Domi31tls
ee34042179
- 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%)
545 lines
19 KiB
Python
545 lines
19 KiB
Python
#!/usr/bin/env python3
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"""
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Évaluateur de qualité d'anonymisation.
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Compare les annotations manuelles (ground truth) avec les détections automatiques
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pour calculer les métriques de qualité (Précision, Rappel, F1-Score).
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"""
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import json
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from dataclasses import dataclass, field
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from pathlib import Path
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from typing import List, Dict, Tuple, Optional
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import re
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@dataclass
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class EvaluationResult:
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"""Résultat d'évaluation pour un document."""
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pdf_path: str
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true_positives: int = 0
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false_positives: int = 0
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false_negatives: int = 0
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precision: float = 0.0
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recall: float = 0.0
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f1_score: float = 0.0
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missed_pii: List[Dict] = field(default_factory=list) # Faux négatifs détaillés
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false_detections: List[Dict] = field(default_factory=list) # Faux positifs détaillés
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by_type: Dict[str, Dict] = field(default_factory=dict) # Métriques par type de PII
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def to_dict(self) -> Dict:
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"""Convertit en dictionnaire."""
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return {
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"pdf_path": self.pdf_path,
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"true_positives": self.true_positives,
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"false_positives": self.false_positives,
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"false_negatives": self.false_negatives,
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"precision": round(self.precision, 4),
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"recall": round(self.recall, 4),
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"f1_score": round(self.f1_score, 4),
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"missed_pii": self.missed_pii,
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"false_detections": self.false_detections,
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"by_type": self.by_type
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}
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class QualityEvaluator:
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"""Évaluateur de qualité d'anonymisation."""
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# Mapping des types de PII entre annotations et détections
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TYPE_MAPPING = {
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# Annotations → Détections possibles
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"NOM": ["NOM", "NOM_GLOBAL", "PRENOM", "PRENOM_GLOBAL"],
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"PRENOM": ["PRENOM", "PRENOM_GLOBAL", "NOM", "NOM_GLOBAL"],
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"TEL": ["TEL", "TEL_GLOBAL"],
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"EMAIL": ["EMAIL", "EMAIL_GLOBAL"],
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"ADRESSE": ["ADRESSE", "ADRESSE_GLOBAL"],
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"CODE_POSTAL": ["CODE_POSTAL", "CODE_POSTAL_GLOBAL"],
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"VILLE": ["VILLE", "VILLE_GLOBAL"],
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"NIR": ["NIR", "NIR_GLOBAL"],
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"IPP": ["IPP", "IPP_GLOBAL"],
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"NDA": ["NDA", "NDA_GLOBAL"],
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"RPPS": ["RPPS", "RPPS_GLOBAL"],
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"FINESS": ["FINESS", "FINESS_GLOBAL"],
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"OGC": ["OGC", "OGC_GLOBAL"],
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"ETABLISSEMENT": ["ETAB", "ETAB_GLOBAL", "VLM_ETAB"],
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"SERVICE": ["SERVICE", "SERVICE_GLOBAL", "VLM_SERVICE"],
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"DATE": ["DATE", "DATE_GLOBAL"],
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"DATE_NAISSANCE": ["DATE_NAISSANCE", "DATE_NAISSANCE_GLOBAL"],
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"AGE": ["AGE", "AGE_GLOBAL"],
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"NUMERO_PATIENT": ["VLM_NUM_PATIENT", "IPP"],
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"NUMERO_LOT": ["VLM_NUM_LOT"],
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"NUMERO_ORDONNANCE": ["VLM_NUM_ORD"],
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"NUMERO_SEJOUR": ["VLM_NUM_SEJOUR", "NDA"],
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}
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def __init__(self, ground_truth_dir: Path):
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"""
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Initialise l'évaluateur.
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Args:
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ground_truth_dir: Répertoire contenant les annotations manuelles
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"""
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self.ground_truth_dir = Path(ground_truth_dir)
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def normalize_text(self, text: str) -> str:
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"""
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Normalise un texte pour la comparaison.
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Args:
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text: Texte à normaliser
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Returns:
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Texte normalisé
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"""
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# Lowercase
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text = text.lower()
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# Supprimer les espaces multiples
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text = re.sub(r'\s+', ' ', text)
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# Strip
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text = text.strip()
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return text
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def load_annotations(self, pdf_path: Path) -> Optional[Dict]:
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"""
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Charge les annotations manuelles d'un document.
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Args:
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pdf_path: Chemin vers le PDF
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Returns:
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Annotations ou None si non trouvées
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"""
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# Chercher dans le répertoire ground_truth configuré
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annotation_file = self.ground_truth_dir / f"{pdf_path.stem}.json"
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if not annotation_file.exists():
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# Fallback: chercher avec le suffixe .annotations.json
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annotation_file = self.ground_truth_dir / f"{pdf_path.stem}.annotations.json"
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if not annotation_file.exists():
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return None
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try:
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with open(annotation_file, 'r', encoding='utf-8') as f:
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data = json.load(f)
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# Convertir le format "pages" en format "annotations" si nécessaire
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if "pages" in data and "annotations" not in data:
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annotations = []
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for page in data["pages"]:
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page_num = page["page_number"]
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for pii_type, texts in page["pii"].items():
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for text in texts:
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annotations.append({
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"page": page_num,
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"type": pii_type,
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"text": text,
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"context": ""
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})
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data["annotations"] = annotations
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return data
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except Exception as e:
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print(f"✗ Erreur lors du chargement des annotations {annotation_file}: {e}")
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return None
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def load_audit(self, audit_path: Path) -> Optional[List[Dict]]:
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"""
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Charge l'audit de détection automatique.
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Args:
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audit_path: Chemin vers le fichier .audit.jsonl
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Returns:
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Liste des détections ou None si non trouvé
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"""
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if not audit_path.exists():
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return None
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try:
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detections = []
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with open(audit_path, 'r', encoding='utf-8') as f:
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for line in f:
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if line.strip():
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detections.append(json.loads(line))
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return detections
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except Exception as e:
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print(f"✗ Erreur lors du chargement de l'audit {audit_path}: {e}")
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return None
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def types_match(self, ann_type: str, det_type: str) -> bool:
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"""
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Vérifie si deux types de PII correspondent.
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Args:
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ann_type: Type dans l'annotation
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det_type: Type dans la détection
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Returns:
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True si les types correspondent
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"""
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# Mapping direct
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if ann_type in self.TYPE_MAPPING:
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return det_type in self.TYPE_MAPPING[ann_type]
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# Correspondance exacte
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return ann_type == det_type
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def compare(self, annotations: List[Dict], detections: List[Dict]) -> Tuple[List, List, List]:
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"""
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Compare les annotations avec les détections.
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Args:
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annotations: Liste des annotations manuelles
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detections: Liste des détections automatiques
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Returns:
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Tuple (true_positives, false_negatives, false_positives)
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"""
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true_positives = []
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false_negatives = []
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false_positives = []
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# Créer des clés de comparaison pour les annotations
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ann_keys = {}
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for ann in annotations:
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page = ann.get("page", 0)
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pii_type = ann.get("type", "")
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text = self.normalize_text(ann.get("text", ""))
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key = (page, text)
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if key not in ann_keys:
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ann_keys[key] = []
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ann_keys[key].append(ann)
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# Créer des clés de comparaison pour les détections
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det_keys = {}
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for det in detections:
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page = det.get("page", 0)
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text = self.normalize_text(det.get("original", ""))
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key = (page, text)
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if key not in det_keys:
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det_keys[key] = []
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det_keys[key].append(det)
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# Trouver les true positives et false negatives
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matched_det_keys = set()
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for key, anns in ann_keys.items():
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page, text = key
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if key in det_keys:
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# Vérifier si au moins une détection correspond au type
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dets = det_keys[key]
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matched = False
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for ann in anns:
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ann_type = ann.get("type", "")
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for det in dets:
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det_type = det.get("kind", "")
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if self.types_match(ann_type, det_type):
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true_positives.append({
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"page": page,
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"type": ann_type,
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"text": ann.get("text", ""),
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"detected_as": det_type,
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"context": ann.get("context", "")
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})
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matched = True
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matched_det_keys.add(key)
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break
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if matched:
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break
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if not matched:
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# Détecté mais type incorrect
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for ann in anns:
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false_negatives.append({
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"page": page,
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"type": ann.get("type", ""),
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"text": ann.get("text", ""),
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"context": ann.get("context", ""),
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"reason": "type_mismatch",
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"detected_as": [d.get("kind", "") for d in dets]
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})
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else:
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# Non détecté
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for ann in anns:
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false_negatives.append({
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"page": page,
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"type": ann.get("type", ""),
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"text": ann.get("text", ""),
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"context": ann.get("context", ""),
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"reason": "not_detected"
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})
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# Trouver les false positives
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for key, dets in det_keys.items():
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if key not in matched_det_keys:
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page, text = key
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for det in dets:
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false_positives.append({
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"page": page,
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"type": det.get("kind", ""),
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"text": det.get("original", ""),
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"placeholder": det.get("placeholder", "")
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})
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return true_positives, false_negatives, false_positives
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def calculate_metrics(self, tp: int, fp: int, fn: int) -> Tuple[float, float, float]:
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"""
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Calcule les métriques de qualité.
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Args:
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tp: True positives
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fp: False positives
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fn: False negatives
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Returns:
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Tuple (precision, recall, f1_score)
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"""
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# Précision
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precision = tp / (tp + fp) if (tp + fp) > 0 else 0.0
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# Rappel
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recall = tp / (tp + fn) if (tp + fn) > 0 else 0.0
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# F1-Score
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f1_score = 2 * (precision * recall) / (precision + recall) if (precision + recall) > 0 else 0.0
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return precision, recall, f1_score
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def calculate_metrics_by_type(self, tp_list: List[Dict], fn_list: List[Dict], fp_list: List[Dict]) -> Dict[str, Dict]:
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"""
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Calcule les métriques par type de PII.
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Args:
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tp_list: Liste des true positives
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fn_list: Liste des false negatives
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fp_list: Liste des false positives
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Returns:
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Dictionnaire des métriques par type
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"""
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by_type = {}
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# Compter par type
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for tp in tp_list:
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pii_type = tp["type"]
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if pii_type not in by_type:
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by_type[pii_type] = {"tp": 0, "fp": 0, "fn": 0}
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by_type[pii_type]["tp"] += 1
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for fn in fn_list:
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pii_type = fn["type"]
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if pii_type not in by_type:
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by_type[pii_type] = {"tp": 0, "fp": 0, "fn": 0}
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by_type[pii_type]["fn"] += 1
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for fp in fp_list:
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pii_type = fp["type"]
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if pii_type not in by_type:
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by_type[pii_type] = {"tp": 0, "fp": 0, "fn": 0}
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by_type[pii_type]["fp"] += 1
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# Calculer les métriques
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for pii_type, counts in by_type.items():
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tp = counts["tp"]
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fp = counts["fp"]
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fn = counts["fn"]
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precision, recall, f1 = self.calculate_metrics(tp, fp, fn)
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counts["precision"] = round(precision, 4)
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counts["recall"] = round(recall, 4)
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counts["f1_score"] = round(f1, 4)
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return by_type
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def evaluate(self, pdf_path: Path, audit_path: Path) -> Optional[EvaluationResult]:
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"""
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Évalue la qualité d'anonymisation d'un document.
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Args:
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pdf_path: Chemin vers le PDF original
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audit_path: Chemin vers le fichier .audit.jsonl
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Returns:
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Résultat d'évaluation ou None si erreur
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"""
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# Charger les annotations
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annotations_data = self.load_annotations(pdf_path)
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if not annotations_data:
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print(f"✗ Annotations introuvables pour {pdf_path.name}")
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return None
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annotations = annotations_data.get("annotations", [])
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# Charger l'audit
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detections = self.load_audit(audit_path)
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if detections is None:
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print(f"✗ Audit introuvable: {audit_path}")
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return None
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# Comparer
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tp_list, fn_list, fp_list = self.compare(annotations, detections)
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# Calculer les métriques globales
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tp = len(tp_list)
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fp = len(fp_list)
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fn = len(fn_list)
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precision, recall, f1_score = self.calculate_metrics(tp, fp, fn)
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# Calculer les métriques par type
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by_type = self.calculate_metrics_by_type(tp_list, fn_list, fp_list)
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# Créer le résultat
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result = EvaluationResult(
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pdf_path=str(pdf_path),
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true_positives=tp,
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false_positives=fp,
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false_negatives=fn,
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precision=precision,
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recall=recall,
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f1_score=f1_score,
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missed_pii=fn_list,
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false_detections=fp_list,
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by_type=by_type
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)
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return result
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def evaluate_batch(self, pdf_list: List[Path], audit_list: List[Path]) -> List[EvaluationResult]:
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"""
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Évalue un batch de documents.
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Args:
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pdf_list: Liste des PDFs
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audit_list: Liste des audits
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Returns:
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Liste des résultats d'évaluation
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"""
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results = []
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for pdf_path, audit_path in zip(pdf_list, audit_list):
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result = self.evaluate(pdf_path, audit_path)
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if result:
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results.append(result)
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return results
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def generate_report(self, results: List[EvaluationResult]) -> str:
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"""
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Génère un rapport texte des résultats.
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Args:
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results: Liste des résultats d'évaluation
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Returns:
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Rapport texte
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"""
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if not results:
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return "Aucun résultat à afficher."
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# Calculer les métriques globales
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total_tp = sum(r.true_positives for r in results)
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total_fp = sum(r.false_positives for r in results)
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total_fn = sum(r.false_negatives for r in results)
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avg_precision = sum(r.precision for r in results) / len(results)
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avg_recall = sum(r.recall for r in results) / len(results)
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avg_f1 = sum(r.f1_score for r in results) / len(results)
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# Générer le rapport
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report = []
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report.append("=" * 80)
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report.append("RAPPORT D'ÉVALUATION DE LA QUALITÉ D'ANONYMISATION")
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report.append("=" * 80)
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report.append("")
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report.append(f"Documents évalués: {len(results)}")
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report.append("")
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report.append("MÉTRIQUES GLOBALES:")
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report.append(f" True Positives: {total_tp}")
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report.append(f" False Positives: {total_fp}")
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report.append(f" False Negatives: {total_fn}")
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report.append("")
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report.append(f" Précision moyenne: {avg_precision:.4f} ({avg_precision*100:.2f}%)")
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report.append(f" Rappel moyen: {avg_recall:.4f} ({avg_recall*100:.2f}%)")
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report.append(f" F1-Score moyen: {avg_f1:.4f}")
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report.append("")
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# Résultats par document
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report.append("RÉSULTATS PAR DOCUMENT:")
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report.append("")
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for result in results:
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pdf_name = Path(result.pdf_path).name
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report.append(f" {pdf_name}")
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report.append(f" Précision: {result.precision:.4f} Rappel: {result.recall:.4f} F1: {result.f1_score:.4f}")
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report.append(f" TP: {result.true_positives} FP: {result.false_positives} FN: {result.false_negatives}")
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report.append("")
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# Faux négatifs critiques
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critical_fn = []
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for result in results:
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for fn in result.missed_pii:
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if fn.get("reason") == "not_detected":
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critical_fn.append((Path(result.pdf_path).name, fn))
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if critical_fn:
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report.append(f"FAUX NÉGATIFS CRITIQUES ({len(critical_fn)}):")
|
|
report.append("")
|
|
for pdf_name, fn in critical_fn[:10]: # Limiter à 10
|
|
report.append(f" {pdf_name} - Page {fn['page']+1}")
|
|
report.append(f" Type: {fn['type']}")
|
|
report.append(f" Texte: {fn['text']}")
|
|
report.append(f" Contexte: {fn['context'][:80]}...")
|
|
report.append("")
|
|
|
|
report.append("=" * 80)
|
|
|
|
return "\n".join(report)
|
|
|
|
def export_json(self, results: List[EvaluationResult], output_path: Path):
|
|
"""
|
|
Exporte les résultats en JSON.
|
|
|
|
Args:
|
|
results: Liste des résultats
|
|
output_path: Chemin du fichier de sortie
|
|
"""
|
|
data = {
|
|
"evaluation_date": Path(__file__).stat().st_mtime,
|
|
"documents_count": len(results),
|
|
"results": [r.to_dict() for r in results]
|
|
}
|
|
|
|
with open(output_path, 'w', encoding='utf-8') as f:
|
|
json.dump(data, f, indent=2, ensure_ascii=False)
|
|
|
|
print(f"✓ Résultats exportés: {output_path}")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
# Test basique
|
|
evaluator = QualityEvaluator(Path("tests/ground_truth/pdfs"))
|
|
|
|
# Exemple d'utilisation
|
|
pdf_path = Path("tests/ground_truth/pdfs/001_simple_unknown_BACTERIO_23018396.pdf")
|
|
audit_path = Path("tests/ground_truth/pdfs/001_simple_unknown_BACTERIO_23018396.audit.jsonl")
|
|
|
|
if pdf_path.exists() and audit_path.exists():
|
|
result = evaluator.evaluate(pdf_path, audit_path)
|
|
if result:
|
|
print(evaluator.generate_report([result]))
|