File size: 6,960 Bytes
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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()
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