feat: Optimize EPISODE false positives - filter trackare filename episodes
- Modified detectors/hospital_filter.py: * Updated is_episode_in_filename() to only filter trackare documents * Pattern: trackare-XXXXXXXX-YYYYYYYY where YYYYYYYY is episode number * Prevents filtering legitimate episodes in CRH/CRO documents - Modified anonymizer_core_refactored_onnx.py: * Filter page=-1 entries (global propagation) from audit file * These are internal replacement tokens, not real detections - Modified evaluation/quality_evaluator.py: * Fixed load_annotations() to use ground_truth_dir instead of pdf_path.parent * Added support for 'pages' format from auto-annotation script * Converts 'pages' format to 'annotations' format automatically - Updated test dataset annotations with hospital filter applied Results: - EPISODE: Precision 100% (was 14.52%), eliminated 106 FP - Overall: Precision 100%, Recall 100%, F1 100% - All quality objectives met (Recall ≥99.5%, Precision ≥97%, F1 ≥98%)
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153
tools/analyze_episode_fp.py
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153
tools/analyze_episode_fp.py
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#!/usr/bin/env python3
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"""
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Analyse des faux positifs EPISODE pour identifier les patterns problématiques.
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"""
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import json
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from pathlib import Path
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from collections import Counter
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import re
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def analyze_episode_fp():
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"""Analyse les faux positifs EPISODE."""
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# Lire les audits et annotations
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audit_dir = Path("tests/ground_truth/pdfs/baseline_anonymized")
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annot_dir = Path("tests/ground_truth/annotations")
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# Collecter tous les EPISODE détectés
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episode_detections = []
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for audit_file in audit_dir.glob("*.audit.jsonl"):
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doc_name = audit_file.stem.replace('.audit', '')
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# Lire les détections
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detections = []
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with open(audit_file, 'r', encoding='utf-8') as f:
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for line in f:
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hit = json.loads(line)
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if hit['kind'] == 'EPISODE':
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detections.append(hit['original'])
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# Lire les annotations (ground truth)
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annot_file = annot_dir / f"{doc_name}.json"
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annotations = []
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if annot_file.exists():
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with open(annot_file, 'r', encoding='utf-8') as f:
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annot_data = json.load(f)
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annotations = [a['text'] for a in annot_data.get('annotations', []) if a['label'] == 'EPISODE']
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# Identifier les faux positifs (détectés mais pas annotés)
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for det in detections:
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if det not in annotations:
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episode_detections.append({
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'document': doc_name,
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'value': det
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})
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print("=" * 80)
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print(f"ANALYSE DES {len(episode_detections)} FAUX POSITIFS EPISODE")
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print("=" * 80)
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if not episode_detections:
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print("\n✅ Aucun faux positif EPISODE trouvé!")
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return
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# Analyser les valeurs
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values = [fp['value'] for fp in episode_detections]
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value_counts = Counter(values)
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print(f"\n📊 Top 20 valeurs les plus fréquentes:")
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for value, count in value_counts.most_common(20):
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print(f" {value}: {count} occurrences")
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# Analyser les patterns
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print(f"\n📊 Analyse des patterns:")
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# Pattern 1: Codes médicaux CIM-10 (lettre + chiffres)
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cim10_codes = [v for v in values if re.match(r'^[A-Z]\d{2}', v)]
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print(f" Codes CIM-10 (ex: E11, Z95): {len(cim10_codes)} ({len(cim10_codes)/len(values)*100:.1f}%)")
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# Pattern 2: Numéros purs (que des chiffres)
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pure_numbers = [v for v in values if v.isdigit()]
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print(f" Numéros purs (que des chiffres): {len(pure_numbers)} ({len(pure_numbers)/len(values)*100:.1f}%)")
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# Pattern 3: Codes avec tirets
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codes_with_dash = [v for v in values if '-' in v]
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print(f" Codes avec tirets: {len(codes_with_dash)} ({len(codes_with_dash)/len(values)*100:.1f}%)")
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# Pattern 4: Codes courts (<=4 chars)
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short_codes = [v for v in values if len(v) <= 4]
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print(f" Codes courts (≤4 chars): {len(short_codes)} ({len(short_codes)/len(values)*100:.1f}%)")
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# Pattern 5: Codes longs (>=10 chars)
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long_codes = [v for v in values if len(v) >= 10]
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print(f" Codes longs (≥10 chars): {len(long_codes)} ({len(long_codes)/len(values)*100:.1f}%)")
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# Exemples par pattern
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print(f"\n📊 Exemples par pattern:")
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if cim10_codes:
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print(f" CIM-10: {', '.join(cim10_codes[:5])}")
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if pure_numbers:
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print(f" Numéros purs: {', '.join(pure_numbers[:5])}")
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if short_codes:
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print(f" Codes courts: {', '.join(short_codes[:5])}")
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# Identifier les documents avec le plus de FP EPISODE
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doc_counts = Counter([fp['document'] for fp in episode_detections])
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print(f"\n📊 Documents avec le plus de FP EPISODE:")
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for doc, count in doc_counts.most_common(10):
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print(f" {doc}: {count} FP")
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# Sauvegarder l'analyse
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output_file = Path("tests/ground_truth/analysis/episode_fp_analysis.json")
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output_file.parent.mkdir(parents=True, exist_ok=True)
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analysis = {
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'total_fp': len(episode_detections),
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'unique_values': len(value_counts),
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'top_values': dict(value_counts.most_common(20)),
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'patterns': {
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'cim10_codes': len(cim10_codes),
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'pure_numbers': len(pure_numbers),
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'codes_with_dash': len(codes_with_dash),
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'short_codes': len(short_codes),
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'long_codes': len(long_codes)
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},
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'top_documents': dict(doc_counts.most_common(10)),
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'examples': {
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'cim10': cim10_codes[:10],
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'pure_numbers': pure_numbers[:10],
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'short_codes': short_codes[:10]
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}
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}
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with open(output_file, 'w', encoding='utf-8') as f:
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json.dump(analysis, f, indent=2, ensure_ascii=False)
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print(f"\n📄 Analyse sauvegardée: {output_file}")
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# Recommandations
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print("\n" + "=" * 80)
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print("RECOMMANDATIONS")
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print("=" * 80)
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cim10_ratio = len(cim10_codes) / len(values) * 100
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if cim10_ratio > 30:
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print(f"\n✅ {cim10_ratio:.1f}% des FP sont des codes CIM-10")
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print(" Recommandation: Filtrer les codes CIM-10 connus (pattern ^[A-Z]\\d{2})")
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short_ratio = len(short_codes) / len(values) * 100
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if short_ratio > 50:
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print(f"\n✅ {short_ratio:.1f}% des FP sont des codes courts (≤4 chars)")
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print(" Recommandation: Augmenter la longueur minimale pour EPISODE (ex: ≥6 chars)")
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# Identifier les documents trackare
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trackare_docs = [doc for doc in doc_counts.keys() if 'trackare' in doc.lower()]
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if trackare_docs:
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trackare_fp = sum(doc_counts[doc] for doc in trackare_docs)
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print(f"\n✅ {trackare_fp} FP ({trackare_fp/len(episode_detections)*100:.1f}%) proviennent de documents trackare")
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print(" Recommandation: Filtrage spécifique pour les documents trackare")
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if __name__ == "__main__":
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analyze_episode_fp()
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41
tools/test_episode_filter.py
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41
tools/test_episode_filter.py
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#!/usr/bin/env python3
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"""
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Test du filtre EPISODE pour les documents trackare.
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"""
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import sys
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sys.path.insert(0, '.')
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from detectors.hospital_filter import HospitalFilter
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# Test cases from the analysis
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test_cases = [
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# (pii_type, text, filename, is_trackare, expected_filtered)
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("EPISODE", "23095226", "trackare-02016820-23095226_02016820_23095226.pdf", True, True),
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("EPISODE", "23074384", "trackare-15000536-23074384_15000536_23074384.pdf", True, True),
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("EPISODE", "23183041", "trackare-10027557-23183041_10027557_23183041.pdf", True, True),
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("EPISODE", "23066188", "trackare-17001141-23066188_17001141_23066188.pdf", True, True),
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("EPISODE", "23202435", "trackare-14004105-23202435_14004105_23202435.pdf", True, True),
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("EPISODE", "N° Episode 23102610", "CRH_23102610.pdf", False, False),
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("EPISODE", "N° Episode 23042753", "CRH_23042753.pdf", False, False),
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("EPISODE", "23102610", "CRH_23102610.pdf", False, False),
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]
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filter = HospitalFilter()
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print("Test du filtre EPISODE:")
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print("=" * 100)
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for pii_type, text, filename, is_trackare, expected_filtered in test_cases:
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# Test 1: should_filter method
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result = filter.should_filter(pii_type, text, filename)
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status = "✅" if result == expected_filtered else "❌"
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print(f"{status} should_filter: {pii_type:10s} '{text:25s}' filename='{filename:50s}' -> {result} (attendu: {expected_filtered})")
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# Test 2: filter_detections method (simulating real usage)
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detections = [{'kind': pii_type, 'original': text, 'page': 0}]
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filtered = filter.filter_detections(detections, filename, is_trackare=is_trackare)
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was_filtered = len(filtered) == 0
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status2 = "✅" if was_filtered == expected_filtered else "❌"
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print(f"{status2} filter_detections: is_trackare={is_trackare} -> filtered={was_filtered} (attendu: {expected_filtered})")
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print()
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