feat: Filtre hospitalier pour éliminer les faux positifs
- Ajout config/hospital_stopwords.yml avec adresses/téléphones hôpitaux - Ajout detectors/hospital_filter.py pour filtrer les FP - Intégration dans anonymizer_core_refactored_onnx.py - Test sur document: 40 -> 32 détections (-8 FP) - Élimine: adresses hôpitaux, codes postaux CEDEX, épisodes dans noms de fichiers
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tools/extract_false_positives.py
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155
tools/extract_false_positives.py
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#!/usr/bin/env python3
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"""
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Extrait les exemples de faux positifs en comparant annotations et détections.
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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 defaultdict
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def load_annotations(pdf_name):
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"""Charge les annotations pour un PDF."""
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# Essayer différents formats de noms
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possible_names = [
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pdf_name,
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pdf_name.replace('.redacted_raster', ''),
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pdf_name.split('.')[0]
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]
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for name in possible_names:
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annotation_file = Path(f"tests/ground_truth/annotations/{name}.json")
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if annotation_file.exists():
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with open(annotation_file, 'r', encoding='utf-8') as f:
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return json.load(f)
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return None
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def load_detections(pdf_name):
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"""Charge les détections pour un PDF."""
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audit_file = Path(f"tests/ground_truth/pdfs/baseline_anonymized/{pdf_name}.audit.jsonl")
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if not audit_file.exists():
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return []
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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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detections.append(json.loads(line))
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return detections
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def normalize_text(text):
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"""Normalise le texte pour la comparaison."""
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return text.lower().strip()
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def is_match(detection, annotation, tolerance=5):
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"""Vérifie si une détection correspond à une annotation."""
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# Même page
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if detection.get('page') != annotation.get('page'):
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return False
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# Même type (ou compatible)
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det_type = detection.get('type', '')
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ann_type = annotation.get('type', '')
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# Normaliser les types
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type_mapping = {
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'NOM': ['NOM', 'PRENOM'],
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'PRENOM': ['NOM', 'PRENOM'],
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}
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det_types = type_mapping.get(det_type, [det_type])
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ann_types = type_mapping.get(ann_type, [ann_type])
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if not any(dt in ann_types for dt in det_types):
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return False
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# Texte similaire
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det_text = normalize_text(detection.get('text', ''))
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ann_text = normalize_text(annotation.get('text', ''))
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return det_text == ann_text or det_text in ann_text or ann_text in det_text
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def extract_false_positives():
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"""Extrait les faux positifs de chaque document."""
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eval_file = Path("tests/ground_truth/quality_evaluation/baseline_quality_evaluation.json")
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with open(eval_file, 'r', encoding='utf-8') as f:
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eval_data = json.load(f)
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false_positives = defaultdict(list)
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# Parcourir chaque document
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for doc_result in eval_data['per_document']:
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pdf_name = doc_result['pdf']
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# Charger annotations et détections
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annotations = load_annotations(pdf_name)
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detections = load_detections(pdf_name)
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if not annotations or not detections:
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continue
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# Identifier les faux positifs
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for detection in detections:
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# Vérifier si cette détection correspond à une annotation
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is_true_positive = False
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for annotation in annotations.get('pii', []):
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if is_match(detection, annotation):
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is_true_positive = True
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break
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# Si pas de correspondance, c'est un faux positif
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if not is_true_positive:
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pii_type = detection.get('type', 'UNKNOWN')
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false_positives[pii_type].append({
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'text': detection.get('text', ''),
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'page': detection.get('page', 0),
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'file': pdf_name,
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'method': detection.get('method', 'unknown')
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})
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# Afficher les résultats
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print("=" * 80)
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print("EXEMPLES DE FAUX POSITIFS")
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print("=" * 80)
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print()
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problematic_types = ['EPISODE', 'VILLE', 'CODE_POSTAL', 'ADRESSE', 'TEL']
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for pii_type in problematic_types:
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fps = false_positives.get(pii_type, [])
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if not fps:
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continue
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print(f"\n{'=' * 80}")
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print(f"Type: {pii_type} ({len(fps)} faux positifs)")
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print(f"{'=' * 80}")
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# Grouper par texte pour voir les patterns
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text_counts = defaultdict(int)
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for fp in fps:
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text_counts[fp['text']] += 1
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# Afficher les plus fréquents
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sorted_texts = sorted(text_counts.items(), key=lambda x: x[1], reverse=True)
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print(f"\nTextes les plus fréquents:")
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for text, count in sorted_texts[:20]:
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print(f" {count:3d}x '{text}'")
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# Afficher quelques exemples avec contexte
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print(f"\nExemples avec contexte:")
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for i, fp in enumerate(fps[:10], 1):
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print(f" {i:2d}. '{fp['text']}' (page {fp['page']}, méthode: {fp['method']})")
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print(f" Fichier: {fp['file']}")
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# Sauvegarder les résultats
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output_file = Path("tests/ground_truth/analysis/false_positives_examples.json")
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output_file.parent.mkdir(parents=True, exist_ok=True)
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with open(output_file, 'w', encoding='utf-8') as f:
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json.dump(dict(false_positives), f, indent=2, ensure_ascii=False)
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print(f"\n✅ Résultats sauvegardés dans: {output_file}")
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if __name__ == "__main__":
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extract_false_positives()
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