpTN2dEbGNKaXBkNnZDYnRLajVhRE8lMkJJZEhEQm5VRUk5aXdlemlSYW02c2VQaGpUV0JuVmRSYWdlS3lNOGMzUTlGaTY2c2JmWkN3MWpiQ1pUMHJWMFZuVWI4S04zTmlKcnNidWI3Mmk3JTJCMkxVTFE0bnV1bUZpb09uZWdkcUVuVzJPV0pRUVZTU0VKbjFucFloUTVyenFJV2tqTk1UM2NVQkVFS1I2OHFOcDklMkYzMXhiUHpSNU5KOFVyRXBmYUxtOWZ2WXg3anZ4aU02QjZobXIlMkJqYmxYdkd3eUJiTkZUSG8wZE10bkNMYTVjeDkwTDN5UDdQYk41SU5PRk9DOXRQaDNCSjR6TXpQa2hpclNERUN3MDdvJTJGV09tRGolMkI1ZFkxRzk4M0FXOHl3Z1VONkNObWthMTVicEtXVzN5NUgxWVVWclE3UUh6bk5TVHVjYThlSTFlZiUyRnJQQjhjSHAxOWdHMFBibFVYOERkR09DMjBjS0laWktWM3lMR3NaSTQlMkI3SXUzcHQ1MXF5Y2c3UmtVejJwYTh6T0FYRXpvJTJGVjNBQWg3d3RuMFNpZGolMkY1emMweDhzVG9UVFVIVEVjTzN4OW9TMTNObXdPM0tVSUNIaW1Lb01vcm5MeHBobmVUdUtrZTB6ZEQ5ayUyRlo4R1c2dzIwZXk3NGZ6c2RDZ0RQTXE2Z3czUzBwOVBSeWhiZ25GZEZTRnFVem0zbVpPVFV3ZSUyRjM4cTVCNm9rc2hVR0t2aCUyQnFmMkpXaml6OEh4Y3ZUNWZobGdQREhQamhpRzdXTTBydUlsS252dW80Mk9JeVg5SG5oOTZXdDM1eEIlMkZ3TGNXOWlYMFlMdjJ5UjNydjlzJTJGT3MyME44d3BWYTNZZ0RLTFh4S0x2NlBINUVsT2RVbDR1RnpUbTJVQzN6NHk3ZEdmeVhjenlDcnBGdVJUTURaM3ZLU3ltTVN6cW5FN3FBTWEzNGVzYW1rbnBtVEwyZGZIQTJHQURmdDlUNE9vOFNFZ1d5dUtqcjIlMkZiOHR5bUp0bzNKd3F2ZGswcGl4USUyRlFrRGdHazhSNmIwSW9vYU1KV1UweG9URlNVWnBLem9xUE9idVRIVHAyaGlpczlwOFdvdnhFNkh0VWl2dXV1Wm1nRm1ONXlHM3U2UjRWN3p1SE9PN1RYTWYxOWNOMlZlMzhLMG5tVWdWdUFUYkIyUkRqelRlWjltZEFpJTJCcHFQdEN6bCUyRmp2TVdsJTJCeGhINGFyZjVkNENqbjk2ZVVSYTlMeW0wNWpBeU9BYmdjWTd4SlNZYXNSQnNiUzN2cmN2eWRVcGoydiUyRk85TVVja3YlMkZrZDJmNzE3JTJCbXVrOVBPbFdWenJVaHZQS0IlMkI3aTJYVWFZT3h0MHp4QmRVQmQzelclMkZTc1JjWDFkZG01NXV3dVJ3OHYyd0RseXVSUTlhQkE2SVROZjNwRGU0OFZZd3d2M04lMkJwREh3U08ybXpiMjQxSCUyQnROdjE5SWZraVJrV0VWJTJCSkhlMlBMYUZmUVhISFF3Zk1NVm5SRklPSnoyRkJobTBHbnc5cjh0JTJCUk9GYlM3V2MyYVBmT0tQQU9lb0hnamd5RHZlWUtuZFVGJTJCTEFvQ2JRdjVVb3hwVzNJdXNPRGRQa2JhSEdxMFo4OFZVTEY3OXFSTXV6eDhvJTJCUHdDYmNIeG9jZjY3dW8lMkZ3UUFaVVd2WCUyRmV4U2d6T0FvaWxOTVFMQllFRnNXSjgyczVMUlVQSTBadyUyRjM2ZUlydm5ieVhSN1hhR3c2NXZoTyUyRjNhaXdpJTJCcmpUanZzM2Jrc0tXJTJCOWU2eHRCaGZPek82ZE43MDRUVTdvU2QzODhScXNyUG0lMkYxOTc3N1gwT3JOa2lUM05pWkF1WmdMZVhNSURoQWNJZTZPQXQ0UW4zTk1MeFgzJTJCTTlOOU9ucWtVV3RHb1poOXNja1BKSUVDS2l0enJhdzBQWHN5RDM1N080ZDR1YmF3OFMlMkJ1ZnhlODA1clY2VDFQbzN1JTJCSmVVUDglMkJnMDRmcjZjQTUyRlZtbmFieEt1blVNV2xPajRjbllNMXp4dVlXVEF4JTJGdkNpZm9lS1JmTGkwN2xkR3BiZFJSY3NPdklVVTRFeG1sdmxnSU5zbUJaWjVmMTF3eHBpZ1hRWUJPMmpJU1pzdTUyaGxrMlBhMVZiaGVzSjMzTDV4RkJ6bFpaTlJVR3IlMkJUcDVXV3oxUzlrSVFxS2o0NkY3VERnUTV5eXZLRGx3TmVQS1JPTXdJbW9xJTJGVmRyYUxXOGxIM0RPSUxzNXY0blJ0dXBpMzR1cFhiSFo2SmRzbnJtVGY1JTJGVTkzb1Fxclp1NkYzd3FmMXVrT3ltNWRZV2g4cDQxTGZGSzBZRWFZNEhkc0FCOUFNMDFvQVZGNG85TzYlMkZMZzBnVHRWJTJCUUg1MTUzMFo1bzN1ZTF5cGdqNHpLOWdnQjMxJTJGRU9BajdpdnpDV1FqVndHNTlYZVBJVlBGbkxlYkcyY2VGblJhJTJGUUdXNmNlQUlRMXJHTmE4RDRoeUZ2N2xsQk5vaXB6S1R2ZzlwOEpxelVualBaJTJCRHpQMXdrS2dCJTJCb2ZtZXR4ZzBtVHk3dHBLTXBta1JYaWx2bEYzVkZDbEx6RlUxQnB0QzdGUmRRZzZJSVlHU3clMkJDa1lnRWlPSWs4cU5mRDQ0alNIJTJCWGxqNnFHWGNFTGNxblJlNTRmNUFwd1dvbEtlbHlGMGdEakN1THBoQXljS1RVM0p5amoxd1haakVmSnBwMTNDc0tWT21UbWZMWUlLZ21hU3ZaaFpORThnU2RiUThlY0QzamdUV0IlMkYlMkZrenQzVm9FdFVmSzdWMG40YTBYUHZTMGg5bmJFSHNhNDd3NTlEOFElMkJDJTJCdnBaeThZMGJVMEhORVMxamtZTjl4U1JwM0M2Q0pHeG54VG9xMiUyQk1OcjhBandreXl3ZDVCS3NJdnQ2dmVCeEF3WkhMRmZkcjY0alhaRThQU3ZUJTJGSEwyWXY4eVdXOVRrTGFYQXVzQmVub2UzZlklMkIxc3V5QTdZYzJaaElwN1ppb1BpaXZxbzY0bXE2eDdaREw4ZTh2TEdXbllhQVkwN29NZHl0NExzZ2Q5Z3NRTUJYdldhU3lJbmdUamY3NVFyT3N1UmgxWW9UVEZCZ2E0JTJCWWhCcHhqa1NIMDFvcGhmdGxUSzRJbWJQZGJwSVNSZUhuNmd0aFRJWDMzVFBiZ2hvT0lYeCUyRlhlS1dCNThCRUwlMkJ2WlA1cHZXRW85bFlLYzZ6ZDFnampQTlUxcFlFVll1RUw5bVFFWWhsbyUyQjVJY2R2VHE5dTNURWNSdkVzNUVNMyUyRk9oOW8xV2JWaGxudzBJJTJCJTJCQnlxVG5tc3dIa1Fma3h0NWlKM1BVa2lJeGJhY2YwJTJGUWd0V05EQ0NpbWZtRHFGeEl2NzBCekVRblhRYUlXeGdrS2t6aU5hN1hlY2NpeEFwa1BtJTJCUyUyQmhPcXplSm5EUVFRM2lwVHVTWXpWczF1JTJGOGR4NVhoWFJwRm9jSTJ5OWNiR0ZQYXQ0dGpoVDFmWmhQTTZwbFNuMGlqZEZyYyUyQmlkR0xzVjBIbDAxNXRmamlWQ2Q4R0duN1ElMkZZVzhGS2Z2cmxEU2M3NndiNm5MbDc2WkNiMkw1eU1velUwaHElMkZ0bmRNRzBpTERTaTVMeHk1MExNYUZtdkFvRiUyRnREOWJYUzRXQWVhMzc5JTJCbFp3QiUyRmVxWXg3aCUyRkdTVmJ0MVlob2VxU2xoemxkblRtQyUyRjN3bUs2Yk1xVWxiSkZqY2RKZDYlMkJQalRvRlJ2JTJCNFhFMG1ZeGRPS3klMkZqS1o4R1hwcUNOa01Gb2NxRjM3YUslMkZxQW5iUlB1ck5YbWs3NXk1bW5ncnVHdGhHdERqakw4NnIlMkJPeU5YMFdkTENPVk5MRFJvem5yV2YlMkIlMkZFbUclMkJzdCUyQnV3UFpTaDg2RG56cndLTlNidlQ2WHQ5cWExRDVFS051eFAlMkZhS2FPM251dTJnVWY4YTdEcjZ0dyUyRkljcTF5WHolMkJPcjB4Wm1oSTcyZUtPMHpkSHV2TWdjaUNPVWdLbnR6ZVp1dGdENnhoQTNMOEd0WEZCMElaMnBsVmFYSUV1MkZ2UzN1c1Y4Rlk2YzgwSDdUJTJCdFhOWlBjaDQ4U3ptZXMlMkJXM2JvOFZUR3Y5bFclMkIzSjRYUm9xWjdLJTJCOURBMjJZRzJKNWRSUzMwakJDTmxqcERKVk1idUFWaCUyQmRMTHFaZUV6S01ZZ2phdktXNzhXdHVZNVh6T2R0M1o3dHFCcEw5eEswVk1LQjhnaGZHNnV6R3NObU1VN0EwMFJoYjNxbWpwUlRXazN4STM4T0xDOXZDJTJCTFgyeEdmOHN5ME9lM0lkT29FeVRMa3hza0RWdEJVVzAwS1dVeHcybGxCSEFhJTJCZyUyQjJsJTJCcWpFRlBXYzgxMEk1WXBSWjVzUlJwSEJjNHRJSElrZ2RzRnduYURQJTJCb05YMUZTMDBIZzFBdHVydENpRkhoYU9CSSUyRjFpRXNjalFJdGpJU1dBSTNUWVJBQ0pIOWZrcSUyQnhVUGF4RmFFZE9tS3FYT0h0UFdER1R0NTlXM2MyTk9JaUo3MDBRN1B3VU00bkglMkJPRlMlMkJHTiUyRnVLJTJGUVQlMkZwRlZ5U0lwbGJHNW5JY2JjcHhTZU0yS3d1RVZ4QXElMkY3VlBZWmFOWHZjODltSWd5RFM0eVlSR0tuZ1laVktmeFVkTExCRVFGYzJuSDF6T254TFh6Z29oZ1AwYUlBdTV1eGhjMW1jdGs4JTJGbnJSTEI5TkM1NiUyRnFHQ29aJTJCOFQzNlZhRTJiYTJOcVZLdTVndCUyQjhOb1VTbkdLdjFiSjI5MDVCdnBjNUZNTGdhbE16OThZSGFuZUFCYmxKYzJoRiUyRlExMElsMHlqUmM2Q3JqVXQlMkJqUEczRkZjOFRFV21zQ2V0czBtU29YNzhScCUyRjclMkJDVUJoWW1wJTJGQ1NzRTRJemgyam10dklxRkhwYXFwbzl5M2N3Y0FjMk8lMkJUV1A2QUw0RE52d1Vvb0NFb2Q5SWgzTk1kVDhvOGVTc3lranN3Y1E3JTJGZVc0eEFSallEclQxZmdHMktUMyUyQkdsdFpUQ1lQTE00cXN5Q002RXpleDBTU0QxZWFDMURBY09CZjJLOXNUc3Blczh4WEFpOEEyMGZXdGRIdG02Tkw4eHFjNWgxTjdHYSUyQlE1NVh2NktEMGwwRTVydmlWWVBuQVg3VHY4b3ZaamlsTFZTNWRxYmRBeExTTWMzVUxmS05hcEJDUml3MDEyWTdGWmRBUVduN28yajAlMkZQenc0UVYzc1Nwa0U0WUl3TkFCMmlSenhPaSUyQkIlMkZjYVpLUEZSJTJCcTQlMkJhJTJCbVJwbDZqclFKWnQlMkZ3cWRIJTJGNlhOS1lRYXk5Y3JoaGJSMkYyTklyaEo3MXV1RURKSmZ5OFp4TWJBdTQyeVVucmlwWnkyJTJCY1lYRFJLU1gyNWRqSWc1eURBSlkwcmVqcnp4VzVaRm0xb1FFbUx5NVg2OElEdmFKQjR6VnRTOW52Y3ROUyUyRmJHdkNZTUlsQnFWbDBFRWJ4SHZXMVA1VnElMkZya1VmUHE2b0tTVkxTSXl2aktwYjhGSmFRJTJCJTJCMEN5aFd1NEU1anNWNWpHVHcxcWRxSHYyN1AlMkJLYmw4UG8lMkJxbThidyUyRjE1YjNCdiUyRjlQY1JIYVM2OXIlMkZ3dndYczVVY1JkT0x1eGk3c2xtM3p6YWhOMEpyRXV6eFloUGtBWDNWYTBUMU80TWZObGFLMzJHQnh6VG0lMkZTUjVIWnY1OXQlMkZpJTJGaFg2NXBiZUhhVUh3ZHlZSk1na1lkNERaTmZ3SWhuMDdMOXV5OU53S0ZCdW8wd3N1V21pTUxFRmR1bW5EcWVESHEyYjUxbEQ5WHdTVW0yS0g2WVJ2STZISHFxSGU0JTJCaXNQMUlyNk5EZFNQRDlTOERiSFpXT0dCVEZTWTJESDEwejM5YVZoS05uMjIlMkZqQVhKTkNCMUR3WW9BNWtwRmI1WVNlVjNVY1FyaEhBZEIlMkZDMTlNSFF2dlJWelhFcEdLcXp1dDdOdGJ5Q1Mxd2RPNnBSZSUyRk1XU3pYTE0lMkZQbDYwckt2RjElMkJBWTg1dXR6cnZKQlolMkZJTE9pMWZHQjBSJTJGZkduNTQwMENRQTIwbmx4U3BaWWlwNGpSaSUyQiUyRkZWM3VaZGU0JTJGV05peiUyRiUyQlI0UmlDaFJVSVZQU1JlVHVKWHRyaTRYMGJDUmNaUnZxZCUyRmsydndpdE9nQ0RpbHFyeXM5YnA4ZU9pd3ZPTElaNncwVjQzYTMlMkJLYmVoOEFZNWhoRlRPOEVEMGhWcmJKcU9sOVNuYjJuVmtGNUlTa0E3dkp1NUFCTUppdGF0WWdQVHdUT2RTMzFucW0zMGdiRCUyRk1ieVZueHEySGtJbGNoV1lkWHlncDdWNExrdmo4MDdvMHh1STViQzlHdGN2Z3I1U1Zrb1dUc05mdVk0N0loUW4zZ1kzaVRKcHcwRnBia0x4a1YlMkJNQnZsTFRyQlBCZ3hOTnRJQ0ozOGNyMFB4eFFpdllKTFcxejdrbzAzVUxDeFJoakRnMVpYdm9TMVlzZFVRMkdLTHBrdjd5UFZ0bEhQTXBzNm1SS3dmQmRyUmVPeEdCYzlsbFZaaGtQSFNaZ044cGEybCUyRnp5STh5WFNxZGlNJTJCYTJoTjQ1SGl4TGNQR1A3OE5kanAweHpOTTM1SzliZm1aV3E0a1ROZTlNQ3ZNUXhLQWMzJTJGRlhXNHlpbUZFWlJIM3poY1JwQ0hIayUyQksxSHN2aTdmaWJWNmRZZG1INnRPZGglMkZGd3AzZnpxSlJhWW1rRmk5NzUlMkZrYlZYN2NZWW9lVW11OHMlMkJFcmdQc3VOUHpBdkhvQWNRNmNXNktwR3FVQ0sxS04zZEwzYnVaT2tGMmVKcTdENFU1MzZQRjV5RVJ3MlBsaSUyRnNraExqRmZUUW93MmNvTmlqclVSWEl0RFRsbjJEek1pdk84YlUlMkJWbEVuc0psVlltejZPWGdteHlveGFMWGZTUmdJRzJCMUpSdjJGTjdxTzBFSk15WVVUa0olMkZpQyUyRlZTJTJCVFlGdW45ZGMlMkZpMzFZNFlFdHQyZHpMODVXaUklMkJlQ1RnYnNYT3V2SzZUWXdSR2ludE9ObWppU3pCVzc3RGNHOCUyRndUVEVNazhTSVVKNEtsa0V1V3clMkJLZEdXNVJMZlBtUmxrUGFoa3MlMkZ4NTdUbDZ3SUtoNzZ5OE55Q1dJSWFDTUdiYkpaMks1a2klMkZXVXF5Nm0ybCUyQjNhTzNHJTJGN2g1ZEUweiUyQmYxYzg5dEpRTkhBcFRJbjBtUWolMkZxUWMwJTJGcFRTUTNxWm5tNHlMbjYxWkZWcXBTbnBON0dROGNhaGZhT214TXpUWHc3M0FtajJ0M0ZWU3NWZm1pVXdseFIwbnh5cmlXdlVtYkUwajdrS0JOV0luJTJCQmhLVjQ5SUE1QlpTTUx0SW0wSGw3Wk9IJTJGMkxIYUtta2h3T2k2c3kwMmVMSHo4dUNidnRjZUd1a3gzQXA1RnZydFdabFhiamRVOGclMkZhVHVOaTklMkJyeGRGU0lsNDM5VUxJZDM4b2FsJTJCWDclMkJOciUyRmdsdjRHZHltenlXdXRtSFVOY3J4JTJGJTJCdHhoeGtPWWNSemd5VThUNlpSTzlJT0FQTWFkQVhFRm5OZmE3NDRxNkVpJTJCaXBVeWJDb3k0RHlvQmY0M01wY0NTaEdSQnRZY2ZXQkozWHVpWnUwTmRQOU9GVSUyRkFZbkduNXZ0TFpUOHpVc0RlempUUjBOYXA3aWlTaUptQ1NiM1NtdVBaYXlZWUptZDZoZCUyRm1GdWtSRkhnM1ROVjkwVDluNkx0Q3cxZHNhMUdNWmpqNFZNQkU5RUVZYmpGZkhvQ1RyOU56QjA4Vm5wY0lkSmRNOVl5cGJRdWdYUWIlMkJZYTBwN2taZDFySWRINVVRNjBncHpST2JmclFQVXEzaWlYWGU5TkRMeVBsJTJGaXZyOG1VQWR3JTJGSXpQNkw5ZnAzSm5FayUyQmI1cGpxM2liOEJjcjFqM1Awc1A5TDkwaVJkUjlqb1hKeDIyT0xDJTJGNGxzWXFMbDFhWXprV3ZMTFI0ZFl5bzkyTlF5czZhSlhSRzJlNVZRNGU5N0t6MGZqR2N3eUhXOHdDMGkzbWJVNTlIeTlINzBhSWVheXlNSEVUZ2d1NHFyaWclMkYlMkJLZHJFOHRDdlpJZUs5N2xsWTdoJTJCNTU1Z1BNNjRveDF0bGNYUXdrclZJVjhWa0x5WnJvZjAlMkZoMU92MU1qa2Zpd1h0aVgyeWlTV0pPSTBxTVlya1RjeiUyRjNRTEJJS3pGRyUyRmkyMnQxN1lPamJkajlIWDRCdExNa2Y2TGpzQ204QzlPbnRTWFlzbjFYNjVGU0txb1l2TXRpZ3R6MGpNdlZ4ektWU3NETEZsTkxvaXI3cHJNVHBmd05XSUNObHN5NGVzN096Z1B1cWVlZXlSUDBnbFZNYWQ1eG91UGFJRzNSJTJCU29sYU1Ta1hUajVSUGRYRzhsc09hOCUyQlFxbFhvOFVxQjVnczdzN3FWQkwySXhOWTRKcnl0RnIyeWJ6ZENYYmtXVzh2ZWloQmMlMkJ3RWx4aUtLNEs4SzAyUFhISVF6JTJGSWo0N1B6bXBiSmZQa2s3bmxlMU00dnZ3VGh1ZWl0NzVKdm16cXFHUHFEaGE4QjVjJTJGWkdKNXp2USUyQmxYaWczczkxcldJaklpWGh5blc2OFRGTWYlMkZRZHFTNjVFY0Zwck1VdXlNakRJcWVES3pXNFNCaWhGcHVyV011RzdxZTAlMkZkTDgwTHYzQiUyRjg2QmdVeWswbFF2RHE4UFdoa1ppZkglMkIzTHRPM3g3Z0NRaU9xUEtncnk3NzNENkJlTHI1ZEtIJTJCV2o5eiUyQmJ2c2p2MThVZjRkVXVnaEhuJTJCNHZxMzZWeEZ3dkF5ZDVTb29XMEl4YlNURnhnQ2xyRENIcGlmTiUyQkJRa1dQM2VVakpQTUZwWE5kRFRoVXk5TGZZc1N6UyUyRmVDdkFwNFY0NlVmdGh4OGhZWTVNQ3o0REVsZ1daV2tJM2c3TjFjJTJGcFpoakNTOWJUdk5wbENETHVCTkpFYlA4YjhYa3ZmTjZzcEVPck1VTWxraElYYjZiTEF6dzBzdmhZREtZbkE1NlcwNlBldVpFc09ESUlHNlBoM2UlMkJqQiUyQkttM1JvUFBVUjI3R1F0amZuaEZhYjc2VEhCYXBXbGtKQURrT3dpeFBjOVZaTVZqNTlBYzhRWVdBSWcycjFLaDdtcHpPRjd0N2VTTTJ4ejIlMkJvWmtnZkhzbDREOU9kSHhhUSUyRmNpRWxGY1BiTSUyRkZrUFdOTTAlMkJVN0F1bFFndFFpZEFtTjlta2Fod2hmdmdoTHJiaGtCaTBWeDdtN3I3JTJGYmhvZTMyc0IwUG1tRjZFN3RVZkUlMkY5NnJzVzNNYiUyQjNWYnpXSm43UkkyJTJGalJFMnZFVDlwdTVac0lidWNEWUpOMUlOY0duOElXdDhzOSUyRkNzaVdCMzFwUTY1S3NFVzBZaHk3bkJwRFc5UXNlZHkyaFhhRmVWN3pRajBFd3NDRlBnNzJ6TzlBZGhCMGYycVNqN2xmclZKODBaczNOd3Q0U2hMMWxsQzJXNUN5TDRzSmkzajkyQTBFJTJGZlFNZjk0ckpsdUxrTG0lMkJUeVRjbHVScWNOODR4OEhqMVJOcUFjUklMakxQOGFsSXRldXNjJTJCSGQ4SFZHUlQlMkZSbEV3ZFEwdUxMVzBCc01kNkkwJTJCSk9DJTJCcXpwU2h4M2QyVDdyZk5YNjNpJTJGekpLdldVeXpVeUFqY2xucVJka1VQS1NkMkQ0SW9DJTJGeUNuJTJCYjgzc1dJT09pRE9vbUpVSERBbDY1Y3p2dVJqMEZjUmZWNiUyQmcxZllONExPM3MxJTJGRHFKQnpJJTJGa0kzVWZSSldrNDZyNmw5MUR6bFNQRnJZYUdJbSUyRlJ5VUElMkZaNVpUMFYzaHIlMkIlMkI2JTJCQlEzQkFuTVRYU09TOFhwa0pOdUhEYTFrVCUyQlJ4MU1uZkozeVFMR1JkSEozVnJYeUJUdWV3SE5LM3JLNWc2YXpXYlR1NTM1UUhLRDRING1ZdGUxaDhVVG9mRTc1VkElMkZtbVV3VTRtaVFVU3NzWFlWYUxqNTM5ZVo2SzNSbGIwZkMyd2dxMzBzVjNrV2N4OXpEUUVoWiUyRm9USyUyRk5xVk53SkROc2ZuOWxyc3RFMzdrUjdPYXRoclhOcHo0NFVtJTJCbFZLdXRVQjNoVGNKRjFSdDJUTlJYZVFoZ2hxaTZkcFhEJTJGb1ZUS0xmUUlLTEdQYXhDN25CcmVqVHJqendleGd2eVlnRTY3S0w2OG9YRzdpTDQ5Zjduc1QxUlM1QktOS2J5NU5RTXBTb2EzdDlBdnRPYXM5MmU4Ym4zQnlHcXo1YyUyQmh6YzR0ZEY2VXlaN2RLNGw1MlYwajQ4YSUyQnVoYjd6SG0wNVZkTERpQVg3YldPc1I0JTJGZkQxclBIOW5TWFhoSHNhMCUyRmI0OUhVekltMzU0QUg1azAySU9DSUx5MkZjR0QwU21mUk9xZ0JBN3ZNWEwlMkJZVmMzU0Q5VXhMMUREUWpROHJvMjZSV2lwazZ4aFRyMkQxNXhwQU9peW5rd1NIQlhWbG9lQ3dOZldsTms1SnVvdER3V3Rncm1QJTJGRE5paXBJTHVKcFJ0VmZsJTJCU3kxZFolMkJsN1djNll4cmolMkZBREpIa1NlQUxVajdRZTltWkl2YUp0bHdpcXZmJTJGb2NoZlFyN0ZOcmVuVEQ3cDd2U05XcG56NmRiMWFOSGk3d2dCZzN5WXFFak9TSGxvJTJGNk0zVVN5NEVLZFN6VWN1b0h0eGxORFNNV042cXY5VHFFbDFIQVdUaXRxZTQzTFduTnBScUVqNzJaQVNIaVFZek5zekJXdW50VzNNNVFKWmZwZTl5V2lJS2lXS3NuWFg5NnQ0JTJCbUtGTng4YWtrSGFlYUVlbElHMGQ0QXQ3U2hyYldOTyUyQlZ0c2FwbXk3M05iRjZ4QlJtVXNOSm90YXNEQkV5aXNRckR3NjJHNDNZR2xmajNnJTJCeFhWYlNjdWdYd3ZIT3dIVk1zemFQTFFMejllYmJwcno1em5zWDBtazl1dEJsYUJWQ3A2QU9ybmZrMVhlNWhVZlJTUW9EJTJCcER5cDJ4bm94JTJGT011WjhlZVV0cjBmU2QwekVkb0FmVHhibVlUblhjbSUyRm14VzN3Z3ZHZHBUY0tqN3F4dFBGOU53MlklMkZtcFhpSjVUMkJIR3dYQ3F3TWMybSUyQjRsJTJCTlZkaWxQQXRVckl6T0U5TUolMkZweDYzcjFUVUlBejMlMkJXaDkxbFdkbko5V0haSmp1cjBBZWZBVHh1NFJ3JTJCZk93Q0JEVWd5TE1YSEIzc1RIeVJENFF2STd5dFFnUURuN2I5WHFabmZGbElpcjRSZFVyRUlmbyUyQkZYQjlkZWg1elBtUDNVbEtBcU1XVFdaOHN4SHRRUUZDdyUyQm02bTZqMDF5Y1h4cEZXT1RBT0ROSWJqZ3gxS1ZyOFZFSWRhYllpTjlUWmpIOGJGVEpHNUdzbkxZQ3BhcXJCNVdWdmdkRFdYMk5CNk5oMEU0JTJGNmtwdm8zY2sxSENFam9RS0xUTHhsUlNDbCUyRnRTJTJGanpRN1VCWG5OZW03QVJBcUJMMHQlMkI3cVh6djJlekglMkI5U09XQSUyQkRzZ0lhSFdoUXlMZTB5JTJGb1pPd21yM0NGbVR4a3NGVTkwUiUyRnpOWDEydUI4VHp5UWtjNnRWckVaeWMxWDNCWkxsemhWJTJGcjd3MFFQZUVkSkZNazExJTJCJTJCVSUyQnV2dW1Jc3JCRDFORWdhOG42M09oekFqbnQybmZUdkFoVEY0THlJTXpVV2pZMVJDdFlkdlJKRE5xc0JkYWhsJTJCQXp0dDRBWHduSjFSWmQ1eDNic1dibkJhTU8xWmxVS016YWpXRWJFUHRHS09xenJSeTI5c0czJTJCNXk0ZW5XVEl3WVJhZlRRZUFKdzdIcDFra1ZlclhKSWhYaDJEODZGcFZrSGQ5YkZIVHgyaWY4RlMybUhkMmxCWVZKenUzOUozN2ZmVjVEbFdISiUyQkZIUFh3Q0plJTJGbGFPdTJNbDhrSSUyRlFpQktlM1MzNTFjcWFBR0NyMkN2bUdjRktkWEkyMkxmWHEzZ0JlTFV0ajFHT2hMWHU3ZzZZUUE5VzB4TzdjZ28xS3BPTktwdEpQc1pjUTRxWGZMYW91RmExaFRwUEJucVR2dzJDR1dGdTBSZFQ0SEJPYkNETFhKaGlBTUhkYklDcFNkYnpITmtuSUFQcWJaWHlzam5yaUhtWlRoS09wWlIzellxT3JlZFJkNDN4Y0xTJTJCd0dwcWU1JTJCWVFONnNnRllRcTFyYmF1RkpMWVlMbDZzb20lMkJySG5HSzVpMWZHZyUyQnJKeFViQWIlMkZ2TUpqSiUyQkgySmVDJTJCa3dXWGU3WVFxME83SXh0eTh1VDAlMkZZZUxTVHRkM09pVXlhV1FlTjc3bHVIY2R6ZGFqZmIxeG8lMkJCakI4ZUt2JTJGSUJ3dXE5aElhQ3NxZzBCUGdnJTJGSHFzeWtyRldKSlRvbk5MU25PckdYbllZZzZCOXFVWWtVZ1lvNSUyQnVWMkpQSjhLYXhldSUyRklnc3N0QzI1NmxHQjMxRWZvN0ppS2RjeDl5d0l6TU4lMkZtUWkxNk9tciUyQlU0bU9IaVl6V0lkQTExY3lnT0dEYUVxTyUyQktnUkxaZGNENUhxTU1XODVISFJxMlpkdE9UeFQ3VGl0RFBqcyUyQm9vSkYlMkZpY0Q5JTJCOXJHaCUyRjdKN3N4TXFoWEh5bmYlMkJoVUk1SURoTjRXQWxhNjJ4OFBHbyUyRjBMJTJCYlJzaFQ5bGp0Mk5oQnhabTdkZmRTdCUyRlVKQzRYaXhSeThVRHd1RXlRbVpPT3pyVCUyQnduNFNqbVluZXM0UEN6VER2aGpmcU5RWHlzQnk5TXNSUDdyZjZCNzR1UUgxWnlKJTJGUnNEaHFxUGV5MDdtN3J3c2ZCUHRRM1EzRzJYTnM0RDBNNjUzR0V1SjVuV0Q4NiUyQlpwNDdMRmVtS2RUJTJCY0V3MG9DJTJGSEVIMHVrbGRQaU9WTTJPSnZTcHdGdzQ2WGF6QU5LelhWbGRHaUFyWXUzbXMwcU1CNXRwVm9vZTZCZzhCRHlPTU51VUF0b2d2SXFkbFclMkZuM05lc0hjZE9raW53bHpYaWwlMkJBdE9rd3hLbE01c1RUSHoyaUtaM05MeGtwbHlUUXBJNyUyRlhZcU84WEYzS2pOZUtnekJVcUU3aVBjMjd1amolMkJTJTJCT0IxT01QT0hXeUs1OFhvRmwlMkZpYlhSbWM4MzE4bWpyWnVYREIzc3BxczZ0VFlmaFhHRmdHMzF4WHVkbmM2aTRDQTZnUWh2YnZxWnh6c1Q4a3F6NDVseDI2d3NpVlJvJTJGMGlrRHolMkJROHc5clFSVUprbjAzQnQlMkZ2T2liV2k1eGV5dlpabFpOeXdhcFlvUUVSbWtKVVk0VmlnbWNvRFlCYWVUZ0JsSjZWWHg5TWdyc1ZueVp3alQ1YW83cGRyNmVmeTRpZ3VUQ2dIaGJuRGtObkQ2VzRHOFk4aGF3elFjSnpGZk9iN0pzZ3RLdFBzd1dwZXh4THZFWUp1NTBKR2RtMSUyQm9TTjlwMEx2YjZ0dVhQWnlQT1d5ZXR1VzBsZW5ldnc1a3U1YjIxbWUlMkJUNW9ldm02eGhlU2NnMzFoQnUxdWIzbXJ2SU1nN3pIcFpqZmxwcUptZHlzc0xIbjBKUklPeVBoSWNwMDljOVFoRVF4VEk4R2Y5MXE3VXAlMkJ0bzdyeklURWFldnNOMG5rem9VM2M4aXUyS3NlQXBwSmN2V1FhMHFaV2dURWVhWlJES0h4SVRPY0s4bWRSWjB2dkFTcDIxcVpDTVFFJTJGJTJCcWVxUjRoYiUyRlhGdXZRQ3VnSzUlMkJ0NXo1NVZ2NSUyQmxxUm5SOUEwNkpJZGd3d1VaUlg2U3RoTWdLS1BZak1rWlQ2WE9TeE9NTWdyJTJGYTZCdUFtSkRQZTlNZlRVMGFWTUhpRGp2YVZEU3kxdkRKanRiJTJCWkk5eUElMkZZTTIlMkZZYXEwQnFVQVNxbElrM1BrY1BhViUyRjJDJTJGRWg2VmVzMXYlMkJWNWh4TVJyN0JKczEwaCUyRjNINTliUThJcEV0aHRwRiUyRlhNakJqOUc0WUVhRk5EYUlySmRkWk1MTGw0VTFoNzRXblpTQzV4UW9kVlpBMiUyRlU0TjVrYk42dmFVM29lTFhtSU9NT1hHa2wwUVJhUVIzcVVVanliNWVkNWpPc3M4akNvcldnYkc2Sjl1RWRzeXR0WjVtMWMzT2JIdXVaSzBFNldkQVZMJTJCS2dEQUE1RzQ5akQycjliZUN0ejdURHJmQzY2aDZQMWlmSGZwUW1GRFEyV3g0QlNXZmRQQ0xNUzczVjFvNnRmVlk3eUxnWUpTSVdaOFUzbHRlYzBSQWtjTHMzc0VYdCUyQlNlRlZOckd2VGdJdURheGxDaFdMd3JkeVg4b2ZXQkdZSkswVmNIOGYwbiUyRjlwck0lMkZNWmF0NktoemlXQkM5bG0zVCUyQkpDYTk4UERGTm5KT0hSaWp0M2VNSElPREVVd25vbkViV1JOZiUyRlVVVmpWSHRWR2FwbFM2NVV6MTZ3dlhpS2Y4YlVpYW9lb0R3Yk1ic0U0RUthY1ZONG11ZXFtdGlsRzYzSU95MkcwRGxGR3RuT20xcW5QeiUyQm5OTWo1MVozVHk5dGtjanZDa1UzQXpvTmtsS3dSV2FNUHJDQVZaTWp1ZWV4bEhFbTFsM0hxdzQlMkJ6R3RJdXlzSlJEQkxGSnZhcGF5MnlEYlAlMkJhZ2p2dkxaU1lyNzdMQ29iMkhoWUJtZXN0VWxNeHNPQWg1c3R3V0NBOSUyRllwWEJCR1ZjZzJZV2d2NHBrWXR6OVlkeE1tRGpIV1hnZlpxcnQ5YmFmaDFoaiUyRm12Ym9vQmpzbFk3SFhCUHJ3aTIydThjc3A0eHMzYThzbGhYajJRbkE1U1h1ZGR5eE54RHVwOFo3bjc1NnBQT09UYml4anc3dWQwUVAxYWNuaTAzalIlMkZVNkdKb2xieVVSSWUlMkZuNTRNc2xYdGRMNjFlYlg3VEtZSWRsa2VtbzJORm5jY3BvM0Y0VXh4TjZoVE02OFJwcnVNYmticHlIMHpOUERVanYlMkI4bFVSdUVSVHZOU0hQQXZNdEdJMzM1VUpDdHhCc3hOaDFTS2MzWkJLT2U5NHYwRkh5QUdzSDZhV2djcUZITXclMkZzc20lMkIlMkY1OVBUV1lVcXpEMTE3ckxxc3Jlckp4NmtnelptanVCazZQdnJpSmVLMU1vMW82SkNISEoyT0U2UW5PcENZQ0FhZU9TQTV3Y1F5QzVVMVBwN05WTWwxaGVIdTJaSFBWekVhYlJyMjVkS3dJSFhJRGNuOGh2anRUMTZXQXhaYnR2WW1XUUxUdEElMkZEQWIwTmtkcWFldnI3ODFlMURUNFRGSW90bSUyQndrdWZ6N0tlVFhPQSUyQmtaVEMyMWlsMWJDcUZ0QWZDeXlPRCUyQmtCQkpsNkl2MSUyQm01S2hIWkI3cnRmVXRETTFLaldHTkZ1a1Z4aFNxOTkzRHVyaFgxeENhQTBGdmJGd0JlTDFBQmJLZU9aVHUlMkJQdXczQkZkdmg4VWR0dTBWZEhyRDVyWXFLZTVkbDBEMkVNbFFkeGVMTGM1encxU3BwS3R2T3hOTU5qaU0lMkJ3ZVglMkZGUGs4JTJCJTJGY0JjTnVLRjgxZDF0VUpramxod3hQbVo4M3E4V3EyUUUwVkoxeXY3emdPaE0wanV5RUthaVRZVWV5SkhMUkwlMkZ5b0tjMHV4ZndQOEQ1OVNqcmFucCUyRlJuYTYlMkZYbG52c2E1OWNSdkFVVDhmd1FwU2dzRmg3cDdqYk5zeCUyQmM3OURWOUN5JTJGNlU0SWtzMTBLU0VxZzJyc2JoV1FVZ2JlREFKc3lOWXB2dVJJcE5RZzh1YUxQRSUyRm4lMkIzM002OE1nUVl1NE1JOCUyQkE0ZEZIdXklMkJCckU2SmdJS2g3MlRwTGU4aTR5S3FLZng1WlRFWEI0VXIyakxRcFRQZWhBZjB5RWk2R3VpcVk4TlRiZHBmVzNqUHRjUGU5S0YlMkJCWWNtQ3RzYnl5U0VIZjdBdDlMS0FKa3AlMkJ4MzRzdWhqTG9aeTRUSE9Jb0ZJTCUyRjFHVVdENVpSckg5SDR2c2hhazE4d0RPTGpHM1Fkdm03MEFkRldielVOMGMlMkJ2b2wlMkYlMkZWMFclMkZ2c3Z3aDdaYllnOUFNY0FtRG1LZHI4dWs3MmM2VmRRczVicTlUS0dDWGFxcldzWlhGaWhGWWNYbzZiVnQxWVh1ZTdhMEZobmpnT25zT2dKTEtlZzZ6NXhPRGNwWlR0SWpYdGhPdFVneVVvZlFjTEN3d1NtUGElMkZrTCUyQndMZGFMZ3RaVlpMcFNoMTZZNm5Yalg3NzY1ZGg3bWFCNTFRdyUyQlc4Nlc4NmtJYldmQ09BU2lqUlBpWjFqR2VBV2N4RjFhaXNiY0VlYllrZXlpJTJGVmk5Qkw0JTJGWW9taHNCSXp1ZFRDZDg4OVVsSWl1RkhFSFNuNkpwZEZDVGE3T0VKdmpsYlozZ1lSbEx2ZzN6biUyQkFxdjlCVnR4TFYlMkJYWDlxcjBBVnFsUVlGZTdaOWZYY3dkOWc1SVphdDBlaUFMbFJ3JTJCOTJZNG5mT2toJTJCZjJEU0olMkZmNE9RMnZ5dFhNY1BnR0EyUXpKQW93Qk1OWEU5VHNlMGdpR1YlMkZ0SmJxemxMeXNlNzVhbjVYd1E3QjFUNE94WUZpaWExOFo3QmwyYk5sclY3SW0lMkYyN2lhWGFkdnJqQzhZVUQlMkJQWWdwcE4ydnRVZXZCN0lndXRPMEhUbU5oM09HeTVibVRkdWNrJTJCJTJCN0tmbDZXJTJGUU9tWGxBTUJHaTRlQVlDUEdqY0ZQN1NURUEzcSUyQkxVOTZKMHFsaWFiQmR0dDM0Sk1PZ3hVTUlKWGxZJTJCdFQlMkZNTUNOMlJKRnhTdkNTZmxjOTFjJTJGMlpXb3BZeCUyQmNLcGc2cU1uVWxVd1VwT1o0VWFyeHZTRzF2VWhTUGUlMkZLd2olMkZPWXh6alRsaDJKQ1ZVQk5kUWFpSEpmRWZEbERnOElRVFlFS1JhUzlyRm5saGx2SG9mWFFwRFBITiUyQmNxM0FnVWNIcXZvRldibmYybCUyRnpVNzFLWGJHdnlJc21aUFNZcHllR0xUQ2FyVkhpZWNoWHl4c2Y2R2NnejhROGRCMkZXJTJGNTRVeGZMJTJGSFNsaXNuMnluSEFocGIzYVh3MFFhYkJIQ1VKbFQwQ0swViUyQkp4OGwzejh4RnF5N1lDZ0I4UkttdVBJV2ZnMCUyRkZQRU9lbSUyRlByUzAyVkg1QlhtV2RlVTE3JTJCREx1YlJvSHIxTSUyQjBMclV6Um5KaCUyQkF5ekRsa3VEdGxvdkxpRTdTOUcwNjRWeFl3RmdFdnUybGZZRlo4aklPSExwMVUxeGlKclREelR0RUZkSmZiaE1wWWsxYnlrJTJGSkxtVG9ic1JNcXhMZjZEcyUyQk9wREw5SWRRMGw0dmJSd1E4cXp6aldSVWxNbDlqR2NhZUQlMkZzMFJSTnVWMDFrZm1LZHlHM3JHZE1TdkpTRiUyRmpjNmNYMkJCaUtBJTJGNE5NaHJBZm94UjBxSVpNRGUwVFBMT2IxVWFJJTJCNEVPbDRFOEJVODZ2VGRPZHVwOHUlMkY4d1ZzTGZHUnBwNzFTcXA4V1d3TDBsREFzOTJGbXBuJTJCMklKakl6bno3cXlONlhpSGZ2UjJHNThaRmpJeEdDQmtlTkswdjNLczU2RmdISkkxbVFKbElCTEhLcEVQdVM5d1E5c1Q3UFJ3bkNWQ3FLZTFSSm4xSjk3SUJOZ28lMkZGVmlEa2VSakJQV0VNZmZEMTdlNGhrRkZvbFhDYXdDR3BZRWMlMkY0RzBHZTNIZHp1UUtYdFh6U2RLb2F0ciUyRno3OGk2Nzl1bFBDWlVJcjd5YUlQejR4d1h1RU1ST3dMJTJGOEY1TXB5Zjk1MGdaSVZMc3BReGhSSzJ1cjBoWlB6NW9wbURBdGNXd0lmOHIwdiUyQiUyQkxacDRUQnJnRHdBWURNVzgzdnV2RnRadWw3Y1pmSDhaZnZDdlpEWnU1QjVwWXI2ViUyRmJvalNhR0dkUmZSbGloJTJCRVpaR2lUaEtXMUQlMkJWZHZmRnNvWFhNQzNwbElkY2kyNEZ5QVhqJTJCNnRtOWZ6QURBQWVoRWFSQTREMiUyRk4lMkIlMkZ5clRvcDZZbHZCYmMzYThoT28zVk1LSGZVRGozeVZmeEtMWGclMkJKS282aW95ejclMkJITVhiSXBEJTJCdkFXUUg4UzhnTyUyRjdFYXFEZ1pQZlc0QkJxUzJ4SG43YUZNUzhsQzB0blpKZGdYbzFwcWY5cXJKVUxXNWdtTjZCbFclMkIxWlRiMUlVbE9sdUVXcjhHS1MyamNFdlFuaHRTRWJyZHZ6VkcxeU1CM1hsM0MlMkZSMCUyQlZsU0JvaUhqN29ON3p3S1hzZzVyS0x0ZGxBanJqUUx6ViUyQjUxaU5FcEVBWG94czh2S1UlMkJ5MGpHdGFqak5RQmJvbGNCTmlBRkY2eDNVJTJCRkpMbUlmVkJHelNFJTJGRU8lMkJEUFlJT2h5YzElMkZUMXJKNWZxQmVCUHA4cSUyQmxwNFlOT0FOajFTJTJGeXV3WU1PUURyOVBXQ2hzenFkMmlGdjRnTkolMkJFdkFUOUM3V1B4Z1NLayUyRno0RVBTRXIwTkhGZW9KR3U4WU9jY05nUEZVUlYlMkZCWkZsbjNIdUdzR0JpZnhPRkZtQ1FRU2klMkJ2V0ZpdFl3VU9mZWglMkZDMFJiTUpVclNHRXglMkZTdEwzUGwwUzV2blA0QXZjQiUyRldjUGdPUHBzNmclMkZhMEpab2U5YWYlMkYyTlZvRzN2RnZQNVM1NWI1djJpSmdjQSUyRmlHd0VoYzBUV1RkOE1xU2pFenBzZFhhOFBDZ005dzVveEd5Q0pBUGFlaWczNiUyQnZQRkxjJTJCUzUlMkI0VXpLJTJGMXl4JTJGcHNmRlY4SzhEQ08yeXk1ZElQd0NxS0d0a2NYJTJCR3AwU0IzaFYlMkZLeUFEZGRpS2g5VWdxMUYzcDh3R1ExazdyWmN1Mm1UaGRuT1RmalBUM0tXdUVFUkxleHZXZm5yRTdJZkFTNFA5JTJCa0NkaCUyQkZvMWIlMkJkckhGJTJGeEYyVWF5cEkzSDBtT1U5R3FsRUVZNHJ1cTJMb0Y5ODZ2UDNyYmtqbzZmMGlRZ1ZGZlo2JTJCcUNEb3BwN0ZVdTk5aTklMkZ3TWZqSUIxa29sT2hPalVxYUNXZ0gwckhNUU1QSUE3aWIlMkJkJTJGdTI4TDZIczFvcjBiZGxza0g5b0Mzd1BIY3FwaFNLSmpuNlhsR2NVenIyNDNSZU1nTmklMkJqUU1hWWk2T0huZGVXS2FWSTJRVEIlMkZjYXR2bTRvR3RqdTlZRjZJbXAwRUtwbnBkSm84WkNKaWp6M01pcnlzUzd3Q0VhZm1yN3A5VUZ6NGNSQTIlMkZwRDdDVWpvMk5MdGxnU1NBVEh6TDhlQWF5Z1NXWWNOZXIyNUxwZDJhMEVWV254QkJKZlB6V2JNa0dUbFRyRUJGUyUyRkhWWUhNeXRwUjN4RmtETlBMYU56YW93NmU5MkJIQ0xieVgxZjZCcEZ1SWdGUHBDTHpUcUs0aDY4NiUyQlpkV0xiWmg5NUh0WFNMTE1wbTdmRWh6cGdaWENqbSUyQlBQZXJsWXdkeUNRNEhlbkN5ekF3MWphUUxzdlZtNnQ5YlZibHFHMWxhaVg1aElkQTdZYjllUllvWlNXSVlzam5WUU1EZG0zQXBuc1U3eEZnS0g1Y2dqZzUzUHd0cVRlbTRhZTcwY0hQZlQlMkZ2Y3VKbVZaQXJFaGIwSVlyQiUyRmZzMDJvT0Ezb0pQUWlBbSUyQnhObVlXJTJCQmJuQzRDUnBzc0ZnaDRFd05IMmt4S2RUd1dMeDVZMGdFYmlvMERVOXZ5MSUyRjFlNTQ0VHdpWHdqWiUyRldTcjIlMkJ2Nll2ODBDd3NXUkNVNjVadlJWNWZaNDJFRUpZJTJGVlpjakl3MHdZbCUyQmlxOUtIRU5kcTglMkYlMkJwS2RFZnIzZ3RkZmxXNG01M2txUUM0Rk1IclIyMHowWUlZN25aeEM3TzdVJTJGJTJCalp2dVhub1kzVnZ2QXNnNkZvJTJGVXlwNFRIUDQlMkZQWkZzdlliTGpCQXA3WUtrNDBtdmpnTWdQV21RMlBHR1NQbSUyQjRsc3M0ckh1bkxxWVU2ciUyQkNWcCUyQlkxNnFkczRtWFVra1FXSlR6WCUyQjZBazR3NjRrVlVXWm1jbHhnVEhGdWhuaVRhOWkzWUtzY1Z0c2Fnc1JETHF1M1UlMkZldm51ZlpvTnNrcmlRJTJGRlRXR1JkVW9WWmVMclY2UVQyTVBYMzFyJTJCaU43aXl2NW5WOVNyUWJiZkVWbFlheHRqdG05akpKU09GS2hHYmpkYUpTWWZ6OU9Zbm1yTyUyQnl2UiUyQmkwY3J2OFkzTGVOZk8lMkJyTTBEakF4Z2h6dng2eTNmZVBHb3VkYyUyRkZjbWZFJTJCZXdMMmlPaWQ0cm5qWmtOWGYwVEh2aWF5cHE4OEo1NFpaVXglMkZXcDJYQW4yTkglMkJ1UEVpbm92VndXazlQNW1saXBIRlNNQVlVN0s4WVpVbmNVMk9ncnFYb3h6NDE4V3MxYzNLc0hadWslMkZDWU5YTkgzNWo3YnhLM1diY3NtY2hVQldzWkFGZTFRc3VwQWNseHYyQzZTTUR6aEV6dkxhS05ad1ZUZ04yWDdEWDAzdEhZVFI5a0REaTJMWnZERlFlVUkwMGZONzJtTEU4NSUyQlZ3UG40c0FGeENRbDhRQ3VXdDRHTEpBaVBIUGdKazh1WmxjV2E3cU5ST1VZY2VqN0VpTnZvZEFIbDBocmRTVU8wR1h3R1psNEtydXRBVFJpVFclMkZjYSUyQlBtaGp1Zm5FY2N5dGllYzdrc3FqRVRkMkNsVjJ0alFKM0NpRlhuSGMlMkJjWXhQcUJmWHh4Tm8lMkZNUWcwTjR5Z01Sd2ZxemVzZDNwa1N3Tnc3RmI1SjhwblBpZjR5dzB6JTJGWlB6dXQ2T0tDcmxJWnIzMUVWU2xhanp2ZjJtVlZITWV0Nm5pZ2dYYmdmbWVieHFpNUxRNFZ6M0p5c1hUWlZ1RHhlZXB6aTJJSUREMUY2MWp2dWsxdzBmNDNKOGwlMkJ2Vmt0SU0wdXRwQnVUJTJCMVo0cnp5SG1NdjRncW5qTGNZTzBycVBvdmp2NkV3a3VlNTQxSnpVcDRXZVE1bWdqTk9xdHJMaVlubzNUaiUyRmZSRkx5cjlHY1ZqOE1paTlxdDFCTnRPSnlTZmxuN3R2blVkUCUyQldNQnFkUTMlMkJwSXpVRmlIS2o5RCUyQlNUbHRXa1JEVDZLdTVxdiUyQldYQWFJZjVxU3M1UnRoSTNHa3o5TGtqM2RQS2NlSkx6V3k3WkFjRWdCS1U4Y0pBeHM0UEJ3aCUyQlRBbVRWazVFMyUyRjloOSUyQmhiN3pma0NMOHdDUiUyRjZjaERhMHl5RlV6QVdqVUFjeG8ycGJRcnF2WlVXaUZrJTJCaXdoT28wbVpCaUNsVnlMd2x4QlY3eHEzbThINUxXRzRDTHBiVkx2ZW9mYkdUcnA3MDNYYjFJSzdhOEVxJTJGQzc3MWRrSkZLWDl0bE94QklJaHhYYzdTMEE0ejV6TWtDRWs0VXJQVkJ1Um9iQUk3MDhTMXppVFclMkZ3TXd1MmNQVEUyYnA3enlzM0NxJTJGWDhkYzU5M3hVTklsJTJCSFZaJTJGUUpQRmZ6MGxDaVFwOG9GWFliYXBOSHVidjJiUDdvSHp3WXE5QjBLSmZuM1dIcUxiMVd6bUJkMzd3NzMxaXhtdzF1b0tyMW9UNklNOFZzWFhIWEZrdTNzV2JVTU5HUWpacE5EMElYaHZYY2t3eFVoMDFHJTJCZkI5MlJhb2xNUU5vT3ZHejJiZ1pzdEVXNGslMkJRJTJGcnZSSEdZT2NXcmFrVW1XSE43dHY0R1hyZ0ZTTTR1NEhYYnJJMTJRaUlZb3JMRUxyVVFMR3ZxYkJIWUIlMkJ1S3hLV3ozb0JpMXlEVUZnVm1EY3V3eXpCTkRtSHlIRWtIY0xvVGtBSVYlMkZqdlBZeGY4aTZWVGQ4MHNtejE5M0xCY0lVZVcwR3A5TDE5U2JwcWYxT3dFS0p5eFVMSTFnd0doanBZY2twYWhXbHMlMkJwbWNSNUVVOFBTR0ZZcVZ0dXh2VWc3Q1d3eWZDM0JvRzROOXY3azhJdWdUQVVBMDNlS3A0RDlvSW9XclgyUGNSS3JqTEZ3NXdGYUhqVCUyQkJqc2ZKeCUyQkJqVFZyRlVwMU5ScEc2eVA0d3VHRHR2QXFVekQ1MHdqYkhnamVsVHVUY2RFTEl5UlRwdHJNSG40YkdBM1BOUkNLaHprJTJCckMlMkJoNVpJeFhPcmxKY2d6RlRMMUVPaiUyQmZGSFV5MTd4NGhhSThFWCUyQlVrVXdlTFU1OUVTNVI2R1JwZFNKcUt5RWZseVFzYTJObSUyRm5lTURqUER2ZFVwcEhCTiUyQkclMkY2TExtQ21vUFZHTjJpOGJJJTJGU0k3dmlEcDVZRGpQbVBmVVg1Y2YyJTJGZU9UNnNNM2pJV1FsMDZOdHJ0OERtb1RwJTJCaUpYSTZjJTJGNmVlTlpWVXNtUG0lMkZkOWVMbkRpanJtU1ZhRUtoaklpVTN4NzllZWh5NkFTN0xLUW5ZMmVCamdCYjAyVjNrQ2cwcXUxWmVpQ0xuWU1uJTJGZWoweTVSJTJCNVdwN1ZtY0R6R2gyWnA0NlpYdElBTXl6RkVIVFo3RnVGUUtmVzMzdCUyQm9PSnB2Z0VnUmx1ZlJ0dGZwQkZBa2glMkZIYnFzaVVRTG9BZW9BQVdrNDBJZ2pMcjlaWjY3OFU4QjIxbnlYWGhuJTJCTmxTQXJ5OG9LUUQ0TVpTeTFBcWdRdnNNRDFEeGtUVkh2cWMlMkJyaVFDN1pHczRBZFRCdkpkeGR0VG15cEg5SVlJNzc1R3dZNmVLQTJnYmozNVo0QlJjeXlSTlFxN21lY1doWktZQUIyVEtsWTllNmpzOG9QVXNEeWtDNUxPbXl2aU5ESFBWTGo4UEpWZTZvZSUyQko5QTFqTVZ4QmVaUm04ckM1eXhTVnR2YWFpMWZjJTJGRFlkTEg2WFdwSERFJTJCeTJyJTJGUjJrS3M2SkslMkYyZTZXYW9zNm1IRmFCcVBQOXF3Vnl3c1p4UjQlMkJwZGNSYjJ2NGtjSUJTWnJTRE1qMnl4T1liTDRLR2IwR0d0aGhwYkpjaWxhWVl0eE9uZllOeG4lMkJyVXJuWmpyNmZ6JTJGb0FrM1lPd25CQ0F5bHZTYkdQSVc3cHBWaVJJVm5YclF1MWlMY1k5dHhQTU43N1lJYk81NElwcG5uZ3dUeUtvMnZ0c3h5R0J1T2huZElyZk9HaFZyS0QxaklWUUhaT3ppVElZJTJCaGRZT1JmSFhHU2MxelkyJTJGZTI1bFk3UHl2cGJMV3IlMkJxRk5aSUZYYVJDeGNkVHVmWHY5OEphSG0lMkZ4MkEzbU1XbTZyVVhndnFuNzhxdkhyV2FFdlpCeWhkdUxyM3c1RTg3c1JlWENPZ1F5MzZFZUhBdlJCQjVsMVVEZU5jSXFIYnNFVnloRU1FMFg4YTRhb1BmaEFtazhXM1JZTWt0Z0RuYXc4Y3hwSzJaNUZEYndZVVp3a05PYlFnTGR2SCUyRmhqZVJnSjZJZjB1dHVxa1ExbE1kVDJFN1FBa1hGZUIyZXVFTE1KcWNzRk1qWHRZQ1dhNUdVbW1YJTJGM0VVUkVYQmx4MEJqdXdJbDVZQzdOVUQlMkI1WVpleTB1OEx2Y3ptaUtCbVBKeFRIcUhCNlBpOCUyQlhKQ3RkRWZkayUyRmZ4NWlKamVwN0dSSk0wQjc1WkhzYkdrbUF2aUlzOUtqamgxdFhTMWI4MHJkWDdWSFVxYTQ2Y1N5bXJ6VTZJV201TDZoV1g5WEQ5bjhsaElLenZudVlvenVWQlR3VjJXWTBoSXd5TnZPb29LS0pvUGxvTGVpYUxQbzBBeTd3NSUyQm1ZMnM1Z1d0M2FjSXA3V1l1R0UlMkI1N014cFRDOU9ybXBYWHFYUlFxbXdrNU56b1FRWFRGSFglMkJ2SGVwNWUxY3VSWndoYTRuMGY0cU50Tkh3V1RpY1U2QXhtZk5rUTUyTWd6YTVmNlhlWWJYS01OVHA3ejUlMkJrb0FvbUQ1RUhHR3dOcWtwRmh0eEpLRFNQdW9JYVNscXo4VUV1SEFpUHdZNzRRMW80bnBjYXhOS0JaeFBIWmU3WndFc01GZ3E4eHdGMzYlMkJvR0tpdUZKUFlqd3RmS0tFcXVjTHBwWXg2UTFMbG8lMkJYcHR5bSUyRjlVTWQ3OFo5bnM4VmxqVkRncVNRNDF2TkU3b1kzd0FlTUZnT3ZualhuY1dRbWwzR1drTk00aEtxUjBnS3dFaVUxYjlma3JQNVhEWmozVFZuWXBrN3dyMTFEU0Nkb2tTeiUyRjRoT1VOdFl4cGYlMkJBNXlTdGJkZk9nU2UlMkJOTmo3ZHdqVklQVXl1OWlBNGZDdjQ4c3U5angxYXFXYnVQRlJPOGplR0Y2enVNMjRuRDk5anU5S09xSlIydHlhJTJCRDFJNkFFVFlheVo3TGZkZTk3SXprWFN2UXozSHpyJTJCZDRzZFZiRklUdmd4a0MyMFRVNCUyQlUyTnhvRjUycHg5UlZFVyUyQkMwZmpxNGVCdWFtZ1h0N2R3RHFYb0g1OUtKaGZuTThpNFR4eXFuWXZXUEdXNmZHZDUzelJHVWhvTVJ4YVMzdHlpYnBmZXNrVVhleVJRaDg5SVc2UTZISiUyRnk4MmhIS3Z5Y1p5RHNoYnBjSzdBU2ZLY0JieDFlTmFOeiUyQjJwJTJCSWtSZkR4M2YzY2dDNThYOUpUY0l6WXdrdjMxazVDeVpFeVZzcUtXVTFDZ29namVlVWQzTExzNTZUNWMxZzUlMkJIem44NyUyRjlNNnRPWFBKcW02eHZ6aWwydktHMzFQdmRPNWhFMiUyRlRsSTElMkZYaDlabkZTRnQlMkZ5cm1YWDhTTUVhU0EySGk1MlAwSG1VNGZkJTJCVk15RWVTWjNpT3JYVERXYkNDbVZPcWRQVDlQZVZDYVBOSzNZZG9BZWViOG10T0pCRnR2bnI3blZZJTJCTHRZSGppOVVJbFRhZERya3NFNGRmY2w1ZkklMkZBcXVCM0Q2QzFEZTE5WmN5MFQzNktSZUElMkJ2Qk5zc2I3SVE4TmxGSkIzSG9jakFmeVliTnBrTXUwUHBkZk8zbnJ6eiUyQmtwbW9FUGxteldGZjFwamtEelkwSXhDWlVSdWZPZXdmbzFRYzZTZWluUllGbUJHejJNZHdQVnJydUFEQ2NDS2pIWTEzMjRPd0pYYk92SUpVRWNQTXBtbkg2eE0zdUg5N2pTeTZjUHZXdnpIUDBzWU11bGU1R08zUUNVUGFNN05ZUk85S2RNbWcxTjRud0k2WCUyQjNlbk1wTSUyQmdLdlNEN2J6ejZodnd5VXlZTjB5QUptd3NMN296SWE5aXFTNEgzb3RTWGtOOTRQdmFKamt6dkpMcXpia1ZmTDVQbjBQNUVPUThESXklMkY5Nm5CJTJGUHUlMkZtQ0Q4dThOSTNid25NNUhPd3RjWGZrSFRKbSUyRjhuUzQ3ajhHcVJHZGNUUjQ1cENhaWpIZnJPalp0ZnRudTBBeHhRZW9xUXprOFh4dm43NmdVSVR4dTNvTFkzeXpaeVg0ckdLdkVXc2FRRDF5QWZZWSUyRm5JSWlqM0wxZXFaNk1HaGowcDIzdURlekNwUkdSNGY2WUxCSHBybCUyRmpxc1dhY3NPOVJWbkRSaE1lQ2YwSXZ2enYzYUg0NzdHJTJGcE15Z09Vb0wxWXR1TDhHM0FYQXk4b09EUWxEMTdiJTJGcXREcVBBM2xQdWNVakYlMkJpZzAwdVlEJTJCJTJGZ01VZ3Y3ODVQcnpONEwlMkIlMkZlJTJCanliZjZyMlBFZjhiJTJGZnVhNmFLcjZyek1UZiUyRiUyRnVReTElMkJCNnAlMkZuUDduZVA1ZEZNREJreXY2JTJGcTh4JTJGTjRqVUpQJTJGJTJCUTMlMkI5NXRJJTJCbSUyRng1OGpmRUtKJTJGcnNDVzQzTUxDTFJ1ViUyRiUyRm5FMkwlMkJqbjk5OEolMkZXNW42T01zOFhZSG82Znc4RCUyQnE3RjhwJTJGV29pJTJCeTdjOUh5WGNiZnglMkY5OWRQblhmWDMxMyUyRiUyRkd2OVhUZ1dWU2ZZdmYlMkZWTzZoRzBidnJYbCUyQk9mbzgyd2d0dCUyQkVCWDAxOVdCRlA0RzhQJTJGcW9MUnYxdVRQb0NCdUhOYnhPZVUlMkZEVTgzRlBmTkpkODE2WlVoWjdadGFkTHZWdnczaHZsUGg1RiUyRk1XamtFVXB3dk40JTJCejl6ejhQTTI2WnRxZU41bmozQVd5M01BaUc2VEpUM3o5dzglMkJUWjZEbno5MDQ1bmZKUDJkQ25yJTJCbnNabTJINVNock4lMkZ3M2x3cnVmVyUyRjhqQTc5VHJ0b3hkd1kzOSUyQkp5WEg4WUJuS1ZzJTJCdjVmSGZvUFdETFBldmpQQ1BFdlZnMzJiNndhbEliJTJCZWNsZyUyRndFcmh2aCUyRnZtS3clMkY1NFY4N0EwYkZzZU9mNGIlMkZpZ2ZKdiUyRko5SiUyRjM4SiUyQlglMkYwMzRQJTJCRCUyRiUyRmM4WCUyRnhwU3V2eDNTJTJGTCUyRkQ2UU5qRmhNUGswUDVNUXZsandaa3Y4WUljVCUyQnBkckdvSDlMYmY4YkFvaiUyQkJ3Z2clMkJmODlBVVQlMkJJWURJJTJGeExBJTJGeUVDaU1MJTJGRXlXUSUyQnA4cGdjVzYlMkZiTUFQcEtIJTJGaSUyRkolMkJ4OGllVGlOJTJGN2NsRCUyRnFQa1R4QTAwWXdLZiUyRjRURnFTcWRiSHZBRGYlMkJEOEIlM0MlMkZkaWFncmFtJTNFJTNDJTJGbXhmaWxlJTNF+X1mawAAIABJREFUeF7snQm4XFWV71fdzCBkAiEJsy2YCaS1FbFR4oAIaPtMaFEbx2c3qB0UJAlKEugWWsBnt9qvHQIICSTIKCQyEwZNCJnTrYAMr1sBCTKoQObcW+/77bPXqV279pnq1r1VFWrz8d2bW3XO2WcPa/3/a9qlG66/vvzob34jndYZgc4ItPYIfPWrX5Vhw4a1RCeXLVsm999/f0v0pdOJzgh0RiB5BE499VTZf//9W2KIOnKjJaah04nOCGSOwKc+9SnZb7/9zPdK06ZOLV9/ww2ZF3W+0BmBzgg0dwReeOF5GT16r+Z2wj79ggsukHPPPbcl+tLpRGcEOiOQPAIPPPCAHHPMMS0xRBdeeKF84xvfaIm+dDrRGYHOCCSPAKT+6KOPrhCFDevXyMMr7+iM2S42Al8//9syeNAgOe/rZ+xib/baep0vfe08+fFPFkmrEYW5c+fI5o2PvLYm4zXwtqvXbZAvfW2uPHTPz14Db7vrvuKS25fK1FNPl1YjCrNnz5EnNr5iBr5cLkupVMr3e7ksZaybpVK/XEffjDW14PNeq9etW7tK5px9hixZ+qD09PQkzmtXV5eZP3dsdR1krQf/uiLz435Xd715XvRBzZrSfrp9y/s8t5/Roo2emPZ+NePSw3qP9kdDx6Us8X11HMwzesqmn3f+/BY57bOnSIco7Lq6oerNZs69WIYNHSLnndMhCu085R2i0M6z13597xCF9puzUI/bgSgYkuAAlwi4lQxgyQJHNde1KOGQfiY4zXre+rWrZfbZ02XxPctrlqOC3RiYSsmAVQXRLlkMAfoQmVTg7T8sBtYAc4PRnWcp8TOfRK2mbxaYu5/7IF/HOPRsl4Qk9S3x2YZMVIhF34xLNB6VcbGjxHtLWe689RY57TM5iUIVI7JvFf2tBPmKXiYiYnFLviaajHhdFLnGzFBl4NzFoYOY/LdK/zL7Fr+jJX9WUOlC8ie/9tnuuFSsJHn6W9dY2gWVNv6z5l4kQ4cMNR6Faibez3NnBjF6Zt/OQ+W9ojHNOw/1z11frmldcx2i4Ivbzr/7cgQ6RKEvR7f/7t26RGG2PPncplgXoBOwphorNP91lXJZpBtyXQnZX8fzOteZOWMOdB7WrVkls2dMl8V31xKF0KpXLKAYyyWGSd/XdeLjMsUWvhfAvY/2U/vsYrjEXWlJHp8XvbdLgos8O8ZIAcLc1+PC/Tsehf6T0S3xpJlzLpJhQyOi0GntOwIdotC+c9eOPe8QhXactdo+ty5RmCNPPvdqFVF49dUoFKmr1CU9ZQhDZFzs8UKTuqy1FzuQfrd310XP2H3318VA0LXidnfvlK1bthhbZfXzbN8ixFobtuL205KgqJ/FrgOcDh06rOrZ9G/nzp2ydesWM2YGhDboeZC1PP0cOHCgDBu6m7FAK3gn9GjuzK/IknsejOdWV2XIMs5n8fO6uuJ3cUmAb1gMfV89UDoWLvEIEQrtU5l3tYTPvzbUt4jIipRy9tXfkUlhU/psDYFq3LhULPlpz477aT17qUThVytul64ux9TvvOWOHTutWb//BGip1CUDBw6oeSCW4p07d/RfR6qeVJJBgwYGn92MMTIbuqtLBgyoHSc+c4mCsvUTpn1eVq/9z34fv8MnvknuXrwg+NxDj3yv/OnPL/d7n3jgXbcskCMmvanm2eP/6jh58cU/9nufIHa/ffgXsdBEwHaIQr9Pw2v6gR2isGtMfysTBXIU1MoK4B1/0KgocqAZrSyyfP3jMmZsVOHFbdctmi8zvvIPzeiVeeaU9x0vl199U83z16x8UKZ9+D1NG7PD3jRRbr9/dVW/jEdh5nRZfOdyE0GA7jr98x+XX/3n+iiiwI0MCf2e93N3neh96YnCV3ufyxbcKIceNr6GjNx8wzXy7YvOj/quz+zNs13YnNU3EVl8x3IZPmJEHPLEPti+fZu8/11/GR6j3vQtMC5V72w///ndK2TPPYebIbnj5zfnDz1yV8ABE4+R5/7wQr9ulpOOnyI3LPiPmmdu/MMLcuDE5lRwIDH4ld+HQfbBk98tv9/4h34dIx72D5/9uHzv4jnB54Y8Cscc/zFZsWp9v/fzyMMnyMp7awUeHXn9G94mf/zTn/u9Tzxw5dIb5cgjJtY8e+xh75DnX3ip3/s0bNhQefnpDfFzESJfPvv8TjJzv8/Ea/eBHaKwa8x9qxMFg1FKJWMZH3/gKJHImN3/rSTyy7WPydhxtUTh+msWyIzpTSQKx0VEwbWkoxPWrnpIpp00pXlEYcJEufXelWauMFbSlCjccueyOJF56olTZN2qh5oyp0vuWSETJk42HgAdPzqyaMFlcu7Xpvd/n8yCF1n18O/i6oWaH4HXasLBo5u2B9Y9/nsZMWKkGae7blucjyh0d3dXWan3G//OJhCF98hNV/8gnkxegAW58bnnZf8Jf92UScabsHnjr6tAnLrUDpr0Lnnm2ef6vV+nfe4T8v1L5iYThd2GyXmzKpuiWUThzZPHy0NLbwp6rVqBKOj60oFsFaJAf9rRo7D3IW+Rl195NXM/DBIR/qdtzvx29heOmDxeVi6trdaDTHvduEnS090cNHLyR06Qq+b9a80LXL7gWjn9zNnZL9YH34gMH7+qubNPFH65fJV85O9O64MeZN9y0vhD5f5bF8U5ce4Vh731vfLiS3/KvkngGwPfKrLjb3pk4Owu6a7jDnf9bL4ceXitgWH8X71fnn+x/w0M5Ko9suou2WvUqPhtWp0oaAz31m1bZPwBTfQolPAoPCH77DumJvzo2kXzZWaTicKlC26IwLgNDWGC165aIdNOaqJHYcJEue3elXEYDvpzw7o1kUfhruWxtbzZRGHi5CMM+FUyw9hdfeW85hEFEVn9yO9k1Oi9qpKoIcsTDhzdNOK37rFnZMTIaA8aj0JS1aNfP3R7zAJ9udkoovB3IvIvIoLd/XYR+ab1/GwNCOoTjjtWbl70o5pPWoko0DlCoRDSrUAU/GSgVvIoQBRW3Rcut9hrojBCRP5SRDg38JliWt94FFD4XtTdvm98e90gpFgPqr/tehR0Pk8/c45ceuVP26o8al6iwNTta73GjzYgwDGNKOwxbrJAGJrR2pko3PeLFfKBj366GcMmh096k6y+7+YgURh72FHy4kt1hAdeKCLHRYqodGIUX160LbvzOnnrkYfXXLbfhKPl+edfLHq7hnz/qUeWy+v3Gt0GRGG2PLGxYkTYtnVr5FEoPg0NGTdkP6FH+44ZZ+7nJp62ikchXqM2DGXNyhVy8oeaSBTGT5TbH1gdg/AkojDtxCnG+9HvrSRiPAqToj2q4wdhuPqKeXLu2U3yKIjImkefMkRBS8jSN/bAhIOaRxTW/OZpGQlRyJvMHMo87w1R2F1EdhORz4jIx0TkSBEhwwA70O9E5PcicouIXO6tpJOOr/Yo6MfNJQqDZPPGWgscfWs2UQjN28w5F8uwYdXlUZvlUdDQI90cbsWBuojCEGuKZnFNEJFPicjdIrJKRDZaxLkpG3m6oUfuGGZ6FAbbhdwb5cYhyyBl2rPRDz/0iL/tyh4F4Od5uK1FhOPbHu6lRkkiCiT/jTjgCIlyibLbGy2BIcWyEYF6bUcUzporD1nPTD1E4XUikmP7ZU5EnxCFH4vINBEhmna8SD0uhQ5RyJy64Bc4cM2co/DcK5GFnPjsbdvyEQVkLg5BuH5v5G6gZ8s3RDkKJFJXEqpLkkgUMCzhCqU/IZFC2iC6aWAp+s6f6+vwlOOOl8uuujEuZxkVwikJRKHZoUe33bfKJpxHYxbnKNwVVT1Cx3/0hGObG3rkEAX1KjTbo6BEQckLP/EoTDxor4av67y7dN1jv5cRI0ea/VioPCoP0BCk3hCFqSKC0n23iLxZRCAOj9l9zn6/HjeaiCz13ujE446Vny36UVxNQEFcc4lCFHoUAuWFiQJAFwHSy7xsQo/IUQiV+jJEgXMUqHpk3Zbv+uApTclRaLhH4a9E5BAReYeIfEBEDnXcU6eLCG6qxfZnym6BKLz58Ak147fvGzOslUQdsJB7M39TSDKxigZ322uQKBwlIh8mT0VEDragnIwbQGY9rRGhR3da/Ejk7f8TEYJucFbloxjhXjeCKIBLWG4HiAjBLdmBXekjmBp61AuiQLkH7HXABfr4pIhE9VmKt4YTBYwKOKrJ2/upiOBdqAPDdYhC8bnkCkMU5syWJ5/dFNXRF5Ft27Zmhx7tLyIn2E34uIg8Ya2M9XWj5qpl6x+rTWYui1z/06tkxvS/r33KJBF5i7V44gpFQCAw3iMikIS9rAFrT6uDADgQU8BOAaemEoXqDmiOQvM9CvTLAN6SyPo1q+PQI+1v0zwKIrJkqfUo0D1K7pajsNNF85uYo2A9CiNHVTx/YLfNmzc1mSg8I3uSYC2l9PKofuiRG9dVL1GAUP9QRMgoQHnwbxQHBh0IOYr3u3bf+HjL9SgoMOcnSdVpOQo8Q41EOIDxXOxjn8E+3SYi2+sULoMG1elR4GUjr2bUALGgIiwkxGGh9fGgIzcLKq3UHIXAgWupHgWEGxPFIDE5CD4QHL9j8ebvCME6LDp4FB5aemPNKYMMR2GPwhEiQj77P9rJBYkQyzbLjh+TTp9RKICB+0Qk7AiKk5n9Mmw1HgWIydmW9cJ8mdOviMhllvDVs6Y+LiKErdNXm0fnehR0D37xrLky74prdsnQI/ToQSLyZRH5W4vhFokIujcUkpg1zGlEYc/9JsvOndlamqWidbA4dxoi8z/Ft2ZVVxtBFBAXeGapNQLwpk/1AnA612iiAG461oqNv7HeIebw3yzhypq70OcNJQooB/YuNTKwWKGIautl5OpmYaIALviolff3NIDlBXrZPqFHlfKoJpl5i01mTtN9p1ghgdxdISK/FJH7LXOOKqv2qi3f8ITsO2Zs1T3o27UL58vMM7xkZjbiJ+z/gAoYMUQA3YlO4nPiKQmfGGmTrwibgExAUgEmOVOlqoiCrfBDv0zVo2bmKIyfKLfdj/ve6sKyiDlwzeYotAxRmHy4Ab/qKaJf9XoUGH7skkw9U4n8xaBFVAz/zgvdNEfBGTrZsmVzIlEgIGiyiLzNLpv/a5c9SyjvM7M2x9rHnpYRI0YZfJar6pFrLS/qUQBX7i0iuJ15uXdayxJ7BvWMde5mEXFr9LC3Qpa6Ez8wRX628Ic11nvfo0D0BkQAjHugJfGEMjCB7F8I/Il2r95rlRd1i36LNzBr9LzPQ0RBx6vKowCY5sXwGmD+Y0CIq9AyV+Q8v1dEkEusMEJmbrVKpFLwJlfvXI+CAkvt06y5F8vQIZFHQf+WShSoyoll/ud29dNvBB+CDYs3coE+6w7J1cPoSw2tesREEnqIiRUk9192EWBx0gYBQzkDBK4TkWXhziZ6FA49qrY8KqhWQ6KZS9AtYCOn0K/pARIHFMXmGBN9+loLPeKd0avoVPYxvIm9i1OIfQoQLmLJTw496pbh++cjCoRBYXgmufopEbmNUsMF1nroq70hCogTbJqftOIEbILSusCKD3ASWzKpsQ0wjvh4qtFE4SwRuchTXjzzsxZH1YPnGkoUUEQQ8ktsyN98u+DqmNvCRAEl9GOR0s0iZZLzQBcNbu1EFCiPqiUztxKfnZWjgDwHdGtDh5LwiCJHd9Yrg+39NPTINUoCmkx5VD+Z+Q0iMsMiRgAI8luFFDo/XGU+Mlb9HwuCcoKPsEehBZKZlSg45UkJPZoz6wyTzKwn/zbbozB+0uToED976jDzW69HAQj0HSs69LUxcKMvKCcD/8/TVj/6Oxk1aq94/TNW27aEcxSwbXzJLnVdViy3n0B4ROSBPA/M8R2IwsiRo3OEHj10e3yMsR9ak9ejwB75XxawY9V/v2U+7HEUPvjuigylpu/kexT4O5PtE4W/sN6+o21+GoQBoAHGjYp2Rc8GtyNLsFQyoVeJSEFMLrk9CoA+UA+sibAYNDUx9G4DcDLze1iCAKN50ALHAmbCNI/CrLmXyNChg+W8c6ID15jXOPQIyxpWGbfhjgeVsPpV8CII+R1XEJYc3ondgjWnQOIwoUeUR9VTMN0qBLk9CkwoBIvjGLAI0o877OTyk/FEAQPiP28RKF4GPCEQHtzVXvNzFHSdBXMUGk0U2DC421iMtANFhg3YNcqj5k1mNuNtp5Pf11gnFmnvLK9rLWHIIevMVxoRegSOgy8T7QAmgIey7HrT6iUKcGGW+smW84KPECfIM3g9HJ4IOHK82LbkfbkNMfQha5Fi+7JNtCZPI4kChiEw0KkeUUCsIepwvN1Qh9O0oUTh7dY9hGxA9n3VMsE6JrYQUWCB8/LHiwhJOQDGrMx9ZAOyGFkGy8t2hEnbEAVCjzZWTmbOVfGFhY13W48MYlwYE/TSPxNPYhdaEauCM+/LNpDMPNbE2rvJ7cEcBQxmhL1GZeerm0sSNH9BQxjQX/yOgwK3ILl0/DuF5ChR0DKa5mElkbUmR8ELPeLZPIP/ERC48xplcvZe8zDXo2Cx2drVK2OioP2cdkLfJzMDoZCRLIfnnX7GoUclkXJPdDAcc7tw/qV1VT3Cpve/ReTfrREaHYH6xvvMULMEMZRkNXIURo4aJSXpisPvkvYAOgiDt1+4F0xNPh9GrIuzHpjjc3IUONuBlitHQUmCWyI1jSiwHvUIMhTYfzt7mYkDooLrisbThjwKodCjr4nIR6wiZLFgbUMmE8YAG2PvkHOPwtQwdixbfA5xySF/42FWouCOTY1HgQfiPcAkykO1AVbdTctqU2bF9xCAIBKALR3LGfsOUfjuRbPjGHs3V2HWeZfI0MGDaz0Kr6wXwZXrV2Vk8BBarBfMvAwOpkreCQbGv/k7riFYFowLq1yO5nsU3DHMQxRKe4iUcd1Drw+zY8muxKWLwIXS807QexALk84OxnqIJYpA84BXQYmCT477hSgwbiAs4lrJefgnMSdpu+co8JVdOZnZXzp4GwmvYZmxxPAwgOnyhiGlexQONyeaZrW32nBoCuOALeGXWPSjY/Dqa/USBcYBuUqUG0t4od2K9Ms6oUwOwLcsd0f+0jRCEJc117LEcKyxZbQmT6OIAkuYHDRIndsQF4rr2JJEXXCsYpGaQA0lCgwESgCrErL26/1EFBgUkqeJw4XVIfsJifSVDwAP8Mn/Gi+L7GUB5iju1DZEYXZ16NEWashneRRAQyQ84qH3G5ZBjGwQBioPYGgrCJAfJPRo7Lg43t48ghyFawI5CixiwEZWI1yXaup3WfCO1QGDIZsa9Pc9DvKxXpGEewWJQlJ5VPQcwgLrKR73/yiJbDLJiQ1vLlFQ459bHlW9RVkeBZY3qu9dVp4BNzBkqJSm6wyX5o0ztVyDx5cp4HdwHlCK63AuYRChxUTBe3uXKCi3whAE/IGPauSyexnPIBqGiGc8C0Qzs00xiBNuCZ5km2KPrBTQDw97RBRGVx0Ql0QUWO4sFTdyXe+KUwqH2rctxu7NJFeFHi1JKY/68Mo74vAUv+5sElFgXX7O4mKtha4uGbDbEmv09a1ceV4olKPAdb5HAdyI0ZsB/aANNaK6En9jkfF3FgHMDKschgD+jqWLs/lYWHn3ke9RcMFlHHoExcRiBcpQiwOrG03tYhQGBQHHINJhkjXUCrBQpPT3UenVrJbqUYAoDKl4FLjXMR/8mKw4eL3I9+2zia3QRv/YDVBj9YqoxR7XOTFdmoPDbuR7KL0cjYThVfbANf+cjlxEgYIAX7Rxa5Aunk3sBWZnrDJY+yE4fIZZAcIDmoIoHCFSOl2kDMrymht65OYpjDn0HfKCXw+dxc0i4tk0JMsXLLHz4yqQYiRTM6+EcjG2WMTY3fTRbcz70IhNV4Ue2VC1XTlHIbR0SCYmHhNhDCflJ3wwT0stj7rfZOnOkaPAc7AcEeoDaaBhRGQL19vqJQpEyxAXSwQdywTxgkLEKEloJ45BQi7hwGApyAxihb+DiRlHKgdjdbvROiz1HRpFFBAlbE01jGqNBpY69gXwL5gJnYARJ4/VTfvYUKJA5jwTC9CjikZUnKWuVtijgCsdhIEMhfUCEn05gC7A4EEmOLKGRYdcwBrGpshobUMU5syWJ56NTIem6tH2bdnJzCh62Hrt0RXRwmNM2RjzRAR3ZBZa88Zy2YbHZMyYyG6rep2QleuuCYQeAXoIGwU1qpU0NDd4jtCf6CkYM6AEyymxeMwruhb2zs+EBlHQcxRi/UTVo1WBHAWEAkiVcAqeBbjB1bihJPJqudfhWW4XlSiop4OfftUj1nCaRwF1Dach9Q+iwL+1SIPLodUYjQpVCAU8YQi1IWNQwfBFPKg0iAKhR66XiH66OQrAGUA+Q0eoKYYW1DX2Rn5XAxXGEPQQNkq8pnA/uDt9A1PyN2QgqZLcK63iG6FHo0fvXSmPSuiRCb+rLY8KhIT/Mq1mbdpoa0gJYwdhwqtwpnVSZsmIpM85R2H4iCi2L5dHIXSjNI8ChVuIAIHAgtcgBwwuXgSiU3xZmPdFTvrAFLnJy1FISmbmHANAtaYFIDfc6B3wGQNNDC3Mk/4hr8GQRVrIo6CkKiYKWLIJuIbqaWgPg4CiQPD7ViQYFqSC6htUUqDhTVBfWkYHQzkKesk5510iQ0IehZfWizBxoAmbjxQ/BvMkcf9a1xYigcsGJUc/+TsanwkGeIcPqq7pda+qHiERNNuVjEnGB2sgWT1pC0yJArubXQxK8Vrhk5m5J6gVBERDiTMWkAZMHygy5hFTLoSR8SGehjHFosTh1FgzE0zkr8UcBX9OCP9F8CIINWqD7YG71XUth7ZG+jkKk6Q754FrLBnCKMGVGBvYzljNCf0p4oXUPtZLFFhOOMowRGJVwkmGAmProqDgrXB4lhO4BEKBskXxkrJDShE2CdzUiKNrnEFrBFFAuSNHwT4oMc0NY/6IoYVogVvASeBfcBFbJW9rKFEg/gm5B6LQikd5O+J9rxBR4Fp0AuEmNJQPShNkosYgBghFBVFg0aHEGEwIAsYG9ErGwmsbouB7FDZvzq4hz5igA5CtoCTcjchRxgwPEX9Hl2Kh5zM8N+iyFBDuTinnKIwZt19NlcVgMjObj7hqGDhsXMs56g2ZV+YKoA5pUVcbwoy+kvDExoBJg/LABgl6tCZHwYbRrFvNycxe6BEbDH3EpudZCAoMestLIg+Xo/XE/+j0PMlfgCVM5nyfvjqGTj/0CGxW5VEwY1GWaSe+J3iOAlOFAZdhBKhXp5HXbkonFSL+0P0bcplusjQw8NCMR8EmM2tlJkKQ3JOZmUrEAmqdaYNfIi9R13A87omRA9iDIQY5hwyF52vZbD5jCcBR4WcYlJ5OCQohmXn0XntX5d8mVT1inCA+37DLDXGBl51ljihDR7HM8dgG4I2RvRiJmO60oyn1wLXc5VGx9hLL5YawpBEFDKpYlHghiAJGVMh2toM/XULn9SjkkfNgOnAlZB75i+sbS13RlsujwE0p48ImI1GZFlrl7sMx+51jVwTAF+ECMsiBSNI8Cuec920ZMnhQVB7Vz1GgfyANdkRWgxCohwTqjnBjV0C9GVAGGBSX0jT0KFRaNtWjgALFfMCYoGxpmE1JZGMnpzWAOSFgjC9eAO5hzyvQy/zQI+1f0KPARRAAghVBjcwrCp+dS24Epl3iQ3geVB8lRsOCCYrjPdggSBjWBmPP+DneCCUK7ji9lkKPGC74Fu5U+KnmCWJ/vNSGr/CdJPnSqHMU2Booj5MsUGeKwGuA4qJhlPS3XqKAkIcc4DFguYEvCG9H1qI0UBQQCXASoB1jCdYvPud/LFzgUwzokB23NYIosIRRtjSWMkQOI+Y/WSCAIRhshLghAY9+sH3z5p42lCgQEvo+kdIPS1K+qVy/FYv5KHrgGvEJgH6NdQDN8G9NGIERElqjtYHxlGImZMEhK1BggN+U1jZEwclRQM7lqiHPRsSCgFcITxCM8wdW17IB8Dag1DXWDSUPmmIMQZFpmfQlkV+ue0zGjrWR4I6+TjxHAWRLvh5uPeYNtx+LnKZn+bDY2bgaD8hn9A8dQZ8RMJAdctQAUQGhFhMFp0+JVY+wDGA5oEIfpmhFiWxSjFWMAesNNIy+5n/GCSGLedzHG2phxZTO9Q7SVKJg9FRXSUplkXVe1SP6OfWEY4NEQfPBkfHIK2QZ0CIpD5yh07QUfue7WiSSYSNaGys/RAFYQoMocDIzfeT/0DkKqHNqydAHlgkwga2I7MSmB3/Du0BjWIFCTDtwjahr5foMNeGfOJswkjBkSSGWfjIzhu4o/C584JpGw0AGkJssN+6PMYgwXfqDzEW+uo134l0wekFc0ANJRprcOQr1nszMmif6BHxLmABuF9w/btQMewKL1mMlkf3LkcEEbwOEO6n19sA1wtlZTFTSROlD/mF7GBvIFGfQizYlCsQ5DxxY7XOsqnrEg2h5kpIRLiQ8s8qwTsD48TWxOvlfFUlCZ5Uo+OE8fP2c878tQwZViAJ/yzxwjZ2KBR/mp6+IoFELOjdh8FByqrjoNxOMpyEBAdR4FLQCVFZ5VOg6O5R8CO7NbsY1hLJww6aSJpMFQBlSlAi73HPhFz5wDRKIUsJfygLDvIASB4UR+3alE55FnxDAzDFjqtn1CGzeB48IyAoSYTdMyKOwK5/MHJo2ypGSPoMRC689nAuQjq5i6nEogQFCkXmNSGZ2+wQ3VWMuwhnBjH7NERVY9Wr1EgWIATKWJazpQSgilgxKCr5Kfiz/q+Jkm+DZRZFRtTEJIzWCKIB3keeERDFO4DfmShtAAG8QZAJFxVZAgWmoEkQmrTWUKLBPAZzIVKy8OfOrQv0rTBQOEin9m0gZGYFcRYOjMN1a3biMYFEgA4wNADcGlL/BvDJieNuGKHgeBYjC+AMyTmZG7hKzhnkVNIgcx3pOI2ICUy8mauQ8BhkWGRuBBYasBfWB3BI80Jqj4B4ICrhA+08PAAAgAElEQVS8btGC2vKo7oKAyYPEMEtrRAC5KAgL0CvP9IUFBiW8I2wcGmgUweISCvuRhh4Bxk2z5yGtW7UyfOAawfQgSPrEOoKMAIC4HAECNuF3+oVeR5DgjqSvGNEwavE/wpcS7rwHQgRzunMapht6pMOhoUe33LXMVBqiJeUocDu2ItCBR7El4TZ0n24q3+MeGsqIvFOgy/c0FYW/MfyAfNdRr0RBoz7MOQplqfIosFxwxGBK5blwUUI02WohGU9kCv/TZ8QJMg19RH/Zsshd8iyw8wbPoy6JrHmkNkchrTyqOhu1TgsOSd4XWwLTzJbAsEZDtCh0QyeAveGyTB3QBNkc2gKao8A98oUemcMzqnldkkeBvQgxoEoIeIf1hOdUMSUTS6fZE2BKLGFMDHsCsE6OQJISq5coaPIfAINBY+9iJyCEnX0AwWchsP4JbaBfTHoexe96FPw8jsIHrunuosPEOkJR8SVhrmRAlShkZP5pMrNbRUhv/fXzv21qpatHgb9nEgUECQKXWDJoP43V5gYE8jdWGyAY4YaVnDwLdhsILrASXaLA6biDBg2MD/RL9SgQ98ECAiGyWFj9SBWsbBleDCOrkEb0DamC5QaG6jQ/9Egt+YknM6OESEzEfYzFBUHL7zBQfI7sRiVYSDikEO5bCAs/6RNoCQLENXhmcD1bUqlEwSV+rzWPAnIF/gUuQCEA1jEk4qZmygHN4Aamk+F060mn5yhMlO6deXZ6tECYJixFbAWsTliu0J30oajHtF6iAEfGKQY5oIF3UFTwZXgnhmqUEpGBWiAHpQGXZ2ywOyT1tbdEAQ8GDjXEFjKWrQ8Xh/9qQ6GxBZlTxAV9Zlz5LjiYraNxtyEF1lCigLUIcEYnIAr+KZ/VoiH1X4WJAmAW9w+AHxMgngIAJaY+GgPIZGJZ1vgCXGgsPHQEpkqUV4orpt2IAsANQJnrwDWs5aA4LPcsHjYCi89tgGIIGId44MbSmDf0BZ4FCJduXtd6XhJZtu4xk8xMPDvNWKGlLDeYZGbvHAV/ZTC3IDCs+cwVQBuwg7XjFyWRbQGZw4YFAEEaQLoYnwKJWBCFy6/mZSuNvpGjcPJJ700FLqWhXVJ+f0+k/9BJADP0F4RUEaVWSWJtQVjoNwQC3UTf8MKDmqkQxrjbBlG4/YEolk51phKFJXdjKo5a0snM2NVY8ux5DM0ECKAegR4YF/gd7IgMwx7Ik1CtAHH+xrUA5TTPpFv1CLxGG9A1oCpHAf1C2BA2A4YGjAiGxTuhtV386YaTsiK4lr4phgUOAMoZUrYrEck1rSSy2hAFqh6V4jKyhB5NOnjv4HzC84hihpiwbNGDjEcIO/MZ8pitQJQ2PJY3Z8zAuSzJ0NExmqOQ+xwF98W2b98hgwcPkiSiwMAC+omfQiGBbWE94CWNV2U98j0GlZfUamDgYTyH3yqJdAf2UYgoJOUouH0+oCRydjkKf4qKPUUNpskE6h7GmIMVjLWPqwrvRloMF/fIXR41tEDS/gYSwcRGHJfW2WIlFgg96jHlv6oJnksUOGhq4MAB2USBfkJaQGOhxDE+15Wnj3PLm0BxCZrz5rTuHAWQGdVKIAtIFV3pebw1LDrMmVBudj4oxis8rETB98gkEgXen0WNNCGuAjMIpIWAbBYYdJ/G+9NflBW/I6SRiDBW3glTCoIZCeWYMDo5CtHyASOhn9Dt/GTIcLrBY5lOpp/hRxhCKjQdKD306M2yY0fOcmJ2GjWFCH3JsldygjEzw9lXtePrJQp4RnErs7R4d8YC+YqixZqPAkGOYZTR77DsSNLLyg/rLVFArhMXi1KH92q4tQtrmD84sjpZGRSNokBssF3g2niLID++4m8oUUA2MWg8GOMGFS3qbIWJAoYWFjGDA8tlUgG6KCJeGh3AZCP3mUgs4chhlBP/xggD/kqZ1HYjCuhzyMKO7duzPQqao0CsCI1FA8IL8X6sg2wSgDvIje+AkiBdIE3kLkDYuVY9CkZ0E07DKi2JXLdwvszwD1zz1wwLenJJ5LxyxIrVHE5kwHyR0rNdUt7irWzWAPoAZEw/bhApfaFLylurv6ehR+6BYVHoEeVRp+SzcNr+lgaXpIxg+MtytCnpK2SAClsudMCyAApl3YE0+YknzPHGK1FwQ2RdomCAeUnk5IQcBeSYVvJBBWIHVHlKF4nmxUjCd5AtAHLyAnxumLZ9Fy99UCZOOiIOo9f1ds2Cy2vKo2J8QZYiy8CwvCr6JuDkMY/E601uGOpcG1scWwRRaegJCEWorXn0aRk1enR8CBxzm3SOAtczHgB/RAGiC0gRSnFkCuk3S19Ds/BuoCsgMXjmCcRAX/jbhtCjPYbvacKz7vz5LXLaZ06RZcuWydFHw7hFStOmTi1vWL9GqHqU1JKIAp3BwEvINp3nJTgMF3lMp/k3HYKkatI/2A2DCoQBOf2+hJeu16OAERwCwqRp7CzvhSEBpQ9eA9sBNFBeYD5AANZLyA7RMxCGEEZvOFFgkFgFbEJCWQCPeBCUYudQYmk5CnV5FHgm7hi8CAizUCMREAHDrsCC4jYEOqDZO/5aiQLJRLEb1V6X6FFASLFAsLLB+gD5p4qUXspXEcogSpTtG0VKXxYpY0X0dghEAXDJBiHRdcCAyKKUShRYzCAcJQpc4JInUBvIh3hjl5hwa4gOY6u1ejENwGpZC0++Ng9c85cYxjl0FXIFg6vGr7Jf2S4sB4YSDzs/GWJyFjn7LjX0aOxEgVAXbehRODzkQMv0oVDUGJznfvUSBdzZKDGWDA2jNJEUyC9iVTFEgp0wToMjUSYQLf6W5fXoLVFg/BlzIhyQoyhxnKHIUW1waJyCfM4SR76C05D9eogdodzMH3LYV4ANJQogD0xuaH88i7049KwwUdABwViBe4U8JSy3xCegIJGnuM2YVEyCDBxWOMJSkYPIEwYONpXQ2okocOCa5kHmCj1C5mIKxRKPzmGR4RHCdOtb+FhofB+dgQVQc9sUkGA0wtxLWBeNHIW1vzHJzP45Chy4NvMMgv8yGl4FIgMARMQD8m82IAgXNu2f2EgfMX5hkAMFQhjRF1pRzz5OPQoaZ8+Y9epkZs2R0TEC1aKzWWcAM9x9/FQxyfcgDaxL50SxtNAjPApKIJJyFBgWHs1+B+6gTtX2xyORs+RmwZvxqPIZMoPhyit3IQqTJsMmRbp7uo03gRY6mRn5hCMKBz/gH3MS8ggoA7f0G3fycyrQE+gsPCIsL7ZvqEEU8ChoYz63pCT0Mx58W8OuGAtkr9ZG4D70H0gPUdBUGTwwEBaMNfBRxhjDGmIPme22tVQ9Gj5curoGyO1Lfianf/bjyUQhlGjKzdLKozKR4Eq3sR+1aAN4ETxFaBKdo6Ncg9LHOAKYD53bVW8yM5OFmx7mBE7UeuIsMvrEmTdMJMSG9Q9bReZgwKEvWDKhTAymb91yiYKGz6ilvq7QI2YUQYe2h+Hjm8fqwc7Iaa7sC6JQ2lOkjPCiH+wedqyGQGnVCVxCsD58gMhRgqn5DJCMEkT4OMl3dXkUmBAUA0KWCYXyQ5HzYj2kDcoFywnkBiHsBQ4mnaMw5rB3yAsvJEwCEoIQAsbHdVvpJkBxYebGCuijNcgVrmAWJ4yaXQ8AQBA/2yEK4Dg4M1OFHvcTh5EbTCeCEVcsQhRlw/4224iD/ZYi4j2Z1N0te+w3Sbp35k2jrVyPkKYyB1YujSpDx4M3WI557lgvUcAKxNJA0GMcZVuBOxgXotogEMg4IgNYdsgu/s11WSGVvSUKkBbCnLTkPxY4wo7w2moDk2MV1BKp5HmC3VC+mlzNFkGHQAB9d3pDiQIsk73HQ4jJZoCQwZjbmEgWUp7JrCeZWQcE5UcsAd4DrNqYU5H5yFLIAnFkgE0GDNmKp4GFjQmWidfMygACaSei8ORzr8ZgMhdR4H2PK4n8sBwBCXQACwcdg9JmUYE+XYchY8iYwrYZQ/QTa4B5ZrPAWmG314g8uO4J2WfMWBMKomQhd+gRfbPAu7RHl5RP64mQHMQPgIFO1OIfkELAECAJYISRiO+x/ghF8vJm/HMUzJkF5R5ZtypQ9SgBmKb+WVE5go01CCllzbFG6R+omfElhhALhG1pRIGTmbVsalKOgnoUmEYALUYY/6gQusR2Ra7QLXgKcjhHoIXp5RI8CpPfXFVdiL9ffcU8OfdspGd1Q7cAtgkW4KfmRKCewZTI3DToQVg9Mgx8ydbW0tr+c7TqEX/XkrdZhw6CV9E3cF5EFmOBDOVv5KbzOTYIvByaCon9GTEHzkYGozcxKCH2wMHYHnS74FEYMTIqj3rHz2/O51Fw0xT4ff8J75Tn/lCbL020DHINQ4029i6GW0LeUArgS0C3GphZl4QbEm1BQ0YS8u1HkihR0FwA7ZN/jkKevaGubr7LusfCBeknJJDPcBHhpcSAjrGCNyUckoF39YYShRChqosoAMI1iZnOsbroDLPnJA6lvaNPFNxE67qSmfVhoA0Gg93q7hD+7gaFq0LDpQ7Z0aBxiIbj2tdzFApVPQJMIzwJgWJnsAvykgTegyQUsqZQEN8QKd0kUvaAe+HyqNyXMWCB4C+N9lZ1o4+gJcwfjqOuNECkzO5m8UFiGDtc6GwkiFC5QxQIY0Q/IdTSIkMYOmxFGPFQMBjkCD9qVNUjd0LVugX/VXkA34QrIr/yYMt6iQK6G8sb+JICKTyXd0VE4Gzj78g0foJJ0OnkesLdXct+SIb0ligQesT2xJWNzGTOXJKA+EAsgINQZGwX5D76gD6DOxhb3pEQUAiRbzBvKFHA0AD45n86ipLCBQ1mwCABiANAZsVs9YYoMBHkJCnDA0BiVGBQAIu4qtDgyFEIDMoU+YfMQVfAVhNa2xAF5xwFAOXWLVtkfNaBa7wzlm6C2clT0JgUNgJzhhzFW4sOdQ83ZdyGipT27ZLyp3siwILMJuSAjcvCe7/I8lWPyz77jo1P74Uk0K5fdFV6MrM/F8wbxiP6hzDDUs9iB52hR38pUjq3JOXflStVCnifFKJAjoKW96wkMzeIKLj9V28DSJTNyzghaBlf9otDwvxzFFiv69esltkzp4smMxO+NfXEcNUjN/QIMEtYUeisHLAh+VbIGrYIhcuwkudp7jkKfF/ndOEVlwaJAt/h1YFlGEGAG/yb5UQfOTASi32Sp9YlCugiDcH3+wpRGDW6Uh2GPZBUHlWvZRrQA9g96Rd4GvmOrGcJQwpYcm6IJ30lwpLPmUL4MWNJ2BQwBBs1y5K25jdPyahRexkSeteti7OJQgjIJXkUsGLhXtFQdvYdHUIOe5En8VihzOggRJ5OguNCR1D7oUfKvCAs+09ANTW2QRQIKcQAAWlhj0PuXWXrehTUk6C9qIsoMGPEPBGGQgMZQZmTAuMCrxydozDXzz8333TPUdBLM5OZ6xlWdohmh2usI1YcFJ5tIY+CrrXE0CMELGgI9IOQYtVrpYg8/VSUskOk9I8i5UBNbffANTf0aN83HiUvvpRwHCrvyD5nsaC4IEXuSZ0sIPyCSDnl15qRC1oiVpnGzmXcQEg25mJXy1EABDKNGO+yGt8DB2H0w8KEEMxq6Fi2i94/verRROkJJURlPcT2idAesBtTjV7HCk5cPn6nrMyHeokCXUNnY1lDuLsrkuWF0YWG7EKZMG6EVoIpsTSlWd96SxR02DQf0lWgYCW8CxjOIQO45AlTQj8QBso7gdnI6WU8CT9Cn/gVpRpKFDB6YOkFlQDIGRzMhpgracgXJtU/WyawPuoOPeJegFQmB0VJn5AXmPrIp4JRAXrR+ChJvoO6w61F3/hbgnJtG6JQz8nMjBtjhRzFXYZ10s0xBkyAiCALGNl8yyP5e4eWI/RGaBd6hbHmurERUeDANcCkGhYBcIQeZSYzh+QHCJPkdOYOVyDGIRpMmHgWCA16g76wFhFgAA5CZZ3mhh7pCcj8XLuKHAXvHIUccqxRX4nLo1oPDAbddWtXydxZXzFEgYZnJin0yCUK8HNkmx+dxT2QcXgtldsDlCELeZp7jgLfV7wRCj1y74f+YYnhhMLIgQEZnsSyQo7pWQuIDm3wUaYYmYd9ElKB1znU1KOgFbb4TlrVI/ceBCRwNIYecMwyRyfoORQETmDQguzgeMN5ijghYgY/u5ZwxYDPOyo8gSiMHDnaeILuyDqZWTukGeJaSSctRwFjLcoK1kWsMAOKgk8y/IKRyOMiBImXwvIVUmYnHjdFfrbohzVuo3o8CnkWFYsBaxd7FiWG4gKf4trRpkTBr3jE54WJAsoCizlmSYgCg8DDoItucxKNSlgk9hApswpYiRglPvcJ+f4lOMsqG0E3hE8U+Pu7PniKrFilR4XkGZmM7yBV2RmQAgXLCEOs6Zg/LQCu52Rmc5AewhQFDuLkfnmPxwXRaQ1NQDtDFPDSJJ2jkJqjYIekNEikDMMkMBvSoI3dSbC4noWBpIM4MZdIPJAl5lTMDpikIQu2uURB57Fdqx5tfuVVMywYEpANmlMYWlEsIzzeYDRct3y/nvMK0ojCnvtNFgh+PQ1MQQgxuh/HGeAYvU7EgxZTSbtvb4hC6L7gEEQA/WL5IEsxaqCs4MMo06RSeHq/RhGFUP9wuDFW4CDwHQoWMqMeBxQdRnX6yDsQZoZh3y/y1lCiQEcBbiSxEU6JhmWfkuDMAkSuooVzhH32iigwKMTYIR8wlW4RKS0TKSMLiLnDlU3sg6tEQSUEPoOUErzNbUMUrEdBcxSyrKk16wvZDmFA7mKwAaUhW0E9hG0xh0nCA/kLYIHBoneZ98MgCk/ImDHjIqJQKsUW/OsWZpRHTdv0WOQBPOgsvFl4r+gruSnkoNB3dCeGNhg0ORfoE6dp6JGGRJk1QYnNlYGTmesRbHVeo8nMbpJ1VXlUO4ZJyczIKPQCoBzey5DAk1HVLHXwJDKOaYIfEwXCduX3FKda1dtUnaOghKbcI4vmX1aTzOwPA0YrnEHIU+y5WOuBMoBt+op3F4ID1GGpsSSxAWIER54RdRyqLsRz3NAjfS5EIekcBbdvLFdwKTiV6DCWPZWaIC/0gS1B33CQYhNhiWPAR7QAowheQC7TuFYLR1L1aM8RIwy5yyQKoco53DDtwDUNa0P9MmB0KmTwQDHAzpCNAAG+Q4wx8Wmhph4F18ORp+pRneveLEQ8wIAVPf0c4k/kiFrxQgeuERJFtaFCRAEmgmWcXYBCgBZC+/ALQa0reS7R7KrFC9MpAoWZRpl9WOS090ZEIeQJmnXeJTJ0yGCZO2t6nDjWcI8CK5fVSL8Rivyb2AhWKvG0djHUlaPASoZMIRlAZnmIAguNHQOaY6eyu6HT7KDAaSNu6JG7/vMQBfOu7FhQDsAj3vUW8WIipcE6NeOTf4OGWVh4PFAYjvUr5FH44llzZd4V18gLLzwvox2XZb1rvRHXXXDBBTJ37hzZvDF8Yt/eh7xFxr3yqrFaMBUsDV4TI2DIiMBQkoCLosAqDlYqcoKvvlNy6FG3DN+/fqLA/ekj7wK/Q4nxb0KksYSxxNK8Co0mCvSHJUc+B25ntggueSxahAAxluQAJBwAboarL4kChhfGBBxEKg/iwT3rkLEDI9NHfsfVj3jjf9eL23CiwMNgLgh5TYoFqGE+1IFEEWSEH/WKKFBQZ6RIGW8kB1m+3hIFZD0TCAgGSaFMCbehv1jXCFkCBCdY4dqGKMyeIyQza8uKzw7KK+YO4M1CB5mhE7EuQPzIAUjL5MegBUjHCEW7QWT52sdNjkK0z9UtLpJ44FoRIYpOgixQvY/+8mzN7tf7gI7xMnjBEuYchauiM3e7pMsQGf5bu+ohObnJHgVTHtWUkY0aRGHOrDPEJDNDuMoiU0+cEjxwDU6OTRT7GYSB7Qb/JeGWZGU8tswGsgzLPECdv2NcCJ5PEJiPuDyq/UwJ4ML5l2YSBS6B0wFtZtnlBalB7iNTyVtAhbMd8Y4QBksaB58zlZALx/5X1Ts/9IgP83oU9EbYYYGPLGH8N4gNciLIkQCL43ngf+AT+W0YaHBaIVq0ICOYl61i5s4lCmk5Cnrgmht+oZ1KIwpsKZiNxvfjig81Jh03EvHHgAZwLosCj2uoVSUzO5VyGu1RMFbrcmQMR1lRbhkwAENDX+DpVWXbsKpHkASyY/Q4VVYamhSzGztGT4NjcJlprSzEbDOzaFx20kyR0074hHz3otk1VXsY01lzL5GhQwfLeedEJzPTGkYUWHG4hdg9AHrYHyuPiaX/HBPonHOARwFQTmPDuussMfQIJcn7Y3ZkDFjp0GnGSkmTu3iId6A8DTsGyQJK1e+TAxBQHm7oEbdSwjXm0KPkhRcTQo/cZ8KUeXfoO7sWpslORFpo3Wk+05J59IHdjFWTd/AC3H2iQH++fPb58uOfLGo7orDplVcNv2Pvw6XAZkwDggzVR9ynFotC1xOjyrARzohlOU/svy87+ir0SJ/D+yAbkGHoengwyx1rWFrF3kYTBfqB4YWKSDYFx0QuYqhGaUC2WPpJIaC8T18SBUgM2BaRAIEh1N7NOUGhYt3CAocyhtODm/3a5Q0nCu6CYRBZZMgwZAoTiNuDhZnRekUUuDfPRrYxUKAMzJeYKDEcsVGweoOgYFkAS1xXyIyUE+ralSjkOkchbT6Qrch79EPagk+5x7L1j8uYsePisqga6twQomCUniWFCDf0EzoT3aGchBAo1h1hqE5zPQpKYPi5xoQeFSuPmrWmi3zuehTMci51GaJAjsJiDlyzHoVpCeVRuQZViXMNTy3hkjoUQBw8pGwLZBuygoI3cGc8lXmbEgXtiyaC5/Eo+M9gqohqg+8BS7DlutHGqHwM5cgvHH94e5OOeVrzqD1wzeZNdJkchc0y8aC9iuVg5hgIyAKkhbow8GrGGSgFTifKTek6RGHEyFH5cxRCz04jCgwWygqGyFoH/MP4XKMHhJpBJsEfxcG+BuvCiJwzPKoefeIHpsjPFlZCj+rNUXhdVA7ZnHkCe0XmMpmr7KqcXY48BvyTsEUGFqyOkvVLbStRcEGuAstCHgWoKAqB1UcDHeHDgjCwK0AAbgOZaCk/zJZ8F4X2e5HTTq2EHvlzN2vuxTJ0yBBz4JqGS+UiCjYBzASXM5EIXwaGxspiBzB5uEmx3qPY+EnoFOCXvAvovwPMj4Qo3Ft9cIz2N5EoqLeCxYMA5d9IEczNlDdkIjVYj5vRB8ZFQ7noJ8TBO43ZHSf1KPjetFweBb0R/UL400fGhDHTPAS+g9cAdAzRYx4hUQnHklcOXKuUam3X0KOXX6n2/+NIAfvood4AWq3xrDnf/Buvo5sUm0Mexl9J9SgcMFl27qgv9Mjvgx4YxHZgGRLMhyEkqTWaKPAc8jNwWoE1Efosd4yqYElyGrIwU18SBaIu2Apgb0QC4ahungqRl0Rd4nhk+6BDkLe+Ib9PiAJKAE1PJzAN8jsdRK5ivnTKQCbNZ6+JAjfGkzFSpESoOUail0RKs23pZ0QlcfgwKMJlcL+DnlJOZ24bojBntjy5cVNslCkcelREIOT87rL1j8nYseQoRE3BZcOIgtsPNi4sGYGhRkAMf4An75Quv+pR1DlCj1Y03aNw2/2rokgF61VQjwJVj7QlVT3Sz1naGFrAXsAhPWSNz5kL/sfRAo8nnymrQIM7zHGOQjmqFKWhbnk9CqGlQ7oJ9kAcgRAZLUUKLMOyD9djCsHCSYYuJQoGgtrDjdPKo+ZcwplfQ9xBzIBH2F7dplWPciUzh2LvuVkaUYBdETeGGwkgAOPjLAU6A1YCc+JtgEiDNRlQPsd9j0tHs679t6z3HAW9D9Y+jAyUgcLQDDFB7tInnolewKhD7jCghN+JIkFXECqI0deLCqk5cM0N9ylEFAhqxhqvq8x9eXAMoTtQP/5Hy2KIB4nozmEFYn51chTMgWFdXWbh6TzOnHuxDBs6xHgUtK+5iAKhTgSXQ/lZzZh32aU8n8kDjTCw7Bbd0Wh4yA+KjPAozz2uRKFQ1SMdFxQ5ORD0izFhEYGGGAclM3yXnYBrlzEEvdFPFC2SJqEZj8LkCfHZDtq/QkSBYeoSKUPwQGj0DwQEgWIcyFHQSlawaMYzAa8qUdB+tLNHwScKGElxg+K5Y2goGMBywrjG7+xRACM6s1443xflUUNLB0MC062HSSJTWG7Ik1DrC6KAUxKLPFuAJDWMNqTisE1QwGlhR/SRwzRfeYaYx+q2et0G+dJZc+UhW2b2vl+skA98lODvYg0vEYrdj5Shf3BqdAWWLkQcdgdCD/r0wDXtPvFQhKpgHsQVhHGBfxMaAmPNkXnfEKJglKuVs3gOcAehgFCkGFn0pCU8Cbi2U85Q4FZtQxTiZGbAW1f+qkfFll+hby/f8ITxKLhyt1fJzGlPBxQx7wg+rWWJ/qS8qxdvqUTBvR1hPetWrWwJj4IacE2C9eqVJvTIlEe1pw5nEQXeC/mFHCDQAimjsAhDB1uRympE3RVtldCjKLFDiUJWMnOe54B7iSbWKkNMn3uuQdo9Yo+C4VjR4X5FQ4/y9LHId+KTmU151FvktM828MA1OgLeBUMy0WBXTdpHCcC4IMyQBqxd4CMsTFQPAYw7RXFq3sn3KOgX8oYeoTQBJbi1mFD2IPtSs8O5n1Y3cB+OsRdGCJZPOkfBLUGqJ/oWIgr40MiSQWPizsAsaZOUjHanAyAQLOT466HTeBUCLap6NCfeBO5XZs65SIYNHWo8CtpyEQV8VKA5GCCMCyblHkHIzfib0ns9iIJsnoTYUEMUlt5UdQKkjmOiR0E7rVlNUHmUKcxTq4VggoAg0FCkxFywyEBtfmZkYPySzlFIrXqUtAPp00CRUo9IWZPY+K5Wbcqxc3e1qkfuK7P/ODoCGcHvkHWcaUwfS50lBYCslyTwrL4OPdL3gQdiyUf+YQ1j2bP8mPZQCHlfEAU8pIwXUYCQLMQJ5AXoT1RLVk0YiYwAACAASURBVJXPviYK7tzr4USMDbwfI5FKJYzn/BvC4I9dn3gUtP4h5AA3BiAdWcFA5jyErWFEAcTB/+gDPT8BmUbMBQqKRUX+BLH3Ga2diAI5Chrdvn3btuyTmbNevjefl0RM6NEYPSu4crPrFy3IPpm5N8/OuNbkKCy4IS7bGuGWkqxe9WDTPQq33r/SbFiK3gB416+NyqPmOUfBlw2oTryQyC2WP/CHSDyMtRiT6vEwazKz8SY4eScLr0wuj5p3KuF6vvcjyzCj9179qC2PWo5KthK21R8ehbR3w6MwfMQIM4933ZZWHvWhO6qRcyWfJ9WjQHIi1newIl5clWcYaTS5GdZF7D8KQU8Hzzo1tLceBZ6J7KXYAMocIAJZoE9ubBlkxVVOuGSSCEwomVlZaiGiQAwW6AJUAQFQo57NBC8tECnbcwxKm9JPIdaqR74lhEXhEoVCHgVQHN4CWBa/w/Z0F6Dx6RuZOmTy0EB7xJPwHgmJdm7VI3/BZhIFvQBpAkJjMdEv8mghMBrmgylaT+HMueP9cxSU+BX1KOR8XObXXKKgROqLZ86ReVf+tO1yFHyPgr48qSPwPf7Xoh/kKsGfU3I1M8cujSgwliMOOEI4KLERDVAOX4X4gDuJZuGE0SQ81xdEARFN6BHVgtgONEQIFjjc+VmEqz+JAtsUjy3iAxEBSdBQLXQFYxcqNtQnRAGlxcM1mZUAaOQwf89hXGCcG0YUdDGykEhkRcZxlg4NuYtVjbqyKV5RvUX7EIXZ8uRzUegR/23fui3fOQqN2LgJ99Achbi6EN8ri1x3zQKZMR2m1pwWPHCNUqSr++AchQKvqDkK7iVu1SPALwA9qepRgUfV/VU/mZkbgdca4VGou1NO1aNyOcrQY81t27JVJhw0uuE5Cnn7GXsUKI+a98A1s0dwidj4qbTQIyItqLihJYk1KxxXPEwQtYwSxTWeEipe8z5+1SPtT16Pgt6Q0HGwJQnKlIjCYKRFB+gbCjVv0qQShR07dpgwJLcVIgpciGsDkyQx99SzqrOlncw8Y85FspvjUUgtjxpyr0AIcAuRgc7/NNxDhB0RkEf8WM4WylHQccxNFJxnlTBD7xApZ6GhjP65RMFd83V5FHKORdrXdmWPgvveYCIi27AcsRUoO4fjrMh5eqFxTPcoTJKe7ry7PXsy6TfWcQAwhghyLpJaXxAFnsW2JNwZroyrnkJjyGPGMutN+4soIFrwdGCEIZoGPk9+vzYcl3wWKh3QF0ShtFtJyu8tRyWjUFJ4bdMmLzCpDScK+gyYFO55FCq/oxtymirbhyjYqkcmfzD7sKnsndj7bxB6tK9WPbK4h7vWfY5C77tk7uCGHrlJua1QHpUcBQz1hDoP6BoQJzObqkdUiIGPJ1Q9atDwpN6mqjwqeJazNEhxTDlwrT/6peVRozM7oj5t2vyqTDpo794rwDpfoBBRCMWP89w0osDnWLUI86FKEEoT4wx5V73BcL31KPjjhXGG/vSmT65Hwc/nKEwU6pxQ/zKIglY98j+bMedbstuw3eS8c6an5yiwVkkgwdcXyBYyJwoPFiltjbwbWimqyCtAFFbcE7lQ/VYPUSjy7LTv+h4FnddmexTcvbirJDM3as7S7pOeozBRunf2lorUPh3OinxJc4/3FVGAbFHVk3wFjDSE2eeIUjEv0V9EASM5ZQ3xHBCaTdMzNfA0QB6Syt73BVEwHcCVRU4VicsoLw7FKND6jChoHwg9wmNaQGG1D1GIPApmHZTLTY/Pph+/XPeYjNtv/xjgaljUDddc3XSPAiczd/d0mxAVJQuUR22Fqke6XP3QI/17M4nC4qUPyqTJb67JO7n6inmJJzMXEAF1f3XNo0/LiFEjzXyqV23b1q25zlGo+6EZF0ZVj0ZKd0+P3H3bkuyTmUP3yyIKhBIQXxadx9eYpkTBB+RFPQqN6U10l1B51LqqHjWwU6kehdkQhWH5chTQ1iQhJyimesiB+5p+6JELhFuFKLh9ajZRcMeuQxTyb5jkqkeNDT3K36Pom31FFLQfRAqSDEjUSt7KIP1FFOgjtRFI1dEDaukj508Q3UjUTVKf+4woFJ1A7/t9ThTq6F/7EIU58uRzrxrwiyWacxTGHzCqadZUhvrB/3xC9tl3bFWuH6EzNyy6quk5ChAFmmIhxm39apKZm3syM+co6By6ROGWO6PyqLRmEoXYo2At90q2elP1qI5tWXMJycyjRo2WHtsvxqr5OQpReVRaauiRnqMQqnyURRQaMXj+PRrtUWhEHxt2jkIjOmPvoUTBTbDW28+AKOy+m5w3iyjmqOVKZm5g//RWDclR6IN+4VE4YtJ46RoQeTpaxaPgvmqHKOSf+DSPwu5jJ0q5p/EehTy962uiQB9C0YNpfetPokA/CPPEeE8kDRVCSDWiGFjajHSIQp7VFX2nnYgCycxqHa/rwLX8w5L9TZKZ1z0m+44dF1t5TUhIidCj5ucoqEcBUqX6ifKozfYoEHoEmaJfNJOjMGO63HLXMhM5QF85FA7vRzOae46CPh+ycM2Cy3MduNYnfS6JrHr4tzJ69N7x+mcfbNr0qkw6uHmhR2sfe0ZGjhxlPBwQhdM/+3FZtmyZHH00bleR0rSpU8sb1q+Rh1dqJfMKWNKBaiZRaHWPgo5RM0OPOJk51AxRsB4FHcfrbrpVnvtDPWfe9m7b7DV6pJwyjZoGte2y+dfKli05A3F7142aqz829STZe6+ISbsehXlXXiPbtmZVom9wZ8hlHDhATvv8J82Ndc46RCH/OPdXedT8PYq+2R9EoWif+psoUCedMCQajss8no8OUcg/q+1EFPAoRPLWwBDZvr3/Za07shgBXflv0qyJsy2LdHeTzehUd8k/Jb3+JodxDfTyIekXuqF5/YryV+kXhhd+5/81qx6SubO+IkvuIQM/alNPOFbWNYso3PuQvGnipDgPwC2POvtrFeNpryepwA3KnIHxCAeujTKlgdUjY4jCIc0jClr1CIJcKJlZ351F+dQzz5qTdPuz7TZsqOzzetKOndKhKJjubvnd0xQD7P+GUDvoAI2yrX7+U08/Kzu7CwSUNqj7e+6xu4weReBXNdDl326OQoMet8vfJilPp5kvfvqZc+TSNqt69MKLL5l8loq9WO3drt27yO/5vjtg4AAZNYLDNmrb8y9oXZ34WKVA/9zr8j2z1iZee92QIUNk+J6vs2NSecbWrdvklVeJ0C/6rAaMZ0lk79ERSXZbo85RaMSe6RCF/KPYTkRBy6Oq5V4TYk3ByLxOP2fLFAqNDVxXXe3IJuI5Q6/Jufyp0LOce9RzXcViX5YeW3OfMQJkMmYKfuPHJImRrGVU4Do94ZgYe8Ud/NTyqCQza0lS8FpSK+oB1fvkvW7AgAHx+DBWjFtX1wDZ2b1TShEHzNXyPs+/WdJ19EtzYLQ6FD+JCjHrq86iHr25rmtA5BVibu9YcnPyOQoaeqQTX7MAcw1pY7+kpSq1T2zWUDJsY5+afTfDom0GfSuNl9/zUHnUVgHCrdiPVumTzqOGk7WjRyF7F732vlEPUGjGKPlE4dVXN8mT/5NyJHAfdpJzYA79C/eY88rDHn70Mdmxs/+NM/TgjW84yHhr/dbMPk0a/0YZMEB9NiJLbl8qU089XR544AE55phj+nCW8t/6wgsvlNn2wDWuUq9p/LPcY8J/DLCzVZGqLP22eo1a+zXEhe8mXxeRD3R20nWxHjflKqOylTEwdQ4xjQBndHZAah9LUdlLPWeg3uu0D0pUYlxma/Ab0G4JQyOel6ufFmKbQ9VsdUz3wDVDFHqYRybQjrkBv854UBrUnsEQjyPftR4KF1fVc52e7VBFAJ1l2h9zlzSW+m4ullVipfPZ32tMk6qZszuW5DhwzQdLO3fslIGDKsInv0jo3Te1H35/enogC81xA6a9UbNAZtJp2vSV8qi7Dxsmc8+ZLu644R0aYGPyEwWRWc0ZHldHWMVj4xprnQHT8TH9MALEG80czzJXONeZe9pY0sS58e7rClyjPHhJpxxeVT+tYqkizTn6acqe+feso596jw5R6J0saZWr25UotMr4dfpRbARalyjMlic2VupcqZyrAcPFXrfub+tzXR2u+RNNNU5a/ap98PsZ99eQp9pqgnUPSJ4Lbd9cQO96FPTvLtnKc9tGfUcJIWrXJTM+MW3U8/LeJ17jAa9Bs4zzPoYsHHrkg1/DOiyuiphPhAoj4hghOJdFRQTSXsP3LPDSe0TXRoyYFjMsB4xV9cH5u/9y7j1cN2HiM7y+hq/hgZGbz8F98XsqazVuU+u6cccoOjbcjgns2YJzE/poD9uA9LiLumh/fUCq1/PTP5k5RBDyLvC++F6zyFXSu9SS0p6me7DoE0Rh3hXXtNWBa32xXtr9nh2i0O4z2F79b12iEJ2jEKUnRPrPBeT67zTg5MrqpN9DsxX6rhqczPOsvvYNUNo/30rskpyk32Pvg+OJSPyu8URE4FavU1zkE6lqo1clkCZxPFzgFRic5OtqDYYhArV29UqZM/MMWXz38pq7670bOW8uSfEf6BO92OviRKX4mM/cIwF7JuHQ+Lk5rwMDuyFj8TgGiFfN+4WMlM6cFpm/GMg7a03H485bb0kvjxoCbjt3dpsEy2aAOn1mu3gRmjFGOjahZ8+cc7GQ64FHoTKWEfhVopVn48bfybEZgvcrSxSzaJOeXHnF+lLvRo3w7MXz1POQ9H4u0awSwPaZIU9NlkIqNJaOZMu6b8ej0F4ALam3HaKwa8xju7xF6xKFysnMaigsBqYr9ecLXdfVZUJbTDiKhg4B3om1x/KM4dN6rJUoxAC3h2KWTshRHtBvnlffda7lW0FviDjpZwoqi4yHq2/zXqdr3zUM67Ub1q0xVY9comC+54BgE65kE8XzPtOAac4cyHmdGnBD+zSJQPigPI0Y8g6KZ4pep7kJ7jhWrTvPcO6TikaQ0Ur4WtQbHw9mEgV/YH3wWVm8EXvFGq4vwsPdnAL9nXtUBj3fdXTcHxD3Pjw76VkAZ4CnLxAUpNZ7nT8WSeRc8xew3jM++calx4TQwDaLXufnbLj9nDn3Yhk2ZEh8jkIziIy7ppr9/KIKvtn91ed3iELRmWvN73eIQmvOy67aq9YlChWPQiTjIhAYA2LraYgsvE5oaOh3zXzWzaWu/wLXAeY1ZMCX+VUgXYszFH6WNVUXuM7Yq+z3TRKzH9IQWLQKyg0yL/Cs6Lv5+qjejlAkA6FHc2adIeQoMG9usrCOq3qR8j4vDuWIT3rN7meVUdTmSagnKFpalg3ae+o41zzLXX9mndmQ6ay1lXCdC/rj6fM8AiYsW9d/oedkj4u/l9y5dAl7ajIz5VGTgFHYSlzZwEUsqVnW0zzAshHPy7pHnn72QAbicCJH0AVi3kPMs7JgozWY1Sf/HpEgja4NWcBnzb1Yhg4dak5mDo1rFSnBsj8wynx3vUhKvFyXa9HrlKSlKeSoqhYkj1rRUciXMt0QyavuYxT6VfS6WGgExjC0F9i3PRwSRNWCKgLsEr1u02/6XOljynXWyxJVhyjV5I7Qxy+eOUfmtVnVo10VfPXmvTpEoTej17m26Ai0OlHQ93Gtt5oA64aK+Bb12MpvSYJrVa+QjQjUhXQqf6PFlucYA9oE5ISB1uuM3rCGPdWLSX1UwKzVbVTn6L3c6/y/+ZZx18vhdtH3Kuhnqjez+xiFiIfG0e2v3s8dB/dZ5hyFmdMNUfBBsXtv9TDkeV4V5nFyIkPjF+GhiKBUYY6YsUS/6D8Vf/lrzX1mvFaw9Hvrzfc6ha5z3zEUQua+h17vv5s/B1Vj4lS7LHpdaA3R30I5Cs2KZ9f4fddS3mzLrivQfE+H66HQ7zVv7KotD7PmXiJDhwyuOpk5JhRWOEaLK8ql8Mc5/ncoNs5ZZYnzE7guNF55FKCrHHxrhi+kk+6Xto6yQo38e4aJRNjyk7V+0+7FZ18++3z58U8WdXIU8iyUFv5Ohyi08OTsgl1rXaJQCT0KyVUFRnmmxLe0h0BsnvskyWglMe49YkBmw2jUkm/AdtT5+OtJoNr5QhwWHwNYtf85FYMUHPpj41qC/fj6JIBp/u4U18jsowPA9VrXX6D61yQzz4AoLDfGaw3xcgFwKALCnx8NT/KBbGgO9G/mfbSykv2jmTvPWxWcR2e+Qs8OrUd/zLKu0++bdWbydSvh337+hBsWnmftFt0DLoZ114jO0123Lk4uj/qrFbdnVhPSsBoftKmHxI+ri9mUU5os7qRXYlQ76b+03lPBt7uhE59nGWX1d8PVdvxYff/dNAcgdPKxTmISiHU3rg9Ea8cwApk8z08Oj2PINJbSYbb0wQ3zchf1rPMsUTjnjLjqURZo1XdKet8QoK6MQ5WMjNc4c8c7qZCINkZtgjgXZFVn4js7duyUQYFKXEkkJA2I+/kvPsP3BYu/PqM+F096LnJNJ/Qoj7hs/e90iELrz9Gu1MPWJQpzRA9ci4GeB4pdAFNlebbgXD93QZar+6os2BYYK7hNAkox2LNVhFwd7fZHdYTr8fb1BJ4RDRnyDVtBHeMZ1ZKe4b+Xr9v083x97KrKHYzfweYUunPjA8rQPtGqR7fcGZ3MrLqRn/6/XeOe71XRd6qcFBPlBPhroqpPjuGTsVevQhVAj3MkKwa9CLhXqjC6BMCdtySvTGhcIuJYoVIRYYn6lNTn2ANSZcD1vq/Xe16RPHvAX8t+v/Vdc3sUKIdKCIqCVsBXCJT1pUDlpQCXJFG3UsubVF0EBPbV++3YsUPmXvhdGTxoUOxR8IF0IsFxOhWHBnpxkknXpt0zNC4h0O8Lv8RnWbKZ9P3M8q72PZPIU1XfAonVtc/1iFLAo5LYV38hOALji2fNbbsD1/pqXbfzfTtEoZ1nr/363i5EwQfSjLQBVyQDO0Yx36in36sB6fY6N7TEvZfeW4Gs+/z4ADiMW1qR0AG66FC+r/cL9dNN3nXBtpvP6QLU+O8OCQqB4iqCEQCUeg0/QzmiPkFxQbFe6/bR1dehUCJ9N71P1YFr9myHGIRb8qHj5h8Opp4RBcyqJ2vHzEZbW2LpWvLduXAJU8U4Tah4hCld4G/WUcBwndSneBwtwQiNoz82ulZdkmrGwhqGIZVuaBLfc+cwtJZ90ug+Mx5nHSd7RoUfjqfv6K6dO3+ecY5CmtW8YlmvrYLkL0C3w9oRt+MK4ABitIEDB9aUH+XvGuNdg6EsaGWy+c7gwYPMV9zFVfU8ewMFfj5g1b+7II7fXYHi9sG/Xi3kUSnVarej+2/Ae81R8V7cu7uQ3Gv9MU0Ct24/Z8y+yFQ9Ou/rZ+TKf0gaZ//vSfOi71dUpSa9Sz0hXEnX5OmbrwRC45/0bkljsn37jnh9utemhWD513Q8CkVXVGt+v0MUWnNedtVetTJRMCczO7kCWGAVLPl62J0fVz/6OtoFTj6IqtKr1iJr9LsXe666PZb91lLmAtaQLlZMYwCwjaCoAt3d3QakukYvBWdJf9PnqIUakORiD5KwNYRF76UVeXzQqWNlgLit2hPMAQS8Ogm/LkGI+qEl76vLt3L/kEcBPadYzH9eiJS4INz1APmE0bWA+9jPnXufbMZrxklyNmMVmOeqsdakeQdnutjWJbEx6NZ8CSdErfJ8e6q1ixm96pDue/nzp4Dfx94h8uCTJv23/qTvAxgDi11vX3KznP7ZU2TZsmVy9NFHm2VYmjZ1annD+jVCMrN2LH5REXFBSxqRcF+qRvBmxLhnCeokIpJ4XS+f5963qrqRd9+kEJis9ynyeRohyIr3P3v2t8yBaxAFmpYiTQPASd6QPMQkiWjkvtaxkhQB6WlrL+vZ+nloLNOuDVm33PcvMo7u+g49s0MUiuyY1v1uhyi07tzsij1rZaLw+LMvxwDSB/gKLl2g7MpT/dzVEa6Rh+tqAG5CiG6SjA9Z012AFfQCuOEvTugNz9Bzluib9tUFmvq7ftf0y8tVCD3fxWo+mTE6zfSjkqTtkhI3HKjquQlhYDomru5TAEs//nP9WpPMrKFHfn9UzxmCZsfHjIVNDHfJnAGvztlUquNDBChojLaLxyeTPsatEBNKuFfIjz9nOm/aJ/VImNK6gdyImMg4Xgf+pjjDJVCGvLmWf0tK8DZQLCdpLbvVQbV/bg6IPk/v7+4ndx34z04tj/qrFbelHi6VBHz04du3b5fBgwfXyNskS27W/ZIEt7uxQ5u8nuclCYs8eQk+SNVrQv1Iek63U3HIfe/t27bL4CG1Y5pFEPQeZ5/7Ldl9twpRaAdlWO+6aMS7Za2tRjyjnnt0iEI9o9Z613SIQuvNya7co9YlCpWTmRXM6Dy4FeUUwLjx/i7IDBkQfR3rglusxljgXWDkgvY0y6v2TwGeAtkkI4/fjzSDlOtNUTyh9/eBZUwMTMB7dRy8Cwh5XqiP/kFbLtHQa1wA6gJKd6+4QF7/rlWPIAq+oc/11LhEI0QI4mvd8xestTs05zUhOk6YU4isuADarXxl5j8QFeKPUfDfbmJ7QKi468HHOG4f/XXiYj2fPOpjQrjFJ0j6DJ8guPOk19x12+L0A9fc99MbugSgr0FcFlBLAtr9Key3bdsmQ4YMSX1kf/bTJTM+OfGJQtF+6Xz7G1Gfo2vDXyv6ufYtz3N9oqnP9IlX/CxLovw+6PeT8jF8Ypenb6EN6f+tx7qWQ3tEn+F+liY4/MV1+ldny6Xzr+1UPerPjd4Hz+oQhT4Y1M4tE0egdYmCPUfBsaL62MMcBdBVqXfv13x3AZVaUdWy7IIf83uCpzoqSS22tHYlWTY6hqBihdf7ud93vR76jLI9VJTHucCYZ9QAPjeMyAl74buutR3iFFndo2pKrvXcLTNq+mvj7BPJhQ2bccdayZFLTEJjEoVUVUriu2Os4+J7FCJgWz2O/ryFgKqfT+ICc3cudGxccmnGz7Hy6/Nccujqbj53802q1g7zxhkbzpzofRRHVHIuKgnRLhHyyaf7PPe9zDU2HMrf0C5+8KtqufcIrTEF/+47KNnyyZ4+J7M8qg80k7wEIclU1JIPoFM3js8+88r+UP+SyEySdyCp36FEEu1X2rjoc/x+JF2T1K80ABt/lhBipZ+7oUf6t29//1L5798+bcPRSBar3gi8o7rVfGBT2WyV63QBVllu7EC587r/uDEy8yt/XzW1bj+3bNkaH12vlhFd0Lr5qXygcY56o0rcY3WfKn2Nwg9dl5y+IwJl5lf/XvYbu6+5nbvRZs69SF7dtKWqv/6z3U3vChB3TKqFsoZCVvJY3IoLjCEk9DsXnGMu0/HpeBTySoTW/l6HKLT2/OxqvWtdolB9MvO2rVvl5A+/L3ioWNLBppF8DFfZc+fRCS0PlgA395GyXH71jbLXXq+P5a7e/O47fi7//q8XJT6rUuwj/Ywzt79+v9Mipd/ytqNkzj9fUrM0H334v2TmV78Y/72mHzZ/Ie25ecdQH+L288CDDpHv/ejKqpwJ36Og+tEFwUnGMtV1/DQA1sbMK9B19aviAxej5cFLLjD2+8E7+kTTJSWhXIc4DMqe9q3fd8mXf52Opf8+rr6Px1tDkPxzP7zVYPrtnOml3gPzTs4p0i5pcfMyDB5ycmgyk5mrN1glMfc9J31SXnzpT1Un7UXxc7a0lHNhTS1gp5RT1PGY4Fe9buVMjOi57/7rt8v3Lp5TU9v/hRdfkvd+6NTg4XC6kON7ec/r7d8hNmt/cYuzOSuHlBz3kU/Lc8+/WPV+7lhEAqnS3HHI8/eksfvbj54o3/jaF4MlOt1kZn3yMcd/TFasWt/vOvHIwyfIyntvCs7b69/wNvnjn/7c733igSuX3ihHHjGxpl9jD3uHPP/CS/3ep2HDhsrLT2+oWmPteI7CMcefLJs2b47fo0KNdCVHHyUdOOoqeHfnuHulakPZ2x76hkPkmp98v2beEJ5vnfJhiSJKa1voeanPqn4NW8U7+d3ef+wx8i/nz6x58I2Lb5dvXvz9KpCUOCZVQqTSu6wx0Yf6c0A1uxX3/KymT6vXbZAvnTVXHlpa+1m/b4jOA+segdYlClF5VLOcy2XZsnmzTDxkL5HIZlVpfmmcukci+8JfrvuNjB23v5VJpfhAzRt+erXMmG4NXD54qdp4KX3XjZfn++47l0SmvO94uezqG2teYO2qFTLtQ++pHTMVQSEwlD0MFYCW8d3DJkyUW+9dWVWZyk1m5nLA6Oc/NVV+tW5dhH6MYCMuyAKzGC35c26/546be5pyLG1NbBIuhOi+HpCa/9Mlctj4ibUy99qr5aJvzq4gsrKehkwfNdPd/qRIEv3lGWZ92oHtsu8Rj7crXbFM6vftNbFnqyy3379GRowYWYU5tm7dIsceNanyDBRVdAZr1CoJK5X3MeyQLlF7vlz9fb2wxLjg7bDjo9Yq12pVFrnjF2tkxMhRpk+5cxRcdsbv+0/4a3nuDy/kWWYN+85Jx79Hbrr6BzX32/jc86Y/zWhULNq88VcS8kIcNOld8syzz/V7t0773Cfk+5fMjZ/rzp0hCrtxMjPnKETW+GYRhTcfPkFW3XtT1fjoOLYCUfAnbsyh7xBIaX83lyi0s0dh70PeIi+/EoGB/mxHTB4vKwMAF+vTHvtNku6dPhrpn96d/JET5Kp5/1rzsMsXXCunn4nS6v8GUXj197+uebBPFNZt+LWc9Y1vGhNuFJKB3omUoUkAZEhJusPjZxV3VPavZHSU6O91XPeGQw6US/89suj67SOnfEFeeeUVKZtKLjY0oqsU6UQTjx4p655SdPCSdPdIT5cNTejpqeM67leWnpLID75zgbzp0DfU9Omjn/wH+fPLLwfGBWVuyzAmjosdw6rxzH/djVf9SEaOGB73qXWJwmx5cuOrBjiil7Zv2ybjDxoVBr39tC2WrX9cxo7bL9aTUdiMyHXXXFUhCv3UF/cxU95/vFy+8KaoX3o2+sPsVgAAIABJREFUg5RkzaoVcjJEIY189GF/IQq33786JlRgi7VrVsqcGWfIknseNE9mDKedNEXWrnyoD3uScOuSyJJ7VsiESYfXYKOF8y+Vc782vf/7ZJ+4+tGnZPTovWKXGOO0dcsWmXDw6KbtgXWPPSPDR4w0Mjsz9Ij38OOr+Nt+49/ZNKLgu5RagSgwJn7IULOJQsj1NmPORbLb0Kg8qm7cd33wlOZ5FJbeFDNkt7+tSBRaxaPAvJ1+5py2O0eh1YgC+3X4/oebyl/NaO1MFO77xQr5wEc/3Yxhk8MnvUlW33dzkCiMPewoefGlPzalX8vuvE7eemQFhGgn9ptwtDz//ItN6dNTjyyX1+81ug2IwhzRqkfgjW3btsqEg5oHkmAED254QvbZd2zFxm0Nx9e3AFG49KobakJn16x8UKad1FyicNt9q8xa05AW9SgsvouTmSNDwbQTp8jaVU0gCiKyZGmFKLhh0FdfMU/OPbt5RGHNo0/JyFGVfcoY4lGYcODophG/tRCF4SPMnGWHHiUEyzWTKPgSt1WIgt+vZhOFkGaaOediGTZsiPEoqMesWR6FRoYeHSUie4oIKeXY/DeKyJN1qmZCj7BC+zkMYw49Sl54sQAIOVhEdhORp6yrsk5juh96xGu1Y45CqxEFPAq7j5so5e7mmOA6RKG+DdohCvnHrZ2IAucoqPFq+/ZtMv6AUU0DSfTjl+sek3H77V91wBtgtxWIAh4Fk6/n4DPjUWgmURg/UW67PyIKmougOQoQBSUQU084tqlEYfzEyVVleCGmi+Zf1jyiUBJZ/chTMmrU6KrE9M2bN8nEg/Ay5N/vjfwmHgVCjxifQlWP3E7sakSB8K9RsDgRKYLnNPQoqk88QLp7uuOE7MJEYYqIvEVECIt8WUT+H4WIRWRNsenX0CNl8FUJuRCFoUNijwJ3bjZR0LeLk5YGDJCiHgVO/PhLERlBjoGIkDXyPTufRfeZ5ij4o57LozBQRDjrjzjGL4vIQSJytYgQmk+aAZ9v85JTMqZXiYI7jx2ikH9PJIUe4UkYfsBk2bkj3aOwu11bfxCR522YaCOyZxpJFJBdLDHkV29a3tCjXcWj8FYR+ZPdlm8UkbEi8gsRebiOQex4FOoYNBG58MILZfZsW/XI1pgnR8F4FIoK7/q6ELwKj8Lr9x1TczrutYvmy8zp/9DAJxW7lQk9uvqmuGynFvgwOQotQBQ0AZjQPhN6NOsMWXznchP2B+iEzPgehf1EhBiHPURkk4jcLSIrRKSAWS7XIOJRmDj5CBPiYziWrbLV9NCjR34nowg9ctqWLZvrIgpAj0NE5EgReZe9H3rhlyLyqIg8KyJ59JcJPRpO6JHIHWknM//6odvNQIZq9OclCsT0gZu255rG9C81MkfhOBGhngEOYTA5IaBYpVG2d4nIby0gyOq2EgX3exqqVZgoULDgEyLCwXdoL7TVVSLyw6xeVH/u5yi4n86ae7EMJfTonOmxpaSZoUcPLb0xWNkiL1Fgfb3ObgJOl7jV/hvo9w2L09kk/23XYJ5odCUKfsjdvm/MCGsAZXxSRP6XiEwWkWE24Qhi8LSI/EREjhWRL1gvQ04l6HoUdC9+8ay5Mu+Ka9qqPGqreRQgCnvun56jgEwAhJP+tsOuo/+y8uJ/im3Lmm83iigAdpFbZEOhWPOs8aSuvxaIAvqIeX2HiEwSkd+LCLLjTSKyj4iQrv3PdcxtYaKA8MJwMNK6QXfW8dCMS9rJo0Ays1bXo+pRXxEFxSQMHXsladiXb3hc9h0zzoyw5idw5sJ1ixb0LkeBDoDmWIiamEon+D/H5oUoXHbVDTWlUZseemQ9Clr+FOzoehQ093bqiVNknRd6hD3t2yLydhEho4Y9uMBisUbuigpRqJw4wdw21aOALdgJPYqWREkgCvXsASIrCAoFSmI8pQFBsDdfJyJLLWHIGtd1j/3ehB5Bqe66dbGclnUyc+iGSUSBKCuiLRQDwRA/bIUxoSCkybE/wMFF5aISBb/MaN7QIwYPJgWIvNAa7lESeBAoegnOQ1FQT+DHIkIdoEqNlvCwukTBLUfFtwsRBQTHeSIyVUQwbbFaoNb/bhFv1qw6n8dEwbol3dwJQxSG9NKjwAQa6Wknsc5KFKFkZvWC5CUKKPgTReRHInKZiHzTdo0QJIjgNBH53yLyIRF5zG6YrKF0PQruWkv1KDB/F9sdiuKnscBZbHyGy4r/YS10GLNlztD4TuhR1oylf56WzJwVenSWiBBBhmzAQkNYG9YuhDFrDpkWBUwUb70lCmNE5KMiMt7KU5TBFZaDFu9NdEWfEwX2QA4wlNb/3oYeoZ+wXlJvCtHFnCLSIIS0a0Tkn0SkKBEsRBR48HsiS1XpTyUpP10urhBzTHK7EQW1jpOj0FehR9htcfbScNjfnqDnSWYeM2acsYRrv/hZVfUoxxxUfYV5Z6HB7s+0Vi32Ay7x2/A6l0R2pluQDFG4+sa4T1qie93qh5ruUbj1vpVVhj+IAh6FJXc9aAobJOUoDLVDArhlW2BYpsxJpSRL0YEOf19zFKrKtHZ1ydVXzut1MnOeQlZJb1FNFCgI0WNyFIqGHmE0JUvqImsE+bld22BwsPm9IvIflohljeja3zxt8iYyqx49vPKOYOlKHpBEFD5gLTOKgQBrZ1tr1322gwhqimNhtQeIozsAfFluc9ej4Ca+5iEKuE8eL4tQp4lFCJDEAofRFxCAolhiXTUTLD7HOk2uPgo4qYU8CvrdQkQBc9b/FZHDLMBUsNkbouAkomufZs29xOQozJ0VeRTYuIVCj2B+7F5kGQMI7WeX83vWBHqDSI6CehT8xOs8RIGQkOMtOPq6FfiP22dgqPsISb/WiP82EXkip8stKfQoKUfBVBVjXFhckAJtoA200AetlwgJSHtERAgzw52Vw6tQCT3iCPuomGcn9ChLzFU+Tw492inDDzhCdu5INlkQBfhmEeEUC2QZghiBSwgSzj5IA3KNhschx3TGHestUcDx+IDdjjix9rayltA7DCL1uO37lCgweOwPBO5fiAibFV94wdYbosDj3ysinxWRd1o9xLY9RkSoVwTpo0I9bvr7C/YrN1FgHHgYjAT3NWyUxdMHrZ2IguYooJM2Ux61D0KPIPgMOfqAhncQEBUihZqjUFWDvlSS6xbNlxn1hB4BMtio/M9D0ZuIc4QG/2NFJaqAxZfSqqoedXUZLwxjliv0CFca3m0spL0k7H4XKTuqOQp8FnsUZkyXxXcvj8N80pKZGRb+R+YSsVtbE653m0SJQhweZcdv0YLL6iYKTCOhU9GJG5FaR6ZgfM4yNOvbrLahR0pgyj09smXLFpl4cLEcBeAjRtE5Nk8T4sV0Qw6IomHKgZkYyrMMXHgU9hw+3ITe3bHk5mSPwq9W3FYVn7djx04ZPDgyJ/tEAeDNmseq+zFvLlV58vN3No6cRXClja6BOKDksnTGSR94j9y08Ac15wPkIQp0Cb2Ei4vBwruAIuBtsMhhLfw/1suw2E402I89DaFJao0gCqUhImVmDz8RuUBoeGYVUyauDSolFojdSg09Ou8SGTp4cHqOAtRY6TGWDjQq7hVM8sRpnWYHUQUcg7NQROZb5JJzL2syM1/3w3yyiAJrjbABonmI8gHQuYVoIQqnisgsq5OpsUC3UQxZzSUK3d0VcF7jUcBHCuDBDXWENUPSsesdlxQWQ0ogglBYUJTfJq4BCfJdkdJlIuW0BQaZ9c5RoP+d0KOsWax83huPgt4F+UAQwvsgac6joRgoNQwjyLYC21R6SxTYlliIIMOsawwc4AyU1A1WFucfpeibfUoUAMfsF6xJeE4xgvybHbwCoKVeooBCZ5si5wlxwNp2vvXAEN2JrEe5I4pRtGkGotC45iYKoIq/sR5klBDavMjCganiBs/RwXYiCoQeAZRMacg6rKlpa529gRGfPQMQjY7TjAz5n7O42b+eHIV9x46LDWrqVUhMZkZn7m49AqE8NIDGVywrJYgcw5omI2OxRO+jRwEgeJwT7BcxUejuiU+qpm+pHgXWzF+JyN85VgQqfhCHkpSQwyChp9D5OZoSBReEuweu6UFjaUQBAgf0Ya6IDlid47lFvrJk6YMycTISu7rV61FArTOtbGdCGcEda62BGTyCxyqP/ZRk5tF77RWTPnpXT9UjDgn4W2sIgSDQL3Ava/xbFnsz5XhTs07OwstBMrOpenTrLXLaZ06RZcuWydFHY6ISKU2bOrW8Yf0awaOQ1FyigPH7O9YNznrXCAv2CvIPxQVGwk3PurvZ6ov3iwhuQAaUcB9eJK31xqPAfRESKAZAI2EDKPZQA6+D9egnRuE0y5wShe3bd8QkCkKFws3jUTDW6H+0JktMHb8SkTtt3OoFdqDIzCV+JmdziYJvqT8HojBUPQrEXvbIu0/4eKU8KswJuolQwcyWt6G4SApA+edskUfhpuABW1lEgUfs3SUyqyeSvURrUVxIDXMoBlYvGxdPEV6tPzPWOUy+/oFrSmKCRIHkc7wG6klgsc+wgh5B7JoU2ByXisgpdoDOFiktFCnzvZSmREEVFRt3V/IooF/xKGqkVs7lk/traUThdeMmSk/OqkesM1JQwLt4s9yD2nD3Il/g+XiuWIfIvrSovN4QBQwzJOuzTVFQyFoi2ljnWIrusc6+okU5+4woMBBsSoQqBhD+jX8cVE4caoEKEvUSBZy2iFfmjfmhK3iKnrFjhQeSRD/UIHNH8nqRlpso6IKnuAHB2OcWiMOF7WBsgCHyMhnyrH2Iwmx5YmNEFACU1JAff2Bjqh4x71+1YajgED26EvJ/rcXuoblevv5x2WcM7q/ooDDVA9cuvFJmnoHZyWnoTTzH/BlgAVNHn7vtM1a3ogcA58S/sRAxOEGcAUSQSD5jcX6nJLKtdoIhCm55VNXxqTkK6CnwBO40BC2bAFYMEkd/MRAKiBAuYBL6pBYQXKd8jmBJWHNKFGKreLksWh51yd0PxiA4jSgAbCFyFCXBnuaDWf6OQw69TjfoHvgxr0MuMfSozvKorC2GDvyBDGZIGT70GbgRssMyABYQSZ4Ubcw5Clr1SPMQN216VSYdvLfsYc9Q4x35Py1kH24H0cLwwfe+Zvt3grXJwBeBJBiUCAZJa4XPUeBmTL6CYYCKSxSQe3QMCy6DhKJicCiIhRGaEB68qzAZLF+8LGF6J1v2g6JbJCKfz+g4ROHGq/6jJvk1r0dBbz8I0m+Pe3cfyVk8g8tRnC+LFRcO8jjN2NUIj4JBHpADBgXFQWgPypSYd35iYcgaHOdF0g5cO+f8b8uQQYNqPQpr1ke+M9xBsCN2Iqs96dhaf64YJPqOmwhknqO5HgUVdnlzFNCXsGRkHxvy3dbVxtrCOwVooyoyHi7WX5EwjNxVjzB7gDRAGNpAkgh5pAZjws7le0gItNTlNt6N7yN88XljVWSzJARF78o5CiwvVDHVGVDg6MgcXC7H6qp8JYkoQOhHHniE8DNPQ7axRfHek3iHF0sdb7+xnJ5QRSw4kFRibAl1AAeHCENviAL9Ramy3XA3EwpF3+D4RLhhxUKMgFeKtD4hCgwc7hhMbYAkOocbGUslFk6ASoFJr4coIM4Qs7jgsbqhh/AqsC2RJYg8cBFrcZ7FcAW6ZIY4N1EADCInkK+AxH/JmYWOMYKFRseIOSNGisWWkuvUPkTBVj2yVWhCOQq6jELrGfAD1wzxTewyeN3AGRBn9gT5Peh4bHCAptCxqCZHYew4sUf3mQPzaMEchQPsWib5gRAiDEI/9XrKZ6BfWCjA4tOWCCAk2MwsQIA8+2KZXYQwV69pjoKeA5Ar9AihRZ8wf6u3g/UHS8a6QeQCwosBZBOwUQiPpYFy0VWEK11WEnmuHETmEIXbH4gOXKNPVIFct3aVzJ45XSAKaujKSxTYk0rqNO8fna98CqlNl7BPYpzJs1+rQo+6u801AwYMqCtHgT5hmef56DHWEgnD2EOoWUKgAX/HQI5NBIiXdPzumkfJBxgV6RN74rRJZj5wtHykHMkoPAQYQ/nfDeZwlwciAq8pwR2H2ihnjPiIGzAR92HcWPNAkbQxU6LA/Tlw7fTPfrx3HgXkPOsQHETDYg+TiSrnJjeAOPgXkIchOuss0lDVI5Q8B+z09mRmBhiLHEXPWIgYaxhcwsnTmksUfOt9Ho+CubdLFDBtoOEREBAFlArMhdWYs6WFHn0dojBksMlR0GZyFB5dH2XBwOgQWm6jT+xIbSg3BJ0mAiDoaKAjQDJB0iShZDSXKPhfzfIogDewEpH/zSORwWxCrA24L8HuDB0hBMxlkQZRmDzxMBk4kBestH3+4u3y0h+BfbbhesIqBNihsQNJmmBsaJidseSwkGgI5k85N0S6wWBg0/zOYoMsIKydhRfyKJz+1dly6fxr277qETIDwQduRI6gt1AOLCUsI4C2AlEpwWlO8yjsNnZC4QdgPEQJIIjV5+rn6AJEmVK2FIAEJxPrla2OZQl81xuigLxCTCBnsbJp5Ar9wGtKyB2ACHmWpxSeDlyfEAVd+2xUOos8Q87AbEjoJIkTppOz1UMU2IaMFcYplDEGhQPt9mMssa4RegSQRCfB3TF45QEf2u3cRIELWETEsCE7MLD8wD48bQxgptSMxBLCROOJYJIp6ZbAdduHKMyWJ5/bFIPMHdu3V3kUkBNgXLA08wc3cm1YyBA4FGqHuWWPMSToCXAIwSaoVPAuS4/9iAbkmqQcBXPgmvUoaBlNdPx11yyQWb5Hgc0NI0EnYHUnZAFLgdtIpGNzMl9YsECP2tChCDzmlBclwx7vBOjTW4QQhXkLrjcAV6tE4fFY/dByOflD7w0vWvQSsXY0rDEMIHHY6CgGChT7Xbvw0VfE9NIQvgZN238zcOxXdJa35tzQI30tE3o0Y7o5mbkoUcDooZXh2avY5HgNtoE2usAcYjEHSGc1iIKeo6DfpV8Lr7y08DkKiC+4FbZcQDfcilAjCC32QQA7QwoOYbhJScKpFGqE+VAeVatrMeQaerSxHG15oALGdqLT0JEso1BDxCL/8S6HGn1hGbIE04IZTNWjEdGBa5knM/sVhvTBrkeBzYlVC7cLGxPFREcXRKfbJ74MQhtFitLF3UvpJvZbUmtkeVT/GeyDj1tiTS4R+wPwibJPaw3xKIBuoc8nRZaE0jdEyviyIApgVQSQZulm7QTky+c+Id+/JFwvAKIwOORReHZ9JIUJ4FVJrAiNCWaF0rRILyU+6TeSl3hL+snAYTXEIpKjXAFE4cG7b4gTdLdt225IDC2LKAAseQTCHvkLg8aqxIYiURGAhCcBL31RoJnbo0C8NegCiyAN7WTn0IwhVlQWOfOYpyHx6DSLDguP7fiu7FFAT/Ha8FAEHE4t5IjKEvQmw4DHMU+sZ2iYU0OP9pskPTuLrpAoZBLOp7reJwrqJgYPgoHh1gh6tgmuavTtRz9yglw1rzZd7/IF18rpZ2aZTSKZy1pHZxMqqaHEKFSUBJECYHLIFkszT+sTooCWBzhp2Q3YPV5SQhuJ50Lw9zFRAGiS0wQuB3+hlghrQF5A9hBhGFD5OyINQFK0FSIK3BwkoSW0SLRiItMaY6iJgLwIuQ1qDU5wHbUPUah4FBgCQJJb9Qgip/aYtCECOOJswdNMmD+kD2MRHgR4KgYkCD4yh5aWzOyWR1WiwM9geVR0AJZPQD6oke3rozAUFR4FjEDk5mDhd1ppcEnK3yxHpfroMPHYXyiJbK4GUepRcPP6IAprVnKOwpRsokAQO8Y83KKYvhFmeA2w8MKiELbEZdEQKgA6F52DjgkfAZk7MT/qUeAyJQXuycw6hkU9CshMeDT8BSIPeQBqgDXZAqhhOBDBEFlpO1oeVcN7+MnYhc5RAMMiN1R9u3VKeEe4HZFbyHas9fTLde6pdR9bCH3DmANeIdrGD3sn9GjkyFFVB8HpWSILyxGMUGKMAYOITcKa0JN+47lEwdF/vGihxl7AG83PpMbJzHvuGSUzp+YoVCczxx4Rc1+XKLAhYex42iCpeK5Y46y3R7oi3PnXPRH+4SXAT6xVrDzoDhgYaxWZx6AmJZz2NVFgMgCcTAK4GKtSVh5PI4iCSXZlMHBfYvaALrJCsTAg6bAqAOBzNojC9y6eE4do4TG13ixJ9ChsWx/F8hAmAwrHfcrE0jDJJ7mHmDhoM4PH5LJyUXhawDelzyGPQt7QIwAYnh/kGnyFTQvDB3DyGd1SvI2SKWIZjIlCWaoO0KvxKKgvnAXDwkdKgHyZK8wMSDL+TvKm3/gunho6iysEaQhq4XoQDErFltPxiUJPT1n+ccb58uOfLGp7jwKu0P/P3p2AW3ZWdcJfpypkICFDBTQTYVDBTAQaGwEb2qDNbLct0K1+n1+LPRFoAzJkUFIJ+oEEtG1bv7YbIjKGeY4MQRCRhCSVEZkFB0BECbOZU3W+57f3Xue+d9+9z9nn3FN1q8J989RTlXv38O53WGv91/qv9VIC9KJlx16y9DPHj87lABNt4EGZpQi6ltuyqEfls02V7UJYz1Px0/okY4DcJ60TKPAlAFLGRb4CJ6RGtjKGk7U4T1Gd3QoU8MtoMHKFtgQUyDuWuonmLR/QFokoZOl6YDTpDOYwowtEly1IfLHHUWnn8M1UvZ4bKABQDEcTJqrAemU8co+XTSe5KClH+gB3yjU81uSIDlO4HW3fAQp1RMHGV7e9TT3iUHSWCbDXbmwPdA+iVjMkAAP2DnvXnHOYq1LmTwkU2POgeheQRj065tjjJkZvvteBa2siCui2XsbTwRufZWfKzvIsEBgWG8uWF6vd3M8i5jhkiGfUqLiujCiUlF05CoMiCoC5dUTX2INqLLNkGf10EgOOUEPzgLq4yf2eUwy4JZzpJt9izzZCOYFCaYR//LprKurRuy4xG3XrOnAtf5c5CuaZ84NNabiIB8b47zU+BstfF3XHEMmTBe6ZHtOqjpc5CsZOVEZ/3/DaV6ypekREGQrPh6esm2yGwFAxe7g2OZnpKS4nYIbfI2m1HOEcSkw8AIEzPP2u+TzUo23btlWGWkaJUI/kKDxwXNs6/LhAEf3IPONjoEuIjXaOBlMSy8ee4bsk13yPJWrMgBZ7Ydp5MRlRME6DTmYuE3Xzw0qgwPAnqwyEzhOwDhU2WDYxMOojU8kn17ckdhh4H4t5QZd00bWf+JjT4u0X1aePzVsetUuI5s8MqkUoLGRf/MEo4pvN6ejT7vO7Eii0x2kw9ajrJQaHJUBxGEwIfmDriiikt743R+Fz19UTZSLyFKIECtPea9XKQufaNLELAIXbb7+9ovkk59LrZkUUXEN/HjKK+D/jejNg7BAYbG+fgSQkwCEgs1tyFHJcbACCVKw0yZTlmKXVmz+jxYQ/KHfWCiOBW8LCZwTYQOC+WOYt3VWP7kzJzBSBpFzK2tKjKyl3ulbjqTGElAS7kpCckf+9asUum3pkijkOTVFZP4PsoywoF07BSk413wXM5uHcIgp08HqoR56NxsTQIfjZmmQmGQqfWkpC1MAydhy5NiThb+lAgZbk1uJBtQkZt7SmkB+ZRuboKI/swKjCIkAhFwSxykai/IFU2670FBJfOL28iHPkV1ePnxsokJ00NyoHZwEXIG3eFlYWnEUlPATl6JwEC9EY1gqLigzpsJD2HaCwPVQ9Sg9vu+JLFuLLnKByg+fv2L2YCSJq9Lm9l15YBpyh4ki3/ziP3Id2JLjV1S697nNx9DE1UNjC09YYcZ05CtYvJZTZrXQBQVU2JdPklRBwkAud2dUkGUH3NjbQ0QouZtWjSr40eRN11aMr+yMK+Lj0CYFFGPhwC9zi12dRPoAhPWq8Do8a1Zb37eMKRY8O2hLjS3bV/WId29OszYZm3EU9umrHFXH+Oc+a5Cjo5zSgAA+bQ7KeHENFJTfJW/MLs7AA2ZsaPQE/2zrmFfd+mnNX1aMTTz61cKLWJeK7qh4ZLnLckNiqzJuykafkiXVEdPl/68y/rTU/Z4/YzqK/nGGGzTPbrLRMZi6ff9NNN1ZAQQfIKlFPS8w0mQLvYxpaanleQk6fda9vbCFOVD/PgpVMFWKH2O2jQumHHIVDDzu8Lo/6x++cXvUoK/i0+fft8qhQEuEL3VDiBsoA0ROUPwdzucktgFRs9o0/kj6sWYvER2R4MAcvIwrtvsybzJzPy3J5AA7EaFIZJ0DyUA90O6JQ9m1dQMGq5CYBFFgf6wQK+c1l1aP82VznKHQJNhUSCBq7FhpU53bBiEI+fghQcK0NAWUTFl7LScOTCjHzUBMyBAgHPdk35IwzEQWHwVUJWVtXzIjeA9fsfga/zKbMFMpD6ZLjmRrLwiL47VqLPFMeLHweJ0TqfKV7D4k4aMuB8Z0vpx+0HqHTn709LnzVG/f5iEJ7OYkKWfqGM52kPIVkh60AKPi5qOPQPToNKNztuOknM3ctd32hb0U5RK5ME6HNO0N2WI/WG0Hte6xLsoUtKMLPLqSE1gsUvFvQkd1N+WA00P3e6+fGiXcLw4BHbghYXjZQGB08itHJEbt+f1xvTFmlOsY7abOKMtgHBmwg+lsPUDCfxoenTRMMTC8dfWA74u9SwANrMkyWyNxAIe/Ek9Ep2p8Rh35YlnvJwx9YS4+OGD1nFOObxzVgIG8IuA4uu8fvS0DBOQpZWvPWW2+NE+esekTEMi7JDrJfEBwQzEb2Z7XP+rzlmi3UN88l9ai6uHH4OEdhTdWjEiig8KBTWFxlk6hsM1pwwoCMjgwDltfhwAEKkjdF81v8kaQe5S0JFqaWR7V25MQINZZAwUPoLxaod3JD43gywvSVwMpmgH9oFPE/xnXFBJQk3+jPDRHlOQppB5XUo3zMNOqRPcg/SpXCxoxc4IGsy4N6edGzGqXr4S94RZVxQzpNziX1KBPAkw7VRT3ibBE1lrvq3QI80g/JcTpAlMvtHVrgAAAgAElEQVRU2bqGzB/rL/2ArvH/SaNFGcIQRAVtY0jlUbcdiV9TN+MnqiaZORWdNc1MsBz0jbHPASUgSwQI8sw6mgn1ieOUn4HYnWZWZkRBfy7543fNdzJzes3bQIHi5NigGK073kFrDGDwOx9ZNh/FA2fwyEcfzZkC4ZCL9IawU5ZMpIyXTT2y+OwbYR0DJyI3hz1efU4ChTKXI09CXhdQAJ8Z26Azl6ryqCXvwiosD2sprCYRhd+94NzJGRgJ9vT37CHnKLTmaub/gvEQlnWe+Q2kdFZN6HkA6tHlH3zrpJ/lZUOBQt5jzZGDusEJI1HHWRnChYaJzCXPCJhZXPf+A9ceFjd8vUuyN70gMexiOzfdD4wiwEBHLDiNpNPKZLCMRBDYroeuRRYeGHHQfjVQKEHonSmi0LU8yABOBs0wENTm0TARdpS77ZBCe1qWwdQD1+75gLjjjiHwcXUvKTHg1B/TyLCkO1GLKDc2sSlndFqHmm1c9nO9QMEYMYTIzXTIg5OMXQqT8rQkOfyyLPQscLVsoFBl8gEDNibPj1AKbWt9S6Tg3iLouYH76lW3Fsh6gUL5OIENnj7GANobwAfI0V98H0kxmCUzPHNhoMD9bWETXJDwyyJG7xjFmCfXIs9DYxiYFBbKB08bgjbLITnvHSGQfQko5DkK9YFrN3aeSkvOE49kguTQruMnDKXlJSBuf5AVDDYRuKQnmS/OBjqjL3B+mXMUjjqm9j5XpbXr3dN5jgIFw2gBBoAA3BKLq2wJFETRhDaA5TaX0oa1GDOpmQu9Lls/aavKoxbR6qtQj574qG4PCmEklEZg+eCMKORTrS1hSd4Olq39yrtQ1O6oLrUWgQnKlEXPkGP5vnMFKGR5VJfnOQrv/sCwA9fcY844OogOgB5zl6Olzl5cKXfOsaw7QB/sw/wwx9MCkwkUci4TmPYduAYoCAKJKHACEVkpx00vbz5jPZ1AQCr5n05xf6fMBQ7Qo4DV9rSLKBzZJDP7RpW2qj3QceCatYydJk2U3ewdRKhIKKcUWdWnD6139w4BCuhQhx9xRGwZNRGFp/aco/CJy98XW9QMbVF9/H/XycyQnbVPSVpDQiXVvc1HWHOZQM+Q66j8VYEEScTArEXAaSJMY1+1IwppOC0aUQAQrHcgBkVFtTKUo3naMnIUOt9ntQl12LgGwuqXiaV/Zhn6spNYJoTJCgVwajLz2ee9pDrAa+GTmfsGB9rjkbAK2V2yfFoCrn1rO0ehNIS//wcfEt/45jz1Wmqh4iA/jklReorEMPK2SqOoDtcZRXxnxhwDCqeecuJk7We/eyMK5YcRopREAgUvhT6HFLRnGcty5XWlMLhIToo4aOvaiMKdHSiUQ5reP+FVxlw2zjZOabYSGlDv0jzlhLjyQ7gZqxuaw8HHnBTjvooLPQ+E4elUEQ5LncICZJJyP1R+rBco5Hu8W9CRIhWWpsgoWl9sqfOwUWAU2iz60dKBAuAsUkarIvJSAoACC4/By3XIHbZBQCHHUO0AmMUYaQJ+uqtx7g8Jdlz2gTfHgx/Iz7i6HXfiw+NrXxsgACxoypM3939HbPn2KHaVCgmBmyNBVZ3MjEeSPiJi9PRRjP9urWDbl4BCdTJzk1BX5SiIKLQsHh5k0ThURXYBn0u74Ah1yYiSKsaAw9DitBTw5p1WQJBtQtRybvZUpQ5A4fuPOnpCU0lqbGcyM/4yS42OZmgDC6xKiZfUmO8gNCB6lrCf/8zaROVKYdGhokw8EKJLKErF1HZRj/RtKlAgRIVPrC0fzGKlX9JZxfPBTW0j6D+Pmn+3gYKlTdDYs2iyNoZ//3oNFJRH1drJzHIU8iyKadSjcuekkc34h3OYE+aWLUitJnjXHcNE1JB7nCV9tMEyopD98c4+oAAgcBiQ7RwKfBlyuUWTAU35dYYt6VBAQJvOmN/EVGPGdZkfqh4dse3IVfTrqjyq08k7rH7fzFzQN0DKv+khfTM2Aoxdp0KXEQVBr2kFAq7+rLyJOspRUY8AhY9OOXCNx22//Wo+RHqnu4CC39svJsseAVI132mS2x61LoVqcfhw3n0GPKAgMxua64ooUPZfu+EbC5VH5egidEwcIwTPbd7WBgrlWK0roqAjoCMhATVxG2bZUisBLcnqsGtIO3zBppU5Ck4WBvZSyAEKBx5wwPSTmecdBNcvASiUr10UKNi4hseQQe3WEAALQPg5p8os+JERhV3GbuvKQTtH3++hccPXZxA4GPiUhLmBhLmzeGdm2QlAnwiMncxNQNKRAr8XcdD+a4HC05+9PV5+J6Qe9S09QpqSMIei6Nk4qTPC34705zX9EYWdcdjxp8Qdt/dHFAh93uXMMWDfAgrAJ4dbynD6vasCxbSttCygwFb0h41JUVAAcKql5N/0ueVHRMwq+blUoFBVsWhi3RBdO8kTeOA5zdJXUE6X96g1iMuMKOSj6Ssh+bVnttbGKFBorZVMjPbcrhcojAir50aMGXS8zqwPHrVMbqaCdebjEaO/GsX4v45rT8iU00r3NaCQeqrPSDIElgm6neVlfQMEpXglgqlLgRoBeV7fXFYMLHY4u9i9gERfRbA8R6GcZwbwW9742jjrjNXhgtHhoxj/xnglV48Vy3Mv2uNvHeTFtw/8TtkeSLQdEiHo6HwhQJvVhhZhKFr7HIU0yq/ecXl/RCETRbnHWZSPHUV8edwPFOgei74LKLBLcGg4LFnOPHIXrACFZFf4+/prr56co5Cf8KTH/3hcswM/aznNfNsq2Ch8lezFPvCX5VGrszGqtJPaEf66KQeuYcnw9cGAxJn1wjHDz5E/p5esM+bYIu2az345Dj8c4q9tNXNa5en0AAXv0HP1EOwFS8TapoMAGKuzTW9yzzxAQZ+AF32pqEe/NAModH14H1Bw7YGjiDPGNb9LZIEazvJRQwoRQpC8OOQmsMozJrRSAoWS6rNoRAFQEMkVTfjvWyJ2DelcazB2W0TBe2xIFgAPMw5XnhMOzjIkRRJ4HkBoQqVp08qjnrW9jiicf07rHIUdsw70nrH820Dh2oiRKOiUuoz1ycxvW3OAnjctAhQ4bNA/UAey7Bc8LH+GAGGzY3ANAQqMSx6Hsg2KKHB9kIFcEZloKO+gK0aeD8cTsRhFIySdibPa8TLunhNx0Ph7O6JQzoFcJh50AtrSzwbsJ4WAPi62Q0yjHh16z1Ni5x27KiFr+PmD4TyKx7aynlQUMi08mPQ+BgFvZKafEMyciX7f5cXp2znLAgr5fM4VRhSvFQOIB0t/6XyEOcaS75t+0vx+8U9fWatirrr2+njGc86LK5rIzIf//PJ4zM8kqarnC20fsotLl0eTYcHazr0gFIS2wIskabJEgFPEze4ACsZFwQ24pd3IC0ERNts0vL9eoFC9l/VqnCwu9E2l94AFDXVRqMP4WaD0gTEm8HqyEvc1oJB5AE5mPvE+3d5URr49yQGENy56YMWyv/PATWvd0PCYzrsvc/7lKGQy86ryqG94TZz5y6tLFo0O3BLjZ+yqLTRKh/FCUPHYM14Y1OY1TwaloFZU8MqSExaHZCBXH8QidV/RMqIwqXi0axyjLaPp5VHtLe8zWJz+TxhFfKOo2FJGFAgx70di78oMZjlTpvhdzIYGSWdEobTNkno09MC1GVZG56/JbkE2EQVd5hBh03W1jCj4XUk/mgYU8jk+lywlrkRoy0bVszPgp/mJrBFlRCHndVpEoXw3wIBSJFUUtY4uZCJieTJFSl2IrWN50WezIgqrgMJ7mhyFaRGFrgGfBhRc/19GEc8Y10BBGOQXRxG3DKD1pBNKdDp5XuRlVe7vsY+Kt7+Oz3ilWZT/+LWvzx1R4HGj7EVACBKhq0XaUg5c63sxSYd+JOxBqdJS4qdCLl+MGH07YtxhmZTlUUU4gOZMyj1r+wVx0IEHTiIKFuUjH/ezcfl6gQJBw0JjrTN6ecGEOXkbWC0duyepR+3kdMMxL1Aglyl5CkJBiUyEZ69z0IkWwSyYDgzAaWChL0fhqPs9NL4+K6JAiki+scBoMbuxK1RlHmm1TFRkc4kocF3rnBCachyv2gQK5fZAPSBGUAcIbvYn/WcoDSnuslDwEKBQUY+OPSnusnNc2au2GOe2SJR/Wy8iCqbROuJNtt3oSZ6c9CtYczxNaOPzKIllAwXjoR/YKQJa6HYUBcDAw2aPiKjxtnIUdvV1qRGFnDihZYaRfYCWl84Dg8zla8+gMMh16itBUyyC3QEU0P1FiYgw4orD1x9lbOF2Dis5H/RSHxhcClDwnRa5RSxSDOERbCaQYEsdCihAy9yYJWJu6ZJ9Dig0/e+jHvm1dayKt+XkbwwajAC2K9COWmTI2A+oivMeuJlDWB64ljqq9xyFSmk11iqeCh3NOC+djzrEwYdaIcpc0IUn02ZOLUYoCLgwtyJxLerRH75utYNN/6ZGFHg87C1rC/hs5yiUQOFbEaPjtsT41l3dZHf7Vt8JXMYZ3ueFa6lH+lRGFCrD3DERTzxtqREFQMEnoaP52561d7ta+8A1ffKnj3pUPoM5xlHFr8eLn5RY15geLHHG+ZQsxl4Tc3LgGm91EVGochQGOLBFVZhdbB2MHiKXr5HzjMVsPOhEgMLysof4IadV3V914NrF71x+RMFo6AzvrUgCdEfudUWx2hNBKOOK+gALAKCmY1D7TmuAQk5uenwXiSjYh5Qmp73n20PTwsp9M5xAAcVn69YtVWJk0rTWTT0i7A6NGFOkwssWDC+Fws+UiCzFDm0/LaJw5vYL4uC7HrT2ZOb1AgUDxHqSpGLy9MvKtHtJ8ilAodpo47pMWbZFgAJnDsOOPs1jHzzxbqOIt47r3HBGJVtlGsMBUGCMlBWP9GtQRAH30+IXC+T5w0XlcfmriNEnI8a3RowOixhDqZQGYEDgmmOKhQdHaFjY1323ry6Pmuvs6c85L17+yjfc6aoeDQXrcFiegMlryHGHjWep8fqUNKCpyczHPyC23L6zwrOcBvSoUnbZTBEZUZbQbNcQsA2FpRmYA2T65NnLBgq5BSkKDAaOTIDZOKlgjJrEHueNNU5dwb7dAhR0rMzqKyeZtuLK55JjJA0oybw7gAIbD1AwNpykFCoageRAXjrzb1sSw30U1aUBBeNDkFnQSPXclCwRnPEmcjC6PmKsY/h2U4qh70tAYXUy801xUk9EoVw+cJK5wsCyhNjUnEEalg0Kqp8v0sqIQt5fAYWOiMKq528d1fN2dnOkroVl/dMHwn4W0dqUqfoR9gCXrwRKC5FBzwNRtDKikDkd+jX1wDV0VnQmjkcg0+C0M/RFrOglFjCh2ufYBRR4GxhMfxAxelUNKsochdTpVdWjM8+Id/9JrZH1c9nUowQK9LtEd8Pbd1BuO0ch+9lV9ahrzdAFWcIVTRFLzFY1vd4LoM46N7Hrue2qR66ZRT1qP4cMMzWcRQK5ppOdw4ZmE+mfXHanWPM7EL2cH31tMFAoD1xrP2xWRMH1vHMQfeZ28szZ2GzcdI67JssLUm68veX6xBsVWaWwl131KCvhGFzv5H2TE8EjIcIgjEU5TFP+u416xGLBWzcAYKINTsi8NmJ0ScR4SlZiAgUHc9mYhf0dZ557QdwV9ehXn1mV/wS21l0eNRdHGyiYNEKnh7zddeBaPmpeoOA+w2SDkMF0qgY587CaV7gFLZQQmVbFZOGqR16Y9dlYHIQzC0NcnBfJorLwGQB+z3VN8gB/yuawSkRibBQAgzB2yUGb1KMp8mzyK15/rYwm+P+pJzMfe1Ls2jmuthf6CZAAMMB5bFa2mOVLRgACmmWejWzADJH7xyDZaKDQN04UGeekvcDQtbzo+bYtsNuAQk/HRk8axfinx7VDgfCH8vClprTdARSIWg4qcsL2y4qtlK61gGhiDEXH+whXSwUKXIScCP6gn+KLoTCyiHnTyBKaH42Ekupp+xpQ8Bn0UlUacgo/u/xcYlYwimNSs6ZVwcHMmkUznbbOyhyFpB65vrPq0fQlW2+8PMhh2rVAMyOdZ5UbGHBoebX6chTw/ntPZp7Vv3l/T8+xNn1TE2Lroh5dfdUVcd5Zz4qLP4i4V8/t0GTmIV2iXu1dQXyyOivitc8szGeV1KPsDxuo6xyFWe+nunnx+QXRUZEHUFAHpFmteXRV9Ug+QPGbafS7vr5xYnEM2ROoWPwK/I90IkoqVhw9yTEOVE8rAQ0oqHqkDUpm7urUEKAAWB8zrp0eSQcQXZD4wXbCkxIiSeezD5TX4P8pXPvknFHEtU3OTQkU8vQ/fVskouA+uRDeRxkYWBEPYUr/FvWTAqDi2bRyWyVQcHCY/8+2cETB6rMRJSCxXvK0R3vtsxGjL9XFIfratIjC8859cRxy8F33TESBZcUjkscl8qgUltQygQK9Kg8BruKIZ7yRX3AKnYpTiIphI89KOi2BQrnOBkUUGBRbI8aquFDwWbAdsOPOtZhYpdmAKR1FLwPv8URatdM2gcIskT3990OAgifIUZBiYopQHTlyORM49Dj44DhTw3NFSbjWEseJhfvKardDerw7Igp972XkYvlwxqBpkMNkcDuqsKeBQsXjYuEJGTOGs4TwlAHcHUCBkSEYyhYXhUlmBh1Bf5l/4osIBhq6PJZLBQrl95s8HfFS7kAIto2Ge8ZrXwIKqh4lb/y2227trHrUtyxEy1DvUJGoRswYlTtnVfmatk+riMLRx06oIK4dFFHoeiiLNpOaprx0tP8oxoTOvxnXCsuct3R9RhTKXACPnBpRGCKQ5r2mDK1ysjRVj/KMggn16Mwz4l0fqHlWSm0+6QnLS2am9zlxAEOefR5zjkIR066WJzOXOSf6O4R61PU88hSri1EuT8KfaQe+9Q3xVZ/+Yhx59zrzIffAvBGF8tnEKpEBLBMZ9kZGxdm56Egc9tMyU/PANc+tTmaeVfWo7MBtt90W+++/f2d51K5BoFChfUYcRUoP6BxFxRZOGh97iRJmMzHkXMtu8jGSC7V2RCGNuEWBgoUFrJho1AE0Pf3V9AMybNIBerdQO6JQgoXBQIHbWwQhmTc0kew5Vq7ZhVqeqNLFdICQnVyJKNQRg7I97/kvrqhHIgrZlhZRsGvwHpAEgRxkPVaW0DjyKJhfGMElUFgv9ci64g1E80cZwCVmCGH18KSKSom45lqaJhPXFVHIByec5440t1xbvIGaeba4KAAbwYbQ4dVnqk26mEDhe+kchWnzM+/v+oDC7XfcEUccf2pVyW1WQ1XkuBUQIicIYXQetEXLfZ7chHzXngQK3smFQXEQLxyB9gtnSNn2NFDYcsCW2PXUXTVQIOtoejHxdqHxopO7CyhwZnk9PA8kstNEvClV0QagkNMBO6PLU71bgQJlRcMLZ3EmDLSA9yWggHqU7aYbb4yT7juMn+0ech71Qu4SsSpAy8aeJ8LXlgEf+7jyqM05CgU9dib1aJYwmfb7PEGUYcT67ag8sOochcKwvNo5Cj/1E8NPolxPPzvunUY9yogCHTbtwLV5u5RFKKhPbTBQaKoepU5dJKJQ9lXgj8gauC3XfGYChUmC+ngcN99001x7oP1QZgaTQ4CKtUeW+RmZLwKe9Oy+MQcUDj8C5BgYUehKNh0SUcgObN0S8fhdNQBgeJN5vHGMNuFvkQR2JTFBX7Avqe52zkAJFMpcgEWBgv4BCrhm2CCoKzxLcnAJGp4le3WaB7ormTnBwmCgYCa5rLKIN4kHsRgA0k68WxhmYJsWUXju838z7nbIwbsnoqB/VqbEFLGt5CUniZsGLiyqBAptz4jHzEs9grWEHVHd8hwPGxeLwdDO08ochTw8z/1DIwqTd0EvXJM6xc3BEtEseETaaZWQig53RRRO/5Vz48JXv+l7NkdhnvmcFlE4+JgTY7yANWF7stks6Vm5V3193dNAIfsh/YWHCdBpU6UXAgr2uTVtww0oWLFmPHC4m5Pdt7x/FLuePa7dcj3P2h1Agf7hyAWiiGGvpw/IFXRuQAHTAoAQZe5quw0ozLPYW9fuS0AhT2bmALz9ttvihHuvPUdhHUMx960iCoBC6Wyjq976xtfFmWesrno098PXcUMVUXjd26pIRzY22jU7Lo8n9x24to73Db21DRTcd83VO2L7WWdEVj1ado4CoJDefHaaiKCIKfpgV2tTj/KaIVWPho7DItcBCtuOvPskT3Ne+t2sd6JGJcUeW3FIGdcECvoyd0Th1ltujQMOPGBwRKH9AZzkwkXCIahFHM2Ag1xOvPFEhl0fnkAhIwk779gZW/fbOjf1KE949Q7Obn3hDLftMq8CaEnAMm0SlpKjoBMyq4E3nQP5hJcNzg0RWy6er3TrVKDwa79ZUY8yomAsf/wJP7/+qkc5SDQrKwT5DSGOFYUop15XC263qUe33npbHHBAff7ivEBh1kaZ5/d9EYW5gcI8L51y7Sb1aH0DOZV6dNxJseuORazb9fXJ3RsFFKbLswXKo2b8nyUtFDsv8OIOFnUHNpLgS/juQaBgTPg4UFdEiXwS3jOWoO7pGqeR3AUVsrraJlBYbE+86EUvinPP3R5lMjPaRdeBa4u9YbG7Mpm5otKwD7ZsqWghzlFol0dd7A2L3TWhHu3cGVu2bq14/wzwPZqj0NF1QOG9f7ZjVVGSqjzqmWfsthwF1oLqscABzr0cWFHfvhhxu+rR3gIUsuqR/phPbZ48ncVW0vS75o4odD1unojCsj5iWcnMohoEP4evEt8UAdtcMgomXd9hHV3fkUChNHLzusERBTfwwstNkLQhgpAHASwweAkUbrvt9th//5WcCY967q/9ZtztbrsxopAaV1hG9QbgB+rqMB6m5Sh83w88JL75rfWkoi0wcM0tCRTakbRNoDB9TF/4whfGeedtj5u+2p2Neo/7Pji+892+MzMXn69Zd/ZSj26/I4641zDq0ax3LPL7Ow1QyI8nauzzRXhYBDLZ13VKUGtwd0dEwSsksaOgSvYW1NVIIMxB3UNrQEfqa5de8ub4kQet42TmRRbRjHv2pYjCqqpHqEdKQ24Mhq9GdVUysx80HvzOk5l3w9z1PbI8mdk1WQXy2quu3HPJzB2dmwCFhtbjkhIocEqKziyTejTvsGeOgjHTl3Q6r5d6NG8/2tdPzlEoKFE33XRjnHyfe2zYHgAUDnMIXEM9Ov2pPxeXXjrlZOauQdhIoFAmmOIXf/0b35zrHAXObhEMHiSeAmWAOfQXkUlLiSjkAGckcZGOFJNURhTGzWEs+WvUo0PuuhJR8POl5SjkS0RFRBPE8KGunu/pAgppnG84UHjASSs5I8137S1AQTWrXz7zBfGyP3r9JvVogISeFlG46zEnzu8BH/DOIZfc6YDCkI9ewjW7CyjoGtGlMpp8JsFRDVjwb2zBTaCwhAlsPSIjCkk9ogOqcxSO37aYUl5SF5N6VK2LLVsmVQLfeNGr4uxnKnewMa0ECpW+zHS3vYB6lBEFNpr28euuqU5mfvcHaka8/i6z6tG8M/DuD34sTn7AAyfl2JeVozBvP9rXV9SjbXevDs6bHLh200115a912oOL9i2BgmhVVfXoF392GFAoPawbCRTywzNPYd4cBRhJyCrtcty2RX3XCRS6vPdzRRQWnc2O+6ZWPXr+i+OQQ+qqRzmfSwcKA79l2oFrGw4UTs1yRSsfc/T9HhY3fH2R41QGDkjPZZvJzOsbv2lA4W7HnVydzLwRbRMoLDbquxMo6BEHkriAanjqpAMHrxiQgrEZUVhsPkug4AmjGMXNt9wcJ95r44HCUaoeNY3BtNfkKFz09lVnD1UHru0Fyczv+fCVq0DVVTuuiPPPeVaVo5BAYW+IKFhj41gBWRe96sJ4/vO6jspebE3Pe5fyqNu2HVnRtvYWoHDN5/4ujjh8W2UozwUUfHwaxBsJFPLQqZyMeYHCvJM47fqlRhSW1LFZ5VEPPmh11aMd13w8vv3tgfX2ltRHj0GB+tEfkUoeVeUZiZS5ST7851eEROKNaA/5kVOrhG+bNsOl/v1nH70yJKrv6cab9ah/KWXLONUleJ/x3PM3IwoDJ2Ja1aPD73nqhq2zTaAwcAJbl+1uoCCqgEWFcpSJzQhzsxx7m0BhsfkscxTSmLz1llvqHIVZg77YKwfdddn1qh4dPUlmTt20t1CPkstenZUUo7iqAgqP2rAxQz0CFCqwV53fNKqpR01EIcdvo4HCCSedsiqPQn83GihkjkKyZcytcxQ2kn7nHIVDD5NB3JRH7YsofPKK900GNA253GEf/+Rn9riCPfyww+K+91afYnW77fY74hOfUqNoz0sVm+FBDzipMuCc5FtWSPiLT30ubr99YGmbQaJr2EV3P3JbHH+cIo51OdX2gWsHH7z6ZOZhT909V5UVj7qqa+2et+6bTy3H6vRnb48LX/XGfYp69I6LLwklSfd0O+KwQ+MnT+MfXt2st7e9+32ByrUR7fjjjo4f/RGntqxuf/O3X4od16pksOcb+fWkf+148dXtqmuvj2c857y44kN1naQP//nl8Zif6Tt6bPf2e3cDhUV7vwkUFhu5VdSjxtPLYProRz5U1d3fNZZMXBue1b8bhVbxzCsPLM3v35KNd1VWwOz76sTkvvvc/6MP/xdxwAEHTpKF8+u++vdfib/8bJ2HpT+a67NlHfzKO9zYJPpf/j7f23VP/TV1y/vKZ6qOc9Ipp64Z7O9+59txzdVXTrzS9XisfGPZ1+ljuth9d7vbofGgBz+ksoGyv9XJzC3q0RUf+2h86xt7PjJvwH7skT8edzv0sFWee3390hf/Jj758Z765Ist67nuetSjH1c5AHPsKiflHXfEJe+7eCPM2qrvP/EYfaqLy1zynnfNph61PfhzjcCSLy6Npa6ymkt+3WKPs8tX5MJiz9hNd525/YK464H1ycxame+xm14512MB0v3227oG8c/1kCVcnGvL2qeX2udRLOEVCz0iy9FMoPUAACAASURBVLXuixGFhT5486a9YgTaQOHb3/lufPwTM45Q3k09P+SQgyvnTFe7fMe1GxL10xd90rd2u+Kqa6tI/Ea0h/7zf1ZFarNd/L4PxZN+4fT4yEc+Eo94xCM2oktr3tmmHrkgwYB/rzK8cfILoFD9uwEK5enJXf+e977saPu+7E8mxLqu9O6XDq+uf+f9mffg/vx3Xx8BIopoMhb1INUR+MbYqH5XXdYYH1PGpbqioLnkOM8az2n35bdWuhNwclhuE1EoqUeTcW1AYc53e57bP1/GfQk02+OclaN631mO/YB/l2tn1nflOpqslQrc1W3Wmi4305Bry2tm9rFYI4OAguvLevKrFtUeEjXluQltgLA3eKJzfDa6L7PG5qztF8RBqEfnrOQo5BRKfE5Bk8lIoiTZ6p+NYuvWxuNQreZaUEnCYVT7/7X3RPWzLgFbvrO9lMrrS2FaKpKkB9lZSuXm2uQlTgO/FOJ575rvG0fs3LXyfdOWtXvJZIAmldR631d7tWuEWeuEteNZgrpNoLCHBM/ma6oRaAOFzWHZN0dgbwYKk6pHsRI5KI2oSnYXxuVMQ2eVYb3i3Z91X74zdWlpRE4M80ZGp44qjbo1xn5xWNs0UDPtvjW6M0FAcu0L8DTr+1YZlB08jEEGZ/u+hrlQabBm3PX5+muvrsuj/slllc5kX8wNnlqHo5X251TQ1bqvhk8N2GrmL+e2ilY1ZWbb9m32d5rBP2TuBoHXQqz0vbcChjPGcei7pn1bFZFq7LsqR8HJzPNWPRosJhfxrrfvKf5/FWptnTpcgQmG7bze/CnvG/ydPRd2Rj0aA3sVJ2jIi5bQz7O2vyQOOvCAVSczD3n1Hrkmv2+RNbMbOriiKJRSq/MVShC0G17Z+cgSJKcQe8Zzzo+Xv+oN+xT1aE+N1+Z7lj8Cm0Bh+WO6EU/cq4HCV79buUpKQzKBwVAvfRpIU4314h1t4899E6Oxtv5XVcgpaTUr1zWJseW97ahH8868vwQjq97ZcV+5Tsp3dtk5bWDV9X19hnCXAT50PEtDO8GUT7kW9ag5R6EdDam+q+RG578TUVTlnAru9Kx/z7hvAuqK57QBQh1RmfHO0n6bdW35jT33jROgFGAl53HVGO2mcembh3LtvP/iKUChzFHwsDJPYU97zsv3pQHXLv25EYJ3zTs32MidRcc6+zxA4cA4T0ShVTrVt5TJu2UUqYyYeAeDOT31WY/Y3zbakPtKKk/XWlr2+3zbkH7OWkOl0X4HmlSTgL2MMenqY19/NiMKs2Zq8/fLHIFNoLDM0dy4Z+3NQEF51NQFSYGZ2+m3pKFte1r7DPuJHmvoIn2ApgkWd3rTpxnu+TldkYuJx7flDc97JhSkJY1J32NqYFCDqvTKZ3+riEKRo1B+TwnmOmlYDVNhmqe/976OfJZyPMrxTL07K6KQ8zSIMtYCvOX6yfetAnG76lySBI35rq779kQ/vbeM1gyuejQvMJjlAZi66Gag6va9E+DQ8Bd7N2vHS5fRz/Zjy2dmfkfljd6yEu2Yp48pZIYIlPZGbM8boHCgiMI5dY5C+5m7WaZMHr835b0M+eZ51/+QZw69pmusNoHC0NHbvG4ZI7AJFJYxihv/jL0ZKKAelfqoNDp7Ofzpqa8Mw5rZPY8hl0nP5X2TdxUHh6XnuR1RSIOqNPCWOculMdk2Hv1/l+1Tvr809krH3ixjc9H7Sg+9f1991RVx3tnPqs5RKOkw02ymVbq2y/HawTBZY8e07lsVGWl+1853nQqsisjBfLbbSnSifV/XWuqjfQ211ZZhz5bgJIHw1IjCp658/yoE3GVUtpH3ZEKaTdaVfLwKSTWbe9p95aKah/5RbiLP6ORtLdrPBsyU39fO4+jy7neNR3vDd/V1nvvaY1QunnPOf2kcsP/+FfVoTf8GbMBOg3me+1obeJoBPktgDNoUHYJm5kZvl4oqFmDJaZwlnNq/733vjD527bt9serRMhXo5rP27AhsAoU9O9676217NVBoqEdpsJWGbdsD3DU+qd8ncnZodJ8tVxx01WU3lD9LQypBQhkdzz63dXi7v/mNZZ/La0rDtn1Nvm+V7dVENPJ3JRBIR+M0Q71zvTXjN+2+NhVqcj5BQ9nKqkdVMnPTx3J+91SRkAmgYwc2lPVcU23DeBYrY1l7swTC7fVQAoPysL9lvXvWc9ogfTpQuOL98/P9Z/Vgyb9vG5pZZ37Jr1n4cWm0t0HEwg+c48auBV8BhQP2XxVRKB/ZHs9SGLVDd6Uw6zP4l+GJ7+rD0Pe17+37nmljUP1uDmO+753l5l/l4eip7NFeM+Xa3owozLERNi9d9whsAoV1D+Fe8YC9Gyj804R+UQ5W2xPelSfQjiJM7m+SfkvjPo34VcZ04Uic5fkuDbc0QPOZ5d+duqblhCqvmfSxoQQnNbjU4xODt8mHWPOOljGev581hiUQq2lftSe89z7sjeK06nIcyvm59podsf2sZ8a7Lrl0VdnUac66NYCv0L+L3lfSnKpxzkTd4hvLa6oIUaP227ZGV/+67JFp9+U6KSMwq8BBUxa4LJe6Zr329K8arq7qYC0J1Nfnqm8t2tfgqkdtI2eW1BtqyM16TvujV10/wGOwDEO179unGZ2MPDz+0rjuAwvL7OOsiMs5L/itOOAud1mdzNwzjsucwxzDadWhhizuIetl6DWDxr1HsA99x6x9M6sP7d/n/28ChXlnYPP69YzAJlBYz+jtPffuzUBBjkI2epMuS+576YXuraDXlApVvS7z5UoDuGsW0mgsq8iUer3sT1sWtw34tFXaRqDSpjubvD6/y/tSV7eN9Pz/NgOij2I9ub/RVWUCeAlc8t9rvi8jB0VFqVnGcRrRpX2WRm8ZJWgfuFYavuV8lGNZHjpWRk+65qLrvrYdkffltV1OumodFJGGEgy0PfrtecgxyPeWz+9j1ORYubek1XXe28NwyGe331FGJ9rfXq6/IfflN73v4nfG6bOqHu0NXvpZBtVGiuJZfZv1+2X3PTeaedtvv/1qsNIIg199wW/F/m2gMKMDt912W+y/f33wRtmWBXymjc+0tdf3u76fd4KoKTSjacOSfe4bm+53TT9foyzZOiv8efqvnBsXvvpNm1WPlr15Np/XOQKbQOHOsTD2XqBwbnzhH26sBrltOO/auTO2NGW61/xuV3PgWVEBscugT4OsyzgtDfO2YeV3bYdgXp/FMcpKeF26rOv57Z/l/0/uL6hE5fvphRIotY3nyf93lNAsDdqSolQZ70Vhkza9tryvPY5935bGagkUsm9toFJGIPL7ygh+BRibCEr5DP+eBiBLJ20JbPK+1NFlAZe28d2moU36VyykNf1rolhd/WuPQb3gV47Ym4Ct5hyKcj2V/W2Ph8dMgFCRW5M2RRsEde2DcrxKEOvnM3MU8oFpEE01eufgn08MoZahlkbetPeURlhpFDvZLu+79dZb44ADDpiMRxcK9Mvbb7s97rL/yn2zvrdtAN52662xf/GePsOxnJi24Mnf9X37NKOx/btZoKQPKCyDGjXr3b6z7z05buYNKCkXrfvaC7ettvvePaRP057VdX/fHC/yriHmR9dzNyMKQ0Zu85pljcAmUFjWSG7sc/ZeoLA9Juco5IFhxVDt2rkrtmxde/rxKr26qz6VOQ3pUjeWHuK8Zyg/vm3Y5v0Z9fC+NDbTOEzKTlI40pDzrHaJ7bzX7/qoJm0veBqYq+rpt84paNsZpUHcNm4Z0pLBs39dACC/od2X0lhve7PLA9eyPyWFpnxPm/pT2dBFDmtpZJdGeLkGyrFs2xD5/122RAUIm3Okcg5yPsr+dkUj1vSlRf8q+93uUwmCy/kpv70EJ+V4rLILWnPfN3b580k/OtZMrl3X5lodVPVob4godInX3WWYbawoX/zt5QbIRd5+WhsotA33eQBDH81pCFjKfvWBoFlzOw089d3b+20DKGzzzkrfuyoAu/8Ba3J/uihZZUSofd8mUJh3RjavX88IbAKF9Yze3nPv3gwUVpVHra2vycBVhlzDuy696m0jtTTE0qhK46grtyH1ZPu+0kjMyEHpla3+PR5XlKL8eQlGJgbzFAO8BBddAAMIaIOdtpG7Cgw0h4lJzJ4AlSKXoTRoJzqypxBJ29DsG0tAKE87LgFU/vuaq3fE9jPPiHd94NLqR20wUIKjtsHdti+6DOlcF23w1dXfciyr72+tsXb0IPtTPqvL657fmuCrXCd5b7mG2qA1/z9P3y7nrs+OK+2nyf1NVK0aN/8u9k+7bwl+yuf3AUs/l6Nw+lN/rvvAtU9c/t7JJhgi6toTWd7Ta/jNMNK6jM5Zp0RPNTKXaBR2RQC82zig/PS1LiS45to58wamzU/5vs4chdb5Cfms9ji2F9KQNdG+ppy7rnnqAxllOHRovxbpb9+zy+/QR9GrtnLp9PosMkite7rGZBMoLGFgNx8xeAQ2gcLgodqrL9x7gcJq6lElW1uHWdXFT+uWv6/UZOtQtC653NZppRFayvzSKCzvkfyaBBHPbxvirm17nv3MYVppsLX7md9RGai+oSj1Wp0g3NTV77Kj0kBO8LMKPLUqOVbX7hpXEZkub3qfAT6JbhRUlhyfNpDKn6/ymI8duLZjco5C+/tz3EvDOQ1qP8s/k2+1JpqxLw1c17VBxbR+lmAl5y2jUNmX/HmZ6zL55p07q7malgfTtiO65n7V/Df0L3OeP8/3TaIkDSgFTvMbasAqGlT/rA1kPKsrclbvn5X7yrlon/mRQOFpv7iOk5mrF9RftlcIyHb0Yypo2EM97vO8t4XXeroz9DtVPXKOwnlnn1F5aIS+tnaAmnxeGwzlz3fe4b6ta7o8tB954zwRjPJlbSpZX39T2cyag/aHDP0O1/mGkvbW+64pILUrvDlRiB0L4xnPOS9e9srNk5nXs2c27x0+AptAYfhY7c1X7r1AYYV6VBpvlbHd0E9WycOkdzSDnSCiNPRK46w0kEsDLY2rNcChsWnoR9V9VjmBCk559q3k/K8ylls8+jKq0QYAbdpRGnklKCmNyAQs5X2lgVz2uTSmS6O+yk2ogNau6juTflOOY1sPtY3w8nltr7yTmbefdUZc/IGP1Z9bRElSx3axA/r0YfWMQo+2DeM8q6rU39Wzmio+uZ6mHdZbfXszLvXr6jFq2x/5s3Z1pHLs+gBDMxQrdnOzpkoQmknOOf7ttV0DzC0rc1ecI9Ze42v6UUWfmjmfQu/K+y7543fF02YlM7cFXx+XfpWBNMUoype3O79q4TTocYgBN8uo66u0016off/fJfjbC7GN2qb1qStfYtaYDFE+fV6S8t7yHIX8+a/9+m/H5z7/12vOzBjyzvVcc5973TNe8htnTR5RCoz//MvnxLe+vVIFYz3vmffeC379rLjvve9Zy6SizNgvPeOc+M53vrMiNNoZUvO+aOD1++9/l3jdhb9TXZ1reTOiMHDwNi9byghsAoWlDOOGP2TvBQrnxue/Wh+4lkZOCRDyZ23vaWm0tu9LKkclw7OUZJO0W54k7L4uA35iaLfKUJb9Kmkm1fuLJOKub3FvGxCkQdimRpW6pwsslTZDubDaHvq2Hiv/f/KOBniVY9gFurL/kwTcJupT/bxIwM3+VOcotKhH+c7Mi5i8ZxyTPJQuR2DZ77Yx3p6HVWCoGPOucZysrSIqlOsDYGh7+fPb+gBaHuK3Zm0UBwnnt5RgoFqHhd1bjkFGhBI0ZoRLXkkf+CyBZBvAVfuhKXHbtYfa62lqjsInr3jfGiSVD3jsz/xifP0b31ol+NoodBGpmPiiGrCG35fPfeTDHxK//aJfXfPYG77xrdCfKnxXeB8akVMFO9oLpH2dxbB1tCV2Qlmtc+P7vAnEwl322xqX/clbqle1veOPf/J/iq/dcEMt+PKZTV/yffVpksuxOLOfT/7px8VZz/qv9fi1qkKcff5L46AmopAD+YjH/vu4fMd1i0zXuu550ANOjCv/9O2TkG05zt/3Aw+Jb37r2+t6/qI3X/mht8UDH3DiZO0nADvm/g+Lr93wjUUfu/B9Bx10YHzny9evWl/74oFrL/uj18dtt9++8DgseuO2Iw6Ln3/Kv1lzu73xf15xUcUz3oj2g/e9dzz2Jx+55tWf/Mzn4k8/cvlGdKnitp7+n/7vNe/eBAobMh1Lf+neCxTqiEKZSHnF5R+t9Galq1NFpmO3OfC21p41TWeVnq3+b4WsVF43ORRsMrordkOlg3iQI+JBD/7RqiBKSXt1yz/+w1fjC1/43Mp7Sw9388yVH9UdTzWfdsAaEIBVU9w7OSStMujS8Vz37dC7HRYnnXLqxKbJZ/3Td78bn/iLWo/nd3hOsrhyDEtWVzVC5TWtFVc+Z5VZVDqCm/sPuuvBceqpD57MhX6hHk1OZm5A1Mte/jvxpS/+TYy34L60ra2I8dZxjHYm9ar+/sD8Kq4djyoXfGlZ1TPe3Ft+RklZe8bpZ8bRRx9TD29jL+rn1Tsuj3f98RubCkTN3aNxjMb9TJmMNFTrU0rArpo+Vvcqqu+rutn8cGXd1ZNd/3xc3Xf2OS+Mgw85ZNWc7tx5R7zg1587sR1XfdvWcYx3Nn0rxrFeu/n8+t/5s3JcJ6utujd7vNJ3//rVX/3NOPDAg6ofDjpHob2oDfBxJ/xY/MM/1kbwnmpPfOyj4u2v+4Pqdbtw7rbUA/XVf/ha3PPEf7GnurHqPSgnN331E6s8z4ne7n3yI+Pv/v4f9ni/nvZLPx//8yXbW2c4yJvYGmef95I48IADqnMUcl43Gih0DdBGA4UHnXrSmm4dfb+Hxg1f/+Yen88ECuWL98WIwj3u++D4zndXvIZ7aiAfeMoJccWH3rHmdYD9Yfd8QNxxx865uoJwdwjHQETUBR0Xa0/56cfHa19eR4rK9orXvClOf/a5iz10nXeJXn337z6x5iltoHDdxz8VZ27/zXW+bbHbf+A+x8cf/M4LO29+8i+cviFrTGd+76UviPv/0H3X9Osp/+Hp8e0Nio6+6ZW/H4cfftikT3svUGhyFBoKxq233BIn3GdbbcS0fWmLsJyn5SauSn5ohmoUcdm1fxlHHX1s9YOajlLnG7zlDa+JM5/5X0sc0hiyRfnr9vvyHXWqw+oDPLv61nV/892nPfqx8YrXvL3ipZfRiKuuuCye8q9/oh6zVahjyj4rAVg5zuX9bZDmcR19vv8JJ8X7PrxjJddiy5Yoy6NOHJlPPC2uufKKxTb/eu4aRVz8wcvjxJMfUD2ljO5c9KoL4/nPO2M9T1/83lHE1Z/+Uhx+xLY6z6BJELcHTrz3kavX2eJvmfvOaz775TjiiCMroDETKOzcqW7vSlmyfNuGAIXHnBZvv+h/r/pgkw2wbDRQyE6VoGojgcLvvfS8NfWf9bECCgceGOefs7IpNgoo8Nrv+NO317kSRX1om2WjgUIZUci53eiIQrnwN4HCcLl36iknxJUdQEGk6JBjT4pdO0tLYfpzt0WEGMALIuILEfEzw7ux1oDch4HCh//88njMz/yHdXz94rc+4OQfjqs+/M6Jp6580jH3f2h8/Rt7Hszrw6WXvDl+5EG1EVK24058eHzta19f/IPXceeXPn1ZfN/dj5w8Ye8FCtvjC1+tT2bWbrn55jjxPkc23s4BA8CYrbzOqwIJA27sv+TS6/4yjj6mBgpaGrpveeNr4sxfriP2G9FO+1ePjT983dtWvMVNiOCqHZfHU574qA0zLO9/4knx3g/vqCMUTeWl66+9epLMnLbRk55wWly7Y36gcHREnBAR91B8JSLE9q+OiHl4Bxd/6PJJNGbC4xhHXPSaC+P5z90goOA7PvOl2Hbk3WubrV5tcfPNN+0VQMGEvn+eHIWSA78hQKGJKLT5a3tDRMHUZohwb4goAAply7k7a/tLKurRZkShX8SjHmVEoQR+R93vofH1vSSi8PTnnBcv38eSmTcqotAHFKqIwvGnxh23iw0Ma4+LCH70UyNCrPCoYbd1XrUvRxQ2gcLaKd0ECotthhe96EVx7rnbI09mJnOdT3TCvbYNN3r/r4h4orBORFy2WD/adyVQKCmx+lZFFM7YC4BCAxCSp371lR+LJ28wUHjPn165EuUYbVmpenTJZRV40J70+B+PaxYACsijz42IhzfR3Osj4tkRgaQJOAxx9wAKIgplrgXL/HWvfPnGRRQaoJARhbQ5brnl5jjxXhscUdhWOxkG5SiUSaYZYdgQoLDEiMIPRMQREXFgs8g4IjgkPhkR35lDziT1KG/ZmyIKXZ9RAYWDDojzz3nm5Nd7Q0SBMEYBEb3aGyIKDzj5hKovKG4amtveElGwxv7b814QOP833PC1OPLIu8+xYnffpS984QvjvPO2x01f/XTnS+YCCneJiB/lph2oAaZ8Vi9Q2LkzDj325CDThrZ/FhG/EhGIjpTU24fe2HHdJlBYbPA2IwrDx23fiSislEf1dTffNKc39ZyI2B4R6l/8WUT8+ya6MHyo1lx52fUiCsdNzjLIXIU3v+E1cdYGA4VXXFRLnrI60DU7Lt9YoHDCSfHeP9sxYTLQ4+0D1+guUY9FgALSOSx4TDNTt0XEZyPiORFx1cDIQhlR8JgEDK971cs3PKJwRGOU50KsomobTT3admQ1RjOpR337bEOAwmMfFW977f+qDMnypMZFIgpnNnYIvCS98paIYJuowfPxOWyTrhyFHLONph51zd2Z2y+Iu6Ie/epAoJAVUOejcQ8Sz5nMXIKrvHGjqUdLyVGAPEvWHlT6ksYd8oqI+FREkHYzWuYolON0p6Ue/duI+OGI+P6I+LmI+LVG4V/UbNJZg9Xx++nUo5MrWTKkYXpzXP5WM4Xvi4h/N+TGnmvWCxSAll+OiP8UEQ+LiM9HBKIEksvfRYRz6efNCBmao7BbIgocjngF/zh9UDeBwvBFt+8AhdXJzLfeest83tTHRoQcfDLjSxHx2xHx8sVlhhEWUTjq6GMmlKOsYvPm1796wyMKgELqgwQLewNQeN9HrloFrJJ69K5LLp1U53nyE05bCCggnT+hAApceCS3PLE/joj/ZwA2BBROOOmUSdSjygkYjfaKiEIChQq8jLbEzbfcNN8eGC4WBl15zef+Lg4/nDt9QDJz6eUtn94HFBwzxr/5fY2S+kpzE0N8va1MZs5nLZKjQIG+NSKkq/5tAxAoWEr11U30Un+HhLLaEQX9ykTrjQQKZTLz7ber818fAHfmuRfEXQ+aAyi8sUFSik19cb0zuPr+BAr502rcGAt7QY4C43JSJ7mpwDV3ROH8iDg9IhDbSbNrGoK7hXVTRDyjkXAzKNVlMnMqh33xHIVBEYWfj4ifjIh/3hBS/zwiFBUDqnYsYPmiCfXkKKAeHX78qWF/DGkcCU+NiP8TEX/VGOnvGXLjkoHC3SLi+Ij4wYj4xYh4WUTsHxE/1Cwzy8uS+khjJ83D2N9QoGCAWQJ/UnxIx9htAoXhi25fAwpZjRBQOOH4OahHDA4RyAsi4v4RofgP4AA9L1ho7dLrPhdHV8nMownnnk54yxteG2ee8V+GT8KSr8wchdQFqac2nHrURBR8bnrqy2TmNICf9IT5qEdknVkQNDLF7fg5effliIAV/7JhiPQNeTuikGO3N1CPjpDM3JyuDPyJKJx0n7sPM0RnrTGOt5+KiI9GxN8MA9DXAgpHbFs8omDCJQ93VT2ScPK0iHhWQxXkEGT7sZGGGN7TvvcJjzkt3tFKZnb9vBEF6WaSEREkeAd5Cn88IqTmoRRYjPhvQ8yHLqCQ37CRQKHKUSgqEyR9rAQKM6sesUhYQiwQHpoPNAabAXJ+ypABmjKhbaBQXjp3RIFigAA/1BgaspwY5wvkEJY5CiUgPfaHHz68PKqNqTALg/fDEXFcRPxYRFwYEdCzhYdWY3M8LyKu7R+oTqDw3PPvfNQjYJTwsO6UFcqWc/jfG28hDjKEP7BNjSgcc9KEXjbrcfITyAisBvLswbNumPH7RSMK/yoiXtw43w0Tp4bA1OHNlqzqkzdggfODtw11e4ajvurthgEFkTeCWHa4xI+X9kfb9ghQyMTYOeZ4GTkKArjpKZ3j1b2X7jtAoT5HIUs73nTjjfMbSeS/TfnaiLhPRHBB/3pE/P1iI5nUI8mHpe3y5te/Js5S9WiDGqCQ1KM0ylVkuvbqKxenHlnvmd07LMC65uurqkcfQQKqgYLa/9dfVyczX/wn9YFr7JAh1CM+A8HF/xYRj2rUJ/YHd+fa0jq1KfI/mumeVokugULdyeYAuJ074/WvfcUGU4++HNu2basrRjlIbevWbvod3Whgakd/xM0NGJ7FTDB4HJdCz+wPulQ+uft7GqBw2OFHVA7TqTkKn7j8vZ3HP3tuX0TBMVVC4c9vFBjlxPsG7S0I7CefkRGFshLTPBEFjuH9x7Uc4WXD8ntv83QLUL//v4bqaBynjOGkT11AIQ3wjQQKv3vBuZ1z97xzXxyHHHzX6mTmbL05CiwPA/TAiPhM80eplxOhs8ZlefFiXl7vzqpH/p1Rjxy7qUDhro3XyKK3YSgFIWeCDufCH5mmJhB6TuN8oADsAgr6OFdEAZEdjYbR87qI4Bm3wSXBkGSnNWgaQKDMjGtP+54oj/rkiPjPjVbIcSA1oXmhP3NrLQIN74qIzzXjSLjMaFOBwnEnx647hi0MMowH//GNLfL0WS+e8ftFgILgFFsaUFgpernyonZVRCAChv7DJlo660t3O1BgCXfRGCEeHi/2F63v3z0CeCGg4L3CLlkVp0uxskDyOv2Zs7L1MoAC/SnYuIB/o3O17TtAYXUy88LeVGCB7H1RQ1z/hUZWLOCl/Oi1n61yFMrI8t6SzAwoKFCS1BmTj/f/5CeeNp9HlsAwZgxJa/72UcSXxgsZa4BC5ihkZOjj111TAYV5qUeipY9p5O29GllXcyK6G5GCCkp8jC24bQAAIABJREFUsDn7mNIXf+hjceLJ9RkUk4TmiLjo1d1Vj+xHflGiQ2NaUDnYbctsOz71t3Hk3RkIddO3NfQ7cwQI82pnooaST69qHI3T1rhBdN3Jjd5E5aVHmwOzu77l2s99JQ4/4oiqL6oenT7vycwe2gcUfqmZXJnp1iB5zPtG+P3bUcSNeU7GAqNcUo/KCkxDIwrGiiOXZ1Ck4y8aJZpHaFkM7E7hK1RyIf1ZXrh5IwoJ2ukjc8aOpbtuGUU8cFyH1STo/PU6IjDOUciqR+3j0Z/3/BfHwXc9aFiOgk5amITtrU0tMgtUEoeQkUnmwRfe/Z/NLppjXkug0L5tKlCg0MUiTaBJI+hYTiSJkBApc2iT2OZ3nBwMD4twQFsKUMBRQZq0eRHJUWfKjSwT9uymjMObpocCEyiUc3n6r5wbF776TXeeZGZzCYCmJWusCA/CzVhyMVW0tAZ4Xdl4Rt42e0L7gMLtd9wRRxz/wHDS/JBmudsKmFFYY5cMuWnKNYsABdtR9Q8JfKm88hWGhoLMYUrPG7vb8D50QBBwtwGFu0SM7jaKMSEHNLeHnJHypEZAG2jAuQfVLAQUnlKjpRE+1rcixmvPlKsFMiToWmeJslQ0CmKWx25J5VHf2QQaOcPnKarRt8z2NaCQjqKZ1KPM/8rwSylbyQrOGH8zSN68mDML9eiYY5mKK40MfusbX7d7qEd950O0DMCMKKQxnkCmOkfhp35iPqDA28Dhxtv8oMYByMHFaOtrZT+LvmVEoezXGurRli2Dqh6JIlCRbLUhTTeYJXLIGPLMla6WVY/Kw4GrHIWOZGZHjT2k8V9JL/QOfj0EAX93iYQ8IqNts09Ksfb0C1C4+93vseoguDVVj9g1ZOR/jAjVeLyEbMLJ4ozMRm6Sr2UnJLQBBig/mqg8Wrk8Htd2yFo5Coceelhs3bp1ekThU1e+vwofZVmr8hv7gIL9S5EC9PeLiIOL/jLSlRacp+5t+c715igwwpXXYreJJryyYam05874OXLIGHKcT2slUGiXbW1HFDiX0Z5EM7B5jJUIBlu23HvsbrRsQMU6GBLZKPsIKPRGFJ7/4jjkkIERBQ9leFOqIgq4w3YPNyVKjQWq45Qqy8lOnaOzbaBQGsK9QMGgWey4zDzxKCg8fx9scigIPgsP1Yf7lece/LfjB2peQOGUk364OqCubN//gw+Jb3xzwOo10ZAoQ0MEgbIqieLCh6+JiJ9ouCzvnh5uu7NHFEYHjmL8kXFtqFHumrlCd2Plyo2hyJBV8/evjwhnkn1tNgCcFlE4+JgTYzzLzd50SW6CKWVrovJImVhPWwQowFKwuqH4/WI4bAteaAE+y5/OpywtNX9L/bBFZh0Qt3SgQAHcL2L0rIixkIxQM1oeAJhuOaA/+VRcgvYMbb9soECwq4rD46yYQLtZf5QwWUbJutYe/UrE6BujGM84b2O9EYU3RMQjmmGBfzmr1tv2NaDgexlujKTeHAVynRJnSVongB2lmY3YVh0cmrbOfmMx+hHq0fcfVbtuq2OwUBJGEW+66FVx1jNxJJfY7t149Mk3kXzeYx5Lzi1/Fw1QePlr3lIZcNkY53IU5gYKB40ifnxcU/0cUmDP0Ud9B8S4flt9EnHVyOlGJZbUo+xXBRTOPCMu/uDHJqduD6Ee8bGRb8THPI3KYNfxy3W1VdSj5gJO5zd0UI/sR7ZssnzyeY4ZVlir6x1+B0RgPpd5ucwRZkqfS+qqz3wp5ChIYs4Tn6tzFMryqOwtZZ94fMqGX2oeknPKyULOssfIME0HeB/Yc5RC5Z1uHDIovx3JdiIKdzv00IqZgnp0+lN/Li699NJ4+MN5ciJGT37Sk8a4ZYBCuyXin1b1iA35LyMCFscxy8b5jOOLMrhAJDC6gIJnD40oAApsC7pIMrMFhXbfbmwQdh1QMyvENE9EATpmYPD6QahanSq10uw5SBWNTCgNep3Xc1lGFNrfdua5L467HnRQXR61efHU8qgsDCj2/REjJ5xblJLFhFoIYadPoQIp/6KzGTNPg04HenYHoHDFB9+6iiI1k3pk4LhVgQRRA8hYQon20YjRX0eMCV2WVNawBGT0c4BR6THrjijw0KA7oUTxZrXPpeINYG1KwCOQkcinGKt3aqBA4fxIk/TNSjJm6ZYBEgg1fytHxoBLj4iNKTHrfzWFtDv2cf5oGlC423GnxM6BJzNzdHA4w6brzU/Qt0WAgvuIaTXLyFjB6iztjF7EJkKJsh2xsyg6DYMLXkVDmpZatHSg8OCI0b+LGOtwenrNubkU5qDUKDOVriCv322E9JQqawtHFGh+awt/QP4QT0w2DpCfbvYtGUOJuv7RtXNk9JhRjK8fTx289QIFQ8K2pdPNHf1EnP7RlLU961f7ElD4wj/UNbp27toZt992Wz9QYERjzvL6mStca+DSxrS4yQU/4+WjIxgkFv6crap6dMwxtfHmkKRq+YzjrVUy8wI5CgAO3QAoa/Tvv24cWnQCqzgjp37vW6B8TjoNKLog4rRjHxt/+Nq3TaoeVY8ajSqgsNA5CpxrhAbjyPv/JGL0b7fE+JYOpYSfDYT5Fo1HuDkkPYGC+du6ZWs1jx+/tqEefeDS2OJ063HEkKpHVCaSQpmqNmT6GOhS/hjyXQ1Q+OGTTp7MaVay6oooWI3EwjyN/cYHiLpeptHxHTI/+sQaoHCkUqS5NEbNgWslUGDr4EHNarxBOgFplZXKCRcGOdsoWzrk/t+1Dx2co1BFFJwU11R9KR81DSgwyA0MBw1jO/sFUbGL7NlFqiCt5Ciot1+jaWjwazd8Y9DJzPrFvqQwTSSwYGGVzade3tCj6BG6YhpjZWiOgjxWi5/DqmzYJxkm4y1k07LDgUb6TBGHt4zqw9yGtmkRhbnLowIKvH2MWXHxxuivznphkAtnCYmRm1yWOk1442jgeglh4pZ3RBoeeMqJsePD3ZXoeyMKBhLtiZUknKbcQVcjYQg/VYcsQOPHo6ifM2pG9gGFo35o9qmv1bhAmgwMYAoX0KLLRjkQtgwioSR9FDac0qeuZObTn709LnzVG+8c1KM8VdVi5ylkSDIeCTqRBPrKWMkiJtA4+cwn3p7x60L7xZAvo+oRTGfKyGnLmWyAT3UddlnE+bEoUMCwY0xyvMDo+mY45MZzlusLWpJlz+/J4er3tu+fNkuSnOlqSwUKBC5DgleewLdvKStRPlqUQkvjxLwzoEQKzeuUthBQAEKtHc4DMsw7GG00u/VlIiWf/E7TDyFlFgcjSI4MOUb58t70JBCsByjwWpobc4cpqUu8qsRp0i/SOThUD7huXwQK+i1HoffANfKB151yhtgZ2JRoImC6xnqjn5gJBpK3bZ6ck1HEpdd+Lo5qTmbeEqOJETc4mRmKxxPEKafcgRZyzAa2IX0HryA9oZ+ZUMwtDSlyhlgIrvE76/Z9Eacd/9h4xevePjlDIWk0C5VH3Tqq+/WkcQ3eUR4Yj4SFv9uW7SnNvklPJ92mMgy7pUlm7qIevesDl9aJ6jEMKJgyTtUSKBAXukXm2ROGst2GAIWTTqlzFLQcu4tedeGaA9e8z2daNqJ7wAeR8bNNXZK+fWj5cTTzgXAszRBnley56lNfjCOPbHIURjVYdujgqoiCfALMCGub8cqDIBJEAZC1hH56nQkL3maUFJTLN22J8XN21XmAmd+QH0BxcKS22tWf/VJs21bXmJqazLxoRMGDTaIcWHoAzYYytZ8ZxuIUwDEDfGDUv+psGVEoAcw8EQVrmg3L4ctubB8kvt8o4p3j+uRVzmD7YFp5waERBeDEAgOgfHfa/RwKaUMzQixO70bLFpHhMBUJFEUa2sqIQhvonbX9gmB4iijMrHrkhQw1wgO8FzEom0VpcTKKWVGsD9YL/gOBQvBYyAznDrTVFVHIx3cCBYJfmMyg8RqlBdQ3MAQyDzXLjtVkQhmWM9hDJVAo6VCDkplxPkQL0hXxjmbBlUAJN0SfbFieVmBiAFDI+fL3ne7ANQLCWJgfY5fUD8ZjGgCkNgEJSYs08ITxsgiz8rplslFrPUynHp1U0StntXSCy7m2lLEeOEJgaLaAvUt+kxV9/Nj2OxYFCp7D8ScKTaaKJOgfeQ8HJ1PB1qR6LDf2rf5xVFh6fUbn0oCCwUFlAIoZ57xDPxkxAhLuGzEGGuxNwo2yYMgDD4zyGW0hoEDDkx0mikeaAUQZkE2cDYwxzgchGREu1gE5Zi9TzjhnqJc4oT0n/64HKFja5o8DkJjCtrPGBFoMnS7SRbNX6urB29eAQsq4mx02NetkZsaNuQQuedb61g5vJRkL/M3RLrv+83F0AxRKw3LmOQqUvE1nTQPJ0B8k6A9jiMEvTwdHBeg02aUhZKPSUXRCm9z+jxGnPawGClrZr96IAmtbX6x5e40DL1lL/p+MFaYU1TCe+gK82BPtBedZvoMA0T8eV+WGOoCCn113zVV1MnMDFBjmXSczY1bAfoaKGO+iHmHzUqc884ZVAJDvqGzULFMFBakLzyf1aFfDN03w0pXMnBEFw8GXB5fan4aKmiJSNIErflTmz30b08dQmmZ7Vwoi0GDK+xw0V32mBgp5JoZxWkM9yoiCeeGg5fiAQgh/jhYdInd5kTJ3RwcpJEpCwReRoLJsFGXGGOZhajVAQXlUz/rAe94dT+tLZv7kFe/rjCZ43pAD1+hy+4LeJ5PJYX0EjIAhIH8Wtafs+xMf86h4+0V/sCrKMU9EgVJFk6Xc2RhsDnK/3TgAcEQ5NNENplHb1wAFSLWJwJQ5CqKfbCEL7SujFdvn1h7JDzE7iZCiBxJ+bIvSYsOkHKBQnqNQ3lUBheLANYLmkY/72bh8R09s1oRBoSwMgyJpuN0IF9EEi1QjkBhujAI7o6ffXcnMatvvt99+0RtRsBlsCpJCdKHkprb6VU3DvSLGFDzhaMfOARQSJOTfR9/vYXHD13us0fLdNL1sLGiYhGkEaXWJPjFOJIwxgIEeYzel3ampR/ndNAXD0abLKkfWDUONkZaeLQCU98EmprQoLInN8mh44jraNKBw6HGnVCeCD2kS5UyXrl7esB04yHWXTBO8UohgaLR0PUDBMrL8DRdnJb1OMYpa5k5OHC/gxzlOT+DVUq4cll1taUCBd14HGShAOnQCCKJ5HxAxprjIh1RakA9Xup/NaAsBBbKJHGNUJr3t8ojRM0cx/otxbRTxbJEPZJ3/54ww6RAZq11UgdLg1uxo6wEKnILEgnXl8VhZ6GOcWnQHcWpuzdtANVD1cN8BCnV51KxEs8ZImrYmyALOKoCUEcsYWikgUw+sA5IgsTnCMlV51KOPC0YlikpVJHUcs89R4Okjs1iyJpYxTc/z3DOCOGlNqCRI+oulOQcCPO3RDfUoxpPciSpHYcflVenRNc/ikGKAsVwZY6zYBAp58BUn3LSWkQ2WOJ1ugbLqLdaGoZ5Vj8rHtIGC3yX1KANDpkvkk2jHICO/dEtwLyMKhoffEXb3WqDCdoX9S5HhOnYTUA1QtFt14NrJp6wat74D10rqEYeyapnEFjuSOZH+QMPKnjUUfB2WI0cvkE+zkM18rJlS2TXMqEfbth05iXDo00033Rgn3bs4R0GOArnK2SFaQOmQqUwS60ynzC2B4ZiPspYsx4d1l/OexWl8FABiPlvt6s8q2cq4WyCikM8aAhRcS1nZH8JIPOr6CSxzJPlZXXV3WFtvRCHfYhyNGTuOYi8b43L7uFb6ToQ3jkOoR2ngetZ6y6MaI7pJNMHYGaOf3CIcNWycpkYUzmuAghyFpk3NUWAF0VYmkgHcmrDR/g0FCdSHCi04UQRKlVabIgAXqnpkF+Kr8/oBDOgAfc3GMZAMdVKJS2AOoNB+7KCIgpt8OylBquCoFJtwtDVinMpKtAaQwC+Y0sqqR1kO7053MvN+EdXYkKaMOeOXa4cmgLApKJKXsATErAHGKHcra7jH3u+nHu2Mw+45HChQTph4GFJ5FAaMwgjXONDJXDbxkIPY1gMUvI9ni5eKWKArbE2OoRIE2LYUGxuZY5Xu8A3C6F1MjKUABRofx5TxZg/i1JAh2YQ6KDTGN/RibzJAeH3PbC6aUhRhIaDgsd7JO5WlIJWW834cstujLtpx73GM9ZciZWCyBkQuoSva3n7lMOmQxYsCBcYQFiVdg0JsbuhIW8DSt+4EZkSr6FAGkMjzkGNs9h2gUJ/MrDHMb7vl1n7qUZesBBAYS/ce1W7dM8c1QLW2yAfeNh58m4SumtUa6lGWR63AwqhGtTMjCjaYKKe1L1oAwJhQE2ZviH7QSz73JaOIT4z7yesd/QQULnztW6v+ZL/8fe0O5yh0lEfl2eBi79LFFheDg2AgwIyhflr3FlzKVN4PeovBRF9BtDwTRf5hST2q82rHcd3VO2L72c+Kiz9wWeVAZRslUNAtBASvtiVF1bwSUBAvIUKo+2xUqXzcTEnMHFipKmVLoNBVsTKrHrm+rHzURT3KFCVOZeIAWABMyHyNz5Q5od9UE1lMpbMrTTOw4I9hN9Wupzu6piEjCglGPX9N1SMODuvGQ72AjPRSdBXC3ED6g5tFN0pQoxC6Dp4AVMlbaMzcdsizsjzqJe95VzztF3+2O5l5vRGFnDyLhqwF6jPHlSxmE1iPQ9sTH3NavN2Ba0Z6VB/eAekPpR7Neg+QcOi4VvZCRWhSfdnz+ax2RKGk+ix6jgIQg+JgfOxNCl50aGibFlE4+7yXxIEHHFCVRx1EPcIBtfCEflJDGf4tEWM71S61aC1MO4ZQFqaBfmdUnXTg2hUfetskaqU/5lT+ycwcBeVOeTLsvIYKUOUHlLsQJwRH2gZjnPA48+6ILEwhDqIeMS4rL1KRo3PU/R4aX//6gHNuKQnfzoOVtYFNHoFsnCx6/TwnYvT6iPGM0lpdEYWnP+e8ePkr33DnyFEoFzbXDAOTwsrGVUMgMtxI/2MjRo+IGNsk5piVjMrCCOyIN089R+HYk2LXjGo2ZfeygidDjUEnHM6BqKEyWmIUTEdu2Jrtu16gwEnO2YLtZmnR3yIMaEYZaPNzHjsKV6Ef8pedxDEuet1mvC0FKPBmcX+nK1yHEgCUo5CVaxh5rGWhWyFfzb6hvDoU2MJAgeEoQ5icyqRAE4rYTNGKIuiLQSXzgND3RYxujBhz7wP8lCsHwBKBAuYHmW+YGCJSbkqRwAnMvhWh57xiiOjKAEm0D0UUaqCQ8naNN3Wo8nMdY4mxYW7LGsLkhLnOiFEaSDyBHU6GLI/KCE+KCh0jR2FqMjMvI2eZd9tgot8MbO/BbTbhjxjX68i+EFkYUH43hwBQqHIUWvSZ3hwFa5n7vav8qj2BfmTd4/ugGeunUCkhkjxK/TN+Fl+P829yjkIR6cjyqBd/4GO1zTbeFU95wqOqMx/gqPYxOF7jZ0ABFcAGyybaIDCSNEpyF80oKUB53VCgkGutrzyqYCggIDBkKn0+B5D9SdaTv8wPQSJLjuznkKGGDDlfluuyUfv8EkBFO+o8AQqNvWGcbr3lltU5CgaE3WP9kl9kkPCKcAzvVDkv0A3nGVABqOpsOf/kGF0LqfW0TGb266lAIXMU0HsYTWVS89CIQvaBZ9ygsTuBWFETdidbjwIbEvwvIwold3xRoGDcKE6LwB97yd41xiaarWvBTispODSZeaicI0PY3pwS/k1fsoWSEug59nZGkKwV/bVuyBtt1TkKO53ytwIpK6Bw4AF11aMm+jGVepTIrm304ztQqIwBu4R3kPtLBMLumebpb/pZRhR2tfrZCxTwhcUg7TibxEAQZrx83HIaY5Kws1Eoey03iRqXrKkpCLCdzJwCZXBEweKxoHlmyoVtGkhHLl9WJqkiVjkjUvQ9QT3KDSJ5FPC0ISnYbDxY3KqfjRjdJWLMO0yCs8hpF95CGxagaCmyqRGF40+JO24fIn3qjljenMvkGGchRZYpL5YgL7Cptbtm6f71AgXGJZqkrWCoRA44FeD0zGmyPdgAaI+MTVuVESpaKa1otwAFESEggFCCpLysq/H8Ev40sr0C5GVFGL8jeEuvZvOMhYFCui45DoB4jQyDsKCmDC8DLfreLjeHVfrBiDFXZ4dSWDSioBscvl5LdLaNJ6ILewTtTVKldafozwzGYvV5+05EoaYepRdw5jkKPUtq1Y+tp6zOU/6CbuAWRi0je62BDtrix67/fHz/0ces8jx7zFveMAMo4A2bIB5dooWQ4OQoQ3g2K2I7ipS1NqPARtn9pB5VKq3x0vNEX1sduNZBPZo1VhKaydLnj2ugwNjwDWTqUB5lk6PgwLUVT/04rrumPpn53R+4bFL288mPP20CFORTlUXITQd5ausxqMvaUhzoyAFsXHtEiorIQ+ZV+0yin7iRatRHPTrx5Aes2LLVqdvjuOjVf7jmZGbq2p4TCeAT9R7PpIpgQWaGZGVqR1oAc4RpwvY27cSMf2ehPt/Dxmf7tqO5V3/mS3H4tm2rKFHod6uoRwQE3ciZglVBnhkUzlCdbAM4A2s+6U1Rh7KiFjQFKLSr+RRrZeGqR+UhZ/MChXw/o5cwxFIhazl3Vduawb6obp9EFMqFP4746j9+bVDVo/Z+Ee6yl/0R0WFvWrjsXQuWY4zyt2/6GDTLjihYA1Azg4QniYPeWQsqkSa4B/Szti/7Xf8sUkBHv9dEFJoIjO8/+/yXxIH71xGFbFOpR31CJrO9xePQQghavHsdIyQzPjdFSLUjCuWlvUDBpEkUAwpMkN1ooEQI6gT9WrgxPAwepMUYBzBAf/SpAUABiEmBlwB5MFDo+2aeGygQqPInF9cMQf49BRTMJ++H+bKezDWNaA4pdBuBxKVgbVx8Db+3EYwpAdgCXlPLox57cjjpfZ6mi2SYbliC2eA+wRBdlUYx60iR9QIFiszS57wEXjhj5FSJaGRFJluffuDgp0DpDkqKbrEN2m1dEQXzYN5o8vR2AQK8+O1G8+Js8YYZSCiGQcUSYNz5vY7/VMToMxFj1zQCcGGgoA/JxfLerOPNAsD5YXVQSq7hzdJvaywtEQRpDpCuxLZ1HLhGFwICbEnzwn7EiiqbLrELAAoMEfNO7tsOutNHQ9p3gEJ9MjPPPcNtDe0iB8Oizzry/rZGGDxdPBNeViCUUZ6ncBk8m8KApfOLzDDoaA5FE1FAPdKq2vbNWQpvuujVcdYzp5RHtX7pR/LdphSNZ/gkZ8YD6QJRULQBfBUeyYHHcU+Awmg0oR5xZi10MrO+HDaKeMa4NsQsMguLIONtWAAo5BAas+sLoJD6NKlHRDiRzva13cszEwQXdQM9qWzAsW1K3Zta9nLZ3GOPkG1dEbekHlUUH6ycpvRt38nM+gQUcNxyApk29hfRhT7PSQssZH4FezZtWaaQ/88cCm8kn+3ZsmqpfgAKKgxlvzpzFFJ+WZLWq6IeeFrokAayz0iFqCAn8i5LRbHPhJYpi7KOazGYgIJkZmvrkj9+V38yc0YUSvpFevIXBQrCRPYEh7TvsncMnBDSrNaOKCRfe9GIAptTBIFdYi/n/kgnOkBNCZMffSc0J1AoIxzryVEAFBghmc9pjLLaW44PHZYxAr+3ViBfvGltWo7COee/NA44YP9JRMH1CwEFYV3uTHE4q54Hws8IFtZTz+Ir5zgjCl0leGeezAwUELAEMYWh0oRNQDpw5dKsBsZGEPOzm4XhxA4HAIUHnXrSJAyehw6uCyjom4UG9ZGKpAflNYBovAYoqGT3vPPjZX/0+jsf9ahakE20wBqyAVm33ODcNNw3eBc2AZI+DiaNYxxRkUjvVptOPTo5RLPmaekIZ3OkA9z99i5sA7QLdMya2vUCBXLK8kc3NRyWPBAAEKAW+SpLzNITtmeMkhuGUuTBUmy3dQEFrjNzJapjrsgGWrariazR8rxdjG8A2t71UZwP3IoUG4tYYijXXDNN6wIK2RcGEWucHCFECU+Dpk8GjAUgauVnOcnkCWuetu9o64ko0IPmUQBmSn2G6q2JX3SZLQy/9B3+t68BhTTIVT066d5HrjV+WG4cQDyhosXmEchk7KMblY37l5IHVv3BJ3aPOS2btSrCxALNwhNVjsJfxjHHNkCh8TwDC2+pzlEoXQQdi8EaIo8YN9aX/jLMhP0BAvsEZxBt1vegtqnKNMAwT+pRVjzKyki9ycyzhBtQQ84KS+qrXJyfHkXcNJ4Z7S4fXVKPMp9j5WTmSxvG+GiSo2Crk6VSzgTpBFZWH3G6tuMAgsCf4eVDalf6ZIoA26a4q/pceTJzmd/x+lf94ZryqPl26oUfAZXI+8h3NiyxZbj0BTbkYyidyqiCcirkFWmzgMIR1TkKNSD19xrqUXbIgzlj6T9OGfpwVllM4WM2UGaHU04Gy/0+pqMcUwIFr527PGr2dVGgACAYPLadRZFJIOhxsw7OfcJjTot3yFFotXmBAnDP8WWhiSAABNggbDdoFMXc4mB8K5gDrFG0Xa2MKLQN3nlzFNAXTx/XpbunnUhozoEHC4/iF4rGqElA2Y4olInWFVDYf/8qopARooWAAg6jwdLQRAg6f3SI0s9DYqYIKRGFK/909TkKM6se5fMoCFIGgiOzLSjuBxuGwZEWkcnlhrDAuHxZSZQGzdpTJQf1KCMKJQAcco5C5+fuHzG6Z8SYFcC1S1FwS886Jrd52PdURKEcQAKCpctQSw3COkpvCAPBZqDgCD6VOxIoFs9ZxjkKZbcw2uj/rIKYxCV7lnTigBZiniXP1gsUsk8YFJxLbNu008k1oEGuGgDBDuAVyzKq7KqSP5vPWjdQYGhxkcsF4lHtqwQBzaAoscMMJnmSLkCoByhQycpHXRMxun4U46/WEm4pQMGgmEihZJaKaCNjkSFnMAlh3F4eLHKGNWCNcTRAgx1tUaDAQDJPRBmc0i6w0X4VXe+PYBqxh7PNbmAYtZ3S+xpQyG/tpR4BjkAjg5qhRPf4N0M7f+XDAAAgAElEQVSC4UP3iCJrST1iCPEIMtbdLxqNwyVfLOVKAoXUaWx4JzOjHhXkbs7JmRGF/AgyXuSKg8p7yTJAkzFB93C08fQCCEAFIDOl+Ec+NpOZ06DMSMdVTmbuox4RTjjuZaiKoAISGER0KK8DIn5ydwb0pVybq89RqH3jGVFQHjW99+0D10whf4K1z+Cm2rvSKaaYEtWvLAMefFuYr6GLUFoCBfckWOg6cK18H7q3ofIH/uR/nFFlvaKvyzni29KMB/2gf2Vwye9EFACFZC+wJddQj8oOHTyKuOu4nq8hzFlrn/e5PAiXPGM8UlYdycKSmQ897LBqMqryqH3JzOs5R6FvUilX3ixg2uATeFAi41w0ZZrttCqiUHDahwIFsp4O4IRE07FPoE/2hsUqmIjKxYFloQIKFgbOMZDNtmzTCYaeozBrkfu9ohv/cVzrTJsFcOTwyvM1XAPAYM+IgDBEKH5/l0AmIwpdnvpzXvBbccBd7jKhHkn+feTj/n1/edQhHSeAGOl4ycjbrBJabEZbqOpRzzNH+0WMCWZwv4/vCSjwBiIJ2uldlIiOk5mz4u3CEQWTCJlCc1wSlJrFvx6g8Jzz42WvvJNGFMo5Vk7zHRFjoXCbNJ3/NiXPNcGRzUYR1RICLHJqpkYUjjm5SqCfp9G5DDSOBt7cLPEMBzL6OJ4lnc5qywIKEu7Y5mxeWIns8ocDE7MBb5atUjZyjxxuOzD3v8td4rtfWVtw/qprr49nPOe8uOJDdcmYD//55fGYn2kdOU5wGRgRMwYXTkyfsQHgSczzOIiFImuHYEQokofLVbcM6lE5CJQmgWrgGJiMOYrX34xOABXfwCDhEdivEBjjzsS32qJAwaNVYYV/jfy0Knv5SnLfnEpS103dlnaF8la2fQkoVMnMjUe1N5nZnLEqLXBAgefRRiQb8hhrRo/5YYyzPMldzqGkwwKiHEf4epnszHIjN9A4mgYoHHV0TX6pDLhGEcysepQPAHJFxfBUbFB7wfrh7KADhCRFTyE9NKS+w0Nbc1pSj/wqjd3ecxQYOkAvoydrePp/uoiBo48El0i8pFHAiod0zgYovOfPrpxUh2J/ZHlUOQrZuk5mhtlhFcQEW66rUM+07pDg8CMVzwToq6NSAYVTHrAil0zrrnG8/jVrcxTm/Pw1lwMKpr7UBfpJRUmJKlsChfpnDjnesvYchfV0CEjllGmHbBiPPOEdCmtwMvOyqh61vw8Dw5q135NuSOfPOsXu8Y8+Ld75+sUjCiYOiGcf0j+cCKKVBC07JA8QJXeyWj6nmL1MjylQ0EaCCRRuv/2OuMtdPHWlzRtRcKe5ZO8CNQA+G9ziesUoYsu4/v88LbCvJPRU6lEBFAZVPRqyOAltyVB5ABq0NeDUqQeeckLs+PA7qjr2+23dWocmG1fCVOrRkD51XZNAgedS3LCtVZt76ojCSVVfSrB19P0fFjfcMOAchfa7KSNeSZ4ai4lhhHIxIMzsUd+zEYUcR5vTfLGoUoPQBFkX3HW8KpT/NyO2/Okodt2xYqFOzVE47uTYecd8QMHr2CuZR59vYn9wQOsmp0NSAfuW67KAAnnAJhElYPOmN46HmhOGU6Z0JMHx5FpXaep1AQW4IT2XshFl/XUNLQCgExQBo4SHaABVMcdxKRGFclIQjPWdUsLntS8NaipV1jtPrGgkY5QR1eKze9yiQMG9hkIQdhZdrew2G5mTkE6DuQAFuBlHmrGk7WtAodrKu3bG7bfeNrs8KqcUzgp9wxlD/ZID1h1ZQGnblBlRaOfNMUASKBgwHv1M7hxFXHbd6ohCevAHUY9yoqwhwoChQ7FzEHknBU9w8OiKqDFEBqqWpB55RVZkqpOZe8qjbhtFnDOu0eiLIkYfGsX4vCZx2V7kkSRLrXFgv11BZ6C+rahHH9lRGd7+27plS1y948o4T3nUD35scjhcF1Ag1skoQUaOWtvR0KXTY1oXMuWEZUilTzsqI89RSGpUZZaPJTNfuCaZeeBn917GfjOt9mY2fRU8alfoverTX4wjjjxyEr0yfuh3J5fnKKynQxQDJylPUqkMzDvUgg3Salke1RqbGVFoe6XTIF6UepR9AWAJNca5PQNvitL1UXzcV0YUqk0yZ3lUOgy9SBSSvAcUKHNRBjLHmAH1UG1Gcyw8EyvM5N8cmWVbZkShax3YQMZnVmJkeW8ChZI2k7//1Rf8VjAG1p3MXL7QwFKeBjPrjA9Y1AkUui7drUCB54RwbhdgbjrSrnqU/Vs4osBjA0TJH7eQhHrFRgfap9/zQIHxxpIyX7zVNkVdqHvFM0QCU/S8dK22bOpR39JmlJNf2AUDAmqxLKCgP8L2bCXLLBMA20o2l5tiCXRDF611XUAhB4aGnxYOJ/RFFCAZFCWG+JRyxe3xXjpQ2Bqx5bBR7LrfuBb+jE/7FLhXl3pHxOiyUYy/PZ5qya8HKAwQl72XmG9bQ/UrjkP6CqYx3/sMUNh+bnz+7+uIAs/9bbfdFiccX58KO7MJj8k9IBt4A8vEITfbjKzIrHQ184H1BdWBa5nMXNTdngsolO8CWoAF+TloTyIenEYLHLiWJzPn4+tk5su7qx7Rz8KKNn7W8SQsyFFGD6celPnWiNHvb4nxLQMVU2scV52j0Hj8JuVR/6QGCn0nM+ejdInTQ7DH0SvEOYO7j4qkp5ziZJnrmSDTet+mHnlvBRRedWFvjsLA5bLmMt9h6WE2lk1EoQ0USurR5HTym26KE7vydBbtECFhHQCr2QwYwYG/2mrXfO7v4rDDDq/mbFB51C4Ky3qBAiYGp6qiEvYNT7113HPoZfUJZUQhE0z9fCj1SKSNHUumeA/6OqEqggA4iBi0ebtZfpAnX4SmXXZ2dwOFRdZEGVFo398GCjNPZh7SASuf4ofAcJSHFPjmwDvlhOocBWKAXLEgM29itwAFkoTLl9sO1LdxOhTRusujtsdMJlSetCLEPM+hGK2IQo7Pne7AtWnrjHeXFwRnHXdR9IortajmVSk8G7njqPfp1KOTYteuIdbI7I3AQ8++tKK7EoXbT1gmUMCwoEw5higmNGz2UuIp705li3WAxt1VvnUpQGHWUAkdQzY8mGg8jJRZhN/imUsHCp6NA3vXiNEREWPEYhY37xGloJ8DIqQbBRR0nx6T54DRgk2SxtI+AxTOraseZbvp5pvm96YmZSBpYaV1KYmDAh/aigPXKq/9aDTh2C8MFIa+e8Z1GVEQedm6ZevEAL/qisviKT/1E2t1mnFg9ADlwovluEiIYQi9JWL03i0xvnUxkKDLGVGoIi8NKKiAwplnVBGFbE96/I9XFZr6GrBgKmEZ3ngUSWzArkZsECECztb+rIjcuz/0sTj5lAdOnMz5zFk5CotMXRdQYJdLCWhXXm7nKHjfXKeTD+kg/aiMKspenlw+BShkRMGj33fxO+L0p/5c94FrXTkKbrII7nniv4h/+MeebNABncbH/5VxjRwVl+DJt4+n2VAZUUhP+bwRBRMnCkhmZPRFpI0znMceMu0yGcp91f79PgkUVD062yjUbaFk5rxZVQmhVNCZJ1HYklE8oHUlM+dtuwUomEguCqBGSEvoueCk5rsTKCibubU4g2LhiIKyncYkcycYuULdA+3T7/mIQk4M8iqDTkSG0KMVgAjz2pTS7Fp2u4N6lO/xaqWqsaOk6IgmcDZkPuW0bbBMoJDvER01NByWnOPUPtsAs0dkUn/x2vsSZvcIUMCxMV+42ewHnekr29MxgLsFKLTfY6B4qPF4hiQLrpN6NEBcTr2ESMEuIIoZTtn2NaBQOYtQj+aJKJQjY12JQEpOZxCR89aX5PTVdTNmDvml14koNDkKTSUaEY+3vP41cdYz8R82pk0OXGtyq5xxZcyuu2pH98nMurk1YnTQlhj/m111Er+cjVsjRreOYoxqd5tIzvq+JyMKCWD8/fFrr6nOUXjXJZdWYGtWRKHsAXNCUI9jYzWpe+Uq+J0IkfoxpIkonHTKqRVQyMRhQPANr3nF0qlHGF0YlpzN0psMLyIDE2lVZbNRxNWf/nIcsa0+9KMibomq3Xrr6gPXhnzgtGvoTqWbeOc5QTSZ2fITOsLLV3/2S3H44UdU+RKqHs0ECnUZrtrrm229EYV8jmicPqP1kMtAQ19rU49cx8P6tRu+MegcBf2v8tccnNO8RCRhHlpPu2/7JFBoqEdVbsB+W9cHFMB/O4IQ5smRODYwSbfMUWCQ7/aIgsljSUm0BHBgpQ4DpR1RSC/+QkDB+ECjkDyppuQB4DDQ+NDlTaDQ2nW8IT8QMToqYpwWuY2NetTRdgf1SDRfqWfGOJtExBHdV4LpENqRbu4OoJCfL1xPJ+D7spXQn+UwYPugYfclzO4RoHD3iNHtoxgfM67pF/7MYaTsEaBgICmJOfq1kRGFkmZW+oT3JaBQnsx8y803z85R6DMUoKU8SdXfHAp00oCoUPlIQOGoY46ZRBLyHICZB66t16CbcX+Zo8A2ywTwXupRPs8iMTZZIMDPre851vi0riVQcM2uXTsrAzOTmS9uqEd+15WjsJuHbPL4BArt9+2OiAKgw7fFxk2gkIddtyMfOz79t3HkkfdYdXbT0iMKPlqnKKs8iFBHROE7FAKgcMQRdSWmmeVR2zz39Z6j0J6gFHBZEmvaXu4CCp43lHpU7pf893r3SAKFrGlcnl69SDLzMjYM6tHvXrA9tgjbtFq76pFfryui4AEmj0uLwh9ycl7Tpz2eo+C9di4rCu/Cru2okFSWRy2jCgslM1NU6AvoDLjYwEl/1LVz+jeBQsewoCDh1rSrC8wBFADAQ45ZjHokNM5hKT9dVyx7XRGsWgmyT9/NuxMoeDMqKhADwKQ3iwNpmszbI0BhnUJujwGFOfu5kUChr6v7KlDorXo055wsfHlDPTrm2HtOqgpldaE3v97JzDPOUVj4xbNvzKpHlc2BstscBNdb9Wj2I5dyRZnMnHZQAgXlUbPM7N4AFCqANR6HaMzuylEwqPyE/syiRO349Bfj7ne/xwT0WWuAwsn3vsfSgNy8kwwo5CFwl1w89MC1ekVO2rIiCvN0vg0Ukgc3L1CY552zrl32ycyz3jfk99NyFMpzFBLcPPJxP7u+8qhDOtVxTXkyc5tOtluoR/qgJA1w0Fc/raM8Kg470LVQRAGIUmf30REjxMT3R4wHRlxyyEqgkGteqcqXvfINd84D1xZcT323TaMeHXrcKVXVrUWawAYqO9okT738AGcXDG27GygM7Ud53SZQWGTU6ns2gcJiY/eiF70ozm1yFNJr33uOwmKvWOiuj173uTj22HtO+PYVUIgt8eY3vDrOPGPKycwLvW34Te3yqBlVuHbHFd3JzMMfva4r88A1D0k9VQIFlYaM4VOe8KipOQrr6sSMm8uqR1kxyi27o+rRPN9Rncx85N0nVazcK6q21GTmeTok8twABbbZB9474xyF3R1RmKfvT3j0afGOpjxqLkR/y5WQM7ERrYt6lF7ojYwo/M+XbK9CRuaPoYtipC3tZOYlDHY7olCutd0GFAb0u4wolIn8CwEF7xPmQ8+aowRk2c12REGf/tvzXnDnPZl5wBzNc0k/9WhnHHbPxYGCPmREVKEcZ3ENqYGffd8ECvPM4sq1mxGF4eO270QUzo0vfPXGuuoRltAttyxOPRo+PFOvvOz6z1c5CiVbgHH51upk5o0HCmXn6/KoewdQKFkVWfUoIwrz5CgsaRpXPSarHlVgpjmzQ59e98qXL73q0Tz9v+ozX6y895VOGdXJ4BsNFJyjcOhhh1dRl5nUIx3vKrO5kRGFEiQY1L0polAujo0ECr/30vM61+nZ5780DmxOZs5x/N3//cr42y/OwRmaZwdMufbYY46KZz/jl+qNIbloy8oxK7/2G78dN9888KCBJfUnH6NPxx2LsFG3HKdzX/g7ceON85iCy+mY8zkueIEyMSvte6rq0TqHcU9UPZIojM02D5VxEygsNrGbQGH4uO07QGF7yFFIeXvLLTcvN5Fz+JBNrnSOwtHHHlcDBYSK5qCfwQeuLfDOIbdkRKF97dU7Lu8/mXnIg9d5TR64RghW5xSMIq656orYfvazwoFraZhvNPXoxJMfMDHGk360O3IU5hlOQEGOQq5/f288UPhKHHr4YdVcvv/id8bTnvqz/VWPukqj+og/fPWb4qab1pMGPM8w1tfe+173jJ96nBS91Qdh6Yf+bESDtp7xX35hFZjKMXvFa98SN/7TnDyTJXzEKSfdP/7lv/jRVYkx+dizz3tpHHjg/nH+OYr6rx7HJbx6XY/ISgSlR2JdD1zHzX3rfh2PXOqtT3/OefHyfYx69EvPOHNDwN+9jz8ufvN8iSKrm8jffzj9ObFzQerReif0YQ/5Z3HG09QKXd0+9JHLqrndiObgw9e8XKHN1a19MvM/3XhT/PXfdtSi3QOdPujAA+IH7+vkoLXt05/9fNyxczEq2Xq7/oP3uVdVeKDdPv25L8Qdd8xiKa/37d33n3C/H4j99lupGXPx+z4UT/qF0+MjH/lIPOIRTn/c+FZSj7I3qh496/T2eeJ7sK+jiBe+9Pfi8CNWKtHUpzNHXH7pR+I1r1C1Y2Payac+ME4/43krh4E2Sfd//YW/jN960fkb0ynntxx3z/i1X79gEoHRkfJk5orhMN4V//1FL4i/+sLQOkX/f3vXASZFsXUvICAiJgSRqGIAAyKggoI554SRp/jjM6BiRiSqGMGMGXPCrGBCQVAEJUhGwYDyRCUpOcf/OzV9e+/UVIfZndmZHW7z8e3uTFc6VV19Tt17qzLbnNt63UP1GuySVEeUMPLrYTTgFezvnoOrHNH9jzxNW28Nt4OiK9fPQN/HnqGtqm4dL5gZ1XZZFHIApynSrku+E7pc4STLlRh16dWHtqxc2Ry4lmvsuPx8Gl/ATeJiW69y1Z82RmXRopAr7LTckiNgC4WS56g55AKBvBUKPT3XI+9gM3a/4J8ctCsDeP252RzS5rlsyN9FsG+66ezy/XpYnSY/d9XV0xbB9ROuJrKNJi9Hu5KK9wKZpa+9+d4TDil5WNjGKS+6fcn15IBvdteC61HPLtcZi4K8gtqX1f4Wh+bZzx6wxsp5uuMkI+PSq4wZKzyOrWDrrOIS9vx4dYs8cC2ITOaC3OUjgeMBZ5Ne6ddY2i+EMHJ7a68+hBU5tigwMbYf4o0bNiaOYa+QiG3AhV1iMGB5pwDzoXfoFcdDoGw7DW7DZ4wJ109aDngLUi6Lx5dMg+9QNr4z86G3Xy9vq+oqJ24axoHzlOPbtnSgrlwWp+N6xqmfbCPXD+nl+GY8kZ/rUqFQ2k/V5l2eCoXC6P+8FQoIZp63LMl3j1egfQInSbXoDieZte51Ef2gdHZPS9FgPNv5necdLMbvIvnO5985L3MImeeYGKe+gek8UsffszuP3MrXkF4qn5nybHER1QdiS2G0c8L3YxNCYWhCKJh3nNgVJw4W6fRdWJ8yEU/qX6u+fh863Ehj1bU46TzBlzRmxGdxyvXHixffEDYm5VgMSifFJ8bTkE8jgpmRkU3igsicLJTJnCQ68rOUB8lrID4PSmOXGzR122XLsuxBZwZtuYSlgkmb/xDixFbvxGBXfeNaN+ThHoHlmZPro8uz62jyxr/yiQAYHiB+G7wTEvnvW3t6QqFrwvUol1eQoMmFCGUc5FjPZT3s/pN1AW5lMZg5l2NNyy4ZAioUSoZfvqTOX6HQg2bOS+w2Lxd++X3NhNgnxiIY1RBQLzA16Peo75PSeeRRppH9Z3OIdPJO515ZJxl4i8/9lXuv3T4/cVgZXGX6osUTMEzeo+oXJ53EynY9YitJVDmpbTfMJrKfg9JxnWzxKUWg5HhBmCU2/ix+PYLaHVQ/e96Iwi1O/8QaDzgcT5ywHRnMHLY6XZqTn8uyIetmE+Rs183GRdaPyWbOiKa34uHCwAiFKgmLggs/1B1CjVfp2TrAbUIatAufS3EVO115HBGZWjNX/+Iuv9yNsHAUWRRQP7uO8AOGP25QHaVlQAoCmY4tCrI+tnWBhWwmynNhmWLBWL+BKni7VjFyalHI9hOu+UsEVCgUxnjIX6FQZFFwEXR/Xo4QBC5yHUdIJKXzFteY1DK3YHJuVlvx/tlUjjaVS6yQRxG4jAgYz/Wa3322m48/QlF/zxJdVK8EhUy/rvHT2Xjh7wnfj6Ne7HqE9/5GV92k0OPtINLFNDqd32+CAG8y9UleZE2nL9O5N3Bs8ngzCnmTOahOLvom1TuN8V/cZ6GoH4uI5BefBJyjMHDQQKpft3bo7Mj7y+fVFBpCkvOqnlmoTBDZlkX9u3Cx+bP6DjgvMP+ujRs2UfkKDiWRg6ryJJKDoiOLxGnkS5ctz7tzFLr36E4Nd8FRX3oVEgJr1qwljLm6dXC8nF5lFYEVK1bR3PkL8i6YuVv3btSgYUNsl5MKLSz7rs+z0QkpZQlC4RFd8lxes1F8aJ42tym/iWhDOSL83OTh5nlB5KyOsgEbN5ltbv/9ZwHVrle/6KTzXLze4/DC0hxnFk4p4xtbbHGflvpA8wpkPDZuopUrVtCCOXNTdz364MMPacfqOJ1Kr0JCYPnyFWY1pGpVnC2rV1lFYNmy5bRy1er8Ewrdu9NONRP7QetVOAisW7feCNN8XWAoHKSz2xIIvsVLluafUOjWjWrUxFHrehUSAuvWraPly5bS9jtUL6RmbXZtWbNmDS1dsjh4e9TNDpECb3DXOx4gnL6KXY/0KrsIqOtR2e27slhzdT0qi72WWue8dj2au8x4pW70ghTkxhD8u8s11MQ1eqf+osXZTOdb7bH5RJplmboXM529WYbfRkfQq8TIhYXByMMrEE+xaUhSHnHSbdroB6VPnjieet7SiQYhmNmzJCS56yS53aTbd9huNeFylFafo22oo7xgtCqfCAA3ngSe1Uh6aXD8rHS59n83W7+mn87lUodqGYw8o4LpKxFDK2Ne7fqVZIyFjc3Ik5kLY3rUVjACKhQKYyyoUCiMfiwrrVChUFZ6KryeeS0UzK5HHgGXxJ/JqbfxiNw5xxAqScDTTOcT8JB09imKKDNJmKRRZsbSCeLIPuUyyJpxidM+F54lSWcOW/Ou8ePGUK/brqdBX4wysQAcIFvUbwlSHAsXJtDenrPh6Tzi7qmToPJ8Eg4PLq/eRiyI3ZlKOsYix7SHlS+gWFDZAliMMz8IO6J9UhTi9+Kmi9z1KGXHI8vXK2cBuzmet5PiAeL4v+WovnbcQrGEQnHb50hXVsdLSr0ziEmsoWGVd9WNPem5l9/KO9ejXr160sq502M1SW8qOwioUCg7fRVW03wWCnwyc74hLXfD4RXgfKijWRX3VsKTzlLwrAz5UEeco9Cjcyd/e1SuUz7hiDq5tlwvDfzssWVbCFgMlUZd7DLk+Io8mTluBZlI4bTTChXKm91qePec9es30BZbYB99dMgGf099BEPj3kQnYX/+cs50cevguq9o15yESiwfo7ykem40+tIzbeFnySJxErhUSIk1SuzUw3gkot+BH/BEmXiwGEe0k39PZztPlNHtzgeN61Gv2zr55rWS4Bs3rWxf3DS5vi9fRQ2w1O1Rcz06Nq/yjVC4uReN+fLDzavhBdbafBYKv85d5u/24nQlSSyJBh6IaVZPHS4z5o1dwnTy7CB7t0D77BynO4g4yNN2IwqrH6ruKo+Hpf1doqkJjMLcpOS2oLKMoPIkkY7TPt4Z0AiFWzulHLiWVGaIO5ZsT4o7UDHT2fgzllHubdJiU9I+53aFTS9y9Z9dpdLqN5cFIsClKqltXjruQ/R9pOsRE/+wwcS+W9meU4sGvrePcPny9PPMWfToky+aLaX63tWFtqxcyVTjtbc/ptHjvqftt92Wene/IWOk+J9/F1G33g/SD9N/pmrVqtENV19Gxx3ZypTpeoC8oxGcW4JmCq8o64b83hy45p3MLEUIH5wW1I7AzxNHCYa0v8hvr2hyS/yWsBgnn/1wa8/7afmKlbTrLvXopms6eFugbqRbetxLCMRr364tHXjAvoHlhY3TIL/Dr0eNp1cGvEPtLzqX2rRqljJWXGdshOHEWEZNqHYe6aRT16PMPD2PP/sS/fL7LKq+3fZmGz/ew73rHffTytWr6bKLz6d9G++VmcKIaPDQr6nfMy/R8pUrqcnejeihe7pTxYoVM5Z/tjIqC0Lhrj6P0b+LFlH9enXoho6X+VDcenti7jj/rNOo5YEHZBSiceMn07Mvv0nnnnkSHXtkm4zmnY3MyoJQYFLOvEJuFx3mn52NdK4V32efeYT+mPU71a5Tjzpec3NiAbJ8eerdqzOtXrOa/nvFdVS/wa6hfu7wgy9agPS2WyW4yyR+l++Gzz8bRK+99CytWrWCmjRrTr1uf5A2wZrgbRGeOIOtSBjwe4e3Qk3C0TtzKRRHbM/p5eeT5zjp4OPvTaCmTBy4NuF76ukJBT5DCm2zy89G36UzVhB03fv2zoSDZjtccS3tsktD0wdjxoykjwe+S9tuuz3d0uV2X4BJUYH+SqcsOZ773t+T5s+dRz3u6EPVqm3jizzuf8aFx4SfNmCsJKVLY4w503njAN9FnszMk5XtgmS7tMiBbZM/Ow+pgplnMtjcAUEn0tqT57r16+mIEy+gsROm0CknHEUfvP6U6bTt6h9Aq1atpqGDXqPDDz3Qf/DMUe/lEx2Lyy7PnD4M64ZneZArCXPn/UMtDj+d5v/zL1WqVJHWrl1nLCJvvfgYnX7ysX7VolaiXdglKugFsWBFQBz2BkKPuSNhYUhYZGySKcsMzJ+IuvTqQ1taJzNzWv5pp98g9vFP7ruiw/FsixLqJ08wDsrb7s8ZP8+kJoecbB7I36Z8TfXq1KI77+9Hvfs8Ts3235dGfv6mT6y4LoyJjY2cIH11DCw9C83sv+fT40+/RC+8+jYtXrqMnut3D/ZvnBwAACAASURBVF1y4dl+lZw4ei5APibe33yvjSUmHowlHudFlqCE5Y37saiuRVarhJDDmRZFFiQuR4VCZmjUpKk/0sFHnWksnn/PGEPbbluNnuj/Ct3Y9W4jVid+8zFV2XLLjBT2/KtvUccbe5qXQeUtK9HqVWuo1k416MexX1DVrfJ7F7KyIBT+mP0n7X3Q8YR3Avpt70Z7+H3ZeK/dadzwDzMmyhYuWkQPP/E8vfTG+zR/wb90/52d6fqrOmRknGQzk7wVCj170My5OHDNrLgl1kwcp8wGLfbIOIVMpnP1xbjRo+i804+lSpUr07gfZlG1atvSU/0eoL539aRdd9udPvtqHFXeckvnwmHSO9469ZiJtHzHPv/MY3R3zy6Gk2xRqSKtW7OWdqpbm74ZO8OcGRR1ubBAmnRWptPBU+aN3+WBa7KuSeVL3hOBSRLvyWA65HvxeafSyOFfUsvDDqc33v3McMRjWu9Ps36dSd3v6kOX/vfqRMBzSh2DLVZBvPjFF5+kcd+OpC8+GkQbNm6gMdNm0Y471vDjbbDwLQ8C5nGfFGPgLbSmWloEjywhnqY/qRyZA9cuPd+969EPYwabioedlyDVETKNIslRA9v1vSxDPkQ8KKf88BM1P+w0Q95/nvAlPfrUS/TwEy/QkYe1os/efcEn2TLvRYuXmheK69pum2omL/t6oN/zdNvtfahli6b01adv0KdffE1ntbvKEO/Ff0zyiR+nSxlQYv9lkEB0p4v0p42R8F8PWsXmPI1QqFyZena51h/00l8wTGQE1au4abj/bJFzctvLaMjwkXTYoQfTx289S7vtfwTBkjPs49epTasW3rukyFKB9P9450NswknV3mEziUFOVGPHHZImbW7HN2Om0lEnneM3i4WC7DeMfT4EJWr77CAcMvV5ouGJRnW8qRf1f+lNjVFI+2FJToC+ueK6rvTygPep0Z670aSRn9DOexxMi5YspeEfv0GHHNw8pQQI0oWLlgSWXAPbSluDZfXqNVS3cStatnwFjfjsTWredD+q26glLVqyjD566zk67qj8Xo0uC0IBHXLhZdfRewMH0yEHHUCff/AK7dXiGPp7zrxQjBf8u9DfpUV2Klw+q+/g3iL8p19nUpNWJ/m3q1Ao/oN4zz33UI8ePUm6Htk+7K73ftC7zvW5+SzxwnET+ACPAFer8I657sr29PGH79IBBx1M7340jBrX34HWrllDHw8bTXvv2yTlHYUVa2wv6Vvui84HM59Vr1EjSSAhg9WrV1GLvevTqhUr6f3BX1Ojvfc1f69Ytpze/XQ4NWtxcCzQJXY2qUzgYoIa0sIlKB1XiAktXI96egeuSXGA9PZ70dmflhgIFYqyD9NMh3ovXbKEDmvRyPx84a0Pafb/ZlGvztfTPk2a0qAho1Lc2lBf9Cn6Vnpk8O9VqmxFVbbaKkmUMQat9m9I8+bMScBVjmjstN9pxxru7YFlmw0FEDtFxRF8Mo0twm08pSiUgyvwwLXJk8bTj2M/dw7EIDHgGpDIAPdD2PAKKlc88RMr5UUPbxjpDFuVvqXHffTIky/S7rs1oF9/+5+p9+zpI6lWzRp+G2Tex5/ZnoaN+M7Zvi8Gvk6HH9Lcr1difilH513aid4f9Dl1ufEq6t3terOSVKfRISaPpX9OpipVEiuPSQLBO/mPcYA5LugwFNk+LhOTkjExeg8zV9jGwsfeKy9IwLksCrK+fj0F2Y4s0xu4QYKSPw8TTrIjVq5aY8jaylWr6NCWLWjU6O/p0ovOoWcfuztlAkY7cYhc7b0S7l/2td1229CCmeOS0sl6Iv3J53SgoV9/S/373UPthUXBmaH40LYepPS92C4t6JkJ7Ecf04RVQV7AUWMUonon/vfYV37nPQ8289QF55xKA979iM45/QR6rf8jTpfF6T/9Sk1bn+wsAGRk0R9FcwHf9PPM32m/lifQNttsTQtmjjcfd7yxBz3/6tv03/bn0+N974hf4RzcWVaEwtq1a6nBvq2NkGvdsgWNHP09nXPGifR6/0ecqKHPt955H9/CLG9CX833+ioI8gs7XEfvDRpsLAo3dOzgu67loItiFZnvFgWbLNqNSlgOEiumfLlERUnT2eQM+UkSNX/eXGrVpKFZlDrt7HPpw7cH0FnnXUQP9OufRIDZ/WfypO/pzBMOcx86Vo5oxqxFVLlyEX9Auh9/mEwnH9WStt++Ok346U+T7w0d/48GvvsmXXrFNdTzrr7JpNFbR/LryoevWWAE4cVCSr7HZNKkdKIPkM7XPcIKhLS2RYE9R/CTsXEN3LA+lcLG2c/iQ+YcBhMhiOx28X2PP3w/PXTvHdRwz71oxfLlNH/uHHr7ky+pWfOEKLOJdbu2J9GoEcOdz95/r76euva81/9O8mMur+ketWnZsiU0ZurvVLNmraRxHfZAB43PWH0nOWqiUaYoiZUsG59HxigggXTTsTOwH1b/QRe+63aDw1ZYTXfyIYNyC7AiV/gk4LnzFi5aTLs2OYJWrlxlvu/d7QbqcuOVvoXDFjFDho+iOXPnO/vimCNbU+1aNVIsKSwuHry7K3W68hL6d9FiqrV7YgAt+mMibR1wiJnrhF+bOMa1xLjuCxJo3DiZpsvtfU0cx+23Jc5RCFP1Ehy73HTKxCxijpvnY+Ud+w3b+T/05Et0a4/EQ1Zt66o0bcxgql2rZoqaxvdYrX37g0+dfQnL0Plnn5LSTlnecWe0p+HffEf9H7uH2l90tueHmCxgo04ht/s4UDR7rkgpz4QQlEHPh6yzuh6FTaPpf/fGOwPp0o6dTUKI+f/9MJJq1nAfHAdh8dFnXwYWcmHb03xLId805YfpdOARZ9CO1Xegv2YkFijueuBx6n1/Pzr5+CPp/deeTr/SpZiirAgFQNL/5Tfpmpt7GXSqblWFJo78hBrUq+NEC8/aG28P9Pdjlzdh04fzzj4lFOULL7ue3hv4GfW5swtd3/FSFQrFGJNsUcCuRy6y4lrldM2RciHKXr3masVNZzeDyTDSs/vJ6y/1px6dE+9RuLNO/mUObV1tGycCCxf+Q8OHuBdekeDMtuebTU7kNX7cd9T25KNp59p16dvJvxhs7u/djZ7p9zCdePqZ9ET/1/3bk9yDPP916SbjqlQYDzNEXlhfZL+40rmIK8qUQkGKhDjDJKh+KX1jiZM46ewxJf8+rnUz+vXnGaaYthdeTPc/8rQfh8I4cBnffDWU5s+b52zOno0a0377NzPfybHJf6PM/XffOSEUYFHYsabZolWK4KC2xMHSbmMULpwn6mePndAYBXY9kijI4GYbHZ8c2avoaaxOhw0gaA8E8dgBHlzu+x99Tue172S+X/bXlBT3IQnUtOk/05Ily5KCjBnY/fbei7aptrWpilzxPbtdR/po8DC6vesN1O2mK2nBPwv9lewlsyfRVltV8e8HsUwYD5IDk5xEPwmvonT24GJs/DyMqk81ULC7jI0T0t92e1+qXKmSOXBNklkz2YmJIWoFXA52WU+ZLkzUyH4OSlN/nzZGzD18bw+65vJ2SUMjEcOREJU4NXbshMmJ+htXoaK2wPf84BZN/bRyDHC5x51xCQ3/ZnRKjAInihZFibgC+76ivxN95Hp2cA+/fCSm9vixrRcqFOK8auLfs379eqq+WzMTN3BXz5uoc6fLA0kfTsSePO3HwMwPbt40xQr00y+IvTmJtt9uW5r7y1iT9pYe99BjT79MF513Or3weJ/4lc3BnWVJKACeJoecSD/98hv16HwNdb/l2kDE8OyNGT/JSVC3qFCBDmy2fyjaLBTuv6Mz3XC1WhSKMzSNUPBiFMKEgk2GzXxpHXKQdI9FIM17wTgg2amSrQWuNriIGVyNmjTciWDFuv2+h+ji/7vST2qTtBUrltP0H6cm3hGIkfPO+9oEY/EmMivWcEP2PQGoHE2dPIFOO+5Q45IyZupvZk7pess1NOCl5+nci9vTvX2fSLF42qv0YYRYvidlm6MwDErH7zFuA+5zWRRcfczlhxHgpLZ4sabJ78yiQPCg9tify7py/sOHDqYOF55l8DYEvkZNk0zeyxjMmDGNli9N7Na1qZznxuUtku+0c22qV7eBU3AxL2zSsBYtW7qExv4wyy/HVXeXWJaYSUxdnNO20PCzwE+CFMCusmIFMwcaBzx7U9RqccqDZ+0J75M36/OiINWiwE87L0ky4XLU+MDjCBP88jlT/W1YOX9JhKNcjw5r1Swl5gC7HfV55Fk66bgjaOCAZ2jKtBnU/PDTTZVWzv3BDCze7lW6i9h+8y7CaH8mCW2IccaHI0hU4AaZtxEKwqIg8YxSnMV5CXCa5PaAHCfIs+wTvpf7fb+DTqAZM3+nd199gk4/6RjfwmPXE8HldfZKuIDZF4jZ/Jljk3YrsNOzUIBF4eILzkwJWDJ5irEZJpZLglFUWmnV0BiFKLTS/75Oo5YmFubdV56g0046xh+jvBMS5xjmeoR7Fjtcj5YuXUo77XGwGfOzpn5DNXasTnsfdCz9Nms2Pfvo3dT+onPyejW6rAmFI04+n74bO5Geerg3/V+7cwMHA7sebfCs1/JGLBYt+C3hJhZ0+UJBXY/Sf+C8FDJGAR9JshhGkOIUWNL0dhl2fs0a1aHFCxfRgA8HU8tDD08WnOLFPXmi53rkqjRcj/5YRJUqVS5a7CpHtGjRQjqwcX3j3jR66kzaYfvq1ObAxvT37Nn05AsD6IRTTnfG3tkLfkwSUwSA59YchWMUhiZ/LwDd7r8UoeCdvI00+GdzR66LLDOQl3j42hyCuVBQOv5eLqRKoj3n77/o0KZ7UMVKleinPxf78Nj4Ye64+NxTAl2PLu94A3XplXCXDsLQWBSMUPidqsOiYAVLu4RMVH+4+K6hMGJHKikcWFwGiUzca4KZ2wcEMyNGwV4VlucjyAFmE0GXgrEHZGCaJFJWdO6CS1FKgsxCoWLFLYxFgQOFXQ/CpCk/0uKlS53PSJN9G9MO221rvvMVPrb6mjyNWh9/nlm1PuPU42jIsJG0ZOkyuubyS+jhe7sm5WWvLst85I02nnKFOYq4B+Fnrz7L8qRFgQeP60EL6j9Xu+ytwoImHt4BKGjc2On2O+h4mjFzFr33aoK4uS6QdpzNMWqM+4WOnSHgq+y6pkz/jbrdfh+NHT+Z4Lq23957Up3aO1O/vj1pl/p1/SR2m1152ZjbaVxiL/WeRIA255UIeE+N7cE9alGIer2l/71LKLhyWbFyJX0/Yaq7gHJEbVodmGJRwM1X39SDnnvlbRNcf8ShB9M7Az8zCwv/+2EU1TQB9+nXubRSlCWhgDXjwz2h8OTDvalDiFDAMzji23GJYDnrwvujtbd5gv3dX3/PpY439aCJU36kefP/oUZ7NqRd6tWhPr270F57JLZWzMcrb2MUevSgmfNW+As6YfzBRZSCxIUkhfy+k/0i0+Fzf/61XEDkO1KSymaN69DifyEUPqeDWrX2V/jZVYnbATI4dfJEY3lOWPyTz3xoeehhqdaBcuXoxqs70Advv0G1atemfQ44gL785BOqsMUWNHXmPBMoy20Kqh+fhMw7PUbhKnmK304vHoHjK23yat5XsN44BrwRCp070cdDv/PtOIF1dViAuE9skhtGlm2ibFfLJ8bC2wNpmKTP/fsvOqTpHlSpUiWaMXux8TqR72W0l/vvx6mTaenSJUXWBmgm401SjnauU5ca7LJbUvFc7xuv7UCLFi6kUcO/JFizWx5+OO3ftDnd2vWuRHli+1OXaDKfOWJQJNm3+WMULnIsJfXRxk2J7VGDdj2CUAhbqUbGcnVVigoXyeUtJYPSIL8glxeJdhCB/uPPv+m08y+nihUq0OhhH/i7CvHA5sEQdwJ31WXI8G/p5u5309Kly6lipYp0ROuDzYpgAmS3i4ld35TTrr0KudoVh6Rye2R66TIlJ4fb7niAKleq6McoyMFh4xJEZIPEQhCuLnEZJJyQB28heuaFV9KsP/6ih+/tRke0aRlrbLjqwP0i+xN1mvTDTOrQ8caUJO+8/Dg13LW+k+zJ+tm4++M6yGLmqFzQwxzkpsR9efVNt9OzLw3QXY/iPswx7jv61Ito0ZIl5myDI9u0jCTu3kJajJwT88LqNWvpiuu60XfjJtC6tetou+22pZef6kv77dPIO1MkVlY5uaksCQUAdGnHW2jytOnUo/O1dOYpx2ccs1mzZ9NZF3WUYZzm95eeeoCa7NMo4+VlKsP8FQqJXY9w+SSF34vez6QVXblTj2/wTezeY94tJm6gvG8Jlgtw/jvPSidXxWU97HlekqhzTj2Kli9eSvc/9jTtf0CLxDtKHFiV4q5ibUrC87/LrQWfwb2p83VXEHYP2rB+HW23/Q70xPOv064N90hxhQniSFIsSZJryrYCfOXfPl8S7zOb4zFOdv0Zy8kTxycduCbPduB3mU1uWXSY72XQdEggu4vvuESPLTCShKRX1j8L5lO7c06iShUr06ChoxKuwd4mNP772ltY2OgJDB97xw6UEhsur905J9PCBQsSvNFzH8cZGfc99FSSa70/bsWY4s98/CyB5fM9ry48JhPbfiUvfqeIQS9MgJ8h3kgndHtU165HUcIhakJzkWF7NdYm0kGnD9vEz2VBCCuPewl+WkFKO45wiWqzPzGVS5w8HWbpkOXFKTtINMky5T1d73jAnMyMGAU7/zChEqeNLAiQb1AbXW2KwsRVNvLhiUz2XdGD7O2NGqfiIfcEid8UrCxxUMJiE0PTC/p24aMWhUwgnJoHk/84IiDOPUG1tNOWJK/sIJGca1kTCgkC4zQUZAwu13bJ+WwVQsPzVij07EG/zlmetHJrk68UMmr1JL8PfDIM66xYKQ56VyaIYGLfer5cZcn0Pon2EkiiynnwPTZnssWELVDsAcrkVpbpKk+mS5DshAe6XVebG3C7XCRanjNlE1S7nUxCZZoJ2B6VD1yzzsWQGPOKPfIwbttiAxSfzAb0t3QLDhoj9qo81zEFM++DJFEqD1Rj64oVd2uLAclJEqv/iJ8oOhvBF7KiAtwP3N6gMSZxD+o77mO+Vx5o5+JL/BkfBMjpjINY+RiuR0ggD86yB7F0J4kk7iFkyiaQxSGPdt1cB3Gl85aIFBkiM1nfqLpHfR9HIKTTDnmvEQqIUejSKa0swuocVV8X4Q1LA1McBm6YoEqr8tZkzmM6rmALGldx6xCEXdDnciWEywAm8oAdFQpx0c/efZkkopnMKxstLotCIRs4JBOL7AqRbNQ/f4VCT5o5N7HrERMl+zDUIMIInCTR9MmmByDn6doFiFetbZEgd+jj/H3y5fnYsDuPf5gnrAnCx5zT2fWWgsYuxydoglTbAsEmfz7Js84mcAkLFlFsObDFgSSh9rbctjgJEhZy3MIS0uPWTjToi1FO1yrca3MBe7E0aBGX+9xVT3s7ebsPnWPMqPyi07E5f/wMW0i2t5V13ZsihBKDyQjZFFERsq2633/WKoUUGRIPvp+fCZeQ4vLlgr0cY/h8yKcfhbseZWOySidPSZCiCLb83iZWsswoYssTggtA1xafnLdLLMQpKx080r3XxkxaFOLmFYalnQcP2KA0LjyCVnqQN9ffZf2QDz9+5zI5jSvfQNLuOH06Lj5B97m2xZUTcdDkE3YPt0mFQkl7p+TpM0nuM5lXyVuWmoMKhVRM8r3PXOMgn4XCr3MSrkc2kQ4jqExWJVlzvettawDfg3RsdZCkiomVTfLlnB30bg+zfLhEQhzyzj7rXC+/jd7KuySzfp3F6rdvZRGuJa7YQpNW7Cgk08l+kCRa4m2TdxYKJkYhxNwWRwzJ9P4qfciOmvI96qqj/XzYIi+FP3gWGhcRT9q8xmonj00XaZd94OJS/JldpusUb38MeGNCpo0SefK5w+9SNKDsyGBmSdZcEw+DaQ/EoJeVi6hxpaKEQHFfgLLD0yXu9mCxA3JtQhy4Shywf35x2iTLDCPZrrylUAhy6fLzdASVc572QIpqRzp9axP+qLzxfTrCJGxMxxnPHGsj6xUmaIIEQcrY8lak+KENS6cHrsUZFdm9J5NEMZN5ZaPVKhRSUc33PnONg3wXCnJxLokkCV91SbaZHNmLMjYpcxF+e65nEm6v2vpixFvl53SSFPu8wguIkyu8QcJH9o9tRZbpXSSO68rf2e+MsBVw2U7XfZK026SRSa/TEmG2fi2fdHghxyi4hIKPn+ciJttUJJ5S93/n+rGbjHQj8om347RpxjtQlFgB7PbzE4QLYy/f54yPHCsSS/k7j80UTMVOUhIbu89TRIpIJ61yUsC6+k8+I9JKhvJKZFEIIqnpknHZIa480yGZcV+SrjpGrZzLNGEEPVZ9HUEvsdL5oz3YB1/WM8qiENRX6QoQrlZabYjRWbZ1IKxtLuJuP5BJE2OG4woixVPM8uyVG/kS4H5Ri0KMwZPlWzJJFDOZVzaarUJBhUI2xhXyTJyjkHA9kvO7nE+ZKAWRKknmk/iE2AKU537kYZP3sLbJ+dgmfjKdfZ/0C7dX7+13hdzBRpYRVFcphGyyn0JSPXcaWVe7PpIkuoQN0vqiTKyY2yLK9sJgi8JHQ75NgtjVXylcRGwVaAs9ro/EJ4mIW5YWKQrNoWbeeRZ2P0StvLssSq66yfqZ3xMfFB1enMbBatwu1zMgRQMDbI+HItGVCPYPswZJDCU2sU5mth+iFDIooqu5MUHEMdCi4EWV+zzY8t2SZMn1cOIzO+8wFxRZDneq3U57ECYNRO9mGb8hyXXSoA8giLK+UcTc9b2cNMMmOpnW7HrkBTMHlWl/zn/bP7nMIILM9we208PFLg/3YyILWhWRbY0SJkG4Ba2kyAnMXiXiMYjPEd8QZ3yF9Yv8zh5rst6uNqhQiIts9u7LJLnPZF7ZaLEKhVRU873PXOMgfy0KPWjm3BVFB5xy8KfHspKInhUHkMQNzM6DiajkqD36nau+luVCviecBF7wFl7hloLEFgs2R5DkL5DAee5R5l6vPLk6zPxFvp8TsCWIoYvH2PdyHklE2XNBssmzXZ5dFyk05K5HNpY2pzPfC05gvx8D4z8MVyzv97ctOCSRlyvpXE8ZDC3rxLjZ/cnE29QH4zQkLkWOMblK78eIELaVTQhXiY90ieNYBu5TPiSN7/ctM149uF1Bz4wsK8qqExnM/P4HH5iThl0X7xEc5zv73rC0xXnByfyyXVZQe83gKk7lc5Bm7Zq1ZvLAHsGldaXb5+ncn869mWqvv0d2Dvt9zZo15kTqf/5ZQNWr75ipppUon7vvvpu6d+9O1bauWqJ8NHH+IYADyVavWUNVq7jfCflXY62RCwFsPb1q9WoaMWIEtWnTJi9AgkWhW7duVLVataSdh0zl7EU2+aIVuxQl3cdpXGk5TRzrLpcVlCbO90F1lG2TdQmrcxJzjrGrXxB2rheYrKcLdy7bhZvEm+/jrTjXbyC8q6pUTZz54BMliWmCARddUf1m32+P4qi+lf0Whr1sM5dp97nrCQrDKG4fugilPQ6j2hE0PoPwCfl8w4b1tHrlKho1ahQdckjicNtyAz98f9PUaT/mxSSilVAEFAE3Ajis6/rrrqMtq3iTcI6B+vbbUTTi669pg3egUI6ro8UrAoqAA4EK5cvRRe3+Q/Xq1csLfL4bPYaGDRuGk6ryoj5aCUVAEXAgUH4LuuTidlS3Tp2EUNgU5HSl6CkCioAioAgoAoqAIqAIKAKKwGaLgAqFzbbrteGKgCKgCCgCioAioAgoAopAMAIqFHR0KAKKgCKgCCgCioAioAgoAopACgIqFHRQKAKKgCKgCCgCioAioAgoAoqACgUdA4qAIqAIKAKKgCKgCCgCioAiEI2AWhSiMdI7FAFFQBFQBBQBRUARUAQUgc0OARUKm12Xa4MVAUVAEVAEFAFFQBFQBBSBaARUKERjpHcoAoqAIqAIKAKKgCKgCCgCmx0CKhQ2uy7XBisCioAioAgoAoqAIqAIKALRCKhQiMZI71AEFAFFQBFQBBQBRUARUAQ2OwRUKGx2Xa4NVgQUAUVAEVAEFAFFQBFQBKIRUKEQjZHeoQgoAoqAIqAIKAKKgCKgCGx2CKhQ2Oy6XBusCCgCioAioAgoAoqAIqAIRCOgQiEaI71DEVAEFAFFQBFQBBQBRUAR2OwQUKGw2XW5NlgRUAQUAUVAEVAEFAFFQBGIRkCFQjRGeocioAgoAoqAIqAIKAKKgCKw2SGgQmGz63JtsCKgCCgCioAioAgoAoqAIhCNgAqFaIz0DkVAEVAEFAFFQBFQBBQBRWCzQ0CFwmbX5dpgRUARUAQUAUVAEVAEFAFFIBoBFQrRGOkdeYLA4MGD6cQTT6RNmzZlpUZHHnkk/ec//6H/+7//y0r+mqkioAiUPgI6b5Q+5lqiIpCPCHzxxRd0/PHHK4dIs3NUKKQJ2MyZM+m1116jXr16pZky+vajjz6ahg0bZm7MFhmOroX7jmeffZZat25Ne++9d3GzSEq3ePFievTRR2PjOHr0aGrVqhXh58EHH5yU15lnnknffPMN7bzzzub/unXr/O/LlSvnY4k+O+KIIwLrv2bNGtpyyy3pww8/pNNPPz0j7dRMFAEgkM1545hjjqEvv/yy4OeNZcuW0ccff0yLFi0yc8EBBxwQObh03oiESG/YjBG444476PbbbzcIDB8+PPT9WNowvfPOO/Tjjz/G5ghR9fv222/pggsuMO3cbbfdkm4/99xz6Z9//nHOoZJD3H///XTQQQdtdhyiIIUCOn2fffbJ2ACTowKrzl999RXNnTuXdtppp6ixmfb3eHD79+9Pf/75Z9pps5kAD0vHjh3piSeeKFExV199NS1dupSmTZtGkyZNii2IunTpQlWqVAns0wkTJlDz5s3pqquuoieffNKv47x582jAgAF0ww030E8//UR77rlnaP3ff/99euSRR2jEiBElaqcmLnsIH0HNdQAAIABJREFU6LyR+T7L1LyBebdly5Z07LHHmgWGoUOHUufOnSPneJ03Mt+nmmPJEMjWPANiDf6Ad2s6F/gMnq9ffvmFdt9993SSZvVe1GnFihU0duzYjJQDbwEsMl5zzTXO/MaNG2dEQBiHiLOAW4gcoiCFAl5OUMnZWPXHCJs1axbtsssuGRm8diZ4OFjdZ6WAYma6YMECWr9+vVmxL8mFyWybbbYxlgH0UZwHb86cOdSiRQuaMmUKVa9e3Vk8rDyYCF555RXz076w+jhx4sRYVe/QoYOxKJx22mmx7tebCgMBnTcy34+ZmjfgLnD55ZfT2WefbSp52WWX0fPPP09wJYB4cF06b2S+PzXHkiOQrXkGAgTPG1bM07luu+02uu+++2K9i9PJNxP3/vzzz5GLe3HKGTNmDB122GH0999/B3KIHj160F133RVoWdl1113p999/j1McFRqHKDih8Oqrr9LFF1+cd+o4zujC6netWrUI5i2slhXyxSbPOEIB90Ltw+0g6ILFAJYAiIn99tvP3Lbvvvv6qytbbLGFETpxrn79+pnx89hjj8W5Xe8pAAR03sjfTuQVT7gNMgnCggOI0cMPP0zXX3+9s/I6b+Rvn26uNcvmPIMFOKyGgz+kcx166KG07bbb0qeffppOsjJ1L+aC77//nj766KPAeh944IE0ffp0Wr58uX+PcogEFAUjFPDSqFq1qlnpB9EDMcRDA6sCCOapp57qWxngpwaf3hkzZtAOO+zgD4o333zT+LDxCwkvo7Zt25rvBw0aZFyCYJqC8uQLyvG3334zabDi1b17d3rhhRfMywur0mE+8ciDX2YNGzY0K2OoJwY03Gj4gpn9gw8+MP56+PyBBx4wX8HvGabCk046iT755BMTOwFffahwlIu2d+rUyXzHZvv27dsnPShLliwhtPuZZ54xqwr4G25bHIvAhB6k+dprr/XTwvJRt25dwmrE2rVrTZvh43fKKafQhRdeGDmJpCMUUBb8sLt16xaYLywG6HMIClwgFwh8XrVqlf93VF9w5jAdIiYDQZB6FTYCOm/k/7wB94Onn36aateubeZnfr4xLwRZEHGPzhuF/eyWpdaFzTPcjpEjR5p3MN7XuOxFNLz/33rrLUN2TzjhBLrnnnvMew6utnCDfvzxx/14gzBvCvAa8ImVK1dS165dCbGRyEu+3+Gm+9RTT5n3epMmTejmm2+mM844I0Eay5Wjpk2bmmcSrjy33nqrcQ9izvH111/73gLgRC4+gHbgP9px3HHHGS8Nfj9ng2vFmQsqV65MzZo1o++++86fY9AXffv23ew5RMEIBTwwGMAQB3vttZchyPgbg++ss84yJBPuKyDBL7/8svm+UqVK/grVpZdeanzm4Z7CJiiQ6hdffJEWLlxoCDoCjREEg8/w3Q8//GBWrd977z1jEkeAHR48kGWo+/POO8+Ii6ALZPnzzz8nBNmA4ILwY7DK+ARMHCD/8MuFKJAEGw8pVtJB8jGpoEzcP3/+fPOSbNeunXGfAQaIDYCggQjhCw/BTTfdZNJgEuC8n3vuOWM6Q1AvhA8mIEx0yBcXHiYIKJj9q1WrZrDBZIA2XHTRRc6AYxuDdIQC+hGT0hVXXOGEEpNao0aN/IkG5le09ZJLLjGTXboXzJToz3yLE0m3HXp/NAI6b5SteYN7FPNHnz59COQqKKhZ543o8a93lA4CPM8gzg9xcpKfoAbgHCDvb7/9tlmku+WWW2jHHXc0JBwXRALewXg3Y9EP4x/X4YcfbngO8gfXgMUCF7sw261jSxy4C8oBf8HviPHj5wiWBfAKcA4QeU4Da97kyZMNDwIHwEJanTp1qGfPnkZMsEsVysS7999//zX1wCo97sMFSz24CNpw5513+nmDT6Dt2eJaUXMB6gg8WKzARemPP/4wi6jF2dik0DhEwQgFDMK//vrLrHJDcYOs48IDBIXbpk0bo5wx2GF1gDBgBQzzNfxd8dLZbrvtTDoMLJDMK6+80tyLtFj1P+SQQwgrzthpBw8uBjh24QCJ5YcP6aG4MSFg8LsutnLINPvvv795ACFEcOEntuq0LQyoGwJ0sboGgQDfOQgXXkVgUz3KZosILBwQNLNnzzZ5I6AYbUE9YYnAxRMC7gGOEFKwFgCfd999l0aNGuVjg3LteAqsCqAuCHjGhBh2pSsUUD77J9v5woKEiRf1RHtwQSBhtZFXQWQa1BPlM852fjyO4ApWs2bN0Hbol2UfAZ03Eju4lYV5A/W0LcRBIxDzZCbnDVhVQZLOP/9881/njbL/7JdmC8aPH28WKyU/QfmwXoM/QCjw+2qPPfYwxJ+JK6wGCMwfMmSIWcUHOQfZxuIiLvAR7CQYFp/AMTt333234Qu48N787LPPDIHHBa4B7gQSD2sAX3jXQ1Rg0fDXX381vvx4j8ry7NgL3Id2yE1EbrzxRuPNAcHBF9KBm2BBNxtcC+VEzQWwnGBxVLYH3h1Y/MQipLwwT0JM4Tv2HCn0uaCghAJUHB4eqEE76BYWBZjXbHMeFCwIOuIa8ADhgnqH5QEPEBQ1X4gbAJmGa0/FihX9z11BRCDaeLCDIuxBZOGqA2sCLiYrmBwQqIcLKh1WClgM+OLJhsk4zGQg/LBMwISH68EHHzSmQtlWWBfKly9vyDMuJuoDBw70g3bRDqyic53kJAFc77333qTnASseEFMsRiCgQOZdW5jaD1K6QiFs6zZMlphA0e81atQwRUEYYkKG8ENZW221lVmlgZjC9qloS1h8BCaWfNsuzsZQ/84MAjpvlJ15A3MmLJjYCAGkIuyKeoZd8wa2gIYQsecNlNW7d2/jmgmfblhbXa6MUWVmZsRqLmURAV7QsvkJ3lVYpJQXVrExxviCiACRxgXiCiLP7118tv322xvyj8WyoIvfubJ8kH8IDyyU4oJgAceQngf4HBYBjHte+KxXr57hKbw4ybxEvjPRXrgms88/uBZW7UHImWux6LcX5TLJtVD/qOcSwh+uUHDHwu6KuGCtAa/ClunADhwImypcd911ZpEYeITlG1VmWRrDBSUUpCuP3Qkg3HjA7FXk119/3bjoQOUeddRRJhl80jBQ4WrDxBN+snio8HDC5C0vPDR4IHgVHWrz5JNPNqYrfGdf2FoVQgZuPxxvgAcVDx5bGHh1Hgoepjq+uG58HyYG3IN4Bb7gBoTAXX6o4YqDtmPlglfl8RPEHg8xYjtwQTDBcvLQQw/5efEqoy2a4KIF6wdbH5AA+OM+CIWoKx2hAMGECSnIooAHEoIOZfPF8SVYzUDfT5061Td/cpuChAKLNhUKUb1YGN/rvJHox7Iwb2CugvsFrKkgHiAjQf7YmZw35Gop5mPMr3IBB/jpvFEY80G2WhG0EAerNcZU1C6N4CpY2ANXqVChgtmBB88DL2zalgq7HbBmwEuAFwJ5xZ9djXE/LAAQ4nC54Yvfl3wfL05KbwhwC7gps3sy0kKIQ9jz9qYuroW4CCzUyC1QM8m1uA0NGjQwvMbFIWCJgUs5+I+0KMATBWIBHAIu7XzGFRYN4JWC/sIujPDIQDykvAptLigooQAij1V1DAgoVahcfvgw0XNsgexQmPTwkErSCDPb6tWrDdGGpQGDh91yoMaxYg/zHFaU+KGBKQ0vJlwYSBgo8OGXh3VwuexTL4koRAIINvZARhr492EQQnTwIITaxYoXYjDgeoQLkw92SpI79MCaAd9GDk5CG2EiwwOIekOg4KGXW6nBLAnXKsRvQBzhezwAME1CFCBOArjwg4QXNMpkkyXqwkIK25PaZjx70kpHKCDuAVubuWIUGEsILrTLviDeNm7caGIc7IkvSChg0sIqiwqFbL0y8ytfnTcS/ZHv84Y9l+LFjfmRF1vsUZXJeUNu04h5EKSLgxy5XJ038uu5zrfaYNEQC402P8FKNd7xUijgXQ1BAM8DvMuxOMnvK7yPwQPAORC3yIuMbClwcQ5gYbsoczqQe3AWeCHACwLkWO6chHc53I5B6LE7EhYn4XoEjsIXFluxcQhciHCxeAGvgfs2+Bh4hc21wNcw/yImIhtci+sHqw3q6OIQsNzAdStot0nUGeIJbuDywqIq5kxwJvsqtLmgoIQCm3oaN25s1CEGLcxD2DUIOwpBgdvnH/DKOKtjFgQwbWPFCrsi4QEGsYWyBnnES4JNc3ho4HcnTVa8+oRBhC07sZptX1DuIOEg81wmhAdcYvBwQTBgYMPkhwGMiQMBT3gA4T7EvvOYfEDa2bcR24Oi7VJgyFgCnkTgkwfBg3YjT7QPwgiTEe7BqgNW4vE7Podwgk8eCxIEamOnBf6bVx2QH9qB1QW0L+hioYAXMLAIuzApwW0KgeL2xf6dNqnHpAlXAQgE4I/JKK5QYDMv3AwQIK5XYSOg8waZbYXzed7gjSnkSMTmE5ibg7ZHzfS8gbJBKuDCCFdH+12i80ZhzxMlbV3QPIP3NzgKrAW4ePGRz4KyD2iTXAT3y7+xig8C61o0A5/AhQVADo7G+xqLmhARa9asMVwCC6oIbsb1xhtvGJdtcBB4SeBCfeAtIT0HsKgJ3sICg/kAXKRBtFEm3I7ghcBcizkAVvnBc7LFtVBnWEvhTu7aOZHrEcQhwH+wICp3yERfQvABA7gs2VehzQUFJRSgukEcQaqxow8GJT9IGJxBgcUgzfD5A9nnrUjxkOKBkIMAg4MDb6BA+aGBDxv7/nN5UOtwywlatUb9UF9+2dWvX98QYQxIGYQMpY28EBOB+6HubcIrBzgeUDwMyJ8Ds/EZTybynAE81BAmwAmxEXgBYoDD6sD7DfNDhMAmaWoHDlKMLFu2zJj18ALF6kjQnszID2ZNuFZBfOBC0DfyDtq+FH0A1S7zRD7oaz5pGa4ACLyW/2GehVBk/8u4QgGuXLAGYZs2vQofgdKYN4CiDKzF3FKW5g0ZA1Xa8wbPX66RCKFQWvMGrxJiJVWSBq6XzhuFP1eUpIVB8wzyxMKb5CfyIMERI0aYVX6+4CcP4o0YQVx4l2JlHkQfq95BLkxwTwbPAVcBqQe3wP2wFMCiAB6EC+MY4gBeBv/973+NxU4umLksIOAD0vWJ+QAW7FAfjqdgroXv8VzD/dnmWmg72pipORP5wFqC7fDlWUw2hwB3QPyi/AkOAT4TFPsBPoa5wLYuFtpcUFBCoSQPsaZNDwG4dcmzHtJLnd7dWL2AqRWrHDw5ppdD8t1RMQogHohR4R0lSlKWplUEFIEiBMrqvAHCg1VPXvjB6ihWZeWl84aOdEUgPxGAQEAwOMQPrB/FvZAe57nw4ix4Aly47djXQpsLVCgUd8RoulJFAMFEMB0G7SIVtzIQCbjgjgWzKPwm5e4RMLUipsPehSJu/nqfIqAI5A8CmZo3sGIKCxCCR/Efrp7YZ54vzBvYAQ6xXHopAopA/iEAqwtiPkrKIbDLJDwxECANTwt4Nkg3xEKcC1Qo5N941hoFIICXtX2mRLpgcZA7p8PKIAsFuEIh5gMm4qitF9MtV+9XBBSB3CCQiXmDFxhkC9jlSeeN3PSrlqoIpIMAb+GKWEvEmRb3wi5J2EgB1gW4HmEBga9CnQtUKBR3tGi6nCAQtKNDJiqDnZWwg1RQLEsmytA8FAFFoPQRgI81DpnMxqXzRjZQ1TwVgcwjgI1jsAuT3LEpk6UU6lygQiGTo0TzUgQUAUVAEVAEFAFFQBFQBAoEARUKBdKR2gxFQBFQBBQBRUARUAQUAUUgkwioUMgkmpqXIqAIKAKKgCKgCCgCioAiUCAIqFAokI7UZigCioAioAgoAoqAIqAIKAKZRECFQibR1LwUAUVAEVAEFAFFQBFQBBSBAkFAhUKBdKQ2QxFQBBQBRUARUAQUAUVAEcgkAioUMomm5qUIKAKKgCKgCCgCioAioAgUCAIqFAqkI7UZioAioAgoAoqAIqAIKAKKQCYRUKGQSTQ1L0VAEVAEFAFFQBFQBBQBRaBAEFChUCAdqc1QBBQBRUARUAQUAUVAEVAEMomACoVMoql5KQKKgCKgCCgCioAioAgoAgWCgAqFAulIbYYioAgoAoqAIqAIKAKKgCKQSQRUKGQSTc1LEVAEFAFFQBFQBBQBRUARKBAEVCgUSEdqMxQBRUARUAQUAUVAEVAEFIFMIqBCIZNoal6KgCKgCCgCioAioAgoAopAgSCgQqFAOlKboQgoAoqAIqAIKAKKgCKgCGQSARUKmURT81IEFAFFQBFQBBQBRUARUAQKBAEVCgXSkdoMRUARUAQKBYFRo0ZR69atU5ozcuRIOvTQQ/Oima+//jq1a9eO8qlO2Qbm33//pYsuuoh22WUXevjhh6lKlSrZLlLzVwQUgRwjoEIhxx2gxZdtBFyEpkGDBrT//vvTaaedRm3btqVtttkmrxvJhOe1114zJKA0LiYcKAvlV69evTSK1TLKCAIqFHLXUTwfXHHFFSliQIVC7vpFS1YEcoWACoVcIa/lFgQCTGiwsrjvvvuaNuFlOnToUJo4cSLttdde9PTTT9Phhx9O5cqVy8s2Dxw4kJ588knq2LEjnX766aVSx0WLFlHnzp1NWX369KHtt9++VMrVQsomAvm4ep+PdcpE76pQyASKmociUDgIqFAonL7UluQAARYK9mr8xo0b6csvv6RbbrmFFi9ebFbN88VlIgcwlUqRYQSH+wmWnrfeeot23HFH33piWzRWrVpFN9xwA9WpU4d69Ohh6s55BzXkzjvv9O/le1yr4scff3ySBSUqX1mey+LTu3dv6tmzZ1K1XHUplQ7IYiFhpDwKw2zhESUU+Puo8tGH7733nhmXWFgIu3766Sc677zz6Oyzz04Zb1mE38+6NCwKpVFGaWClZSgChYKACoVC6UltR04QCBIKXJkpU6bQ+eefTwceeCD169cv792QcgJihgplYsZiQJIuJtS2UPj888/JJnKZEAph5FW6dESR3CChEOSaw/cXmt98WRMKTHYxvmxxaA93FQrJiKhQyNCEqNkoAhlCQIVChoDUbDZPBKKEwoYNG+iOO+6g5557jj766CNq3ry5AWrq1KnG/xduPwsXLqSjjz7auP4grmGLLbYw93DeX331Fc2ZM4fuuece+uuvv+jSSy+lW2+9lbbeemvq37+/cRtasGABXXnllXTTTTfRDjvsYNL/8ssvJs13331HWInEBRcoCJdLLrnED0TkcphcMlHG/Z06dTJ1f//99016uFjdeOONfhnr16+nQYMGmTrAgoKy0ZYOHTqYn9wWe3TIMjgoEoQJdWFcXnrpJdMuXj2tV69e6CADmXzllVdo3rx5SSuuTDxg0eGVW7YogMjhksTaJRRkwVF9zulnzZqVEn+BugCrm2++2RkIGpaW68CrypMnT04RObgHOCDYVFqwXJYHW0wErVZH1cmVt8u/neuGMRQkgII6OGr1ntNF9Y1NQjGuZX2CVv8l5igL1h1+HlyiDPW4+uqrzTMNLGxrkJ2fq92yLi6MZZogcczjHffCEoExg0v2jxQ1Uf1i4/fAAw/4Fi1Xnwf1m2usxRHNLqxd9S80oRw68emXikCWEVChkGWANfvCRiCKmKD1INLw/Wey8PXXXxuivuWWWxphUK1aNRoyZAh98803dN999xmyD4LNeR9yyCG0bNkycy9IMFwUQPZB0n/99Vc69thjafr06fTxxx/T9ddfT7169aIKFSoYgQBB0bJlSz9YGEQB9912220mRgD3BQkF1LtSpUp0wAEHGIvItGnTTNrLL7+c7r77bvPdY489Zlwg0B7Ub8mSJfTyyy/TunXrQoOUg4TCI488QlWrVqU999zTiBoIo3feeceIjmeeeSY0lgFEAxgieByWHHYpQvtAtK666iqDjXQ9atKkCX3xxRcGIxYsJRUKJQnUjiLlGE9MGuMEn3N+wM6+bHJZHKEQRmBtspbOvXZdsyEUME769u2bgouNa3GsN2grxm737t3psssuS9klqDSFAosV2dCSCgWOKXrzzTeT8LPFQraFQhiOKhYK+92rrSs9BFQolB7WWlIBIhBHKPA9WCG85pprzGo7YhieeOIJ4wePa82aNSao96mnnqIPP/yQDjroIJ/Ad+nShbp27WoEBSwUIOaPP/64IfoQFdiiMA7BRDmbNm0yFg7U6Y033qAaNWoECoVJkyYZd6kWLVqYQGwIk27dupnVd6QF0frPf/5DO++8Mz366KO01VZbmbbgvsGDB5sV7aAg5SCh8PzzzxtcTjzxRCpfvrypL4LBQbykRcY1lFgowLLSvn17k0+zZs38eIOjjjrKrPJKoYD6//bbb2ZVlAliSYWCJPOuld6wxyCqH9N1y0B+WPUFWZQ7SzFpl2QqXaEQVheUu3LlyqQyIcROOumkJD/8uDtuZVoouCxJ/JwGkWiJlRQPQZYZxCdhF7Eo16Ko7+V4SSdGQQoz2zoBERO0vWnYnBbkUiUFqcQjHaHA7Yw7xmVdXONYiv8CfPVokxSBUkNAhUKpQa0FFSIC6QoFrP4fd9xxhuhffPHFSZBgFfycc84xrkVY8bdX+vlmuNc89NBDKcGPLsKxdOlSAin69NNP6X//+59xPYCrkySwYa5HNpmQZdeqVcuslsLagfbsvvvusbs4zPXIDi4eMWKEsS5ErRCyUIC1A4IGIuzcc881LhcQDbhcQuGEE04whG7u3LkG0/r166cEM8uGxelzKRY4bRzRECUU0iGKYZ3hInDpCgVJDqMCdoPqEhfLTAsF1MceZy6CKkU+B7ZzW4LqhM9fffXVJIsWzoQIwijbQiHqubH7Jo5QcJ2j4MIqm0IhrJ7sxmj3cewJSm9UBBQBHwEVCjoYFIESIBCH6MCtCOIgyq+ZiUrDhg3pwQcfpPHjx5tDp+wXPV5+cJmwd0mxCQcINtyE4OcP152mTZtS48aN6bPPPqOxY8f66dMRCnbZcAvCCj7EB1bw2rRpQ8ccc4z5GXYYUzpCIUgw2d3GQgHiBr7nwAi4QyzhswkTJjiFAkSCJDmIH7B3PSqOUOA0tutKWHBrtoRCkP93SSwKaJ/L9SPMJSrIjSfKjSrTQsFFdF1CIU4QdVR8S5QrWjaFQtzdlOKO77DV/rC4g7jxMKhHXItCVExDHGFegqlfkyoCmw0CKhQ2m67WhmYDgSihYAczz5gxI/A010wKBV7tnz17tglG5jMeeKVbEoiSCAXkBzcGxC4MGzaMxowZYywXZ555pokpgGuT68q2UPjjjz/84E0moRxgarseQSjI1XHELAAfuT1qXCIVNsbirMBHCYUo0mmXH3R/JiwKsixXLISLQI8ePTpJ4EY9P1Gr9+mshkeR0EwIheLGM8Ql9OlYlNIRIHHHtwqFbLxFNE9FIL8RUKGQ3/2jtctzBKKIDoKML7jgAj9YFq4/WOXGajd8oeUV1/UojkUB+Qbtt24TiJIKBdkGxFogpgBB1djRCQHOuRAKsGa42ulyPeLTqJmEob477bSTsebY7ib4LqrPw4Zs1GpplFBgoYeYijjuPkFjJR2hkK44cfn6B1mF4mKZK4tCWP1cdYranci1M1A6hD6fhYILq6B+C3PpinpG+PmKO3by/BWi1VME8h4BFQp530VawXxGIOhlhWDlcePGmWBjuOVgdxDssIMTiRHMvHbtWkOo69ata5oXFsxcHNcjBD6DAMPfXp7fgMBdbHn6559/ltj1qGbNmmbHoDPOOIMqV67sdxPHFOA7xGTkSijY5YZZFPhe6c4QRMSjCAoTHQRKswix8w/KO45QkO4+Ljcm5AHssdOWa4ckmV66/PDnsEaxb3dYWcABvvh2HAunkcGkjKtstwxGjRI9uRIKLkzQl3Kc8PMZ1u8s8FyWg3R2sQqqj+sZS0eAyPRxYhTs80ekyJYuka5+j3rGGEeOGQo6hC4omFkKCfyuB13m8xtU61YWEFChUBZ6SeuYtwjwSxV7sbN7D15gQ4cOpYkTJ5qgYexk1KpVK78NvD0qPmjbtq3ZPShse9TiCAUEFiPgGTsjnXLKKaZ8uOMgqBkXzjvgF3pxLQp8FgGEEAKwEf8AC8qLL75oBFAuXY9c8RFxhEIQeY3yhw4iwK6BG+Y7HUcoIM8oFxeuT9R9yItXufE7YjPsrVSxew/iPOxzIaLydsU/8D7+LlzCYjfixAoETRKuvokbo8AE3z79Gn3IlkFuZ5D1RhJXWKnibr/qsj6EbXfr2o42rktTcYSCC++450VgjOE8kaATpoMsM3Gx47qlG8idty8arZgikEMEVCjkEHwtuuwj4CJLDRo0MAIBbjcsBGRLseUn3IywpWicA9eKIxSwCgdS8eyzz5oVX5B5xA0gUBfbr2YiRmGPPfYwMQkDBgwwZ0UgNgHlYmtSWE2C4hOARbZjFIorFCQJlwQzHaHAfe0iO1Er53GFQjpl2GOUyRbXTxJ0OzgZ9eXgbtcBcq5g5iAh5Mobrl2MbZiAyqVQsMUC44UtgLFAgOeTt+HFvUHbjoa5cLnGV9ChdUEHpJWmUIC1DGILwoevoIB0u22oJxYZglwj5fxgi1ZXGXHxKPtvG22BIpAbBFQo5AZ3LVURUAQUAUVAEVAEFAFFQBHIawRUKOR192jlFAFFQBFQBBQBRUARUAQUgdwgoEIhN7hrqYqAIqAIKAKKgCKgCCgCikBeI6BCIa+7RyunCCgCioAioAgoAoqAIqAI5AYBFQq5wV1LVQQUAUVAEVAEFAFFQBFQBPIaARUKed09WjlFQBFQBBQBRUARUAQUAUUgNwioUMgN7lqqIqAIKAKKgCKgCCgCioAikNcIqFDI6+7RyikCioAioAgoAoqAIqAIKAK5QUCFQm5w11IVAUVAEVAEFAFFQBFQBBSBvEZAhUJed49WThFQBBQBRUARUAQUAUVAEcgNAioUcoO7lqoIKAKKgCKgCCgCioAioAjkNQIqFPK6e7RyioAioAgoAoqAIqAIKAKKQG7iB4nDAAAAbElEQVQQUKGQG9y1VEVAEVAEFAFFQBFQBBQBRSCvEVChkNfdo5VTBBQBRUARUAQUAUVAEVAEcoOACoXc4K6lKgKKgCKgCCgCioAioAgoAnmNgAqFvO4erZwioAgoAoqAIqAIKAKKgCKQGwT+H70NuwC+pAveAAAAAElFTkSuQmCC" } }, "cell_type": "markdown", "id": "9ee1890a", "metadata": {}, "source": [ "" ] }, { "cell_type": "code", "execution_count": null, "id": "8c441b37", "metadata": { "scrolled": true }, "outputs": [], "source": [ "import torch\n", "import pytorch_lightning as pl" ] }, { "cell_type": "markdown", "id": "1bca038c", "metadata": {}, "source": [ "## Initialize dataset" ] }, { "cell_type": "code", "execution_count": null, "id": "23c5c320", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.datasets.mnist import MNISTCausalAttribute\n", "\n", "# dataset class initialization requires mandatory param `data_dir`\n", "# `download` is passed to torchvision.datasets.MNIST and downloads data if not present \n", "data_dir = 'data'\n", "dataset = MNISTCausalAttribute(data_dir, download=True)" ] }, { "cell_type": "markdown", "id": "eb49277b", "metadata": {}, "source": [ "## Initialize data loaders\n", "\n", "`get_loaders` returns data loaders for training, validation, and test. `loaders` returned is a dictionary of `train_loaders`, `val_loaders`, `test_loaders`. There are two scenarios supported currently to initialize validation domains:\n", "\n", "**Method 1**: When a domain(s) from the dataset is explicitly specified as the validation domain <br>\n", "**Method 2**: When no specific validation domain is present, a subset of the training domain(s) is used to create the validation set\n", "\n", "Run either cell below Method 1 or Method 2 as required." ] }, { "cell_type": "code", "execution_count": null, "id": "64dd7f3c", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.dataloaders.get_data_loader import get_loaders" ] }, { "cell_type": "markdown", "id": "51cbb17d", "metadata": {}, "source": [ "### Method 1: Provide validation domain explicitly\n", "Provide index of validation domains as `val_envs`. `test_envs` is an optional parameter." ] }, { "cell_type": "code", "execution_count": null, "id": "a75b74fa", "metadata": {}, "outputs": [], "source": [ "loaders = get_loaders(dataset, train_envs=[0, 1], batch_size=64,\n", " val_envs=[2], test_envs=[3])" ] }, { "cell_type": "markdown", "id": "9da42119", "metadata": {}, "source": [ "### Method 2: Validation set using subset of training data\n", "\n", "`val_envs`, `test_envs` are optional parameters. If `val_envs` is not provided, a subset of training data is used for creating the validation set. The fraction of training data used is determined by `holdout_fraction`." ] }, { "cell_type": "code", "execution_count": null, "id": "5201cd08", "metadata": {}, "outputs": [], "source": [ "loaders = get_loaders(dataset, train_envs=[0, 1], batch_size=64,\n", " holdout_fraction=0.2, test_envs=[3])" ] }, { "cell_type": "markdown", "id": "2b599691", "metadata": {}, "source": [ "The code below handles more than one validation or test domains, if present. Run the cell below irrespective of Method 1 or 2 used above." ] }, { "cell_type": "code", "execution_count": null, "id": "afbd74ef", "metadata": {}, "outputs": [], "source": [ "# handle multiple validation and test domains if present \n", "from pytorch_lightning.trainer.supporters import CombinedLoader\n", "\n", "if len(loaders['val_loaders']) > 1:\n", " val_loaders = loaders['val_loaders']\n", " loaders['val_loaders'] = CombinedLoader(val_loaders)\n", " \n", "if len(loaders['test_loaders']) > 1:\n", " test_loaders = loaders['test_loaders']\n", " loaders['test_loaders'] = CombinedLoader(test_loaders)" ] }, { "cell_type": "markdown", "id": "106cea0d", "metadata": {}, "source": [ "## Initialize model and algorithm " ] }, { "cell_type": "code", "execution_count": null, "id": "5b977c7f", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.models.networks import MNIST_MLP, Classifier" ] }, { "cell_type": "markdown", "id": "cbe00718", "metadata": {}, "source": [ "`model` below is expected to be of type `torch.nn.Sequential` with two `torch.nn.Module` elements (feature extractor and classifier). We provide sample networks (MLP, ResNet) in `dowhy.causal_prediction.models.networks` but the user can flexibly use any model.\n" ] }, { "cell_type": "code", "execution_count": null, "id": "9a663fe7", "metadata": {}, "outputs": [], "source": [ "featurizer = MNIST_MLP(dataset.input_shape)\n", "classifier = Classifier(\n", " featurizer.n_outputs,\n", " dataset.num_classes)\n", "\n", "model = torch.nn.Sequential(featurizer, classifier)" ] }, { "cell_type": "markdown", "id": "adf11498", "metadata": {}, "source": [ "### Initialize algorithm class: ERM\n", "\n", "We have implemented Empirical Risk Minimization (ERM) in `dowhy.causal_prediction.algorithms` as a baseline." ] }, { "cell_type": "code", "execution_count": null, "id": "51fadf20", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.algorithms.erm import ERM" ] }, { "cell_type": "code", "execution_count": null, "id": "633da7eb", "metadata": {}, "outputs": [], "source": [ "algorithm = ERM(model, lr=1e-3)" ] }, { "cell_type": "markdown", "id": "94828541", "metadata": {}, "source": [ "## Fit predictor and start training\n", "\n", "Note: The optimal accuracy for `MNISTCausalAttribute` (and other MNIST variants introduced) is **75%** as we introduce 25% noise following previous work." ] }, { "cell_type": "code", "execution_count": null, "id": "27f43e54", "metadata": {}, "outputs": [], "source": [ "trainer = pl.Trainer(devices=1, max_epochs=5) \n", "\n", "# val_loaders is optional param\n", "trainer.fit(algorithm, loaders['train_loaders'], loaders['val_loaders'])" ] }, { "cell_type": "markdown", "id": "8635befc", "metadata": {}, "source": [ "## Evaluate on test domain\n", "\n", "Perform an evaluation epoch over the test set using `trainer.test`. `ckpt_path` determines the model to be used for evaluation -- 'best', 'last', or path to a specific checkpoint. If `ckpt_path` is not passed, best model checkpoint from the previous `trainer.fit` is loaded (https://pytorch-lightning.readthedocs.io/en/stable/_modules/pytorch_lightning/trainer/trainer.html#Trainer.test).\n", "\n", "We report accuracy (`test_acc`) and cross-entropy loss (`test_loss`) on the test domains/test set." ] }, { "cell_type": "code", "execution_count": null, "id": "4f382191", "metadata": { "scrolled": true }, "outputs": [], "source": [ "if 'test_loaders' in loaders:\n", " trainer.test(dataloaders=loaders['test_loaders'], ckpt_path='best')" ] }, { "cell_type": "markdown", "id": "bb318eea", "metadata": {}, "source": [ "## Prediction with *CACM*\n", "\n", "We now train and evaluate the above dataset with *CACM*. We specify the type of shifts present using list `attr_types` provided as input to *CACM*. Further instructions regarding using *CACM* with multi-attribute shifts is provided in the next section." ] }, { "cell_type": "code", "execution_count": null, "id": "08881e8d", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.algorithms.cacm import CACM" ] }, { "cell_type": "code", "execution_count": null, "id": "48c87949", "metadata": {}, "outputs": [], "source": [ "# `attr_types` list contains type of attributes present (supports 'causal', 'conf', ind', and 'sel' currently)\n", "algorithm = CACM(model, lr=1e-3, gamma=1e-2, attr_types=['causal'], lambda_causal=100.)" ] }, { "cell_type": "code", "execution_count": null, "id": "2a9b1e8a", "metadata": {}, "outputs": [], "source": [ "trainer = pl.Trainer(devices=1, max_epochs=5) \n", "\n", "trainer.fit(algorithm, loaders['train_loaders'], loaders['val_loaders'])" ] }, { "cell_type": "code", "execution_count": null, "id": "cf1640b9", "metadata": {}, "outputs": [], "source": [ "if 'test_loaders' in loaders:\n", " trainer.test(dataloaders=loaders['test_loaders'], ckpt_path='best')" ] }, { "cell_type": "markdown", "id": "72066ad0", "metadata": {}, "source": [ "## Extending to different datasets and algorithms" ] }, { "cell_type": "markdown", "id": "a249be81", "metadata": {}, "source": [ "### MNIST Independent and Causal+Independent datasets\n", "\n", "We show how to perform the above evaluation for `MNISTIndAttribute` and`MNISTCausalIndAttribute` datasets. Additional `attr_types` should be provided to *CACM* algorithm for handling multiple shifts. We currently support *Causal*, *Confounded*, *Independent*, and *Selected* distribution shifts in the data." ] }, { "cell_type": "markdown", "id": "9a025cb3", "metadata": {}, "source": [ "#### `MNISTIndAttribute`: Single-attribute *Independent* shift" ] }, { "cell_type": "code", "execution_count": null, "id": "8c5cd482", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.datasets.mnist import MNISTIndAttribute\n", "\n", "data_dir = 'data'\n", "dataset = MNISTIndAttribute(data_dir)" ] }, { "cell_type": "code", "execution_count": null, "id": "4b28707a", "metadata": {}, "outputs": [], "source": [ "algorithm = CACM(model, lr=1e-3, gamma=1e-2, attr_types=['ind'], lambda_ind=10., E_eq_A=[0])" ] }, { "cell_type": "markdown", "id": "aa460184", "metadata": {}, "source": [ "#### `MNISTCausalIndAttribute`: Multi-attribute *Causal*+*Independent* shift" ] }, { "cell_type": "code", "execution_count": null, "id": "17b446be", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.datasets.mnist import MNISTCausalIndAttribute\n", "\n", "data_dir = 'data'\n", "dataset = MNISTCausalIndAttribute(data_dir)" ] }, { "cell_type": "code", "execution_count": null, "id": "9448cc87", "metadata": {}, "outputs": [], "source": [ "# `attr_types` should be ordered consistent with the attribute order in dataset class\n", "algorithm = CACM(model, lr=1e-3, gamma=1e-2, attr_types=['causal', 'ind'], lambda_causal=100., lambda_ind=10., E_eq_A=[1])" ] }, { "cell_type": "markdown", "id": "829479bc", "metadata": {}, "source": [ "### Additional datasets and algorithms\n", "\n", "We provide our demo on MNIST using ERM and *CACM* algorithms. It is possible to extend the evaluation to new datasets and algorithms for evaluation.\n", "\n", "\n", "New datasets can be added to `dowhy.causal_prediction.datasets` and imported here, as we did for MNIST. We provide description of the MNIST dataset (and variants) in `dowhy.causal_prediction.datasets.mnist` that will be helpful in creating new dataset classes. We currently support *Causal*, *Confounded*, *Independent*, and *Selected* distribution shifts in the data. \n", "\n", "We have implemented ERM in `dowhy.causal_prediction.algorithms` as a baseline. Additional algorithms can be added by overriding the `training_step` function in base class `PredictionAlgorithm`." ] }, { "cell_type": "markdown", "id": "3b9e1b05", "metadata": {}, "source": [ "## References\n", "\n", "[1] Kaur, J.N., Kıcıman, E., & Sharma, A. (2022). Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization. ArXiv, abs/2206.07837.\n", "\n", "[2] Ghifary, M., Kleijn, W., Zhang, M., & Balduzzi, D. (2015). Domain Generalization for Object Recognition with Multi-task Autoencoders. 2015 IEEE International Conference on Computer Vision (ICCV), 2551-2559.<br>\n", "\n", "[3] Arjovsky, M., Bottou, L., Gulrajani, I., & Lopez-Paz, D. (2019). Invariant Risk Minimization. ArXiv, abs/1907.02893.\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.16" } }, "nbformat": 4, "nbformat_minor": 5 }

jivatneet

c87b511bfd95caec2bc50e4686ed21ca448e9c37

0fb1314c5ddaeb6805109453433ac6248063f3c5

This seems to be an issue with tqdm and jupyter, not PL (https://github.com/Lightning-AI/lightning/issues/330, https://github.com/Lightning-AI/lightning/issues/765). A potential solution I could see in these issues is using custom progress bar as a callback in Jupyter, but these don't seem to be working here.

jivatneet

py-why/dowhy

Adding general version of CACM

This PR is the first step toward a general causal prediction API. The API supports _Causal, Independent, Confounded,_ and _Selected_ shifts (individual and multi-attribute settings) currently. The regularization has been implemented using `unconditional_reg` and `conditional_reg` functions, which can be used for the general _CACM_ API. Follow up: implement Phase I of _CACM_ for deriving conditional independence constraints given arbitrary graphs.

2023-04-18 10:36:38+00:00

2023-06-15 11:59:01+00:00

docs/source/example_notebooks/prediction/dowhy_causal_prediction_demo.ipynb

{ "cells": [ { "cell_type": "markdown", "id": "7a84e574", "metadata": {}, "source": [ "# Demo for DoWhy Causal Prediction on MNIST\n", "\n", "We're adding prediction functionality to DoWhy. The goal of this notebook is to demonstrate an example of causal prediction using *Causally Adaptive Constraint Minimization (CACM)* [1]. \n", "\n", "[1] Kaur, J.N., Kıcıman, E., & Sharma, A. (2022). Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization. ArXiv, abs/2206.07837." ] }, { "cell_type": "markdown", "id": "900de7ec", "metadata": {}, "source": [ "## Multi-attribute distribution shift datasets \n", "\n", "Domain generalization literature has largely focused on datasets with a single kind of distribution shift over one attribute. Using MNIST as an example, domains are created either by adding new values of a spurious attribute like rotation (e.g., Rotated-MNIST dataset [2]) or domains exhibit different values of correlation between the class label and a spurious attribute like color (e.g., Colored-MNIST [3]). However, real-world data often has multiple distribution shifts over different attributes. For example, satellite imagery data demonstrates distribution shifts over time as well as the region captured.\n", "\n", "[2] Ghifary, M., Kleijn, W., Zhang, M., & Balduzzi, D. (2015). Domain Generalization for Object Recognition with Multi-task Autoencoders. 2015 IEEE International Conference on Computer Vision (ICCV), 2551-2559.<br>\n", "[3] Arjovsky, M., Bottou, L., Gulrajani, I., & Lopez-Paz, D. (2019). Invariant Risk Minimization. ArXiv, abs/1907.02893.\n", "\n", "\n", "### Multi-attribute MNIST\n", "\n", "We create a *multi-attribute* shift variant of MNIST, where both the color and rotation angle of digits can shift across data distributions. Hence, we create three variants of MNIST -- `MNISTCausalAttribute` (single-attribute shift), `MNISTIndAttribute` (single-attribute shift), `MNISTCausalIndAttribute` (multi-attribute shift). To describe, `Causal`, `Ind`, and `CausalInd` datasets better, consider the causal graph for the data generating process below:\n", "\n" ] }, { "attachments": { "main_fig_mnist.drawio.png": { "image/png": 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" } }, "cell_type": "markdown", "id": "4d22f37d", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "id": "f2667794", "metadata": {}, "source": [ "Distribution shifts are characterized based on the relationship between spurious attributes ***A*** and the classification label *Y*.\n", "1. `Causal`: Attribute has a direct-*Causal* relationship with the class label i.e., *Y* causing attribute (e.g., *Color* here)\n", "2. `Ind`: Attribute is *Independent* of the class label (e.g., *Rotation* here)\n", "3. `CausalInd`: Different attributes having *Causal* and *Independent* relationships with *Y* co-exist in the data" ] }, { "cell_type": "markdown", "id": "56115bbd", "metadata": {}, "source": [ "### Domains in multi-attribute MNIST" ] }, { "cell_type": "markdown", "id": "403765c4", "metadata": {}, "source": [ "We describe the domains for our *multi-attribute* shift dataset `MNISTCausalIndAttribute`.<br> \n", "Each domain E_i has a specific *Rotation* angle r_i and a specific correlation corr_i between *Color C* and label *Y* . Our setup consists of 3 domains:\n", "E₁, E₂ are training domains, E₃ is the test domain. We define corr_i = P(*Y* = 1|*C* = 1) = P(*Y* = 0|*C* = 0) in E_i. In our setup, r₁ = 15◦, r₂ = 60◦, r₃ = 90◦ and corr₁ = 0.9, corr₂ = 0.8, corr₃ = 0.1. All environments have 25% label noise, as in [3]\n", "\n", "\n", "Other dataset-related details can be found in `dowhy.causal_prediction.datasets`." ] }, { "attachments": { "MNIST_visualize.drawio%20%283%29.png": { "image/png": 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" } }, "cell_type": "markdown", "id": "9ee1890a", "metadata": {}, "source": [ "" ] }, { "cell_type": "code", "execution_count": null, "id": "8c441b37", "metadata": { "scrolled": true }, "outputs": [], "source": [ "import torch\n", "import pytorch_lightning as pl" ] }, { "cell_type": "markdown", "id": "1bca038c", "metadata": {}, "source": [ "## Initialize dataset" ] }, { "cell_type": "code", "execution_count": null, "id": "23c5c320", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.datasets.mnist import MNISTCausalAttribute\n", "\n", "# dataset class initialization requires mandatory param `data_dir`\n", "# `download` is passed to torchvision.datasets.MNIST and downloads data if not present \n", "data_dir = 'data'\n", "dataset = MNISTCausalAttribute(data_dir, download=True)" ] }, { "cell_type": "markdown", "id": "eb49277b", "metadata": {}, "source": [ "## Initialize data loaders\n", "\n", "`get_loaders` returns data loaders for training, validation, and test. `loaders` returned is a dictionary of `train_loaders`, `val_loaders`, `test_loaders`. There are two scenarios supported currently to initialize validation domains:\n", "\n", "**Method 1**: When a domain(s) from the dataset is explicitly specified as the validation domain <br>\n", "**Method 2**: When no specific validation domain is present, a subset of the training domain(s) is used to create the validation set\n", "\n", "Run either cell below Method 1 or Method 2 as required." ] }, { "cell_type": "code", "execution_count": null, "id": "64dd7f3c", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.dataloaders.get_data_loader import get_loaders" ] }, { "cell_type": "markdown", "id": "51cbb17d", "metadata": {}, "source": [ "### Method 1: Provide validation domain explicitly\n", "Provide index of validation domains as `val_envs`. `test_envs` is an optional parameter." ] }, { "cell_type": "code", "execution_count": null, "id": "a75b74fa", "metadata": {}, "outputs": [], "source": [ "loaders = get_loaders(dataset, train_envs=[0, 1], batch_size=64,\n", " val_envs=[2], test_envs=[3])" ] }, { "cell_type": "markdown", "id": "9da42119", "metadata": {}, "source": [ "### Method 2: Validation set using subset of training data\n", "\n", "`val_envs`, `test_envs` are optional parameters. If `val_envs` is not provided, a subset of training data is used for creating the validation set. The fraction of training data used is determined by `holdout_fraction`." ] }, { "cell_type": "code", "execution_count": null, "id": "5201cd08", "metadata": {}, "outputs": [], "source": [ "loaders = get_loaders(dataset, train_envs=[0, 1], batch_size=64,\n", " holdout_fraction=0.2, test_envs=[3])" ] }, { "cell_type": "markdown", "id": "2b599691", "metadata": {}, "source": [ "The code below handles more than one validation or test domains, if present. Run the cell below irrespective of Method 1 or 2 used above." ] }, { "cell_type": "code", "execution_count": null, "id": "afbd74ef", "metadata": {}, "outputs": [], "source": [ "# handle multiple validation and test domains if present \n", "from pytorch_lightning.trainer.supporters import CombinedLoader\n", "\n", "if len(loaders['val_loaders']) > 1:\n", " val_loaders = loaders['val_loaders']\n", " loaders['val_loaders'] = CombinedLoader(val_loaders)\n", " \n", "if len(loaders['test_loaders']) > 1:\n", " test_loaders = loaders['test_loaders']\n", " loaders['test_loaders'] = CombinedLoader(test_loaders)" ] }, { "cell_type": "markdown", "id": "106cea0d", "metadata": {}, "source": [ "## Initialize model and algorithm " ] }, { "cell_type": "code", "execution_count": null, "id": "5b977c7f", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.models.networks import MNIST_MLP, Classifier" ] }, { "cell_type": "markdown", "id": "cbe00718", "metadata": {}, "source": [ "`model` below is expected to be of type `torch.nn.Sequential` with two `torch.nn.Module` elements (feature extractor and classifier). We provide sample networks (MLP, ResNet) in `dowhy.causal_prediction.models.networks` but the user can flexibly use any model.\n" ] }, { "cell_type": "code", "execution_count": null, "id": "9a663fe7", "metadata": {}, "outputs": [], "source": [ "featurizer = MNIST_MLP(dataset.input_shape)\n", "classifier = Classifier(\n", " featurizer.n_outputs,\n", " dataset.num_classes)\n", "\n", "model = torch.nn.Sequential(featurizer, classifier)" ] }, { "cell_type": "markdown", "id": "adf11498", "metadata": {}, "source": [ "### Initialize algorithm class: ERM" ] }, { "cell_type": "code", "execution_count": null, "id": "51fadf20", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.algorithms.erm import ERM" ] }, { "cell_type": "code", "execution_count": null, "id": "633da7eb", "metadata": {}, "outputs": [], "source": [ "algorithm = ERM(model, lr=1e-3)" ] }, { "cell_type": "markdown", "id": "94828541", "metadata": {}, "source": [ "## Fit predictor and start training\n", "\n", "Note: The optimal accuracy for `MNISTCausalAttribute` (and other MNIST variants introduced) is **75%** as we introduce 25% noise following previous work." ] }, { "cell_type": "code", "execution_count": null, "id": "27f43e54", "metadata": {}, "outputs": [], "source": [ "trainer = pl.Trainer(devices=1, max_epochs=5) \n", "\n", "# val_loaders is optional param\n", "trainer.fit(algorithm, loaders['train_loaders'], loaders['val_loaders'])" ] }, { "cell_type": "markdown", "id": "8635befc", "metadata": {}, "source": [ "## Evaluate on test domain\n", "\n", "Perform an evaluation epoch over the test set using `trainer.test`. `ckpt_path` determines the model to be used for evaluation -- 'best', 'last', or path to a specific checkpoint. If `ckpt_path` is not passed, best model checkpoint from the previous `trainer.fit` is loaded (https://pytorch-lightning.readthedocs.io/en/stable/_modules/pytorch_lightning/trainer/trainer.html#Trainer.test).\n", "\n", "We report accuracy (`test_acc`) and cross-entropy loss (`test_loss`) on the test domains/test set." ] }, { "cell_type": "code", "execution_count": null, "id": "4f382191", "metadata": { "scrolled": true }, "outputs": [], "source": [ "if 'test_loaders' in loaders:\n", " trainer.test(dataloaders=loaders['test_loaders'], ckpt_path='best')" ] }, { "cell_type": "markdown", "id": "bb318eea", "metadata": {}, "source": [ "## Prediction with CACM\n", "\n", "We now train and evaluate the above dataset with CACM. We specify the type of shifts present using list `attr_types` provided as input to CACM. Further instructions regarding using CACM with multi-attribute shifts is provided in the next section." ] }, { "cell_type": "code", "execution_count": null, "id": "08881e8d", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.algorithms.cacm import CACM" ] }, { "cell_type": "code", "execution_count": null, "id": "48c87949", "metadata": {}, "outputs": [], "source": [ "# `attr_types` list contains type of attributes present (supports 'causal' and 'ind' currently)\n", "algorithm = CACM(model, lr=1e-3, gamma=1e-2, attr_types=['causal'], lambda_causal=100.)" ] }, { "cell_type": "code", "execution_count": null, "id": "2a9b1e8a", "metadata": {}, "outputs": [], "source": [ "trainer = pl.Trainer(devices=1, max_epochs=5) \n", "\n", "trainer.fit(algorithm, loaders['train_loaders'], loaders['val_loaders'])" ] }, { "cell_type": "code", "execution_count": null, "id": "cf1640b9", "metadata": {}, "outputs": [], "source": [ "if 'test_loaders' in loaders:\n", " trainer.test(dataloaders=loaders['test_loaders'], ckpt_path='best')" ] }, { "cell_type": "markdown", "id": "72066ad0", "metadata": {}, "source": [ "## Extending to different datasets and algorithms" ] }, { "cell_type": "markdown", "id": "a249be81", "metadata": {}, "source": [ "### MNIST Independent and Causal+Independent datasets\n", "\n", "We show how to perform the above evaluation for `MNISTIndAttribute` and`MNISTCausalIndAttribute` datasets. Additional `attr_types` should be provided to CACM algorithm for handling multiple shifts. We currently support `Causal` and `Independent` distribution shifts in the data." ] }, { "cell_type": "markdown", "id": "9a025cb3", "metadata": {}, "source": [ "#### `MNISTIndAttribute`: Single-attribute *Independent* shift" ] }, { "cell_type": "code", "execution_count": null, "id": "8c5cd482", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.datasets.mnist import MNISTIndAttribute\n", "\n", "data_dir = 'data'\n", "dataset = MNISTIndAttribute(data_dir)" ] }, { "cell_type": "code", "execution_count": null, "id": "4b28707a", "metadata": {}, "outputs": [], "source": [ "algorithm = CACM(model, lr=1e-3, gamma=1e-2, attr_types=['ind'], lambda_ind=10.)" ] }, { "cell_type": "markdown", "id": "aa460184", "metadata": {}, "source": [ "#### `MNISTCausalIndAttribute`: Multi-attribute *Causal*+*Independent* shift" ] }, { "cell_type": "code", "execution_count": null, "id": "17b446be", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.datasets.mnist import MNISTCausalIndAttribute\n", "\n", "data_dir = 'data'\n", "dataset = MNISTCausalIndAttribute(data_dir)" ] }, { "cell_type": "code", "execution_count": null, "id": "9448cc87", "metadata": {}, "outputs": [], "source": [ "# `attr_types` can be provided in any order\n", "algorithm = CACM(model, lr=1e-3, gamma=1e-2, attr_types=['ind', 'causal'], lambda_causal=100., lambda_ind=10.)" ] }, { "cell_type": "markdown", "id": "829479bc", "metadata": {}, "source": [ "### Additional datasets and algorithms\n", "\n", "We provide our demo on MNIST using ERM and *CACM* algorithms. It is possible to extend the evaluation to new datasets and algorithms for evaluation.\n", "\n", "\n", "New datasets can be added to `dowhy.causal_prediction.datasets` and imported here, as we did for MNIST. We provide description of the MNIST dataset (and variants) in `dowhy.causal_prediction.datasets.mnist` that will be helpful in creating new dataset classes. We currently support `Causal` and `Independent` distribution shifts in the data.\n", "\n", "We have implemented ERM in `dowhy.causal_prediction.algorithms` as a baseline. Additional algorithms can be added by overriding the `training_step` function in base class `PredictionAlgorithm`." ] }, { "cell_type": "code", "execution_count": null, "id": "6206a050", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.11" } }, "nbformat": 4, "nbformat_minor": 5 }

{ "cells": [ { "cell_type": "markdown", "id": "7a84e574", "metadata": {}, "source": [ "# Demo for DoWhy Causal Prediction on MNIST\n", "\n", "The goal of this notebook is to demonstrate an example of causal prediction using *Causally Adaptive Constraint Minimization (CACM)* (https://arxiv.org/abs/2206.07837) [1]. " ] }, { "cell_type": "markdown", "id": "900de7ec", "metadata": {}, "source": [ "## Multi-attribute distribution shift datasets \n", "\n", "Domain generalization literature has largely focused on datasets with a single kind of distribution shift over one attribute. Using MNIST as an example, domains are created either by adding new values of a spurious attribute like rotation (e.g., Rotated-MNIST dataset [2]) or domains exhibit different values of correlation between the class label and a spurious attribute like color (e.g., Colored-MNIST [3]). However, real-world data often has multiple distribution shifts over different attributes. For example, satellite imagery data demonstrates distribution shifts over time as well as the region captured.\n", "\n", "\n", "### Multi-attribute MNIST\n", "\n", "We create a *multi-attribute* shift variant of MNIST, where both the color and rotation angle of digits can shift across data distributions. Hence, we create three variants of MNIST -- `MNISTCausalAttribute` (single-attribute shift), `MNISTIndAttribute` (single-attribute shift), `MNISTCausalIndAttribute` (multi-attribute shift). To describe, `Causal`, `Ind`, and `CausalInd` datasets better, consider the causal graph for the data generating process below:\n", "\n" ] }, { "attachments": { "main_fig_mnist.drawio.png": { "image/png": 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" } }, "cell_type": "markdown", "id": "4d22f37d", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "id": "f2667794", "metadata": {}, "source": [ "Distribution shifts are characterized based on the relationship between spurious attributes ***A*** and the classification label *Y*.\n", "1. `Causal`: Attribute has a direct-*Causal* relationship with the class label i.e., *Y* causing attribute (e.g., *Color* here)\n", "2. `Ind`: Attribute is *Independent* of the class label (e.g., *Rotation* here)\n", "3. `CausalInd`: Different attributes having *Causal* and *Independent* relationships with *Y* co-exist in the data" ] }, { "cell_type": "markdown", "id": "56115bbd", "metadata": {}, "source": [ "### Domains in multi-attribute MNIST" ] }, { "cell_type": "markdown", "id": "403765c4", "metadata": {}, "source": [ "We describe the domains for our *multi-attribute* shift dataset `MNISTCausalIndAttribute`.<br> \n", "Each domain E_i has a specific *Rotation* angle r_i and a specific correlation corr_i between *Color C* and label *Y* . Our setup consists of 3 domains:\n", "E₁, E₂ are training domains, E₃ is the test domain. We define corr_i = P(*Y* = 1|*C* = 1) = P(*Y* = 0|*C* = 0) in E_i. In our setup, r₁ = 15◦, r₂ = 60◦, r₃ = 90◦ and corr₁ = 0.9, corr₂ = 0.8, corr₃ = 0.1. All environments have 25% label noise, as in [3]\n", "\n", "\n", "Other dataset-related details can be found in `dowhy.causal_prediction.datasets`." ] }, { "attachments": { "MNIST_visualize.drawio%20%283%29.png": { "image/png": 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" } }, "cell_type": "markdown", "id": "9ee1890a", "metadata": {}, "source": [ "" ] }, { "cell_type": "code", "execution_count": null, "id": "8c441b37", "metadata": { "scrolled": true }, "outputs": [], "source": [ "import torch\n", "import pytorch_lightning as pl" ] }, { "cell_type": "markdown", "id": "1bca038c", "metadata": {}, "source": [ "## Initialize dataset" ] }, { "cell_type": "code", "execution_count": null, "id": "23c5c320", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.datasets.mnist import MNISTCausalAttribute\n", "\n", "# dataset class initialization requires mandatory param `data_dir`\n", "# `download` is passed to torchvision.datasets.MNIST and downloads data if not present \n", "data_dir = 'data'\n", "dataset = MNISTCausalAttribute(data_dir, download=True)" ] }, { "cell_type": "markdown", "id": "eb49277b", "metadata": {}, "source": [ "## Initialize data loaders\n", "\n", "`get_loaders` returns data loaders for training, validation, and test. `loaders` returned is a dictionary of `train_loaders`, `val_loaders`, `test_loaders`. There are two scenarios supported currently to initialize validation domains:\n", "\n", "**Method 1**: When a domain(s) from the dataset is explicitly specified as the validation domain <br>\n", "**Method 2**: When no specific validation domain is present, a subset of the training domain(s) is used to create the validation set\n", "\n", "Run either cell below Method 1 or Method 2 as required." ] }, { "cell_type": "code", "execution_count": null, "id": "64dd7f3c", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.dataloaders.get_data_loader import get_loaders" ] }, { "cell_type": "markdown", "id": "51cbb17d", "metadata": {}, "source": [ "### Method 1: Provide validation domain explicitly\n", "Provide index of validation domains as `val_envs`. `test_envs` is an optional parameter." ] }, { "cell_type": "code", "execution_count": null, "id": "a75b74fa", "metadata": {}, "outputs": [], "source": [ "loaders = get_loaders(dataset, train_envs=[0, 1], batch_size=64,\n", " val_envs=[2], test_envs=[3])" ] }, { "cell_type": "markdown", "id": "9da42119", "metadata": {}, "source": [ "### Method 2: Validation set using subset of training data\n", "\n", "`val_envs`, `test_envs` are optional parameters. If `val_envs` is not provided, a subset of training data is used for creating the validation set. The fraction of training data used is determined by `holdout_fraction`." ] }, { "cell_type": "code", "execution_count": null, "id": "5201cd08", "metadata": {}, "outputs": [], "source": [ "loaders = get_loaders(dataset, train_envs=[0, 1], batch_size=64,\n", " holdout_fraction=0.2, test_envs=[3])" ] }, { "cell_type": "markdown", "id": "2b599691", "metadata": {}, "source": [ "The code below handles more than one validation or test domains, if present. Run the cell below irrespective of Method 1 or 2 used above." ] }, { "cell_type": "code", "execution_count": null, "id": "afbd74ef", "metadata": {}, "outputs": [], "source": [ "# handle multiple validation and test domains if present \n", "from pytorch_lightning.trainer.supporters import CombinedLoader\n", "\n", "if len(loaders['val_loaders']) > 1:\n", " val_loaders = loaders['val_loaders']\n", " loaders['val_loaders'] = CombinedLoader(val_loaders)\n", " \n", "if len(loaders['test_loaders']) > 1:\n", " test_loaders = loaders['test_loaders']\n", " loaders['test_loaders'] = CombinedLoader(test_loaders)" ] }, { "cell_type": "markdown", "id": "106cea0d", "metadata": {}, "source": [ "## Initialize model and algorithm " ] }, { "cell_type": "code", "execution_count": null, "id": "5b977c7f", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.models.networks import MNIST_MLP, Classifier" ] }, { "cell_type": "markdown", "id": "cbe00718", "metadata": {}, "source": [ "`model` below is expected to be of type `torch.nn.Sequential` with two `torch.nn.Module` elements (feature extractor and classifier). We provide sample networks (MLP, ResNet) in `dowhy.causal_prediction.models.networks` but the user can flexibly use any model.\n" ] }, { "cell_type": "code", "execution_count": null, "id": "9a663fe7", "metadata": {}, "outputs": [], "source": [ "featurizer = MNIST_MLP(dataset.input_shape)\n", "classifier = Classifier(\n", " featurizer.n_outputs,\n", " dataset.num_classes)\n", "\n", "model = torch.nn.Sequential(featurizer, classifier)" ] }, { "cell_type": "markdown", "id": "adf11498", "metadata": {}, "source": [ "### Initialize algorithm class: ERM\n", "\n", "We have implemented Empirical Risk Minimization (ERM) in `dowhy.causal_prediction.algorithms` as a baseline." ] }, { "cell_type": "code", "execution_count": null, "id": "51fadf20", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.algorithms.erm import ERM" ] }, { "cell_type": "code", "execution_count": null, "id": "633da7eb", "metadata": {}, "outputs": [], "source": [ "algorithm = ERM(model, lr=1e-3)" ] }, { "cell_type": "markdown", "id": "94828541", "metadata": {}, "source": [ "## Fit predictor and start training\n", "\n", "Note: The optimal accuracy for `MNISTCausalAttribute` (and other MNIST variants introduced) is **75%** as we introduce 25% noise following previous work." ] }, { "cell_type": "code", "execution_count": null, "id": "27f43e54", "metadata": {}, "outputs": [], "source": [ "trainer = pl.Trainer(devices=1, max_epochs=5) \n", "\n", "# val_loaders is optional param\n", "trainer.fit(algorithm, loaders['train_loaders'], loaders['val_loaders'])" ] }, { "cell_type": "markdown", "id": "8635befc", "metadata": {}, "source": [ "## Evaluate on test domain\n", "\n", "Perform an evaluation epoch over the test set using `trainer.test`. `ckpt_path` determines the model to be used for evaluation -- 'best', 'last', or path to a specific checkpoint. If `ckpt_path` is not passed, best model checkpoint from the previous `trainer.fit` is loaded (https://pytorch-lightning.readthedocs.io/en/stable/_modules/pytorch_lightning/trainer/trainer.html#Trainer.test).\n", "\n", "We report accuracy (`test_acc`) and cross-entropy loss (`test_loss`) on the test domains/test set." ] }, { "cell_type": "code", "execution_count": null, "id": "4f382191", "metadata": { "scrolled": true }, "outputs": [], "source": [ "if 'test_loaders' in loaders:\n", " trainer.test(dataloaders=loaders['test_loaders'], ckpt_path='best')" ] }, { "cell_type": "markdown", "id": "bb318eea", "metadata": {}, "source": [ "## Prediction with *CACM*\n", "\n", "We now train and evaluate the above dataset with *CACM*. We specify the type of shifts present using list `attr_types` provided as input to *CACM*. Further instructions regarding using *CACM* with multi-attribute shifts is provided in the next section." ] }, { "cell_type": "code", "execution_count": null, "id": "08881e8d", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.algorithms.cacm import CACM" ] }, { "cell_type": "code", "execution_count": null, "id": "48c87949", "metadata": {}, "outputs": [], "source": [ "# `attr_types` list contains type of attributes present (supports 'causal', 'conf', ind', and 'sel' currently)\n", "algorithm = CACM(model, lr=1e-3, gamma=1e-2, attr_types=['causal'], lambda_causal=100.)" ] }, { "cell_type": "code", "execution_count": null, "id": "2a9b1e8a", "metadata": {}, "outputs": [], "source": [ "trainer = pl.Trainer(devices=1, max_epochs=5) \n", "\n", "trainer.fit(algorithm, loaders['train_loaders'], loaders['val_loaders'])" ] }, { "cell_type": "code", "execution_count": null, "id": "cf1640b9", "metadata": {}, "outputs": [], "source": [ "if 'test_loaders' in loaders:\n", " trainer.test(dataloaders=loaders['test_loaders'], ckpt_path='best')" ] }, { "cell_type": "markdown", "id": "72066ad0", "metadata": {}, "source": [ "## Extending to different datasets and algorithms" ] }, { "cell_type": "markdown", "id": "a249be81", "metadata": {}, "source": [ "### MNIST Independent and Causal+Independent datasets\n", "\n", "We show how to perform the above evaluation for `MNISTIndAttribute` and`MNISTCausalIndAttribute` datasets. Additional `attr_types` should be provided to *CACM* algorithm for handling multiple shifts. We currently support *Causal*, *Confounded*, *Independent*, and *Selected* distribution shifts in the data." ] }, { "cell_type": "markdown", "id": "9a025cb3", "metadata": {}, "source": [ "#### `MNISTIndAttribute`: Single-attribute *Independent* shift" ] }, { "cell_type": "code", "execution_count": null, "id": "8c5cd482", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.datasets.mnist import MNISTIndAttribute\n", "\n", "data_dir = 'data'\n", "dataset = MNISTIndAttribute(data_dir)" ] }, { "cell_type": "code", "execution_count": null, "id": "4b28707a", "metadata": {}, "outputs": [], "source": [ "algorithm = CACM(model, lr=1e-3, gamma=1e-2, attr_types=['ind'], lambda_ind=10., E_eq_A=[0])" ] }, { "cell_type": "markdown", "id": "aa460184", "metadata": {}, "source": [ "#### `MNISTCausalIndAttribute`: Multi-attribute *Causal*+*Independent* shift" ] }, { "cell_type": "code", "execution_count": null, "id": "17b446be", "metadata": {}, "outputs": [], "source": [ "from dowhy.causal_prediction.datasets.mnist import MNISTCausalIndAttribute\n", "\n", "data_dir = 'data'\n", "dataset = MNISTCausalIndAttribute(data_dir)" ] }, { "cell_type": "code", "execution_count": null, "id": "9448cc87", "metadata": {}, "outputs": [], "source": [ "# `attr_types` should be ordered consistent with the attribute order in dataset class\n", "algorithm = CACM(model, lr=1e-3, gamma=1e-2, attr_types=['causal', 'ind'], lambda_causal=100., lambda_ind=10., E_eq_A=[1])" ] }, { "cell_type": "markdown", "id": "829479bc", "metadata": {}, "source": [ "### Additional datasets and algorithms\n", "\n", "We provide our demo on MNIST using ERM and *CACM* algorithms. It is possible to extend the evaluation to new datasets and algorithms for evaluation.\n", "\n", "\n", "New datasets can be added to `dowhy.causal_prediction.datasets` and imported here, as we did for MNIST. We provide description of the MNIST dataset (and variants) in `dowhy.causal_prediction.datasets.mnist` that will be helpful in creating new dataset classes. We currently support *Causal*, *Confounded*, *Independent*, and *Selected* distribution shifts in the data. \n", "\n", "We have implemented ERM in `dowhy.causal_prediction.algorithms` as a baseline. Additional algorithms can be added by overriding the `training_step` function in base class `PredictionAlgorithm`." ] }, { "cell_type": "markdown", "id": "3b9e1b05", "metadata": {}, "source": [ "## References\n", "\n", "[1] Kaur, J.N., Kıcıman, E., & Sharma, A. (2022). Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization. ArXiv, abs/2206.07837.\n", "\n", "[2] Ghifary, M., Kleijn, W., Zhang, M., & Balduzzi, D. (2015). Domain Generalization for Object Recognition with Multi-task Autoencoders. 2015 IEEE International Conference on Computer Vision (ICCV), 2551-2559.<br>\n", "\n", "[3] Arjovsky, M., Bottou, L., Gulrajani, I., & Lopez-Paz, D. (2019). Invariant Risk Minimization. ArXiv, abs/1907.02893.\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.16" } }, "nbformat": 4, "nbformat_minor": 5 }

jivatneet

c87b511bfd95caec2bc50e4686ed21ca448e9c37

0fb1314c5ddaeb6805109453433ac6248063f3c5

okay, it is fine then

amit-sharma

py-why/dowhy

Adding general version of CACM

This PR is the first step toward a general causal prediction API. The API supports _Causal, Independent, Confounded,_ and _Selected_ shifts (individual and multi-attribute settings) currently. The regularization has been implemented using `unconditional_reg` and `conditional_reg` functions, which can be used for the general _CACM_ API. Follow up: implement Phase I of _CACM_ for deriving conditional independence constraints given arbitrary graphs.

2023-04-18 10:36:38+00:00

2023-06-15 11:59:01+00:00

dowhy/causal_prediction/algorithms/cacm.py

import torch from torch import nn from torch.nn import functional as F from dowhy.causal_prediction.algorithms.base_algorithm import PredictionAlgorithm from dowhy.causal_prediction.algorithms.utils import mmd_compute class CACM(PredictionAlgorithm): def __init__( self, model, optimizer="Adam", lr=1e-3, weight_decay=0.0, betas=(0.9, 0.999), momentum=0.9, kernel_type="gaussian", ci_test="mmd", attr_types=[], E_conditioned=True, E_eq_Aind=True, gamma=1e-6, lambda_causal=1.0, lambda_ind=1.0, ): """Class for Causally Adaptive Constraint Minimization (CACM) Algorithm. @article{Kaur2022ModelingTD, title={Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization}, author={Jivat Neet Kaur and Emre Kıcıman and Amit Sharma}, journal={ArXiv}, year={2022}, volume={abs/2206.07837}, url={https://arxiv.org/abs/2206.07837} } :param model: Networks used for training. `model` type expected is torch.nn.Sequential(featurizer, classifier) where featurizer and classifier are of type torch.nn.Module. :param optimizer: Optimization algorithm used for training. Currently supports "Adam" and "SGD". :param lr: learning rate for CACM :param weight_decay: Value of weight decay for optimizer :param betas: Adam configuration parameters (beta1, beta2), exponential decay rate for the first moment and second-moment estimates, respectively. :param momentum: Value of momentum for SGD optimzer :param kernel_type: Kernel type for MMD penalty. Currently, supports "gaussian" (RBF). If None, distance between mean and second-order statistics (covariances) is used. :param ci_test: Conditional independence metric used for regularization penalty. Currently, MMD is supported. :param attr_types: list of attribute types (based on relationship with label Y); can be in any order. Currently, only 'causal' and 'ind' are supported. For single-shift datasets, use: ['causal'], ['ind'] For multi-shift datasets, use: ['causal', 'ind'] :param E_conditioned: Binary flag indicating whether E-conditioned regularization has to be applied :param E_eq_Aind: Binary flag indicating whether environment (E) and Aind (Independent attribute) coincide :param gamma: kernel bandwidth for MMD (due to implementation, the kernel bandwdith will actually be the reciprocal of gamma i.e., gamma=1e-6 implies kernel bandwidth=1e6. See `mmd_compute` in utils.py) :param lambda_causal: MMD penalty hyperparameter for Causal shift :param lambda_ind: MMD penalty hyperparameter for Independent shift :returns: an instance of PredictionAlgorithm class """ super().__init__(model, optimizer, lr, weight_decay, betas, momentum) self.kernel_type = kernel_type self.attr_types = attr_types self.E_conditioned = E_conditioned # E-conditioned regularization by default self.E_eq_Aind = E_eq_Aind self.gamma = gamma self.lambda_causal = lambda_causal self.lambda_ind = lambda_ind def mmd(self, x, y): """ Compute MMD penalty between x and y. """ return mmd_compute(x, y, self.kernel_type, self.gamma) def training_step(self, train_batch, batch_idx): """ Override `training_step` from PredictionAlgorithm class for CACM-specific training loop. """ self.featurizer = self.model[0] self.classifier = self.model[1] minibatches = train_batch objective = 0 correct, total = 0, 0 penalty_causal, penalty_ind = 0, 0 nmb = len(minibatches) if len(minibatches[0]) == 4: features = [self.featurizer(xi) for xi, _, _, _ in minibatches] classifs = [self.classifier(fi) for fi in features] targets = [yi for _, yi, _, _ in minibatches] causal_attribute_labels = [ai for _, _, ai, _ in minibatches] ind_attribute_labels = [ai for _, _, _, ai in minibatches] elif len(minibatches[0]) == 3: # redundant for now since enforcing 4-dim output from dataset features = [self.featurizer(xi) for xi, _, _ in minibatches] classifs = [self.classifier(fi) for fi in features] targets = [yi for _, yi, _ in minibatches] causal_attribute_labels = [ai for _, _, ai in minibatches] for i in range(nmb): objective += F.cross_entropy(classifs[i], targets[i]) correct += (torch.argmax(classifs[i], dim=1) == targets[i]).float().sum().item() total += classifs[i].shape[0] # Acause regularization if "causal" in self.attr_types: if self.E_conditioned: for i in range(nmb): unique_labels = torch.unique(targets[i]) # find distinct labels in environment unique_label_indices = [] for label in unique_labels: label_ind = [ind for ind, j in enumerate(targets[i]) if j == label] unique_label_indices.append(label_ind) nulabels = unique_labels.shape[0] for idx in range(nulabels): unique_attrs = torch.unique( causal_attribute_labels[i][unique_label_indices[idx]] ) # find distinct attributes in environment with same label unique_attr_indices = [] for attr in unique_attrs: single_attr = [] for y_attr_idx in unique_label_indices[idx]: if causal_attribute_labels[i][y_attr_idx] == attr: single_attr.append(y_attr_idx) unique_attr_indices.append(single_attr) nuattr = unique_attrs.shape[0] for aidx in range(nuattr): for bidx in range(aidx + 1, nuattr): penalty_causal += self.mmd( classifs[i][unique_attr_indices[aidx]], classifs[i][unique_attr_indices[bidx]] ) else: overall_label_attr_vindices = {} # storing attribute indices overall_label_attr_eindices = {} # storing corresponding environment indices for i in range(nmb): unique_labels = torch.unique(targets[i]) # find distinct labels in environment unique_label_indices = [] for label in unique_labels: label_ind = [ind for ind, j in enumerate(targets[i]) if j == label] unique_label_indices.append(label_ind) nulabels = unique_labels.shape[0] for idx in range(nulabels): label = unique_labels[idx] if label not in overall_label_attr_vindices: overall_label_attr_vindices[label] = {} overall_label_attr_eindices[label] = {} unique_attrs = torch.unique( causal_attribute_labels[i][unique_label_indices[idx]] ) # find distinct attributes in environment with same label unique_attr_indices = [] for attr in unique_attrs: # storing indices with same attribute value and label if attr not in overall_label_attr_vindices[label]: overall_label_attr_vindices[label][attr] = [] overall_label_attr_eindices[label][attr] = [] single_attr = [] for y_attr_idx in unique_label_indices[idx]: if causal_attribute_labels[i][y_attr_idx] == attr: single_attr.append(y_attr_idx) overall_label_attr_vindices[label][attr].append(single_attr) overall_label_attr_eindices[label][attr].append(i) unique_attr_indices.append(single_attr) for ( y_val ) in ( overall_label_attr_vindices ): # applying MMD penalty between distributions P(φ(x)|ai, y), P(φ(x)|aj, y) i.e samples with different attribute values but same label tensors_list = [] for attr in overall_label_attr_vindices[y_val]: attrs_list = [] if overall_label_attr_vindices[y_val][attr] != []: for il_ind, indices_list in enumerate(overall_label_attr_vindices[y_val][attr]): attrs_list.append( classifs[overall_label_attr_eindices[y_val][attr][il_ind]][indices_list] ) if len(attrs_list) > 0: tensor_attrs = torch.cat(attrs_list, 0) tensors_list.append(tensor_attrs) nuattr = len(tensors_list) for aidx in range(nuattr): for bidx in range(aidx + 1, nuattr): penalty_causal += self.mmd(tensors_list[aidx], tensors_list[bidx]) # Aind regularization if "ind" in self.attr_types: if self.E_eq_Aind: # Environment (E) and Independent attribute (Aind) coincide for i in range(nmb): for j in range(i + 1, nmb): penalty_ind += self.mmd(classifs[i], classifs[j]) else: if self.E_conditioned: for i in range(nmb): unique_aind_labels = torch.unique(ind_attribute_labels[i]) unique_aind_label_indices = [] for label in unique_aind_labels: label_ind = [ind for ind, j in enumerate(ind_attribute_labels[i]) if j == label] unique_aind_label_indices.append(label_ind) nulabels = unique_aind_labels.shape[0] for aidx in range(nulabels): for bidx in range(aidx + 1, nulabels): penalty_ind += self.mmd( classifs[i][unique_aind_label_indices[aidx]], classifs[i][unique_aind_label_indices[bidx]], ) else: # this currently assumes we have a disjoint set of attributes (Aind) across environments i.e., environment is defined by multiple closely related values of the attribute overall_nmb_indices, nmb_id = [], [] for i in range(nmb): unique_attrs = torch.unique(ind_attribute_labels[i]) unique_attr_indices = [] for attr in unique_attrs: attr_ind = [ind for ind, j in enumerate(ind_attribute_labels[i]) if j == attr] unique_attr_indices.append(attr_ind) overall_nmb_indices.append(attr_ind) nmb_id.append(i) nuattr = len(overall_nmb_indices) for aidx in range(nuattr): for bidx in range(aidx + 1, nuattr): a_nmb_id = nmb_id[aidx] b_nmb_id = nmb_id[bidx] penalty_ind += self.mmd( classifs[a_nmb_id][overall_nmb_indices[aidx]], classifs[b_nmb_id][overall_nmb_indices[bidx]], ) objective /= nmb if nmb > 1: penalty_causal /= nmb * (nmb - 1) / 2 penalty_ind /= nmb * (nmb - 1) / 2 # Compile loss loss = objective loss += self.lambda_causal * penalty_causal loss += self.lambda_ind * penalty_ind if torch.is_tensor(penalty_causal): penalty_causal = penalty_causal.item() self.log("penalty_causal", penalty_causal, on_step=False, on_epoch=True, prog_bar=True) if torch.is_tensor(penalty_ind): penalty_ind = penalty_ind.item() self.log("penalty_ind", penalty_ind, on_step=False, on_epoch=True, prog_bar=True) acc = correct / total metrics = {"train_acc": acc, "train_loss": loss} self.log_dict(metrics, on_step=False, on_epoch=True, prog_bar=True) return loss

import torch from torch import nn from torch.nn import functional as F from dowhy.causal_prediction.algorithms.base_algorithm import PredictionAlgorithm from dowhy.causal_prediction.algorithms.regularization import Regularizer class CACM(PredictionAlgorithm): def __init__( self, model, optimizer="Adam", lr=1e-3, weight_decay=0.0, betas=(0.9, 0.999), momentum=0.9, kernel_type="gaussian", ci_test="mmd", attr_types=[], E_conditioned=True, E_eq_A=[], gamma=1e-6, lambda_causal=1.0, lambda_conf=1.0, lambda_ind=1.0, lambda_sel=1.0, ): """Class for Causally Adaptive Constraint Minimization (CACM) Algorithm. @article{Kaur2022ModelingTD, title={Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization}, author={Jivat Neet Kaur and Emre Kıcıman and Amit Sharma}, journal={ArXiv}, year={2022}, volume={abs/2206.07837}, url={https://arxiv.org/abs/2206.07837} } :param model: Networks used for training. `model` type expected is torch.nn.Sequential(featurizer, classifier) where featurizer and classifier are of type torch.nn.Module. :param optimizer: Optimization algorithm used for training. Currently supports "Adam" and "SGD". :param lr: learning rate for CACM :param weight_decay: Value of weight decay for optimizer :param betas: Adam configuration parameters (beta1, beta2), exponential decay rate for the first moment and second-moment estimates, respectively. :param momentum: Value of momentum for SGD optimzer :param kernel_type: Kernel type for MMD penalty. Currently, supports "gaussian" (RBF). If None, distance between mean and second-order statistics (covariances) is used. :param ci_test: Conditional independence metric used for regularization penalty. Currently, MMD is supported. :param attr_types: list of attribute types (based on relationship with label Y); should be ordered according to attribute order in loaded dataset. Currently, 'causal' (Causal), 'conf' (Confounded), 'ind' (Independent) and 'sel' (Selected) are supported. For single-shift datasets, use: ['causal'], ['ind'] For multi-shift datasets, use: ['causal', 'ind'] :param E_conditioned: Binary flag indicating whether E-conditioned regularization has to be applied :param E_eq_A: list indicating indices of attributes that coincide with environment (E) definition; default is empty. :param gamma: kernel bandwidth for MMD (due to implementation, the kernel bandwdith will actually be the reciprocal of gamma i.e., gamma=1e-6 implies kernel bandwidth=1e6. See `mmd_compute` in utils.py) :param lambda_causal: MMD penalty hyperparameter for Causal shift :param lambda_conf: MMD penalty hyperparameter for Confounded shift :param lambda_ind: MMD penalty hyperparameter for Independent shift :param lambda_sel: MMD penalty hyperparameter for Selected shift :returns: an instance of PredictionAlgorithm class """ super().__init__(model, optimizer, lr, weight_decay, betas, momentum) self.CACMRegularizer = Regularizer(E_conditioned, ci_test, kernel_type, gamma) self.attr_types = attr_types self.E_eq_A = E_eq_A self.lambda_causal = lambda_causal self.lambda_conf = lambda_conf self.lambda_ind = lambda_ind self.lambda_sel = lambda_sel def training_step(self, train_batch, batch_idx): """ Override `training_step` from PredictionAlgorithm class for CACM-specific training loop. """ self.featurizer = self.model[0] self.classifier = self.model[1] minibatches = train_batch objective = 0 correct, total = 0, 0 penalty_causal, penalty_conf, penalty_ind, penalty_sel = 0, 0, 0, 0 nmb = len(minibatches) features = [self.featurizer(xi) for xi, _, _ in minibatches] classifs = [self.classifier(fi) for fi in features] targets = [yi for _, yi, _ in minibatches] for i in range(nmb): objective += F.cross_entropy(classifs[i], targets[i]) correct += (torch.argmax(classifs[i], dim=1) == targets[i]).float().sum().item() total += classifs[i].shape[0] objective /= nmb loss = objective if self.attr_types != []: for attr_type_idx, attr_type in enumerate(self.attr_types): attribute_labels = [ ai for _, _, ai in minibatches ] # [(batch_size, num_attrs)_1, batch_size, num_attrs)_2, ..., (batch_size, num_attrs)_(num_environments)] E_eq_A_attr = attr_type_idx in self.E_eq_A # Acause regularization if attr_type == "causal": penalty_causal += self.CACMRegularizer.conditional_reg( classifs, [a[:, attr_type_idx] for a in attribute_labels], [targets], nmb, E_eq_A_attr ) # Aconf regularization elif attr_type == "conf": penalty_conf += self.CACMRegularizer.unconditional_reg( classifs, [a[:, attr_type_idx] for a in attribute_labels], nmb, E_eq_A_attr ) # Aind regularization elif attr_type == "ind": penalty_ind += self.CACMRegularizer.unconditional_reg( classifs, [a[:, attr_type_idx] for a in attribute_labels], nmb, E_eq_A_attr ) # Asel regularization elif attr_type == "sel": penalty_sel += self.CACMRegularizer.conditional_reg( classifs, [a[:, attr_type_idx] for a in attribute_labels], [targets], nmb, E_eq_A_attr ) if nmb > 1: penalty_causal /= nmb * (nmb - 1) / 2 penalty_conf /= nmb * (nmb - 1) / 2 penalty_ind /= nmb * (nmb - 1) / 2 penalty_sel /= nmb * (nmb - 1) / 2 # Compile loss loss += self.lambda_causal * penalty_causal loss += self.lambda_conf * penalty_conf loss += self.lambda_ind * penalty_ind loss += self.lambda_sel * penalty_sel if torch.is_tensor(penalty_causal): penalty_causal = penalty_causal.item() self.log("penalty_causal", penalty_causal, on_step=False, on_epoch=True, prog_bar=True) if torch.is_tensor(penalty_conf): penalty_conf = penalty_conf.item() self.log("penalty_conf", penalty_conf, on_step=False, on_epoch=True, prog_bar=True) if torch.is_tensor(penalty_ind): penalty_ind = penalty_ind.item() self.log("penalty_ind", penalty_ind, on_step=False, on_epoch=True, prog_bar=True) if torch.is_tensor(penalty_sel): penalty_sel = penalty_sel.item() self.log("penalty_sel", penalty_sel, on_step=False, on_epoch=True, prog_bar=True) elif self.graph is not None: pass # TODO else: raise ValueError("No attribute types or graph provided.") acc = correct / total metrics = {"train_acc": acc, "train_loss": loss} self.log_dict(metrics, on_step=False, on_epoch=True, prog_bar=True) return loss

jivatneet

c87b511bfd95caec2bc50e4686ed21ca448e9c37

0fb1314c5ddaeb6805109453433ac6248063f3c5

remove the commented code if not needed

amit-sharma

py-why/dowhy

Adding general version of CACM

This PR is the first step toward a general causal prediction API. The API supports _Causal, Independent, Confounded,_ and _Selected_ shifts (individual and multi-attribute settings) currently. The regularization has been implemented using `unconditional_reg` and `conditional_reg` functions, which can be used for the general _CACM_ API. Follow up: implement Phase I of _CACM_ for deriving conditional independence constraints given arbitrary graphs.

2023-04-18 10:36:38+00:00

2023-06-15 11:59:01+00:00

dowhy/causal_prediction/algorithms/cacm.py

import torch from torch import nn from torch.nn import functional as F from dowhy.causal_prediction.algorithms.base_algorithm import PredictionAlgorithm from dowhy.causal_prediction.algorithms.utils import mmd_compute class CACM(PredictionAlgorithm): def __init__( self, model, optimizer="Adam", lr=1e-3, weight_decay=0.0, betas=(0.9, 0.999), momentum=0.9, kernel_type="gaussian", ci_test="mmd", attr_types=[], E_conditioned=True, E_eq_Aind=True, gamma=1e-6, lambda_causal=1.0, lambda_ind=1.0, ): """Class for Causally Adaptive Constraint Minimization (CACM) Algorithm. @article{Kaur2022ModelingTD, title={Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization}, author={Jivat Neet Kaur and Emre Kıcıman and Amit Sharma}, journal={ArXiv}, year={2022}, volume={abs/2206.07837}, url={https://arxiv.org/abs/2206.07837} } :param model: Networks used for training. `model` type expected is torch.nn.Sequential(featurizer, classifier) where featurizer and classifier are of type torch.nn.Module. :param optimizer: Optimization algorithm used for training. Currently supports "Adam" and "SGD". :param lr: learning rate for CACM :param weight_decay: Value of weight decay for optimizer :param betas: Adam configuration parameters (beta1, beta2), exponential decay rate for the first moment and second-moment estimates, respectively. :param momentum: Value of momentum for SGD optimzer :param kernel_type: Kernel type for MMD penalty. Currently, supports "gaussian" (RBF). If None, distance between mean and second-order statistics (covariances) is used. :param ci_test: Conditional independence metric used for regularization penalty. Currently, MMD is supported. :param attr_types: list of attribute types (based on relationship with label Y); can be in any order. Currently, only 'causal' and 'ind' are supported. For single-shift datasets, use: ['causal'], ['ind'] For multi-shift datasets, use: ['causal', 'ind'] :param E_conditioned: Binary flag indicating whether E-conditioned regularization has to be applied :param E_eq_Aind: Binary flag indicating whether environment (E) and Aind (Independent attribute) coincide :param gamma: kernel bandwidth for MMD (due to implementation, the kernel bandwdith will actually be the reciprocal of gamma i.e., gamma=1e-6 implies kernel bandwidth=1e6. See `mmd_compute` in utils.py) :param lambda_causal: MMD penalty hyperparameter for Causal shift :param lambda_ind: MMD penalty hyperparameter for Independent shift :returns: an instance of PredictionAlgorithm class """ super().__init__(model, optimizer, lr, weight_decay, betas, momentum) self.kernel_type = kernel_type self.attr_types = attr_types self.E_conditioned = E_conditioned # E-conditioned regularization by default self.E_eq_Aind = E_eq_Aind self.gamma = gamma self.lambda_causal = lambda_causal self.lambda_ind = lambda_ind def mmd(self, x, y): """ Compute MMD penalty between x and y. """ return mmd_compute(x, y, self.kernel_type, self.gamma) def training_step(self, train_batch, batch_idx): """ Override `training_step` from PredictionAlgorithm class for CACM-specific training loop. """ self.featurizer = self.model[0] self.classifier = self.model[1] minibatches = train_batch objective = 0 correct, total = 0, 0 penalty_causal, penalty_ind = 0, 0 nmb = len(minibatches) if len(minibatches[0]) == 4: features = [self.featurizer(xi) for xi, _, _, _ in minibatches] classifs = [self.classifier(fi) for fi in features] targets = [yi for _, yi, _, _ in minibatches] causal_attribute_labels = [ai for _, _, ai, _ in minibatches] ind_attribute_labels = [ai for _, _, _, ai in minibatches] elif len(minibatches[0]) == 3: # redundant for now since enforcing 4-dim output from dataset features = [self.featurizer(xi) for xi, _, _ in minibatches] classifs = [self.classifier(fi) for fi in features] targets = [yi for _, yi, _ in minibatches] causal_attribute_labels = [ai for _, _, ai in minibatches] for i in range(nmb): objective += F.cross_entropy(classifs[i], targets[i]) correct += (torch.argmax(classifs[i], dim=1) == targets[i]).float().sum().item() total += classifs[i].shape[0] # Acause regularization if "causal" in self.attr_types: if self.E_conditioned: for i in range(nmb): unique_labels = torch.unique(targets[i]) # find distinct labels in environment unique_label_indices = [] for label in unique_labels: label_ind = [ind for ind, j in enumerate(targets[i]) if j == label] unique_label_indices.append(label_ind) nulabels = unique_labels.shape[0] for idx in range(nulabels): unique_attrs = torch.unique( causal_attribute_labels[i][unique_label_indices[idx]] ) # find distinct attributes in environment with same label unique_attr_indices = [] for attr in unique_attrs: single_attr = [] for y_attr_idx in unique_label_indices[idx]: if causal_attribute_labels[i][y_attr_idx] == attr: single_attr.append(y_attr_idx) unique_attr_indices.append(single_attr) nuattr = unique_attrs.shape[0] for aidx in range(nuattr): for bidx in range(aidx + 1, nuattr): penalty_causal += self.mmd( classifs[i][unique_attr_indices[aidx]], classifs[i][unique_attr_indices[bidx]] ) else: overall_label_attr_vindices = {} # storing attribute indices overall_label_attr_eindices = {} # storing corresponding environment indices for i in range(nmb): unique_labels = torch.unique(targets[i]) # find distinct labels in environment unique_label_indices = [] for label in unique_labels: label_ind = [ind for ind, j in enumerate(targets[i]) if j == label] unique_label_indices.append(label_ind) nulabels = unique_labels.shape[0] for idx in range(nulabels): label = unique_labels[idx] if label not in overall_label_attr_vindices: overall_label_attr_vindices[label] = {} overall_label_attr_eindices[label] = {} unique_attrs = torch.unique( causal_attribute_labels[i][unique_label_indices[idx]] ) # find distinct attributes in environment with same label unique_attr_indices = [] for attr in unique_attrs: # storing indices with same attribute value and label if attr not in overall_label_attr_vindices[label]: overall_label_attr_vindices[label][attr] = [] overall_label_attr_eindices[label][attr] = [] single_attr = [] for y_attr_idx in unique_label_indices[idx]: if causal_attribute_labels[i][y_attr_idx] == attr: single_attr.append(y_attr_idx) overall_label_attr_vindices[label][attr].append(single_attr) overall_label_attr_eindices[label][attr].append(i) unique_attr_indices.append(single_attr) for ( y_val ) in ( overall_label_attr_vindices ): # applying MMD penalty between distributions P(φ(x)|ai, y), P(φ(x)|aj, y) i.e samples with different attribute values but same label tensors_list = [] for attr in overall_label_attr_vindices[y_val]: attrs_list = [] if overall_label_attr_vindices[y_val][attr] != []: for il_ind, indices_list in enumerate(overall_label_attr_vindices[y_val][attr]): attrs_list.append( classifs[overall_label_attr_eindices[y_val][attr][il_ind]][indices_list] ) if len(attrs_list) > 0: tensor_attrs = torch.cat(attrs_list, 0) tensors_list.append(tensor_attrs) nuattr = len(tensors_list) for aidx in range(nuattr): for bidx in range(aidx + 1, nuattr): penalty_causal += self.mmd(tensors_list[aidx], tensors_list[bidx]) # Aind regularization if "ind" in self.attr_types: if self.E_eq_Aind: # Environment (E) and Independent attribute (Aind) coincide for i in range(nmb): for j in range(i + 1, nmb): penalty_ind += self.mmd(classifs[i], classifs[j]) else: if self.E_conditioned: for i in range(nmb): unique_aind_labels = torch.unique(ind_attribute_labels[i]) unique_aind_label_indices = [] for label in unique_aind_labels: label_ind = [ind for ind, j in enumerate(ind_attribute_labels[i]) if j == label] unique_aind_label_indices.append(label_ind) nulabels = unique_aind_labels.shape[0] for aidx in range(nulabels): for bidx in range(aidx + 1, nulabels): penalty_ind += self.mmd( classifs[i][unique_aind_label_indices[aidx]], classifs[i][unique_aind_label_indices[bidx]], ) else: # this currently assumes we have a disjoint set of attributes (Aind) across environments i.e., environment is defined by multiple closely related values of the attribute overall_nmb_indices, nmb_id = [], [] for i in range(nmb): unique_attrs = torch.unique(ind_attribute_labels[i]) unique_attr_indices = [] for attr in unique_attrs: attr_ind = [ind for ind, j in enumerate(ind_attribute_labels[i]) if j == attr] unique_attr_indices.append(attr_ind) overall_nmb_indices.append(attr_ind) nmb_id.append(i) nuattr = len(overall_nmb_indices) for aidx in range(nuattr): for bidx in range(aidx + 1, nuattr): a_nmb_id = nmb_id[aidx] b_nmb_id = nmb_id[bidx] penalty_ind += self.mmd( classifs[a_nmb_id][overall_nmb_indices[aidx]], classifs[b_nmb_id][overall_nmb_indices[bidx]], ) objective /= nmb if nmb > 1: penalty_causal /= nmb * (nmb - 1) / 2 penalty_ind /= nmb * (nmb - 1) / 2 # Compile loss loss = objective loss += self.lambda_causal * penalty_causal loss += self.lambda_ind * penalty_ind if torch.is_tensor(penalty_causal): penalty_causal = penalty_causal.item() self.log("penalty_causal", penalty_causal, on_step=False, on_epoch=True, prog_bar=True) if torch.is_tensor(penalty_ind): penalty_ind = penalty_ind.item() self.log("penalty_ind", penalty_ind, on_step=False, on_epoch=True, prog_bar=True) acc = correct / total metrics = {"train_acc": acc, "train_loss": loss} self.log_dict(metrics, on_step=False, on_epoch=True, prog_bar=True) return loss

import torch from torch import nn from torch.nn import functional as F from dowhy.causal_prediction.algorithms.base_algorithm import PredictionAlgorithm from dowhy.causal_prediction.algorithms.regularization import Regularizer class CACM(PredictionAlgorithm): def __init__( self, model, optimizer="Adam", lr=1e-3, weight_decay=0.0, betas=(0.9, 0.999), momentum=0.9, kernel_type="gaussian", ci_test="mmd", attr_types=[], E_conditioned=True, E_eq_A=[], gamma=1e-6, lambda_causal=1.0, lambda_conf=1.0, lambda_ind=1.0, lambda_sel=1.0, ): """Class for Causally Adaptive Constraint Minimization (CACM) Algorithm. @article{Kaur2022ModelingTD, title={Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization}, author={Jivat Neet Kaur and Emre Kıcıman and Amit Sharma}, journal={ArXiv}, year={2022}, volume={abs/2206.07837}, url={https://arxiv.org/abs/2206.07837} } :param model: Networks used for training. `model` type expected is torch.nn.Sequential(featurizer, classifier) where featurizer and classifier are of type torch.nn.Module. :param optimizer: Optimization algorithm used for training. Currently supports "Adam" and "SGD". :param lr: learning rate for CACM :param weight_decay: Value of weight decay for optimizer :param betas: Adam configuration parameters (beta1, beta2), exponential decay rate for the first moment and second-moment estimates, respectively. :param momentum: Value of momentum for SGD optimzer :param kernel_type: Kernel type for MMD penalty. Currently, supports "gaussian" (RBF). If None, distance between mean and second-order statistics (covariances) is used. :param ci_test: Conditional independence metric used for regularization penalty. Currently, MMD is supported. :param attr_types: list of attribute types (based on relationship with label Y); should be ordered according to attribute order in loaded dataset. Currently, 'causal' (Causal), 'conf' (Confounded), 'ind' (Independent) and 'sel' (Selected) are supported. For single-shift datasets, use: ['causal'], ['ind'] For multi-shift datasets, use: ['causal', 'ind'] :param E_conditioned: Binary flag indicating whether E-conditioned regularization has to be applied :param E_eq_A: list indicating indices of attributes that coincide with environment (E) definition; default is empty. :param gamma: kernel bandwidth for MMD (due to implementation, the kernel bandwdith will actually be the reciprocal of gamma i.e., gamma=1e-6 implies kernel bandwidth=1e6. See `mmd_compute` in utils.py) :param lambda_causal: MMD penalty hyperparameter for Causal shift :param lambda_conf: MMD penalty hyperparameter for Confounded shift :param lambda_ind: MMD penalty hyperparameter for Independent shift :param lambda_sel: MMD penalty hyperparameter for Selected shift :returns: an instance of PredictionAlgorithm class """ super().__init__(model, optimizer, lr, weight_decay, betas, momentum) self.CACMRegularizer = Regularizer(E_conditioned, ci_test, kernel_type, gamma) self.attr_types = attr_types self.E_eq_A = E_eq_A self.lambda_causal = lambda_causal self.lambda_conf = lambda_conf self.lambda_ind = lambda_ind self.lambda_sel = lambda_sel def training_step(self, train_batch, batch_idx): """ Override `training_step` from PredictionAlgorithm class for CACM-specific training loop. """ self.featurizer = self.model[0] self.classifier = self.model[1] minibatches = train_batch objective = 0 correct, total = 0, 0 penalty_causal, penalty_conf, penalty_ind, penalty_sel = 0, 0, 0, 0 nmb = len(minibatches) features = [self.featurizer(xi) for xi, _, _ in minibatches] classifs = [self.classifier(fi) for fi in features] targets = [yi for _, yi, _ in minibatches] for i in range(nmb): objective += F.cross_entropy(classifs[i], targets[i]) correct += (torch.argmax(classifs[i], dim=1) == targets[i]).float().sum().item() total += classifs[i].shape[0] objective /= nmb loss = objective if self.attr_types != []: for attr_type_idx, attr_type in enumerate(self.attr_types): attribute_labels = [ ai for _, _, ai in minibatches ] # [(batch_size, num_attrs)_1, batch_size, num_attrs)_2, ..., (batch_size, num_attrs)_(num_environments)] E_eq_A_attr = attr_type_idx in self.E_eq_A # Acause regularization if attr_type == "causal": penalty_causal += self.CACMRegularizer.conditional_reg( classifs, [a[:, attr_type_idx] for a in attribute_labels], [targets], nmb, E_eq_A_attr ) # Aconf regularization elif attr_type == "conf": penalty_conf += self.CACMRegularizer.unconditional_reg( classifs, [a[:, attr_type_idx] for a in attribute_labels], nmb, E_eq_A_attr ) # Aind regularization elif attr_type == "ind": penalty_ind += self.CACMRegularizer.unconditional_reg( classifs, [a[:, attr_type_idx] for a in attribute_labels], nmb, E_eq_A_attr ) # Asel regularization elif attr_type == "sel": penalty_sel += self.CACMRegularizer.conditional_reg( classifs, [a[:, attr_type_idx] for a in attribute_labels], [targets], nmb, E_eq_A_attr ) if nmb > 1: penalty_causal /= nmb * (nmb - 1) / 2 penalty_conf /= nmb * (nmb - 1) / 2 penalty_ind /= nmb * (nmb - 1) / 2 penalty_sel /= nmb * (nmb - 1) / 2 # Compile loss loss += self.lambda_causal * penalty_causal loss += self.lambda_conf * penalty_conf loss += self.lambda_ind * penalty_ind loss += self.lambda_sel * penalty_sel if torch.is_tensor(penalty_causal): penalty_causal = penalty_causal.item() self.log("penalty_causal", penalty_causal, on_step=False, on_epoch=True, prog_bar=True) if torch.is_tensor(penalty_conf): penalty_conf = penalty_conf.item() self.log("penalty_conf", penalty_conf, on_step=False, on_epoch=True, prog_bar=True) if torch.is_tensor(penalty_ind): penalty_ind = penalty_ind.item() self.log("penalty_ind", penalty_ind, on_step=False, on_epoch=True, prog_bar=True) if torch.is_tensor(penalty_sel): penalty_sel = penalty_sel.item() self.log("penalty_sel", penalty_sel, on_step=False, on_epoch=True, prog_bar=True) elif self.graph is not None: pass # TODO else: raise ValueError("No attribute types or graph provided.") acc = correct / total metrics = {"train_acc": acc, "train_loss": loss} self.log_dict(metrics, on_step=False, on_epoch=True, prog_bar=True) return loss

jivatneet

c87b511bfd95caec2bc50e4686ed21ca448e9c37

0fb1314c5ddaeb6805109453433ac6248063f3c5

Added this to provide greater clarity on the variable shape. Can remove if you feel it's not needed.

jivatneet

py-why/dowhy

Adding general version of CACM

This PR is the first step toward a general causal prediction API. The API supports _Causal, Independent, Confounded,_ and _Selected_ shifts (individual and multi-attribute settings) currently. The regularization has been implemented using `unconditional_reg` and `conditional_reg` functions, which can be used for the general _CACM_ API. Follow up: implement Phase I of _CACM_ for deriving conditional independence constraints given arbitrary graphs.

2023-04-18 10:36:38+00:00

2023-06-15 11:59:01+00:00

dowhy/causal_prediction/algorithms/cacm.py

import torch from torch import nn from torch.nn import functional as F from dowhy.causal_prediction.algorithms.base_algorithm import PredictionAlgorithm from dowhy.causal_prediction.algorithms.utils import mmd_compute class CACM(PredictionAlgorithm): def __init__( self, model, optimizer="Adam", lr=1e-3, weight_decay=0.0, betas=(0.9, 0.999), momentum=0.9, kernel_type="gaussian", ci_test="mmd", attr_types=[], E_conditioned=True, E_eq_Aind=True, gamma=1e-6, lambda_causal=1.0, lambda_ind=1.0, ): """Class for Causally Adaptive Constraint Minimization (CACM) Algorithm. @article{Kaur2022ModelingTD, title={Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization}, author={Jivat Neet Kaur and Emre Kıcıman and Amit Sharma}, journal={ArXiv}, year={2022}, volume={abs/2206.07837}, url={https://arxiv.org/abs/2206.07837} } :param model: Networks used for training. `model` type expected is torch.nn.Sequential(featurizer, classifier) where featurizer and classifier are of type torch.nn.Module. :param optimizer: Optimization algorithm used for training. Currently supports "Adam" and "SGD". :param lr: learning rate for CACM :param weight_decay: Value of weight decay for optimizer :param betas: Adam configuration parameters (beta1, beta2), exponential decay rate for the first moment and second-moment estimates, respectively. :param momentum: Value of momentum for SGD optimzer :param kernel_type: Kernel type for MMD penalty. Currently, supports "gaussian" (RBF). If None, distance between mean and second-order statistics (covariances) is used. :param ci_test: Conditional independence metric used for regularization penalty. Currently, MMD is supported. :param attr_types: list of attribute types (based on relationship with label Y); can be in any order. Currently, only 'causal' and 'ind' are supported. For single-shift datasets, use: ['causal'], ['ind'] For multi-shift datasets, use: ['causal', 'ind'] :param E_conditioned: Binary flag indicating whether E-conditioned regularization has to be applied :param E_eq_Aind: Binary flag indicating whether environment (E) and Aind (Independent attribute) coincide :param gamma: kernel bandwidth for MMD (due to implementation, the kernel bandwdith will actually be the reciprocal of gamma i.e., gamma=1e-6 implies kernel bandwidth=1e6. See `mmd_compute` in utils.py) :param lambda_causal: MMD penalty hyperparameter for Causal shift :param lambda_ind: MMD penalty hyperparameter for Independent shift :returns: an instance of PredictionAlgorithm class """ super().__init__(model, optimizer, lr, weight_decay, betas, momentum) self.kernel_type = kernel_type self.attr_types = attr_types self.E_conditioned = E_conditioned # E-conditioned regularization by default self.E_eq_Aind = E_eq_Aind self.gamma = gamma self.lambda_causal = lambda_causal self.lambda_ind = lambda_ind def mmd(self, x, y): """ Compute MMD penalty between x and y. """ return mmd_compute(x, y, self.kernel_type, self.gamma) def training_step(self, train_batch, batch_idx): """ Override `training_step` from PredictionAlgorithm class for CACM-specific training loop. """ self.featurizer = self.model[0] self.classifier = self.model[1] minibatches = train_batch objective = 0 correct, total = 0, 0 penalty_causal, penalty_ind = 0, 0 nmb = len(minibatches) if len(minibatches[0]) == 4: features = [self.featurizer(xi) for xi, _, _, _ in minibatches] classifs = [self.classifier(fi) for fi in features] targets = [yi for _, yi, _, _ in minibatches] causal_attribute_labels = [ai for _, _, ai, _ in minibatches] ind_attribute_labels = [ai for _, _, _, ai in minibatches] elif len(minibatches[0]) == 3: # redundant for now since enforcing 4-dim output from dataset features = [self.featurizer(xi) for xi, _, _ in minibatches] classifs = [self.classifier(fi) for fi in features] targets = [yi for _, yi, _ in minibatches] causal_attribute_labels = [ai for _, _, ai in minibatches] for i in range(nmb): objective += F.cross_entropy(classifs[i], targets[i]) correct += (torch.argmax(classifs[i], dim=1) == targets[i]).float().sum().item() total += classifs[i].shape[0] # Acause regularization if "causal" in self.attr_types: if self.E_conditioned: for i in range(nmb): unique_labels = torch.unique(targets[i]) # find distinct labels in environment unique_label_indices = [] for label in unique_labels: label_ind = [ind for ind, j in enumerate(targets[i]) if j == label] unique_label_indices.append(label_ind) nulabels = unique_labels.shape[0] for idx in range(nulabels): unique_attrs = torch.unique( causal_attribute_labels[i][unique_label_indices[idx]] ) # find distinct attributes in environment with same label unique_attr_indices = [] for attr in unique_attrs: single_attr = [] for y_attr_idx in unique_label_indices[idx]: if causal_attribute_labels[i][y_attr_idx] == attr: single_attr.append(y_attr_idx) unique_attr_indices.append(single_attr) nuattr = unique_attrs.shape[0] for aidx in range(nuattr): for bidx in range(aidx + 1, nuattr): penalty_causal += self.mmd( classifs[i][unique_attr_indices[aidx]], classifs[i][unique_attr_indices[bidx]] ) else: overall_label_attr_vindices = {} # storing attribute indices overall_label_attr_eindices = {} # storing corresponding environment indices for i in range(nmb): unique_labels = torch.unique(targets[i]) # find distinct labels in environment unique_label_indices = [] for label in unique_labels: label_ind = [ind for ind, j in enumerate(targets[i]) if j == label] unique_label_indices.append(label_ind) nulabels = unique_labels.shape[0] for idx in range(nulabels): label = unique_labels[idx] if label not in overall_label_attr_vindices: overall_label_attr_vindices[label] = {} overall_label_attr_eindices[label] = {} unique_attrs = torch.unique( causal_attribute_labels[i][unique_label_indices[idx]] ) # find distinct attributes in environment with same label unique_attr_indices = [] for attr in unique_attrs: # storing indices with same attribute value and label if attr not in overall_label_attr_vindices[label]: overall_label_attr_vindices[label][attr] = [] overall_label_attr_eindices[label][attr] = [] single_attr = [] for y_attr_idx in unique_label_indices[idx]: if causal_attribute_labels[i][y_attr_idx] == attr: single_attr.append(y_attr_idx) overall_label_attr_vindices[label][attr].append(single_attr) overall_label_attr_eindices[label][attr].append(i) unique_attr_indices.append(single_attr) for ( y_val ) in ( overall_label_attr_vindices ): # applying MMD penalty between distributions P(φ(x)|ai, y), P(φ(x)|aj, y) i.e samples with different attribute values but same label tensors_list = [] for attr in overall_label_attr_vindices[y_val]: attrs_list = [] if overall_label_attr_vindices[y_val][attr] != []: for il_ind, indices_list in enumerate(overall_label_attr_vindices[y_val][attr]): attrs_list.append( classifs[overall_label_attr_eindices[y_val][attr][il_ind]][indices_list] ) if len(attrs_list) > 0: tensor_attrs = torch.cat(attrs_list, 0) tensors_list.append(tensor_attrs) nuattr = len(tensors_list) for aidx in range(nuattr): for bidx in range(aidx + 1, nuattr): penalty_causal += self.mmd(tensors_list[aidx], tensors_list[bidx]) # Aind regularization if "ind" in self.attr_types: if self.E_eq_Aind: # Environment (E) and Independent attribute (Aind) coincide for i in range(nmb): for j in range(i + 1, nmb): penalty_ind += self.mmd(classifs[i], classifs[j]) else: if self.E_conditioned: for i in range(nmb): unique_aind_labels = torch.unique(ind_attribute_labels[i]) unique_aind_label_indices = [] for label in unique_aind_labels: label_ind = [ind for ind, j in enumerate(ind_attribute_labels[i]) if j == label] unique_aind_label_indices.append(label_ind) nulabels = unique_aind_labels.shape[0] for aidx in range(nulabels): for bidx in range(aidx + 1, nulabels): penalty_ind += self.mmd( classifs[i][unique_aind_label_indices[aidx]], classifs[i][unique_aind_label_indices[bidx]], ) else: # this currently assumes we have a disjoint set of attributes (Aind) across environments i.e., environment is defined by multiple closely related values of the attribute overall_nmb_indices, nmb_id = [], [] for i in range(nmb): unique_attrs = torch.unique(ind_attribute_labels[i]) unique_attr_indices = [] for attr in unique_attrs: attr_ind = [ind for ind, j in enumerate(ind_attribute_labels[i]) if j == attr] unique_attr_indices.append(attr_ind) overall_nmb_indices.append(attr_ind) nmb_id.append(i) nuattr = len(overall_nmb_indices) for aidx in range(nuattr): for bidx in range(aidx + 1, nuattr): a_nmb_id = nmb_id[aidx] b_nmb_id = nmb_id[bidx] penalty_ind += self.mmd( classifs[a_nmb_id][overall_nmb_indices[aidx]], classifs[b_nmb_id][overall_nmb_indices[bidx]], ) objective /= nmb if nmb > 1: penalty_causal /= nmb * (nmb - 1) / 2 penalty_ind /= nmb * (nmb - 1) / 2 # Compile loss loss = objective loss += self.lambda_causal * penalty_causal loss += self.lambda_ind * penalty_ind if torch.is_tensor(penalty_causal): penalty_causal = penalty_causal.item() self.log("penalty_causal", penalty_causal, on_step=False, on_epoch=True, prog_bar=True) if torch.is_tensor(penalty_ind): penalty_ind = penalty_ind.item() self.log("penalty_ind", penalty_ind, on_step=False, on_epoch=True, prog_bar=True) acc = correct / total metrics = {"train_acc": acc, "train_loss": loss} self.log_dict(metrics, on_step=False, on_epoch=True, prog_bar=True) return loss

import torch from torch import nn from torch.nn import functional as F from dowhy.causal_prediction.algorithms.base_algorithm import PredictionAlgorithm from dowhy.causal_prediction.algorithms.regularization import Regularizer class CACM(PredictionAlgorithm): def __init__( self, model, optimizer="Adam", lr=1e-3, weight_decay=0.0, betas=(0.9, 0.999), momentum=0.9, kernel_type="gaussian", ci_test="mmd", attr_types=[], E_conditioned=True, E_eq_A=[], gamma=1e-6, lambda_causal=1.0, lambda_conf=1.0, lambda_ind=1.0, lambda_sel=1.0, ): """Class for Causally Adaptive Constraint Minimization (CACM) Algorithm. @article{Kaur2022ModelingTD, title={Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization}, author={Jivat Neet Kaur and Emre Kıcıman and Amit Sharma}, journal={ArXiv}, year={2022}, volume={abs/2206.07837}, url={https://arxiv.org/abs/2206.07837} } :param model: Networks used for training. `model` type expected is torch.nn.Sequential(featurizer, classifier) where featurizer and classifier are of type torch.nn.Module. :param optimizer: Optimization algorithm used for training. Currently supports "Adam" and "SGD". :param lr: learning rate for CACM :param weight_decay: Value of weight decay for optimizer :param betas: Adam configuration parameters (beta1, beta2), exponential decay rate for the first moment and second-moment estimates, respectively. :param momentum: Value of momentum for SGD optimzer :param kernel_type: Kernel type for MMD penalty. Currently, supports "gaussian" (RBF). If None, distance between mean and second-order statistics (covariances) is used. :param ci_test: Conditional independence metric used for regularization penalty. Currently, MMD is supported. :param attr_types: list of attribute types (based on relationship with label Y); should be ordered according to attribute order in loaded dataset. Currently, 'causal' (Causal), 'conf' (Confounded), 'ind' (Independent) and 'sel' (Selected) are supported. For single-shift datasets, use: ['causal'], ['ind'] For multi-shift datasets, use: ['causal', 'ind'] :param E_conditioned: Binary flag indicating whether E-conditioned regularization has to be applied :param E_eq_A: list indicating indices of attributes that coincide with environment (E) definition; default is empty. :param gamma: kernel bandwidth for MMD (due to implementation, the kernel bandwdith will actually be the reciprocal of gamma i.e., gamma=1e-6 implies kernel bandwidth=1e6. See `mmd_compute` in utils.py) :param lambda_causal: MMD penalty hyperparameter for Causal shift :param lambda_conf: MMD penalty hyperparameter for Confounded shift :param lambda_ind: MMD penalty hyperparameter for Independent shift :param lambda_sel: MMD penalty hyperparameter for Selected shift :returns: an instance of PredictionAlgorithm class """ super().__init__(model, optimizer, lr, weight_decay, betas, momentum) self.CACMRegularizer = Regularizer(E_conditioned, ci_test, kernel_type, gamma) self.attr_types = attr_types self.E_eq_A = E_eq_A self.lambda_causal = lambda_causal self.lambda_conf = lambda_conf self.lambda_ind = lambda_ind self.lambda_sel = lambda_sel def training_step(self, train_batch, batch_idx): """ Override `training_step` from PredictionAlgorithm class for CACM-specific training loop. """ self.featurizer = self.model[0] self.classifier = self.model[1] minibatches = train_batch objective = 0 correct, total = 0, 0 penalty_causal, penalty_conf, penalty_ind, penalty_sel = 0, 0, 0, 0 nmb = len(minibatches) features = [self.featurizer(xi) for xi, _, _ in minibatches] classifs = [self.classifier(fi) for fi in features] targets = [yi for _, yi, _ in minibatches] for i in range(nmb): objective += F.cross_entropy(classifs[i], targets[i]) correct += (torch.argmax(classifs[i], dim=1) == targets[i]).float().sum().item() total += classifs[i].shape[0] objective /= nmb loss = objective if self.attr_types != []: for attr_type_idx, attr_type in enumerate(self.attr_types): attribute_labels = [ ai for _, _, ai in minibatches ] # [(batch_size, num_attrs)_1, batch_size, num_attrs)_2, ..., (batch_size, num_attrs)_(num_environments)] E_eq_A_attr = attr_type_idx in self.E_eq_A # Acause regularization if attr_type == "causal": penalty_causal += self.CACMRegularizer.conditional_reg( classifs, [a[:, attr_type_idx] for a in attribute_labels], [targets], nmb, E_eq_A_attr ) # Aconf regularization elif attr_type == "conf": penalty_conf += self.CACMRegularizer.unconditional_reg( classifs, [a[:, attr_type_idx] for a in attribute_labels], nmb, E_eq_A_attr ) # Aind regularization elif attr_type == "ind": penalty_ind += self.CACMRegularizer.unconditional_reg( classifs, [a[:, attr_type_idx] for a in attribute_labels], nmb, E_eq_A_attr ) # Asel regularization elif attr_type == "sel": penalty_sel += self.CACMRegularizer.conditional_reg( classifs, [a[:, attr_type_idx] for a in attribute_labels], [targets], nmb, E_eq_A_attr ) if nmb > 1: penalty_causal /= nmb * (nmb - 1) / 2 penalty_conf /= nmb * (nmb - 1) / 2 penalty_ind /= nmb * (nmb - 1) / 2 penalty_sel /= nmb * (nmb - 1) / 2 # Compile loss loss += self.lambda_causal * penalty_causal loss += self.lambda_conf * penalty_conf loss += self.lambda_ind * penalty_ind loss += self.lambda_sel * penalty_sel if torch.is_tensor(penalty_causal): penalty_causal = penalty_causal.item() self.log("penalty_causal", penalty_causal, on_step=False, on_epoch=True, prog_bar=True) if torch.is_tensor(penalty_conf): penalty_conf = penalty_conf.item() self.log("penalty_conf", penalty_conf, on_step=False, on_epoch=True, prog_bar=True) if torch.is_tensor(penalty_ind): penalty_ind = penalty_ind.item() self.log("penalty_ind", penalty_ind, on_step=False, on_epoch=True, prog_bar=True) if torch.is_tensor(penalty_sel): penalty_sel = penalty_sel.item() self.log("penalty_sel", penalty_sel, on_step=False, on_epoch=True, prog_bar=True) elif self.graph is not None: pass # TODO else: raise ValueError("No attribute types or graph provided.") acc = correct / total metrics = {"train_acc": acc, "train_loss": loss} self.log_dict(metrics, on_step=False, on_epoch=True, prog_bar=True) return loss

jivatneet

c87b511bfd95caec2bc50e4686ed21ca448e9c37

0fb1314c5ddaeb6805109453433ac6248063f3c5

Okay, it is good then.

amit-sharma

py-why/dowhy

Fix auto assign model unit test

2023-04-03 14:24:14+00:00

2023-04-03 15:38:01+00:00

tests/gcm/test_auto.py

import networkx as nx import numpy as np import pandas as pd from _pytest.python_api import approx from flaky import flaky from pytest import mark from sklearn.ensemble import HistGradientBoostingClassifier, HistGradientBoostingRegressor from sklearn.linear_model import ElasticNetCV, LassoCV, LinearRegression, LogisticRegression, RidgeCV from sklearn.naive_bayes import GaussianNB from sklearn.pipeline import Pipeline from dowhy.gcm import ProbabilisticCausalModel, draw_samples, fit from dowhy.gcm.auto import AssignmentQuality, assign_causal_mechanisms, has_linear_relationship def _generate_linear_regression_data(num_samples=1000): X = np.random.normal(0, 1, (num_samples, 5)) Y = np.sum(X * np.random.uniform(-5, 5, X.shape[1]), axis=1) return X, Y def _generate_non_linear_regression_data(): X = np.random.normal(0, 1, (1000, 5)) Y = np.sum(np.log(abs(X)), axis=1) return X, Y def _generate_linear_classification_data(): X = np.random.normal(0, 1, (1000, 5)) Y = (np.sum(X * np.random.uniform(-5, 5, X.shape[1]), axis=1) > 0).astype(str) return X, Y def _generate_non_classification_data(): X = np.random.normal(0, 1, (1000, 5)) Y = (np.sum(np.exp(X), axis=1) > np.median(np.sum(np.exp(X), axis=1))).astype(str) return X, Y @flaky(max_runs=3) def test_given_linear_regression_problem_when_auto_assign_causal_models_with_good_quality_returns_linear_model(): X, Y = _generate_linear_regression_data() causal_model = ProbabilisticCausalModel( nx.DiGraph([("X0", "Y"), ("X1", "Y"), ("X2", "Y"), ("X3", "Y"), ("X4", "Y")]) ) data = {"X" + str(i): X[:, i] for i in range(X.shape[1])} data.update({"Y": Y}) assign_causal_mechanisms(causal_model, pd.DataFrame(data), quality=AssignmentQuality.GOOD) assert isinstance(causal_model.causal_mechanism("Y").prediction_model.sklearn_model, LinearRegression) @flaky(max_runs=3) def test_given_linear_regression_problem_when_auto_assign_causal_models_with_better_quality_returns_linear_model(): X, Y = _generate_linear_regression_data(5000) causal_model = ProbabilisticCausalModel( nx.DiGraph([("X0", "Y"), ("X1", "Y"), ("X2", "Y"), ("X3", "Y"), ("X4", "Y")]) ) data = {"X" + str(i): X[:, i] for i in range(X.shape[1])} data.update({"Y": Y}) assign_causal_mechanisms(causal_model, pd.DataFrame(data), quality=AssignmentQuality.BETTER) assert isinstance(causal_model.causal_mechanism("Y").prediction_model.sklearn_model, LinearRegression) @flaky(max_runs=3) def test_given_non_linear_regression_problem_when_auto_assign_causal_models_with_good_quality_returns_non_linear_model(): X, Y = _generate_non_linear_regression_data() causal_model = ProbabilisticCausalModel( nx.DiGraph([("X0", "Y"), ("X1", "Y"), ("X2", "Y"), ("X3", "Y"), ("X4", "Y")]) ) data = {"X" + str(i): X[:, i] for i in range(X.shape[1])} data.update({"Y": Y}) assign_causal_mechanisms(causal_model, pd.DataFrame(data), quality=AssignmentQuality.GOOD) assert isinstance( causal_model.causal_mechanism("Y").prediction_model.sklearn_model, HistGradientBoostingRegressor ) or isinstance(causal_model.causal_mechanism("Y").prediction_model.sklearn_model, Pipeline) @flaky(max_runs=3) def test_given_non_linear_regression_problem_when_auto_assign_causal_models_with_better_quality_returns_non_linear_model(): X, Y = _generate_non_linear_regression_data() causal_model = ProbabilisticCausalModel( nx.DiGraph([("X0", "Y"), ("X1", "Y"), ("X2", "Y"), ("X3", "Y"), ("X4", "Y")]) ) data = {"X" + str(i): X[:, i] for i in range(X.shape[1])} data.update({"Y": Y}) assign_causal_mechanisms(causal_model, pd.DataFrame(data), quality=AssignmentQuality.BETTER) assert not isinstance(causal_model.causal_mechanism("Y").prediction_model.sklearn_model, LinearRegression) assert not isinstance(causal_model.causal_mechanism("Y").prediction_model.sklearn_model, LassoCV) assert not isinstance(causal_model.causal_mechanism("Y").prediction_model.sklearn_model, ElasticNetCV) assert not isinstance(causal_model.causal_mechanism("Y").prediction_model.sklearn_model, RidgeCV) @flaky(max_runs=3) def test_given_linear_classification_problem_when_auto_assign_causal_models_with_good_quality_returns_linear_model(): X, Y = _generate_linear_classification_data() causal_model = ProbabilisticCausalModel( nx.DiGraph([("X0", "Y"), ("X1", "Y"), ("X2", "Y"), ("X3", "Y"), ("X4", "Y")]) ) data = {"X" + str(i): X[:, i] for i in range(X.shape[1])} data.update({"Y": Y}) assign_causal_mechanisms(causal_model, pd.DataFrame(data), quality=AssignmentQuality.GOOD) assert isinstance(causal_model.causal_mechanism("Y").classifier_model.sklearn_model, LogisticRegression) @flaky(max_runs=3) def test_given_linear_classification_problem_when_auto_assign_causal_models_with_better_quality_returns_linear_model(): X, Y = _generate_linear_classification_data() causal_model = ProbabilisticCausalModel( nx.DiGraph([("X0", "Y"), ("X1", "Y"), ("X2", "Y"), ("X3", "Y"), ("X4", "Y")]) ) data = {"X" + str(i): X[:, i] for i in range(X.shape[1])} data.update({"Y": Y}) assign_causal_mechanisms(causal_model, pd.DataFrame(data), quality=AssignmentQuality.BETTER) assert isinstance(causal_model.causal_mechanism("Y").classifier_model.sklearn_model, LogisticRegression) @flaky(max_runs=3) def test_given_non_linear_classification_problem_when_auto_assign_causal_models_with_good_quality_returns_non_linear_model(): X, Y = _generate_non_classification_data() causal_model = ProbabilisticCausalModel( nx.DiGraph([("X0", "Y"), ("X1", "Y"), ("X2", "Y"), ("X3", "Y"), ("X4", "Y")]) ) data = {"X" + str(i): X[:, i] for i in range(X.shape[1])} data.update({"Y": Y}) assign_causal_mechanisms(causal_model, pd.DataFrame(data), quality=AssignmentQuality.GOOD) assert isinstance( causal_model.causal_mechanism("Y").classifier_model.sklearn_model, HistGradientBoostingClassifier ) or isinstance(causal_model.causal_mechanism("Y").classifier_model.sklearn_model, Pipeline) @flaky(max_runs=3) def test_given_non_linear_classification_problem_when_auto_assign_causal_models_with_better_quality_returns_non_linear_model(): X, Y = _generate_non_classification_data() causal_model = ProbabilisticCausalModel( nx.DiGraph([("X0", "Y"), ("X1", "Y"), ("X2", "Y"), ("X3", "Y"), ("X4", "Y")]) ) data = {"X" + str(i): X[:, i] for i in range(X.shape[1])} data.update({"Y": Y}) assign_causal_mechanisms(causal_model, pd.DataFrame(data), quality=AssignmentQuality.BETTER) assert not isinstance(causal_model.causal_mechanism("Y").classifier_model.sklearn_model, LogisticRegression) assert not isinstance(causal_model.causal_mechanism("Y").classifier_model.sklearn_model, GaussianNB) @flaky(max_runs=3) def test_given_polynomial_regression_data_with_categorical_input_when_auto_assign_causal_models_then_does_not_raise_error(): X = np.column_stack( [np.random.choice(2, 100, replace=True).astype(str), np.random.normal(0, 1, (100, 2)).astype(object)] ).astype(object) Y = [] for i in range(X.shape[0]): Y.append(X[i, 1] * X[i, 2] if X[i, 0] == "0" else X[i, 1] + X[i, 2]) Y = np.array(Y) causal_model = ProbabilisticCausalModel(nx.DiGraph([("X0", "Y"), ("X1", "Y"), ("X2", "Y")])) data = {"X" + str(i): X[:, i] for i in range(X.shape[1])} data.update({"Y": Y}) assign_causal_mechanisms(causal_model, pd.DataFrame(data), quality=AssignmentQuality.GOOD) assign_causal_mechanisms(causal_model, pd.DataFrame(data), quality=AssignmentQuality.BETTER, override_models=True) @flaky(max_runs=3) def test_given_polynomial_classification_data_with_categorical_input_when_auto_assign_causal_models_then_does_not_raise_error(): X = np.random.normal(0, 1, (100, 2)) Y = [] for x in X: if x[0] * x[1] > 0: Y.append("Class 0") else: Y.append("Class 1") Y = np.array(Y) causal_model = ProbabilisticCausalModel(nx.DiGraph([("X0", "Y"), ("X1", "Y")])) data = {"X" + str(i): X[:, i] for i in range(X.shape[1])} data.update({"Y": Y}) assign_causal_mechanisms(causal_model, pd.DataFrame(data), quality=AssignmentQuality.BETTER) assign_causal_mechanisms(causal_model, pd.DataFrame(data), quality=AssignmentQuality.GOOD, override_models=True) def test_when_auto_called_from_main_namespace_returns_no_attribute_error(): from dowhy import gcm _ = gcm.auto.AssignmentQuality.GOOD @mark.skip("Not running AutoGluon-based tests as part of CI yet.") def test_when_using_best_quality_then_returns_auto_gluon_model(): from dowhy.gcm.ml import AutoGluonClassifier, AutoGluonRegressor causal_model = ProbabilisticCausalModel(nx.DiGraph([("X", "Y")])) assign_causal_mechanisms(causal_model, pd.DataFrame({"X": [1], "Y": [1]}), quality=AssignmentQuality.BEST) assert isinstance(causal_model.causal_mechanism("Y").prediction_model, AutoGluonRegressor) assign_causal_mechanisms( causal_model, pd.DataFrame({"X": [1], "Y": ["Class 1"]}), quality=AssignmentQuality.BEST, override_models=True ) assert isinstance(causal_model.causal_mechanism("Y").classifier_model, AutoGluonClassifier) @flaky(max_runs=3) def test_given_linear_gaussian_data_when_fit_scm_with_auto_assigned_models_with_default_parameters_then_generate_samples_with_correct_statistics(): X0 = np.random.normal(0, 1, 2000) X1 = 2 * X0 + np.random.normal(0, 0.2, 2000) X2 = 0.5 * X0 + np.random.normal(0, 0.2, 2000) X3 = 0.5 * X2 + np.random.normal(0, 0.2, 2000) original_observations = pd.DataFrame({"X0": X0, "X1": X1, "X2": X2, "X3": X3}) causal_model = ProbabilisticCausalModel(nx.DiGraph([("X0", "X1"), ("X0", "X2"), ("X2", "X3")])) assign_causal_mechanisms(causal_model, original_observations) fit(causal_model, original_observations) generated_samples = draw_samples(causal_model, 2000) assert np.mean(generated_samples["X0"]) == approx(np.mean(X0), abs=0.1) assert np.std(generated_samples["X0"]) == approx(np.std(X0), abs=0.1) assert np.mean(generated_samples["X1"]) == approx(np.mean(X1), abs=0.1) assert np.std(generated_samples["X1"]) == approx(np.std(X1), abs=0.1) assert np.mean(generated_samples["X2"]) == approx(np.mean(X2), abs=0.1) assert np.std(generated_samples["X2"]) == approx(np.std(X2), abs=0.1) assert np.mean(generated_samples["X3"]) == approx(np.mean(X3), abs=0.1) assert np.std(generated_samples["X3"]) == approx(np.std(X3), abs=0.1) @flaky(max_runs=3) def test_given_nonlinear_gaussian_data_when_fit_scm_with_auto_assigned_models_with_default_parameters_then_generate_samples_with_correct_statistics(): X0 = np.random.normal(0, 1, 2000) X1 = np.sin(2 * X0) + np.random.normal(0, 0.2, 2000) X2 = 0.5 * X0**2 + np.random.normal(0, 0.2, 2000) X3 = 0.5 * X2 + np.random.normal(0, 0.2, 2000) original_observations = pd.DataFrame({"X0": X0, "X1": X1, "X2": X2, "X3": X3}) causal_model = ProbabilisticCausalModel(nx.DiGraph([("X0", "X1"), ("X0", "X2"), ("X2", "X3")])) assign_causal_mechanisms(causal_model, original_observations) fit(causal_model, original_observations) generated_samples = draw_samples(causal_model, 2000) assert np.mean(generated_samples["X0"]) == approx(np.mean(X0), abs=0.1) assert np.std(generated_samples["X0"]) == approx(np.std(X0), abs=0.1) assert np.mean(generated_samples["X1"]) == approx(np.mean(X1), abs=0.1) assert np.std(generated_samples["X1"]) == approx(np.std(X1), abs=0.1) assert np.mean(generated_samples["X2"]) == approx(np.mean(X2), abs=0.1) assert np.std(generated_samples["X2"]) == approx(np.std(X2), abs=0.1) assert np.mean(generated_samples["X3"]) == approx(np.mean(X3), abs=0.1) assert np.std(generated_samples["X3"]) == approx(np.std(X3), abs=0.1) def test_givne_simple_data_when_apply_has_linear_relationship_then_returns_expected_results(): X = np.random.random(1000) assert has_linear_relationship(X, 2 * X) assert not has_linear_relationship(X, X**2) @flaky(max_runs=3) def test_given_categorical_data_when_calling_has_linear_relationship_then_returns_correct_results(): X1 = np.random.normal(0, 1, 1000) X2 = np.random.normal(0, 1, 1000) assert has_linear_relationship(np.column_stack([X1, X2]), (X1 + X2 > 0).astype(str)) assert not has_linear_relationship(np.column_stack([X1, X2]), (X1 * X2 > 0).astype(str)) def test_given_imbalanced_categorical_data_when_calling_has_linear_relationship_then_does_not_raise_exception(): X = np.random.normal(0, 1, 1000) Y = np.array(["OneClass"] * 1000) assert has_linear_relationship(np.append(X, 0), np.append(Y, "RareClass")) X = np.random.normal(0, 1, 100000) Y = np.array(["OneClass"] * 100000) assert has_linear_relationship( np.append(X, np.random.normal(0, 0.000001, 100)), np.append(Y, np.array(["RareClass"] * 100)) ) def test_given_data_with_rare_categorical_features_when_calling_has_linear_relationship_then_does_not_raise_exception(): X = np.array(["Feature" + str(i) for i in range(20)]) Y = np.append(np.array(["Class1"] * 10), np.array(["Class2"] * 10)) assert has_linear_relationship(X, Y)

import networkx as nx import numpy as np import pandas as pd from _pytest.python_api import approx from flaky import flaky from pytest import mark from sklearn.ensemble import HistGradientBoostingClassifier, HistGradientBoostingRegressor from sklearn.linear_model import ElasticNetCV, LassoCV, LinearRegression, LogisticRegression, RidgeCV from sklearn.naive_bayes import GaussianNB from sklearn.pipeline import Pipeline from dowhy.gcm import ProbabilisticCausalModel, draw_samples, fit from dowhy.gcm.auto import AssignmentQuality, assign_causal_mechanisms, has_linear_relationship def _generate_linear_regression_data(num_samples=1000): X = np.random.normal(0, 1, (num_samples, 5)) Y = np.sum(X * np.random.uniform(-5, 5, X.shape[1]), axis=1) return X, Y def _generate_non_linear_regression_data(): X = np.random.normal(0, 1, (1000, 5)) Y = np.sum(np.log(abs(X)), axis=1) return X, Y def _generate_linear_classification_data(): X = np.random.normal(0, 1, (1000, 5)) Y = (np.sum(X * np.random.uniform(-5, 5, X.shape[1]), axis=1) > 0).astype(str) return X, Y def _generate_non_classification_data(): X = np.random.normal(0, 1, (1000, 5)) Y = (np.sum(np.exp(X), axis=1) > np.median(np.sum(np.exp(X), axis=1))).astype(str) return X, Y @flaky(max_runs=3) def test_given_linear_regression_problem_when_auto_assign_causal_models_with_good_quality_returns_linear_model(): X, Y = _generate_linear_regression_data() causal_model = ProbabilisticCausalModel( nx.DiGraph([("X0", "Y"), ("X1", "Y"), ("X2", "Y"), ("X3", "Y"), ("X4", "Y")]) ) data = {"X" + str(i): X[:, i] for i in range(X.shape[1])} data.update({"Y": Y}) assign_causal_mechanisms(causal_model, pd.DataFrame(data), quality=AssignmentQuality.GOOD) assert isinstance( causal_model.causal_mechanism("Y").prediction_model.sklearn_model, LinearRegression ) or isinstance(causal_model.causal_mechanism("Y").prediction_model.sklearn_model, Pipeline) @flaky(max_runs=3) def test_given_linear_regression_problem_when_auto_assign_causal_models_with_better_quality_returns_linear_model(): X, Y = _generate_linear_regression_data(5000) causal_model = ProbabilisticCausalModel( nx.DiGraph([("X0", "Y"), ("X1", "Y"), ("X2", "Y"), ("X3", "Y"), ("X4", "Y")]) ) data = {"X" + str(i): X[:, i] for i in range(X.shape[1])} data.update({"Y": Y}) assign_causal_mechanisms(causal_model, pd.DataFrame(data), quality=AssignmentQuality.BETTER) assert isinstance( causal_model.causal_mechanism("Y").prediction_model.sklearn_model, LinearRegression ) or isinstance(causal_model.causal_mechanism("Y").prediction_model.sklearn_model, Pipeline) @flaky(max_runs=3) def test_given_non_linear_regression_problem_when_auto_assign_causal_models_with_good_quality_returns_non_linear_model(): X, Y = _generate_non_linear_regression_data() causal_model = ProbabilisticCausalModel( nx.DiGraph([("X0", "Y"), ("X1", "Y"), ("X2", "Y"), ("X3", "Y"), ("X4", "Y")]) ) data = {"X" + str(i): X[:, i] for i in range(X.shape[1])} data.update({"Y": Y}) assign_causal_mechanisms(causal_model, pd.DataFrame(data), quality=AssignmentQuality.GOOD) assert isinstance( causal_model.causal_mechanism("Y").prediction_model.sklearn_model, HistGradientBoostingRegressor ) or isinstance(causal_model.causal_mechanism("Y").prediction_model.sklearn_model, Pipeline) @flaky(max_runs=3) def test_given_non_linear_regression_problem_when_auto_assign_causal_models_with_better_quality_returns_non_linear_model(): X, Y = _generate_non_linear_regression_data() causal_model = ProbabilisticCausalModel( nx.DiGraph([("X0", "Y"), ("X1", "Y"), ("X2", "Y"), ("X3", "Y"), ("X4", "Y")]) ) data = {"X" + str(i): X[:, i] for i in range(X.shape[1])} data.update({"Y": Y}) assign_causal_mechanisms(causal_model, pd.DataFrame(data), quality=AssignmentQuality.BETTER) assert not isinstance(causal_model.causal_mechanism("Y").prediction_model.sklearn_model, LinearRegression) assert not isinstance(causal_model.causal_mechanism("Y").prediction_model.sklearn_model, LassoCV) assert not isinstance(causal_model.causal_mechanism("Y").prediction_model.sklearn_model, ElasticNetCV) assert not isinstance(causal_model.causal_mechanism("Y").prediction_model.sklearn_model, RidgeCV) @flaky(max_runs=3) def test_given_linear_classification_problem_when_auto_assign_causal_models_with_good_quality_returns_linear_model(): X, Y = _generate_linear_classification_data() causal_model = ProbabilisticCausalModel( nx.DiGraph([("X0", "Y"), ("X1", "Y"), ("X2", "Y"), ("X3", "Y"), ("X4", "Y")]) ) data = {"X" + str(i): X[:, i] for i in range(X.shape[1])} data.update({"Y": Y}) assign_causal_mechanisms(causal_model, pd.DataFrame(data), quality=AssignmentQuality.GOOD) assert isinstance(causal_model.causal_mechanism("Y").classifier_model.sklearn_model, LogisticRegression) @flaky(max_runs=3) def test_given_linear_classification_problem_when_auto_assign_causal_models_with_better_quality_returns_linear_model(): X, Y = _generate_linear_classification_data() causal_model = ProbabilisticCausalModel( nx.DiGraph([("X0", "Y"), ("X1", "Y"), ("X2", "Y"), ("X3", "Y"), ("X4", "Y")]) ) data = {"X" + str(i): X[:, i] for i in range(X.shape[1])} data.update({"Y": Y}) assign_causal_mechanisms(causal_model, pd.DataFrame(data), quality=AssignmentQuality.BETTER) assert isinstance(causal_model.causal_mechanism("Y").classifier_model.sklearn_model, LogisticRegression) @flaky(max_runs=3) def test_given_non_linear_classification_problem_when_auto_assign_causal_models_with_good_quality_returns_non_linear_model(): X, Y = _generate_non_classification_data() causal_model = ProbabilisticCausalModel( nx.DiGraph([("X0", "Y"), ("X1", "Y"), ("X2", "Y"), ("X3", "Y"), ("X4", "Y")]) ) data = {"X" + str(i): X[:, i] for i in range(X.shape[1])} data.update({"Y": Y}) assign_causal_mechanisms(causal_model, pd.DataFrame(data), quality=AssignmentQuality.GOOD) assert isinstance( causal_model.causal_mechanism("Y").classifier_model.sklearn_model, HistGradientBoostingClassifier ) or isinstance(causal_model.causal_mechanism("Y").classifier_model.sklearn_model, Pipeline) @flaky(max_runs=3) def test_given_non_linear_classification_problem_when_auto_assign_causal_models_with_better_quality_returns_non_linear_model(): X, Y = _generate_non_classification_data() causal_model = ProbabilisticCausalModel( nx.DiGraph([("X0", "Y"), ("X1", "Y"), ("X2", "Y"), ("X3", "Y"), ("X4", "Y")]) ) data = {"X" + str(i): X[:, i] for i in range(X.shape[1])} data.update({"Y": Y}) assign_causal_mechanisms(causal_model, pd.DataFrame(data), quality=AssignmentQuality.BETTER) assert not isinstance(causal_model.causal_mechanism("Y").classifier_model.sklearn_model, LogisticRegression) assert not isinstance(causal_model.causal_mechanism("Y").classifier_model.sklearn_model, GaussianNB) @flaky(max_runs=3) def test_given_polynomial_regression_data_with_categorical_input_when_auto_assign_causal_models_then_does_not_raise_error(): X = np.column_stack( [np.random.choice(2, 100, replace=True).astype(str), np.random.normal(0, 1, (100, 2)).astype(object)] ).astype(object) Y = [] for i in range(X.shape[0]): Y.append(X[i, 1] * X[i, 2] if X[i, 0] == "0" else X[i, 1] + X[i, 2]) Y = np.array(Y) causal_model = ProbabilisticCausalModel(nx.DiGraph([("X0", "Y"), ("X1", "Y"), ("X2", "Y")])) data = {"X" + str(i): X[:, i] for i in range(X.shape[1])} data.update({"Y": Y}) assign_causal_mechanisms(causal_model, pd.DataFrame(data), quality=AssignmentQuality.GOOD) assign_causal_mechanisms(causal_model, pd.DataFrame(data), quality=AssignmentQuality.BETTER, override_models=True) @flaky(max_runs=3) def test_given_polynomial_classification_data_with_categorical_input_when_auto_assign_causal_models_then_does_not_raise_error(): X = np.random.normal(0, 1, (100, 2)) Y = [] for x in X: if x[0] * x[1] > 0: Y.append("Class 0") else: Y.append("Class 1") Y = np.array(Y) causal_model = ProbabilisticCausalModel(nx.DiGraph([("X0", "Y"), ("X1", "Y")])) data = {"X" + str(i): X[:, i] for i in range(X.shape[1])} data.update({"Y": Y}) assign_causal_mechanisms(causal_model, pd.DataFrame(data), quality=AssignmentQuality.BETTER) assign_causal_mechanisms(causal_model, pd.DataFrame(data), quality=AssignmentQuality.GOOD, override_models=True) def test_when_auto_called_from_main_namespace_returns_no_attribute_error(): from dowhy import gcm _ = gcm.auto.AssignmentQuality.GOOD @mark.skip("Not running AutoGluon-based tests as part of CI yet.") def test_when_using_best_quality_then_returns_auto_gluon_model(): from dowhy.gcm.ml import AutoGluonClassifier, AutoGluonRegressor causal_model = ProbabilisticCausalModel(nx.DiGraph([("X", "Y")])) assign_causal_mechanisms(causal_model, pd.DataFrame({"X": [1], "Y": [1]}), quality=AssignmentQuality.BEST) assert isinstance(causal_model.causal_mechanism("Y").prediction_model, AutoGluonRegressor) assign_causal_mechanisms( causal_model, pd.DataFrame({"X": [1], "Y": ["Class 1"]}), quality=AssignmentQuality.BEST, override_models=True ) assert isinstance(causal_model.causal_mechanism("Y").classifier_model, AutoGluonClassifier) @flaky(max_runs=3) def test_given_linear_gaussian_data_when_fit_scm_with_auto_assigned_models_with_default_parameters_then_generate_samples_with_correct_statistics(): X0 = np.random.normal(0, 1, 2000) X1 = 2 * X0 + np.random.normal(0, 0.2, 2000) X2 = 0.5 * X0 + np.random.normal(0, 0.2, 2000) X3 = 0.5 * X2 + np.random.normal(0, 0.2, 2000) original_observations = pd.DataFrame({"X0": X0, "X1": X1, "X2": X2, "X3": X3}) causal_model = ProbabilisticCausalModel(nx.DiGraph([("X0", "X1"), ("X0", "X2"), ("X2", "X3")])) assign_causal_mechanisms(causal_model, original_observations) fit(causal_model, original_observations) generated_samples = draw_samples(causal_model, 2000) assert np.mean(generated_samples["X0"]) == approx(np.mean(X0), abs=0.1) assert np.std(generated_samples["X0"]) == approx(np.std(X0), abs=0.1) assert np.mean(generated_samples["X1"]) == approx(np.mean(X1), abs=0.1) assert np.std(generated_samples["X1"]) == approx(np.std(X1), abs=0.1) assert np.mean(generated_samples["X2"]) == approx(np.mean(X2), abs=0.1) assert np.std(generated_samples["X2"]) == approx(np.std(X2), abs=0.1) assert np.mean(generated_samples["X3"]) == approx(np.mean(X3), abs=0.1) assert np.std(generated_samples["X3"]) == approx(np.std(X3), abs=0.1) @flaky(max_runs=3) def test_given_nonlinear_gaussian_data_when_fit_scm_with_auto_assigned_models_with_default_parameters_then_generate_samples_with_correct_statistics(): X0 = np.random.normal(0, 1, 2000) X1 = np.sin(2 * X0) + np.random.normal(0, 0.2, 2000) X2 = 0.5 * X0**2 + np.random.normal(0, 0.2, 2000) X3 = 0.5 * X2 + np.random.normal(0, 0.2, 2000) original_observations = pd.DataFrame({"X0": X0, "X1": X1, "X2": X2, "X3": X3}) causal_model = ProbabilisticCausalModel(nx.DiGraph([("X0", "X1"), ("X0", "X2"), ("X2", "X3")])) assign_causal_mechanisms(causal_model, original_observations) fit(causal_model, original_observations) generated_samples = draw_samples(causal_model, 2000) assert np.mean(generated_samples["X0"]) == approx(np.mean(X0), abs=0.1) assert np.std(generated_samples["X0"]) == approx(np.std(X0), abs=0.1) assert np.mean(generated_samples["X1"]) == approx(np.mean(X1), abs=0.1) assert np.std(generated_samples["X1"]) == approx(np.std(X1), abs=0.1) assert np.mean(generated_samples["X2"]) == approx(np.mean(X2), abs=0.1) assert np.std(generated_samples["X2"]) == approx(np.std(X2), abs=0.1) assert np.mean(generated_samples["X3"]) == approx(np.mean(X3), abs=0.1) assert np.std(generated_samples["X3"]) == approx(np.std(X3), abs=0.1) def test_givne_simple_data_when_apply_has_linear_relationship_then_returns_expected_results(): X = np.random.random(1000) assert has_linear_relationship(X, 2 * X) assert not has_linear_relationship(X, X**2) @flaky(max_runs=3) def test_given_categorical_data_when_calling_has_linear_relationship_then_returns_correct_results(): X1 = np.random.normal(0, 1, 1000) X2 = np.random.normal(0, 1, 1000) assert has_linear_relationship(np.column_stack([X1, X2]), (X1 + X2 > 0).astype(str)) assert not has_linear_relationship(np.column_stack([X1, X2]), (X1 * X2 > 0).astype(str)) def test_given_imbalanced_categorical_data_when_calling_has_linear_relationship_then_does_not_raise_exception(): X = np.random.normal(0, 1, 1000) Y = np.array(["OneClass"] * 1000) assert has_linear_relationship(np.append(X, 0), np.append(Y, "RareClass")) X = np.random.normal(0, 1, 100000) Y = np.array(["OneClass"] * 100000) assert has_linear_relationship( np.append(X, np.random.normal(0, 0.000001, 100)), np.append(Y, np.array(["RareClass"] * 100)) ) def test_given_data_with_rare_categorical_features_when_calling_has_linear_relationship_then_does_not_raise_exception(): X = np.array(["Feature" + str(i) for i in range(20)]) Y = np.append(np.array(["Class1"] * 10), np.array(["Class2"] * 10)) assert has_linear_relationship(X, Y)

bloebp

13ed13e8adf7a26a995b2e219103d7a25dacb426

5edb89192864d1684b5b2bde4eb86450a00b093e

Where does pipeline sneak in [here](https://github.com/py-why/dowhy/blob/main/dowhy/gcm/auto.py#L150)?

kailashbuki

py-why/dowhy

Fix auto assign model unit test

2023-04-03 14:24:14+00:00

2023-04-03 15:38:01+00:00

tests/gcm/test_auto.py

import networkx as nx import numpy as np import pandas as pd from _pytest.python_api import approx from flaky import flaky from pytest import mark from sklearn.ensemble import HistGradientBoostingClassifier, HistGradientBoostingRegressor from sklearn.linear_model import ElasticNetCV, LassoCV, LinearRegression, LogisticRegression, RidgeCV from sklearn.naive_bayes import GaussianNB from sklearn.pipeline import Pipeline from dowhy.gcm import ProbabilisticCausalModel, draw_samples, fit from dowhy.gcm.auto import AssignmentQuality, assign_causal_mechanisms, has_linear_relationship def _generate_linear_regression_data(num_samples=1000): X = np.random.normal(0, 1, (num_samples, 5)) Y = np.sum(X * np.random.uniform(-5, 5, X.shape[1]), axis=1) return X, Y def _generate_non_linear_regression_data(): X = np.random.normal(0, 1, (1000, 5)) Y = np.sum(np.log(abs(X)), axis=1) return X, Y def _generate_linear_classification_data(): X = np.random.normal(0, 1, (1000, 5)) Y = (np.sum(X * np.random.uniform(-5, 5, X.shape[1]), axis=1) > 0).astype(str) return X, Y def _generate_non_classification_data(): X = np.random.normal(0, 1, (1000, 5)) Y = (np.sum(np.exp(X), axis=1) > np.median(np.sum(np.exp(X), axis=1))).astype(str) return X, Y @flaky(max_runs=3) def test_given_linear_regression_problem_when_auto_assign_causal_models_with_good_quality_returns_linear_model(): X, Y = _generate_linear_regression_data() causal_model = ProbabilisticCausalModel( nx.DiGraph([("X0", "Y"), ("X1", "Y"), ("X2", "Y"), ("X3", "Y"), ("X4", "Y")]) ) data = {"X" + str(i): X[:, i] for i in range(X.shape[1])} data.update({"Y": Y}) assign_causal_mechanisms(causal_model, pd.DataFrame(data), quality=AssignmentQuality.GOOD) assert isinstance(causal_model.causal_mechanism("Y").prediction_model.sklearn_model, LinearRegression) @flaky(max_runs=3) def test_given_linear_regression_problem_when_auto_assign_causal_models_with_better_quality_returns_linear_model(): X, Y = _generate_linear_regression_data(5000) causal_model = ProbabilisticCausalModel( nx.DiGraph([("X0", "Y"), ("X1", "Y"), ("X2", "Y"), ("X3", "Y"), ("X4", "Y")]) ) data = {"X" + str(i): X[:, i] for i in range(X.shape[1])} data.update({"Y": Y}) assign_causal_mechanisms(causal_model, pd.DataFrame(data), quality=AssignmentQuality.BETTER) assert isinstance(causal_model.causal_mechanism("Y").prediction_model.sklearn_model, LinearRegression) @flaky(max_runs=3) def test_given_non_linear_regression_problem_when_auto_assign_causal_models_with_good_quality_returns_non_linear_model(): X, Y = _generate_non_linear_regression_data() causal_model = ProbabilisticCausalModel( nx.DiGraph([("X0", "Y"), ("X1", "Y"), ("X2", "Y"), ("X3", "Y"), ("X4", "Y")]) ) data = {"X" + str(i): X[:, i] for i in range(X.shape[1])} data.update({"Y": Y}) assign_causal_mechanisms(causal_model, pd.DataFrame(data), quality=AssignmentQuality.GOOD) assert isinstance( causal_model.causal_mechanism("Y").prediction_model.sklearn_model, HistGradientBoostingRegressor ) or isinstance(causal_model.causal_mechanism("Y").prediction_model.sklearn_model, Pipeline) @flaky(max_runs=3) def test_given_non_linear_regression_problem_when_auto_assign_causal_models_with_better_quality_returns_non_linear_model(): X, Y = _generate_non_linear_regression_data() causal_model = ProbabilisticCausalModel( nx.DiGraph([("X0", "Y"), ("X1", "Y"), ("X2", "Y"), ("X3", "Y"), ("X4", "Y")]) ) data = {"X" + str(i): X[:, i] for i in range(X.shape[1])} data.update({"Y": Y}) assign_causal_mechanisms(causal_model, pd.DataFrame(data), quality=AssignmentQuality.BETTER) assert not isinstance(causal_model.causal_mechanism("Y").prediction_model.sklearn_model, LinearRegression) assert not isinstance(causal_model.causal_mechanism("Y").prediction_model.sklearn_model, LassoCV) assert not isinstance(causal_model.causal_mechanism("Y").prediction_model.sklearn_model, ElasticNetCV) assert not isinstance(causal_model.causal_mechanism("Y").prediction_model.sklearn_model, RidgeCV) @flaky(max_runs=3) def test_given_linear_classification_problem_when_auto_assign_causal_models_with_good_quality_returns_linear_model(): X, Y = _generate_linear_classification_data() causal_model = ProbabilisticCausalModel( nx.DiGraph([("X0", "Y"), ("X1", "Y"), ("X2", "Y"), ("X3", "Y"), ("X4", "Y")]) ) data = {"X" + str(i): X[:, i] for i in range(X.shape[1])} data.update({"Y": Y}) assign_causal_mechanisms(causal_model, pd.DataFrame(data), quality=AssignmentQuality.GOOD) assert isinstance(causal_model.causal_mechanism("Y").classifier_model.sklearn_model, LogisticRegression) @flaky(max_runs=3) def test_given_linear_classification_problem_when_auto_assign_causal_models_with_better_quality_returns_linear_model(): X, Y = _generate_linear_classification_data() causal_model = ProbabilisticCausalModel( nx.DiGraph([("X0", "Y"), ("X1", "Y"), ("X2", "Y"), ("X3", "Y"), ("X4", "Y")]) ) data = {"X" + str(i): X[:, i] for i in range(X.shape[1])} data.update({"Y": Y}) assign_causal_mechanisms(causal_model, pd.DataFrame(data), quality=AssignmentQuality.BETTER) assert isinstance(causal_model.causal_mechanism("Y").classifier_model.sklearn_model, LogisticRegression) @flaky(max_runs=3) def test_given_non_linear_classification_problem_when_auto_assign_causal_models_with_good_quality_returns_non_linear_model(): X, Y = _generate_non_classification_data() causal_model = ProbabilisticCausalModel( nx.DiGraph([("X0", "Y"), ("X1", "Y"), ("X2", "Y"), ("X3", "Y"), ("X4", "Y")]) ) data = {"X" + str(i): X[:, i] for i in range(X.shape[1])} data.update({"Y": Y}) assign_causal_mechanisms(causal_model, pd.DataFrame(data), quality=AssignmentQuality.GOOD) assert isinstance( causal_model.causal_mechanism("Y").classifier_model.sklearn_model, HistGradientBoostingClassifier ) or isinstance(causal_model.causal_mechanism("Y").classifier_model.sklearn_model, Pipeline) @flaky(max_runs=3) def test_given_non_linear_classification_problem_when_auto_assign_causal_models_with_better_quality_returns_non_linear_model(): X, Y = _generate_non_classification_data() causal_model = ProbabilisticCausalModel( nx.DiGraph([("X0", "Y"), ("X1", "Y"), ("X2", "Y"), ("X3", "Y"), ("X4", "Y")]) ) data = {"X" + str(i): X[:, i] for i in range(X.shape[1])} data.update({"Y": Y}) assign_causal_mechanisms(causal_model, pd.DataFrame(data), quality=AssignmentQuality.BETTER) assert not isinstance(causal_model.causal_mechanism("Y").classifier_model.sklearn_model, LogisticRegression) assert not isinstance(causal_model.causal_mechanism("Y").classifier_model.sklearn_model, GaussianNB) @flaky(max_runs=3) def test_given_polynomial_regression_data_with_categorical_input_when_auto_assign_causal_models_then_does_not_raise_error(): X = np.column_stack( [np.random.choice(2, 100, replace=True).astype(str), np.random.normal(0, 1, (100, 2)).astype(object)] ).astype(object) Y = [] for i in range(X.shape[0]): Y.append(X[i, 1] * X[i, 2] if X[i, 0] == "0" else X[i, 1] + X[i, 2]) Y = np.array(Y) causal_model = ProbabilisticCausalModel(nx.DiGraph([("X0", "Y"), ("X1", "Y"), ("X2", "Y")])) data = {"X" + str(i): X[:, i] for i in range(X.shape[1])} data.update({"Y": Y}) assign_causal_mechanisms(causal_model, pd.DataFrame(data), quality=AssignmentQuality.GOOD) assign_causal_mechanisms(causal_model, pd.DataFrame(data), quality=AssignmentQuality.BETTER, override_models=True) @flaky(max_runs=3) def test_given_polynomial_classification_data_with_categorical_input_when_auto_assign_causal_models_then_does_not_raise_error(): X = np.random.normal(0, 1, (100, 2)) Y = [] for x in X: if x[0] * x[1] > 0: Y.append("Class 0") else: Y.append("Class 1") Y = np.array(Y) causal_model = ProbabilisticCausalModel(nx.DiGraph([("X0", "Y"), ("X1", "Y")])) data = {"X" + str(i): X[:, i] for i in range(X.shape[1])} data.update({"Y": Y}) assign_causal_mechanisms(causal_model, pd.DataFrame(data), quality=AssignmentQuality.BETTER) assign_causal_mechanisms(causal_model, pd.DataFrame(data), quality=AssignmentQuality.GOOD, override_models=True) def test_when_auto_called_from_main_namespace_returns_no_attribute_error(): from dowhy import gcm _ = gcm.auto.AssignmentQuality.GOOD @mark.skip("Not running AutoGluon-based tests as part of CI yet.") def test_when_using_best_quality_then_returns_auto_gluon_model(): from dowhy.gcm.ml import AutoGluonClassifier, AutoGluonRegressor causal_model = ProbabilisticCausalModel(nx.DiGraph([("X", "Y")])) assign_causal_mechanisms(causal_model, pd.DataFrame({"X": [1], "Y": [1]}), quality=AssignmentQuality.BEST) assert isinstance(causal_model.causal_mechanism("Y").prediction_model, AutoGluonRegressor) assign_causal_mechanisms( causal_model, pd.DataFrame({"X": [1], "Y": ["Class 1"]}), quality=AssignmentQuality.BEST, override_models=True ) assert isinstance(causal_model.causal_mechanism("Y").classifier_model, AutoGluonClassifier) @flaky(max_runs=3) def test_given_linear_gaussian_data_when_fit_scm_with_auto_assigned_models_with_default_parameters_then_generate_samples_with_correct_statistics(): X0 = np.random.normal(0, 1, 2000) X1 = 2 * X0 + np.random.normal(0, 0.2, 2000) X2 = 0.5 * X0 + np.random.normal(0, 0.2, 2000) X3 = 0.5 * X2 + np.random.normal(0, 0.2, 2000) original_observations = pd.DataFrame({"X0": X0, "X1": X1, "X2": X2, "X3": X3}) causal_model = ProbabilisticCausalModel(nx.DiGraph([("X0", "X1"), ("X0", "X2"), ("X2", "X3")])) assign_causal_mechanisms(causal_model, original_observations) fit(causal_model, original_observations) generated_samples = draw_samples(causal_model, 2000) assert np.mean(generated_samples["X0"]) == approx(np.mean(X0), abs=0.1) assert np.std(generated_samples["X0"]) == approx(np.std(X0), abs=0.1) assert np.mean(generated_samples["X1"]) == approx(np.mean(X1), abs=0.1) assert np.std(generated_samples["X1"]) == approx(np.std(X1), abs=0.1) assert np.mean(generated_samples["X2"]) == approx(np.mean(X2), abs=0.1) assert np.std(generated_samples["X2"]) == approx(np.std(X2), abs=0.1) assert np.mean(generated_samples["X3"]) == approx(np.mean(X3), abs=0.1) assert np.std(generated_samples["X3"]) == approx(np.std(X3), abs=0.1) @flaky(max_runs=3) def test_given_nonlinear_gaussian_data_when_fit_scm_with_auto_assigned_models_with_default_parameters_then_generate_samples_with_correct_statistics(): X0 = np.random.normal(0, 1, 2000) X1 = np.sin(2 * X0) + np.random.normal(0, 0.2, 2000) X2 = 0.5 * X0**2 + np.random.normal(0, 0.2, 2000) X3 = 0.5 * X2 + np.random.normal(0, 0.2, 2000) original_observations = pd.DataFrame({"X0": X0, "X1": X1, "X2": X2, "X3": X3}) causal_model = ProbabilisticCausalModel(nx.DiGraph([("X0", "X1"), ("X0", "X2"), ("X2", "X3")])) assign_causal_mechanisms(causal_model, original_observations) fit(causal_model, original_observations) generated_samples = draw_samples(causal_model, 2000) assert np.mean(generated_samples["X0"]) == approx(np.mean(X0), abs=0.1) assert np.std(generated_samples["X0"]) == approx(np.std(X0), abs=0.1) assert np.mean(generated_samples["X1"]) == approx(np.mean(X1), abs=0.1) assert np.std(generated_samples["X1"]) == approx(np.std(X1), abs=0.1) assert np.mean(generated_samples["X2"]) == approx(np.mean(X2), abs=0.1) assert np.std(generated_samples["X2"]) == approx(np.std(X2), abs=0.1) assert np.mean(generated_samples["X3"]) == approx(np.mean(X3), abs=0.1) assert np.std(generated_samples["X3"]) == approx(np.std(X3), abs=0.1) def test_givne_simple_data_when_apply_has_linear_relationship_then_returns_expected_results(): X = np.random.random(1000) assert has_linear_relationship(X, 2 * X) assert not has_linear_relationship(X, X**2) @flaky(max_runs=3) def test_given_categorical_data_when_calling_has_linear_relationship_then_returns_correct_results(): X1 = np.random.normal(0, 1, 1000) X2 = np.random.normal(0, 1, 1000) assert has_linear_relationship(np.column_stack([X1, X2]), (X1 + X2 > 0).astype(str)) assert not has_linear_relationship(np.column_stack([X1, X2]), (X1 * X2 > 0).astype(str)) def test_given_imbalanced_categorical_data_when_calling_has_linear_relationship_then_does_not_raise_exception(): X = np.random.normal(0, 1, 1000) Y = np.array(["OneClass"] * 1000) assert has_linear_relationship(np.append(X, 0), np.append(Y, "RareClass")) X = np.random.normal(0, 1, 100000) Y = np.array(["OneClass"] * 100000) assert has_linear_relationship( np.append(X, np.random.normal(0, 0.000001, 100)), np.append(Y, np.array(["RareClass"] * 100)) ) def test_given_data_with_rare_categorical_features_when_calling_has_linear_relationship_then_does_not_raise_exception(): X = np.array(["Feature" + str(i) for i in range(20)]) Y = np.append(np.array(["Class1"] * 10), np.array(["Class2"] * 10)) assert has_linear_relationship(X, Y)

import networkx as nx import numpy as np import pandas as pd from _pytest.python_api import approx from flaky import flaky from pytest import mark from sklearn.ensemble import HistGradientBoostingClassifier, HistGradientBoostingRegressor from sklearn.linear_model import ElasticNetCV, LassoCV, LinearRegression, LogisticRegression, RidgeCV from sklearn.naive_bayes import GaussianNB from sklearn.pipeline import Pipeline from dowhy.gcm import ProbabilisticCausalModel, draw_samples, fit from dowhy.gcm.auto import AssignmentQuality, assign_causal_mechanisms, has_linear_relationship def _generate_linear_regression_data(num_samples=1000): X = np.random.normal(0, 1, (num_samples, 5)) Y = np.sum(X * np.random.uniform(-5, 5, X.shape[1]), axis=1) return X, Y def _generate_non_linear_regression_data(): X = np.random.normal(0, 1, (1000, 5)) Y = np.sum(np.log(abs(X)), axis=1) return X, Y def _generate_linear_classification_data(): X = np.random.normal(0, 1, (1000, 5)) Y = (np.sum(X * np.random.uniform(-5, 5, X.shape[1]), axis=1) > 0).astype(str) return X, Y def _generate_non_classification_data(): X = np.random.normal(0, 1, (1000, 5)) Y = (np.sum(np.exp(X), axis=1) > np.median(np.sum(np.exp(X), axis=1))).astype(str) return X, Y @flaky(max_runs=3) def test_given_linear_regression_problem_when_auto_assign_causal_models_with_good_quality_returns_linear_model(): X, Y = _generate_linear_regression_data() causal_model = ProbabilisticCausalModel( nx.DiGraph([("X0", "Y"), ("X1", "Y"), ("X2", "Y"), ("X3", "Y"), ("X4", "Y")]) ) data = {"X" + str(i): X[:, i] for i in range(X.shape[1])} data.update({"Y": Y}) assign_causal_mechanisms(causal_model, pd.DataFrame(data), quality=AssignmentQuality.GOOD) assert isinstance( causal_model.causal_mechanism("Y").prediction_model.sklearn_model, LinearRegression ) or isinstance(causal_model.causal_mechanism("Y").prediction_model.sklearn_model, Pipeline) @flaky(max_runs=3) def test_given_linear_regression_problem_when_auto_assign_causal_models_with_better_quality_returns_linear_model(): X, Y = _generate_linear_regression_data(5000) causal_model = ProbabilisticCausalModel( nx.DiGraph([("X0", "Y"), ("X1", "Y"), ("X2", "Y"), ("X3", "Y"), ("X4", "Y")]) ) data = {"X" + str(i): X[:, i] for i in range(X.shape[1])} data.update({"Y": Y}) assign_causal_mechanisms(causal_model, pd.DataFrame(data), quality=AssignmentQuality.BETTER) assert isinstance( causal_model.causal_mechanism("Y").prediction_model.sklearn_model, LinearRegression ) or isinstance(causal_model.causal_mechanism("Y").prediction_model.sklearn_model, Pipeline) @flaky(max_runs=3) def test_given_non_linear_regression_problem_when_auto_assign_causal_models_with_good_quality_returns_non_linear_model(): X, Y = _generate_non_linear_regression_data() causal_model = ProbabilisticCausalModel( nx.DiGraph([("X0", "Y"), ("X1", "Y"), ("X2", "Y"), ("X3", "Y"), ("X4", "Y")]) ) data = {"X" + str(i): X[:, i] for i in range(X.shape[1])} data.update({"Y": Y}) assign_causal_mechanisms(causal_model, pd.DataFrame(data), quality=AssignmentQuality.GOOD) assert isinstance( causal_model.causal_mechanism("Y").prediction_model.sklearn_model, HistGradientBoostingRegressor ) or isinstance(causal_model.causal_mechanism("Y").prediction_model.sklearn_model, Pipeline) @flaky(max_runs=3) def test_given_non_linear_regression_problem_when_auto_assign_causal_models_with_better_quality_returns_non_linear_model(): X, Y = _generate_non_linear_regression_data() causal_model = ProbabilisticCausalModel( nx.DiGraph([("X0", "Y"), ("X1", "Y"), ("X2", "Y"), ("X3", "Y"), ("X4", "Y")]) ) data = {"X" + str(i): X[:, i] for i in range(X.shape[1])} data.update({"Y": Y}) assign_causal_mechanisms(causal_model, pd.DataFrame(data), quality=AssignmentQuality.BETTER) assert not isinstance(causal_model.causal_mechanism("Y").prediction_model.sklearn_model, LinearRegression) assert not isinstance(causal_model.causal_mechanism("Y").prediction_model.sklearn_model, LassoCV) assert not isinstance(causal_model.causal_mechanism("Y").prediction_model.sklearn_model, ElasticNetCV) assert not isinstance(causal_model.causal_mechanism("Y").prediction_model.sklearn_model, RidgeCV) @flaky(max_runs=3) def test_given_linear_classification_problem_when_auto_assign_causal_models_with_good_quality_returns_linear_model(): X, Y = _generate_linear_classification_data() causal_model = ProbabilisticCausalModel( nx.DiGraph([("X0", "Y"), ("X1", "Y"), ("X2", "Y"), ("X3", "Y"), ("X4", "Y")]) ) data = {"X" + str(i): X[:, i] for i in range(X.shape[1])} data.update({"Y": Y}) assign_causal_mechanisms(causal_model, pd.DataFrame(data), quality=AssignmentQuality.GOOD) assert isinstance(causal_model.causal_mechanism("Y").classifier_model.sklearn_model, LogisticRegression) @flaky(max_runs=3) def test_given_linear_classification_problem_when_auto_assign_causal_models_with_better_quality_returns_linear_model(): X, Y = _generate_linear_classification_data() causal_model = ProbabilisticCausalModel( nx.DiGraph([("X0", "Y"), ("X1", "Y"), ("X2", "Y"), ("X3", "Y"), ("X4", "Y")]) ) data = {"X" + str(i): X[:, i] for i in range(X.shape[1])} data.update({"Y": Y}) assign_causal_mechanisms(causal_model, pd.DataFrame(data), quality=AssignmentQuality.BETTER) assert isinstance(causal_model.causal_mechanism("Y").classifier_model.sklearn_model, LogisticRegression) @flaky(max_runs=3) def test_given_non_linear_classification_problem_when_auto_assign_causal_models_with_good_quality_returns_non_linear_model(): X, Y = _generate_non_classification_data() causal_model = ProbabilisticCausalModel( nx.DiGraph([("X0", "Y"), ("X1", "Y"), ("X2", "Y"), ("X3", "Y"), ("X4", "Y")]) ) data = {"X" + str(i): X[:, i] for i in range(X.shape[1])} data.update({"Y": Y}) assign_causal_mechanisms(causal_model, pd.DataFrame(data), quality=AssignmentQuality.GOOD) assert isinstance( causal_model.causal_mechanism("Y").classifier_model.sklearn_model, HistGradientBoostingClassifier ) or isinstance(causal_model.causal_mechanism("Y").classifier_model.sklearn_model, Pipeline) @flaky(max_runs=3) def test_given_non_linear_classification_problem_when_auto_assign_causal_models_with_better_quality_returns_non_linear_model(): X, Y = _generate_non_classification_data() causal_model = ProbabilisticCausalModel( nx.DiGraph([("X0", "Y"), ("X1", "Y"), ("X2", "Y"), ("X3", "Y"), ("X4", "Y")]) ) data = {"X" + str(i): X[:, i] for i in range(X.shape[1])} data.update({"Y": Y}) assign_causal_mechanisms(causal_model, pd.DataFrame(data), quality=AssignmentQuality.BETTER) assert not isinstance(causal_model.causal_mechanism("Y").classifier_model.sklearn_model, LogisticRegression) assert not isinstance(causal_model.causal_mechanism("Y").classifier_model.sklearn_model, GaussianNB) @flaky(max_runs=3) def test_given_polynomial_regression_data_with_categorical_input_when_auto_assign_causal_models_then_does_not_raise_error(): X = np.column_stack( [np.random.choice(2, 100, replace=True).astype(str), np.random.normal(0, 1, (100, 2)).astype(object)] ).astype(object) Y = [] for i in range(X.shape[0]): Y.append(X[i, 1] * X[i, 2] if X[i, 0] == "0" else X[i, 1] + X[i, 2]) Y = np.array(Y) causal_model = ProbabilisticCausalModel(nx.DiGraph([("X0", "Y"), ("X1", "Y"), ("X2", "Y")])) data = {"X" + str(i): X[:, i] for i in range(X.shape[1])} data.update({"Y": Y}) assign_causal_mechanisms(causal_model, pd.DataFrame(data), quality=AssignmentQuality.GOOD) assign_causal_mechanisms(causal_model, pd.DataFrame(data), quality=AssignmentQuality.BETTER, override_models=True) @flaky(max_runs=3) def test_given_polynomial_classification_data_with_categorical_input_when_auto_assign_causal_models_then_does_not_raise_error(): X = np.random.normal(0, 1, (100, 2)) Y = [] for x in X: if x[0] * x[1] > 0: Y.append("Class 0") else: Y.append("Class 1") Y = np.array(Y) causal_model = ProbabilisticCausalModel(nx.DiGraph([("X0", "Y"), ("X1", "Y")])) data = {"X" + str(i): X[:, i] for i in range(X.shape[1])} data.update({"Y": Y}) assign_causal_mechanisms(causal_model, pd.DataFrame(data), quality=AssignmentQuality.BETTER) assign_causal_mechanisms(causal_model, pd.DataFrame(data), quality=AssignmentQuality.GOOD, override_models=True) def test_when_auto_called_from_main_namespace_returns_no_attribute_error(): from dowhy import gcm _ = gcm.auto.AssignmentQuality.GOOD @mark.skip("Not running AutoGluon-based tests as part of CI yet.") def test_when_using_best_quality_then_returns_auto_gluon_model(): from dowhy.gcm.ml import AutoGluonClassifier, AutoGluonRegressor causal_model = ProbabilisticCausalModel(nx.DiGraph([("X", "Y")])) assign_causal_mechanisms(causal_model, pd.DataFrame({"X": [1], "Y": [1]}), quality=AssignmentQuality.BEST) assert isinstance(causal_model.causal_mechanism("Y").prediction_model, AutoGluonRegressor) assign_causal_mechanisms( causal_model, pd.DataFrame({"X": [1], "Y": ["Class 1"]}), quality=AssignmentQuality.BEST, override_models=True ) assert isinstance(causal_model.causal_mechanism("Y").classifier_model, AutoGluonClassifier) @flaky(max_runs=3) def test_given_linear_gaussian_data_when_fit_scm_with_auto_assigned_models_with_default_parameters_then_generate_samples_with_correct_statistics(): X0 = np.random.normal(0, 1, 2000) X1 = 2 * X0 + np.random.normal(0, 0.2, 2000) X2 = 0.5 * X0 + np.random.normal(0, 0.2, 2000) X3 = 0.5 * X2 + np.random.normal(0, 0.2, 2000) original_observations = pd.DataFrame({"X0": X0, "X1": X1, "X2": X2, "X3": X3}) causal_model = ProbabilisticCausalModel(nx.DiGraph([("X0", "X1"), ("X0", "X2"), ("X2", "X3")])) assign_causal_mechanisms(causal_model, original_observations) fit(causal_model, original_observations) generated_samples = draw_samples(causal_model, 2000) assert np.mean(generated_samples["X0"]) == approx(np.mean(X0), abs=0.1) assert np.std(generated_samples["X0"]) == approx(np.std(X0), abs=0.1) assert np.mean(generated_samples["X1"]) == approx(np.mean(X1), abs=0.1) assert np.std(generated_samples["X1"]) == approx(np.std(X1), abs=0.1) assert np.mean(generated_samples["X2"]) == approx(np.mean(X2), abs=0.1) assert np.std(generated_samples["X2"]) == approx(np.std(X2), abs=0.1) assert np.mean(generated_samples["X3"]) == approx(np.mean(X3), abs=0.1) assert np.std(generated_samples["X3"]) == approx(np.std(X3), abs=0.1) @flaky(max_runs=3) def test_given_nonlinear_gaussian_data_when_fit_scm_with_auto_assigned_models_with_default_parameters_then_generate_samples_with_correct_statistics(): X0 = np.random.normal(0, 1, 2000) X1 = np.sin(2 * X0) + np.random.normal(0, 0.2, 2000) X2 = 0.5 * X0**2 + np.random.normal(0, 0.2, 2000) X3 = 0.5 * X2 + np.random.normal(0, 0.2, 2000) original_observations = pd.DataFrame({"X0": X0, "X1": X1, "X2": X2, "X3": X3}) causal_model = ProbabilisticCausalModel(nx.DiGraph([("X0", "X1"), ("X0", "X2"), ("X2", "X3")])) assign_causal_mechanisms(causal_model, original_observations) fit(causal_model, original_observations) generated_samples = draw_samples(causal_model, 2000) assert np.mean(generated_samples["X0"]) == approx(np.mean(X0), abs=0.1) assert np.std(generated_samples["X0"]) == approx(np.std(X0), abs=0.1) assert np.mean(generated_samples["X1"]) == approx(np.mean(X1), abs=0.1) assert np.std(generated_samples["X1"]) == approx(np.std(X1), abs=0.1) assert np.mean(generated_samples["X2"]) == approx(np.mean(X2), abs=0.1) assert np.std(generated_samples["X2"]) == approx(np.std(X2), abs=0.1) assert np.mean(generated_samples["X3"]) == approx(np.mean(X3), abs=0.1) assert np.std(generated_samples["X3"]) == approx(np.std(X3), abs=0.1) def test_givne_simple_data_when_apply_has_linear_relationship_then_returns_expected_results(): X = np.random.random(1000) assert has_linear_relationship(X, 2 * X) assert not has_linear_relationship(X, X**2) @flaky(max_runs=3) def test_given_categorical_data_when_calling_has_linear_relationship_then_returns_correct_results(): X1 = np.random.normal(0, 1, 1000) X2 = np.random.normal(0, 1, 1000) assert has_linear_relationship(np.column_stack([X1, X2]), (X1 + X2 > 0).astype(str)) assert not has_linear_relationship(np.column_stack([X1, X2]), (X1 * X2 > 0).astype(str)) def test_given_imbalanced_categorical_data_when_calling_has_linear_relationship_then_does_not_raise_exception(): X = np.random.normal(0, 1, 1000) Y = np.array(["OneClass"] * 1000) assert has_linear_relationship(np.append(X, 0), np.append(Y, "RareClass")) X = np.random.normal(0, 1, 100000) Y = np.array(["OneClass"] * 100000) assert has_linear_relationship( np.append(X, np.random.normal(0, 0.000001, 100)), np.append(Y, np.array(["RareClass"] * 100)) ) def test_given_data_with_rare_categorical_features_when_calling_has_linear_relationship_then_does_not_raise_exception(): X = np.array(["Feature" + str(i) for i in range(20)]) Y = np.append(np.array(["Class1"] * 10), np.array(["Class2"] * 10)) assert has_linear_relationship(X, Y)

bloebp

13ed13e8adf7a26a995b2e219103d7a25dacb426

5edb89192864d1684b5b2bde4eb86450a00b093e

Here: https://github.com/py-why/dowhy/blob/main/dowhy/gcm/ml/regression.py#L121 It adds polynomial features, which can be equally good as a linear model when the coefficients are zero accordingly.

bloebp

py-why/dowhy

Add method to estimate ICC for single samples

Before, it was only possible to estimate intrinsic causal contributions with respect to the distribution. This new method allows to estimate the influences of upstream nodes on a particular observation of a target node. As for now, this requires that all models are invertible with respect to the noise.

2023-03-29 21:42:31+00:00

2023-03-31 13:39:16+00:00

dowhy/gcm/influence.py

"""This module provides functions to estimate causal influences. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging import warnings from typing import Any, Callable, Dict, List, Optional, Tuple, Union, cast import numpy as np import pandas as pd from joblib import Parallel, delayed from numpy.matlib import repmat import dowhy.gcm.auto as auto from dowhy.gcm._noise import compute_data_from_noise, noise_samples_of_ancestors from dowhy.gcm.cms import ProbabilisticCausalModel, StructuralCausalModel from dowhy.gcm.divergence import estimate_kl_divergence_of_probabilities from dowhy.gcm.fcms import ClassificationModel, ClassifierFCM, PredictionModel, ProbabilityEstimatorModel from dowhy.gcm.fitting_sampling import draw_samples from dowhy.gcm.graph import ( ConditionalStochasticModel, get_ordered_predecessors, is_root_node, node_connected_subgraph_view, validate_causal_dag, validate_node, ) from dowhy.gcm.shapley import ShapleyConfig, estimate_shapley_values from dowhy.gcm.stats import marginal_expectation from dowhy.gcm.uncertainty import estimate_entropy_of_probabilities, estimate_variance from dowhy.gcm.util.general import is_categorical, set_random_seed, shape_into_2d _logger = logging.getLogger(__name__) def arrow_strength( causal_model: ProbabilisticCausalModel, target_node: Any, parent_samples: Optional[pd.DataFrame] = None, num_samples_conditional: int = 2000, max_num_runs: int = 5000, tolerance: float = 0.01, n_jobs: int = -1, difference_estimation_func: Optional[Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]]] = None, ) -> Dict[Tuple[Any, Any], float]: """Computes the causal strength of each edge directed to the target node. The strength of an edge is quantified in terms of distance between conditional distributions of the target node in the original graph and the imputed graph wherein the edge has been removed and the target node is fed a random permutation of the observations of the source node. For more scientific details behind this API, please refer to the research paper below. **Research Paper**: Dominik Janzing, David Balduzzi, Moritz Grosse-Wentrup, Bernhard Schölkopf. *Quantifying Causal Influences*. The Annals of Statistics, Vol. 41, No. 5, 2324-2358, 2013. :param causal_model: The probabilistic causal model for whose target node we compute the strength of incoming edges for. :param target_node: The target node whose incoming edges' strength is to be computed. :param parent_samples: Optional samples from the parents of the target_node. If None are given, they are generated based on the provided causal model. Providing observational data can help to mitigate misspecifications in the graph, such as missing interactions between root nodes or confounders. :param num_samples_conditional: Sample size to use for estimating the distance between distributions. The more more samples, the higher the accuracy. :param max_num_runs: The maximum number of times to resample and estimate the strength to report the average strength. :param tolerance: If the percentage change in the estimated strength between two consecutive runs falls below the specified tolerance, the algorithm will terminate before reaching the maximum number of runs. A value of 0.01 would indicate a change of less than 1%. However, in order to minimize the impact of randomness, there must be at least three consecutive runs where the change is below the threshold. :param n_jobs: The number of jobs to run in parallel. Set it to -1 to use all processors. :param difference_estimation_func: Optional: How to measure the distance between two distributions. By default, the difference of the variance is estimated for a continuous target node and the KL divergence for a categorical target node. :return: Causal strength of each edge. """ if target_node not in causal_model.graph.nodes: raise ValueError("Target node %s can not be found in given graph!" % target_node) if is_root_node(causal_model.graph, target_node): raise ValueError("Target node %s is a root node, but it requires to have ancestors!" % target_node) # Creating a smaller subgraph, which only contains upstream nodes that are connected to the target node. sub_causal_model = ProbabilisticCausalModel(node_connected_subgraph_view(causal_model.graph, target_node)) validate_node(sub_causal_model.graph, target_node) ordered_predecessors = get_ordered_predecessors(sub_causal_model.graph, target_node) if parent_samples is None: parent_samples = draw_samples(sub_causal_model, num_samples_conditional * 20)[ordered_predecessors] direct_influences = arrow_strength_of_model( sub_causal_model.causal_mechanism(target_node), parent_samples[ordered_predecessors].to_numpy(), num_samples_from_conditional=num_samples_conditional, max_num_runs=max_num_runs, tolerance=tolerance, n_jobs=n_jobs, difference_estimation_func=difference_estimation_func, ) return {(predecessor, target_node): direct_influences[i] for i, predecessor in enumerate(ordered_predecessors)} def arrow_strength_of_model( conditional_stochastic_model: ConditionalStochasticModel, input_samples: np.ndarray, num_samples_from_conditional: int = 2000, max_num_runs: int = 5000, tolerance: float = 0.01, n_jobs: int = -1, difference_estimation_func: Optional[Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]]] = None, input_subsets: Optional[List[List[int]]] = None, ) -> np.ndarray: input_samples = shape_into_2d(input_samples) if input_subsets is None: input_subsets = [[i] for i in range(input_samples.shape[1])] if difference_estimation_func is None: if isinstance(conditional_stochastic_model, ProbabilityEstimatorModel): difference_estimation_func = estimate_kl_divergence_of_probabilities else: def difference_estimation_func(old, new): return np.var(new) - np.var(old) if isinstance(conditional_stochastic_model, ProbabilityEstimatorModel): samples_creation_method = conditional_stochastic_model.estimate_probabilities else: samples_creation_method = conditional_stochastic_model.draw_samples with warnings.catch_warnings(): warnings.filterwarnings("ignore") def parallel_job(subset: List[int], parallel_random_seed: int): set_random_seed(parallel_random_seed) return _estimate_direct_strength( samples_creation_method, input_samples, subset, difference_estimation_func, num_samples_from_conditional, max_num_runs, tolerance, ) random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(input_subsets)) results = Parallel(n_jobs=n_jobs)( delayed(parallel_job)(subset, random_seed) for subset, random_seed in zip(input_subsets, random_seeds) ) if np.any(results == np.inf): _logger.warning( "At least one arrow strength is infinite. This typically happens if the causal models are " "deterministic, i.e. there is no noise or it is extremely small." ) return np.array(results) def _estimate_direct_strength( draw_samples_func: Callable[[np.ndarray], np.ndarray], distribution_samples: np.ndarray, parents_subset: List[int], difference_estimation_func: Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]], num_samples_conditional: int, max_num_runs: int, tolerance: float, ) -> float: distribution_samples = shape_into_2d(distribution_samples) num_samples_conditional = min(num_samples_conditional, distribution_samples.shape[0]) aggregated_conditional_difference_result = 0 average_difference_result = 0 converged_run = 0 for run, sample in enumerate(distribution_samples): tmp_samples = repmat(sample, num_samples_conditional, 1) rnd_permutation = np.random.choice(distribution_samples.shape[0], num_samples_conditional, replace=False) # Sampling from the conditional distribution based on the current sample. conditional_distribution_samples = draw_samples_func(tmp_samples) # Sampling from the conditional based on the current sample, but randomizing the inputs of all variables that # are in the given subset. By this, we can simulate the impact on the conditional distribution when removing # only the incoming edges of the variables in the subset. tmp_samples[:, parents_subset] = distribution_samples[:, parents_subset][rnd_permutation] cond_dist_removed_arr_samples = draw_samples_func(tmp_samples) old_average_difference_result = average_difference_result aggregated_conditional_difference_result += difference_estimation_func( conditional_distribution_samples, cond_dist_removed_arr_samples ) average_difference_result = aggregated_conditional_difference_result / (run + 1) if run >= max_num_runs: break elif run > 0: if old_average_difference_result == 0: converging = average_difference_result == 0 else: converging = abs(1 - average_difference_result / old_average_difference_result) < tolerance if converging: converged_run += 1 if converged_run >= 3: break else: converged_run = 0 return average_difference_result def intrinsic_causal_influence( causal_model: StructuralCausalModel, target_node: Any, prediction_model: Union[PredictionModel, ClassificationModel, str] = "approx", attribution_func: Optional[Callable[[np.ndarray, np.ndarray], float]] = None, num_training_samples: int = 100000, num_samples_randomization: int = 1000, num_samples_baseline: int = 1000, max_batch_size: int = 250, auto_assign_quality: auto.AssignmentQuality = auto.AssignmentQuality.GOOD, shapley_config: Optional[ShapleyConfig] = None, ) -> Dict[Any, float]: """Computes the causal contribution of each upstream noise term of the target node (including the noise of the target itself) to the statistical property (e.g. mean, variance) of the target. We call this contribution *intrinsic* as noise terms, by definition, do not inherit properties of observed parents. The contribution of each noise term is then the *intrinsic* causal contribution of the corresponding node. For more scientific details, please refer to the paper below. **Research Paper**: Janzing et al. *Quantifying causal contributions via structure preserving interventions*. arXiv:2007.00714, 2021. :param causal_model: The structural causal model for whose target node we compute the intrinsic causal influence of its ancestors. :param target_node: Target node whose statistical property is to be attributed. :param prediction_model: Prediction model for estimating the functional relationship between subsets of ancestor noise terms and the target node. This can be an instance of a PredictionModel, the string 'approx' or the string 'exact'. With 'exact', the underlying causal models in the graph are utilized directly by propagating given noise inputs through the graph. This is generally more accurate but slow. With 'approx', an appropriate model is selected and trained based on sampled data from the graph, which is less accurate but faster. A more detailed treatment on why we need this parameter is also provided in :ref:`icc`. :param attribution_func: Optional attribution function to measure the statistical property of the target node. This function expects two inputs; predictions after the randomization of certain features (i.e. samples from noise nodes) and a baseline where no features were randomized. The baseline predictions can be typically ignored if one is interested in uncertainty measures such as entropy or variance, but they might be relevant if, for instance, these shall be estimated based on the residuals. By default, entropy is used if prediction model is a classifier, variance otherwise. :param num_training_samples: Number of samples drawn from the graphical causal model that are used for fitting the prediction_model (if necessary). :param num_samples_randomization: Number of noise samples drawn from the graphical causal model that are used for evaluating the set function. Here, these samples are samples from the noise distributions used for randomizing features that are not in the subset. :param num_samples_baseline: Number of noise samples drawn from the graphical causal model that are used for evaluating the set function. Here, these samples are used as fixed observations for features that are in the subset. :param max_batch_size: Maximum batch size for estimating the predictions from evaluation samples. This has a significant impact on the overall memory usage. If set to -1, all samples are used in one batch. :param auto_assign_quality: Auto assign quality for the 'approx' prediction_model option. :param shapley_config: :class:`~dowhy.gcm.shapley.ShapleyConfig` for the Shapley estimator. :return: Intrinsic causal contribution of each ancestor node to the statistical property defined by the attribution_func of the target node. """ validate_causal_dag(causal_model.graph) # Creating a smaller subgraph, which only contains upstream nodes that are connected to the target node. sub_causal_model = StructuralCausalModel(node_connected_subgraph_view(causal_model.graph, target_node)) data_samples, noise_samples = noise_samples_of_ancestors(sub_causal_model, target_node, num_training_samples) node_names = noise_samples.columns noise_samples, target_samples = shape_into_2d(noise_samples.to_numpy(), data_samples[target_node].to_numpy()) target_is_categorical = is_categorical(data_samples[target_node].to_numpy()) if prediction_model == "approx": prediction_model = auto.select_model(noise_samples, target_samples, auto_assign_quality) prediction_model.fit(noise_samples, target_samples) if target_is_categorical: prediction_method = prediction_model.predict_probabilities else: prediction_method = prediction_model.predict elif prediction_model == "exact": def exact_model(X: np.ndarray) -> np.ndarray: return compute_data_from_noise(sub_causal_model, pd.DataFrame(X, columns=[x for x in node_names]))[ target_node ].to_numpy() if target_is_categorical: list_of_classes = cast( ClassifierFCM, sub_causal_model.causal_mechanism(target_node) ).classifier_model.classes def prediction_method(X): return (shape_into_2d(exact_model(X)) == list_of_classes).astype(float) else: prediction_method = exact_model elif isinstance(prediction_model, str): raise ValueError( "Invalid value for prediction_model: %s! This should either be an instance of a PredictionModel or" "one of the two string options 'exact' or 'approx'." % prediction_model ) else: prediction_model.fit(noise_samples, target_samples) prediction_method = prediction_model.predict if attribution_func is None: if target_is_categorical: def attribution_func(x, _): return -estimate_entropy_of_probabilities(x) else: def attribution_func(x, _): return estimate_variance(x) _, noise_samples = noise_samples_of_ancestors( sub_causal_model, target_node, num_samples_randomization + num_samples_baseline ) noise_samples = shape_into_2d(noise_samples.to_numpy()) iccs = _estimate_iccs( attribution_func, prediction_method, noise_samples[:num_samples_randomization], noise_samples[num_samples_randomization : num_samples_randomization + num_samples_baseline], max_batch_size, ShapleyConfig() if shapley_config is None else shapley_config, ) return {node: iccs[i] for i, node in enumerate(node_names)} def _estimate_iccs( attribution_func: Callable[[np.ndarray, np.ndarray], float], prediction_method: Callable[[np.ndarray], np.ndarray], noise_samples: np.ndarray, baseline_noise_samples: np.ndarray, max_batch_size: int, shapley_config: ShapleyConfig, ): target_values = shape_into_2d(prediction_method(baseline_noise_samples)) def icc_set_function(subset: np.ndarray) -> Union[np.ndarray, float]: if np.all(subset == 1): # In case of the full subset (no randomization), we get the same predictions as when we apply the # prediction method to the samples of interest, since all noise samples are replaced with a sample of # interest. predictions = target_values elif np.all(subset == 0): # In case of the empty subset (all are jointly randomize), it boils down to taking the average over all # predictions, seeing that the randomization yields the same values for each sample of interest (none of the # samples of interest are used to replace a (jointly) 'randomized' sample). predictions = repmat(np.mean(prediction_method(noise_samples), axis=0), baseline_noise_samples.shape[0], 1) else: predictions = marginal_expectation( prediction_method, feature_samples=noise_samples, baseline_samples=baseline_noise_samples, baseline_feature_indices=np.arange(0, noise_samples.shape[1])[subset == 1], return_averaged_results=True, feature_perturbation="randomize_columns_jointly", max_batch_size=max_batch_size, ) return attribution_func(shape_into_2d(predictions), target_values) return estimate_shapley_values(icc_set_function, noise_samples.shape[1], shapley_config)

"""This module provides functions to estimate causal influences. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging import warnings from typing import Any, Callable, Dict, Iterator, List, Optional, Tuple, Union, cast import numpy as np import pandas as pd from joblib import Parallel, delayed from numpy.matlib import repmat import dowhy.gcm.auto as auto from dowhy.gcm import feature_relevance_sample from dowhy.gcm._noise import compute_data_from_noise, compute_noise_from_data, noise_samples_of_ancestors from dowhy.gcm.cms import InvertibleStructuralCausalModel, ProbabilisticCausalModel, StructuralCausalModel from dowhy.gcm.divergence import estimate_kl_divergence_of_probabilities from dowhy.gcm.fcms import ClassificationModel, ClassifierFCM, PredictionModel, ProbabilityEstimatorModel from dowhy.gcm.fitting_sampling import draw_samples from dowhy.gcm.graph import ( ConditionalStochasticModel, get_ordered_predecessors, is_root_node, node_connected_subgraph_view, validate_causal_dag, validate_node, ) from dowhy.gcm.shapley import ShapleyConfig, estimate_shapley_values from dowhy.gcm.stats import marginal_expectation from dowhy.gcm.uncertainty import estimate_entropy_of_probabilities, estimate_variance from dowhy.gcm.util.general import has_categorical, is_categorical, means_difference, set_random_seed, shape_into_2d _logger = logging.getLogger(__name__) def arrow_strength( causal_model: ProbabilisticCausalModel, target_node: Any, parent_samples: Optional[pd.DataFrame] = None, num_samples_conditional: int = 2000, max_num_runs: int = 5000, tolerance: float = 0.01, n_jobs: int = -1, difference_estimation_func: Optional[Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]]] = None, ) -> Dict[Tuple[Any, Any], float]: """Computes the causal strength of each edge directed to the target node. The strength of an edge is quantified in terms of distance between conditional distributions of the target node in the original graph and the imputed graph wherein the edge has been removed and the target node is fed a random permutation of the observations of the source node. For more scientific details behind this API, please refer to the research paper below. **Research Paper**: Dominik Janzing, David Balduzzi, Moritz Grosse-Wentrup, Bernhard Schölkopf. *Quantifying Causal Influences*. The Annals of Statistics, Vol. 41, No. 5, 2324-2358, 2013. :param causal_model: The probabilistic causal model for whose target node we compute the strength of incoming edges for. :param target_node: The target node whose incoming edges' strength is to be computed. :param parent_samples: Optional samples from the parents of the target_node. If None are given, they are generated based on the provided causal model. Providing observational data can help to mitigate misspecifications in the graph, such as missing interactions between root nodes or confounders. :param num_samples_conditional: Sample size to use for estimating the distance between distributions. The more more samples, the higher the accuracy. :param max_num_runs: The maximum number of times to resample and estimate the strength to report the average strength. :param tolerance: If the percentage change in the estimated strength between two consecutive runs falls below the specified tolerance, the algorithm will terminate before reaching the maximum number of runs. A value of 0.01 would indicate a change of less than 1%. However, in order to minimize the impact of randomness, there must be at least three consecutive runs where the change is below the threshold. :param n_jobs: The number of jobs to run in parallel. Set it to -1 to use all processors. :param difference_estimation_func: Optional: How to measure the distance between two distributions. By default, the difference of the variance is estimated for a continuous target node and the KL divergence for a categorical target node. :return: Causal strength of each edge. """ if target_node not in causal_model.graph.nodes: raise ValueError("Target node %s can not be found in given graph!" % target_node) if is_root_node(causal_model.graph, target_node): raise ValueError("Target node %s is a root node, but it requires to have ancestors!" % target_node) # Creating a smaller subgraph, which only contains upstream nodes that are connected to the target node. sub_causal_model = ProbabilisticCausalModel(node_connected_subgraph_view(causal_model.graph, target_node)) validate_node(sub_causal_model.graph, target_node) ordered_predecessors = get_ordered_predecessors(sub_causal_model.graph, target_node) if parent_samples is None: parent_samples = draw_samples(sub_causal_model, num_samples_conditional * 20)[ordered_predecessors] direct_influences = arrow_strength_of_model( sub_causal_model.causal_mechanism(target_node), parent_samples[ordered_predecessors].to_numpy(), num_samples_from_conditional=num_samples_conditional, max_num_runs=max_num_runs, tolerance=tolerance, n_jobs=n_jobs, difference_estimation_func=difference_estimation_func, ) return {(predecessor, target_node): direct_influences[i] for i, predecessor in enumerate(ordered_predecessors)} def arrow_strength_of_model( conditional_stochastic_model: ConditionalStochasticModel, input_samples: np.ndarray, num_samples_from_conditional: int = 2000, max_num_runs: int = 5000, tolerance: float = 0.01, n_jobs: int = -1, difference_estimation_func: Optional[Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]]] = None, input_subsets: Optional[List[List[int]]] = None, ) -> np.ndarray: input_samples = shape_into_2d(input_samples) if input_subsets is None: input_subsets = [[i] for i in range(input_samples.shape[1])] if difference_estimation_func is None: if isinstance(conditional_stochastic_model, ProbabilityEstimatorModel): difference_estimation_func = estimate_kl_divergence_of_probabilities else: def difference_estimation_func(old, new): return np.var(new) - np.var(old) if isinstance(conditional_stochastic_model, ProbabilityEstimatorModel): samples_creation_method = conditional_stochastic_model.estimate_probabilities else: samples_creation_method = conditional_stochastic_model.draw_samples with warnings.catch_warnings(): warnings.filterwarnings("ignore") def parallel_job(subset: List[int], parallel_random_seed: int): set_random_seed(parallel_random_seed) return _estimate_direct_strength( samples_creation_method, input_samples, subset, difference_estimation_func, num_samples_from_conditional, max_num_runs, tolerance, ) random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(input_subsets)) results = Parallel(n_jobs=n_jobs)( delayed(parallel_job)(subset, random_seed) for subset, random_seed in zip(input_subsets, random_seeds) ) if np.any(results == np.inf): _logger.warning( "At least one arrow strength is infinite. This typically happens if the causal models are " "deterministic, i.e. there is no noise or it is extremely small." ) return np.array(results) def _estimate_direct_strength( draw_samples_func: Callable[[np.ndarray], np.ndarray], distribution_samples: np.ndarray, parents_subset: List[int], difference_estimation_func: Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]], num_samples_conditional: int, max_num_runs: int, tolerance: float, ) -> float: distribution_samples = shape_into_2d(distribution_samples) num_samples_conditional = min(num_samples_conditional, distribution_samples.shape[0]) aggregated_conditional_difference_result = 0 average_difference_result = 0 converged_run = 0 for run, sample in enumerate(distribution_samples): tmp_samples = repmat(sample, num_samples_conditional, 1) rnd_permutation = np.random.choice(distribution_samples.shape[0], num_samples_conditional, replace=False) # Sampling from the conditional distribution based on the current sample. conditional_distribution_samples = draw_samples_func(tmp_samples) # Sampling from the conditional based on the current sample, but randomizing the inputs of all variables that # are in the given subset. By this, we can simulate the impact on the conditional distribution when removing # only the incoming edges of the variables in the subset. tmp_samples[:, parents_subset] = distribution_samples[:, parents_subset][rnd_permutation] cond_dist_removed_arr_samples = draw_samples_func(tmp_samples) old_average_difference_result = average_difference_result aggregated_conditional_difference_result += difference_estimation_func( conditional_distribution_samples, cond_dist_removed_arr_samples ) average_difference_result = aggregated_conditional_difference_result / (run + 1) if run >= max_num_runs: break elif run > 0: if old_average_difference_result == 0: converging = average_difference_result == 0 else: converging = abs(1 - average_difference_result / old_average_difference_result) < tolerance if converging: converged_run += 1 if converged_run >= 3: break else: converged_run = 0 return average_difference_result def intrinsic_causal_influence( causal_model: StructuralCausalModel, target_node: Any, prediction_model: Union[PredictionModel, ClassificationModel, str] = "approx", attribution_func: Optional[Callable[[np.ndarray, np.ndarray], float]] = None, num_training_samples: int = 100000, num_samples_randomization: int = 1000, num_samples_baseline: int = 1000, max_batch_size: int = 250, auto_assign_quality: auto.AssignmentQuality = auto.AssignmentQuality.GOOD, shapley_config: Optional[ShapleyConfig] = None, ) -> Dict[Any, float]: """Computes the causal contribution of each upstream noise term of the target node (including the noise of the target itself) to the statistical property (e.g. mean, variance) of the target. We call this contribution *intrinsic* as noise terms, by definition, do not inherit properties of observed parents. The contribution of each noise term is then the *intrinsic* causal contribution of the corresponding node. For more scientific details, please refer to the paper below. **Research Paper**: Janzing et al. *Quantifying causal contributions via structure preserving interventions*. arXiv:2007.00714, 2021. :param causal_model: The structural causal model for whose target node we compute the intrinsic causal influence of its ancestors. :param target_node: Target node whose statistical property is to be attributed. :param prediction_model: Prediction model for estimating the functional relationship between subsets of ancestor noise terms and the target node. This can be an instance of a PredictionModel, the string 'approx' or the string 'exact'. With 'exact', the underlying causal models in the graph are utilized directly by propagating given noise inputs through the graph. This is generally more accurate but slow. With 'approx', an appropriate model is selected and trained based on sampled data from the graph, which is less accurate but faster. A more detailed treatment on why we need this parameter is also provided in :ref:`icc`. :param attribution_func: Optional attribution function to measure the statistical property of the target node. This function expects two inputs; predictions after the randomization of certain features (i.e. samples from noise nodes) and a baseline where no features were randomized. The baseline predictions can be typically ignored if one is interested in uncertainty measures such as entropy or variance, but they might be relevant if, for instance, these shall be estimated based on the residuals. By default, entropy is used if prediction model is a classifier, variance otherwise. :param num_training_samples: Number of samples drawn from the graphical causal model that are used for fitting the prediction_model (if necessary). :param num_samples_randomization: Number of noise samples drawn from the graphical causal model that are used for evaluating the set function. Here, these samples are samples from the noise distributions used for randomizing features that are not in the subset. :param num_samples_baseline: Number of noise samples drawn from the graphical causal model that are used for evaluating the set function. Here, these samples are used as fixed observations for features that are in the subset. :param max_batch_size: Maximum batch size for estimating the predictions from evaluation samples. This has a significant impact on the overall memory usage. If set to -1, all samples are used in one batch. :param auto_assign_quality: Auto assign quality for the 'approx' prediction_model option. :param shapley_config: :class:`~dowhy.gcm.shapley.ShapleyConfig` for the Shapley estimator. :return: Intrinsic causal contribution of each ancestor node to the statistical property defined by the attribution_func of the target node. """ validate_causal_dag(causal_model.graph) # Creating a smaller subgraph, which only contains upstream nodes that are connected to the target node. sub_causal_model = StructuralCausalModel(node_connected_subgraph_view(causal_model.graph, target_node)) data_samples, noise_samples = noise_samples_of_ancestors(sub_causal_model, target_node, num_training_samples) node_names = noise_samples.columns noise_samples, target_samples = shape_into_2d(noise_samples.to_numpy(), data_samples[target_node].to_numpy()) target_is_categorical = is_categorical(data_samples[target_node].to_numpy()) prediction_method = _get_icc_noise_function( causal_model, target_node, prediction_model, noise_samples, node_names, target_samples, auto_assign_quality, target_is_categorical, ) if attribution_func is None: if target_is_categorical: def attribution_func(x, _): return -estimate_entropy_of_probabilities(x) else: def attribution_func(x, _): return estimate_variance(x) _, noise_samples = noise_samples_of_ancestors( sub_causal_model, target_node, num_samples_randomization + num_samples_baseline ) noise_samples = shape_into_2d(noise_samples.to_numpy()) iccs = _estimate_iccs( attribution_func, prediction_method, noise_samples[:num_samples_randomization], noise_samples[num_samples_randomization : num_samples_randomization + num_samples_baseline], max_batch_size, ShapleyConfig() if shapley_config is None else shapley_config, ) return {node: iccs[i] for i, node in enumerate(node_names)} def intrinsic_causal_influence_sample( causal_model: InvertibleStructuralCausalModel, target_node: Any, baseline_samples: pd.DataFrame, noise_feature_samples: Optional[pd.DataFrame] = None, subset_scoring_func: Optional[Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]]] = None, num_noise_feature_samples: int = 5000, max_batch_size: int = 100, shapley_config: Optional[ShapleyConfig] = None, ) -> List[Dict[Any, Any]]: """Estimates the intrinsic causal impact of upstream nodes on a specified target_node, using the provided baseline_samples as a reference. In this context, observed values are attributed to the noise factors present in upstream nodes. Compared to intrinsic_causal_influence, this method quantifies the influences with respect to single observations instead of the distribution. Note that the current implementation only supports non-categorical data, since the noise terms need to be reconstructed. **Research Paper**: Janzing et al. *Quantifying causal contributions via structure preserving interventions*. arXiv:2007.00714, 2021. :param causal_model: The fitted invertible structural causal model. :param target_node: Node of interest. :param baseline_samples: Samples for which the influence should be estimated. :param noise_feature_samples: Optional noise samples of upstream nodes used as 'background' samples.. If None is given, new noise samples are generated based on the graph. These samples are used for randomizing features that are not in the subset. :param subset_scoring_func: Set function for estimating the quantity of interest based. This function expects two inputs; the outcome of the model for some samples if certain features are permuted and the outcome of the model for the same samples when no features were permuted. By default, the difference between means of these samples are estimated. :param num_noise_feature_samples: If no noise_feature_samples are given, noise samples are drawn from the graph. This parameter indicates how many. :param max_batch_size: Maximum batch size for estimating multiple predictions at once. This has a significant influence on the overall memory usage. If set to -1, all samples are used in one batch. :param shapley_config: :class:`~dowhy.gcm.shapley.ShapleyConfig` for the Shapley estimator. :return: A list of dictionaries indicating the intrinsic causal influence of a node on the target for a particular sample. This is, each dictionary belongs to one baseline sample. """ validate_node(causal_model.graph, target_node) causal_model = InvertibleStructuralCausalModel(node_connected_subgraph_view(causal_model.graph, target_node)) feature_samples, tmp_noise_feature_samples = noise_samples_of_ancestors( causal_model, target_node, num_noise_feature_samples ) if has_categorical(feature_samples.to_numpy()): raise ValueError( "The current implementation requires all variables to be numeric, i.e., non-categorical! " "There is at least one node in the graph that is categorical." ) if noise_feature_samples is None: noise_feature_samples = tmp_noise_feature_samples if subset_scoring_func is None: subset_scoring_func = means_difference shapley_vales = feature_relevance_sample( _get_icc_noise_function( causal_model, target_node, "exact", noise_feature_samples, noise_feature_samples.columns, None, None, False ), feature_samples=noise_feature_samples.to_numpy(), baseline_samples=compute_noise_from_data(causal_model, baseline_samples)[ noise_feature_samples.columns ].to_numpy(), subset_scoring_func=subset_scoring_func, max_batch_size=max_batch_size, shapley_config=shapley_config, ) return [ {(predecessor, target_node): shapley_vales[i][q] for q, predecessor in enumerate(noise_feature_samples.columns)} for i in range(shapley_vales.shape[0]) ] def _estimate_iccs( attribution_func: Callable[[np.ndarray, np.ndarray], float], prediction_method: Callable[[np.ndarray], np.ndarray], noise_samples: np.ndarray, baseline_noise_samples: np.ndarray, max_batch_size: int, shapley_config: ShapleyConfig, ): target_values = shape_into_2d(prediction_method(baseline_noise_samples)) def icc_set_function(subset: np.ndarray) -> Union[np.ndarray, float]: if np.all(subset == 1): # In case of the full subset (no randomization), we get the same predictions as when we apply the # prediction method to the samples of interest, since all noise samples are replaced with a sample of # interest. predictions = target_values elif np.all(subset == 0): # In case of the empty subset (all are jointly randomize), it boils down to taking the average over all # predictions, seeing that the randomization yields the same values for each sample of interest (none of the # samples of interest are used to replace a (jointly) 'randomized' sample). predictions = repmat(np.mean(prediction_method(noise_samples), axis=0), baseline_noise_samples.shape[0], 1) else: predictions = marginal_expectation( prediction_method, feature_samples=noise_samples, baseline_samples=baseline_noise_samples, baseline_feature_indices=np.arange(0, noise_samples.shape[1])[subset == 1], return_averaged_results=True, feature_perturbation="randomize_columns_jointly", max_batch_size=max_batch_size, ) return attribution_func(shape_into_2d(predictions), target_values) return estimate_shapley_values(icc_set_function, noise_samples.shape[1], shapley_config) def _get_icc_noise_function( causal_model: InvertibleStructuralCausalModel, target_node: Any, prediction_model: Union[PredictionModel, ClassificationModel, str], noise_samples: np.ndarray, node_names: Iterator[Any], target_samples: np.ndarray, auto_assign_quality: auto.AssignmentQuality, target_is_categorical: bool, ) -> Callable[[np.ndarray], np.ndarray]: if isinstance(prediction_model, str) and prediction_model not in ("approx", "exact"): raise ValueError( "Invalid value for prediction_model: %s! This should either be an instance of a PredictionModel or" "one of the two string options 'exact' or 'approx'." % prediction_model ) if not isinstance(prediction_model, str): prediction_model.fit(noise_samples, target_samples) return prediction_model.predict if prediction_model == "approx": prediction_model = auto.select_model(noise_samples, target_samples, auto_assign_quality) prediction_model.fit(noise_samples, target_samples) if target_is_categorical: return prediction_model.predict_probabilities else: return prediction_model.predict else: # Exact model def exact_model(X: np.ndarray) -> np.ndarray: return compute_data_from_noise(causal_model, pd.DataFrame(X, columns=[x for x in node_names]))[ target_node ].to_numpy() if target_is_categorical: list_of_classes = cast(ClassifierFCM, causal_model.causal_mechanism(target_node)).classifier_model.classes def prediction_method(X): return (shape_into_2d(exact_model(X)) == list_of_classes).astype(float) return prediction_method else: return exact_model

bloebp

45046e141ebd701820ca8a692c8f03ed5dce314c

e734f4e6245d404218b0ba11b14f7cc621bec7a0

One statement

petergtz

py-why/dowhy

Add method to estimate ICC for single samples

Before, it was only possible to estimate intrinsic causal contributions with respect to the distribution. This new method allows to estimate the influences of upstream nodes on a particular observation of a target node. As for now, this requires that all models are invertible with respect to the noise.

2023-03-29 21:42:31+00:00

2023-03-31 13:39:16+00:00

dowhy/gcm/influence.py

"""This module provides functions to estimate causal influences. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging import warnings from typing import Any, Callable, Dict, List, Optional, Tuple, Union, cast import numpy as np import pandas as pd from joblib import Parallel, delayed from numpy.matlib import repmat import dowhy.gcm.auto as auto from dowhy.gcm._noise import compute_data_from_noise, noise_samples_of_ancestors from dowhy.gcm.cms import ProbabilisticCausalModel, StructuralCausalModel from dowhy.gcm.divergence import estimate_kl_divergence_of_probabilities from dowhy.gcm.fcms import ClassificationModel, ClassifierFCM, PredictionModel, ProbabilityEstimatorModel from dowhy.gcm.fitting_sampling import draw_samples from dowhy.gcm.graph import ( ConditionalStochasticModel, get_ordered_predecessors, is_root_node, node_connected_subgraph_view, validate_causal_dag, validate_node, ) from dowhy.gcm.shapley import ShapleyConfig, estimate_shapley_values from dowhy.gcm.stats import marginal_expectation from dowhy.gcm.uncertainty import estimate_entropy_of_probabilities, estimate_variance from dowhy.gcm.util.general import is_categorical, set_random_seed, shape_into_2d _logger = logging.getLogger(__name__) def arrow_strength( causal_model: ProbabilisticCausalModel, target_node: Any, parent_samples: Optional[pd.DataFrame] = None, num_samples_conditional: int = 2000, max_num_runs: int = 5000, tolerance: float = 0.01, n_jobs: int = -1, difference_estimation_func: Optional[Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]]] = None, ) -> Dict[Tuple[Any, Any], float]: """Computes the causal strength of each edge directed to the target node. The strength of an edge is quantified in terms of distance between conditional distributions of the target node in the original graph and the imputed graph wherein the edge has been removed and the target node is fed a random permutation of the observations of the source node. For more scientific details behind this API, please refer to the research paper below. **Research Paper**: Dominik Janzing, David Balduzzi, Moritz Grosse-Wentrup, Bernhard Schölkopf. *Quantifying Causal Influences*. The Annals of Statistics, Vol. 41, No. 5, 2324-2358, 2013. :param causal_model: The probabilistic causal model for whose target node we compute the strength of incoming edges for. :param target_node: The target node whose incoming edges' strength is to be computed. :param parent_samples: Optional samples from the parents of the target_node. If None are given, they are generated based on the provided causal model. Providing observational data can help to mitigate misspecifications in the graph, such as missing interactions between root nodes or confounders. :param num_samples_conditional: Sample size to use for estimating the distance between distributions. The more more samples, the higher the accuracy. :param max_num_runs: The maximum number of times to resample and estimate the strength to report the average strength. :param tolerance: If the percentage change in the estimated strength between two consecutive runs falls below the specified tolerance, the algorithm will terminate before reaching the maximum number of runs. A value of 0.01 would indicate a change of less than 1%. However, in order to minimize the impact of randomness, there must be at least three consecutive runs where the change is below the threshold. :param n_jobs: The number of jobs to run in parallel. Set it to -1 to use all processors. :param difference_estimation_func: Optional: How to measure the distance between two distributions. By default, the difference of the variance is estimated for a continuous target node and the KL divergence for a categorical target node. :return: Causal strength of each edge. """ if target_node not in causal_model.graph.nodes: raise ValueError("Target node %s can not be found in given graph!" % target_node) if is_root_node(causal_model.graph, target_node): raise ValueError("Target node %s is a root node, but it requires to have ancestors!" % target_node) # Creating a smaller subgraph, which only contains upstream nodes that are connected to the target node. sub_causal_model = ProbabilisticCausalModel(node_connected_subgraph_view(causal_model.graph, target_node)) validate_node(sub_causal_model.graph, target_node) ordered_predecessors = get_ordered_predecessors(sub_causal_model.graph, target_node) if parent_samples is None: parent_samples = draw_samples(sub_causal_model, num_samples_conditional * 20)[ordered_predecessors] direct_influences = arrow_strength_of_model( sub_causal_model.causal_mechanism(target_node), parent_samples[ordered_predecessors].to_numpy(), num_samples_from_conditional=num_samples_conditional, max_num_runs=max_num_runs, tolerance=tolerance, n_jobs=n_jobs, difference_estimation_func=difference_estimation_func, ) return {(predecessor, target_node): direct_influences[i] for i, predecessor in enumerate(ordered_predecessors)} def arrow_strength_of_model( conditional_stochastic_model: ConditionalStochasticModel, input_samples: np.ndarray, num_samples_from_conditional: int = 2000, max_num_runs: int = 5000, tolerance: float = 0.01, n_jobs: int = -1, difference_estimation_func: Optional[Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]]] = None, input_subsets: Optional[List[List[int]]] = None, ) -> np.ndarray: input_samples = shape_into_2d(input_samples) if input_subsets is None: input_subsets = [[i] for i in range(input_samples.shape[1])] if difference_estimation_func is None: if isinstance(conditional_stochastic_model, ProbabilityEstimatorModel): difference_estimation_func = estimate_kl_divergence_of_probabilities else: def difference_estimation_func(old, new): return np.var(new) - np.var(old) if isinstance(conditional_stochastic_model, ProbabilityEstimatorModel): samples_creation_method = conditional_stochastic_model.estimate_probabilities else: samples_creation_method = conditional_stochastic_model.draw_samples with warnings.catch_warnings(): warnings.filterwarnings("ignore") def parallel_job(subset: List[int], parallel_random_seed: int): set_random_seed(parallel_random_seed) return _estimate_direct_strength( samples_creation_method, input_samples, subset, difference_estimation_func, num_samples_from_conditional, max_num_runs, tolerance, ) random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(input_subsets)) results = Parallel(n_jobs=n_jobs)( delayed(parallel_job)(subset, random_seed) for subset, random_seed in zip(input_subsets, random_seeds) ) if np.any(results == np.inf): _logger.warning( "At least one arrow strength is infinite. This typically happens if the causal models are " "deterministic, i.e. there is no noise or it is extremely small." ) return np.array(results) def _estimate_direct_strength( draw_samples_func: Callable[[np.ndarray], np.ndarray], distribution_samples: np.ndarray, parents_subset: List[int], difference_estimation_func: Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]], num_samples_conditional: int, max_num_runs: int, tolerance: float, ) -> float: distribution_samples = shape_into_2d(distribution_samples) num_samples_conditional = min(num_samples_conditional, distribution_samples.shape[0]) aggregated_conditional_difference_result = 0 average_difference_result = 0 converged_run = 0 for run, sample in enumerate(distribution_samples): tmp_samples = repmat(sample, num_samples_conditional, 1) rnd_permutation = np.random.choice(distribution_samples.shape[0], num_samples_conditional, replace=False) # Sampling from the conditional distribution based on the current sample. conditional_distribution_samples = draw_samples_func(tmp_samples) # Sampling from the conditional based on the current sample, but randomizing the inputs of all variables that # are in the given subset. By this, we can simulate the impact on the conditional distribution when removing # only the incoming edges of the variables in the subset. tmp_samples[:, parents_subset] = distribution_samples[:, parents_subset][rnd_permutation] cond_dist_removed_arr_samples = draw_samples_func(tmp_samples) old_average_difference_result = average_difference_result aggregated_conditional_difference_result += difference_estimation_func( conditional_distribution_samples, cond_dist_removed_arr_samples ) average_difference_result = aggregated_conditional_difference_result / (run + 1) if run >= max_num_runs: break elif run > 0: if old_average_difference_result == 0: converging = average_difference_result == 0 else: converging = abs(1 - average_difference_result / old_average_difference_result) < tolerance if converging: converged_run += 1 if converged_run >= 3: break else: converged_run = 0 return average_difference_result def intrinsic_causal_influence( causal_model: StructuralCausalModel, target_node: Any, prediction_model: Union[PredictionModel, ClassificationModel, str] = "approx", attribution_func: Optional[Callable[[np.ndarray, np.ndarray], float]] = None, num_training_samples: int = 100000, num_samples_randomization: int = 1000, num_samples_baseline: int = 1000, max_batch_size: int = 250, auto_assign_quality: auto.AssignmentQuality = auto.AssignmentQuality.GOOD, shapley_config: Optional[ShapleyConfig] = None, ) -> Dict[Any, float]: """Computes the causal contribution of each upstream noise term of the target node (including the noise of the target itself) to the statistical property (e.g. mean, variance) of the target. We call this contribution *intrinsic* as noise terms, by definition, do not inherit properties of observed parents. The contribution of each noise term is then the *intrinsic* causal contribution of the corresponding node. For more scientific details, please refer to the paper below. **Research Paper**: Janzing et al. *Quantifying causal contributions via structure preserving interventions*. arXiv:2007.00714, 2021. :param causal_model: The structural causal model for whose target node we compute the intrinsic causal influence of its ancestors. :param target_node: Target node whose statistical property is to be attributed. :param prediction_model: Prediction model for estimating the functional relationship between subsets of ancestor noise terms and the target node. This can be an instance of a PredictionModel, the string 'approx' or the string 'exact'. With 'exact', the underlying causal models in the graph are utilized directly by propagating given noise inputs through the graph. This is generally more accurate but slow. With 'approx', an appropriate model is selected and trained based on sampled data from the graph, which is less accurate but faster. A more detailed treatment on why we need this parameter is also provided in :ref:`icc`. :param attribution_func: Optional attribution function to measure the statistical property of the target node. This function expects two inputs; predictions after the randomization of certain features (i.e. samples from noise nodes) and a baseline where no features were randomized. The baseline predictions can be typically ignored if one is interested in uncertainty measures such as entropy or variance, but they might be relevant if, for instance, these shall be estimated based on the residuals. By default, entropy is used if prediction model is a classifier, variance otherwise. :param num_training_samples: Number of samples drawn from the graphical causal model that are used for fitting the prediction_model (if necessary). :param num_samples_randomization: Number of noise samples drawn from the graphical causal model that are used for evaluating the set function. Here, these samples are samples from the noise distributions used for randomizing features that are not in the subset. :param num_samples_baseline: Number of noise samples drawn from the graphical causal model that are used for evaluating the set function. Here, these samples are used as fixed observations for features that are in the subset. :param max_batch_size: Maximum batch size for estimating the predictions from evaluation samples. This has a significant impact on the overall memory usage. If set to -1, all samples are used in one batch. :param auto_assign_quality: Auto assign quality for the 'approx' prediction_model option. :param shapley_config: :class:`~dowhy.gcm.shapley.ShapleyConfig` for the Shapley estimator. :return: Intrinsic causal contribution of each ancestor node to the statistical property defined by the attribution_func of the target node. """ validate_causal_dag(causal_model.graph) # Creating a smaller subgraph, which only contains upstream nodes that are connected to the target node. sub_causal_model = StructuralCausalModel(node_connected_subgraph_view(causal_model.graph, target_node)) data_samples, noise_samples = noise_samples_of_ancestors(sub_causal_model, target_node, num_training_samples) node_names = noise_samples.columns noise_samples, target_samples = shape_into_2d(noise_samples.to_numpy(), data_samples[target_node].to_numpy()) target_is_categorical = is_categorical(data_samples[target_node].to_numpy()) if prediction_model == "approx": prediction_model = auto.select_model(noise_samples, target_samples, auto_assign_quality) prediction_model.fit(noise_samples, target_samples) if target_is_categorical: prediction_method = prediction_model.predict_probabilities else: prediction_method = prediction_model.predict elif prediction_model == "exact": def exact_model(X: np.ndarray) -> np.ndarray: return compute_data_from_noise(sub_causal_model, pd.DataFrame(X, columns=[x for x in node_names]))[ target_node ].to_numpy() if target_is_categorical: list_of_classes = cast( ClassifierFCM, sub_causal_model.causal_mechanism(target_node) ).classifier_model.classes def prediction_method(X): return (shape_into_2d(exact_model(X)) == list_of_classes).astype(float) else: prediction_method = exact_model elif isinstance(prediction_model, str): raise ValueError( "Invalid value for prediction_model: %s! This should either be an instance of a PredictionModel or" "one of the two string options 'exact' or 'approx'." % prediction_model ) else: prediction_model.fit(noise_samples, target_samples) prediction_method = prediction_model.predict if attribution_func is None: if target_is_categorical: def attribution_func(x, _): return -estimate_entropy_of_probabilities(x) else: def attribution_func(x, _): return estimate_variance(x) _, noise_samples = noise_samples_of_ancestors( sub_causal_model, target_node, num_samples_randomization + num_samples_baseline ) noise_samples = shape_into_2d(noise_samples.to_numpy()) iccs = _estimate_iccs( attribution_func, prediction_method, noise_samples[:num_samples_randomization], noise_samples[num_samples_randomization : num_samples_randomization + num_samples_baseline], max_batch_size, ShapleyConfig() if shapley_config is None else shapley_config, ) return {node: iccs[i] for i, node in enumerate(node_names)} def _estimate_iccs( attribution_func: Callable[[np.ndarray, np.ndarray], float], prediction_method: Callable[[np.ndarray], np.ndarray], noise_samples: np.ndarray, baseline_noise_samples: np.ndarray, max_batch_size: int, shapley_config: ShapleyConfig, ): target_values = shape_into_2d(prediction_method(baseline_noise_samples)) def icc_set_function(subset: np.ndarray) -> Union[np.ndarray, float]: if np.all(subset == 1): # In case of the full subset (no randomization), we get the same predictions as when we apply the # prediction method to the samples of interest, since all noise samples are replaced with a sample of # interest. predictions = target_values elif np.all(subset == 0): # In case of the empty subset (all are jointly randomize), it boils down to taking the average over all # predictions, seeing that the randomization yields the same values for each sample of interest (none of the # samples of interest are used to replace a (jointly) 'randomized' sample). predictions = repmat(np.mean(prediction_method(noise_samples), axis=0), baseline_noise_samples.shape[0], 1) else: predictions = marginal_expectation( prediction_method, feature_samples=noise_samples, baseline_samples=baseline_noise_samples, baseline_feature_indices=np.arange(0, noise_samples.shape[1])[subset == 1], return_averaged_results=True, feature_perturbation="randomize_columns_jointly", max_batch_size=max_batch_size, ) return attribution_func(shape_into_2d(predictions), target_values) return estimate_shapley_values(icc_set_function, noise_samples.shape[1], shapley_config)

"""This module provides functions to estimate causal influences. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging import warnings from typing import Any, Callable, Dict, Iterator, List, Optional, Tuple, Union, cast import numpy as np import pandas as pd from joblib import Parallel, delayed from numpy.matlib import repmat import dowhy.gcm.auto as auto from dowhy.gcm import feature_relevance_sample from dowhy.gcm._noise import compute_data_from_noise, compute_noise_from_data, noise_samples_of_ancestors from dowhy.gcm.cms import InvertibleStructuralCausalModel, ProbabilisticCausalModel, StructuralCausalModel from dowhy.gcm.divergence import estimate_kl_divergence_of_probabilities from dowhy.gcm.fcms import ClassificationModel, ClassifierFCM, PredictionModel, ProbabilityEstimatorModel from dowhy.gcm.fitting_sampling import draw_samples from dowhy.gcm.graph import ( ConditionalStochasticModel, get_ordered_predecessors, is_root_node, node_connected_subgraph_view, validate_causal_dag, validate_node, ) from dowhy.gcm.shapley import ShapleyConfig, estimate_shapley_values from dowhy.gcm.stats import marginal_expectation from dowhy.gcm.uncertainty import estimate_entropy_of_probabilities, estimate_variance from dowhy.gcm.util.general import has_categorical, is_categorical, means_difference, set_random_seed, shape_into_2d _logger = logging.getLogger(__name__) def arrow_strength( causal_model: ProbabilisticCausalModel, target_node: Any, parent_samples: Optional[pd.DataFrame] = None, num_samples_conditional: int = 2000, max_num_runs: int = 5000, tolerance: float = 0.01, n_jobs: int = -1, difference_estimation_func: Optional[Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]]] = None, ) -> Dict[Tuple[Any, Any], float]: """Computes the causal strength of each edge directed to the target node. The strength of an edge is quantified in terms of distance between conditional distributions of the target node in the original graph and the imputed graph wherein the edge has been removed and the target node is fed a random permutation of the observations of the source node. For more scientific details behind this API, please refer to the research paper below. **Research Paper**: Dominik Janzing, David Balduzzi, Moritz Grosse-Wentrup, Bernhard Schölkopf. *Quantifying Causal Influences*. The Annals of Statistics, Vol. 41, No. 5, 2324-2358, 2013. :param causal_model: The probabilistic causal model for whose target node we compute the strength of incoming edges for. :param target_node: The target node whose incoming edges' strength is to be computed. :param parent_samples: Optional samples from the parents of the target_node. If None are given, they are generated based on the provided causal model. Providing observational data can help to mitigate misspecifications in the graph, such as missing interactions between root nodes or confounders. :param num_samples_conditional: Sample size to use for estimating the distance between distributions. The more more samples, the higher the accuracy. :param max_num_runs: The maximum number of times to resample and estimate the strength to report the average strength. :param tolerance: If the percentage change in the estimated strength between two consecutive runs falls below the specified tolerance, the algorithm will terminate before reaching the maximum number of runs. A value of 0.01 would indicate a change of less than 1%. However, in order to minimize the impact of randomness, there must be at least three consecutive runs where the change is below the threshold. :param n_jobs: The number of jobs to run in parallel. Set it to -1 to use all processors. :param difference_estimation_func: Optional: How to measure the distance between two distributions. By default, the difference of the variance is estimated for a continuous target node and the KL divergence for a categorical target node. :return: Causal strength of each edge. """ if target_node not in causal_model.graph.nodes: raise ValueError("Target node %s can not be found in given graph!" % target_node) if is_root_node(causal_model.graph, target_node): raise ValueError("Target node %s is a root node, but it requires to have ancestors!" % target_node) # Creating a smaller subgraph, which only contains upstream nodes that are connected to the target node. sub_causal_model = ProbabilisticCausalModel(node_connected_subgraph_view(causal_model.graph, target_node)) validate_node(sub_causal_model.graph, target_node) ordered_predecessors = get_ordered_predecessors(sub_causal_model.graph, target_node) if parent_samples is None: parent_samples = draw_samples(sub_causal_model, num_samples_conditional * 20)[ordered_predecessors] direct_influences = arrow_strength_of_model( sub_causal_model.causal_mechanism(target_node), parent_samples[ordered_predecessors].to_numpy(), num_samples_from_conditional=num_samples_conditional, max_num_runs=max_num_runs, tolerance=tolerance, n_jobs=n_jobs, difference_estimation_func=difference_estimation_func, ) return {(predecessor, target_node): direct_influences[i] for i, predecessor in enumerate(ordered_predecessors)} def arrow_strength_of_model( conditional_stochastic_model: ConditionalStochasticModel, input_samples: np.ndarray, num_samples_from_conditional: int = 2000, max_num_runs: int = 5000, tolerance: float = 0.01, n_jobs: int = -1, difference_estimation_func: Optional[Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]]] = None, input_subsets: Optional[List[List[int]]] = None, ) -> np.ndarray: input_samples = shape_into_2d(input_samples) if input_subsets is None: input_subsets = [[i] for i in range(input_samples.shape[1])] if difference_estimation_func is None: if isinstance(conditional_stochastic_model, ProbabilityEstimatorModel): difference_estimation_func = estimate_kl_divergence_of_probabilities else: def difference_estimation_func(old, new): return np.var(new) - np.var(old) if isinstance(conditional_stochastic_model, ProbabilityEstimatorModel): samples_creation_method = conditional_stochastic_model.estimate_probabilities else: samples_creation_method = conditional_stochastic_model.draw_samples with warnings.catch_warnings(): warnings.filterwarnings("ignore") def parallel_job(subset: List[int], parallel_random_seed: int): set_random_seed(parallel_random_seed) return _estimate_direct_strength( samples_creation_method, input_samples, subset, difference_estimation_func, num_samples_from_conditional, max_num_runs, tolerance, ) random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(input_subsets)) results = Parallel(n_jobs=n_jobs)( delayed(parallel_job)(subset, random_seed) for subset, random_seed in zip(input_subsets, random_seeds) ) if np.any(results == np.inf): _logger.warning( "At least one arrow strength is infinite. This typically happens if the causal models are " "deterministic, i.e. there is no noise or it is extremely small." ) return np.array(results) def _estimate_direct_strength( draw_samples_func: Callable[[np.ndarray], np.ndarray], distribution_samples: np.ndarray, parents_subset: List[int], difference_estimation_func: Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]], num_samples_conditional: int, max_num_runs: int, tolerance: float, ) -> float: distribution_samples = shape_into_2d(distribution_samples) num_samples_conditional = min(num_samples_conditional, distribution_samples.shape[0]) aggregated_conditional_difference_result = 0 average_difference_result = 0 converged_run = 0 for run, sample in enumerate(distribution_samples): tmp_samples = repmat(sample, num_samples_conditional, 1) rnd_permutation = np.random.choice(distribution_samples.shape[0], num_samples_conditional, replace=False) # Sampling from the conditional distribution based on the current sample. conditional_distribution_samples = draw_samples_func(tmp_samples) # Sampling from the conditional based on the current sample, but randomizing the inputs of all variables that # are in the given subset. By this, we can simulate the impact on the conditional distribution when removing # only the incoming edges of the variables in the subset. tmp_samples[:, parents_subset] = distribution_samples[:, parents_subset][rnd_permutation] cond_dist_removed_arr_samples = draw_samples_func(tmp_samples) old_average_difference_result = average_difference_result aggregated_conditional_difference_result += difference_estimation_func( conditional_distribution_samples, cond_dist_removed_arr_samples ) average_difference_result = aggregated_conditional_difference_result / (run + 1) if run >= max_num_runs: break elif run > 0: if old_average_difference_result == 0: converging = average_difference_result == 0 else: converging = abs(1 - average_difference_result / old_average_difference_result) < tolerance if converging: converged_run += 1 if converged_run >= 3: break else: converged_run = 0 return average_difference_result def intrinsic_causal_influence( causal_model: StructuralCausalModel, target_node: Any, prediction_model: Union[PredictionModel, ClassificationModel, str] = "approx", attribution_func: Optional[Callable[[np.ndarray, np.ndarray], float]] = None, num_training_samples: int = 100000, num_samples_randomization: int = 1000, num_samples_baseline: int = 1000, max_batch_size: int = 250, auto_assign_quality: auto.AssignmentQuality = auto.AssignmentQuality.GOOD, shapley_config: Optional[ShapleyConfig] = None, ) -> Dict[Any, float]: """Computes the causal contribution of each upstream noise term of the target node (including the noise of the target itself) to the statistical property (e.g. mean, variance) of the target. We call this contribution *intrinsic* as noise terms, by definition, do not inherit properties of observed parents. The contribution of each noise term is then the *intrinsic* causal contribution of the corresponding node. For more scientific details, please refer to the paper below. **Research Paper**: Janzing et al. *Quantifying causal contributions via structure preserving interventions*. arXiv:2007.00714, 2021. :param causal_model: The structural causal model for whose target node we compute the intrinsic causal influence of its ancestors. :param target_node: Target node whose statistical property is to be attributed. :param prediction_model: Prediction model for estimating the functional relationship between subsets of ancestor noise terms and the target node. This can be an instance of a PredictionModel, the string 'approx' or the string 'exact'. With 'exact', the underlying causal models in the graph are utilized directly by propagating given noise inputs through the graph. This is generally more accurate but slow. With 'approx', an appropriate model is selected and trained based on sampled data from the graph, which is less accurate but faster. A more detailed treatment on why we need this parameter is also provided in :ref:`icc`. :param attribution_func: Optional attribution function to measure the statistical property of the target node. This function expects two inputs; predictions after the randomization of certain features (i.e. samples from noise nodes) and a baseline where no features were randomized. The baseline predictions can be typically ignored if one is interested in uncertainty measures such as entropy or variance, but they might be relevant if, for instance, these shall be estimated based on the residuals. By default, entropy is used if prediction model is a classifier, variance otherwise. :param num_training_samples: Number of samples drawn from the graphical causal model that are used for fitting the prediction_model (if necessary). :param num_samples_randomization: Number of noise samples drawn from the graphical causal model that are used for evaluating the set function. Here, these samples are samples from the noise distributions used for randomizing features that are not in the subset. :param num_samples_baseline: Number of noise samples drawn from the graphical causal model that are used for evaluating the set function. Here, these samples are used as fixed observations for features that are in the subset. :param max_batch_size: Maximum batch size for estimating the predictions from evaluation samples. This has a significant impact on the overall memory usage. If set to -1, all samples are used in one batch. :param auto_assign_quality: Auto assign quality for the 'approx' prediction_model option. :param shapley_config: :class:`~dowhy.gcm.shapley.ShapleyConfig` for the Shapley estimator. :return: Intrinsic causal contribution of each ancestor node to the statistical property defined by the attribution_func of the target node. """ validate_causal_dag(causal_model.graph) # Creating a smaller subgraph, which only contains upstream nodes that are connected to the target node. sub_causal_model = StructuralCausalModel(node_connected_subgraph_view(causal_model.graph, target_node)) data_samples, noise_samples = noise_samples_of_ancestors(sub_causal_model, target_node, num_training_samples) node_names = noise_samples.columns noise_samples, target_samples = shape_into_2d(noise_samples.to_numpy(), data_samples[target_node].to_numpy()) target_is_categorical = is_categorical(data_samples[target_node].to_numpy()) prediction_method = _get_icc_noise_function( causal_model, target_node, prediction_model, noise_samples, node_names, target_samples, auto_assign_quality, target_is_categorical, ) if attribution_func is None: if target_is_categorical: def attribution_func(x, _): return -estimate_entropy_of_probabilities(x) else: def attribution_func(x, _): return estimate_variance(x) _, noise_samples = noise_samples_of_ancestors( sub_causal_model, target_node, num_samples_randomization + num_samples_baseline ) noise_samples = shape_into_2d(noise_samples.to_numpy()) iccs = _estimate_iccs( attribution_func, prediction_method, noise_samples[:num_samples_randomization], noise_samples[num_samples_randomization : num_samples_randomization + num_samples_baseline], max_batch_size, ShapleyConfig() if shapley_config is None else shapley_config, ) return {node: iccs[i] for i, node in enumerate(node_names)} def intrinsic_causal_influence_sample( causal_model: InvertibleStructuralCausalModel, target_node: Any, baseline_samples: pd.DataFrame, noise_feature_samples: Optional[pd.DataFrame] = None, subset_scoring_func: Optional[Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]]] = None, num_noise_feature_samples: int = 5000, max_batch_size: int = 100, shapley_config: Optional[ShapleyConfig] = None, ) -> List[Dict[Any, Any]]: """Estimates the intrinsic causal impact of upstream nodes on a specified target_node, using the provided baseline_samples as a reference. In this context, observed values are attributed to the noise factors present in upstream nodes. Compared to intrinsic_causal_influence, this method quantifies the influences with respect to single observations instead of the distribution. Note that the current implementation only supports non-categorical data, since the noise terms need to be reconstructed. **Research Paper**: Janzing et al. *Quantifying causal contributions via structure preserving interventions*. arXiv:2007.00714, 2021. :param causal_model: The fitted invertible structural causal model. :param target_node: Node of interest. :param baseline_samples: Samples for which the influence should be estimated. :param noise_feature_samples: Optional noise samples of upstream nodes used as 'background' samples.. If None is given, new noise samples are generated based on the graph. These samples are used for randomizing features that are not in the subset. :param subset_scoring_func: Set function for estimating the quantity of interest based. This function expects two inputs; the outcome of the model for some samples if certain features are permuted and the outcome of the model for the same samples when no features were permuted. By default, the difference between means of these samples are estimated. :param num_noise_feature_samples: If no noise_feature_samples are given, noise samples are drawn from the graph. This parameter indicates how many. :param max_batch_size: Maximum batch size for estimating multiple predictions at once. This has a significant influence on the overall memory usage. If set to -1, all samples are used in one batch. :param shapley_config: :class:`~dowhy.gcm.shapley.ShapleyConfig` for the Shapley estimator. :return: A list of dictionaries indicating the intrinsic causal influence of a node on the target for a particular sample. This is, each dictionary belongs to one baseline sample. """ validate_node(causal_model.graph, target_node) causal_model = InvertibleStructuralCausalModel(node_connected_subgraph_view(causal_model.graph, target_node)) feature_samples, tmp_noise_feature_samples = noise_samples_of_ancestors( causal_model, target_node, num_noise_feature_samples ) if has_categorical(feature_samples.to_numpy()): raise ValueError( "The current implementation requires all variables to be numeric, i.e., non-categorical! " "There is at least one node in the graph that is categorical." ) if noise_feature_samples is None: noise_feature_samples = tmp_noise_feature_samples if subset_scoring_func is None: subset_scoring_func = means_difference shapley_vales = feature_relevance_sample( _get_icc_noise_function( causal_model, target_node, "exact", noise_feature_samples, noise_feature_samples.columns, None, None, False ), feature_samples=noise_feature_samples.to_numpy(), baseline_samples=compute_noise_from_data(causal_model, baseline_samples)[ noise_feature_samples.columns ].to_numpy(), subset_scoring_func=subset_scoring_func, max_batch_size=max_batch_size, shapley_config=shapley_config, ) return [ {(predecessor, target_node): shapley_vales[i][q] for q, predecessor in enumerate(noise_feature_samples.columns)} for i in range(shapley_vales.shape[0]) ] def _estimate_iccs( attribution_func: Callable[[np.ndarray, np.ndarray], float], prediction_method: Callable[[np.ndarray], np.ndarray], noise_samples: np.ndarray, baseline_noise_samples: np.ndarray, max_batch_size: int, shapley_config: ShapleyConfig, ): target_values = shape_into_2d(prediction_method(baseline_noise_samples)) def icc_set_function(subset: np.ndarray) -> Union[np.ndarray, float]: if np.all(subset == 1): # In case of the full subset (no randomization), we get the same predictions as when we apply the # prediction method to the samples of interest, since all noise samples are replaced with a sample of # interest. predictions = target_values elif np.all(subset == 0): # In case of the empty subset (all are jointly randomize), it boils down to taking the average over all # predictions, seeing that the randomization yields the same values for each sample of interest (none of the # samples of interest are used to replace a (jointly) 'randomized' sample). predictions = repmat(np.mean(prediction_method(noise_samples), axis=0), baseline_noise_samples.shape[0], 1) else: predictions = marginal_expectation( prediction_method, feature_samples=noise_samples, baseline_samples=baseline_noise_samples, baseline_feature_indices=np.arange(0, noise_samples.shape[1])[subset == 1], return_averaged_results=True, feature_perturbation="randomize_columns_jointly", max_batch_size=max_batch_size, ) return attribution_func(shape_into_2d(predictions), target_values) return estimate_shapley_values(icc_set_function, noise_samples.shape[1], shapley_config) def _get_icc_noise_function( causal_model: InvertibleStructuralCausalModel, target_node: Any, prediction_model: Union[PredictionModel, ClassificationModel, str], noise_samples: np.ndarray, node_names: Iterator[Any], target_samples: np.ndarray, auto_assign_quality: auto.AssignmentQuality, target_is_categorical: bool, ) -> Callable[[np.ndarray], np.ndarray]: if isinstance(prediction_model, str) and prediction_model not in ("approx", "exact"): raise ValueError( "Invalid value for prediction_model: %s! This should either be an instance of a PredictionModel or" "one of the two string options 'exact' or 'approx'." % prediction_model ) if not isinstance(prediction_model, str): prediction_model.fit(noise_samples, target_samples) return prediction_model.predict if prediction_model == "approx": prediction_model = auto.select_model(noise_samples, target_samples, auto_assign_quality) prediction_model.fit(noise_samples, target_samples) if target_is_categorical: return prediction_model.predict_probabilities else: return prediction_model.predict else: # Exact model def exact_model(X: np.ndarray) -> np.ndarray: return compute_data_from_noise(causal_model, pd.DataFrame(X, columns=[x for x in node_names]))[ target_node ].to_numpy() if target_is_categorical: list_of_classes = cast(ClassifierFCM, causal_model.causal_mechanism(target_node)).classifier_model.classes def prediction_method(X): return (shape_into_2d(exact_model(X)) == list_of_classes).astype(float) return prediction_method else: return exact_model

bloebp

45046e141ebd701820ca8a692c8f03ed5dce314c

e734f4e6245d404218b0ba11b14f7cc621bec7a0

Can we use type hints here?

petergtz

py-why/dowhy

Add method to estimate ICC for single samples

Before, it was only possible to estimate intrinsic causal contributions with respect to the distribution. This new method allows to estimate the influences of upstream nodes on a particular observation of a target node. As for now, this requires that all models are invertible with respect to the noise.

2023-03-29 21:42:31+00:00

2023-03-31 13:39:16+00:00

dowhy/gcm/influence.py

"""This module provides functions to estimate causal influences. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging import warnings from typing import Any, Callable, Dict, List, Optional, Tuple, Union, cast import numpy as np import pandas as pd from joblib import Parallel, delayed from numpy.matlib import repmat import dowhy.gcm.auto as auto from dowhy.gcm._noise import compute_data_from_noise, noise_samples_of_ancestors from dowhy.gcm.cms import ProbabilisticCausalModel, StructuralCausalModel from dowhy.gcm.divergence import estimate_kl_divergence_of_probabilities from dowhy.gcm.fcms import ClassificationModel, ClassifierFCM, PredictionModel, ProbabilityEstimatorModel from dowhy.gcm.fitting_sampling import draw_samples from dowhy.gcm.graph import ( ConditionalStochasticModel, get_ordered_predecessors, is_root_node, node_connected_subgraph_view, validate_causal_dag, validate_node, ) from dowhy.gcm.shapley import ShapleyConfig, estimate_shapley_values from dowhy.gcm.stats import marginal_expectation from dowhy.gcm.uncertainty import estimate_entropy_of_probabilities, estimate_variance from dowhy.gcm.util.general import is_categorical, set_random_seed, shape_into_2d _logger = logging.getLogger(__name__) def arrow_strength( causal_model: ProbabilisticCausalModel, target_node: Any, parent_samples: Optional[pd.DataFrame] = None, num_samples_conditional: int = 2000, max_num_runs: int = 5000, tolerance: float = 0.01, n_jobs: int = -1, difference_estimation_func: Optional[Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]]] = None, ) -> Dict[Tuple[Any, Any], float]: """Computes the causal strength of each edge directed to the target node. The strength of an edge is quantified in terms of distance between conditional distributions of the target node in the original graph and the imputed graph wherein the edge has been removed and the target node is fed a random permutation of the observations of the source node. For more scientific details behind this API, please refer to the research paper below. **Research Paper**: Dominik Janzing, David Balduzzi, Moritz Grosse-Wentrup, Bernhard Schölkopf. *Quantifying Causal Influences*. The Annals of Statistics, Vol. 41, No. 5, 2324-2358, 2013. :param causal_model: The probabilistic causal model for whose target node we compute the strength of incoming edges for. :param target_node: The target node whose incoming edges' strength is to be computed. :param parent_samples: Optional samples from the parents of the target_node. If None are given, they are generated based on the provided causal model. Providing observational data can help to mitigate misspecifications in the graph, such as missing interactions between root nodes or confounders. :param num_samples_conditional: Sample size to use for estimating the distance between distributions. The more more samples, the higher the accuracy. :param max_num_runs: The maximum number of times to resample and estimate the strength to report the average strength. :param tolerance: If the percentage change in the estimated strength between two consecutive runs falls below the specified tolerance, the algorithm will terminate before reaching the maximum number of runs. A value of 0.01 would indicate a change of less than 1%. However, in order to minimize the impact of randomness, there must be at least three consecutive runs where the change is below the threshold. :param n_jobs: The number of jobs to run in parallel. Set it to -1 to use all processors. :param difference_estimation_func: Optional: How to measure the distance between two distributions. By default, the difference of the variance is estimated for a continuous target node and the KL divergence for a categorical target node. :return: Causal strength of each edge. """ if target_node not in causal_model.graph.nodes: raise ValueError("Target node %s can not be found in given graph!" % target_node) if is_root_node(causal_model.graph, target_node): raise ValueError("Target node %s is a root node, but it requires to have ancestors!" % target_node) # Creating a smaller subgraph, which only contains upstream nodes that are connected to the target node. sub_causal_model = ProbabilisticCausalModel(node_connected_subgraph_view(causal_model.graph, target_node)) validate_node(sub_causal_model.graph, target_node) ordered_predecessors = get_ordered_predecessors(sub_causal_model.graph, target_node) if parent_samples is None: parent_samples = draw_samples(sub_causal_model, num_samples_conditional * 20)[ordered_predecessors] direct_influences = arrow_strength_of_model( sub_causal_model.causal_mechanism(target_node), parent_samples[ordered_predecessors].to_numpy(), num_samples_from_conditional=num_samples_conditional, max_num_runs=max_num_runs, tolerance=tolerance, n_jobs=n_jobs, difference_estimation_func=difference_estimation_func, ) return {(predecessor, target_node): direct_influences[i] for i, predecessor in enumerate(ordered_predecessors)} def arrow_strength_of_model( conditional_stochastic_model: ConditionalStochasticModel, input_samples: np.ndarray, num_samples_from_conditional: int = 2000, max_num_runs: int = 5000, tolerance: float = 0.01, n_jobs: int = -1, difference_estimation_func: Optional[Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]]] = None, input_subsets: Optional[List[List[int]]] = None, ) -> np.ndarray: input_samples = shape_into_2d(input_samples) if input_subsets is None: input_subsets = [[i] for i in range(input_samples.shape[1])] if difference_estimation_func is None: if isinstance(conditional_stochastic_model, ProbabilityEstimatorModel): difference_estimation_func = estimate_kl_divergence_of_probabilities else: def difference_estimation_func(old, new): return np.var(new) - np.var(old) if isinstance(conditional_stochastic_model, ProbabilityEstimatorModel): samples_creation_method = conditional_stochastic_model.estimate_probabilities else: samples_creation_method = conditional_stochastic_model.draw_samples with warnings.catch_warnings(): warnings.filterwarnings("ignore") def parallel_job(subset: List[int], parallel_random_seed: int): set_random_seed(parallel_random_seed) return _estimate_direct_strength( samples_creation_method, input_samples, subset, difference_estimation_func, num_samples_from_conditional, max_num_runs, tolerance, ) random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(input_subsets)) results = Parallel(n_jobs=n_jobs)( delayed(parallel_job)(subset, random_seed) for subset, random_seed in zip(input_subsets, random_seeds) ) if np.any(results == np.inf): _logger.warning( "At least one arrow strength is infinite. This typically happens if the causal models are " "deterministic, i.e. there is no noise or it is extremely small." ) return np.array(results) def _estimate_direct_strength( draw_samples_func: Callable[[np.ndarray], np.ndarray], distribution_samples: np.ndarray, parents_subset: List[int], difference_estimation_func: Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]], num_samples_conditional: int, max_num_runs: int, tolerance: float, ) -> float: distribution_samples = shape_into_2d(distribution_samples) num_samples_conditional = min(num_samples_conditional, distribution_samples.shape[0]) aggregated_conditional_difference_result = 0 average_difference_result = 0 converged_run = 0 for run, sample in enumerate(distribution_samples): tmp_samples = repmat(sample, num_samples_conditional, 1) rnd_permutation = np.random.choice(distribution_samples.shape[0], num_samples_conditional, replace=False) # Sampling from the conditional distribution based on the current sample. conditional_distribution_samples = draw_samples_func(tmp_samples) # Sampling from the conditional based on the current sample, but randomizing the inputs of all variables that # are in the given subset. By this, we can simulate the impact on the conditional distribution when removing # only the incoming edges of the variables in the subset. tmp_samples[:, parents_subset] = distribution_samples[:, parents_subset][rnd_permutation] cond_dist_removed_arr_samples = draw_samples_func(tmp_samples) old_average_difference_result = average_difference_result aggregated_conditional_difference_result += difference_estimation_func( conditional_distribution_samples, cond_dist_removed_arr_samples ) average_difference_result = aggregated_conditional_difference_result / (run + 1) if run >= max_num_runs: break elif run > 0: if old_average_difference_result == 0: converging = average_difference_result == 0 else: converging = abs(1 - average_difference_result / old_average_difference_result) < tolerance if converging: converged_run += 1 if converged_run >= 3: break else: converged_run = 0 return average_difference_result def intrinsic_causal_influence( causal_model: StructuralCausalModel, target_node: Any, prediction_model: Union[PredictionModel, ClassificationModel, str] = "approx", attribution_func: Optional[Callable[[np.ndarray, np.ndarray], float]] = None, num_training_samples: int = 100000, num_samples_randomization: int = 1000, num_samples_baseline: int = 1000, max_batch_size: int = 250, auto_assign_quality: auto.AssignmentQuality = auto.AssignmentQuality.GOOD, shapley_config: Optional[ShapleyConfig] = None, ) -> Dict[Any, float]: """Computes the causal contribution of each upstream noise term of the target node (including the noise of the target itself) to the statistical property (e.g. mean, variance) of the target. We call this contribution *intrinsic* as noise terms, by definition, do not inherit properties of observed parents. The contribution of each noise term is then the *intrinsic* causal contribution of the corresponding node. For more scientific details, please refer to the paper below. **Research Paper**: Janzing et al. *Quantifying causal contributions via structure preserving interventions*. arXiv:2007.00714, 2021. :param causal_model: The structural causal model for whose target node we compute the intrinsic causal influence of its ancestors. :param target_node: Target node whose statistical property is to be attributed. :param prediction_model: Prediction model for estimating the functional relationship between subsets of ancestor noise terms and the target node. This can be an instance of a PredictionModel, the string 'approx' or the string 'exact'. With 'exact', the underlying causal models in the graph are utilized directly by propagating given noise inputs through the graph. This is generally more accurate but slow. With 'approx', an appropriate model is selected and trained based on sampled data from the graph, which is less accurate but faster. A more detailed treatment on why we need this parameter is also provided in :ref:`icc`. :param attribution_func: Optional attribution function to measure the statistical property of the target node. This function expects two inputs; predictions after the randomization of certain features (i.e. samples from noise nodes) and a baseline where no features were randomized. The baseline predictions can be typically ignored if one is interested in uncertainty measures such as entropy or variance, but they might be relevant if, for instance, these shall be estimated based on the residuals. By default, entropy is used if prediction model is a classifier, variance otherwise. :param num_training_samples: Number of samples drawn from the graphical causal model that are used for fitting the prediction_model (if necessary). :param num_samples_randomization: Number of noise samples drawn from the graphical causal model that are used for evaluating the set function. Here, these samples are samples from the noise distributions used for randomizing features that are not in the subset. :param num_samples_baseline: Number of noise samples drawn from the graphical causal model that are used for evaluating the set function. Here, these samples are used as fixed observations for features that are in the subset. :param max_batch_size: Maximum batch size for estimating the predictions from evaluation samples. This has a significant impact on the overall memory usage. If set to -1, all samples are used in one batch. :param auto_assign_quality: Auto assign quality for the 'approx' prediction_model option. :param shapley_config: :class:`~dowhy.gcm.shapley.ShapleyConfig` for the Shapley estimator. :return: Intrinsic causal contribution of each ancestor node to the statistical property defined by the attribution_func of the target node. """ validate_causal_dag(causal_model.graph) # Creating a smaller subgraph, which only contains upstream nodes that are connected to the target node. sub_causal_model = StructuralCausalModel(node_connected_subgraph_view(causal_model.graph, target_node)) data_samples, noise_samples = noise_samples_of_ancestors(sub_causal_model, target_node, num_training_samples) node_names = noise_samples.columns noise_samples, target_samples = shape_into_2d(noise_samples.to_numpy(), data_samples[target_node].to_numpy()) target_is_categorical = is_categorical(data_samples[target_node].to_numpy()) if prediction_model == "approx": prediction_model = auto.select_model(noise_samples, target_samples, auto_assign_quality) prediction_model.fit(noise_samples, target_samples) if target_is_categorical: prediction_method = prediction_model.predict_probabilities else: prediction_method = prediction_model.predict elif prediction_model == "exact": def exact_model(X: np.ndarray) -> np.ndarray: return compute_data_from_noise(sub_causal_model, pd.DataFrame(X, columns=[x for x in node_names]))[ target_node ].to_numpy() if target_is_categorical: list_of_classes = cast( ClassifierFCM, sub_causal_model.causal_mechanism(target_node) ).classifier_model.classes def prediction_method(X): return (shape_into_2d(exact_model(X)) == list_of_classes).astype(float) else: prediction_method = exact_model elif isinstance(prediction_model, str): raise ValueError( "Invalid value for prediction_model: %s! This should either be an instance of a PredictionModel or" "one of the two string options 'exact' or 'approx'." % prediction_model ) else: prediction_model.fit(noise_samples, target_samples) prediction_method = prediction_model.predict if attribution_func is None: if target_is_categorical: def attribution_func(x, _): return -estimate_entropy_of_probabilities(x) else: def attribution_func(x, _): return estimate_variance(x) _, noise_samples = noise_samples_of_ancestors( sub_causal_model, target_node, num_samples_randomization + num_samples_baseline ) noise_samples = shape_into_2d(noise_samples.to_numpy()) iccs = _estimate_iccs( attribution_func, prediction_method, noise_samples[:num_samples_randomization], noise_samples[num_samples_randomization : num_samples_randomization + num_samples_baseline], max_batch_size, ShapleyConfig() if shapley_config is None else shapley_config, ) return {node: iccs[i] for i, node in enumerate(node_names)} def _estimate_iccs( attribution_func: Callable[[np.ndarray, np.ndarray], float], prediction_method: Callable[[np.ndarray], np.ndarray], noise_samples: np.ndarray, baseline_noise_samples: np.ndarray, max_batch_size: int, shapley_config: ShapleyConfig, ): target_values = shape_into_2d(prediction_method(baseline_noise_samples)) def icc_set_function(subset: np.ndarray) -> Union[np.ndarray, float]: if np.all(subset == 1): # In case of the full subset (no randomization), we get the same predictions as when we apply the # prediction method to the samples of interest, since all noise samples are replaced with a sample of # interest. predictions = target_values elif np.all(subset == 0): # In case of the empty subset (all are jointly randomize), it boils down to taking the average over all # predictions, seeing that the randomization yields the same values for each sample of interest (none of the # samples of interest are used to replace a (jointly) 'randomized' sample). predictions = repmat(np.mean(prediction_method(noise_samples), axis=0), baseline_noise_samples.shape[0], 1) else: predictions = marginal_expectation( prediction_method, feature_samples=noise_samples, baseline_samples=baseline_noise_samples, baseline_feature_indices=np.arange(0, noise_samples.shape[1])[subset == 1], return_averaged_results=True, feature_perturbation="randomize_columns_jointly", max_batch_size=max_batch_size, ) return attribution_func(shape_into_2d(predictions), target_values) return estimate_shapley_values(icc_set_function, noise_samples.shape[1], shapley_config)

"""This module provides functions to estimate causal influences. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging import warnings from typing import Any, Callable, Dict, Iterator, List, Optional, Tuple, Union, cast import numpy as np import pandas as pd from joblib import Parallel, delayed from numpy.matlib import repmat import dowhy.gcm.auto as auto from dowhy.gcm import feature_relevance_sample from dowhy.gcm._noise import compute_data_from_noise, compute_noise_from_data, noise_samples_of_ancestors from dowhy.gcm.cms import InvertibleStructuralCausalModel, ProbabilisticCausalModel, StructuralCausalModel from dowhy.gcm.divergence import estimate_kl_divergence_of_probabilities from dowhy.gcm.fcms import ClassificationModel, ClassifierFCM, PredictionModel, ProbabilityEstimatorModel from dowhy.gcm.fitting_sampling import draw_samples from dowhy.gcm.graph import ( ConditionalStochasticModel, get_ordered_predecessors, is_root_node, node_connected_subgraph_view, validate_causal_dag, validate_node, ) from dowhy.gcm.shapley import ShapleyConfig, estimate_shapley_values from dowhy.gcm.stats import marginal_expectation from dowhy.gcm.uncertainty import estimate_entropy_of_probabilities, estimate_variance from dowhy.gcm.util.general import has_categorical, is_categorical, means_difference, set_random_seed, shape_into_2d _logger = logging.getLogger(__name__) def arrow_strength( causal_model: ProbabilisticCausalModel, target_node: Any, parent_samples: Optional[pd.DataFrame] = None, num_samples_conditional: int = 2000, max_num_runs: int = 5000, tolerance: float = 0.01, n_jobs: int = -1, difference_estimation_func: Optional[Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]]] = None, ) -> Dict[Tuple[Any, Any], float]: """Computes the causal strength of each edge directed to the target node. The strength of an edge is quantified in terms of distance between conditional distributions of the target node in the original graph and the imputed graph wherein the edge has been removed and the target node is fed a random permutation of the observations of the source node. For more scientific details behind this API, please refer to the research paper below. **Research Paper**: Dominik Janzing, David Balduzzi, Moritz Grosse-Wentrup, Bernhard Schölkopf. *Quantifying Causal Influences*. The Annals of Statistics, Vol. 41, No. 5, 2324-2358, 2013. :param causal_model: The probabilistic causal model for whose target node we compute the strength of incoming edges for. :param target_node: The target node whose incoming edges' strength is to be computed. :param parent_samples: Optional samples from the parents of the target_node. If None are given, they are generated based on the provided causal model. Providing observational data can help to mitigate misspecifications in the graph, such as missing interactions between root nodes or confounders. :param num_samples_conditional: Sample size to use for estimating the distance between distributions. The more more samples, the higher the accuracy. :param max_num_runs: The maximum number of times to resample and estimate the strength to report the average strength. :param tolerance: If the percentage change in the estimated strength between two consecutive runs falls below the specified tolerance, the algorithm will terminate before reaching the maximum number of runs. A value of 0.01 would indicate a change of less than 1%. However, in order to minimize the impact of randomness, there must be at least three consecutive runs where the change is below the threshold. :param n_jobs: The number of jobs to run in parallel. Set it to -1 to use all processors. :param difference_estimation_func: Optional: How to measure the distance between two distributions. By default, the difference of the variance is estimated for a continuous target node and the KL divergence for a categorical target node. :return: Causal strength of each edge. """ if target_node not in causal_model.graph.nodes: raise ValueError("Target node %s can not be found in given graph!" % target_node) if is_root_node(causal_model.graph, target_node): raise ValueError("Target node %s is a root node, but it requires to have ancestors!" % target_node) # Creating a smaller subgraph, which only contains upstream nodes that are connected to the target node. sub_causal_model = ProbabilisticCausalModel(node_connected_subgraph_view(causal_model.graph, target_node)) validate_node(sub_causal_model.graph, target_node) ordered_predecessors = get_ordered_predecessors(sub_causal_model.graph, target_node) if parent_samples is None: parent_samples = draw_samples(sub_causal_model, num_samples_conditional * 20)[ordered_predecessors] direct_influences = arrow_strength_of_model( sub_causal_model.causal_mechanism(target_node), parent_samples[ordered_predecessors].to_numpy(), num_samples_from_conditional=num_samples_conditional, max_num_runs=max_num_runs, tolerance=tolerance, n_jobs=n_jobs, difference_estimation_func=difference_estimation_func, ) return {(predecessor, target_node): direct_influences[i] for i, predecessor in enumerate(ordered_predecessors)} def arrow_strength_of_model( conditional_stochastic_model: ConditionalStochasticModel, input_samples: np.ndarray, num_samples_from_conditional: int = 2000, max_num_runs: int = 5000, tolerance: float = 0.01, n_jobs: int = -1, difference_estimation_func: Optional[Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]]] = None, input_subsets: Optional[List[List[int]]] = None, ) -> np.ndarray: input_samples = shape_into_2d(input_samples) if input_subsets is None: input_subsets = [[i] for i in range(input_samples.shape[1])] if difference_estimation_func is None: if isinstance(conditional_stochastic_model, ProbabilityEstimatorModel): difference_estimation_func = estimate_kl_divergence_of_probabilities else: def difference_estimation_func(old, new): return np.var(new) - np.var(old) if isinstance(conditional_stochastic_model, ProbabilityEstimatorModel): samples_creation_method = conditional_stochastic_model.estimate_probabilities else: samples_creation_method = conditional_stochastic_model.draw_samples with warnings.catch_warnings(): warnings.filterwarnings("ignore") def parallel_job(subset: List[int], parallel_random_seed: int): set_random_seed(parallel_random_seed) return _estimate_direct_strength( samples_creation_method, input_samples, subset, difference_estimation_func, num_samples_from_conditional, max_num_runs, tolerance, ) random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(input_subsets)) results = Parallel(n_jobs=n_jobs)( delayed(parallel_job)(subset, random_seed) for subset, random_seed in zip(input_subsets, random_seeds) ) if np.any(results == np.inf): _logger.warning( "At least one arrow strength is infinite. This typically happens if the causal models are " "deterministic, i.e. there is no noise or it is extremely small." ) return np.array(results) def _estimate_direct_strength( draw_samples_func: Callable[[np.ndarray], np.ndarray], distribution_samples: np.ndarray, parents_subset: List[int], difference_estimation_func: Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]], num_samples_conditional: int, max_num_runs: int, tolerance: float, ) -> float: distribution_samples = shape_into_2d(distribution_samples) num_samples_conditional = min(num_samples_conditional, distribution_samples.shape[0]) aggregated_conditional_difference_result = 0 average_difference_result = 0 converged_run = 0 for run, sample in enumerate(distribution_samples): tmp_samples = repmat(sample, num_samples_conditional, 1) rnd_permutation = np.random.choice(distribution_samples.shape[0], num_samples_conditional, replace=False) # Sampling from the conditional distribution based on the current sample. conditional_distribution_samples = draw_samples_func(tmp_samples) # Sampling from the conditional based on the current sample, but randomizing the inputs of all variables that # are in the given subset. By this, we can simulate the impact on the conditional distribution when removing # only the incoming edges of the variables in the subset. tmp_samples[:, parents_subset] = distribution_samples[:, parents_subset][rnd_permutation] cond_dist_removed_arr_samples = draw_samples_func(tmp_samples) old_average_difference_result = average_difference_result aggregated_conditional_difference_result += difference_estimation_func( conditional_distribution_samples, cond_dist_removed_arr_samples ) average_difference_result = aggregated_conditional_difference_result / (run + 1) if run >= max_num_runs: break elif run > 0: if old_average_difference_result == 0: converging = average_difference_result == 0 else: converging = abs(1 - average_difference_result / old_average_difference_result) < tolerance if converging: converged_run += 1 if converged_run >= 3: break else: converged_run = 0 return average_difference_result def intrinsic_causal_influence( causal_model: StructuralCausalModel, target_node: Any, prediction_model: Union[PredictionModel, ClassificationModel, str] = "approx", attribution_func: Optional[Callable[[np.ndarray, np.ndarray], float]] = None, num_training_samples: int = 100000, num_samples_randomization: int = 1000, num_samples_baseline: int = 1000, max_batch_size: int = 250, auto_assign_quality: auto.AssignmentQuality = auto.AssignmentQuality.GOOD, shapley_config: Optional[ShapleyConfig] = None, ) -> Dict[Any, float]: """Computes the causal contribution of each upstream noise term of the target node (including the noise of the target itself) to the statistical property (e.g. mean, variance) of the target. We call this contribution *intrinsic* as noise terms, by definition, do not inherit properties of observed parents. The contribution of each noise term is then the *intrinsic* causal contribution of the corresponding node. For more scientific details, please refer to the paper below. **Research Paper**: Janzing et al. *Quantifying causal contributions via structure preserving interventions*. arXiv:2007.00714, 2021. :param causal_model: The structural causal model for whose target node we compute the intrinsic causal influence of its ancestors. :param target_node: Target node whose statistical property is to be attributed. :param prediction_model: Prediction model for estimating the functional relationship between subsets of ancestor noise terms and the target node. This can be an instance of a PredictionModel, the string 'approx' or the string 'exact'. With 'exact', the underlying causal models in the graph are utilized directly by propagating given noise inputs through the graph. This is generally more accurate but slow. With 'approx', an appropriate model is selected and trained based on sampled data from the graph, which is less accurate but faster. A more detailed treatment on why we need this parameter is also provided in :ref:`icc`. :param attribution_func: Optional attribution function to measure the statistical property of the target node. This function expects two inputs; predictions after the randomization of certain features (i.e. samples from noise nodes) and a baseline where no features were randomized. The baseline predictions can be typically ignored if one is interested in uncertainty measures such as entropy or variance, but they might be relevant if, for instance, these shall be estimated based on the residuals. By default, entropy is used if prediction model is a classifier, variance otherwise. :param num_training_samples: Number of samples drawn from the graphical causal model that are used for fitting the prediction_model (if necessary). :param num_samples_randomization: Number of noise samples drawn from the graphical causal model that are used for evaluating the set function. Here, these samples are samples from the noise distributions used for randomizing features that are not in the subset. :param num_samples_baseline: Number of noise samples drawn from the graphical causal model that are used for evaluating the set function. Here, these samples are used as fixed observations for features that are in the subset. :param max_batch_size: Maximum batch size for estimating the predictions from evaluation samples. This has a significant impact on the overall memory usage. If set to -1, all samples are used in one batch. :param auto_assign_quality: Auto assign quality for the 'approx' prediction_model option. :param shapley_config: :class:`~dowhy.gcm.shapley.ShapleyConfig` for the Shapley estimator. :return: Intrinsic causal contribution of each ancestor node to the statistical property defined by the attribution_func of the target node. """ validate_causal_dag(causal_model.graph) # Creating a smaller subgraph, which only contains upstream nodes that are connected to the target node. sub_causal_model = StructuralCausalModel(node_connected_subgraph_view(causal_model.graph, target_node)) data_samples, noise_samples = noise_samples_of_ancestors(sub_causal_model, target_node, num_training_samples) node_names = noise_samples.columns noise_samples, target_samples = shape_into_2d(noise_samples.to_numpy(), data_samples[target_node].to_numpy()) target_is_categorical = is_categorical(data_samples[target_node].to_numpy()) prediction_method = _get_icc_noise_function( causal_model, target_node, prediction_model, noise_samples, node_names, target_samples, auto_assign_quality, target_is_categorical, ) if attribution_func is None: if target_is_categorical: def attribution_func(x, _): return -estimate_entropy_of_probabilities(x) else: def attribution_func(x, _): return estimate_variance(x) _, noise_samples = noise_samples_of_ancestors( sub_causal_model, target_node, num_samples_randomization + num_samples_baseline ) noise_samples = shape_into_2d(noise_samples.to_numpy()) iccs = _estimate_iccs( attribution_func, prediction_method, noise_samples[:num_samples_randomization], noise_samples[num_samples_randomization : num_samples_randomization + num_samples_baseline], max_batch_size, ShapleyConfig() if shapley_config is None else shapley_config, ) return {node: iccs[i] for i, node in enumerate(node_names)} def intrinsic_causal_influence_sample( causal_model: InvertibleStructuralCausalModel, target_node: Any, baseline_samples: pd.DataFrame, noise_feature_samples: Optional[pd.DataFrame] = None, subset_scoring_func: Optional[Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]]] = None, num_noise_feature_samples: int = 5000, max_batch_size: int = 100, shapley_config: Optional[ShapleyConfig] = None, ) -> List[Dict[Any, Any]]: """Estimates the intrinsic causal impact of upstream nodes on a specified target_node, using the provided baseline_samples as a reference. In this context, observed values are attributed to the noise factors present in upstream nodes. Compared to intrinsic_causal_influence, this method quantifies the influences with respect to single observations instead of the distribution. Note that the current implementation only supports non-categorical data, since the noise terms need to be reconstructed. **Research Paper**: Janzing et al. *Quantifying causal contributions via structure preserving interventions*. arXiv:2007.00714, 2021. :param causal_model: The fitted invertible structural causal model. :param target_node: Node of interest. :param baseline_samples: Samples for which the influence should be estimated. :param noise_feature_samples: Optional noise samples of upstream nodes used as 'background' samples.. If None is given, new noise samples are generated based on the graph. These samples are used for randomizing features that are not in the subset. :param subset_scoring_func: Set function for estimating the quantity of interest based. This function expects two inputs; the outcome of the model for some samples if certain features are permuted and the outcome of the model for the same samples when no features were permuted. By default, the difference between means of these samples are estimated. :param num_noise_feature_samples: If no noise_feature_samples are given, noise samples are drawn from the graph. This parameter indicates how many. :param max_batch_size: Maximum batch size for estimating multiple predictions at once. This has a significant influence on the overall memory usage. If set to -1, all samples are used in one batch. :param shapley_config: :class:`~dowhy.gcm.shapley.ShapleyConfig` for the Shapley estimator. :return: A list of dictionaries indicating the intrinsic causal influence of a node on the target for a particular sample. This is, each dictionary belongs to one baseline sample. """ validate_node(causal_model.graph, target_node) causal_model = InvertibleStructuralCausalModel(node_connected_subgraph_view(causal_model.graph, target_node)) feature_samples, tmp_noise_feature_samples = noise_samples_of_ancestors( causal_model, target_node, num_noise_feature_samples ) if has_categorical(feature_samples.to_numpy()): raise ValueError( "The current implementation requires all variables to be numeric, i.e., non-categorical! " "There is at least one node in the graph that is categorical." ) if noise_feature_samples is None: noise_feature_samples = tmp_noise_feature_samples if subset_scoring_func is None: subset_scoring_func = means_difference shapley_vales = feature_relevance_sample( _get_icc_noise_function( causal_model, target_node, "exact", noise_feature_samples, noise_feature_samples.columns, None, None, False ), feature_samples=noise_feature_samples.to_numpy(), baseline_samples=compute_noise_from_data(causal_model, baseline_samples)[ noise_feature_samples.columns ].to_numpy(), subset_scoring_func=subset_scoring_func, max_batch_size=max_batch_size, shapley_config=shapley_config, ) return [ {(predecessor, target_node): shapley_vales[i][q] for q, predecessor in enumerate(noise_feature_samples.columns)} for i in range(shapley_vales.shape[0]) ] def _estimate_iccs( attribution_func: Callable[[np.ndarray, np.ndarray], float], prediction_method: Callable[[np.ndarray], np.ndarray], noise_samples: np.ndarray, baseline_noise_samples: np.ndarray, max_batch_size: int, shapley_config: ShapleyConfig, ): target_values = shape_into_2d(prediction_method(baseline_noise_samples)) def icc_set_function(subset: np.ndarray) -> Union[np.ndarray, float]: if np.all(subset == 1): # In case of the full subset (no randomization), we get the same predictions as when we apply the # prediction method to the samples of interest, since all noise samples are replaced with a sample of # interest. predictions = target_values elif np.all(subset == 0): # In case of the empty subset (all are jointly randomize), it boils down to taking the average over all # predictions, seeing that the randomization yields the same values for each sample of interest (none of the # samples of interest are used to replace a (jointly) 'randomized' sample). predictions = repmat(np.mean(prediction_method(noise_samples), axis=0), baseline_noise_samples.shape[0], 1) else: predictions = marginal_expectation( prediction_method, feature_samples=noise_samples, baseline_samples=baseline_noise_samples, baseline_feature_indices=np.arange(0, noise_samples.shape[1])[subset == 1], return_averaged_results=True, feature_perturbation="randomize_columns_jointly", max_batch_size=max_batch_size, ) return attribution_func(shape_into_2d(predictions), target_values) return estimate_shapley_values(icc_set_function, noise_samples.shape[1], shapley_config) def _get_icc_noise_function( causal_model: InvertibleStructuralCausalModel, target_node: Any, prediction_model: Union[PredictionModel, ClassificationModel, str], noise_samples: np.ndarray, node_names: Iterator[Any], target_samples: np.ndarray, auto_assign_quality: auto.AssignmentQuality, target_is_categorical: bool, ) -> Callable[[np.ndarray], np.ndarray]: if isinstance(prediction_model, str) and prediction_model not in ("approx", "exact"): raise ValueError( "Invalid value for prediction_model: %s! This should either be an instance of a PredictionModel or" "one of the two string options 'exact' or 'approx'." % prediction_model ) if not isinstance(prediction_model, str): prediction_model.fit(noise_samples, target_samples) return prediction_model.predict if prediction_model == "approx": prediction_model = auto.select_model(noise_samples, target_samples, auto_assign_quality) prediction_model.fit(noise_samples, target_samples) if target_is_categorical: return prediction_model.predict_probabilities else: return prediction_model.predict else: # Exact model def exact_model(X: np.ndarray) -> np.ndarray: return compute_data_from_noise(causal_model, pd.DataFrame(X, columns=[x for x in node_names]))[ target_node ].to_numpy() if target_is_categorical: list_of_classes = cast(ClassifierFCM, causal_model.causal_mechanism(target_node)).classifier_model.classes def prediction_method(X): return (shape_into_2d(exact_model(X)) == list_of_classes).astype(float) return prediction_method else: return exact_model

bloebp

45046e141ebd701820ca8a692c8f03ed5dce314c

e734f4e6245d404218b0ba11b14f7cc621bec7a0

Put this at the top as ```python if isinstance(prediction_model, str) and prediction_model not in ("approx", "exact") ```

petergtz

py-why/dowhy

Add method to estimate ICC for single samples

Before, it was only possible to estimate intrinsic causal contributions with respect to the distribution. This new method allows to estimate the influences of upstream nodes on a particular observation of a target node. As for now, this requires that all models are invertible with respect to the noise.

2023-03-29 21:42:31+00:00

2023-03-31 13:39:16+00:00

dowhy/gcm/influence.py

"""This module provides functions to estimate causal influences. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging import warnings from typing import Any, Callable, Dict, List, Optional, Tuple, Union, cast import numpy as np import pandas as pd from joblib import Parallel, delayed from numpy.matlib import repmat import dowhy.gcm.auto as auto from dowhy.gcm._noise import compute_data_from_noise, noise_samples_of_ancestors from dowhy.gcm.cms import ProbabilisticCausalModel, StructuralCausalModel from dowhy.gcm.divergence import estimate_kl_divergence_of_probabilities from dowhy.gcm.fcms import ClassificationModel, ClassifierFCM, PredictionModel, ProbabilityEstimatorModel from dowhy.gcm.fitting_sampling import draw_samples from dowhy.gcm.graph import ( ConditionalStochasticModel, get_ordered_predecessors, is_root_node, node_connected_subgraph_view, validate_causal_dag, validate_node, ) from dowhy.gcm.shapley import ShapleyConfig, estimate_shapley_values from dowhy.gcm.stats import marginal_expectation from dowhy.gcm.uncertainty import estimate_entropy_of_probabilities, estimate_variance from dowhy.gcm.util.general import is_categorical, set_random_seed, shape_into_2d _logger = logging.getLogger(__name__) def arrow_strength( causal_model: ProbabilisticCausalModel, target_node: Any, parent_samples: Optional[pd.DataFrame] = None, num_samples_conditional: int = 2000, max_num_runs: int = 5000, tolerance: float = 0.01, n_jobs: int = -1, difference_estimation_func: Optional[Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]]] = None, ) -> Dict[Tuple[Any, Any], float]: """Computes the causal strength of each edge directed to the target node. The strength of an edge is quantified in terms of distance between conditional distributions of the target node in the original graph and the imputed graph wherein the edge has been removed and the target node is fed a random permutation of the observations of the source node. For more scientific details behind this API, please refer to the research paper below. **Research Paper**: Dominik Janzing, David Balduzzi, Moritz Grosse-Wentrup, Bernhard Schölkopf. *Quantifying Causal Influences*. The Annals of Statistics, Vol. 41, No. 5, 2324-2358, 2013. :param causal_model: The probabilistic causal model for whose target node we compute the strength of incoming edges for. :param target_node: The target node whose incoming edges' strength is to be computed. :param parent_samples: Optional samples from the parents of the target_node. If None are given, they are generated based on the provided causal model. Providing observational data can help to mitigate misspecifications in the graph, such as missing interactions between root nodes or confounders. :param num_samples_conditional: Sample size to use for estimating the distance between distributions. The more more samples, the higher the accuracy. :param max_num_runs: The maximum number of times to resample and estimate the strength to report the average strength. :param tolerance: If the percentage change in the estimated strength between two consecutive runs falls below the specified tolerance, the algorithm will terminate before reaching the maximum number of runs. A value of 0.01 would indicate a change of less than 1%. However, in order to minimize the impact of randomness, there must be at least three consecutive runs where the change is below the threshold. :param n_jobs: The number of jobs to run in parallel. Set it to -1 to use all processors. :param difference_estimation_func: Optional: How to measure the distance between two distributions. By default, the difference of the variance is estimated for a continuous target node and the KL divergence for a categorical target node. :return: Causal strength of each edge. """ if target_node not in causal_model.graph.nodes: raise ValueError("Target node %s can not be found in given graph!" % target_node) if is_root_node(causal_model.graph, target_node): raise ValueError("Target node %s is a root node, but it requires to have ancestors!" % target_node) # Creating a smaller subgraph, which only contains upstream nodes that are connected to the target node. sub_causal_model = ProbabilisticCausalModel(node_connected_subgraph_view(causal_model.graph, target_node)) validate_node(sub_causal_model.graph, target_node) ordered_predecessors = get_ordered_predecessors(sub_causal_model.graph, target_node) if parent_samples is None: parent_samples = draw_samples(sub_causal_model, num_samples_conditional * 20)[ordered_predecessors] direct_influences = arrow_strength_of_model( sub_causal_model.causal_mechanism(target_node), parent_samples[ordered_predecessors].to_numpy(), num_samples_from_conditional=num_samples_conditional, max_num_runs=max_num_runs, tolerance=tolerance, n_jobs=n_jobs, difference_estimation_func=difference_estimation_func, ) return {(predecessor, target_node): direct_influences[i] for i, predecessor in enumerate(ordered_predecessors)} def arrow_strength_of_model( conditional_stochastic_model: ConditionalStochasticModel, input_samples: np.ndarray, num_samples_from_conditional: int = 2000, max_num_runs: int = 5000, tolerance: float = 0.01, n_jobs: int = -1, difference_estimation_func: Optional[Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]]] = None, input_subsets: Optional[List[List[int]]] = None, ) -> np.ndarray: input_samples = shape_into_2d(input_samples) if input_subsets is None: input_subsets = [[i] for i in range(input_samples.shape[1])] if difference_estimation_func is None: if isinstance(conditional_stochastic_model, ProbabilityEstimatorModel): difference_estimation_func = estimate_kl_divergence_of_probabilities else: def difference_estimation_func(old, new): return np.var(new) - np.var(old) if isinstance(conditional_stochastic_model, ProbabilityEstimatorModel): samples_creation_method = conditional_stochastic_model.estimate_probabilities else: samples_creation_method = conditional_stochastic_model.draw_samples with warnings.catch_warnings(): warnings.filterwarnings("ignore") def parallel_job(subset: List[int], parallel_random_seed: int): set_random_seed(parallel_random_seed) return _estimate_direct_strength( samples_creation_method, input_samples, subset, difference_estimation_func, num_samples_from_conditional, max_num_runs, tolerance, ) random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(input_subsets)) results = Parallel(n_jobs=n_jobs)( delayed(parallel_job)(subset, random_seed) for subset, random_seed in zip(input_subsets, random_seeds) ) if np.any(results == np.inf): _logger.warning( "At least one arrow strength is infinite. This typically happens if the causal models are " "deterministic, i.e. there is no noise or it is extremely small." ) return np.array(results) def _estimate_direct_strength( draw_samples_func: Callable[[np.ndarray], np.ndarray], distribution_samples: np.ndarray, parents_subset: List[int], difference_estimation_func: Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]], num_samples_conditional: int, max_num_runs: int, tolerance: float, ) -> float: distribution_samples = shape_into_2d(distribution_samples) num_samples_conditional = min(num_samples_conditional, distribution_samples.shape[0]) aggregated_conditional_difference_result = 0 average_difference_result = 0 converged_run = 0 for run, sample in enumerate(distribution_samples): tmp_samples = repmat(sample, num_samples_conditional, 1) rnd_permutation = np.random.choice(distribution_samples.shape[0], num_samples_conditional, replace=False) # Sampling from the conditional distribution based on the current sample. conditional_distribution_samples = draw_samples_func(tmp_samples) # Sampling from the conditional based on the current sample, but randomizing the inputs of all variables that # are in the given subset. By this, we can simulate the impact on the conditional distribution when removing # only the incoming edges of the variables in the subset. tmp_samples[:, parents_subset] = distribution_samples[:, parents_subset][rnd_permutation] cond_dist_removed_arr_samples = draw_samples_func(tmp_samples) old_average_difference_result = average_difference_result aggregated_conditional_difference_result += difference_estimation_func( conditional_distribution_samples, cond_dist_removed_arr_samples ) average_difference_result = aggregated_conditional_difference_result / (run + 1) if run >= max_num_runs: break elif run > 0: if old_average_difference_result == 0: converging = average_difference_result == 0 else: converging = abs(1 - average_difference_result / old_average_difference_result) < tolerance if converging: converged_run += 1 if converged_run >= 3: break else: converged_run = 0 return average_difference_result def intrinsic_causal_influence( causal_model: StructuralCausalModel, target_node: Any, prediction_model: Union[PredictionModel, ClassificationModel, str] = "approx", attribution_func: Optional[Callable[[np.ndarray, np.ndarray], float]] = None, num_training_samples: int = 100000, num_samples_randomization: int = 1000, num_samples_baseline: int = 1000, max_batch_size: int = 250, auto_assign_quality: auto.AssignmentQuality = auto.AssignmentQuality.GOOD, shapley_config: Optional[ShapleyConfig] = None, ) -> Dict[Any, float]: """Computes the causal contribution of each upstream noise term of the target node (including the noise of the target itself) to the statistical property (e.g. mean, variance) of the target. We call this contribution *intrinsic* as noise terms, by definition, do not inherit properties of observed parents. The contribution of each noise term is then the *intrinsic* causal contribution of the corresponding node. For more scientific details, please refer to the paper below. **Research Paper**: Janzing et al. *Quantifying causal contributions via structure preserving interventions*. arXiv:2007.00714, 2021. :param causal_model: The structural causal model for whose target node we compute the intrinsic causal influence of its ancestors. :param target_node: Target node whose statistical property is to be attributed. :param prediction_model: Prediction model for estimating the functional relationship between subsets of ancestor noise terms and the target node. This can be an instance of a PredictionModel, the string 'approx' or the string 'exact'. With 'exact', the underlying causal models in the graph are utilized directly by propagating given noise inputs through the graph. This is generally more accurate but slow. With 'approx', an appropriate model is selected and trained based on sampled data from the graph, which is less accurate but faster. A more detailed treatment on why we need this parameter is also provided in :ref:`icc`. :param attribution_func: Optional attribution function to measure the statistical property of the target node. This function expects two inputs; predictions after the randomization of certain features (i.e. samples from noise nodes) and a baseline where no features were randomized. The baseline predictions can be typically ignored if one is interested in uncertainty measures such as entropy or variance, but they might be relevant if, for instance, these shall be estimated based on the residuals. By default, entropy is used if prediction model is a classifier, variance otherwise. :param num_training_samples: Number of samples drawn from the graphical causal model that are used for fitting the prediction_model (if necessary). :param num_samples_randomization: Number of noise samples drawn from the graphical causal model that are used for evaluating the set function. Here, these samples are samples from the noise distributions used for randomizing features that are not in the subset. :param num_samples_baseline: Number of noise samples drawn from the graphical causal model that are used for evaluating the set function. Here, these samples are used as fixed observations for features that are in the subset. :param max_batch_size: Maximum batch size for estimating the predictions from evaluation samples. This has a significant impact on the overall memory usage. If set to -1, all samples are used in one batch. :param auto_assign_quality: Auto assign quality for the 'approx' prediction_model option. :param shapley_config: :class:`~dowhy.gcm.shapley.ShapleyConfig` for the Shapley estimator. :return: Intrinsic causal contribution of each ancestor node to the statistical property defined by the attribution_func of the target node. """ validate_causal_dag(causal_model.graph) # Creating a smaller subgraph, which only contains upstream nodes that are connected to the target node. sub_causal_model = StructuralCausalModel(node_connected_subgraph_view(causal_model.graph, target_node)) data_samples, noise_samples = noise_samples_of_ancestors(sub_causal_model, target_node, num_training_samples) node_names = noise_samples.columns noise_samples, target_samples = shape_into_2d(noise_samples.to_numpy(), data_samples[target_node].to_numpy()) target_is_categorical = is_categorical(data_samples[target_node].to_numpy()) if prediction_model == "approx": prediction_model = auto.select_model(noise_samples, target_samples, auto_assign_quality) prediction_model.fit(noise_samples, target_samples) if target_is_categorical: prediction_method = prediction_model.predict_probabilities else: prediction_method = prediction_model.predict elif prediction_model == "exact": def exact_model(X: np.ndarray) -> np.ndarray: return compute_data_from_noise(sub_causal_model, pd.DataFrame(X, columns=[x for x in node_names]))[ target_node ].to_numpy() if target_is_categorical: list_of_classes = cast( ClassifierFCM, sub_causal_model.causal_mechanism(target_node) ).classifier_model.classes def prediction_method(X): return (shape_into_2d(exact_model(X)) == list_of_classes).astype(float) else: prediction_method = exact_model elif isinstance(prediction_model, str): raise ValueError( "Invalid value for prediction_model: %s! This should either be an instance of a PredictionModel or" "one of the two string options 'exact' or 'approx'." % prediction_model ) else: prediction_model.fit(noise_samples, target_samples) prediction_method = prediction_model.predict if attribution_func is None: if target_is_categorical: def attribution_func(x, _): return -estimate_entropy_of_probabilities(x) else: def attribution_func(x, _): return estimate_variance(x) _, noise_samples = noise_samples_of_ancestors( sub_causal_model, target_node, num_samples_randomization + num_samples_baseline ) noise_samples = shape_into_2d(noise_samples.to_numpy()) iccs = _estimate_iccs( attribution_func, prediction_method, noise_samples[:num_samples_randomization], noise_samples[num_samples_randomization : num_samples_randomization + num_samples_baseline], max_batch_size, ShapleyConfig() if shapley_config is None else shapley_config, ) return {node: iccs[i] for i, node in enumerate(node_names)} def _estimate_iccs( attribution_func: Callable[[np.ndarray, np.ndarray], float], prediction_method: Callable[[np.ndarray], np.ndarray], noise_samples: np.ndarray, baseline_noise_samples: np.ndarray, max_batch_size: int, shapley_config: ShapleyConfig, ): target_values = shape_into_2d(prediction_method(baseline_noise_samples)) def icc_set_function(subset: np.ndarray) -> Union[np.ndarray, float]: if np.all(subset == 1): # In case of the full subset (no randomization), we get the same predictions as when we apply the # prediction method to the samples of interest, since all noise samples are replaced with a sample of # interest. predictions = target_values elif np.all(subset == 0): # In case of the empty subset (all are jointly randomize), it boils down to taking the average over all # predictions, seeing that the randomization yields the same values for each sample of interest (none of the # samples of interest are used to replace a (jointly) 'randomized' sample). predictions = repmat(np.mean(prediction_method(noise_samples), axis=0), baseline_noise_samples.shape[0], 1) else: predictions = marginal_expectation( prediction_method, feature_samples=noise_samples, baseline_samples=baseline_noise_samples, baseline_feature_indices=np.arange(0, noise_samples.shape[1])[subset == 1], return_averaged_results=True, feature_perturbation="randomize_columns_jointly", max_batch_size=max_batch_size, ) return attribution_func(shape_into_2d(predictions), target_values) return estimate_shapley_values(icc_set_function, noise_samples.shape[1], shapley_config)

"""This module provides functions to estimate causal influences. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging import warnings from typing import Any, Callable, Dict, Iterator, List, Optional, Tuple, Union, cast import numpy as np import pandas as pd from joblib import Parallel, delayed from numpy.matlib import repmat import dowhy.gcm.auto as auto from dowhy.gcm import feature_relevance_sample from dowhy.gcm._noise import compute_data_from_noise, compute_noise_from_data, noise_samples_of_ancestors from dowhy.gcm.cms import InvertibleStructuralCausalModel, ProbabilisticCausalModel, StructuralCausalModel from dowhy.gcm.divergence import estimate_kl_divergence_of_probabilities from dowhy.gcm.fcms import ClassificationModel, ClassifierFCM, PredictionModel, ProbabilityEstimatorModel from dowhy.gcm.fitting_sampling import draw_samples from dowhy.gcm.graph import ( ConditionalStochasticModel, get_ordered_predecessors, is_root_node, node_connected_subgraph_view, validate_causal_dag, validate_node, ) from dowhy.gcm.shapley import ShapleyConfig, estimate_shapley_values from dowhy.gcm.stats import marginal_expectation from dowhy.gcm.uncertainty import estimate_entropy_of_probabilities, estimate_variance from dowhy.gcm.util.general import has_categorical, is_categorical, means_difference, set_random_seed, shape_into_2d _logger = logging.getLogger(__name__) def arrow_strength( causal_model: ProbabilisticCausalModel, target_node: Any, parent_samples: Optional[pd.DataFrame] = None, num_samples_conditional: int = 2000, max_num_runs: int = 5000, tolerance: float = 0.01, n_jobs: int = -1, difference_estimation_func: Optional[Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]]] = None, ) -> Dict[Tuple[Any, Any], float]: """Computes the causal strength of each edge directed to the target node. The strength of an edge is quantified in terms of distance between conditional distributions of the target node in the original graph and the imputed graph wherein the edge has been removed and the target node is fed a random permutation of the observations of the source node. For more scientific details behind this API, please refer to the research paper below. **Research Paper**: Dominik Janzing, David Balduzzi, Moritz Grosse-Wentrup, Bernhard Schölkopf. *Quantifying Causal Influences*. The Annals of Statistics, Vol. 41, No. 5, 2324-2358, 2013. :param causal_model: The probabilistic causal model for whose target node we compute the strength of incoming edges for. :param target_node: The target node whose incoming edges' strength is to be computed. :param parent_samples: Optional samples from the parents of the target_node. If None are given, they are generated based on the provided causal model. Providing observational data can help to mitigate misspecifications in the graph, such as missing interactions between root nodes or confounders. :param num_samples_conditional: Sample size to use for estimating the distance between distributions. The more more samples, the higher the accuracy. :param max_num_runs: The maximum number of times to resample and estimate the strength to report the average strength. :param tolerance: If the percentage change in the estimated strength between two consecutive runs falls below the specified tolerance, the algorithm will terminate before reaching the maximum number of runs. A value of 0.01 would indicate a change of less than 1%. However, in order to minimize the impact of randomness, there must be at least three consecutive runs where the change is below the threshold. :param n_jobs: The number of jobs to run in parallel. Set it to -1 to use all processors. :param difference_estimation_func: Optional: How to measure the distance between two distributions. By default, the difference of the variance is estimated for a continuous target node and the KL divergence for a categorical target node. :return: Causal strength of each edge. """ if target_node not in causal_model.graph.nodes: raise ValueError("Target node %s can not be found in given graph!" % target_node) if is_root_node(causal_model.graph, target_node): raise ValueError("Target node %s is a root node, but it requires to have ancestors!" % target_node) # Creating a smaller subgraph, which only contains upstream nodes that are connected to the target node. sub_causal_model = ProbabilisticCausalModel(node_connected_subgraph_view(causal_model.graph, target_node)) validate_node(sub_causal_model.graph, target_node) ordered_predecessors = get_ordered_predecessors(sub_causal_model.graph, target_node) if parent_samples is None: parent_samples = draw_samples(sub_causal_model, num_samples_conditional * 20)[ordered_predecessors] direct_influences = arrow_strength_of_model( sub_causal_model.causal_mechanism(target_node), parent_samples[ordered_predecessors].to_numpy(), num_samples_from_conditional=num_samples_conditional, max_num_runs=max_num_runs, tolerance=tolerance, n_jobs=n_jobs, difference_estimation_func=difference_estimation_func, ) return {(predecessor, target_node): direct_influences[i] for i, predecessor in enumerate(ordered_predecessors)} def arrow_strength_of_model( conditional_stochastic_model: ConditionalStochasticModel, input_samples: np.ndarray, num_samples_from_conditional: int = 2000, max_num_runs: int = 5000, tolerance: float = 0.01, n_jobs: int = -1, difference_estimation_func: Optional[Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]]] = None, input_subsets: Optional[List[List[int]]] = None, ) -> np.ndarray: input_samples = shape_into_2d(input_samples) if input_subsets is None: input_subsets = [[i] for i in range(input_samples.shape[1])] if difference_estimation_func is None: if isinstance(conditional_stochastic_model, ProbabilityEstimatorModel): difference_estimation_func = estimate_kl_divergence_of_probabilities else: def difference_estimation_func(old, new): return np.var(new) - np.var(old) if isinstance(conditional_stochastic_model, ProbabilityEstimatorModel): samples_creation_method = conditional_stochastic_model.estimate_probabilities else: samples_creation_method = conditional_stochastic_model.draw_samples with warnings.catch_warnings(): warnings.filterwarnings("ignore") def parallel_job(subset: List[int], parallel_random_seed: int): set_random_seed(parallel_random_seed) return _estimate_direct_strength( samples_creation_method, input_samples, subset, difference_estimation_func, num_samples_from_conditional, max_num_runs, tolerance, ) random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(input_subsets)) results = Parallel(n_jobs=n_jobs)( delayed(parallel_job)(subset, random_seed) for subset, random_seed in zip(input_subsets, random_seeds) ) if np.any(results == np.inf): _logger.warning( "At least one arrow strength is infinite. This typically happens if the causal models are " "deterministic, i.e. there is no noise or it is extremely small." ) return np.array(results) def _estimate_direct_strength( draw_samples_func: Callable[[np.ndarray], np.ndarray], distribution_samples: np.ndarray, parents_subset: List[int], difference_estimation_func: Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]], num_samples_conditional: int, max_num_runs: int, tolerance: float, ) -> float: distribution_samples = shape_into_2d(distribution_samples) num_samples_conditional = min(num_samples_conditional, distribution_samples.shape[0]) aggregated_conditional_difference_result = 0 average_difference_result = 0 converged_run = 0 for run, sample in enumerate(distribution_samples): tmp_samples = repmat(sample, num_samples_conditional, 1) rnd_permutation = np.random.choice(distribution_samples.shape[0], num_samples_conditional, replace=False) # Sampling from the conditional distribution based on the current sample. conditional_distribution_samples = draw_samples_func(tmp_samples) # Sampling from the conditional based on the current sample, but randomizing the inputs of all variables that # are in the given subset. By this, we can simulate the impact on the conditional distribution when removing # only the incoming edges of the variables in the subset. tmp_samples[:, parents_subset] = distribution_samples[:, parents_subset][rnd_permutation] cond_dist_removed_arr_samples = draw_samples_func(tmp_samples) old_average_difference_result = average_difference_result aggregated_conditional_difference_result += difference_estimation_func( conditional_distribution_samples, cond_dist_removed_arr_samples ) average_difference_result = aggregated_conditional_difference_result / (run + 1) if run >= max_num_runs: break elif run > 0: if old_average_difference_result == 0: converging = average_difference_result == 0 else: converging = abs(1 - average_difference_result / old_average_difference_result) < tolerance if converging: converged_run += 1 if converged_run >= 3: break else: converged_run = 0 return average_difference_result def intrinsic_causal_influence( causal_model: StructuralCausalModel, target_node: Any, prediction_model: Union[PredictionModel, ClassificationModel, str] = "approx", attribution_func: Optional[Callable[[np.ndarray, np.ndarray], float]] = None, num_training_samples: int = 100000, num_samples_randomization: int = 1000, num_samples_baseline: int = 1000, max_batch_size: int = 250, auto_assign_quality: auto.AssignmentQuality = auto.AssignmentQuality.GOOD, shapley_config: Optional[ShapleyConfig] = None, ) -> Dict[Any, float]: """Computes the causal contribution of each upstream noise term of the target node (including the noise of the target itself) to the statistical property (e.g. mean, variance) of the target. We call this contribution *intrinsic* as noise terms, by definition, do not inherit properties of observed parents. The contribution of each noise term is then the *intrinsic* causal contribution of the corresponding node. For more scientific details, please refer to the paper below. **Research Paper**: Janzing et al. *Quantifying causal contributions via structure preserving interventions*. arXiv:2007.00714, 2021. :param causal_model: The structural causal model for whose target node we compute the intrinsic causal influence of its ancestors. :param target_node: Target node whose statistical property is to be attributed. :param prediction_model: Prediction model for estimating the functional relationship between subsets of ancestor noise terms and the target node. This can be an instance of a PredictionModel, the string 'approx' or the string 'exact'. With 'exact', the underlying causal models in the graph are utilized directly by propagating given noise inputs through the graph. This is generally more accurate but slow. With 'approx', an appropriate model is selected and trained based on sampled data from the graph, which is less accurate but faster. A more detailed treatment on why we need this parameter is also provided in :ref:`icc`. :param attribution_func: Optional attribution function to measure the statistical property of the target node. This function expects two inputs; predictions after the randomization of certain features (i.e. samples from noise nodes) and a baseline where no features were randomized. The baseline predictions can be typically ignored if one is interested in uncertainty measures such as entropy or variance, but they might be relevant if, for instance, these shall be estimated based on the residuals. By default, entropy is used if prediction model is a classifier, variance otherwise. :param num_training_samples: Number of samples drawn from the graphical causal model that are used for fitting the prediction_model (if necessary). :param num_samples_randomization: Number of noise samples drawn from the graphical causal model that are used for evaluating the set function. Here, these samples are samples from the noise distributions used for randomizing features that are not in the subset. :param num_samples_baseline: Number of noise samples drawn from the graphical causal model that are used for evaluating the set function. Here, these samples are used as fixed observations for features that are in the subset. :param max_batch_size: Maximum batch size for estimating the predictions from evaluation samples. This has a significant impact on the overall memory usage. If set to -1, all samples are used in one batch. :param auto_assign_quality: Auto assign quality for the 'approx' prediction_model option. :param shapley_config: :class:`~dowhy.gcm.shapley.ShapleyConfig` for the Shapley estimator. :return: Intrinsic causal contribution of each ancestor node to the statistical property defined by the attribution_func of the target node. """ validate_causal_dag(causal_model.graph) # Creating a smaller subgraph, which only contains upstream nodes that are connected to the target node. sub_causal_model = StructuralCausalModel(node_connected_subgraph_view(causal_model.graph, target_node)) data_samples, noise_samples = noise_samples_of_ancestors(sub_causal_model, target_node, num_training_samples) node_names = noise_samples.columns noise_samples, target_samples = shape_into_2d(noise_samples.to_numpy(), data_samples[target_node].to_numpy()) target_is_categorical = is_categorical(data_samples[target_node].to_numpy()) prediction_method = _get_icc_noise_function( causal_model, target_node, prediction_model, noise_samples, node_names, target_samples, auto_assign_quality, target_is_categorical, ) if attribution_func is None: if target_is_categorical: def attribution_func(x, _): return -estimate_entropy_of_probabilities(x) else: def attribution_func(x, _): return estimate_variance(x) _, noise_samples = noise_samples_of_ancestors( sub_causal_model, target_node, num_samples_randomization + num_samples_baseline ) noise_samples = shape_into_2d(noise_samples.to_numpy()) iccs = _estimate_iccs( attribution_func, prediction_method, noise_samples[:num_samples_randomization], noise_samples[num_samples_randomization : num_samples_randomization + num_samples_baseline], max_batch_size, ShapleyConfig() if shapley_config is None else shapley_config, ) return {node: iccs[i] for i, node in enumerate(node_names)} def intrinsic_causal_influence_sample( causal_model: InvertibleStructuralCausalModel, target_node: Any, baseline_samples: pd.DataFrame, noise_feature_samples: Optional[pd.DataFrame] = None, subset_scoring_func: Optional[Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]]] = None, num_noise_feature_samples: int = 5000, max_batch_size: int = 100, shapley_config: Optional[ShapleyConfig] = None, ) -> List[Dict[Any, Any]]: """Estimates the intrinsic causal impact of upstream nodes on a specified target_node, using the provided baseline_samples as a reference. In this context, observed values are attributed to the noise factors present in upstream nodes. Compared to intrinsic_causal_influence, this method quantifies the influences with respect to single observations instead of the distribution. Note that the current implementation only supports non-categorical data, since the noise terms need to be reconstructed. **Research Paper**: Janzing et al. *Quantifying causal contributions via structure preserving interventions*. arXiv:2007.00714, 2021. :param causal_model: The fitted invertible structural causal model. :param target_node: Node of interest. :param baseline_samples: Samples for which the influence should be estimated. :param noise_feature_samples: Optional noise samples of upstream nodes used as 'background' samples.. If None is given, new noise samples are generated based on the graph. These samples are used for randomizing features that are not in the subset. :param subset_scoring_func: Set function for estimating the quantity of interest based. This function expects two inputs; the outcome of the model for some samples if certain features are permuted and the outcome of the model for the same samples when no features were permuted. By default, the difference between means of these samples are estimated. :param num_noise_feature_samples: If no noise_feature_samples are given, noise samples are drawn from the graph. This parameter indicates how many. :param max_batch_size: Maximum batch size for estimating multiple predictions at once. This has a significant influence on the overall memory usage. If set to -1, all samples are used in one batch. :param shapley_config: :class:`~dowhy.gcm.shapley.ShapleyConfig` for the Shapley estimator. :return: A list of dictionaries indicating the intrinsic causal influence of a node on the target for a particular sample. This is, each dictionary belongs to one baseline sample. """ validate_node(causal_model.graph, target_node) causal_model = InvertibleStructuralCausalModel(node_connected_subgraph_view(causal_model.graph, target_node)) feature_samples, tmp_noise_feature_samples = noise_samples_of_ancestors( causal_model, target_node, num_noise_feature_samples ) if has_categorical(feature_samples.to_numpy()): raise ValueError( "The current implementation requires all variables to be numeric, i.e., non-categorical! " "There is at least one node in the graph that is categorical." ) if noise_feature_samples is None: noise_feature_samples = tmp_noise_feature_samples if subset_scoring_func is None: subset_scoring_func = means_difference shapley_vales = feature_relevance_sample( _get_icc_noise_function( causal_model, target_node, "exact", noise_feature_samples, noise_feature_samples.columns, None, None, False ), feature_samples=noise_feature_samples.to_numpy(), baseline_samples=compute_noise_from_data(causal_model, baseline_samples)[ noise_feature_samples.columns ].to_numpy(), subset_scoring_func=subset_scoring_func, max_batch_size=max_batch_size, shapley_config=shapley_config, ) return [ {(predecessor, target_node): shapley_vales[i][q] for q, predecessor in enumerate(noise_feature_samples.columns)} for i in range(shapley_vales.shape[0]) ] def _estimate_iccs( attribution_func: Callable[[np.ndarray, np.ndarray], float], prediction_method: Callable[[np.ndarray], np.ndarray], noise_samples: np.ndarray, baseline_noise_samples: np.ndarray, max_batch_size: int, shapley_config: ShapleyConfig, ): target_values = shape_into_2d(prediction_method(baseline_noise_samples)) def icc_set_function(subset: np.ndarray) -> Union[np.ndarray, float]: if np.all(subset == 1): # In case of the full subset (no randomization), we get the same predictions as when we apply the # prediction method to the samples of interest, since all noise samples are replaced with a sample of # interest. predictions = target_values elif np.all(subset == 0): # In case of the empty subset (all are jointly randomize), it boils down to taking the average over all # predictions, seeing that the randomization yields the same values for each sample of interest (none of the # samples of interest are used to replace a (jointly) 'randomized' sample). predictions = repmat(np.mean(prediction_method(noise_samples), axis=0), baseline_noise_samples.shape[0], 1) else: predictions = marginal_expectation( prediction_method, feature_samples=noise_samples, baseline_samples=baseline_noise_samples, baseline_feature_indices=np.arange(0, noise_samples.shape[1])[subset == 1], return_averaged_results=True, feature_perturbation="randomize_columns_jointly", max_batch_size=max_batch_size, ) return attribution_func(shape_into_2d(predictions), target_values) return estimate_shapley_values(icc_set_function, noise_samples.shape[1], shapley_config) def _get_icc_noise_function( causal_model: InvertibleStructuralCausalModel, target_node: Any, prediction_model: Union[PredictionModel, ClassificationModel, str], noise_samples: np.ndarray, node_names: Iterator[Any], target_samples: np.ndarray, auto_assign_quality: auto.AssignmentQuality, target_is_categorical: bool, ) -> Callable[[np.ndarray], np.ndarray]: if isinstance(prediction_model, str) and prediction_model not in ("approx", "exact"): raise ValueError( "Invalid value for prediction_model: %s! This should either be an instance of a PredictionModel or" "one of the two string options 'exact' or 'approx'." % prediction_model ) if not isinstance(prediction_model, str): prediction_model.fit(noise_samples, target_samples) return prediction_model.predict if prediction_model == "approx": prediction_model = auto.select_model(noise_samples, target_samples, auto_assign_quality) prediction_model.fit(noise_samples, target_samples) if target_is_categorical: return prediction_model.predict_probabilities else: return prediction_model.predict else: # Exact model def exact_model(X: np.ndarray) -> np.ndarray: return compute_data_from_noise(causal_model, pd.DataFrame(X, columns=[x for x in node_names]))[ target_node ].to_numpy() if target_is_categorical: list_of_classes = cast(ClassifierFCM, causal_model.causal_mechanism(target_node)).classifier_model.classes def prediction_method(X): return (shape_into_2d(exact_model(X)) == list_of_classes).astype(float) return prediction_method else: return exact_model

bloebp

45046e141ebd701820ca8a692c8f03ed5dce314c

e734f4e6245d404218b0ba11b14f7cc621bec7a0

directly return this.

petergtz

py-why/dowhy

Add method to estimate ICC for single samples

Before, it was only possible to estimate intrinsic causal contributions with respect to the distribution. This new method allows to estimate the influences of upstream nodes on a particular observation of a target node. As for now, this requires that all models are invertible with respect to the noise.

2023-03-29 21:42:31+00:00

2023-03-31 13:39:16+00:00

dowhy/gcm/influence.py

"""This module provides functions to estimate causal influences. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging import warnings from typing import Any, Callable, Dict, List, Optional, Tuple, Union, cast import numpy as np import pandas as pd from joblib import Parallel, delayed from numpy.matlib import repmat import dowhy.gcm.auto as auto from dowhy.gcm._noise import compute_data_from_noise, noise_samples_of_ancestors from dowhy.gcm.cms import ProbabilisticCausalModel, StructuralCausalModel from dowhy.gcm.divergence import estimate_kl_divergence_of_probabilities from dowhy.gcm.fcms import ClassificationModel, ClassifierFCM, PredictionModel, ProbabilityEstimatorModel from dowhy.gcm.fitting_sampling import draw_samples from dowhy.gcm.graph import ( ConditionalStochasticModel, get_ordered_predecessors, is_root_node, node_connected_subgraph_view, validate_causal_dag, validate_node, ) from dowhy.gcm.shapley import ShapleyConfig, estimate_shapley_values from dowhy.gcm.stats import marginal_expectation from dowhy.gcm.uncertainty import estimate_entropy_of_probabilities, estimate_variance from dowhy.gcm.util.general import is_categorical, set_random_seed, shape_into_2d _logger = logging.getLogger(__name__) def arrow_strength( causal_model: ProbabilisticCausalModel, target_node: Any, parent_samples: Optional[pd.DataFrame] = None, num_samples_conditional: int = 2000, max_num_runs: int = 5000, tolerance: float = 0.01, n_jobs: int = -1, difference_estimation_func: Optional[Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]]] = None, ) -> Dict[Tuple[Any, Any], float]: """Computes the causal strength of each edge directed to the target node. The strength of an edge is quantified in terms of distance between conditional distributions of the target node in the original graph and the imputed graph wherein the edge has been removed and the target node is fed a random permutation of the observations of the source node. For more scientific details behind this API, please refer to the research paper below. **Research Paper**: Dominik Janzing, David Balduzzi, Moritz Grosse-Wentrup, Bernhard Schölkopf. *Quantifying Causal Influences*. The Annals of Statistics, Vol. 41, No. 5, 2324-2358, 2013. :param causal_model: The probabilistic causal model for whose target node we compute the strength of incoming edges for. :param target_node: The target node whose incoming edges' strength is to be computed. :param parent_samples: Optional samples from the parents of the target_node. If None are given, they are generated based on the provided causal model. Providing observational data can help to mitigate misspecifications in the graph, such as missing interactions between root nodes or confounders. :param num_samples_conditional: Sample size to use for estimating the distance between distributions. The more more samples, the higher the accuracy. :param max_num_runs: The maximum number of times to resample and estimate the strength to report the average strength. :param tolerance: If the percentage change in the estimated strength between two consecutive runs falls below the specified tolerance, the algorithm will terminate before reaching the maximum number of runs. A value of 0.01 would indicate a change of less than 1%. However, in order to minimize the impact of randomness, there must be at least three consecutive runs where the change is below the threshold. :param n_jobs: The number of jobs to run in parallel. Set it to -1 to use all processors. :param difference_estimation_func: Optional: How to measure the distance between two distributions. By default, the difference of the variance is estimated for a continuous target node and the KL divergence for a categorical target node. :return: Causal strength of each edge. """ if target_node not in causal_model.graph.nodes: raise ValueError("Target node %s can not be found in given graph!" % target_node) if is_root_node(causal_model.graph, target_node): raise ValueError("Target node %s is a root node, but it requires to have ancestors!" % target_node) # Creating a smaller subgraph, which only contains upstream nodes that are connected to the target node. sub_causal_model = ProbabilisticCausalModel(node_connected_subgraph_view(causal_model.graph, target_node)) validate_node(sub_causal_model.graph, target_node) ordered_predecessors = get_ordered_predecessors(sub_causal_model.graph, target_node) if parent_samples is None: parent_samples = draw_samples(sub_causal_model, num_samples_conditional * 20)[ordered_predecessors] direct_influences = arrow_strength_of_model( sub_causal_model.causal_mechanism(target_node), parent_samples[ordered_predecessors].to_numpy(), num_samples_from_conditional=num_samples_conditional, max_num_runs=max_num_runs, tolerance=tolerance, n_jobs=n_jobs, difference_estimation_func=difference_estimation_func, ) return {(predecessor, target_node): direct_influences[i] for i, predecessor in enumerate(ordered_predecessors)} def arrow_strength_of_model( conditional_stochastic_model: ConditionalStochasticModel, input_samples: np.ndarray, num_samples_from_conditional: int = 2000, max_num_runs: int = 5000, tolerance: float = 0.01, n_jobs: int = -1, difference_estimation_func: Optional[Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]]] = None, input_subsets: Optional[List[List[int]]] = None, ) -> np.ndarray: input_samples = shape_into_2d(input_samples) if input_subsets is None: input_subsets = [[i] for i in range(input_samples.shape[1])] if difference_estimation_func is None: if isinstance(conditional_stochastic_model, ProbabilityEstimatorModel): difference_estimation_func = estimate_kl_divergence_of_probabilities else: def difference_estimation_func(old, new): return np.var(new) - np.var(old) if isinstance(conditional_stochastic_model, ProbabilityEstimatorModel): samples_creation_method = conditional_stochastic_model.estimate_probabilities else: samples_creation_method = conditional_stochastic_model.draw_samples with warnings.catch_warnings(): warnings.filterwarnings("ignore") def parallel_job(subset: List[int], parallel_random_seed: int): set_random_seed(parallel_random_seed) return _estimate_direct_strength( samples_creation_method, input_samples, subset, difference_estimation_func, num_samples_from_conditional, max_num_runs, tolerance, ) random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(input_subsets)) results = Parallel(n_jobs=n_jobs)( delayed(parallel_job)(subset, random_seed) for subset, random_seed in zip(input_subsets, random_seeds) ) if np.any(results == np.inf): _logger.warning( "At least one arrow strength is infinite. This typically happens if the causal models are " "deterministic, i.e. there is no noise or it is extremely small." ) return np.array(results) def _estimate_direct_strength( draw_samples_func: Callable[[np.ndarray], np.ndarray], distribution_samples: np.ndarray, parents_subset: List[int], difference_estimation_func: Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]], num_samples_conditional: int, max_num_runs: int, tolerance: float, ) -> float: distribution_samples = shape_into_2d(distribution_samples) num_samples_conditional = min(num_samples_conditional, distribution_samples.shape[0]) aggregated_conditional_difference_result = 0 average_difference_result = 0 converged_run = 0 for run, sample in enumerate(distribution_samples): tmp_samples = repmat(sample, num_samples_conditional, 1) rnd_permutation = np.random.choice(distribution_samples.shape[0], num_samples_conditional, replace=False) # Sampling from the conditional distribution based on the current sample. conditional_distribution_samples = draw_samples_func(tmp_samples) # Sampling from the conditional based on the current sample, but randomizing the inputs of all variables that # are in the given subset. By this, we can simulate the impact on the conditional distribution when removing # only the incoming edges of the variables in the subset. tmp_samples[:, parents_subset] = distribution_samples[:, parents_subset][rnd_permutation] cond_dist_removed_arr_samples = draw_samples_func(tmp_samples) old_average_difference_result = average_difference_result aggregated_conditional_difference_result += difference_estimation_func( conditional_distribution_samples, cond_dist_removed_arr_samples ) average_difference_result = aggregated_conditional_difference_result / (run + 1) if run >= max_num_runs: break elif run > 0: if old_average_difference_result == 0: converging = average_difference_result == 0 else: converging = abs(1 - average_difference_result / old_average_difference_result) < tolerance if converging: converged_run += 1 if converged_run >= 3: break else: converged_run = 0 return average_difference_result def intrinsic_causal_influence( causal_model: StructuralCausalModel, target_node: Any, prediction_model: Union[PredictionModel, ClassificationModel, str] = "approx", attribution_func: Optional[Callable[[np.ndarray, np.ndarray], float]] = None, num_training_samples: int = 100000, num_samples_randomization: int = 1000, num_samples_baseline: int = 1000, max_batch_size: int = 250, auto_assign_quality: auto.AssignmentQuality = auto.AssignmentQuality.GOOD, shapley_config: Optional[ShapleyConfig] = None, ) -> Dict[Any, float]: """Computes the causal contribution of each upstream noise term of the target node (including the noise of the target itself) to the statistical property (e.g. mean, variance) of the target. We call this contribution *intrinsic* as noise terms, by definition, do not inherit properties of observed parents. The contribution of each noise term is then the *intrinsic* causal contribution of the corresponding node. For more scientific details, please refer to the paper below. **Research Paper**: Janzing et al. *Quantifying causal contributions via structure preserving interventions*. arXiv:2007.00714, 2021. :param causal_model: The structural causal model for whose target node we compute the intrinsic causal influence of its ancestors. :param target_node: Target node whose statistical property is to be attributed. :param prediction_model: Prediction model for estimating the functional relationship between subsets of ancestor noise terms and the target node. This can be an instance of a PredictionModel, the string 'approx' or the string 'exact'. With 'exact', the underlying causal models in the graph are utilized directly by propagating given noise inputs through the graph. This is generally more accurate but slow. With 'approx', an appropriate model is selected and trained based on sampled data from the graph, which is less accurate but faster. A more detailed treatment on why we need this parameter is also provided in :ref:`icc`. :param attribution_func: Optional attribution function to measure the statistical property of the target node. This function expects two inputs; predictions after the randomization of certain features (i.e. samples from noise nodes) and a baseline where no features were randomized. The baseline predictions can be typically ignored if one is interested in uncertainty measures such as entropy or variance, but they might be relevant if, for instance, these shall be estimated based on the residuals. By default, entropy is used if prediction model is a classifier, variance otherwise. :param num_training_samples: Number of samples drawn from the graphical causal model that are used for fitting the prediction_model (if necessary). :param num_samples_randomization: Number of noise samples drawn from the graphical causal model that are used for evaluating the set function. Here, these samples are samples from the noise distributions used for randomizing features that are not in the subset. :param num_samples_baseline: Number of noise samples drawn from the graphical causal model that are used for evaluating the set function. Here, these samples are used as fixed observations for features that are in the subset. :param max_batch_size: Maximum batch size for estimating the predictions from evaluation samples. This has a significant impact on the overall memory usage. If set to -1, all samples are used in one batch. :param auto_assign_quality: Auto assign quality for the 'approx' prediction_model option. :param shapley_config: :class:`~dowhy.gcm.shapley.ShapleyConfig` for the Shapley estimator. :return: Intrinsic causal contribution of each ancestor node to the statistical property defined by the attribution_func of the target node. """ validate_causal_dag(causal_model.graph) # Creating a smaller subgraph, which only contains upstream nodes that are connected to the target node. sub_causal_model = StructuralCausalModel(node_connected_subgraph_view(causal_model.graph, target_node)) data_samples, noise_samples = noise_samples_of_ancestors(sub_causal_model, target_node, num_training_samples) node_names = noise_samples.columns noise_samples, target_samples = shape_into_2d(noise_samples.to_numpy(), data_samples[target_node].to_numpy()) target_is_categorical = is_categorical(data_samples[target_node].to_numpy()) if prediction_model == "approx": prediction_model = auto.select_model(noise_samples, target_samples, auto_assign_quality) prediction_model.fit(noise_samples, target_samples) if target_is_categorical: prediction_method = prediction_model.predict_probabilities else: prediction_method = prediction_model.predict elif prediction_model == "exact": def exact_model(X: np.ndarray) -> np.ndarray: return compute_data_from_noise(sub_causal_model, pd.DataFrame(X, columns=[x for x in node_names]))[ target_node ].to_numpy() if target_is_categorical: list_of_classes = cast( ClassifierFCM, sub_causal_model.causal_mechanism(target_node) ).classifier_model.classes def prediction_method(X): return (shape_into_2d(exact_model(X)) == list_of_classes).astype(float) else: prediction_method = exact_model elif isinstance(prediction_model, str): raise ValueError( "Invalid value for prediction_model: %s! This should either be an instance of a PredictionModel or" "one of the two string options 'exact' or 'approx'." % prediction_model ) else: prediction_model.fit(noise_samples, target_samples) prediction_method = prediction_model.predict if attribution_func is None: if target_is_categorical: def attribution_func(x, _): return -estimate_entropy_of_probabilities(x) else: def attribution_func(x, _): return estimate_variance(x) _, noise_samples = noise_samples_of_ancestors( sub_causal_model, target_node, num_samples_randomization + num_samples_baseline ) noise_samples = shape_into_2d(noise_samples.to_numpy()) iccs = _estimate_iccs( attribution_func, prediction_method, noise_samples[:num_samples_randomization], noise_samples[num_samples_randomization : num_samples_randomization + num_samples_baseline], max_batch_size, ShapleyConfig() if shapley_config is None else shapley_config, ) return {node: iccs[i] for i, node in enumerate(node_names)} def _estimate_iccs( attribution_func: Callable[[np.ndarray, np.ndarray], float], prediction_method: Callable[[np.ndarray], np.ndarray], noise_samples: np.ndarray, baseline_noise_samples: np.ndarray, max_batch_size: int, shapley_config: ShapleyConfig, ): target_values = shape_into_2d(prediction_method(baseline_noise_samples)) def icc_set_function(subset: np.ndarray) -> Union[np.ndarray, float]: if np.all(subset == 1): # In case of the full subset (no randomization), we get the same predictions as when we apply the # prediction method to the samples of interest, since all noise samples are replaced with a sample of # interest. predictions = target_values elif np.all(subset == 0): # In case of the empty subset (all are jointly randomize), it boils down to taking the average over all # predictions, seeing that the randomization yields the same values for each sample of interest (none of the # samples of interest are used to replace a (jointly) 'randomized' sample). predictions = repmat(np.mean(prediction_method(noise_samples), axis=0), baseline_noise_samples.shape[0], 1) else: predictions = marginal_expectation( prediction_method, feature_samples=noise_samples, baseline_samples=baseline_noise_samples, baseline_feature_indices=np.arange(0, noise_samples.shape[1])[subset == 1], return_averaged_results=True, feature_perturbation="randomize_columns_jointly", max_batch_size=max_batch_size, ) return attribution_func(shape_into_2d(predictions), target_values) return estimate_shapley_values(icc_set_function, noise_samples.shape[1], shapley_config)

"""This module provides functions to estimate causal influences. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging import warnings from typing import Any, Callable, Dict, Iterator, List, Optional, Tuple, Union, cast import numpy as np import pandas as pd from joblib import Parallel, delayed from numpy.matlib import repmat import dowhy.gcm.auto as auto from dowhy.gcm import feature_relevance_sample from dowhy.gcm._noise import compute_data_from_noise, compute_noise_from_data, noise_samples_of_ancestors from dowhy.gcm.cms import InvertibleStructuralCausalModel, ProbabilisticCausalModel, StructuralCausalModel from dowhy.gcm.divergence import estimate_kl_divergence_of_probabilities from dowhy.gcm.fcms import ClassificationModel, ClassifierFCM, PredictionModel, ProbabilityEstimatorModel from dowhy.gcm.fitting_sampling import draw_samples from dowhy.gcm.graph import ( ConditionalStochasticModel, get_ordered_predecessors, is_root_node, node_connected_subgraph_view, validate_causal_dag, validate_node, ) from dowhy.gcm.shapley import ShapleyConfig, estimate_shapley_values from dowhy.gcm.stats import marginal_expectation from dowhy.gcm.uncertainty import estimate_entropy_of_probabilities, estimate_variance from dowhy.gcm.util.general import has_categorical, is_categorical, means_difference, set_random_seed, shape_into_2d _logger = logging.getLogger(__name__) def arrow_strength( causal_model: ProbabilisticCausalModel, target_node: Any, parent_samples: Optional[pd.DataFrame] = None, num_samples_conditional: int = 2000, max_num_runs: int = 5000, tolerance: float = 0.01, n_jobs: int = -1, difference_estimation_func: Optional[Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]]] = None, ) -> Dict[Tuple[Any, Any], float]: """Computes the causal strength of each edge directed to the target node. The strength of an edge is quantified in terms of distance between conditional distributions of the target node in the original graph and the imputed graph wherein the edge has been removed and the target node is fed a random permutation of the observations of the source node. For more scientific details behind this API, please refer to the research paper below. **Research Paper**: Dominik Janzing, David Balduzzi, Moritz Grosse-Wentrup, Bernhard Schölkopf. *Quantifying Causal Influences*. The Annals of Statistics, Vol. 41, No. 5, 2324-2358, 2013. :param causal_model: The probabilistic causal model for whose target node we compute the strength of incoming edges for. :param target_node: The target node whose incoming edges' strength is to be computed. :param parent_samples: Optional samples from the parents of the target_node. If None are given, they are generated based on the provided causal model. Providing observational data can help to mitigate misspecifications in the graph, such as missing interactions between root nodes or confounders. :param num_samples_conditional: Sample size to use for estimating the distance between distributions. The more more samples, the higher the accuracy. :param max_num_runs: The maximum number of times to resample and estimate the strength to report the average strength. :param tolerance: If the percentage change in the estimated strength between two consecutive runs falls below the specified tolerance, the algorithm will terminate before reaching the maximum number of runs. A value of 0.01 would indicate a change of less than 1%. However, in order to minimize the impact of randomness, there must be at least three consecutive runs where the change is below the threshold. :param n_jobs: The number of jobs to run in parallel. Set it to -1 to use all processors. :param difference_estimation_func: Optional: How to measure the distance between two distributions. By default, the difference of the variance is estimated for a continuous target node and the KL divergence for a categorical target node. :return: Causal strength of each edge. """ if target_node not in causal_model.graph.nodes: raise ValueError("Target node %s can not be found in given graph!" % target_node) if is_root_node(causal_model.graph, target_node): raise ValueError("Target node %s is a root node, but it requires to have ancestors!" % target_node) # Creating a smaller subgraph, which only contains upstream nodes that are connected to the target node. sub_causal_model = ProbabilisticCausalModel(node_connected_subgraph_view(causal_model.graph, target_node)) validate_node(sub_causal_model.graph, target_node) ordered_predecessors = get_ordered_predecessors(sub_causal_model.graph, target_node) if parent_samples is None: parent_samples = draw_samples(sub_causal_model, num_samples_conditional * 20)[ordered_predecessors] direct_influences = arrow_strength_of_model( sub_causal_model.causal_mechanism(target_node), parent_samples[ordered_predecessors].to_numpy(), num_samples_from_conditional=num_samples_conditional, max_num_runs=max_num_runs, tolerance=tolerance, n_jobs=n_jobs, difference_estimation_func=difference_estimation_func, ) return {(predecessor, target_node): direct_influences[i] for i, predecessor in enumerate(ordered_predecessors)} def arrow_strength_of_model( conditional_stochastic_model: ConditionalStochasticModel, input_samples: np.ndarray, num_samples_from_conditional: int = 2000, max_num_runs: int = 5000, tolerance: float = 0.01, n_jobs: int = -1, difference_estimation_func: Optional[Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]]] = None, input_subsets: Optional[List[List[int]]] = None, ) -> np.ndarray: input_samples = shape_into_2d(input_samples) if input_subsets is None: input_subsets = [[i] for i in range(input_samples.shape[1])] if difference_estimation_func is None: if isinstance(conditional_stochastic_model, ProbabilityEstimatorModel): difference_estimation_func = estimate_kl_divergence_of_probabilities else: def difference_estimation_func(old, new): return np.var(new) - np.var(old) if isinstance(conditional_stochastic_model, ProbabilityEstimatorModel): samples_creation_method = conditional_stochastic_model.estimate_probabilities else: samples_creation_method = conditional_stochastic_model.draw_samples with warnings.catch_warnings(): warnings.filterwarnings("ignore") def parallel_job(subset: List[int], parallel_random_seed: int): set_random_seed(parallel_random_seed) return _estimate_direct_strength( samples_creation_method, input_samples, subset, difference_estimation_func, num_samples_from_conditional, max_num_runs, tolerance, ) random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(input_subsets)) results = Parallel(n_jobs=n_jobs)( delayed(parallel_job)(subset, random_seed) for subset, random_seed in zip(input_subsets, random_seeds) ) if np.any(results == np.inf): _logger.warning( "At least one arrow strength is infinite. This typically happens if the causal models are " "deterministic, i.e. there is no noise or it is extremely small." ) return np.array(results) def _estimate_direct_strength( draw_samples_func: Callable[[np.ndarray], np.ndarray], distribution_samples: np.ndarray, parents_subset: List[int], difference_estimation_func: Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]], num_samples_conditional: int, max_num_runs: int, tolerance: float, ) -> float: distribution_samples = shape_into_2d(distribution_samples) num_samples_conditional = min(num_samples_conditional, distribution_samples.shape[0]) aggregated_conditional_difference_result = 0 average_difference_result = 0 converged_run = 0 for run, sample in enumerate(distribution_samples): tmp_samples = repmat(sample, num_samples_conditional, 1) rnd_permutation = np.random.choice(distribution_samples.shape[0], num_samples_conditional, replace=False) # Sampling from the conditional distribution based on the current sample. conditional_distribution_samples = draw_samples_func(tmp_samples) # Sampling from the conditional based on the current sample, but randomizing the inputs of all variables that # are in the given subset. By this, we can simulate the impact on the conditional distribution when removing # only the incoming edges of the variables in the subset. tmp_samples[:, parents_subset] = distribution_samples[:, parents_subset][rnd_permutation] cond_dist_removed_arr_samples = draw_samples_func(tmp_samples) old_average_difference_result = average_difference_result aggregated_conditional_difference_result += difference_estimation_func( conditional_distribution_samples, cond_dist_removed_arr_samples ) average_difference_result = aggregated_conditional_difference_result / (run + 1) if run >= max_num_runs: break elif run > 0: if old_average_difference_result == 0: converging = average_difference_result == 0 else: converging = abs(1 - average_difference_result / old_average_difference_result) < tolerance if converging: converged_run += 1 if converged_run >= 3: break else: converged_run = 0 return average_difference_result def intrinsic_causal_influence( causal_model: StructuralCausalModel, target_node: Any, prediction_model: Union[PredictionModel, ClassificationModel, str] = "approx", attribution_func: Optional[Callable[[np.ndarray, np.ndarray], float]] = None, num_training_samples: int = 100000, num_samples_randomization: int = 1000, num_samples_baseline: int = 1000, max_batch_size: int = 250, auto_assign_quality: auto.AssignmentQuality = auto.AssignmentQuality.GOOD, shapley_config: Optional[ShapleyConfig] = None, ) -> Dict[Any, float]: """Computes the causal contribution of each upstream noise term of the target node (including the noise of the target itself) to the statistical property (e.g. mean, variance) of the target. We call this contribution *intrinsic* as noise terms, by definition, do not inherit properties of observed parents. The contribution of each noise term is then the *intrinsic* causal contribution of the corresponding node. For more scientific details, please refer to the paper below. **Research Paper**: Janzing et al. *Quantifying causal contributions via structure preserving interventions*. arXiv:2007.00714, 2021. :param causal_model: The structural causal model for whose target node we compute the intrinsic causal influence of its ancestors. :param target_node: Target node whose statistical property is to be attributed. :param prediction_model: Prediction model for estimating the functional relationship between subsets of ancestor noise terms and the target node. This can be an instance of a PredictionModel, the string 'approx' or the string 'exact'. With 'exact', the underlying causal models in the graph are utilized directly by propagating given noise inputs through the graph. This is generally more accurate but slow. With 'approx', an appropriate model is selected and trained based on sampled data from the graph, which is less accurate but faster. A more detailed treatment on why we need this parameter is also provided in :ref:`icc`. :param attribution_func: Optional attribution function to measure the statistical property of the target node. This function expects two inputs; predictions after the randomization of certain features (i.e. samples from noise nodes) and a baseline where no features were randomized. The baseline predictions can be typically ignored if one is interested in uncertainty measures such as entropy or variance, but they might be relevant if, for instance, these shall be estimated based on the residuals. By default, entropy is used if prediction model is a classifier, variance otherwise. :param num_training_samples: Number of samples drawn from the graphical causal model that are used for fitting the prediction_model (if necessary). :param num_samples_randomization: Number of noise samples drawn from the graphical causal model that are used for evaluating the set function. Here, these samples are samples from the noise distributions used for randomizing features that are not in the subset. :param num_samples_baseline: Number of noise samples drawn from the graphical causal model that are used for evaluating the set function. Here, these samples are used as fixed observations for features that are in the subset. :param max_batch_size: Maximum batch size for estimating the predictions from evaluation samples. This has a significant impact on the overall memory usage. If set to -1, all samples are used in one batch. :param auto_assign_quality: Auto assign quality for the 'approx' prediction_model option. :param shapley_config: :class:`~dowhy.gcm.shapley.ShapleyConfig` for the Shapley estimator. :return: Intrinsic causal contribution of each ancestor node to the statistical property defined by the attribution_func of the target node. """ validate_causal_dag(causal_model.graph) # Creating a smaller subgraph, which only contains upstream nodes that are connected to the target node. sub_causal_model = StructuralCausalModel(node_connected_subgraph_view(causal_model.graph, target_node)) data_samples, noise_samples = noise_samples_of_ancestors(sub_causal_model, target_node, num_training_samples) node_names = noise_samples.columns noise_samples, target_samples = shape_into_2d(noise_samples.to_numpy(), data_samples[target_node].to_numpy()) target_is_categorical = is_categorical(data_samples[target_node].to_numpy()) prediction_method = _get_icc_noise_function( causal_model, target_node, prediction_model, noise_samples, node_names, target_samples, auto_assign_quality, target_is_categorical, ) if attribution_func is None: if target_is_categorical: def attribution_func(x, _): return -estimate_entropy_of_probabilities(x) else: def attribution_func(x, _): return estimate_variance(x) _, noise_samples = noise_samples_of_ancestors( sub_causal_model, target_node, num_samples_randomization + num_samples_baseline ) noise_samples = shape_into_2d(noise_samples.to_numpy()) iccs = _estimate_iccs( attribution_func, prediction_method, noise_samples[:num_samples_randomization], noise_samples[num_samples_randomization : num_samples_randomization + num_samples_baseline], max_batch_size, ShapleyConfig() if shapley_config is None else shapley_config, ) return {node: iccs[i] for i, node in enumerate(node_names)} def intrinsic_causal_influence_sample( causal_model: InvertibleStructuralCausalModel, target_node: Any, baseline_samples: pd.DataFrame, noise_feature_samples: Optional[pd.DataFrame] = None, subset_scoring_func: Optional[Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]]] = None, num_noise_feature_samples: int = 5000, max_batch_size: int = 100, shapley_config: Optional[ShapleyConfig] = None, ) -> List[Dict[Any, Any]]: """Estimates the intrinsic causal impact of upstream nodes on a specified target_node, using the provided baseline_samples as a reference. In this context, observed values are attributed to the noise factors present in upstream nodes. Compared to intrinsic_causal_influence, this method quantifies the influences with respect to single observations instead of the distribution. Note that the current implementation only supports non-categorical data, since the noise terms need to be reconstructed. **Research Paper**: Janzing et al. *Quantifying causal contributions via structure preserving interventions*. arXiv:2007.00714, 2021. :param causal_model: The fitted invertible structural causal model. :param target_node: Node of interest. :param baseline_samples: Samples for which the influence should be estimated. :param noise_feature_samples: Optional noise samples of upstream nodes used as 'background' samples.. If None is given, new noise samples are generated based on the graph. These samples are used for randomizing features that are not in the subset. :param subset_scoring_func: Set function for estimating the quantity of interest based. This function expects two inputs; the outcome of the model for some samples if certain features are permuted and the outcome of the model for the same samples when no features were permuted. By default, the difference between means of these samples are estimated. :param num_noise_feature_samples: If no noise_feature_samples are given, noise samples are drawn from the graph. This parameter indicates how many. :param max_batch_size: Maximum batch size for estimating multiple predictions at once. This has a significant influence on the overall memory usage. If set to -1, all samples are used in one batch. :param shapley_config: :class:`~dowhy.gcm.shapley.ShapleyConfig` for the Shapley estimator. :return: A list of dictionaries indicating the intrinsic causal influence of a node on the target for a particular sample. This is, each dictionary belongs to one baseline sample. """ validate_node(causal_model.graph, target_node) causal_model = InvertibleStructuralCausalModel(node_connected_subgraph_view(causal_model.graph, target_node)) feature_samples, tmp_noise_feature_samples = noise_samples_of_ancestors( causal_model, target_node, num_noise_feature_samples ) if has_categorical(feature_samples.to_numpy()): raise ValueError( "The current implementation requires all variables to be numeric, i.e., non-categorical! " "There is at least one node in the graph that is categorical." ) if noise_feature_samples is None: noise_feature_samples = tmp_noise_feature_samples if subset_scoring_func is None: subset_scoring_func = means_difference shapley_vales = feature_relevance_sample( _get_icc_noise_function( causal_model, target_node, "exact", noise_feature_samples, noise_feature_samples.columns, None, None, False ), feature_samples=noise_feature_samples.to_numpy(), baseline_samples=compute_noise_from_data(causal_model, baseline_samples)[ noise_feature_samples.columns ].to_numpy(), subset_scoring_func=subset_scoring_func, max_batch_size=max_batch_size, shapley_config=shapley_config, ) return [ {(predecessor, target_node): shapley_vales[i][q] for q, predecessor in enumerate(noise_feature_samples.columns)} for i in range(shapley_vales.shape[0]) ] def _estimate_iccs( attribution_func: Callable[[np.ndarray, np.ndarray], float], prediction_method: Callable[[np.ndarray], np.ndarray], noise_samples: np.ndarray, baseline_noise_samples: np.ndarray, max_batch_size: int, shapley_config: ShapleyConfig, ): target_values = shape_into_2d(prediction_method(baseline_noise_samples)) def icc_set_function(subset: np.ndarray) -> Union[np.ndarray, float]: if np.all(subset == 1): # In case of the full subset (no randomization), we get the same predictions as when we apply the # prediction method to the samples of interest, since all noise samples are replaced with a sample of # interest. predictions = target_values elif np.all(subset == 0): # In case of the empty subset (all are jointly randomize), it boils down to taking the average over all # predictions, seeing that the randomization yields the same values for each sample of interest (none of the # samples of interest are used to replace a (jointly) 'randomized' sample). predictions = repmat(np.mean(prediction_method(noise_samples), axis=0), baseline_noise_samples.shape[0], 1) else: predictions = marginal_expectation( prediction_method, feature_samples=noise_samples, baseline_samples=baseline_noise_samples, baseline_feature_indices=np.arange(0, noise_samples.shape[1])[subset == 1], return_averaged_results=True, feature_perturbation="randomize_columns_jointly", max_batch_size=max_batch_size, ) return attribution_func(shape_into_2d(predictions), target_values) return estimate_shapley_values(icc_set_function, noise_samples.shape[1], shapley_config) def _get_icc_noise_function( causal_model: InvertibleStructuralCausalModel, target_node: Any, prediction_model: Union[PredictionModel, ClassificationModel, str], noise_samples: np.ndarray, node_names: Iterator[Any], target_samples: np.ndarray, auto_assign_quality: auto.AssignmentQuality, target_is_categorical: bool, ) -> Callable[[np.ndarray], np.ndarray]: if isinstance(prediction_model, str) and prediction_model not in ("approx", "exact"): raise ValueError( "Invalid value for prediction_model: %s! This should either be an instance of a PredictionModel or" "one of the two string options 'exact' or 'approx'." % prediction_model ) if not isinstance(prediction_model, str): prediction_model.fit(noise_samples, target_samples) return prediction_model.predict if prediction_model == "approx": prediction_model = auto.select_model(noise_samples, target_samples, auto_assign_quality) prediction_model.fit(noise_samples, target_samples) if target_is_categorical: return prediction_model.predict_probabilities else: return prediction_model.predict else: # Exact model def exact_model(X: np.ndarray) -> np.ndarray: return compute_data_from_noise(causal_model, pd.DataFrame(X, columns=[x for x in node_names]))[ target_node ].to_numpy() if target_is_categorical: list_of_classes = cast(ClassifierFCM, causal_model.causal_mechanism(target_node)).classifier_model.classes def prediction_method(X): return (shape_into_2d(exact_model(X)) == list_of_classes).astype(float) return prediction_method else: return exact_model

bloebp

45046e141ebd701820ca8a692c8f03ed5dce314c

e734f4e6245d404218b0ba11b14f7cc621bec7a0

dito

petergtz

py-why/dowhy

Add method to estimate ICC for single samples

Before, it was only possible to estimate intrinsic causal contributions with respect to the distribution. This new method allows to estimate the influences of upstream nodes on a particular observation of a target node. As for now, this requires that all models are invertible with respect to the noise.

2023-03-29 21:42:31+00:00

2023-03-31 13:39:16+00:00

dowhy/gcm/influence.py

"""This module provides functions to estimate causal influences. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging import warnings from typing import Any, Callable, Dict, List, Optional, Tuple, Union, cast import numpy as np import pandas as pd from joblib import Parallel, delayed from numpy.matlib import repmat import dowhy.gcm.auto as auto from dowhy.gcm._noise import compute_data_from_noise, noise_samples_of_ancestors from dowhy.gcm.cms import ProbabilisticCausalModel, StructuralCausalModel from dowhy.gcm.divergence import estimate_kl_divergence_of_probabilities from dowhy.gcm.fcms import ClassificationModel, ClassifierFCM, PredictionModel, ProbabilityEstimatorModel from dowhy.gcm.fitting_sampling import draw_samples from dowhy.gcm.graph import ( ConditionalStochasticModel, get_ordered_predecessors, is_root_node, node_connected_subgraph_view, validate_causal_dag, validate_node, ) from dowhy.gcm.shapley import ShapleyConfig, estimate_shapley_values from dowhy.gcm.stats import marginal_expectation from dowhy.gcm.uncertainty import estimate_entropy_of_probabilities, estimate_variance from dowhy.gcm.util.general import is_categorical, set_random_seed, shape_into_2d _logger = logging.getLogger(__name__) def arrow_strength( causal_model: ProbabilisticCausalModel, target_node: Any, parent_samples: Optional[pd.DataFrame] = None, num_samples_conditional: int = 2000, max_num_runs: int = 5000, tolerance: float = 0.01, n_jobs: int = -1, difference_estimation_func: Optional[Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]]] = None, ) -> Dict[Tuple[Any, Any], float]: """Computes the causal strength of each edge directed to the target node. The strength of an edge is quantified in terms of distance between conditional distributions of the target node in the original graph and the imputed graph wherein the edge has been removed and the target node is fed a random permutation of the observations of the source node. For more scientific details behind this API, please refer to the research paper below. **Research Paper**: Dominik Janzing, David Balduzzi, Moritz Grosse-Wentrup, Bernhard Schölkopf. *Quantifying Causal Influences*. The Annals of Statistics, Vol. 41, No. 5, 2324-2358, 2013. :param causal_model: The probabilistic causal model for whose target node we compute the strength of incoming edges for. :param target_node: The target node whose incoming edges' strength is to be computed. :param parent_samples: Optional samples from the parents of the target_node. If None are given, they are generated based on the provided causal model. Providing observational data can help to mitigate misspecifications in the graph, such as missing interactions between root nodes or confounders. :param num_samples_conditional: Sample size to use for estimating the distance between distributions. The more more samples, the higher the accuracy. :param max_num_runs: The maximum number of times to resample and estimate the strength to report the average strength. :param tolerance: If the percentage change in the estimated strength between two consecutive runs falls below the specified tolerance, the algorithm will terminate before reaching the maximum number of runs. A value of 0.01 would indicate a change of less than 1%. However, in order to minimize the impact of randomness, there must be at least three consecutive runs where the change is below the threshold. :param n_jobs: The number of jobs to run in parallel. Set it to -1 to use all processors. :param difference_estimation_func: Optional: How to measure the distance between two distributions. By default, the difference of the variance is estimated for a continuous target node and the KL divergence for a categorical target node. :return: Causal strength of each edge. """ if target_node not in causal_model.graph.nodes: raise ValueError("Target node %s can not be found in given graph!" % target_node) if is_root_node(causal_model.graph, target_node): raise ValueError("Target node %s is a root node, but it requires to have ancestors!" % target_node) # Creating a smaller subgraph, which only contains upstream nodes that are connected to the target node. sub_causal_model = ProbabilisticCausalModel(node_connected_subgraph_view(causal_model.graph, target_node)) validate_node(sub_causal_model.graph, target_node) ordered_predecessors = get_ordered_predecessors(sub_causal_model.graph, target_node) if parent_samples is None: parent_samples = draw_samples(sub_causal_model, num_samples_conditional * 20)[ordered_predecessors] direct_influences = arrow_strength_of_model( sub_causal_model.causal_mechanism(target_node), parent_samples[ordered_predecessors].to_numpy(), num_samples_from_conditional=num_samples_conditional, max_num_runs=max_num_runs, tolerance=tolerance, n_jobs=n_jobs, difference_estimation_func=difference_estimation_func, ) return {(predecessor, target_node): direct_influences[i] for i, predecessor in enumerate(ordered_predecessors)} def arrow_strength_of_model( conditional_stochastic_model: ConditionalStochasticModel, input_samples: np.ndarray, num_samples_from_conditional: int = 2000, max_num_runs: int = 5000, tolerance: float = 0.01, n_jobs: int = -1, difference_estimation_func: Optional[Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]]] = None, input_subsets: Optional[List[List[int]]] = None, ) -> np.ndarray: input_samples = shape_into_2d(input_samples) if input_subsets is None: input_subsets = [[i] for i in range(input_samples.shape[1])] if difference_estimation_func is None: if isinstance(conditional_stochastic_model, ProbabilityEstimatorModel): difference_estimation_func = estimate_kl_divergence_of_probabilities else: def difference_estimation_func(old, new): return np.var(new) - np.var(old) if isinstance(conditional_stochastic_model, ProbabilityEstimatorModel): samples_creation_method = conditional_stochastic_model.estimate_probabilities else: samples_creation_method = conditional_stochastic_model.draw_samples with warnings.catch_warnings(): warnings.filterwarnings("ignore") def parallel_job(subset: List[int], parallel_random_seed: int): set_random_seed(parallel_random_seed) return _estimate_direct_strength( samples_creation_method, input_samples, subset, difference_estimation_func, num_samples_from_conditional, max_num_runs, tolerance, ) random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(input_subsets)) results = Parallel(n_jobs=n_jobs)( delayed(parallel_job)(subset, random_seed) for subset, random_seed in zip(input_subsets, random_seeds) ) if np.any(results == np.inf): _logger.warning( "At least one arrow strength is infinite. This typically happens if the causal models are " "deterministic, i.e. there is no noise or it is extremely small." ) return np.array(results) def _estimate_direct_strength( draw_samples_func: Callable[[np.ndarray], np.ndarray], distribution_samples: np.ndarray, parents_subset: List[int], difference_estimation_func: Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]], num_samples_conditional: int, max_num_runs: int, tolerance: float, ) -> float: distribution_samples = shape_into_2d(distribution_samples) num_samples_conditional = min(num_samples_conditional, distribution_samples.shape[0]) aggregated_conditional_difference_result = 0 average_difference_result = 0 converged_run = 0 for run, sample in enumerate(distribution_samples): tmp_samples = repmat(sample, num_samples_conditional, 1) rnd_permutation = np.random.choice(distribution_samples.shape[0], num_samples_conditional, replace=False) # Sampling from the conditional distribution based on the current sample. conditional_distribution_samples = draw_samples_func(tmp_samples) # Sampling from the conditional based on the current sample, but randomizing the inputs of all variables that # are in the given subset. By this, we can simulate the impact on the conditional distribution when removing # only the incoming edges of the variables in the subset. tmp_samples[:, parents_subset] = distribution_samples[:, parents_subset][rnd_permutation] cond_dist_removed_arr_samples = draw_samples_func(tmp_samples) old_average_difference_result = average_difference_result aggregated_conditional_difference_result += difference_estimation_func( conditional_distribution_samples, cond_dist_removed_arr_samples ) average_difference_result = aggregated_conditional_difference_result / (run + 1) if run >= max_num_runs: break elif run > 0: if old_average_difference_result == 0: converging = average_difference_result == 0 else: converging = abs(1 - average_difference_result / old_average_difference_result) < tolerance if converging: converged_run += 1 if converged_run >= 3: break else: converged_run = 0 return average_difference_result def intrinsic_causal_influence( causal_model: StructuralCausalModel, target_node: Any, prediction_model: Union[PredictionModel, ClassificationModel, str] = "approx", attribution_func: Optional[Callable[[np.ndarray, np.ndarray], float]] = None, num_training_samples: int = 100000, num_samples_randomization: int = 1000, num_samples_baseline: int = 1000, max_batch_size: int = 250, auto_assign_quality: auto.AssignmentQuality = auto.AssignmentQuality.GOOD, shapley_config: Optional[ShapleyConfig] = None, ) -> Dict[Any, float]: """Computes the causal contribution of each upstream noise term of the target node (including the noise of the target itself) to the statistical property (e.g. mean, variance) of the target. We call this contribution *intrinsic* as noise terms, by definition, do not inherit properties of observed parents. The contribution of each noise term is then the *intrinsic* causal contribution of the corresponding node. For more scientific details, please refer to the paper below. **Research Paper**: Janzing et al. *Quantifying causal contributions via structure preserving interventions*. arXiv:2007.00714, 2021. :param causal_model: The structural causal model for whose target node we compute the intrinsic causal influence of its ancestors. :param target_node: Target node whose statistical property is to be attributed. :param prediction_model: Prediction model for estimating the functional relationship between subsets of ancestor noise terms and the target node. This can be an instance of a PredictionModel, the string 'approx' or the string 'exact'. With 'exact', the underlying causal models in the graph are utilized directly by propagating given noise inputs through the graph. This is generally more accurate but slow. With 'approx', an appropriate model is selected and trained based on sampled data from the graph, which is less accurate but faster. A more detailed treatment on why we need this parameter is also provided in :ref:`icc`. :param attribution_func: Optional attribution function to measure the statistical property of the target node. This function expects two inputs; predictions after the randomization of certain features (i.e. samples from noise nodes) and a baseline where no features were randomized. The baseline predictions can be typically ignored if one is interested in uncertainty measures such as entropy or variance, but they might be relevant if, for instance, these shall be estimated based on the residuals. By default, entropy is used if prediction model is a classifier, variance otherwise. :param num_training_samples: Number of samples drawn from the graphical causal model that are used for fitting the prediction_model (if necessary). :param num_samples_randomization: Number of noise samples drawn from the graphical causal model that are used for evaluating the set function. Here, these samples are samples from the noise distributions used for randomizing features that are not in the subset. :param num_samples_baseline: Number of noise samples drawn from the graphical causal model that are used for evaluating the set function. Here, these samples are used as fixed observations for features that are in the subset. :param max_batch_size: Maximum batch size for estimating the predictions from evaluation samples. This has a significant impact on the overall memory usage. If set to -1, all samples are used in one batch. :param auto_assign_quality: Auto assign quality for the 'approx' prediction_model option. :param shapley_config: :class:`~dowhy.gcm.shapley.ShapleyConfig` for the Shapley estimator. :return: Intrinsic causal contribution of each ancestor node to the statistical property defined by the attribution_func of the target node. """ validate_causal_dag(causal_model.graph) # Creating a smaller subgraph, which only contains upstream nodes that are connected to the target node. sub_causal_model = StructuralCausalModel(node_connected_subgraph_view(causal_model.graph, target_node)) data_samples, noise_samples = noise_samples_of_ancestors(sub_causal_model, target_node, num_training_samples) node_names = noise_samples.columns noise_samples, target_samples = shape_into_2d(noise_samples.to_numpy(), data_samples[target_node].to_numpy()) target_is_categorical = is_categorical(data_samples[target_node].to_numpy()) if prediction_model == "approx": prediction_model = auto.select_model(noise_samples, target_samples, auto_assign_quality) prediction_model.fit(noise_samples, target_samples) if target_is_categorical: prediction_method = prediction_model.predict_probabilities else: prediction_method = prediction_model.predict elif prediction_model == "exact": def exact_model(X: np.ndarray) -> np.ndarray: return compute_data_from_noise(sub_causal_model, pd.DataFrame(X, columns=[x for x in node_names]))[ target_node ].to_numpy() if target_is_categorical: list_of_classes = cast( ClassifierFCM, sub_causal_model.causal_mechanism(target_node) ).classifier_model.classes def prediction_method(X): return (shape_into_2d(exact_model(X)) == list_of_classes).astype(float) else: prediction_method = exact_model elif isinstance(prediction_model, str): raise ValueError( "Invalid value for prediction_model: %s! This should either be an instance of a PredictionModel or" "one of the two string options 'exact' or 'approx'." % prediction_model ) else: prediction_model.fit(noise_samples, target_samples) prediction_method = prediction_model.predict if attribution_func is None: if target_is_categorical: def attribution_func(x, _): return -estimate_entropy_of_probabilities(x) else: def attribution_func(x, _): return estimate_variance(x) _, noise_samples = noise_samples_of_ancestors( sub_causal_model, target_node, num_samples_randomization + num_samples_baseline ) noise_samples = shape_into_2d(noise_samples.to_numpy()) iccs = _estimate_iccs( attribution_func, prediction_method, noise_samples[:num_samples_randomization], noise_samples[num_samples_randomization : num_samples_randomization + num_samples_baseline], max_batch_size, ShapleyConfig() if shapley_config is None else shapley_config, ) return {node: iccs[i] for i, node in enumerate(node_names)} def _estimate_iccs( attribution_func: Callable[[np.ndarray, np.ndarray], float], prediction_method: Callable[[np.ndarray], np.ndarray], noise_samples: np.ndarray, baseline_noise_samples: np.ndarray, max_batch_size: int, shapley_config: ShapleyConfig, ): target_values = shape_into_2d(prediction_method(baseline_noise_samples)) def icc_set_function(subset: np.ndarray) -> Union[np.ndarray, float]: if np.all(subset == 1): # In case of the full subset (no randomization), we get the same predictions as when we apply the # prediction method to the samples of interest, since all noise samples are replaced with a sample of # interest. predictions = target_values elif np.all(subset == 0): # In case of the empty subset (all are jointly randomize), it boils down to taking the average over all # predictions, seeing that the randomization yields the same values for each sample of interest (none of the # samples of interest are used to replace a (jointly) 'randomized' sample). predictions = repmat(np.mean(prediction_method(noise_samples), axis=0), baseline_noise_samples.shape[0], 1) else: predictions = marginal_expectation( prediction_method, feature_samples=noise_samples, baseline_samples=baseline_noise_samples, baseline_feature_indices=np.arange(0, noise_samples.shape[1])[subset == 1], return_averaged_results=True, feature_perturbation="randomize_columns_jointly", max_batch_size=max_batch_size, ) return attribution_func(shape_into_2d(predictions), target_values) return estimate_shapley_values(icc_set_function, noise_samples.shape[1], shapley_config)

"""This module provides functions to estimate causal influences. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging import warnings from typing import Any, Callable, Dict, Iterator, List, Optional, Tuple, Union, cast import numpy as np import pandas as pd from joblib import Parallel, delayed from numpy.matlib import repmat import dowhy.gcm.auto as auto from dowhy.gcm import feature_relevance_sample from dowhy.gcm._noise import compute_data_from_noise, compute_noise_from_data, noise_samples_of_ancestors from dowhy.gcm.cms import InvertibleStructuralCausalModel, ProbabilisticCausalModel, StructuralCausalModel from dowhy.gcm.divergence import estimate_kl_divergence_of_probabilities from dowhy.gcm.fcms import ClassificationModel, ClassifierFCM, PredictionModel, ProbabilityEstimatorModel from dowhy.gcm.fitting_sampling import draw_samples from dowhy.gcm.graph import ( ConditionalStochasticModel, get_ordered_predecessors, is_root_node, node_connected_subgraph_view, validate_causal_dag, validate_node, ) from dowhy.gcm.shapley import ShapleyConfig, estimate_shapley_values from dowhy.gcm.stats import marginal_expectation from dowhy.gcm.uncertainty import estimate_entropy_of_probabilities, estimate_variance from dowhy.gcm.util.general import has_categorical, is_categorical, means_difference, set_random_seed, shape_into_2d _logger = logging.getLogger(__name__) def arrow_strength( causal_model: ProbabilisticCausalModel, target_node: Any, parent_samples: Optional[pd.DataFrame] = None, num_samples_conditional: int = 2000, max_num_runs: int = 5000, tolerance: float = 0.01, n_jobs: int = -1, difference_estimation_func: Optional[Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]]] = None, ) -> Dict[Tuple[Any, Any], float]: """Computes the causal strength of each edge directed to the target node. The strength of an edge is quantified in terms of distance between conditional distributions of the target node in the original graph and the imputed graph wherein the edge has been removed and the target node is fed a random permutation of the observations of the source node. For more scientific details behind this API, please refer to the research paper below. **Research Paper**: Dominik Janzing, David Balduzzi, Moritz Grosse-Wentrup, Bernhard Schölkopf. *Quantifying Causal Influences*. The Annals of Statistics, Vol. 41, No. 5, 2324-2358, 2013. :param causal_model: The probabilistic causal model for whose target node we compute the strength of incoming edges for. :param target_node: The target node whose incoming edges' strength is to be computed. :param parent_samples: Optional samples from the parents of the target_node. If None are given, they are generated based on the provided causal model. Providing observational data can help to mitigate misspecifications in the graph, such as missing interactions between root nodes or confounders. :param num_samples_conditional: Sample size to use for estimating the distance between distributions. The more more samples, the higher the accuracy. :param max_num_runs: The maximum number of times to resample and estimate the strength to report the average strength. :param tolerance: If the percentage change in the estimated strength between two consecutive runs falls below the specified tolerance, the algorithm will terminate before reaching the maximum number of runs. A value of 0.01 would indicate a change of less than 1%. However, in order to minimize the impact of randomness, there must be at least three consecutive runs where the change is below the threshold. :param n_jobs: The number of jobs to run in parallel. Set it to -1 to use all processors. :param difference_estimation_func: Optional: How to measure the distance between two distributions. By default, the difference of the variance is estimated for a continuous target node and the KL divergence for a categorical target node. :return: Causal strength of each edge. """ if target_node not in causal_model.graph.nodes: raise ValueError("Target node %s can not be found in given graph!" % target_node) if is_root_node(causal_model.graph, target_node): raise ValueError("Target node %s is a root node, but it requires to have ancestors!" % target_node) # Creating a smaller subgraph, which only contains upstream nodes that are connected to the target node. sub_causal_model = ProbabilisticCausalModel(node_connected_subgraph_view(causal_model.graph, target_node)) validate_node(sub_causal_model.graph, target_node) ordered_predecessors = get_ordered_predecessors(sub_causal_model.graph, target_node) if parent_samples is None: parent_samples = draw_samples(sub_causal_model, num_samples_conditional * 20)[ordered_predecessors] direct_influences = arrow_strength_of_model( sub_causal_model.causal_mechanism(target_node), parent_samples[ordered_predecessors].to_numpy(), num_samples_from_conditional=num_samples_conditional, max_num_runs=max_num_runs, tolerance=tolerance, n_jobs=n_jobs, difference_estimation_func=difference_estimation_func, ) return {(predecessor, target_node): direct_influences[i] for i, predecessor in enumerate(ordered_predecessors)} def arrow_strength_of_model( conditional_stochastic_model: ConditionalStochasticModel, input_samples: np.ndarray, num_samples_from_conditional: int = 2000, max_num_runs: int = 5000, tolerance: float = 0.01, n_jobs: int = -1, difference_estimation_func: Optional[Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]]] = None, input_subsets: Optional[List[List[int]]] = None, ) -> np.ndarray: input_samples = shape_into_2d(input_samples) if input_subsets is None: input_subsets = [[i] for i in range(input_samples.shape[1])] if difference_estimation_func is None: if isinstance(conditional_stochastic_model, ProbabilityEstimatorModel): difference_estimation_func = estimate_kl_divergence_of_probabilities else: def difference_estimation_func(old, new): return np.var(new) - np.var(old) if isinstance(conditional_stochastic_model, ProbabilityEstimatorModel): samples_creation_method = conditional_stochastic_model.estimate_probabilities else: samples_creation_method = conditional_stochastic_model.draw_samples with warnings.catch_warnings(): warnings.filterwarnings("ignore") def parallel_job(subset: List[int], parallel_random_seed: int): set_random_seed(parallel_random_seed) return _estimate_direct_strength( samples_creation_method, input_samples, subset, difference_estimation_func, num_samples_from_conditional, max_num_runs, tolerance, ) random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(input_subsets)) results = Parallel(n_jobs=n_jobs)( delayed(parallel_job)(subset, random_seed) for subset, random_seed in zip(input_subsets, random_seeds) ) if np.any(results == np.inf): _logger.warning( "At least one arrow strength is infinite. This typically happens if the causal models are " "deterministic, i.e. there is no noise or it is extremely small." ) return np.array(results) def _estimate_direct_strength( draw_samples_func: Callable[[np.ndarray], np.ndarray], distribution_samples: np.ndarray, parents_subset: List[int], difference_estimation_func: Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]], num_samples_conditional: int, max_num_runs: int, tolerance: float, ) -> float: distribution_samples = shape_into_2d(distribution_samples) num_samples_conditional = min(num_samples_conditional, distribution_samples.shape[0]) aggregated_conditional_difference_result = 0 average_difference_result = 0 converged_run = 0 for run, sample in enumerate(distribution_samples): tmp_samples = repmat(sample, num_samples_conditional, 1) rnd_permutation = np.random.choice(distribution_samples.shape[0], num_samples_conditional, replace=False) # Sampling from the conditional distribution based on the current sample. conditional_distribution_samples = draw_samples_func(tmp_samples) # Sampling from the conditional based on the current sample, but randomizing the inputs of all variables that # are in the given subset. By this, we can simulate the impact on the conditional distribution when removing # only the incoming edges of the variables in the subset. tmp_samples[:, parents_subset] = distribution_samples[:, parents_subset][rnd_permutation] cond_dist_removed_arr_samples = draw_samples_func(tmp_samples) old_average_difference_result = average_difference_result aggregated_conditional_difference_result += difference_estimation_func( conditional_distribution_samples, cond_dist_removed_arr_samples ) average_difference_result = aggregated_conditional_difference_result / (run + 1) if run >= max_num_runs: break elif run > 0: if old_average_difference_result == 0: converging = average_difference_result == 0 else: converging = abs(1 - average_difference_result / old_average_difference_result) < tolerance if converging: converged_run += 1 if converged_run >= 3: break else: converged_run = 0 return average_difference_result def intrinsic_causal_influence( causal_model: StructuralCausalModel, target_node: Any, prediction_model: Union[PredictionModel, ClassificationModel, str] = "approx", attribution_func: Optional[Callable[[np.ndarray, np.ndarray], float]] = None, num_training_samples: int = 100000, num_samples_randomization: int = 1000, num_samples_baseline: int = 1000, max_batch_size: int = 250, auto_assign_quality: auto.AssignmentQuality = auto.AssignmentQuality.GOOD, shapley_config: Optional[ShapleyConfig] = None, ) -> Dict[Any, float]: """Computes the causal contribution of each upstream noise term of the target node (including the noise of the target itself) to the statistical property (e.g. mean, variance) of the target. We call this contribution *intrinsic* as noise terms, by definition, do not inherit properties of observed parents. The contribution of each noise term is then the *intrinsic* causal contribution of the corresponding node. For more scientific details, please refer to the paper below. **Research Paper**: Janzing et al. *Quantifying causal contributions via structure preserving interventions*. arXiv:2007.00714, 2021. :param causal_model: The structural causal model for whose target node we compute the intrinsic causal influence of its ancestors. :param target_node: Target node whose statistical property is to be attributed. :param prediction_model: Prediction model for estimating the functional relationship between subsets of ancestor noise terms and the target node. This can be an instance of a PredictionModel, the string 'approx' or the string 'exact'. With 'exact', the underlying causal models in the graph are utilized directly by propagating given noise inputs through the graph. This is generally more accurate but slow. With 'approx', an appropriate model is selected and trained based on sampled data from the graph, which is less accurate but faster. A more detailed treatment on why we need this parameter is also provided in :ref:`icc`. :param attribution_func: Optional attribution function to measure the statistical property of the target node. This function expects two inputs; predictions after the randomization of certain features (i.e. samples from noise nodes) and a baseline where no features were randomized. The baseline predictions can be typically ignored if one is interested in uncertainty measures such as entropy or variance, but they might be relevant if, for instance, these shall be estimated based on the residuals. By default, entropy is used if prediction model is a classifier, variance otherwise. :param num_training_samples: Number of samples drawn from the graphical causal model that are used for fitting the prediction_model (if necessary). :param num_samples_randomization: Number of noise samples drawn from the graphical causal model that are used for evaluating the set function. Here, these samples are samples from the noise distributions used for randomizing features that are not in the subset. :param num_samples_baseline: Number of noise samples drawn from the graphical causal model that are used for evaluating the set function. Here, these samples are used as fixed observations for features that are in the subset. :param max_batch_size: Maximum batch size for estimating the predictions from evaluation samples. This has a significant impact on the overall memory usage. If set to -1, all samples are used in one batch. :param auto_assign_quality: Auto assign quality for the 'approx' prediction_model option. :param shapley_config: :class:`~dowhy.gcm.shapley.ShapleyConfig` for the Shapley estimator. :return: Intrinsic causal contribution of each ancestor node to the statistical property defined by the attribution_func of the target node. """ validate_causal_dag(causal_model.graph) # Creating a smaller subgraph, which only contains upstream nodes that are connected to the target node. sub_causal_model = StructuralCausalModel(node_connected_subgraph_view(causal_model.graph, target_node)) data_samples, noise_samples = noise_samples_of_ancestors(sub_causal_model, target_node, num_training_samples) node_names = noise_samples.columns noise_samples, target_samples = shape_into_2d(noise_samples.to_numpy(), data_samples[target_node].to_numpy()) target_is_categorical = is_categorical(data_samples[target_node].to_numpy()) prediction_method = _get_icc_noise_function( causal_model, target_node, prediction_model, noise_samples, node_names, target_samples, auto_assign_quality, target_is_categorical, ) if attribution_func is None: if target_is_categorical: def attribution_func(x, _): return -estimate_entropy_of_probabilities(x) else: def attribution_func(x, _): return estimate_variance(x) _, noise_samples = noise_samples_of_ancestors( sub_causal_model, target_node, num_samples_randomization + num_samples_baseline ) noise_samples = shape_into_2d(noise_samples.to_numpy()) iccs = _estimate_iccs( attribution_func, prediction_method, noise_samples[:num_samples_randomization], noise_samples[num_samples_randomization : num_samples_randomization + num_samples_baseline], max_batch_size, ShapleyConfig() if shapley_config is None else shapley_config, ) return {node: iccs[i] for i, node in enumerate(node_names)} def intrinsic_causal_influence_sample( causal_model: InvertibleStructuralCausalModel, target_node: Any, baseline_samples: pd.DataFrame, noise_feature_samples: Optional[pd.DataFrame] = None, subset_scoring_func: Optional[Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]]] = None, num_noise_feature_samples: int = 5000, max_batch_size: int = 100, shapley_config: Optional[ShapleyConfig] = None, ) -> List[Dict[Any, Any]]: """Estimates the intrinsic causal impact of upstream nodes on a specified target_node, using the provided baseline_samples as a reference. In this context, observed values are attributed to the noise factors present in upstream nodes. Compared to intrinsic_causal_influence, this method quantifies the influences with respect to single observations instead of the distribution. Note that the current implementation only supports non-categorical data, since the noise terms need to be reconstructed. **Research Paper**: Janzing et al. *Quantifying causal contributions via structure preserving interventions*. arXiv:2007.00714, 2021. :param causal_model: The fitted invertible structural causal model. :param target_node: Node of interest. :param baseline_samples: Samples for which the influence should be estimated. :param noise_feature_samples: Optional noise samples of upstream nodes used as 'background' samples.. If None is given, new noise samples are generated based on the graph. These samples are used for randomizing features that are not in the subset. :param subset_scoring_func: Set function for estimating the quantity of interest based. This function expects two inputs; the outcome of the model for some samples if certain features are permuted and the outcome of the model for the same samples when no features were permuted. By default, the difference between means of these samples are estimated. :param num_noise_feature_samples: If no noise_feature_samples are given, noise samples are drawn from the graph. This parameter indicates how many. :param max_batch_size: Maximum batch size for estimating multiple predictions at once. This has a significant influence on the overall memory usage. If set to -1, all samples are used in one batch. :param shapley_config: :class:`~dowhy.gcm.shapley.ShapleyConfig` for the Shapley estimator. :return: A list of dictionaries indicating the intrinsic causal influence of a node on the target for a particular sample. This is, each dictionary belongs to one baseline sample. """ validate_node(causal_model.graph, target_node) causal_model = InvertibleStructuralCausalModel(node_connected_subgraph_view(causal_model.graph, target_node)) feature_samples, tmp_noise_feature_samples = noise_samples_of_ancestors( causal_model, target_node, num_noise_feature_samples ) if has_categorical(feature_samples.to_numpy()): raise ValueError( "The current implementation requires all variables to be numeric, i.e., non-categorical! " "There is at least one node in the graph that is categorical." ) if noise_feature_samples is None: noise_feature_samples = tmp_noise_feature_samples if subset_scoring_func is None: subset_scoring_func = means_difference shapley_vales = feature_relevance_sample( _get_icc_noise_function( causal_model, target_node, "exact", noise_feature_samples, noise_feature_samples.columns, None, None, False ), feature_samples=noise_feature_samples.to_numpy(), baseline_samples=compute_noise_from_data(causal_model, baseline_samples)[ noise_feature_samples.columns ].to_numpy(), subset_scoring_func=subset_scoring_func, max_batch_size=max_batch_size, shapley_config=shapley_config, ) return [ {(predecessor, target_node): shapley_vales[i][q] for q, predecessor in enumerate(noise_feature_samples.columns)} for i in range(shapley_vales.shape[0]) ] def _estimate_iccs( attribution_func: Callable[[np.ndarray, np.ndarray], float], prediction_method: Callable[[np.ndarray], np.ndarray], noise_samples: np.ndarray, baseline_noise_samples: np.ndarray, max_batch_size: int, shapley_config: ShapleyConfig, ): target_values = shape_into_2d(prediction_method(baseline_noise_samples)) def icc_set_function(subset: np.ndarray) -> Union[np.ndarray, float]: if np.all(subset == 1): # In case of the full subset (no randomization), we get the same predictions as when we apply the # prediction method to the samples of interest, since all noise samples are replaced with a sample of # interest. predictions = target_values elif np.all(subset == 0): # In case of the empty subset (all are jointly randomize), it boils down to taking the average over all # predictions, seeing that the randomization yields the same values for each sample of interest (none of the # samples of interest are used to replace a (jointly) 'randomized' sample). predictions = repmat(np.mean(prediction_method(noise_samples), axis=0), baseline_noise_samples.shape[0], 1) else: predictions = marginal_expectation( prediction_method, feature_samples=noise_samples, baseline_samples=baseline_noise_samples, baseline_feature_indices=np.arange(0, noise_samples.shape[1])[subset == 1], return_averaged_results=True, feature_perturbation="randomize_columns_jointly", max_batch_size=max_batch_size, ) return attribution_func(shape_into_2d(predictions), target_values) return estimate_shapley_values(icc_set_function, noise_samples.shape[1], shapley_config) def _get_icc_noise_function( causal_model: InvertibleStructuralCausalModel, target_node: Any, prediction_model: Union[PredictionModel, ClassificationModel, str], noise_samples: np.ndarray, node_names: Iterator[Any], target_samples: np.ndarray, auto_assign_quality: auto.AssignmentQuality, target_is_categorical: bool, ) -> Callable[[np.ndarray], np.ndarray]: if isinstance(prediction_model, str) and prediction_model not in ("approx", "exact"): raise ValueError( "Invalid value for prediction_model: %s! This should either be an instance of a PredictionModel or" "one of the two string options 'exact' or 'approx'." % prediction_model ) if not isinstance(prediction_model, str): prediction_model.fit(noise_samples, target_samples) return prediction_model.predict if prediction_model == "approx": prediction_model = auto.select_model(noise_samples, target_samples, auto_assign_quality) prediction_model.fit(noise_samples, target_samples) if target_is_categorical: return prediction_model.predict_probabilities else: return prediction_model.predict else: # Exact model def exact_model(X: np.ndarray) -> np.ndarray: return compute_data_from_noise(causal_model, pd.DataFrame(X, columns=[x for x in node_names]))[ target_node ].to_numpy() if target_is_categorical: list_of_classes = cast(ClassifierFCM, causal_model.causal_mechanism(target_node)).classifier_model.classes def prediction_method(X): return (shape_into_2d(exact_model(X)) == list_of_classes).astype(float) return prediction_method else: return exact_model

bloebp

45046e141ebd701820ca8a692c8f03ed5dce314c

e734f4e6245d404218b0ba11b14f7cc621bec7a0

this can now be a simple if

petergtz

py-why/dowhy

Add method to estimate ICC for single samples

Before, it was only possible to estimate intrinsic causal contributions with respect to the distribution. This new method allows to estimate the influences of upstream nodes on a particular observation of a target node. As for now, this requires that all models are invertible with respect to the noise.

2023-03-29 21:42:31+00:00

2023-03-31 13:39:16+00:00

dowhy/gcm/influence.py

"""This module provides functions to estimate causal influences. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging import warnings from typing import Any, Callable, Dict, List, Optional, Tuple, Union, cast import numpy as np import pandas as pd from joblib import Parallel, delayed from numpy.matlib import repmat import dowhy.gcm.auto as auto from dowhy.gcm._noise import compute_data_from_noise, noise_samples_of_ancestors from dowhy.gcm.cms import ProbabilisticCausalModel, StructuralCausalModel from dowhy.gcm.divergence import estimate_kl_divergence_of_probabilities from dowhy.gcm.fcms import ClassificationModel, ClassifierFCM, PredictionModel, ProbabilityEstimatorModel from dowhy.gcm.fitting_sampling import draw_samples from dowhy.gcm.graph import ( ConditionalStochasticModel, get_ordered_predecessors, is_root_node, node_connected_subgraph_view, validate_causal_dag, validate_node, ) from dowhy.gcm.shapley import ShapleyConfig, estimate_shapley_values from dowhy.gcm.stats import marginal_expectation from dowhy.gcm.uncertainty import estimate_entropy_of_probabilities, estimate_variance from dowhy.gcm.util.general import is_categorical, set_random_seed, shape_into_2d _logger = logging.getLogger(__name__) def arrow_strength( causal_model: ProbabilisticCausalModel, target_node: Any, parent_samples: Optional[pd.DataFrame] = None, num_samples_conditional: int = 2000, max_num_runs: int = 5000, tolerance: float = 0.01, n_jobs: int = -1, difference_estimation_func: Optional[Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]]] = None, ) -> Dict[Tuple[Any, Any], float]: """Computes the causal strength of each edge directed to the target node. The strength of an edge is quantified in terms of distance between conditional distributions of the target node in the original graph and the imputed graph wherein the edge has been removed and the target node is fed a random permutation of the observations of the source node. For more scientific details behind this API, please refer to the research paper below. **Research Paper**: Dominik Janzing, David Balduzzi, Moritz Grosse-Wentrup, Bernhard Schölkopf. *Quantifying Causal Influences*. The Annals of Statistics, Vol. 41, No. 5, 2324-2358, 2013. :param causal_model: The probabilistic causal model for whose target node we compute the strength of incoming edges for. :param target_node: The target node whose incoming edges' strength is to be computed. :param parent_samples: Optional samples from the parents of the target_node. If None are given, they are generated based on the provided causal model. Providing observational data can help to mitigate misspecifications in the graph, such as missing interactions between root nodes or confounders. :param num_samples_conditional: Sample size to use for estimating the distance between distributions. The more more samples, the higher the accuracy. :param max_num_runs: The maximum number of times to resample and estimate the strength to report the average strength. :param tolerance: If the percentage change in the estimated strength between two consecutive runs falls below the specified tolerance, the algorithm will terminate before reaching the maximum number of runs. A value of 0.01 would indicate a change of less than 1%. However, in order to minimize the impact of randomness, there must be at least three consecutive runs where the change is below the threshold. :param n_jobs: The number of jobs to run in parallel. Set it to -1 to use all processors. :param difference_estimation_func: Optional: How to measure the distance between two distributions. By default, the difference of the variance is estimated for a continuous target node and the KL divergence for a categorical target node. :return: Causal strength of each edge. """ if target_node not in causal_model.graph.nodes: raise ValueError("Target node %s can not be found in given graph!" % target_node) if is_root_node(causal_model.graph, target_node): raise ValueError("Target node %s is a root node, but it requires to have ancestors!" % target_node) # Creating a smaller subgraph, which only contains upstream nodes that are connected to the target node. sub_causal_model = ProbabilisticCausalModel(node_connected_subgraph_view(causal_model.graph, target_node)) validate_node(sub_causal_model.graph, target_node) ordered_predecessors = get_ordered_predecessors(sub_causal_model.graph, target_node) if parent_samples is None: parent_samples = draw_samples(sub_causal_model, num_samples_conditional * 20)[ordered_predecessors] direct_influences = arrow_strength_of_model( sub_causal_model.causal_mechanism(target_node), parent_samples[ordered_predecessors].to_numpy(), num_samples_from_conditional=num_samples_conditional, max_num_runs=max_num_runs, tolerance=tolerance, n_jobs=n_jobs, difference_estimation_func=difference_estimation_func, ) return {(predecessor, target_node): direct_influences[i] for i, predecessor in enumerate(ordered_predecessors)} def arrow_strength_of_model( conditional_stochastic_model: ConditionalStochasticModel, input_samples: np.ndarray, num_samples_from_conditional: int = 2000, max_num_runs: int = 5000, tolerance: float = 0.01, n_jobs: int = -1, difference_estimation_func: Optional[Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]]] = None, input_subsets: Optional[List[List[int]]] = None, ) -> np.ndarray: input_samples = shape_into_2d(input_samples) if input_subsets is None: input_subsets = [[i] for i in range(input_samples.shape[1])] if difference_estimation_func is None: if isinstance(conditional_stochastic_model, ProbabilityEstimatorModel): difference_estimation_func = estimate_kl_divergence_of_probabilities else: def difference_estimation_func(old, new): return np.var(new) - np.var(old) if isinstance(conditional_stochastic_model, ProbabilityEstimatorModel): samples_creation_method = conditional_stochastic_model.estimate_probabilities else: samples_creation_method = conditional_stochastic_model.draw_samples with warnings.catch_warnings(): warnings.filterwarnings("ignore") def parallel_job(subset: List[int], parallel_random_seed: int): set_random_seed(parallel_random_seed) return _estimate_direct_strength( samples_creation_method, input_samples, subset, difference_estimation_func, num_samples_from_conditional, max_num_runs, tolerance, ) random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(input_subsets)) results = Parallel(n_jobs=n_jobs)( delayed(parallel_job)(subset, random_seed) for subset, random_seed in zip(input_subsets, random_seeds) ) if np.any(results == np.inf): _logger.warning( "At least one arrow strength is infinite. This typically happens if the causal models are " "deterministic, i.e. there is no noise or it is extremely small." ) return np.array(results) def _estimate_direct_strength( draw_samples_func: Callable[[np.ndarray], np.ndarray], distribution_samples: np.ndarray, parents_subset: List[int], difference_estimation_func: Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]], num_samples_conditional: int, max_num_runs: int, tolerance: float, ) -> float: distribution_samples = shape_into_2d(distribution_samples) num_samples_conditional = min(num_samples_conditional, distribution_samples.shape[0]) aggregated_conditional_difference_result = 0 average_difference_result = 0 converged_run = 0 for run, sample in enumerate(distribution_samples): tmp_samples = repmat(sample, num_samples_conditional, 1) rnd_permutation = np.random.choice(distribution_samples.shape[0], num_samples_conditional, replace=False) # Sampling from the conditional distribution based on the current sample. conditional_distribution_samples = draw_samples_func(tmp_samples) # Sampling from the conditional based on the current sample, but randomizing the inputs of all variables that # are in the given subset. By this, we can simulate the impact on the conditional distribution when removing # only the incoming edges of the variables in the subset. tmp_samples[:, parents_subset] = distribution_samples[:, parents_subset][rnd_permutation] cond_dist_removed_arr_samples = draw_samples_func(tmp_samples) old_average_difference_result = average_difference_result aggregated_conditional_difference_result += difference_estimation_func( conditional_distribution_samples, cond_dist_removed_arr_samples ) average_difference_result = aggregated_conditional_difference_result / (run + 1) if run >= max_num_runs: break elif run > 0: if old_average_difference_result == 0: converging = average_difference_result == 0 else: converging = abs(1 - average_difference_result / old_average_difference_result) < tolerance if converging: converged_run += 1 if converged_run >= 3: break else: converged_run = 0 return average_difference_result def intrinsic_causal_influence( causal_model: StructuralCausalModel, target_node: Any, prediction_model: Union[PredictionModel, ClassificationModel, str] = "approx", attribution_func: Optional[Callable[[np.ndarray, np.ndarray], float]] = None, num_training_samples: int = 100000, num_samples_randomization: int = 1000, num_samples_baseline: int = 1000, max_batch_size: int = 250, auto_assign_quality: auto.AssignmentQuality = auto.AssignmentQuality.GOOD, shapley_config: Optional[ShapleyConfig] = None, ) -> Dict[Any, float]: """Computes the causal contribution of each upstream noise term of the target node (including the noise of the target itself) to the statistical property (e.g. mean, variance) of the target. We call this contribution *intrinsic* as noise terms, by definition, do not inherit properties of observed parents. The contribution of each noise term is then the *intrinsic* causal contribution of the corresponding node. For more scientific details, please refer to the paper below. **Research Paper**: Janzing et al. *Quantifying causal contributions via structure preserving interventions*. arXiv:2007.00714, 2021. :param causal_model: The structural causal model for whose target node we compute the intrinsic causal influence of its ancestors. :param target_node: Target node whose statistical property is to be attributed. :param prediction_model: Prediction model for estimating the functional relationship between subsets of ancestor noise terms and the target node. This can be an instance of a PredictionModel, the string 'approx' or the string 'exact'. With 'exact', the underlying causal models in the graph are utilized directly by propagating given noise inputs through the graph. This is generally more accurate but slow. With 'approx', an appropriate model is selected and trained based on sampled data from the graph, which is less accurate but faster. A more detailed treatment on why we need this parameter is also provided in :ref:`icc`. :param attribution_func: Optional attribution function to measure the statistical property of the target node. This function expects two inputs; predictions after the randomization of certain features (i.e. samples from noise nodes) and a baseline where no features were randomized. The baseline predictions can be typically ignored if one is interested in uncertainty measures such as entropy or variance, but they might be relevant if, for instance, these shall be estimated based on the residuals. By default, entropy is used if prediction model is a classifier, variance otherwise. :param num_training_samples: Number of samples drawn from the graphical causal model that are used for fitting the prediction_model (if necessary). :param num_samples_randomization: Number of noise samples drawn from the graphical causal model that are used for evaluating the set function. Here, these samples are samples from the noise distributions used for randomizing features that are not in the subset. :param num_samples_baseline: Number of noise samples drawn from the graphical causal model that are used for evaluating the set function. Here, these samples are used as fixed observations for features that are in the subset. :param max_batch_size: Maximum batch size for estimating the predictions from evaluation samples. This has a significant impact on the overall memory usage. If set to -1, all samples are used in one batch. :param auto_assign_quality: Auto assign quality for the 'approx' prediction_model option. :param shapley_config: :class:`~dowhy.gcm.shapley.ShapleyConfig` for the Shapley estimator. :return: Intrinsic causal contribution of each ancestor node to the statistical property defined by the attribution_func of the target node. """ validate_causal_dag(causal_model.graph) # Creating a smaller subgraph, which only contains upstream nodes that are connected to the target node. sub_causal_model = StructuralCausalModel(node_connected_subgraph_view(causal_model.graph, target_node)) data_samples, noise_samples = noise_samples_of_ancestors(sub_causal_model, target_node, num_training_samples) node_names = noise_samples.columns noise_samples, target_samples = shape_into_2d(noise_samples.to_numpy(), data_samples[target_node].to_numpy()) target_is_categorical = is_categorical(data_samples[target_node].to_numpy()) if prediction_model == "approx": prediction_model = auto.select_model(noise_samples, target_samples, auto_assign_quality) prediction_model.fit(noise_samples, target_samples) if target_is_categorical: prediction_method = prediction_model.predict_probabilities else: prediction_method = prediction_model.predict elif prediction_model == "exact": def exact_model(X: np.ndarray) -> np.ndarray: return compute_data_from_noise(sub_causal_model, pd.DataFrame(X, columns=[x for x in node_names]))[ target_node ].to_numpy() if target_is_categorical: list_of_classes = cast( ClassifierFCM, sub_causal_model.causal_mechanism(target_node) ).classifier_model.classes def prediction_method(X): return (shape_into_2d(exact_model(X)) == list_of_classes).astype(float) else: prediction_method = exact_model elif isinstance(prediction_model, str): raise ValueError( "Invalid value for prediction_model: %s! This should either be an instance of a PredictionModel or" "one of the two string options 'exact' or 'approx'." % prediction_model ) else: prediction_model.fit(noise_samples, target_samples) prediction_method = prediction_model.predict if attribution_func is None: if target_is_categorical: def attribution_func(x, _): return -estimate_entropy_of_probabilities(x) else: def attribution_func(x, _): return estimate_variance(x) _, noise_samples = noise_samples_of_ancestors( sub_causal_model, target_node, num_samples_randomization + num_samples_baseline ) noise_samples = shape_into_2d(noise_samples.to_numpy()) iccs = _estimate_iccs( attribution_func, prediction_method, noise_samples[:num_samples_randomization], noise_samples[num_samples_randomization : num_samples_randomization + num_samples_baseline], max_batch_size, ShapleyConfig() if shapley_config is None else shapley_config, ) return {node: iccs[i] for i, node in enumerate(node_names)} def _estimate_iccs( attribution_func: Callable[[np.ndarray, np.ndarray], float], prediction_method: Callable[[np.ndarray], np.ndarray], noise_samples: np.ndarray, baseline_noise_samples: np.ndarray, max_batch_size: int, shapley_config: ShapleyConfig, ): target_values = shape_into_2d(prediction_method(baseline_noise_samples)) def icc_set_function(subset: np.ndarray) -> Union[np.ndarray, float]: if np.all(subset == 1): # In case of the full subset (no randomization), we get the same predictions as when we apply the # prediction method to the samples of interest, since all noise samples are replaced with a sample of # interest. predictions = target_values elif np.all(subset == 0): # In case of the empty subset (all are jointly randomize), it boils down to taking the average over all # predictions, seeing that the randomization yields the same values for each sample of interest (none of the # samples of interest are used to replace a (jointly) 'randomized' sample). predictions = repmat(np.mean(prediction_method(noise_samples), axis=0), baseline_noise_samples.shape[0], 1) else: predictions = marginal_expectation( prediction_method, feature_samples=noise_samples, baseline_samples=baseline_noise_samples, baseline_feature_indices=np.arange(0, noise_samples.shape[1])[subset == 1], return_averaged_results=True, feature_perturbation="randomize_columns_jointly", max_batch_size=max_batch_size, ) return attribution_func(shape_into_2d(predictions), target_values) return estimate_shapley_values(icc_set_function, noise_samples.shape[1], shapley_config)

"""This module provides functions to estimate causal influences. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging import warnings from typing import Any, Callable, Dict, Iterator, List, Optional, Tuple, Union, cast import numpy as np import pandas as pd from joblib import Parallel, delayed from numpy.matlib import repmat import dowhy.gcm.auto as auto from dowhy.gcm import feature_relevance_sample from dowhy.gcm._noise import compute_data_from_noise, compute_noise_from_data, noise_samples_of_ancestors from dowhy.gcm.cms import InvertibleStructuralCausalModel, ProbabilisticCausalModel, StructuralCausalModel from dowhy.gcm.divergence import estimate_kl_divergence_of_probabilities from dowhy.gcm.fcms import ClassificationModel, ClassifierFCM, PredictionModel, ProbabilityEstimatorModel from dowhy.gcm.fitting_sampling import draw_samples from dowhy.gcm.graph import ( ConditionalStochasticModel, get_ordered_predecessors, is_root_node, node_connected_subgraph_view, validate_causal_dag, validate_node, ) from dowhy.gcm.shapley import ShapleyConfig, estimate_shapley_values from dowhy.gcm.stats import marginal_expectation from dowhy.gcm.uncertainty import estimate_entropy_of_probabilities, estimate_variance from dowhy.gcm.util.general import has_categorical, is_categorical, means_difference, set_random_seed, shape_into_2d _logger = logging.getLogger(__name__) def arrow_strength( causal_model: ProbabilisticCausalModel, target_node: Any, parent_samples: Optional[pd.DataFrame] = None, num_samples_conditional: int = 2000, max_num_runs: int = 5000, tolerance: float = 0.01, n_jobs: int = -1, difference_estimation_func: Optional[Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]]] = None, ) -> Dict[Tuple[Any, Any], float]: """Computes the causal strength of each edge directed to the target node. The strength of an edge is quantified in terms of distance between conditional distributions of the target node in the original graph and the imputed graph wherein the edge has been removed and the target node is fed a random permutation of the observations of the source node. For more scientific details behind this API, please refer to the research paper below. **Research Paper**: Dominik Janzing, David Balduzzi, Moritz Grosse-Wentrup, Bernhard Schölkopf. *Quantifying Causal Influences*. The Annals of Statistics, Vol. 41, No. 5, 2324-2358, 2013. :param causal_model: The probabilistic causal model for whose target node we compute the strength of incoming edges for. :param target_node: The target node whose incoming edges' strength is to be computed. :param parent_samples: Optional samples from the parents of the target_node. If None are given, they are generated based on the provided causal model. Providing observational data can help to mitigate misspecifications in the graph, such as missing interactions between root nodes or confounders. :param num_samples_conditional: Sample size to use for estimating the distance between distributions. The more more samples, the higher the accuracy. :param max_num_runs: The maximum number of times to resample and estimate the strength to report the average strength. :param tolerance: If the percentage change in the estimated strength between two consecutive runs falls below the specified tolerance, the algorithm will terminate before reaching the maximum number of runs. A value of 0.01 would indicate a change of less than 1%. However, in order to minimize the impact of randomness, there must be at least three consecutive runs where the change is below the threshold. :param n_jobs: The number of jobs to run in parallel. Set it to -1 to use all processors. :param difference_estimation_func: Optional: How to measure the distance between two distributions. By default, the difference of the variance is estimated for a continuous target node and the KL divergence for a categorical target node. :return: Causal strength of each edge. """ if target_node not in causal_model.graph.nodes: raise ValueError("Target node %s can not be found in given graph!" % target_node) if is_root_node(causal_model.graph, target_node): raise ValueError("Target node %s is a root node, but it requires to have ancestors!" % target_node) # Creating a smaller subgraph, which only contains upstream nodes that are connected to the target node. sub_causal_model = ProbabilisticCausalModel(node_connected_subgraph_view(causal_model.graph, target_node)) validate_node(sub_causal_model.graph, target_node) ordered_predecessors = get_ordered_predecessors(sub_causal_model.graph, target_node) if parent_samples is None: parent_samples = draw_samples(sub_causal_model, num_samples_conditional * 20)[ordered_predecessors] direct_influences = arrow_strength_of_model( sub_causal_model.causal_mechanism(target_node), parent_samples[ordered_predecessors].to_numpy(), num_samples_from_conditional=num_samples_conditional, max_num_runs=max_num_runs, tolerance=tolerance, n_jobs=n_jobs, difference_estimation_func=difference_estimation_func, ) return {(predecessor, target_node): direct_influences[i] for i, predecessor in enumerate(ordered_predecessors)} def arrow_strength_of_model( conditional_stochastic_model: ConditionalStochasticModel, input_samples: np.ndarray, num_samples_from_conditional: int = 2000, max_num_runs: int = 5000, tolerance: float = 0.01, n_jobs: int = -1, difference_estimation_func: Optional[Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]]] = None, input_subsets: Optional[List[List[int]]] = None, ) -> np.ndarray: input_samples = shape_into_2d(input_samples) if input_subsets is None: input_subsets = [[i] for i in range(input_samples.shape[1])] if difference_estimation_func is None: if isinstance(conditional_stochastic_model, ProbabilityEstimatorModel): difference_estimation_func = estimate_kl_divergence_of_probabilities else: def difference_estimation_func(old, new): return np.var(new) - np.var(old) if isinstance(conditional_stochastic_model, ProbabilityEstimatorModel): samples_creation_method = conditional_stochastic_model.estimate_probabilities else: samples_creation_method = conditional_stochastic_model.draw_samples with warnings.catch_warnings(): warnings.filterwarnings("ignore") def parallel_job(subset: List[int], parallel_random_seed: int): set_random_seed(parallel_random_seed) return _estimate_direct_strength( samples_creation_method, input_samples, subset, difference_estimation_func, num_samples_from_conditional, max_num_runs, tolerance, ) random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(input_subsets)) results = Parallel(n_jobs=n_jobs)( delayed(parallel_job)(subset, random_seed) for subset, random_seed in zip(input_subsets, random_seeds) ) if np.any(results == np.inf): _logger.warning( "At least one arrow strength is infinite. This typically happens if the causal models are " "deterministic, i.e. there is no noise or it is extremely small." ) return np.array(results) def _estimate_direct_strength( draw_samples_func: Callable[[np.ndarray], np.ndarray], distribution_samples: np.ndarray, parents_subset: List[int], difference_estimation_func: Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]], num_samples_conditional: int, max_num_runs: int, tolerance: float, ) -> float: distribution_samples = shape_into_2d(distribution_samples) num_samples_conditional = min(num_samples_conditional, distribution_samples.shape[0]) aggregated_conditional_difference_result = 0 average_difference_result = 0 converged_run = 0 for run, sample in enumerate(distribution_samples): tmp_samples = repmat(sample, num_samples_conditional, 1) rnd_permutation = np.random.choice(distribution_samples.shape[0], num_samples_conditional, replace=False) # Sampling from the conditional distribution based on the current sample. conditional_distribution_samples = draw_samples_func(tmp_samples) # Sampling from the conditional based on the current sample, but randomizing the inputs of all variables that # are in the given subset. By this, we can simulate the impact on the conditional distribution when removing # only the incoming edges of the variables in the subset. tmp_samples[:, parents_subset] = distribution_samples[:, parents_subset][rnd_permutation] cond_dist_removed_arr_samples = draw_samples_func(tmp_samples) old_average_difference_result = average_difference_result aggregated_conditional_difference_result += difference_estimation_func( conditional_distribution_samples, cond_dist_removed_arr_samples ) average_difference_result = aggregated_conditional_difference_result / (run + 1) if run >= max_num_runs: break elif run > 0: if old_average_difference_result == 0: converging = average_difference_result == 0 else: converging = abs(1 - average_difference_result / old_average_difference_result) < tolerance if converging: converged_run += 1 if converged_run >= 3: break else: converged_run = 0 return average_difference_result def intrinsic_causal_influence( causal_model: StructuralCausalModel, target_node: Any, prediction_model: Union[PredictionModel, ClassificationModel, str] = "approx", attribution_func: Optional[Callable[[np.ndarray, np.ndarray], float]] = None, num_training_samples: int = 100000, num_samples_randomization: int = 1000, num_samples_baseline: int = 1000, max_batch_size: int = 250, auto_assign_quality: auto.AssignmentQuality = auto.AssignmentQuality.GOOD, shapley_config: Optional[ShapleyConfig] = None, ) -> Dict[Any, float]: """Computes the causal contribution of each upstream noise term of the target node (including the noise of the target itself) to the statistical property (e.g. mean, variance) of the target. We call this contribution *intrinsic* as noise terms, by definition, do not inherit properties of observed parents. The contribution of each noise term is then the *intrinsic* causal contribution of the corresponding node. For more scientific details, please refer to the paper below. **Research Paper**: Janzing et al. *Quantifying causal contributions via structure preserving interventions*. arXiv:2007.00714, 2021. :param causal_model: The structural causal model for whose target node we compute the intrinsic causal influence of its ancestors. :param target_node: Target node whose statistical property is to be attributed. :param prediction_model: Prediction model for estimating the functional relationship between subsets of ancestor noise terms and the target node. This can be an instance of a PredictionModel, the string 'approx' or the string 'exact'. With 'exact', the underlying causal models in the graph are utilized directly by propagating given noise inputs through the graph. This is generally more accurate but slow. With 'approx', an appropriate model is selected and trained based on sampled data from the graph, which is less accurate but faster. A more detailed treatment on why we need this parameter is also provided in :ref:`icc`. :param attribution_func: Optional attribution function to measure the statistical property of the target node. This function expects two inputs; predictions after the randomization of certain features (i.e. samples from noise nodes) and a baseline where no features were randomized. The baseline predictions can be typically ignored if one is interested in uncertainty measures such as entropy or variance, but they might be relevant if, for instance, these shall be estimated based on the residuals. By default, entropy is used if prediction model is a classifier, variance otherwise. :param num_training_samples: Number of samples drawn from the graphical causal model that are used for fitting the prediction_model (if necessary). :param num_samples_randomization: Number of noise samples drawn from the graphical causal model that are used for evaluating the set function. Here, these samples are samples from the noise distributions used for randomizing features that are not in the subset. :param num_samples_baseline: Number of noise samples drawn from the graphical causal model that are used for evaluating the set function. Here, these samples are used as fixed observations for features that are in the subset. :param max_batch_size: Maximum batch size for estimating the predictions from evaluation samples. This has a significant impact on the overall memory usage. If set to -1, all samples are used in one batch. :param auto_assign_quality: Auto assign quality for the 'approx' prediction_model option. :param shapley_config: :class:`~dowhy.gcm.shapley.ShapleyConfig` for the Shapley estimator. :return: Intrinsic causal contribution of each ancestor node to the statistical property defined by the attribution_func of the target node. """ validate_causal_dag(causal_model.graph) # Creating a smaller subgraph, which only contains upstream nodes that are connected to the target node. sub_causal_model = StructuralCausalModel(node_connected_subgraph_view(causal_model.graph, target_node)) data_samples, noise_samples = noise_samples_of_ancestors(sub_causal_model, target_node, num_training_samples) node_names = noise_samples.columns noise_samples, target_samples = shape_into_2d(noise_samples.to_numpy(), data_samples[target_node].to_numpy()) target_is_categorical = is_categorical(data_samples[target_node].to_numpy()) prediction_method = _get_icc_noise_function( causal_model, target_node, prediction_model, noise_samples, node_names, target_samples, auto_assign_quality, target_is_categorical, ) if attribution_func is None: if target_is_categorical: def attribution_func(x, _): return -estimate_entropy_of_probabilities(x) else: def attribution_func(x, _): return estimate_variance(x) _, noise_samples = noise_samples_of_ancestors( sub_causal_model, target_node, num_samples_randomization + num_samples_baseline ) noise_samples = shape_into_2d(noise_samples.to_numpy()) iccs = _estimate_iccs( attribution_func, prediction_method, noise_samples[:num_samples_randomization], noise_samples[num_samples_randomization : num_samples_randomization + num_samples_baseline], max_batch_size, ShapleyConfig() if shapley_config is None else shapley_config, ) return {node: iccs[i] for i, node in enumerate(node_names)} def intrinsic_causal_influence_sample( causal_model: InvertibleStructuralCausalModel, target_node: Any, baseline_samples: pd.DataFrame, noise_feature_samples: Optional[pd.DataFrame] = None, subset_scoring_func: Optional[Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]]] = None, num_noise_feature_samples: int = 5000, max_batch_size: int = 100, shapley_config: Optional[ShapleyConfig] = None, ) -> List[Dict[Any, Any]]: """Estimates the intrinsic causal impact of upstream nodes on a specified target_node, using the provided baseline_samples as a reference. In this context, observed values are attributed to the noise factors present in upstream nodes. Compared to intrinsic_causal_influence, this method quantifies the influences with respect to single observations instead of the distribution. Note that the current implementation only supports non-categorical data, since the noise terms need to be reconstructed. **Research Paper**: Janzing et al. *Quantifying causal contributions via structure preserving interventions*. arXiv:2007.00714, 2021. :param causal_model: The fitted invertible structural causal model. :param target_node: Node of interest. :param baseline_samples: Samples for which the influence should be estimated. :param noise_feature_samples: Optional noise samples of upstream nodes used as 'background' samples.. If None is given, new noise samples are generated based on the graph. These samples are used for randomizing features that are not in the subset. :param subset_scoring_func: Set function for estimating the quantity of interest based. This function expects two inputs; the outcome of the model for some samples if certain features are permuted and the outcome of the model for the same samples when no features were permuted. By default, the difference between means of these samples are estimated. :param num_noise_feature_samples: If no noise_feature_samples are given, noise samples are drawn from the graph. This parameter indicates how many. :param max_batch_size: Maximum batch size for estimating multiple predictions at once. This has a significant influence on the overall memory usage. If set to -1, all samples are used in one batch. :param shapley_config: :class:`~dowhy.gcm.shapley.ShapleyConfig` for the Shapley estimator. :return: A list of dictionaries indicating the intrinsic causal influence of a node on the target for a particular sample. This is, each dictionary belongs to one baseline sample. """ validate_node(causal_model.graph, target_node) causal_model = InvertibleStructuralCausalModel(node_connected_subgraph_view(causal_model.graph, target_node)) feature_samples, tmp_noise_feature_samples = noise_samples_of_ancestors( causal_model, target_node, num_noise_feature_samples ) if has_categorical(feature_samples.to_numpy()): raise ValueError( "The current implementation requires all variables to be numeric, i.e., non-categorical! " "There is at least one node in the graph that is categorical." ) if noise_feature_samples is None: noise_feature_samples = tmp_noise_feature_samples if subset_scoring_func is None: subset_scoring_func = means_difference shapley_vales = feature_relevance_sample( _get_icc_noise_function( causal_model, target_node, "exact", noise_feature_samples, noise_feature_samples.columns, None, None, False ), feature_samples=noise_feature_samples.to_numpy(), baseline_samples=compute_noise_from_data(causal_model, baseline_samples)[ noise_feature_samples.columns ].to_numpy(), subset_scoring_func=subset_scoring_func, max_batch_size=max_batch_size, shapley_config=shapley_config, ) return [ {(predecessor, target_node): shapley_vales[i][q] for q, predecessor in enumerate(noise_feature_samples.columns)} for i in range(shapley_vales.shape[0]) ] def _estimate_iccs( attribution_func: Callable[[np.ndarray, np.ndarray], float], prediction_method: Callable[[np.ndarray], np.ndarray], noise_samples: np.ndarray, baseline_noise_samples: np.ndarray, max_batch_size: int, shapley_config: ShapleyConfig, ): target_values = shape_into_2d(prediction_method(baseline_noise_samples)) def icc_set_function(subset: np.ndarray) -> Union[np.ndarray, float]: if np.all(subset == 1): # In case of the full subset (no randomization), we get the same predictions as when we apply the # prediction method to the samples of interest, since all noise samples are replaced with a sample of # interest. predictions = target_values elif np.all(subset == 0): # In case of the empty subset (all are jointly randomize), it boils down to taking the average over all # predictions, seeing that the randomization yields the same values for each sample of interest (none of the # samples of interest are used to replace a (jointly) 'randomized' sample). predictions = repmat(np.mean(prediction_method(noise_samples), axis=0), baseline_noise_samples.shape[0], 1) else: predictions = marginal_expectation( prediction_method, feature_samples=noise_samples, baseline_samples=baseline_noise_samples, baseline_feature_indices=np.arange(0, noise_samples.shape[1])[subset == 1], return_averaged_results=True, feature_perturbation="randomize_columns_jointly", max_batch_size=max_batch_size, ) return attribution_func(shape_into_2d(predictions), target_values) return estimate_shapley_values(icc_set_function, noise_samples.shape[1], shapley_config) def _get_icc_noise_function( causal_model: InvertibleStructuralCausalModel, target_node: Any, prediction_model: Union[PredictionModel, ClassificationModel, str], noise_samples: np.ndarray, node_names: Iterator[Any], target_samples: np.ndarray, auto_assign_quality: auto.AssignmentQuality, target_is_categorical: bool, ) -> Callable[[np.ndarray], np.ndarray]: if isinstance(prediction_model, str) and prediction_model not in ("approx", "exact"): raise ValueError( "Invalid value for prediction_model: %s! This should either be an instance of a PredictionModel or" "one of the two string options 'exact' or 'approx'." % prediction_model ) if not isinstance(prediction_model, str): prediction_model.fit(noise_samples, target_samples) return prediction_model.predict if prediction_model == "approx": prediction_model = auto.select_model(noise_samples, target_samples, auto_assign_quality) prediction_model.fit(noise_samples, target_samples) if target_is_categorical: return prediction_model.predict_probabilities else: return prediction_model.predict else: # Exact model def exact_model(X: np.ndarray) -> np.ndarray: return compute_data_from_noise(causal_model, pd.DataFrame(X, columns=[x for x in node_names]))[ target_node ].to_numpy() if target_is_categorical: list_of_classes = cast(ClassifierFCM, causal_model.causal_mechanism(target_node)).classifier_model.classes def prediction_method(X): return (shape_into_2d(exact_model(X)) == list_of_classes).astype(float) return prediction_method else: return exact_model

bloebp

45046e141ebd701820ca8a692c8f03ed5dce314c

e734f4e6245d404218b0ba11b14f7cc621bec7a0

else is not needed anymore See also: [Why You Shouldn't Nest Your Code ](https://www.youtube.com/watch?v=CFRhGnuXG-4) Oh, just saw: this code was actually just moved. Feel free to postpone my proposed refactoring. Either way you choose, I think it's worth doing it.

petergtz

py-why/dowhy

Add method to estimate ICC for single samples

Before, it was only possible to estimate intrinsic causal contributions with respect to the distribution. This new method allows to estimate the influences of upstream nodes on a particular observation of a target node. As for now, this requires that all models are invertible with respect to the noise.

2023-03-29 21:42:31+00:00

2023-03-31 13:39:16+00:00

dowhy/gcm/influence.py

"""This module provides functions to estimate causal influences. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging import warnings from typing import Any, Callable, Dict, List, Optional, Tuple, Union, cast import numpy as np import pandas as pd from joblib import Parallel, delayed from numpy.matlib import repmat import dowhy.gcm.auto as auto from dowhy.gcm._noise import compute_data_from_noise, noise_samples_of_ancestors from dowhy.gcm.cms import ProbabilisticCausalModel, StructuralCausalModel from dowhy.gcm.divergence import estimate_kl_divergence_of_probabilities from dowhy.gcm.fcms import ClassificationModel, ClassifierFCM, PredictionModel, ProbabilityEstimatorModel from dowhy.gcm.fitting_sampling import draw_samples from dowhy.gcm.graph import ( ConditionalStochasticModel, get_ordered_predecessors, is_root_node, node_connected_subgraph_view, validate_causal_dag, validate_node, ) from dowhy.gcm.shapley import ShapleyConfig, estimate_shapley_values from dowhy.gcm.stats import marginal_expectation from dowhy.gcm.uncertainty import estimate_entropy_of_probabilities, estimate_variance from dowhy.gcm.util.general import is_categorical, set_random_seed, shape_into_2d _logger = logging.getLogger(__name__) def arrow_strength( causal_model: ProbabilisticCausalModel, target_node: Any, parent_samples: Optional[pd.DataFrame] = None, num_samples_conditional: int = 2000, max_num_runs: int = 5000, tolerance: float = 0.01, n_jobs: int = -1, difference_estimation_func: Optional[Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]]] = None, ) -> Dict[Tuple[Any, Any], float]: """Computes the causal strength of each edge directed to the target node. The strength of an edge is quantified in terms of distance between conditional distributions of the target node in the original graph and the imputed graph wherein the edge has been removed and the target node is fed a random permutation of the observations of the source node. For more scientific details behind this API, please refer to the research paper below. **Research Paper**: Dominik Janzing, David Balduzzi, Moritz Grosse-Wentrup, Bernhard Schölkopf. *Quantifying Causal Influences*. The Annals of Statistics, Vol. 41, No. 5, 2324-2358, 2013. :param causal_model: The probabilistic causal model for whose target node we compute the strength of incoming edges for. :param target_node: The target node whose incoming edges' strength is to be computed. :param parent_samples: Optional samples from the parents of the target_node. If None are given, they are generated based on the provided causal model. Providing observational data can help to mitigate misspecifications in the graph, such as missing interactions between root nodes or confounders. :param num_samples_conditional: Sample size to use for estimating the distance between distributions. The more more samples, the higher the accuracy. :param max_num_runs: The maximum number of times to resample and estimate the strength to report the average strength. :param tolerance: If the percentage change in the estimated strength between two consecutive runs falls below the specified tolerance, the algorithm will terminate before reaching the maximum number of runs. A value of 0.01 would indicate a change of less than 1%. However, in order to minimize the impact of randomness, there must be at least three consecutive runs where the change is below the threshold. :param n_jobs: The number of jobs to run in parallel. Set it to -1 to use all processors. :param difference_estimation_func: Optional: How to measure the distance between two distributions. By default, the difference of the variance is estimated for a continuous target node and the KL divergence for a categorical target node. :return: Causal strength of each edge. """ if target_node not in causal_model.graph.nodes: raise ValueError("Target node %s can not be found in given graph!" % target_node) if is_root_node(causal_model.graph, target_node): raise ValueError("Target node %s is a root node, but it requires to have ancestors!" % target_node) # Creating a smaller subgraph, which only contains upstream nodes that are connected to the target node. sub_causal_model = ProbabilisticCausalModel(node_connected_subgraph_view(causal_model.graph, target_node)) validate_node(sub_causal_model.graph, target_node) ordered_predecessors = get_ordered_predecessors(sub_causal_model.graph, target_node) if parent_samples is None: parent_samples = draw_samples(sub_causal_model, num_samples_conditional * 20)[ordered_predecessors] direct_influences = arrow_strength_of_model( sub_causal_model.causal_mechanism(target_node), parent_samples[ordered_predecessors].to_numpy(), num_samples_from_conditional=num_samples_conditional, max_num_runs=max_num_runs, tolerance=tolerance, n_jobs=n_jobs, difference_estimation_func=difference_estimation_func, ) return {(predecessor, target_node): direct_influences[i] for i, predecessor in enumerate(ordered_predecessors)} def arrow_strength_of_model( conditional_stochastic_model: ConditionalStochasticModel, input_samples: np.ndarray, num_samples_from_conditional: int = 2000, max_num_runs: int = 5000, tolerance: float = 0.01, n_jobs: int = -1, difference_estimation_func: Optional[Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]]] = None, input_subsets: Optional[List[List[int]]] = None, ) -> np.ndarray: input_samples = shape_into_2d(input_samples) if input_subsets is None: input_subsets = [[i] for i in range(input_samples.shape[1])] if difference_estimation_func is None: if isinstance(conditional_stochastic_model, ProbabilityEstimatorModel): difference_estimation_func = estimate_kl_divergence_of_probabilities else: def difference_estimation_func(old, new): return np.var(new) - np.var(old) if isinstance(conditional_stochastic_model, ProbabilityEstimatorModel): samples_creation_method = conditional_stochastic_model.estimate_probabilities else: samples_creation_method = conditional_stochastic_model.draw_samples with warnings.catch_warnings(): warnings.filterwarnings("ignore") def parallel_job(subset: List[int], parallel_random_seed: int): set_random_seed(parallel_random_seed) return _estimate_direct_strength( samples_creation_method, input_samples, subset, difference_estimation_func, num_samples_from_conditional, max_num_runs, tolerance, ) random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(input_subsets)) results = Parallel(n_jobs=n_jobs)( delayed(parallel_job)(subset, random_seed) for subset, random_seed in zip(input_subsets, random_seeds) ) if np.any(results == np.inf): _logger.warning( "At least one arrow strength is infinite. This typically happens if the causal models are " "deterministic, i.e. there is no noise or it is extremely small." ) return np.array(results) def _estimate_direct_strength( draw_samples_func: Callable[[np.ndarray], np.ndarray], distribution_samples: np.ndarray, parents_subset: List[int], difference_estimation_func: Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]], num_samples_conditional: int, max_num_runs: int, tolerance: float, ) -> float: distribution_samples = shape_into_2d(distribution_samples) num_samples_conditional = min(num_samples_conditional, distribution_samples.shape[0]) aggregated_conditional_difference_result = 0 average_difference_result = 0 converged_run = 0 for run, sample in enumerate(distribution_samples): tmp_samples = repmat(sample, num_samples_conditional, 1) rnd_permutation = np.random.choice(distribution_samples.shape[0], num_samples_conditional, replace=False) # Sampling from the conditional distribution based on the current sample. conditional_distribution_samples = draw_samples_func(tmp_samples) # Sampling from the conditional based on the current sample, but randomizing the inputs of all variables that # are in the given subset. By this, we can simulate the impact on the conditional distribution when removing # only the incoming edges of the variables in the subset. tmp_samples[:, parents_subset] = distribution_samples[:, parents_subset][rnd_permutation] cond_dist_removed_arr_samples = draw_samples_func(tmp_samples) old_average_difference_result = average_difference_result aggregated_conditional_difference_result += difference_estimation_func( conditional_distribution_samples, cond_dist_removed_arr_samples ) average_difference_result = aggregated_conditional_difference_result / (run + 1) if run >= max_num_runs: break elif run > 0: if old_average_difference_result == 0: converging = average_difference_result == 0 else: converging = abs(1 - average_difference_result / old_average_difference_result) < tolerance if converging: converged_run += 1 if converged_run >= 3: break else: converged_run = 0 return average_difference_result def intrinsic_causal_influence( causal_model: StructuralCausalModel, target_node: Any, prediction_model: Union[PredictionModel, ClassificationModel, str] = "approx", attribution_func: Optional[Callable[[np.ndarray, np.ndarray], float]] = None, num_training_samples: int = 100000, num_samples_randomization: int = 1000, num_samples_baseline: int = 1000, max_batch_size: int = 250, auto_assign_quality: auto.AssignmentQuality = auto.AssignmentQuality.GOOD, shapley_config: Optional[ShapleyConfig] = None, ) -> Dict[Any, float]: """Computes the causal contribution of each upstream noise term of the target node (including the noise of the target itself) to the statistical property (e.g. mean, variance) of the target. We call this contribution *intrinsic* as noise terms, by definition, do not inherit properties of observed parents. The contribution of each noise term is then the *intrinsic* causal contribution of the corresponding node. For more scientific details, please refer to the paper below. **Research Paper**: Janzing et al. *Quantifying causal contributions via structure preserving interventions*. arXiv:2007.00714, 2021. :param causal_model: The structural causal model for whose target node we compute the intrinsic causal influence of its ancestors. :param target_node: Target node whose statistical property is to be attributed. :param prediction_model: Prediction model for estimating the functional relationship between subsets of ancestor noise terms and the target node. This can be an instance of a PredictionModel, the string 'approx' or the string 'exact'. With 'exact', the underlying causal models in the graph are utilized directly by propagating given noise inputs through the graph. This is generally more accurate but slow. With 'approx', an appropriate model is selected and trained based on sampled data from the graph, which is less accurate but faster. A more detailed treatment on why we need this parameter is also provided in :ref:`icc`. :param attribution_func: Optional attribution function to measure the statistical property of the target node. This function expects two inputs; predictions after the randomization of certain features (i.e. samples from noise nodes) and a baseline where no features were randomized. The baseline predictions can be typically ignored if one is interested in uncertainty measures such as entropy or variance, but they might be relevant if, for instance, these shall be estimated based on the residuals. By default, entropy is used if prediction model is a classifier, variance otherwise. :param num_training_samples: Number of samples drawn from the graphical causal model that are used for fitting the prediction_model (if necessary). :param num_samples_randomization: Number of noise samples drawn from the graphical causal model that are used for evaluating the set function. Here, these samples are samples from the noise distributions used for randomizing features that are not in the subset. :param num_samples_baseline: Number of noise samples drawn from the graphical causal model that are used for evaluating the set function. Here, these samples are used as fixed observations for features that are in the subset. :param max_batch_size: Maximum batch size for estimating the predictions from evaluation samples. This has a significant impact on the overall memory usage. If set to -1, all samples are used in one batch. :param auto_assign_quality: Auto assign quality for the 'approx' prediction_model option. :param shapley_config: :class:`~dowhy.gcm.shapley.ShapleyConfig` for the Shapley estimator. :return: Intrinsic causal contribution of each ancestor node to the statistical property defined by the attribution_func of the target node. """ validate_causal_dag(causal_model.graph) # Creating a smaller subgraph, which only contains upstream nodes that are connected to the target node. sub_causal_model = StructuralCausalModel(node_connected_subgraph_view(causal_model.graph, target_node)) data_samples, noise_samples = noise_samples_of_ancestors(sub_causal_model, target_node, num_training_samples) node_names = noise_samples.columns noise_samples, target_samples = shape_into_2d(noise_samples.to_numpy(), data_samples[target_node].to_numpy()) target_is_categorical = is_categorical(data_samples[target_node].to_numpy()) if prediction_model == "approx": prediction_model = auto.select_model(noise_samples, target_samples, auto_assign_quality) prediction_model.fit(noise_samples, target_samples) if target_is_categorical: prediction_method = prediction_model.predict_probabilities else: prediction_method = prediction_model.predict elif prediction_model == "exact": def exact_model(X: np.ndarray) -> np.ndarray: return compute_data_from_noise(sub_causal_model, pd.DataFrame(X, columns=[x for x in node_names]))[ target_node ].to_numpy() if target_is_categorical: list_of_classes = cast( ClassifierFCM, sub_causal_model.causal_mechanism(target_node) ).classifier_model.classes def prediction_method(X): return (shape_into_2d(exact_model(X)) == list_of_classes).astype(float) else: prediction_method = exact_model elif isinstance(prediction_model, str): raise ValueError( "Invalid value for prediction_model: %s! This should either be an instance of a PredictionModel or" "one of the two string options 'exact' or 'approx'." % prediction_model ) else: prediction_model.fit(noise_samples, target_samples) prediction_method = prediction_model.predict if attribution_func is None: if target_is_categorical: def attribution_func(x, _): return -estimate_entropy_of_probabilities(x) else: def attribution_func(x, _): return estimate_variance(x) _, noise_samples = noise_samples_of_ancestors( sub_causal_model, target_node, num_samples_randomization + num_samples_baseline ) noise_samples = shape_into_2d(noise_samples.to_numpy()) iccs = _estimate_iccs( attribution_func, prediction_method, noise_samples[:num_samples_randomization], noise_samples[num_samples_randomization : num_samples_randomization + num_samples_baseline], max_batch_size, ShapleyConfig() if shapley_config is None else shapley_config, ) return {node: iccs[i] for i, node in enumerate(node_names)} def _estimate_iccs( attribution_func: Callable[[np.ndarray, np.ndarray], float], prediction_method: Callable[[np.ndarray], np.ndarray], noise_samples: np.ndarray, baseline_noise_samples: np.ndarray, max_batch_size: int, shapley_config: ShapleyConfig, ): target_values = shape_into_2d(prediction_method(baseline_noise_samples)) def icc_set_function(subset: np.ndarray) -> Union[np.ndarray, float]: if np.all(subset == 1): # In case of the full subset (no randomization), we get the same predictions as when we apply the # prediction method to the samples of interest, since all noise samples are replaced with a sample of # interest. predictions = target_values elif np.all(subset == 0): # In case of the empty subset (all are jointly randomize), it boils down to taking the average over all # predictions, seeing that the randomization yields the same values for each sample of interest (none of the # samples of interest are used to replace a (jointly) 'randomized' sample). predictions = repmat(np.mean(prediction_method(noise_samples), axis=0), baseline_noise_samples.shape[0], 1) else: predictions = marginal_expectation( prediction_method, feature_samples=noise_samples, baseline_samples=baseline_noise_samples, baseline_feature_indices=np.arange(0, noise_samples.shape[1])[subset == 1], return_averaged_results=True, feature_perturbation="randomize_columns_jointly", max_batch_size=max_batch_size, ) return attribution_func(shape_into_2d(predictions), target_values) return estimate_shapley_values(icc_set_function, noise_samples.shape[1], shapley_config)

"""This module provides functions to estimate causal influences. Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import logging import warnings from typing import Any, Callable, Dict, Iterator, List, Optional, Tuple, Union, cast import numpy as np import pandas as pd from joblib import Parallel, delayed from numpy.matlib import repmat import dowhy.gcm.auto as auto from dowhy.gcm import feature_relevance_sample from dowhy.gcm._noise import compute_data_from_noise, compute_noise_from_data, noise_samples_of_ancestors from dowhy.gcm.cms import InvertibleStructuralCausalModel, ProbabilisticCausalModel, StructuralCausalModel from dowhy.gcm.divergence import estimate_kl_divergence_of_probabilities from dowhy.gcm.fcms import ClassificationModel, ClassifierFCM, PredictionModel, ProbabilityEstimatorModel from dowhy.gcm.fitting_sampling import draw_samples from dowhy.gcm.graph import ( ConditionalStochasticModel, get_ordered_predecessors, is_root_node, node_connected_subgraph_view, validate_causal_dag, validate_node, ) from dowhy.gcm.shapley import ShapleyConfig, estimate_shapley_values from dowhy.gcm.stats import marginal_expectation from dowhy.gcm.uncertainty import estimate_entropy_of_probabilities, estimate_variance from dowhy.gcm.util.general import has_categorical, is_categorical, means_difference, set_random_seed, shape_into_2d _logger = logging.getLogger(__name__) def arrow_strength( causal_model: ProbabilisticCausalModel, target_node: Any, parent_samples: Optional[pd.DataFrame] = None, num_samples_conditional: int = 2000, max_num_runs: int = 5000, tolerance: float = 0.01, n_jobs: int = -1, difference_estimation_func: Optional[Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]]] = None, ) -> Dict[Tuple[Any, Any], float]: """Computes the causal strength of each edge directed to the target node. The strength of an edge is quantified in terms of distance between conditional distributions of the target node in the original graph and the imputed graph wherein the edge has been removed and the target node is fed a random permutation of the observations of the source node. For more scientific details behind this API, please refer to the research paper below. **Research Paper**: Dominik Janzing, David Balduzzi, Moritz Grosse-Wentrup, Bernhard Schölkopf. *Quantifying Causal Influences*. The Annals of Statistics, Vol. 41, No. 5, 2324-2358, 2013. :param causal_model: The probabilistic causal model for whose target node we compute the strength of incoming edges for. :param target_node: The target node whose incoming edges' strength is to be computed. :param parent_samples: Optional samples from the parents of the target_node. If None are given, they are generated based on the provided causal model. Providing observational data can help to mitigate misspecifications in the graph, such as missing interactions between root nodes or confounders. :param num_samples_conditional: Sample size to use for estimating the distance between distributions. The more more samples, the higher the accuracy. :param max_num_runs: The maximum number of times to resample and estimate the strength to report the average strength. :param tolerance: If the percentage change in the estimated strength between two consecutive runs falls below the specified tolerance, the algorithm will terminate before reaching the maximum number of runs. A value of 0.01 would indicate a change of less than 1%. However, in order to minimize the impact of randomness, there must be at least three consecutive runs where the change is below the threshold. :param n_jobs: The number of jobs to run in parallel. Set it to -1 to use all processors. :param difference_estimation_func: Optional: How to measure the distance between two distributions. By default, the difference of the variance is estimated for a continuous target node and the KL divergence for a categorical target node. :return: Causal strength of each edge. """ if target_node not in causal_model.graph.nodes: raise ValueError("Target node %s can not be found in given graph!" % target_node) if is_root_node(causal_model.graph, target_node): raise ValueError("Target node %s is a root node, but it requires to have ancestors!" % target_node) # Creating a smaller subgraph, which only contains upstream nodes that are connected to the target node. sub_causal_model = ProbabilisticCausalModel(node_connected_subgraph_view(causal_model.graph, target_node)) validate_node(sub_causal_model.graph, target_node) ordered_predecessors = get_ordered_predecessors(sub_causal_model.graph, target_node) if parent_samples is None: parent_samples = draw_samples(sub_causal_model, num_samples_conditional * 20)[ordered_predecessors] direct_influences = arrow_strength_of_model( sub_causal_model.causal_mechanism(target_node), parent_samples[ordered_predecessors].to_numpy(), num_samples_from_conditional=num_samples_conditional, max_num_runs=max_num_runs, tolerance=tolerance, n_jobs=n_jobs, difference_estimation_func=difference_estimation_func, ) return {(predecessor, target_node): direct_influences[i] for i, predecessor in enumerate(ordered_predecessors)} def arrow_strength_of_model( conditional_stochastic_model: ConditionalStochasticModel, input_samples: np.ndarray, num_samples_from_conditional: int = 2000, max_num_runs: int = 5000, tolerance: float = 0.01, n_jobs: int = -1, difference_estimation_func: Optional[Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]]] = None, input_subsets: Optional[List[List[int]]] = None, ) -> np.ndarray: input_samples = shape_into_2d(input_samples) if input_subsets is None: input_subsets = [[i] for i in range(input_samples.shape[1])] if difference_estimation_func is None: if isinstance(conditional_stochastic_model, ProbabilityEstimatorModel): difference_estimation_func = estimate_kl_divergence_of_probabilities else: def difference_estimation_func(old, new): return np.var(new) - np.var(old) if isinstance(conditional_stochastic_model, ProbabilityEstimatorModel): samples_creation_method = conditional_stochastic_model.estimate_probabilities else: samples_creation_method = conditional_stochastic_model.draw_samples with warnings.catch_warnings(): warnings.filterwarnings("ignore") def parallel_job(subset: List[int], parallel_random_seed: int): set_random_seed(parallel_random_seed) return _estimate_direct_strength( samples_creation_method, input_samples, subset, difference_estimation_func, num_samples_from_conditional, max_num_runs, tolerance, ) random_seeds = np.random.randint(np.iinfo(np.int32).max, size=len(input_subsets)) results = Parallel(n_jobs=n_jobs)( delayed(parallel_job)(subset, random_seed) for subset, random_seed in zip(input_subsets, random_seeds) ) if np.any(results == np.inf): _logger.warning( "At least one arrow strength is infinite. This typically happens if the causal models are " "deterministic, i.e. there is no noise or it is extremely small." ) return np.array(results) def _estimate_direct_strength( draw_samples_func: Callable[[np.ndarray], np.ndarray], distribution_samples: np.ndarray, parents_subset: List[int], difference_estimation_func: Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]], num_samples_conditional: int, max_num_runs: int, tolerance: float, ) -> float: distribution_samples = shape_into_2d(distribution_samples) num_samples_conditional = min(num_samples_conditional, distribution_samples.shape[0]) aggregated_conditional_difference_result = 0 average_difference_result = 0 converged_run = 0 for run, sample in enumerate(distribution_samples): tmp_samples = repmat(sample, num_samples_conditional, 1) rnd_permutation = np.random.choice(distribution_samples.shape[0], num_samples_conditional, replace=False) # Sampling from the conditional distribution based on the current sample. conditional_distribution_samples = draw_samples_func(tmp_samples) # Sampling from the conditional based on the current sample, but randomizing the inputs of all variables that # are in the given subset. By this, we can simulate the impact on the conditional distribution when removing # only the incoming edges of the variables in the subset. tmp_samples[:, parents_subset] = distribution_samples[:, parents_subset][rnd_permutation] cond_dist_removed_arr_samples = draw_samples_func(tmp_samples) old_average_difference_result = average_difference_result aggregated_conditional_difference_result += difference_estimation_func( conditional_distribution_samples, cond_dist_removed_arr_samples ) average_difference_result = aggregated_conditional_difference_result / (run + 1) if run >= max_num_runs: break elif run > 0: if old_average_difference_result == 0: converging = average_difference_result == 0 else: converging = abs(1 - average_difference_result / old_average_difference_result) < tolerance if converging: converged_run += 1 if converged_run >= 3: break else: converged_run = 0 return average_difference_result def intrinsic_causal_influence( causal_model: StructuralCausalModel, target_node: Any, prediction_model: Union[PredictionModel, ClassificationModel, str] = "approx", attribution_func: Optional[Callable[[np.ndarray, np.ndarray], float]] = None, num_training_samples: int = 100000, num_samples_randomization: int = 1000, num_samples_baseline: int = 1000, max_batch_size: int = 250, auto_assign_quality: auto.AssignmentQuality = auto.AssignmentQuality.GOOD, shapley_config: Optional[ShapleyConfig] = None, ) -> Dict[Any, float]: """Computes the causal contribution of each upstream noise term of the target node (including the noise of the target itself) to the statistical property (e.g. mean, variance) of the target. We call this contribution *intrinsic* as noise terms, by definition, do not inherit properties of observed parents. The contribution of each noise term is then the *intrinsic* causal contribution of the corresponding node. For more scientific details, please refer to the paper below. **Research Paper**: Janzing et al. *Quantifying causal contributions via structure preserving interventions*. arXiv:2007.00714, 2021. :param causal_model: The structural causal model for whose target node we compute the intrinsic causal influence of its ancestors. :param target_node: Target node whose statistical property is to be attributed. :param prediction_model: Prediction model for estimating the functional relationship between subsets of ancestor noise terms and the target node. This can be an instance of a PredictionModel, the string 'approx' or the string 'exact'. With 'exact', the underlying causal models in the graph are utilized directly by propagating given noise inputs through the graph. This is generally more accurate but slow. With 'approx', an appropriate model is selected and trained based on sampled data from the graph, which is less accurate but faster. A more detailed treatment on why we need this parameter is also provided in :ref:`icc`. :param attribution_func: Optional attribution function to measure the statistical property of the target node. This function expects two inputs; predictions after the randomization of certain features (i.e. samples from noise nodes) and a baseline where no features were randomized. The baseline predictions can be typically ignored if one is interested in uncertainty measures such as entropy or variance, but they might be relevant if, for instance, these shall be estimated based on the residuals. By default, entropy is used if prediction model is a classifier, variance otherwise. :param num_training_samples: Number of samples drawn from the graphical causal model that are used for fitting the prediction_model (if necessary). :param num_samples_randomization: Number of noise samples drawn from the graphical causal model that are used for evaluating the set function. Here, these samples are samples from the noise distributions used for randomizing features that are not in the subset. :param num_samples_baseline: Number of noise samples drawn from the graphical causal model that are used for evaluating the set function. Here, these samples are used as fixed observations for features that are in the subset. :param max_batch_size: Maximum batch size for estimating the predictions from evaluation samples. This has a significant impact on the overall memory usage. If set to -1, all samples are used in one batch. :param auto_assign_quality: Auto assign quality for the 'approx' prediction_model option. :param shapley_config: :class:`~dowhy.gcm.shapley.ShapleyConfig` for the Shapley estimator. :return: Intrinsic causal contribution of each ancestor node to the statistical property defined by the attribution_func of the target node. """ validate_causal_dag(causal_model.graph) # Creating a smaller subgraph, which only contains upstream nodes that are connected to the target node. sub_causal_model = StructuralCausalModel(node_connected_subgraph_view(causal_model.graph, target_node)) data_samples, noise_samples = noise_samples_of_ancestors(sub_causal_model, target_node, num_training_samples) node_names = noise_samples.columns noise_samples, target_samples = shape_into_2d(noise_samples.to_numpy(), data_samples[target_node].to_numpy()) target_is_categorical = is_categorical(data_samples[target_node].to_numpy()) prediction_method = _get_icc_noise_function( causal_model, target_node, prediction_model, noise_samples, node_names, target_samples, auto_assign_quality, target_is_categorical, ) if attribution_func is None: if target_is_categorical: def attribution_func(x, _): return -estimate_entropy_of_probabilities(x) else: def attribution_func(x, _): return estimate_variance(x) _, noise_samples = noise_samples_of_ancestors( sub_causal_model, target_node, num_samples_randomization + num_samples_baseline ) noise_samples = shape_into_2d(noise_samples.to_numpy()) iccs = _estimate_iccs( attribution_func, prediction_method, noise_samples[:num_samples_randomization], noise_samples[num_samples_randomization : num_samples_randomization + num_samples_baseline], max_batch_size, ShapleyConfig() if shapley_config is None else shapley_config, ) return {node: iccs[i] for i, node in enumerate(node_names)} def intrinsic_causal_influence_sample( causal_model: InvertibleStructuralCausalModel, target_node: Any, baseline_samples: pd.DataFrame, noise_feature_samples: Optional[pd.DataFrame] = None, subset_scoring_func: Optional[Callable[[np.ndarray, np.ndarray], Union[np.ndarray, float]]] = None, num_noise_feature_samples: int = 5000, max_batch_size: int = 100, shapley_config: Optional[ShapleyConfig] = None, ) -> List[Dict[Any, Any]]: """Estimates the intrinsic causal impact of upstream nodes on a specified target_node, using the provided baseline_samples as a reference. In this context, observed values are attributed to the noise factors present in upstream nodes. Compared to intrinsic_causal_influence, this method quantifies the influences with respect to single observations instead of the distribution. Note that the current implementation only supports non-categorical data, since the noise terms need to be reconstructed. **Research Paper**: Janzing et al. *Quantifying causal contributions via structure preserving interventions*. arXiv:2007.00714, 2021. :param causal_model: The fitted invertible structural causal model. :param target_node: Node of interest. :param baseline_samples: Samples for which the influence should be estimated. :param noise_feature_samples: Optional noise samples of upstream nodes used as 'background' samples.. If None is given, new noise samples are generated based on the graph. These samples are used for randomizing features that are not in the subset. :param subset_scoring_func: Set function for estimating the quantity of interest based. This function expects two inputs; the outcome of the model for some samples if certain features are permuted and the outcome of the model for the same samples when no features were permuted. By default, the difference between means of these samples are estimated. :param num_noise_feature_samples: If no noise_feature_samples are given, noise samples are drawn from the graph. This parameter indicates how many. :param max_batch_size: Maximum batch size for estimating multiple predictions at once. This has a significant influence on the overall memory usage. If set to -1, all samples are used in one batch. :param shapley_config: :class:`~dowhy.gcm.shapley.ShapleyConfig` for the Shapley estimator. :return: A list of dictionaries indicating the intrinsic causal influence of a node on the target for a particular sample. This is, each dictionary belongs to one baseline sample. """ validate_node(causal_model.graph, target_node) causal_model = InvertibleStructuralCausalModel(node_connected_subgraph_view(causal_model.graph, target_node)) feature_samples, tmp_noise_feature_samples = noise_samples_of_ancestors( causal_model, target_node, num_noise_feature_samples ) if has_categorical(feature_samples.to_numpy()): raise ValueError( "The current implementation requires all variables to be numeric, i.e., non-categorical! " "There is at least one node in the graph that is categorical." ) if noise_feature_samples is None: noise_feature_samples = tmp_noise_feature_samples if subset_scoring_func is None: subset_scoring_func = means_difference shapley_vales = feature_relevance_sample( _get_icc_noise_function( causal_model, target_node, "exact", noise_feature_samples, noise_feature_samples.columns, None, None, False ), feature_samples=noise_feature_samples.to_numpy(), baseline_samples=compute_noise_from_data(causal_model, baseline_samples)[ noise_feature_samples.columns ].to_numpy(), subset_scoring_func=subset_scoring_func, max_batch_size=max_batch_size, shapley_config=shapley_config, ) return [ {(predecessor, target_node): shapley_vales[i][q] for q, predecessor in enumerate(noise_feature_samples.columns)} for i in range(shapley_vales.shape[0]) ] def _estimate_iccs( attribution_func: Callable[[np.ndarray, np.ndarray], float], prediction_method: Callable[[np.ndarray], np.ndarray], noise_samples: np.ndarray, baseline_noise_samples: np.ndarray, max_batch_size: int, shapley_config: ShapleyConfig, ): target_values = shape_into_2d(prediction_method(baseline_noise_samples)) def icc_set_function(subset: np.ndarray) -> Union[np.ndarray, float]: if np.all(subset == 1): # In case of the full subset (no randomization), we get the same predictions as when we apply the # prediction method to the samples of interest, since all noise samples are replaced with a sample of # interest. predictions = target_values elif np.all(subset == 0): # In case of the empty subset (all are jointly randomize), it boils down to taking the average over all # predictions, seeing that the randomization yields the same values for each sample of interest (none of the # samples of interest are used to replace a (jointly) 'randomized' sample). predictions = repmat(np.mean(prediction_method(noise_samples), axis=0), baseline_noise_samples.shape[0], 1) else: predictions = marginal_expectation( prediction_method, feature_samples=noise_samples, baseline_samples=baseline_noise_samples, baseline_feature_indices=np.arange(0, noise_samples.shape[1])[subset == 1], return_averaged_results=True, feature_perturbation="randomize_columns_jointly", max_batch_size=max_batch_size, ) return attribution_func(shape_into_2d(predictions), target_values) return estimate_shapley_values(icc_set_function, noise_samples.shape[1], shapley_config) def _get_icc_noise_function( causal_model: InvertibleStructuralCausalModel, target_node: Any, prediction_model: Union[PredictionModel, ClassificationModel, str], noise_samples: np.ndarray, node_names: Iterator[Any], target_samples: np.ndarray, auto_assign_quality: auto.AssignmentQuality, target_is_categorical: bool, ) -> Callable[[np.ndarray], np.ndarray]: if isinstance(prediction_model, str) and prediction_model not in ("approx", "exact"): raise ValueError( "Invalid value for prediction_model: %s! This should either be an instance of a PredictionModel or" "one of the two string options 'exact' or 'approx'." % prediction_model ) if not isinstance(prediction_model, str): prediction_model.fit(noise_samples, target_samples) return prediction_model.predict if prediction_model == "approx": prediction_model = auto.select_model(noise_samples, target_samples, auto_assign_quality) prediction_model.fit(noise_samples, target_samples) if target_is_categorical: return prediction_model.predict_probabilities else: return prediction_model.predict else: # Exact model def exact_model(X: np.ndarray) -> np.ndarray: return compute_data_from_noise(causal_model, pd.DataFrame(X, columns=[x for x in node_names]))[ target_node ].to_numpy() if target_is_categorical: list_of_classes = cast(ClassifierFCM, causal_model.causal_mechanism(target_node)).classifier_model.classes def prediction_method(X): return (shape_into_2d(exact_model(X)) == list_of_classes).astype(float) return prediction_method else: return exact_model

bloebp

45046e141ebd701820ca8a692c8f03ed5dce314c

e734f4e6245d404218b0ba11b14f7cc621bec7a0

return this directly

petergtz

py-why/dowhy

Change default parameter for estimating continous KL divergence

This should reduce variance in the estimation, increase runtime and, generally, accuracy.

2023-03-13 21:42:18+00:00

2023-03-15 19:04:17+00:00

dowhy/gcm/divergence.py

"""Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import numpy as np from scipy.stats import entropy from sklearn.neighbors import NearestNeighbors from dowhy.gcm.constant import EPS from dowhy.gcm.util.general import is_categorical, shape_into_2d def auto_estimate_kl_divergence(X: np.ndarray, Y: np.ndarray) -> float: if is_categorical(X): return estimate_kl_divergence_categorical(X, Y) elif is_probability_matrix(X): return estimate_kl_divergence_of_probabilities(X, Y) else: return estimate_kl_divergence_continuous(X, Y) def estimate_kl_divergence_continuous(X: np.ndarray, Y: np.ndarray) -> float: """Estimates KL-Divergence using k-nearest neighbours (Wang et al., 2009). Q. Wang, S. R. Kulkarni, and S. Verdú, "Divergence estimation for multidimensional densities via k-nearest-neighbor distances", IEEE Transactions on Information Theory, vol. 55, no. 5, pp. 2392-2405, May 2009. :param X: (N_1,D) Sample drawn from distribution P_X :param Y: (N_2,D) Sample drawn from distribution P_Y return: Estimated value of D(P_X||P_Y). """ X, Y = shape_into_2d(X, Y) if X.shape[1] != Y.shape[1]: raise RuntimeError( "Samples from X and Y need to have the same dimension, but X has dimension %d and Y has " "dimension %d." % (X.shape[1], Y.shape[1]) ) n, m = X.shape[0], Y.shape[0] d = float(X.shape[1]) k = int(np.sqrt(n)) x_neighbourhood = NearestNeighbors(n_neighbors=k + 1).fit(X) y_neighbourhood = NearestNeighbors(n_neighbors=k).fit(Y) distances_x, _ = x_neighbourhood.kneighbors(X, n_neighbors=k + 1) distances_y, _ = y_neighbourhood.kneighbors(X, n_neighbors=k) rho = distances_x[:, -1] nu = distances_y[:, -1] result = np.sum((d / n) * np.log((nu + EPS) / (rho + EPS))) + np.log(m / (n - 1)) if result < 0: result = 0 return result def estimate_kl_divergence_categorical(X: np.ndarray, Y: np.ndarray) -> float: X, Y = shape_into_2d(X, Y) if X.shape[1] != Y.shape[1]: raise RuntimeError( "Samples from X and Y need to have the same dimension, but X has dimension %d and Y has " "dimension %d." % (X.shape[1], Y.shape[1]) ) all_uniques = np.unique(np.vstack([X, Y])) p = np.array([(np.sum(X == i) + EPS) / (X.shape[0] + EPS) for i in all_uniques]) q = np.array([(np.sum(Y == i) + EPS) / (Y.shape[0] + EPS) for i in all_uniques]) return float(np.sum(p * np.log(p / q))) def estimate_kl_divergence_of_probabilities(X: np.ndarray, Y: np.ndarray) -> float: """Estimates the Kullback-Leibler divergence between each pair of probability vectors (row wise) in X and Y separately and returns the mean over all results.""" X, Y = shape_into_2d(X, Y) if X.shape[1] != Y.shape[1]: raise RuntimeError( "Samples from X and Y need to have the same dimension, but X has dimension %d and Y has " "dimension %d." % (X.shape[1], Y.shape[1]) ) return float(np.mean(entropy(X + EPS, Y + EPS, axis=1))) def is_probability_matrix(X: np.ndarray) -> bool: if X.ndim == 1: return np.all(np.isclose(np.sum(abs(X.astype(np.float64)), axis=0), 1)) else: return np.all(np.isclose(np.sum(abs(X.astype(np.float64)), axis=1), 1))

"""Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import numpy as np from scipy.stats import entropy from sklearn.neighbors import NearestNeighbors from dowhy.gcm.constant import EPS from dowhy.gcm.util.general import is_categorical, setdiff2d, shape_into_2d def auto_estimate_kl_divergence(X: np.ndarray, Y: np.ndarray) -> float: if is_categorical(X): return estimate_kl_divergence_categorical(X, Y) elif is_probability_matrix(X): return estimate_kl_divergence_of_probabilities(X, Y) else: return estimate_kl_divergence_continuous(X, Y) def estimate_kl_divergence_continuous( X: np.ndarray, Y: np.ndarray, k: int = 1, remove_common_elements: bool = True ) -> float: """Estimates KL-Divergence using k-nearest neighbours (Wang et al., 2009). Q. Wang, S. R. Kulkarni, and S. Verdú, "Divergence estimation for multidimensional densities via k-nearest-neighbor distances", IEEE Transactions on Information Theory, vol. 55, no. 5, pp. 2392-2405, May 2009. :param X: (N_1,D) Sample drawn from distribution P_X :param Y: (N_2,D) Sample drawn from distribution P_Y :param k: Number of neighbors to consider. :param remove_common_elements: If true, common values in X and Y are removed. This would otherwise lead to a KNN distance of zero for these values if k is set to 1, which would cause a division by zero error. return: Estimated value of D(P_X||P_Y). """ X, Y = shape_into_2d(X, Y) if X.shape[1] != Y.shape[1]: raise RuntimeError( "Samples from X and Y need to have the same dimension, but X has dimension %d and Y has " "dimension %d." % (X.shape[1], Y.shape[1]) ) X = X.astype(np.float64) Y = Y.astype(np.float64) # Making sure that X and Y have no overlapping values, which would lead to a distance of 0 with k=1 and, thus, to # a division by zero. if remove_common_elements: X = setdiff2d(X, Y, assume_unique=True) n, m = X.shape[0], Y.shape[0] if n == 0: return 0 d = float(X.shape[1]) x_neighbourhood = NearestNeighbors(n_neighbors=k + 1).fit(X) y_neighbourhood = NearestNeighbors(n_neighbors=k).fit(Y) distances_x, _ = x_neighbourhood.kneighbors(X, n_neighbors=k + 1) distances_y, _ = y_neighbourhood.kneighbors(X, n_neighbors=k) rho = distances_x[:, -1] nu = distances_y[:, -1] result = np.sum((d / n) * np.log(nu / rho)) + np.log(m / (n - 1)) if ~np.isfinite(result): raise RuntimeError( "Got a non-finite KL divergence! This can happen if both data sets have overlapping " "elements. Since these are normally removed by this method, double check whether the arrays " "are numeric." ) if result < 0: result = 0 return result def estimate_kl_divergence_categorical(X: np.ndarray, Y: np.ndarray) -> float: X, Y = shape_into_2d(X, Y) if X.shape[1] != Y.shape[1]: raise RuntimeError( "Samples from X and Y need to have the same dimension, but X has dimension %d and Y has " "dimension %d." % (X.shape[1], Y.shape[1]) ) all_uniques = np.unique(np.vstack([X, Y])) p = np.array([(np.sum(X == i) + EPS) / (X.shape[0] + EPS) for i in all_uniques]) q = np.array([(np.sum(Y == i) + EPS) / (Y.shape[0] + EPS) for i in all_uniques]) return float(np.sum(p * np.log(p / q))) def estimate_kl_divergence_of_probabilities(X: np.ndarray, Y: np.ndarray) -> float: """Estimates the Kullback-Leibler divergence between each pair of probability vectors (row wise) in X and Y separately and returns the mean over all results.""" X, Y = shape_into_2d(X, Y) if X.shape[1] != Y.shape[1]: raise RuntimeError( "Samples from X and Y need to have the same dimension, but X has dimension %d and Y has " "dimension %d." % (X.shape[1], Y.shape[1]) ) return float(np.mean(entropy(X + EPS, Y + EPS, axis=1))) def is_probability_matrix(X: np.ndarray) -> bool: if X.ndim == 1: return np.all(np.isclose(np.sum(abs(X.astype(np.float64)), axis=0), 1)) else: return np.all(np.isclose(np.sum(abs(X.astype(np.float64)), axis=1), 1))

bloebp

b8fb120033656688621477768fc7002aba4d3601

58f314c81c98c4e417b49fce990c82f1c535fe42

BTW, there is an elegant construct in Python for this: ```python X, Y = setdiff2d(X, Y, assume_unique=True), setdiff2d(Y, X, assume_unique=True) ```

petergtz

py-why/dowhy

Change default parameter for estimating continous KL divergence

This should reduce variance in the estimation, increase runtime and, generally, accuracy.

2023-03-13 21:42:18+00:00

2023-03-15 19:04:17+00:00

dowhy/gcm/util/general.py

"""Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import random from typing import Dict import numpy as np from scipy.optimize import minimize from sklearn.preprocessing import OneHotEncoder def shape_into_2d(*args): """If necessary, shapes the numpy inputs into 2D matrices. Example: array([1, 2, 3]) -> array([[1], [2], [3]]) 2 -> array([[2]]) :param args: The function expects numpy arrays as inputs and returns a reshaped (2D) version of them (if necessary). :return: Reshaped versions of the input numpy arrays. For instance, given 1D inputs X, Y and Z, then shape_into_2d(X, Y, Z) reshapes them into 2D and returns them. If an input is already 2D, it will not be modified and returned as it is. """ def shaping(X: np.ndarray): if X.ndim < 2: return np.column_stack([X]) elif X.ndim > 2: raise ValueError("Cannot reshape a %dD array into a 2D array!" % X.ndim) return X result = [shaping(x) for x in args] if len(result) == 1: return result[0] else: return result def set_random_seed(random_seed: int) -> None: """Sets random seed in numpy and the random module. :param random_seed: Random see for the numpy and random module. :return: None """ np.random.seed(random_seed) random.seed(random_seed) def fit_one_hot_encoders(X: np.ndarray) -> Dict[int, OneHotEncoder]: """Fits one-hot encoders to each categorical column in X. A categorical input needs to be a string, i.e. a categorical column consists only of strings. :param X: Input data matrix. :return: Dictionary that maps a column index to a scikit OneHotEncoder. """ X = shape_into_2d(X) one_hot_encoders = {} for column in range(X.shape[1]): if isinstance(X[0, column], str): one_hot_encoders[column] = OneHotEncoder(handle_unknown="ignore") one_hot_encoders[column].fit(X[:, column].reshape(-1, 1)) return one_hot_encoders def apply_one_hot_encoding(X: np.ndarray, one_hot_encoder_map: Dict[int, OneHotEncoder]) -> np.ndarray: X = shape_into_2d(X) if not one_hot_encoder_map: return X one_hot_features = [] for column in range(X.shape[1]): if column in one_hot_encoder_map: one_hot_features.append(one_hot_encoder_map[column].transform(X[:, column].reshape(-1, 1)).toarray()) else: one_hot_features.append(X[:, column].reshape(-1, 1)) return np.hstack(one_hot_features).astype(float) def is_categorical(X: np.ndarray) -> bool: """Checks if all of the given columns are categorical, i.e. either a string or a boolean. Only if all of the columns are categorical, this method will return True. Alternatively, consider has_categorical for checking if any of the columns is categorical. Note: A np matrix with mixed data types might internally convert numeric columns to strings and vice versa. To ensure that the given given data keeps the original data type, consider converting/initializing it with the dtype 'object'. For instance: np.array([[1, 'True', '0', 0.2], [3, 'False', '1', 2.3]], dtype=object) :param X: Input array to check if all columns are categorical. :return: True if all columns of the input are categorical, False otherwise. """ X = shape_into_2d(X) status = True for column in range(X.shape[1]): if (isinstance(X[0, column], int) or isinstance(X[0, column], float)) and np.isnan(X[0, column]): raise ValueError( "Input contains NaN values! This is currently not supported. " "Consider imputing missing values." ) status &= isinstance(X[0, column], str) or isinstance(X[0, column], bool) or isinstance(X[0, column], np.bool_) if not status: break return status def has_categorical(X: np.ndarray) -> bool: """Checks if any of the given columns are categorical, i.e. either a string or a boolean. If any of the columns is categorical, this method will return True. Alternatively, consider is_categorical for checking if all columns are categorical. Note: A np matrix with mixed data types might internally convert numeric columns to strings and vice versa. To ensure that the given given data keeps the original data type, consider converting/initializing it with the dtype 'object'. For instance: np.array([[1, 'True', '0', 0.2], [3, 'False', '1', 2.3]], dtype=object) :param X: Input array to check if all columns are categorical. :return: True if all columns of the input are categorical, False otherwise. """ X = shape_into_2d(X) for column in range(X.shape[1]): if is_categorical(X[:, column]): return True return False def means_difference(randomized_predictions: np.ndarray, baseline_values: np.ndarray) -> np.ndarray: return np.mean(randomized_predictions).squeeze() - np.mean(baseline_values).squeeze() def variance_of_deviations(randomized_predictions: np.ndarray, baseline_values: np.ndarray) -> np.ndarray: # Using the negative value here seeing that the Shapley estimation evaluates v(S u {i}) - v(S) for a subset S. In # case of variance, we have v(S u {i}) <= v(S), which would result in a negative contribution of players to the # target quantity (here, variance). return -np.var((randomized_predictions - baseline_values).squeeze()) def variance_of_matching_values(randomized_predictions: np.ndarray, baseline_values: np.ndarray) -> np.ndarray: # Using the negative value here seeing that the Shapley estimation evaluates v(S u {i}) - v(S) for a subset S. In # case of variance, we have v(S u {i}) <= v(S), which would result in a negative contribution of players to the # target quantity (here, variance). return -np.var((randomized_predictions == baseline_values).squeeze()) def geometric_median(x: np.ndarray) -> np.ndarray: def distance_function(x_input: np.ndarray) -> np.ndarray: return np.sum(np.sqrt(np.sum((x_input - x) ** 2, axis=1))) return minimize(distance_function, np.sum(x, axis=0) / x.shape[0]).x

"""Functions in this module should be considered experimental, meaning there might be breaking API changes in the future. """ import random from typing import Dict import numpy as np from scipy.optimize import minimize from sklearn.preprocessing import OneHotEncoder def shape_into_2d(*args): """If necessary, shapes the numpy inputs into 2D matrices. Example: array([1, 2, 3]) -> array([[1], [2], [3]]) 2 -> array([[2]]) :param args: The function expects numpy arrays as inputs and returns a reshaped (2D) version of them (if necessary). :return: Reshaped versions of the input numpy arrays. For instance, given 1D inputs X, Y and Z, then shape_into_2d(X, Y, Z) reshapes them into 2D and returns them. If an input is already 2D, it will not be modified and returned as it is. """ def shaping(X: np.ndarray): if X.ndim < 2: return np.column_stack([X]) elif X.ndim > 2: raise ValueError("Cannot reshape a %dD array into a 2D array!" % X.ndim) return X result = [shaping(x) for x in args] if len(result) == 1: return result[0] else: return result def set_random_seed(random_seed: int) -> None: """Sets random seed in numpy and the random module. :param random_seed: Random see for the numpy and random module. :return: None """ np.random.seed(random_seed) random.seed(random_seed) def fit_one_hot_encoders(X: np.ndarray) -> Dict[int, OneHotEncoder]: """Fits one-hot encoders to each categorical column in X. A categorical input needs to be a string, i.e. a categorical column consists only of strings. :param X: Input data matrix. :return: Dictionary that maps a column index to a scikit OneHotEncoder. """ X = shape_into_2d(X) one_hot_encoders = {} for column in range(X.shape[1]): if isinstance(X[0, column], str): one_hot_encoders[column] = OneHotEncoder(handle_unknown="ignore") one_hot_encoders[column].fit(X[:, column].reshape(-1, 1)) return one_hot_encoders def apply_one_hot_encoding(X: np.ndarray, one_hot_encoder_map: Dict[int, OneHotEncoder]) -> np.ndarray: X = shape_into_2d(X) if not one_hot_encoder_map: return X one_hot_features = [] for column in range(X.shape[1]): if column in one_hot_encoder_map: one_hot_features.append(one_hot_encoder_map[column].transform(X[:, column].reshape(-1, 1)).toarray()) else: one_hot_features.append(X[:, column].reshape(-1, 1)) return np.hstack(one_hot_features).astype(float) def is_categorical(X: np.ndarray) -> bool: """Checks if all of the given columns are categorical, i.e. either a string or a boolean. Only if all of the columns are categorical, this method will return True. Alternatively, consider has_categorical for checking if any of the columns is categorical. Note: A np matrix with mixed data types might internally convert numeric columns to strings and vice versa. To ensure that the given given data keeps the original data type, consider converting/initializing it with the dtype 'object'. For instance: np.array([[1, 'True', '0', 0.2], [3, 'False', '1', 2.3]], dtype=object) :param X: Input array to check if all columns are categorical. :return: True if all columns of the input are categorical, False otherwise. """ X = shape_into_2d(X) status = True for column in range(X.shape[1]): if (isinstance(X[0, column], int) or isinstance(X[0, column], float)) and np.isnan(X[0, column]): raise ValueError( "Input contains NaN values! This is currently not supported. " "Consider imputing missing values." ) status &= isinstance(X[0, column], str) or isinstance(X[0, column], bool) or isinstance(X[0, column], np.bool_) if not status: break return status def has_categorical(X: np.ndarray) -> bool: """Checks if any of the given columns are categorical, i.e. either a string or a boolean. If any of the columns is categorical, this method will return True. Alternatively, consider is_categorical for checking if all columns are categorical. Note: A np matrix with mixed data types might internally convert numeric columns to strings and vice versa. To ensure that the given given data keeps the original data type, consider converting/initializing it with the dtype 'object'. For instance: np.array([[1, 'True', '0', 0.2], [3, 'False', '1', 2.3]], dtype=object) :param X: Input array to check if all columns are categorical. :return: True if all columns of the input are categorical, False otherwise. """ X = shape_into_2d(X) for column in range(X.shape[1]): if is_categorical(X[:, column]): return True return False def setdiff2d(ar1: np.ndarray, ar2: np.ndarray, assume_unique: bool = False) -> np.ndarray: """This method generalizes numpy's setdiff1d to 2d, i.e., it compares vectors for arbitrary length. See https://numpy.org/doc/stable/reference/generated/numpy.setdiff1d.html for more details.""" if ar1.ndim == ar2.ndim != 2: raise ValueError("Only support 2D arrays!") if ar1.shape[1] != ar2.shape[1]: return ar1 dtype = {"names": ["f{}".format(i) for i in range(ar1.shape[1])], "formats": ar1.shape[1] * [ar1.dtype]} if not ar1.flags["C_CONTIGUOUS"]: ar1 = np.ascontiguousarray(ar1) if not ar2.flags["C_CONTIGUOUS"]: ar2 = np.ascontiguousarray(ar2) return ( np.setdiff1d(ar1.view(dtype), ar2.view(dtype), assume_unique=assume_unique) .view(ar1.dtype) .reshape(-1, ar1.shape[1]) ) def means_difference(randomized_predictions: np.ndarray, baseline_values: np.ndarray) -> np.ndarray: return np.mean(randomized_predictions).squeeze() - np.mean(baseline_values).squeeze() def variance_of_deviations(randomized_predictions: np.ndarray, baseline_values: np.ndarray) -> np.ndarray: # Using the negative value here seeing that the Shapley estimation evaluates v(S u {i}) - v(S) for a subset S. In # case of variance, we have v(S u {i}) <= v(S), which would result in a negative contribution of players to the # target quantity (here, variance). return -np.var((randomized_predictions - baseline_values).squeeze()) def variance_of_matching_values(randomized_predictions: np.ndarray, baseline_values: np.ndarray) -> np.ndarray: # Using the negative value here seeing that the Shapley estimation evaluates v(S u {i}) - v(S) for a subset S. In # case of variance, we have v(S u {i}) <= v(S), which would result in a negative contribution of players to the # target quantity (here, variance). return -np.var((randomized_predictions == baseline_values).squeeze()) def geometric_median(x: np.ndarray) -> np.ndarray: def distance_function(x_input: np.ndarray) -> np.ndarray: return np.sum(np.sqrt(np.sum((x_input - x) ** 2, axis=1))) return minimize(distance_function, np.sum(x, axis=0) / x.shape[0]).x

bloebp

b8fb120033656688621477768fc7002aba4d3601

58f314c81c98c4e417b49fce990c82f1c535fe42

Every local variable is temporary ;-). Just `dtype`?

petergtz

py-why/dowhy

Revise user guide entry for intrinsic causal influence

2023-02-24 21:36:44+00:00

2023-03-07 16:13:45+00:00

docs/source/user_guide/gcm_based_inference/answering_causal_questions/quantify_intrinsic_causal_influence.rst

Quantifying Intrinsic Causal Influence ====================================== By quantifying intrinsic causal influence, we answer the question: How strong is the causal influence of a source node to a target node that is not inherited from the parents of the source node? Naturally, descendants will have a zero intrinsic influence on the target node. How to use it ^^^^^^^^^^^^^^ To see how the method works, let us generate some data. >>> import numpy as np, pandas as pd, networkx as nx >>> from dowhy import gcm >>> from dowhy.gcm.uncertainty import estimate_variance >>> np.random.seed(10) # to reproduce these results >>> X = np.random.normal(loc=0, scale=1, size=1000) >>> Y = 2*X + np.random.normal(loc=0, scale=1, size=1000) >>> Z = 3*Y + np.random.normal(loc=0, scale=1, size=1000) >>> data = pd.DataFrame(data=dict(X=X, Y=Y, Z=Z)) Next, we will model cause-effect relationships as a structural causal model and fit it to the data. >>> causal_model = gcm.StructuralCausalModel(nx.DiGraph([('X', 'Y'), ('Y', 'Z')])) # X -> Y -> Z >>> causal_model.set_causal_mechanism('X', gcm.EmpiricalDistribution()) >>> causal_model.set_causal_mechanism('Y', gcm.AdditiveNoiseModel(gcm.ml.create_linear_regressor())) >>> causal_model.set_causal_mechanism('Z', gcm.AdditiveNoiseModel(gcm.ml.create_linear_regressor())) >>> gcm.fit(causal_model, data) .. Todo: Use auto module for automatic assignment! Finally, we can ask for the intrinsic causal influences of ancestors to a node of interest (e.g., :math:`Z`). >>> contributions = gcm.intrinsic_causal_influence(causal_model, 'Z', >>> gcm.ml.create_linear_regressor(), >>> lambda x, _: estimate_variance(x)) >>> contributions {'X': 33.34300732332951, 'Y': 9.599478688607254, 'Z': 0.9750701113403872} **Interpreting the results:** We estimated the intrinsic influence of ancestors of :math:`Z`, including itself, to its variance. These contributions sum up to the variance of :math:`Z`. We observe that ~76% of the variance of :math:`Z` comes from :math:`X`. Understanding the method ^^^^^^^^^^^^^^^^^^^^^^^^^ Consider the following example to get the intuition behind the notion of "intrinsic" causal influence we seek to measure here. A charity event is organised to collect funds to help an orphanage. At the end of the event, a donation box is passed around to each participant. Since the donation is voluntary, some may not donate for various reasons. For instance, they may not have the cash. In this scenario, a participant that simply passes the donation box to the other participant does not contribute anything to the collective donation after all. Each person's contribution then is simply the amount they donated. To measure the `intrinsic causal influence <https://arxiv.org/pdf/2007.00714.pdf>`_ of a source node to a target node, we need a functional causal model. In particular, we assume that the causal model of each node follows an additive noise model (ANM), i.e. :math:`X_j := f_j (\textrm{PA}_j) + N_j`, where :math:`\textrm{PA}_j` are the parents of node :math:`X_j` in the causal graph, and :math:`N_j` is the independent unobserved noise term. To compute the "intrinsic" contribution of ancestors of :math:`X_n` to some property (e.g. entropy, variance) of the marginal distribution of :math:`X_n`, we first have to set up our causal graph, and learn the causal model of each node from the dataset. Consider a causal graph :math:`X \rightarrow Y \rightarrow Z` as in the code example above, induced by the following ANMs. .. math:: X &:= N_X\\ Y &:= 2 X + N_Y\\ Z &:= 3 Y + N_Z \;, where :math:`N_w \sim \mathcal{N}(0, 1)`, for all :math:`w \in \{X, Y, Z\}`, are standard Normal noise variables. Suppose that we are interested in the contribution of each variable to the *variance* of the target :math:`Z`, i.e. :math:`\mathrm{Var}[Z]`. If there were no noise variables, everything can be contributed to the root node :math:`X` as all other variables would then be its deterministic function. The intrinsic contribution of each variable to the target quantity :math:`\mathrm{Var}[Z]` is then really the contribution of corresponding noise term. To compute "intrinsic" contribution, we also require conditional distributions of :math:`Z` given subsets of noise variables :math:`N_T`, i.e., :math:`P_{Z \mid N_T}`, where :math:`T \subseteq \{X, Y, Z\}`. We estimate them using an ANM. To this end, we have to specify the prediction model from a subset of noise variables to the target. Below, we quantify the intrinsic causal influence of :math:`X, Y` and :math:`Z` to :math:`\mathrm{Var}[Z]` using a linear prediction model from noise variables to :math:`Z`. >>> from dowhy.gcm.uncertainty import estimate_variance >>> prediction_model_from_noises_to_target = gcm.ml.create_linear_regressor() >>> node_to_contribution = gcm.intrinsic_causal_influence(causal_model, 'Z', >>> prediction_model_from_noises_to_target, >>> lambda x, _: estimate_variance(x)) .. note:: While using variance as uncertainty estimator gives valuable information about the contribution of nodes to the squared deviations in the target, one might be rather interested in other quantities, such as absolute deviations. This can also be simply computed by replacing the uncertainty estimator with a custom function: >>> mean_absolute_deviation_estimator = lambda x: np.mean(abs(x)) >>> node_to_contribution = gcm.intrinsic_causal_influence(causal_model, 'Z', >>> prediction_model_from_noises_to_target, >>> mean_absolute_deviation_estimator) If the choice of a prediction model is unclear, the prediction model parameter can also be set to "auto". .. Todo: Add this once confidence intervals is added! Above, we report point estimates of Shapley values from a sample drawn from the estimated joint distribution :math:`\hat{P}_{X, Y, Z}`. To quantify the uncertainty of those point estimates, we now compute their `bootstrap confidence intervals <https://ocw.mit .edu/courses/mathematics/18-05-introduction-to-probability-and-statistics-spring-2014/readings /MIT18_05S14_Reading24.pdf>`_ by simply running the above a number of times, and aggregating the results. >>> from gcm import confidence_intervals, bootstrap_sampling >>> >>> node_to_mean_contrib, node_to_contrib_conf = confidence_intervals( >>> bootstrap_sampling(gcm.intrinsic_causal_influence, causal_model, 'Z', >>> prediction_model_from_noises_to_target, lambda x, _: estimate_variance(x)), >>> confidence_level=0.95, >>> num_bootstrap_resamples=200) Note that the higher the number of repetitions, the better we are able to approximate the sampling distribution of Shapley values.

Quantifying Intrinsic Causal Influence ====================================== By quantifying intrinsic causal influence, we answer the question: How strong is the causal influence of a source node to a target node that is not inherited from the parents of the source node? Naturally, descendants will have a zero intrinsic causal influence on the target node. This method is based on the paper: Dominik Janzing, Patrick Blöbaum, Lenon Minorics, Philipp Faller, Atalanti Mastakouri. `Quantifying intrinsic causal contributions via structure preserving interventions <https://arxiv.org/abs/2007.00714>`_ arXiv:2007.00714, 2021 Let's consider an example from the paper to understand the type of influence being measured here. Imagine a schedule of three trains, ``Train A, Train B`` and ``Train C``, where the departure time of ``Train C`` depends on the arrival time of ``Train B``, and the departure time of ``Train B`` depends on the arrival time of ``Train A``. Suppose ``Train A`` typically experiences much longer delays than ``Train B`` and ``Train C``. The question we want to answer is: How strong is the influence of each train on the delay of ``Train C``? While there are various definitions of influence in the literature, we are interested in the *intrinsic causal influence*, which measures the influence of a node that has not been inherited from its parents, that is, the influence of the noise of a node. The reason for this is that, while ``Train C`` has to wait for ``Train B``, ``Train B`` mostly inherits the delay from ``Train A``. Thus, ``Train A`` should be identified as the node that contributes the most to the delay of ``Train C``. See the :ref:`Understanding the method <understand-method>` section for another example and more details. How to use it ^^^^^^^^^^^^^^ To see how the method works, let us generate some data following the example above: >>> import numpy as np, pandas as pd, networkx as nx >>> from dowhy import gcm >>> X = abs(np.random.normal(loc=0, scale=5, size=1000)) >>> Y = X + abs(np.random.normal(loc=0, scale=1, size=1000)) >>> Z = Y + abs(np.random.normal(loc=0, scale=1, size=1000)) >>> data = pd.DataFrame(data=dict(X=X, Y=Y, Z=Z)) Note the larger standard deviation of the 'noise' in :math:`X`. Next, we will model cause-effect relationships as a structural causal model and fit it to the data. Here, we are using the auto module to automatically assign causal mechanisms: >>> causal_model = gcm.StructuralCausalModel(nx.DiGraph([('X', 'Y'), ('Y', 'Z')])) # X -> Y -> Z >>> gcm.auto.assign_causal_mechanisms(causal_model, data) >>> gcm.fit(causal_model, data) Finally, we can ask for the intrinsic causal influences of ancestors to a node of interest (e.g., :math:`Z`). >>> contributions = gcm.intrinsic_causal_influence(causal_model, 'Z') >>> contributions {'X': 8.736841722582117, 'Y': 0.4491606897202768, 'Z': 0.35377942123477574} Note that, although we use a linear relationship here, the method can also handle arbitrary non-linear relationships. **Interpreting the results:** We estimated the intrinsic causal influence of ancestors of :math:`Z`, including itself, to its variance (the default measure). These contributions sum up to the variance of :math:`Z`. As we see here, we observe that ~92% of the variance of :math:`Z` comes from :math:`X`. .. _understand-method: Understanding the method ^^^^^^^^^^^^^^^^^^^^^^^^^ Let's look at a different example to explain the intuition behind the notion of "intrinsic" causal influence further: A charity event is organised to collect funds to help an orphanage. At the end of the event, a donation box is passed around to each participant. Since the donation is voluntary, some may not donate for various reasons. For instance, they may not have the cash. In this scenario, a participant that simply passes the donation box to the other participant does not contribute anything to the collective donation after all. Each person's contribution then is simply the amount they donated. To measure the intrinsic causal influence of a source node to a target node, we need a functional causal model. For instance, we can assume that the causal model of each node follows an additive noise model (ANM), i.e. :math:`X_j := f_j (\textrm{PA}_j) + N_j`, where :math:`\textrm{PA}_j` are the parents of node :math:`X_j` in the causal graph, and :math:`N_j` is the independent unobserved noise term. To compute the "intrinsic" contribution of ancestors of :math:`X_n` to some property (e.g. variance or entropy) of the marginal distribution of :math:`X_n`, we first have to set up our causal graph, and learn the causal model of each node from the dataset. Consider a causal graph :math:`X \rightarrow Y \rightarrow Z` as in the code example above, induced by the following ANMs. .. math:: X &:= N_X\\ Y &:= 2 X + N_Y\\ Z &:= 3 Y + N_Z \;, where :math:`N_w \sim \mathcal{N}(0, 1)`, for all :math:`w \in \{X, Y, Z\}`, are standard Normal noise variables. Suppose that we are interested in the contribution of each variable to the *variance* of the target :math:`Z`, i.e. :math:`\mathrm{Var}[Z]`. If there were no noise variables, everything can be contributed to the root node :math:`X` as all other variables would then be its deterministic function. The intrinsic contribution of each variable to the target quantity :math:`\mathrm{Var}[Z]` is then really the contribution of corresponding noise term. To compute "intrinsic" contribution, we also require conditional distributions of :math:`Z` given subsets of noise variables :math:`N_T`, i.e., :math:`P_{Z \mid N_T}`, where :math:`T \subseteq \{X, Y, Z\}`. We estimate them using an ANM. To this end, we have to specify the prediction model from a subset of noise variables to the target. Below, we quantify the intrinsic causal influence of :math:`X, Y` and :math:`Z` to :math:`\mathrm{Var}[Z]` using a linear prediction model from noise variables to :math:`Z`. >>> from dowhy.gcm.uncertainty import estimate_variance >>> prediction_model_from_noises_to_target = gcm.ml.create_linear_regressor() >>> node_to_contribution = gcm.intrinsic_causal_influence(causal_model, 'Z', >>> prediction_model_from_noises_to_target, >>> attribution_func=lambda x, _: estimate_variance(x)) Here, we explicitly defined the variance in the parameter ``attribution_func`` as the property we are interested in. .. note:: While using variance as uncertainty estimator gives valuable information about the contribution of nodes to the squared deviations in the target, one might be rather interested in other quantities, such as absolute deviations. This can also be simply computed by replacing the ``attribution_func`` with a custom function: >>> mean_absolute_deviation_estimator = lambda x: np.mean(abs(x)) >>> node_to_contribution = gcm.intrinsic_causal_influence(causal_model, 'Z', >>> prediction_model_from_noises_to_target, >>> attribution_func=mean_absolute_deviation_estimator) If the choice of a prediction model is unclear, the prediction model parameter can also be set to "auto". **Remark on using the mean for the attribution:** Although the ``attribution_func`` can be customized for a given use case, not all definitions make sense. For instance, using the **mean** does not provide any meaningful results. This is because the way influences are estimated is based on the concept of Shapley values. To understand this better, we can look at a general property of Shapley values, which states that the sum of Shapley values, in our case the sum of the attributions, adds up to :math:`\nu(T) - \nu(\{\})`. Here, :math:`\nu` is a set function (in our case, the expectation of the ``attribution_func``), and :math:`T` is the full set of all players (in our case, all noise variables). Now, if we use the mean, :math:`\nu(T)` becomes :math:`\mathbb{E}_\mathbf{N}[\mathbb{E}[Y | \mathbf{N}]] = \mathbb{E}[Y]`, because the target variable :math:`Y` depends deterministically on all noise variables :math:`\mathbf{N}` in the graphical causal model. Similarly, :math:`\nu(\{\})` becomes :math:`\mathbb{E}[Y | \{\}] = \mathbb{E}[Y]`. This would result in :math:`\mathbb{E}_\mathbb{N}[\mathbb{E}[Y | \mathbb{N}]] - \mathbb{E}[Y | \{\}] = 0`, i.e. the resulting attributions are close to 0. For more details, see Section 3.3 of the paper.

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Do X, Y, Z represent **delays**? If so, then delays can also be negative, trains may arrive faster than usual. A realistic distribution would then be a Gaussian distribution which has a non-zero mean with negative observations ~3 standard deviations away.

kailashbuki