""" Process full Card (Basic Card Test) dataset and export to Parquet. Processes all Card files and exports cleaned data. """ import sys import argparse from pathlib import Path from datetime import datetime sys.path.insert(0, str(Path(__file__).parent.parent)) from src.core.config import Config from src.core.output_lock import seal_output, unlock_for_write from src.processors.card_processor import CardProcessor def main(): parser = argparse.ArgumentParser(description='Process Card dataset') parser.add_argument('--audit', action='store_true', help='Include source_file and source_row_number columns in output') parser.add_argument('--data-dir', help='Override raw data directory (default: data/)') args = parser.parse_args() print("="*70) print("Card (Basic Card Test) Full Dataset Processing") print("="*70) print() start_time = datetime.now() # Load config config = Config('config/cleaning_config.yaml') # Override audit mode if --audit flag is provided if args.audit: config.set('processing.audit_mode', True) print("Audit mode: ENABLED (including source_file and source_row_number columns)") print() # Override data directory if --data-dir flag is provided if args.data_dir: config.set('directories.raw_data', args.data_dir) # Create output directory output_dir = config.output_dir / 'parquet' output_dir.mkdir(parents=True, exist_ok=True) output_file = output_dir / 'card_cleaned.parquet' print(f"Output file: {output_file}") print() # Create processor processor = CardProcessor(config) # Get file count try: all_files = processor.get_file_list() print(f"Total files to process: {len(all_files)}") print() except Exception as e: print(f"✗ Error getting file list: {e}") print() print("Expected directory: data/card_data/") print("Make sure the raw data is in the correct location.") sys.exit(1) # Process all files print("Processing all Card files...") print("This may take some time depending on dataset size...") print() try: df = processor.process() elapsed = datetime.now() - start_time print() print("="*70) print("✓ Processing Complete!") print("="*70) print() # Show stats stats = processor.get_stats() print("Processing Statistics:") print(f" Files processed: {stats['files_processed']:,}") print(f" Files failed: {stats['files_failed']:,}") print(f" Success rate: {stats['files_processed'] / len(all_files) * 100:.1f}%") print() print(f" Schema v1 files (pre 2006-01-10): {stats['schema_v1_files']:,}") print(f" Schema v2 files (post 2006-01-10): {stats['schema_v2_files']:,}") print() print(f" Total rows: {len(df):,}") print(f" Valid rows: {stats['rows_valid']:,}") if stats['rows_cheaters_filtered'] > 0: print(f" Known cheaters filtered (2001): {stats['rows_cheaters_filtered']:,}") print() # Data quality metrics print("Data Quality:") completeness = df.notna().sum() / len(df) print(f" Completeness (avg): {completeness.mean():.1%}") print(f" Missing timestamps: {df['timestamp'].isna().sum():,} ({df['timestamp'].isna().sum() / len(df) * 100:.1f}%)") print() # Date range if 'timestamp' in df.columns: valid_ts = df['timestamp'].dropna() if not valid_ts.empty: print(f"Date range: {valid_ts.min()} to {valid_ts.max()}") print(f"Span: {(valid_ts.max() - valid_ts.min()).days} days") print() # User statistics print("User Statistics:") n_users = df['user_id'].nunique() print(f" Unique users: {n_users:,}") if n_users > 0: print(f" Trials per user (mean): {len(df) / n_users:.0f}") print() # Target/Response distribution if 'target2' in df.columns and 'response' in df.columns: print("Target/Response Distribution:") print(f" Target2 (actual target) distribution:") for i in range(1, 6): count = (df['target2'] == i).sum() pct = count / len(df) * 100 print(f" Card {i}: {count:,} ({pct:.1f}%)") print() print(f" Response distribution:") for i in range(1, 6): count = (df['response'] == i).sum() pct = count / len(df) * 100 print(f" Card {i}: {count:,} ({pct:.1f}%)") print() # Hit rate if 'is_hit' in df.columns: total_hits = df['is_hit'].sum() hit_rate = total_hits / len(df) * 100 print(f"Overall hit rate: {total_hits:,} / {len(df):,} = {hit_rate:.2f}%") print(f"Expected chance rate: 20.0%") print() # Performance stats print(f"Processing time: {elapsed}") print(f"Speed: {len(df) / elapsed.total_seconds():.0f} rows/second") print() # Export to Parquet print("Exporting to Parquet...") unlock_for_write(output_file) df.to_parquet( output_file, engine='pyarrow', compression='snappy', index=False ) seal_output(output_file) file_size_mb = output_file.stat().st_size / (1024 * 1024) print(f"✓ Exported to: {output_file}") print(f" File size: {file_size_mb:.1f} MB") print() # Compression ratio raw_size_estimate = len(df) * 200 # Rough estimate of ~200 bytes/row in CSV compression_ratio = raw_size_estimate / output_file.stat().st_size print(f" Compression ratio: {compression_ratio:.1f}x") print() # Show errata summary errata_summary = processor.errata_logger.get_summary() print("Error Summary:") print(f" Total errors: {errata_summary['total_errors']:,}") print(f" Files with errors: {errata_summary['files_with_errors']:,}") if errata_summary['error_types']: print(f" Error types:") for error_type, count in errata_summary['error_types'].items(): print(f" - {error_type}: {count:,}") print(f" Errata log: {errata_summary['log_file']}") print() print("="*70) print("✓ Card Dataset Processing Complete!") print("="*70) except KeyboardInterrupt: print("\n\n✗ Processing interrupted by user") sys.exit(1) except Exception as e: print(f"\n✗ Processing failed: {e}") import traceback traceback.print_exc() sys.exit(1) if __name__ == '__main__': main()