diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..ca458198b1bf18a1dd3c9d07a69cfab051ca6b91 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +data/**/*.npy filter=lfs diff=lfs merge=lfs -text +data/**/*.jsonl filter=lfs diff=lfs merge=lfs -text +data/**/*.json filter=lfs diff=lfs merge=lfs -text +assets/*.png filter=lfs diff=lfs merge=lfs -text +data/**/*.npz filter=lfs diff=lfs merge=lfs -text diff --git a/.huggingface.yaml b/.huggingface.yaml new file mode 100644 index 0000000000000000000000000000000000000000..23f293f7177e70e3af9c761c24fcd755ce30b864 --- /dev/null +++ b/.huggingface.yaml @@ -0,0 +1,3 @@ +# .huggingface.yaml +sdk: streamlit # or gradio +app_file: ./app/ui.py diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..c518600715b03dc515e239eff496934aa82f1e16 --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2025 Suman Adhya + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/app/ui_updated.py b/app/ui_updated.py new file mode 100644 index 0000000000000000000000000000000000000000..d3db765ca7ea28997b3ba41649362e57558148a7 --- /dev/null +++ b/app/ui_updated.py @@ -0,0 +1,450 @@ +import streamlit as st +import plotly.graph_objects as go +import plotly.colors as pc +import sys +import os +import base64 +import streamlit.components.v1 as components +import html + +# Absolute path to the repo root (assuming `ui.py` is in /app) +REPO_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), '..')) +sys.path.append(REPO_ROOT) +ASSETS_DIR = os.path.join(REPO_ROOT, 'assets') +DATA_DIR = os.path.join(REPO_ROOT, 'data') + +# Import functions from the backend +from backend.inference.process_beta import ( + load_beta_matrix, + get_top_words_over_time, + load_time_labels + ) +from backend.inference.word_selector import get_interesting_words, get_word_trend +from backend.inference.indexing_utils import load_index +from backend.inference.doc_retriever import ( + load_length_stats, + get_yearly_counts_for_word, + deduplicate_docs, + get_all_documents_for_word_year, + highlight_words, + extract_snippet +) +from backend.llm_utils.summarizer import summarize_multiword_docs, ask_multiturn_followup +from backend.llm_utils.label_generator import get_topic_labels +from backend.llm.llm_router import get_llm, list_supported_models +from backend.llm_utils.token_utils import estimate_k_max_from_word_stats + +def get_base64_image(image_path): + with open(image_path, "rb") as img_file: + return base64.b64encode(img_file.read()).decode() + +# --- Page Configuration --- +st.set_page_config( + page_title="DTECT", + page_icon="🔍", + layout="wide" +) + +# Sidebar branding and repo link +st.sidebar.markdown( + """ +
+ + + +
+
+ """.format(get_base64_image(os.path.join(ASSETS_DIR, 'Logo_light.png'))), + unsafe_allow_html=True +) + +# 1. Sidebar: Model and Dataset Selection +st.sidebar.title("Configuration") + +AVAILABLE_MODELS = ["DTM", "DETM", "CFDTM"] +ENV_VAR_MAP = { + "OpenAI": "OPENAI_API_KEY", + "Anthropic": "ANTHROPIC_API_KEY", + "Gemini": "GEMINI_API_KEY", + "Mistral": "MISTRAL_API_KEY" +} + +def list_datasets(data_dir): + return sorted([ + name for name in os.listdir(data_dir) + if os.path.isdir(os.path.join(data_dir, name)) + ]) + +with st.sidebar.expander("Select Dataset & Topic Model", expanded=True): + datasets = list_datasets(DATA_DIR) + selected_dataset = st.selectbox("Dataset", datasets, help="Choose an available dataset.") + selected_model = st.selectbox("Model", AVAILABLE_MODELS, help="Select topic model architecture.") + +# Resolve paths +dataset_path = os.path.join(DATA_DIR, selected_dataset) +model_path = os.path.join(dataset_path, selected_model) +docs_path = os.path.join(dataset_path, "docs.jsonl") +vocab_path = os.path.join(dataset_path, "processed/vocab.txt") +time2id_path = os.path.join(dataset_path, "processed/time2id.txt") +index_path = os.path.join(dataset_path, "inverted_index.json") +beta_path = os.path.join(model_path, "beta.npy") +label_cache_path = os.path.join(model_path, "topic_label_cache.json") +length_stats_path = os.path.join(dataset_path, "processed/length_stats.json") +lemma_map_path = os.path.join(dataset_path, "processed/lemma_to_forms.json") + +with st.sidebar.expander("LLM Settings", expanded=True): + provider = st.selectbox("LLM Provider", options=list(ENV_VAR_MAP.keys()), help="Choose the LLM backend.") + available_models = list_supported_models(provider) + model = st.selectbox("LLM Model", options=available_models) + env_var = ENV_VAR_MAP[provider] + api_key = os.getenv(env_var) + + if "llm_configured" not in st.session_state: + st.session_state.llm_configured = False + + if api_key: + st.session_state.llm_configured = True + else: + st.session_state.llm_configured = False + with st.form(key="api_key_form"): + entered_key = st.text_input(f"Enter your {provider} API Key", type="password") + submitted = st.form_submit_button("Submit and Confirm") + if submitted: + if entered_key: + os.environ[env_var] = entered_key + api_key = entered_key + st.session_state.llm_configured = True + st.rerun() + else: + st.warning("Please enter a key.") + + if not st.session_state.llm_configured: + st.warning("Please configure your LLM settings in the sidebar.") + st.stop() + + if api_key and not st.session_state.llm_configured: + st.session_state.llm_configured = True + + if not api_key: + st.session_state.llm_configured = False + + if not st.session_state.llm_configured: + st.warning("Please configure your LLM settings in the sidebar.") + st.stop() + +# Initialize LLM with the provided key +llm = get_llm(provider=provider, model=model, api_key=api_key) + +# 3. Load Data +@st.cache_resource +def load_resources(beta_path, vocab_path, docs_path, index_path, time2id_path, length_stats_path, lemma_map_path): + beta, vocab = load_beta_matrix(beta_path, vocab_path) + index, docs, lemma_to_forms = load_index(docs_file_path=docs_path, vocab=vocab, index_path=index_path, lemma_map_path=lemma_map_path) + time_labels = load_time_labels(time2id_path) + length_stats = load_length_stats(length_stats_path) + return beta, vocab, index, docs, lemma_to_forms, time_labels, length_stats + +# --- Main Title and Paper-aligned Intro --- +st.markdown("""# 🔍 DTECT: Dynamic Topic Explorer & Context Tracker""") + +# --- Load resources --- +try: + beta, vocab, index, docs, lemma_to_forms, time_labels, length_stats = load_resources( + beta_path, + vocab_path, + docs_path, + index_path, + time2id_path, + length_stats_path, + lemma_map_path + ) +except FileNotFoundError as e: + st.error(f"Missing required file: {e}") + st.stop() +except Exception as e: + st.error(f"Failed to load data: {str(e)}") + st.stop() + +timestamps = list(range(len(time_labels))) +num_topics = beta.shape[1] +# Estimate max_k based on document length stats and selected LLM +suggested_max_k = estimate_k_max_from_word_stats(length_stats.get("avg_len"), model_name=model, provider=provider) + + +# ============================================================================== +# 1. 🏷 TOPIC LABELING +# ============================================================================== +st.markdown("## 1️⃣ 🏷️ Topic Labeling") +st.info("Topics are automatically labeled using LLMs by analyzing their temporal word distributions.") + +topic_labels = get_topic_labels(beta, vocab, time_labels, llm, label_cache_path) +topic_options = list(topic_labels.values()) +selected_topic_label = st.selectbox("Select a Topic", topic_options, help="LLM-generated topic label") +label_to_topic = {v: k for k, v in topic_labels.items()} +selected_topic = label_to_topic[selected_topic_label] + +# ============================================================================== +# 2. 💡 INFORMATIVE WORD DETECTION & 📊 TREND VISUALIZATION +# ============================================================================== +st.markdown("---") +st.markdown("## 2️⃣ 💡 Informative Word Detection & 📊 Trend Visualization") +st.info("Explore top/interesting words for each topic, and visualize their trends over time.") + +top_n_words = st.slider("Number of Top Words per Topic", min_value=5, max_value=500, value=10) +top_words = get_top_words_over_time( + beta=beta, + vocab=vocab, + topic_id=selected_topic, + top_n=top_n_words +) + +st.write(f"### Top {top_n_words} Words for Topic '{selected_topic_label}' (Ranked):") +scrollable_top_words = "
" +words_per_col = (top_n_words + 3) // 4 +columns = [top_words[i:i+words_per_col] for i in range(0, len(top_words), words_per_col)] +scrollable_top_words += "
" +word_rank = 1 +for col in columns: + scrollable_top_words += "
" + for word in col: + scrollable_top_words += f"
{word_rank}. {word}
" + word_rank += 1 + scrollable_top_words += "
" +scrollable_top_words += "
" +st.markdown(scrollable_top_words, unsafe_allow_html=True) + +st.markdown("
", unsafe_allow_html=True) + +if st.button("💡 Suggest Informative Words", key="suggest_topic_words"): + top_words = get_top_words_over_time( + beta=beta, + vocab=vocab, + topic_id=selected_topic, + top_n=top_n_words + ) + interesting_words = get_interesting_words(beta, vocab, topic_id=selected_topic, restrict_to=top_words) + st.session_state.interesting_words = interesting_words + st.session_state.selected_words = interesting_words[:15] # pre-fill multiselect + styled_words = " ".join([ + f"{w}" + for w in interesting_words + ]) + st.markdown( + f"**Top Informative Words from Topic '{selected_topic_label}':**
{styled_words}", + unsafe_allow_html=True + ) + +st.markdown("#### 📈 Plot Word Trends Over Time") +all_word_options = vocab +interesting_words = st.session_state.get("interesting_words", []) + +if "selected_words" not in st.session_state: + st.session_state.selected_words = interesting_words[:15] # initial default + +selected_words = st.multiselect( + "Select words to visualize trends", + options=all_word_options, + default=st.session_state.selected_words, + key="selected_words" +) +if selected_words: + fig = go.Figure() + color_cycle = pc.qualitative.Plotly + for i, word in enumerate(selected_words): + trend = get_word_trend(beta, vocab, word, topic_id=selected_topic) + color = color_cycle[i % len(color_cycle)] + fig.add_trace(go.Scatter( + x=time_labels, + y=trend, + name=word, + line=dict(color=color), + legendgroup=word, + showlegend=True + )) + fig.update_layout(title="", xaxis_title="Year", yaxis_title="Importance") + st.plotly_chart(fig, use_container_width=True) + +# ============================================================================== +# 3. 🔍 DOCUMENT RETRIEVAL & 📃 SUMMARIZATION +# ============================================================================== +st.markdown("---") +st.markdown("## 3️⃣ 🔍 Document Retrieval & 📃 Summarization") +st.info("Retrieve and summarize documents matching selected words and years.") + +if selected_words: + st.markdown("#### 📊 Document Frequency Over Time") + selected_words_for_counts = st.multiselect( + "Select word(s) to show document frequencies over time", + options=selected_words, + default=selected_words[:3], + key="word_counts_multiselect" + ) + + if selected_words_for_counts: + color_cycle = pc.qualitative.Set2 + bar_fig = go.Figure() + for i, word in enumerate(selected_words_for_counts): + doc_years, doc_counts = get_yearly_counts_for_word(index=index, word=word) + bar_fig.add_trace(go.Bar( + x=doc_years, + y=doc_counts, + name=word, + marker_color=color_cycle[i % len(color_cycle)], + opacity=0.85 + )) + bar_fig.update_layout( + barmode="group", + title="Document Frequency Over Time", + xaxis_title="Year", + yaxis_title="Document Count", + xaxis=dict( + tickmode='linear', + dtick=1, + tickformat='d' + ), + bargap=0.2 + ) + st.plotly_chart(bar_fig, use_container_width=True) + + st.markdown("#### 📄 Inspect Documents for Word-Year Pairs") + # selected_year = st.slider("Select year", min_value=int(time_labels[0]), max_value=int(time_labels[-1]), key="inspect_year_slider") + selected_year = st.selectbox( + "Select year", + options=time_labels, # Use the list of available time labels (years) + index=0, # Default to the first year in the list + key="inspect_year_selectbox" + ) + collected_docs_raw = [] + for word in selected_words_for_counts: + docs_for_word_year = get_all_documents_for_word_year( + index=index, + docs_file_path=docs_path, + word=word, + year=selected_year + ) + for doc in docs_for_word_year: + doc["__word__"] = word + collected_docs_raw.extend(docs_for_word_year) + + if collected_docs_raw: + st.session_state.collected_deduplicated_docs = deduplicate_docs(collected_docs_raw) + st.write(f"Found {len(collected_docs_raw)} matching documents, {len(st.session_state.collected_deduplicated_docs)} after deduplication.") + + html_blocks = "" + for doc in st.session_state.collected_deduplicated_docs: + word = doc["__word__"] + full_text = html.escape(doc["text"]) + snippet_text = extract_snippet(doc["text"], word) + highlighted_snippet = highlight_words( + snippet_text, + query_words=selected_words_for_counts, + lemma_to_forms=lemma_to_forms + ) + html_blocks += f""" +
+
Match: {word} | Doc ID: {doc['id']} | Timestamp: {doc['timestamp']}
+
Snippet: {highlighted_snippet}
+
+ Show full document + 
{full_text}
+
+
+ """ + min_height = 120 + max_height = 700 + per_doc_height = 130 + dynamic_height = min_height + per_doc_height * max(len(st.session_state.collected_deduplicated_docs) - 1, 0) + container_height = min(dynamic_height, max_height) + scrollable_html = f""" +
+ {html_blocks} +
+ """ + components.html(scrollable_html, height=container_height, scrolling=True) + else: + st.warning("No documents found for the selected words and year.") + +# ============================================================================== +# 4. 💬 CHAT ASSISTANT (Summary & Follow-up) +# ============================================================================== +st.markdown("---") +st.markdown("## 4️⃣ 💬 Chat Assistant") +st.info("Generate summaries from the inspected documents and ask follow-up questions.") + +if "summary" not in st.session_state: + st.session_state.summary = None +if "context_for_followup" not in st.session_state: + st.session_state.context_for_followup = "" +if "followup_history" not in st.session_state: + st.session_state.followup_history = [] + +# MMR K selection +st.markdown(f"**Max documents for summarization (k):**") +st.markdown(f"The suggested maximum number of documents for summarization (k) based on the average document length and the selected LLM is **{suggested_max_k}**.") +mmr_k = st.slider( + "Select the maximum number of documents (k) for MMR (Maximum Marginal Relevance) selection for summarization.", + min_value=1, + max_value=20, # Set a reasonable max for k, can be adjusted + value=min(suggested_max_k, 20), # Use suggested_max_k as default, capped at 20 + help="This value determines how many relevant and diverse documents will be selected for summarization." +) + +if st.button("📃 Summarize These Documents"): + if st.session_state.get("collected_deduplicated_docs"): + st.session_state.summary = None + st.session_state.context_for_followup = "" + st.session_state.followup_history = [] + with st.spinner("Selecting and summarizing documents..."): + summary, mmr_docs = summarize_multiword_docs( + selected_words_for_counts, + selected_year, + st.session_state.collected_deduplicated_docs, + llm, + k=mmr_k + ) + st.session_state.summary = summary + st.session_state.context_for_followup = "\n".join( + f"Document {i+1}:\n{doc.page_content.strip()}" for i, doc in enumerate(mmr_docs) + ) + st.session_state.followup_history.append( + {"role": "user", "content": f"Please summarize the context of the words '{', '.join(selected_words_for_counts)}' in {selected_year} based on the provided documents."} + ) + st.session_state.followup_history.append( + {"role": "assistant", "content": st.session_state.summary} + ) + st.success(f"✅ Summary generated from {len(mmr_docs)} MMR-selected documents.") + else: + st.warning("⚠️ No documents collected to summarize. Please inspect some documents first.") + +if st.session_state.summary: + st.markdown(f"**Summary for words `{', '.join(selected_words_for_counts)}` in `{selected_year}`:**") + st.write(st.session_state.summary) + + if st.checkbox("💬 Ask follow-up questions about this summary", key="enable_followup"): + with st.expander("View the documents used for this conversation"): + st.text_area("Context Documents", st.session_state.context_for_followup, height=200) + st.info("Ask a question based on the summary and the documents above.") + for msg in st.session_state.followup_history[2:]: + with st.chat_message(msg["role"], avatar="🧑" if msg["role"] == "user" else "🤖"): + st.markdown(msg["content"]) + if user_query := st.chat_input("Ask a follow-up question..."): + with st.chat_message("user", avatar="🧑"): + st.markdown(user_query) + st.session_state.followup_history.append({"role": "user", "content": user_query}) + with st.spinner("Thinking..."): + followup_response = ask_multiturn_followup( + history=st.session_state.followup_history, + question=user_query, + llm=llm, + context_texts=st.session_state.context_for_followup + ) + st.session_state.followup_history.append({"role": "assistant", "content": followup_response}) + if followup_response.startswith("[Error"): + st.error(followup_response) + else: + with st.chat_message("assistant", avatar="🤖"): + st.markdown(followup_response) + st.rerun() \ No newline at end of file diff --git a/assets/Logo_light.png b/assets/Logo_light.png new file mode 100644 index 0000000000000000000000000000000000000000..607d48a9948f9798871e324b3b6e34fef8fec1ce --- /dev/null +++ b/assets/Logo_light.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4237eeb306339868507feb9ae60b6c5ab5980abe769d8b26d3635eed55e9714f +size 317450 diff --git a/backend/__init__.py b/backend/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6b5517720eef30b7c7e6af35a6bd163682c9302c --- /dev/null +++ b/backend/__init__.py @@ -0,0 +1,81 @@ +# === Inference components === +from .inference.process_beta import ( + load_beta_matrix, + get_top_words_at_time, + get_top_words_over_time, + load_time_labels + ) + +from .inference.indexing_utils import load_index +from .inference.word_selector import ( + get_interesting_words, + get_word_trend +) +from .inference.peak_detector import detect_peaks +from .inference.doc_retriever import ( + load_length_stats, + get_yearly_counts_for_word, + get_all_documents_for_word_year, + deduplicate_docs, + extract_snippet, + highlight, + get_docs_by_ids, +) + +# === LLM components === +from .llm_utils.label_generator import label_topic_temporal, get_topic_labels +from .llm_utils.token_utils import ( + get_token_limit_for_model, + count_tokens, + estimate_avg_tokens_per_doc, + estimate_max_k, + estimate_max_k_fast + ) +from .llm_utils.summarizer import ( + summarize_docs, + summarize_multiword_docs, + ask_multiturn_followup +) +from .llm.llm_router import ( + list_supported_models, + get_llm +) + +# === Dataset utilities === +from .datasets import dynamic_dataset +from .datasets import preprocess +from .datasets.utils import logger, _utils +from .datasets.data import file_utils, download + +# === Evaluation === +from .evaluation.CoherenceModel_ttc import CoherenceModel_ttc +from .evaluation.eval import TopicQualityAssessor + +# === Models === +from .models.DETM import DETM +from .models.DTM_trainer import DTMTrainer +from .models.CFDTM.CFDTM import CFDTM +from .models.dynamic_trainer import DynamicTrainer + +__all__ = [ + # Inference + "load_beta_matrix", "load_time_labels", "get_top_words_at_time", "get_top_words_over_time", + "load_index", "get_interesting_words", "get_word_trend", "detect_peaks", + "load_length_stats", "get_yearly_counts_for_word", "get_all_documents_for_word_year", + "deduplicate_docs", "extract_snippet", "highlight", "get_docs_by_ids", + + # LLM + "summarize_docs", "summarize_multiword_docs", "ask_multiturn_followup", + "get_token_limit_for_model", "list_supported_models", "get_llm", + "label_topic_temporal", "get_topic_labels", "count_tokens", + "estimate_avg_tokens_per_doc", "estimate_max_k", "estimate_max_k_fast", + + # Dataset + "dynamic_dataset", "preprocess", "logger","_utils", "file_utils", "download", + + # Evaluation + "CoherenceModel_ttc", "TopicQualityAssessor", + + # Models + "DETM", "DTMTrainer", "CFDTM", "DynamicTrainer" +] diff --git a/backend/datasets/_preprocess.py b/backend/datasets/_preprocess.py new file mode 100644 index 0000000000000000000000000000000000000000..e77e9e56f25986b63fb2bbaf2dba56f1f7264450 --- /dev/null +++ b/backend/datasets/_preprocess.py @@ -0,0 +1,447 @@ +import os +import re +import string +import gensim.downloader +from collections import Counter +import numpy as np +import scipy.sparse +from tqdm import tqdm +from sklearn.feature_extraction.text import CountVectorizer + +from backend.datasets.data import file_utils +from backend.datasets.utils._utils import get_stopwords_set +from backend.datasets.utils.logger import Logger +import json +import nltk +from nltk.stem import WordNetLemmatizer + +logger = Logger("WARNING") + +try: + nltk.data.find('corpora/wordnet') +except LookupError: + nltk.download('wordnet', quiet=True) +try: + nltk.data.find('corpora/omw-1.4') +except LookupError: + nltk.download('omw-1.4', quiet=True) + +# compile some regexes +punct_chars = list(set(string.punctuation) - set("'")) +punct_chars.sort() +punctuation = ''.join(punct_chars) +replace = re.compile('[%s]' % re.escape(punctuation)) +alpha = re.compile('^[a-zA-Z_]+$') +alpha_or_num = re.compile('^[a-zA-Z_]+|[0-9_]+$') +alphanum = re.compile('^[a-zA-Z0-9_]+$') + + +class Tokenizer: + def __init__(self, + stopwords="English", + keep_num=False, + keep_alphanum=False, + strip_html=False, + no_lower=False, + min_length=3, + lemmatize=True, + ): + self.keep_num = keep_num + self.keep_alphanum = keep_alphanum + self.strip_html = strip_html + self.lower = not no_lower + self.min_length = min_length + + self.stopword_set = get_stopwords_set(stopwords) + + self.lemmatize = lemmatize + if lemmatize: + self.lemmatizer = WordNetLemmatizer() + + def clean_text(self, text, strip_html=False, lower=True, keep_emails=False, keep_at_mentions=False): + # remove html tags + if strip_html: + text = re.sub(r'<[^>]+>', '', text) + else: + # replace angle brackets + text = re.sub(r'<', '(', text) + text = re.sub(r'>', ')', text) + # lower case + if lower: + text = text.lower() + # eliminate email addresses + if not keep_emails: + text = re.sub(r'\S+@\S+', ' ', text) + # eliminate @mentions + if not keep_at_mentions: + text = re.sub(r'\s@\S+', ' ', text) + # replace underscores with spaces + text = re.sub(r'_', ' ', text) + # break off single quotes at the ends of words + text = re.sub(r'\s\'', ' ', text) + text = re.sub(r'\'\s', ' ', text) + # remove periods + text = re.sub(r'\.', '', text) + # replace all other punctuation (except single quotes) with spaces + text = replace.sub(' ', text) + # remove single quotes + text = re.sub(r'\'', '', text) + # replace all whitespace with a single space + text = re.sub(r'\s', ' ', text) + # strip off spaces on either end + text = text.strip() + return text + + def tokenize(self, text): + text = self.clean_text(text, self.strip_html, self.lower) + tokens = text.split() + + tokens = ['_' if t in self.stopword_set else t for t in tokens] + + # remove tokens that contain numbers + if not self.keep_alphanum and not self.keep_num: + tokens = [t if alpha.match(t) else '_' for t in tokens] + + # or just remove tokens that contain a combination of letters and numbers + elif not self.keep_alphanum: + tokens = [t if alpha_or_num.match(t) else '_' for t in tokens] + + # drop short tokens + if self.min_length > 0: + tokens = [t if len(t) >= self.min_length else '_' for t in tokens] + + if getattr(self, "lemmatize", False): + tokens = [self.lemmatizer.lemmatize(t) if t != '_' else t for t in tokens] + + unigrams = [t for t in tokens if t != '_'] + return unigrams + + +def make_word_embeddings(vocab): + glove_vectors = gensim.downloader.load('glove-wiki-gigaword-200') + word_embeddings = np.zeros((len(vocab), glove_vectors.vectors.shape[1])) + + num_found = 0 + + try: + key_word_list = glove_vectors.index_to_key + except: + key_word_list = glove_vectors.index2word + + for i, word in enumerate(tqdm(vocab, desc="loading word embeddings")): + if word in key_word_list: + word_embeddings[i] = glove_vectors[word] + num_found += 1 + + logger.info(f'number of found embeddings: {num_found}/{len(vocab)}') + + return scipy.sparse.csr_matrix(word_embeddings) + + +class Preprocess: + def __init__(self, + tokenizer=None, + test_sample_size=None, + test_p=0.2, + stopwords="English", + min_doc_count=0, + max_doc_freq=1.0, + keep_num=False, + keep_alphanum=False, + strip_html=False, + no_lower=False, + min_length=3, + min_term=0, + vocab_size=None, + seed=42, + verbose=True, + lemmatize=True, + ): + """ + Args: + test_sample_size: + Size of the test set. + test_p: + Proportion of the test set. This helps sample the train set based on the size of the test set. + stopwords: + List of stopwords to exclude. + min-doc-count: + Exclude words that occur in less than this number of documents. + max_doc_freq: + Exclude words that occur in more than this proportion of documents. + keep-num: + Keep tokens made of only numbers. + keep-alphanum: + Keep tokens made of a mixture of letters and numbers. + strip_html: + Strip HTML tags. + no-lower: + Do not lowercase text + min_length: + Minimum token length. + min_term: + Minimum term number + vocab-size: + Size of the vocabulary (by most common in the union of train and test sets, following above exclusions) + seed: + Random integer seed (only relevant for choosing test set) + lemmatize: + Whether to apply lemmatization to the tokens. + """ + + self.test_sample_size = test_sample_size + self.min_doc_count = min_doc_count + self.max_doc_freq = max_doc_freq + self.min_term = min_term + self.test_p = test_p + self.vocab_size = vocab_size + self.seed = seed + + if tokenizer is not None: + self.tokenizer = tokenizer + else: + self.tokenizer = Tokenizer( + stopwords, + keep_num, + keep_alphanum, + strip_html, + no_lower, + min_length, + lemmatize=lemmatize + ).tokenize + + if verbose: + logger.set_level("DEBUG") + else: + logger.set_level("WARNING") + + def parse(self, texts, vocab): + if not isinstance(texts, list): + texts = [texts] + + vocab_set = set(vocab) + parsed_texts = list() + for i, text in enumerate(tqdm(texts, desc="parsing texts")): + tokens = self.tokenizer(text) + tokens = [t for t in tokens if t in vocab_set] + parsed_texts.append(" ".join(tokens)) + + vectorizer = CountVectorizer(vocabulary=vocab, tokenizer=lambda x: x.split()) + sparse_bow = vectorizer.fit_transform(parsed_texts) + return parsed_texts, sparse_bow + + def preprocess_jsonlist(self, dataset_dir, label_name=None, use_partition=True): + if use_partition: + train_items = file_utils.read_jsonlist(os.path.join(dataset_dir, 'train.jsonlist')) + test_items = file_utils.read_jsonlist(os.path.join(dataset_dir, 'test.jsonlist')) + else: + raw_path = os.path.join(dataset_dir, 'docs.jsonl') + with open(raw_path, 'r', encoding='utf-8') as f: + train_items = [json.loads(line.strip()) for line in f if line.strip()] + test_items = [] + + logger.info(f"Found training documents {len(train_items)} testing documents {len(test_items)}") + + # Initialize containers + raw_train_texts, train_labels, raw_train_times = [], [], [] + raw_test_texts, test_labels, raw_test_times = [], [], [] + + # Process train items + for item in train_items: + raw_train_texts.append(item['text']) + raw_train_times.append(str(item['timestamp'])) + if label_name and label_name in item: + train_labels.append(item[label_name]) + + # Process test items + for item in test_items: + raw_test_texts.append(item['text']) + raw_test_times.append(str(item['timestamp'])) + if label_name and label_name in item: + test_labels.append(item[label_name]) + + # Create and apply time2id mapping + all_times = sorted(set(raw_train_times + raw_test_times)) + time2id = {t: i for i, t in enumerate(all_times)} + + train_times = np.array([time2id[t] for t in raw_train_times], dtype=np.int32) + test_times = np.array([time2id[t] for t in raw_test_times], dtype=np.int32) if raw_test_times else None + + # Preprocess and get sample indices + rst = self.preprocess(raw_train_texts, train_labels, raw_test_texts, test_labels) + train_idx = rst.get("train_idx") + test_idx = rst.get("test_idx") + + # Add filtered timestamps to result for saving later + rst["train_times"] = train_times[train_idx] + if test_times is not None and test_idx is not None: + rst["test_times"] = test_times[test_idx] + + # Add time2id to result dict + rst["time2id"] = time2id + + return rst + + + def convert_labels(self, train_labels, test_labels): + if train_labels: + label_list = list(set(train_labels).union(set(test_labels))) + label_list.sort() + n_labels = len(label_list) + label2id = dict(zip(label_list, range(n_labels))) + + logger.info(f"label2id: {label2id}") + + train_labels = [label2id[label] for label in train_labels] + + if test_labels: + test_labels = [label2id[label] for label in test_labels] + + return train_labels, test_labels + + def preprocess( + self, + raw_train_texts, + train_labels=None, + raw_test_texts=None, + test_labels=None, + pretrained_WE=True + ): + np.random.seed(self.seed) + + train_texts = list() + test_texts = list() + word_counts = Counter() + doc_counts_counter = Counter() + + train_labels, test_labels = self.convert_labels(train_labels, test_labels) + + for text in tqdm(raw_train_texts, desc="loading train texts"): + tokens = self.tokenizer(text) + word_counts.update(tokens) + doc_counts_counter.update(set(tokens)) + parsed_text = ' '.join(tokens) + train_texts.append(parsed_text) + + if raw_test_texts: + for text in tqdm(raw_test_texts, desc="loading test texts"): + tokens = self.tokenizer(text) + word_counts.update(tokens) + doc_counts_counter.update(set(tokens)) + parsed_text = ' '.join(tokens) + test_texts.append(parsed_text) + + words, doc_counts = zip(*doc_counts_counter.most_common()) + doc_freqs = np.array(doc_counts) / float(len(train_texts) + len(test_texts)) + + vocab = [word for i, word in enumerate(words) if doc_counts[i] >= self.min_doc_count and doc_freqs[i] <= self.max_doc_freq] + + # filter vocabulary + if self.vocab_size is not None: + vocab = vocab[:self.vocab_size] + + vocab.sort() + + train_idx = [i for i, text in enumerate(train_texts) if len(text.split()) >= self.min_term] + train_idx = np.asarray(train_idx) + + if raw_test_texts is not None: + test_idx = [i for i, text in enumerate(test_texts) if len(text.split()) >= self.min_term] + test_idx = np.asarray(test_idx) + else: + test_idx = None + + # randomly sample + if self.test_sample_size and raw_test_texts is not None: + logger.info("sample train and test sets...") + + train_num = len(train_idx) + test_num = len(test_idx) + test_sample_size = min(test_num, self.test_sample_size) + train_sample_size = int((test_sample_size / self.test_p) * (1 - self.test_p)) + if train_sample_size > train_num: + test_sample_size = int((train_num / (1 - self.test_p)) * self.test_p) + train_sample_size = train_num + + train_idx = train_idx[np.sort(np.random.choice(train_num, train_sample_size, replace=False))] + test_idx = test_idx[np.sort(np.random.choice(test_num, test_sample_size, replace=False))] + + logger.info(f"sampled train size: {len(train_idx)}") + logger.info(f"sampled test size: {len(test_idx)}") + + train_texts, train_bow = self.parse([train_texts[i] for i in train_idx], vocab) + + rst = { + 'vocab': vocab, + 'train_bow': train_bow, + "train_texts": train_texts, + "train_idx": train_idx, # <--- NEW: indices of kept train samples + } + + if train_labels: + rst['train_labels'] = np.asarray(train_labels)[train_idx] + + logger.info(f"Real vocab size: {len(vocab)}") + logger.info(f"Real training size: {len(train_texts)} \t avg length: {rst['train_bow'].sum() / len(train_texts):.3f}") + + if raw_test_texts: + rst['test_texts'], rst['test_bow'] = self.parse(np.asarray(test_texts)[test_idx].tolist(), vocab) + rst["test_idx"] = test_idx # <--- NEW: indices of kept test samples + + if test_labels: + rst['test_labels'] = np.asarray(test_labels)[test_idx] + + logger.info(f"Real testing size: {len(rst['test_texts'])} \t avg length: {rst['test_bow'].sum() / len(rst['test_texts']):.3f}") + + if pretrained_WE: + rst['word_embeddings'] = make_word_embeddings(vocab) + + return rst + + def save( + self, + output_dir, + vocab, + train_texts, + train_bow, + word_embeddings=None, + train_labels=None, + test_texts=None, + test_bow=None, + test_labels=None, + train_times=None, + test_times=None, + time2id=None # <-- new parameter + ): + file_utils.make_dir(output_dir) + + file_utils.save_text(vocab, f"{output_dir}/vocab.txt") + file_utils.save_text(train_texts, f"{output_dir}/train_texts.txt") + scipy.sparse.save_npz(f"{output_dir}/train_bow.npz", scipy.sparse.csr_matrix(train_bow)) + + if word_embeddings is not None: + scipy.sparse.save_npz(f"{output_dir}/word_embeddings.npz", word_embeddings) + + if train_labels: + np.savetxt(f"{output_dir}/train_labels.txt", train_labels, fmt='%i') + + if train_times is not None: + np.savetxt(f"{output_dir}/train_times.txt", train_times, fmt='%i') + + if test_bow is not None: + scipy.sparse.save_npz(f"{output_dir}/test_bow.npz", scipy.sparse.csr_matrix(test_bow)) + + if test_texts is not None: + file_utils.save_text(test_texts, f"{output_dir}/test_texts.txt") + + if test_labels: + np.savetxt(f"{output_dir}/test_labels.txt", test_labels, fmt='%i') + + if test_times is not None: + np.savetxt(f"{output_dir}/test_times.txt", test_times, fmt='%i') + + # Save time2id mapping if provided + if time2id is not None: + with open(f"{output_dir}/time2id.txt", "w", encoding="utf-8") as f: + json.dump(time2id, f, indent=2) + diff --git a/backend/datasets/data/download.py b/backend/datasets/data/download.py new file mode 100644 index 0000000000000000000000000000000000000000..e6fc2a357811eb3d79cc0eb82a76f190d93c2374 --- /dev/null +++ b/backend/datasets/data/download.py @@ -0,0 +1,32 @@ +import os +import zipfile +from torchvision.datasets.utils import download_url +from backend.datasets.utils.logger import Logger + + +logger = Logger("WARNING") + + +def download_dataset(dataset_name, cache_path="~/.topmost"): + cache_path = os.path.expanduser(cache_path) + raw_filename = f'{dataset_name}.zip' + + if dataset_name in ['Wikitext-103']: + # download from Git LFS. + zipped_dataset_url = f"https://media.githubusercontent.com/media/BobXWu/TopMost/main/data/{raw_filename}" + else: + zipped_dataset_url = f"https://raw.githubusercontent.com/BobXWu/TopMost/master/data/{raw_filename}" + + logger.info(zipped_dataset_url) + + download_url(zipped_dataset_url, root=cache_path, filename=raw_filename, md5=None) + + path = f'{cache_path}/{raw_filename}' + with zipfile.ZipFile(path, 'r') as zip_ref: + zip_ref.extractall(cache_path) + + os.remove(path) + + +if __name__ == '__main__': + download_dataset('20NG') diff --git a/backend/datasets/data/file_utils.py b/backend/datasets/data/file_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..0defcaf4e54b965b6b8a3ff361bac5722330f5e3 --- /dev/null +++ b/backend/datasets/data/file_utils.py @@ -0,0 +1,39 @@ +import os +import json + + +def make_dir(path): + os.makedirs(path, exist_ok=True) + + +def read_text(path): + texts = list() + with open(path, 'r', encoding='utf-8', errors='ignore') as file: + for line in file: + texts.append(line.strip()) + return texts + + +def save_text(texts, path): + with open(path, 'w', encoding='utf-8') as file: + for text in texts: + file.write(text.strip() + '\n') + + +def read_jsonlist(path): + data = list() + with open(path, 'r', encoding='utf-8') as input_file: + for line in input_file: + data.append(json.loads(line)) + return data + + +def save_jsonlist(list_of_json_objects, path, sort_keys=True): + with open(path, 'w', encoding='utf-8') as output_file: + for obj in list_of_json_objects: + output_file.write(json.dumps(obj, sort_keys=sort_keys) + '\n') + + +def split_text_word(texts): + texts = [text.split() for text in texts] + return texts diff --git a/backend/datasets/dynamic_dataset.py b/backend/datasets/dynamic_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..5462e56ca43e357f955a1bf2d1b9bb76a5e01ed1 --- /dev/null +++ b/backend/datasets/dynamic_dataset.py @@ -0,0 +1,90 @@ +import torch +from torch.utils.data import Dataset, DataLoader +import numpy as np +import scipy.sparse +import scipy.io +from backend.datasets.data import file_utils + + +class _SequentialDataset(Dataset): + def __init__(self, bow, times, time_wordfreq): + super().__init__() + self.bow = bow + self.times = times + self.time_wordfreq = time_wordfreq + + def __len__(self): + return len(self.bow) + + def __getitem__(self, index): + return_dict = { + 'bow': self.bow[index], + 'times': self.times[index], + 'time_wordfreq': self.time_wordfreq[self.times[index]], + } + + return return_dict + + +class DynamicDataset: + def __init__(self, dataset_dir, batch_size=200, read_labels=False, use_partition=False, device='cuda', as_tensor=True): + + self.load_data(dataset_dir, read_labels, use_partition) + + self.vocab_size = len(self.vocab) + self.train_size = len(self.train_bow) + self.num_times = int(self.train_times.max()) + 1 # assuming train_times is a numpy array + self.train_time_wordfreq = self.get_time_wordfreq(self.train_bow, self.train_times) + + print('train size: ', len(self.train_bow)) + if use_partition: + print('test size: ', len(self.test_bow)) + print('vocab size: ', len(self.vocab)) + print('average length: {:.3f}'.format(self.train_bow.sum(1).mean().item())) + print('num of each time slice: ', self.num_times, np.bincount(self.train_times)) + + if as_tensor: + self.train_bow = torch.from_numpy(self.train_bow).float().to(device) + self.train_times = torch.from_numpy(self.train_times).long().to(device) + self.train_time_wordfreq = torch.from_numpy(self.train_time_wordfreq).float().to(device) + + if use_partition: + self.test_bow = torch.from_numpy(self.test_bow).float().to(device) + self.test_times = torch.from_numpy(self.test_times).long().to(device) + + self.train_dataset = _SequentialDataset(self.train_bow, self.train_times, self.train_time_wordfreq) + + if use_partition: + self.test_dataset = _SequentialDataset(self.test_bow, self.test_times, self.train_time_wordfreq) + + self.train_dataloader = DataLoader(self.train_dataset, batch_size=batch_size, shuffle=True) + + def load_data(self, path, read_labels, use_partition=False): + self.train_bow = scipy.sparse.load_npz(f'{path}/train_bow.npz').toarray().astype('float32') + self.train_texts = file_utils.read_text(f'{path}/train_texts.txt') + self.train_times = np.loadtxt(f'{path}/train_times.txt').astype('int32') + self.vocab = file_utils.read_text(f'{path}/vocab.txt') + self.word_embeddings = scipy.sparse.load_npz(f'{path}/word_embeddings.npz').toarray().astype('float32') + + self.pretrained_WE = self.word_embeddings # preserve compatibility + + if read_labels: + self.train_labels = np.loadtxt(f'{path}/train_labels.txt').astype('int32') + + if use_partition: + self.test_bow = scipy.sparse.load_npz(f'{path}/test_bow.npz').toarray().astype('float32') + self.test_texts = file_utils.read_text(f'{path}/test_texts.txt') + self.test_times = np.loadtxt(f'{path}/test_times.txt').astype('int32') + if read_labels: + self.test_labels = np.loadtxt(f'{path}/test_labels.txt').astype('int32') + + # word frequency at each time slice. + def get_time_wordfreq(self, bow, times): + train_time_wordfreq = np.zeros((self.num_times, self.vocab_size)) + for time in range(self.num_times): + idx = np.where(times == time)[0] + train_time_wordfreq[time] += bow[idx].sum(0) + cnt_times = np.bincount(times) + + train_time_wordfreq = train_time_wordfreq / cnt_times[:, np.newaxis] + return train_time_wordfreq diff --git a/backend/datasets/preprocess.py b/backend/datasets/preprocess.py new file mode 100644 index 0000000000000000000000000000000000000000..8518ec3ca64bf46b3586e291b50f8b0d97c93fc0 --- /dev/null +++ b/backend/datasets/preprocess.py @@ -0,0 +1,362 @@ +import json +import os +import numpy as np +from collections import OrderedDict +import tempfile +import gensim.downloader +from tqdm import tqdm +from backend.datasets.utils.logger import Logger +import scipy.sparse +from gensim.models.phrases import Phrases, Phraser +from typing import List, Union +from octis.preprocessing.preprocessing import Preprocessing + +logger = Logger("WARNING") + +class Preprocessor: + def __init__(self, + docs_jsonl_path: str, + output_folder: str, + use_partition: bool = False, + use_bigrams: bool = False, + min_count_bigram: int = 5, + threshold_bigram: int = 10, + remove_punctuation: bool = True, + lemmatize: bool = True, + stopword_list: Union[str, List[str]] = None, + min_chars: int = 3, + min_words_docs: int = 10, + min_df: Union[int, float] = 0.0, + max_df: Union[int, float] = 1.0, + max_features: int = None, + language: str = 'english'): + + self.docs_jsonl_path = docs_jsonl_path + self.output_folder = output_folder + self.use_partition = use_partition + self.use_bigrams = use_bigrams + self.min_count_bigram = min_count_bigram + self.threshold_bigram = threshold_bigram + + os.makedirs(self.output_folder, exist_ok=True) + + self.preprocessing_params = { + 'remove_punctuation': remove_punctuation, + 'lemmatize': lemmatize, + 'stopword_list': stopword_list, + 'min_chars': min_chars, + 'min_words_docs': min_words_docs, + 'min_df': min_df, + 'max_df': max_df, + 'max_features': max_features, + 'language': language + } + self.preprocessor_octis = Preprocessing(**self.preprocessing_params) + + def _load_data_to_temp_files(self): + """Loads data from JSONL and writes to temporary files for OCTIS preprocessor.""" + raw_texts = [] + raw_timestamps = [] + raw_labels = [] + has_labels = False + + with open(self.docs_jsonl_path, 'r', encoding='utf-8') as f: + for line in f: + data = json.loads(line.strip()) + # Remove newlines from text + clean_text = data.get('text', '').replace('\n', ' ').replace('\r', ' ') + clean_text = " ".join(clean_text.split()) + raw_texts.append(clean_text) + raw_timestamps.append(data.get('timestamp', '')) + label = data.get('label', '') + if label: + has_labels = True + raw_labels.append(label) + + # Create temporary files + temp_dir = tempfile.mkdtemp() + temp_docs_path = os.path.join(temp_dir, "temp_docs.txt") + temp_labels_path = None + + with open(temp_docs_path, 'w', encoding='utf-8') as f_docs: + for text in raw_texts: + f_docs.write(f"{text}\n") + + if has_labels: + temp_labels_path = os.path.join(temp_dir, "temp_labels.txt") + with open(temp_labels_path, 'w', encoding='utf-8') as f_labels: + for label in raw_labels: + f_labels.write(f"{label}\n") + + print(f"Loaded {len(raw_texts)} raw documents and created temporary files in {temp_dir}.") + return raw_texts, raw_timestamps, raw_labels, temp_docs_path, temp_labels_path, temp_dir + + def _make_word_embeddings(self, vocab): + """ + Generates word embeddings for the given vocabulary using GloVe. + For n-grams (e.g., "wordA_wordB", "wordX_wordY_wordZ" for n>=2), + the resultant embedding is the sum of the embeddings of its constituent + single words (wordA + wordB + ...). + """ + print("Loading GloVe word embeddings...") + glove_vectors = gensim.downloader.load('glove-wiki-gigaword-200') + + # Initialize word_embeddings matrix with zeros. + # This ensures that words not found (single or n-gram constituents) + # will have a zero vector embedding. + word_embeddings = np.zeros((len(vocab), glove_vectors.vectors.shape[1]), dtype=np.float32) + + num_found = 0 + + try: + # Using a set for key_word_list for O(1) average time complexity lookup + key_word_list = set(glove_vectors.index_to_key) + except AttributeError: # For older gensim versions + key_word_list = set(glove_vectors.index2word) + + print("Generating word embeddings for vocabulary (including n-grams)...") + for i, word in enumerate(tqdm(vocab, desc="Processing vocabulary words")): + if '_' in word: # Check if it's a potential n-gram (n >= 2) + parts = word.split('_') + + # Check if *all* constituent words are present in GloVe + all_parts_in_glove = True + for part in parts: + if part not in key_word_list: + all_parts_in_glove = False + break # One part not found, stop checking + + if all_parts_in_glove: + # If all parts are found, sum their embeddings + resultant_vector = np.zeros(glove_vectors.vectors.shape[1], dtype=np.float32) + for part in parts: + resultant_vector += glove_vectors[part] + + word_embeddings[i] = resultant_vector + num_found += 1 + # Else: one or more constituent words not found, embedding remains zero + else: # It's a single word (n=1) + if word in key_word_list: + word_embeddings[i] = glove_vectors[word] + num_found += 1 + # Else: single word not found, embedding remains zero + + logger.info(f'Number of found embeddings (including n-grams): {num_found}/{len(vocab)}') + return word_embeddings # Return as dense NumPy array + + + def _save_doc_length_stats(self, filepath: str, output_path: str): + doc_lengths = [] + try: + with open(filepath, 'r', encoding='utf-8') as f: + for line in f: + doc = line.strip() + if doc: + doc_lengths.append(len(doc)) + except Exception as e: + print(f"Error processing '{filepath}': {e}") + return + + if not doc_lengths: + print(f"No documents found in '{filepath}'.") + return + + stats = { + "avg_len": float(np.mean(doc_lengths)), + "std_len": float(np.std(doc_lengths)), + "max_len": int(np.max(doc_lengths)), + "min_len": int(np.min(doc_lengths)), + "num_docs": int(len(doc_lengths)) + } + + with open(output_path, 'w', encoding='utf-8') as f: + json.dump(stats, f, indent=4) + print(f"Saved document length stats to: {output_path}") + + + def preprocess(self): + print("Loading data and creating temporary files for OCTIS...") + _, raw_timestamps, _, temp_docs_path, temp_labels_path, temp_dir = \ + self._load_data_to_temp_files() + + print("Starting OCTIS pre-processing using file paths and specified parameters...") + octis_dataset = self.preprocessor_octis.preprocess_dataset( + documents_path=temp_docs_path, + labels_path=temp_labels_path + ) + + # Clean up temporary files immediately + os.remove(temp_docs_path) + if temp_labels_path: + os.remove(temp_labels_path) + os.rmdir(temp_dir) + print(f"Temporary files in {temp_dir} cleaned up.") + + # --- Proxy: Save __original_indexes and then manually load it --- + temp_indexes_dir = tempfile.mkdtemp() + temp_indexes_file = os.path.join(temp_indexes_dir, "temp_original_indexes.txt") + + print(f"Saving __original_indexes to {temp_indexes_file}...") + octis_dataset._save_document_indexes(temp_indexes_file) + + # Manually load the indexes from the file + original_indexes_after_octis = [] + with open(temp_indexes_file, 'r') as f_indexes: + for line in f_indexes: + original_indexes_after_octis.append(int(line.strip())) # Read as int + + # Clean up the temporary indexes file and its directory + os.remove(temp_indexes_file) + os.rmdir(temp_indexes_dir) + print("Temporary indexes file cleaned up.") + # --- End Proxy --- + + # Get processed data from OCTIS Dataset object + processed_corpus_octis_list = octis_dataset.get_corpus() # List of list of tokens + processed_labels_octis = octis_dataset.get_labels() # List of labels + + print("Max index in original_indexes_after_octis:", max(original_indexes_after_octis)) + print("Length of raw_timestamps:", len(raw_timestamps)) + + # Filter timestamps based on documents that survived OCTIS preprocessing + filtered_timestamps = [raw_timestamps[i] for i in original_indexes_after_octis] + + print(f"OCTIS preprocessing complete. {len(processed_corpus_octis_list)} documents remaining.") + + if self.use_bigrams: + print("Generating bigrams with Gensim...") + phrases = Phrases(processed_corpus_octis_list, min_count=self.min_count_bigram, threshold=self.threshold_bigram) + bigram_phraser = Phraser(phrases) + bigrammed_corpus_list = [bigram_phraser[doc] for doc in processed_corpus_octis_list] + print("Bigram generation complete.") + else: + print("Skipping bigram generation as 'use_bigrams' is False.") + bigrammed_corpus_list = processed_corpus_octis_list # Use the original processed list + + + # Convert back to list of strings for easier handling if needed later, but keep as list of lists for BOW + bigrammed_texts_for_file = [" ".join(doc) for doc in bigrammed_corpus_list] + print("Bigram generation complete.") + + # Build Vocabulary from OCTIS output (after bigrams) + # We need a flat list of all tokens to build the vocabulary + all_tokens = [token for doc in bigrammed_corpus_list for token in doc] + vocab = sorted(list(set(all_tokens))) # Sorted unique words form the vocabulary + word_to_id = {word: i for i, word in enumerate(vocab)} + + # Create BOW matrix manually + print("Creating Bag-of-Words representations...") + rows, cols, data = [], [], [] + for i, doc_tokens in enumerate(bigrammed_corpus_list): + doc_word_counts = {} + for token in doc_tokens: + if token in word_to_id: # Ensure token is in our final vocab + doc_word_counts[word_to_id[token]] = doc_word_counts.get(word_to_id[token], 0) + 1 + for col_id, count in doc_word_counts.items(): + rows.append(i) + cols.append(col_id) + data.append(count) + + # Shape is (num_documents, vocab_size) + bow_matrix = scipy.sparse.csc_matrix((data, (rows, cols)), shape=(len(bigrammed_corpus_list), len(vocab))) + print("Bag-of-Words complete.") + + # Handle partitioning if required + if self.use_partition: + num_docs = len(bigrammed_corpus_list) + train_size = int(0.8 * num_docs) + + train_texts = bigrammed_texts_for_file[:train_size] + train_bow_matrix = bow_matrix[:train_size] + train_timestamps = filtered_timestamps[:train_size] + train_labels = processed_labels_octis[:train_size] if processed_labels_octis else [] + + test_texts = bigrammed_texts_for_file[train_size:] + test_bow_matrix = bow_matrix[train_size:] + test_timestamps = filtered_timestamps[train_size:] + test_labels = processed_labels_octis[train_size:] if processed_labels_octis else [] + + else: + train_texts = bigrammed_texts_for_file + train_bow_matrix = bow_matrix + train_timestamps = filtered_timestamps + train_labels = processed_labels_octis + test_texts = [] + test_timestamps = [] + test_labels = [] + + # Generate word embeddings using the provided function + word_embeddings = self._make_word_embeddings(vocab) + + # Process timestamps to 0, 1, 2...T and create time2id.txt + print("Processing timestamps...") + unique_timestamps = sorted(list(set(train_timestamps + test_timestamps))) + time_to_id = {timestamp: i for i, timestamp in enumerate(unique_timestamps)} + + train_times_ids = [time_to_id[ts] for ts in train_timestamps] + test_times_ids = [time_to_id[ts] for ts in test_timestamps] if self.use_partition else [] + print("Timestamps processed.") + + # Save files + print(f"Saving preprocessed files to {self.output_folder}...") + + # 1. vocab.txt + with open(os.path.join(self.output_folder, "vocab.txt"), "w", encoding="utf-8") as f: + for word in vocab: + f.write(f"{word}\n") + + # 2. train_texts.txt + train_text_path = os.path.join(self.output_folder, "train_texts.txt") + with open(train_text_path, "w", encoding="utf-8") as f: + for doc in train_texts: + f.write(f"{doc}\n") + + # Save document length stats + doc_stats_path = os.path.join(self.output_folder, "length_stats.json") + self._save_doc_length_stats(train_text_path, doc_stats_path) + + # 3. train_bow.npz + scipy.sparse.save_npz(os.path.join(self.output_folder, "train_bow.npz"), train_bow_matrix) + + # 4. word_embeddings.npz + sparse_word_embeddings = scipy.sparse.csr_matrix(word_embeddings) + scipy.sparse.save_npz(os.path.join(self.output_folder, "word_embeddings.npz"), sparse_word_embeddings) + + # 5. train_labels.txt (if labels exist) + if train_labels: + with open(os.path.join(self.output_folder, "train_labels.txt"), "w", encoding="utf-8") as f: + for label in train_labels: + f.write(f"{label}\n") + + # 6. train_times.txt + with open(os.path.join(self.output_folder, "train_times.txt"), "w", encoding="utf-8") as f: + for time_id in train_times_ids: + f.write(f"{time_id}\n") + + # Files for test set (if use_partition=True) + if self.use_partition: + # 7. test_bow.npz + scipy.sparse.save_npz(os.path.join(self.output_folder, "test_bow.npz"), test_bow_matrix) + + # 8. test_texts.txt + with open(os.path.join(self.output_folder, "test_texts.txt"), "w", encoding="utf-8") as f: + for doc in test_texts: + f.write(f"{doc}\n") + + # 9. test_labels.txt (if labels exist) + if test_labels: + with open(os.path.join(self.output_folder, "test_labels.txt"), "w", encoding="utf-8") as f: + for label in test_labels: + f.write(f"{label}\n") + + # 10. test_times.txt + with open(os.path.join(self.output_folder, "test_times.txt"), "w", encoding="utf-8") as f: + for time_id in test_times_ids: + f.write(f"{time_id}\n") + + # 11. time2id.txt + sorted_time_to_id = OrderedDict(sorted(time_to_id.items(), key=lambda item: item[1])) + with open(os.path.join(self.output_folder, "time2id.txt"), "w", encoding="utf-8") as f: + json.dump(sorted_time_to_id, f, indent=4) + + print("All files saved successfully.") \ No newline at end of file diff --git a/backend/datasets/utils/_utils.py b/backend/datasets/utils/_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..89ffa0e4465f2a07063d1caeabb386bc7b80c221 --- /dev/null +++ b/backend/datasets/utils/_utils.py @@ -0,0 +1,37 @@ +import numpy as np +from backend.datasets.data import file_utils + + +def get_top_words(beta, vocab, num_top_words, verbose=False): + topic_str_list = list() + for i, topic_dist in enumerate(beta): + topic_words = np.array(vocab)[np.argsort(topic_dist)][:-(num_top_words + 1):-1] + topic_str = ' '.join(topic_words) + topic_str_list.append(topic_str) + if verbose: + print('Topic {}: {}'.format(i, topic_str)) + + return topic_str_list + + +def get_stopwords_set(stopwords=[]): + from backend.datasets.data.download import download_dataset + + if stopwords == 'English': + from gensim.parsing.preprocessing import STOPWORDS as stopwords + + elif stopwords in ['mallet', 'snowball']: + download_dataset('stopwords', cache_path='./') + path = f'./stopwords/{stopwords}_stopwords.txt' + stopwords = file_utils.read_text(path) + + stopword_set = frozenset(stopwords) + + return stopword_set + + +if __name__ == '__main__': + print(list(get_stopwords_set('English'))[:10]) + print(list(get_stopwords_set('mallet'))[:10]) + print(list(get_stopwords_set('snowball'))[:10]) + print(list(get_stopwords_set())[:10]) diff --git a/backend/datasets/utils/logger.py b/backend/datasets/utils/logger.py new file mode 100644 index 0000000000000000000000000000000000000000..4878a9e063cba7c57f6873143f1e0f7ed8ba6cd3 --- /dev/null +++ b/backend/datasets/utils/logger.py @@ -0,0 +1,29 @@ +import logging + + +class Logger: + def __init__(self, level): + self.logger = logging.getLogger('TopMost') + self.set_level(level) + self._add_handler() + self.logger.propagate = False + + def info(self, message): + self.logger.info(f"{message}") + + def warning(self, message): + self.logger.warning(f"WARNING: {message}") + + def set_level(self, level): + levels = ["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"] + if level in levels: + self.logger.setLevel(level) + + def _add_handler(self): + sh = logging.StreamHandler() + sh.setFormatter(logging.Formatter('%(asctime)s - %(name)s - %(message)s')) + self.logger.addHandler(sh) + + # Remove duplicate handlers + if len(self.logger.handlers) > 1: + self.logger.handlers = [self.logger.handlers[0]] diff --git a/backend/evaluation/CoherenceModel_ttc.py b/backend/evaluation/CoherenceModel_ttc.py new file mode 100644 index 0000000000000000000000000000000000000000..07e58faf82f9a6240d50e457899014dab5745f3a --- /dev/null +++ b/backend/evaluation/CoherenceModel_ttc.py @@ -0,0 +1,862 @@ +import logging +import multiprocessing as mp +from collections import namedtuple + +import numpy as np + +from gensim import interfaces, matutils +from gensim import utils +from gensim.topic_coherence import ( + segmentation, probability_estimation, + direct_confirmation_measure, indirect_confirmation_measure, + aggregation, +) +from gensim.topic_coherence.probability_estimation import unique_ids_from_segments + +# Set up logging for this module +logger = logging.getLogger(__name__) + +# Define sets for categorizing coherence measures based on their probability estimation method +BOOLEAN_DOCUMENT_BASED = {'u_mass'} +SLIDING_WINDOW_BASED = {'c_v', 'c_uci', 'c_npmi', 'c_w2v'} + +# Create a namedtuple to define the structure of a coherence measure pipeline +# Each pipeline consists of a segmentation (seg), probability estimation (prob), +# confirmation measure (conf), and aggregation (aggr) function. +_make_pipeline = namedtuple('Coherence_Measure', 'seg, prob, conf, aggr') + +# Define the supported coherence measures and their respective pipeline components +COHERENCE_MEASURES = { + 'u_mass': _make_pipeline( + segmentation.s_one_pre, + probability_estimation.p_boolean_document, + direct_confirmation_measure.log_conditional_probability, + aggregation.arithmetic_mean + ), + 'c_v': _make_pipeline( + segmentation.s_one_set, + probability_estimation.p_boolean_sliding_window, + indirect_confirmation_measure.cosine_similarity, + aggregation.arithmetic_mean + ), + 'c_w2v': _make_pipeline( + segmentation.s_one_set, + probability_estimation.p_word2vec, + indirect_confirmation_measure.word2vec_similarity, + aggregation.arithmetic_mean + ), + 'c_uci': _make_pipeline( + segmentation.s_one_one, + probability_estimation.p_boolean_sliding_window, + direct_confirmation_measure.log_ratio_measure, + aggregation.arithmetic_mean + ), + 'c_npmi': _make_pipeline( + segmentation.s_one_one, + probability_estimation.p_boolean_sliding_window, + direct_confirmation_measure.log_ratio_measure, + aggregation.arithmetic_mean + ), +} + +# Define default sliding window sizes for different coherence measures +SLIDING_WINDOW_SIZES = { + 'c_v': 110, + 'c_w2v': 5, + 'c_uci': 10, + 'c_npmi': 10, + 'u_mass': None # u_mass does not use a sliding window +} + + +class CoherenceModel_ttc(interfaces.TransformationABC): + """Objects of this class allow for building and maintaining a model for topic coherence. + + Examples + --------- + One way of using this feature is through providing a trained topic model. A dictionary has to be explicitly provided + if the model does not contain a dictionary already + + .. sourcecode:: pycon + + >>> from gensim.test.utils import common_corpus, common_dictionary + >>> from gensim.models.ldamodel import LdaModel + >>> # Assuming CoherenceModel_ttc is imported or defined in the current scope + >>> # from your_module import CoherenceModel_ttc # if saved in a file + >>> + >>> model = LdaModel(common_corpus, 5, common_dictionary) + >>> + >>> cm = CoherenceModel_ttc(model=model, corpus=common_corpus, coherence='u_mass') + >>> coherence = cm.get_coherence() # get coherence value + + Another way of using this feature is through providing tokenized topics such as: + + .. sourcecode:: pycon + + >>> from gensim.test.utils import common_corpus, common_dictionary + >>> # Assuming CoherenceModel_ttc is imported or defined in the current scope + >>> # from your_module import CoherenceModel_ttc # if saved in a file + >>> topics = [ + ... ['human', 'computer', 'system', 'interface'], + ... ['graph', 'minors', 'trees', 'eps'] + ... ] + >>> + >>> cm = CoherenceModel_ttc(topics=topics, corpus=common_corpus, dictionary=common_dictionary, coherence='u_mass') + >>> coherence = cm.get_coherence() # get coherence value + + """ + def __init__(self, model=None, topics=None, texts=None, corpus=None, dictionary=None, + window_size=None, keyed_vectors=None, coherence='c_v', topn=20, processes=-1): + """ + Initializes the CoherenceModel_ttc. + + Parameters + ---------- + model : :class:`~gensim.models.basemodel.BaseTopicModel`, optional + Pre-trained topic model. Should be provided if `topics` is not provided. + Supports models that implement the `get_topics` method. + topics : list of list of str, optional + List of tokenized topics. If provided, `dictionary` must also be provided. + texts : list of list of str, optional + Tokenized texts, needed for coherence models that use sliding window based (e.g., `c_v`, `c_uci`, `c_npmi`). + corpus : iterable of list of (int, number), optional + Corpus in Bag-of-Words format. + dictionary : :class:`~gensim.corpora.dictionary.Dictionary`, optional + Gensim dictionary mapping of id word to create corpus. + If `model.id2word` is present and `dictionary` is None, `model.id2word` will be used. + window_size : int, optional + The size of the window to be used for coherence measures using boolean sliding window as their + probability estimator. For 'u_mass' this doesn't matter. + If None, default window sizes from `SLIDING_WINDOW_SIZES` are used. + keyed_vectors : :class:`~gensim.models.keyedvectors.KeyedVectors`, optional + Pre-trained word embeddings (e.g., Word2Vec model) for 'c_w2v' coherence. + coherence : {'u_mass', 'c_v', 'c_uci', 'c_npmi', 'c_w2v'}, optional + Coherence measure to be used. + 'u_mass' requires `corpus` (or `texts` which will be converted to corpus). + 'c_v', 'c_uci', 'c_npmi', 'c_w2v' require `texts`. + topn : int, optional + Integer corresponding to the number of top words to be extracted from each topic. Defaults to 20. + processes : int, optional + Number of processes to use for probability estimation phase. Any value less than 1 will be interpreted as + `num_cpus - 1`. Defaults to -1. + """ + # Ensure either a model or explicit topics are provided + if model is None and topics is None: + raise ValueError("One of 'model' or 'topics' has to be provided.") + # If topics are provided, a dictionary is mandatory to convert tokens to IDs + elif topics is not None and dictionary is None: + raise ValueError("Dictionary has to be provided if 'topics' are to be used.") + + self.keyed_vectors = keyed_vectors + # Ensure a data source (keyed_vectors, texts, or corpus) is provided for coherence calculation + if keyed_vectors is None and texts is None and corpus is None: + raise ValueError("One of 'texts', 'corpus', or 'keyed_vectors' has to be provided.") + + # Determine the dictionary to use + if dictionary is None: + # If no explicit dictionary, try to use the model's dictionary + if isinstance(model.id2word, utils.FakeDict): + # If model's id2word is a FakeDict, it means no proper dictionary is associated + raise ValueError( + "The associated dictionary should be provided with the corpus or 'id2word'" + " for topic model should be set as the associated dictionary.") + else: + self.dictionary = model.id2word + else: + self.dictionary = dictionary + + # Store coherence type and window size + self.coherence = coherence + self.window_size = window_size + if self.window_size is None: + # Use default window size if not specified + self.window_size = SLIDING_WINDOW_SIZES[self.coherence] + + # Store texts and corpus + self.texts = texts + self.corpus = corpus + + # Validate inputs based on coherence type + if coherence in BOOLEAN_DOCUMENT_BASED: + # For document-based measures (e.g., u_mass), corpus is preferred + if utils.is_corpus(corpus)[0]: + self.corpus = corpus + elif self.texts is not None: + # If texts are provided, convert them to corpus format + self.corpus = [self.dictionary.doc2bow(text) for text in self.texts] + else: + raise ValueError( + "Either 'corpus' with 'dictionary' or 'texts' should " + "be provided for %s coherence." % coherence) + + elif coherence == 'c_w2v' and keyed_vectors is not None: + # For c_w2v, keyed_vectors are needed + pass + elif coherence in SLIDING_WINDOW_BASED: + # For sliding window-based measures, texts are required + if self.texts is None: + raise ValueError("'texts' should be provided for %s coherence." % coherence) + else: + # Raise error if coherence type is not supported + raise ValueError("%s coherence is not currently supported." % coherence) + + self._topn = topn + self._model = model + self._accumulator = None # Cached accumulator for probability estimation + self._topics = None # Store topics internally + self.topics = topics # Call the setter to initialize topics and accumulator state + + # Determine the number of processes to use for parallelization + self.processes = processes if processes >= 1 else max(1, mp.cpu_count() - 1) + + @classmethod + def for_models(cls, models, dictionary, topn=20, **kwargs): + """ + Initialize a CoherenceModel_ttc with estimated probabilities for all of the given models. + This method extracts topics from each model and then uses `for_topics`. + + Parameters + ---------- + models : list of :class:`~gensim.models.basemodel.BaseTopicModel` + List of models to evaluate coherence of. Each model should implement + the `get_topics` method. + dictionary : :class:`~gensim.corpora.dictionary.Dictionary` + Gensim dictionary mapping of id word. + topn : int, optional + Integer corresponding to the number of top words to be extracted from each topic. Defaults to 20. + kwargs : object + Additional arguments passed to the `CoherenceModel_ttc` constructor (e.g., `corpus`, `texts`, `coherence`). + + Returns + ------- + :class:`~gensim.models.coherencemodel.CoherenceModel` + CoherenceModel_ttc instance with estimated probabilities for all given models. + + Example + ------- + .. sourcecode:: pycon + + >>> from gensim.test.utils import common_corpus, common_dictionary + >>> from gensim.models.ldamodel import LdaModel + >>> # from your_module import CoherenceModel_ttc + >>> + >>> m1 = LdaModel(common_corpus, 3, common_dictionary) + >>> m2 = LdaModel(common_corpus, 5, common_dictionary) + >>> + >>> cm = CoherenceModel_ttc.for_models([m1, m2], common_dictionary, corpus=common_corpus, coherence='u_mass') + >>> # To get coherences for each model: + >>> # model_coherences = cm.compare_model_topics([ + >>> # CoherenceModel_ttc._get_topics_from_model(m1, topn=cm.topn), + >>> # CoherenceModel_ttc._get_topics_from_model(m2, topn=cm.topn) + >>> # ]) + """ + # Extract top words as lists for each model's topics + topics = [cls.top_topics_as_word_lists(model, dictionary, topn) for model in models] + kwargs['dictionary'] = dictionary + kwargs['topn'] = topn + # Use for_topics to initialize the coherence model with these topics + return cls.for_topics(topics, **kwargs) + + @staticmethod + def top_topics_as_word_lists(model, dictionary, topn=20): + """ + Get `topn` topics from a model as lists of words. + + Parameters + ---------- + model : :class:`~gensim.models.basemodel.BaseTopicModel` + Pre-trained topic model. + dictionary : :class:`~gensim.corpora.dictionary.Dictionary` + Gensim dictionary mapping of id word. + topn : int, optional + Integer corresponding to the number of top words to be extracted from each topic. Defaults to 20. + + Returns + ------- + list of list of str + Top topics in list-of-list-of-words format. + """ + # Ensure id2token mapping exists in the dictionary + if not dictionary.id2token: + dictionary.id2token = {v: k for k, v in dictionary.token2id.items()} + + str_topics = [] + for topic_distribution in model.get_topics(): + # Get the indices of the topN words based on their probabilities + bestn_indices = matutils.argsort(topic_distribution, topn=topn, reverse=True) + # Convert word IDs back to words using the dictionary + best_words = [dictionary.id2token[_id] for _id in bestn_indices] + str_topics.append(best_words) + return str_topics + + @classmethod + def for_topics(cls, topics_as_topn_terms, **kwargs): + """ + Initialize a CoherenceModel_ttc with estimated probabilities for all of the given topics. + This is useful when you have raw topics (list of lists of words) and not a Gensim model object. + + Parameters + ---------- + topics_as_topn_terms : list of list of str + Each element in the top-level list should be a list of top-N words, one per topic. + For example: `[['word1', 'word2'], ['word3', 'word4']]`. + + Returns + ------- + :class:`~gensim.models.coherencemodel.CoherenceModel` + CoherenceModel_ttc with estimated probabilities for the given topics. + """ + if not topics_as_topn_terms: + raise ValueError("len(topics_as_topn_terms) must be > 0.") + if any(len(topic_list) == 0 for topic_list in topics_as_topn_terms): + raise ValueError("Found an empty topic listing in `topics_as_topn_terms`.") + + # Determine the maximum 'topn' value among the provided topics + # This will be used to initialize the CoherenceModel_ttc correctly for probability estimation + actual_topn_in_data = 0 + for topic_list in topics_as_topn_terms: + for topic in topic_list: + actual_topn_in_data = max(actual_topn_in_data, len(topic)) + + # Use the provided 'topn' from kwargs, or the determined 'actual_topn_in_data', + # ensuring it's not greater than the actual data available. + # This allows for precomputing probabilities for a wider set of words if needed. + topn_for_prob_estimation = min(kwargs.pop('topn', actual_topn_in_data), actual_topn_in_data) + + # Flatten all topics into a single "super topic" for initial probability estimation. + # This ensures that all words relevant to *any* topic in the comparison set + # are included in the accumulator. + super_topic = utils.flatten(topics_as_topn_terms) + + logger.info( + "Number of relevant terms for all %d models (or topic sets): %d", + len(topics_as_topn_terms), len(super_topic)) + + # Initialize CoherenceModel_ttc with the super topic to pre-estimate probabilities + # for all relevant words across all models. + # We pass `topics=[super_topic]` and `topn=len(super_topic)` to ensure all words + # are considered during the probability estimation phase. + cm = CoherenceModel_ttc(topics=[super_topic], topn=len(super_topic), **kwargs) + cm.estimate_probabilities() # Perform the actual probability estimation + + # After estimation, set the 'topn' back to the desired value for coherence calculation. + cm.topn = topn_for_prob_estimation + return cm + + def __str__(self): + """Returns a string representation of the coherence measure pipeline.""" + return str(self.measure) + + @property + def model(self): + """ + Get the current topic model used by the instance. + + Returns + ------- + :class:`~gensim.models.basemodel.BaseTopicModel` + The currently set topic model. + """ + return self._model + + @model.setter + def model(self, model): + """ + Set the topic model for the instance. When a new model is set, + it triggers an update of the internal topics and checks if the accumulator needs recomputing. + + Parameters + ---------- + model : :class:`~gensim.models.basemodel.BaseTopicModel` + The new topic model to set. + """ + self._model = model + if model is not None: + new_topics = self._get_topics() # Get topics from the new model + self._update_accumulator(new_topics) # Check and update accumulator if needed + self._topics = new_topics # Store the new topics + + @property + def topn(self): + """ + Get the number of top words (`_topn`) used for coherence calculation. + + Returns + ------- + int + The number of top words. + """ + return self._topn + + @topn.setter + def topn(self, topn): + """ + Set the number of top words (`_topn`) to consider for coherence calculation. + If the new `topn` requires more words than currently loaded topics, and a model is available, + it will attempt to re-extract topics from the model. + + Parameters + ---------- + topn : int + The new number of top words. + """ + # Get the length of the first topic to check current topic length + current_topic_length = len(self._topics[0]) + # Determine if the new 'topn' requires more words than currently available in topics + requires_expansion = current_topic_length < topn + + if self.model is not None: + self._topn = topn + if requires_expansion: + # If expansion is needed and a model is available, re-extract topics from the model. + # This call to the setter property `self.model = self._model` effectively re-runs + # the logic that extracts topics and updates the accumulator based on the new `_topn`. + self.model = self._model + else: + # If no model is available and expansion is required, raise an error + if requires_expansion: + raise ValueError("Model unavailable and topic sizes are less than topn=%d" % topn) + self._topn = topn # Topics will be truncated by the `topics` getter if needed + + @property + def measure(self): + """ + Returns the namedtuple representing the coherence pipeline functions + (segmentation, probability estimation, confirmation, aggregation) + based on the `self.coherence` type. + + Returns + ------- + namedtuple + Pipeline that contains needed functions/method for calculating coherence. + """ + return COHERENCE_MEASURES[self.coherence] + + @property + def topics(self): + """ + Get the current topics. If the internally stored topics have more words + than `self._topn`, they are truncated to `self._topn` words. + + Returns + ------- + list of list of str + Topics as lists of word tokens. + """ + # If the stored topics contain more words than `_topn`, truncate them + if len(self._topics[0]) > self._topn: + return [topic[:self._topn] for topic in self._topics] + else: + return self._topics + + @topics.setter + def topics(self, topics): + """ + Set the topics for the instance. This method converts topic words to their + corresponding dictionary IDs and updates the accumulator state. + + Parameters + ---------- + topics : list of list of str or list of list of int + Topics, either as lists of word tokens or lists of word IDs. + """ + if topics is not None: + new_topics = [] + for topic in topics: + # Ensure topic elements are converted to dictionary IDs (numpy array for efficiency) + topic_token_ids = self._ensure_elements_are_ids(topic) + new_topics.append(topic_token_ids) + + if self.model is not None: + # Warn if both model and explicit topics are set, as they might be inconsistent + logger.warning( + "The currently set model '%s' may be inconsistent with the newly set topics", + self.model) + elif self.model is not None: + # If topics are None but a model exists, extract topics from the model + new_topics = self._get_topics() + logger.debug("Setting topics to those of the model: %s", self.model) + else: + new_topics = None + + # Check if the accumulator needs to be recomputed based on the new topics + self._update_accumulator(new_topics) + self._topics = new_topics # Store the (ID-converted) topics + + def _ensure_elements_are_ids(self, topic): + """ + Internal helper to ensure that topic elements are converted to dictionary IDs. + Handles cases where input topic might be tokens or already IDs. + + Parameters + ---------- + topic : list of str or list of int + A single topic, either as a list of word tokens or word IDs. + + Returns + ------- + :class:`numpy.ndarray` + A numpy array of word IDs for the topic. + + Raises + ------ + KeyError + If a token is not found in the dictionary or an ID is not a valid key in id2token. + """ + try: + # Try to convert tokens to IDs. This is the common case if `topic` contains strings. + return np.array([self.dictionary.token2id[token] for token in topic if token in self.dictionary.token2id]) + except KeyError: + # If `KeyError` occurs, assume `topic` might already be a list of IDs. + # Attempt to convert IDs to tokens and then back to IDs, ensuring they are valid dictionary entries. + # This handles cases where `topic` might contain integer IDs that are not present in the dictionary. + try: + # Convert IDs to tokens (via id2token) and then tokens to IDs (via token2id) + # This filters out invalid IDs. + return np.array([self.dictionary.token2id[self.dictionary.id2token[_id]] + for _id in topic if _id in self.dictionary]) + except KeyError: + raise ValueError("Unable to interpret topic as either a list of tokens or a list of valid IDs within the dictionary.") + + def _update_accumulator(self, new_topics): + """ + Internal helper to determine if the cached `_accumulator` (probability statistics) + needs to be wiped and recomputed due to changes in topics. + """ + if self._relevant_ids_will_differ(new_topics): + logger.debug("Wiping cached accumulator since it does not contain all relevant ids.") + self._accumulator = None + + def _relevant_ids_will_differ(self, new_topics): + """ + Internal helper to check if the set of unique word IDs relevant to the new topics + is different from the IDs already covered by the current accumulator. + + Parameters + ---------- + new_topics : list of list of int + The new set of topics (as word IDs). + + Returns + ------- + bool + True if the relevant IDs will differ, False otherwise. + """ + if self._accumulator is None or not self._topics_differ(new_topics): + return False + + # Get unique IDs from the segmented new topics + new_set = unique_ids_from_segments(self.measure.seg(new_topics)) + # Check if the current accumulator's relevant IDs are a superset of the new set. + # If not, it means the new topics introduce words not covered, so the accumulator needs updating. + return not self._accumulator.relevant_ids.issuperset(new_set) + + def _topics_differ(self, new_topics): + """ + Internal helper to check if the new topics are different from the currently stored topics. + + Parameters + ---------- + new_topics : list of list of int + The new set of topics (as word IDs). + + Returns + ------- + bool + True if topics are different, False otherwise. + """ + # Compare topic arrays using numpy.array_equal for efficient comparison + return (new_topics is not None + and self._topics is not None + and not np.array_equal(new_topics, self._topics)) + + def _get_topics(self): + """ + Internal helper function to extract top words (as IDs) from a trained topic model. + """ + return self._get_topics_from_model(self.model, self.topn) + + @staticmethod + def _get_topics_from_model(model, topn): + """ + Internal static method to extract top `topn` words (as IDs) from a trained topic model. + + Parameters + ---------- + model : :class:`~gensim.models.basemodel.BaseTopicModel` + Pre-trained topic model (must implement `get_topics` method). + topn : int + Integer corresponding to the number of top words to extract. + + Returns + ------- + list of :class:`numpy.ndarray` + A list where each element is a numpy array of word IDs representing a topic's top words. + + Raises + ------ + AttributeError + If the provided model does not implement a `get_topics` method. + """ + try: + # Iterate over the topic distributions from the model + # Use matutils.argsort to get the indices (word IDs) of the top `topn` words + return [ + matutils.argsort(topic, topn=topn, reverse=True) for topic in + model.get_topics() + ] + except AttributeError: + raise ValueError( + "This topic model is not currently supported. Supported topic models" + " should implement the `get_topics` method.") + + def segment_topics(self): + """ + Segments the current topics using the segmentation function defined by the + chosen coherence measure (`self.measure.seg`). + + Returns + ------- + list of list of tuple + Segmented topics. The structure depends on the segmentation method (e.g., pairs of word IDs). + """ + # Apply the segmentation function from the pipeline to the current topics + return self.measure.seg(self.topics) + + def estimate_probabilities(self, segmented_topics=None): + """ + Accumulates word occurrences and co-occurrences from texts or corpus + using the optimal probability estimation method for the chosen coherence metric. + This operation can be computationally intensive, especially for sliding window methods. + + Parameters + ---------- + segmented_topics : list of list of tuple, optional + Segmented topics. If None, `self.segment_topics()` is called internally. + + Returns + ------- + :class:`~gensim.topic_coherence.text_analysis.CorpusAccumulator` + An object that holds the accumulated statistics (word frequencies, co-occurrence frequencies). + """ + if segmented_topics is None: + segmented_topics = self.segment_topics() + + # Choose the appropriate probability estimation method based on the coherence type + if self.coherence in BOOLEAN_DOCUMENT_BASED: + self._accumulator = self.measure.prob(self.corpus, segmented_topics) + else: + kwargs = dict( + texts=self.texts, segmented_topics=segmented_topics, + dictionary=self.dictionary, window_size=self.window_size, + processes=self.processes) + if self.coherence == 'c_w2v': + kwargs['model'] = self.keyed_vectors # Pass keyed_vectors for word2vec based coherence + + self._accumulator = self.measure.prob(**kwargs) + + return self._accumulator + + def get_coherence_per_topic(self, segmented_topics=None, with_std=False, with_support=False): + """ + Calculates and returns a list of coherence values, one for each topic, + based on the pipeline's confirmation measure. + + Parameters + ---------- + segmented_topics : list of list of tuple, optional + Segmented topics. If None, `self.segment_topics()` is called internally. + with_std : bool, optional + If True, also includes the standard deviation across topic segment sets in addition + to the mean coherence for each topic. Defaults to False. + with_support : bool, optional + If True, also includes the "support" (number of pairwise similarity comparisons) + used to compute each topic's coherence. Defaults to False. + + Returns + ------- + list of float or list of tuple + A sequence of similarity measures for each topic. + If `with_std` or `with_support` is True, each element in the list will be a tuple + containing the coherence value and the requested additional statistics. + """ + measure = self.measure + if segmented_topics is None: + segmented_topics = measure.seg(self.topics) + + # Ensure probabilities are estimated before calculating coherence + if self._accumulator is None: + self.estimate_probabilities(segmented_topics) + + kwargs = dict(with_std=with_std, with_support=with_support) + if self.coherence in BOOLEAN_DOCUMENT_BASED or self.coherence == 'c_w2v': + # These coherence types don't require specific additional kwargs for confirmation measure + pass + elif self.coherence == 'c_v': + # Specific kwargs for c_v's confirmation measure (cosine_similarity) + kwargs['topics'] = self.topics + kwargs['measure'] = 'nlr' # Normalized Log Ratio + kwargs['gamma'] = 1 + else: + # For c_uci and c_npmi, 'normalize' parameter is relevant + kwargs['normalize'] = (self.coherence == 'c_npmi') + + return measure.conf(segmented_topics, self._accumulator, **kwargs) + + def aggregate_measures(self, topic_coherences): + """ + Aggregates the individual topic coherence measures into a single overall score + using the pipeline's aggregation function (`self.measure.aggr`). + + Parameters + ---------- + topic_coherences : list of float + List of coherence values for each topic. + + Returns + ------- + float + The aggregated coherence value (e.g., arithmetic mean). + """ + # Apply the aggregation function from the pipeline to the list of topic coherences + return self.measure.aggr(topic_coherences) + + def get_coherence(self): + """ + Calculates and returns the overall coherence value for the entire set of topics. + This is the main entry point for getting a single coherence score. + + Returns + ------- + float + The aggregated coherence value. + """ + # First, get coherence values for each individual topic + confirmed_measures = self.get_coherence_per_topic() + # Then, aggregate these topic-level coherences into a single score + return self.aggregate_measures(confirmed_measures) + + def compare_models(self, models): + """ + Compares multiple topic models by their coherence values. + It extracts topics from each model and then calls `compare_model_topics`. + + Parameters + ---------- + models : list of :class:`~gensim.models.basemodel.BaseTopicModel` + A sequence of topic models to compare. + + Returns + ------- + list of (list of float, float) + A sequence where each element is a pair: + (list of average topic coherences for the model, overall model coherence). + """ + # Extract topics (as word IDs) for each model using the internal helper + model_topics = [self._get_topics_from_model(model, self.topn) for model in models] + # Delegate to compare_model_topics for the actual coherence comparison + return self.compare_model_topics(model_topics) + + def compare_model_topics(self, model_topics): + """ + Performs coherence evaluation for each set of topics provided in `model_topics`. + This method is designed to be efficient by precomputing probabilities once if needed, + and then evaluating coherence for each set of topics. + + Parameters + ---------- + model_topics : list of list of list of int + A list where each element is itself a list of topics (each topic being a list of word IDs) + representing a set of topics (e.g., from a single model). + + Returns + ------- + list of (list of float, float) + A sequence where each element is a pair: + (list of average topic coherences for the topic set, overall topic set coherence). + + Notes + ----- + This method uses a heuristic of evaluating coherence at various `topn` values (e.g., 20, 15, 10, 5) + and averaging the results for robustness, as suggested in some research. + """ + # Store original topics and topn to restore them after comparison + orig_topics = self._topics + orig_topn = self.topn + + try: + # Perform the actual comparison + coherences = self._compare_model_topics(model_topics) + finally: + # Ensure original topics and topn are restored even if an error occurs + self.topics = orig_topics + self.topn = orig_topn + + return coherences + + def _compare_model_topics(self, model_topics): + """ + Internal helper to get average topic and model coherences across multiple sets of topics. + + Parameters + ---------- + model_topics : list of list of list of int + A list where each element is a set of topics (list of lists of word IDs). + + Returns + ------- + list of (list of float, float) + A sequence of pairs: + (average topic coherences across different `topn` values for each topic, + overall model coherence averaged across different `topn` values). + """ + coherences = [] + # Define a grid of `topn` values to evaluate coherence. + # This provides a more robust average coherence value. + # It goes from `self.topn` down to `min(self.topn - 1, 4)` in steps of -5. + # e.g., if self.topn is 20, grid might be [20, 15, 10, 5]. + # The `min(self.topn - 1, 4)` ensures at least some lower values are included, + # but also prevents trying `topn` values that are too small or negative. + last_topn_value = min(self.topn - 1, 4) + topn_grid = list(range(self.topn, last_topn_value, -5)) + if not topn_grid or max(topn_grid) < 1: # Ensure at least one valid topn if range is empty or too small + topn_grid = [max(1, min(self.topn, 5))] # Use min of self.topn and 5, ensure at least 1 + + for model_num, topics in enumerate(model_topics): + # Set the current topics for the instance to the topics of the model being evaluated + self.topics = topics + + coherence_at_n = {} # Dictionary to store coherence results for different `topn` values + for n in topn_grid: + self.topn = n # Set the `topn` for the current evaluation round + topic_coherences = self.get_coherence_per_topic() + + # Handle NaN values in topic coherences by imputing with the mean + filled_coherences = np.array(topic_coherences, dtype=float) + # Check for NaN values and replace them with the mean of non-NaN values. + # np.nanmean handles arrays with all NaNs gracefully by returning NaN. + if np.any(np.isnan(filled_coherences)): + mean_val = np.nanmean(filled_coherences) + if np.isnan(mean_val): # If all are NaN, mean_val will also be NaN. In this case, replace with 0 or a very small number. + filled_coherences[np.isnan(filled_coherences)] = 0.0 # Or another sensible default + else: + filled_coherences[np.isnan(filled_coherences)] = mean_val + + + # Store the topic-level coherences and the aggregated (overall) coherence for this `topn` + coherence_at_n[n] = (topic_coherences, self.aggregate_measures(filled_coherences)) + + # Unpack the stored coherences for different `topn` values + all_topic_coherences_at_n, all_avg_coherences_at_n = zip(*coherence_at_n.values()) + + # Calculate the average topic coherence across all `topn` values + # np.vstack stacks lists of topic coherences into a 2D array, then mean(0) computes mean for each topic. + avg_topic_coherences = np.vstack(all_topic_coherences_at_n).mean(axis=0) + + # Calculate the overall model coherence by averaging the aggregated coherences from all `topn` values + model_coherence = np.mean(all_avg_coherences_at_n) + + logging.info("Avg coherence for model %d: %.5f" % (model_num, model_coherence)) + coherences.append((avg_topic_coherences.tolist(), model_coherence)) # Convert numpy array back to list for output + + return coherences \ No newline at end of file diff --git a/backend/evaluation/eval.py b/backend/evaluation/eval.py new file mode 100644 index 0000000000000000000000000000000000000000..3d39ed50eff2c3af84d6ca43343d9d96fb018982 --- /dev/null +++ b/backend/evaluation/eval.py @@ -0,0 +1,179 @@ +# dynamic_topic_quality.py +import numpy as np +import pandas as pd +from gensim.corpora.dictionary import Dictionary +from gensim.models.coherencemodel import CoherenceModel +from backend.evaluation.CoherenceModel_ttc import CoherenceModel_ttc +from typing import List, Dict + +class TopicQualityAssessor: + """ + Calculates various quality metrics for dynamic topic models from in-memory data. + + This class provides methods to compute: + - Temporal Topic Coherence (TTC) + - Temporal Topic Smoothness (TTS) + - Temporal Topic Quality (TTQ) + - Yearly Topic Coherence (TC) + - Yearly Topic Diversity (TD) + - Yearly Topic Quality (TQ) + """ + + def __init__(self, topics: List[List[List[str]]], train_texts: List[List[str]], topn: int, coherence_type: str): + """ + Initializes the TopicQualityAssessor with data in memory. + + Args: + topics (List[List[List[str]]]): A nested list of topics with structure (T, K, W), + where T is time slices, K is topics, and W is words. + train_texts (List[List[str]]): A list of tokenized documents for the reference corpus. + topn (int): Number of top words per topic to consider for calculations. + coherence_type (str): The type of coherence to calculate (e.g., 'c_npmi', 'c_v'). + """ + # 1. Set texts and dictionary + self.texts = train_texts + self.dictionary = Dictionary(self.texts) + + # 2. Process topics + # User provides topics as (T, K, W) -> List[timestamps][topics][words] + # Internal representation for temporal evolution is (K, T, W) + topics_array_T_K_W = np.array(topics, dtype=object) + if topics_array_T_K_W.ndim != 3: + raise ValueError(f"Input 'topics' must be a 3-dimensional list/array. Got {topics_array_T_K_W.ndim} dimensions.") + self.total_topics = topics_array_T_K_W.transpose(1, 0, 2) # Shape: (K, T, W) + + # 3. Get dimensions + self.K, self.T, _ = self.total_topics.shape + + # 4. Create topic groups for smoothness calculation (pairs of topics over time) + groups = [] + for k in range(self.K): + time_pairs = [] + for t in range(self.T - 1): + time_pairs.append([self.total_topics[k, t].tolist(), self.total_topics[k, t+1].tolist()]) + groups.append(time_pairs) + self.group_topics = np.array(groups, dtype=object) + + # 5. Create yearly topics (T, K, W) for TC/TD calculation + self.yearly_topics = self.total_topics.transpose(1, 0, 2) + + # 6. Set parameters + self.topn = topn + self.coherence_type = coherence_type + + def _compute_coherence(self, topics: List[List[str]]) -> List[float]: + cm = CoherenceModel( + topics=topics, texts=self.texts, dictionary=self.dictionary, + coherence=self.coherence_type, topn=self.topn + ) + return cm.get_coherence_per_topic() + + def _compute_coherence_ttc(self, topics: List[List[str]]) -> List[float]: + cm = CoherenceModel_ttc( + topics=topics, texts=self.texts, dictionary=self.dictionary, + coherence=self.coherence_type, topn=self.topn + ) + return cm.get_coherence_per_topic() + + def _topic_smoothness(self, topics: List[List[str]]) -> float: + K = len(topics) + if K <= 1: + return 1.0 # Or 0.0, depending on definition. A single topic has no other topic to be dissimilar to. + scores = [] + for i, base in enumerate(topics): + base_set = set(base[:self.topn]) + others = [other for j, other in enumerate(topics) if j != i] + if not others: + return 1.0 + overlaps = [len(base_set & set(other[:self.topn])) / self.topn for other in others] + scores.append(sum(overlaps) / len(overlaps)) + return float(sum(scores) / K) + + def get_ttq_dataframe(self) -> pd.DataFrame: + """Computes and returns a DataFrame with detailed TTQ metrics per topic chain.""" + all_coh_scores, avg_coh_scores = [], [] + for k in range(self.K): + coh_per_topic = self._compute_coherence_ttc(self.total_topics[k].tolist()) + all_coh_scores.append(coh_per_topic) + avg_coh_scores.append(float(np.mean(coh_per_topic))) + + all_smooth_scores, avg_smooth_scores = [], [] + for k in range(self.K): + pair_scores = [self._topic_smoothness(pair) for pair in self.group_topics[k]] + all_smooth_scores.append(pair_scores) + avg_smooth_scores.append(float(np.mean(pair_scores))) + + df = pd.DataFrame({ + 'topic_idx': list(range(self.K)), + 'temporal_coherence': all_coh_scores, + 'temporal_smoothness': all_smooth_scores, + 'avg_temporal_coherence': avg_coh_scores, + 'avg_temporal_smoothness': avg_smooth_scores + }) + df['ttq_product'] = df['avg_temporal_coherence'] * df['avg_temporal_smoothness'] + return df + + def get_tq_dataframe(self) -> pd.DataFrame: + """Computes and returns a DataFrame with detailed TQ metrics per time slice.""" + all_coh, avg_coh, div = [], [], [] + for t in range(self.T): + yearly_t_topics = self.yearly_topics[t].tolist() + coh_per_topic = self._compute_coherence(yearly_t_topics) + all_coh.append(coh_per_topic) + avg_coh.append(float(np.mean(coh_per_topic))) + div.append(1 - self._topic_smoothness(yearly_t_topics)) + + df = pd.DataFrame({ + 'year': list(range(self.T)), + 'all_coherence': all_coh, + 'avg_coherence': avg_coh, + 'diversity': div + }) + df['tq_product'] = df['avg_coherence'] * df['diversity'] + return df + + def get_ttc_score(self) -> float: + """Calculates the overall Temporal Topic Coherence (TTC).""" + ttq_df = self.get_ttq_dataframe() + return ttq_df['avg_temporal_coherence'].mean() + + def get_tts_score(self) -> float: + """Calculates the overall Temporal Topic Smoothness (TTS).""" + ttq_df = self.get_ttq_dataframe() + return ttq_df['avg_temporal_smoothness'].mean() + + def get_ttq_score(self) -> float: + """Calculates the overall Temporal Topic Quality (TTQ).""" + ttq_df = self.get_ttq_dataframe() + return ttq_df['ttq_product'].mean() + + def get_tc_score(self) -> float: + """Calculates the overall yearly Topic Coherence (TC).""" + tq_df = self.get_tq_dataframe() + return tq_df['avg_coherence'].mean() + + def get_td_score(self) -> float: + """Calculates the overall yearly Topic Diversity (TD).""" + tq_df = self.get_tq_dataframe() + return tq_df['diversity'].mean() + + def get_tq_score(self) -> float: + """Calculates the overall yearly Topic Quality (TQ).""" + tq_df = self.get_tq_dataframe() + return tq_df['tq_product'].mean() + + def get_dtq_summary(self) -> Dict[str, float]: + """ + Computes all dynamic topic quality metrics and returns them in a dictionary. + """ + ttq_df = self.get_ttq_dataframe() + tq_df = self.get_tq_dataframe() + summary = { + 'TTC': ttq_df['avg_temporal_coherence'].mean(), + 'TTS': ttq_df['avg_temporal_smoothness'].mean(), + 'TTQ': ttq_df['ttq_product'].mean(), + 'TC': tq_df['avg_coherence'].mean(), + 'TD': tq_df['diversity'].mean(), + 'TQ': tq_df['tq_product'].mean() + } + return summary \ No newline at end of file diff --git a/backend/inference/doc_retriever.py b/backend/inference/doc_retriever.py new file mode 100644 index 0000000000000000000000000000000000000000..e0c03c06b614c7a2681cda2096c034a82470b532 --- /dev/null +++ b/backend/inference/doc_retriever.py @@ -0,0 +1,219 @@ +import html +import json +import re +import os +from hashlib import md5 + +def deduplicate_docs(collected_docs): + seen = set() + unique_docs = [] + for doc in collected_docs: + # Prefer unique ID if available + key = doc.get("id", md5(doc["text"].encode()).hexdigest()) + if key not in seen: + seen.add(key) + unique_docs.append(doc) + return unique_docs + +def load_length_stats(length_stats_path): + """ + Loads length statistics from a JSON file for a given model path. + + Args: + path (str): Path to the model directory containing 'length_stats.json'. + + Returns: + dict: A dictionary containing document length statistics. + """ + if not os.path.exists(length_stats_path): + raise FileNotFoundError(f"'length_stats.json' not found at: {length_stats_path}") + + with open(length_stats_path, "r") as f: + length_stats = json.load(f) + + return length_stats + +def get_yearly_counts_for_word(index, word): + if word not in index: + print(f"[ERROR] Word '{word}' not found in index.") + return [], [] + + year_counts = index[word] + sorted_items = sorted((int(year), len(doc_ids)) for year, doc_ids in year_counts.items()) + years, counts = zip(*sorted_items) if sorted_items else ([], []) + return list(years), list(counts) + + +def get_all_documents_for_word_year(index, docs_file_path, word, year): + """ + Returns all full documents (text + metadata) that contain a given word in a given year. + + Parameters: + index (dict): Inverted index. + docs_file_path (str): Path to original jsonl corpus. + word (str): Word (unigram or bigram). + year (int): Year to retrieve docs for. + + Returns: + List[Dict]: List of documents with 'id', 'timestamp', and 'text'. + """ + year = int(year) + + if word not in index or year not in index[word]: + return [] + + doc_ids = set(index[word][year]) + results = [] + + try: + with open(docs_file_path, 'r', encoding='utf-8') as f: + for doc_id, line in enumerate(f): + if doc_id in doc_ids: + doc = json.loads(line) + results.append({ + "id": doc_id, + "timestamp": doc.get("timestamp", "N/A"), + "text": doc["text"] + }) + except Exception as e: + print(f"[ERROR] Could not load documents: {e}") + + return results + + +def get_documents_with_all_words_for_year(index, docs_path, words, year): + doc_sets = [] + all_doc_occurrences = {} + + for word in words: + word_docs = get_all_documents_for_word_year(index, docs_path, word, year) + doc_sets.append(set(doc["id"] for doc in word_docs)) + for doc in word_docs: + all_doc_occurrences.setdefault(doc["id"], doc) + + common_doc_ids = set.intersection(*doc_sets) if doc_sets else set() + return [all_doc_occurrences[doc_id] for doc_id in common_doc_ids] + + +def get_intersection_doc_counts_by_year(index, docs_path, words, all_years): + year_counts = {} + for y in all_years: + docs = get_documents_with_all_words_for_year(index, docs_path, words, y) + year_counts[y] = len(docs) + return year_counts + + +def extract_snippet(text, query, window=30): + """ + Return a short snippet around the first occurrence of the query word. + """ + pattern = re.compile(re.escape(query.replace('_', ' ')), re.IGNORECASE) + match = pattern.search(text) + if not match: + return text[:200] + "..." + + start = max(match.start() - window, 0) + end = min(match.end() + window, len(text)) + snippet = text[start:end].strip() + + return f"...{snippet}..." + +def highlight(text, query, highlight_color="#FFD54F"): + """ + Highlight all instances of the query term in text using a colored tag. + """ + escaped_query = re.escape(query.replace('_', ' ')) + pattern = re.compile(f"({escaped_query})", flags=re.IGNORECASE) + + def replacer(match): + matched_text = html.escape(match.group(1)) + return f"{matched_text}" + + return pattern.sub(replacer, html.escape(text)) + +def highlight_words(text, query_words, highlight_color="#24F31D", lemma_to_forms=None): + """ + Highlight all surface forms of each query lemma in the text using a colored tag. + + Args: + text (str): The input raw document text. + query_words (List[str]): Lemmatized query tokens to highlight. + highlight_color (str): Color to use for highlighting. + lemma_to_forms (Dict[str, Set[str]]): Maps a lemma to its surface forms. + """ + # Escape HTML special characters first + escaped_text = html.escape(text) + + # Expand query words to include all surface forms + expanded_forms = set() + for lemma in query_words: + if lemma_to_forms and lemma in lemma_to_forms: + expanded_forms.update(lemma_to_forms[lemma]) + else: + expanded_forms.add(lemma) # Fallback if map is missing + + # Sort by length to avoid partial overlaps (e.g., "run" before "running") + sorted_queries = sorted(expanded_forms, key=lambda w: -len(w)) + + for word in sorted_queries: + # Match full word, case insensitive + pattern = re.compile(rf'\b({re.escape(word)})\b', flags=re.IGNORECASE) + + def replacer(match): + matched_text = match.group(1) + return f"{matched_text}" + + escaped_text = pattern.sub(replacer, escaped_text) + + return escaped_text + +def get_docs_by_ids(docs_file_path, doc_ids): + """ + Efficiently retrieves specific documents from a .jsonl file by their line number (ID). + + This function reads the file line-by-line and only parses the lines that match + the requested document IDs, avoiding loading the entire file into memory. + + Args: + docs_file_path (str): The path to the documents.jsonl file. + doc_ids (list or set): A collection of document IDs (0-indexed line numbers) to retrieve. + + Returns: + list[dict]: A list of document dictionaries that were found. Each dictionary + is augmented with an 'id' key corresponding to its line number. + """ + # Use a set for efficient O(1) lookups. + doc_ids_to_find = set(doc_ids) + found_docs = {} + + if not doc_ids_to_find: + return [] + + try: + with open(docs_file_path, 'r', encoding='utf-8') as f: + for i, line in enumerate(f): + # If the current line number is one we're looking for + if i in doc_ids_to_find: + try: + doc = json.loads(line) + # Explicitly add the line number as the 'id' + doc['id'] = i + found_docs[i] = doc + # Optimization: stop reading the file once all docs are found + if len(found_docs) == len(doc_ids_to_find): + break + except json.JSONDecodeError: + # Skip malformed lines but inform the user + print(f"[WARNING] Skipping malformed JSON on line {i+1} in {docs_file_path}") + continue + + except FileNotFoundError: + print(f"[ERROR] Document file not found at: {docs_file_path}") + return [] + except Exception as e: + print(f"[ERROR] An unexpected error occurred while reading documents: {e}") + return [] + + # Return the documents in the same order as the original doc_ids list + # This ensures consistency for downstream processing. + return [found_docs[doc_id] for doc_id in doc_ids if doc_id in found_docs] \ No newline at end of file diff --git a/backend/inference/indexing_utils.py b/backend/inference/indexing_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..0a904773a7e727afd88f8ca3663a3f166fd47eb7 --- /dev/null +++ b/backend/inference/indexing_utils.py @@ -0,0 +1,146 @@ +import json +import os +import re +import spacy +from collections import defaultdict + +# Load spaCy once +nlp = spacy.load("en_core_web_sm", disable=["parser", "ner"]) + +def tokenize(text): + return re.findall(r"\b\w+\b", text.lower()) + +def has_bigram(tokens, bigram): + parts = bigram.split('_') + for i in range(len(tokens) - len(parts) + 1): + if tokens[i:i + len(parts)] == parts: + return True + return False + +def build_inverse_lemma_map(docs_file_path, cache_path=None): + """ + Build or load a mapping from lemma -> set of surface forms seen in corpus. + If cache_path is provided and exists, loads from it. + Else builds from scratch and saves to cache_path. + """ + if cache_path and os.path.exists(cache_path): + print(f"[INFO] Loading cached lemma_to_forms from {cache_path}") + with open(cache_path, "r", encoding="utf-8") as f: + raw_map = json.load(f) + return {lemma: set(forms) for lemma, forms in raw_map.items()} + + print(f"[INFO] Building inverse lemma map from {docs_file_path}...") + lemma_to_forms = defaultdict(set) + + with open(docs_file_path, 'r', encoding='utf-8') as f: + for line in f: + doc = json.loads(line) + tokens = tokenize(doc['text']) + spacy_doc = nlp(" ".join(tokens)) + for token in spacy_doc: + lemma_to_forms[token.lemma_].add(token.text.lower()) + + if cache_path: + print(f"[INFO] Saving lemma_to_forms to {cache_path}") + os.makedirs(os.path.dirname(cache_path), exist_ok=True) + with open(cache_path, "w", encoding="utf-8") as f: + json.dump({k: list(v) for k, v in lemma_to_forms.items()}, f, indent=2) + + return lemma_to_forms + +def build_inverted_index(docs_file_path, vocab_set, lemma_map_path=None): + vocab_unigrams = {w for w in vocab_set if '_' not in w} + vocab_bigrams = {w for w in vocab_set if '_' in w} + + # Load or build lemma map + lemma_to_forms = build_inverse_lemma_map(docs_file_path, cache_path=lemma_map_path) + + index = defaultdict(lambda: defaultdict(list)) + docs = [] + global_seen_words = set() + + with open(docs_file_path, 'r', encoding='utf-8') as f: + for doc_id, line in enumerate(f): + doc = json.loads(line) + text = doc['text'] + timestamp = int(doc['timestamp']) + docs.append({"text": text, "timestamp": timestamp}) + + tokens = tokenize(text) + token_set = set(tokens) + seen_words = set() + + # Match all lemma queries using surface forms + for lemma in vocab_unigrams: + surface_forms = lemma_to_forms.get(lemma, set()) + if token_set & surface_forms: + index[lemma][timestamp].append(doc_id) + seen_words.add(lemma) + + for bigram in vocab_bigrams: + if bigram not in seen_words and has_bigram(tokens, bigram): + index[bigram][timestamp].append(doc_id) + seen_words.add(bigram) + + global_seen_words.update(seen_words) + + if (doc_id + 1) % 500 == 0: + missing = vocab_set - global_seen_words + print(f"[INFO] After {doc_id+1} docs, {len(missing)} vocab words still not seen.") + print("Example missing words:", list(missing)[:5]) + + missing_final = vocab_set - global_seen_words + if missing_final: + print(f"[WARNING] {len(missing_final)} vocab words were never found in any document.") + print("Examples:", list(missing_final)[:10]) + + return index, docs, lemma_to_forms + +def save_index_to_disk(index, index_path): + index_clean = { + word: {str(ts): doc_ids for ts, doc_ids in ts_dict.items()} + for word, ts_dict in index.items() + } + os.makedirs(os.path.dirname(index_path), exist_ok=True) + with open(index_path, "w", encoding='utf-8') as f: + json.dump(index_clean, f, ensure_ascii=False) + +def load_index_from_disk(index_path): + with open(index_path, 'r', encoding='utf-8') as f: + raw_index = json.load(f) + + index = defaultdict(lambda: defaultdict(list)) + for word, ts_dict in raw_index.items(): + for ts, doc_ids in ts_dict.items(): + index[word][int(ts)] = doc_ids + + return index + +def load_docs(docs_file_path): + docs = [] + with open(docs_file_path, 'r', encoding='utf-8') as f: + for line in f: + doc = json.loads(line) + docs.append({ + "text": doc["text"], + "timestamp": int(doc["timestamp"]) + }) + return docs + +def load_index(docs_file_path, vocab, index_path=None, lemma_map_path=None): + if index_path and os.path.exists(index_path): + index = load_index_from_disk(index_path) + docs = load_docs(docs_file_path) + lemma_to_forms = build_inverse_lemma_map(docs_file_path, cache_path=lemma_map_path) + return index, docs, lemma_to_forms + + index, docs, lemma_to_forms = build_inverted_index( + docs_file_path, + set(vocab), + lemma_map_path=lemma_map_path + ) + + if index_path: + save_index_to_disk(index, index_path) + + return index, docs, lemma_to_forms diff --git a/backend/inference/peak_detector.py b/backend/inference/peak_detector.py new file mode 100644 index 0000000000000000000000000000000000000000..2a023111fd6fe7225f3485df32ba85cfd8dfdc6b --- /dev/null +++ b/backend/inference/peak_detector.py @@ -0,0 +1,18 @@ +import numpy as np +from scipy.signal import find_peaks + +def detect_peaks(trend, prominence=0.001, distance=2): + """ + Detect peaks in a word's trend over time. + + Args: + trend: List or np.array of floats (word importance over time) + prominence: Required prominence of peaks (tune based on scale) + distance: Minimum distance between peaks + + Returns: + List of indices (timestamps) where peaks occur + """ + trend = np.array(trend) + peaks, _ = find_peaks(trend, prominence=prominence, distance=distance) + return peaks.tolist() diff --git a/backend/inference/process_beta.py b/backend/inference/process_beta.py new file mode 100644 index 0000000000000000000000000000000000000000..86eca5e79cdb48175c59d9ef56d603f4e90fb62e --- /dev/null +++ b/backend/inference/process_beta.py @@ -0,0 +1,33 @@ +import numpy as np +import json + +def load_beta_matrix(beta_path: str, vocab_path: str): + """ + Loads the beta matrix (T x K x V) and vocab list. + + Returns: + beta: np.ndarray of shape (T, K, V) + vocab: list of words + """ + beta = np.load(beta_path) # shape: T x K x V + with open(vocab_path, 'r') as f: + vocab = [line.strip() for line in f.readlines()] + return beta, vocab + +def get_top_words_at_time(beta, vocab, topic_id, time, top_n): + topic_beta = beta[time, topic_id, :] + top_indices = topic_beta.argsort()[-top_n:][::-1] + return [vocab[i] for i in top_indices] + +def get_top_words_over_time(beta, vocab, topic_id, top_n): + topic_beta = beta[:, topic_id, :] + mean_beta = topic_beta.mean(axis=0) + top_indices = mean_beta.argsort()[-top_n:][::-1] + return [vocab[i] for i in top_indices] + +def load_time_labels(time2id_path): + with open(time2id_path, 'r') as f: + time2id = json.load(f) + # Invert and sort by id + id2time = {v: k for k, v in time2id.items()} + return [id2time[i] for i in sorted(id2time)] \ No newline at end of file diff --git a/backend/inference/word_selector.py b/backend/inference/word_selector.py new file mode 100644 index 0000000000000000000000000000000000000000..06a35a4b5fe7a685e4355ad31a8c33feb432c4f9 --- /dev/null +++ b/backend/inference/word_selector.py @@ -0,0 +1,102 @@ +import numpy as np +from scipy.special import softmax + +def get_interesting_words(beta, vocab, topic_id, top_k_final=10, restrict_to=None): + """ + Suggests interesting words by prioritizing "bursty" or "emerging" terms, + making it effective at capturing important low-probability words. + + This algorithm focuses on the ratio of a word's peak probability to its mean, + capturing words that show significant growth or have a sudden moment of high + relevance, even if their average probability is low. + + Parameters: + - beta: np.ndarray (T, K, V) - Topic-word distributions for each timestamp. + - vocab: list of V words - The vocabulary. + - topic_id: int - The ID of the topic to analyze. + - top_k_final: int - The number of words to return. + - restrict_to: optional list of str - Restricts scoring to a subset of words. + + Returns: + - list of top_k_final interesting words (strings). + """ + T, K, V = beta.shape + + # --- 1. Detect whether softmax is needed --- + row_sums = beta.sum(axis=2) + is_prob_dist = np.allclose(row_sums, 1.0, atol=1e-2) + + if not is_prob_dist: + print("🔁 Beta is not normalized — applying softmax across words per topic.") + beta = softmax(beta / 1e-3, axis=2) + + # --- 2. Now extract normalized topic slice --- + topic_beta = beta[:, topic_id, :] # Shape: (T, V) + + # Mean and Peak probability within the topic for each word + mean_topic = topic_beta.mean(axis=0) # Shape: (V,) + peak_topic = topic_beta.max(axis=0) # Shape: (V,) + + # Corpus-wide mean for baseline comparison + mean_all = beta.mean(axis=(0, 1)) # Shape: (V,) + + # Epsilon to prevent division by zero for words that never appear + epsilon = 1e-9 + + # --- 3. Calculate the three core components of the new score --- + + # a) Burstiness Score: How much a word's peak stands out from its own average. + # This is the key to finding "surprising" words. + burstiness_score = peak_topic / (mean_topic + epsilon) + + # b) Peak Specificity: How much the word's peak in this topic stands out from + # its average presence in the entire corpus. + peak_specificity_score = peak_topic / (mean_all + epsilon) + + # c) Uniqueness Score (same as before): Penalizes words active in many topics. + active_in_topics = (beta > 1e-5).mean(axis=0) # Shape: (K, V) + idf_like = np.log((K + 1) / (active_in_topics.sum(axis=0) + 1)) # Shape: (V,) + + # --- 4. Compute Final Interestingness Score --- + # This score is high for words that are unique, have a high peak relative + # to their baseline, and whose peak is an unusual event for that word. + final_scores = burstiness_score * peak_specificity_score * idf_like + + # --- 5. Rank and select top words --- + if restrict_to is not None: + restrict_set = set(restrict_to) + word_indices = [i for i, w in enumerate(vocab) if w in restrict_set] + else: + word_indices = np.arange(V) + + if not word_indices: + return [] + + # Rank the filtered indices by the final score in descending order + sorted_indices = sorted(word_indices, key=lambda i: -final_scores[i]) + + return [vocab[i] for i in sorted_indices[:top_k_final]] + + +def get_word_trend(beta, vocab, word, topic_id): + """ + Get the time trend of a word's probability under a specific topic. + + Args: + beta: np.ndarray of shape (T, K, V) + vocab: list of vocab words + word: word to search + topic_id: index of topic to inspect (0 <= topic_id < K) + + Returns: + List of word probabilities over time (length T) + """ + T, K, V = beta.shape + if word not in vocab: + raise ValueError(f"Word '{word}' not found in vocab.") + if not (0 <= topic_id < K): + raise ValueError(f"Invalid topic_id {topic_id}. Must be between 0 and {K - 1}.") + + word_index = vocab.index(word) + trend = beta[:, topic_id, word_index] # shape (T,) + return trend.tolist() \ No newline at end of file diff --git a/backend/llm/custom_gemini.py b/backend/llm/custom_gemini.py new file mode 100644 index 0000000000000000000000000000000000000000..776d7d183e5d3b431f6b9300d7e12b7b7e6a8946 --- /dev/null +++ b/backend/llm/custom_gemini.py @@ -0,0 +1,28 @@ +from langchain_google_genai import ChatGoogleGenerativeAI +from langchain_core.messages import AIMessage, HumanMessage +from langchain_core.language_models.chat_models import BaseChatModel +from typing import List + + +class ChatGemini(BaseChatModel): + def __init__(self, api_key: str, model: str = "gemini-pro", temperature: float = 0.7): + self.model = model + self.temperature = temperature + self.api_key = api_key + self.client = ChatGoogleGenerativeAI( + model=model, + temperature=temperature, + google_api_key=api_key + ) + + def _generate(self, messages: List, stop: List[str] = None): + # Convert LangChain messages to string + prompt = "\n".join( + msg.content for msg in messages if isinstance(msg, (HumanMessage, AIMessage)) + ) + response = self.client.invoke(prompt) + return response + + @property + def _llm_type(self) -> str: + return "gemini" diff --git a/backend/llm/custom_mistral.py b/backend/llm/custom_mistral.py new file mode 100644 index 0000000000000000000000000000000000000000..c6d2eb781c2d7c1fbf0cd3cf92a02fd5b3354bf8 --- /dev/null +++ b/backend/llm/custom_mistral.py @@ -0,0 +1,27 @@ +from langchain_core.language_models.chat_models import BaseChatModel +from langchain_core.messages import HumanMessage, AIMessage +from langchain_core.outputs import ChatResult, ChatGeneration +import requests +import os + +class ChatMistral(BaseChatModel): + def __init__(self, hf_token=None, model_url=None): + self.hf_token = hf_token or os.getenv("HF_TOKEN") + self.model_url = model_url or "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.1" + self.headers = {"Authorization": f"Bearer {self.hf_token}"} + + def _call(self, prompt: str) -> str: + response = requests.post( + self.model_url, + headers=self.headers, + json={"inputs": prompt, "parameters": {"max_new_tokens": 256}}, + ) + return response.json()[0]["generated_text"] + + def invoke(self, messages, **kwargs): + prompt = "\n".join([msg.content for msg in messages if isinstance(msg, HumanMessage)]) + response = self._call(prompt) + return AIMessage(content=response) + + def _generate(self, messages, stop=None, **kwargs) -> ChatResult: + return ChatResult(generations=[ChatGeneration(message=self.invoke(messages))]) diff --git a/backend/llm/llm_router.py b/backend/llm/llm_router.py new file mode 100644 index 0000000000000000000000000000000000000000..d8bf66d9932e6152399a8dd38b597460ce100667 --- /dev/null +++ b/backend/llm/llm_router.py @@ -0,0 +1,73 @@ +from langchain_anthropic import ChatAnthropic +from backend.llm.custom_mistral import ChatMistral +from langchain_google_genai import ChatGoogleGenerativeAI +from langchain_openai import ChatOpenAI +import os +import google.auth.transport.requests +import requests + +resp = requests.get("https://www.google.com", proxies={ + "http": os.getenv("http_proxy"), + "https": os.getenv("https_proxy") +}) + +def list_supported_models(provider=None): + if provider == "OpenAI": + return ["gpt-4.1-nano", "gpt-4o-mini"] + elif provider == "Anthropic": + return ["claude-3-opus-20240229", "claude-3-sonnet-20240229"] + elif provider == "Gemini": + return ["gemini-2.0-flash-lite", "gemini-1.5-flash"] + elif provider == "Mistral": + return ["mistral-small", "mistral-medium"] + else: + # Default fallback: all models grouped by provider + return { + "OpenAI": ["gpt-4.1-nano", "gpt-4o-mini"], + "Anthropic": ["claude-3-opus-20240229", "claude-3-sonnet-20240229"], + "Gemini": ["gemini-2.0-flash-lite", "gemini-1.5-flash"], + "Mistral": ["mistral-small", "mistral-medium"] + } + + +def get_llm(provider: str, model: str, api_key: str = None): + if provider == "OpenAI": + api_key = api_key or os.getenv("OPENAI_API_KEY") + if not api_key: + raise ValueError("Missing OpenAI API key.") + return ChatOpenAI(model_name=model, temperature=0, openai_api_key=api_key) + + elif provider == "Anthropic": + api_key = api_key or os.getenv("ANTHROPIC_API_KEY") + if not api_key: + raise ValueError("Missing Anthropic API key.") + return ChatAnthropic(model=model, temperature=0, anthropic_api_key=api_key) + + elif provider == "Gemini": + api_key = api_key or os.getenv("GEMINI_API_KEY") + if not api_key: + raise ValueError("Missing Gemini API key.") + # --- Patch: Set proxy if available --- + if "HTTP_PROXY" in os.environ or "http_proxy" in os.environ: + + proxies = { + "http": os.getenv("http_proxy") or os.getenv("HTTP_PROXY"), + "https": os.getenv("https_proxy") or os.getenv("HTTPS_PROXY") + } + + google.auth.transport.requests.requests.Request = lambda *args, **kwargs: requests.Request( + *args, **kwargs, proxies=proxies + ) + + return ChatGoogleGenerativeAI(model=model, temperature=0, google_api_key=api_key) + + + elif provider == "Mistral": + api_key = api_key or os.getenv("MISTRAL_API_KEY") + if not api_key: + raise ValueError("Missing Mistral API key.") + return ChatMistral(model=model, temperature=0, mistral_api_key=api_key) + + else: + raise ValueError(f"Unsupported provider: {provider}") + diff --git a/backend/llm_utils/label_generator.py b/backend/llm_utils/label_generator.py new file mode 100644 index 0000000000000000000000000000000000000000..441a61e5fbcddb79184f90f232f6e292dff18199 --- /dev/null +++ b/backend/llm_utils/label_generator.py @@ -0,0 +1,72 @@ +from hashlib import sha256 +import json +from langchain_core.prompts import ChatPromptTemplate +from langchain_core.output_parsers import StrOutputParser +from typing import Optional +import os + +#get_top_words_at_time +from backend.inference.process_beta import get_top_words_at_time + +def label_topic_temporal(word_trajectory_str: str, llm, cache_path: Optional[str] = None) -> str: + """ + Label a dynamic topic by providing the LLM with the top words over time. + + Args: + word_trajectory_str (str): Formatted keyword evolution string. + llm: LangChain-compatible LLM instance. + cache_path (Optional[str]): Path to the cache file (JSON). + + Returns: + str: Short label for the topic. + """ + topic_key = sha256(word_trajectory_str.encode()).hexdigest() + + # Load cache + if cache_path is not None and os.path.exists(cache_path): + with open(cache_path, "r") as f: + label_cache = json.load(f) + else: + label_cache = {} + + # Return cached result + if topic_key in label_cache: + return label_cache[topic_key] + + # Prompt template + prompt = ChatPromptTemplate.from_template( + "You are an expert in topic modeling and temporal data analysis. " + "Given the top words for a topic across multiple time points, your task is to return a short, specific, descriptive topic label. " + "Avoid vague, generic, or overly broad labels. Focus on consistent themes in the top words over time. " + "Use concise noun phrases, 2–5 words max. Do NOT include any explanation, justification, or extra output.\n\n" + "Top words over time:\n{trajectory}\n\n" + "Return ONLY the label (no quotes, no extra text):" + ) + chain = prompt | llm | StrOutputParser() + + try: + label = chain.invoke({"trajectory": word_trajectory_str}).strip() + except Exception as e: + label = "Unknown Topic" + print(f"[Labeling Error] {e}") + + # Update cache and save + label_cache[topic_key] = label + if cache_path is not None: + os.makedirs(os.path.dirname(cache_path), exist_ok=True) + with open(cache_path, "w") as f: + json.dump(label_cache, f, indent=2) + + return label + + +def get_topic_labels(beta, vocab, time_labels, llm, cache_path): + topic_labels = {} + for topic_id in range(beta.shape[1]): + word_trajectory_str = "\n".join([ + f"{time_labels[t]}: {', '.join(get_top_words_at_time(beta, vocab, topic_id, t, top_n=10))}" + for t in range(beta.shape[0]) + ]) + label = label_topic_temporal(word_trajectory_str, llm=llm, cache_path=cache_path) + topic_labels[topic_id] = label + return topic_labels \ No newline at end of file diff --git a/backend/llm_utils/summarizer.py b/backend/llm_utils/summarizer.py new file mode 100644 index 0000000000000000000000000000000000000000..6188e9cef0e099b832fd579cc130db8bcdde7b60 --- /dev/null +++ b/backend/llm_utils/summarizer.py @@ -0,0 +1,192 @@ +import hashlib +import numpy as np +from sklearn.metrics.pairwise import cosine_similarity +from sentence_transformers import SentenceTransformer +import faiss + +from langchain.prompts import ChatPromptTemplate +from langchain.docstore.document import Document +from langchain.memory import ConversationBufferMemory +from langchain.chains import ConversationChain + +import os +os.environ["TOKENIZERS_PARALLELISM"] = "false" + + +# --- MMR Utilities --- +def build_mmr_index(docs): + texts = [doc['text'] for doc in docs if 'text' in doc] + documents = [Document(page_content=text) for text in texts] + + model = SentenceTransformer("all-MiniLM-L6-v2") + embeddings = model.encode([doc.page_content for doc in documents], convert_to_numpy=True) + faiss.normalize_L2(embeddings) + + index = faiss.IndexFlatIP(embeddings.shape[1]) + index.add(embeddings) + + return model, index, embeddings, documents + +def get_mmr_sample(model, index, embeddings, documents, query, k=15, lambda_mult=0.7): + if len(documents) == 0: + print("Warning: No documents available, returning empty list.") + return [] + + if len(documents) <= k: + print(f"Warning: Only {len(documents)} documents available, returning all.") + return documents + + else: + query_vec = model.encode(query, convert_to_numpy=True) + query_vec = query_vec / np.linalg.norm(query_vec) + + # Get candidate indices from FAISS (k * 4 or less if not enough documents) + num_candidates = min(k * 4, len(documents)) + D, I = index.search(np.expand_dims(query_vec, axis=0), num_candidates) + candidate_idxs = list(I[0]) + + selected = [] + while len(selected) < k and candidate_idxs: + if not selected: + selected.append(candidate_idxs.pop(0)) + continue + + mmr_scores = [] + for idx in candidate_idxs: + relevance = cosine_similarity([query_vec], [embeddings[idx]])[0][0] + diversity = max([ + cosine_similarity([embeddings[idx]], [embeddings[sel]])[0][0] + for sel in selected + ]) + mmr_score = lambda_mult * relevance - (1 - lambda_mult) * diversity + mmr_scores.append((idx, mmr_score)) + + next_best = max(mmr_scores, key=lambda x: x[1])[0] + selected.append(next_best) + candidate_idxs.remove(next_best) + + return [documents[i] for i in selected] + + +# --- Summarization --- +def summarize_docs(word, timestamp, docs, llm, k): + if not docs: + return "No documents available for this word at this time.", [], 0 + + try: + model, index, embeddings, documents = build_mmr_index(docs) + mmr_docs = get_mmr_sample(model, index, embeddings, documents, query=word, k=k) + + context_texts = "\n".join(f"- {doc.page_content}" for doc in mmr_docs) + + prompt_template = ChatPromptTemplate.from_template( + "Given the following documents from {timestamp} containing the word '{word}', " + "identify the key themes or distinct discussion points that were prevalent during that time. " + "Do NOT describe each bullet in detail. Be concise. Each bullet should be a short phrase or sentence " + "capturing a unique, non-overlapping theme. Avoid any elaboration, examples, or justification.\n\n" + "Return no more than 5–7 bullets.\n\n" + "{context_texts}\n\nSummary:" + ) + + chain = prompt_template | llm + summary = chain.invoke({ + "word": word, + "timestamp": timestamp, + "context_texts": context_texts + }).content.strip() + + return summary, mmr_docs + + except Exception as e: + return f"[Error summarizing: {e}]", [], 0 + + +def summarize_multiword_docs(words, timestamp, docs, llm, k): + if not docs: + return "No common documents available for these words at this time.", [] + + try: + model, index, embeddings, documents = build_mmr_index(docs) + query = " ".join(words) + mmr_docs = get_mmr_sample(model, index, embeddings, documents, query=query, k=k) + + context_texts = "\n".join(f"- {doc.page_content}" for doc in mmr_docs) + + prompt_template = ChatPromptTemplate.from_template( + "Given the following documents from {timestamp} that all mention the words: '{word_list}', " + "identify the key themes or distinct discussion points that were prevalent during that time. " + "Do NOT describe each bullet in detail. Be concise. Each bullet should be a short phrase or sentence " + "capturing a unique, non-overlapping theme. Avoid any elaboration, examples, or justification.\n\n" + "Return no more than 5–7 bullets.\n\n" + "{context_texts}\n\n" + "Concise Thematic Summary:" + ) + + chain = prompt_template | llm + summary = chain.invoke({ + "word_list": ", ".join(words), + "timestamp": timestamp, + "context_texts": context_texts + }).content.strip() + + return summary, mmr_docs + + except Exception as e: + return f"[Error summarizing: {e}]", [] + + +# --- Follow-up Question Handler (Improved) --- +def ask_multiturn_followup(history: list, question: str, llm, context_texts: str) -> str: + """ + Handles multi-turn follow-up questions based on a provided set of documents. + + This function now REQUIRES context_texts to be provided, ensuring the LLM + is always grounded in the source documents for follow-up questions. + + Args: + history (list): A list of dictionaries representing the conversation history + (e.g., [{"role": "user", "content": "..."}]). + question (str): The user's new follow-up question. + llm: The initialized language model instance. + context_texts (str): A single string containing all the numbered documents + for context. + + Returns: + str: The AI's response to the follow-up question. + """ + try: + # 1. Reconstruct conversation memory from the history provided from the UI + memory = ConversationBufferMemory(return_messages=True) + for turn in history: + if turn["role"] == "user": + memory.chat_memory.add_user_message(turn["content"]) + elif turn["role"] == "assistant": + memory.chat_memory.add_ai_message(turn["content"]) + + # 2. Define the system instruction that grounds the LLM + system_instruction = ( + "You are an assistant answering questions strictly based on the provided sample documents below. " + "Your memory contains the previous turns of this conversation. " + "If the answer is not clearly available in the text, respond with: " + "'The information is not available in the documents provided.'\n\n" + ) + + # 3. Create the full prompt. No more conditional logic, as context is required. + # The `ConversationChain` will automatically use the memory, so we only need + # to provide the current input, which includes the grounding documents. + full_prompt = ( + f"{system_instruction}" + f"--- DOCUMENTS ---\n{context_texts.strip()}\n\n" + f"--- QUESTION ---\n{question}" + ) + + # 4. Create and run the conversation chain + conversation = ConversationChain(llm=llm, memory=memory, verbose=False) + response = conversation.predict(input=full_prompt) + + return response.strip() + + except Exception as e: + # Good practice to log the full exception for easier debugging + print(f"[ERROR] in ask_multiturn_followup: {e}") + return f"[Error during multi-turn follow-up. Please check the logs.]" \ No newline at end of file diff --git a/backend/llm_utils/token_utils.py b/backend/llm_utils/token_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..48c14f08a823432d49d24e7f6f3781687efe31c0 --- /dev/null +++ b/backend/llm_utils/token_utils.py @@ -0,0 +1,167 @@ +from typing import Literal +import tiktoken +import anthropic +from typing import List + +# Gemini requires the Vertex AI SDK +try: + from vertexai.preview import tokenization as vertex_tokenization +except ImportError: + vertex_tokenization = None + +# Mistral requires the SentencePiece tokenizer +try: + import sentencepiece as spm +except ImportError: + spm = None + +# --------------------------- +# Individual Token Counters +# --------------------------- + +def count_tokens_openai(text: str, model_name: str) -> int: + try: + encoding = tiktoken.encoding_for_model(model_name) + except KeyError: + encoding = tiktoken.get_encoding("cl100k_base") # fallback + return len(encoding.encode(text)) + +def count_tokens_anthropic(text: str, model_name: str) -> int: + try: + client = anthropic.Anthropic() + response = client.messages.count_tokens( + model=model_name, + messages=[{"role": "user", "content": text}] + ) + return response['input_tokens'] + except Exception as e: + raise RuntimeError(f"Anthropic token counting failed: {e}") + +def count_tokens_gemini(text: str, model_name: str) -> int: + if vertex_tokenization is None: + raise ImportError("Please install vertexai: pip install google-cloud-aiplatform[tokenization]") + try: + tokenizer = vertex_tokenization.get_tokenizer_for_model("gemini-1.5-flash-002") + result = tokenizer.count_tokens(text) + return result.total_tokens + except Exception as e: + raise RuntimeError(f"Gemini token counting failed: {e}") + +def count_tokens_mistral(text: str) -> int: + if spm is None: + raise ImportError("Please install sentencepiece: pip install sentencepiece") + try: + sp = spm.SentencePieceProcessor() + # IMPORTANT: You must provide the correct path to the tokenizer model file + sp.load("mistral_tokenizer.model") + tokens = sp.encode(text, out_type=str) + return len(tokens) + except Exception as e: + raise RuntimeError(f"Mistral token counting failed: {e}") + +# --------------------------- +# Unified Token Counter +# --------------------------- + +def count_tokens(text: str, model_name: str, provider: Literal["OpenAI", "Anthropic", "Gemini", "Mistral"]) -> int: + if provider == "OpenAI": + return count_tokens_openai(text, model_name) + elif provider == "Anthropic": + return count_tokens_anthropic(text, model_name) + elif provider == "Gemini": + return count_tokens_gemini(text, model_name) + elif provider == "Mistral": + return count_tokens_mistral(text) + else: + raise ValueError(f"Unsupported provider: {provider}") + + +def get_token_limit_for_model(model_name, provider): + # Example values; update as needed for your providers + if provider == "openai": + if "gpt-4.1-nano" in model_name: + return 1047576 # Based on search results + elif "gpt-4o-mini" in model_name: + return 128000 # Based on search results + elif provider == "anthropic": + if "claude-3-opus" in model_name: + return 200000 # Based on search results + elif "claude-3-sonnet" in model_name: + return 200000 # Based on search results + elif provider == "gemini": + if "gemini-2.0-flash-lite" in model_name: + return 1048576 # Based on search results + elif "gemini-1.5-flash" in model_name: + return 1048576 # Based on search results + elif provider == "mistral": + if "mistral-small" in model_name: + return 32000 # Based on search results + elif "mistral-medium" in model_name: + return 32000 # Based on search results + return 8000 # default fallback + + +def estimate_avg_tokens_per_doc( + docs: List[str], + model_name: str, + provider: Literal["OpenAI", "Anthropic", "Gemini", "Mistral"] +) -> float: + """ + Estimate the average number of tokens per document for the given model. + + Args: + docs (List[str]): List of documents. + model_name (str): Model name. + provider (Literal): LLM provider. + + Returns: + float: Average number of tokens per document. + """ + if not docs: + return 0.0 + token_counts = [count_tokens(doc, model_name, provider) for doc in docs] + return sum(token_counts) / len(token_counts) + +def estimate_max_k( + docs: List[str], + model_name: str, + provider: Literal["OpenAI", "Anthropic", "Gemini", "Mistral"], + margin_ratio: float = 0.1, +) -> int: + """ + Estimate the maximum number of documents that can fit in the context window. + + Returns: + int: Estimated K. + """ + if not docs: + return 0 + + max_tokens = get_token_limit_for_model(model_name, provider) + margin = int(max_tokens * margin_ratio) + available_tokens = max_tokens - margin + + avg_tokens_per_doc = estimate_avg_tokens_per_doc(docs, model_name, provider) + if avg_tokens_per_doc == 0: + return 0 + + return min(len(docs), int(available_tokens // avg_tokens_per_doc)) + +def estimate_max_k_fast(docs, margin_ratio=0.1, max_tokens=8000, model_name="gpt-3.5-turbo"): + enc = tiktoken.encoding_for_model(model_name) + avg_len = sum(len(enc.encode(doc)) for doc in docs[:20]) / min(20, len(docs)) + margin = int(max_tokens * margin_ratio) + available = max_tokens - margin + return min(len(docs), int(available // avg_len)) + +def estimate_k_max_from_word_stats( + avg_words_per_doc: float, + margin_ratio: float = 0.1, + avg_tokens_per_word: float = 1.3, + model_name=None, + provider=None +) -> int: + model_token_limit = get_token_limit_for_model(model_name, provider) + effective_limit = int(model_token_limit * (1 - margin_ratio)) + est_tokens_per_doc = avg_words_per_doc * avg_tokens_per_word + return int(effective_limit // est_tokens_per_doc) \ No newline at end of file diff --git a/backend/models/CFDTM/CFDTM.py b/backend/models/CFDTM/CFDTM.py new file mode 100644 index 0000000000000000000000000000000000000000..4324004f7674bd2d53785488f69e5f7287b72b21 --- /dev/null +++ b/backend/models/CFDTM/CFDTM.py @@ -0,0 +1,127 @@ +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + +from .ETC import ETC +from .UWE import UWE +from .Encoder import MLPEncoder + + +class CFDTM(nn.Module): + ''' + Modeling Dynamic Topics in Chain-Free Fashion by Evolution-Tracking Contrastive Learning and Unassociated Word Exclusion. ACL 2024 Findings + + Xiaobao Wu, Xinshuai Dong, Liangming Pan, Thong Nguyen, Anh Tuan Luu. + ''' + + def __init__(self, + vocab_size, + train_time_wordfreq, + num_times, + pretrained_WE=None, + num_topics=50, + en_units=100, + temperature=0.1, + beta_temp=1.0, + weight_neg=1.0e+7, + weight_pos=1.0e+1, + weight_UWE=1.0e+3, + neg_topk=15, + dropout=0., + embed_size=200 + ): + super().__init__() + + self.num_topics = num_topics + self.beta_temp = beta_temp + self.train_time_wordfreq = train_time_wordfreq + self.encoder = MLPEncoder(vocab_size, num_topics, en_units, dropout) + + self.a = 1 * np.ones((1, num_topics)).astype(np.float32) + self.mu2 = nn.Parameter(torch.as_tensor((np.log(self.a).T - np.mean(np.log(self.a), 1)).T)) + self.var2 = nn.Parameter(torch.as_tensor((((1.0 / self.a) * (1 - (2.0 / num_topics))).T + (1.0 / (num_topics * num_topics)) * np.sum(1.0 / self.a, 1)).T)) + + self.mu2.requires_grad = False + self.var2.requires_grad = False + + self.decoder_bn = nn.BatchNorm1d(vocab_size, affine=False) + + if pretrained_WE is None: + self.word_embeddings = nn.init.trunc_normal_(torch.empty(vocab_size, embed_size), std=0.1) + self.word_embeddings = nn.Parameter(F.normalize(self.word_embeddings)) + + else: + self.word_embeddings = nn.Parameter(torch.from_numpy(pretrained_WE).float()) + + # topic_embeddings: TxKxD + self.topic_embeddings = nn.init.xavier_normal_(torch.zeros(num_topics, self.word_embeddings.shape[1])).repeat(num_times, 1, 1) + self.topic_embeddings = nn.Parameter(self.topic_embeddings) + + self.ETC = ETC(num_times, temperature, weight_neg, weight_pos) + self.UWE = UWE(self.ETC, num_times, temperature, weight_UWE, neg_topk) + + def get_beta(self): + dist = self.pairwise_euclidean_dist(F.normalize(self.topic_embeddings, dim=-1), F.normalize(self.word_embeddings, dim=-1)) + beta = F.softmax(-dist / self.beta_temp, dim=1) + + return beta + + def pairwise_euclidean_dist(self, x, y): + cost = torch.sum(x ** 2, axis=-1, keepdim=True) + torch.sum(y ** 2, axis=-1) - 2 * torch.matmul(x, y.t()) + return cost + + def get_theta(self, x, times=None): + theta, mu, logvar = self.encoder(x) + if self.training: + return theta, mu, logvar + + return theta + + def get_KL(self, mu, logvar): + var = logvar.exp() + var_division = var / self.var2 + diff = mu - self.mu2 + diff_term = diff * diff / self.var2 + logvar_division = self.var2.log() - logvar + KLD = 0.5 * ((var_division + diff_term + logvar_division).sum(axis=1) - self.num_topics) + + return KLD.mean() + + def get_NLL(self, theta, beta, x, recon_x=None): + if recon_x is None: + recon_x = self.decode(theta, beta) + recon_loss = -(x * recon_x.log()).sum(axis=1) + + return recon_loss + + def decode(self, theta, beta): + d1 = F.softmax(self.decoder_bn(torch.bmm(theta.unsqueeze(1), beta).squeeze(1)), dim=-1) + return d1 + + def forward(self, x, times): + loss = 0. + + theta, mu, logvar = self.get_theta(x) + kl_theta = self.get_KL(mu, logvar) + + loss += kl_theta + + beta = self.get_beta() + time_index_beta = beta[times] + recon_x = self.decode(theta, time_index_beta) + NLL = self.get_NLL(theta, time_index_beta, x, recon_x) + NLL = NLL.mean() + loss += NLL + + loss_ETC = self.ETC(self.topic_embeddings) + loss += loss_ETC + + loss_UWE = self.UWE(self.train_time_wordfreq, beta, self.topic_embeddings, self.word_embeddings) + loss += loss_UWE + + rst_dict = { + 'loss': loss, + } + + return rst_dict diff --git a/backend/models/CFDTM/ETC.py b/backend/models/CFDTM/ETC.py new file mode 100644 index 0000000000000000000000000000000000000000..fa1eb74ac062078680c5ddea70eb1aec9827f41d --- /dev/null +++ b/backend/models/CFDTM/ETC.py @@ -0,0 +1,62 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class ETC(nn.Module): + def __init__(self, num_times, temperature, weight_neg, weight_pos): + super().__init__() + self.num_times = num_times + self.weight_neg = weight_neg + self.weight_pos = weight_pos + self.temperature = temperature + + def forward(self, topic_embeddings): + loss = 0. + loss_neg = 0. + loss_pos = 0. + + for t in range(self.num_times): + loss_neg += self.compute_loss(topic_embeddings[t], topic_embeddings[t], self.temperature, self_contrast=True) + + for t in range(1, self.num_times): + loss_pos += self.compute_loss(topic_embeddings[t], topic_embeddings[t - 1].detach(), self.temperature, self_contrast=False, only_pos=True) + + loss_neg *= (self.weight_neg / self.num_times) + loss_pos *= (self.weight_pos / (self.num_times - 1)) + loss = loss_neg + loss_pos + + return loss + + def compute_loss(self, anchor_feature, contrast_feature, temperature, self_contrast=False, only_pos=False, all_neg=False): + # KxK + anchor_dot_contrast = torch.div( + torch.matmul(F.normalize(anchor_feature, dim=1), F.normalize(contrast_feature, dim=1).T), + temperature + ) + + logits_max, _ = torch.max(anchor_dot_contrast, dim=1, keepdim=True) + logits = anchor_dot_contrast - logits_max.detach() + + pos_mask = torch.eye(anchor_dot_contrast.shape[0]).to(anchor_dot_contrast.device) + + if self_contrast is False: + if only_pos is False: + if all_neg is True: + exp_logits = torch.exp(logits) + sum_exp_logits = exp_logits.sum(1) + log_prob = -torch.log(sum_exp_logits + 1e-12) + + mean_log_prob = -log_prob.sum() / (logits.shape[0] * logits.shape[1]) + else: + # only pos + mean_log_prob = -(logits * pos_mask).sum() / pos_mask.sum() + else: + # self contrast: push away from each other in the same time slice. + exp_logits = torch.exp(logits) * (1 - pos_mask) + sum_exp_logits = exp_logits.sum(1) + log_prob = -torch.log(sum_exp_logits + 1e-12) + + mean_log_prob = -log_prob.sum() / (1 - pos_mask).sum() + + return mean_log_prob diff --git a/backend/models/CFDTM/Encoder.py b/backend/models/CFDTM/Encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..92510dcdaf233fda0cb2d2b56df532f52d6de657 --- /dev/null +++ b/backend/models/CFDTM/Encoder.py @@ -0,0 +1,40 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class MLPEncoder(nn.Module): + def __init__(self, vocab_size, num_topic, hidden_dim, dropout): + super().__init__() + + self.fc11 = nn.Linear(vocab_size, hidden_dim) + self.fc12 = nn.Linear(hidden_dim, hidden_dim) + self.fc21 = nn.Linear(hidden_dim, num_topic) + self.fc22 = nn.Linear(hidden_dim, num_topic) + + self.fc1_drop = nn.Dropout(dropout) + self.z_drop = nn.Dropout(dropout) + + self.mean_bn = nn.BatchNorm1d(num_topic, affine=True) + self.mean_bn.weight.requires_grad = False + self.logvar_bn = nn.BatchNorm1d(num_topic, affine=True) + self.logvar_bn.weight.requires_grad = False + + def reparameterize(self, mu, logvar): + if self.training: + std = torch.exp(0.5 * logvar) + eps = torch.randn_like(std) + return mu + (eps * std) + else: + return mu + + def forward(self, x): + e1 = F.softplus(self.fc11(x)) + e1 = F.softplus(self.fc12(e1)) + e1 = self.fc1_drop(e1) + mu = self.mean_bn(self.fc21(e1)) + logvar = self.logvar_bn(self.fc22(e1)) + theta = self.reparameterize(mu, logvar) + theta = F.softmax(theta, dim=1) + theta = self.z_drop(theta) + return theta, mu, logvar diff --git a/backend/models/CFDTM/UWE.py b/backend/models/CFDTM/UWE.py new file mode 100644 index 0000000000000000000000000000000000000000..dda97d8ee8a78eaeb1f22528c4b0a542afa1c91b --- /dev/null +++ b/backend/models/CFDTM/UWE.py @@ -0,0 +1,48 @@ +import torch +import torch.nn as nn + + +class UWE(nn.Module): + def __init__(self, ETC, num_times, temperature, weight_UWE, neg_topk): + super().__init__() + + self.ETC = ETC + self.weight_UWE = weight_UWE + self.num_times = num_times + self.temperature = temperature + self.neg_topk = neg_topk + + def forward(self, time_wordcount, beta, topic_embeddings, word_embeddings): + assert(self.num_times == time_wordcount.shape[0]) + + topk_indices = self.get_topk_indices(beta) + + loss_UWE = 0. + cnt_valid_times = 0. + for t in range(self.num_times): + neg_idx = torch.where(time_wordcount[t] == 0)[0] + + time_topk_indices = topk_indices[t] + neg_idx = list(set(neg_idx.cpu().tolist()).intersection(set(time_topk_indices.cpu().tolist()))) + neg_idx = torch.tensor(neg_idx).long().to(time_wordcount.device) + + if len(neg_idx) == 0: + continue + + time_neg_WE = word_embeddings[neg_idx] + + # topic_embeddings[t]: K x D + # word_embeddings[neg_idx]: |V_{neg}| x D + loss_UWE += self.ETC.compute_loss(topic_embeddings[t], time_neg_WE, temperature=self.temperature, all_neg=True) + cnt_valid_times += 1 + + if cnt_valid_times > 0: + loss_UWE *= (self.weight_UWE / cnt_valid_times) + + return loss_UWE + + def get_topk_indices(self, beta): + # topk_indices: T x K x neg_topk + topk_indices = torch.topk(beta, k=self.neg_topk, dim=-1).indices + topk_indices = torch.flatten(topk_indices, start_dim=1) + return topk_indices diff --git a/backend/models/CFDTM/__init__.py b/backend/models/CFDTM/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/backend/models/CFDTM/__pycache__/CFDTM.cpython-39.pyc b/backend/models/CFDTM/__pycache__/CFDTM.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..cf1b7309ac980d553f4139b5135edfd5c6e4843f Binary files /dev/null and b/backend/models/CFDTM/__pycache__/CFDTM.cpython-39.pyc differ diff --git a/backend/models/CFDTM/__pycache__/ETC.cpython-39.pyc b/backend/models/CFDTM/__pycache__/ETC.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..eb2c8b35b98314d8f97bef53afcce509d005638d Binary files /dev/null and b/backend/models/CFDTM/__pycache__/ETC.cpython-39.pyc differ diff --git a/backend/models/CFDTM/__pycache__/Encoder.cpython-39.pyc b/backend/models/CFDTM/__pycache__/Encoder.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3c99da65b2b8d0fc2d04079fc2da48a55ac13adb Binary files /dev/null and b/backend/models/CFDTM/__pycache__/Encoder.cpython-39.pyc differ diff --git a/backend/models/CFDTM/__pycache__/UWE.cpython-39.pyc b/backend/models/CFDTM/__pycache__/UWE.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..aec5ec4377c2267065c0061f47fe07426df368bc Binary files /dev/null and b/backend/models/CFDTM/__pycache__/UWE.cpython-39.pyc differ diff --git a/backend/models/CFDTM/__pycache__/__init__.cpython-39.pyc b/backend/models/CFDTM/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..5e3c079a46d26c4300245cf6a9e638bae4729558 Binary files /dev/null and b/backend/models/CFDTM/__pycache__/__init__.cpython-39.pyc differ diff --git a/backend/models/DBERTopic_trainer.py b/backend/models/DBERTopic_trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..5e1ae71820c5c0b18817bf04f87243908484a9e5 --- /dev/null +++ b/backend/models/DBERTopic_trainer.py @@ -0,0 +1,99 @@ +import numpy as np +from bertopic import BERTopic +from backend.datasets.utils import _utils +from backend.datasets.utils.logger import Logger + +logger = Logger("WARNING") + + +class DBERTopicTrainer: + def __init__(self, + dataset, + num_topics=20, + num_top_words=15, + nr_bins=20, + global_tuning=True, + evolution_tuning=True, + datetime_format=None, + verbose=False): + + self.dataset = dataset + self.docs = dataset.raw_documents + self.num_topics=num_topics + # self.timestamps = dataset.train_times + self.vocab = dataset.vocab + self.num_top_words = num_top_words + # self.nr_bins = nr_bins + # self.global_tuning = global_tuning + # self.evolution_tuning = evolution_tuning + # self.datetime_format = datetime_format + self.verbose = verbose + + if verbose: + logger.set_level("DEBUG") + else: + logger.set_level("WARNING") + + def train(self, timestamps, datetime_format='%Y'): + logger.info("Fitting BERTopic...") + self.model = BERTopic(nr_topics=self.num_topics, verbose=self.verbose) + self.topics, _ = self.model.fit_transform(self.docs) + + logger.info("Running topics_over_time...") + self.topics_over_time_df = self.model.topics_over_time( + docs=self.docs, + timestamps=timestamps, + nr_bins=len(set(timestamps)), + datetime_format=datetime_format + ) + + self.unique_timestamps = sorted(self.topics_over_time_df["Timestamp"].unique()) + self.unique_topics = sorted(self.topics_over_time_df["Topic"].unique()) + self.vocab = self.model.vectorizer_model.get_feature_names_out() + self.V = len(self.vocab) + self.K = len(self.unique_topics) + self.T = len(self.unique_timestamps) + + def get_beta(self): + logger.info("Generating β matrix...") + + beta = np.zeros((self.T, self.K, self.V)) + topic_to_index = {topic: idx for idx, topic in enumerate(self.unique_topics)} + timestamp_to_index = {timestamp: idx for idx, timestamp in enumerate(self.unique_timestamps)} + + # Extract topic representations at each time + for t_idx, timestamp in enumerate(self.unique_timestamps): + selection = self.topics_over_time_df[self.topics_over_time_df["Timestamp"] == timestamp] + for _, row in selection.iterrows(): + topic = row["Topic"] + words = row["Words"].split(", ") + if topic not in topic_to_index: + continue + k = topic_to_index[topic] + for word in words: + if word in self.vocab: + v = np.where(self.vocab == word)[0][0] + beta[t_idx, k, v] += 1.0 + + # Normalize each β_tk to be a probability distribution + beta = beta / (beta.sum(axis=2, keepdims=True) + 1e-10) + return beta + + def get_top_words(self, num_top_words=None): + if num_top_words is None: + num_top_words = self.num_top_words + beta = self.get_beta() + top_words_list = list() + for time in range(beta.shape[0]): + top_words = _utils.get_top_words(beta[time], self.vocab, num_top_words, self.verbose) + top_words_list.append(top_words) + return top_words_list + + def get_theta(self): + # Not applicable for BERTopic; can return topic assignments or soft topic distributions if required + logger.warning("get_theta is not implemented for BERTopic.") + return None + + def export_theta(self): + logger.warning("export_theta is not implemented for BERTopic.") + return None, None diff --git a/backend/models/DETM.py b/backend/models/DETM.py new file mode 100644 index 0000000000000000000000000000000000000000..f11796194532d1691c4a1588fa8c1e6de9b153f9 --- /dev/null +++ b/backend/models/DETM.py @@ -0,0 +1,259 @@ + +import torch +from torch import nn +import torch.nn.functional as F + + +class DETM(nn.Module): + """ + The Dynamic Embedded Topic Model. 2019 + + Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei + """ + def __init__(self, vocab_size, num_times, train_size, train_time_wordfreq, + num_topics=50, train_WE=True, pretrained_WE=None, en_units=800, + eta_hidden_size=200, rho_size=300, enc_drop=0.0, eta_nlayers=3, + eta_dropout=0.0, delta=0.005, theta_act='relu', device='cpu'): + super().__init__() + + ## define hyperparameters + self.num_topics = num_topics + self.num_times = num_times + self.vocab_size = vocab_size + self.eta_hidden_size = eta_hidden_size + self.rho_size = rho_size + self.enc_drop = enc_drop + self.eta_nlayers = eta_nlayers + self.t_drop = nn.Dropout(enc_drop) + self.eta_dropout = eta_dropout + self.delta = delta + self.train_WE = train_WE + self.train_size = train_size + self.rnn_inp = train_time_wordfreq + self.device = device + + self.theta_act = self.get_activation(theta_act) + + ## define the word embedding matrix \rho + if self.train_WE: + self.rho = nn.Linear(self.rho_size, self.vocab_size, bias=False) + else: + rho = nn.Embedding(pretrained_WE.size()) + rho.weight.data = torch.from_numpy(pretrained_WE) + self.rho = rho.weight.data.clone().float().to(self.device) + + ## define the variational parameters for the topic embeddings over time (alpha) ... alpha is K x T x L + self.mu_q_alpha = nn.Parameter(torch.randn(self.num_topics, self.num_times, self.rho_size)) + self.logsigma_q_alpha = nn.Parameter(torch.randn(self.num_topics, self.num_times, self.rho_size)) + + ## define variational distribution for \theta_{1:D} via amortizartion... theta is K x D + self.q_theta = nn.Sequential( + nn.Linear(self.vocab_size + self.num_topics, en_units), + self.theta_act, + nn.Linear(en_units, en_units), + self.theta_act, + ) + self.mu_q_theta = nn.Linear(en_units, self.num_topics, bias=True) + self.logsigma_q_theta = nn.Linear(en_units, self.num_topics, bias=True) + + ## define variational distribution for \eta via amortizartion... eta is K x T + self.q_eta_map = nn.Linear(self.vocab_size, self.eta_hidden_size) + self.q_eta = nn.LSTM(self.eta_hidden_size, self.eta_hidden_size, self.eta_nlayers, dropout=self.eta_dropout) + self.mu_q_eta = nn.Linear(self.eta_hidden_size + self.num_topics, self.num_topics, bias=True) + self.logsigma_q_eta = nn.Linear(self.eta_hidden_size + self.num_topics, self.num_topics, bias=True) + + self.decoder_bn = nn.BatchNorm1d(vocab_size) + self.decoder_bn.weight.requires_grad = False + + def get_activation(self, act): + activations = { + 'tanh': nn.Tanh(), + 'relu': nn.ReLU(), + 'softplus': nn.Softplus(), + 'rrelu': nn.RReLU(), + 'leakyrelu': nn.LeakyReLU(), + 'elu': nn.ELU(), + 'selu': nn.SELU(), + 'glu': nn.GLU(), + } + + if act in activations: + act = activations[act] + else: + print('Defaulting to tanh activations...') + act = nn.Tanh() + return act + + def reparameterize(self, mu, logvar): + """Returns a sample from a Gaussian distribution via reparameterization. + """ + if self.training: + std = torch.exp(0.5 * logvar) + eps = torch.randn_like(std) + return eps.mul_(std).add_(mu) + else: + return mu + + def get_kl(self, q_mu, q_logsigma, p_mu=None, p_logsigma=None): + """Returns KL( N(q_mu, q_logsigma) || N(p_mu, p_logsigma) ). + """ + if p_mu is not None and p_logsigma is not None: + sigma_q_sq = torch.exp(q_logsigma) + sigma_p_sq = torch.exp(p_logsigma) + kl = ( sigma_q_sq + (q_mu - p_mu)**2 ) / ( sigma_p_sq + 1e-6 ) + kl = kl - 1 + p_logsigma - q_logsigma + kl = 0.5 * torch.sum(kl, dim=-1) + else: + kl = -0.5 * torch.sum(1 + q_logsigma - q_mu.pow(2) - q_logsigma.exp(), dim=-1) + return kl + + def get_alpha(self): ## mean field + alphas = torch.zeros(self.num_times, self.num_topics, self.rho_size).to(self.device) + kl_alpha = [] + + alphas[0] = self.reparameterize(self.mu_q_alpha[:, 0, :], self.logsigma_q_alpha[:, 0, :]) + + # TODO: why logsigma_p_0 is zero? + p_mu_0 = torch.zeros(self.num_topics, self.rho_size).to(self.device) + logsigma_p_0 = torch.zeros(self.num_topics, self.rho_size).to(self.device) + kl_0 = self.get_kl(self.mu_q_alpha[:, 0, :], self.logsigma_q_alpha[:, 0, :], p_mu_0, logsigma_p_0) + kl_alpha.append(kl_0) + for t in range(1, self.num_times): + alphas[t] = self.reparameterize(self.mu_q_alpha[:, t, :], self.logsigma_q_alpha[:, t, :]) + + p_mu_t = alphas[t - 1] + logsigma_p_t = torch.log(self.delta * torch.ones(self.num_topics, self.rho_size).to(self.device)) + kl_t = self.get_kl(self.mu_q_alpha[:, t, :], self.logsigma_q_alpha[:, t, :], p_mu_t, logsigma_p_t) + kl_alpha.append(kl_t) + kl_alpha = torch.stack(kl_alpha).sum() + return alphas, kl_alpha.sum() + + def get_eta(self, rnn_inp): ## structured amortized inference + inp = self.q_eta_map(rnn_inp).unsqueeze(1) + hidden = self.init_hidden() + output, _ = self.q_eta(inp, hidden) + output = output.squeeze() + + etas = torch.zeros(self.num_times, self.num_topics).to(self.device) + kl_eta = [] + + inp_0 = torch.cat([output[0], torch.zeros(self.num_topics,).to(self.device)], dim=0) + mu_0 = self.mu_q_eta(inp_0) + logsigma_0 = self.logsigma_q_eta(inp_0) + etas[0] = self.reparameterize(mu_0, logsigma_0) + + p_mu_0 = torch.zeros(self.num_topics,).to(self.device) + logsigma_p_0 = torch.zeros(self.num_topics,).to(self.device) + kl_0 = self.get_kl(mu_0, logsigma_0, p_mu_0, logsigma_p_0) + kl_eta.append(kl_0) + + for t in range(1, self.num_times): + inp_t = torch.cat([output[t], etas[t-1]], dim=0) + mu_t = self.mu_q_eta(inp_t) + logsigma_t = self.logsigma_q_eta(inp_t) + etas[t] = self.reparameterize(mu_t, logsigma_t) + + p_mu_t = etas[t-1] + logsigma_p_t = torch.log(self.delta * torch.ones(self.num_topics,).to(self.device)) + kl_t = self.get_kl(mu_t, logsigma_t, p_mu_t, logsigma_p_t) + kl_eta.append(kl_t) + kl_eta = torch.stack(kl_eta).sum() + + return etas, kl_eta + + def get_theta(self, bows, times, eta=None): ## amortized inference + """Returns the topic proportions. + """ + + normalized_bows = bows / bows.sum(1, keepdims=True) + + if eta is None and self.training is False: + eta, kl_eta = self.get_eta(self.rnn_inp) + + eta_td = eta[times] + inp = torch.cat([normalized_bows, eta_td], dim=1) + q_theta = self.q_theta(inp) + if self.enc_drop > 0: + q_theta = self.t_drop(q_theta) + mu_theta = self.mu_q_theta(q_theta) + logsigma_theta = self.logsigma_q_theta(q_theta) + z = self.reparameterize(mu_theta, logsigma_theta) + theta = F.softmax(z, dim=-1) + kl_theta = self.get_kl(mu_theta, logsigma_theta, eta_td, torch.zeros(self.num_topics).to(self.device)) + + if self.training: + return theta, kl_theta + else: + return theta + + @property + def word_embeddings(self): + return self.rho.weight + + @property + def topic_embeddings(self): + alpha, _ = self.get_alpha() + return alpha + + def get_beta(self, alpha=None): + """Returns the topic matrix \beta of shape T x K x V + """ + + if alpha is None and self.training is False: + alpha, kl_alpha = self.get_alpha() + + if self.train_WE: + logit = self.rho(alpha.view(alpha.size(0) * alpha.size(1), self.rho_size)) + else: + tmp = alpha.view(alpha.size(0) * alpha.size(1), self.rho_size) + logit = torch.mm(tmp, self.rho.permute(1, 0)) + logit = logit.view(alpha.size(0), alpha.size(1), -1) + + beta = F.softmax(logit, dim=-1) + + return beta + + def get_NLL(self, theta, beta, bows): + theta = theta.unsqueeze(1) + loglik = torch.bmm(theta, beta).squeeze(1) + loglik = torch.log(loglik + 1e-12) + nll = -loglik * bows + nll = nll.sum(-1) + return nll + + def forward(self, bows, times): + bsz = bows.size(0) + coeff = self.train_size / bsz + eta, kl_eta = self.get_eta(self.rnn_inp) + theta, kl_theta = self.get_theta(bows, times, eta) + kl_theta = kl_theta.sum() * coeff + + alpha, kl_alpha = self.get_alpha() + beta = self.get_beta(alpha) + + beta = beta[times] + # beta = beta[times.type('torch.LongTensor')] + nll = self.get_NLL(theta, beta, bows) + nll = nll.sum() * coeff + + loss = nll + kl_eta + kl_theta + + rst_dict = { + 'loss': loss, + 'nll': nll, + 'kl_eta': kl_eta, + 'kl_theta': kl_theta + } + + loss += kl_alpha + rst_dict['kl_alpha'] = kl_alpha + + return rst_dict + + def init_hidden(self): + """Initializes the first hidden state of the RNN used as inference network for \\eta. + """ + weight = next(self.parameters()) + nlayers = self.eta_nlayers + nhid = self.eta_hidden_size + return (weight.new_zeros(nlayers, 1, nhid), weight.new_zeros(nlayers, 1, nhid)) diff --git a/backend/models/DTM_trainer.py b/backend/models/DTM_trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..01f989da11bff985cbc8ce9aede60958d957cdfa --- /dev/null +++ b/backend/models/DTM_trainer.py @@ -0,0 +1,148 @@ +import gensim +import numpy as np +from gensim.models import ldaseqmodel +from tqdm import tqdm +import datetime +from multiprocessing.pool import Pool +from backend.datasets.utils import _utils +from backend.datasets.utils.logger import Logger + + +logger = Logger("WARNING") + + +def work(arguments): + model, docs = arguments + theta_list = list() + for doc in tqdm(docs): + theta_list.append(model[doc]) + return theta_list + + +class DTMTrainer: + def __init__(self, + dataset, + num_topics=50, + num_top_words=15, + alphas=0.01, + chain_variance=0.005, + passes=10, + lda_inference_max_iter=25, + em_min_iter=6, + em_max_iter=20, + verbose=False + ): + + self.dataset = dataset + self.vocab_size = dataset.vocab_size + self.num_topics = num_topics + self.num_top_words = num_top_words + self.alphas = alphas + self.chain_variance = chain_variance + self.passes = passes + self.lda_inference_max_iter = lda_inference_max_iter + self.em_min_iter = em_min_iter + self.em_max_iter = em_max_iter + + self.verbose = verbose + if verbose: + logger.set_level("DEBUG") + else: + logger.set_level("WARNING") + + def train(self): + id2word = dict(zip(range(self.vocab_size), self.dataset.vocab)) + train_bow = self.dataset.train_bow + train_times = self.dataset.train_times.astype('int32') + + # order documents by time slices + self.doc_order_idx = np.argsort(train_times) + train_bow = train_bow[self.doc_order_idx] + time_slices = np.bincount(train_times) + + corpus = gensim.matutils.Dense2Corpus(train_bow, documents_columns=False) + + self.model = ldaseqmodel.LdaSeqModel( + corpus=corpus, + id2word=id2word, + time_slice=time_slices, + num_topics=self.num_topics, + alphas=self.alphas, + chain_variance=self.chain_variance, + em_min_iter=self.em_min_iter, + em_max_iter=self.em_max_iter, + lda_inference_max_iter=self.lda_inference_max_iter, + passes=self.passes + ) + + def test(self, bow): + # bow = dataset.bow.cpu().numpy() + # times = dataset.times.cpu().numpy() + corpus = gensim.matutils.Dense2Corpus(bow, documents_columns=False) + + num_workers = 20 + split_idx_list = np.array_split(np.arange(len(bow)), num_workers) + worker_size_list = [len(x) for x in split_idx_list] + + worker_id = 0 + docs_list = [list() for i in range(num_workers)] + for i, doc in enumerate(corpus): + docs_list[worker_id].append(doc) + if len(docs_list[worker_id]) >= worker_size_list[worker_id]: + worker_id += 1 + + args_list = list() + for docs in docs_list: + args_list.append([self.model, docs]) + + starttime = datetime.datetime.now() + + pool = Pool(processes=num_workers) + results = pool.map(work, args_list) + + pool.close() + pool.join() + + theta_list = list() + for rst in results: + theta_list.extend(rst) + + endtime = datetime.datetime.now() + + print("DTM test time: {}s".format((endtime - starttime).seconds)) + + return np.asarray(theta_list) + + def get_theta(self): + theta = self.model.gammas / self.model.gammas.sum(axis=1)[:, np.newaxis] + # NOTE: MUST transform gamma to original order. + return theta[np.argsort(self.doc_order_idx)] + + def get_beta(self): + beta = list() + # K x V x T + for item in self.model.topic_chains: + # V x T + beta.append(item.e_log_prob) + + # T x K x V + beta = np.transpose(np.asarray(beta), (2, 0, 1)) + # use softmax + beta = np.exp(beta) + beta = beta / beta.sum(-1, keepdims=True) + return beta + + def get_top_words(self, num_top_words=None): + if num_top_words is None: + num_top_words = self.num_top_words + beta = self.get_beta() + top_words_list = list() + for time in range(beta.shape[0]): + top_words = _utils.get_top_words(beta[time], self.dataset.vocab, num_top_words, self.verbose) + top_words_list.append(top_words) + return top_words_list + + def export_theta(self): + train_theta = self.get_theta() + test_theta = self.test(self.dataset.test_bow) + return train_theta, test_theta diff --git a/backend/models/dynamic_trainer.py b/backend/models/dynamic_trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..a9d79f3c7b18e61249fa9f296412cf02bec4c51b --- /dev/null +++ b/backend/models/dynamic_trainer.py @@ -0,0 +1,177 @@ +import numpy as np +from tqdm import tqdm +from collections import defaultdict + +import torch +from torch.optim.lr_scheduler import StepLR +from backend.datasets.utils import _utils +from backend.datasets.utils.logger import Logger + +logger = Logger("WARNING") + +class DynamicTrainer: + def __init__(self, + model, + dataset, + num_top_words=15, + epochs=200, + learning_rate=0.002, + batch_size=200, + lr_scheduler=None, + lr_step_size=125, + log_interval=5, + verbose=False + ): + + self.model = model + self.dataset = dataset + self.num_top_words = num_top_words + self.epochs = epochs + self.learning_rate = learning_rate + self.batch_size = batch_size + self.lr_scheduler = lr_scheduler + self.lr_step_size = lr_step_size + self.log_interval = log_interval + + self.verbose = verbose + if verbose: + logger.set_level("DEBUG") + else: + logger.set_level("WARNING") + + def make_optimizer(self,): + args_dict = { + 'params': self.model.parameters(), + 'lr': self.learning_rate, + } + + optimizer = torch.optim.Adam(**args_dict) + return optimizer + + def make_lr_scheduler(self, optimizer): + lr_scheduler = StepLR(optimizer, step_size=self.lr_step_size, gamma=0.5, verbose=False) + return lr_scheduler + + def train(self): + optimizer = self.make_optimizer() + + if self.lr_scheduler: + logger.info("using lr_scheduler") + lr_scheduler = self.make_lr_scheduler(optimizer) + + data_size = len(self.dataset.train_dataloader.dataset) + + for epoch in tqdm(range(1, self.epochs + 1)): + self.model.train() + loss_rst_dict = defaultdict(float) + + for batch_data in self.dataset.train_dataloader: + + rst_dict = self.model(batch_data['bow'], batch_data['times']) + batch_loss = rst_dict['loss'] + + optimizer.zero_grad() + batch_loss.backward() + optimizer.step() + + for key in rst_dict: + loss_rst_dict[key] += rst_dict[key] * len(batch_data) + + if self.lr_scheduler: + lr_scheduler.step() + + if epoch % self.log_interval == 0: + output_log = f'Epoch: {epoch:03d}' + for key in loss_rst_dict: + output_log += f' {key}: {loss_rst_dict[key] / data_size :.3f}' + + logger.info(output_log) + + top_words = self.get_top_words() + train_theta = self.test(self.dataset.train_bow, self.dataset.train_times) + + return top_words, train_theta + + def test(self, bow, times): + data_size = bow.shape[0] + theta = list() + all_idx = torch.split(torch.arange(data_size), self.batch_size) + + with torch.no_grad(): + self.model.eval() + for idx in all_idx: + batch_theta = self.model.get_theta(bow[idx], times[idx]) + theta.extend(batch_theta.cpu().tolist()) + + theta = np.asarray(theta) + return theta + + def get_beta(self): + self.model.eval() + beta = self.model.get_beta().detach().cpu().numpy() + return beta + + def get_top_words(self, num_top_words=None): + if num_top_words is None: + num_top_words = self.num_top_words + + beta = self.get_beta() + top_words_list = list() + for time in range(beta.shape[0]): + if self.verbose: + print(f"======= Time: {time} =======") + top_words = _utils.get_top_words(beta[time], self.dataset.vocab, num_top_words, self.verbose) + top_words_list.append(top_words) + return top_words_list + + def export_theta(self): + train_theta = self.test(self.dataset.train_bow, self.dataset.train_times) + test_theta = self.test(self.dataset.test_bow, self.dataset.test_times) + + return train_theta, test_theta + + def get_top_words_at_time(self, topic_id, time, top_n): + beta = self.get_beta() # shape: [T, K, V] + topic_beta = beta[time, topic_id, :] + top_indices = topic_beta.argsort()[-top_n:][::-1] + return [self.dataset.vocab[i] for i in top_indices] + + + def get_topic_words_over_time(self, topic_id, top_n): + """ + Returns top_n words for the given topic_id over all time steps. + Output: List[List[str]], each inner list is the top_n words at a time step. + """ + beta = self.get_beta() # shape: [T, K, V] + T = beta.shape[0] + return [ + self.get_top_words_at_time(topic_id=topic_id, time=t, top_n=top_n) + for t in range(T) + ] + + def get_all_topics_at_time(self, time, top_n): + """ + Returns top_n words for each topic at the given time step. + Output: List[List[str]], each inner list is the top_n words for a topic. + """ + beta = self.get_beta() # shape: [T, K, V] + K = beta.shape[1] + return [ + self.get_top_words_at_time(topic_id=k, time=time, top_n=top_n) + for k in range(K) + ] + + def get_all_topics_over_time(self, top_n=10): + """ + Returns the top_n words for all topics over all time steps. + Output shape: List[List[List[str]]] = T x K x top_n + """ + beta = self.get_beta() # shape: [T, K, V] + T, K, _ = beta.shape + return [ + [ + self.get_top_words_at_time(topic_id=k, time=t, top_n=top_n) + for k in range(K) + ] + for t in range(T) + ] diff --git a/data/ACL_Anthology/CFDTM/beta.npy b/data/ACL_Anthology/CFDTM/beta.npy new file mode 100644 index 0000000000000000000000000000000000000000..0eedd28c5e295c33837df476fb51415355a3484c --- /dev/null +++ b/data/ACL_Anthology/CFDTM/beta.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:34984bfb432a10733161a9dfed834a9ef4f366a28a6cb2ecd6e8351997f1599a +size 16645248 diff --git a/data/ACL_Anthology/DETM/beta.npy b/data/ACL_Anthology/DETM/beta.npy new file mode 100644 index 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0000000000000000000000000000000000000000..791630808d4a3ddf72aec0d8b142877031124d29 --- /dev/null +++ b/data/ACL_Anthology/processed/time2id.txt @@ -0,0 +1,18 @@ +{ + "2010": 0, + "2011": 1, + "2012": 2, + "2013": 3, + "2014": 4, + "2015": 5, + "2016": 6, + "2017": 7, + "2018": 8, + "2019": 9, + "2020": 10, + "2021": 11, + "2022": 12, + "2023": 13, + "2024": 14, + "2025": 15 +} \ No newline at end of file diff --git a/data/ACL_Anthology/processed/vocab.txt b/data/ACL_Anthology/processed/vocab.txt new file mode 100644 index 0000000000000000000000000000000000000000..ea58cd372b21400f4001d1177b27dbc709105aed --- /dev/null +++ b/data/ACL_Anthology/processed/vocab.txt @@ -0,0 +1,13004 @@ +abbreviation +abbreviation_acronym +abbreviation_expansion +abductive +abductive_reasoning +ability +ablation +ablation_study +able +abord +absa +absence +absent +absent_keyphrase +absolute +absolute_gain +absolute_improvement +absolute_position +absolute_relative +abstain +abstract +abstract_anaphora 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0000000000000000000000000000000000000000..afee23ff63e49951556ca5ffc4e0c3b330fb82c6 --- /dev/null +++ b/environment.yml @@ -0,0 +1,27 @@ +name: dtect +channels: + - defaults + - conda-forge + - pytorch +dependencies: + - python=3.9 + - pip + - pip: + - anthropic==0.55.0 + - faiss-cpu==1.11.0 + - gensim==3.8.3 + - langchain==0.3.26 + - langchain-community==0.3.26 + - langchain-core==0.3.66 + - langchain-google-genai==2.1.5 + - langchain-openai==0.3.25 + - octis==1.14.0 + - plotly==6.1.2 + - scikit-learn==1.1.0 + - scipy==1.10.1 + - sentence-transformers==4.1.0 + - streamlit==1.46.0 + - tiktoken==0.9.0 + - torch==2.7.1 + - torchvision==0.22.1 + diff --git a/main.py b/main.py new file mode 100644 index 0000000000000000000000000000000000000000..31354ec1389994b5f6708c7d915fdcc6bb76ba6e --- /dev/null +++ b/main.py @@ -0,0 +1 @@ +_ diff --git a/requirements.txt b/requirements.txt index 28d994e22f8dd432b51df193562052e315ad95f7..4a0bc09aab19930a5e572fcc114f50942b1cd5d3 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,3 +1,19 @@ altair pandas -streamlit \ No newline at end of file +anthropic==0.55.0 +faiss-cpu==1.11.0 +gensim +langchain==0.3.26 +langchain-community==0.3.26 +langchain-core==0.3.66 +langchain-google-genai==2.1.5 +langchain-openai==0.3.25 +octis +plotly==6.1.2 +scikit-learn==1.1.0 +scipy==1.10.1 +sentence-transformers==4.1.0 +streamlit==1.46.0 +tiktoken==0.9.0 +torch==2.7.1 +torchvision==0.22.1