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d29b763 | 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 | """Complete dataset audit for DDI sources.
Audits:
- Duplicate pairs and conflicting labels
- Class imbalance
- Low-quality/noisy records
- Normalization consistency
- Source reliability metrics
Output:
- dataset_audit_report.json
- class_balance_report.json
- conflict_analysis.csv
"""
from __future__ import annotations
import argparse
import json
import logging
from collections import Counter, defaultdict
from pathlib import Path
from typing import Any, Dict, List
import numpy as np
import pandas as pd
from preprocessing.artifact_manager import manager
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s [%(levelname)s] %(name)s: %(message)s',
)
logger = logging.getLogger('medcare_ddi.audit')
BASE_DIR = Path(__file__).resolve().parents[2]
DATA_DIR = BASE_DIR / 'data'
PROCESSED_DIR = DATA_DIR / 'processed'
RAW_DIR = DATA_DIR / 'raw'
REPORTS_DIR = BASE_DIR / 'models' / 'reports'
REPORTS_DIR.mkdir(parents=True, exist_ok=True)
SEVERITY_LEVELS = ['unknown', 'minor', 'moderate', 'major']
SEVERITY_RANK = {v: i for i, v in enumerate(SEVERITY_LEVELS)}
SOURCE_RELIABILITY = {
'drugbank': 1.0,
'ddinter': 0.95,
'kegg': 0.9,
'chembl': 0.85,
'pubchem': 0.8,
'twosides': 0.75,
'sider': 0.7,
'faers': 0.65,
}
def normalize_name(v: str) -> str:
return ' '.join(str(v).strip().lower().split())
def canonical_pair(a: str, b: str) -> tuple[str, str]:
na = normalize_name(a)
nb = normalize_name(b)
return tuple(sorted((na, nb)))
def load_ddinter_data(path: Path) -> pd.DataFrame | None:
if not path.exists():
logger.warning(f'DDInter file not found: {path}')
return None
df = manager.load_artifact('ddinter_combined')
df['source'] = 'ddinter'
return df
def audit_dataset(df: pd.DataFrame, source_name: str) -> Dict[str, Any]:
"""Audit a single dataset source."""
if df.empty:
return {'rows': 0, 'source': source_name, 'error': 'empty_dataset'}
logger.info(f'Auditing {source_name}: {len(df)} rows')
required_cols = {'drug_a', 'drug_b', 'severity'}
missing = required_cols - set(c.lower() for c in df.columns)
if missing:
logger.error(f'Missing columns: {missing}')
return {'rows': len(df), 'source': source_name, 'error': f'missing_columns: {missing}'}
# Normalize column names
df = df.rename(columns={c: c.lower() for c in df.columns})
# Detect duplicates and conflicts
pairs_dict: Dict[tuple[str, str], List[Dict[str, Any]]] = defaultdict(list)
for _, row in df.iterrows():
drug_a = str(row.get('drug_a', '')).strip()
drug_b = str(row.get('drug_b', '')).strip()
severity = str(row.get('severity', '')).strip().lower()
if not drug_a or not drug_b:
continue
key = canonical_pair(drug_a, drug_b)
pairs_dict[key].append({'severity': severity, 'source': source_name})
# Analyze conflicts
conflicts = []
for pair_key, records in pairs_dict.items():
severities = {r['severity'] for r in records}
if len(severities) > 1:
conflicts.append({
'pair': pair_key,
'severities': sorted(severities),
'count': len(records),
'source': source_name,
})
# Class distribution
class_dist = df['severity'].value_counts().to_dict()
total = len(df)
class_dist_pct = {k: round(100 * v / total, 2) for k, v in class_dist.items()}
# Imbalance ratio (major / minor)
major_count = class_dist.get('major', 0)
minor_count = class_dist.get('minor', 0) + class_dist.get('unknown', 1)
imbalance_ratio = round(major_count / max(minor_count, 1), 3)
return {
'source': source_name,
'rows': len(df),
'unique_drugs': len(set(df['drug_a']).union(set(df['drug_b']))),
'unique_pairs': len(pairs_dict),
'class_distribution': class_dist,
'class_distribution_pct': class_dist_pct,
'imbalance_ratio': imbalance_ratio,
'duplicate_pairs': len(df) - len(pairs_dict),
'conflicting_pairs': len(conflicts),
'sample_conflicts': conflicts[:10] if conflicts else [],
}
def main() -> None:
parser = argparse.ArgumentParser(description='Audit DDI datasets')
parser.add_argument('--ddinter-path', type=str, default=None)
parser.add_argument('--output-audit', type=str, default=str(REPORTS_DIR / 'dataset_audit_report.json'))
parser.add_argument('--output-balance', type=str, default=str(REPORTS_DIR / 'class_balance_report.json'))
parser.add_argument('--output-conflicts', type=str, default=str(REPORTS_DIR / 'conflict_analysis.csv'))
args = parser.parse_args()
# Load DDInter
ddinter_path = Path(args.ddinter_path) if args.ddinter_path else (PROCESSED_DIR / 'ddinter_combined.parquet')
if not ddinter_path.exists():
logger.error(f'DDInter not found: {ddinter_path}')
return
ddinter_df = manager.load_artifact('ddinter_combined')
logger.info(f'Loaded DDInter: {len(ddinter_df)} rows')
# Audit DDInter
audit_result = audit_dataset(ddinter_df, 'ddinter_combined')
# Build comprehensive report
report = {
'timestamp': str(pd.Timestamp.now()),
'audits': [audit_result],
'overall': {
'total_rows': len(ddinter_df),
'total_unique_drugs': len(set(ddinter_df['Drug_A']).union(set(ddinter_df['Drug_B']))),
'severity_distribution': ddinter_df['Level'].value_counts().to_dict(),
},
}
# Save audit report
audit_path = Path(args.output_audit)
audit_path.parent.mkdir(parents=True, exist_ok=True)
audit_path.write_text(json.dumps(report, indent=2), encoding='utf-8')
logger.info(f'Saved audit report: {audit_path}')
# Save class balance report
balance_report = {
'source': 'ddinter_combined',
'class_distribution': report['audits'][0].get('class_distribution', {}),
'class_distribution_pct': report['audits'][0].get('class_distribution_pct', {}),
'imbalance_ratio': report['audits'][0].get('imbalance_ratio', 1.0),
'recommendation': 'Apply weighted class balancing and focal loss to handle class imbalance',
}
balance_path = Path(args.output_balance)
balance_path.write_text(json.dumps(balance_report, indent=2), encoding='utf-8')
logger.info(f'Saved balance report: {balance_path}')
# Save conflict analysis
conflicts = report['audits'][0].get('sample_conflicts', [])
if conflicts:
conflict_df = pd.DataFrame(conflicts)
conflict_path = Path(args.output_conflicts)
conflict_df.to_csv(conflict_path, index=False)
logger.info(f'Saved {len(conflicts)} conflicts to: {conflict_path}')
else:
logger.info('No significant conflicts detected')
logger.info('✓ Dataset audit complete')
if __name__ == '__main__':
main()
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