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Diffstat (limited to 'scripts/test_historical_fetch.py')
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diff --git a/scripts/test_historical_fetch.py b/scripts/test_historical_fetch.py new file mode 100644 index 0000000..d4e8a6b --- /dev/null +++ b/scripts/test_historical_fetch.py @@ -0,0 +1,117 @@ +#!/usr/bin/env python3 +""" +Test script for historical paper fetching functionality. + +This script tests the ArxivPaperFetcher class with a smaller date range +to verify the historical fetching works correctly before running on 2 years of data. +""" + +import os +import sys +from datetime import datetime, timezone, timedelta + +# Add the parent directory to the path so we can import the main module +sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +from scripts.fetch_papers import ArxivPaperFetcher + + +def test_recent_historical_fetch(): + """Test fetching papers from the last 30 days as a historical test.""" + + # Check for OpenAI API key + openai_api_key = os.getenv("OPENAI_API_KEY") + if not openai_api_key: + print("ERROR: OPENAI_API_KEY environment variable is required") + sys.exit(1) + + print("Testing historical paper fetching (last 30 days)...") + + # Initialize fetcher + fetcher = ArxivPaperFetcher(openai_api_key) + + # Test with last 30 days + end_date = datetime.now(timezone.utc) + start_date = end_date - timedelta(days=30) + + print(f"Fetching papers from {start_date.date()} to {end_date.date()}") + + # Fetch papers (limit to 200 for testing) + papers = fetcher.fetch_papers_by_date_range(start_date, end_date, max_papers=200) + + print(f"\nFetched {len(papers)} papers total") + + if papers: + print("\nSample papers:") + for i, paper in enumerate(papers[:3], 1): + print(f"\n{i}. {paper['title']}") + print(f" Authors: {', '.join(paper['authors'][:2])}") + print(f" Categories: {', '.join(paper['categories'])}") + print(f" Published: {paper['published']}") + print(f" Abstract: {paper['abstract'][:150]}...") + + # Test GPT filtering on a smaller subset + print(f"\nTesting GPT-4o filtering on first 10 papers...") + sample_papers = papers[:10] + filtered_papers = fetcher.filter_papers_with_gpt(sample_papers) + + print(f"\nFiltering results: {len(filtered_papers)}/{len(sample_papers)} papers are relevant") + + if filtered_papers: + print("\nRelevant papers found:") + for i, paper in enumerate(filtered_papers, 1): + print(f"\n{i}. {paper['title']}") + print(f" Abstract: {paper['abstract'][:200]}...") + else: + print("No relevant papers found in the sample.") + + else: + print("No papers found in the date range.") + + +def test_specific_date_range(): + """Test fetching papers from a specific date range known to have bias papers.""" + + openai_api_key = os.getenv("OPENAI_API_KEY") + if not openai_api_key: + print("ERROR: OPENAI_API_KEY environment variable is required") + sys.exit(1) + + print("\nTesting specific date range (January 2024)...") + + fetcher = ArxivPaperFetcher(openai_api_key) + + # Test January 2024 (likely to have some relevant papers) + start_date = datetime(2024, 1, 1, tzinfo=timezone.utc) + end_date = datetime(2024, 1, 31, tzinfo=timezone.utc) + + print(f"Fetching papers from {start_date.date()} to {end_date.date()}") + + papers = fetcher.fetch_papers_by_date_range(start_date, end_date, max_papers=500) + print(f"Fetched {len(papers)} papers from January 2024") + + if papers: + # Filter for bias-related papers + filtered_papers = fetcher.filter_papers_with_gpt(papers) + + print(f"\nFound {len(filtered_papers)} bias-related papers in January 2024") + + for i, paper in enumerate(filtered_papers[:5], 1): + print(f"\n{i}. {paper['title']}") + print(f" arXiv ID: {paper['arxiv_id']}") + print(f" Link: {paper['link']}") + + +if __name__ == "__main__": + print("ArXiv Historical Paper Fetcher Test") + print("=" * 40) + + try: + test_recent_historical_fetch() + test_specific_date_range() + print("\n" + "=" * 40) + print("Test completed successfully!") + + except Exception as e: + print(f"\nError during testing: {e}") + sys.exit(1)
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