#!/usr/bin/env python3
"""
Analyse des faux positifs EPISODE pour identifier les patterns problématiques.
"""

import json
from pathlib import Path
from collections import Counter
import re

def analyze_episode_fp():
    """Analyse les faux positifs EPISODE."""
    
    # Lire les audits et annotations
    audit_dir = Path("tests/ground_truth/pdfs/baseline_anonymized")
    annot_dir = Path("tests/ground_truth/annotations")
    
    # Collecter tous les EPISODE détectés
    episode_detections = []
    
    for audit_file in audit_dir.glob("*.audit.jsonl"):
        doc_name = audit_file.stem.replace('.audit', '')
        
        # Lire les détections
        detections = []
        with open(audit_file, 'r', encoding='utf-8') as f:
            for line in f:
                hit = json.loads(line)
                if hit['kind'] == 'EPISODE':
                    detections.append(hit['original'])
        
        # Lire les annotations (ground truth)
        annot_file = annot_dir / f"{doc_name}.json"
        annotations = []
        if annot_file.exists():
            with open(annot_file, 'r', encoding='utf-8') as f:
                annot_data = json.load(f)
                annotations = [a['text'] for a in annot_data.get('annotations', []) if a['label'] == 'EPISODE']
        
        # Identifier les faux positifs (détectés mais pas annotés)
        for det in detections:
            if det not in annotations:
                episode_detections.append({
                    'document': doc_name,
                    'value': det
                })
    
    print("=" * 80)
    print(f"ANALYSE DES {len(episode_detections)} FAUX POSITIFS EPISODE")
    print("=" * 80)
    
    if not episode_detections:
        print("\n✅ Aucun faux positif EPISODE trouvé!")
        return
    
    # Analyser les valeurs
    values = [fp['value'] for fp in episode_detections]
    value_counts = Counter(values)
    
    print(f"\n📊 Top 20 valeurs les plus fréquentes:")
    for value, count in value_counts.most_common(20):
        print(f"  {value}: {count} occurrences")
    
    # Analyser les patterns
    print(f"\n📊 Analyse des patterns:")
    
    # Pattern 1: Codes médicaux CIM-10 (lettre + chiffres)
    cim10_codes = [v for v in values if re.match(r'^[A-Z]\d{2}', v)]
    print(f"  Codes CIM-10 (ex: E11, Z95): {len(cim10_codes)} ({len(cim10_codes)/len(values)*100:.1f}%)")
    
    # Pattern 2: Numéros purs (que des chiffres)
    pure_numbers = [v for v in values if v.isdigit()]
    print(f"  Numéros purs (que des chiffres): {len(pure_numbers)} ({len(pure_numbers)/len(values)*100:.1f}%)")
    
    # Pattern 3: Codes avec tirets
    codes_with_dash = [v for v in values if '-' in v]
    print(f"  Codes avec tirets: {len(codes_with_dash)} ({len(codes_with_dash)/len(values)*100:.1f}%)")
    
    # Pattern 4: Codes courts (<=4 chars)
    short_codes = [v for v in values if len(v) <= 4]
    print(f"  Codes courts (≤4 chars): {len(short_codes)} ({len(short_codes)/len(values)*100:.1f}%)")
    
    # Pattern 5: Codes longs (>=10 chars)
    long_codes = [v for v in values if len(v) >= 10]
    print(f"  Codes longs (≥10 chars): {len(long_codes)} ({len(long_codes)/len(values)*100:.1f}%)")
    
    # Exemples par pattern
    print(f"\n📊 Exemples par pattern:")
    if cim10_codes:
        print(f"  CIM-10: {', '.join(cim10_codes[:5])}")
    if pure_numbers:
        print(f"  Numéros purs: {', '.join(pure_numbers[:5])}")
    if short_codes:
        print(f"  Codes courts: {', '.join(short_codes[:5])}")
    
    # Identifier les documents avec le plus de FP EPISODE
    doc_counts = Counter([fp['document'] for fp in episode_detections])
    print(f"\n📊 Documents avec le plus de FP EPISODE:")
    for doc, count in doc_counts.most_common(10):
        print(f"  {doc}: {count} FP")
    
    # Sauvegarder l'analyse
    output_file = Path("tests/ground_truth/analysis/episode_fp_analysis.json")
    output_file.parent.mkdir(parents=True, exist_ok=True)
    
    analysis = {
        'total_fp': len(episode_detections),
        'unique_values': len(value_counts),
        'top_values': dict(value_counts.most_common(20)),
        'patterns': {
            'cim10_codes': len(cim10_codes),
            'pure_numbers': len(pure_numbers),
            'codes_with_dash': len(codes_with_dash),
            'short_codes': len(short_codes),
            'long_codes': len(long_codes)
        },
        'top_documents': dict(doc_counts.most_common(10)),
        'examples': {
            'cim10': cim10_codes[:10],
            'pure_numbers': pure_numbers[:10],
            'short_codes': short_codes[:10]
        }
    }
    
    with open(output_file, 'w', encoding='utf-8') as f:
        json.dump(analysis, f, indent=2, ensure_ascii=False)
    
    print(f"\n📄 Analyse sauvegardée: {output_file}")
    
    # Recommandations
    print("\n" + "=" * 80)
    print("RECOMMANDATIONS")
    print("=" * 80)
    
    cim10_ratio = len(cim10_codes) / len(values) * 100
    if cim10_ratio > 30:
        print(f"\n✅ {cim10_ratio:.1f}% des FP sont des codes CIM-10")
        print("   Recommandation: Filtrer les codes CIM-10 connus (pattern ^[A-Z]\\d{2})")
    
    short_ratio = len(short_codes) / len(values) * 100
    if short_ratio > 50:
        print(f"\n✅ {short_ratio:.1f}% des FP sont des codes courts (≤4 chars)")
        print("   Recommandation: Augmenter la longueur minimale pour EPISODE (ex: ≥6 chars)")
    
    # Identifier les documents trackare
    trackare_docs = [doc for doc in doc_counts.keys() if 'trackare' in doc.lower()]
    if trackare_docs:
        trackare_fp = sum(doc_counts[doc] for doc in trackare_docs)
        print(f"\n✅ {trackare_fp} FP ({trackare_fp/len(episode_detections)*100:.1f}%) proviennent de documents trackare")
        print("   Recommandation: Filtrage spécifique pour les documents trackare")

if __name__ == "__main__":
    analyze_episode_fp()