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"cells": [
{
"cell_type": "code",
"execution_count": 4,
"id": "72d11d95",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"✅ Evidently importe\n"
]
}
],
"source": [
"# EXPLICATION : Imports Evidently pour comparaison de distributions\n",
"# - Report : genere les rapports automatiques\n",
"# - DataDriftPreset : ensemble de metriques pour detecter le drift (Distribution, KS Test, etc.)\n",
"# - ColumnMapping : informe Evidently du type de chaque colonne (numerique/categorique)\n",
"\n",
"import pandas as pd\n",
"import json\n",
"from pathlib import Path\n",
"\n",
"try:\n",
" from evidently.legacy.report import Report\n",
" from evidently.legacy.metric_preset import DataDriftPreset\n",
" from evidently.legacy.pipeline.column_mapping import ColumnMapping\n",
"except ImportError:\n",
" # Fallback for older/newer Evidently layouts\n",
" from evidently.report import Report\n",
" from evidently.metric_preset import DataDriftPreset\n",
" from evidently.pipeline.column_mapping import ColumnMapping\n",
"\n",
"print(\"✅ Evidently importe\")\n"
]
},
{
"cell_type": "markdown",
"id": "9b33c429",
"metadata": {},
"source": [
"## Chargement référence et données production"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "61a259c2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"⚠️ Colonnes vides supprimées : 31\n",
"✅ Référence : 10000 lignes | Production : 500 lignes\n",
" Colonnes analysées : 711\n"
]
}
],
"source": [
"# EXPLICATION : \n",
"# 1. Référence = distribution d'entraînement (dataset pristine)\n",
"# 2. Production = features réelles extraites des logs d'inférence\n",
"# 3. Nettoyage : convertir \"\" en NaN (valeurs vides)\n",
"# 4. Aligner : garder seulement colonnes communes (peut y avoir des différences en production)\n",
"\n",
"# Référence (entraînement)\n",
"reference = pd.read_csv(\"../reference/reference.csv\")\n",
"\n",
"# Production : extraire input_features des logs\n",
"LOG_FILE = Path(\"../logs/predictions.jsonl\")\n",
"logs = pd.read_json(LOG_FILE, lines=True)\n",
"production = pd.json_normalize(logs['input_features'])\n",
"\n",
"# Nettoyage (\"\" → NaN, aligner colonnes)\n",
"production = production.replace(\"\", pd.NA).infer_objects()\n",
"# EXPLICATION : infer_objects() détecte automatiquement les vrais types (ex: strings → objects)\n",
"\n",
"# Garder seulement les colonnes communes avec la référence\n",
"# (en production, certaines colonnes peuvent être absentes ou ajoutées)\n",
"common_cols = list(set(reference.columns) & set(production.columns))\n",
"reference = reference[common_cols]\n",
"production = production[common_cols]\n",
"\n",
"# Supprimer les colonnes vides (100% NaN) pour éviter les erreurs Evidently\n",
"empty_ref = reference.columns[reference.isna().all()].tolist()\n",
"empty_prod = production.columns[production.isna().all()].tolist()\n",
"empty_cols = sorted(set(empty_ref) | set(empty_prod))\n",
"if empty_cols:\n",
" reference = reference.drop(columns=empty_cols)\n",
" production = production.drop(columns=empty_cols)\n",
" print(f\"⚠️ Colonnes vides supprimées : {len(empty_cols)}\")\n",
"\n",
"print(f\"✅ Référence : {len(reference)} lignes | Production : {len(production)} lignes\")\n",
"print(f\" Colonnes analysées : {len(reference.columns)}\")"
]
},
{
"cell_type": "markdown",
"id": "8a5feb72",
"metadata": {},
"source": [
"## Calcul du data drift + génération du rapport"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "8e4c48a8",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Numériques : 580 | Catégorique : 131\n",
"✅ Rapport généré : reports/data_drift_report.html\n"
]
}
],
"source": [
"# EXPLICATION : ColumnMapping aide Evidently à utiliser les bonnes métriques\n",
"# - Features numériques : test KS (Kolmogorov-Smirnov) pour comparaison de distributions\n",
"# - Features catégorique : test Chi-Squared pour comparer les fréquences\n",
"\n",
"column_mapping = ColumnMapping()\n",
"column_mapping.numerical_features = reference.select_dtypes(include=['number']).columns.tolist()\n",
"column_mapping.categorical_features = reference.select_dtypes(include=['object', 'bool']).columns.tolist()\n",
"\n",
"print(f\" Numériques : {len(column_mapping.numerical_features)} | Catégorique : {len(column_mapping.categorical_features)}\")\n",
"\n",
"# EXPLICATION : DataDriftPreset inclut :\n",
"# - Drift per column (KS test pour numériques, Chi2 pour catégories)\n",
"# - Dataset drift ratio\n",
"# - Détection automatique pour seuil default (0.95 confiance)\n",
"data_drift_report = Report(metrics=[DataDriftPreset()])\n",
"data_drift_report.run(reference_data=reference, current_data=production, column_mapping=column_mapping)\n",
"\n",
"# Sauvegarde HTML (dashboard interactif)\n",
"REPORT_DIR = Path(\"../reports\")\n",
"REPORT_DIR.mkdir(exist_ok=True)\n",
"report_path = REPORT_DIR / \"data_drift_report.html\"\n",
"data_drift_report.save_html(str(report_path))\n",
"print(\"✅ Rapport généré : reports/data_drift_report.html\")"
]
},
{
"cell_type": "markdown",
"id": "e6e9f4c5",
"metadata": {},
"source": [
"## Alertes automatiques"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "c5497ce9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"🔴 ALERTE : Drift détecté sur 1 features !\n",
" Exemples : ['AMT_INCOME_TOTAL']\n",
"\n",
" 📋 Recommandations : \n",
" - Vérifier source des données (anomalie/changement)\n",
" - Envisager réentraînement du modèle\n",
" - Ajouter monitoring continu sur ces features\n",
"\n",
"📊 Ouvre le fichier reports/data_drift_report.html dans ton navigateur pour le dashboard complet\n"
]
}
],
"source": [
"# EXPLICATION : \n",
"# - Extraire les résultats du rapport (dictionnaire structuré)\n",
"# - Seuil 0.3 : drift_score > 0.3 = **drift modéré à fort** (sensibilité équilibrée)\n",
"# * 0.1-0.3 = léger (toléré)\n",
"# * > 0.3 = alerte (intervention recommandée)\n",
"# - Ce seuil est a : selon besoin métier (plus strict = plus d'alertes)\n",
"\n",
"# Exemple d'alerte sur features qui driftent fortement\n",
"report_dict = data_drift_report.as_dict()\n",
"drift_summary = None\n",
"for metric in report_dict.get(\"metrics\", []):\n",
" result = metric.get(\"result\", {})\n",
" if \"drift_by_columns\" in result:\n",
" drift_summary = result[\"drift_by_columns\"]\n",
" break\n",
"\n",
"if drift_summary is None:\n",
" sample_keys = [list(m.get(\"result\", {}).keys()) for m in report_dict.get(\"metrics\", [])[:3]]\n",
" print(\"⚠️ Impossible de trouver 'drift_by_columns' dans le rapport Evidently\")\n",
" print(f\" Exemples de clés disponibles : {sample_keys}\")\n",
"else:\n",
" drifted_features = [col for col, info in drift_summary.items()\n",
" if info.get(\"drift_detected\") and info.get(\"drift_score\", 0) > 0.3]\n",
"\n",
" if len(drifted_features) > 0:\n",
" print(f\"🔴 ALERTE : Drift détecté sur {len(drifted_features)} features !\")\n",
" print(f\" Exemples : {drifted_features[:5]}\")\n",
" print(\"\\n 📋 Recommandations : \")\n",
" print(\" - Vérifier source des données (anomalie/changement)\") \n",
" print(\" - Envisager réentraînement du modèle\")\n",
" print(\" - Ajouter monitoring continu sur ces features\")\n",
" else:\n",
" print(\"✅ Aucun drift majeur détecté\")\n",
"\n",
"print(\"\\n📊 Ouvre le fichier reports/data_drift_report.html dans ton navigateur pour le dashboard complet\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "OC_P6",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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