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| # ########################################################################### | |
| # | |
| # CLOUDERA APPLIED MACHINE LEARNING PROTOTYPE (AMP) | |
| # (C) Cloudera, Inc. 2022 | |
| # All rights reserved. | |
| # | |
| # Applicable Open Source License: Apache 2.0 | |
| # | |
| # NOTE: Cloudera open source products are modular software products | |
| # made up of hundreds of individual components, each of which was | |
| # individually copyrighted. Each Cloudera open source product is a | |
| # collective work under U.S. Copyright Law. Your license to use the | |
| # collective work is as provided in your written agreement with | |
| # Cloudera. Used apart from the collective work, this file is | |
| # licensed for your use pursuant to the open source license | |
| # identified above. | |
| # | |
| # This code is provided to you pursuant a written agreement with | |
| # (i) Cloudera, Inc. or (ii) a third-party authorized to distribute | |
| # this code. If you do not have a written agreement with Cloudera nor | |
| # with an authorized and properly licensed third party, you do not | |
| # have any rights to access nor to use this code. | |
| # | |
| # Absent a written agreement with Cloudera, Inc. (“Cloudera”) to the | |
| # contrary, A) CLOUDERA PROVIDES THIS CODE TO YOU WITHOUT WARRANTIES OF ANY | |
| # KIND; (B) CLOUDERA DISCLAIMS ANY AND ALL EXPRESS AND IMPLIED | |
| # WARRANTIES WITH RESPECT TO THIS CODE, INCLUDING BUT NOT LIMITED TO | |
| # IMPLIED WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY AND | |
| # FITNESS FOR A PARTICULAR PURPOSE; (C) CLOUDERA IS NOT LIABLE TO YOU, | |
| # AND WILL NOT DEFEND, INDEMNIFY, NOR HOLD YOU HARMLESS FOR ANY CLAIMS | |
| # ARISING FROM OR RELATED TO THE CODE; AND (D)WITH RESPECT TO YOUR EXERCISE | |
| # OF ANY RIGHTS GRANTED TO YOU FOR THE CODE, CLOUDERA IS NOT LIABLE FOR ANY | |
| # DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, PUNITIVE OR | |
| # CONSEQUENTIAL DAMAGES INCLUDING, BUT NOT LIMITED TO, DAMAGES | |
| # RELATED TO LOST REVENUE, LOST PROFITS, LOSS OF INCOME, LOSS OF | |
| # BUSINESS ADVANTAGE OR UNAVAILABILITY, OR LOSS OR CORRUPTION OF | |
| # DATA. | |
| # | |
| # ########################################################################### | |
| from typing import Iterable | |
| import altair as alt | |
| from captum.attr._utils.visualization import ( | |
| VisualizationDataRecord, | |
| format_word_importances, | |
| _get_color, | |
| ) | |
| try: | |
| from IPython.display import display, HTML | |
| HAS_IPYTHON = True | |
| except ImportError: | |
| HAS_IPYTHON = False | |
| def format_classname(classname): | |
| return f'<td>{classname}</td>' | |
| def visualize_text( | |
| datarecords: Iterable[VisualizationDataRecord], legend: bool = True | |
| ) -> "HTML": # In quotes because this type doesn't exist in standalone mode | |
| assert HAS_IPYTHON, ( | |
| "IPython must be available to visualize text. " | |
| "Please run 'pip install ipython'." | |
| ) | |
| dom = [] | |
| dom.append( | |
| '<head><link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/bootstrap@4.0.0/dist/css/bootstrap.min.css" integrity="sha384-Gn5384xqQ1aoWXA+058RXPxPg6fy4IWvTNh0E263XmFcJlSAwiGgFAW/dAiS6JXm" crossorigin="anonymous"></head>' | |
| ) | |
| dom.append("""<table width:100; class="table">""") | |
| rows = [ | |
| "<thead>" | |
| "<tr>" | |
| "<th scope='col'><span class='text-nowrap'>Predicted Label</span></th>" | |
| "<th scope='col'><span class='text-nowrap'>Attribution Score</span></th>" | |
| "<th scope='col'><span class='text-nowrap'>Feature Importance</span></th>" | |
| "</tr>" | |
| "</thead>" | |
| ] | |
| for datarecord in datarecords: | |
| rows.append( | |
| "".join( | |
| [ | |
| "<tbody>", | |
| "<tr>", | |
| format_classname( | |
| f"{datarecord.pred_class.capitalize()}" | |
| ), | |
| format_classname(f"{round(datarecord.attr_score.item(), 2)}"), | |
| format_word_importances( | |
| datarecord.raw_input_ids, datarecord.word_attributions | |
| ), | |
| "<tr>", | |
| "</tbody>", | |
| ] | |
| ) | |
| ) | |
| dom.append("".join(rows)) | |
| dom.append("</table>") | |
| if legend: | |
| dom.append("<div class='row'>") | |
| dom.append("<div class='col-6'>") | |
| dom.append("<b>Legend: </b>") | |
| for value, label in zip([-1, 0, 1], ["Negative", "Neutral", "Positive"]): | |
| dom.append( | |
| '<span style="display: inline-block; width: 10px; height: 10px; \ | |
| border: 1px solid; background-color: \ | |
| {value}"></span> {label} '.format( | |
| value=_get_color(value), label=label | |
| ) | |
| ) | |
| dom.append("</div>") | |
| dom.append("<div class='col-6'></div>") | |
| dom.append("</div>") | |
| html = HTML("".join(dom)) | |
| display(html) | |
| return html | |
| def build_altair_classification_plot(format_cls_result): | |
| """ | |
| Builds Altair bar chart for classification results. | |
| Args: | |
| format_cls_result (List): Output from `format_classification_results()` | |
| """ | |
| source = alt.pd.DataFrame(format_cls_result) | |
| color_scale = alt.Scale( | |
| domain=[record["type"] for record in format_cls_result], | |
| range=["#00A3AF", "#F96702"], | |
| ) | |
| c = ( | |
| alt.Chart(source) | |
| .mark_bar(size=50) | |
| .encode( | |
| x=alt.X( | |
| "percentage_start:Q", axis=alt.Axis(title="Style Distribution (%)") | |
| ), | |
| x2=alt.X2("percentage_end:Q"), | |
| color=alt.Color( | |
| "type:N", | |
| legend=alt.Legend(title="Attribute"), | |
| scale=color_scale, | |
| ), | |
| ) | |
| .properties(height=150) | |
| ) | |
| return c | |