Delete usage_example.py

usage_example.py

@@ -1,175 +0,0 @@
-#!/usr/bin/env python3
-"""
-Usage example for the DeepScholarBench dataset.
-
-This shows how to use the dataset builder directly, which is the recommended approach
-for local development and testing.
-"""
-
-import sys
-import os
-from pathlib import Path
-
-# Add the current directory to Python path
-sys.path.insert(0, str(Path(__file__).parent))
-
-from DeepScholarBench import DeepScholarBench
-import pandas as pd
-
-
-def load_papers_dataset():
-    """Load the papers dataset."""
-    print("Loading papers dataset...")
-    
-    # Create dataset builder
-    builder = DeepScholarBench(config_name="papers")
-    
-    # Mock download manager for local files
-    class MockDownloadManager:
-        def download_and_extract(self, url_or_path):
-            return url_or_path
-    
-    dl_manager = MockDownloadManager()
-    
-    # Get split generators and load data
-    split_generators = builder._split_generators(dl_manager)
-    split_gen = split_generators[0]  # Get train split
-    
-    # Collect all examples
-    papers_data = []
-    for key, example in builder._generate_examples(
-        split_gen.gen_kwargs["filepath"], 
-        split_gen.gen_kwargs["split"]
-    ):
-        papers_data.append(example)
-    
-    print(f"Loaded {len(papers_data)} papers")
-    return papers_data
-
-
-def load_citations_dataset():
-    """Load the citations dataset."""
-    print("Loading citations dataset...")
-    
-    # Create dataset builder
-    builder = DeepScholarBench(config_name="citations")
-    
-    # Mock download manager for local files
-    class MockDownloadManager:
-        def download_and_extract(self, url_or_path):
-            return url_or_path
-    
-    dl_manager = MockDownloadManager()
-    
-    # Get split generators and load data
-    split_generators = builder._split_generators(dl_manager)
-    split_gen = split_generators[0]  # Get train split
-    
-    # Collect all examples
-    citations_data = []
-    for key, example in builder._generate_examples(
-        split_gen.gen_kwargs["filepath"], 
-        split_gen.gen_kwargs["split"]
-    ):
-        citations_data.append(example)
-    
-    print(f"Loaded {len(citations_data)} citations")
-    return citations_data
-
-
-def load_important_citations_dataset():
-    """Load the important citations dataset."""
-    print("Loading important citations dataset...")
-    
-    # Create dataset builder
-    builder = DeepScholarBench(config_name="important_citations")
-    
-    # Mock download manager for local files
-    class MockDownloadManager:
-        def download_and_extract(self, url_or_path):
-            return url_or_path
-    
-    dl_manager = MockDownloadManager()
-    
-    # Get split generators and load data
-    split_generators = builder._split_generators(dl_manager)
-    split_gen = split_generators[0]  # Get train split
-    
-    # Collect all examples
-    important_citations_data = []
-    for key, example in builder._generate_examples(
-        split_gen.gen_kwargs["filepath"], 
-        split_gen.gen_kwargs["split"]
-    ):
-        important_citations_data.append(example)
-    
-    print(f"Loaded {len(important_citations_data)} important citations")
-    return important_citations_data
-
-
-def main():
-    """Main example function."""
-    print("DeepScholarBench Dataset - Usage Example")
-    print("=" * 50)
-    
-    # Load datasets
-    papers = load_papers_dataset()
-    citations = load_citations_dataset()
-    important_citations = load_important_citations_dataset()
-    
-    print("\n📊 Dataset Statistics:")
-    print(f"  - Papers: {len(papers)}")
-    print(f"  - Citations: {len(citations)}")
-    print(f"  - Important Citations: {len(important_citations)}")
-    
-    # Show sample paper
-    if papers:
-        sample_paper = papers[0]
-        print(f"\n📄 Sample Paper:")
-        print(f"  - Title: {sample_paper['title']}")
-        print(f"  - ArXiv ID: {sample_paper['arxiv_id']}")
-        print(f"  - Authors: {sample_paper['authors'][:100]}...")
-        print(f"  - Abstract: {sample_paper['abstract'][:200]}...")
-    
-    # Show sample citation
-    if citations:
-        sample_citation = citations[0]
-        print(f"\n📚 Sample Citation:")
-        print(f"  - Parent Paper: {sample_citation['parent_paper_title']}")
-        print(f"  - Cited Paper: {sample_citation['cited_paper_title']}")
-        print(f"  - Has Metadata: {sample_citation['has_metadata']}")
-        print(f"  - Is ArXiv Paper: {sample_citation['is_arxiv_paper']}")
-    
-    # Show sample important citation
-    if important_citations:
-        sample_important_citation = important_citations[0]
-        print(f"\n⭐ Sample Important Citation:")
-        print(f"  - Parent Paper: {sample_important_citation['parent_paper_title']}")
-        print(f"  - Cited Paper: {sample_important_citation['cited_paper_title']}")
-        print(f"  - Has Metadata: {sample_important_citation['has_metadata']}")
-        print(f"  - Is ArXiv Paper: {sample_important_citation['is_arxiv_paper']}")
-    
-    # Convert to pandas for easier analysis
-    print(f"\n🐼 Converting to Pandas DataFrames...")
-    papers_df = pd.DataFrame(papers)
-    citations_df = pd.DataFrame(citations)
-    important_citations_df = pd.DataFrame(important_citations)
-    
-    print(f"  - Papers DataFrame: {papers_df.shape}")
-    print(f"  - Citations DataFrame: {citations_df.shape}")
-    print(f"  - Important Citations DataFrame: {important_citations_df.shape}")
-    
-    # Show some analysis
-    print(f"\n📈 Quick Analysis:")
-    print(f"  - Unique parent papers in citations: {citations_df['parent_paper_arxiv_id'].nunique()}")
-    print(f"  - Citations with metadata: {citations_df['has_metadata'].sum()}")
-    print(f"  - ArXiv citations: {citations_df['is_arxiv_paper'].sum()}")
-    print(f"  - Unique parent papers in important citations: {important_citations_df['parent_paper_arxiv_id'].nunique()}")
-    print(f"  - Important citations with metadata: {important_citations_df['has_metadata'].sum()}")
-    print(f"  - ArXiv important citations: {important_citations_df['is_arxiv_paper'].sum()}")
-    
-    return papers_df, citations_df, important_citations_df
-
-
-if __name__ == "__main__":
-    papers_df, citations_df, important_citations_df = main()