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| import argparse | |
| import pandas as pd | |
| from nltk.corpus import stopwords | |
| from nltk.stem import PorterStemmer | |
| from nltk.tokenize import word_tokenize | |
| from collections import Counter | |
| import string | |
| import os | |
| import streamlit as st | |
| # Ensure you've downloaded the set of stop words the first time you run this | |
| import nltk | |
| # only download if they don't exist | |
| if not os.path.exists(os.path.join(nltk.data.find('corpora'), 'stopwords')): | |
| nltk.download('punkt') | |
| nltk.download('stopwords') | |
| def preprocess_document(doc): | |
| """ | |
| Tokenizes, removes punctuation, stopwords, and stems words in a single document. | |
| """ | |
| # Lowercase | |
| doc = doc.lower() | |
| # Remove punctuation | |
| doc = doc.translate(str.maketrans('', '', string.punctuation)) | |
| # Tokenize | |
| tokens = word_tokenize(doc) | |
| # Remove stop words | |
| stop_words = set(stopwords.words('english')) | |
| filtered_tokens = [word for word in tokens if word not in stop_words] | |
| # Stemming | |
| stemmer = PorterStemmer() | |
| stemmed_tokens = [stemmer.stem(word) for word in filtered_tokens] | |
| return stemmed_tokens | |
| def find_dividing_words(documents): | |
| """ | |
| Identifies candidate words that might split the set of documents into two groups. | |
| """ | |
| all_words = [] | |
| per_doc_word_counts = [] | |
| i = 0 | |
| for doc in documents: | |
| print(i) | |
| preprocessed_doc = preprocess_document(doc) | |
| all_words.extend(preprocessed_doc) | |
| per_doc_word_counts.append(Counter(preprocessed_doc)) | |
| i += 1 | |
| # Overall word frequency | |
| overall_word_counts = Counter(all_words) | |
| # Find words that appear in roughly half the documents | |
| num_docs = len(documents) | |
| candidate_words = [] | |
| for word, count in overall_word_counts.items(): | |
| doc_frequency = sum(1 for doc_count in per_doc_word_counts if doc_count[word] > 0) | |
| if 0.35 * num_docs <= doc_frequency <= 0.75 * num_docs: | |
| candidate_words.append(word) | |
| print("Done with dividing words") | |
| return candidate_words | |
| def make_contents(doc): | |
| """ | |
| Returns the contents of a document as a single string. | |
| """ | |
| if "title" in doc and "contents" in doc: | |
| return doc["title"] + " " + doc["contents"] | |
| if "headline" in doc and "text" in doc: | |
| return doc["headline"] + " " + doc["text"] | |
| if "title" in doc and "text" in doc: | |
| return doc["title"] + " " + doc["text"] | |
| if "contents" in doc: | |
| return doc["contents"] | |
| if "text" in doc: | |
| return doc["text"] | |
| def main(args): | |
| # read in the qrels and docs file from the `args.dataset` directory for the `.relevant_only` files | |
| base_dir = os.path.join("data", args.dataset) | |
| qrels = pd.read_csv(os.path.join(base_dir, "qrels.relevant_only.trec"), sep="\t", header=None, names=["qid", "docid", "rel"]) | |
| docs = pd.read_json(os.path.join(base_dir, "docs.relevant_only.jsonl"), lines=True) | |
| for qid in qrels.groupby("qid").groups.keys(): | |
| # get the relevant documents for the current query | |
| relevant_docids = qrels[qrels["qid"] == qid]["docid"].tolist() | |
| # get the text for the relevant documents | |
| relevant_docs_text = docs[docs["doc_id"].isin(relevant_docids)].apply(lambda x: make_contents(x), axis=1).tolist() | |
| splitting_words = find_dividing_words(relevant_docs_text) | |
| breakpoint() | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser(description='Find words that might split the set of documents into two groups.') | |
| parser.add_argument('dataset', type=str, help='The dataset to use (e.g. "robust04")') | |
| args = parser.parse_args() | |
| main(args) |