File size: 8,515 Bytes
9deebf2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 | """
Process full Users dataset and export to Parquet.
Processes all user survey files (users14.dat and questions.dat) 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.users_processor import UsersProcessor
def main():
parser = argparse.ArgumentParser(description='Process Users 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("Users Dataset Full 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 / 'users.parquet'
print(f"Output file: {output_file}")
print()
# Create processor
processor = UsersProcessor(config)
# Get file count
try:
all_files = processor.get_file_list()
print(f"Total files to process: {len(all_files)}")
for f in all_files:
file_type = processor.detect_file_type(f)
print(f" - {f.name} ({file_type})")
print()
except Exception as e:
print(f"✗ Error getting file list: {e}")
print()
print("Expected directory: data/user_data/")
print("Make sure the raw data is in the correct location.")
print("Files expected: users14.dat and/or questions.dat")
sys.exit(1)
# Process all files
print("Processing all user files...")
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" users14.dat rows: {stats['users14_rows']:,}")
print(f" questions.dat rows: {stats['questions_rows']:,}")
print()
print(f" Total rows (pre-dedup): {stats['rows_total']:,}")
print(f" Duplicates removed: {stats['duplicates_removed']:,}")
print(f" Final rows: {len(df):,}")
print(f" NA usernames (kept): {stats['na_usernames_count']:,}")
print()
# Data quality metrics
print("Data Quality:")
completeness = df.notna().sum() / len(df)
print(f" Overall completeness (avg): {completeness.mean():.1%}")
print()
# Column completeness
print("Column Completeness:")
key_cols = ['username', 'timestamp', 'email', 'state',
'coordinates', 'country', 'na_count_psi_and_hemi']
for col in key_cols:
if col in df.columns:
complete = df[col].notna().sum()
pct = complete / len(df) * 100
print(f" {col:25s}: {complete:6,} / {len(df):6,} ({pct:5.1f}%)")
print()
# Psi columns completeness
psi_cols = [f'psi_{i:02d}' for i in range(1, 16)]
existing_psi = [col for col in psi_cols if col in df.columns]
if existing_psi:
psi_complete = df[existing_psi].notna().sum().sum()
psi_total = len(df) * len(existing_psi)
print(f"Psi columns (1-15) completeness: {psi_complete:,} / {psi_total:,} ({psi_complete/psi_total*100:.1f}%)")
print()
# Hemi columns completeness
hemi_cols = [f'hemi_{i:02d}' for i in range(1, 11)]
existing_hemi = [col for col in hemi_cols if col in df.columns]
if existing_hemi:
hemi_complete = df[existing_hemi].notna().sum().sum()
hemi_total = len(df) * len(existing_hemi)
print(f"Hemi columns (1-10) completeness: {hemi_complete:,} / {hemi_total:,} ({hemi_complete/hemi_total*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()
# File type distribution
if 'file_type' in df.columns:
print("File Type Distribution:")
for file_type in df['file_type'].unique():
count = (df['file_type'] == file_type).sum()
pct = count / len(df) * 100
print(f" {file_type}: {count:,} rows ({pct:.1f}%)")
print()
# Country distribution (top 10)
if 'country' in df.columns:
print("Top 10 Countries:")
country_counts = df['country'].value_counts().head(10)
for country, count in country_counts.items():
pct = count / len(df) * 100
print(f" {country}: {count:,} ({pct:.1f}%)")
print()
# NA count distribution
if 'na_count_psi_and_hemi' in df.columns:
print("NA Count (Psi + Hemi) Distribution:")
na_stats = df['na_count_psi_and_hemi'].describe()
print(f" Min: {na_stats['min']:.0f}")
print(f" Max: {na_stats['max']:.0f}")
print(f" Mean: {na_stats['mean']:.1f}")
print(f" Median: {na_stats['50%']:.0f}")
print()
# Performance stats
print(f"Processing time: {elapsed}")
if elapsed.total_seconds() > 0:
print(f"Speed: {len(df) / elapsed.total_seconds():.0f} rows/second")
print()
# Drop demographic columns scrubbed at the .dat level (see sanitize_raw.py)
df = df.drop(columns=['city', 'how_find'], errors='ignore')
# 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) * 400 # Rough estimate of ~400 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 sorted(errata_summary['error_types'].items(),
key=lambda x: x[1], reverse=True)[:10]:
print(f" - {error_type}: {count:,}")
print(f" Errata log: {errata_summary['log_file']}")
print()
print("="*70)
print("✓ Users 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()
|